\define extract(tiddler,start:"""ç-noStart-ç""",end:"""ç-noEnd-ç""",prefix:"""""",suffix:"""""",
limit:"yes",class:"", rmQuotes:'no' mode:"block")
<$vars start="""$start$""" end="""$end$""" prefix="""$prefix$""" suffix="""$suffix$""" startorend="""$start$""">
<$set name="tid" filter="[field:title[$tiddler$]]" value="""$tiddler$""" emptyValue=<<currentTiddler>>>
<$list variable="fulltext" filter="""[<tid>get[text]regexp<startorend>addprefix[ç-noStart-ç]addsuffix[ç-noEnd-ç]]""" emptyValue="ç-noStart-ç $start$ error: no text field $end$ ç-noEnd-ç">
<$list variable="beforeStart" emptyMessage="filter error"
filter="""[<fulltext>splitbefore<start>]""">
<$list variable="firstRest" filter="[<fulltext>removeprefix<beforeStart>]">
<span class="te-summary $class$">
<$macrocall $name="extractSnippet" rest=<<firstRest>> start=<<start>> end=<<end>> prefix=<<prefix>> suffix=<<suffix>> limit="$limit$" rmQuotes='$rmQuotes$' mode="$mode$"/>
</span>
</$list>
</$list>
</$list>
</$set>
</$vars>
\end
\define extractSnippet(rest,start,end,prefix,suffix,limit,rmQuotes,mode)
<$vars text="""$rest$""" start="""$start$""" end="""$end$""" prefix="""$prefix$""" suffix="""$suffix$""" startorend="""$start$""" linkstart="""$linkstart$""" linkend="""$linkend$""">
<$list variable="snippet" filter="""[<text>splitbefore<end>removesuffix<end>]""" emptyValue="summary empty">
<$set name="mymode" filter="""[[$mode$]removeprefix[block]]""" value="blockOutput" emptyValue="inlineOutput">
<$set name="snipify" filter="""[[$mode$]removeprefix[link]]""" value="linkedOutput" emptyValue=<<mymode>>>
<$set name="extracted" filter="""[[$rmQuotes$]removeprefix[no]]""" value=<<noQuotes>> emptyValue=<<removeQuotes>>>
<$macrocall $name=<<snipify>>/>
</$set>
</$set>
</$set>
<$list variable="newRest" filter="""[<text>removeprefix<snippet>removeprefix<end>]"""
emptyValue="never empty">
<$set name="unlimited" filter="""[[$limit$]removeprefix[y]]""" emptyValue="checkRest">
<$macrocall $name=<<unlimited>> rest=<<newRest>> start=<<start>> end=<<end>> prefix=<<prefix>> suffix=<<suffix>> limit="$limit$" rmQuotes='$rmQuotes$' mode="$mode$"/>
</$set>
</$list>
</$list>
</$vars>
\end
\define linkedOutput()
$(prefix)$[[$(extracted)$]]$(suffix)$
\end
\define inlineOutput()
$(prefix)$$(extracted)$$(suffix)$
\end
\define blockOutput()
<span>
$(prefix)$$(extracted)$$(suffix)$
</span>
\end
\define checkRest(rest,start,end,prefix,suffix,limit,rmQuotes,mode,linkstart,linkend)
<$set name="text" value="""$rest$""">
<$list variable="beforeStart" filter="""[<text>splitbefore[$start$]]""">
<$set name="proceed" filter="""[<beforeStart>removesuffix[$start$]] +[addsuffix[ç-TestPassed-ç]]"""
emptyValue="es">
<$set name="proceedto" filter="""[<proceed>removesuffix[ç-TestPassed-ç]regexp[es]]""" emptyValue="extractSnippet">
<$list variable="newRest" filter="""[<text>removeprefix<beforeStart>]""">
<$macrocall $name=<<proceedto>> rest=<<newRest>> start="""$start$""" end="""$end$""" prefix="""$prefix$""" suffix="""$suffix$""" limit="$limit$" rmQuotes='$rmQuotes$' mode="$mode$" linkstart=<<linkstart>> linkend=<<linkend>>/>
</$list>
</$set>
</$set>
</$list>
</$set>
\end
\define es()
<span class='summaryend'></span>
\end
\define removeQuotes()
<$vars output=$(snippet)$>
<<output>>
</$vars>
\end
\define noQuotes()
$(snippet)$
\end
<!-- !! Extract Macro Documentation
* Version: 0.9.2
* parameters
** tiddler – if not provided, the current tiddler is used
** start and end – text-identifiers that sourround the snippet you want to extract
** prefix and suffix – text to attach to the result
** limit – defaults to "yes" and produces one result, collects all matches if set to "no"
** class – CSS class(es) to append to the surrounding span
** rmQuotes – defaults to 'no', removes surrounding quotes (") when set to "yes"
** mode – defaults to "block", set to "inline" to omit surrounding tags, set to "link" to get links in inline mode
!!! Advantages
* find content to extract based on common markup or comments
* handle wiki syntax where start and end are the same, e.g. `''` or `//`
* ''start or end can be omitted when extracting the beginning or to the end of the text''
* to collect several snippets from the same tiddler set limit to "no"
-->
<!-- !!! FAQ
; The output is something like " end:" – what can I do?
: This happens, when you try to extract from the tiddler, where the extract macro is called: the macro call contains the start marker. Solutions:
* if you are using a filter, exclude the calling tiddler using ![tiddler]
* transclude the macrocall from somewhere else, e.g. from a field in the tiddler
; The list widget produces results only for tiddlers without spaces in the title?
: Lists need to be constructed carefully according to the examples below. Macro calls from lists with the parameter `tiddler=<<tiddler>>` and similar work only for titles without spaces.
* see: http://tid.li/tw5/hacks.html#Tweeting for a working example
* or: http://tid.li/tw5/numbers.html
; Can I use start or end markers containing " (quotes)?
: This will not work – but you can remove surrounding quotes by setting rmQuotes to "yes".
-->
<!-- !!! Procedure – How it Works
* get the text field of the tiddler
** cut off everything before the first start tag, including start tag
* extract a snippet from the rest
** ''put texts in variables to avoid problems with square brackets''
** cut after end tag, remove end tag
** prepend prefix, output snippet, append the suffix
* check, if limit is "no", else exit via es()
** es() prints an empty span which can be used for design purposes
* check the rest
** cut off everything before start tag, including start tag and save in //beforeStart// (contains all text if no start tag is found)
** try to remove start tag from beforeStart, if found, add a confirmation suffix
*** no start tag? => exit via es()
** if a start tag exists, proceed with extracting
!!! Missing – Ideas for Improvement
* allow users to specify an empty-message
* support for some kinds of transclusion (e.g. from a tiddler’s own fields)
* Possible optimisations (low priority)
** limit to a defined number of loops
** limit to a defined number of chars (possible with length param from 5.1.14?) – this might not be useful as tags could get cut in pieces.
-->
\define strex(content:"TextStretch", label:"…", start:"[", end:"]", class:"", id:"_false_")
<$vars content="""$content$""" id="""$id$""">
<$set name="uid" filter="[<id>!prefix[_false_]]" value=<<id>> emptyValue=<<content>> >
<span class="strex-container $class$"><$macrocall $name="strexx" content=<<content>> label="""$label$""" start="""$start$""" end="""$end$""" class="""$class$""" uid=<<uid>>/></span>
</$set>
</$vars>
\end
\define strexx(content, label, start, end, class, uid)
<$set name="xuid" filter="[<uid>prefix[_false_]]" value="error: xuid hashing" emptyValue=<<HashStr """$uid$""">> >
<$macrocall $name="strexxx" content="""$content$""" label="""$label$""" start="""$start$""" end="""$end$""" class="""$class$""" xuid=<<xuid>>/>
</$set>
\end
\define strexxx(content, label, start, end, class, xuid)
<$vars content="""$content$""" label="""$label$""" start="""$start$""" end="""$end$""" class="""$class$""" xuid="""$xuid$""">
<$set name="qualstate" value=<<qualify "$:/state/strex_$xuid$_">> >
<$vars openclass="strex-open $class$" contentclass="strex-content $class$" startclass="strex-close strex-start $class$" endclass="strex-close strex-end $class$">
<$reveal type="nomatch" state=<<qualstate>> text="visible" animate="yes"><$button set=<<qualstate>> setTo="visible" class=<<openclass>> tooltip="show text part"><<label>></$button></$reveal><$reveal type="match" state=<<qualstate>> text="visible" animate="yes">
<span class="strex-all $class$"><span class="strex-inner $class$"><$button set=<<qualstate>> setTo="hidden" class=<<startclass>> tooltip="hide text part">$start$<$action-setfield $tiddler=<<qualstate>>/></$button><span class=<<contentclass>> > <<content>> </span></span><$button set=<<qualstate>> setTo="hidden" class=<<endclass>> tooltip="hide text part">$end$<$action-setfield $tiddler=<<qualstate>>/></$button></span></$reveal>
</$vars>
</$set>
</$vars>
\end
\define ref(content:"empty")
<$macrocall $name="strex" content="""$content$""" label="​" start="(" end=")" tooltip="""$content$""" class="numbers"/>
\end
<!-- step 1 (x): check for id, replace with content if param is empty -->
<!-- step 2 (xx): hash id -->
<!-- step 3 (xxx): generate output, use state with hashed id -->
/* strex standard styling */
.strex-container, .strex-container .tc-reveal, .strex-all {
position:relative;
}
.strex-open, .strex-start, .strex-end {
color: <<colour tiddler-link-foreground>>;
padding: 0 2px 2px 2px;
margin:-5px -3px -5px -5px;
line-height: 96%;
background-color: rgba(135, 135, 135, 0.0);
border: 0px solid lightgray;
border-radius:10px
}
.strex-open:hover, .strex-start:hover, .strex-end:hover {
border: 1px solid rgba(135, 135, 135, 0.60);
}
.strex-open:active, .strex-start:active, .strex-end:active,
.strex-open:focus, .strex-start:focus, .strex-end:focus {
border: 1px solid lightgray;
}
.strex-content .tc-reveal .strex-close {
color: <<colour foreground>>;
}
.strex-content {
color: #c44;
display:inline;
-webkit-animation: expandtext 0.1s ease 0s running;
animation-name: expandtext;
animation-duration: 0.1s;
animation-timing-function: ease;
animation-delay: 0s;
animation-iteration-count: 1;
animation-direction: normal;
}
.strex-content .tc-reveal .strex-content {
color: #766;
}
/* * * * * * * * * * * *
** MODIFICATION
* * * * * * * * * * * * */
.stretch-open {
display: inline-block;
padding: 0px 3px;
margin: 0px -3px;
background: rgba(137, 137, 137, 0.2) none repeat scroll 0% 0%;}
/* * * * * * * * * * * *
** Footnotes with Numbers
* * * * * * * * * * * * */
/* Footer Collection with Numbers */
footer.footnotes {
counter-reset: fnotenr; /* set counter to 0 */
}
.footnotes p span.summary span {
counter-increment: fnotenr; /* counter +1 */
}
.footnotes p span.summary span p::before {
content: counter(fnotenr); /* Display the counter */
font-size: xx-small;
vertical-align: top;
line-height: 1.5;
margin-left: -1em;
}
.footnotes p span.summary span p {
padding: 0.175em 0 0 0;
margin: 0;
}
/* * * * * * * * * * * *
** Special Styles
* * * * * * * * * * * * */
/* hidden parts */
.strex-content.nocontent, .strex-start.nostart, .strex-end.noend, .strex-close.noclose {
display: none;
}
/* standard text color */
.strex-content.standardcolor {
color: <<colour foreground>>;
}
/* block */
.strex-content.block, .strex-inner.blockinner,
.strex-container.blockcontainer {
display: block;
}
/* hint */
.strex-inner.hint {
position: absolute;
min-width: 220px;
background-color: rgb(252, 254, 211);
border: 1px solid black;
box-shadow: 5px 5px 10px #aaa;
padding: 15px 13px 12px 15px;
margin: 24px 0 0 -5px;
z-index: 998;
}
.strexXX-inner.hint {
display: block;
}
.strex-start.hint {
letter-spacing: -0.5em;
color: rgba(1,1,1,0) !important;
background-color: transparent;
border: 0;
position: absolute;
padding: 0 6px 3px;
right: 10px;
top: 5px;
}
.strex-inner.hint button::before {
content: " ×";
font-size: 1.2em;
color: <<colour tiddler-link-foreground>>;
}
.strex-content.hint {
padding-right: 10px;
}
/* note top right */
.strex-inner.note {
background-color: rgb(252, 254, 211);
border: 1px solid black;
box-shadow: 5px 5px 10px #aaa;
display: block;
min-width: 220px;
padding: 26px 10px 15px 15px;
position: fixed;
right: 5%;
top: 5%;
z-index: 998;
}
.strex-start.note {
position: absolute;
padding: 0 6px 3px;
right: 5px;
top: 5px;
}
.strex-content.note {
padding-right: 10px;
}
/* note flex */
.strex-inner.noteflex {
background-color: rgb(252, 254, 211);
border: 1px solid black;
box-shadow: 5px 5px 10px #aaa;
display: flex;
flex-flow: column wrap;
min-width: 220px;
padding: 10px 15px 15px 15px;
position: fixed;
right: 5%;
top: 5%;
z-index: 999;
justify-content: center;
}
.strex-start.noteflex {
display: flex;
order: 2;
margin: 10px auto 1px;
order: 2;
padding: 3px 10px 5px;
}
.strex-content.noteflex {
display: flex;
order: 1;
margin-top: 8px;
width: 100%;
}
/* * * * * * * * * * * *
** Footnote Styles for Numbers
* * * * * * * * * * * * */
body {
counter-reset: notenr; /* set counter to 0 */
}
.strex-container.numbers {
counter-increment: notenr; /* counter +1 */
}
button.strex-open.numbers::before {
content: counter(notenr); /* Display the counter */font-size:xx-small; vertical-align:top;}
}
/* * * * * * * * * * * *
** stretch animation
* * * * * * * * * * * * */
@keyframes expandtext {
0% {
letter-spacing: -0.48em;
rotateY(88deg);
opacity: 0;
}
70.0% {
opacity: 0.35;
}
100.0% {
letter-spacing: 0;
rotateY(0deg);
opacity: 1;
}
}
@-webkit-keyframes expandtext {
0% {
letter-spacing: -0.48em;
rotateY(88deg);
opacity: 0;
}
100.0% {
letter-spacing: 0;
rotateY(0deg);
opacity: 1;
}
}
/*\
title: $:/core/modules/macros/HashStr.js
type: application/javascript
module-type: macro
Generate a numeric hash from a string
uses $:/core/modules/utils/utils.js
\*/
(function(){
/*jslint node: true, browser: true */
/*global $tw: false */
"use strict";
/*
Information about this macro
*/
exports.name = "HashStr";
exports.params = [
{name: "str"}
];
/*
Run the macro
*/
exports.run = function(str) {
var hash = $tw.utils.hashString(str);
return hash;
};
})();
$:/core/ui/SideBar/Recent
<div class="tc-control-panel">
<<tabs "[all[shadows+tiddlers]tag[$:/tags/ControlPanel]!has[draft.of]]" "$:/core/ui/ControlPanel/Info">>
</div>
{
"tiddlers": {
"$:/Acknowledgements": {
"title": "$:/Acknowledgements",
"text": "TiddlyWiki incorporates code from these fine OpenSource projects:\n\n* [[The Stanford Javascript Crypto Library|http://bitwiseshiftleft.github.io/sjcl/]]\n* [[The Jasmine JavaScript Test Framework|http://pivotal.github.io/jasmine/]]\n* [[Normalize.css by Nicolas Gallagher|http://necolas.github.io/normalize.css/]]\n\nAnd media from these projects:\n\n* World flag icons from [[Wikipedia|http://commons.wikimedia.org/wiki/Category:SVG_flags_by_country]]\n"
},
"$:/core/copyright.txt": {
"title": "$:/core/copyright.txt",
"type": "text/plain",
"text": "TiddlyWiki created by Jeremy Ruston, (jeremy [at] jermolene [dot] com)\n\nCopyright (c) 2004-2007, Jeremy Ruston\nCopyright (c) 2007-2020, UnaMesa Association\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 'AS IS'\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."
},
"$:/core/icon": {
"title": "$:/core/icon",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" viewBox=\"0 0 128 128\"><path d=\"M64 0l54.56 32v64L64 128 9.44 96V32L64 0zm21.127 95.408c-3.578-.103-5.15-.094-6.974-3.152l-1.42.042c-1.653-.075-.964-.04-2.067-.097-1.844-.07-1.548-1.86-1.873-2.8-.52-3.202.687-6.43.65-9.632-.014-1.14-1.593-5.17-2.157-6.61-1.768.34-3.546.406-5.34.497-4.134-.01-8.24-.527-12.317-1.183-.8 3.35-3.16 8.036-1.21 11.44 2.37 3.52 4.03 4.495 6.61 4.707 2.572.212 3.16 3.18 2.53 4.242-.55.73-1.52.864-2.346 1.04l-1.65.08c-1.296-.046-2.455-.404-3.61-.955-1.93-1.097-3.925-3.383-5.406-5.024.345.658.55 1.938.24 2.53-.878 1.27-4.665 1.26-6.4.47-1.97-.89-6.73-7.162-7.468-11.86 1.96-3.78 4.812-7.07 6.255-11.186-3.146-2.05-4.83-5.384-4.61-9.16l.08-.44c-3.097.59-1.49.37-4.82.628-10.608-.032-19.935-7.37-14.68-18.774.34-.673.664-1.287 1.243-.994.466.237.4 1.18.166 2.227-3.005 13.627 11.67 13.732 20.69 11.21.89-.25 2.67-1.936 3.905-2.495 2.016-.91 4.205-1.282 6.376-1.55 5.4-.63 11.893 2.276 15.19 2.37 3.3.096 7.99-.805 10.87-.615 2.09.098 4.143.483 6.16 1.03 1.306-6.49 1.4-11.27 4.492-12.38 1.814.293 3.213 2.818 4.25 4.167 2.112-.086 4.12.46 6.115 1.066 3.61-.522 6.642-2.593 9.833-4.203-3.234 2.69-3.673 7.075-3.303 11.127.138 2.103-.444 4.386-1.164 6.54-1.348 3.507-3.95 7.204-6.97 7.014-1.14-.036-1.805-.695-2.653-1.4-.164 1.427-.81 2.7-1.434 3.96-1.44 2.797-5.203 4.03-8.687 7.016-3.484 2.985 1.114 13.65 2.23 15.594 1.114 1.94 4.226 2.652 3.02 4.406-.37.58-.936.785-1.54 1.01l-.82.11zm-40.097-8.85l.553.14c.694-.27 2.09.15 2.83.353-1.363-1.31-3.417-3.24-4.897-4.46-.485-1.47-.278-2.96-.174-4.46l.02-.123c-.582 1.205-1.322 2.376-1.72 3.645-.465 1.71 2.07 3.557 3.052 4.615l.336.3z\" fill-rule=\"evenodd\"/></svg>"
},
"$:/core/images/add-comment": {
"title": "$:/core/images/add-comment",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-add-comment tc-image-button\" viewBox=\"0 0 128 128\"><path fill-rule=\"evenodd\" d=\"M56 56H36a8 8 0 100 16h20v20a8 8 0 1016 0V72h20a8 8 0 100-16H72V36a8 8 0 10-16 0v20zm-12.595 58.362c-6.683 7.659-20.297 12.903-36.006 12.903-2.196 0-4.35-.102-6.451-.3 9.652-3.836 17.356-12.24 21.01-22.874C8.516 94.28 0 79.734 0 63.5 0 33.953 28.206 10 63 10s63 23.953 63 53.5S97.794 117 63 117c-6.841 0-13.428-.926-19.595-2.638z\"/></svg>"
},
"$:/core/images/advanced-search-button": {
"title": "$:/core/images/advanced-search-button",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-advanced-search-button tc-image-button\" viewBox=\"0 0 128 128\"><g fill-rule=\"evenodd\"><path d=\"M74.565 87.985A47.776 47.776 0 0148 96C21.49 96 0 74.51 0 48S21.49 0 48 0s48 21.49 48 48c0 9.854-2.97 19.015-8.062 26.636l34.347 34.347a9.443 9.443 0 010 13.36 9.446 9.446 0 01-13.36 0l-34.36-34.358zM48 80c17.673 0 32-14.327 32-32 0-17.673-14.327-32-32-32-17.673 0-32 14.327-32 32 0 17.673 14.327 32 32 32z\"/><circle cx=\"48\" cy=\"48\" r=\"8\"/><circle cx=\"28\" cy=\"48\" r=\"8\"/><circle cx=\"68\" cy=\"48\" r=\"8\"/></g></svg>"
},
"$:/core/images/auto-height": {
"title": "$:/core/images/auto-height",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-auto-height tc-image-button\" viewBox=\"0 0 128 128\"><path d=\"M67.987 114.356l-.029-14.477a4 4 0 00-2.067-3.494l-15.966-8.813-1.933 7.502H79.9c4.222 0 5.564-5.693 1.786-7.58L49.797 71.572 48.01 79.15h31.982c4.217 0 5.564-5.682 1.795-7.575L49.805 55.517l-1.795 7.575h31.982c4.212 0 5.563-5.67 1.805-7.57l-16.034-8.105 2.195 3.57V35.614l9.214 9.213a4 4 0 105.656-5.656l-16-16a4 4 0 00-5.656 0l-16 16a4 4 0 105.656 5.656l9.13-9.13v15.288a4 4 0 002.195 3.57l16.035 8.106 1.804-7.57H48.01c-4.217 0-5.564 5.682-1.795 7.574l31.982 16.059 1.795-7.575H48.01c-4.222 0-5.564 5.693-1.787 7.579l31.89 15.923 1.787-7.578H47.992c-4.133 0-5.552 5.504-1.933 7.501l15.966 8.813-2.067-3.494.029 14.436-9.159-9.158a4 4 0 00-5.656 5.656l16 16a4 4 0 005.656 0l16-16a4 4 0 10-5.656-5.656l-9.185 9.184zM16 20h96a4 4 0 100-8H16a4 4 0 100 8z\"/></svg>"
},
"$:/core/images/blank": {
"title": "$:/core/images/blank",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-blank tc-image-button\" viewBox=\"0 0 128 128\"/>"
},
"$:/core/images/bold": {
"title": "$:/core/images/bold",
"tags": "$:/tags/Image",
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"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-twitter tc-image-button\" viewBox=\"0 0 128 128\"><path fill-rule=\"evenodd\" d=\"M41.626 115.803A73.376 73.376 0 012 104.235c2.022.238 4.08.36 6.166.36 12.111 0 23.258-4.117 32.105-11.023-11.312-.208-20.859-7.653-24.148-17.883a25.98 25.98 0 0011.674-.441C15.971 72.881 7.061 62.474 7.061 49.997c0-.108 0-.216.002-.323a25.824 25.824 0 0011.709 3.22c-6.936-4.617-11.5-12.5-11.5-21.433 0-4.719 1.274-9.142 3.5-12.945 12.75 15.579 31.797 25.83 53.281 26.904-.44-1.884-.67-3.85-.67-5.868 0-14.22 11.575-25.75 25.852-25.75a25.865 25.865 0 0118.869 8.132 51.892 51.892 0 0016.415-6.248c-1.93 6.012-6.029 11.059-11.366 14.246A51.844 51.844 0 00128 25.878a52.428 52.428 0 01-12.9 13.33c.05 1.104.075 2.214.075 3.33 0 34.028-26 73.265-73.549 73.265\"/></svg>"
},
"$:/core/images/underline": {
"title": "$:/core/images/underline",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-underline tc-image-button\" viewBox=\"0 0 128 128\"><path fill-rule=\"evenodd\" d=\"M7 117.421h114.248V128H7v-10.579zm97.871-18.525V0h-16.26v55.856c0 4.463-.605 8.576-1.816 12.338-1.212 3.762-3.03 7.046-5.452 9.851-2.423 2.806-5.452 4.974-9.086 6.504-3.635 1.53-7.939 2.296-12.912 2.296-6.25 0-11.159-1.786-14.73-5.356-3.57-3.571-5.356-8.417-5.356-14.538V0H23v65.038c0 5.356.542 10.234 1.626 14.633 1.084 4.4 2.965 8.194 5.643 11.382 2.678 3.188 6.185 5.643 10.52 7.365 4.337 1.721 9.756 2.582 16.26 2.582 7.27 0 13.582-1.435 18.938-4.304 5.356-2.87 9.755-7.365 13.199-13.486h.382v15.686h15.303z\"/></svg>"
},
"$:/core/images/unfold-all-button": {
"title": "$:/core/images/unfold-all-button",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-unfold-all tc-image-button\" viewBox=\"0 0 128 128\"><g fill-rule=\"evenodd\"><rect width=\"128\" height=\"16\" rx=\"8\"/><rect width=\"128\" height=\"16\" y=\"64\" rx=\"8\"/><path d=\"M63.945 60.624c-2.05 0-4.101-.78-5.666-2.345L35.662 35.662c-3.125-3.125-3.13-8.195-.005-11.319 3.118-3.118 8.192-3.122 11.319.005L63.94 41.314l16.966-16.966c3.124-3.124 8.194-3.129 11.318-.005 3.118 3.118 3.122 8.192-.005 11.319L69.603 58.279a7.986 7.986 0 01-5.663 2.346zM64.004 124.565c-2.05 0-4.102-.78-5.666-2.345L35.721 99.603c-3.125-3.125-3.13-8.195-.005-11.319 3.118-3.118 8.191-3.122 11.318.005L64 105.255l16.966-16.966c3.124-3.124 8.194-3.129 11.318-.005 3.118 3.118 3.122 8.192-.005 11.319L69.662 122.22a7.986 7.986 0 01-5.663 2.346z\"/></g></svg>"
},
"$:/core/images/unfold-button": {
"title": "$:/core/images/unfold-button",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-unfold tc-image-button\" viewBox=\"0 0 128 128\"><g fill-rule=\"evenodd\"><rect width=\"128\" height=\"16\" rx=\"8\"/><path d=\"M63.945 63.624c-2.05 0-4.101-.78-5.666-2.345L35.662 38.662c-3.125-3.125-3.13-8.195-.005-11.319 3.118-3.118 8.192-3.122 11.319.005L63.94 44.314l16.966-16.966c3.124-3.124 8.194-3.129 11.318-.005 3.118 3.118 3.122 8.192-.005 11.319L69.603 61.279a7.986 7.986 0 01-5.663 2.346zM64.004 105.682c-2.05.001-4.102-.78-5.666-2.344L35.721 80.721c-3.125-3.125-3.13-8.195-.005-11.319 3.118-3.118 8.191-3.122 11.318.005L64 86.373l16.966-16.966c3.124-3.125 8.194-3.13 11.318-.005 3.118 3.118 3.122 8.192-.005 11.319l-22.617 22.617a7.986 7.986 0 01-5.663 2.346z\"/></g></svg>"
},
"$:/core/images/unlocked-padlock": {
"title": "$:/core/images/unlocked-padlock",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-unlocked-padlock tc-image-button\" viewBox=\"0 0 128 128\"><path fill-rule=\"evenodd\" d=\"M48.627 64H105v32.01C105 113.674 90.674 128 73.001 128H56C38.318 128 24 113.677 24 96.01V64h6.136c-10.455-12.651-27.364-35.788-4.3-55.142 24.636-20.672 45.835 4.353 55.777 16.201 9.943 11.85-2.676 22.437-12.457 9.892-9.78-12.545-21.167-24.146-33.207-14.043-12.041 10.104-1.757 22.36 8.813 34.958 2.467 2.94 3.641 5.732 3.865 8.134zm19.105 28.364A8.503 8.503 0 0064.5 76a8.5 8.5 0 00-3.498 16.25l-5.095 22.77H72.8l-5.07-22.656z\"/></svg>"
},
"$:/core/images/up-arrow": {
"title": "$:/core/images/up-arrow",
"created": "20150316000544368",
"modified": "20150316000831867",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-up-arrow tc-image-button\" viewBox=\"0 0 128 128\"><path d=\"M63.892.281c2.027 0 4.054.77 5.6 2.316l55.98 55.98a7.92 7.92 0 010 11.196c-3.086 3.085-8.104 3.092-11.196 0L63.894 19.393 13.513 69.774a7.92 7.92 0 01-11.196 0c-3.085-3.086-3.092-8.105 0-11.196l55.98-55.98A7.892 7.892 0 0163.893.28z\"/></svg>"
},
"$:/core/images/video": {
"title": "$:/core/images/video",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-video tc-image-button\" viewBox=\"0 0 128 128\"><path fill-rule=\"evenodd\" d=\"M64 12c-34.91 0-55.273 2.917-58.182 5.833C2.91 20.75 0 41.167 0 64.5c0 23.333 2.91 43.75 5.818 46.667C8.728 114.083 29.091 117 64 117c34.91 0 55.273-2.917 58.182-5.833C125.09 108.25 128 87.833 128 64.5c0-23.333-2.91-43.75-5.818-46.667C119.272 14.917 98.909 12 64 12zm-9.084 32.618c-3.813-2.542-6.905-.879-6.905 3.698v31.368c0 4.585 3.099 6.235 6.905 3.698l22.168-14.779c3.813-2.542 3.806-6.669 0-9.206L54.916 44.618z\"/></svg>"
},
"$:/core/images/warning": {
"title": "$:/core/images/warning",
"tags": "$:/tags/Image",
"text": "<svg width=\"22pt\" height=\"22pt\" class=\"tc-image-warning tc-image-button\" viewBox=\"0 0 128 128\"><path fill-rule=\"evenodd\" d=\"M57.072 11c3.079-5.333 10.777-5.333 13.856 0l55.426 96c3.079 5.333-.77 12-6.928 12H8.574c-6.158 0-10.007-6.667-6.928-12l55.426-96zM64 37c-4.418 0-8 3.582-8 7.994v28.012C56 77.421 59.59 81 64 81c4.418 0 8-3.582 8-7.994V44.994C72 40.579 68.41 37 64 37zm0 67a8 8 0 100-16 8 8 0 000 16z\"/></svg>"
},
"$:/language/Buttons/AdvancedSearch/Caption": {
"title": "$:/language/Buttons/AdvancedSearch/Caption",
"text": "advanced search"
},
"$:/language/Buttons/AdvancedSearch/Hint": {
"title": "$:/language/Buttons/AdvancedSearch/Hint",
"text": "Advanced search"
},
"$:/language/Buttons/Cancel/Caption": {
"title": "$:/language/Buttons/Cancel/Caption",
"text": "cancel"
},
"$:/language/Buttons/Cancel/Hint": {
"title": "$:/language/Buttons/Cancel/Hint",
"text": "Discard changes to this tiddler"
},
"$:/language/Buttons/Clone/Caption": {
"title": "$:/language/Buttons/Clone/Caption",
"text": "clone"
},
"$:/language/Buttons/Clone/Hint": {
"title": "$:/language/Buttons/Clone/Hint",
"text": "Clone this tiddler"
},
"$:/language/Buttons/Close/Caption": {
"title": "$:/language/Buttons/Close/Caption",
"text": "close"
},
"$:/language/Buttons/Close/Hint": {
"title": "$:/language/Buttons/Close/Hint",
"text": "Close this tiddler"
},
"$:/language/Buttons/CloseAll/Caption": {
"title": "$:/language/Buttons/CloseAll/Caption",
"text": "close all"
},
"$:/language/Buttons/CloseAll/Hint": {
"title": "$:/language/Buttons/CloseAll/Hint",
"text": "Close all tiddlers"
},
"$:/language/Buttons/CloseOthers/Caption": {
"title": "$:/language/Buttons/CloseOthers/Caption",
"text": "close others"
},
"$:/language/Buttons/CloseOthers/Hint": {
"title": "$:/language/Buttons/CloseOthers/Hint",
"text": "Close other tiddlers"
},
"$:/language/Buttons/ControlPanel/Caption": {
"title": "$:/language/Buttons/ControlPanel/Caption",
"text": "control panel"
},
"$:/language/Buttons/ControlPanel/Hint": {
"title": "$:/language/Buttons/ControlPanel/Hint",
"text": "Open control panel"
},
"$:/language/Buttons/CopyToClipboard/Caption": {
"title": "$:/language/Buttons/CopyToClipboard/Caption",
"text": "copy to clipboard"
},
"$:/language/Buttons/CopyToClipboard/Hint": {
"title": "$:/language/Buttons/CopyToClipboard/Hint",
"text": "Copy this text to the clipboard"
},
"$:/language/Buttons/Delete/Caption": {
"title": "$:/language/Buttons/Delete/Caption",
"text": "delete"
},
"$:/language/Buttons/Delete/Hint": {
"title": "$:/language/Buttons/Delete/Hint",
"text": "Delete this tiddler"
},
"$:/language/Buttons/Edit/Caption": {
"title": "$:/language/Buttons/Edit/Caption",
"text": "edit"
},
"$:/language/Buttons/Edit/Hint": {
"title": "$:/language/Buttons/Edit/Hint",
"text": "Edit this tiddler"
},
"$:/language/Buttons/Encryption/Caption": {
"title": "$:/language/Buttons/Encryption/Caption",
"text": "encryption"
},
"$:/language/Buttons/Encryption/Hint": {
"title": "$:/language/Buttons/Encryption/Hint",
"text": "Set or clear a password for saving this wiki"
},
"$:/language/Buttons/Encryption/ClearPassword/Caption": {
"title": "$:/language/Buttons/Encryption/ClearPassword/Caption",
"text": "clear password"
},
"$:/language/Buttons/Encryption/ClearPassword/Hint": {
"title": "$:/language/Buttons/Encryption/ClearPassword/Hint",
"text": "Clear the password and save this wiki without encryption"
},
"$:/language/Buttons/Encryption/SetPassword/Caption": {
"title": "$:/language/Buttons/Encryption/SetPassword/Caption",
"text": "set password"
},
"$:/language/Buttons/Encryption/SetPassword/Hint": {
"title": "$:/language/Buttons/Encryption/SetPassword/Hint",
"text": "Set a password for saving this wiki with encryption"
},
"$:/language/Buttons/ExportPage/Caption": {
"title": "$:/language/Buttons/ExportPage/Caption",
"text": "export all"
},
"$:/language/Buttons/ExportPage/Hint": {
"title": "$:/language/Buttons/ExportPage/Hint",
"text": "Export all tiddlers"
},
"$:/language/Buttons/ExportTiddler/Caption": {
"title": "$:/language/Buttons/ExportTiddler/Caption",
"text": "export tiddler"
},
"$:/language/Buttons/ExportTiddler/Hint": {
"title": "$:/language/Buttons/ExportTiddler/Hint",
"text": "Export tiddler"
},
"$:/language/Buttons/ExportTiddlers/Caption": {
"title": "$:/language/Buttons/ExportTiddlers/Caption",
"text": "export tiddlers"
},
"$:/language/Buttons/ExportTiddlers/Hint": {
"title": "$:/language/Buttons/ExportTiddlers/Hint",
"text": "Export tiddlers"
},
"$:/language/Buttons/SidebarSearch/Hint": {
"title": "$:/language/Buttons/SidebarSearch/Hint",
"text": "Select the sidebar search field"
},
"$:/language/Buttons/Fold/Caption": {
"title": "$:/language/Buttons/Fold/Caption",
"text": "fold tiddler"
},
"$:/language/Buttons/Fold/Hint": {
"title": "$:/language/Buttons/Fold/Hint",
"text": "Fold the body of this tiddler"
},
"$:/language/Buttons/Fold/FoldBar/Caption": {
"title": "$:/language/Buttons/Fold/FoldBar/Caption",
"text": "fold-bar"
},
"$:/language/Buttons/Fold/FoldBar/Hint": {
"title": "$:/language/Buttons/Fold/FoldBar/Hint",
"text": "Optional bars to fold and unfold tiddlers"
},
"$:/language/Buttons/Unfold/Caption": {
"title": "$:/language/Buttons/Unfold/Caption",
"text": "unfold tiddler"
},
"$:/language/Buttons/Unfold/Hint": {
"title": "$:/language/Buttons/Unfold/Hint",
"text": "Unfold the body of this tiddler"
},
"$:/language/Buttons/FoldOthers/Caption": {
"title": "$:/language/Buttons/FoldOthers/Caption",
"text": "fold other tiddlers"
},
"$:/language/Buttons/FoldOthers/Hint": {
"title": "$:/language/Buttons/FoldOthers/Hint",
"text": "Fold the bodies of other opened tiddlers"
},
"$:/language/Buttons/FoldAll/Caption": {
"title": "$:/language/Buttons/FoldAll/Caption",
"text": "fold all tiddlers"
},
"$:/language/Buttons/FoldAll/Hint": {
"title": "$:/language/Buttons/FoldAll/Hint",
"text": "Fold the bodies of all opened tiddlers"
},
"$:/language/Buttons/UnfoldAll/Caption": {
"title": "$:/language/Buttons/UnfoldAll/Caption",
"text": "unfold all tiddlers"
},
"$:/language/Buttons/UnfoldAll/Hint": {
"title": "$:/language/Buttons/UnfoldAll/Hint",
"text": "Unfold the bodies of all opened tiddlers"
},
"$:/language/Buttons/FullScreen/Caption": {
"title": "$:/language/Buttons/FullScreen/Caption",
"text": "full-screen"
},
"$:/language/Buttons/FullScreen/Hint": {
"title": "$:/language/Buttons/FullScreen/Hint",
"text": "Enter or leave full-screen mode"
},
"$:/language/Buttons/Help/Caption": {
"title": "$:/language/Buttons/Help/Caption",
"text": "help"
},
"$:/language/Buttons/Help/Hint": {
"title": "$:/language/Buttons/Help/Hint",
"text": "Show help panel"
},
"$:/language/Buttons/Import/Caption": {
"title": "$:/language/Buttons/Import/Caption",
"text": "import"
},
"$:/language/Buttons/Import/Hint": {
"title": "$:/language/Buttons/Import/Hint",
"text": "Import many types of file including text, image, TiddlyWiki or JSON"
},
"$:/language/Buttons/Info/Caption": {
"title": "$:/language/Buttons/Info/Caption",
"text": "info"
},
"$:/language/Buttons/Info/Hint": {
"title": "$:/language/Buttons/Info/Hint",
"text": "Show information for this tiddler"
},
"$:/language/Buttons/Home/Caption": {
"title": "$:/language/Buttons/Home/Caption",
"text": "home"
},
"$:/language/Buttons/Home/Hint": {
"title": "$:/language/Buttons/Home/Hint",
"text": "Open the default tiddlers"
},
"$:/language/Buttons/Language/Caption": {
"title": "$:/language/Buttons/Language/Caption",
"text": "language"
},
"$:/language/Buttons/Language/Hint": {
"title": "$:/language/Buttons/Language/Hint",
"text": "Choose the user interface language"
},
"$:/language/Buttons/Manager/Caption": {
"title": "$:/language/Buttons/Manager/Caption",
"text": "tiddler manager"
},
"$:/language/Buttons/Manager/Hint": {
"title": "$:/language/Buttons/Manager/Hint",
"text": "Open tiddler manager"
},
"$:/language/Buttons/More/Caption": {
"title": "$:/language/Buttons/More/Caption",
"text": "more"
},
"$:/language/Buttons/More/Hint": {
"title": "$:/language/Buttons/More/Hint",
"text": "More actions"
},
"$:/language/Buttons/NewHere/Caption": {
"title": "$:/language/Buttons/NewHere/Caption",
"text": "new here"
},
"$:/language/Buttons/NewHere/Hint": {
"title": "$:/language/Buttons/NewHere/Hint",
"text": "Create a new tiddler tagged with this one"
},
"$:/language/Buttons/NewJournal/Caption": {
"title": "$:/language/Buttons/NewJournal/Caption",
"text": "new journal"
},
"$:/language/Buttons/NewJournal/Hint": {
"title": "$:/language/Buttons/NewJournal/Hint",
"text": "Create a new journal tiddler"
},
"$:/language/Buttons/NewJournalHere/Caption": {
"title": "$:/language/Buttons/NewJournalHere/Caption",
"text": "new journal here"
},
"$:/language/Buttons/NewJournalHere/Hint": {
"title": "$:/language/Buttons/NewJournalHere/Hint",
"text": "Create a new journal tiddler tagged with this one"
},
"$:/language/Buttons/NewImage/Caption": {
"title": "$:/language/Buttons/NewImage/Caption",
"text": "new image"
},
"$:/language/Buttons/NewImage/Hint": {
"title": "$:/language/Buttons/NewImage/Hint",
"text": "Create a new image tiddler"
},
"$:/language/Buttons/NewMarkdown/Caption": {
"title": "$:/language/Buttons/NewMarkdown/Caption",
"text": "new Markdown tiddler"
},
"$:/language/Buttons/NewMarkdown/Hint": {
"title": "$:/language/Buttons/NewMarkdown/Hint",
"text": "Create a new Markdown tiddler"
},
"$:/language/Buttons/NewTiddler/Caption": {
"title": "$:/language/Buttons/NewTiddler/Caption",
"text": "new tiddler"
},
"$:/language/Buttons/NewTiddler/Hint": {
"title": "$:/language/Buttons/NewTiddler/Hint",
"text": "Create a new tiddler"
},
"$:/language/Buttons/OpenWindow/Caption": {
"title": "$:/language/Buttons/OpenWindow/Caption",
"text": "open in new window"
},
"$:/language/Buttons/OpenWindow/Hint": {
"title": "$:/language/Buttons/OpenWindow/Hint",
"text": "Open tiddler in new window"
},
"$:/language/Buttons/Palette/Caption": {
"title": "$:/language/Buttons/Palette/Caption",
"text": "palette"
},
"$:/language/Buttons/Palette/Hint": {
"title": "$:/language/Buttons/Palette/Hint",
"text": "Choose the colour palette"
},
"$:/language/Buttons/Permalink/Caption": {
"title": "$:/language/Buttons/Permalink/Caption",
"text": "permalink"
},
"$:/language/Buttons/Permalink/Hint": {
"title": "$:/language/Buttons/Permalink/Hint",
"text": "Set browser address bar to a direct link to this tiddler"
},
"$:/language/Buttons/Permaview/Caption": {
"title": "$:/language/Buttons/Permaview/Caption",
"text": "permaview"
},
"$:/language/Buttons/Permaview/Hint": {
"title": "$:/language/Buttons/Permaview/Hint",
"text": "Set browser address bar to a direct link to all the tiddlers in this story"
},
"$:/language/Buttons/Print/Caption": {
"title": "$:/language/Buttons/Print/Caption",
"text": "print page"
},
"$:/language/Buttons/Print/Hint": {
"title": "$:/language/Buttons/Print/Hint",
"text": "Print the current page"
},
"$:/language/Buttons/Refresh/Caption": {
"title": "$:/language/Buttons/Refresh/Caption",
"text": "refresh"
},
"$:/language/Buttons/Refresh/Hint": {
"title": "$:/language/Buttons/Refresh/Hint",
"text": "Perform a full refresh of the wiki"
},
"$:/language/Buttons/Save/Caption": {
"title": "$:/language/Buttons/Save/Caption",
"text": "ok"
},
"$:/language/Buttons/Save/Hint": {
"title": "$:/language/Buttons/Save/Hint",
"text": "Confirm changes to this tiddler"
},
"$:/language/Buttons/SaveWiki/Caption": {
"title": "$:/language/Buttons/SaveWiki/Caption",
"text": "save changes"
},
"$:/language/Buttons/SaveWiki/Hint": {
"title": "$:/language/Buttons/SaveWiki/Hint",
"text": "Save changes"
},
"$:/language/Buttons/StoryView/Caption": {
"title": "$:/language/Buttons/StoryView/Caption",
"text": "storyview"
},
"$:/language/Buttons/StoryView/Hint": {
"title": "$:/language/Buttons/StoryView/Hint",
"text": "Choose the story visualisation"
},
"$:/language/Buttons/HideSideBar/Caption": {
"title": "$:/language/Buttons/HideSideBar/Caption",
"text": "hide sidebar"
},
"$:/language/Buttons/HideSideBar/Hint": {
"title": "$:/language/Buttons/HideSideBar/Hint",
"text": "Hide sidebar"
},
"$:/language/Buttons/ShowSideBar/Caption": {
"title": "$:/language/Buttons/ShowSideBar/Caption",
"text": "show sidebar"
},
"$:/language/Buttons/ShowSideBar/Hint": {
"title": "$:/language/Buttons/ShowSideBar/Hint",
"text": "Show sidebar"
},
"$:/language/Buttons/TagManager/Caption": {
"title": "$:/language/Buttons/TagManager/Caption",
"text": "tag manager"
},
"$:/language/Buttons/TagManager/Hint": {
"title": "$:/language/Buttons/TagManager/Hint",
"text": "Open tag manager"
},
"$:/language/Buttons/Timestamp/Caption": {
"title": "$:/language/Buttons/Timestamp/Caption",
"text": "timestamps"
},
"$:/language/Buttons/Timestamp/Hint": {
"title": "$:/language/Buttons/Timestamp/Hint",
"text": "Choose whether modifications update timestamps"
},
"$:/language/Buttons/Timestamp/On/Caption": {
"title": "$:/language/Buttons/Timestamp/On/Caption",
"text": "timestamps are on"
},
"$:/language/Buttons/Timestamp/On/Hint": {
"title": "$:/language/Buttons/Timestamp/On/Hint",
"text": "Update timestamps when tiddlers are modified"
},
"$:/language/Buttons/Timestamp/Off/Caption": {
"title": "$:/language/Buttons/Timestamp/Off/Caption",
"text": "timestamps are off"
},
"$:/language/Buttons/Timestamp/Off/Hint": {
"title": "$:/language/Buttons/Timestamp/Off/Hint",
"text": "Don't update timestamps when tiddlers are modified"
},
"$:/language/Buttons/Theme/Caption": {
"title": "$:/language/Buttons/Theme/Caption",
"text": "theme"
},
"$:/language/Buttons/Theme/Hint": {
"title": "$:/language/Buttons/Theme/Hint",
"text": "Choose the display theme"
},
"$:/language/Buttons/Bold/Caption": {
"title": "$:/language/Buttons/Bold/Caption",
"text": "bold"
},
"$:/language/Buttons/Bold/Hint": {
"title": "$:/language/Buttons/Bold/Hint",
"text": "Apply bold formatting to selection"
},
"$:/language/Buttons/Clear/Caption": {
"title": "$:/language/Buttons/Clear/Caption",
"text": "clear"
},
"$:/language/Buttons/Clear/Hint": {
"title": "$:/language/Buttons/Clear/Hint",
"text": "Clear image to solid colour"
},
"$:/language/Buttons/EditorHeight/Caption": {
"title": "$:/language/Buttons/EditorHeight/Caption",
"text": "editor height"
},
"$:/language/Buttons/EditorHeight/Caption/Auto": {
"title": "$:/language/Buttons/EditorHeight/Caption/Auto",
"text": "Automatically adjust height to fit content"
},
"$:/language/Buttons/EditorHeight/Caption/Fixed": {
"title": "$:/language/Buttons/EditorHeight/Caption/Fixed",
"text": "Fixed height:"
},
"$:/language/Buttons/EditorHeight/Hint": {
"title": "$:/language/Buttons/EditorHeight/Hint",
"text": "Choose the height of the text editor"
},
"$:/language/Buttons/Excise/Caption": {
"title": "$:/language/Buttons/Excise/Caption",
"text": "excise"
},
"$:/language/Buttons/Excise/Caption/Excise": {
"title": "$:/language/Buttons/Excise/Caption/Excise",
"text": "Perform excision"
},
"$:/language/Buttons/Excise/Caption/MacroName": {
"title": "$:/language/Buttons/Excise/Caption/MacroName",
"text": "Macro name:"
},
"$:/language/Buttons/Excise/Caption/NewTitle": {
"title": "$:/language/Buttons/Excise/Caption/NewTitle",
"text": "Title of new tiddler:"
},
"$:/language/Buttons/Excise/Caption/Replace": {
"title": "$:/language/Buttons/Excise/Caption/Replace",
"text": "Replace excised text with:"
},
"$:/language/Buttons/Excise/Caption/Replace/Macro": {
"title": "$:/language/Buttons/Excise/Caption/Replace/Macro",
"text": "macro"
},
"$:/language/Buttons/Excise/Caption/Replace/Link": {
"title": "$:/language/Buttons/Excise/Caption/Replace/Link",
"text": "link"
},
"$:/language/Buttons/Excise/Caption/Replace/Transclusion": {
"title": "$:/language/Buttons/Excise/Caption/Replace/Transclusion",
"text": "transclusion"
},
"$:/language/Buttons/Excise/Caption/Tag": {
"title": "$:/language/Buttons/Excise/Caption/Tag",
"text": "Tag new tiddler with the title of this tiddler"
},
"$:/language/Buttons/Excise/Caption/TiddlerExists": {
"title": "$:/language/Buttons/Excise/Caption/TiddlerExists",
"text": "Warning: tiddler already exists"
},
"$:/language/Buttons/Excise/Hint": {
"title": "$:/language/Buttons/Excise/Hint",
"text": "Excise the selected text into a new tiddler"
},
"$:/language/Buttons/Heading1/Caption": {
"title": "$:/language/Buttons/Heading1/Caption",
"text": "heading 1"
},
"$:/language/Buttons/Heading1/Hint": {
"title": "$:/language/Buttons/Heading1/Hint",
"text": "Apply heading level 1 formatting to lines containing selection"
},
"$:/language/Buttons/Heading2/Caption": {
"title": "$:/language/Buttons/Heading2/Caption",
"text": "heading 2"
},
"$:/language/Buttons/Heading2/Hint": {
"title": "$:/language/Buttons/Heading2/Hint",
"text": "Apply heading level 2 formatting to lines containing selection"
},
"$:/language/Buttons/Heading3/Caption": {
"title": "$:/language/Buttons/Heading3/Caption",
"text": "heading 3"
},
"$:/language/Buttons/Heading3/Hint": {
"title": "$:/language/Buttons/Heading3/Hint",
"text": "Apply heading level 3 formatting to lines containing selection"
},
"$:/language/Buttons/Heading4/Caption": {
"title": "$:/language/Buttons/Heading4/Caption",
"text": "heading 4"
},
"$:/language/Buttons/Heading4/Hint": {
"title": "$:/language/Buttons/Heading4/Hint",
"text": "Apply heading level 4 formatting to lines containing selection"
},
"$:/language/Buttons/Heading5/Caption": {
"title": "$:/language/Buttons/Heading5/Caption",
"text": "heading 5"
},
"$:/language/Buttons/Heading5/Hint": {
"title": "$:/language/Buttons/Heading5/Hint",
"text": "Apply heading level 5 formatting to lines containing selection"
},
"$:/language/Buttons/Heading6/Caption": {
"title": "$:/language/Buttons/Heading6/Caption",
"text": "heading 6"
},
"$:/language/Buttons/Heading6/Hint": {
"title": "$:/language/Buttons/Heading6/Hint",
"text": "Apply heading level 6 formatting to lines containing selection"
},
"$:/language/Buttons/Italic/Caption": {
"title": "$:/language/Buttons/Italic/Caption",
"text": "italic"
},
"$:/language/Buttons/Italic/Hint": {
"title": "$:/language/Buttons/Italic/Hint",
"text": "Apply italic formatting to selection"
},
"$:/language/Buttons/LineWidth/Caption": {
"title": "$:/language/Buttons/LineWidth/Caption",
"text": "line width"
},
"$:/language/Buttons/LineWidth/Hint": {
"title": "$:/language/Buttons/LineWidth/Hint",
"text": "Set line width for painting"
},
"$:/language/Buttons/Link/Caption": {
"title": "$:/language/Buttons/Link/Caption",
"text": "link"
},
"$:/language/Buttons/Link/Hint": {
"title": "$:/language/Buttons/Link/Hint",
"text": "Create wikitext link"
},
"$:/language/Buttons/Linkify/Caption": {
"title": "$:/language/Buttons/Linkify/Caption",
"text": "wikilink"
},
"$:/language/Buttons/Linkify/Hint": {
"title": "$:/language/Buttons/Linkify/Hint",
"text": "Wrap selection in square brackets"
},
"$:/language/Buttons/ListBullet/Caption": {
"title": "$:/language/Buttons/ListBullet/Caption",
"text": "bulleted list"
},
"$:/language/Buttons/ListBullet/Hint": {
"title": "$:/language/Buttons/ListBullet/Hint",
"text": "Apply bulleted list formatting to lines containing selection"
},
"$:/language/Buttons/ListNumber/Caption": {
"title": "$:/language/Buttons/ListNumber/Caption",
"text": "numbered list"
},
"$:/language/Buttons/ListNumber/Hint": {
"title": "$:/language/Buttons/ListNumber/Hint",
"text": "Apply numbered list formatting to lines containing selection"
},
"$:/language/Buttons/MonoBlock/Caption": {
"title": "$:/language/Buttons/MonoBlock/Caption",
"text": "monospaced block"
},
"$:/language/Buttons/MonoBlock/Hint": {
"title": "$:/language/Buttons/MonoBlock/Hint",
"text": "Apply monospaced block formatting to lines containing selection"
},
"$:/language/Buttons/MonoLine/Caption": {
"title": "$:/language/Buttons/MonoLine/Caption",
"text": "monospaced"
},
"$:/language/Buttons/MonoLine/Hint": {
"title": "$:/language/Buttons/MonoLine/Hint",
"text": "Apply monospaced character formatting to selection"
},
"$:/language/Buttons/Opacity/Caption": {
"title": "$:/language/Buttons/Opacity/Caption",
"text": "opacity"
},
"$:/language/Buttons/Opacity/Hint": {
"title": "$:/language/Buttons/Opacity/Hint",
"text": "Set painting opacity"
},
"$:/language/Buttons/Paint/Caption": {
"title": "$:/language/Buttons/Paint/Caption",
"text": "paint colour"
},
"$:/language/Buttons/Paint/Hint": {
"title": "$:/language/Buttons/Paint/Hint",
"text": "Set painting colour"
},
"$:/language/Buttons/Picture/Caption": {
"title": "$:/language/Buttons/Picture/Caption",
"text": "picture"
},
"$:/language/Buttons/Picture/Hint": {
"title": "$:/language/Buttons/Picture/Hint",
"text": "Insert picture"
},
"$:/language/Buttons/Preview/Caption": {
"title": "$:/language/Buttons/Preview/Caption",
"text": "preview"
},
"$:/language/Buttons/Preview/Hint": {
"title": "$:/language/Buttons/Preview/Hint",
"text": "Show preview pane"
},
"$:/language/Buttons/PreviewType/Caption": {
"title": "$:/language/Buttons/PreviewType/Caption",
"text": "preview type"
},
"$:/language/Buttons/PreviewType/Hint": {
"title": "$:/language/Buttons/PreviewType/Hint",
"text": "Choose preview type"
},
"$:/language/Buttons/Quote/Caption": {
"title": "$:/language/Buttons/Quote/Caption",
"text": "quote"
},
"$:/language/Buttons/Quote/Hint": {
"title": "$:/language/Buttons/Quote/Hint",
"text": "Apply quoted text formatting to lines containing selection"
},
"$:/language/Buttons/RotateLeft/Caption": {
"title": "$:/language/Buttons/RotateLeft/Caption",
"text": "rotate left"
},
"$:/language/Buttons/RotateLeft/Hint": {
"title": "$:/language/Buttons/RotateLeft/Hint",
"text": "Rotate image left by 90 degrees"
},
"$:/language/Buttons/Size/Caption": {
"title": "$:/language/Buttons/Size/Caption",
"text": "image size"
},
"$:/language/Buttons/Size/Caption/Height": {
"title": "$:/language/Buttons/Size/Caption/Height",
"text": "Height:"
},
"$:/language/Buttons/Size/Caption/Resize": {
"title": "$:/language/Buttons/Size/Caption/Resize",
"text": "Resize image"
},
"$:/language/Buttons/Size/Caption/Width": {
"title": "$:/language/Buttons/Size/Caption/Width",
"text": "Width:"
},
"$:/language/Buttons/Size/Hint": {
"title": "$:/language/Buttons/Size/Hint",
"text": "Set image size"
},
"$:/language/Buttons/Stamp/Caption": {
"title": "$:/language/Buttons/Stamp/Caption",
"text": "stamp"
},
"$:/language/Buttons/Stamp/Caption/New": {
"title": "$:/language/Buttons/Stamp/Caption/New",
"text": "Add your own"
},
"$:/language/Buttons/Stamp/Hint": {
"title": "$:/language/Buttons/Stamp/Hint",
"text": "Insert a preconfigured snippet of text"
},
"$:/language/Buttons/Stamp/New/Title": {
"title": "$:/language/Buttons/Stamp/New/Title",
"text": "Name as shown in menu"
},
"$:/language/Buttons/Stamp/New/Text": {
"title": "$:/language/Buttons/Stamp/New/Text",
"text": "Text of snippet. (Remember to add a descriptive title in the caption field)."
},
"$:/language/Buttons/Strikethrough/Caption": {
"title": "$:/language/Buttons/Strikethrough/Caption",
"text": "strikethrough"
},
"$:/language/Buttons/Strikethrough/Hint": {
"title": "$:/language/Buttons/Strikethrough/Hint",
"text": "Apply strikethrough formatting to selection"
},
"$:/language/Buttons/Subscript/Caption": {
"title": "$:/language/Buttons/Subscript/Caption",
"text": "subscript"
},
"$:/language/Buttons/Subscript/Hint": {
"title": "$:/language/Buttons/Subscript/Hint",
"text": "Apply subscript formatting to selection"
},
"$:/language/Buttons/Superscript/Caption": {
"title": "$:/language/Buttons/Superscript/Caption",
"text": "superscript"
},
"$:/language/Buttons/Superscript/Hint": {
"title": "$:/language/Buttons/Superscript/Hint",
"text": "Apply superscript formatting to selection"
},
"$:/language/Buttons/ToggleSidebar/Hint": {
"title": "$:/language/Buttons/ToggleSidebar/Hint",
"text": "Toggle the sidebar visibility"
},
"$:/language/Buttons/Transcludify/Caption": {
"title": "$:/language/Buttons/Transcludify/Caption",
"text": "transclusion"
},
"$:/language/Buttons/Transcludify/Hint": {
"title": "$:/language/Buttons/Transcludify/Hint",
"text": "Wrap selection in curly brackets"
},
"$:/language/Buttons/Underline/Caption": {
"title": "$:/language/Buttons/Underline/Caption",
"text": "underline"
},
"$:/language/Buttons/Underline/Hint": {
"title": "$:/language/Buttons/Underline/Hint",
"text": "Apply underline formatting to selection"
},
"$:/language/ControlPanel/Advanced/Caption": {
"title": "$:/language/ControlPanel/Advanced/Caption",
"text": "Advanced"
},
"$:/language/ControlPanel/Advanced/Hint": {
"title": "$:/language/ControlPanel/Advanced/Hint",
"text": "Internal information about this TiddlyWiki"
},
"$:/language/ControlPanel/Appearance/Caption": {
"title": "$:/language/ControlPanel/Appearance/Caption",
"text": "Appearance"
},
"$:/language/ControlPanel/Appearance/Hint": {
"title": "$:/language/ControlPanel/Appearance/Hint",
"text": "Ways to customise the appearance of your TiddlyWiki."
},
"$:/language/ControlPanel/Basics/AnimDuration/Prompt": {
"title": "$:/language/ControlPanel/Basics/AnimDuration/Prompt",
"text": "Animation duration"
},
"$:/language/ControlPanel/Basics/AutoFocus/Prompt": {
"title": "$:/language/ControlPanel/Basics/AutoFocus/Prompt",
"text": "Default focus field for new tiddlers"
},
"$:/language/ControlPanel/Basics/Caption": {
"title": "$:/language/ControlPanel/Basics/Caption",
"text": "Basics"
},
"$:/language/ControlPanel/Basics/DefaultTiddlers/BottomHint": {
"title": "$:/language/ControlPanel/Basics/DefaultTiddlers/BottomHint",
"text": "Use [[double square brackets]] for titles with spaces. Or you can choose to <$button set=\"$:/DefaultTiddlers\" setTo=\"[list[$:/StoryList]]\">retain story ordering</$button>"
},
"$:/language/ControlPanel/Basics/DefaultTiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/DefaultTiddlers/Prompt",
"text": "Default tiddlers"
},
"$:/language/ControlPanel/Basics/DefaultTiddlers/TopHint": {
"title": "$:/language/ControlPanel/Basics/DefaultTiddlers/TopHint",
"text": "Choose which tiddlers are displayed at startup"
},
"$:/language/ControlPanel/Basics/Language/Prompt": {
"title": "$:/language/ControlPanel/Basics/Language/Prompt",
"text": "Hello! Current language:"
},
"$:/language/ControlPanel/Basics/NewJournal/Title/Prompt": {
"title": "$:/language/ControlPanel/Basics/NewJournal/Title/Prompt",
"text": "Title of new journal tiddlers"
},
"$:/language/ControlPanel/Basics/NewJournal/Text/Prompt": {
"title": "$:/language/ControlPanel/Basics/NewJournal/Text/Prompt",
"text": "Text for new journal tiddlers"
},
"$:/language/ControlPanel/Basics/NewJournal/Tags/Prompt": {
"title": "$:/language/ControlPanel/Basics/NewJournal/Tags/Prompt",
"text": "Tags for new journal tiddlers"
},
"$:/language/ControlPanel/Basics/NewTiddler/Title/Prompt": {
"title": "$:/language/ControlPanel/Basics/NewTiddler/Title/Prompt",
"text": "Title of new tiddlers"
},
"$:/language/ControlPanel/Basics/NewTiddler/Tags/Prompt": {
"title": "$:/language/ControlPanel/Basics/NewTiddler/Tags/Prompt",
"text": "Tags for new tiddlers"
},
"$:/language/ControlPanel/Basics/OverriddenShadowTiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/OverriddenShadowTiddlers/Prompt",
"text": "Number of overridden shadow tiddlers"
},
"$:/language/ControlPanel/Basics/ShadowTiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/ShadowTiddlers/Prompt",
"text": "Number of shadow tiddlers"
},
"$:/language/ControlPanel/Basics/Subtitle/Prompt": {
"title": "$:/language/ControlPanel/Basics/Subtitle/Prompt",
"text": "Subtitle"
},
"$:/language/ControlPanel/Basics/SystemTiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/SystemTiddlers/Prompt",
"text": "Number of system tiddlers"
},
"$:/language/ControlPanel/Basics/Tags/Prompt": {
"title": "$:/language/ControlPanel/Basics/Tags/Prompt",
"text": "Number of tags"
},
"$:/language/ControlPanel/Basics/Tiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/Tiddlers/Prompt",
"text": "Number of tiddlers"
},
"$:/language/ControlPanel/Basics/Title/Prompt": {
"title": "$:/language/ControlPanel/Basics/Title/Prompt",
"text": "Title of this ~TiddlyWiki"
},
"$:/language/ControlPanel/Basics/Username/Prompt": {
"title": "$:/language/ControlPanel/Basics/Username/Prompt",
"text": "Username for signing edits"
},
"$:/language/ControlPanel/Basics/Version/Prompt": {
"title": "$:/language/ControlPanel/Basics/Version/Prompt",
"text": "~TiddlyWiki version"
},
"$:/language/ControlPanel/EditorTypes/Caption": {
"title": "$:/language/ControlPanel/EditorTypes/Caption",
"text": "Editor Types"
},
"$:/language/ControlPanel/EditorTypes/Editor/Caption": {
"title": "$:/language/ControlPanel/EditorTypes/Editor/Caption",
"text": "Editor"
},
"$:/language/ControlPanel/EditorTypes/Hint": {
"title": "$:/language/ControlPanel/EditorTypes/Hint",
"text": "These tiddlers determine which editor is used to edit specific tiddler types."
},
"$:/language/ControlPanel/EditorTypes/Type/Caption": {
"title": "$:/language/ControlPanel/EditorTypes/Type/Caption",
"text": "Type"
},
"$:/language/ControlPanel/Info/Caption": {
"title": "$:/language/ControlPanel/Info/Caption",
"text": "Info"
},
"$:/language/ControlPanel/Info/Hint": {
"title": "$:/language/ControlPanel/Info/Hint",
"text": "Information about this TiddlyWiki"
},
"$:/language/ControlPanel/KeyboardShortcuts/Add/Prompt": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Add/Prompt",
"text": "Type shortcut here"
},
"$:/language/ControlPanel/KeyboardShortcuts/Add/Caption": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Add/Caption",
"text": "add shortcut"
},
"$:/language/ControlPanel/KeyboardShortcuts/Caption": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Caption",
"text": "Keyboard Shortcuts"
},
"$:/language/ControlPanel/KeyboardShortcuts/Hint": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Hint",
"text": "Manage keyboard shortcut assignments"
},
"$:/language/ControlPanel/KeyboardShortcuts/NoShortcuts/Caption": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/NoShortcuts/Caption",
"text": "No keyboard shortcuts assigned"
},
"$:/language/ControlPanel/KeyboardShortcuts/Remove/Hint": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Remove/Hint",
"text": "remove keyboard shortcut"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/All": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/All",
"text": "All platforms"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/Mac": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/Mac",
"text": "Macintosh platform only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/NonMac": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/NonMac",
"text": "Non-Macintosh platforms only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/Linux": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/Linux",
"text": "Linux platform only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/NonLinux": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/NonLinux",
"text": "Non-Linux platforms only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/Windows": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/Windows",
"text": "Windows platform only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/NonWindows": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/NonWindows",
"text": "Non-Windows platforms only"
},
"$:/language/ControlPanel/LoadedModules/Caption": {
"title": "$:/language/ControlPanel/LoadedModules/Caption",
"text": "Loaded Modules"
},
"$:/language/ControlPanel/LoadedModules/Hint": {
"title": "$:/language/ControlPanel/LoadedModules/Hint",
"text": "These are the currently loaded tiddler modules linked to their source tiddlers. Any italicised modules lack a source tiddler, typically because they were setup during the boot process."
},
"$:/language/ControlPanel/Palette/Caption": {
"title": "$:/language/ControlPanel/Palette/Caption",
"text": "Palette"
},
"$:/language/ControlPanel/Palette/Editor/Clone/Caption": {
"title": "$:/language/ControlPanel/Palette/Editor/Clone/Caption",
"text": "clone"
},
"$:/language/ControlPanel/Palette/Editor/Clone/Prompt": {
"title": "$:/language/ControlPanel/Palette/Editor/Clone/Prompt",
"text": "It is recommended that you clone this shadow palette before editing it"
},
"$:/language/ControlPanel/Palette/Editor/Delete/Hint": {
"title": "$:/language/ControlPanel/Palette/Editor/Delete/Hint",
"text": "delete this entry from the current palette"
},
"$:/language/ControlPanel/Palette/Editor/Names/External/Show": {
"title": "$:/language/ControlPanel/Palette/Editor/Names/External/Show",
"text": "Show color names that are not part of the current palette"
},
"$:/language/ControlPanel/Palette/Editor/Prompt/Modified": {
"title": "$:/language/ControlPanel/Palette/Editor/Prompt/Modified",
"text": "This shadow palette has been modified"
},
"$:/language/ControlPanel/Palette/Editor/Prompt": {
"title": "$:/language/ControlPanel/Palette/Editor/Prompt",
"text": "Editing"
},
"$:/language/ControlPanel/Palette/Editor/Reset/Caption": {
"title": "$:/language/ControlPanel/Palette/Editor/Reset/Caption",
"text": "reset"
},
"$:/language/ControlPanel/Palette/HideEditor/Caption": {
"title": "$:/language/ControlPanel/Palette/HideEditor/Caption",
"text": "hide editor"
},
"$:/language/ControlPanel/Palette/Prompt": {
"title": "$:/language/ControlPanel/Palette/Prompt",
"text": "Current palette:"
},
"$:/language/ControlPanel/Palette/ShowEditor/Caption": {
"title": "$:/language/ControlPanel/Palette/ShowEditor/Caption",
"text": "show editor"
},
"$:/language/ControlPanel/Parsing/Caption": {
"title": "$:/language/ControlPanel/Parsing/Caption",
"text": "Parsing"
},
"$:/language/ControlPanel/Parsing/Hint": {
"title": "$:/language/ControlPanel/Parsing/Hint",
"text": "Here you can globally disable/enable wiki parser rules. For changes to take effect, save and reload your wiki. Disabling certain parser rules can prevent <$text text=\"TiddlyWiki\"/> from functioning correctly. Use [[safe mode|https://tiddlywiki.com/#SafeMode]] to restore normal operation."
},
"$:/language/ControlPanel/Parsing/Block/Caption": {
"title": "$:/language/ControlPanel/Parsing/Block/Caption",
"text": "Block Parse Rules"
},
"$:/language/ControlPanel/Parsing/Inline/Caption": {
"title": "$:/language/ControlPanel/Parsing/Inline/Caption",
"text": "Inline Parse Rules"
},
"$:/language/ControlPanel/Parsing/Pragma/Caption": {
"title": "$:/language/ControlPanel/Parsing/Pragma/Caption",
"text": "Pragma Parse Rules"
},
"$:/language/ControlPanel/Plugins/Add/Caption": {
"title": "$:/language/ControlPanel/Plugins/Add/Caption",
"text": "Get more plugins"
},
"$:/language/ControlPanel/Plugins/Add/Hint": {
"title": "$:/language/ControlPanel/Plugins/Add/Hint",
"text": "Install plugins from the official library"
},
"$:/language/ControlPanel/Plugins/AlreadyInstalled/Hint": {
"title": "$:/language/ControlPanel/Plugins/AlreadyInstalled/Hint",
"text": "This plugin is already installed at version <$text text=<<installedVersion>>/>"
},
"$:/language/ControlPanel/Plugins/AlsoRequires": {
"title": "$:/language/ControlPanel/Plugins/AlsoRequires",
"text": "Also requires:"
},
"$:/language/ControlPanel/Plugins/Caption": {
"title": "$:/language/ControlPanel/Plugins/Caption",
"text": "Plugins"
},
"$:/language/ControlPanel/Plugins/Disable/Caption": {
"title": "$:/language/ControlPanel/Plugins/Disable/Caption",
"text": "disable"
},
"$:/language/ControlPanel/Plugins/Disable/Hint": {
"title": "$:/language/ControlPanel/Plugins/Disable/Hint",
"text": "Disable this plugin when reloading page"
},
"$:/language/ControlPanel/Plugins/Disabled/Status": {
"title": "$:/language/ControlPanel/Plugins/Disabled/Status",
"text": "(disabled)"
},
"$:/language/ControlPanel/Plugins/Downgrade/Caption": {
"title": "$:/language/ControlPanel/Plugins/Downgrade/Caption",
"text": "downgrade"
},
"$:/language/ControlPanel/Plugins/Empty/Hint": {
"title": "$:/language/ControlPanel/Plugins/Empty/Hint",
"text": "None"
},
"$:/language/ControlPanel/Plugins/Enable/Caption": {
"title": "$:/language/ControlPanel/Plugins/Enable/Caption",
"text": "enable"
},
"$:/language/ControlPanel/Plugins/Enable/Hint": {
"title": "$:/language/ControlPanel/Plugins/Enable/Hint",
"text": "Enable this plugin when reloading page"
},
"$:/language/ControlPanel/Plugins/Install/Caption": {
"title": "$:/language/ControlPanel/Plugins/Install/Caption",
"text": "install"
},
"$:/language/ControlPanel/Plugins/Installed/Hint": {
"title": "$:/language/ControlPanel/Plugins/Installed/Hint",
"text": "Currently installed plugins:"
},
"$:/language/ControlPanel/Plugins/Languages/Caption": {
"title": "$:/language/ControlPanel/Plugins/Languages/Caption",
"text": "Languages"
},
"$:/language/ControlPanel/Plugins/Languages/Hint": {
"title": "$:/language/ControlPanel/Plugins/Languages/Hint",
"text": "Language pack plugins"
},
"$:/language/ControlPanel/Plugins/NoInfoFound/Hint": {
"title": "$:/language/ControlPanel/Plugins/NoInfoFound/Hint",
"text": "No ''\"<$text text=<<currentTab>>/>\"'' found"
},
"$:/language/ControlPanel/Plugins/NotInstalled/Hint": {
"title": "$:/language/ControlPanel/Plugins/NotInstalled/Hint",
"text": "This plugin is not currently installed"
},
"$:/language/ControlPanel/Plugins/OpenPluginLibrary": {
"title": "$:/language/ControlPanel/Plugins/OpenPluginLibrary",
"text": "open plugin library"
},
"$:/language/ControlPanel/Plugins/ClosePluginLibrary": {
"title": "$:/language/ControlPanel/Plugins/ClosePluginLibrary",
"text": "close plugin library"
},
"$:/language/ControlPanel/Plugins/PluginWillRequireReload": {
"title": "$:/language/ControlPanel/Plugins/PluginWillRequireReload",
"text": "(requires reload)"
},
"$:/language/ControlPanel/Plugins/Plugins/Caption": {
"title": "$:/language/ControlPanel/Plugins/Plugins/Caption",
"text": "Plugins"
},
"$:/language/ControlPanel/Plugins/Plugins/Hint": {
"title": "$:/language/ControlPanel/Plugins/Plugins/Hint",
"text": "Plugins"
},
"$:/language/ControlPanel/Plugins/Reinstall/Caption": {
"title": "$:/language/ControlPanel/Plugins/Reinstall/Caption",
"text": "reinstall"
},
"$:/language/ControlPanel/Plugins/Themes/Caption": {
"title": "$:/language/ControlPanel/Plugins/Themes/Caption",
"text": "Themes"
},
"$:/language/ControlPanel/Plugins/Themes/Hint": {
"title": "$:/language/ControlPanel/Plugins/Themes/Hint",
"text": "Theme plugins"
},
"$:/language/ControlPanel/Plugins/Update/Caption": {
"title": "$:/language/ControlPanel/Plugins/Update/Caption",
"text": "update"
},
"$:/language/ControlPanel/Plugins/Updates/Caption": {
"title": "$:/language/ControlPanel/Plugins/Updates/Caption",
"text": "Updates"
},
"$:/language/ControlPanel/Plugins/Updates/Hint": {
"title": "$:/language/ControlPanel/Plugins/Updates/Hint",
"text": "Available updates to installed plugins"
},
"$:/language/ControlPanel/Plugins/Updates/UpdateAll/Caption": {
"title": "$:/language/ControlPanel/Plugins/Updates/UpdateAll/Caption",
"text": "Update <<update-count>> plugins"
},
"$:/language/ControlPanel/Plugins/SubPluginPrompt": {
"title": "$:/language/ControlPanel/Plugins/SubPluginPrompt",
"text": "With <<count>> sub-plugins available"
},
"$:/language/ControlPanel/Saving/Caption": {
"title": "$:/language/ControlPanel/Saving/Caption",
"text": "Saving"
},
"$:/language/ControlPanel/Saving/DownloadSaver/AutoSave/Description": {
"title": "$:/language/ControlPanel/Saving/DownloadSaver/AutoSave/Description",
"text": "Permit automatic saving for the download saver"
},
"$:/language/ControlPanel/Saving/DownloadSaver/AutoSave/Hint": {
"title": "$:/language/ControlPanel/Saving/DownloadSaver/AutoSave/Hint",
"text": "Enable Autosave for Download Saver"
},
"$:/language/ControlPanel/Saving/DownloadSaver/Caption": {
"title": "$:/language/ControlPanel/Saving/DownloadSaver/Caption",
"text": "Download Saver"
},
"$:/language/ControlPanel/Saving/DownloadSaver/Hint": {
"title": "$:/language/ControlPanel/Saving/DownloadSaver/Hint",
"text": "These settings apply to the HTML5-compatible download saver"
},
"$:/language/ControlPanel/Saving/General/Caption": {
"title": "$:/language/ControlPanel/Saving/General/Caption",
"text": "General"
},
"$:/language/ControlPanel/Saving/General/Hint": {
"title": "$:/language/ControlPanel/Saving/General/Hint",
"text": "These settings apply to all the loaded savers"
},
"$:/language/ControlPanel/Saving/Hint": {
"title": "$:/language/ControlPanel/Saving/Hint",
"text": "Settings used for saving the entire TiddlyWiki as a single file via a saver module"
},
"$:/language/ControlPanel/Saving/GitService/Branch": {
"title": "$:/language/ControlPanel/Saving/GitService/Branch",
"text": "Target branch for saving"
},
"$:/language/ControlPanel/Saving/GitService/CommitMessage": {
"title": "$:/language/ControlPanel/Saving/GitService/CommitMessage",
"text": "Saved by TiddlyWiki"
},
"$:/language/ControlPanel/Saving/GitService/Description": {
"title": "$:/language/ControlPanel/Saving/GitService/Description",
"text": "These settings are only used when saving to <<service-name>>"
},
"$:/language/ControlPanel/Saving/GitService/Filename": {
"title": "$:/language/ControlPanel/Saving/GitService/Filename",
"text": "Filename of target file (e.g. `index.html`)"
},
"$:/language/ControlPanel/Saving/GitService/Path": {
"title": "$:/language/ControlPanel/Saving/GitService/Path",
"text": "Path to target file (e.g. `/wiki/`)"
},
"$:/language/ControlPanel/Saving/GitService/Repo": {
"title": "$:/language/ControlPanel/Saving/GitService/Repo",
"text": "Target repository (e.g. `Jermolene/TiddlyWiki5`)"
},
"$:/language/ControlPanel/Saving/GitService/ServerURL": {
"title": "$:/language/ControlPanel/Saving/GitService/ServerURL",
"text": "Server API URL"
},
"$:/language/ControlPanel/Saving/GitService/UserName": {
"title": "$:/language/ControlPanel/Saving/GitService/UserName",
"text": "Username"
},
"$:/language/ControlPanel/Saving/GitService/GitHub/Caption": {
"title": "$:/language/ControlPanel/Saving/GitService/GitHub/Caption",
"text": "~GitHub Saver"
},
"$:/language/ControlPanel/Saving/GitService/GitHub/Password": {
"title": "$:/language/ControlPanel/Saving/GitService/GitHub/Password",
"text": "Password, OAUTH token, or personal access token (see [[GitHub help page|https://help.github.com/en/articles/creating-a-personal-access-token-for-the-command-line]] for details)"
},
"$:/language/ControlPanel/Saving/GitService/GitLab/Caption": {
"title": "$:/language/ControlPanel/Saving/GitService/GitLab/Caption",
"text": "~GitLab Saver"
},
"$:/language/ControlPanel/Saving/GitService/GitLab/Password": {
"title": "$:/language/ControlPanel/Saving/GitService/GitLab/Password",
"text": "Personal access token for API (see [[GitLab help page|https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html]] for details)"
},
"$:/language/ControlPanel/Saving/GitService/Gitea/Caption": {
"title": "$:/language/ControlPanel/Saving/GitService/Gitea/Caption",
"text": "Gitea Saver"
},
"$:/language/ControlPanel/Saving/GitService/Gitea/Password": {
"title": "$:/language/ControlPanel/Saving/GitService/Gitea/Password",
"text": "Personal access token for API (via Gitea’s web interface: `Settings | Applications | Generate New Token`)"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Advanced/Heading": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Advanced/Heading",
"text": "Advanced Settings"
},
"$:/language/ControlPanel/Saving/TiddlySpot/BackupDir": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/BackupDir",
"text": "Backup Directory"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Backups": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Backups",
"text": "Backups"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Caption": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Caption",
"text": "~TiddlySpot Saver"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Description": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Description",
"text": "These settings are only used when saving to http://tiddlyspot.com or a compatible remote server"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Filename": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Filename",
"text": "Upload Filename"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Heading": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Heading",
"text": "~TiddlySpot"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Hint": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Hint",
"text": "//The server URL defaults to `http://<wikiname>.tiddlyspot.com/store.cgi` and can be changed to use a custom server address, e.g. `http://example.com/store.php`.//"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Password": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Password",
"text": "Password"
},
"$:/language/ControlPanel/Saving/TiddlySpot/ServerURL": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/ServerURL",
"text": "Server URL"
},
"$:/language/ControlPanel/Saving/TiddlySpot/UploadDir": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/UploadDir",
"text": "Upload Directory"
},
"$:/language/ControlPanel/Saving/TiddlySpot/UserName": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/UserName",
"text": "Wiki Name"
},
"$:/language/ControlPanel/Settings/AutoSave/Caption": {
"title": "$:/language/ControlPanel/Settings/AutoSave/Caption",
"text": "Autosave"
},
"$:/language/ControlPanel/Settings/AutoSave/Disabled/Description": {
"title": "$:/language/ControlPanel/Settings/AutoSave/Disabled/Description",
"text": "Do not save changes automatically"
},
"$:/language/ControlPanel/Settings/AutoSave/Enabled/Description": {
"title": "$:/language/ControlPanel/Settings/AutoSave/Enabled/Description",
"text": "Save changes automatically"
},
"$:/language/ControlPanel/Settings/AutoSave/Hint": {
"title": "$:/language/ControlPanel/Settings/AutoSave/Hint",
"text": "Attempt to automatically save changes during editing when using a supporting saver"
},
"$:/language/ControlPanel/Settings/CamelCase/Caption": {
"title": "$:/language/ControlPanel/Settings/CamelCase/Caption",
"text": "Camel Case Wiki Links"
},
"$:/language/ControlPanel/Settings/CamelCase/Hint": {
"title": "$:/language/ControlPanel/Settings/CamelCase/Hint",
"text": "You can globally disable automatic linking of ~CamelCase phrases. Requires reload to take effect"
},
"$:/language/ControlPanel/Settings/CamelCase/Description": {
"title": "$:/language/ControlPanel/Settings/CamelCase/Description",
"text": "Enable automatic ~CamelCase linking"
},
"$:/language/ControlPanel/Settings/Caption": {
"title": "$:/language/ControlPanel/Settings/Caption",
"text": "Settings"
},
"$:/language/ControlPanel/Settings/EditorToolbar/Caption": {
"title": "$:/language/ControlPanel/Settings/EditorToolbar/Caption",
"text": "Editor Toolbar"
},
"$:/language/ControlPanel/Settings/EditorToolbar/Hint": {
"title": "$:/language/ControlPanel/Settings/EditorToolbar/Hint",
"text": "Enable or disable the editor toolbar:"
},
"$:/language/ControlPanel/Settings/EditorToolbar/Description": {
"title": "$:/language/ControlPanel/Settings/EditorToolbar/Description",
"text": "Show editor toolbar"
},
"$:/language/ControlPanel/Settings/InfoPanelMode/Caption": {
"title": "$:/language/ControlPanel/Settings/InfoPanelMode/Caption",
"text": "Tiddler Info Panel Mode"
},
"$:/language/ControlPanel/Settings/InfoPanelMode/Hint": {
"title": "$:/language/ControlPanel/Settings/InfoPanelMode/Hint",
"text": "Control when the tiddler info panel closes:"
},
"$:/language/ControlPanel/Settings/InfoPanelMode/Popup/Description": {
"title": "$:/language/ControlPanel/Settings/InfoPanelMode/Popup/Description",
"text": "Tiddler info panel closes automatically"
},
"$:/language/ControlPanel/Settings/InfoPanelMode/Sticky/Description": {
"title": "$:/language/ControlPanel/Settings/InfoPanelMode/Sticky/Description",
"text": "Tiddler info panel stays open until explicitly closed"
},
"$:/language/ControlPanel/Settings/Hint": {
"title": "$:/language/ControlPanel/Settings/Hint",
"text": "These settings let you customise the behaviour of TiddlyWiki."
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/Caption": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/Caption",
"text": "Navigation Address Bar"
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/Hint": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/Hint",
"text": "Behaviour of the browser address bar when navigating to a tiddler:"
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/No/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/No/Description",
"text": "Do not update the address bar"
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/Permalink/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/Permalink/Description",
"text": "Include the target tiddler"
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/Permaview/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/Permaview/Description",
"text": "Include the target tiddler and the current story sequence"
},
"$:/language/ControlPanel/Settings/NavigationHistory/Caption": {
"title": "$:/language/ControlPanel/Settings/NavigationHistory/Caption",
"text": "Navigation History"
},
"$:/language/ControlPanel/Settings/NavigationHistory/Hint": {
"title": "$:/language/ControlPanel/Settings/NavigationHistory/Hint",
"text": "Update browser history when navigating to a tiddler:"
},
"$:/language/ControlPanel/Settings/NavigationHistory/No/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationHistory/No/Description",
"text": "Do not update history"
},
"$:/language/ControlPanel/Settings/NavigationHistory/Yes/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationHistory/Yes/Description",
"text": "Update history"
},
"$:/language/ControlPanel/Settings/NavigationPermalinkviewMode/Caption": {
"title": "$:/language/ControlPanel/Settings/NavigationPermalinkviewMode/Caption",
"text": "Permalink/permaview Mode"
},
"$:/language/ControlPanel/Settings/NavigationPermalinkviewMode/Hint": {
"title": "$:/language/ControlPanel/Settings/NavigationPermalinkviewMode/Hint",
"text": "Choose how permalink/permaview is handled:"
},
"$:/language/ControlPanel/Settings/NavigationPermalinkviewMode/CopyToClipboard/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationPermalinkviewMode/CopyToClipboard/Description",
"text": "Copy permalink/permaview URL to clipboard"
},
"$:/language/ControlPanel/Settings/NavigationPermalinkviewMode/UpdateAddressBar/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationPermalinkviewMode/UpdateAddressBar/Description",
"text": "Update address bar with permalink/permaview URL"
},
"$:/language/ControlPanel/Settings/PerformanceInstrumentation/Caption": {
"title": "$:/language/ControlPanel/Settings/PerformanceInstrumentation/Caption",
"text": "Performance Instrumentation"
},
"$:/language/ControlPanel/Settings/PerformanceInstrumentation/Hint": {
"title": "$:/language/ControlPanel/Settings/PerformanceInstrumentation/Hint",
"text": "Displays performance statistics in the browser developer console. Requires reload to take effect"
},
"$:/language/ControlPanel/Settings/PerformanceInstrumentation/Description": {
"title": "$:/language/ControlPanel/Settings/PerformanceInstrumentation/Description",
"text": "Enable performance instrumentation"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Caption": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Caption",
"text": "Toolbar Button Style"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Hint": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Hint",
"text": "Choose the style for toolbar buttons:"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Borderless": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Borderless",
"text": "Borderless"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Boxed": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Boxed",
"text": "Boxed"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Rounded": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Rounded",
"text": "Rounded"
},
"$:/language/ControlPanel/Settings/ToolbarButtons/Caption": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtons/Caption",
"text": "Toolbar Buttons"
},
"$:/language/ControlPanel/Settings/ToolbarButtons/Hint": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtons/Hint",
"text": "Default toolbar button appearance:"
},
"$:/language/ControlPanel/Settings/ToolbarButtons/Icons/Description": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtons/Icons/Description",
"text": "Include icon"
},
"$:/language/ControlPanel/Settings/ToolbarButtons/Text/Description": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtons/Text/Description",
"text": "Include text"
},
"$:/language/ControlPanel/Settings/DefaultSidebarTab/Caption": {
"title": "$:/language/ControlPanel/Settings/DefaultSidebarTab/Caption",
"text": "Default Sidebar Tab"
},
"$:/language/ControlPanel/Settings/DefaultSidebarTab/Hint": {
"title": "$:/language/ControlPanel/Settings/DefaultSidebarTab/Hint",
"text": "Specify which sidebar tab is displayed by default"
},
"$:/language/ControlPanel/Settings/DefaultMoreSidebarTab/Caption": {
"title": "$:/language/ControlPanel/Settings/DefaultMoreSidebarTab/Caption",
"text": "Default More Sidebar Tab"
},
"$:/language/ControlPanel/Settings/DefaultMoreSidebarTab/Hint": {
"title": "$:/language/ControlPanel/Settings/DefaultMoreSidebarTab/Hint",
"text": "Specify which More sidebar tab is displayed by default"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/Caption": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/Caption",
"text": "Tiddler Opening Behaviour"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/InsideRiver/Hint": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/InsideRiver/Hint",
"text": "Navigation from //within// the story river"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OutsideRiver/Hint": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OutsideRiver/Hint",
"text": "Navigation from //outside// the story river"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAbove": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAbove",
"text": "Open above the current tiddler"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OpenBelow": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OpenBelow",
"text": "Open below the current tiddler"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAtTop": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAtTop",
"text": "Open at the top of the story river"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAtBottom": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAtBottom",
"text": "Open at the bottom of the story river"
},
"$:/language/ControlPanel/Settings/TitleLinks/Caption": {
"title": "$:/language/ControlPanel/Settings/TitleLinks/Caption",
"text": "Tiddler Titles"
},
"$:/language/ControlPanel/Settings/TitleLinks/Hint": {
"title": "$:/language/ControlPanel/Settings/TitleLinks/Hint",
"text": "Optionally display tiddler titles as links"
},
"$:/language/ControlPanel/Settings/TitleLinks/No/Description": {
"title": "$:/language/ControlPanel/Settings/TitleLinks/No/Description",
"text": "Do not display tiddler titles as links"
},
"$:/language/ControlPanel/Settings/TitleLinks/Yes/Description": {
"title": "$:/language/ControlPanel/Settings/TitleLinks/Yes/Description",
"text": "Display tiddler titles as links"
},
"$:/language/ControlPanel/Settings/MissingLinks/Caption": {
"title": "$:/language/ControlPanel/Settings/MissingLinks/Caption",
"text": "Wiki Links"
},
"$:/language/ControlPanel/Settings/MissingLinks/Hint": {
"title": "$:/language/ControlPanel/Settings/MissingLinks/Hint",
"text": "Choose whether to link to tiddlers that do not exist yet"
},
"$:/language/ControlPanel/Settings/MissingLinks/Description": {
"title": "$:/language/ControlPanel/Settings/MissingLinks/Description",
"text": "Enable links to missing tiddlers"
},
"$:/language/ControlPanel/StoryView/Caption": {
"title": "$:/language/ControlPanel/StoryView/Caption",
"text": "Story View"
},
"$:/language/ControlPanel/StoryView/Prompt": {
"title": "$:/language/ControlPanel/StoryView/Prompt",
"text": "Current view:"
},
"$:/language/ControlPanel/Stylesheets/Caption": {
"title": "$:/language/ControlPanel/Stylesheets/Caption",
"text": "Stylesheets"
},
"$:/language/ControlPanel/Stylesheets/Expand/Caption": {
"title": "$:/language/ControlPanel/Stylesheets/Expand/Caption",
"text": "Expand All"
},
"$:/language/ControlPanel/Stylesheets/Hint": {
"title": "$:/language/ControlPanel/Stylesheets/Hint",
"text": "This is the rendered CSS of the current stylesheet tiddlers tagged with <<tag \"$:/tags/Stylesheet\">>"
},
"$:/language/ControlPanel/Stylesheets/Restore/Caption": {
"title": "$:/language/ControlPanel/Stylesheets/Restore/Caption",
"text": "Restore"
},
"$:/language/ControlPanel/Theme/Caption": {
"title": "$:/language/ControlPanel/Theme/Caption",
"text": "Theme"
},
"$:/language/ControlPanel/Theme/Prompt": {
"title": "$:/language/ControlPanel/Theme/Prompt",
"text": "Current theme:"
},
"$:/language/ControlPanel/TiddlerFields/Caption": {
"title": "$:/language/ControlPanel/TiddlerFields/Caption",
"text": "Tiddler Fields"
},
"$:/language/ControlPanel/TiddlerFields/Hint": {
"title": "$:/language/ControlPanel/TiddlerFields/Hint",
"text": "This is the full set of TiddlerFields in use in this wiki (including system tiddlers but excluding shadow tiddlers)."
},
"$:/language/ControlPanel/Toolbars/Caption": {
"title": "$:/language/ControlPanel/Toolbars/Caption",
"text": "Toolbars"
},
"$:/language/ControlPanel/Toolbars/EditToolbar/Caption": {
"title": "$:/language/ControlPanel/Toolbars/EditToolbar/Caption",
"text": "Edit Toolbar"
},
"$:/language/ControlPanel/Toolbars/EditToolbar/Hint": {
"title": "$:/language/ControlPanel/Toolbars/EditToolbar/Hint",
"text": "Choose which buttons are displayed for tiddlers in edit mode. Drag and drop to change the ordering"
},
"$:/language/ControlPanel/Toolbars/Hint": {
"title": "$:/language/ControlPanel/Toolbars/Hint",
"text": "Select which toolbar buttons are displayed"
},
"$:/language/ControlPanel/Toolbars/PageControls/Caption": {
"title": "$:/language/ControlPanel/Toolbars/PageControls/Caption",
"text": "Page Toolbar"
},
"$:/language/ControlPanel/Toolbars/PageControls/Hint": {
"title": "$:/language/ControlPanel/Toolbars/PageControls/Hint",
"text": "Choose which buttons are displayed on the main page toolbar. Drag and drop to change the ordering"
},
"$:/language/ControlPanel/Toolbars/EditorToolbar/Caption": {
"title": "$:/language/ControlPanel/Toolbars/EditorToolbar/Caption",
"text": "Editor Toolbar"
},
"$:/language/ControlPanel/Toolbars/EditorToolbar/Hint": {
"title": "$:/language/ControlPanel/Toolbars/EditorToolbar/Hint",
"text": "Choose which buttons are displayed in the editor toolbar. Note that some buttons will only appear when editing tiddlers of a certain type. Drag and drop to change the ordering"
},
"$:/language/ControlPanel/Toolbars/ViewToolbar/Caption": {
"title": "$:/language/ControlPanel/Toolbars/ViewToolbar/Caption",
"text": "View Toolbar"
},
"$:/language/ControlPanel/Toolbars/ViewToolbar/Hint": {
"title": "$:/language/ControlPanel/Toolbars/ViewToolbar/Hint",
"text": "Choose which buttons are displayed for tiddlers in view mode. Drag and drop to change the ordering"
},
"$:/language/ControlPanel/Tools/Download/Full/Caption": {
"title": "$:/language/ControlPanel/Tools/Download/Full/Caption",
"text": "Download full wiki"
},
"$:/language/Date/DaySuffix/1": {
"title": "$:/language/Date/DaySuffix/1",
"text": "st"
},
"$:/language/Date/DaySuffix/2": {
"title": "$:/language/Date/DaySuffix/2",
"text": "nd"
},
"$:/language/Date/DaySuffix/3": {
"title": "$:/language/Date/DaySuffix/3",
"text": "rd"
},
"$:/language/Date/DaySuffix/4": {
"title": "$:/language/Date/DaySuffix/4",
"text": "th"
},
"$:/language/Date/DaySuffix/5": {
"title": "$:/language/Date/DaySuffix/5",
"text": "th"
},
"$:/language/Date/DaySuffix/6": {
"title": "$:/language/Date/DaySuffix/6",
"text": "th"
},
"$:/language/Date/DaySuffix/7": {
"title": "$:/language/Date/DaySuffix/7",
"text": "th"
},
"$:/language/Date/DaySuffix/8": {
"title": "$:/language/Date/DaySuffix/8",
"text": "th"
},
"$:/language/Date/DaySuffix/9": {
"title": "$:/language/Date/DaySuffix/9",
"text": "th"
},
"$:/language/Date/DaySuffix/10": {
"title": "$:/language/Date/DaySuffix/10",
"text": "th"
},
"$:/language/Date/DaySuffix/11": {
"title": "$:/language/Date/DaySuffix/11",
"text": "th"
},
"$:/language/Date/DaySuffix/12": {
"title": "$:/language/Date/DaySuffix/12",
"text": "th"
},
"$:/language/Date/DaySuffix/13": {
"title": "$:/language/Date/DaySuffix/13",
"text": "th"
},
"$:/language/Date/DaySuffix/14": {
"title": "$:/language/Date/DaySuffix/14",
"text": "th"
},
"$:/language/Date/DaySuffix/15": {
"title": "$:/language/Date/DaySuffix/15",
"text": "th"
},
"$:/language/Date/DaySuffix/16": {
"title": "$:/language/Date/DaySuffix/16",
"text": "th"
},
"$:/language/Date/DaySuffix/17": {
"title": "$:/language/Date/DaySuffix/17",
"text": "th"
},
"$:/language/Date/DaySuffix/18": {
"title": "$:/language/Date/DaySuffix/18",
"text": "th"
},
"$:/language/Date/DaySuffix/19": {
"title": "$:/language/Date/DaySuffix/19",
"text": "th"
},
"$:/language/Date/DaySuffix/20": {
"title": "$:/language/Date/DaySuffix/20",
"text": "th"
},
"$:/language/Date/DaySuffix/21": {
"title": "$:/language/Date/DaySuffix/21",
"text": "st"
},
"$:/language/Date/DaySuffix/22": {
"title": "$:/language/Date/DaySuffix/22",
"text": "nd"
},
"$:/language/Date/DaySuffix/23": {
"title": "$:/language/Date/DaySuffix/23",
"text": "rd"
},
"$:/language/Date/DaySuffix/24": {
"title": "$:/language/Date/DaySuffix/24",
"text": "th"
},
"$:/language/Date/DaySuffix/25": {
"title": "$:/language/Date/DaySuffix/25",
"text": "th"
},
"$:/language/Date/DaySuffix/26": {
"title": "$:/language/Date/DaySuffix/26",
"text": "th"
},
"$:/language/Date/DaySuffix/27": {
"title": "$:/language/Date/DaySuffix/27",
"text": "th"
},
"$:/language/Date/DaySuffix/28": {
"title": "$:/language/Date/DaySuffix/28",
"text": "th"
},
"$:/language/Date/DaySuffix/29": {
"title": "$:/language/Date/DaySuffix/29",
"text": "th"
},
"$:/language/Date/DaySuffix/30": {
"title": "$:/language/Date/DaySuffix/30",
"text": "th"
},
"$:/language/Date/DaySuffix/31": {
"title": "$:/language/Date/DaySuffix/31",
"text": "st"
},
"$:/language/Date/Long/Day/0": {
"title": "$:/language/Date/Long/Day/0",
"text": "Sunday"
},
"$:/language/Date/Long/Day/1": {
"title": "$:/language/Date/Long/Day/1",
"text": "Monday"
},
"$:/language/Date/Long/Day/2": {
"title": "$:/language/Date/Long/Day/2",
"text": "Tuesday"
},
"$:/language/Date/Long/Day/3": {
"title": "$:/language/Date/Long/Day/3",
"text": "Wednesday"
},
"$:/language/Date/Long/Day/4": {
"title": "$:/language/Date/Long/Day/4",
"text": "Thursday"
},
"$:/language/Date/Long/Day/5": {
"title": "$:/language/Date/Long/Day/5",
"text": "Friday"
},
"$:/language/Date/Long/Day/6": {
"title": "$:/language/Date/Long/Day/6",
"text": "Saturday"
},
"$:/language/Date/Long/Month/1": {
"title": "$:/language/Date/Long/Month/1",
"text": "January"
},
"$:/language/Date/Long/Month/2": {
"title": "$:/language/Date/Long/Month/2",
"text": "February"
},
"$:/language/Date/Long/Month/3": {
"title": "$:/language/Date/Long/Month/3",
"text": "March"
},
"$:/language/Date/Long/Month/4": {
"title": "$:/language/Date/Long/Month/4",
"text": "April"
},
"$:/language/Date/Long/Month/5": {
"title": "$:/language/Date/Long/Month/5",
"text": "May"
},
"$:/language/Date/Long/Month/6": {
"title": "$:/language/Date/Long/Month/6",
"text": "June"
},
"$:/language/Date/Long/Month/7": {
"title": "$:/language/Date/Long/Month/7",
"text": "July"
},
"$:/language/Date/Long/Month/8": {
"title": "$:/language/Date/Long/Month/8",
"text": "August"
},
"$:/language/Date/Long/Month/9": {
"title": "$:/language/Date/Long/Month/9",
"text": "September"
},
"$:/language/Date/Long/Month/10": {
"title": "$:/language/Date/Long/Month/10",
"text": "October"
},
"$:/language/Date/Long/Month/11": {
"title": "$:/language/Date/Long/Month/11",
"text": "November"
},
"$:/language/Date/Long/Month/12": {
"title": "$:/language/Date/Long/Month/12",
"text": "December"
},
"$:/language/Date/Period/am": {
"title": "$:/language/Date/Period/am",
"text": "am"
},
"$:/language/Date/Period/pm": {
"title": "$:/language/Date/Period/pm",
"text": "pm"
},
"$:/language/Date/Short/Day/0": {
"title": "$:/language/Date/Short/Day/0",
"text": "Sun"
},
"$:/language/Date/Short/Day/1": {
"title": "$:/language/Date/Short/Day/1",
"text": "Mon"
},
"$:/language/Date/Short/Day/2": {
"title": "$:/language/Date/Short/Day/2",
"text": "Tue"
},
"$:/language/Date/Short/Day/3": {
"title": "$:/language/Date/Short/Day/3",
"text": "Wed"
},
"$:/language/Date/Short/Day/4": {
"title": "$:/language/Date/Short/Day/4",
"text": "Thu"
},
"$:/language/Date/Short/Day/5": {
"title": "$:/language/Date/Short/Day/5",
"text": "Fri"
},
"$:/language/Date/Short/Day/6": {
"title": "$:/language/Date/Short/Day/6",
"text": "Sat"
},
"$:/language/Date/Short/Month/1": {
"title": "$:/language/Date/Short/Month/1",
"text": "Jan"
},
"$:/language/Date/Short/Month/2": {
"title": "$:/language/Date/Short/Month/2",
"text": "Feb"
},
"$:/language/Date/Short/Month/3": {
"title": "$:/language/Date/Short/Month/3",
"text": "Mar"
},
"$:/language/Date/Short/Month/4": {
"title": "$:/language/Date/Short/Month/4",
"text": "Apr"
},
"$:/language/Date/Short/Month/5": {
"title": "$:/language/Date/Short/Month/5",
"text": "May"
},
"$:/language/Date/Short/Month/6": {
"title": "$:/language/Date/Short/Month/6",
"text": "Jun"
},
"$:/language/Date/Short/Month/7": {
"title": "$:/language/Date/Short/Month/7",
"text": "Jul"
},
"$:/language/Date/Short/Month/8": {
"title": "$:/language/Date/Short/Month/8",
"text": "Aug"
},
"$:/language/Date/Short/Month/9": {
"title": "$:/language/Date/Short/Month/9",
"text": "Sep"
},
"$:/language/Date/Short/Month/10": {
"title": "$:/language/Date/Short/Month/10",
"text": "Oct"
},
"$:/language/Date/Short/Month/11": {
"title": "$:/language/Date/Short/Month/11",
"text": "Nov"
},
"$:/language/Date/Short/Month/12": {
"title": "$:/language/Date/Short/Month/12",
"text": "Dec"
},
"$:/language/RelativeDate/Future/Days": {
"title": "$:/language/RelativeDate/Future/Days",
"text": "<<period>> days from now"
},
"$:/language/RelativeDate/Future/Hours": {
"title": "$:/language/RelativeDate/Future/Hours",
"text": "<<period>> hours from now"
},
"$:/language/RelativeDate/Future/Minutes": {
"title": "$:/language/RelativeDate/Future/Minutes",
"text": "<<period>> minutes from now"
},
"$:/language/RelativeDate/Future/Months": {
"title": "$:/language/RelativeDate/Future/Months",
"text": "<<period>> months from now"
},
"$:/language/RelativeDate/Future/Second": {
"title": "$:/language/RelativeDate/Future/Second",
"text": "1 second from now"
},
"$:/language/RelativeDate/Future/Seconds": {
"title": "$:/language/RelativeDate/Future/Seconds",
"text": "<<period>> seconds from now"
},
"$:/language/RelativeDate/Future/Years": {
"title": "$:/language/RelativeDate/Future/Years",
"text": "<<period>> years from now"
},
"$:/language/RelativeDate/Past/Days": {
"title": "$:/language/RelativeDate/Past/Days",
"text": "<<period>> days ago"
},
"$:/language/RelativeDate/Past/Hours": {
"title": "$:/language/RelativeDate/Past/Hours",
"text": "<<period>> hours ago"
},
"$:/language/RelativeDate/Past/Minutes": {
"title": "$:/language/RelativeDate/Past/Minutes",
"text": "<<period>> minutes ago"
},
"$:/language/RelativeDate/Past/Months": {
"title": "$:/language/RelativeDate/Past/Months",
"text": "<<period>> months ago"
},
"$:/language/RelativeDate/Past/Second": {
"title": "$:/language/RelativeDate/Past/Second",
"text": "1 second ago"
},
"$:/language/RelativeDate/Past/Seconds": {
"title": "$:/language/RelativeDate/Past/Seconds",
"text": "<<period>> seconds ago"
},
"$:/language/RelativeDate/Past/Years": {
"title": "$:/language/RelativeDate/Past/Years",
"text": "<<period>> years ago"
},
"$:/language/Docs/ModuleTypes/allfilteroperator": {
"title": "$:/language/Docs/ModuleTypes/allfilteroperator",
"text": "A sub-operator for the ''all'' filter operator."
},
"$:/language/Docs/ModuleTypes/animation": {
"title": "$:/language/Docs/ModuleTypes/animation",
"text": "Animations that may be used with the RevealWidget."
},
"$:/language/Docs/ModuleTypes/authenticator": {
"title": "$:/language/Docs/ModuleTypes/authenticator",
"text": "Defines how requests are authenticated by the built-in HTTP server."
},
"$:/language/Docs/ModuleTypes/bitmapeditoroperation": {
"title": "$:/language/Docs/ModuleTypes/bitmapeditoroperation",
"text": "A bitmap editor toolbar operation."
},
"$:/language/Docs/ModuleTypes/command": {
"title": "$:/language/Docs/ModuleTypes/command",
"text": "Commands that can be executed under Node.js."
},
"$:/language/Docs/ModuleTypes/config": {
"title": "$:/language/Docs/ModuleTypes/config",
"text": "Data to be inserted into `$tw.config`."
},
"$:/language/Docs/ModuleTypes/filteroperator": {
"title": "$:/language/Docs/ModuleTypes/filteroperator",
"text": "Individual filter operator methods."
},
"$:/language/Docs/ModuleTypes/global": {
"title": "$:/language/Docs/ModuleTypes/global",
"text": "Global data to be inserted into `$tw`."
},
"$:/language/Docs/ModuleTypes/info": {
"title": "$:/language/Docs/ModuleTypes/info",
"text": "Publishes system information via the [[$:/temp/info-plugin]] pseudo-plugin."
},
"$:/language/Docs/ModuleTypes/isfilteroperator": {
"title": "$:/language/Docs/ModuleTypes/isfilteroperator",
"text": "Operands for the ''is'' filter operator."
},
"$:/language/Docs/ModuleTypes/library": {
"title": "$:/language/Docs/ModuleTypes/library",
"text": "Generic module type for general purpose JavaScript modules."
},
"$:/language/Docs/ModuleTypes/macro": {
"title": "$:/language/Docs/ModuleTypes/macro",
"text": "JavaScript macro definitions."
},
"$:/language/Docs/ModuleTypes/parser": {
"title": "$:/language/Docs/ModuleTypes/parser",
"text": "Parsers for different content types."
},
"$:/language/Docs/ModuleTypes/route": {
"title": "$:/language/Docs/ModuleTypes/route",
"text": "Defines how individual URL patterns are handled by the built-in HTTP server."
},
"$:/language/Docs/ModuleTypes/saver": {
"title": "$:/language/Docs/ModuleTypes/saver",
"text": "Savers handle different methods for saving files from the browser."
},
"$:/language/Docs/ModuleTypes/startup": {
"title": "$:/language/Docs/ModuleTypes/startup",
"text": "Startup functions."
},
"$:/language/Docs/ModuleTypes/storyview": {
"title": "$:/language/Docs/ModuleTypes/storyview",
"text": "Story views customise the animation and behaviour of list widgets."
},
"$:/language/Docs/ModuleTypes/texteditoroperation": {
"title": "$:/language/Docs/ModuleTypes/texteditoroperation",
"text": "A text editor toolbar operation."
},
"$:/language/Docs/ModuleTypes/tiddlerdeserializer": {
"title": "$:/language/Docs/ModuleTypes/tiddlerdeserializer",
"text": "Converts different content types into tiddlers."
},
"$:/language/Docs/ModuleTypes/tiddlerfield": {
"title": "$:/language/Docs/ModuleTypes/tiddlerfield",
"text": "Defines the behaviour of an individual tiddler field."
},
"$:/language/Docs/ModuleTypes/tiddlermethod": {
"title": "$:/language/Docs/ModuleTypes/tiddlermethod",
"text": "Adds methods to the `$tw.Tiddler` prototype."
},
"$:/language/Docs/ModuleTypes/upgrader": {
"title": "$:/language/Docs/ModuleTypes/upgrader",
"text": "Applies upgrade processing to tiddlers during an upgrade/import."
},
"$:/language/Docs/ModuleTypes/utils": {
"title": "$:/language/Docs/ModuleTypes/utils",
"text": "Adds methods to `$tw.utils`."
},
"$:/language/Docs/ModuleTypes/utils-node": {
"title": "$:/language/Docs/ModuleTypes/utils-node",
"text": "Adds Node.js-specific methods to `$tw.utils`."
},
"$:/language/Docs/ModuleTypes/widget": {
"title": "$:/language/Docs/ModuleTypes/widget",
"text": "Widgets encapsulate DOM rendering and refreshing."
},
"$:/language/Docs/ModuleTypes/wikimethod": {
"title": "$:/language/Docs/ModuleTypes/wikimethod",
"text": "Adds methods to `$tw.Wiki`."
},
"$:/language/Docs/ModuleTypes/wikirule": {
"title": "$:/language/Docs/ModuleTypes/wikirule",
"text": "Individual parser rules for the main WikiText parser."
},
"$:/language/Docs/PaletteColours/alert-background": {
"title": "$:/language/Docs/PaletteColours/alert-background",
"text": "Alert background"
},
"$:/language/Docs/PaletteColours/alert-border": {
"title": "$:/language/Docs/PaletteColours/alert-border",
"text": "Alert border"
},
"$:/language/Docs/PaletteColours/alert-highlight": {
"title": "$:/language/Docs/PaletteColours/alert-highlight",
"text": "Alert highlight"
},
"$:/language/Docs/PaletteColours/alert-muted-foreground": {
"title": "$:/language/Docs/PaletteColours/alert-muted-foreground",
"text": "Alert muted foreground"
},
"$:/language/Docs/PaletteColours/background": {
"title": "$:/language/Docs/PaletteColours/background",
"text": "General background"
},
"$:/language/Docs/PaletteColours/blockquote-bar": {
"title": "$:/language/Docs/PaletteColours/blockquote-bar",
"text": "Blockquote bar"
},
"$:/language/Docs/PaletteColours/button-background": {
"title": "$:/language/Docs/PaletteColours/button-background",
"text": "Default button background"
},
"$:/language/Docs/PaletteColours/button-border": {
"title": "$:/language/Docs/PaletteColours/button-border",
"text": "Default button border"
},
"$:/language/Docs/PaletteColours/button-foreground": {
"title": "$:/language/Docs/PaletteColours/button-foreground",
"text": "Default button foreground"
},
"$:/language/Docs/PaletteColours/dirty-indicator": {
"title": "$:/language/Docs/PaletteColours/dirty-indicator",
"text": "Unsaved changes indicator"
},
"$:/language/Docs/PaletteColours/code-background": {
"title": "$:/language/Docs/PaletteColours/code-background",
"text": "Code background"
},
"$:/language/Docs/PaletteColours/code-border": {
"title": "$:/language/Docs/PaletteColours/code-border",
"text": "Code border"
},
"$:/language/Docs/PaletteColours/code-foreground": {
"title": "$:/language/Docs/PaletteColours/code-foreground",
"text": "Code foreground"
},
"$:/language/Docs/PaletteColours/download-background": {
"title": "$:/language/Docs/PaletteColours/download-background",
"text": "Download button background"
},
"$:/language/Docs/PaletteColours/download-foreground": {
"title": "$:/language/Docs/PaletteColours/download-foreground",
"text": "Download button foreground"
},
"$:/language/Docs/PaletteColours/dragger-background": {
"title": "$:/language/Docs/PaletteColours/dragger-background",
"text": "Dragger background"
},
"$:/language/Docs/PaletteColours/dragger-foreground": {
"title": "$:/language/Docs/PaletteColours/dragger-foreground",
"text": "Dragger foreground"
},
"$:/language/Docs/PaletteColours/dropdown-background": {
"title": "$:/language/Docs/PaletteColours/dropdown-background",
"text": "Dropdown background"
},
"$:/language/Docs/PaletteColours/dropdown-border": {
"title": "$:/language/Docs/PaletteColours/dropdown-border",
"text": "Dropdown border"
},
"$:/language/Docs/PaletteColours/dropdown-tab-background-selected": {
"title": "$:/language/Docs/PaletteColours/dropdown-tab-background-selected",
"text": "Dropdown tab background for selected tabs"
},
"$:/language/Docs/PaletteColours/dropdown-tab-background": {
"title": "$:/language/Docs/PaletteColours/dropdown-tab-background",
"text": "Dropdown tab background"
},
"$:/language/Docs/PaletteColours/dropzone-background": {
"title": "$:/language/Docs/PaletteColours/dropzone-background",
"text": "Dropzone background"
},
"$:/language/Docs/PaletteColours/external-link-background-hover": {
"title": "$:/language/Docs/PaletteColours/external-link-background-hover",
"text": "External link background hover"
},
"$:/language/Docs/PaletteColours/external-link-background-visited": {
"title": "$:/language/Docs/PaletteColours/external-link-background-visited",
"text": "External link background visited"
},
"$:/language/Docs/PaletteColours/external-link-background": {
"title": "$:/language/Docs/PaletteColours/external-link-background",
"text": "External link background"
},
"$:/language/Docs/PaletteColours/external-link-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/external-link-foreground-hover",
"text": "External link foreground hover"
},
"$:/language/Docs/PaletteColours/external-link-foreground-visited": {
"title": "$:/language/Docs/PaletteColours/external-link-foreground-visited",
"text": "External link foreground visited"
},
"$:/language/Docs/PaletteColours/external-link-foreground": {
"title": "$:/language/Docs/PaletteColours/external-link-foreground",
"text": "External link foreground"
},
"$:/language/Docs/PaletteColours/foreground": {
"title": "$:/language/Docs/PaletteColours/foreground",
"text": "General foreground"
},
"$:/language/Docs/PaletteColours/menubar-background": {
"title": "$:/language/Docs/PaletteColours/menubar-background",
"text": "Menu bar background"
},
"$:/language/Docs/PaletteColours/menubar-foreground": {
"title": "$:/language/Docs/PaletteColours/menubar-foreground",
"text": "Menu bar foreground"
},
"$:/language/Docs/PaletteColours/message-background": {
"title": "$:/language/Docs/PaletteColours/message-background",
"text": "Message box background"
},
"$:/language/Docs/PaletteColours/message-border": {
"title": "$:/language/Docs/PaletteColours/message-border",
"text": "Message box border"
},
"$:/language/Docs/PaletteColours/message-foreground": {
"title": "$:/language/Docs/PaletteColours/message-foreground",
"text": "Message box foreground"
},
"$:/language/Docs/PaletteColours/modal-backdrop": {
"title": "$:/language/Docs/PaletteColours/modal-backdrop",
"text": "Modal backdrop"
},
"$:/language/Docs/PaletteColours/modal-background": {
"title": "$:/language/Docs/PaletteColours/modal-background",
"text": "Modal background"
},
"$:/language/Docs/PaletteColours/modal-border": {
"title": "$:/language/Docs/PaletteColours/modal-border",
"text": "Modal border"
},
"$:/language/Docs/PaletteColours/modal-footer-background": {
"title": "$:/language/Docs/PaletteColours/modal-footer-background",
"text": "Modal footer background"
},
"$:/language/Docs/PaletteColours/modal-footer-border": {
"title": "$:/language/Docs/PaletteColours/modal-footer-border",
"text": "Modal footer border"
},
"$:/language/Docs/PaletteColours/modal-header-border": {
"title": "$:/language/Docs/PaletteColours/modal-header-border",
"text": "Modal header border"
},
"$:/language/Docs/PaletteColours/muted-foreground": {
"title": "$:/language/Docs/PaletteColours/muted-foreground",
"text": "General muted foreground"
},
"$:/language/Docs/PaletteColours/notification-background": {
"title": "$:/language/Docs/PaletteColours/notification-background",
"text": "Notification background"
},
"$:/language/Docs/PaletteColours/notification-border": {
"title": "$:/language/Docs/PaletteColours/notification-border",
"text": "Notification border"
},
"$:/language/Docs/PaletteColours/page-background": {
"title": "$:/language/Docs/PaletteColours/page-background",
"text": "Page background"
},
"$:/language/Docs/PaletteColours/pre-background": {
"title": "$:/language/Docs/PaletteColours/pre-background",
"text": "Preformatted code background"
},
"$:/language/Docs/PaletteColours/pre-border": {
"title": "$:/language/Docs/PaletteColours/pre-border",
"text": "Preformatted code border"
},
"$:/language/Docs/PaletteColours/primary": {
"title": "$:/language/Docs/PaletteColours/primary",
"text": "General primary"
},
"$:/language/Docs/PaletteColours/select-tag-background": {
"title": "$:/language/Docs/PaletteColours/select-tag-background",
"text": "`<select>` element background"
},
"$:/language/Docs/PaletteColours/select-tag-foreground": {
"title": "$:/language/Docs/PaletteColours/select-tag-foreground",
"text": "`<select>` element text"
},
"$:/language/Docs/PaletteColours/sidebar-button-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-button-foreground",
"text": "Sidebar button foreground"
},
"$:/language/Docs/PaletteColours/sidebar-controls-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/sidebar-controls-foreground-hover",
"text": "Sidebar controls foreground hover"
},
"$:/language/Docs/PaletteColours/sidebar-controls-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-controls-foreground",
"text": "Sidebar controls foreground"
},
"$:/language/Docs/PaletteColours/sidebar-foreground-shadow": {
"title": "$:/language/Docs/PaletteColours/sidebar-foreground-shadow",
"text": "Sidebar foreground shadow"
},
"$:/language/Docs/PaletteColours/sidebar-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-foreground",
"text": "Sidebar foreground"
},
"$:/language/Docs/PaletteColours/sidebar-muted-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/sidebar-muted-foreground-hover",
"text": "Sidebar muted foreground hover"
},
"$:/language/Docs/PaletteColours/sidebar-muted-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-muted-foreground",
"text": "Sidebar muted foreground"
},
"$:/language/Docs/PaletteColours/sidebar-tab-background-selected": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-background-selected",
"text": "Sidebar tab background for selected tabs"
},
"$:/language/Docs/PaletteColours/sidebar-tab-background": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-background",
"text": "Sidebar tab background"
},
"$:/language/Docs/PaletteColours/sidebar-tab-border-selected": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-border-selected",
"text": "Sidebar tab border for selected tabs"
},
"$:/language/Docs/PaletteColours/sidebar-tab-border": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-border",
"text": "Sidebar tab border"
},
"$:/language/Docs/PaletteColours/sidebar-tab-divider": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-divider",
"text": "Sidebar tab divider"
},
"$:/language/Docs/PaletteColours/sidebar-tab-foreground-selected": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-foreground-selected",
"text": "Sidebar tab foreground for selected tabs"
},
"$:/language/Docs/PaletteColours/sidebar-tab-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-foreground",
"text": "Sidebar tab foreground"
},
"$:/language/Docs/PaletteColours/sidebar-tiddler-link-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/sidebar-tiddler-link-foreground-hover",
"text": "Sidebar tiddler link foreground hover"
},
"$:/language/Docs/PaletteColours/sidebar-tiddler-link-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-tiddler-link-foreground",
"text": "Sidebar tiddler link foreground"
},
"$:/language/Docs/PaletteColours/site-title-foreground": {
"title": "$:/language/Docs/PaletteColours/site-title-foreground",
"text": "Site title foreground"
},
"$:/language/Docs/PaletteColours/static-alert-foreground": {
"title": "$:/language/Docs/PaletteColours/static-alert-foreground",
"text": "Static alert foreground"
},
"$:/language/Docs/PaletteColours/tab-background-selected": {
"title": "$:/language/Docs/PaletteColours/tab-background-selected",
"text": "Tab background for selected tabs"
},
"$:/language/Docs/PaletteColours/tab-background": {
"title": "$:/language/Docs/PaletteColours/tab-background",
"text": "Tab background"
},
"$:/language/Docs/PaletteColours/tab-border-selected": {
"title": "$:/language/Docs/PaletteColours/tab-border-selected",
"text": "Tab border for selected tabs"
},
"$:/language/Docs/PaletteColours/tab-border": {
"title": "$:/language/Docs/PaletteColours/tab-border",
"text": "Tab border"
},
"$:/language/Docs/PaletteColours/tab-divider": {
"title": "$:/language/Docs/PaletteColours/tab-divider",
"text": "Tab divider"
},
"$:/language/Docs/PaletteColours/tab-foreground-selected": {
"title": "$:/language/Docs/PaletteColours/tab-foreground-selected",
"text": "Tab foreground for selected tabs"
},
"$:/language/Docs/PaletteColours/tab-foreground": {
"title": "$:/language/Docs/PaletteColours/tab-foreground",
"text": "Tab foreground"
},
"$:/language/Docs/PaletteColours/table-border": {
"title": "$:/language/Docs/PaletteColours/table-border",
"text": "Table border"
},
"$:/language/Docs/PaletteColours/table-footer-background": {
"title": "$:/language/Docs/PaletteColours/table-footer-background",
"text": "Table footer background"
},
"$:/language/Docs/PaletteColours/table-header-background": {
"title": "$:/language/Docs/PaletteColours/table-header-background",
"text": "Table header background"
},
"$:/language/Docs/PaletteColours/tag-background": {
"title": "$:/language/Docs/PaletteColours/tag-background",
"text": "Tag background"
},
"$:/language/Docs/PaletteColours/tag-foreground": {
"title": "$:/language/Docs/PaletteColours/tag-foreground",
"text": "Tag foreground"
},
"$:/language/Docs/PaletteColours/tiddler-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-background",
"text": "Tiddler background"
},
"$:/language/Docs/PaletteColours/tiddler-border": {
"title": "$:/language/Docs/PaletteColours/tiddler-border",
"text": "Tiddler border"
},
"$:/language/Docs/PaletteColours/tiddler-controls-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/tiddler-controls-foreground-hover",
"text": "Tiddler controls foreground hover"
},
"$:/language/Docs/PaletteColours/tiddler-controls-foreground-selected": {
"title": "$:/language/Docs/PaletteColours/tiddler-controls-foreground-selected",
"text": "Tiddler controls foreground for selected controls"
},
"$:/language/Docs/PaletteColours/tiddler-controls-foreground": {
"title": "$:/language/Docs/PaletteColours/tiddler-controls-foreground",
"text": "Tiddler controls foreground"
},
"$:/language/Docs/PaletteColours/tiddler-editor-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-background",
"text": "Tiddler editor background"
},
"$:/language/Docs/PaletteColours/tiddler-editor-border-image": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-border-image",
"text": "Tiddler editor border image"
},
"$:/language/Docs/PaletteColours/tiddler-editor-border": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-border",
"text": "Tiddler editor border"
},
"$:/language/Docs/PaletteColours/tiddler-editor-fields-even": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-fields-even",
"text": "Tiddler editor background for even fields"
},
"$:/language/Docs/PaletteColours/tiddler-editor-fields-odd": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-fields-odd",
"text": "Tiddler editor background for odd fields"
},
"$:/language/Docs/PaletteColours/tiddler-info-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-info-background",
"text": "Tiddler info panel background"
},
"$:/language/Docs/PaletteColours/tiddler-info-border": {
"title": "$:/language/Docs/PaletteColours/tiddler-info-border",
"text": "Tiddler info panel border"
},
"$:/language/Docs/PaletteColours/tiddler-info-tab-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-info-tab-background",
"text": "Tiddler info panel tab background"
},
"$:/language/Docs/PaletteColours/tiddler-link-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-link-background",
"text": "Tiddler link background"
},
"$:/language/Docs/PaletteColours/tiddler-link-foreground": {
"title": "$:/language/Docs/PaletteColours/tiddler-link-foreground",
"text": "Tiddler link foreground"
},
"$:/language/Docs/PaletteColours/tiddler-subtitle-foreground": {
"title": "$:/language/Docs/PaletteColours/tiddler-subtitle-foreground",
"text": "Tiddler subtitle foreground"
},
"$:/language/Docs/PaletteColours/tiddler-title-foreground": {
"title": "$:/language/Docs/PaletteColours/tiddler-title-foreground",
"text": "Tiddler title foreground"
},
"$:/language/Docs/PaletteColours/toolbar-new-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-new-button",
"text": "Toolbar 'new tiddler' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-options-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-options-button",
"text": "Toolbar 'options' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-save-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-save-button",
"text": "Toolbar 'save' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-info-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-info-button",
"text": "Toolbar 'info' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-edit-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-edit-button",
"text": "Toolbar 'edit' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-close-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-close-button",
"text": "Toolbar 'close' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-delete-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-delete-button",
"text": "Toolbar 'delete' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-cancel-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-cancel-button",
"text": "Toolbar 'cancel' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-done-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-done-button",
"text": "Toolbar 'done' button foreground"
},
"$:/language/Docs/PaletteColours/untagged-background": {
"title": "$:/language/Docs/PaletteColours/untagged-background",
"text": "Untagged pill background"
},
"$:/language/Docs/PaletteColours/very-muted-foreground": {
"title": "$:/language/Docs/PaletteColours/very-muted-foreground",
"text": "Very muted foreground"
},
"$:/language/EditTemplate/Body/External/Hint": {
"title": "$:/language/EditTemplate/Body/External/Hint",
"text": "This tiddler shows content stored outside of the main TiddlyWiki file. You can edit the tags and fields but cannot directly edit the content itself"
},
"$:/language/EditTemplate/Body/Placeholder": {
"title": "$:/language/EditTemplate/Body/Placeholder",
"text": "Type the text for this tiddler"
},
"$:/language/EditTemplate/Body/Preview/Type/Output": {
"title": "$:/language/EditTemplate/Body/Preview/Type/Output",
"text": "output"
},
"$:/language/EditTemplate/Field/Remove/Caption": {
"title": "$:/language/EditTemplate/Field/Remove/Caption",
"text": "remove field"
},
"$:/language/EditTemplate/Field/Remove/Hint": {
"title": "$:/language/EditTemplate/Field/Remove/Hint",
"text": "Remove field"
},
"$:/language/EditTemplate/Field/Dropdown/Caption": {
"title": "$:/language/EditTemplate/Field/Dropdown/Caption",
"text": "field list"
},
"$:/language/EditTemplate/Field/Dropdown/Hint": {
"title": "$:/language/EditTemplate/Field/Dropdown/Hint",
"text": "Show field list"
},
"$:/language/EditTemplate/Fields/Add/Button": {
"title": "$:/language/EditTemplate/Fields/Add/Button",
"text": "add"
},
"$:/language/EditTemplate/Fields/Add/Button/Hint": {
"title": "$:/language/EditTemplate/Fields/Add/Button/Hint",
"text": "Add the new field to the tiddler"
},
"$:/language/EditTemplate/Fields/Add/Name/Placeholder": {
"title": "$:/language/EditTemplate/Fields/Add/Name/Placeholder",
"text": "field name"
},
"$:/language/EditTemplate/Fields/Add/Prompt": {
"title": "$:/language/EditTemplate/Fields/Add/Prompt",
"text": "Add a new field:"
},
"$:/language/EditTemplate/Fields/Add/Value/Placeholder": {
"title": "$:/language/EditTemplate/Fields/Add/Value/Placeholder",
"text": "field value"
},
"$:/language/EditTemplate/Fields/Add/Dropdown/System": {
"title": "$:/language/EditTemplate/Fields/Add/Dropdown/System",
"text": "System fields"
},
"$:/language/EditTemplate/Fields/Add/Dropdown/User": {
"title": "$:/language/EditTemplate/Fields/Add/Dropdown/User",
"text": "User fields"
},
"$:/language/EditTemplate/Shadow/Warning": {
"title": "$:/language/EditTemplate/Shadow/Warning",
"text": "This is a shadow tiddler. Any changes you make will override the default version from the plugin <<pluginLink>>"
},
"$:/language/EditTemplate/Shadow/OverriddenWarning": {
"title": "$:/language/EditTemplate/Shadow/OverriddenWarning",
"text": "This is a modified shadow tiddler. You can revert to the default version in the plugin <<pluginLink>> by deleting this tiddler"
},
"$:/language/EditTemplate/Tags/Add/Button": {
"title": "$:/language/EditTemplate/Tags/Add/Button",
"text": "add"
},
"$:/language/EditTemplate/Tags/Add/Button/Hint": {
"title": "$:/language/EditTemplate/Tags/Add/Button/Hint",
"text": "add tag"
},
"$:/language/EditTemplate/Tags/Add/Placeholder": {
"title": "$:/language/EditTemplate/Tags/Add/Placeholder",
"text": "tag name"
},
"$:/language/EditTemplate/Tags/Dropdown/Caption": {
"title": "$:/language/EditTemplate/Tags/Dropdown/Caption",
"text": "tag list"
},
"$:/language/EditTemplate/Tags/Dropdown/Hint": {
"title": "$:/language/EditTemplate/Tags/Dropdown/Hint",
"text": "Show tag list"
},
"$:/language/EditTemplate/Title/BadCharacterWarning": {
"title": "$:/language/EditTemplate/Title/BadCharacterWarning",
"text": "Warning: avoid using any of the characters <<bad-chars>> in tiddler titles"
},
"$:/language/EditTemplate/Title/Exists/Prompt": {
"title": "$:/language/EditTemplate/Title/Exists/Prompt",
"text": "Target tiddler already exists"
},
"$:/language/EditTemplate/Title/Relink/Prompt": {
"title": "$:/language/EditTemplate/Title/Relink/Prompt",
"text": "Update ''<$text text=<<fromTitle>>/>'' to ''<$text text=<<toTitle>>/>'' in the //tags// and //list// fields of other tiddlers"
},
"$:/language/EditTemplate/Title/References/Prompt": {
"title": "$:/language/EditTemplate/Title/References/Prompt",
"text": "The following references to this tiddler will not be automatically updated:"
},
"$:/language/EditTemplate/Type/Dropdown/Caption": {
"title": "$:/language/EditTemplate/Type/Dropdown/Caption",
"text": "content type list"
},
"$:/language/EditTemplate/Type/Dropdown/Hint": {
"title": "$:/language/EditTemplate/Type/Dropdown/Hint",
"text": "Show content type list"
},
"$:/language/EditTemplate/Type/Delete/Caption": {
"title": "$:/language/EditTemplate/Type/Delete/Caption",
"text": "delete content type"
},
"$:/language/EditTemplate/Type/Delete/Hint": {
"title": "$:/language/EditTemplate/Type/Delete/Hint",
"text": "Delete content type"
},
"$:/language/EditTemplate/Type/Placeholder": {
"title": "$:/language/EditTemplate/Type/Placeholder",
"text": "content type"
},
"$:/language/EditTemplate/Type/Prompt": {
"title": "$:/language/EditTemplate/Type/Prompt",
"text": "Type:"
},
"$:/language/Exporters/StaticRiver": {
"title": "$:/language/Exporters/StaticRiver",
"text": "Static HTML"
},
"$:/language/Exporters/JsonFile": {
"title": "$:/language/Exporters/JsonFile",
"text": "JSON file"
},
"$:/language/Exporters/CsvFile": {
"title": "$:/language/Exporters/CsvFile",
"text": "CSV file"
},
"$:/language/Exporters/TidFile": {
"title": "$:/language/Exporters/TidFile",
"text": "\".tid\" file"
},
"$:/language/Docs/Fields/_canonical_uri": {
"title": "$:/language/Docs/Fields/_canonical_uri",
"text": "The full URI of an external image tiddler"
},
"$:/language/Docs/Fields/bag": {
"title": "$:/language/Docs/Fields/bag",
"text": "The name of the bag from which a tiddler came"
},
"$:/language/Docs/Fields/caption": {
"title": "$:/language/Docs/Fields/caption",
"text": "The text to be displayed on a tab or button"
},
"$:/language/Docs/Fields/color": {
"title": "$:/language/Docs/Fields/color",
"text": "The CSS color value associated with a tiddler"
},
"$:/language/Docs/Fields/component": {
"title": "$:/language/Docs/Fields/component",
"text": "The name of the component responsible for an [[alert tiddler|AlertMechanism]]"
},
"$:/language/Docs/Fields/current-tiddler": {
"title": "$:/language/Docs/Fields/current-tiddler",
"text": "Used to cache the top tiddler in a [[history list|HistoryMechanism]]"
},
"$:/language/Docs/Fields/created": {
"title": "$:/language/Docs/Fields/created",
"text": "The date a tiddler was created"
},
"$:/language/Docs/Fields/creator": {
"title": "$:/language/Docs/Fields/creator",
"text": "The name of the person who created a tiddler"
},
"$:/language/Docs/Fields/dependents": {
"title": "$:/language/Docs/Fields/dependents",
"text": "For a plugin, lists the dependent plugin titles"
},
"$:/language/Docs/Fields/description": {
"title": "$:/language/Docs/Fields/description",
"text": "The descriptive text for a plugin, or a modal dialogue"
},
"$:/language/Docs/Fields/draft.of": {
"title": "$:/language/Docs/Fields/draft.of",
"text": "For draft tiddlers, contains the title of the tiddler of which this is a draft"
},
"$:/language/Docs/Fields/draft.title": {
"title": "$:/language/Docs/Fields/draft.title",
"text": "For draft tiddlers, contains the proposed new title of the tiddler"
},
"$:/language/Docs/Fields/footer": {
"title": "$:/language/Docs/Fields/footer",
"text": "The footer text for a wizard"
},
"$:/language/Docs/Fields/hide-body": {
"title": "$:/language/Docs/Fields/hide-body",
"text": "The view template will hide bodies of tiddlers if set to: ''yes''"
},
"$:/language/Docs/Fields/icon": {
"title": "$:/language/Docs/Fields/icon",
"text": "The title of the tiddler containing the icon associated with a tiddler"
},
"$:/language/Docs/Fields/library": {
"title": "$:/language/Docs/Fields/library",
"text": "Indicates that a tiddler should be saved as a JavaScript library if set to: ''yes''"
},
"$:/language/Docs/Fields/list": {
"title": "$:/language/Docs/Fields/list",
"text": "An ordered list of tiddler titles associated with a tiddler"
},
"$:/language/Docs/Fields/list-before": {
"title": "$:/language/Docs/Fields/list-before",
"text": "If set, the title of a tiddler before which this tiddler should be added to the ordered list of tiddler titles, or at the start of the list if this field is present but empty"
},
"$:/language/Docs/Fields/list-after": {
"title": "$:/language/Docs/Fields/list-after",
"text": "If set, the title of the tiddler after which this tiddler should be added to the ordered list of tiddler titles, or at the end of the list if this field is present but empty"
},
"$:/language/Docs/Fields/modified": {
"title": "$:/language/Docs/Fields/modified",
"text": "The date and time at which a tiddler was last modified"
},
"$:/language/Docs/Fields/modifier": {
"title": "$:/language/Docs/Fields/modifier",
"text": "The tiddler title associated with the person who last modified a tiddler"
},
"$:/language/Docs/Fields/name": {
"title": "$:/language/Docs/Fields/name",
"text": "The human readable name associated with a plugin tiddler"
},
"$:/language/Docs/Fields/plugin-priority": {
"title": "$:/language/Docs/Fields/plugin-priority",
"text": "A numerical value indicating the priority of a plugin tiddler"
},
"$:/language/Docs/Fields/plugin-type": {
"title": "$:/language/Docs/Fields/plugin-type",
"text": "The type of plugin in a plugin tiddler"
},
"$:/language/Docs/Fields/revision": {
"title": "$:/language/Docs/Fields/revision",
"text": "The revision of the tiddler held at the server"
},
"$:/language/Docs/Fields/released": {
"title": "$:/language/Docs/Fields/released",
"text": "Date of a TiddlyWiki release"
},
"$:/language/Docs/Fields/source": {
"title": "$:/language/Docs/Fields/source",
"text": "The source URL associated with a tiddler"
},
"$:/language/Docs/Fields/subtitle": {
"title": "$:/language/Docs/Fields/subtitle",
"text": "The subtitle text for a wizard"
},
"$:/language/Docs/Fields/tags": {
"title": "$:/language/Docs/Fields/tags",
"text": "A list of tags associated with a tiddler"
},
"$:/language/Docs/Fields/text": {
"title": "$:/language/Docs/Fields/text",
"text": "The body text of a tiddler"
},
"$:/language/Docs/Fields/throttle.refresh": {
"title": "$:/language/Docs/Fields/throttle.refresh",
"text": "If present, throttles refreshes of this tiddler"
},
"$:/language/Docs/Fields/title": {
"title": "$:/language/Docs/Fields/title",
"text": "The unique name of a tiddler"
},
"$:/language/Docs/Fields/toc-link": {
"title": "$:/language/Docs/Fields/toc-link",
"text": "Suppresses the tiddler's link in a Table of Contents tree if set to: ''no''"
},
"$:/language/Docs/Fields/type": {
"title": "$:/language/Docs/Fields/type",
"text": "The content type of a tiddler"
},
"$:/language/Docs/Fields/version": {
"title": "$:/language/Docs/Fields/version",
"text": "Version information for a plugin"
},
"$:/language/Docs/Fields/_is_skinny": {
"title": "$:/language/Docs/Fields/_is_skinny",
"text": "If present, indicates that the tiddler text field must be loaded from the server"
},
"$:/language/Filters/AllTiddlers": {
"title": "$:/language/Filters/AllTiddlers",
"text": "All tiddlers except system tiddlers"
},
"$:/language/Filters/RecentSystemTiddlers": {
"title": "$:/language/Filters/RecentSystemTiddlers",
"text": "Recently modified tiddlers, including system tiddlers"
},
"$:/language/Filters/RecentTiddlers": {
"title": "$:/language/Filters/RecentTiddlers",
"text": "Recently modified tiddlers"
},
"$:/language/Filters/AllTags": {
"title": "$:/language/Filters/AllTags",
"text": "All tags except system tags"
},
"$:/language/Filters/Missing": {
"title": "$:/language/Filters/Missing",
"text": "Missing tiddlers"
},
"$:/language/Filters/Drafts": {
"title": "$:/language/Filters/Drafts",
"text": "Draft tiddlers"
},
"$:/language/Filters/Orphans": {
"title": "$:/language/Filters/Orphans",
"text": "Orphan tiddlers"
},
"$:/language/Filters/SystemTiddlers": {
"title": "$:/language/Filters/SystemTiddlers",
"text": "System tiddlers"
},
"$:/language/Filters/ShadowTiddlers": {
"title": "$:/language/Filters/ShadowTiddlers",
"text": "Shadow tiddlers"
},
"$:/language/Filters/OverriddenShadowTiddlers": {
"title": "$:/language/Filters/OverriddenShadowTiddlers",
"text": "Overridden shadow tiddlers"
},
"$:/language/Filters/SessionTiddlers": {
"title": "$:/language/Filters/SessionTiddlers",
"text": "Tiddlers modified since the wiki was loaded"
},
"$:/language/Filters/SystemTags": {
"title": "$:/language/Filters/SystemTags",
"text": "System tags"
},
"$:/language/Filters/StoryList": {
"title": "$:/language/Filters/StoryList",
"text": "Tiddlers in the story river, excluding <$text text=\"$:/AdvancedSearch\"/>"
},
"$:/language/Filters/TypedTiddlers": {
"title": "$:/language/Filters/TypedTiddlers",
"text": "Non wiki-text tiddlers"
},
"GettingStarted": {
"title": "GettingStarted",
"text": "\\define lingo-base() $:/language/ControlPanel/Basics/\nWelcome to ~TiddlyWiki and the ~TiddlyWiki community\n\nBefore you start storing important information in ~TiddlyWiki it is vital to make sure that you can reliably save changes. See https://tiddlywiki.com/#GettingStarted for details\n\n!! Set up this ~TiddlyWiki\n\n<div class=\"tc-control-panel\">\n\n|<$link to=\"$:/SiteTitle\"><<lingo Title/Prompt>></$link> |<$edit-text tiddler=\"$:/SiteTitle\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/SiteSubtitle\"><<lingo Subtitle/Prompt>></$link> |<$edit-text tiddler=\"$:/SiteSubtitle\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/DefaultTiddlers\"><<lingo DefaultTiddlers/Prompt>></$link> |<<lingo DefaultTiddlers/TopHint>><br> <$edit tag=\"textarea\" tiddler=\"$:/DefaultTiddlers\"/><br>//<<lingo DefaultTiddlers/BottomHint>>// |\n</div>\n\nSee the [[control panel|$:/ControlPanel]] for more options.\n"
},
"$:/language/Help/build": {
"title": "$:/language/Help/build",
"description": "Automatically run configured commands",
"text": "Build the specified build targets for the current wiki. If no build targets are specified then all available targets will be built.\n\n```\n--build <target> [<target> ...]\n```\n\nBuild targets are defined in the `tiddlywiki.info` file of a wiki folder.\n\n"
},
"$:/language/Help/clearpassword": {
"title": "$:/language/Help/clearpassword",
"description": "Clear a password for subsequent crypto operations",
"text": "Clear the password for subsequent crypto operations\n\n```\n--clearpassword\n```\n"
},
"$:/language/Help/default": {
"title": "$:/language/Help/default",
"text": "\\define commandTitle()\n$:/language/Help/$(command)$\n\\end\n```\nusage: tiddlywiki [<wikifolder>] [--<command> [<args>...]...]\n```\n\nAvailable commands:\n\n<ul>\n<$list filter=\"[commands[]sort[title]]\" variable=\"command\">\n<li><$link to=<<commandTitle>>><$macrocall $name=\"command\" $type=\"text/plain\" $output=\"text/plain\"/></$link>: <$transclude tiddler=<<commandTitle>> field=\"description\"/></li>\n</$list>\n</ul>\n\nTo get detailed help on a command:\n\n```\ntiddlywiki --help <command>\n```\n"
},
"$:/language/Help/deletetiddlers": {
"title": "$:/language/Help/deletetiddlers",
"description": "Deletes a group of tiddlers",
"text": "<<.from-version \"5.1.20\">> Deletes a group of tiddlers identified by a filter.\n\n```\n--deletetiddlers <filter>\n```\n"
},
"$:/language/Help/editions": {
"title": "$:/language/Help/editions",
"description": "Lists the available editions of TiddlyWiki",
"text": "Lists the names and descriptions of the available editions. You can create a new wiki of a specified edition with the `--init` command.\n\n```\n--editions\n```\n"
},
"$:/language/Help/fetch": {
"title": "$:/language/Help/fetch",
"description": "Fetch tiddlers from wiki by URL",
"text": "Fetch one or more files over HTTP/HTTPS, and import the tiddlers matching a filter, optionally transforming the incoming titles.\n\n```\n--fetch file <url> <import-filter> <transform-filter>\n--fetch files <url-filter> <import-filter> <transform-filter>\n--fetch raw-file <url> <transform-filter>\n--fetch raw-files <url-filter> <transform-filter>\n```\n\nThe \"file\" and \"files\" variants fetch the specified files and attempt to import the tiddlers within them (the same processing as if the files were dragged into the browser window). The \"raw-file\" and \"raw-files\" variants fetch the specified files and then store the raw file data in tiddlers, without applying the import logic.\n\nWith the \"file\" and \"raw-file\" variants only a single file is fetched and the first parameter is the URL of the file to read.\n\nWith the \"files\" and \"raw-files\" variants, multiple files are fetched and the first parameter is a filter yielding a list of URLs of the files to read. For example, given a set of tiddlers tagged \"remote-server\" that have a field \"url\" the filter `[tag[remote-server]get[url]]` will retrieve all the available URLs.\n\nFor the \"file\" and \"files\" variants, the `<import-filter>` parameter specifies a filter determining which tiddlers are imported. It defaults to `[all[tiddlers]]` if not provided.\n\nFor all variants, the `<transform-filter>` parameter specifies an optional filter that transforms the titles of the imported tiddlers. For example, `[addprefix[$:/myimports/]]` would add the prefix `$:/myimports/` to each title.\n\nPreceding the `--fetch` command with `--verbose` will output progress information during the import.\n\nNote that TiddlyWiki will not fetch an older version of an already loaded plugin.\n\nThe following example retrieves all the non-system tiddlers from https://tiddlywiki.com and saves them to a JSON file:\n\n```\ntiddlywiki --verbose --fetch file \"https://tiddlywiki.com/\" \"[!is[system]]\" \"\" --rendertiddler \"$:/core/templates/exporters/JsonFile\" output.json text/plain \"\" exportFilter \"[!is[system]]\"\n```\n\nThe following example retrieves the \"favicon\" file from tiddlywiki.com and saves it in a file called \"output.ico\". Note that the intermediate tiddler \"Icon Tiddler\" is quoted in the \"--fetch\" command because it is being used as a transformation filter to replace the default title, while there are no quotes for the \"--savetiddler\" command because it is being used directly as a title.\n\n```\ntiddlywiki --verbose --fetch raw-file \"https://tiddlywiki.com/favicon.ico\" \"[[Icon Tiddler]]\" --savetiddler \"Icon Tiddler\" output.ico\n```\n\n"
},
"$:/language/Help/help": {
"title": "$:/language/Help/help",
"description": "Display help for TiddlyWiki commands",
"text": "Displays help text for a command:\n\n```\n--help [<command>]\n```\n\nIf the command name is omitted then a list of available commands is displayed.\n"
},
"$:/language/Help/import": {
"title": "$:/language/Help/import",
"description": "Import tiddlers from a file",
"text": "Import tiddlers from TiddlyWiki (`.html`), `.tiddler`, `.tid`, `.json` or other local files. The deserializer must be explicitly specified, unlike the `load` command which infers the deserializer from the file extension.\n\n```\n--import <filepath> <deserializer> [<title>] [<encoding>]\n```\n\nThe deserializers in the core include:\n\n* application/javascript\n* application/json\n* application/x-tiddler\n* application/x-tiddler-html-div\n* application/x-tiddlers\n* text/html\n* text/plain\n\nThe title of the imported tiddler defaults to the filename.\n\nThe encoding defaults to \"utf8\", but can be \"base64\" for importing binary files.\n\nNote that TiddlyWiki will not import an older version of an already loaded plugin.\n"
},
"$:/language/Help/init": {
"title": "$:/language/Help/init",
"description": "Initialise a new wiki folder",
"text": "Initialise an empty [[WikiFolder|WikiFolders]] with a copy of the specified edition.\n\n```\n--init <edition> [<edition> ...]\n```\n\nFor example:\n\n```\ntiddlywiki ./MyWikiFolder --init empty\n```\n\nNote:\n\n* The wiki folder directory will be created if necessary\n* The \"edition\" defaults to ''empty''\n* The init command will fail if the wiki folder is not empty\n* The init command removes any `includeWikis` definitions in the edition's `tiddlywiki.info` file\n* When multiple editions are specified, editions initialised later will overwrite any files shared with earlier editions (so, the final `tiddlywiki.info` file will be copied from the last edition)\n* `--editions` returns a list of available editions\n"
},
"$:/language/Help/listen": {
"title": "$:/language/Help/listen",
"description": "Provides an HTTP server interface to TiddlyWiki",
"text": "Serves a wiki over HTTP.\n\nThe listen command uses NamedCommandParameters:\n\n```\n--listen [<name>=<value>]...\n```\n\nAll parameters are optional with safe defaults, and can be specified in any order. The recognised parameters are:\n\n* ''host'' - optional hostname to serve from (defaults to \"127.0.0.1\" aka \"localhost\")\n* ''path-prefix'' - optional prefix for paths\n* ''port'' - port number on which to listen; non-numeric values are interpreted as a system environment variable from which the port number is extracted (defaults to \"8080\")\n* ''credentials'' - pathname of credentials CSV file (relative to wiki folder)\n* ''anon-username'' - the username for signing edits for anonymous users\n* ''username'' - optional username for basic authentication\n* ''password'' - optional password for basic authentication\n* ''authenticated-user-header'' - optional name of header to be used for trusted authentication\n* ''readers'' - comma separated list of principals allowed to read from this wiki\n* ''writers'' - comma separated list of principals allowed to write to this wiki\n* ''csrf-disable'' - set to \"yes\" to disable CSRF checks (defaults to \"no\")\n* ''root-tiddler'' - the tiddler to serve at the root (defaults to \"$:/core/save/all\")\n* ''root-render-type'' - the content type to which the root tiddler should be rendered (defaults to \"text/plain\")\n* ''root-serve-type'' - the content type with which the root tiddler should be served (defaults to \"text/html\")\n* ''tls-cert'' - pathname of TLS certificate file (relative to wiki folder)\n* ''tls-key'' - pathname of TLS key file (relative to wiki folder)\n* ''debug-level'' - optional debug level; set to \"debug\" to view request details (defaults to \"none\")\n* ''gzip'' - set to \"yes\" to enable gzip compression for some http endpoints (defaults to \"no\")\n\nFor information on opening up your instance to the entire local network, and possible security concerns, see the WebServer tiddler at TiddlyWiki.com.\n\n"
},
"$:/language/Help/load": {
"title": "$:/language/Help/load",
"description": "Load tiddlers from a file",
"text": "Load tiddlers from TiddlyWiki (`.html`), `.tiddler`, `.tid`, `.json` or other local files. The processing applied to incoming files is determined by the file extension. Use the alternative `import` command if you need to specify the deserializer and encoding explicitly.\n\n```\n--load <filepath> [noerror]\n--load <dirpath> [noerror]\n```\n\nBy default, the load command raises an error if no tiddlers are found. The error can be suppressed by providing the optional \"noerror\" parameter.\n\nTo load tiddlers from an encrypted TiddlyWiki file you should first specify the password with the PasswordCommand. For example:\n\n```\ntiddlywiki ./MyWiki --password pa55w0rd --load my_encrypted_wiki.html\n```\n\nNote that TiddlyWiki will not load an older version of an already loaded plugin.\n"
},
"$:/language/Help/makelibrary": {
"title": "$:/language/Help/makelibrary",
"description": "Construct library plugin required by upgrade process",
"text": "Constructs the `$:/UpgradeLibrary` tiddler for the upgrade process.\n\nThe upgrade library is formatted as an ordinary plugin tiddler with the plugin type `library`. It contains a copy of each of the plugins, themes and language packs available within the TiddlyWiki5 repository.\n\nThis command is intended for internal use; it is only relevant to users constructing a custom upgrade procedure.\n\n```\n--makelibrary <title>\n```\n\nThe title argument defaults to `$:/UpgradeLibrary`.\n"
},
"$:/language/Help/notfound": {
"title": "$:/language/Help/notfound",
"text": "No such help item"
},
"$:/language/Help/output": {
"title": "$:/language/Help/output",
"description": "Set the base output directory for subsequent commands",
"text": "Sets the base output directory for subsequent commands. The default output directory is the `output` subdirectory of the edition directory.\n\n```\n--output <pathname>\n```\n\nIf the specified pathname is relative then it is resolved relative to the current working directory. For example `--output .` sets the output directory to the current working directory.\n\n"
},
"$:/language/Help/password": {
"title": "$:/language/Help/password",
"description": "Set a password for subsequent crypto operations",
"text": "Set a password for subsequent crypto operations\n\n```\n--password <password>\n```\n\n''Note'': This should not be used for serving TiddlyWiki with password protection. Instead, see the password option under the [[ServerCommand]].\n"
},
"$:/language/Help/render": {
"title": "$:/language/Help/render",
"description": "Renders individual tiddlers to files",
"text": "Render individual tiddlers identified by a filter and save the results to the specified files.\n\nOptionally, the title of a template tiddler can be specified. In this case, instead of directly rendering each tiddler, the template tiddler is rendered with the \"currentTiddler\" variable set to the title of the tiddler that is being rendered.\n\nA name and value for an additional variable may optionally also be specified.\n\n```\n--render <tiddler-filter> [<filename-filter>] [<render-type>] [<template>] [<name>] [<value>]\n```\n\n* ''tiddler-filter'': A filter identifying the tiddler(s) to be rendered\n* ''filename-filter'': Optional filter transforming tiddler titles into pathnames. If omitted, defaults to `[is[tiddler]addsuffix[.html]]`, which uses the unchanged tiddler title as the filename\n* ''render-type'': Optional render type: `text/html` (the default) returns the full HTML text and `text/plain` just returns the text content (ie it ignores HTML tags and other unprintable material)\n* ''template'': Optional template through which each tiddler is rendered\n* ''name'': Name of optional variable\n* ''value'': Value of optional variable\n\nBy default, the filename is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nNotes:\n\n* The output directory is not cleared of any existing files\n* Any missing directories in the path to the filename are automatically created.\n* When referring to a tiddler with spaces in its title, take care to use both the quotes required by your shell and also TiddlyWiki's double square brackets : `--render \"[[Motovun Jack.jpg]]\"`\n* The filename filter is evaluated with the selected items being set to the title of the tiddler currently being rendered, allowing the title to be used as the basis for computing the filename. For example `[encodeuricomponent[]addprefix[static/]]` applies URI encoding to each title, and then adds the prefix `static/`\n* The `--render` command is a more flexible replacement for both the `--rendertiddler` and `--rendertiddlers` commands, which are deprecated\n\nExamples:\n\n* `--render \"[!is[system]]\" \"[encodeuricomponent[]addprefix[tiddlers/]addsuffix[.html]]\"` -- renders all non-system tiddlers as files in the subdirectory \"tiddlers\" with URL-encoded titles and the extension HTML\n\n"
},
"$:/language/Help/rendertiddler": {
"title": "$:/language/Help/rendertiddler",
"description": "Render an individual tiddler as a specified ContentType",
"text": "(Note: The `--rendertiddler` command is deprecated in favour of the new, more flexible `--render` command)\n\nRender an individual tiddler as a specified ContentType, defaulting to `text/html` and save it to the specified filename.\n\nOptionally the title of a template tiddler can be specified, in which case the template tiddler is rendered with the \"currentTiddler\" variable set to the tiddler that is being rendered (the first parameter value).\n\nA name and value for an additional variable may optionally also be specified.\n\n```\n--rendertiddler <title> <filename> [<type>] [<template>] [<name>] [<value>]\n```\n\nBy default, the filename is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nAny missing directories in the path to the filename are automatically created.\n\nFor example, the following command saves all tiddlers matching the filter `[tag[done]]` to a JSON file titled `output.json` by employing the core template `$:/core/templates/exporters/JsonFile`.\n\n```\n--rendertiddler \"$:/core/templates/exporters/JsonFile\" output.json text/plain \"\" exportFilter \"[tag[done]]\"\n```\n"
},
"$:/language/Help/rendertiddlers": {
"title": "$:/language/Help/rendertiddlers",
"description": "Render tiddlers matching a filter to a specified ContentType",
"text": "(Note: The `--rendertiddlers` command is deprecated in favour of the new, more flexible `--render` command)\n\nRender a set of tiddlers matching a filter to separate files of a specified ContentType (defaults to `text/html`) and extension (defaults to `.html`).\n\n```\n--rendertiddlers <filter> <template> <pathname> [<type>] [<extension>] [\"noclean\"]\n```\n\nFor example:\n\n```\n--rendertiddlers [!is[system]] $:/core/templates/static.tiddler.html ./static text/plain\n```\n\nBy default, the pathname is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nAny files in the target directory are deleted unless the ''noclean'' flag is specified. The target directory is recursively created if it is missing.\n"
},
"$:/language/Help/save": {
"title": "$:/language/Help/save",
"description": "Saves individual raw tiddlers to files",
"text": "Saves individual tiddlers identified by a filter in their raw text or binary format to the specified files.\n\n```\n--save <tiddler-filter> <filename-filter>\n```\n\n* ''tiddler-filter'': A filter identifying the tiddler(s) to be saved\n* ''filename-filter'': Optional filter transforming tiddler titles into pathnames. If omitted, defaults to `[is[tiddler]]`, which uses the unchanged tiddler title as the filename\n\nBy default, the filename is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nNotes:\n\n* The output directory is not cleared of any existing files\n* Any missing directories in the path to the filename are automatically created.\n* When saving a tiddler with spaces in its title, take care to use both the quotes required by your shell and also TiddlyWiki's double square brackets : `--save \"[[Motovun Jack.jpg]]\"`\n* The filename filter is evaluated with the selected items being set to the title of the tiddler currently being saved, allowing the title to be used as the basis for computing the filename. For example `[encodeuricomponent[]addprefix[static/]]` applies URI encoding to each title, and then adds the prefix `static/`\n* The `--save` command is a more flexible replacement for both the `--savetiddler` and `--savetiddlers` commands, which are deprecated\n\nExamples:\n\n* `--save \"[!is[system]is[image]]\" \"[encodeuricomponent[]addprefix[tiddlers/]]\"` -- saves all non-system image tiddlers as files in the subdirectory \"tiddlers\" with URL-encoded titles\n"
},
"$:/language/Help/savetiddler": {
"title": "$:/language/Help/savetiddler",
"description": "Saves a raw tiddler to a file",
"text": "(Note: The `--savetiddler` command is deprecated in favour of the new, more flexible `--save` command)\n\nSaves an individual tiddler in its raw text or binary format to the specified filename.\n\n```\n--savetiddler <title> <filename>\n```\n\nBy default, the filename is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nAny missing directories in the path to the filename are automatically created.\n"
},
"$:/language/Help/savetiddlers": {
"title": "$:/language/Help/savetiddlers",
"description": "Saves a group of raw tiddlers to a directory",
"text": "(Note: The `--savetiddlers` command is deprecated in favour of the new, more flexible `--save` command)\n\nSaves a group of tiddlers in their raw text or binary format to the specified directory.\n\n```\n--savetiddlers <filter> <pathname> [\"noclean\"]\n```\n\nBy default, the pathname is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nThe output directory is cleared of existing files before saving the specified files. The deletion can be disabled by specifying the ''noclean'' flag.\n\nAny missing directories in the pathname are automatically created.\n"
},
"$:/language/Help/savewikifolder": {
"title": "$:/language/Help/savewikifolder",
"description": "Saves a wiki to a new wiki folder",
"text": "<<.from-version \"5.1.20\">> Saves the current wiki as a wiki folder, including tiddlers, plugins and configuration:\n\n```\n--savewikifolder <wikifolderpath> [<filter>]\n```\n\n* The target wiki folder must be empty or non-existent\n* The filter specifies which tiddlers should be included. It is optional, defaulting to `[all[tiddlers]]`\n* Plugins from the official plugin library are replaced with references to those plugins in the `tiddlywiki.info` file\n* Custom plugins are unpacked into their own folder\n\nA common usage is to convert a TiddlyWiki HTML file into a wiki folder:\n\n```\ntiddlywiki --load ./mywiki.html --savewikifolder ./mywikifolder\n```\n"
},
"$:/language/Help/server": {
"title": "$:/language/Help/server",
"description": "Provides an HTTP server interface to TiddlyWiki (deprecated in favour of the new listen command)",
"text": "Legacy command to serve a wiki over HTTP.\n\n```\n--server <port> <root-tiddler> <root-render-type> <root-serve-type> <username> <password> <host> <path-prefix> <debug-level>\n```\n\nThe parameters are:\n\n* ''port'' - port number on which to listen; non-numeric values are interpreted as a system environment variable from which the port number is extracted (defaults to \"8080\")\n* ''root-tiddler'' - the tiddler to serve at the root (defaults to \"$:/core/save/all\")\n* ''root-render-type'' - the content type to which the root tiddler should be rendered (defaults to \"text/plain\")\n* ''root-serve-type'' - the content type with which the root tiddler should be served (defaults to \"text/html\")\n* ''username'' - the default username for signing edits\n* ''password'' - optional password for basic authentication\n* ''host'' - optional hostname to serve from (defaults to \"127.0.0.1\" aka \"localhost\")\n* ''path-prefix'' - optional prefix for paths\n* ''debug-level'' - optional debug level; set to \"debug\" to view request details (defaults to \"none\")\n\nIf the password parameter is specified then the browser will prompt the user for the username and password. Note that the password is transmitted in plain text so this implementation should only be used on a trusted network or over HTTPS.\n\nFor example:\n\n```\n--server 8080 $:/core/save/all text/plain text/html MyUserName passw0rd\n```\n\nThe username and password can be specified as empty strings if you need to set the hostname or pathprefix and don't want to require a password.\n\n\n```\n--server 8080 $:/core/save/all text/plain text/html \"\" \"\" 192.168.0.245\n```\n\nUsing an address like this exposes your system to the local network. For information on opening up your instance to the entire local network, and possible security concerns, see the WebServer tiddler at TiddlyWiki.com.\n\nTo run multiple TiddlyWiki servers at the same time you'll need to put each one on a different port. It can be useful to use an environment variable to pass the port number to the Node.js process. This example references an environment variable called \"MY_PORT_NUMBER\":\n\n```\n--server MY_PORT_NUMBER $:/core/save/all text/plain text/html MyUserName passw0rd\n```\n"
},
"$:/language/Help/setfield": {
"title": "$:/language/Help/setfield",
"description": "Prepares external tiddlers for use",
"text": "//Note that this command is experimental and may change or be replaced before being finalised//\n\nSets the specified field of a group of tiddlers to the result of wikifying a template tiddler with the `currentTiddler` variable set to the tiddler.\n\n```\n--setfield <filter> <fieldname> <templatetitle> <rendertype>\n```\n\nThe parameters are:\n\n* ''filter'' - filter identifying the tiddlers to be affected\n* ''fieldname'' - the field to modify (defaults to \"text\")\n* ''templatetitle'' - the tiddler to wikify into the specified field. If blank or missing then the specified field is deleted\n* ''rendertype'' - the text type to render (defaults to \"text/plain\"; \"text/html\" can be used to include HTML tags)\n"
},
"$:/language/Help/unpackplugin": {
"title": "$:/language/Help/unpackplugin",
"description": "Unpack the payload tiddlers from a plugin",
"text": "Extract the payload tiddlers from a plugin, creating them as ordinary tiddlers:\n\n```\n--unpackplugin <title>\n```\n"
},
"$:/language/Help/verbose": {
"title": "$:/language/Help/verbose",
"description": "Triggers verbose output mode",
"text": "Triggers verbose output, useful for debugging\n\n```\n--verbose\n```\n"
},
"$:/language/Help/version": {
"title": "$:/language/Help/version",
"description": "Displays the version number of TiddlyWiki",
"text": "Displays the version number of TiddlyWiki.\n\n```\n--version\n```\n"
},
"$:/language/Import/Imported/Hint": {
"title": "$:/language/Import/Imported/Hint",
"text": "The following tiddlers were imported:"
},
"$:/language/Import/Listing/Cancel/Caption": {
"title": "$:/language/Import/Listing/Cancel/Caption",
"text": "Cancel"
},
"$:/language/Import/Listing/Hint": {
"title": "$:/language/Import/Listing/Hint",
"text": "These tiddlers are ready to import:"
},
"$:/language/Import/Listing/Import/Caption": {
"title": "$:/language/Import/Listing/Import/Caption",
"text": "Import"
},
"$:/language/Import/Listing/Select/Caption": {
"title": "$:/language/Import/Listing/Select/Caption",
"text": "Select"
},
"$:/language/Import/Listing/Status/Caption": {
"title": "$:/language/Import/Listing/Status/Caption",
"text": "Status"
},
"$:/language/Import/Listing/Title/Caption": {
"title": "$:/language/Import/Listing/Title/Caption",
"text": "Title"
},
"$:/language/Import/Listing/Preview": {
"title": "$:/language/Import/Listing/Preview",
"text": "Preview:"
},
"$:/language/Import/Listing/Preview/Text": {
"title": "$:/language/Import/Listing/Preview/Text",
"text": "Text"
},
"$:/language/Import/Listing/Preview/TextRaw": {
"title": "$:/language/Import/Listing/Preview/TextRaw",
"text": "Text (Raw)"
},
"$:/language/Import/Listing/Preview/Fields": {
"title": "$:/language/Import/Listing/Preview/Fields",
"text": "Fields"
},
"$:/language/Import/Listing/Preview/Diff": {
"title": "$:/language/Import/Listing/Preview/Diff",
"text": "Diff"
},
"$:/language/Import/Listing/Preview/DiffFields": {
"title": "$:/language/Import/Listing/Preview/DiffFields",
"text": "Diff (Fields)"
},
"$:/language/Import/Upgrader/Plugins/Suppressed/Incompatible": {
"title": "$:/language/Import/Upgrader/Plugins/Suppressed/Incompatible",
"text": "Blocked incompatible or obsolete plugin"
},
"$:/language/Import/Upgrader/Plugins/Suppressed/Version": {
"title": "$:/language/Import/Upgrader/Plugins/Suppressed/Version",
"text": "Blocked plugin (due to incoming <<incoming>> being older than existing <<existing>>)"
},
"$:/language/Import/Upgrader/Plugins/Upgraded": {
"title": "$:/language/Import/Upgrader/Plugins/Upgraded",
"text": "Upgraded plugin from <<incoming>> to <<upgraded>>"
},
"$:/language/Import/Upgrader/State/Suppressed": {
"title": "$:/language/Import/Upgrader/State/Suppressed",
"text": "Blocked temporary state tiddler"
},
"$:/language/Import/Upgrader/System/Suppressed": {
"title": "$:/language/Import/Upgrader/System/Suppressed",
"text": "Blocked system tiddler"
},
"$:/language/Import/Upgrader/System/Warning": {
"title": "$:/language/Import/Upgrader/System/Warning",
"text": "Core module tiddler"
},
"$:/language/Import/Upgrader/System/Alert": {
"title": "$:/language/Import/Upgrader/System/Alert",
"text": "You are about to import a tiddler that will overwrite a core module tiddler. This is not recommended as it may make the system unstable"
},
"$:/language/Import/Upgrader/ThemeTweaks/Created": {
"title": "$:/language/Import/Upgrader/ThemeTweaks/Created",
"text": "Migrated theme tweak from <$text text=<<from>>/>"
},
"$:/language/AboveStory/ClassicPlugin/Warning": {
"title": "$:/language/AboveStory/ClassicPlugin/Warning",
"text": "It looks like you are trying to load a plugin designed for ~TiddlyWiki Classic. Please note that [[these plugins do not work with TiddlyWiki version 5.x.x|https://tiddlywiki.com/#TiddlyWikiClassic]]. ~TiddlyWiki Classic plugins detected:"
},
"$:/language/BinaryWarning/Prompt": {
"title": "$:/language/BinaryWarning/Prompt",
"text": "This tiddler contains binary data"
},
"$:/language/ClassicWarning/Hint": {
"title": "$:/language/ClassicWarning/Hint",
"text": "This tiddler is written in TiddlyWiki Classic wiki text format, which is not fully compatible with TiddlyWiki version 5. See https://tiddlywiki.com/static/Upgrading.html for more details."
},
"$:/language/ClassicWarning/Upgrade/Caption": {
"title": "$:/language/ClassicWarning/Upgrade/Caption",
"text": "upgrade"
},
"$:/language/CloseAll/Button": {
"title": "$:/language/CloseAll/Button",
"text": "close all"
},
"$:/language/ColourPicker/Recent": {
"title": "$:/language/ColourPicker/Recent",
"text": "Recent:"
},
"$:/language/ConfirmCancelTiddler": {
"title": "$:/language/ConfirmCancelTiddler",
"text": "Do you wish to discard changes to the tiddler \"<$text text=<<title>>/>\"?"
},
"$:/language/ConfirmDeleteTiddler": {
"title": "$:/language/ConfirmDeleteTiddler",
"text": "Do you wish to delete the tiddler \"<$text text=<<title>>/>\"?"
},
"$:/language/ConfirmOverwriteTiddler": {
"title": "$:/language/ConfirmOverwriteTiddler",
"text": "Do you wish to overwrite the tiddler \"<$text text=<<title>>/>\"?"
},
"$:/language/ConfirmEditShadowTiddler": {
"title": "$:/language/ConfirmEditShadowTiddler",
"text": "You are about to edit a ShadowTiddler. Any changes will override the default system making future upgrades non-trivial. Are you sure you want to edit \"<$text text=<<title>>/>\"?"
},
"$:/language/Count": {
"title": "$:/language/Count",
"text": "count"
},
"$:/language/DefaultNewTiddlerTitle": {
"title": "$:/language/DefaultNewTiddlerTitle",
"text": "New Tiddler"
},
"$:/language/Diffs/CountMessage": {
"title": "$:/language/Diffs/CountMessage",
"text": "<<diff-count>> differences"
},
"$:/language/DropMessage": {
"title": "$:/language/DropMessage",
"text": "Drop here (or use the 'Escape' key to cancel)"
},
"$:/language/Encryption/Cancel": {
"title": "$:/language/Encryption/Cancel",
"text": "Cancel"
},
"$:/language/Encryption/ConfirmClearPassword": {
"title": "$:/language/Encryption/ConfirmClearPassword",
"text": "Do you wish to clear the password? This will remove the encryption applied when saving this wiki"
},
"$:/language/Encryption/PromptSetPassword": {
"title": "$:/language/Encryption/PromptSetPassword",
"text": "Set a new password for this TiddlyWiki"
},
"$:/language/Encryption/Username": {
"title": "$:/language/Encryption/Username",
"text": "Username"
},
"$:/language/Encryption/Password": {
"title": "$:/language/Encryption/Password",
"text": "Password"
},
"$:/language/Encryption/RepeatPassword": {
"title": "$:/language/Encryption/RepeatPassword",
"text": "Repeat password"
},
"$:/language/Encryption/PasswordNoMatch": {
"title": "$:/language/Encryption/PasswordNoMatch",
"text": "Passwords do not match"
},
"$:/language/Encryption/SetPassword": {
"title": "$:/language/Encryption/SetPassword",
"text": "Set password"
},
"$:/language/Error/Caption": {
"title": "$:/language/Error/Caption",
"text": "Error"
},
"$:/language/Error/EditConflict": {
"title": "$:/language/Error/EditConflict",
"text": "File changed on server"
},
"$:/language/Error/Filter": {
"title": "$:/language/Error/Filter",
"text": "Filter error"
},
"$:/language/Error/FilterSyntax": {
"title": "$:/language/Error/FilterSyntax",
"text": "Syntax error in filter expression"
},
"$:/language/Error/IsFilterOperator": {
"title": "$:/language/Error/IsFilterOperator",
"text": "Filter Error: Unknown operand for the 'is' filter operator"
},
"$:/language/Error/LoadingPluginLibrary": {
"title": "$:/language/Error/LoadingPluginLibrary",
"text": "Error loading plugin library"
},
"$:/language/Error/NetworkErrorAlert": {
"title": "$:/language/Error/NetworkErrorAlert",
"text": "`<h2>''Network Error''</h2>It looks like the connection to the server has been lost. This may indicate a problem with your network connection. Please attempt to restore network connectivity before continuing.<br><br>''Any unsaved changes will be automatically synchronised when connectivity is restored''.`"
},
"$:/language/Error/RecursiveTransclusion": {
"title": "$:/language/Error/RecursiveTransclusion",
"text": "Recursive transclusion error in transclude widget"
},
"$:/language/Error/RetrievingSkinny": {
"title": "$:/language/Error/RetrievingSkinny",
"text": "Error retrieving skinny tiddler list"
},
"$:/language/Error/SavingToTWEdit": {
"title": "$:/language/Error/SavingToTWEdit",
"text": "Error saving to TWEdit"
},
"$:/language/Error/WhileSaving": {
"title": "$:/language/Error/WhileSaving",
"text": "Error while saving"
},
"$:/language/Error/XMLHttpRequest": {
"title": "$:/language/Error/XMLHttpRequest",
"text": "XMLHttpRequest error code"
},
"$:/language/InternalJavaScriptError/Title": {
"title": "$:/language/InternalJavaScriptError/Title",
"text": "Internal JavaScript Error"
},
"$:/language/InternalJavaScriptError/Hint": {
"title": "$:/language/InternalJavaScriptError/Hint",
"text": "Well, this is embarrassing. It is recommended that you restart TiddlyWiki by refreshing your browser"
},
"$:/language/InvalidFieldName": {
"title": "$:/language/InvalidFieldName",
"text": "Illegal characters in field name \"<$text text=<<fieldName>>/>\". Fields can only contain lowercase letters, digits and the characters underscore (`_`), hyphen (`-`) and period (`.`)"
},
"$:/language/LazyLoadingWarning": {
"title": "$:/language/LazyLoadingWarning",
"text": "<p>Trying to load external content from ''<$text text={{!!_canonical_uri}}/>''</p><p>If this message doesn't disappear, either the tiddler content type doesn't match the type of the external content, or you may be using a browser that doesn't support external content for wikis loaded as standalone files. See https://tiddlywiki.com/#ExternalText</p>"
},
"$:/language/LoginToTiddlySpace": {
"title": "$:/language/LoginToTiddlySpace",
"text": "Login to TiddlySpace"
},
"$:/language/Manager/Controls/FilterByTag/None": {
"title": "$:/language/Manager/Controls/FilterByTag/None",
"text": "(none)"
},
"$:/language/Manager/Controls/FilterByTag/Prompt": {
"title": "$:/language/Manager/Controls/FilterByTag/Prompt",
"text": "Filter by tag:"
},
"$:/language/Manager/Controls/Order/Prompt": {
"title": "$:/language/Manager/Controls/Order/Prompt",
"text": "Reverse order"
},
"$:/language/Manager/Controls/Search/Placeholder": {
"title": "$:/language/Manager/Controls/Search/Placeholder",
"text": "Search"
},
"$:/language/Manager/Controls/Search/Prompt": {
"title": "$:/language/Manager/Controls/Search/Prompt",
"text": "Search:"
},
"$:/language/Manager/Controls/Show/Option/Tags": {
"title": "$:/language/Manager/Controls/Show/Option/Tags",
"text": "tags"
},
"$:/language/Manager/Controls/Show/Option/Tiddlers": {
"title": "$:/language/Manager/Controls/Show/Option/Tiddlers",
"text": "tiddlers"
},
"$:/language/Manager/Controls/Show/Prompt": {
"title": "$:/language/Manager/Controls/Show/Prompt",
"text": "Show:"
},
"$:/language/Manager/Controls/Sort/Prompt": {
"title": "$:/language/Manager/Controls/Sort/Prompt",
"text": "Sort by:"
},
"$:/language/Manager/Item/Colour": {
"title": "$:/language/Manager/Item/Colour",
"text": "Colour"
},
"$:/language/Manager/Item/Fields": {
"title": "$:/language/Manager/Item/Fields",
"text": "Fields"
},
"$:/language/Manager/Item/Icon/None": {
"title": "$:/language/Manager/Item/Icon/None",
"text": "(none)"
},
"$:/language/Manager/Item/Icon": {
"title": "$:/language/Manager/Item/Icon",
"text": "Icon"
},
"$:/language/Manager/Item/RawText": {
"title": "$:/language/Manager/Item/RawText",
"text": "Raw text"
},
"$:/language/Manager/Item/Tags": {
"title": "$:/language/Manager/Item/Tags",
"text": "Tags"
},
"$:/language/Manager/Item/Tools": {
"title": "$:/language/Manager/Item/Tools",
"text": "Tools"
},
"$:/language/Manager/Item/WikifiedText": {
"title": "$:/language/Manager/Item/WikifiedText",
"text": "Wikified text"
},
"$:/language/MissingTiddler/Hint": {
"title": "$:/language/MissingTiddler/Hint",
"text": "Missing tiddler \"<$text text=<<currentTiddler>>/>\" -- click {{||$:/core/ui/Buttons/edit}} to create"
},
"$:/language/No": {
"title": "$:/language/No",
"text": "No"
},
"$:/language/OfficialPluginLibrary": {
"title": "$:/language/OfficialPluginLibrary",
"text": "Official ~TiddlyWiki Plugin Library"
},
"$:/language/OfficialPluginLibrary/Hint": {
"title": "$:/language/OfficialPluginLibrary/Hint",
"text": "The official ~TiddlyWiki plugin library at tiddlywiki.com. Plugins, themes and language packs are maintained by the core team."
},
"$:/language/PluginReloadWarning": {
"title": "$:/language/PluginReloadWarning",
"text": "Please save {{$:/core/ui/Buttons/save-wiki}} and reload {{$:/core/ui/Buttons/refresh}} to allow changes to ~JavaScript plugins to take effect"
},
"$:/language/RecentChanges/DateFormat": {
"title": "$:/language/RecentChanges/DateFormat",
"text": "DDth MMM YYYY"
},
"$:/language/SystemTiddler/Tooltip": {
"title": "$:/language/SystemTiddler/Tooltip",
"text": "This is a system tiddler"
},
"$:/language/SystemTiddlers/Include/Prompt": {
"title": "$:/language/SystemTiddlers/Include/Prompt",
"text": "Include system tiddlers"
},
"$:/language/TagManager/Colour/Heading": {
"title": "$:/language/TagManager/Colour/Heading",
"text": "Colour"
},
"$:/language/TagManager/Count/Heading": {
"title": "$:/language/TagManager/Count/Heading",
"text": "Count"
},
"$:/language/TagManager/Icon/Heading": {
"title": "$:/language/TagManager/Icon/Heading",
"text": "Icon"
},
"$:/language/TagManager/Icons/None": {
"title": "$:/language/TagManager/Icons/None",
"text": "None"
},
"$:/language/TagManager/Info/Heading": {
"title": "$:/language/TagManager/Info/Heading",
"text": "Info"
},
"$:/language/TagManager/Tag/Heading": {
"title": "$:/language/TagManager/Tag/Heading",
"text": "Tag"
},
"$:/language/Tiddler/DateFormat": {
"title": "$:/language/Tiddler/DateFormat",
"text": "DDth MMM YYYY at hh12:0mmam"
},
"$:/language/UnsavedChangesWarning": {
"title": "$:/language/UnsavedChangesWarning",
"text": "You have unsaved changes in TiddlyWiki"
},
"$:/language/Yes": {
"title": "$:/language/Yes",
"text": "Yes"
},
"$:/language/Modals/Download": {
"title": "$:/language/Modals/Download",
"subtitle": "Download changes",
"footer": "<$button message=\"tm-close-tiddler\">Close</$button>",
"help": "https://tiddlywiki.com/static/DownloadingChanges.html",
"text": "Your browser only supports manual saving.\n\nTo save your modified wiki, right click on the download link below and select \"Download file\" or \"Save file\", and then choose the folder and filename.\n\n//You can marginally speed things up by clicking the link with the control key (Windows) or the options/alt key (Mac OS X). You will not be prompted for the folder or filename, but your browser is likely to give it an unrecognisable name -- you may need to rename the file to include an `.html` extension before you can do anything useful with it.//\n\nOn smartphones that do not allow files to be downloaded you can instead bookmark the link, and then sync your bookmarks to a desktop computer from where the wiki can be saved normally.\n"
},
"$:/language/Modals/SaveInstructions": {
"title": "$:/language/Modals/SaveInstructions",
"subtitle": "Save your work",
"footer": "<$button message=\"tm-close-tiddler\">Close</$button>",
"help": "https://tiddlywiki.com/static/SavingChanges.html",
"text": "Your changes to this wiki need to be saved as a ~TiddlyWiki HTML file.\n\n!!! Desktop browsers\n\n# Select ''Save As'' from the ''File'' menu\n# Choose a filename and location\n#* Some browsers also require you to explicitly specify the file saving format as ''Webpage, HTML only'' or similar\n# Close this tab\n\n!!! Smartphone browsers\n\n# Create a bookmark to this page\n#* If you've got iCloud or Google Sync set up then the bookmark will automatically sync to your desktop where you can open it and save it as above\n# Close this tab\n\n//If you open the bookmark again in Mobile Safari you will see this message again. If you want to go ahead and use the file, just click the ''close'' button below//\n"
},
"$:/config/NewJournal/Title": {
"title": "$:/config/NewJournal/Title",
"text": "DDth MMM YYYY"
},
"$:/config/NewJournal/Text": {
"title": "$:/config/NewJournal/Text",
"text": ""
},
"$:/config/NewJournal/Tags": {
"title": "$:/config/NewJournal/Tags",
"tags": "Journal"
},
"$:/language/Notifications/Save/Done": {
"title": "$:/language/Notifications/Save/Done",
"text": "Saved wiki"
},
"$:/language/Notifications/Save/Starting": {
"title": "$:/language/Notifications/Save/Starting",
"text": "Starting to save wiki"
},
"$:/language/Notifications/CopiedToClipboard/Succeeded": {
"title": "$:/language/Notifications/CopiedToClipboard/Succeeded",
"text": "Copied to clipboard!"
},
"$:/language/Notifications/CopiedToClipboard/Failed": {
"title": "$:/language/Notifications/CopiedToClipboard/Failed",
"text": "Failed to copy to clipboard!"
},
"$:/language/Search/DefaultResults/Caption": {
"title": "$:/language/Search/DefaultResults/Caption",
"text": "List"
},
"$:/language/Search/Filter/Caption": {
"title": "$:/language/Search/Filter/Caption",
"text": "Filter"
},
"$:/language/Search/Filter/Hint": {
"title": "$:/language/Search/Filter/Hint",
"text": "Search via a [[filter expression|https://tiddlywiki.com/static/Filters.html]]"
},
"$:/language/Search/Filter/Matches": {
"title": "$:/language/Search/Filter/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/Search/Matches": {
"title": "$:/language/Search/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/Search/Matches/All": {
"title": "$:/language/Search/Matches/All",
"text": "All matches:"
},
"$:/language/Search/Matches/Title": {
"title": "$:/language/Search/Matches/Title",
"text": "Title matches:"
},
"$:/language/Search/Search": {
"title": "$:/language/Search/Search",
"text": "Search"
},
"$:/language/Search/Search/TooShort": {
"title": "$:/language/Search/Search/TooShort",
"text": "Search text too short"
},
"$:/language/Search/Shadows/Caption": {
"title": "$:/language/Search/Shadows/Caption",
"text": "Shadows"
},
"$:/language/Search/Shadows/Hint": {
"title": "$:/language/Search/Shadows/Hint",
"text": "Search for shadow tiddlers"
},
"$:/language/Search/Shadows/Matches": {
"title": "$:/language/Search/Shadows/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/Search/Standard/Caption": {
"title": "$:/language/Search/Standard/Caption",
"text": "Standard"
},
"$:/language/Search/Standard/Hint": {
"title": "$:/language/Search/Standard/Hint",
"text": "Search for standard tiddlers"
},
"$:/language/Search/Standard/Matches": {
"title": "$:/language/Search/Standard/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/Search/System/Caption": {
"title": "$:/language/Search/System/Caption",
"text": "System"
},
"$:/language/Search/System/Hint": {
"title": "$:/language/Search/System/Hint",
"text": "Search for system tiddlers"
},
"$:/language/Search/System/Matches": {
"title": "$:/language/Search/System/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/SideBar/All/Caption": {
"title": "$:/language/SideBar/All/Caption",
"text": "All"
},
"$:/language/SideBar/Contents/Caption": {
"title": "$:/language/SideBar/Contents/Caption",
"text": "Contents"
},
"$:/language/SideBar/Drafts/Caption": {
"title": "$:/language/SideBar/Drafts/Caption",
"text": "Drafts"
},
"$:/language/SideBar/Explorer/Caption": {
"title": "$:/language/SideBar/Explorer/Caption",
"text": "Explorer"
},
"$:/language/SideBar/Missing/Caption": {
"title": "$:/language/SideBar/Missing/Caption",
"text": "Missing"
},
"$:/language/SideBar/More/Caption": {
"title": "$:/language/SideBar/More/Caption",
"text": "More"
},
"$:/language/SideBar/Open/Caption": {
"title": "$:/language/SideBar/Open/Caption",
"text": "Open"
},
"$:/language/SideBar/Orphans/Caption": {
"title": "$:/language/SideBar/Orphans/Caption",
"text": "Orphans"
},
"$:/language/SideBar/Recent/Caption": {
"title": "$:/language/SideBar/Recent/Caption",
"text": "Recent"
},
"$:/language/SideBar/Shadows/Caption": {
"title": "$:/language/SideBar/Shadows/Caption",
"text": "Shadows"
},
"$:/language/SideBar/System/Caption": {
"title": "$:/language/SideBar/System/Caption",
"text": "System"
},
"$:/language/SideBar/Tags/Caption": {
"title": "$:/language/SideBar/Tags/Caption",
"text": "Tags"
},
"$:/language/SideBar/Tags/Untagged/Caption": {
"title": "$:/language/SideBar/Tags/Untagged/Caption",
"text": "untagged"
},
"$:/language/SideBar/Tools/Caption": {
"title": "$:/language/SideBar/Tools/Caption",
"text": "Tools"
},
"$:/language/SideBar/Types/Caption": {
"title": "$:/language/SideBar/Types/Caption",
"text": "Types"
},
"$:/SiteSubtitle": {
"title": "$:/SiteSubtitle",
"text": "a non-linear personal web notebook"
},
"$:/SiteTitle": {
"title": "$:/SiteTitle",
"text": "My ~TiddlyWiki"
},
"$:/language/Snippets/ListByTag": {
"title": "$:/language/Snippets/ListByTag",
"tags": "$:/tags/TextEditor/Snippet",
"caption": "List of tiddlers by tag",
"text": "<<list-links \"[tag[task]sort[title]]\">>\n"
},
"$:/language/Snippets/MacroDefinition": {
"title": "$:/language/Snippets/MacroDefinition",
"tags": "$:/tags/TextEditor/Snippet",
"caption": "Macro definition",
"text": "\\define macroName(param1:\"default value\",param2)\nText of the macro\n\\end\n"
},
"$:/language/Snippets/Table4x3": {
"title": "$:/language/Snippets/Table4x3",
"tags": "$:/tags/TextEditor/Snippet",
"caption": "Table with 4 columns by 3 rows",
"text": "|! |!Alpha |!Beta |!Gamma |!Delta |\n|!One | | | | |\n|!Two | | | | |\n|!Three | | | | |\n"
},
"$:/language/Snippets/TableOfContents": {
"title": "$:/language/Snippets/TableOfContents",
"tags": "$:/tags/TextEditor/Snippet",
"caption": "Table of Contents",
"text": "<div class=\"tc-table-of-contents\">\n\n<<toc-selective-expandable 'TableOfContents'>>\n\n</div>"
},
"$:/language/ThemeTweaks/ThemeTweaks": {
"title": "$:/language/ThemeTweaks/ThemeTweaks",
"text": "Theme Tweaks"
},
"$:/language/ThemeTweaks/ThemeTweaks/Hint": {
"title": "$:/language/ThemeTweaks/ThemeTweaks/Hint",
"text": "You can tweak certain aspects of the ''Vanilla'' theme."
},
"$:/language/ThemeTweaks/Options": {
"title": "$:/language/ThemeTweaks/Options",
"text": "Options"
},
"$:/language/ThemeTweaks/Options/SidebarLayout": {
"title": "$:/language/ThemeTweaks/Options/SidebarLayout",
"text": "Sidebar layout"
},
"$:/language/ThemeTweaks/Options/SidebarLayout/Fixed-Fluid": {
"title": "$:/language/ThemeTweaks/Options/SidebarLayout/Fixed-Fluid",
"text": "Fixed story, fluid sidebar"
},
"$:/language/ThemeTweaks/Options/SidebarLayout/Fluid-Fixed": {
"title": "$:/language/ThemeTweaks/Options/SidebarLayout/Fluid-Fixed",
"text": "Fluid story, fixed sidebar"
},
"$:/language/ThemeTweaks/Options/StickyTitles": {
"title": "$:/language/ThemeTweaks/Options/StickyTitles",
"text": "Sticky titles"
},
"$:/language/ThemeTweaks/Options/StickyTitles/Hint": {
"title": "$:/language/ThemeTweaks/Options/StickyTitles/Hint",
"text": "Causes tiddler titles to \"stick\" to the top of the browser window"
},
"$:/language/ThemeTweaks/Options/CodeWrapping": {
"title": "$:/language/ThemeTweaks/Options/CodeWrapping",
"text": "Wrap long lines in code blocks"
},
"$:/language/ThemeTweaks/Settings": {
"title": "$:/language/ThemeTweaks/Settings",
"text": "Settings"
},
"$:/language/ThemeTweaks/Settings/FontFamily": {
"title": "$:/language/ThemeTweaks/Settings/FontFamily",
"text": "Font family"
},
"$:/language/ThemeTweaks/Settings/CodeFontFamily": {
"title": "$:/language/ThemeTweaks/Settings/CodeFontFamily",
"text": "Code font family"
},
"$:/language/ThemeTweaks/Settings/EditorFontFamily": {
"title": "$:/language/ThemeTweaks/Settings/EditorFontFamily",
"text": "Editor font family"
},
"$:/language/ThemeTweaks/Settings/BackgroundImage": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImage",
"text": "Page background image"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageAttachment": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageAttachment",
"text": "Page background image attachment"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageAttachment/Scroll": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageAttachment/Scroll",
"text": "Scroll with tiddlers"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageAttachment/Fixed": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageAttachment/Fixed",
"text": "Fixed to window"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageSize": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageSize",
"text": "Page background image size"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageSize/Auto": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageSize/Auto",
"text": "Auto"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageSize/Cover": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageSize/Cover",
"text": "Cover"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageSize/Contain": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageSize/Contain",
"text": "Contain"
},
"$:/language/ThemeTweaks/Metrics": {
"title": "$:/language/ThemeTweaks/Metrics",
"text": "Sizes"
},
"$:/language/ThemeTweaks/Metrics/FontSize": {
"title": "$:/language/ThemeTweaks/Metrics/FontSize",
"text": "Font size"
},
"$:/language/ThemeTweaks/Metrics/LineHeight": {
"title": "$:/language/ThemeTweaks/Metrics/LineHeight",
"text": "Line height"
},
"$:/language/ThemeTweaks/Metrics/BodyFontSize": {
"title": "$:/language/ThemeTweaks/Metrics/BodyFontSize",
"text": "Font size for tiddler body"
},
"$:/language/ThemeTweaks/Metrics/BodyLineHeight": {
"title": "$:/language/ThemeTweaks/Metrics/BodyLineHeight",
"text": "Line height for tiddler body"
},
"$:/language/ThemeTweaks/Metrics/StoryLeft": {
"title": "$:/language/ThemeTweaks/Metrics/StoryLeft",
"text": "Story left position"
},
"$:/language/ThemeTweaks/Metrics/StoryLeft/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/StoryLeft/Hint",
"text": "how far the left margin of the story river<br>(tiddler area) is from the left of the page"
},
"$:/language/ThemeTweaks/Metrics/StoryTop": {
"title": "$:/language/ThemeTweaks/Metrics/StoryTop",
"text": "Story top position"
},
"$:/language/ThemeTweaks/Metrics/StoryTop/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/StoryTop/Hint",
"text": "how far the top margin of the story river<br>is from the top of the page"
},
"$:/language/ThemeTweaks/Metrics/StoryRight": {
"title": "$:/language/ThemeTweaks/Metrics/StoryRight",
"text": "Story right"
},
"$:/language/ThemeTweaks/Metrics/StoryRight/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/StoryRight/Hint",
"text": "how far the left margin of the sidebar <br>is from the left of the page"
},
"$:/language/ThemeTweaks/Metrics/StoryWidth": {
"title": "$:/language/ThemeTweaks/Metrics/StoryWidth",
"text": "Story width"
},
"$:/language/ThemeTweaks/Metrics/StoryWidth/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/StoryWidth/Hint",
"text": "the overall width of the story river"
},
"$:/language/ThemeTweaks/Metrics/TiddlerWidth": {
"title": "$:/language/ThemeTweaks/Metrics/TiddlerWidth",
"text": "Tiddler width"
},
"$:/language/ThemeTweaks/Metrics/TiddlerWidth/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/TiddlerWidth/Hint",
"text": "within the story river"
},
"$:/language/ThemeTweaks/Metrics/SidebarBreakpoint": {
"title": "$:/language/ThemeTweaks/Metrics/SidebarBreakpoint",
"text": "Sidebar breakpoint"
},
"$:/language/ThemeTweaks/Metrics/SidebarBreakpoint/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/SidebarBreakpoint/Hint",
"text": "the minimum page width at which the story<br>river and sidebar will appear side by side"
},
"$:/language/ThemeTweaks/Metrics/SidebarWidth": {
"title": "$:/language/ThemeTweaks/Metrics/SidebarWidth",
"text": "Sidebar width"
},
"$:/language/ThemeTweaks/Metrics/SidebarWidth/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/SidebarWidth/Hint",
"text": "the width of the sidebar in fluid-fixed layout"
},
"$:/language/TiddlerInfo/Advanced/Caption": {
"title": "$:/language/TiddlerInfo/Advanced/Caption",
"text": "Advanced"
},
"$:/language/TiddlerInfo/Advanced/PluginInfo/Empty/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/PluginInfo/Empty/Hint",
"text": "none"
},
"$:/language/TiddlerInfo/Advanced/PluginInfo/Heading": {
"title": "$:/language/TiddlerInfo/Advanced/PluginInfo/Heading",
"text": "Plugin Details"
},
"$:/language/TiddlerInfo/Advanced/PluginInfo/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/PluginInfo/Hint",
"text": "This plugin contains the following shadow tiddlers:"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/Heading": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/Heading",
"text": "Shadow Status"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/NotShadow/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/NotShadow/Hint",
"text": "The tiddler <$link to=<<infoTiddler>>><$text text=<<infoTiddler>>/></$link> is not a shadow tiddler"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/Shadow/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/Shadow/Hint",
"text": "The tiddler <$link to=<<infoTiddler>>><$text text=<<infoTiddler>>/></$link> is a shadow tiddler"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/Shadow/Source": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/Shadow/Source",
"text": "It is defined in the plugin <$link to=<<pluginTiddler>>><$text text=<<pluginTiddler>>/></$link>"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/OverriddenShadow/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/OverriddenShadow/Hint",
"text": "It is overridden by an ordinary tiddler"
},
"$:/language/TiddlerInfo/Fields/Caption": {
"title": "$:/language/TiddlerInfo/Fields/Caption",
"text": "Fields"
},
"$:/language/TiddlerInfo/List/Caption": {
"title": "$:/language/TiddlerInfo/List/Caption",
"text": "List"
},
"$:/language/TiddlerInfo/List/Empty": {
"title": "$:/language/TiddlerInfo/List/Empty",
"text": "This tiddler does not have a list"
},
"$:/language/TiddlerInfo/Listed/Caption": {
"title": "$:/language/TiddlerInfo/Listed/Caption",
"text": "Listed"
},
"$:/language/TiddlerInfo/Listed/Empty": {
"title": "$:/language/TiddlerInfo/Listed/Empty",
"text": "This tiddler is not listed by any others"
},
"$:/language/TiddlerInfo/References/Caption": {
"title": "$:/language/TiddlerInfo/References/Caption",
"text": "References"
},
"$:/language/TiddlerInfo/References/Empty": {
"title": "$:/language/TiddlerInfo/References/Empty",
"text": "No tiddlers link to this one"
},
"$:/language/TiddlerInfo/Tagging/Caption": {
"title": "$:/language/TiddlerInfo/Tagging/Caption",
"text": "Tagging"
},
"$:/language/TiddlerInfo/Tagging/Empty": {
"title": "$:/language/TiddlerInfo/Tagging/Empty",
"text": "No tiddlers are tagged with this one"
},
"$:/language/TiddlerInfo/Tools/Caption": {
"title": "$:/language/TiddlerInfo/Tools/Caption",
"text": "Tools"
},
"$:/language/Docs/Types/application/javascript": {
"title": "$:/language/Docs/Types/application/javascript",
"description": "JavaScript code",
"name": "application/javascript",
"group": "Developer",
"group-sort": "2"
},
"$:/language/Docs/Types/application/json": {
"title": "$:/language/Docs/Types/application/json",
"description": "JSON data",
"name": "application/json",
"group": "Developer",
"group-sort": "2"
},
"$:/language/Docs/Types/application/x-tiddler-dictionary": {
"title": "$:/language/Docs/Types/application/x-tiddler-dictionary",
"description": "Data dictionary",
"name": "application/x-tiddler-dictionary",
"group": "Developer",
"group-sort": "2"
},
"$:/language/Docs/Types/image/gif": {
"title": "$:/language/Docs/Types/image/gif",
"description": "GIF image",
"name": "image/gif",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/image/jpeg": {
"title": "$:/language/Docs/Types/image/jpeg",
"description": "JPEG image",
"name": "image/jpeg",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/image/png": {
"title": "$:/language/Docs/Types/image/png",
"description": "PNG image",
"name": "image/png",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/image/svg+xml": {
"title": "$:/language/Docs/Types/image/svg+xml",
"description": "Structured Vector Graphics image",
"name": "image/svg+xml",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/image/x-icon": {
"title": "$:/language/Docs/Types/image/x-icon",
"description": "ICO format icon file",
"name": "image/x-icon",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/text/css": {
"title": "$:/language/Docs/Types/text/css",
"description": "Static stylesheet",
"name": "text/css",
"group": "Developer",
"group-sort": "2"
},
"$:/language/Docs/Types/text/html": {
"title": "$:/language/Docs/Types/text/html",
"description": "HTML markup",
"name": "text/html",
"group": "Text",
"group-sort": "0"
},
"$:/language/Docs/Types/text/plain": {
"title": "$:/language/Docs/Types/text/plain",
"description": "Plain text",
"name": "text/plain",
"group": "Text",
"group-sort": "0"
},
"$:/language/Docs/Types/text/vnd.tiddlywiki": {
"title": "$:/language/Docs/Types/text/vnd.tiddlywiki",
"description": "TiddlyWiki 5",
"name": "text/vnd.tiddlywiki",
"group": "Text",
"group-sort": "0"
},
"$:/language/Docs/Types/text/x-tiddlywiki": {
"title": "$:/language/Docs/Types/text/x-tiddlywiki",
"description": "TiddlyWiki Classic",
"name": "text/x-tiddlywiki",
"group": "Text",
"group-sort": "0"
},
"$:/languages/en-GB/icon": {
"title": "$:/languages/en-GB/icon",
"type": "image/svg+xml",
"text": "<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 60 30\" width=\"1200\" height=\"600\">\n<clipPath id=\"t\">\n\t<path d=\"M30,15 h30 v15 z v15 h-30 z h-30 v-15 z v-15 h30 z\"/>\n</clipPath>\n<path d=\"M0,0 v30 h60 v-30 z\" fill=\"#00247d\"/>\n<path d=\"M0,0 L60,30 M60,0 L0,30\" stroke=\"#fff\" stroke-width=\"6\"/>\n<path d=\"M0,0 L60,30 M60,0 L0,30\" clip-path=\"url(#t)\" stroke=\"#cf142b\" stroke-width=\"4\"/>\n<path d=\"M30,0 v30 M0,15 h60\" stroke=\"#fff\" stroke-width=\"10\"/>\n<path d=\"M30,0 v30 M0,15 h60\" stroke=\"#cf142b\" stroke-width=\"6\"/>\n</svg>\n"
},
"$:/languages/en-GB": {
"title": "$:/languages/en-GB",
"name": "en-GB",
"description": "English (British)",
"author": "JeremyRuston",
"core-version": ">=5.0.0\"",
"text": "Stub pseudo-plugin for the default language"
},
"$:/core/modules/commander.js": {
"title": "$:/core/modules/commander.js",
"text": "/*\\\ntitle: $:/core/modules/commander.js\ntype: application/javascript\nmodule-type: global\n\nThe $tw.Commander class is a command interpreter\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nParse a sequence of commands\n\tcommandTokens: an array of command string tokens\n\twiki: reference to the wiki store object\n\tstreams: {output:, error:}, each of which has a write(string) method\n\tcallback: a callback invoked as callback(err) where err is null if there was no error\n*/\nvar Commander = function(commandTokens,callback,wiki,streams) {\n\tvar path = require(\"path\");\n\tthis.commandTokens = commandTokens;\n\tthis.nextToken = 0;\n\tthis.callback = callback;\n\tthis.wiki = wiki;\n\tthis.streams = streams;\n\tthis.outputPath = path.resolve($tw.boot.wikiPath,$tw.config.wikiOutputSubDir);\n};\n\n/*\nLog a string if verbose flag is set\n*/\nCommander.prototype.log = function(str) {\n\tif(this.verbose) {\n\t\tthis.streams.output.write(str + \"\\n\");\n\t}\n};\n\n/*\nWrite a string if verbose flag is set\n*/\nCommander.prototype.write = function(str) {\n\tif(this.verbose) {\n\t\tthis.streams.output.write(str);\n\t}\n};\n\n/*\nAdd a string of tokens to the command queue\n*/\nCommander.prototype.addCommandTokens = function(commandTokens) {\n\tvar params = commandTokens.slice(0);\n\tparams.unshift(0);\n\tparams.unshift(this.nextToken);\n\tArray.prototype.splice.apply(this.commandTokens,params);\n};\n\n/*\nExecute the sequence of commands and invoke a callback on completion\n*/\nCommander.prototype.execute = function() {\n\tthis.executeNextCommand();\n};\n\n/*\nExecute the next command in the sequence\n*/\nCommander.prototype.executeNextCommand = function() {\n\tvar self = this;\n\t// Invoke the callback if there are no more commands\n\tif(this.nextToken >= this.commandTokens.length) {\n\t\tthis.callback(null);\n\t} else {\n\t\t// Get and check the command token\n\t\tvar commandName = this.commandTokens[this.nextToken++];\n\t\tif(commandName.substr(0,2) !== \"--\") {\n\t\t\tthis.callback(\"Missing command: \" + commandName);\n\t\t} else {\n\t\t\tcommandName = commandName.substr(2); // Trim off the --\n\t\t\t// Accumulate the parameters to the command\n\t\t\tvar params = [];\n\t\t\twhile(this.nextToken < this.commandTokens.length && \n\t\t\t\tthis.commandTokens[this.nextToken].substr(0,2) !== \"--\") {\n\t\t\t\tparams.push(this.commandTokens[this.nextToken++]);\n\t\t\t}\n\t\t\t// Get the command info\n\t\t\tvar command = $tw.commands[commandName],\n\t\t\t\tc,err;\n\t\t\tif(!command) {\n\t\t\t\tthis.callback(\"Unknown command: \" + commandName);\n\t\t\t} else {\n\t\t\t\tif(this.verbose) {\n\t\t\t\t\tthis.streams.output.write(\"Executing command: \" + commandName + \" \" + params.join(\" \") + \"\\n\");\n\t\t\t\t}\n\t\t\t\t// Parse named parameters if required\n\t\t\t\tif(command.info.namedParameterMode) {\n\t\t\t\t\tparams = this.extractNamedParameters(params,command.info.mandatoryParameters);\n\t\t\t\t\tif(typeof params === \"string\") {\n\t\t\t\t\t\treturn this.callback(params);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif(command.info.synchronous) {\n\t\t\t\t\t// Synchronous command\n\t\t\t\t\tc = new command.Command(params,this);\n\t\t\t\t\terr = c.execute();\n\t\t\t\t\tif(err) {\n\t\t\t\t\t\tthis.callback(err);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tthis.executeNextCommand();\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\t// Asynchronous command\n\t\t\t\t\tc = new command.Command(params,this,function(err) {\n\t\t\t\t\t\tif(err) {\n\t\t\t\t\t\t\tself.callback(err);\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tself.executeNextCommand();\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t\terr = c.execute();\n\t\t\t\t\tif(err) {\n\t\t\t\t\t\tthis.callback(err);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n};\n\n/*\nGiven an array of parameter strings `params` in name:value format, and an array of mandatory parameter names in `mandatoryParameters`, returns a hashmap of values or a string if error\n*/\nCommander.prototype.extractNamedParameters = function(params,mandatoryParameters) {\n\tmandatoryParameters = mandatoryParameters || [];\n\tvar errors = [],\n\t\tparamsByName = Object.create(null);\n\t// Extract the parameters\n\t$tw.utils.each(params,function(param) {\n\t\tvar index = param.indexOf(\"=\");\n\t\tif(index < 1) {\n\t\t\terrors.push(\"malformed named parameter: '\" + param + \"'\");\n\t\t}\n\t\tparamsByName[param.slice(0,index)] = $tw.utils.trim(param.slice(index+1));\n\t});\n\t// Check the mandatory parameters are present\n\t$tw.utils.each(mandatoryParameters,function(mandatoryParameter) {\n\t\tif(!$tw.utils.hop(paramsByName,mandatoryParameter)) {\n\t\t\terrors.push(\"missing mandatory parameter: '\" + mandatoryParameter + \"'\");\n\t\t}\n\t});\n\t// Return any errors\n\tif(errors.length > 0) {\n\t\treturn errors.join(\" and\\n\");\n\t} else {\n\t\treturn paramsByName;\t\t\n\t}\n};\n\nCommander.initCommands = function(moduleType) {\n\tmoduleType = moduleType || \"command\";\n\t$tw.commands = {};\n\t$tw.modules.forEachModuleOfType(moduleType,function(title,module) {\n\t\tvar c = $tw.commands[module.info.name] = {};\n\t\t// Add the methods defined by the module\n\t\tfor(var f in module) {\n\t\t\tif($tw.utils.hop(module,f)) {\n\t\t\t\tc[f] = module[f];\n\t\t\t}\n\t\t}\n\t});\n};\n\nexports.Commander = Commander;\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/commands/build.js": {
"title": "$:/core/modules/commands/build.js",
"text": "/*\\\ntitle: $:/core/modules/commands/build.js\ntype: application/javascript\nmodule-type: command\n\nCommand to build a build target\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"build\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\t// Get the build targets defined in the wiki\n\tvar buildTargets = $tw.boot.wikiInfo.build;\n\tif(!buildTargets) {\n\t\treturn \"No build targets defined\";\n\t}\n\t// Loop through each of the specified targets\n\tvar targets;\n\tif(this.params.length > 0) {\n\t\ttargets = this.params;\n\t} else {\n\t\ttargets = Object.keys(buildTargets);\n\t}\n\tfor(var targetIndex=0; targetIndex<targets.length; targetIndex++) {\n\t\tvar target = targets[targetIndex],\n\t\t\tcommands = buildTargets[target];\n\t\tif(!commands) {\n\t\t\treturn \"Build target '\" + target + \"' not found\";\n\t\t}\n\t\t// Add the commands to the queue\n\t\tthis.commander.addCommandTokens(commands);\n\t}\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/clearpassword.js": {
"title": "$:/core/modules/commands/clearpassword.js",
"text": "/*\\\ntitle: $:/core/modules/commands/clearpassword.js\ntype: application/javascript\nmodule-type: command\n\nClear password for crypto operations\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"clearpassword\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\t$tw.crypto.setPassword(null);\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/deletetiddlers.js": {
"title": "$:/core/modules/commands/deletetiddlers.js",
"text": "/*\\\ntitle: $:/core/modules/commands/deletetiddlers.js\ntype: application/javascript\nmodule-type: command\n\nCommand to delete tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"deletetiddlers\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing filter\";\n\t}\n\tvar self = this,\n\t\twiki = this.commander.wiki,\n\t\tfilter = this.params[0],\n\t\ttiddlers = wiki.filterTiddlers(filter);\n\t$tw.utils.each(tiddlers,function(title) {\n\t\twiki.deleteTiddler(title);\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/editions.js": {
"title": "$:/core/modules/commands/editions.js",
"text": "/*\\\ntitle: $:/core/modules/commands/editions.js\ntype: application/javascript\nmodule-type: command\n\nCommand to list the available editions\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"editions\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tvar self = this;\n\t// Output the list\n\tthis.commander.streams.output.write(\"Available editions:\\n\\n\");\n\tvar editionInfo = $tw.utils.getEditionInfo();\n\t$tw.utils.each(editionInfo,function(info,name) {\n\t\tself.commander.streams.output.write(\" \" + name + \": \" + info.description + \"\\n\");\n\t});\n\tthis.commander.streams.output.write(\"\\n\");\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/fetch.js": {
"title": "$:/core/modules/commands/fetch.js",
"text": "/*\\\ntitle: $:/core/modules/commands/fetch.js\ntype: application/javascript\nmodule-type: command\n\nCommands to fetch external tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"fetch\",\n\tsynchronous: false\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing subcommand and url\";\n\t}\n\tswitch(this.params[0]) {\n\t\tcase \"raw-file\":\n\t\t\treturn this.fetchFiles({\n\t\t\t\traw: true,\n\t\t\t\turl: this.params[1],\n\t\t\t\ttransformFilter: this.params[2] || \"\",\n\t\t\t\tcallback: this.callback\n\t\t\t});\n\t\t\tbreak;\n\t\tcase \"file\":\n\t\t\treturn this.fetchFiles({\n\t\t\t\turl: this.params[1],\n\t\t\t\timportFilter: this.params[2],\n\t\t\t\ttransformFilter: this.params[3] || \"\",\n\t\t\t\tcallback: this.callback\n\t\t\t});\n\t\t\tbreak;\n\t\tcase \"raw-files\":\n\t\t\treturn this.fetchFiles({\n\t\t\t\traw: true,\n\t\t\t\turlFilter: this.params[1],\n\t\t\t\ttransformFilter: this.params[2] || \"\",\n\t\t\t\tcallback: this.callback\n\t\t\t});\n\t\t\tbreak;\n\t\tcase \"files\":\n\t\t\treturn this.fetchFiles({\n\t\t\t\turlFilter: this.params[1],\n\t\t\t\timportFilter: this.params[2],\n\t\t\t\ttransformFilter: this.params[3] || \"\",\n\t\t\t\tcallback: this.callback\n\t\t\t});\n\t\t\tbreak;\n\t}\n\treturn null;\n};\n\nCommand.prototype.fetchFiles = function(options) {\n\tvar self = this;\n\t// Get the list of URLs\n\tvar urls;\n\tif(options.url) {\n\t\turls = [options.url]\n\t} else if(options.urlFilter) {\n\t\turls = $tw.wiki.filterTiddlers(options.urlFilter);\n\t} else {\n\t\treturn \"Missing URL\";\n\t}\n\t// Process each URL in turn\n\tvar next = 0;\n\tvar getNextFile = function(err) {\n\t\tif(err) {\n\t\t\treturn options.callback(err);\n\t\t}\n\t\tif(next < urls.length) {\n\t\t\tself.fetchFile(urls[next++],options,getNextFile);\n\t\t} else {\n\t\t\toptions.callback(null);\n\t\t}\n\t};\n\tgetNextFile(null);\n\t// Success\n\treturn null;\n};\n\nCommand.prototype.fetchFile = function(url,options,callback,redirectCount) {\n\tif(redirectCount > 10) {\n\t\treturn callback(\"Error too many redirects retrieving \" + url);\n\t}\n\tvar self = this,\n\t\tlib = url.substr(0,8) === \"https://\" ? require(\"https\") : require(\"http\");\n\tlib.get(url).on(\"response\",function(response) {\n\t var type = (response.headers[\"content-type\"] || \"\").split(\";\")[0],\n\t \tdata = [];\n\t self.commander.write(\"Reading \" + url + \": \");\n\t response.on(\"data\",function(chunk) {\n\t data.push(chunk);\n\t self.commander.write(\".\");\n\t });\n\t response.on(\"end\",function() {\n\t self.commander.write(\"\\n\");\n\t if(response.statusCode === 200) {\n\t\t self.processBody(Buffer.concat(data),type,options,url);\n\t\t callback(null);\n\t } else {\n\t \tif(response.statusCode === 302 || response.statusCode === 303 || response.statusCode === 307) {\n\t \t\treturn self.fetchFile(response.headers.location,options,callback,redirectCount + 1);\n\t \t} else {\n\t\t \treturn callback(\"Error \" + response.statusCode + \" retrieving \" + url)\t \t\t\n\t \t}\n\t }\n\t \t});\n\t \tresponse.on(\"error\",function(e) {\n\t\t\tconsole.log(\"Error on GET request: \" + e);\n\t\t\tcallback(e);\n\t \t});\n\t});\n\treturn null;\n};\n\nCommand.prototype.processBody = function(body,type,options,url) {\n\tvar self = this;\n\t// Collect the tiddlers in a wiki\n\tvar incomingWiki = new $tw.Wiki();\n\tif(options.raw) {\n\t\tvar typeInfo = type ? $tw.config.contentTypeInfo[type] : null,\n\t\t\tencoding = typeInfo ? typeInfo.encoding : \"utf8\";\n\t\tincomingWiki.addTiddler(new $tw.Tiddler({\n\t\t\ttitle: url,\n\t\t\ttype: type,\n\t\t\ttext: body.toString(encoding)\n\t\t}));\n\t} else {\n\t\t// Deserialise the file to extract the tiddlers\n\t\tvar tiddlers = this.commander.wiki.deserializeTiddlers(type || \"text/html\",body.toString(\"utf8\"),{});\n\t\t$tw.utils.each(tiddlers,function(tiddler) {\n\t\t\tincomingWiki.addTiddler(new $tw.Tiddler(tiddler));\n\t\t});\n\t}\n\t// Filter the tiddlers to select the ones we want\n\tvar filteredTitles = incomingWiki.filterTiddlers(options.importFilter || \"[all[tiddlers]]\");\n\t// Import the selected tiddlers\n\tvar count = 0;\n\tincomingWiki.each(function(tiddler,title) {\n\t\tif(filteredTitles.indexOf(title) !== -1) {\n\t\t\tvar newTiddler;\n\t\t\tif(options.transformFilter) {\n\t\t\t\tvar transformedTitle = (incomingWiki.filterTiddlers(options.transformFilter,null,self.commander.wiki.makeTiddlerIterator([title])) || [\"\"])[0];\n\t\t\t\tif(transformedTitle) {\n\t\t\t\t\tself.commander.log(\"Importing \" + title + \" as \" + transformedTitle)\n\t\t\t\t\tnewTiddler = new $tw.Tiddler(tiddler,{title: transformedTitle});\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tself.commander.log(\"Importing \" + title)\n\t\t\t\tnewTiddler = tiddler;\n\t\t\t}\n\t\t\tself.commander.wiki.importTiddler(newTiddler);\n\t\t\tcount++;\n\t\t}\n\t});\n\tself.commander.log(\"Imported \" + count + \" tiddlers\")\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/help.js": {
"title": "$:/core/modules/commands/help.js",
"text": "/*\\\ntitle: $:/core/modules/commands/help.js\ntype: application/javascript\nmodule-type: command\n\nHelp command\n\n\\*/\n(function(){\n\n/*jshint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"help\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tvar subhelp = this.params[0] || \"default\",\n\t\thelpBase = \"$:/language/Help/\",\n\t\ttext;\n\tif(!this.commander.wiki.getTiddler(helpBase + subhelp)) {\n\t\tsubhelp = \"notfound\";\n\t}\n\t// Wikify the help as formatted text (ie block elements generate newlines)\n\ttext = this.commander.wiki.renderTiddler(\"text/plain-formatted\",helpBase + subhelp);\n\t// Remove any leading linebreaks\n\ttext = text.replace(/^(\\r?\\n)*/g,\"\");\n\tthis.commander.streams.output.write(text);\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/import.js": {
"title": "$:/core/modules/commands/import.js",
"text": "/*\\\ntitle: $:/core/modules/commands/import.js\ntype: application/javascript\nmodule-type: command\n\nCommand to import tiddlers from a file\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"import\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\");\n\tif(this.params.length < 2) {\n\t\treturn \"Missing parameters\";\n\t}\n\tvar filename = self.params[0],\n\t\tdeserializer = self.params[1],\n\t\ttitle = self.params[2] || filename,\n\t\tencoding = self.params[3] || \"utf8\",\n\t\ttext = fs.readFileSync(filename,encoding),\n\t\ttiddlers = this.commander.wiki.deserializeTiddlers(null,text,{title: title},{deserializer: deserializer});\n\t$tw.utils.each(tiddlers,function(tiddler) {\n\t\tself.commander.wiki.importTiddler(new $tw.Tiddler(tiddler));\n\t});\n\tthis.commander.log(tiddlers.length + \" tiddler(s) imported\");\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/init.js": {
"title": "$:/core/modules/commands/init.js",
"text": "/*\\\ntitle: $:/core/modules/commands/init.js\ntype: application/javascript\nmodule-type: command\n\nCommand to initialise an empty wiki folder\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"init\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tvar fs = require(\"fs\"),\n\t\tpath = require(\"path\");\n\t// Check that we don't already have a valid wiki folder\n\tif($tw.boot.wikiTiddlersPath || ($tw.utils.isDirectory($tw.boot.wikiPath) && !$tw.utils.isDirectoryEmpty($tw.boot.wikiPath))) {\n\t\treturn \"Wiki folder is not empty\";\n\t}\n\t// Loop through each of the specified editions\n\tvar editions = this.params.length > 0 ? this.params : [\"empty\"];\n\tfor(var editionIndex=0; editionIndex<editions.length; editionIndex++) {\n\t\tvar editionName = editions[editionIndex];\n\t\t// Check the edition exists\n\t\tvar editionPath = $tw.findLibraryItem(editionName,$tw.getLibraryItemSearchPaths($tw.config.editionsPath,$tw.config.editionsEnvVar));\n\t\tif(!$tw.utils.isDirectory(editionPath)) {\n\t\t\treturn \"Edition '\" + editionName + \"' not found\";\n\t\t}\n\t\t// Copy the edition content\n\t\tvar err = $tw.utils.copyDirectory(editionPath,$tw.boot.wikiPath);\n\t\tif(!err) {\n\t\t\tthis.commander.streams.output.write(\"Copied edition '\" + editionName + \"' to \" + $tw.boot.wikiPath + \"\\n\");\n\t\t} else {\n\t\t\treturn err;\n\t\t}\n\t}\n\t// Tweak the tiddlywiki.info to remove any included wikis\n\tvar packagePath = $tw.boot.wikiPath + \"/tiddlywiki.info\",\n\t\tpackageJson = JSON.parse(fs.readFileSync(packagePath));\n\tdelete packageJson.includeWikis;\n\tfs.writeFileSync(packagePath,JSON.stringify(packageJson,null,$tw.config.preferences.jsonSpaces));\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/listen.js": {
"title": "$:/core/modules/commands/listen.js",
"text": "/*\\\ntitle: $:/core/modules/commands/listen.js\ntype: application/javascript\nmodule-type: command\n\nListen for HTTP requests and serve tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Server = require(\"$:/core/modules/server/server.js\").Server;\n\nexports.info = {\n\tname: \"listen\",\n\tsynchronous: true,\n\tnamedParameterMode: true,\n\tmandatoryParameters: [],\n};\n\nvar Command = function(params,commander,callback) {\n\tvar self = this;\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tvar self = this;\n\tif(!$tw.boot.wikiTiddlersPath) {\n\t\t$tw.utils.warning(\"Warning: Wiki folder '\" + $tw.boot.wikiPath + \"' does not exist or is missing a tiddlywiki.info file\");\n\t}\n\t// Set up server\n\tthis.server = new Server({\n\t\twiki: this.commander.wiki,\n\t\tvariables: self.params\n\t});\n\tvar nodeServer = this.server.listen();\n\t$tw.hooks.invokeHook(\"th-server-command-post-start\",this.server,nodeServer,\"tiddlywiki\");\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/load.js": {
"title": "$:/core/modules/commands/load.js",
"text": "/*\\\ntitle: $:/core/modules/commands/load.js\ntype: application/javascript\nmodule-type: command\n\nCommand to load tiddlers from a file or directory\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"load\",\n\tsynchronous: false\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\");\n\tif(this.params.length < 1) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar tiddlers = $tw.loadTiddlersFromPath(self.params[0]),\n\t\tcount = 0;\n\t$tw.utils.each(tiddlers,function(tiddlerInfo) {\n\t\t$tw.utils.each(tiddlerInfo.tiddlers,function(tiddler) {\n\t\t\tself.commander.wiki.importTiddler(new $tw.Tiddler(tiddler));\n\t\t\tcount++;\n\t\t});\n\t});\n\tif(!count && self.params[1] !== \"noerror\") {\n\t\tself.callback(\"No tiddlers found in file \\\"\" + self.params[0] + \"\\\"\");\n\t} else {\n\t\tself.callback(null);\n\t}\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/makelibrary.js": {
"title": "$:/core/modules/commands/makelibrary.js",
"text": "/*\\\ntitle: $:/core/modules/commands/makelibrary.js\ntype: application/javascript\nmodule-type: command\n\nCommand to pack all of the plugins in the library into a plugin tiddler of type \"library\"\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"makelibrary\",\n\tsynchronous: true\n};\n\nvar UPGRADE_LIBRARY_TITLE = \"$:/UpgradeLibrary\";\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tvar wiki = this.commander.wiki,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\tupgradeLibraryTitle = this.params[0] || UPGRADE_LIBRARY_TITLE,\n\t\ttiddlers = {};\n\t// Collect up the library plugins\n\tvar collectPlugins = function(folder) {\n\t\t\tvar pluginFolders = fs.readdirSync(folder);\n\t\t\tfor(var p=0; p<pluginFolders.length; p++) {\n\t\t\t\tif(!$tw.boot.excludeRegExp.test(pluginFolders[p])) {\n\t\t\t\t\tpluginFields = $tw.loadPluginFolder(path.resolve(folder,\"./\" + pluginFolders[p]));\n\t\t\t\t\tif(pluginFields && pluginFields.title) {\n\t\t\t\t\t\ttiddlers[pluginFields.title] = pluginFields;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\t\tcollectPublisherPlugins = function(folder) {\n\t\t\tvar publisherFolders = fs.readdirSync(folder);\n\t\t\tfor(var t=0; t<publisherFolders.length; t++) {\n\t\t\t\tif(!$tw.boot.excludeRegExp.test(publisherFolders[t])) {\n\t\t\t\t\tcollectPlugins(path.resolve(folder,\"./\" + publisherFolders[t]));\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t$tw.utils.each($tw.getLibraryItemSearchPaths($tw.config.pluginsPath,$tw.config.pluginsEnvVar),collectPublisherPlugins);\n\t$tw.utils.each($tw.getLibraryItemSearchPaths($tw.config.themesPath,$tw.config.themesEnvVar),collectPublisherPlugins);\n\t$tw.utils.each($tw.getLibraryItemSearchPaths($tw.config.languagesPath,$tw.config.languagesEnvVar),collectPlugins);\n\t// Save the upgrade library tiddler\n\tvar pluginFields = {\n\t\ttitle: upgradeLibraryTitle,\n\t\ttype: \"application/json\",\n\t\t\"plugin-type\": \"library\",\n\t\t\"text\": JSON.stringify({tiddlers: tiddlers})\n\t};\n\twiki.addTiddler(new $tw.Tiddler(pluginFields));\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/output.js": {
"title": "$:/core/modules/commands/output.js",
"text": "/*\\\ntitle: $:/core/modules/commands/output.js\ntype: application/javascript\nmodule-type: command\n\nCommand to set the default output location (defaults to current working directory)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"output\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tvar fs = require(\"fs\"),\n\t\tpath = require(\"path\");\n\tif(this.params.length < 1) {\n\t\treturn \"Missing output path\";\n\t}\n\tthis.commander.outputPath = path.resolve(process.cwd(),this.params[0]);\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/password.js": {
"title": "$:/core/modules/commands/password.js",
"text": "/*\\\ntitle: $:/core/modules/commands/password.js\ntype: application/javascript\nmodule-type: command\n\nSave password for crypto operations\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"password\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing password\";\n\t}\n\t$tw.crypto.setPassword(this.params[0]);\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/render.js": {
"title": "$:/core/modules/commands/render.js",
"text": "/*\\\ntitle: $:/core/modules/commands/render.js\ntype: application/javascript\nmodule-type: command\n\nRender individual tiddlers and save the results to the specified files\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.info = {\n\tname: \"render\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing tiddler filter\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\twiki = this.commander.wiki,\n\t\ttiddlerFilter = this.params[0],\n\t\tfilenameFilter = this.params[1] || \"[is[tiddler]addsuffix[.html]]\",\n\t\ttype = this.params[2] || \"text/html\",\n\t\ttemplate = this.params[3],\n\t\tvarName = this.params[4],\n\t\tvarValue = this.params[5],\n\t\ttiddlers = wiki.filterTiddlers(tiddlerFilter);\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar parser = wiki.parseTiddler(template || title),\n\t\t\tvariables = {currentTiddler: title};\n\t\tif(varName) {\n\t\t\tvariables[varName] = varValue || \"\";\n\t\t}\n\t\tvar widgetNode = wiki.makeWidget(parser,{variables: variables}),\n\t\t\tcontainer = $tw.fakeDocument.createElement(\"div\");\n\t\twidgetNode.render(container,null);\n\t\tvar text = type === \"text/html\" ? container.innerHTML : container.textContent,\n\t\t\tfilepath = path.resolve(self.commander.outputPath,wiki.filterTiddlers(filenameFilter,$tw.rootWidget,wiki.makeTiddlerIterator([title]))[0]);\n\t\tif(self.commander.verbose) {\n\t\t\tconsole.log(\"Rendering \\\"\" + title + \"\\\" to \\\"\" + filepath + \"\\\"\");\n\t\t}\n\t\t$tw.utils.createFileDirectories(filepath);\n\t\tfs.writeFileSync(filepath,text,\"utf8\");\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/rendertiddler.js": {
"title": "$:/core/modules/commands/rendertiddler.js",
"text": "/*\\\ntitle: $:/core/modules/commands/rendertiddler.js\ntype: application/javascript\nmodule-type: command\n\nCommand to render a tiddler and save it to a file\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"rendertiddler\",\n\tsynchronous: false\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\ttitle = this.params[0],\n\t\tfilename = path.resolve(this.commander.outputPath,this.params[1]),\n\t\ttype = this.params[2] || \"text/html\",\n\t\ttemplate = this.params[3],\n\t\tname = this.params[4],\n\t\tvalue = this.params[5],\n\t\tvariables = {};\n\t$tw.utils.createFileDirectories(filename);\n\tif(template) {\n\t\tvariables.currentTiddler = title;\n\t\ttitle = template;\n\t}\n\tif(name && value) {\n\t\tvariables[name] = value;\n\t}\n\tfs.writeFile(filename,this.commander.wiki.renderTiddler(type,title,{variables: variables}),\"utf8\",function(err) {\n\t\tself.callback(err);\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/rendertiddlers.js": {
"title": "$:/core/modules/commands/rendertiddlers.js",
"text": "/*\\\ntitle: $:/core/modules/commands/rendertiddlers.js\ntype: application/javascript\nmodule-type: command\n\nCommand to render several tiddlers to a folder of files\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.info = {\n\tname: \"rendertiddlers\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\twiki = this.commander.wiki,\n\t\tfilter = this.params[0],\n\t\ttemplate = this.params[1],\n\t\toutputPath = this.commander.outputPath,\n\t\tpathname = path.resolve(outputPath,this.params[2]),\t\t\n\t\ttype = this.params[3] || \"text/html\",\n\t\textension = this.params[4] || \".html\",\n\t\tdeleteDirectory = (this.params[5] || \"\").toLowerCase() !== \"noclean\",\n\t\ttiddlers = wiki.filterTiddlers(filter);\n\tif(deleteDirectory) {\n\t\t$tw.utils.deleteDirectory(pathname);\n\t}\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar parser = wiki.parseTiddler(template),\n\t\t\twidgetNode = wiki.makeWidget(parser,{variables: {currentTiddler: title}}),\n\t\t\tcontainer = $tw.fakeDocument.createElement(\"div\");\n\t\twidgetNode.render(container,null);\n\t\tvar text = type === \"text/html\" ? container.innerHTML : container.textContent,\n\t\t\texportPath = null;\n\t\tif($tw.utils.hop($tw.macros,\"tv-get-export-path\")) {\n\t\t\tvar macroPath = $tw.macros[\"tv-get-export-path\"].run.apply(self,[title]);\n\t\t\tif(macroPath) {\n\t\t\t\texportPath = path.resolve(outputPath,macroPath + extension);\n\t\t\t}\n\t\t}\n\t\tvar finalPath = exportPath || path.resolve(pathname,encodeURIComponent(title) + extension);\n\t\t$tw.utils.createFileDirectories(finalPath);\n\t\tfs.writeFileSync(finalPath,text,\"utf8\");\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/save.js": {
"title": "$:/core/modules/commands/save.js",
"text": "/*\\\ntitle: $:/core/modules/commands/save.js\ntype: application/javascript\nmodule-type: command\n\nSaves individual tiddlers in their raw text or binary format to the specified files\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"save\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing filename filter\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\twiki = this.commander.wiki,\n\t\ttiddlerFilter = this.params[0],\n\t\tfilenameFilter = this.params[1] || \"[is[tiddler]]\",\n\t\ttiddlers = wiki.filterTiddlers(tiddlerFilter);\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar tiddler = self.commander.wiki.getTiddler(title),\n\t\t\ttype = tiddler.fields.type || \"text/vnd.tiddlywiki\",\n\t\t\tcontentTypeInfo = $tw.config.contentTypeInfo[type] || {encoding: \"utf8\"},\n\t\t\tfilepath = path.resolve(self.commander.outputPath,wiki.filterTiddlers(filenameFilter,$tw.rootWidget,wiki.makeTiddlerIterator([title]))[0]);\n\t\tif(self.commander.verbose) {\n\t\t\tconsole.log(\"Saving \\\"\" + title + \"\\\" to \\\"\" + filepath + \"\\\"\");\n\t\t}\n\t\t$tw.utils.createFileDirectories(filepath);\n\t\tfs.writeFileSync(filepath,tiddler.fields.text,contentTypeInfo.encoding);\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/savelibrarytiddlers.js": {
"title": "$:/core/modules/commands/savelibrarytiddlers.js",
"text": "/*\\\ntitle: $:/core/modules/commands/savelibrarytiddlers.js\ntype: application/javascript\nmodule-type: command\n\nCommand to save the subtiddlers of a bundle tiddler as a series of JSON files\n\n--savelibrarytiddlers <tiddler> <pathname> <skinnylisting>\n\nThe tiddler identifies the bundle tiddler that contains the subtiddlers.\n\nThe pathname specifies the pathname to the folder in which the JSON files should be saved. The filename is the URL encoded title of the subtiddler.\n\nThe skinnylisting specifies the title of the tiddler to which a JSON catalogue of the subtiddlers will be saved. The JSON file contains the same data as the bundle tiddler but with the `text` field removed.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"savelibrarytiddlers\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\tcontainerTitle = this.params[0],\n\t\tfilter = this.params[1],\n\t\tbasepath = this.params[2],\n\t\tskinnyListTitle = this.params[3];\n\t// Get the container tiddler as data\n\tvar containerData = self.commander.wiki.getTiddlerDataCached(containerTitle,undefined);\n\tif(!containerData) {\n\t\treturn \"'\" + containerTitle + \"' is not a tiddler bundle\";\n\t}\n\t// Filter the list of plugins\n\tvar pluginList = [];\n\t$tw.utils.each(containerData.tiddlers,function(tiddler,title) {\n\t\tpluginList.push(title);\n\t});\n\tvar filteredPluginList;\n\tif(filter) {\n\t\tfilteredPluginList = self.commander.wiki.filterTiddlers(filter,null,self.commander.wiki.makeTiddlerIterator(pluginList));\n\t} else {\n\t\tfilteredPluginList = pluginList;\n\t}\n\t// Iterate through the plugins\n\tvar skinnyList = [];\n\t$tw.utils.each(filteredPluginList,function(title) {\n\t\tvar tiddler = containerData.tiddlers[title];\n\t\t// Save each JSON file and collect the skinny data\n\t\tvar pathname = path.resolve(self.commander.outputPath,basepath + encodeURIComponent(title) + \".json\");\n\t\t$tw.utils.createFileDirectories(pathname);\n\t\tfs.writeFileSync(pathname,JSON.stringify(tiddler),\"utf8\");\n\t\t// Collect the skinny list data\n\t\tvar pluginTiddlers = JSON.parse(tiddler.text),\n\t\t\treadmeContent = (pluginTiddlers.tiddlers[title + \"/readme\"] || {}).text,\n\t\t\tdoesRequireReload = !!$tw.wiki.doesPluginInfoRequireReload(pluginTiddlers),\n\t\t\ticonTiddler = pluginTiddlers.tiddlers[title + \"/icon\"] || {},\n\t\t\ticonType = iconTiddler.type,\n\t\t\ticonText = iconTiddler.text,\n\t\t\ticonContent;\n\t\tif(iconType && iconText) {\n\t\t\ticonContent = $tw.utils.makeDataUri(iconText,iconType);\n\t\t}\n\t\tskinnyList.push($tw.utils.extend({},tiddler,{\n\t\t\ttext: undefined,\n\t\t\treadme: readmeContent,\n\t\t\t\"requires-reload\": doesRequireReload ? \"yes\" : \"no\",\n\t\t\ticon: iconContent\n\t\t}));\n\t});\n\t// Save the catalogue tiddler\n\tif(skinnyListTitle) {\n\t\tself.commander.wiki.setTiddlerData(skinnyListTitle,skinnyList);\n\t}\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/savetiddler.js": {
"title": "$:/core/modules/commands/savetiddler.js",
"text": "/*\\\ntitle: $:/core/modules/commands/savetiddler.js\ntype: application/javascript\nmodule-type: command\n\nCommand to save the content of a tiddler to a file\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"savetiddler\",\n\tsynchronous: false\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\ttitle = this.params[0],\n\t\tfilename = path.resolve(this.commander.outputPath,this.params[1]),\n\t\ttiddler = this.commander.wiki.getTiddler(title);\n\tif(tiddler) {\n\t\tvar type = tiddler.fields.type || \"text/vnd.tiddlywiki\",\n\t\t\tcontentTypeInfo = $tw.config.contentTypeInfo[type] || {encoding: \"utf8\"};\n\t\t$tw.utils.createFileDirectories(filename);\n\t\tfs.writeFile(filename,tiddler.fields.text,contentTypeInfo.encoding,function(err) {\n\t\t\tself.callback(err);\n\t\t});\n\t} else {\n\t\treturn \"Missing tiddler: \" + title;\n\t}\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/savetiddlers.js": {
"title": "$:/core/modules/commands/savetiddlers.js",
"text": "/*\\\ntitle: $:/core/modules/commands/savetiddlers.js\ntype: application/javascript\nmodule-type: command\n\nCommand to save several tiddlers to a folder of files\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.info = {\n\tname: \"savetiddlers\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\twiki = this.commander.wiki,\n\t\tfilter = this.params[0],\n\t\tpathname = path.resolve(this.commander.outputPath,this.params[1]),\n\t\tdeleteDirectory = (this.params[2] || \"\").toLowerCase() !== \"noclean\",\n\t\ttiddlers = wiki.filterTiddlers(filter);\n\tif(deleteDirectory) {\n\t\t$tw.utils.deleteDirectory(pathname);\n\t}\n\t$tw.utils.createDirectory(pathname);\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar tiddler = self.commander.wiki.getTiddler(title),\n\t\t\ttype = tiddler.fields.type || \"text/vnd.tiddlywiki\",\n\t\t\tcontentTypeInfo = $tw.config.contentTypeInfo[type] || {encoding: \"utf8\"},\n\t\t\tfilename = path.resolve(pathname,encodeURIComponent(title));\n\t\tfs.writeFileSync(filename,tiddler.fields.text,contentTypeInfo.encoding);\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/savewikifolder.js": {
"title": "$:/core/modules/commands/savewikifolder.js",
"text": "/*\\\ntitle: $:/core/modules/commands/savewikifolder.js\ntype: application/javascript\nmodule-type: command\n\nCommand to save the current wiki as a wiki folder\n\n--savewikifolder <wikifolderpath> [<filter>]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"savewikifolder\",\n\tsynchronous: true\n};\n\nvar fs,path;\nif($tw.node) {\n\tfs = require(\"fs\");\n\tpath = require(\"path\");\n}\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing wiki folder path\";\n\t}\n\tvar wikifoldermaker = new WikiFolderMaker(this.params[0],this.params[1],this.commander);\n\treturn wikifoldermaker.save();\n};\n\nfunction WikiFolderMaker(wikiFolderPath,wikiFilter,commander) {\n\tthis.wikiFolderPath = wikiFolderPath;\n\tthis.wikiFilter = wikiFilter || \"[all[tiddlers]]\";\n\tthis.commander = commander;\n\tthis.wiki = commander.wiki;\n\tthis.savedPaths = []; // So that we can detect filename clashes\n}\n\nWikiFolderMaker.prototype.log = function(str) {\n\tif(this.commander.verbose) {\n\t\tconsole.log(str);\n\t}\n};\n\nWikiFolderMaker.prototype.tiddlersToIgnore = [\n\t\"$:/boot/boot.css\",\n\t\"$:/boot/boot.js\",\n\t\"$:/boot/bootprefix.js\",\n\t\"$:/core\",\n\t\"$:/library/sjcl.js\",\n\t\"$:/temp/info-plugin\"\n];\n\n/*\nReturns null if successful, or an error string if there was an error\n*/\nWikiFolderMaker.prototype.save = function() {\n\tvar self = this;\n\t// Check that the output directory doesn't exist\n\tif(fs.existsSync(this.wikiFolderPath) && !$tw.utils.isDirectoryEmpty(this.wikiFolderPath)) {\n\t\treturn \"The unpackwiki command requires that the output wiki folder be empty\";\n\t}\n\t// Get the tiddlers from the source wiki\n\tvar tiddlerTitles = this.wiki.filterTiddlers(this.wikiFilter);\n\t// Initialise a new tiddlwiki.info file\n\tvar newWikiInfo = {};\n\t// Process each incoming tiddler in turn\n\t$tw.utils.each(tiddlerTitles,function(title) {\n\t\tvar tiddler = self.wiki.getTiddler(title);\n\t\tif(tiddler) {\n\t\t\tif(self.tiddlersToIgnore.indexOf(title) !== -1) {\n\t\t\t\t// Ignore the core plugin and the ephemeral info plugin\n\t\t\t\tself.log(\"Ignoring tiddler: \" + title);\n\t\t\t} else {\n\t\t\t\tvar type = tiddler.fields.type,\n\t\t\t\t\tpluginType = tiddler.fields[\"plugin-type\"];\n\t\t\t\tif(type === \"application/json\" && pluginType) {\n\t\t\t\t\t// Plugin tiddler\n\t\t\t\t\tvar libraryDetails = self.findPluginInLibrary(title);\n\t\t\t\t\tif(libraryDetails) {\n\t\t\t\t\t\t// A plugin from the core library\n\t\t\t\t\t\tself.log(\"Adding built-in plugin: \" + libraryDetails.name);\n\t\t\t\t\t\tnewWikiInfo[libraryDetails.type] = newWikiInfo[libraryDetails.type] || [];\n\t\t\t\t\t\t$tw.utils.pushTop(newWikiInfo[libraryDetails.type],libraryDetails.name);\n\t\t\t\t\t} else {\n\t\t\t\t\t\t// A custom plugin\n\t\t\t\t\t\tself.log(\"Processing custom plugin: \" + title);\n\t\t\t\t\t\tself.saveCustomPlugin(tiddler);\n\t\t\t\t\t}\t\t\t\t\n\t\t\t\t} else {\n\t\t\t\t\t// Ordinary tiddler\n\t\t\t\t\tself.saveTiddler(\"tiddlers\",tiddler);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n\t// Save the tiddlywiki.info file\n\tthis.saveJSONFile(\"tiddlywiki.info\",newWikiInfo);\n\tself.log(\"Writing tiddlywiki.info: \" + JSON.stringify(newWikiInfo,null,$tw.config.preferences.jsonSpaces));\n\treturn null;\n};\n\n/*\nTest whether the specified tiddler is a plugin in the plugin library\n*/\nWikiFolderMaker.prototype.findPluginInLibrary = function(title) {\n\tvar parts = title.split(\"/\"),\n\t\tpluginPath, type, name;\n\tif(parts[0] === \"$:\") {\n\t\tif(parts[1] === \"languages\" && parts.length === 3) {\n\t\t\tpluginPath = \"languages\" + path.sep + parts[2];\n\t\t\ttype = parts[1];\n\t\t\tname = parts[2];\n\t\t} else if(parts[1] === \"plugins\" || parts[1] === \"themes\" && parts.length === 4) {\n\t\t\tpluginPath = parts[1] + path.sep + parts[2] + path.sep + parts[3];\n\t\t\ttype = parts[1];\n\t\t\tname = parts[2] + \"/\" + parts[3];\n\t\t}\n\t}\n\tif(pluginPath && type && name) {\n\t\tpluginPath = path.resolve($tw.boot.bootPath,\"..\",pluginPath);\n\t\tif(fs.existsSync(pluginPath)) {\n\t\t\treturn {\n\t\t\t\tpluginPath: pluginPath,\n\t\t\t\ttype: type,\n\t\t\t\tname: name\n\t\t\t};\n\t\t}\n\t}\n\treturn false;\n};\n\nWikiFolderMaker.prototype.saveCustomPlugin = function(pluginTiddler) {\n\tvar self = this,\n\t\tpluginTitle = pluginTiddler.fields.title,\n\t\ttitleParts = pluginTitle.split(\"/\"),\n\t\tdirectory = $tw.utils.generateTiddlerFilepath(titleParts[titleParts.length - 1],{\n\t\t\tdirectory: path.resolve(this.wikiFolderPath,pluginTiddler.fields[\"plugin-type\"] + \"s\")\n\t\t}),\n\t\tpluginInfo = pluginTiddler.getFieldStrings({exclude: [\"text\",\"type\"]});\n\tthis.saveJSONFile(directory + path.sep + \"plugin.info\",pluginInfo);\n\tself.log(\"Writing \" + directory + path.sep + \"plugin.info: \" + JSON.stringify(pluginInfo,null,$tw.config.preferences.jsonSpaces));\n\tvar pluginTiddlers = JSON.parse(pluginTiddler.fields.text).tiddlers; // A hashmap of tiddlers in the plugin\n\t$tw.utils.each(pluginTiddlers,function(tiddler) {\n\t\tself.saveTiddler(directory,new $tw.Tiddler(tiddler));\n\t});\n};\n\nWikiFolderMaker.prototype.saveTiddler = function(directory,tiddler) {\n\tvar fileInfo = $tw.utils.generateTiddlerFileInfo(tiddler,{\n\t\tdirectory: path.resolve(this.wikiFolderPath,directory),\n\t\twiki: this.wiki\n\t});\n\t$tw.utils.saveTiddlerToFileSync(tiddler,fileInfo);\n};\n\nWikiFolderMaker.prototype.saveJSONFile = function(filename,json) {\n\tthis.saveTextFile(filename,JSON.stringify(json,null,$tw.config.preferences.jsonSpaces));\n};\n\nWikiFolderMaker.prototype.saveTextFile = function(filename,data) {\n\tthis.saveFile(filename,\"utf8\",data);\n};\n\nWikiFolderMaker.prototype.saveFile = function(filename,encoding,data) {\n\tvar filepath = path.resolve(this.wikiFolderPath,filename);\n\t$tw.utils.createFileDirectories(filepath);\n\tfs.writeFileSync(filepath,data,encoding);\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/server.js": {
"title": "$:/core/modules/commands/server.js",
"text": "/*\\\ntitle: $:/core/modules/commands/server.js\ntype: application/javascript\nmodule-type: command\n\nDeprecated legacy command for serving tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Server = require(\"$:/core/modules/server/server.js\").Server;\n\nexports.info = {\n\tname: \"server\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tvar self = this;\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(!$tw.boot.wikiTiddlersPath) {\n\t\t$tw.utils.warning(\"Warning: Wiki folder '\" + $tw.boot.wikiPath + \"' does not exist or is missing a tiddlywiki.info file\");\n\t}\n\t// Set up server\n\tthis.server = new Server({\n\t\twiki: this.commander.wiki,\n\t\tvariables: {\n\t\t\tport: this.params[0],\n\t\t\thost: this.params[6],\n\t\t\t\"root-tiddler\": this.params[1],\n\t\t\t\"root-render-type\": this.params[2],\n\t\t\t\"root-serve-type\": this.params[3],\n\t\t\tusername: this.params[4],\n\t\t\tpassword: this.params[5],\n\t\t\t\"path-prefix\": this.params[7],\n\t\t\t\"debug-level\": this.params[8]\n\t\t}\n\t});\n\tvar nodeServer = this.server.listen();\n\t$tw.hooks.invokeHook(\"th-server-command-post-start\",this.server,nodeServer,\"tiddlywiki\");\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/setfield.js": {
"title": "$:/core/modules/commands/setfield.js",
"text": "/*\\\ntitle: $:/core/modules/commands/setfield.js\ntype: application/javascript\nmodule-type: command\n\nCommand to modify selected tiddlers to set a field to the text of a template tiddler that has been wikified with the selected tiddler as the current tiddler.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.info = {\n\tname: \"setfield\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 4) {\n\t\treturn \"Missing parameters\";\n\t}\n\tvar self = this,\n\t\twiki = this.commander.wiki,\n\t\tfilter = this.params[0],\n\t\tfieldname = this.params[1] || \"text\",\n\t\ttemplatetitle = this.params[2],\n\t\trendertype = this.params[3] || \"text/plain\",\n\t\ttiddlers = wiki.filterTiddlers(filter);\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar parser = wiki.parseTiddler(templatetitle),\n\t\t\tnewFields = {},\n\t\t\ttiddler = wiki.getTiddler(title);\n\t\tif(parser) {\n\t\t\tvar widgetNode = wiki.makeWidget(parser,{variables: {currentTiddler: title}});\n\t\t\tvar container = $tw.fakeDocument.createElement(\"div\");\n\t\t\twidgetNode.render(container,null);\n\t\t\tnewFields[fieldname] = rendertype === \"text/html\" ? container.innerHTML : container.textContent;\n\t\t} else {\n\t\t\tnewFields[fieldname] = undefined;\n\t\t}\n\t\twiki.addTiddler(new $tw.Tiddler(tiddler,newFields));\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/unpackplugin.js": {
"title": "$:/core/modules/commands/unpackplugin.js",
"text": "/*\\\ntitle: $:/core/modules/commands/unpackplugin.js\ntype: application/javascript\nmodule-type: command\n\nCommand to extract the shadow tiddlers from within a plugin\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"unpackplugin\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing plugin name\";\n\t}\n\tvar self = this,\n\t\ttitle = this.params[0],\n\t\tpluginData = this.commander.wiki.getTiddlerDataCached(title);\n\tif(!pluginData) {\n\t\treturn \"Plugin '\" + title + \"' not found\";\n\t}\n\t$tw.utils.each(pluginData.tiddlers,function(tiddler) {\n\t\tself.commander.wiki.addTiddler(new $tw.Tiddler(tiddler));\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/verbose.js": {
"title": "$:/core/modules/commands/verbose.js",
"text": "/*\\\ntitle: $:/core/modules/commands/verbose.js\ntype: application/javascript\nmodule-type: command\n\nVerbose command\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"verbose\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tthis.commander.verbose = true;\n\t// Output the boot message log\n\tthis.commander.streams.output.write(\"Boot log:\\n \" + $tw.boot.logMessages.join(\"\\n \") + \"\\n\");\n\treturn null; // No error\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/version.js": {
"title": "$:/core/modules/commands/version.js",
"text": "/*\\\ntitle: $:/core/modules/commands/version.js\ntype: application/javascript\nmodule-type: command\n\nVersion command\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"version\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tthis.commander.streams.output.write($tw.version + \"\\n\");\n\treturn null; // No error\n};\n\nexports.Command = Command;\n\n})();\n",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/config.js": {
"title": "$:/core/modules/config.js",
"text": "/*\\\ntitle: $:/core/modules/config.js\ntype: application/javascript\nmodule-type: config\n\nCore configuration constants\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.preferences = {};\n\nexports.preferences.notificationDuration = 3 * 1000;\nexports.preferences.jsonSpaces = 4;\n\nexports.textPrimitives = {\n\tupperLetter: \"[A-Z\\u00c0-\\u00d6\\u00d8-\\u00de\\u0150\\u0170]\",\n\tlowerLetter: \"[a-z\\u00df-\\u00f6\\u00f8-\\u00ff\\u0151\\u0171]\",\n\tanyLetter: \"[A-Za-z0-9\\u00c0-\\u00d6\\u00d8-\\u00de\\u00df-\\u00f6\\u00f8-\\u00ff\\u0150\\u0170\\u0151\\u0171]\",\n\tblockPrefixLetters:\t\"[A-Za-z0-9-_\\u00c0-\\u00d6\\u00d8-\\u00de\\u00df-\\u00f6\\u00f8-\\u00ff\\u0150\\u0170\\u0151\\u0171]\"\n};\n\nexports.textPrimitives.unWikiLink = \"~\";\nexports.textPrimitives.wikiLink = exports.textPrimitives.upperLetter + \"+\" +\n\texports.textPrimitives.lowerLetter + \"+\" +\n\texports.textPrimitives.upperLetter +\n\texports.textPrimitives.anyLetter + \"*\";\n\nexports.htmlEntities = {quot:34, amp:38, apos:39, lt:60, gt:62, nbsp:160, iexcl:161, cent:162, pound:163, curren:164, yen:165, brvbar:166, sect:167, uml:168, copy:169, ordf:170, laquo:171, not:172, shy:173, reg:174, macr:175, deg:176, plusmn:177, sup2:178, sup3:179, acute:180, micro:181, para:182, middot:183, cedil:184, sup1:185, ordm:186, raquo:187, frac14:188, frac12:189, frac34:190, iquest:191, Agrave:192, Aacute:193, Acirc:194, Atilde:195, Auml:196, Aring:197, AElig:198, Ccedil:199, Egrave:200, Eacute:201, Ecirc:202, Euml:203, Igrave:204, Iacute:205, Icirc:206, Iuml:207, ETH:208, Ntilde:209, Ograve:210, Oacute:211, Ocirc:212, Otilde:213, Ouml:214, times:215, Oslash:216, Ugrave:217, Uacute:218, Ucirc:219, Uuml:220, Yacute:221, THORN:222, szlig:223, agrave:224, aacute:225, acirc:226, atilde:227, auml:228, aring:229, aelig:230, ccedil:231, egrave:232, eacute:233, ecirc:234, euml:235, igrave:236, iacute:237, icirc:238, iuml:239, eth:240, ntilde:241, ograve:242, oacute:243, ocirc:244, otilde:245, ouml:246, divide:247, oslash:248, ugrave:249, uacute:250, ucirc:251, uuml:252, yacute:253, thorn:254, yuml:255, OElig:338, oelig:339, Scaron:352, scaron:353, Yuml:376, fnof:402, circ:710, tilde:732, Alpha:913, Beta:914, Gamma:915, Delta:916, Epsilon:917, Zeta:918, Eta:919, Theta:920, Iota:921, Kappa:922, Lambda:923, Mu:924, Nu:925, Xi:926, Omicron:927, Pi:928, Rho:929, Sigma:931, Tau:932, Upsilon:933, Phi:934, Chi:935, Psi:936, Omega:937, alpha:945, beta:946, gamma:947, delta:948, epsilon:949, zeta:950, eta:951, theta:952, iota:953, kappa:954, lambda:955, mu:956, nu:957, xi:958, omicron:959, pi:960, rho:961, sigmaf:962, sigma:963, tau:964, upsilon:965, phi:966, chi:967, psi:968, omega:969, thetasym:977, upsih:978, piv:982, ensp:8194, emsp:8195, thinsp:8201, zwnj:8204, zwj:8205, lrm:8206, rlm:8207, ndash:8211, mdash:8212, lsquo:8216, rsquo:8217, sbquo:8218, ldquo:8220, rdquo:8221, bdquo:8222, dagger:8224, Dagger:8225, bull:8226, hellip:8230, permil:8240, prime:8242, Prime:8243, lsaquo:8249, rsaquo:8250, oline:8254, frasl:8260, euro:8364, image:8465, weierp:8472, real:8476, trade:8482, alefsym:8501, larr:8592, uarr:8593, rarr:8594, darr:8595, harr:8596, crarr:8629, lArr:8656, uArr:8657, rArr:8658, dArr:8659, hArr:8660, forall:8704, part:8706, exist:8707, empty:8709, nabla:8711, isin:8712, notin:8713, ni:8715, prod:8719, sum:8721, minus:8722, lowast:8727, radic:8730, prop:8733, infin:8734, ang:8736, and:8743, or:8744, cap:8745, cup:8746, int:8747, there4:8756, sim:8764, cong:8773, asymp:8776, ne:8800, equiv:8801, le:8804, ge:8805, sub:8834, sup:8835, nsub:8836, sube:8838, supe:8839, oplus:8853, otimes:8855, perp:8869, sdot:8901, lceil:8968, rceil:8969, lfloor:8970, rfloor:8971, lang:9001, rang:9002, loz:9674, spades:9824, clubs:9827, hearts:9829, diams:9830 };\n\nexports.htmlVoidElements = \"area,base,br,col,command,embed,hr,img,input,keygen,link,meta,param,source,track,wbr\".split(\",\");\n\nexports.htmlBlockElements = \"address,article,aside,audio,blockquote,canvas,dd,div,dl,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,hr,li,noscript,ol,output,p,pre,section,table,tfoot,ul,video\".split(\",\");\n\nexports.htmlUnsafeElements = \"script\".split(\",\");\n\n})();\n",
"type": "application/javascript",
"module-type": "config"
},
"$:/core/modules/deserializers.js": {
"title": "$:/core/modules/deserializers.js",
"text": "/*\\\ntitle: $:/core/modules/deserializers.js\ntype: application/javascript\nmodule-type: tiddlerdeserializer\n\nFunctions to deserialise tiddlers from a block of text\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nUtility function to parse an old-style tiddler DIV in a *.tid file. It looks like this:\n\n<div title=\"Title\" creator=\"JoeBloggs\" modifier=\"JoeBloggs\" created=\"201102111106\" modified=\"201102111310\" tags=\"myTag [[my long tag]]\">\n<pre>The text of the tiddler (without the expected HTML encoding).\n</pre>\n</div>\n\nNote that the field attributes are HTML encoded, but that the body of the <PRE> tag is not encoded.\n\nWhen these tiddler DIVs are encountered within a TiddlyWiki HTML file then the body is encoded in the usual way.\n*/\nvar parseTiddlerDiv = function(text /* [,fields] */) {\n\t// Slot together the default results\n\tvar result = {};\n\tif(arguments.length > 1) {\n\t\tfor(var f=1; f<arguments.length; f++) {\n\t\t\tvar fields = arguments[f];\n\t\t\tfor(var t in fields) {\n\t\t\t\tresult[t] = fields[t];\t\t\n\t\t\t}\n\t\t}\n\t}\n\t// Parse the DIV body\n\tvar startRegExp = /^\\s*<div\\s+([^>]*)>(\\s*<pre>)?/gi,\n\t\tendRegExp,\n\t\tmatch = startRegExp.exec(text);\n\tif(match) {\n\t\t// Old-style DIVs don't have the <pre> tag\n\t\tif(match[2]) {\n\t\t\tendRegExp = /<\\/pre>\\s*<\\/div>\\s*$/gi;\n\t\t} else {\n\t\t\tendRegExp = /<\\/div>\\s*$/gi;\n\t\t}\n\t\tvar endMatch = endRegExp.exec(text);\n\t\tif(endMatch) {\n\t\t\t// Extract the text\n\t\t\tresult.text = text.substring(match.index + match[0].length,endMatch.index);\n\t\t\t// Process the attributes\n\t\t\tvar attrRegExp = /\\s*([^=\\s]+)\\s*=\\s*(?:\"([^\"]*)\"|'([^']*)')/gi,\n\t\t\t\tattrMatch;\n\t\t\tdo {\n\t\t\t\tattrMatch = attrRegExp.exec(match[1]);\n\t\t\t\tif(attrMatch) {\n\t\t\t\t\tvar name = attrMatch[1];\n\t\t\t\t\tvar value = attrMatch[2] !== undefined ? attrMatch[2] : attrMatch[3];\n\t\t\t\t\tresult[name] = value;\n\t\t\t\t}\n\t\t\t} while(attrMatch);\n\t\t\treturn result;\n\t\t}\n\t}\n\treturn undefined;\n};\n\nexports[\"application/x-tiddler-html-div\"] = function(text,fields) {\n\treturn [parseTiddlerDiv(text,fields)];\n};\n\nexports[\"application/json\"] = function(text,fields) {\n\tvar incoming,\n\t\tresults = [];\n\ttry {\n\t\tincoming = JSON.parse(text);\n\t} catch(e) {\n\t\tincoming = [{\n\t\t\ttitle: \"JSON error: \" + e,\n\t\t\ttext: \"\"\n\t\t}]\n\t}\n\tif(!$tw.utils.isArray(incoming)) {\n\t\tincoming = [incoming];\n\t}\n\tfor(var t=0; t<incoming.length; t++) {\n\t\tvar incomingFields = incoming[t],\n\t\t\tfields = {};\n\t\tfor(var f in incomingFields) {\n\t\t\tif(typeof incomingFields[f] === \"string\") {\n\t\t\t\tfields[f] = incomingFields[f];\n\t\t\t}\n\t\t}\n\t\tresults.push(fields);\n\t}\n\treturn results;\n};\n\n/*\nParse an HTML file into tiddlers. There are three possibilities:\n# A TiddlyWiki classic HTML file containing `text/x-tiddlywiki` tiddlers\n# A TiddlyWiki5 HTML file containing `text/vnd.tiddlywiki` tiddlers\n# An ordinary HTML file\n*/\nexports[\"text/html\"] = function(text,fields) {\n\t// Check if we've got a store area\n\tvar storeAreaMarkerRegExp = /<div id=[\"']?storeArea['\"]?( style=[\"']?display:none;[\"']?)?>/gi,\n\t\tmatch = storeAreaMarkerRegExp.exec(text);\n\tif(match) {\n\t\t// If so, it's either a classic TiddlyWiki file or an unencrypted TW5 file\n\t\t// First read the normal tiddlers\n\t\tvar results = deserializeTiddlyWikiFile(text,storeAreaMarkerRegExp.lastIndex,!!match[1],fields);\n\t\t// Then any system tiddlers\n\t\tvar systemAreaMarkerRegExp = /<div id=[\"']?systemArea['\"]?( style=[\"']?display:none;[\"']?)?>/gi,\n\t\t\tsysMatch = systemAreaMarkerRegExp.exec(text);\n\t\tif(sysMatch) {\n\t\t\tresults.push.apply(results,deserializeTiddlyWikiFile(text,systemAreaMarkerRegExp.lastIndex,!!sysMatch[1],fields));\n\t\t}\n\t\treturn results;\n\t} else {\n\t\t// Check whether we've got an encrypted file\n\t\tvar encryptedStoreArea = $tw.utils.extractEncryptedStoreArea(text);\n\t\tif(encryptedStoreArea) {\n\t\t\t// If so, attempt to decrypt it using the current password\n\t\t\treturn $tw.utils.decryptStoreArea(encryptedStoreArea);\n\t\t} else {\n\t\t\t// It's not a TiddlyWiki so we'll return the entire HTML file as a tiddler\n\t\t\treturn deserializeHtmlFile(text,fields);\n\t\t}\n\t}\n};\n\nfunction deserializeHtmlFile(text,fields) {\n\tvar result = {};\n\t$tw.utils.each(fields,function(value,name) {\n\t\tresult[name] = value;\n\t});\n\tresult.text = text;\n\tresult.type = \"text/html\";\n\treturn [result];\n}\n\nfunction deserializeTiddlyWikiFile(text,storeAreaEnd,isTiddlyWiki5,fields) {\n\tvar results = [],\n\t\tendOfDivRegExp = /(<\\/div>\\s*)/gi,\n\t\tstartPos = storeAreaEnd,\n\t\tdefaultType = isTiddlyWiki5 ? undefined : \"text/x-tiddlywiki\";\n\tendOfDivRegExp.lastIndex = startPos;\n\tvar match = endOfDivRegExp.exec(text);\n\twhile(match) {\n\t\tvar endPos = endOfDivRegExp.lastIndex,\n\t\t\ttiddlerFields = parseTiddlerDiv(text.substring(startPos,endPos),fields,{type: defaultType});\n\t\tif(!tiddlerFields) {\n\t\t\tbreak;\n\t\t}\n\t\t$tw.utils.each(tiddlerFields,function(value,name) {\n\t\t\tif(typeof value === \"string\") {\n\t\t\t\ttiddlerFields[name] = $tw.utils.htmlDecode(value);\n\t\t\t}\n\t\t});\n\t\tif(tiddlerFields.text !== null) {\n\t\t\tresults.push(tiddlerFields);\n\t\t}\n\t\tstartPos = endPos;\n\t\tmatch = endOfDivRegExp.exec(text);\n\t}\n\treturn results;\n}\n\n})();\n",
"type": "application/javascript",
"module-type": "tiddlerdeserializer"
},
"$:/core/modules/editor/engines/framed.js": {
"title": "$:/core/modules/editor/engines/framed.js",
"text": "/*\\\ntitle: $:/core/modules/editor/engines/framed.js\ntype: application/javascript\nmodule-type: library\n\nText editor engine based on a simple input or textarea within an iframe. This is done so that the selection is preserved even when clicking away from the textarea\n\n\\*/\n(function(){\n\n/*jslint node: true,browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar HEIGHT_VALUE_TITLE = \"$:/config/TextEditor/EditorHeight/Height\";\n\nfunction FramedEngine(options) {\n\t// Save our options\n\toptions = options || {};\n\tthis.widget = options.widget;\n\tthis.value = options.value;\n\tthis.parentNode = options.parentNode;\n\tthis.nextSibling = options.nextSibling;\n\t// Create our hidden dummy text area for reading styles\n\tthis.dummyTextArea = this.widget.document.createElement(\"textarea\");\n\tif(this.widget.editClass) {\n\t\tthis.dummyTextArea.className = this.widget.editClass;\n\t}\n\tthis.dummyTextArea.setAttribute(\"hidden\",\"true\");\n\tthis.parentNode.insertBefore(this.dummyTextArea,this.nextSibling);\n\tthis.widget.domNodes.push(this.dummyTextArea);\n\t// Create the iframe\n\tthis.iframeNode = this.widget.document.createElement(\"iframe\");\n\tthis.parentNode.insertBefore(this.iframeNode,this.nextSibling);\n\tthis.iframeDoc = this.iframeNode.contentWindow.document;\n\t// (Firefox requires us to put some empty content in the iframe)\n\tthis.iframeDoc.open();\n\tthis.iframeDoc.write(\"\");\n\tthis.iframeDoc.close();\n\t// Style the iframe\n\tthis.iframeNode.className = this.dummyTextArea.className;\n\tthis.iframeNode.style.border = \"none\";\n\tthis.iframeNode.style.padding = \"0\";\n\tthis.iframeNode.style.resize = \"none\";\n\tthis.iframeNode.style[\"background-color\"] = this.widget.wiki.extractTiddlerDataItem(this.widget.wiki.getTiddlerText(\"$:/palette\"),\"tiddler-editor-background\");\n\tthis.iframeDoc.body.style.margin = \"0\";\n\tthis.iframeDoc.body.style.padding = \"0\";\n\tthis.widget.domNodes.push(this.iframeNode);\n\t// Construct the textarea or input node\n\tvar tag = this.widget.editTag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"input\";\n\t}\n\tthis.domNode = this.iframeDoc.createElement(tag);\n\t// Set the text\n\tif(this.widget.editTag === \"textarea\") {\n\t\tthis.domNode.appendChild(this.iframeDoc.createTextNode(this.value));\n\t} else {\n\t\tthis.domNode.value = this.value;\n\t}\n\t// Set the attributes\n\tif(this.widget.editType) {\n\t\tthis.domNode.setAttribute(\"type\",this.widget.editType);\n\t}\n\tif(this.widget.editPlaceholder) {\n\t\tthis.domNode.setAttribute(\"placeholder\",this.widget.editPlaceholder);\n\t}\n\tif(this.widget.editSize) {\n\t\tthis.domNode.setAttribute(\"size\",this.widget.editSize);\n\t}\n\tif(this.widget.editRows) {\n\t\tthis.domNode.setAttribute(\"rows\",this.widget.editRows);\n\t}\n\tif(this.widget.editTabIndex) {\n\t\tthis.iframeNode.setAttribute(\"tabindex\",this.widget.editTabIndex);\n\t}\n\t// Copy the styles from the dummy textarea\n\tthis.copyStyles();\n\t// Add event listeners\n\t$tw.utils.addEventListeners(this.domNode,[\n\t\t{name: \"click\",handlerObject: this,handlerMethod: \"handleClickEvent\"},\n\t\t{name: \"input\",handlerObject: this,handlerMethod: \"handleInputEvent\"},\n\t\t{name: \"keydown\",handlerObject: this.widget,handlerMethod: \"handleKeydownEvent\"}\n\t]);\n\t// Insert the element into the DOM\n\tthis.iframeDoc.body.appendChild(this.domNode);\n}\n\n/*\nCopy styles from the dummy text area to the textarea in the iframe\n*/\nFramedEngine.prototype.copyStyles = function() {\n\t// Copy all styles\n\t$tw.utils.copyStyles(this.dummyTextArea,this.domNode);\n\t// Override the ones that should not be set the same as the dummy textarea\n\tthis.domNode.style.display = \"block\";\n\tthis.domNode.style.width = \"100%\";\n\tthis.domNode.style.margin = \"0\";\n\tthis.domNode.style[\"background-color\"] = this.widget.wiki.extractTiddlerDataItem(this.widget.wiki.getTiddlerText(\"$:/palette\"),\"tiddler-editor-background\");\n\t// In Chrome setting -webkit-text-fill-color overrides the placeholder text colour\n\tthis.domNode.style[\"-webkit-text-fill-color\"] = \"currentcolor\";\n};\n\n/*\nSet the text of the engine if it doesn't currently have focus\n*/\nFramedEngine.prototype.setText = function(text,type) {\n\tif(!this.domNode.isTiddlyWikiFakeDom) {\n\t\tif(this.domNode.ownerDocument.activeElement !== this.domNode) {\n\t\t\tthis.domNode.value = text;\n\t\t}\n\t\t// Fix the height if needed\n\t\tthis.fixHeight();\n\t}\n};\n\n/*\nGet the text of the engine\n*/\nFramedEngine.prototype.getText = function() {\n\treturn this.domNode.value;\n};\n\n/*\nFix the height of textarea to fit content\n*/\nFramedEngine.prototype.fixHeight = function() {\n\t// Make sure styles are updated\n\tthis.copyStyles();\n\t// Adjust height\n\tif(this.widget.editTag === \"textarea\") {\n\t\tif(this.widget.editAutoHeight) {\n\t\t\tif(this.domNode && !this.domNode.isTiddlyWikiFakeDom) {\n\t\t\t\tvar newHeight = $tw.utils.resizeTextAreaToFit(this.domNode,this.widget.editMinHeight);\n\t\t\t\tthis.iframeNode.style.height = (newHeight + 14) + \"px\"; // +14 for the border on the textarea\n\t\t\t}\n\t\t} else {\n\t\t\tvar fixedHeight = parseInt(this.widget.wiki.getTiddlerText(HEIGHT_VALUE_TITLE,\"400px\"),10);\n\t\t\tfixedHeight = Math.max(fixedHeight,20);\n\t\t\tthis.domNode.style.height = fixedHeight + \"px\";\n\t\t\tthis.iframeNode.style.height = (fixedHeight + 14) + \"px\";\n\t\t}\n\t}\n};\n\n/*\nFocus the engine node\n*/\nFramedEngine.prototype.focus = function() {\n\tif(this.domNode.focus && this.domNode.select) {\n\t\tthis.domNode.focus();\n\t\tthis.domNode.select();\n\t}\n};\n\n/*\nHandle a click\n*/\nFramedEngine.prototype.handleClickEvent = function(event) {\n\tthis.fixHeight();\n\treturn true;\n};\n\n/*\nHandle a dom \"input\" event which occurs when the text has changed\n*/\nFramedEngine.prototype.handleInputEvent = function(event) {\n\tthis.widget.saveChanges(this.getText());\n\tthis.fixHeight();\n\treturn true;\n};\n\n/*\nCreate a blank structure representing a text operation\n*/\nFramedEngine.prototype.createTextOperation = function() {\n\tvar operation = {\n\t\ttext: this.domNode.value,\n\t\tselStart: this.domNode.selectionStart,\n\t\tselEnd: this.domNode.selectionEnd,\n\t\tcutStart: null,\n\t\tcutEnd: null,\n\t\treplacement: null,\n\t\tnewSelStart: null,\n\t\tnewSelEnd: null\n\t};\n\toperation.selection = operation.text.substring(operation.selStart,operation.selEnd);\n\treturn operation;\n};\n\n/*\nExecute a text operation\n*/\nFramedEngine.prototype.executeTextOperation = function(operation) {\n\t// Perform the required changes to the text area and the underlying tiddler\n\tvar newText = operation.text;\n\tif(operation.replacement !== null) {\n\t\tnewText = operation.text.substring(0,operation.cutStart) + operation.replacement + operation.text.substring(operation.cutEnd);\n\t\t// Attempt to use a execCommand to modify the value of the control\n\t\tif(this.iframeDoc.queryCommandSupported(\"insertText\") && this.iframeDoc.queryCommandSupported(\"delete\") && !$tw.browser.isFirefox) {\n\t\t\tthis.domNode.focus();\n\t\t\tthis.domNode.setSelectionRange(operation.cutStart,operation.cutEnd);\n\t\t\tif(operation.replacement === \"\") {\n\t\t\t\tthis.iframeDoc.execCommand(\"delete\",false,\"\");\n\t\t\t} else {\n\t\t\t\tthis.iframeDoc.execCommand(\"insertText\",false,operation.replacement);\n\t\t\t}\n\t\t} else {\n\t\t\tthis.domNode.value = newText;\n\t\t}\n\t\tthis.domNode.focus();\n\t\tthis.domNode.setSelectionRange(operation.newSelStart,operation.newSelEnd);\n\t}\n\tthis.domNode.focus();\n\treturn newText;\n};\n\nexports.FramedEngine = FramedEngine;\n\n})();\n",
"type": "application/javascript",
"module-type": "library"
},
"$:/core/modules/editor/engines/simple.js": {
"title": "$:/core/modules/editor/engines/simple.js",
"text": "/*\\\ntitle: $:/core/modules/editor/engines/simple.js\ntype: application/javascript\nmodule-type: library\n\nText editor engine based on a simple input or textarea tag\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar HEIGHT_VALUE_TITLE = \"$:/config/TextEditor/EditorHeight/Height\";\n\nfunction SimpleEngine(options) {\n\t// Save our options\n\toptions = options || {};\n\tthis.widget = options.widget;\n\tthis.value = options.value;\n\tthis.parentNode = options.parentNode;\n\tthis.nextSibling = options.nextSibling;\n\t// Construct the textarea or input node\n\tvar tag = this.widget.editTag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"input\";\n\t}\n\tthis.domNode = this.widget.document.createElement(tag);\n\t// Set the text\n\tif(this.widget.editTag === \"textarea\") {\n\t\tthis.domNode.appendChild(this.widget.document.createTextNode(this.value));\n\t} else {\n\t\tthis.domNode.value = this.value;\n\t}\n\t// Set the attributes\n\tif(this.widget.editType) {\n\t\tthis.domNode.setAttribute(\"type\",this.widget.editType);\n\t}\n\tif(this.widget.editPlaceholder) {\n\t\tthis.domNode.setAttribute(\"placeholder\",this.widget.editPlaceholder);\n\t}\n\tif(this.widget.editSize) {\n\t\tthis.domNode.setAttribute(\"size\",this.widget.editSize);\n\t}\n\tif(this.widget.editRows) {\n\t\tthis.domNode.setAttribute(\"rows\",this.widget.editRows);\n\t}\n\tif(this.widget.editClass) {\n\t\tthis.domNode.className = this.widget.editClass;\n\t}\n\tif(this.widget.editTabIndex) {\n\t\tthis.domNode.setAttribute(\"tabindex\",this.widget.editTabIndex);\n\t}\n\t// Add an input event handler\n\t$tw.utils.addEventListeners(this.domNode,[\n\t\t{name: \"focus\", handlerObject: this, handlerMethod: \"handleFocusEvent\"},\n\t\t{name: \"input\", handlerObject: this, handlerMethod: \"handleInputEvent\"}\n\t]);\n\t// Insert the element into the DOM\n\tthis.parentNode.insertBefore(this.domNode,this.nextSibling);\n\tthis.widget.domNodes.push(this.domNode);\n}\n\n/*\nSet the text of the engine if it doesn't currently have focus\n*/\nSimpleEngine.prototype.setText = function(text,type) {\n\tif(!this.domNode.isTiddlyWikiFakeDom) {\n\t\tif(this.domNode.ownerDocument.activeElement !== this.domNode || text === \"\") {\n\t\t\tthis.domNode.value = text;\n\t\t}\n\t\t// Fix the height if needed\n\t\tthis.fixHeight();\n\t}\n};\n\n/*\nGet the text of the engine\n*/\nSimpleEngine.prototype.getText = function() {\n\treturn this.domNode.value;\n};\n\n/*\nFix the height of textarea to fit content\n*/\nSimpleEngine.prototype.fixHeight = function() {\n\tif(this.widget.editTag === \"textarea\") {\n\t\tif(this.widget.editAutoHeight) {\n\t\t\tif(this.domNode && !this.domNode.isTiddlyWikiFakeDom) {\n\t\t\t\t$tw.utils.resizeTextAreaToFit(this.domNode,this.widget.editMinHeight);\n\t\t\t}\n\t\t} else {\n\t\t\tvar fixedHeight = parseInt(this.widget.wiki.getTiddlerText(HEIGHT_VALUE_TITLE,\"400px\"),10);\n\t\t\tfixedHeight = Math.max(fixedHeight,20);\n\t\t\tthis.domNode.style.height = fixedHeight + \"px\";\n\t\t}\n\t}\n};\n\n/*\nFocus the engine node\n*/\nSimpleEngine.prototype.focus = function() {\n\tif(this.domNode.focus && this.domNode.select) {\n\t\tthis.domNode.focus();\n\t\tthis.domNode.select();\n\t}\n};\n\n/*\nHandle a dom \"input\" event which occurs when the text has changed\n*/\nSimpleEngine.prototype.handleInputEvent = function(event) {\n\tthis.widget.saveChanges(this.getText());\n\tthis.fixHeight();\n\treturn true;\n};\n\n/*\nHandle a dom \"focus\" event\n*/\nSimpleEngine.prototype.handleFocusEvent = function(event) {\n\tif(this.widget.editFocusPopup) {\n\t\t$tw.popup.triggerPopup({\n\t\t\tdomNode: this.domNode,\n\t\t\ttitle: this.widget.editFocusPopup,\n\t\t\twiki: this.widget.wiki,\n\t\t\tforce: true\n\t\t});\n\t}\n\treturn true;\n};\n\n/*\nCreate a blank structure representing a text operation\n*/\nSimpleEngine.prototype.createTextOperation = function() {\n\treturn null;\n};\n\n/*\nExecute a text operation\n*/\nSimpleEngine.prototype.executeTextOperation = function(operation) {\n};\n\nexports.SimpleEngine = SimpleEngine;\n\n})();\n",
"type": "application/javascript",
"module-type": "library"
},
"$:/core/modules/editor/factory.js": {
"title": "$:/core/modules/editor/factory.js",
"text": "/*\\\ntitle: $:/core/modules/editor/factory.js\ntype: application/javascript\nmodule-type: library\n\nFactory for constructing text editor widgets with specified engines for the toolbar and non-toolbar cases\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar DEFAULT_MIN_TEXT_AREA_HEIGHT = \"100px\"; // Minimum height of textareas in pixels\n\n// Configuration tiddlers\nvar HEIGHT_MODE_TITLE = \"$:/config/TextEditor/EditorHeight/Mode\";\nvar ENABLE_TOOLBAR_TITLE = \"$:/config/TextEditor/EnableToolbar\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nfunction editTextWidgetFactory(toolbarEngine,nonToolbarEngine) {\n\n\tvar EditTextWidget = function(parseTreeNode,options) {\n\t\t// Initialise the editor operations if they've not been done already\n\t\tif(!this.editorOperations) {\n\t\t\tEditTextWidget.prototype.editorOperations = {};\n\t\t\t$tw.modules.applyMethods(\"texteditoroperation\",this.editorOperations);\n\t\t}\n\t\tthis.initialise(parseTreeNode,options);\n\t};\n\n\t/*\n\tInherit from the base widget class\n\t*/\n\tEditTextWidget.prototype = new Widget();\n\n\t/*\n\tRender this widget into the DOM\n\t*/\n\tEditTextWidget.prototype.render = function(parent,nextSibling) {\n\t\t// Save the parent dom node\n\t\tthis.parentDomNode = parent;\n\t\t// Compute our attributes\n\t\tthis.computeAttributes();\n\t\t// Execute our logic\n\t\tthis.execute();\n\t\t// Create the wrapper for the toolbar and render its content\n\t\tif(this.editShowToolbar) {\n\t\t\tthis.toolbarNode = this.document.createElement(\"div\");\n\t\t\tthis.toolbarNode.className = \"tc-editor-toolbar\";\n\t\t\tparent.insertBefore(this.toolbarNode,nextSibling);\n\t\t\tthis.renderChildren(this.toolbarNode,null);\n\t\t\tthis.domNodes.push(this.toolbarNode);\n\t\t}\n\t\t// Create our element\n\t\tvar editInfo = this.getEditInfo(),\n\t\t\tEngine = this.editShowToolbar ? toolbarEngine : nonToolbarEngine;\n\t\tthis.engine = new Engine({\n\t\t\t\twidget: this,\n\t\t\t\tvalue: editInfo.value,\n\t\t\t\ttype: editInfo.type,\n\t\t\t\tparentNode: parent,\n\t\t\t\tnextSibling: nextSibling\n\t\t\t});\n\t\t// Call the postRender hook\n\t\tif(this.postRender) {\n\t\t\tthis.postRender();\n\t\t}\n\t\t// Fix height\n\t\tthis.engine.fixHeight();\n\t\t// Focus if required\n\t\tif(this.editFocus === \"true\" || this.editFocus === \"yes\") {\n\t\t\tthis.engine.focus();\n\t\t}\n\t\t// Add widget message listeners\n\t\tthis.addEventListeners([\n\t\t\t{type: \"tm-edit-text-operation\", handler: \"handleEditTextOperationMessage\"}\n\t\t]);\n\t};\n\n\t/*\n\tGet the tiddler being edited and current value\n\t*/\n\tEditTextWidget.prototype.getEditInfo = function() {\n\t\t// Get the edit value\n\t\tvar self = this,\n\t\t\tvalue,\n\t\t\ttype = \"text/plain\",\n\t\t\tupdate;\n\t\tif(this.editIndex) {\n\t\t\tvalue = this.wiki.extractTiddlerDataItem(this.editTitle,this.editIndex,this.editDefault);\n\t\t\tupdate = function(value) {\n\t\t\t\tvar data = self.wiki.getTiddlerData(self.editTitle,{});\n\t\t\t\tif(data[self.editIndex] !== value) {\n\t\t\t\t\tdata[self.editIndex] = value;\n\t\t\t\t\tself.wiki.setTiddlerData(self.editTitle,data);\n\t\t\t\t}\n\t\t\t};\n\t\t} else {\n\t\t\t// Get the current tiddler and the field name\n\t\t\tvar tiddler = this.wiki.getTiddler(this.editTitle);\n\t\t\tif(tiddler) {\n\t\t\t\t// If we've got a tiddler, the value to display is the field string value\n\t\t\t\tvalue = tiddler.getFieldString(this.editField);\n\t\t\t\tif(this.editField === \"text\") {\n\t\t\t\t\ttype = tiddler.fields.type || \"text/vnd.tiddlywiki\";\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t// Otherwise, we need to construct a default value for the editor\n\t\t\t\tswitch(this.editField) {\n\t\t\t\t\tcase \"text\":\n\t\t\t\t\t\tvalue = \"Type the text for the tiddler '\" + this.editTitle + \"'\";\n\t\t\t\t\t\ttype = \"text/vnd.tiddlywiki\";\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase \"title\":\n\t\t\t\t\t\tvalue = this.editTitle;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tdefault:\n\t\t\t\t\t\tvalue = \"\";\n\t\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif(this.editDefault !== undefined) {\n\t\t\t\t\tvalue = this.editDefault;\n\t\t\t\t}\n\t\t\t}\n\t\t\tupdate = function(value) {\n\t\t\t\tvar tiddler = self.wiki.getTiddler(self.editTitle),\n\t\t\t\t\tupdateFields = {\n\t\t\t\t\t\ttitle: self.editTitle\n\t\t\t\t\t};\n\t\t\t\tupdateFields[self.editField] = value;\n\t\t\t\tself.wiki.addTiddler(new $tw.Tiddler(self.wiki.getCreationFields(),tiddler,updateFields,self.wiki.getModificationFields()));\n\t\t\t};\n\t\t}\n\t\tif(this.editType) {\n\t\t\ttype = this.editType;\n\t\t}\n\t\treturn {value: value || \"\", type: type, update: update};\n\t};\n\n\t/*\n\tHandle an edit text operation message from the toolbar\n\t*/\n\tEditTextWidget.prototype.handleEditTextOperationMessage = function(event) {\n\t\t// Prepare information about the operation\n\t\tvar operation = this.engine.createTextOperation();\n\t\t// Invoke the handler for the selected operation\n\t\tvar handler = this.editorOperations[event.param];\n\t\tif(handler) {\n\t\t\thandler.call(this,event,operation);\n\t\t}\n\t\t// Execute the operation via the engine\n\t\tvar newText = this.engine.executeTextOperation(operation);\n\t\t// Fix the tiddler height and save changes\n\t\tthis.engine.fixHeight();\n\t\tthis.saveChanges(newText);\n\t};\n\n\t/*\n\tCompute the internal state of the widget\n\t*/\n\tEditTextWidget.prototype.execute = function() {\n\t\t// Get our parameters\n\t\tthis.editTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\t\tthis.editField = this.getAttribute(\"field\",\"text\");\n\t\tthis.editIndex = this.getAttribute(\"index\");\n\t\tthis.editDefault = this.getAttribute(\"default\");\n\t\tthis.editClass = this.getAttribute(\"class\");\n\t\tthis.editPlaceholder = this.getAttribute(\"placeholder\");\n\t\tthis.editSize = this.getAttribute(\"size\");\n\t\tthis.editRows = this.getAttribute(\"rows\");\n\t\tthis.editAutoHeight = this.wiki.getTiddlerText(HEIGHT_MODE_TITLE,\"auto\");\n\t\tthis.editAutoHeight = this.getAttribute(\"autoHeight\",this.editAutoHeight === \"auto\" ? \"yes\" : \"no\") === \"yes\";\n\t\tthis.editMinHeight = this.getAttribute(\"minHeight\",DEFAULT_MIN_TEXT_AREA_HEIGHT);\n\t\tthis.editFocusPopup = this.getAttribute(\"focusPopup\");\n\t\tthis.editFocus = this.getAttribute(\"focus\");\n\t\tthis.editTabIndex = this.getAttribute(\"tabindex\");\n\t\t// Get the default editor element tag and type\n\t\tvar tag,type;\n\t\tif(this.editField === \"text\") {\n\t\t\ttag = \"textarea\";\n\t\t} else {\n\t\t\ttag = \"input\";\n\t\t\tvar fieldModule = $tw.Tiddler.fieldModules[this.editField];\n\t\t\tif(fieldModule && fieldModule.editTag) {\n\t\t\t\ttag = fieldModule.editTag;\n\t\t\t}\n\t\t\tif(fieldModule && fieldModule.editType) {\n\t\t\t\ttype = fieldModule.editType;\n\t\t\t}\n\t\t\ttype = type || \"text\";\n\t\t}\n\t\t// Get the rest of our parameters\n\t\tthis.editTag = this.getAttribute(\"tag\",tag) || \"input\";\n\t\tthis.editType = this.getAttribute(\"type\",type);\n\t\t// Make the child widgets\n\t\tthis.makeChildWidgets();\n\t\t// Determine whether to show the toolbar\n\t\tthis.editShowToolbar = this.wiki.getTiddlerText(ENABLE_TOOLBAR_TITLE,\"yes\");\n\t\tthis.editShowToolbar = (this.editShowToolbar === \"yes\") && !!(this.children && this.children.length > 0) && (!this.document.isTiddlyWikiFakeDom);\n\t};\n\n\t/*\n\tSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n\t*/\n\tEditTextWidget.prototype.refresh = function(changedTiddlers) {\n\t\tvar changedAttributes = this.computeAttributes();\n\t\t// Completely rerender if any of our attributes have changed\n\t\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes[\"default\"] || changedAttributes[\"class\"] || changedAttributes.placeholder || changedAttributes.size || changedAttributes.autoHeight || changedAttributes.minHeight || changedAttributes.focusPopup || changedAttributes.rows || changedAttributes.tabindex || changedTiddlers[HEIGHT_MODE_TITLE] || changedTiddlers[ENABLE_TOOLBAR_TITLE]) {\n\t\t\tthis.refreshSelf();\n\t\t\treturn true;\n\t\t} else if(changedTiddlers[this.editTitle]) {\n\t\t\tvar editInfo = this.getEditInfo();\n\t\t\tthis.updateEditor(editInfo.value,editInfo.type);\n\t\t}\n\t\tthis.engine.fixHeight();\n\t\tif(this.editShowToolbar) {\n\t\t\treturn this.refreshChildren(changedTiddlers);\n\t\t} else {\n\t\t\treturn false;\n\t\t}\n\t};\n\n\t/*\n\tUpdate the editor with new text. This method is separate from updateEditorDomNode()\n\tso that subclasses can override updateEditor() and still use updateEditorDomNode()\n\t*/\n\tEditTextWidget.prototype.updateEditor = function(text,type) {\n\t\tthis.updateEditorDomNode(text,type);\n\t};\n\n\t/*\n\tUpdate the editor dom node with new text\n\t*/\n\tEditTextWidget.prototype.updateEditorDomNode = function(text,type) {\n\t\tthis.engine.setText(text,type);\n\t};\n\n\t/*\n\tSave changes back to the tiddler store\n\t*/\n\tEditTextWidget.prototype.saveChanges = function(text) {\n\t\tvar editInfo = this.getEditInfo();\n\t\tif(text !== editInfo.value) {\n\t\t\teditInfo.update(text);\n\t\t}\n\t};\n\n\t/*\n\tHandle a dom \"keydown\" event, which we'll bubble up to our container for the keyboard widgets benefit\n\t*/\n\tEditTextWidget.prototype.handleKeydownEvent = function(event) {\n\t\t// Check for a keyboard shortcut\n\t\tif(this.toolbarNode) {\n\t\t\tvar shortcutElements = this.toolbarNode.querySelectorAll(\"[data-tw-keyboard-shortcut]\");\n\t\t\tfor(var index=0; index<shortcutElements.length; index++) {\n\t\t\t\tvar el = shortcutElements[index],\n\t\t\t\t\tshortcutData = el.getAttribute(\"data-tw-keyboard-shortcut\"),\n\t\t\t\t\tkeyInfoArray = $tw.keyboardManager.parseKeyDescriptors(shortcutData,{\n\t\t\t\t\t\twiki: this.wiki\n\t\t\t\t\t});\n\t\t\t\tif($tw.keyboardManager.checkKeyDescriptors(event,keyInfoArray)) {\n\t\t\t\t\tvar clickEvent = this.document.createEvent(\"Events\");\n\t\t\t\t clickEvent.initEvent(\"click\",true,false);\n\t\t\t\t el.dispatchEvent(clickEvent);\n\t\t\t\t\tevent.preventDefault();\n\t\t\t\t\tevent.stopPropagation();\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t// Propogate the event to the container\n\t\tif(this.propogateKeydownEvent(event)) {\n\t\t\t// Ignore the keydown if it was already handled\n\t\t\tevent.preventDefault();\n\t\t\tevent.stopPropagation();\n\t\t\treturn true;\n\t\t}\n\t\t// Otherwise, process the keydown normally\n\t\treturn false;\n\t};\n\n\t/*\n\tPropogate keydown events to our container for the keyboard widgets benefit\n\t*/\n\tEditTextWidget.prototype.propogateKeydownEvent = function(event) {\n\t\tvar newEvent = this.document.createEventObject ? this.document.createEventObject() : this.document.createEvent(\"Events\");\n\t\tif(newEvent.initEvent) {\n\t\t\tnewEvent.initEvent(\"keydown\", true, true);\n\t\t}\n\t\tnewEvent.keyCode = event.keyCode;\n\t\tnewEvent.which = event.which;\n\t\tnewEvent.metaKey = event.metaKey;\n\t\tnewEvent.ctrlKey = event.ctrlKey;\n\t\tnewEvent.altKey = event.altKey;\n\t\tnewEvent.shiftKey = event.shiftKey;\n\t\treturn !this.parentDomNode.dispatchEvent(newEvent);\n\t};\n\n\treturn EditTextWidget;\n\n}\n\nexports.editTextWidgetFactory = editTextWidgetFactory;\n\n})();\n",
"type": "application/javascript",
"module-type": "library"
},
"$:/core/modules/editor/operations/bitmap/clear.js": {
"title": "$:/core/modules/editor/operations/bitmap/clear.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/bitmap/clear.js\ntype: application/javascript\nmodule-type: bitmapeditoroperation\n\nBitmap editor operation to clear the image\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"clear\"] = function(event) {\n\tvar ctx = this.canvasDomNode.getContext(\"2d\");\n\tctx.globalAlpha = 1;\n\tctx.fillStyle = event.paramObject.colour || \"white\";\n\tctx.fillRect(0,0,this.canvasDomNode.width,this.canvasDomNode.height);\n\t// Save changes\n\tthis.strokeEnd();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "bitmapeditoroperation"
},
"$:/core/modules/editor/operations/bitmap/resize.js": {
"title": "$:/core/modules/editor/operations/bitmap/resize.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/bitmap/resize.js\ntype: application/javascript\nmodule-type: bitmapeditoroperation\n\nBitmap editor operation to resize the image\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"resize\"] = function(event) {\n\t// Get the new width\n\tvar newWidth = parseInt(event.paramObject.width || this.canvasDomNode.width,10),\n\t\tnewHeight = parseInt(event.paramObject.height || this.canvasDomNode.height,10);\n\t// Update if necessary\n\tif(newWidth > 0 && newHeight > 0 && !(newWidth === this.currCanvas.width && newHeight === this.currCanvas.height)) {\n\t\tthis.changeCanvasSize(newWidth,newHeight);\n\t}\n\t// Update the input controls\n\tthis.refreshToolbar();\n\t// Save the image into the tiddler\n\tthis.saveChanges();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "bitmapeditoroperation"
},
"$:/core/modules/editor/operations/bitmap/rotate-left.js": {
"title": "$:/core/modules/editor/operations/bitmap/rotate-left.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/bitmap/rotate-left.js\ntype: application/javascript\nmodule-type: bitmapeditoroperation\n\nBitmap editor operation to rotate the image left by 90 degrees\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"rotate-left\"] = function(event) {\n\t// Rotate the canvas left by 90 degrees\n\tthis.rotateCanvasLeft();\n\t// Update the input controls\n\tthis.refreshToolbar();\n\t// Save the image into the tiddler\n\tthis.saveChanges();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "bitmapeditoroperation"
},
"$:/core/modules/editor/operations/text/excise.js": {
"title": "$:/core/modules/editor/operations/text/excise.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/excise.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to excise the selection to a new tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"excise\"] = function(event,operation) {\n\tvar editTiddler = this.wiki.getTiddler(this.editTitle),\n\t\teditTiddlerTitle = this.editTitle;\n\tif(editTiddler && editTiddler.fields[\"draft.of\"]) {\n\t\teditTiddlerTitle = editTiddler.fields[\"draft.of\"];\n\t}\n\tvar excisionTitle = event.paramObject.title || this.wiki.generateNewTitle(\"New Excision\");\n\tthis.wiki.addTiddler(new $tw.Tiddler(\n\t\tthis.wiki.getCreationFields(),\n\t\tthis.wiki.getModificationFields(),\n\t\t{\n\t\t\ttitle: excisionTitle,\n\t\t\ttext: operation.selection,\n\t\t\ttags: event.paramObject.tagnew === \"yes\" ? [editTiddlerTitle] : []\n\t\t}\n\t));\n\toperation.replacement = excisionTitle;\n\tswitch(event.paramObject.type || \"transclude\") {\n\t\tcase \"transclude\":\n\t\t\toperation.replacement = \"{{\" + operation.replacement+ \"}}\";\n\t\t\tbreak;\n\t\tcase \"link\":\n\t\t\toperation.replacement = \"[[\" + operation.replacement+ \"]]\";\n\t\t\tbreak;\n\t\tcase \"macro\":\n\t\t\toperation.replacement = \"<<\" + (event.paramObject.macro || \"translink\") + \" \\\"\\\"\\\"\" + operation.replacement + \"\\\"\\\"\\\">>\";\n\t\t\tbreak;\n\t}\n\toperation.cutStart = operation.selStart;\n\toperation.cutEnd = operation.selEnd;\n\toperation.newSelStart = operation.selStart;\n\toperation.newSelEnd = operation.selStart + operation.replacement.length;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/make-link.js": {
"title": "$:/core/modules/editor/operations/text/make-link.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/make-link.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to make a link\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"make-link\"] = function(event,operation) {\n\tif(operation.selection) {\n\t\toperation.replacement = \"[[\" + operation.selection + \"|\" + event.paramObject.text + \"]]\";\n\t\toperation.cutStart = operation.selStart;\n\t\toperation.cutEnd = operation.selEnd;\n\t} else {\n\t\toperation.replacement = \"[[\" + event.paramObject.text + \"]]\";\n\t\toperation.cutStart = operation.selStart;\n\t\toperation.cutEnd = operation.selEnd;\n\t}\n\toperation.newSelStart = operation.selStart + operation.replacement.length;\n\toperation.newSelEnd = operation.newSelStart;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/prefix-lines.js": {
"title": "$:/core/modules/editor/operations/text/prefix-lines.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/prefix-lines.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to add a prefix to the selected lines\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"prefix-lines\"] = function(event,operation) {\n\tvar targetCount = parseInt(event.paramObject.count + \"\",10);\n\t// Cut just past the preceding line break, or the start of the text\n\toperation.cutStart = $tw.utils.findPrecedingLineBreak(operation.text,operation.selStart);\n\t// Cut to just past the following line break, or to the end of the text\n\toperation.cutEnd = $tw.utils.findFollowingLineBreak(operation.text,operation.selEnd);\n\t// Compose the required prefix\n\tvar prefix = $tw.utils.repeat(event.paramObject.character,targetCount);\n\t// Process each line\n\tvar lines = operation.text.substring(operation.cutStart,operation.cutEnd).split(/\\r?\\n/mg);\n\t$tw.utils.each(lines,function(line,index) {\n\t\t// Remove and count any existing prefix characters\n\t\tvar count = 0;\n\t\twhile(line.charAt(0) === event.paramObject.character) {\n\t\t\tline = line.substring(1);\n\t\t\tcount++;\n\t\t}\n\t\t// Remove any whitespace\n\t\twhile(line.charAt(0) === \" \") {\n\t\t\tline = line.substring(1);\n\t\t}\n\t\t// We're done if we removed the exact required prefix, otherwise add it\n\t\tif(count !== targetCount) {\n\t\t\t// Apply the prefix\n\t\t\tline = prefix + \" \" + line;\n\t\t}\n\t\t// Save the modified line\n\t\tlines[index] = line;\n\t});\n\t// Stitch the replacement text together and set the selection\n\toperation.replacement = lines.join(\"\\n\");\n\tif(lines.length === 1) {\n\t\toperation.newSelStart = operation.cutStart + operation.replacement.length;\n\t\toperation.newSelEnd = operation.newSelStart;\n\t} else {\n\t\toperation.newSelStart = operation.cutStart;\n\t\toperation.newSelEnd = operation.newSelStart + operation.replacement.length;\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/replace-all.js": {
"title": "$:/core/modules/editor/operations/text/replace-all.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/replace-all.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to replace the entire text\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"replace-all\"] = function(event,operation) {\n\toperation.cutStart = 0;\n\toperation.cutEnd = operation.text.length;\n\toperation.replacement = event.paramObject.text;\n\toperation.newSelStart = 0;\n\toperation.newSelEnd = operation.replacement.length;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/replace-selection.js": {
"title": "$:/core/modules/editor/operations/text/replace-selection.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/replace-selection.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to replace the selection\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"replace-selection\"] = function(event,operation) {\n\toperation.replacement = event.paramObject.text;\n\toperation.cutStart = operation.selStart;\n\toperation.cutEnd = operation.selEnd;\n\toperation.newSelStart = operation.selStart;\n\toperation.newSelEnd = operation.selStart + operation.replacement.length;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/save-selection.js": {
"title": "$:/core/modules/editor/operations/text/save-selection.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/save-selection.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to save the current selection in a specified tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"save-selection\"] = function(event,operation) {\n\tvar tiddler = event.paramObject.tiddler,\n\t\tfield = event.paramObject.field || \"text\";\n\tif(tiddler && field) {\n\t\tthis.wiki.setText(tiddler,field,null,operation.text.substring(operation.selStart,operation.selEnd));\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/wrap-lines.js": {
"title": "$:/core/modules/editor/operations/text/wrap-lines.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/wrap-lines.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to wrap the selected lines with a prefix and suffix\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"wrap-lines\"] = function(event,operation) {\n\t// Cut just past the preceding line break, or the start of the text\n\toperation.cutStart = $tw.utils.findPrecedingLineBreak(operation.text,operation.selStart);\n\t// Cut to just past the following line break, or to the end of the text\n\toperation.cutEnd = $tw.utils.findFollowingLineBreak(operation.text,operation.selEnd);\n\t// Add the prefix and suffix\n\toperation.replacement = event.paramObject.prefix + \"\\n\" +\n\t\t\t\toperation.text.substring(operation.cutStart,operation.cutEnd) + \"\\n\" +\n\t\t\t\tevent.paramObject.suffix + \"\\n\";\n\toperation.newSelStart = operation.cutStart + event.paramObject.prefix.length + 1;\n\toperation.newSelEnd = operation.newSelStart + (operation.cutEnd - operation.cutStart);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/wrap-selection.js": {
"title": "$:/core/modules/editor/operations/text/wrap-selection.js",
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/wrap-selection.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to wrap the selection with the specified prefix and suffix\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"wrap-selection\"] = function(event,operation) {\n\tif(operation.selStart === operation.selEnd) {\n\t\t// No selection; check if we're within the prefix/suffix\n\t\tif(operation.text.substring(operation.selStart - event.paramObject.prefix.length,operation.selStart + event.paramObject.suffix.length) === event.paramObject.prefix + event.paramObject.suffix) {\n\t\t\t// Remove the prefix and suffix\n\t\t\toperation.cutStart = operation.selStart - event.paramObject.prefix.length;\n\t\t\toperation.cutEnd = operation.selEnd + event.paramObject.suffix.length;\n\t\t\toperation.replacement = \"\";\n\t\t\toperation.newSelStart = operation.cutStart;\n\t\t\toperation.newSelEnd = operation.newSelStart;\n\t\t} else {\n\t\t\t// Wrap the cursor instead\n\t\t\toperation.cutStart = operation.selStart;\n\t\t\toperation.cutEnd = operation.selEnd;\n\t\t\toperation.replacement = event.paramObject.prefix + event.paramObject.suffix;\n\t\t\toperation.newSelStart = operation.selStart + event.paramObject.prefix.length;\n\t\t\toperation.newSelEnd = operation.newSelStart;\n\t\t}\n\t} else if(operation.text.substring(operation.selStart,operation.selStart + event.paramObject.prefix.length) === event.paramObject.prefix && operation.text.substring(operation.selEnd - event.paramObject.suffix.length,operation.selEnd) === event.paramObject.suffix) {\n\t\t// Prefix and suffix are already present, so remove them\n\t\toperation.cutStart = operation.selStart;\n\t\toperation.cutEnd = operation.selEnd;\n\t\toperation.replacement = operation.selection.substring(event.paramObject.prefix.length,operation.selection.length - event.paramObject.suffix.length);\n\t\toperation.newSelStart = operation.selStart;\n\t\toperation.newSelEnd = operation.selStart + operation.replacement.length;\n\t} else {\n\t\t// Add the prefix and suffix\n\t\toperation.cutStart = operation.selStart;\n\t\toperation.cutEnd = operation.selEnd;\n\t\toperation.replacement = event.paramObject.prefix + operation.selection + event.paramObject.suffix;\n\t\toperation.newSelStart = operation.selStart;\n\t\toperation.newSelEnd = operation.selStart + operation.replacement.length;\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/filters/addprefix.js": {
"title": "$:/core/modules/filters/addprefix.js",
"text": "/*\\\ntitle: $:/core/modules/filters/addprefix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for adding a prefix to each title in the list. This is\nespecially useful in contexts where only a filter expression is allowed\nand macro substitution isn't available.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.addprefix = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(operator.operand + title);\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/addsuffix.js": {
"title": "$:/core/modules/filters/addsuffix.js",
"text": "/*\\\ntitle: $:/core/modules/filters/addsuffix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for adding a suffix to each title in the list. This is\nespecially useful in contexts where only a filter expression is allowed\nand macro substitution isn't available.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.addsuffix = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title + operator.operand);\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/after.js": {
"title": "$:/core/modules/filters/after.js",
"text": "/*\\\ntitle: $:/core/modules/filters/after.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddler from the current list that is after the tiddler named in the operand.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.after = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\tvar index = results.indexOf(operator.operand);\n\tif(index === -1 || index > (results.length - 2)) {\n\t\treturn [];\n\t} else {\n\t\treturn [results[index + 1]];\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/all/current.js": {
"title": "$:/core/modules/filters/all/current.js",
"text": "/*\\\ntitle: $:/core/modules/filters/all/current.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[current]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.current = function(source,prefix,options) {\n\tvar currTiddlerTitle = options.widget && options.widget.getVariable(\"currentTiddler\");\n\tif(currTiddlerTitle) {\n\t\treturn [currTiddlerTitle];\n\t} else {\n\t\treturn [];\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/missing.js": {
"title": "$:/core/modules/filters/all/missing.js",
"text": "/*\\\ntitle: $:/core/modules/filters/all/missing.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[missing]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.missing = function(source,prefix,options) {\n\treturn options.wiki.getMissingTitles();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/orphans.js": {
"title": "$:/core/modules/filters/all/orphans.js",
"text": "/*\\\ntitle: $:/core/modules/filters/all/orphans.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[orphans]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.orphans = function(source,prefix,options) {\n\treturn options.wiki.getOrphanTitles();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/shadows.js": {
"title": "$:/core/modules/filters/all/shadows.js",
"text": "/*\\\ntitle: $:/core/modules/filters/all/shadows.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[shadows]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.shadows = function(source,prefix,options) {\n\treturn options.wiki.allShadowTitles();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/tags.js": {
"title": "$:/core/modules/filters/all/tags.js",
"text": "/*\\\ntitle: $:/core/modules/filters/all/tags.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[tags]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tags = function(source,prefix,options) {\n\treturn Object.keys(options.wiki.getTagMap());\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/tiddlers.js": {
"title": "$:/core/modules/filters/all/tiddlers.js",
"text": "/*\\\ntitle: $:/core/modules/filters/all/tiddlers.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[tiddlers]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tiddlers = function(source,prefix,options) {\n\treturn options.wiki.allTitles();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all.js": {
"title": "$:/core/modules/filters/all.js",
"text": "/*\\\ntitle: $:/core/modules/filters/all.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for selecting tiddlers\n\n[all[shadows+tiddlers]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar allFilterOperators;\n\nfunction getAllFilterOperators() {\n\tif(!allFilterOperators) {\n\t\tallFilterOperators = {};\n\t\t$tw.modules.applyMethods(\"allfilteroperator\",allFilterOperators);\n\t}\n\treturn allFilterOperators;\n}\n\n/*\nExport our filter function\n*/\nexports.all = function(source,operator,options) {\n\t// Get our suboperators\n\tvar allFilterOperators = getAllFilterOperators();\n\t// Cycle through the suboperators accumulating their results\n\tvar results = [],\n\t\tsubops = operator.operand.split(\"+\");\n\t// Check for common optimisations\n\tif(subops.length === 1 && subops[0] === \"\") {\n\t\treturn source;\n\t} else if(subops.length === 1 && subops[0] === \"tiddlers\") {\n\t\treturn options.wiki.each;\n\t} else if(subops.length === 1 && subops[0] === \"shadows\") {\n\t\treturn options.wiki.eachShadow;\n\t} else if(subops.length === 2 && subops[0] === \"tiddlers\" && subops[1] === \"shadows\") {\n\t\treturn options.wiki.eachTiddlerPlusShadows;\n\t} else if(subops.length === 2 && subops[0] === \"shadows\" && subops[1] === \"tiddlers\") {\n\t\treturn options.wiki.eachShadowPlusTiddlers;\n\t}\n\t// Do it the hard way\n\tfor(var t=0; t<subops.length; t++) {\n\t\tvar subop = allFilterOperators[subops[t]];\n\t\tif(subop) {\n\t\t\t$tw.utils.pushTop(results,subop(source,operator.prefix,options));\n\t\t}\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/backlinks.js": {
"title": "$:/core/modules/filters/backlinks.js",
"text": "/*\\\ntitle: $:/core/modules/filters/backlinks.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning all the backlinks from a tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.backlinks = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.pushTop(results,options.wiki.getTiddlerBacklinks(title));\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/before.js": {
"title": "$:/core/modules/filters/before.js",
"text": "/*\\\ntitle: $:/core/modules/filters/before.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddler from the current list that is before the tiddler named in the operand.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.before = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\tvar index = results.indexOf(operator.operand);\n\tif(index <= 0) {\n\t\treturn [];\n\t} else {\n\t\treturn [results[index - 1]];\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/commands.js": {
"title": "$:/core/modules/filters/commands.js",
"text": "/*\\\ntitle: $:/core/modules/filters/commands.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the commands available in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.commands = function(source,operator,options) {\n\tvar results = [];\n\t$tw.utils.each($tw.commands,function(commandInfo,name) {\n\t\tresults.push(name);\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/compare.js": {
"title": "$:/core/modules/filters/compare.js",
"text": "/*\\\ntitle: $:/core/modules/filters/compare.js\ntype: application/javascript\nmodule-type: filteroperator\n\nGeneral purpose comparison operator\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.compare = function(source,operator,options) {\n\tvar suffixes = operator.suffixes || [],\n\t\ttype = (suffixes[0] || [])[0],\n\t\tmode = (suffixes[1] || [])[0],\n\t\ttypeFn = types[type] || types.number,\n\t\tmodeFn = modes[mode] || modes.eq,\n\t\tinvert = operator.prefix === \"!\",\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tif(modeFn(typeFn(title,operator.operand)) !== invert) {\n\t\t\tresults.push(title);\n\t\t}\n\t});\n\treturn results;\n};\n\nvar types = {\n\t\"number\": function(a,b) {\n\t\treturn compare($tw.utils.parseNumber(a),$tw.utils.parseNumber(b));\n\t},\n\t\"integer\": function(a,b) {\n\t\treturn compare($tw.utils.parseInt(a),$tw.utils.parseInt(b));\n\t},\n\t\"string\": function(a,b) {\n\t\treturn compare(\"\" + a,\"\" +b);\n\t},\n\t\"date\": function(a,b) {\n\t\tvar dateA = $tw.utils.parseDate(a),\n\t\t\tdateB = $tw.utils.parseDate(b);\n\t\tif(!isFinite(dateA)) {\n\t\t\tdateA = new Date(0);\n\t\t}\n\t\tif(!isFinite(dateB)) {\n\t\t\tdateB = new Date(0);\n\t\t}\n\t\treturn compare(dateA,dateB);\n\t},\n\t\"version\": function(a,b) {\n\t\treturn $tw.utils.compareVersions(a,b);\n\t}\n};\n\nfunction compare(a,b) {\n\tif(a > b) {\n\t\treturn +1;\n\t} else if(a < b) {\n\t\treturn -1;\n\t} else {\n\t\treturn 0;\n\t}\n};\n\nvar modes = {\n\t\"eq\": function(value) {return value === 0;},\n\t\"ne\": function(value) {return value !== 0;},\n\t\"gteq\": function(value) {return value >= 0;},\n\t\"gt\": function(value) {return value > 0;},\n\t\"lteq\": function(value) {return value <= 0;},\n\t\"lt\": function(value) {return value < 0;}\n}\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/contains.js": {
"title": "$:/core/modules/filters/contains.js",
"text": "/*\\\ntitle: $:/core/modules/filters/contains.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for finding values in array fields\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.contains = function(source,operator,options) {\n\tvar results = [],\n\t\tfieldname = (operator.suffix || \"list\").toLowerCase();\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler) {\n\t\t\t\tvar list = tiddler.getFieldList(fieldname);\n\t\t\t\tif(list.indexOf(operator.operand) === -1) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler) {\n\t\t\t\tvar list = tiddler.getFieldList(fieldname);\n\t\t\t\tif(list.indexOf(operator.operand) !== -1) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/count.js": {
"title": "$:/core/modules/filters/count.js",
"text": "/*\\\ntitle: $:/core/modules/filters/count.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the number of entries in the current list.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.count = function(source,operator,options) {\n\tvar count = 0;\n\tsource(function(tiddler,title) {\n\t\tcount++;\n\t});\n\treturn [count + \"\"];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/days.js": {
"title": "$:/core/modules/filters/days.js",
"text": "/*\\\ntitle: $:/core/modules/filters/days.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that selects tiddlers with a specified date field within a specified date interval.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.days = function(source,operator,options) {\n\tvar results = [],\n\t\tfieldName = operator.suffix || \"modified\",\n\t\tdayInterval = (parseInt(operator.operand,10)||0),\n\t\tdayIntervalSign = $tw.utils.sign(dayInterval),\n\t\ttargetTimeStamp = (new Date()).setHours(0,0,0,0) + 1000*60*60*24*dayInterval,\n\t\tisWithinDays = function(dateField) {\n\t\t\tvar sign = $tw.utils.sign(targetTimeStamp - (new Date(dateField)).setHours(0,0,0,0));\n\t\t\treturn sign === 0 || sign === dayIntervalSign;\n\t\t};\n\n\tif(operator.prefix === \"!\") {\n\t\ttargetTimeStamp = targetTimeStamp - 1000*60*60*24*dayIntervalSign;\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler && tiddler.fields[fieldName]) {\n\t\t\t\tif(!isWithinDays($tw.utils.parseDate(tiddler.fields[fieldName]))) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler && tiddler.fields[fieldName]) {\n\t\t\t\tif(isWithinDays($tw.utils.parseDate(tiddler.fields[fieldName]))) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/each.js": {
"title": "$:/core/modules/filters/each.js",
"text": "/*\\\ntitle: $:/core/modules/filters/each.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that selects one tiddler for each unique value of the specified field.\nWith suffix \"list\", selects all tiddlers that are values in a specified list field.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.each = function(source,operator,options) {\n\tvar results =[] ,\n\tvalue,values = {},\n\tfield = operator.operand || \"title\";\n\tif(operator.suffix === \"value\" && field === \"title\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!$tw.utils.hop(values,title)) {\n\t\t\t\tvalues[title] = true;\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else if(operator.suffix !== \"list-item\") {\n\t\tif(field === \"title\") {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler && !$tw.utils.hop(values,title)) {\n\t\t\t\t\tvalues[title] = true;\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler) {\n\t\t\t\t\tvalue = tiddler.getFieldString(field);\n\t\t\t\t\tif(!$tw.utils.hop(values,value)) {\n\t\t\t\t\t\tvalues[value] = true;\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler) {\n\t\t\t\t$tw.utils.each(\n\t\t\t\t\toptions.wiki.getTiddlerList(title,field),\n\t\t\t\t\tfunction(value) {\n\t\t\t\t\t\tif(!$tw.utils.hop(values,value)) {\n\t\t\t\t\t\t\tvalues[value] = true;\n\t\t\t\t\t\t\tresults.push(value);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/eachday.js": {
"title": "$:/core/modules/filters/eachday.js",
"text": "/*\\\ntitle: $:/core/modules/filters/eachday.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that selects one tiddler for each unique day covered by the specified date field\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.eachday = function(source,operator,options) {\n\tvar results = [],\n\t\tvalues = [],\n\t\tfieldName = operator.operand || \"modified\";\n\t// Function to convert a date/time to a date integer\n\tvar toDate = function(value) {\n\t\tvalue = (new Date(value)).setHours(0,0,0,0);\n\t\treturn value+0;\n\t};\n\tsource(function(tiddler,title) {\n\t\tif(tiddler && tiddler.fields[fieldName]) {\n\t\t\tvar value = toDate($tw.utils.parseDate(tiddler.fields[fieldName]));\n\t\t\tif(values.indexOf(value) === -1) {\n\t\t\t\tvalues.push(value);\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/editiondescription.js": {
"title": "$:/core/modules/filters/editiondescription.js",
"text": "/*\\\ntitle: $:/core/modules/filters/editiondescription.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the descriptions of the specified edition names\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.editiondescription = function(source,operator,options) {\n\tvar results = [],\n\t\teditionInfo = $tw.utils.getEditionInfo();\n\tif(editionInfo) {\n\t\tsource(function(tiddler,title) {\n\t\t\tif($tw.utils.hop(editionInfo,title)) {\n\t\t\t\tresults.push(editionInfo[title].description || \"\");\t\t\t\t\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/editions.js": {
"title": "$:/core/modules/filters/editions.js",
"text": "/*\\\ntitle: $:/core/modules/filters/editions.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the available editions in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.editions = function(source,operator,options) {\n\tvar results = [],\n\t\teditionInfo = $tw.utils.getEditionInfo();\n\tif(editionInfo) {\n\t\t$tw.utils.each(editionInfo,function(info,name) {\n\t\t\tresults.push(name);\n\t\t});\n\t}\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/else.js": {
"title": "$:/core/modules/filters/else.js",
"text": "/*\\\ntitle: $:/core/modules/filters/else.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for replacing an empty input list with a constant, passing a non-empty input list straight through\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.else = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\tif(results.length === 0) {\n\t\treturn [operator.operand];\n\t} else {\n\t\treturn results;\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/decodeuricomponent.js": {
"title": "$:/core/modules/filters/decodeuricomponent.js",
"text": "/*\\\ntitle: $:/core/modules/filters/decodeuricomponent.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for applying decodeURIComponent() to each item.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter functions\n*/\n\nexports.decodeuricomponent = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar value = title;\n\t\ttry {\n\t\t\tvalue = decodeURIComponent(title);\n\t\t} catch(e) {\n\t\t}\n\t\tresults.push(value);\n\t});\n\treturn results;\n};\n\nexports.encodeuricomponent = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(encodeURIComponent(title));\n\t});\n\treturn results;\n};\n\nexports.decodeuri = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar value = title;\n\t\ttry {\n\t\t\tvalue = decodeURI(title);\n\t\t} catch(e) {\n\t\t}\n\t\tresults.push(value);\n\t});\n\treturn results;\n};\n\nexports.encodeuri = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(encodeURI(title));\n\t});\n\treturn results;\n};\n\nexports.decodehtml = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push($tw.utils.htmlDecode(title));\n\t});\n\treturn results;\n};\n\nexports.encodehtml = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push($tw.utils.htmlEncode(title));\n\t});\n\treturn results;\n};\n\nexports.stringify = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push($tw.utils.stringify(title));\n\t});\n\treturn results;\n};\n\nexports.jsonstringify = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push($tw.utils.jsonStringify(title));\n\t});\n\treturn results;\n};\n\nexports.escaperegexp = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push($tw.utils.escapeRegExp(title));\n\t});\n\treturn results;\n};\n\nexports.escapecss = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\t// escape any character with a special meaning in CSS using CSS.escape()\n\t\tresults.push(CSS.escape(title));\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/enlist.js": {
"title": "$:/core/modules/filters/enlist.js",
"text": "/*\\\ntitle: $:/core/modules/filters/enlist.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning its operand parsed as a list\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.enlist = function(source,operator,options) {\n\tvar allowDuplicates = false;\n\tswitch(operator.suffix) {\n\t\tcase \"raw\":\n\t\t\tallowDuplicates = true;\n\t\t\tbreak;\n\t\tcase \"dedupe\":\n\t\t\tallowDuplicates = false;\n\t\t\tbreak;\n\t}\n\tvar list = $tw.utils.parseStringArray(operator.operand,allowDuplicates);\n\tif(operator.prefix === \"!\") {\n\t\tvar results = [];\n\t\tsource(function(tiddler,title) {\n\t\t\tif(list.indexOf(title) === -1) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t\treturn results;\n\t} else {\n\t\treturn list;\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/field.js": {
"title": "$:/core/modules/filters/field.js",
"text": "/*\\\ntitle: $:/core/modules/filters/field.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for comparing fields for equality\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.field = function(source,operator,options) {\n\tvar results = [],indexedResults,\n\t\tfieldname = (operator.suffix || operator.operator || \"title\").toLowerCase();\n\tif(operator.prefix === \"!\") {\n\t\tif(operator.regexp) {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler) {\n\t\t\t\t\tvar text = tiddler.getFieldString(fieldname);\n\t\t\t\t\tif(text !== null && !operator.regexp.exec(text)) {\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler) {\n\t\t\t\t\tvar text = tiddler.getFieldString(fieldname);\n\t\t\t\t\tif(text !== null && text !== operator.operand) {\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t} else {\n\t\tif(operator.regexp) {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler) {\n\t\t\t\t\tvar text = tiddler.getFieldString(fieldname);\n\t\t\t\t\tif(text !== null && !!operator.regexp.exec(text)) {\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tif(source.byField && operator.operand) {\n\t\t\t\tindexedResults = source.byField(fieldname,operator.operand);\n\t\t\t\tif(indexedResults) {\n\t\t\t\t\treturn indexedResults\n\t\t\t\t}\n\t\t\t}\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler) {\n\t\t\t\t\tvar text = tiddler.getFieldString(fieldname);\n\t\t\t\t\tif(text !== null && text === operator.operand) {\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/fields.js": {
"title": "$:/core/modules/filters/fields.js",
"text": "/*\\\ntitle: $:/core/modules/filters/fields.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the fields on the selected tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.fields = function(source,operator,options) {\n\tvar results = [],\n\t\tfieldName,\n\t\tsuffixes = (operator.suffixes || [])[0] || [],\n\t\toperand = $tw.utils.parseStringArray(operator.operand);\n\t\n\tsource(function(tiddler,title) {\n\t\tif(tiddler) {\n\t\t\tif(suffixes.indexOf(\"include\") !== -1) {\n\t\t\t\tfor(fieldName in tiddler.fields) {\n\t\t\t\t\t(operand.indexOf(fieldName) !== -1) ? $tw.utils.pushTop(results,fieldName) : \"\";\n\t\t\t\t}\n\t\t\t} else if (suffixes.indexOf(\"exclude\") !== -1) {\n\t\t\t\tfor(fieldName in tiddler.fields) {\n\t\t\t\t\t(operand.indexOf(fieldName) !== -1) ? \"\" : $tw.utils.pushTop(results,fieldName);\n\t\t\t\t}\n\t\t\t} // else if\n\t\t\telse {\n\t\t\t\tfor(fieldName in tiddler.fields) {\n\t\t\t\t\t$tw.utils.pushTop(results,fieldName);\n\t\t\t\t}\n\t\t\t} // else\n\t\t} // if (tiddler)\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/get.js": {
"title": "$:/core/modules/filters/get.js",
"text": "/*\\\ntitle: $:/core/modules/filters/get.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for replacing tiddler titles by the value of the field specified in the operand.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.get = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tif(tiddler) {\n\t\t\tvar value = tiddler.getFieldString(operator.operand);\n\t\t\tif(value) {\n\t\t\t\tresults.push(value);\n\t\t\t}\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/getindex.js": {
"title": "$:/core/modules/filters/getindex.js",
"text": "/*\\\ntitle: $:/core/modules/filters/getindex.js\ntype: application/javascript\nmodule-type: filteroperator\n\nreturns the value at a given index of datatiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.getindex = function(source,operator,options) {\n\tvar data,title,results = [];\n\tif(operator.operand){\n\t\tsource(function(tiddler,title) {\n\t\t\ttitle = tiddler ? tiddler.fields.title : title;\n\t\t\tdata = options.wiki.extractTiddlerDataItem(tiddler,operator.operand);\n\t\t\tif(data) {\n\t\t\t\tresults.push(data);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/getvariable.js": {
"title": "$:/core/modules/filters/getvariable.js",
"text": "/*\\\ntitle: $:/core/modules/filters/getvariable.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for replacing input values by the value of the variable with the same name, or blank if the variable is missing\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.getvariable = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(options.widget.getVariable(title) || \"\");\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/has.js": {
"title": "$:/core/modules/filters/has.js",
"text": "/*\\\ntitle: $:/core/modules/filters/has.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking if a tiddler has the specified field or index\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.has = function(source,operator,options) {\n\tvar results = [],\n\t\tinvert = operator.prefix === \"!\";\n\n\tif(operator.suffix === \"field\") {\n\t\tif(invert) {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(!tiddler || (tiddler && (!$tw.utils.hop(tiddler.fields,operator.operand)))) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler && $tw.utils.hop(tiddler.fields,operator.operand)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t}\n\telse if(operator.suffix === \"index\") {\n\t\tif(invert) {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(!tiddler || (tiddler && (!$tw.utils.hop($tw.wiki.getTiddlerDataCached(tiddler,Object.create(null)),operator.operand)))) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler && $tw.utils.hop($tw.wiki.getTiddlerDataCached(tiddler,Object.create(null)),operator.operand)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t}\n\telse {\n\t\tif(invert) {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(!tiddler || !$tw.utils.hop(tiddler.fields,operator.operand) || (tiddler.fields[operator.operand] === \"\")) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler && $tw.utils.hop(tiddler.fields,operator.operand) && !(tiddler.fields[operator.operand] === \"\" || tiddler.fields[operator.operand].length === 0)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\t\t\t\t\n\t\t}\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/haschanged.js": {
"title": "$:/core/modules/filters/haschanged.js",
"text": "/*\\\ntitle: $:/core/modules/filters/haschanged.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returns tiddlers from the list that have a non-zero changecount.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.haschanged = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.getChangeCount(title) === 0) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.getChangeCount(title) > 0) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/indexes.js": {
"title": "$:/core/modules/filters/indexes.js",
"text": "/*\\\ntitle: $:/core/modules/filters/indexes.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the indexes of a data tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.indexes = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar data = options.wiki.getTiddlerDataCached(title);\n\t\tif(data) {\n\t\t\t$tw.utils.pushTop(results,Object.keys(data));\n\t\t}\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/insertbefore.js": {
"title": "$:/core/modules/filters/insertbefore.js",
"text": "/*\\\ntitle: $:/core/modules/filters/insertbefore.js\ntype: application/javascript\nmodule-type: filteroperator\n\nInsert an item before another item in a list\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nOrder a list\n*/\nexports.insertbefore = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\tvar target = options.widget && options.widget.getVariable(operator.suffix || \"currentTiddler\");\n\tif(target !== operator.operand) {\n\t\t// Remove the entry from the list if it is present\n\t\tvar pos = results.indexOf(operator.operand);\n\t\tif(pos !== -1) {\n\t\t\tresults.splice(pos,1);\n\t\t}\n\t\t// Insert the entry before the target marker\n\t\tpos = results.indexOf(target);\n\t\tif(pos !== -1) {\n\t\t\tresults.splice(pos,0,operator.operand);\n\t\t} else {\n\t\t\tresults.push(operator.operand);\n\t\t}\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/is/binary.js": {
"title": "$:/core/modules/filters/is/binary.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/binary.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[binary]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.binary = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.isBinaryTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.isBinaryTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/blank.js": {
"title": "$:/core/modules/filters/is/blank.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/blank.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[blank]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.blank = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!title) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/current.js": {
"title": "$:/core/modules/filters/is/current.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/current.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[current]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.current = function(source,prefix,options) {\n\tvar results = [],\n\t\tcurrTiddlerTitle = options.widget && options.widget.getVariable(\"currentTiddler\");\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title !== currTiddlerTitle) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title === currTiddlerTitle) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/image.js": {
"title": "$:/core/modules/filters/is/image.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/image.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[image]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.image = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.isImageTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.isImageTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/missing.js": {
"title": "$:/core/modules/filters/is/missing.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/missing.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[missing]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.missing = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.tiddlerExists(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.tiddlerExists(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/orphan.js": {
"title": "$:/core/modules/filters/is/orphan.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/orphan.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[orphan]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.orphan = function(source,prefix,options) {\n\tvar results = [],\n\t\torphanTitles = options.wiki.getOrphanTitles();\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(orphanTitles.indexOf(title) === -1) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(orphanTitles.indexOf(title) !== -1) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/shadow.js": {
"title": "$:/core/modules/filters/is/shadow.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/shadow.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[shadow]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.shadow = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.isShadowTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.isShadowTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/system.js": {
"title": "$:/core/modules/filters/is/system.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/system.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[system]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.system = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.isSystemTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.isSystemTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/tag.js": {
"title": "$:/core/modules/filters/is/tag.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/tag.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[tag]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tag = function(source,prefix,options) {\n\tvar results = [],\n\t\ttagMap = options.wiki.getTagMap();\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!$tw.utils.hop(tagMap,title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif($tw.utils.hop(tagMap,title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/tiddler.js": {
"title": "$:/core/modules/filters/is/tiddler.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/tiddler.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[tiddler]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tiddler = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.tiddlerExists(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.tiddlerExists(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/variable.js": {
"title": "$:/core/modules/filters/is/variable.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is/variable.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[variable]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.variable = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!(title in options.widget.variables)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title in options.widget.variables) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is.js": {
"title": "$:/core/modules/filters/is.js",
"text": "/*\\\ntitle: $:/core/modules/filters/is.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking tiddler properties\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar isFilterOperators;\n\nfunction getIsFilterOperators() {\n\tif(!isFilterOperators) {\n\t\tisFilterOperators = {};\n\t\t$tw.modules.applyMethods(\"isfilteroperator\",isFilterOperators);\n\t}\n\treturn isFilterOperators;\n}\n\n/*\nExport our filter function\n*/\nexports.is = function(source,operator,options) {\n\t// Dispatch to the correct isfilteroperator\n\tvar isFilterOperators = getIsFilterOperators();\n\tif(operator.operand) {\n\t\tvar isFilterOperator = isFilterOperators[operator.operand];\n\t\tif(isFilterOperator) {\n\t\t\treturn isFilterOperator(source,operator.prefix,options);\n\t\t} else {\n\t\t\treturn [$tw.language.getString(\"Error/IsFilterOperator\")];\n\t\t}\n\t} else {\n\t\t// Return all tiddlers if the operand is missing\n\t\tvar results = [];\n\t\tsource(function(tiddler,title) {\n\t\t\tresults.push(title);\n\t\t});\n\t\treturn results;\n\t}\n};\n\n})();",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/limit.js": {
"title": "$:/core/modules/filters/limit.js",
"text": "/*\\\ntitle: $:/core/modules/filters/limit.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for chopping the results to a specified maximum number of entries\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.limit = function(source,operator,options) {\n\tvar results = [];\n\t// Convert to an array\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\t// Slice the array if necessary\n\tvar limit = Math.min(results.length,parseInt(operator.operand,10));\n\tif(operator.prefix === \"!\") {\n\t\tresults = results.slice(-limit);\n\t} else {\n\t\tresults = results.slice(0,limit);\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/links.js": {
"title": "$:/core/modules/filters/links.js",
"text": "/*\\\ntitle: $:/core/modules/filters/links.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning all the links from a tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.links = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.pushTop(results,options.wiki.getTiddlerLinks(title));\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/list.js": {
"title": "$:/core/modules/filters/list.js",
"text": "/*\\\ntitle: $:/core/modules/filters/list.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddlers whose title is listed in the operand tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.list = function(source,operator,options) {\n\tvar results = [],\n\t\ttr = $tw.utils.parseTextReference(operator.operand),\n\t\tcurrTiddlerTitle = options.widget && options.widget.getVariable(\"currentTiddler\"),\n\t\tlist = options.wiki.getTiddlerList(tr.title || currTiddlerTitle,tr.field,tr.index);\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(list.indexOf(title) === -1) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tresults = list;\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/listed.js": {
"title": "$:/core/modules/filters/listed.js",
"text": "/*\\\ntitle: $:/core/modules/filters/listed.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning all tiddlers that have the selected tiddlers in a list\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.listed = function(source,operator,options) {\n\tvar field = operator.operand || \"list\",\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.pushTop(results,options.wiki.findListingsOfTiddler(title,field));\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/listops.js": {
"title": "$:/core/modules/filters/listops.js",
"text": "/*\\\ntitle: $:/core/modules/filters/listops.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operators for manipulating the current selection list\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nOrder a list\n*/\nexports.order = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.operand.toLowerCase() === \"reverse\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tresults.unshift(title);\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tresults.push(title);\n\t\t});\n\t}\n\treturn results;\n};\n\n/*\nReverse list\n*/\nexports.reverse = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.unshift(title);\n\t});\n\treturn results;\n};\n\n/*\nFirst entry/entries in list\n*/\nexports.first = function(source,operator,options) {\n\tvar count = $tw.utils.getInt(operator.operand,1),\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(0,count);\n};\n\n/*\nLast entry/entries in list\n*/\nexports.last = function(source,operator,options) {\n\tvar count = $tw.utils.getInt(operator.operand,1),\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(-count);\n};\n\n/*\nAll but the first entry/entries of the list\n*/\nexports.rest = function(source,operator,options) {\n\tvar count = $tw.utils.getInt(operator.operand,1),\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(count);\n};\nexports.butfirst = exports.rest;\nexports.bf = exports.rest;\n\n/*\nAll but the last entry/entries of the list\n*/\nexports.butlast = function(source,operator,options) {\n\tvar count = $tw.utils.getInt(operator.operand,1),\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(0,-count);\n};\nexports.bl = exports.butlast;\n\n/*\nThe nth member of the list\n*/\nexports.nth = function(source,operator,options) {\n\tvar count = $tw.utils.getInt(operator.operand,1),\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(count - 1,count);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/lookup.js": {
"title": "$:/core/modules/filters/lookup.js",
"text": "/*\\\ntitle: $:/core/modules/filters/lookup.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that looks up values via a title prefix\n\n[lookup:<field>[<prefix>]]\n\nPrepends the prefix to the selected items and returns the specified field value\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.lookup = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(options.wiki.getTiddlerText(operator.operand + title) || options.wiki.getTiddlerText(operator.operand + operator.suffix));\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/match.js": {
"title": "$:/core/modules/filters/match.js",
"text": "/*\\\ntitle: $:/core/modules/filters/match.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking if a title matches a string\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.match = function(source,operator,options) {\n\tvar results = [],\n\t\tsuffixes = (operator.suffixes || [])[0] || [];\n\tif(suffixes.indexOf(\"caseinsensitive\") !== -1) {\n\t\tif(operator.prefix === \"!\") {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(title.toLowerCase() !== (operator.operand || \"\").toLowerCase()) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(title.toLowerCase() === (operator.operand || \"\").toLowerCase()) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t} else {\n\t\tif(operator.prefix === \"!\") {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(title !== operator.operand) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(title === operator.operand) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/math.js": {
"title": "$:/core/modules/filters/math.js",
"text": "/*\\\ntitle: $:/core/modules/filters/math.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operators for math. Unary/binary operators work on each item in turn, and return a new item list.\n\nSum/product/maxall/minall operate on the entire list, returning a single item.\n\nNote that strings are converted to numbers automatically. Trailing non-digits are ignored.\n\n* \"\" converts to 0\n* \"12kk\" converts to 12\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.negate = makeNumericBinaryOperator(\n\tfunction(a) {return -a}\n);\n\nexports.abs = makeNumericBinaryOperator(\n\tfunction(a) {return Math.abs(a)}\n);\n\nexports.ceil = makeNumericBinaryOperator(\n\tfunction(a) {return Math.ceil(a)}\n);\n\nexports.floor = makeNumericBinaryOperator(\n\tfunction(a) {return Math.floor(a)}\n);\n\nexports.round = makeNumericBinaryOperator(\n\tfunction(a) {return Math.round(a)}\n);\n\nexports.trunc = makeNumericBinaryOperator(\n\tfunction(a) {return Math.trunc(a)}\n);\n\nexports.untrunc = makeNumericBinaryOperator(\n\tfunction(a) {return Math.ceil(Math.abs(a)) * Math.sign(a)}\n);\n\nexports.sign = makeNumericBinaryOperator(\n\tfunction(a) {return Math.sign(a)}\n);\n\nexports.add = makeNumericBinaryOperator(\n\tfunction(a,b) {return a + b;}\n);\n\nexports.subtract = makeNumericBinaryOperator(\n\tfunction(a,b) {return a - b;}\n);\n\nexports.multiply = makeNumericBinaryOperator(\n\tfunction(a,b) {return a * b;}\n);\n\nexports.divide = makeNumericBinaryOperator(\n\tfunction(a,b) {return a / b;}\n);\n\nexports.remainder = makeNumericBinaryOperator(\n\tfunction(a,b) {return a % b;}\n);\n\nexports.max = makeNumericBinaryOperator(\n\tfunction(a,b) {return Math.max(a,b);}\n);\n\nexports.min = makeNumericBinaryOperator(\n\tfunction(a,b) {return Math.min(a,b);}\n);\n\nexports.fixed = makeNumericBinaryOperator(\n\tfunction(a,b) {return Number.prototype.toFixed.call(a,Math.min(Math.max(b,0),100));}\n);\n\nexports.precision = makeNumericBinaryOperator(\n\tfunction(a,b) {return Number.prototype.toPrecision.call(a,Math.min(Math.max(b,1),100));}\n);\n\nexports.exponential = makeNumericBinaryOperator(\n\tfunction(a,b) {return Number.prototype.toExponential.call(a,Math.min(Math.max(b,0),100));}\n);\n\nexports.sum = makeNumericReducingOperator(\n\tfunction(accumulator,value) {return accumulator + value},\n\t0 // Initial value\n);\n\nexports.product = makeNumericReducingOperator(\n\tfunction(accumulator,value) {return accumulator * value},\n\t1 // Initial value\n);\n\nexports.maxall = makeNumericReducingOperator(\n\tfunction(accumulator,value) {return Math.max(accumulator,value)},\n\t-Infinity // Initial value\n);\n\nexports.minall = makeNumericReducingOperator(\n\tfunction(accumulator,value) {return Math.min(accumulator,value)},\n\tInfinity // Initial value\n);\n\nfunction makeNumericBinaryOperator(fnCalc) {\n\treturn function(source,operator,options) {\n\t\tvar result = [],\n\t\t\tnumOperand = $tw.utils.parseNumber(operator.operand);\n\t\tsource(function(tiddler,title) {\n\t\t\tresult.push($tw.utils.stringifyNumber(fnCalc($tw.utils.parseNumber(title),numOperand)));\n\t\t});\n\t\treturn result;\n\t};\n}\n\nfunction makeNumericReducingOperator(fnCalc,initialValue) {\n\tinitialValue = initialValue || 0;\n\treturn function(source,operator,options) {\n\t\tvar result = [];\n\t\tsource(function(tiddler,title) {\n\t\t\tresult.push(title);\n\t\t});\n\t\treturn [$tw.utils.stringifyNumber(result.reduce(function(accumulator,currentValue) {\n\t\t\treturn fnCalc(accumulator,$tw.utils.parseNumber(currentValue));\n\t\t},initialValue))];\n\t};\n}\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/minlength.js": {
"title": "$:/core/modules/filters/minlength.js",
"text": "/*\\\ntitle: $:/core/modules/filters/minlength.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for filtering out titles that don't meet the minimum length in the operand\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.minlength = function(source,operator,options) {\n\tvar results = [],\n\t\tminLength = parseInt(operator.operand || \"\",10) || 0;\n\tsource(function(tiddler,title) {\n\t\tif(title.length >= minLength) {\n\t\t\tresults.push(title);\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/modules.js": {
"title": "$:/core/modules/filters/modules.js",
"text": "/*\\\ntitle: $:/core/modules/filters/modules.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the titles of the modules of a given type in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.modules = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.each($tw.modules.types[title],function(moduleInfo,moduleName) {\n\t\t\tresults.push(moduleName);\n\t\t});\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/moduletypes.js": {
"title": "$:/core/modules/filters/moduletypes.js",
"text": "/*\\\ntitle: $:/core/modules/filters/moduletypes.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the module types in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.moduletypes = function(source,operator,options) {\n\tvar results = [];\n\t$tw.utils.each($tw.modules.types,function(moduleInfo,type) {\n\t\tresults.push(type);\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/next.js": {
"title": "$:/core/modules/filters/next.js",
"text": "/*\\\ntitle: $:/core/modules/filters/next.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddler whose title occurs next in the list supplied in the operand tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.next = function(source,operator,options) {\n\tvar results = [],\n\t\tlist = options.wiki.getTiddlerList(operator.operand);\n\tsource(function(tiddler,title) {\n\t\tvar match = list.indexOf(title);\n\t\t// increment match and then test if result is in range\n\t\tmatch++;\n\t\tif(match > 0 && match < list.length) {\n\t\t\tresults.push(list[match]);\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/plugintiddlers.js": {
"title": "$:/core/modules/filters/plugintiddlers.js",
"text": "/*\\\ntitle: $:/core/modules/filters/plugintiddlers.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the titles of the shadow tiddlers within a plugin\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.plugintiddlers = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar pluginInfo = options.wiki.getPluginInfo(title) || options.wiki.getTiddlerDataCached(title,{tiddlers:[]});\n\t\tif(pluginInfo && pluginInfo.tiddlers) {\n\t\t\t$tw.utils.each(pluginInfo.tiddlers,function(fields,title) {\n\t\t\t\tresults.push(title);\n\t\t\t});\n\t\t}\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/prefix.js": {
"title": "$:/core/modules/filters/prefix.js",
"text": "/*\\\ntitle: $:/core/modules/filters/prefix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking if a title starts with a prefix\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.prefix = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title.substr(0,operator.operand.length) !== operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title.substr(0,operator.operand.length) === operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/previous.js": {
"title": "$:/core/modules/filters/previous.js",
"text": "/*\\\ntitle: $:/core/modules/filters/previous.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddler whose title occurs immediately prior in the list supplied in the operand tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.previous = function(source,operator,options) {\n\tvar results = [],\n\t\tlist = options.wiki.getTiddlerList(operator.operand);\n\tsource(function(tiddler,title) {\n\t\tvar match = list.indexOf(title);\n\t\t// increment match and then test if result is in range\n\t\tmatch--;\n\t\tif(match >= 0) {\n\t\t\tresults.push(list[match]);\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/range.js": {
"title": "$:/core/modules/filters/range.js",
"text": "/*\\\ntitle: $:/core/modules/filters/range.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for generating a numeric range.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.range = function(source,operator,options) {\n\tvar results = [];\n\t// Split the operand into numbers delimited by these symbols\n\tvar parts = operator.operand.split(/[,:;]/g),\n\t\tbeg, end, inc, i, fixed = 0;\n\tfor (i=0; i<parts.length; i++) {\n\t\t// Validate real number\n\t\tif(!/^\\s*[+-]?((\\d+(\\.\\d*)?)|(\\.\\d+))\\s*$/.test(parts[i])) {\n\t\t\treturn [\"range: bad number \\\"\" + parts[i] + \"\\\"\"];\n\t\t}\n\t\t// Count digits; the most precise number determines decimal places in output.\n\t\tvar frac = /\\.\\d+/.exec(parts[i]);\n\t\tif(frac) {\n\t\t\tfixed = Math.max(fixed,frac[0].length-1);\n\t\t}\n\t\tparts[i] = parseFloat(parts[i]);\n\t}\n\tswitch(parts.length) {\n\t\tcase 1:\n\t\t\tend = parts[0];\n\t\t\tif (end >= 1) {\n\t\t\t\tbeg = 1;\n\t\t\t}\n\t\t\telse if (end <= -1) {\n\t\t\t\tbeg = -1;\n\t\t\t}\n\t\t\telse {\n\t\t\t\treturn [];\n\t\t\t}\n\t\t\tinc = 1;\n\t\t\tbreak;\n\t\tcase 2:\n\t\t\tbeg = parts[0];\n\t\t\tend = parts[1];\n\t\t\tinc = 1;\n\t\t\tbreak;\n\t\tcase 3:\n\t\t\tbeg = parts[0];\n\t\t\tend = parts[1];\n\t\t\tinc = Math.abs(parts[2]);\n\t\t\tbreak;\n\t}\n\tif(inc === 0) {\n\t\treturn [\"range: increment 0 causes infinite loop\"];\n\t}\n\t// May need to count backwards\n\tvar direction = ((end < beg) ? -1 : 1);\n\tinc *= direction;\n\t// Estimate number of resulting elements\n\tif((end - beg) / inc > 10000) {\n\t\treturn [\"range: too many steps (over 10K)\"];\n\t}\n\t// Avoid rounding error on last step\n\tend += direction * 0.5 * Math.pow(0.1,fixed);\n\tvar safety = 10010;\n\t// Enumerate the range\n\tif (end<beg) {\n\t\tfor(i=beg; i>end; i+=inc) {\n\t\t\tresults.push(i.toFixed(fixed));\n\t\t\tif(--safety<0) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t} else {\n\t\tfor(i=beg; i<end; i+=inc) {\n\t\t\tresults.push(i.toFixed(fixed));\n\t\t\tif(--safety<0) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\tif(safety<0) {\n\t\treturn [\"range: unexpectedly large output\"];\n\t}\n\t// Reverse?\n\tif(operator.prefix === \"!\") {\n\t\tresults.reverse();\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/regexp.js": {
"title": "$:/core/modules/filters/regexp.js",
"text": "/*\\\ntitle: $:/core/modules/filters/regexp.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for regexp matching\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.regexp = function(source,operator,options) {\n\tvar results = [],\n\t\tfieldname = (operator.suffix || \"title\").toLowerCase(),\n\t\tregexpString, regexp, flags = \"\", match,\n\t\tgetFieldString = function(tiddler,title) {\n\t\t\tif(tiddler) {\n\t\t\t\treturn tiddler.getFieldString(fieldname);\n\t\t\t} else if(fieldname === \"title\") {\n\t\t\t\treturn title;\n\t\t\t} else {\n\t\t\t\treturn null;\n\t\t\t}\n\t\t};\n\t// Process flags and construct regexp\n\tregexpString = operator.operand;\n\tmatch = /^\\(\\?([gim]+)\\)/.exec(regexpString);\n\tif(match) {\n\t\tflags = match[1];\n\t\tregexpString = regexpString.substr(match[0].length);\n\t} else {\n\t\tmatch = /\\(\\?([gim]+)\\)$/.exec(regexpString);\n\t\tif(match) {\n\t\t\tflags = match[1];\n\t\t\tregexpString = regexpString.substr(0,regexpString.length - match[0].length);\n\t\t}\n\t}\n\ttry {\n\t\tregexp = new RegExp(regexpString,flags);\n\t} catch(e) {\n\t\treturn [\"\" + e];\n\t}\n\t// Process the incoming tiddlers\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tvar text = getFieldString(tiddler,title);\n\t\t\tif(text !== null) {\n\t\t\t\tif(!regexp.exec(text)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tvar text = getFieldString(tiddler,title);\n\t\t\tif(text !== null) {\n\t\t\t\tif(!!regexp.exec(text)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/removeprefix.js": {
"title": "$:/core/modules/filters/removeprefix.js",
"text": "/*\\\ntitle: $:/core/modules/filters/removeprefix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for removing a prefix from each title in the list. Titles that do not start with the prefix are removed.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.removeprefix = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tif(title.substr(0,operator.operand.length) === operator.operand) {\n\t\t\tresults.push(title.substr(operator.operand.length));\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/removesuffix.js": {
"title": "$:/core/modules/filters/removesuffix.js",
"text": "/*\\\ntitle: $:/core/modules/filters/removesuffix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for removing a suffix from each title in the list. Titles that do not end with the suffix are removed.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.removesuffix = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tif(title && title.substr(-operator.operand.length) === operator.operand) {\n\t\t\tresults.push(title.substr(0,title.length - operator.operand.length));\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/sameday.js": {
"title": "$:/core/modules/filters/sameday.js",
"text": "/*\\\ntitle: $:/core/modules/filters/sameday.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that selects tiddlers with a modified date field on the same day as the provided value.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.sameday = function(source,operator,options) {\n\tvar results = [],\n\t\tfieldName = operator.suffix || \"modified\",\n\t\ttargetDate = (new Date($tw.utils.parseDate(operator.operand))).setHours(0,0,0,0);\n\t// Function to convert a date/time to a date integer\n\tsource(function(tiddler,title) {\n\t\tif(tiddler) {\n\t\t\tif(tiddler.getFieldDay(fieldName) === targetDate) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/search.js": {
"title": "$:/core/modules/filters/search.js",
"text": "/*\\\ntitle: $:/core/modules/filters/search.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for searching for the text in the operand tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.search = function(source,operator,options) {\n\tvar invert = operator.prefix === \"!\";\n\tif(operator.suffixes) {\n\t\tvar hasFlag = function(flag) {\n\t\t\t\treturn (operator.suffixes[1] || []).indexOf(flag) !== -1;\n\t\t\t},\n\t\t\texcludeFields = false,\n\t\t\tfieldList = operator.suffixes[0] || [],\n\t\t\tfirstField = fieldList[0] || \"\", \n\t\t\tfirstChar = firstField.charAt(0),\n\t\t\tfields;\n\t\tif(firstChar === \"-\") {\n\t\t\tfields = [firstField.slice(1)].concat(fieldList.slice(1));\n\t\t\texcludeFields = true;\n\t\t} else if(fieldList[0] === \"*\"){\n\t\t\tfields = [];\n\t\t\texcludeFields = true;\n\t\t} else {\n\t\t\tfields = fieldList.slice(0);\n\t\t}\n\t\treturn options.wiki.search(operator.operand,{\n\t\t\tsource: source,\n\t\t\tinvert: invert,\n\t\t\tfield: fields,\n\t\t\texcludeField: excludeFields,\n\t\t\tcaseSensitive: hasFlag(\"casesensitive\"),\n\t\t\tliteral: hasFlag(\"literal\"),\n\t\t\twhitespace: hasFlag(\"whitespace\"),\n\t\t\tanchored: hasFlag(\"anchored\"),\n\t\t\tregexp: hasFlag(\"regexp\"),\n\t\t\twords: hasFlag(\"words\")\n\t\t});\n\t} else {\n\t\treturn options.wiki.search(operator.operand,{\n\t\t\tsource: source,\n\t\t\tinvert: invert\n\t\t});\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/shadowsource.js": {
"title": "$:/core/modules/filters/shadowsource.js",
"text": "/*\\\ntitle: $:/core/modules/filters/shadowsource.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the source plugins for shadow tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.shadowsource = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar source = options.wiki.getShadowSource(title);\n\t\tif(source) {\n\t\t\t$tw.utils.pushTop(results,source);\n\t\t}\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/sort.js": {
"title": "$:/core/modules/filters/sort.js",
"text": "/*\\\ntitle: $:/core/modules/filters/sort.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for sorting\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.sort = function(source,operator,options) {\n\tvar results = prepare_results(source);\n\toptions.wiki.sortTiddlers(results,operator.operand || \"title\",operator.prefix === \"!\",false,false);\n\treturn results;\n};\n\nexports.nsort = function(source,operator,options) {\n\tvar results = prepare_results(source);\n\toptions.wiki.sortTiddlers(results,operator.operand || \"title\",operator.prefix === \"!\",false,true);\n\treturn results;\n};\n\nexports.sortan = function(source, operator, options) {\n\tvar results = prepare_results(source);\n\toptions.wiki.sortTiddlers(results, operator.operand || \"title\", operator.prefix === \"!\",false,false,true);\n\treturn results;\n};\n\nexports.sortcs = function(source,operator,options) {\n\tvar results = prepare_results(source);\n\toptions.wiki.sortTiddlers(results,operator.operand || \"title\",operator.prefix === \"!\",true,false);\n\treturn results;\n};\n\nexports.nsortcs = function(source,operator,options) {\n\tvar results = prepare_results(source);\n\toptions.wiki.sortTiddlers(results,operator.operand || \"title\",operator.prefix === \"!\",true,true);\n\treturn results;\n};\n\nvar prepare_results = function (source) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/splitbefore.js": {
"title": "$:/core/modules/filters/splitbefore.js",
"text": "/*\\\ntitle: $:/core/modules/filters/splitbefore.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that splits each result on the first occurance of the specified separator and returns the unique values.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.splitbefore = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar parts = title.split(operator.operand);\n\t\tif(parts.length === 1) {\n\t\t\t$tw.utils.pushTop(results,parts[0]);\n\t\t} else {\n\t\t\t$tw.utils.pushTop(results,parts[0] + operator.operand);\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/storyviews.js": {
"title": "$:/core/modules/filters/storyviews.js",
"text": "/*\\\ntitle: $:/core/modules/filters/storyviews.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the story views in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.storyviews = function(source,operator,options) {\n\tvar results = [],\n\t\tstoryviews = {};\n\t$tw.modules.applyMethods(\"storyview\",storyviews);\n\t$tw.utils.each(storyviews,function(info,name) {\n\t\tresults.push(name);\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/strings.js": {
"title": "$:/core/modules/filters/strings.js",
"text": "/*\\\ntitle: $:/core/modules/filters/strings.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operators for strings. Unary/binary operators work on each item in turn, and return a new item list.\n\nSum/product/maxall/minall operate on the entire list, returning a single item.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.length = makeStringBinaryOperator(\n\tfunction(a) {return [\"\" + (\"\" + a).length];}\n);\n\nexports.uppercase = makeStringBinaryOperator(\n\tfunction(a) {return [(\"\" + a).toUpperCase()];}\n);\n\nexports.lowercase = makeStringBinaryOperator(\n\tfunction(a) {return [(\"\" + a).toLowerCase()];}\n);\n\nexports.sentencecase = makeStringBinaryOperator(\n\tfunction(a) {return [$tw.utils.toSentenceCase(a)];}\n);\n\nexports.titlecase = makeStringBinaryOperator(\n\tfunction(a) {return [$tw.utils.toTitleCase(a)];}\n);\n\nexports.trim = makeStringBinaryOperator(\n\tfunction(a) {return [$tw.utils.trim(a)];}\n);\n\nexports.split = makeStringBinaryOperator(\n\tfunction(a,b) {return (\"\" + a).split(b);}\n);\n\nexports.join = makeStringReducingOperator(\n\tfunction(accumulator,value,operand) {\n\t\tif(accumulator === null) {\n\t\t\treturn value;\n\t\t} else {\n\t\t\treturn accumulator + operand + value;\n\t\t}\n\t},null\n);\n\nfunction makeStringBinaryOperator(fnCalc) {\n\treturn function(source,operator,options) {\n\t\tvar result = [];\n\t\tsource(function(tiddler,title) {\n\t\t\tArray.prototype.push.apply(result,fnCalc(title,operator.operand || \"\"));\n\t\t});\n\t\treturn result;\n\t};\n}\n\nfunction makeStringReducingOperator(fnCalc,initialValue) {\n\treturn function(source,operator,options) {\n\t\tvar result = [];\n\t\tsource(function(tiddler,title) {\n\t\t\tresult.push(title);\n\t\t});\n\t\treturn [result.reduce(function(accumulator,currentValue) {\n\t\t\treturn fnCalc(accumulator,currentValue,operator.operand || \"\");\n\t\t},initialValue) || \"\"];\n\t};\n}\n\nexports.splitregexp = function(source,operator,options) {\n\tvar result = [],\n\t\tsuffix = operator.suffix || \"\",\n\t\tflags = (suffix.indexOf(\"m\") !== -1 ? \"m\" : \"\") + (suffix.indexOf(\"i\") !== -1 ? \"i\" : \"\"),\n\t\tregExp;\n\ttry {\n\t\tregExp = new RegExp(operator.operand || \"\",flags);\t\t\n\t} catch(ex) {\n\t\treturn [\"RegExp error: \" + ex];\n\t}\n\tsource(function(tiddler,title) {\n\t\tArray.prototype.push.apply(result,title.split(regExp));\n\t});\t\t\n\treturn result;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/subfilter.js": {
"title": "$:/core/modules/filters/subfilter.js",
"text": "/*\\\ntitle: $:/core/modules/filters/subfilter.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning its operand evaluated as a filter\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.subfilter = function(source,operator,options) {\n\tvar list = options.wiki.filterTiddlers(operator.operand,options.widget,source);\n\tif(operator.prefix === \"!\") {\n\t\tvar results = [];\n\t\tsource(function(tiddler,title) {\n\t\t\tif(list.indexOf(title) === -1) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t\treturn results;\n\t} else {\n\t\treturn list;\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/subtiddlerfields.js": {
"title": "$:/core/modules/filters/subtiddlerfields.js",
"text": "/*\\\ntitle: $:/core/modules/filters/subtiddlerfields.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the fields on the selected subtiddlers of the plugin named in the operand\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.subtiddlerfields = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar subtiddler = options.wiki.getSubTiddler(operator.operand,title);\n\t\tif(subtiddler) {\n\t\t\tfor(var fieldName in subtiddler.fields) {\n\t\t\t\t$tw.utils.pushTop(results,fieldName);\n\t\t\t}\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/suffix.js": {
"title": "$:/core/modules/filters/suffix.js",
"text": "/*\\\ntitle: $:/core/modules/filters/suffix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking if a title ends with a suffix\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.suffix = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title.substr(-operator.operand.length) !== operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title.substr(-operator.operand.length) === operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/tag.js": {
"title": "$:/core/modules/filters/tag.js",
"text": "/*\\\ntitle: $:/core/modules/filters/tag.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking for the presence of a tag\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tag = function(source,operator,options) {\n\tvar results = [],indexedResults;\n\tif((operator.suffix || \"\").toLowerCase() === \"strict\" && !operator.operand) {\n\t\t// New semantics:\n\t\t// Always return copy of input if operator.operand is missing\n\t\tsource(function(tiddler,title) {\n\t\t\tresults.push(title);\n\t\t});\n\t} else {\n\t\t// Old semantics:\n\t\tvar tiddlers;\n\t\tif(operator.prefix === \"!\") {\n\t\t\t// Returns a copy of the input if operator.operand is missing\n\t\t\ttiddlers = options.wiki.getTiddlersWithTag(operator.operand);\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddlers.indexOf(title) === -1) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\t// Returns empty results if operator.operand is missing\n\t\t\tif(source.byTag) {\n\t\t\t\tindexedResults = source.byTag(operator.operand);\n\t\t\t\tif(indexedResults) {\n\t\t\t\t\treturn indexedResults;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\ttiddlers = options.wiki.getTiddlersWithTag(operator.operand);\n\t\t\t\tsource(function(tiddler,title) {\n\t\t\t\t\tif(tiddlers.indexOf(title) !== -1) {\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t\tresults = options.wiki.sortByList(results,operator.operand);\n\t\t\t}\n\t\t}\t\t\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/tagging.js": {
"title": "$:/core/modules/filters/tagging.js",
"text": "/*\\\ntitle: $:/core/modules/filters/tagging.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning all tiddlers that are tagged with the selected tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tagging = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.pushTop(results,options.wiki.getTiddlersWithTag(title));\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/tags.js": {
"title": "$:/core/modules/filters/tags.js",
"text": "/*\\\ntitle: $:/core/modules/filters/tags.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning all the tags of the selected tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tags = function(source,operator,options) {\n\tvar tags = {};\n\tsource(function(tiddler,title) {\n\t\tvar t, length;\n\t\tif(tiddler && tiddler.fields.tags) {\n\t\t\tfor(t=0, length=tiddler.fields.tags.length; t<length; t++) {\n\t\t\t\ttags[tiddler.fields.tags[t]] = true;\n\t\t\t}\n\t\t}\n\t});\n\treturn Object.keys(tags);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/then.js": {
"title": "$:/core/modules/filters/then.js",
"text": "/*\\\ntitle: $:/core/modules/filters/then.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for replacing any titles with a constant\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.then = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(operator.operand);\n\t});\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/title.js": {
"title": "$:/core/modules/filters/title.js",
"text": "/*\\\ntitle: $:/core/modules/filters/title.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for comparing title fields for equality\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.title = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler && tiddler.fields.title !== operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tresults.push(operator.operand);\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/untagged.js": {
"title": "$:/core/modules/filters/untagged.js",
"text": "/*\\\ntitle: $:/core/modules/filters/untagged.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning all the selected tiddlers that are untagged\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.untagged = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler && $tw.utils.isArray(tiddler.fields.tags) && tiddler.fields.tags.length > 0) {\n\t\t\t\t$tw.utils.pushTop(results,title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!tiddler || !tiddler.hasField(\"tags\") || ($tw.utils.isArray(tiddler.fields.tags) && tiddler.fields.tags.length === 0)) {\n\t\t\t\t$tw.utils.pushTop(results,title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/variables.js": {
"title": "$:/core/modules/filters/variables.js",
"text": "/*\\\ntitle: $:/core/modules/filters/variables.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the active variables\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.variables = function(source,operator,options) {\n\tvar names = [];\n\tfor(var variable in options.widget.variables) {\n\t\tnames.push(variable);\n\t}\n\treturn names.sort();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/wikiparserrules.js": {
"title": "$:/core/modules/filters/wikiparserrules.js",
"text": "/*\\\ntitle: $:/core/modules/filters/wikiparserrules.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the wiki parser rules in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.wikiparserrules = function(source,operator,options) {\n\tvar results = [],\n\t\toperand = operator.operand;\n\t$tw.utils.each($tw.modules.types.wikirule,function(mod) {\n\t\tvar exp = mod.exports;\n\t\tif(!operand || exp.types[operand]) {\n\t\t\tresults.push(exp.name);\n\t\t}\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/x-listops.js": {
"title": "$:/core/modules/filters/x-listops.js",
"text": "/*\\\ntitle: $:/core/modules/filters/x-listops.js\ntype: application/javascript\nmodule-type: filteroperator\n\nExtended filter operators to manipulate the current list.\n\n\\*/\n(function () {\n\n /*jslint node: true, browser: true */\n /*global $tw: false */\n \"use strict\";\n\n /*\n Fetch titles from the current list\n */\n var prepare_results = function (source) {\n var results = [];\n source(function (tiddler, title) {\n results.push(title);\n });\n return results;\n };\n\n /*\n Moves a number of items from the tail of the current list before the item named in the operand\n */\n exports.putbefore = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand),\n count = $tw.utils.getInt(operator.suffix,1);\n return (index === -1) ?\n results.slice(0, -1) :\n results.slice(0, index).concat(results.slice(-count)).concat(results.slice(index, -count));\n };\n\n /*\n Moves a number of items from the tail of the current list after the item named in the operand\n */\n exports.putafter = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand),\n count = $tw.utils.getInt(operator.suffix,1);\n return (index === -1) ?\n results.slice(0, -1) :\n results.slice(0, index + 1).concat(results.slice(-count)).concat(results.slice(index + 1, -count));\n };\n\n /*\n Replaces the item named in the operand with a number of items from the tail of the current list\n */\n exports.replace = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand),\n count = $tw.utils.getInt(operator.suffix,1);\n return (index === -1) ?\n results.slice(0, -count) :\n results.slice(0, index).concat(results.slice(-count)).concat(results.slice(index + 1, -count));\n };\n\n /*\n Moves a number of items from the tail of the current list to the head of the list\n */\n exports.putfirst = function (source, operator) {\n var results = prepare_results(source),\n count = $tw.utils.getInt(operator.suffix,1);\n return results.slice(-count).concat(results.slice(0, -count));\n };\n\n /*\n Moves a number of items from the head of the current list to the tail of the list\n */\n exports.putlast = function (source, operator) {\n var results = prepare_results(source),\n count = $tw.utils.getInt(operator.suffix,1);\n return results.slice(count).concat(results.slice(0, count));\n };\n\n /*\n Moves the item named in the operand a number of places forward or backward in the list\n */\n exports.move = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand),\n count = $tw.utils.getInt(operator.suffix,1),\n marker = results.splice(index, 1),\n offset = (index + count) > 0 ? index + count : 0;\n return results.slice(0, offset).concat(marker).concat(results.slice(offset));\n };\n\n /*\n Returns the items from the current list that are after the item named in the operand\n */\n exports.allafter = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand);\n return (index === -1) ? [] :\n (operator.suffix) ? results.slice(index) :\n results.slice(index + 1);\n };\n\n /*\n Returns the items from the current list that are before the item named in the operand\n */\n exports.allbefore = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand);\n return (index === -1) ? [] :\n (operator.suffix) ? results.slice(0, index + 1) :\n results.slice(0, index);\n };\n\n /*\n Appends the items listed in the operand array to the tail of the current list\n */\n exports.append = function (source, operator) {\n var append = $tw.utils.parseStringArray(operator.operand, \"true\"),\n results = prepare_results(source),\n count = parseInt(operator.suffix) || append.length;\n return (append.length === 0) ? results :\n (operator.prefix) ? results.concat(append.slice(-count)) :\n results.concat(append.slice(0, count));\n };\n\n /*\n Prepends the items listed in the operand array to the head of the current list\n */\n exports.prepend = function (source, operator) {\n var prepend = $tw.utils.parseStringArray(operator.operand, \"true\"),\n results = prepare_results(source),\n count = $tw.utils.getInt(operator.suffix,prepend.length);\n return (prepend.length === 0) ? results :\n (operator.prefix) ? prepend.slice(-count).concat(results) :\n prepend.slice(0, count).concat(results);\n };\n\n /*\n Returns all items from the current list except the items listed in the operand array\n */\n exports.remove = function (source, operator) {\n var array = $tw.utils.parseStringArray(operator.operand, \"true\"),\n results = prepare_results(source),\n count = parseInt(operator.suffix) || array.length,\n p,\n len,\n index;\n len = array.length - 1;\n for (p = 0; p < count; ++p) {\n if (operator.prefix) {\n index = results.indexOf(array[len - p]);\n } else {\n index = results.indexOf(array[p]);\n }\n if (index !== -1) {\n results.splice(index, 1);\n }\n }\n return results;\n };\n\n /*\n Returns all items from the current list sorted in the order of the items in the operand array\n */\n exports.sortby = function (source, operator) {\n var results = prepare_results(source);\n if (!results || results.length < 2) {\n return results;\n }\n var lookup = $tw.utils.parseStringArray(operator.operand, \"true\");\n results.sort(function (a, b) {\n return lookup.indexOf(a) - lookup.indexOf(b);\n });\n return results;\n };\n\n /*\n Removes all duplicate items from the current list\n */\n exports.unique = function (source, operator) {\n var results = prepare_results(source);\n var set = results.reduce(function (a, b) {\n if (a.indexOf(b) < 0) {\n a.push(b);\n }\n return a;\n }, []);\n return set;\n };\n})();\n",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters.js": {
"title": "$:/core/modules/filters.js",
"text": "/*\\\ntitle: $:/core/modules/filters.js\ntype: application/javascript\nmodule-type: wikimethod\n\nAdds tiddler filtering methods to the $tw.Wiki object.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nParses an operation (i.e. a run) within a filter string\n\toperators: Array of array of operator nodes into which results should be inserted\n\tfilterString: filter string\n\tp: start position within the string\nReturns the new start position, after the parsed operation\n*/\nfunction parseFilterOperation(operators,filterString,p) {\n\tvar nextBracketPos, operator;\n\t// Skip the starting square bracket\n\tif(filterString.charAt(p++) !== \"[\") {\n\t\tthrow \"Missing [ in filter expression\";\n\t}\n\t// Process each operator in turn\n\tdo {\n\t\toperator = {};\n\t\t// Check for an operator prefix\n\t\tif(filterString.charAt(p) === \"!\") {\n\t\t\toperator.prefix = filterString.charAt(p++);\n\t\t}\n\t\t// Get the operator name\n\t\tnextBracketPos = filterString.substring(p).search(/[\\[\\{<\\/]/);\n\t\tif(nextBracketPos === -1) {\n\t\t\tthrow \"Missing [ in filter expression\";\n\t\t}\n\t\tnextBracketPos += p;\n\t\tvar bracket = filterString.charAt(nextBracketPos);\n\t\toperator.operator = filterString.substring(p,nextBracketPos);\n\t\t// Any suffix?\n\t\tvar colon = operator.operator.indexOf(':');\n\t\tif(colon > -1) {\n\t\t\t// The raw suffix for older filters\n\t\t\toperator.suffix = operator.operator.substring(colon + 1);\n\t\t\toperator.operator = operator.operator.substring(0,colon) || \"field\";\n\t\t\t// The processed suffix for newer filters\n\t\t\toperator.suffixes = [];\n\t\t\t$tw.utils.each(operator.suffix.split(\":\"),function(subsuffix) {\n\t\t\t\toperator.suffixes.push([]);\n\t\t\t\t$tw.utils.each(subsuffix.split(\",\"),function(entry) {\n\t\t\t\t\tentry = $tw.utils.trim(entry);\n\t\t\t\t\tif(entry) {\n\t\t\t\t\t\toperator.suffixes[operator.suffixes.length - 1].push(entry); \n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t});\n\t\t}\n\t\t// Empty operator means: title\n\t\telse if(operator.operator === \"\") {\n\t\t\toperator.operator = \"title\";\n\t\t}\n\n\t\tp = nextBracketPos + 1;\n\t\tswitch (bracket) {\n\t\t\tcase \"{\": // Curly brackets\n\t\t\t\toperator.indirect = true;\n\t\t\t\tnextBracketPos = filterString.indexOf(\"}\",p);\n\t\t\t\tbreak;\n\t\t\tcase \"[\": // Square brackets\n\t\t\t\tnextBracketPos = filterString.indexOf(\"]\",p);\n\t\t\t\tbreak;\n\t\t\tcase \"<\": // Angle brackets\n\t\t\t\toperator.variable = true;\n\t\t\t\tnextBracketPos = filterString.indexOf(\">\",p);\n\t\t\t\tbreak;\n\t\t\tcase \"/\": // regexp brackets\n\t\t\t\tvar rex = /^((?:[^\\\\\\/]*|\\\\.)*)\\/(?:\\(([mygi]+)\\))?/g,\n\t\t\t\t\trexMatch = rex.exec(filterString.substring(p));\n\t\t\t\tif(rexMatch) {\n\t\t\t\t\toperator.regexp = new RegExp(rexMatch[1], rexMatch[2]);\n// DEPRECATION WARNING\nconsole.log(\"WARNING: Filter\",operator.operator,\"has a deprecated regexp operand\",operator.regexp);\n\t\t\t\t\tnextBracketPos = p + rex.lastIndex - 1;\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthrow \"Unterminated regular expression in filter expression\";\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t}\n\n\t\tif(nextBracketPos === -1) {\n\t\t\tthrow \"Missing closing bracket in filter expression\";\n\t\t}\n\t\tif(!operator.regexp) {\n\t\t\toperator.operand = filterString.substring(p,nextBracketPos);\n\t\t}\n\t\tp = nextBracketPos + 1;\n\n\t\t// Push this operator\n\t\toperators.push(operator);\n\t} while(filterString.charAt(p) !== \"]\");\n\t// Skip the ending square bracket\n\tif(filterString.charAt(p++) !== \"]\") {\n\t\tthrow \"Missing ] in filter expression\";\n\t}\n\t// Return the parsing position\n\treturn p;\n}\n\n/*\nParse a filter string\n*/\nexports.parseFilter = function(filterString) {\n\tfilterString = filterString || \"\";\n\tvar results = [], // Array of arrays of operator nodes {operator:,operand:}\n\t\tp = 0, // Current position in the filter string\n\t\tmatch;\n\tvar whitespaceRegExp = /(\\s+)/mg,\n\t\toperandRegExp = /((?:\\+|\\-|~|=)?)(?:(\\[)|(?:\"([^\"]*)\")|(?:'([^']*)')|([^\\s\\[\\]]+))/mg;\n\twhile(p < filterString.length) {\n\t\t// Skip any whitespace\n\t\twhitespaceRegExp.lastIndex = p;\n\t\tmatch = whitespaceRegExp.exec(filterString);\n\t\tif(match && match.index === p) {\n\t\t\tp = p + match[0].length;\n\t\t}\n\t\t// Match the start of the operation\n\t\tif(p < filterString.length) {\n\t\t\toperandRegExp.lastIndex = p;\n\t\t\tmatch = operandRegExp.exec(filterString);\n\t\t\tif(!match || match.index !== p) {\n\t\t\t\tthrow $tw.language.getString(\"Error/FilterSyntax\");\n\t\t\t}\n\t\t\tvar operation = {\n\t\t\t\tprefix: \"\",\n\t\t\t\toperators: []\n\t\t\t};\n\t\t\tif(match[1]) {\n\t\t\t\toperation.prefix = match[1];\n\t\t\t\tp++;\n\t\t\t}\n\t\t\tif(match[2]) { // Opening square bracket\n\t\t\t\tp = parseFilterOperation(operation.operators,filterString,p);\n\t\t\t} else {\n\t\t\t\tp = match.index + match[0].length;\n\t\t\t}\n\t\t\tif(match[3] || match[4] || match[5]) { // Double quoted string, single quoted string or unquoted title\n\t\t\t\toperation.operators.push(\n\t\t\t\t\t{operator: \"title\", operand: match[3] || match[4] || match[5]}\n\t\t\t\t);\n\t\t\t}\n\t\t\tresults.push(operation);\n\t\t}\n\t}\n\treturn results;\n};\n\nexports.getFilterOperators = function() {\n\tif(!this.filterOperators) {\n\t\t$tw.Wiki.prototype.filterOperators = {};\n\t\t$tw.modules.applyMethods(\"filteroperator\",this.filterOperators);\n\t}\n\treturn this.filterOperators;\n};\n\nexports.filterTiddlers = function(filterString,widget,source) {\n\tvar fn = this.compileFilter(filterString);\n\treturn fn.call(this,source,widget);\n};\n\n/*\nCompile a filter into a function with the signature fn(source,widget) where:\nsource: an iterator function for the source tiddlers, called source(iterator), where iterator is called as iterator(tiddler,title)\nwidget: an optional widget node for retrieving the current tiddler etc.\n*/\nexports.compileFilter = function(filterString) {\n\tvar filterParseTree;\n\ttry {\n\t\tfilterParseTree = this.parseFilter(filterString);\n\t} catch(e) {\n\t\treturn function(source,widget) {\n\t\t\treturn [$tw.language.getString(\"Error/Filter\") + \": \" + e];\n\t\t};\n\t}\n\t// Get the hashmap of filter operator functions\n\tvar filterOperators = this.getFilterOperators();\n\t// Assemble array of functions, one for each operation\n\tvar operationFunctions = [];\n\t// Step through the operations\n\tvar self = this;\n\t$tw.utils.each(filterParseTree,function(operation) {\n\t\t// Create a function for the chain of operators in the operation\n\t\tvar operationSubFunction = function(source,widget) {\n\t\t\tvar accumulator = source,\n\t\t\t\tresults = [],\n\t\t\t\tcurrTiddlerTitle = widget && widget.getVariable(\"currentTiddler\");\n\t\t\t$tw.utils.each(operation.operators,function(operator) {\n\t\t\t\tvar operand = operator.operand,\n\t\t\t\t\toperatorFunction;\n\t\t\t\tif(!operator.operator) {\n\t\t\t\t\toperatorFunction = filterOperators.title;\n\t\t\t\t} else if(!filterOperators[operator.operator]) {\n\t\t\t\t\toperatorFunction = filterOperators.field;\n\t\t\t\t} else {\n\t\t\t\t\toperatorFunction = filterOperators[operator.operator];\n\t\t\t\t}\n\t\t\t\tif(operator.indirect) {\n\t\t\t\t\toperand = self.getTextReference(operator.operand,\"\",currTiddlerTitle);\n\t\t\t\t}\n\t\t\t\tif(operator.variable) {\n\t\t\t\t\toperand = widget.getVariable(operator.operand,{defaultValue: \"\"});\n\t\t\t\t}\n\t\t\t\t// Invoke the appropriate filteroperator module\n\t\t\t\tresults = operatorFunction(accumulator,{\n\t\t\t\t\t\t\toperator: operator.operator,\n\t\t\t\t\t\t\toperand: operand,\n\t\t\t\t\t\t\tprefix: operator.prefix,\n\t\t\t\t\t\t\tsuffix: operator.suffix,\n\t\t\t\t\t\t\tsuffixes: operator.suffixes,\n\t\t\t\t\t\t\tregexp: operator.regexp\n\t\t\t\t\t\t},{\n\t\t\t\t\t\t\twiki: self,\n\t\t\t\t\t\t\twidget: widget\n\t\t\t\t\t\t});\n\t\t\t\tif($tw.utils.isArray(results)) {\n\t\t\t\t\taccumulator = self.makeTiddlerIterator(results);\n\t\t\t\t} else {\n\t\t\t\t\taccumulator = results;\n\t\t\t\t}\n\t\t\t});\n\t\t\tif($tw.utils.isArray(results)) {\n\t\t\t\treturn results;\n\t\t\t} else {\n\t\t\t\tvar resultArray = [];\n\t\t\t\tresults(function(tiddler,title) {\n\t\t\t\t\tresultArray.push(title);\n\t\t\t\t});\n\t\t\t\treturn resultArray;\n\t\t\t}\n\t\t};\n\t\t// Wrap the operator functions in a wrapper function that depends on the prefix\n\t\toperationFunctions.push((function() {\n\t\t\tswitch(operation.prefix || \"\") {\n\t\t\t\tcase \"\": // No prefix means that the operation is unioned into the result\n\t\t\t\t\treturn function(results,source,widget) {\n\t\t\t\t\t\t$tw.utils.pushTop(results,operationSubFunction(source,widget));\n\t\t\t\t\t};\n\t\t\t\tcase \"=\": // The results of the operation are pushed into the result without deduplication\n\t\t\t\t\treturn function(results,source,widget) {\n\t\t\t\t\t\tArray.prototype.push.apply(results,operationSubFunction(source,widget));\n\t\t\t\t\t};\n\t\t\t\tcase \"-\": // The results of this operation are removed from the main result\n\t\t\t\t\treturn function(results,source,widget) {\n\t\t\t\t\t\t$tw.utils.removeArrayEntries(results,operationSubFunction(source,widget));\n\t\t\t\t\t};\n\t\t\t\tcase \"+\": // This operation is applied to the main results so far\n\t\t\t\t\treturn function(results,source,widget) {\n\t\t\t\t\t\t// This replaces all the elements of the array, but keeps the actual array so that references to it are preserved\n\t\t\t\t\t\tsource = self.makeTiddlerIterator(results);\n\t\t\t\t\t\tresults.splice(0,results.length);\n\t\t\t\t\t\t$tw.utils.pushTop(results,operationSubFunction(source,widget));\n\t\t\t\t\t};\n\t\t\t\tcase \"~\": // This operation is unioned into the result only if the main result so far is empty\n\t\t\t\t\treturn function(results,source,widget) {\n\t\t\t\t\t\tif(results.length === 0) {\n\t\t\t\t\t\t\t// Main result so far is empty\n\t\t\t\t\t\t\t$tw.utils.pushTop(results,operationSubFunction(source,widget));\n\t\t\t\t\t\t}\n\t\t\t\t\t};\n\t\t\t}\n\t\t})());\n\t});\n\t// Return a function that applies the operations to a source iterator of tiddler titles\n\treturn $tw.perf.measure(\"filter: \" + filterString,function filterFunction(source,widget) {\n\t\tif(!source) {\n\t\t\tsource = self.each;\n\t\t} else if(typeof source === \"object\") { // Array or hashmap\n\t\t\tsource = self.makeTiddlerIterator(source);\n\t\t}\n\t\tvar results = [];\n\t\t$tw.utils.each(operationFunctions,function(operationFunction) {\n\t\t\toperationFunction(results,source,widget);\n\t\t});\n\t\treturn results;\n\t});\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikimethod"
},
"$:/core/modules/indexers/backlinks-indexer.js": {
"title": "$:/core/modules/indexers/backlinks-indexer.js",
"text": "/*\\\ntitle: $:/core/modules/indexers/backlinks-indexer.js\ntype: application/javascript\nmodule-type: indexer\n\nIndexes the tiddlers' backlinks\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global modules: false */\n\"use strict\";\n\n\nfunction BacklinksIndexer(wiki) {\n\tthis.wiki = wiki;\n}\n\nBacklinksIndexer.prototype.init = function() {\n\tthis.index = null;\n}\n\nBacklinksIndexer.prototype.rebuild = function() {\n\tthis.index = null;\n}\n\nBacklinksIndexer.prototype._getLinks = function(tiddler) {\n\tvar parser = this.wiki.parseText(tiddler.fields.type, tiddler.fields.text, {});\n\tif(parser) {\n\t\treturn this.wiki.extractLinks(parser.tree);\n\t}\n\treturn [];\n}\n\nBacklinksIndexer.prototype.update = function(updateDescriptor) {\n\tif(!this.index) {\n\t\treturn;\n\t}\n\tvar newLinks = [],\n\t oldLinks = [],\n\t self = this;\n\tif(updateDescriptor.old.exists) {\n\t\toldLinks = this._getLinks(updateDescriptor.old.tiddler);\n\t}\n\tif(updateDescriptor.new.exists) {\n\t\tnewLinks = this._getLinks(updateDescriptor.new.tiddler);\n\t}\n\n\t$tw.utils.each(oldLinks,function(link) {\n\t\tif(self.index[link]) {\n\t\t\tdelete self.index[link][updateDescriptor.old.tiddler.fields.title];\n\t\t}\n\t});\n\t$tw.utils.each(newLinks,function(link) {\n\t\tif(!self.index[link]) {\n\t\t\tself.index[link] = Object.create(null);\n\t\t}\n\t\tself.index[link][updateDescriptor.new.tiddler.fields.title] = true;\n\t});\n}\n\nBacklinksIndexer.prototype.lookup = function(title) {\n\tif(!this.index) {\n\t\tthis.index = Object.create(null);\n\t\tvar self = this;\n\t\tthis.wiki.forEachTiddler(function(title,tiddler) {\n\t\t\tvar links = self._getLinks(tiddler);\n\t\t\t$tw.utils.each(links, function(link) {\n\t\t\t\tif(!self.index[link]) {\n\t\t\t\t\tself.index[link] = Object.create(null);\n\t\t\t\t}\n\t\t\t\tself.index[link][title] = true;\n\t\t\t});\n\t\t});\n\t}\n\tif(this.index[title]) {\n\t\treturn Object.keys(this.index[title]);\n\t} else {\n\t\treturn [];\n\t}\n}\n\nexports.BacklinksIndexer = BacklinksIndexer;\n\n})();\n",
"type": "application/javascript",
"module-type": "indexer"
},
"$:/core/modules/indexers/field-indexer.js": {
"title": "$:/core/modules/indexers/field-indexer.js",
"text": "/*\\\ntitle: $:/core/modules/indexers/field-indexer.js\ntype: application/javascript\nmodule-type: indexer\n\nIndexes the tiddlers with each field value\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global modules: false */\n\"use strict\";\n\nvar DEFAULT_MAXIMUM_INDEXED_VALUE_LENGTH = 128;\n\nfunction FieldIndexer(wiki) {\n\tthis.wiki = wiki;\n}\n\nFieldIndexer.prototype.init = function() {\n\tthis.index = null;\n\tthis.maxIndexedValueLength = DEFAULT_MAXIMUM_INDEXED_VALUE_LENGTH;\n\tthis.addIndexMethods();\n}\n\n// Provided for testing\nFieldIndexer.prototype.setMaxIndexedValueLength = function(length) {\n\tthis.index = null;\n\tthis.maxIndexedValueLength = length;\n};\n\nFieldIndexer.prototype.addIndexMethods = function() {\n\tvar self = this;\n\tthis.wiki.each.byField = function(name,value) {\n\t\tvar titles = self.wiki.allTitles(),\n\t\t\tlookup = self.lookup(name,value);\n\t\treturn lookup && lookup.filter(function(title) {\n\t\t\treturn titles.indexOf(title) !== -1;\n\t\t});\n\t};\n\tthis.wiki.eachShadow.byField = function(name,value) {\n\t\tvar titles = self.wiki.allShadowTitles(),\n\t\t\tlookup = self.lookup(name,value);\n\t\treturn lookup && lookup.filter(function(title) {\n\t\t\treturn titles.indexOf(title) !== -1;\n\t\t});\n\t};\n\tthis.wiki.eachTiddlerPlusShadows.byField = function(name,value) {\n\t\tvar lookup = self.lookup(name,value);\n\t\treturn lookup ? lookup.slice(0) : null;\n\t};\n\tthis.wiki.eachShadowPlusTiddlers.byField = function(name,value) {\n\t\tvar lookup = self.lookup(name,value);\n\t\treturn lookup ? lookup.slice(0) : null;\n\t};\n};\n\n/*\nTear down and then rebuild the index as if all tiddlers have changed\n*/\nFieldIndexer.prototype.rebuild = function() {\n\t// Invalidate the index so that it will be rebuilt when it is next used\n\tthis.index = null;\n};\n\n/*\nBuild the index for a particular field\n*/\nFieldIndexer.prototype.buildIndexForField = function(name) {\n\tvar self = this;\n\t// Hashmap by field name of hashmap by field value of array of tiddler titles\n\tthis.index = this.index || Object.create(null);\n\tthis.index[name] = Object.create(null);\n\tvar baseIndex = this.index[name];\n\t// Update the index for each tiddler\n\tthis.wiki.eachTiddlerPlusShadows(function(tiddler,title) {\n\t\tif(name in tiddler.fields) {\n\t\t\tvar value = tiddler.getFieldString(name);\n\t\t\t// Skip any values above the maximum length\n\t\t\tif(value.length < self.maxIndexedValueLength) {\n\t\t\t\tbaseIndex[value] = baseIndex[value] || [];\n\t\t\t\tbaseIndex[value].push(title);\n\t\t\t}\n\t\t}\n\t});\n};\n\n/*\nUpdate the index in the light of a tiddler value changing; note that the title must be identical. (Renames are handled as a separate delete and create)\nupdateDescriptor: {old: {tiddler: <tiddler>, shadow: <boolean>, exists: <boolean>},new: {tiddler: <tiddler>, shadow: <boolean>, exists: <boolean>}}\n*/\nFieldIndexer.prototype.update = function(updateDescriptor) {\n\tvar self = this;\n\t// Don't do anything if the index hasn't been built yet\n\tif(this.index === null) {\n\t\treturn;\n\t}\n\t// Remove the old tiddler from the index\n\tif(updateDescriptor.old.tiddler) {\n\t\t$tw.utils.each(this.index,function(indexEntry,name) {\n\t\t\tif(name in updateDescriptor.old.tiddler.fields) {\n\t\t\t\tvar value = updateDescriptor.old.tiddler.getFieldString(name),\n\t\t\t\t\ttiddlerList = indexEntry[value];\n\t\t\t\tif(tiddlerList) {\n\t\t\t\t\tvar index = tiddlerList.indexOf(updateDescriptor.old.tiddler.fields.title);\n\t\t\t\t\tif(index !== -1) {\n\t\t\t\t\t\ttiddlerList.splice(index,1);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n\t// Add the new tiddler to the index\n\tif(updateDescriptor[\"new\"].tiddler) {\n\t\t$tw.utils.each(this.index,function(indexEntry,name) {\n\t\t\tif(name in updateDescriptor[\"new\"].tiddler.fields) {\n\t\t\t\tvar value = updateDescriptor[\"new\"].tiddler.getFieldString(name);\n\t\t\t\tif(value.length < self.maxIndexedValueLength) {\n\t\t\t\t\tindexEntry[value] = indexEntry[value] || [];\n\t\t\t\t\tindexEntry[value].push(updateDescriptor[\"new\"].tiddler.fields.title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\t\t\n\t}\n};\n\n// Lookup the given field returning a list of tiddler titles\nFieldIndexer.prototype.lookup = function(name,value) {\n\t// Fail the lookup if the value is too long\n\tif(value.length >= this.maxIndexedValueLength) {\n\t\treturn null;\n\t}\n\t// Update the index if it has yet to be built\n\tif(this.index === null || !this.index[name]) {\n\t\tthis.buildIndexForField(name);\n\t}\n\treturn this.index[name][value] || [];\n};\n\nexports.FieldIndexer = FieldIndexer;\n\n})();\n",
"type": "application/javascript",
"module-type": "indexer"
},
"$:/core/modules/indexers/tag-indexer.js": {
"title": "$:/core/modules/indexers/tag-indexer.js",
"text": "/*\\\ntitle: $:/core/modules/indexers/tag-indexer.js\ntype: application/javascript\nmodule-type: indexer\n\nIndexes the tiddlers with each tag\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global modules: false */\n\"use strict\";\n\nfunction TagIndexer(wiki) {\n\tthis.wiki = wiki;\n}\n\nTagIndexer.prototype.init = function() {\n\tthis.subIndexers = [\n\t\tnew TagSubIndexer(this,\"each\"),\n\t\tnew TagSubIndexer(this,\"eachShadow\"),\n\t\tnew TagSubIndexer(this,\"eachTiddlerPlusShadows\"),\n\t\tnew TagSubIndexer(this,\"eachShadowPlusTiddlers\")\n\t];\n\t$tw.utils.each(this.subIndexers,function(subIndexer) {\n\t\tsubIndexer.addIndexMethod();\n\t});\n};\n\nTagIndexer.prototype.rebuild = function() {\n\t$tw.utils.each(this.subIndexers,function(subIndexer) {\n\t\tsubIndexer.rebuild();\n\t});\n};\n\nTagIndexer.prototype.update = function(updateDescriptor) {\n\t$tw.utils.each(this.subIndexers,function(subIndexer) {\n\t\tsubIndexer.update(updateDescriptor);\n\t});\n};\n\nfunction TagSubIndexer(indexer,iteratorMethod) {\n\tthis.indexer = indexer;\n\tthis.iteratorMethod = iteratorMethod;\n\tthis.index = null; // Hashmap of tag title to {isSorted: bool, titles: [array]} or null if not yet initialised\n}\n\nTagSubIndexer.prototype.addIndexMethod = function() {\n\tvar self = this;\n\tthis.indexer.wiki[this.iteratorMethod].byTag = function(tag) {\n\t\treturn self.lookup(tag).slice(0);\n\t};\n};\n\nTagSubIndexer.prototype.rebuild = function() {\n\tvar self = this;\n\t// Hashmap by tag of array of {isSorted:, titles:[]}\n\tthis.index = Object.create(null);\n\t// Add all the tags\n\tthis.indexer.wiki[this.iteratorMethod](function(tiddler,title) {\n\t\t$tw.utils.each(tiddler.fields.tags,function(tag) {\n\t\t\tif(!self.index[tag]) {\n\t\t\t\tself.index[tag] = {isSorted: false, titles: [title]};\n\t\t\t} else {\n\t\t\t\tself.index[tag].titles.push(title);\n\t\t\t}\n\t\t});\t\t\n\t});\n};\n\nTagSubIndexer.prototype.update = function(updateDescriptor) {\n\tthis.index = null;\n};\n\nTagSubIndexer.prototype.lookup = function(tag) {\n\t// Update the index if it has yet to be built\n\tif(this.index === null) {\n\t\tthis.rebuild();\n\t}\n\tvar indexRecord = this.index[tag];\n\tif(indexRecord) {\n\t\tif(!indexRecord.isSorted) {\n\t\t\tif(this.indexer.wiki.sortByList) {\n\t\t\t\tindexRecord.titles = this.indexer.wiki.sortByList(indexRecord.titles,tag);\n\t\t\t}\t\t\t\n\t\t\tindexRecord.isSorted = true;\n\t\t}\n\t\treturn indexRecord.titles;\n\t} else {\n\t\treturn [];\n\t}\n};\n\n\nexports.TagIndexer = TagIndexer;\n\n})();\n",
"type": "application/javascript",
"module-type": "indexer"
},
"$:/core/modules/info/platform.js": {
"title": "$:/core/modules/info/platform.js",
"text": "/*\\\ntitle: $:/core/modules/info/platform.js\ntype: application/javascript\nmodule-type: info\n\nInitialise basic platform $:/info/ tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.getInfoTiddlerFields = function() {\n\tvar mapBoolean = function(value) {return value ? \"yes\" : \"no\";},\n\t\tinfoTiddlerFields = [];\n\t// Basics\n\tinfoTiddlerFields.push({title: \"$:/info/browser\", text: mapBoolean(!!$tw.browser)});\n\tinfoTiddlerFields.push({title: \"$:/info/node\", text: mapBoolean(!!$tw.node)});\n\tif($tw.browser) {\n\t\t// Document location\n\t\tvar setLocationProperty = function(name,value) {\n\t\t\t\tinfoTiddlerFields.push({title: \"$:/info/url/\" + name, text: value});\t\t\t\n\t\t\t},\n\t\t\tlocation = document.location;\n\t\tsetLocationProperty(\"full\", (location.toString()).split(\"#\")[0]);\n\t\tsetLocationProperty(\"host\", location.host);\n\t\tsetLocationProperty(\"hostname\", location.hostname);\n\t\tsetLocationProperty(\"protocol\", location.protocol);\n\t\tsetLocationProperty(\"port\", location.port);\n\t\tsetLocationProperty(\"pathname\", location.pathname);\n\t\tsetLocationProperty(\"search\", location.search);\n\t\tsetLocationProperty(\"origin\", location.origin);\n\t\t// Screen size\n\t\tinfoTiddlerFields.push({title: \"$:/info/browser/screen/width\", text: window.screen.width.toString()});\n\t\tinfoTiddlerFields.push({title: \"$:/info/browser/screen/height\", text: window.screen.height.toString()});\n\t\t// Language\n\t\tinfoTiddlerFields.push({title: \"$:/info/browser/language\", text: navigator.language || \"\"});\n\t}\n\treturn infoTiddlerFields;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "info"
},
"$:/core/modules/keyboard.js": {
"title": "$:/core/modules/keyboard.js",
"text": "/*\\\ntitle: $:/core/modules/keyboard.js\ntype: application/javascript\nmodule-type: global\n\nKeyboard handling utilities\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar namedKeys = {\n\t\"cancel\": 3,\n\t\"help\": 6,\n\t\"backspace\": 8,\n\t\"tab\": 9,\n\t\"clear\": 12,\n\t\"return\": 13,\n\t\"enter\": 13,\n\t\"pause\": 19,\n\t\"escape\": 27,\n\t\"space\": 32,\n\t\"page_up\": 33,\n\t\"page_down\": 34,\n\t\"end\": 35,\n\t\"home\": 36,\n\t\"left\": 37,\n\t\"up\": 38,\n\t\"right\": 39,\n\t\"down\": 40,\n\t\"printscreen\": 44,\n\t\"insert\": 45,\n\t\"delete\": 46,\n\t\"0\": 48,\n\t\"1\": 49,\n\t\"2\": 50,\n\t\"3\": 51,\n\t\"4\": 52,\n\t\"5\": 53,\n\t\"6\": 54,\n\t\"7\": 55,\n\t\"8\": 56,\n\t\"9\": 57,\n\t\"firefoxsemicolon\": 59,\n\t\"firefoxequals\": 61,\n\t\"a\": 65,\n\t\"b\": 66,\n\t\"c\": 67,\n\t\"d\": 68,\n\t\"e\": 69,\n\t\"f\": 70,\n\t\"g\": 71,\n\t\"h\": 72,\n\t\"i\": 73,\n\t\"j\": 74,\n\t\"k\": 75,\n\t\"l\": 76,\n\t\"m\": 77,\n\t\"n\": 78,\n\t\"o\": 79,\n\t\"p\": 80,\n\t\"q\": 81,\n\t\"r\": 82,\n\t\"s\": 83,\n\t\"t\": 84,\n\t\"u\": 85,\n\t\"v\": 86,\n\t\"w\": 87,\n\t\"x\": 88,\n\t\"y\": 89,\n\t\"z\": 90,\n\t\"numpad0\": 96,\n\t\"numpad1\": 97,\n\t\"numpad2\": 98,\n\t\"numpad3\": 99,\n\t\"numpad4\": 100,\n\t\"numpad5\": 101,\n\t\"numpad6\": 102,\n\t\"numpad7\": 103,\n\t\"numpad8\": 104,\n\t\"numpad9\": 105,\n\t\"multiply\": 106,\n\t\"add\": 107,\n\t\"separator\": 108,\n\t\"subtract\": 109,\n\t\"decimal\": 110,\n\t\"divide\": 111,\n\t\"f1\": 112,\n\t\"f2\": 113,\n\t\"f3\": 114,\n\t\"f4\": 115,\n\t\"f5\": 116,\n\t\"f6\": 117,\n\t\"f7\": 118,\n\t\"f8\": 119,\n\t\"f9\": 120,\n\t\"f10\": 121,\n\t\"f11\": 122,\n\t\"f12\": 123,\n\t\"f13\": 124,\n\t\"f14\": 125,\n\t\"f15\": 126,\n\t\"f16\": 127,\n\t\"f17\": 128,\n\t\"f18\": 129,\n\t\"f19\": 130,\n\t\"f20\": 131,\n\t\"f21\": 132,\n\t\"f22\": 133,\n\t\"f23\": 134,\n\t\"f24\": 135,\n\t\"firefoxminus\": 173,\n\t\"semicolon\": 186,\n\t\"equals\": 187,\n\t\"comma\": 188,\n\t\"dash\": 189,\n\t\"period\": 190,\n\t\"slash\": 191,\n\t\"backquote\": 192,\n\t\"openbracket\": 219,\n\t\"backslash\": 220,\n\t\"closebracket\": 221,\n\t\"quote\": 222\n};\n\nfunction KeyboardManager(options) {\n\tvar self = this;\n\toptions = options || \"\";\n\t// Save the named key hashmap\n\tthis.namedKeys = namedKeys;\n\t// Create a reverse mapping of code to keyname\n\tthis.keyNames = [];\n\t$tw.utils.each(namedKeys,function(keyCode,name) {\n\t\tself.keyNames[keyCode] = name.substr(0,1).toUpperCase() + name.substr(1);\n\t});\n\t// Save the platform-specific name of the \"meta\" key\n\tthis.metaKeyName = $tw.platform.isMac ? \"cmd-\" : \"win-\";\n\tthis.shortcutKeysList = [], // Stores the shortcut-key descriptors\n\tthis.shortcutActionList = [], // Stores the corresponding action strings\n\tthis.shortcutParsedList = []; // Stores the parsed key descriptors\n\tthis.lookupNames = [\"shortcuts\"];\n\tthis.lookupNames.push($tw.platform.isMac ? \"shortcuts-mac\" : \"shortcuts-not-mac\")\n\tthis.lookupNames.push($tw.platform.isWindows ? \"shortcuts-windows\" : \"shortcuts-not-windows\");\n\tthis.lookupNames.push($tw.platform.isLinux ? \"shortcuts-linux\" : \"shortcuts-not-linux\");\n\tthis.updateShortcutLists(this.getShortcutTiddlerList());\n\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\tself.handleShortcutChanges(changes);\n\t});\n}\n\n/*\nReturn an array of keycodes for the modifier keys ctrl, shift, alt, meta\n*/\nKeyboardManager.prototype.getModifierKeys = function() {\n\treturn [\n\t\t16, // Shift\n\t\t17, // Ctrl\n\t\t18, // Alt\n\t\t20, // CAPS LOCK\n\t\t91, // Meta (left)\n\t\t93, // Meta (right)\n\t\t224 // Meta (Firefox)\n\t]\n};\n\n/*\nParses a key descriptor into the structure:\n{\n\tkeyCode: numeric keycode\n\tshiftKey: boolean\n\taltKey: boolean\n\tctrlKey: boolean\n\tmetaKey: boolean\n}\nKey descriptors have the following format:\n\tctrl+enter\n\tctrl+shift+alt+A\n*/\nKeyboardManager.prototype.parseKeyDescriptor = function(keyDescriptor) {\n\tvar components = keyDescriptor.split(/\\+|\\-/),\n\t\tinfo = {\n\t\t\tkeyCode: 0,\n\t\t\tshiftKey: false,\n\t\t\taltKey: false,\n\t\t\tctrlKey: false,\n\t\t\tmetaKey: false\n\t\t};\n\tfor(var t=0; t<components.length; t++) {\n\t\tvar s = components[t].toLowerCase(),\n\t\t\tc = s.charCodeAt(0);\n\t\t// Look for modifier keys\n\t\tif(s === \"ctrl\") {\n\t\t\tinfo.ctrlKey = true;\n\t\t} else if(s === \"shift\") {\n\t\t\tinfo.shiftKey = true;\n\t\t} else if(s === \"alt\") {\n\t\t\tinfo.altKey = true;\n\t\t} else if(s === \"meta\" || s === \"cmd\" || s === \"win\") {\n\t\t\tinfo.metaKey = true;\n\t\t}\n\t\t// Replace named keys with their code\n\t\tif(this.namedKeys[s]) {\n\t\t\tinfo.keyCode = this.namedKeys[s];\n\t\t}\n\t}\n\tif(info.keyCode) {\n\t\treturn info;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n/*\nParse a list of key descriptors into an array of keyInfo objects. The key descriptors can be passed as an array of strings or a space separated string\n*/\nKeyboardManager.prototype.parseKeyDescriptors = function(keyDescriptors,options) {\n\tvar self = this;\n\toptions = options || {};\n\toptions.stack = options.stack || [];\n\tvar wiki = options.wiki || $tw.wiki;\n\tif(typeof keyDescriptors === \"string\" && keyDescriptors === \"\") {\n\t\treturn [];\n\t}\n\tif(!$tw.utils.isArray(keyDescriptors)) {\n\t\tkeyDescriptors = keyDescriptors.split(\" \");\n\t}\n\tvar result = [];\n\t$tw.utils.each(keyDescriptors,function(keyDescriptor) {\n\t\t// Look for a named shortcut\n\t\tif(keyDescriptor.substr(0,2) === \"((\" && keyDescriptor.substr(-2,2) === \"))\") {\n\t\t\tif(options.stack.indexOf(keyDescriptor) === -1) {\n\t\t\t\toptions.stack.push(keyDescriptor);\n\t\t\t\tvar name = keyDescriptor.substring(2,keyDescriptor.length - 2),\n\t\t\t\t\tlookupName = function(configName) {\n\t\t\t\t\t\tvar keyDescriptors = wiki.getTiddlerText(\"$:/config/\" + configName + \"/\" + name);\n\t\t\t\t\t\tif(keyDescriptors) {\n\t\t\t\t\t\t\tresult.push.apply(result,self.parseKeyDescriptors(keyDescriptors,options));\n\t\t\t\t\t\t}\n\t\t\t\t\t};\n\t\t\t\t$tw.utils.each(self.lookupNames,function(platformDescriptor) {\n\t\t\t\t\tlookupName(platformDescriptor);\n\t\t\t\t});\n\t\t\t}\n\t\t} else {\n\t\t\tresult.push(self.parseKeyDescriptor(keyDescriptor));\n\t\t}\n\t});\n\treturn result;\n};\n\nKeyboardManager.prototype.getPrintableShortcuts = function(keyInfoArray) {\n\tvar self = this,\n\t\tresult = [];\n\t$tw.utils.each(keyInfoArray,function(keyInfo) {\n\t\tif(keyInfo) {\n\t\t\tresult.push((keyInfo.ctrlKey ? \"ctrl-\" : \"\") + \n\t\t\t\t (keyInfo.shiftKey ? \"shift-\" : \"\") + \n\t\t\t\t (keyInfo.altKey ? \"alt-\" : \"\") + \n\t\t\t\t (keyInfo.metaKey ? self.metaKeyName : \"\") + \n\t\t\t\t (self.keyNames[keyInfo.keyCode]));\n\t\t}\n\t});\n\treturn result;\n}\n\nKeyboardManager.prototype.checkKeyDescriptor = function(event,keyInfo) {\n\treturn keyInfo &&\n\t\t\tevent.keyCode === keyInfo.keyCode && \n\t\t\tevent.shiftKey === keyInfo.shiftKey && \n\t\t\tevent.altKey === keyInfo.altKey && \n\t\t\tevent.ctrlKey === keyInfo.ctrlKey && \n\t\t\tevent.metaKey === keyInfo.metaKey;\n};\n\nKeyboardManager.prototype.checkKeyDescriptors = function(event,keyInfoArray) {\n\tfor(var t=0; t<keyInfoArray.length; t++) {\n\t\tif(this.checkKeyDescriptor(event,keyInfoArray[t])) {\n\t\t\treturn true;\n\t\t}\n\t}\n\treturn false;\n};\n\nKeyboardManager.prototype.getShortcutTiddlerList = function() {\n\treturn $tw.wiki.getTiddlersWithTag(\"$:/tags/KeyboardShortcut\");\n};\n\nKeyboardManager.prototype.updateShortcutLists = function(tiddlerList) {\n\tthis.shortcutTiddlers = tiddlerList;\n\tfor(var i=0; i<tiddlerList.length; i++) {\n\t\tvar title = tiddlerList[i],\n\t\t\ttiddlerFields = $tw.wiki.getTiddler(title).fields;\n\t\tthis.shortcutKeysList[i] = tiddlerFields.key !== undefined ? tiddlerFields.key : undefined;\n\t\tthis.shortcutActionList[i] = tiddlerFields.text;\n\t\tthis.shortcutParsedList[i] = this.shortcutKeysList[i] !== undefined ? this.parseKeyDescriptors(this.shortcutKeysList[i]) : undefined;\n\t}\n};\n\nKeyboardManager.prototype.handleKeydownEvent = function(event) {\n\tvar key, action;\n\tfor(var i=0; i<this.shortcutTiddlers.length; i++) {\n\t\tif(this.shortcutParsedList[i] !== undefined && this.checkKeyDescriptors(event,this.shortcutParsedList[i])) {\n\t\t\tkey = this.shortcutParsedList[i];\n\t\t\taction = this.shortcutActionList[i];\n\t\t}\n\t}\n\tif(key !== undefined) {\n\t\tevent.preventDefault();\n\t\tevent.stopPropagation();\n\t\t$tw.rootWidget.invokeActionString(action,$tw.rootWidget);\n\t\treturn true;\n\t}\n\treturn false;\n};\n\nKeyboardManager.prototype.detectNewShortcuts = function(changedTiddlers) {\n\tvar shortcutConfigTiddlers = [],\n\t\thandled = false;\n\t$tw.utils.each(this.lookupNames,function(platformDescriptor) {\n\t\tvar descriptorString = \"$:/config/\" + platformDescriptor + \"/\";\n\t\tObject.keys(changedTiddlers).forEach(function(configTiddler) {\n\t\t\tvar configString = configTiddler.substr(0, configTiddler.lastIndexOf(\"/\") + 1);\n\t\t\tif(configString === descriptorString) {\n\t\t\t\tshortcutConfigTiddlers.push(configTiddler);\n\t\t\t\thandled = true;\n\t\t\t}\n\t\t});\n\t});\n\tif(handled) {\n\t\treturn $tw.utils.hopArray(changedTiddlers,shortcutConfigTiddlers);\n\t} else {\n\t\treturn false;\n\t}\n};\n\nKeyboardManager.prototype.handleShortcutChanges = function(changedTiddlers) {\n\tvar newList = this.getShortcutTiddlerList();\n\tvar hasChanged = $tw.utils.hopArray(changedTiddlers,this.shortcutTiddlers) ? true :\n\t\t($tw.utils.hopArray(changedTiddlers,newList) ? true :\n\t\t(this.detectNewShortcuts(changedTiddlers))\n\t);\n\t// Re-cache shortcuts if something changed\n\tif(hasChanged) {\n\t\tthis.updateShortcutLists(newList);\n\t}\n};\n\nexports.KeyboardManager = KeyboardManager;\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/language.js": {
"title": "$:/core/modules/language.js",
"text": "/*\\\ntitle: $:/core/modules/language.js\ntype: application/javascript\nmodule-type: global\n\nThe $tw.Language() manages translateable strings\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nCreate an instance of the language manager. Options include:\nwiki: wiki from which to retrieve translation tiddlers\n*/\nfunction Language(options) {\n\toptions = options || \"\";\n\tthis.wiki = options.wiki || $tw.wiki;\n}\n\n/*\nReturn a wikified translateable string. The title is automatically prefixed with \"$:/language/\"\nOptions include:\nvariables: optional hashmap of variables to supply to the language wikification\n*/\nLanguage.prototype.getString = function(title,options) {\n\toptions = options || {};\n\ttitle = \"$:/language/\" + title;\n\treturn this.wiki.renderTiddler(\"text/plain\",title,{variables: options.variables});\n};\n\n/*\nReturn a raw, unwikified translateable string. The title is automatically prefixed with \"$:/language/\"\n*/\nLanguage.prototype.getRawString = function(title) {\n\ttitle = \"$:/language/\" + title;\n\treturn this.wiki.getTiddlerText(title);\n};\n\nexports.Language = Language;\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/macros/changecount.js": {
"title": "$:/core/modules/macros/changecount.js",
"text": "/*\\\ntitle: $:/core/modules/macros/changecount.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to return the changecount for the current tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"changecount\";\n\nexports.params = [];\n\n/*\nRun the macro\n*/\nexports.run = function() {\n\treturn this.wiki.getChangeCount(this.getVariable(\"currentTiddler\")) + \"\";\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/contrastcolour.js": {
"title": "$:/core/modules/macros/contrastcolour.js",
"text": "/*\\\ntitle: $:/core/modules/macros/contrastcolour.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to choose which of two colours has the highest contrast with a base colour\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"contrastcolour\";\n\nexports.params = [\n\t{name: \"target\"},\n\t{name: \"fallbackTarget\"},\n\t{name: \"colourA\"},\n\t{name: \"colourB\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(target,fallbackTarget,colourA,colourB) {\n\tvar rgbTarget = $tw.utils.parseCSSColor(target) || $tw.utils.parseCSSColor(fallbackTarget);\n\tif(!rgbTarget) {\n\t\treturn colourA;\n\t}\n\tvar rgbColourA = $tw.utils.parseCSSColor(colourA),\n\t\trgbColourB = $tw.utils.parseCSSColor(colourB);\n\tif(rgbColourA && !rgbColourB) {\n\t\treturn rgbColourA;\n\t}\n\tif(rgbColourB && !rgbColourA) {\n\t\treturn rgbColourB;\n\t}\n\tif(!rgbColourA && !rgbColourB) {\n\t\t// If neither colour is readable, return a crude inverse of the target\n\t\treturn [255 - rgbTarget[0],255 - rgbTarget[1],255 - rgbTarget[2],rgbTarget[3]];\n\t}\n\t// Colour brightness formula derived from http://www.w3.org/WAI/ER/WD-AERT/#color-contrast\n\tvar brightnessTarget = rgbTarget[0] * 0.299 + rgbTarget[1] * 0.587 + rgbTarget[2] * 0.114,\n\t\tbrightnessA = rgbColourA[0] * 0.299 + rgbColourA[1] * 0.587 + rgbColourA[2] * 0.114,\n\t\tbrightnessB = rgbColourB[0] * 0.299 + rgbColourB[1] * 0.587 + rgbColourB[2] * 0.114;\n\treturn Math.abs(brightnessTarget - brightnessA) > Math.abs(brightnessTarget - brightnessB) ? colourA : colourB;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/csvtiddlers.js": {
"title": "$:/core/modules/macros/csvtiddlers.js",
"text": "/*\\\ntitle: $:/core/modules/macros/csvtiddlers.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to output tiddlers matching a filter to CSV\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"csvtiddlers\";\n\nexports.params = [\n\t{name: \"filter\"},\n\t{name: \"format\"},\n];\n\n/*\nRun the macro\n*/\nexports.run = function(filter,format) {\n\tvar self = this,\n\t\ttiddlers = this.wiki.filterTiddlers(filter),\n\t\ttiddler,\n\t\tfields = [],\n\t\tt,f;\n\t// Collect all the fields\n\tfor(t=0;t<tiddlers.length; t++) {\n\t\ttiddler = this.wiki.getTiddler(tiddlers[t]);\n\t\tfor(f in tiddler.fields) {\n\t\t\tif(fields.indexOf(f) === -1) {\n\t\t\t\tfields.push(f);\n\t\t\t}\n\t\t}\n\t}\n\t// Sort the fields and bring the standard ones to the front\n\tfields.sort();\n\t\"title text modified modifier created creator\".split(\" \").reverse().forEach(function(value,index) {\n\t\tvar p = fields.indexOf(value);\n\t\tif(p !== -1) {\n\t\t\tfields.splice(p,1);\n\t\t\tfields.unshift(value)\n\t\t}\n\t});\n\t// Output the column headings\n\tvar output = [], row = [];\n\tfields.forEach(function(value) {\n\t\trow.push(quoteAndEscape(value))\n\t});\n\toutput.push(row.join(\",\"));\n\t// Output each tiddler\n\tfor(var t=0;t<tiddlers.length; t++) {\n\t\trow = [];\n\t\ttiddler = this.wiki.getTiddler(tiddlers[t]);\n\t\t\tfor(f=0; f<fields.length; f++) {\n\t\t\t\trow.push(quoteAndEscape(tiddler ? tiddler.getFieldString(fields[f]) || \"\" : \"\"));\n\t\t\t}\n\t\toutput.push(row.join(\",\"));\n\t}\n\treturn output.join(\"\\n\");\n};\n\nfunction quoteAndEscape(value) {\n\treturn \"\\\"\" + value.replace(/\"/mg,\"\\\"\\\"\") + \"\\\"\";\n}\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/displayshortcuts.js": {
"title": "$:/core/modules/macros/displayshortcuts.js",
"text": "/*\\\ntitle: $:/core/modules/macros/displayshortcuts.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to display a list of keyboard shortcuts in human readable form. Notably, it resolves named shortcuts like `((bold))` to the underlying keystrokes.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"displayshortcuts\";\n\nexports.params = [\n\t{name: \"shortcuts\"},\n\t{name: \"prefix\"},\n\t{name: \"separator\"},\n\t{name: \"suffix\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(shortcuts,prefix,separator,suffix) {\n\tvar shortcutArray = $tw.keyboardManager.getPrintableShortcuts($tw.keyboardManager.parseKeyDescriptors(shortcuts,{\n\t\twiki: this.wiki\n\t}));\n\tif(shortcutArray.length > 0) {\n\t\tshortcutArray.sort(function(a,b) {\n\t\t return a.toLowerCase().localeCompare(b.toLowerCase());\n\t\t})\n\t\treturn prefix + shortcutArray.join(separator) + suffix;\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/jsontiddler.js": {
"title": "$:/core/modules/macros/jsontiddler.js",
"text": "/*\\\ntitle: $:/core/modules/macros/jsontiddler.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to output a single tiddler to JSON\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"jsontiddler\";\n\nexports.params = [\n\t{name: \"title\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(title) {\n\ttitle = title || this.getVariable(\"currentTiddler\");\n\tvar tiddler = !!title && this.wiki.getTiddler(title),\n\t\tfields = new Object();\n\tif(tiddler) {\n\t\tfor(var field in tiddler.fields) {\n\t\t\tfields[field] = tiddler.getFieldString(field);\n\t\t}\n\t}\n\treturn JSON.stringify(fields,null,$tw.config.preferences.jsonSpaces);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/jsontiddlers.js": {
"title": "$:/core/modules/macros/jsontiddlers.js",
"text": "/*\\\ntitle: $:/core/modules/macros/jsontiddlers.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to output tiddlers matching a filter to JSON\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"jsontiddlers\";\n\nexports.params = [\n\t{name: \"filter\"},\n\t{name: \"spaces\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(filter,spaces) {\n\treturn this.wiki.getTiddlersAsJson(filter,$tw.utils.parseInt(spaces));\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/makedatauri.js": {
"title": "$:/core/modules/macros/makedatauri.js",
"text": "/*\\\ntitle: $:/core/modules/macros/makedatauri.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to convert a string of text to a data URI\n\n<<makedatauri text:\"Text to be converted\" type:\"text/vnd.tiddlywiki\">>\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"makedatauri\";\n\nexports.params = [\n\t{name: \"text\"},\n\t{name: \"type\"},\n\t{name: \"_canonical_uri\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(text,type,_canonical_uri) {\n\treturn $tw.utils.makeDataUri(text,type,_canonical_uri);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/now.js": {
"title": "$:/core/modules/macros/now.js",
"text": "/*\\\ntitle: $:/core/modules/macros/now.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to return a formatted version of the current time\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"now\";\n\nexports.params = [\n\t{name: \"format\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(format) {\n\treturn $tw.utils.formatDateString(new Date(),format || \"0hh:0mm, DDth MMM YYYY\");\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/qualify.js": {
"title": "$:/core/modules/macros/qualify.js",
"text": "/*\\\ntitle: $:/core/modules/macros/qualify.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to qualify a state tiddler title according\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"qualify\";\n\nexports.params = [\n\t{name: \"title\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(title) {\n\treturn title + \"-\" + this.getStateQualifier();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/resolvepath.js": {
"title": "$:/core/modules/macros/resolvepath.js",
"text": "/*\\\ntitle: $:/core/modules/macros/resolvepath.js\ntype: application/javascript\nmodule-type: macro\n\nResolves a relative path for an absolute rootpath.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"resolvepath\";\n\nexports.params = [\n\t{name: \"source\"},\n\t{name: \"root\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(source, root) {\n\treturn $tw.utils.resolvePath(source, root);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/unusedtitle.js": {
"title": "$:/core/modules/macros/unusedtitle.js",
"text": "/*\\\ntitle: $:/core/modules/macros/unusedtitle.js\ntype: application/javascript\nmodule-type: macro\nMacro to return a new title that is unused in the wiki. It can be given a name as a base.\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"unusedtitle\";\n\nexports.params = [\n\t{name: \"baseName\"},\n\t{name: \"options\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(baseName, options) {\n\tif(!baseName) {\n\t\tbaseName = $tw.language.getString(\"DefaultNewTiddlerTitle\");\n\t}\n\treturn this.wiki.generateNewTitle(baseName, options);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/version.js": {
"title": "$:/core/modules/macros/version.js",
"text": "/*\\\ntitle: $:/core/modules/macros/version.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to return the TiddlyWiki core version number\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"version\";\n\nexports.params = [];\n\n/*\nRun the macro\n*/\nexports.run = function() {\n\treturn $tw.version;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/parsers/audioparser.js": {
"title": "$:/core/modules/parsers/audioparser.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/audioparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe audio parser parses an audio tiddler into an embeddable HTML element\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar AudioParser = function(type,text,options) {\n\tvar element = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"audio\",\n\t\t\tattributes: {\n\t\t\t\tcontrols: {type: \"string\", value: \"controls\"},\n\t\t\t\tstyle: {type: \"string\", value: \"width: 100%; object-fit: contain\"}\n\t\t\t}\n\t\t},\n\t\tsrc;\n\tif(options._canonical_uri) {\n\t\telement.attributes.src = {type: \"string\", value: options._canonical_uri};\n\t} else if(text) {\n\t\telement.attributes.src = {type: \"string\", value: \"data:\" + type + \";base64,\" + text};\n\t}\n\tthis.tree = [element];\n};\n\nexports[\"audio/ogg\"] = AudioParser;\nexports[\"audio/mpeg\"] = AudioParser;\nexports[\"audio/mp3\"] = AudioParser;\nexports[\"audio/mp4\"] = AudioParser;\n\n})();\n\n",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/binaryparser.js": {
"title": "$:/core/modules/parsers/binaryparser.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/binaryparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe binary parser parses a binary tiddler into a warning message and download link\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar BINARY_WARNING_MESSAGE = \"$:/core/ui/BinaryWarning\";\nvar EXPORT_BUTTON_IMAGE = \"$:/core/images/export-button\";\n\nvar BinaryParser = function(type,text,options) {\n\t// Transclude the binary data tiddler warning message\n\tvar warn = {\n\t\ttype: \"element\",\n\t\ttag: \"p\",\n\t\tchildren: [{\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {type: \"string\", value: BINARY_WARNING_MESSAGE}\n\t\t\t}\n\t\t}]\n\t};\n\t// Create download link based on binary tiddler title\n\tvar link = {\n\t\ttype: \"element\",\n\t\ttag: \"a\",\n\t\tattributes: {\n\t\t\ttitle: {type: \"indirect\", textReference: \"!!title\"},\n\t\t\tdownload: {type: \"indirect\", textReference: \"!!title\"}\n\t\t},\n\t\tchildren: [{\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {type: \"string\", value: EXPORT_BUTTON_IMAGE}\n\t\t\t}\n\t\t}]\n\t};\n\t// Set the link href to external or internal data URI\n\tif(options._canonical_uri) {\n\t\tlink.attributes.href = {\n\t\t\ttype: \"string\", \n\t\t\tvalue: options._canonical_uri\n\t\t};\n\t} else if(text) {\n\t\tlink.attributes.href = {\n\t\t\ttype: \"string\", \n\t\t\tvalue: \"data:\" + type + \";base64,\" + text\n\t\t};\n\t}\n\t// Combine warning message and download link in a div\n\tvar element = {\n\t\ttype: \"element\",\n\t\ttag: \"div\",\n\t\tattributes: {\n\t\t\tclass: {type: \"string\", value: \"tc-binary-warning\"}\n\t\t},\n\t\tchildren: [warn, link]\n\t}\n\tthis.tree = [element];\n};\n\nexports[\"application/octet-stream\"] = BinaryParser;\n\n})();\n\n",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/csvparser.js": {
"title": "$:/core/modules/parsers/csvparser.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/csvparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe CSV text parser processes CSV files into a table wrapped in a scrollable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar CsvParser = function(type,text,options) {\n\t// Table framework\n\tthis.tree = [{\n\t\t\"type\": \"scrollable\", \"children\": [{\n\t\t\t\"type\": \"element\", \"tag\": \"table\", \"children\": [{\n\t\t\t\t\"type\": \"element\", \"tag\": \"tbody\", \"children\": []\n\t\t\t}], \"attributes\": {\n\t\t\t\t\"class\": {\"type\": \"string\", \"value\": \"tc-csv-table\"}\n\t\t\t}\n\t\t}]\n\t}];\n\t// Split the text into lines\n\tvar lines = text.split(/\\r?\\n/mg),\n\t\ttag = \"th\";\n\tfor(var line=0; line<lines.length; line++) {\n\t\tvar lineText = lines[line];\n\t\tif(lineText) {\n\t\t\tvar row = {\n\t\t\t\t\t\"type\": \"element\", \"tag\": \"tr\", \"children\": []\n\t\t\t\t};\n\t\t\tvar columns = lineText.split(\",\");\n\t\t\tfor(var column=0; column<columns.length; column++) {\n\t\t\t\trow.children.push({\n\t\t\t\t\t\t\"type\": \"element\", \"tag\": tag, \"children\": [{\n\t\t\t\t\t\t\t\"type\": \"text\",\n\t\t\t\t\t\t\t\"text\": columns[column]\n\t\t\t\t\t\t}]\n\t\t\t\t\t});\n\t\t\t}\n\t\t\ttag = \"td\";\n\t\t\tthis.tree[0].children[0].children[0].children.push(row);\n\t\t}\n\t}\n};\n\nexports[\"text/csv\"] = CsvParser;\n\n})();\n\n",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/htmlparser.js": {
"title": "$:/core/modules/parsers/htmlparser.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/htmlparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe HTML parser displays text as raw HTML\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar HtmlParser = function(type,text,options) {\n\tvar src;\n\tif(options._canonical_uri) {\n\t\tsrc = options._canonical_uri;\n\t} else if(text) {\n\t\tsrc = \"data:text/html;charset=utf-8,\" + encodeURIComponent(text);\n\t}\n\tthis.tree = [{\n\t\ttype: \"element\",\n\t\ttag: \"iframe\",\n\t\tattributes: {\n\t\t\tsrc: {type: \"string\", value: src},\n\t\t\tsandbox: {type: \"string\", value: \"\"}\n\t\t}\n\t}];\n};\n\nexports[\"text/html\"] = HtmlParser;\n\n})();\n\n",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/imageparser.js": {
"title": "$:/core/modules/parsers/imageparser.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/imageparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe image parser parses an image into an embeddable HTML element\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar ImageParser = function(type,text,options) {\n\tvar element = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"img\",\n\t\t\tattributes: {}\n\t\t};\n\tif(options._canonical_uri) {\n\t\telement.attributes.src = {type: \"string\", value: options._canonical_uri};\n\t} else if(text) {\n\t\tif(type === \"image/svg+xml\" || type === \".svg\") {\n\t\t\telement.attributes.src = {type: \"string\", value: \"data:image/svg+xml,\" + encodeURIComponent(text)};\n\t\t} else {\n\t\t\telement.attributes.src = {type: \"string\", value: \"data:\" + type + \";base64,\" + text};\n\t\t}\n\t}\n\tthis.tree = [element];\n};\n\nexports[\"image/svg+xml\"] = ImageParser;\nexports[\"image/jpg\"] = ImageParser;\nexports[\"image/jpeg\"] = ImageParser;\nexports[\"image/png\"] = ImageParser;\nexports[\"image/gif\"] = ImageParser;\nexports[\"image/webp\"] = ImageParser;\nexports[\"image/heic\"] = ImageParser;\nexports[\"image/heif\"] = ImageParser;\nexports[\"image/x-icon\"] = ImageParser;\n\n})();\n\n",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/utils/parseutils.js": {
"title": "$:/core/modules/utils/parseutils.js",
"text": "/*\\\ntitle: $:/core/modules/utils/parseutils.js\ntype: application/javascript\nmodule-type: utils\n\nUtility functions concerned with parsing text into tokens.\n\nMost functions have the following pattern:\n\n* The parameters are:\n** `source`: the source string being parsed\n** `pos`: the current parse position within the string\n** Any further parameters are used to identify the token that is being parsed\n* The return value is:\n** null if the token was not found at the specified position\n** an object representing the token with the following standard fields:\n*** `type`: string indicating the type of the token\n*** `start`: start position of the token in the source string\n*** `end`: end position of the token in the source string\n*** Any further fields required to describe the token\n\nThe exception is `skipWhiteSpace`, which just returns the position after the whitespace.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nLook for a whitespace token. Returns null if not found, otherwise returns {type: \"whitespace\", start:, end:,}\n*/\nexports.parseWhiteSpace = function(source,pos) {\n\tvar p = pos,c;\n\twhile(true) {\n\t\tc = source.charAt(p);\n\t\tif((c === \" \") || (c === \"\\f\") || (c === \"\\n\") || (c === \"\\r\") || (c === \"\\t\") || (c === \"\\v\") || (c === \"\\u00a0\")) { // Ignores some obscure unicode spaces\n\t\t\tp++;\n\t\t} else {\n\t\t\tbreak;\n\t\t}\n\t}\n\tif(p === pos) {\n\t\treturn null;\n\t} else {\n\t\treturn {\n\t\t\ttype: \"whitespace\",\n\t\t\tstart: pos,\n\t\t\tend: p\n\t\t}\n\t}\n};\n\n/*\nConvenience wrapper for parseWhiteSpace. Returns the position after the whitespace\n*/\nexports.skipWhiteSpace = function(source,pos) {\n\tvar c;\n\twhile(true) {\n\t\tc = source.charAt(pos);\n\t\tif((c === \" \") || (c === \"\\f\") || (c === \"\\n\") || (c === \"\\r\") || (c === \"\\t\") || (c === \"\\v\") || (c === \"\\u00a0\")) { // Ignores some obscure unicode spaces\n\t\t\tpos++;\n\t\t} else {\n\t\t\treturn pos;\n\t\t}\n\t}\n};\n\n/*\nLook for a given string token. Returns null if not found, otherwise returns {type: \"token\", value:, start:, end:,}\n*/\nexports.parseTokenString = function(source,pos,token) {\n\tvar match = source.indexOf(token,pos) === pos;\n\tif(match) {\n\t\treturn {\n\t\t\ttype: \"token\",\n\t\t\tvalue: token,\n\t\t\tstart: pos,\n\t\t\tend: pos + token.length\n\t\t};\n\t}\n\treturn null;\n};\n\n/*\nLook for a token matching a regex. Returns null if not found, otherwise returns {type: \"regexp\", match:, start:, end:,}\n*/\nexports.parseTokenRegExp = function(source,pos,reToken) {\n\tvar node = {\n\t\ttype: \"regexp\",\n\t\tstart: pos\n\t};\n\treToken.lastIndex = pos;\n\tnode.match = reToken.exec(source);\n\tif(node.match && node.match.index === pos) {\n\t\tnode.end = pos + node.match[0].length;\n\t\treturn node;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n/*\nLook for a string literal. Returns null if not found, otherwise returns {type: \"string\", value:, start:, end:,}\n*/\nexports.parseStringLiteral = function(source,pos) {\n\tvar node = {\n\t\ttype: \"string\",\n\t\tstart: pos\n\t};\n\tvar reString = /(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\")|(?:'([^']*)')/g;\n\treString.lastIndex = pos;\n\tvar match = reString.exec(source);\n\tif(match && match.index === pos) {\n\t\tnode.value = match[1] !== undefined ? match[1] :(\n\t\t\tmatch[2] !== undefined ? match[2] : match[3] \n\t\t\t\t\t);\n\t\tnode.end = pos + match[0].length;\n\t\treturn node;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n/*\nLook for a macro invocation parameter. Returns null if not found, or {type: \"macro-parameter\", name:, value:, start:, end:}\n*/\nexports.parseMacroParameter = function(source,pos) {\n\tvar node = {\n\t\ttype: \"macro-parameter\",\n\t\tstart: pos\n\t};\n\t// Define our regexp\n\tvar reMacroParameter = /(?:([A-Za-z0-9\\-_]+)\\s*:)?(?:\\s*(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\"|'([^']*)'|\\[\\[([^\\]]*)\\]\\]|([^\\s>\"'=]+)))/g;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for the parameter\n\tvar token = $tw.utils.parseTokenRegExp(source,pos,reMacroParameter);\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Get the parameter details\n\tnode.value = token.match[2] !== undefined ? token.match[2] : (\n\t\t\t\t\ttoken.match[3] !== undefined ? token.match[3] : (\n\t\t\t\t\t\ttoken.match[4] !== undefined ? token.match[4] : (\n\t\t\t\t\t\t\ttoken.match[5] !== undefined ? token.match[5] : (\n\t\t\t\t\t\t\t\ttoken.match[6] !== undefined ? token.match[6] : (\n\t\t\t\t\t\t\t\t\t\"\"\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t)\n\t\t\t\t\t)\n\t\t\t\t);\n\tif(token.match[1]) {\n\t\tnode.name = token.match[1];\n\t}\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\n/*\nLook for a macro invocation. Returns null if not found, or {type: \"macrocall\", name:, parameters:, start:, end:}\n*/\nexports.parseMacroInvocation = function(source,pos) {\n\tvar node = {\n\t\ttype: \"macrocall\",\n\t\tstart: pos,\n\t\tparams: []\n\t};\n\t// Define our regexps\n\tvar reMacroName = /([^\\s>\"'=]+)/g;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for a double less than sign\n\tvar token = $tw.utils.parseTokenString(source,pos,\"<<\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Get the macro name\n\tvar name = $tw.utils.parseTokenRegExp(source,pos,reMacroName);\n\tif(!name) {\n\t\treturn null;\n\t}\n\tnode.name = name.match[1];\n\tpos = name.end;\n\t// Process parameters\n\tvar parameter = $tw.utils.parseMacroParameter(source,pos);\n\twhile(parameter) {\n\t\tnode.params.push(parameter);\n\t\tpos = parameter.end;\n\t\t// Get the next parameter\n\t\tparameter = $tw.utils.parseMacroParameter(source,pos);\n\t}\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for a double greater than sign\n\ttoken = $tw.utils.parseTokenString(source,pos,\">>\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\n/*\nLook for an HTML attribute definition. Returns null if not found, otherwise returns {type: \"attribute\", name:, valueType: \"string|indirect|macro\", value:, start:, end:,}\n*/\nexports.parseAttribute = function(source,pos) {\n\tvar node = {\n\t\tstart: pos\n\t};\n\t// Define our regexps\n\tvar reAttributeName = /([^\\/\\s>\"'=]+)/g,\n\t\treUnquotedAttribute = /([^\\/\\s<>\"'=]+)/g,\n\t\treFilteredValue = /\\{\\{\\{(.+?)\\}\\}\\}/g,\n\t\treIndirectValue = /\\{\\{([^\\}]+)\\}\\}/g;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Get the attribute name\n\tvar name = $tw.utils.parseTokenRegExp(source,pos,reAttributeName);\n\tif(!name) {\n\t\treturn null;\n\t}\n\tnode.name = name.match[1];\n\tpos = name.end;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for an equals sign\n\tvar token = $tw.utils.parseTokenString(source,pos,\"=\");\n\tif(token) {\n\t\tpos = token.end;\n\t\t// Skip whitespace\n\t\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t\t// Look for a string literal\n\t\tvar stringLiteral = $tw.utils.parseStringLiteral(source,pos);\n\t\tif(stringLiteral) {\n\t\t\tpos = stringLiteral.end;\n\t\t\tnode.type = \"string\";\n\t\t\tnode.value = stringLiteral.value;\n\t\t} else {\n\t\t\t// Look for a filtered value\n\t\t\tvar filteredValue = $tw.utils.parseTokenRegExp(source,pos,reFilteredValue);\n\t\t\tif(filteredValue) {\n\t\t\t\tpos = filteredValue.end;\n\t\t\t\tnode.type = \"filtered\";\n\t\t\t\tnode.filter = filteredValue.match[1];\n\t\t\t} else {\n\t\t\t\t// Look for an indirect value\n\t\t\t\tvar indirectValue = $tw.utils.parseTokenRegExp(source,pos,reIndirectValue);\n\t\t\t\tif(indirectValue) {\n\t\t\t\t\tpos = indirectValue.end;\n\t\t\t\t\tnode.type = \"indirect\";\n\t\t\t\t\tnode.textReference = indirectValue.match[1];\n\t\t\t\t} else {\n\t\t\t\t\t// Look for a unquoted value\n\t\t\t\t\tvar unquotedValue = $tw.utils.parseTokenRegExp(source,pos,reUnquotedAttribute);\n\t\t\t\t\tif(unquotedValue) {\n\t\t\t\t\t\tpos = unquotedValue.end;\n\t\t\t\t\t\tnode.type = \"string\";\n\t\t\t\t\t\tnode.value = unquotedValue.match[1];\n\t\t\t\t\t} else {\n\t\t\t\t\t\t// Look for a macro invocation value\n\t\t\t\t\t\tvar macroInvocation = $tw.utils.parseMacroInvocation(source,pos);\n\t\t\t\t\t\tif(macroInvocation) {\n\t\t\t\t\t\t\tpos = macroInvocation.end;\n\t\t\t\t\t\t\tnode.type = \"macro\";\n\t\t\t\t\t\t\tnode.value = macroInvocation;\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tnode.type = \"string\";\n\t\t\t\t\t\t\tnode.value = \"true\";\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t} else {\n\t\tnode.type = \"string\";\n\t\tnode.value = \"true\";\n\t}\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/parsers/pdfparser.js": {
"title": "$:/core/modules/parsers/pdfparser.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/pdfparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe PDF parser embeds a PDF viewer\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar ImageParser = function(type,text,options) {\n\tvar element = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"embed\",\n\t\t\tattributes: {}\n\t\t},\n\t\tsrc;\n\tif(options._canonical_uri) {\n\t\telement.attributes.src = {type: \"string\", value: options._canonical_uri};\n\t} else if(text) {\n\t\telement.attributes.src = {type: \"string\", value: \"data:application/pdf;base64,\" + text};\n\t}\n\tthis.tree = [element];\n};\n\nexports[\"application/pdf\"] = ImageParser;\n\n})();\n\n",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/textparser.js": {
"title": "$:/core/modules/parsers/textparser.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/textparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe plain text parser processes blocks of source text into a degenerate parse tree consisting of a single text node\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar TextParser = function(type,text,options) {\n\tthis.tree = [{\n\t\ttype: \"codeblock\",\n\t\tattributes: {\n\t\t\tcode: {type: \"string\", value: text},\n\t\t\tlanguage: {type: \"string\", value: type}\n\t\t}\n\t}];\n};\n\nexports[\"text/plain\"] = TextParser;\nexports[\"text/x-tiddlywiki\"] = TextParser;\nexports[\"application/javascript\"] = TextParser;\nexports[\"application/json\"] = TextParser;\nexports[\"text/css\"] = TextParser;\nexports[\"application/x-tiddler-dictionary\"] = TextParser;\n\n})();\n\n",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/videoparser.js": {
"title": "$:/core/modules/parsers/videoparser.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/videoparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe video parser parses a video tiddler into an embeddable HTML element\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar VideoParser = function(type,text,options) {\n\tvar element = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"video\",\n\t\t\tattributes: {\n\t\t\t\tcontrols: {type: \"string\", value: \"controls\"},\n\t\t\t\tstyle: {type: \"string\", value: \"width: 100%; object-fit: contain\"}\n\t\t\t}\n\t\t},\n\t\tsrc;\n\tif(options._canonical_uri) {\n\t\telement.attributes.src = {type: \"string\", value: options._canonical_uri};\n\t} else if(text) {\n\t\telement.attributes.src = {type: \"string\", value: \"data:\" + type + \";base64,\" + text};\n\t}\n\tthis.tree = [element];\n};\n\nexports[\"video/ogg\"] = VideoParser;\nexports[\"video/webm\"] = VideoParser;\nexports[\"video/mp4\"] = VideoParser;\nexports[\"video/quicktime\"] = VideoParser;\n\n})();\n",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/wikiparser/rules/codeblock.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/codeblock.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/codeblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for code blocks. For example:\n\n```\n\t```\n\tThis text will not be //wikified//\n\t```\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"codeblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match and get language if defined\n\tthis.matchRegExp = /```([\\w-]*)\\r?\\n/mg;\n};\n\nexports.parse = function() {\n\tvar reEnd = /(\\r?\\n```$)/mg;\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Look for the end of the block\n\treEnd.lastIndex = this.parser.pos;\n\tvar match = reEnd.exec(this.parser.source),\n\t\ttext;\n\t// Process the block\n\tif(match) {\n\t\ttext = this.parser.source.substring(this.parser.pos,match.index);\n\t\tthis.parser.pos = match.index + match[0].length;\n\t} else {\n\t\ttext = this.parser.source.substr(this.parser.pos);\n\t\tthis.parser.pos = this.parser.sourceLength;\n\t}\n\t// Return the $codeblock widget\n\treturn [{\n\t\t\ttype: \"codeblock\",\n\t\t\tattributes: {\n\t\t\t\t\tcode: {type: \"string\", value: text},\n\t\t\t\t\tlanguage: {type: \"string\", value: this.match[1]}\n\t\t\t}\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/codeinline.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/codeinline.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/codeinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for code runs. For example:\n\n```\n\tThis is a `code run`.\n\tThis is another ``code run``\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"codeinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /(``?)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tvar reEnd = new RegExp(this.match[1], \"mg\");\n\t// Look for the end marker\n\treEnd.lastIndex = this.parser.pos;\n\tvar match = reEnd.exec(this.parser.source),\n\t\ttext;\n\t// Process the text\n\tif(match) {\n\t\ttext = this.parser.source.substring(this.parser.pos,match.index);\n\t\tthis.parser.pos = match.index + match[0].length;\n\t} else {\n\t\ttext = this.parser.source.substr(this.parser.pos);\n\t\tthis.parser.pos = this.parser.sourceLength;\n\t}\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"code\",\n\t\tchildren: [{\n\t\t\ttype: \"text\",\n\t\t\ttext: text\n\t\t}]\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/commentblock.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/commentblock.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/commentblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for HTML comments. For example:\n\n```\n<!-- This is a comment -->\n```\n\nNote that the syntax for comments is simplified to an opening \"<!--\" sequence and a closing \"-->\" sequence -- HTML itself implements a more complex format (see http://ostermiller.org/findhtmlcomment.html)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"commentblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\tthis.matchRegExp = /<!--/mg;\n\tthis.endMatchRegExp = /-->/mg;\n};\n\nexports.findNextMatch = function(startPos) {\n\tthis.matchRegExp.lastIndex = startPos;\n\tthis.match = this.matchRegExp.exec(this.parser.source);\n\tif(this.match) {\n\t\tthis.endMatchRegExp.lastIndex = startPos + this.match[0].length;\n\t\tthis.endMatch = this.endMatchRegExp.exec(this.parser.source);\n\t\tif(this.endMatch) {\n\t\t\treturn this.match.index;\n\t\t}\n\t}\n\treturn undefined;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.endMatchRegExp.lastIndex;\n\t// Don't return any elements\n\treturn [];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/commentinline.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/commentinline.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/commentinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for HTML comments. For example:\n\n```\n<!-- This is a comment -->\n```\n\nNote that the syntax for comments is simplified to an opening \"<!--\" sequence and a closing \"-->\" sequence -- HTML itself implements a more complex format (see http://ostermiller.org/findhtmlcomment.html)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"commentinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\tthis.matchRegExp = /<!--/mg;\n\tthis.endMatchRegExp = /-->/mg;\n};\n\nexports.findNextMatch = function(startPos) {\n\tthis.matchRegExp.lastIndex = startPos;\n\tthis.match = this.matchRegExp.exec(this.parser.source);\n\tif(this.match) {\n\t\tthis.endMatchRegExp.lastIndex = startPos + this.match[0].length;\n\t\tthis.endMatch = this.endMatchRegExp.exec(this.parser.source);\n\t\tif(this.endMatch) {\n\t\t\treturn this.match.index;\n\t\t}\n\t}\n\treturn undefined;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.endMatchRegExp.lastIndex;\n\t// Don't return any elements\n\treturn [];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/dash.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/dash.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/dash.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for dashes. For example:\n\n```\nThis is an en-dash: --\n\nThis is an em-dash: ---\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"dash\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /-{2,3}(?!-)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tvar dash = this.match[0].length === 2 ? \"–\" : \"—\";\n\treturn [{\n\t\ttype: \"entity\",\n\t\tentity: dash\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/bold.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/bold.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/bold.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - bold. For example:\n\n```\n\tThis is ''bold'' text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except bold \n\\rules only bold \n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"bold\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /''/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/''/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"strong\",\n\t\tchildren: tree\n\t}];\n};\n\n})();",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/italic.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/italic.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/italic.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - italic. For example:\n\n```\n\tThis is //italic// text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except italic\n\\rules only italic\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"italic\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\/\\//mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/\\/\\//mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"em\",\n\t\tchildren: tree\n\t}];\n};\n\n})();",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/strikethrough.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/strikethrough.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/strikethrough.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - strikethrough. For example:\n\n```\n\tThis is ~~strikethrough~~ text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except strikethrough \n\\rules only strikethrough \n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"strikethrough\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /~~/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/~~/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"strike\",\n\t\tchildren: tree\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/subscript.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/subscript.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/subscript.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - subscript. For example:\n\n```\n\tThis is ,,subscript,, text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except subscript \n\\rules only subscript \n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"subscript\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /,,/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/,,/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"sub\",\n\t\tchildren: tree\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/superscript.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/superscript.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/superscript.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - superscript. For example:\n\n```\n\tThis is ^^superscript^^ text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except superscript \n\\rules only superscript \n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"superscript\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\^\\^/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/\\^\\^/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"sup\",\n\t\tchildren: tree\n\t}];\n};\n\n})();",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/underscore.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/underscore.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/underscore.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - underscore. For example:\n\n```\n\tThis is __underscore__ text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except underscore \n\\rules only underscore\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"underscore\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /__/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/__/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"u\",\n\t\tchildren: tree\n\t}];\n};\n\n})();",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/entity.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/entity.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/entity.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for HTML entities. For example:\n\n```\n\tThis is a copyright symbol: ©\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"entity\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /(&#?[a-zA-Z0-9]{2,8};)/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get all the details of the match\n\tvar entityString = this.match[1];\n\t// Move past the macro call\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Return the entity\n\treturn [{type: \"entity\", entity: this.match[0]}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/extlink.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/extlink.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/extlink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for external links. For example:\n\n```\nAn external link: https://www.tiddlywiki.com/\n\nA suppressed external link: ~http://www.tiddlyspace.com/\n```\n\nExternal links can be suppressed by preceding them with `~`.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"extlink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /~?(?:file|http|https|mailto|ftp|irc|news|data|skype):[^\\s<>{}\\[\\]`|\"\\\\^]+(?:\\/|\\b)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Create the link unless it is suppressed\n\tif(this.match[0].substr(0,1) === \"~\") {\n\t\treturn [{type: \"text\", text: this.match[0].substr(1)}];\n\t} else {\n\t\treturn [{\n\t\t\ttype: \"element\",\n\t\t\ttag: \"a\",\n\t\t\tattributes: {\n\t\t\t\thref: {type: \"string\", value: this.match[0]},\n\t\t\t\t\"class\": {type: \"string\", value: \"tc-tiddlylink-external\"},\n\t\t\t\ttarget: {type: \"string\", value: \"_blank\"},\n\t\t\t\trel: {type: \"string\", value: \"noopener noreferrer\"}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\", text: this.match[0]\n\t\t\t}]\n\t\t}];\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/filteredtranscludeblock.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/filteredtranscludeblock.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/filteredtranscludeblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for block-level filtered transclusion. For example:\n\n```\n{{{ [tag[docs]] }}}\n{{{ [tag[docs]] |tooltip}}}\n{{{ [tag[docs]] ||TemplateTitle}}}\n{{{ [tag[docs]] |tooltip||TemplateTitle}}}\n{{{ [tag[docs]] }}width:40;height:50;}.class.class\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"filteredtranscludeblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\{\\{\\{([^\\|]+?)(?:\\|([^\\|\\{\\}]+))?(?:\\|\\|([^\\|\\{\\}]+))?\\}\\}([^\\}]*)\\}(?:\\.(\\S+))?(?:\\r?\\n|$)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Get the match details\n\tvar filter = this.match[1],\n\t\ttooltip = this.match[2],\n\t\ttemplate = $tw.utils.trim(this.match[3]),\n\t\tstyle = this.match[4],\n\t\tclasses = this.match[5];\n\t// Return the list widget\n\tvar node = {\n\t\ttype: \"list\",\n\t\tattributes: {\n\t\t\tfilter: {type: \"string\", value: filter}\n\t\t},\n\t\tisBlock: true\n\t};\n\tif(tooltip) {\n\t\tnode.attributes.tooltip = {type: \"string\", value: tooltip};\n\t}\n\tif(template) {\n\t\tnode.attributes.template = {type: \"string\", value: template};\n\t}\n\tif(style) {\n\t\tnode.attributes.style = {type: \"string\", value: style};\n\t}\n\tif(classes) {\n\t\tnode.attributes.itemClass = {type: \"string\", value: classes.split(\".\").join(\" \")};\n\t}\n\treturn [node];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/filteredtranscludeinline.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/filteredtranscludeinline.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/filteredtranscludeinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for inline filtered transclusion. For example:\n\n```\n{{{ [tag[docs]] }}}\n{{{ [tag[docs]] |tooltip}}}\n{{{ [tag[docs]] ||TemplateTitle}}}\n{{{ [tag[docs]] |tooltip||TemplateTitle}}}\n{{{ [tag[docs]] }}width:40;height:50;}.class.class\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"filteredtranscludeinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\{\\{\\{([^\\|]+?)(?:\\|([^\\|\\{\\}]+))?(?:\\|\\|([^\\|\\{\\}]+))?\\}\\}([^\\}]*)\\}(?:\\.(\\S+))?/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Get the match details\n\tvar filter = this.match[1],\n\t\ttooltip = this.match[2],\n\t\ttemplate = $tw.utils.trim(this.match[3]),\n\t\tstyle = this.match[4],\n\t\tclasses = this.match[5];\n\t// Return the list widget\n\tvar node = {\n\t\ttype: \"list\",\n\t\tattributes: {\n\t\t\tfilter: {type: \"string\", value: filter}\n\t\t}\n\t};\n\tif(tooltip) {\n\t\tnode.attributes.tooltip = {type: \"string\", value: tooltip};\n\t}\n\tif(template) {\n\t\tnode.attributes.template = {type: \"string\", value: template};\n\t}\n\tif(style) {\n\t\tnode.attributes.style = {type: \"string\", value: style};\n\t}\n\tif(classes) {\n\t\tnode.attributes.itemClass = {type: \"string\", value: classes.split(\".\").join(\" \")};\n\t}\n\treturn [node];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/hardlinebreaks.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/hardlinebreaks.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/hardlinebreaks.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for marking areas with hard line breaks. For example:\n\n```\n\"\"\"\nThis is some text\nThat is set like\nIt is a Poem\nWhen it is\nClearly\nNot\n\"\"\"\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"hardlinebreaks\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\"\"\"(?:\\r?\\n)?/mg;\n};\n\nexports.parse = function() {\n\tvar reEnd = /(\"\"\")|(\\r?\\n)/mg,\n\t\ttree = [],\n\t\tmatch;\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tdo {\n\t\t// Parse the run up to the terminator\n\t\ttree.push.apply(tree,this.parser.parseInlineRun(reEnd,{eatTerminator: false}));\n\t\t// Redo the terminator match\n\t\treEnd.lastIndex = this.parser.pos;\n\t\tmatch = reEnd.exec(this.parser.source);\n\t\tif(match) {\n\t\t\tthis.parser.pos = reEnd.lastIndex;\n\t\t\t// Add a line break if the terminator was a line break\n\t\t\tif(match[2]) {\n\t\t\t\ttree.push({type: \"element\", tag: \"br\"});\n\t\t\t}\n\t\t}\n\t} while(match && !match[1]);\n\t// Return the nodes\n\treturn tree;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/heading.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/heading.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/heading.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for headings\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"heading\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /(!{1,6})/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get all the details of the match\n\tvar headingLevel = this.match[1].length;\n\t// Move past the !s\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse any classes, whitespace and then the heading itself\n\tvar classes = this.parser.parseClasses();\n\tthis.parser.skipWhitespace({treatNewlinesAsNonWhitespace: true});\n\tvar tree = this.parser.parseInlineRun(/(\\r?\\n)/mg);\n\t// Return the heading\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"h\" + headingLevel, \n\t\tattributes: {\n\t\t\t\"class\": {type: \"string\", value: classes.join(\" \")}\n\t\t},\n\t\tchildren: tree\n\t}];\n};\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/horizrule.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/horizrule.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/horizrule.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for rules. For example:\n\n```\n---\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"horizrule\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /-{3,}\\r?(?:\\n|$)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\treturn [{type: \"element\", tag: \"hr\"}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/html.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/html.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/html.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki rule for HTML elements and widgets. For example:\n\n{{{\n<aside>\nThis is an HTML5 aside element\n</aside>\n\n<$slider target=\"MyTiddler\">\nThis is a widget invocation\n</$slider>\n\n}}}\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"html\";\nexports.types = {inline: true, block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n};\n\nexports.findNextMatch = function(startPos) {\n\t// Find the next tag\n\tthis.nextTag = this.findNextTag(this.parser.source,startPos,{\n\t\trequireLineBreak: this.is.block\n\t});\n\treturn this.nextTag ? this.nextTag.start : undefined;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Retrieve the most recent match so that recursive calls don't overwrite it\n\tvar tag = this.nextTag;\n\tthis.nextTag = null;\n\t// Advance the parser position to past the tag\n\tthis.parser.pos = tag.end;\n\t// Check for an immediately following double linebreak\n\tvar hasLineBreak = !tag.isSelfClosing && !!$tw.utils.parseTokenRegExp(this.parser.source,this.parser.pos,/([^\\S\\n\\r]*\\r?\\n(?:[^\\S\\n\\r]*\\r?\\n|$))/g);\n\t// Set whether we're in block mode\n\ttag.isBlock = this.is.block || hasLineBreak;\n\t// Parse the body if we need to\n\tif(!tag.isSelfClosing && $tw.config.htmlVoidElements.indexOf(tag.tag) === -1) {\n\t\t\tvar reEndString = \"</\" + $tw.utils.escapeRegExp(tag.tag) + \">\",\n\t\t\t\treEnd = new RegExp(\"(\" + reEndString + \")\",\"mg\");\n\t\tif(hasLineBreak) {\n\t\t\ttag.children = this.parser.parseBlocks(reEndString);\n\t\t} else {\n\t\t\ttag.children = this.parser.parseInlineRun(reEnd);\n\t\t}\n\t\treEnd.lastIndex = this.parser.pos;\n\t\tvar endMatch = reEnd.exec(this.parser.source);\n\t\tif(endMatch && endMatch.index === this.parser.pos) {\n\t\t\tthis.parser.pos = endMatch.index + endMatch[0].length;\n\t\t}\n\t}\n\t// Return the tag\n\treturn [tag];\n};\n\n/*\nLook for an HTML tag. Returns null if not found, otherwise returns {type: \"element\", name:, attributes: [], isSelfClosing:, start:, end:,}\n*/\nexports.parseTag = function(source,pos,options) {\n\toptions = options || {};\n\tvar token,\n\t\tnode = {\n\t\t\ttype: \"element\",\n\t\t\tstart: pos,\n\t\t\tattributes: {}\n\t\t};\n\t// Define our regexps\n\tvar reTagName = /([a-zA-Z0-9\\-\\$]+)/g;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for a less than sign\n\ttoken = $tw.utils.parseTokenString(source,pos,\"<\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Get the tag name\n\ttoken = $tw.utils.parseTokenRegExp(source,pos,reTagName);\n\tif(!token) {\n\t\treturn null;\n\t}\n\tnode.tag = token.match[1];\n\tif(node.tag.slice(1).indexOf(\"$\") !== -1) {\n\t\treturn null;\n\t}\n\tif(node.tag.charAt(0) === \"$\") {\n\t\tnode.type = node.tag.substr(1);\n\t}\n\tpos = token.end;\n\t// Check that the tag is terminated by a space, / or >\n\tif(!$tw.utils.parseWhiteSpace(source,pos) && !(source.charAt(pos) === \"/\") && !(source.charAt(pos) === \">\") ) {\n\t\treturn null;\n\t}\n\t// Process attributes\n\tvar attribute = $tw.utils.parseAttribute(source,pos);\n\twhile(attribute) {\n\t\tnode.attributes[attribute.name] = attribute;\n\t\tpos = attribute.end;\n\t\t// Get the next attribute\n\t\tattribute = $tw.utils.parseAttribute(source,pos);\n\t}\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for a closing slash\n\ttoken = $tw.utils.parseTokenString(source,pos,\"/\");\n\tif(token) {\n\t\tpos = token.end;\n\t\tnode.isSelfClosing = true;\n\t}\n\t// Look for a greater than sign\n\ttoken = $tw.utils.parseTokenString(source,pos,\">\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Check for a required line break\n\tif(options.requireLineBreak) {\n\t\ttoken = $tw.utils.parseTokenRegExp(source,pos,/([^\\S\\n\\r]*\\r?\\n(?:[^\\S\\n\\r]*\\r?\\n|$))/g);\n\t\tif(!token) {\n\t\t\treturn null;\n\t\t}\n\t}\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\nexports.findNextTag = function(source,pos,options) {\n\t// A regexp for finding candidate HTML tags\n\tvar reLookahead = /<([a-zA-Z\\-\\$]+)/g;\n\t// Find the next candidate\n\treLookahead.lastIndex = pos;\n\tvar match = reLookahead.exec(source);\n\twhile(match) {\n\t\t// Try to parse the candidate as a tag\n\t\tvar tag = this.parseTag(source,match.index,options);\n\t\t// Return success\n\t\tif(tag && this.isLegalTag(tag)) {\n\t\t\treturn tag;\n\t\t}\n\t\t// Look for the next match\n\t\treLookahead.lastIndex = match.index + 1;\n\t\tmatch = reLookahead.exec(source);\n\t}\n\t// Failed\n\treturn null;\n};\n\nexports.isLegalTag = function(tag) {\n\t// Widgets are always OK\n\tif(tag.type !== \"element\") {\n\t\treturn true;\n\t// If it's an HTML tag that starts with a dash then it's not legal\n\t} else if(tag.tag.charAt(0) === \"-\") {\n\t\treturn false;\n\t} else {\n\t\t// Otherwise it's OK\n\t\treturn true;\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/image.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/image.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/image.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for embedding images. For example:\n\n```\n[img[https://tiddlywiki.com/fractalveg.jpg]]\n[img width=23 height=24 [https://tiddlywiki.com/fractalveg.jpg]]\n[img width={{!!width}} height={{!!height}} [https://tiddlywiki.com/fractalveg.jpg]]\n[img[Description of image|https://tiddlywiki.com/fractalveg.jpg]]\n[img[TiddlerTitle]]\n[img[Description of image|TiddlerTitle]]\n```\n\nGenerates the `<$image>` widget.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"image\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n};\n\nexports.findNextMatch = function(startPos) {\n\t// Find the next tag\n\tthis.nextImage = this.findNextImage(this.parser.source,startPos);\n\treturn this.nextImage ? this.nextImage.start : undefined;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.nextImage.end;\n\tvar node = {\n\t\ttype: \"image\",\n\t\tattributes: this.nextImage.attributes\n\t};\n\treturn [node];\n};\n\n/*\nFind the next image from the current position\n*/\nexports.findNextImage = function(source,pos) {\n\t// A regexp for finding candidate HTML tags\n\tvar reLookahead = /(\\[img)/g;\n\t// Find the next candidate\n\treLookahead.lastIndex = pos;\n\tvar match = reLookahead.exec(source);\n\twhile(match) {\n\t\t// Try to parse the candidate as a tag\n\t\tvar tag = this.parseImage(source,match.index);\n\t\t// Return success\n\t\tif(tag) {\n\t\t\treturn tag;\n\t\t}\n\t\t// Look for the next match\n\t\treLookahead.lastIndex = match.index + 1;\n\t\tmatch = reLookahead.exec(source);\n\t}\n\t// Failed\n\treturn null;\n};\n\n/*\nLook for an image at the specified position. Returns null if not found, otherwise returns {type: \"image\", attributes: [], isSelfClosing:, start:, end:,}\n*/\nexports.parseImage = function(source,pos) {\n\tvar token,\n\t\tnode = {\n\t\t\ttype: \"image\",\n\t\t\tstart: pos,\n\t\t\tattributes: {}\n\t\t};\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for the `[img`\n\ttoken = $tw.utils.parseTokenString(source,pos,\"[img\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Process attributes\n\tif(source.charAt(pos) !== \"[\") {\n\t\tvar attribute = $tw.utils.parseAttribute(source,pos);\n\t\twhile(attribute) {\n\t\t\tnode.attributes[attribute.name] = attribute;\n\t\t\tpos = attribute.end;\n\t\t\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t\t\tif(source.charAt(pos) !== \"[\") {\n\t\t\t\t// Get the next attribute\n\t\t\t\tattribute = $tw.utils.parseAttribute(source,pos);\n\t\t\t} else {\n\t\t\t\tattribute = null;\n\t\t\t}\n\t\t}\n\t}\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for the `[` after the attributes\n\ttoken = $tw.utils.parseTokenString(source,pos,\"[\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Get the source up to the terminating `]]`\n\ttoken = $tw.utils.parseTokenRegExp(source,pos,/(?:([^|\\]]*?)\\|)?([^\\]]+?)\\]\\]/g);\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\tif(token.match[1]) {\n\t\tnode.attributes.tooltip = {type: \"string\", value: token.match[1].trim()};\n\t}\n\tnode.attributes.source = {type: \"string\", value: (token.match[2] || \"\").trim()};\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/import.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/import.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/import.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki pragma rule for importing variable definitions\n\n```\n\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"import\";\nexports.types = {pragma: true};\n\n/*\nInstantiate parse rule\n*/\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /^\\\\import[^\\S\\n]/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\tvar self = this;\n\t// Move past the pragma invocation\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse the filter terminated by a line break\n\tvar reMatch = /(.*)(\\r?\\n)|$/mg;\n\treMatch.lastIndex = this.parser.pos;\n\tvar match = reMatch.exec(this.parser.source);\n\tthis.parser.pos = reMatch.lastIndex;\n\t// Parse tree nodes to return\n\treturn [{\n\t\ttype: \"importvariables\",\n\t\tattributes: {\n\t\t\tfilter: {type: \"string\", value: match[1]}\n\t\t},\n\t\tchildren: []\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/list.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/list.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/list.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for lists. For example:\n\n```\n* This is an unordered list\n* It has two items\n\n# This is a numbered list\n## With a subitem\n# And a third item\n\n; This is a term that is being defined\n: This is the definition of that term\n```\n\nNote that lists can be nested arbitrarily:\n\n```\n#** One\n#* Two\n#** Three\n#**** Four\n#**# Five\n#**## Six\n## Seven\n### Eight\n## Nine\n```\n\nA CSS class can be applied to a list item as follows:\n\n```\n* List item one\n*.active List item two has the class `active`\n* List item three\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"list\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /([\\*#;:>]+)/mg;\n};\n\nvar listTypes = {\n\t\"*\": {listTag: \"ul\", itemTag: \"li\"},\n\t\"#\": {listTag: \"ol\", itemTag: \"li\"},\n\t\";\": {listTag: \"dl\", itemTag: \"dt\"},\n\t\":\": {listTag: \"dl\", itemTag: \"dd\"},\n\t\">\": {listTag: \"blockquote\", itemTag: \"div\"}\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Array of parse tree nodes for the previous row of the list\n\tvar listStack = [];\n\t// Cycle through the items in the list\n\twhile(true) {\n\t\t// Match the list marker\n\t\tvar reMatch = /([\\*#;:>]+)/mg;\n\t\treMatch.lastIndex = this.parser.pos;\n\t\tvar match = reMatch.exec(this.parser.source);\n\t\tif(!match || match.index !== this.parser.pos) {\n\t\t\tbreak;\n\t\t}\n\t\t// Check whether the list type of the top level matches\n\t\tvar listInfo = listTypes[match[0].charAt(0)];\n\t\tif(listStack.length > 0 && listStack[0].tag !== listInfo.listTag) {\n\t\t\tbreak;\n\t\t}\n\t\t// Move past the list marker\n\t\tthis.parser.pos = match.index + match[0].length;\n\t\t// Walk through the list markers for the current row\n\t\tfor(var t=0; t<match[0].length; t++) {\n\t\t\tlistInfo = listTypes[match[0].charAt(t)];\n\t\t\t// Remove any stacked up element if we can't re-use it because the list type doesn't match\n\t\t\tif(listStack.length > t && listStack[t].tag !== listInfo.listTag) {\n\t\t\t\tlistStack.splice(t,listStack.length - t);\n\t\t\t}\n\t\t\t// Construct the list element or reuse the previous one at this level\n\t\t\tif(listStack.length <= t) {\n\t\t\t\tvar listElement = {type: \"element\", tag: listInfo.listTag, children: [\n\t\t\t\t\t{type: \"element\", tag: listInfo.itemTag, children: []}\n\t\t\t\t]};\n\t\t\t\t// Link this list element into the last child item of the parent list item\n\t\t\t\tif(t) {\n\t\t\t\t\tvar prevListItem = listStack[t-1].children[listStack[t-1].children.length-1];\n\t\t\t\t\tprevListItem.children.push(listElement);\n\t\t\t\t}\n\t\t\t\t// Save this element in the stack\n\t\t\t\tlistStack[t] = listElement;\n\t\t\t} else if(t === (match[0].length - 1)) {\n\t\t\t\tlistStack[t].children.push({type: \"element\", tag: listInfo.itemTag, children: []});\n\t\t\t}\n\t\t}\n\t\tif(listStack.length > match[0].length) {\n\t\t\tlistStack.splice(match[0].length,listStack.length - match[0].length);\n\t\t}\n\t\t// Process the body of the list item into the last list item\n\t\tvar lastListChildren = listStack[listStack.length-1].children,\n\t\t\tlastListItem = lastListChildren[lastListChildren.length-1],\n\t\t\tclasses = this.parser.parseClasses();\n\t\tthis.parser.skipWhitespace({treatNewlinesAsNonWhitespace: true});\n\t\tvar tree = this.parser.parseInlineRun(/(\\r?\\n)/mg);\n\t\tlastListItem.children.push.apply(lastListItem.children,tree);\n\t\tif(classes.length > 0) {\n\t\t\t$tw.utils.addClassToParseTreeNode(lastListItem,classes.join(\" \"));\n\t\t}\n\t\t// Consume any whitespace following the list item\n\t\tthis.parser.skipWhitespace();\n\t}\n\t// Return the root element of the list\n\treturn [listStack[0]];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/macrocallblock.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/macrocallblock.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/macrocallblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki rule for block macro calls\n\n```\n<<name value value2>>\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"macrocallblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /<<([^>\\s]+)(?:\\s*)((?:[^>]|(?:>(?!>)))*?)>>(?:\\r?\\n|$)/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get all the details of the match\n\tvar macroName = this.match[1],\n\t\tparamString = this.match[2];\n\t// Move past the macro call\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tvar params = [],\n\t\treParam = /\\s*(?:([A-Za-z0-9\\-_]+)\\s*:)?(?:\\s*(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\"|'([^']*)'|\\[\\[([^\\]]*)\\]\\]|([^\"'\\s]+)))/mg,\n\t\tparamMatch = reParam.exec(paramString);\n\twhile(paramMatch) {\n\t\t// Process this parameter\n\t\tvar paramInfo = {\n\t\t\tvalue: paramMatch[2] || paramMatch[3] || paramMatch[4] || paramMatch[5] || paramMatch[6]\n\t\t};\n\t\tif(paramMatch[1]) {\n\t\t\tparamInfo.name = paramMatch[1];\n\t\t}\n\t\tparams.push(paramInfo);\n\t\t// Find the next match\n\t\tparamMatch = reParam.exec(paramString);\n\t}\n\treturn [{\n\t\ttype: \"macrocall\",\n\t\tname: macroName,\n\t\tparams: params,\n\t\tisBlock: true\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/macrocallinline.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/macrocallinline.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/macrocallinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki rule for macro calls\n\n```\n<<name value value2>>\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"macrocallinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /<<([^\\s>]+)\\s*([\\s\\S]*?)>>/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get all the details of the match\n\tvar macroName = this.match[1],\n\t\tparamString = this.match[2];\n\t// Move past the macro call\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tvar params = [],\n\t\treParam = /\\s*(?:([A-Za-z0-9\\-_]+)\\s*:)?(?:\\s*(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\"|'([^']*)'|\\[\\[([^\\]]*)\\]\\]|([^\"'\\s]+)))/mg,\n\t\tparamMatch = reParam.exec(paramString);\n\twhile(paramMatch) {\n\t\t// Process this parameter\n\t\tvar paramInfo = {\n\t\t\tvalue: paramMatch[2] || paramMatch[3] || paramMatch[4] || paramMatch[5]|| paramMatch[6]\n\t\t};\n\t\tif(paramMatch[1]) {\n\t\t\tparamInfo.name = paramMatch[1];\n\t\t}\n\t\tparams.push(paramInfo);\n\t\t// Find the next match\n\t\tparamMatch = reParam.exec(paramString);\n\t}\n\treturn [{\n\t\ttype: \"macrocall\",\n\t\tname: macroName,\n\t\tparams: params\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/macrodef.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/macrodef.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/macrodef.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki pragma rule for macro definitions\n\n```\n\\define name(param:defaultvalue,param2:defaultvalue)\ndefinition text, including $param$ markers\n\\end\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"macrodef\";\nexports.types = {pragma: true};\n\n/*\nInstantiate parse rule\n*/\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /^\\\\define\\s+([^(\\s]+)\\(\\s*([^)]*)\\)(\\s*\\r?\\n)?/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Move past the macro name and parameters\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse the parameters\n\tvar paramString = this.match[2],\n\t\tparams = [];\n\tif(paramString !== \"\") {\n\t\tvar reParam = /\\s*([A-Za-z0-9\\-_]+)(?:\\s*:\\s*(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\"|'([^']*)'|\\[\\[([^\\]]*)\\]\\]|([^\"'\\s]+)))?/mg,\n\t\t\tparamMatch = reParam.exec(paramString);\n\t\twhile(paramMatch) {\n\t\t\t// Save the parameter details\n\t\t\tvar paramInfo = {name: paramMatch[1]},\n\t\t\t\tdefaultValue = paramMatch[2] || paramMatch[3] || paramMatch[4] || paramMatch[5] || paramMatch[6];\n\t\t\tif(defaultValue) {\n\t\t\t\tparamInfo[\"default\"] = defaultValue;\n\t\t\t}\n\t\t\tparams.push(paramInfo);\n\t\t\t// Look for the next parameter\n\t\t\tparamMatch = reParam.exec(paramString);\n\t\t}\n\t}\n\t// Is this a multiline definition?\n\tvar reEnd;\n\tif(this.match[3]) {\n\t\t// If so, the end of the body is marked with \\end\n\t\treEnd = /(\\r?\\n\\\\end[^\\S\\n\\r]*(?:$|\\r?\\n))/mg;\n\t} else {\n\t\t// Otherwise, the end of the definition is marked by the end of the line\n\t\treEnd = /($|\\r?\\n)/mg;\n\t\t// Move past any whitespace\n\t\tthis.parser.pos = $tw.utils.skipWhiteSpace(this.parser.source,this.parser.pos);\n\t}\n\t// Find the end of the definition\n\treEnd.lastIndex = this.parser.pos;\n\tvar text,\n\t\tendMatch = reEnd.exec(this.parser.source);\n\tif(endMatch) {\n\t\ttext = this.parser.source.substring(this.parser.pos,endMatch.index);\n\t\tthis.parser.pos = endMatch.index + endMatch[0].length;\n\t} else {\n\t\t// We didn't find the end of the definition, so we'll make it blank\n\t\ttext = \"\";\n\t}\n\t// Save the macro definition\n\treturn [{\n\t\ttype: \"set\",\n\t\tattributes: {\n\t\t\tname: {type: \"string\", value: this.match[1]},\n\t\t\tvalue: {type: \"string\", value: text}\n\t\t},\n\t\tchildren: [],\n\t\tparams: params,\n\t\tisMacroDefinition: true\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/prettyextlink.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/prettyextlink.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/prettyextlink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for external links. For example:\n\n```\n[ext[https://tiddlywiki.com/fractalveg.jpg]]\n[ext[Tooltip|https://tiddlywiki.com/fractalveg.jpg]]\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"prettyextlink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n};\n\nexports.findNextMatch = function(startPos) {\n\t// Find the next tag\n\tthis.nextLink = this.findNextLink(this.parser.source,startPos);\n\treturn this.nextLink ? this.nextLink.start : undefined;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.nextLink.end;\n\treturn [this.nextLink];\n};\n\n/*\nFind the next link from the current position\n*/\nexports.findNextLink = function(source,pos) {\n\t// A regexp for finding candidate links\n\tvar reLookahead = /(\\[ext\\[)/g;\n\t// Find the next candidate\n\treLookahead.lastIndex = pos;\n\tvar match = reLookahead.exec(source);\n\twhile(match) {\n\t\t// Try to parse the candidate as a link\n\t\tvar link = this.parseLink(source,match.index);\n\t\t// Return success\n\t\tif(link) {\n\t\t\treturn link;\n\t\t}\n\t\t// Look for the next match\n\t\treLookahead.lastIndex = match.index + 1;\n\t\tmatch = reLookahead.exec(source);\n\t}\n\t// Failed\n\treturn null;\n};\n\n/*\nLook for an link at the specified position. Returns null if not found, otherwise returns {type: \"element\", tag: \"a\", attributes: [], isSelfClosing:, start:, end:,}\n*/\nexports.parseLink = function(source,pos) {\n\tvar token,\n\t\ttextNode = {\n\t\t\ttype: \"text\"\n\t\t},\n\t\tnode = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"a\",\n\t\t\tstart: pos,\n\t\t\tattributes: {\n\t\t\t\t\"class\": {type: \"string\", value: \"tc-tiddlylink-external\"},\n\t\t\t},\n\t\t\tchildren: [textNode]\n\t\t};\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for the `[ext[`\n\ttoken = $tw.utils.parseTokenString(source,pos,\"[ext[\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Look ahead for the terminating `]]`\n\tvar closePos = source.indexOf(\"]]\",pos);\n\tif(closePos === -1) {\n\t\treturn null;\n\t}\n\t// Look for a `|` separating the tooltip\n\tvar splitPos = source.indexOf(\"|\",pos);\n\tif(splitPos === -1 || splitPos > closePos) {\n\t\tsplitPos = null;\n\t}\n\t// Pull out the tooltip and URL\n\tvar tooltip, URL;\n\tif(splitPos) {\n\t\tURL = source.substring(splitPos + 1,closePos).trim();\n\t\ttextNode.text = source.substring(pos,splitPos).trim();\n\t} else {\n\t\tURL = source.substring(pos,closePos).trim();\n\t\ttextNode.text = URL;\n\t}\n\tnode.attributes.href = {type: \"string\", value: URL};\n\tnode.attributes.target = {type: \"string\", value: \"_blank\"};\n\tnode.attributes.rel = {type: \"string\", value: \"noopener noreferrer\"};\n\t// Update the end position\n\tnode.end = closePos + 2;\n\treturn node;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/prettylink.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/prettylink.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/prettylink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for pretty links. For example:\n\n```\n[[Introduction]]\n\n[[Link description|TiddlerTitle]]\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"prettylink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\[\\[(.*?)(?:\\|(.*?))?\\]\\]/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Process the link\n\tvar text = this.match[1],\n\t\tlink = this.match[2] || text;\n\tif($tw.utils.isLinkExternal(link)) {\n\t\treturn [{\n\t\t\ttype: \"element\",\n\t\t\ttag: \"a\",\n\t\t\tattributes: {\n\t\t\t\thref: {type: \"string\", value: link},\n\t\t\t\t\"class\": {type: \"string\", value: \"tc-tiddlylink-external\"},\n\t\t\t\ttarget: {type: \"string\", value: \"_blank\"},\n\t\t\t\trel: {type: \"string\", value: \"noopener noreferrer\"}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\", text: text\n\t\t\t}]\n\t\t}];\n\t} else {\n\t\treturn [{\n\t\t\ttype: \"link\",\n\t\t\tattributes: {\n\t\t\t\tto: {type: \"string\", value: link}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\", text: text\n\t\t\t}]\n\t\t}];\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/quoteblock.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/quoteblock.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/quoteblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for quote blocks. For example:\n\n```\n\t<<<.optionalClass(es) optional cited from\n\ta quote\n\t<<<\n\t\n\t<<<.optionalClass(es)\n\ta quote\n\t<<< optional cited from\n```\n\nQuotes can be quoted by putting more <s\n\n```\n\t<<<\n\tQuote Level 1\n\t\n\t<<<<\n\tQuoteLevel 2\n\t<<<<\n\t\n\t<<<\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"quoteblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /(<<<+)/mg;\n};\n\nexports.parse = function() {\n\tvar classes = [\"tc-quote\"];\n\t// Get all the details of the match\n\tvar reEndString = \"^\" + this.match[1] + \"(?!<)\";\n\t// Move past the <s\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t\n\t// Parse any classes, whitespace and then the optional cite itself\n\tclasses.push.apply(classes, this.parser.parseClasses());\n\tthis.parser.skipWhitespace({treatNewlinesAsNonWhitespace: true});\n\tvar cite = this.parser.parseInlineRun(/(\\r?\\n)/mg);\n\t// before handling the cite, parse the body of the quote\n\tvar tree= this.parser.parseBlocks(reEndString);\n\t// If we got a cite, put it before the text\n\tif(cite.length > 0) {\n\t\ttree.unshift({\n\t\t\ttype: \"element\",\n\t\t\ttag: \"cite\",\n\t\t\tchildren: cite\n\t\t});\n\t}\n\t// Parse any optional cite\n\tthis.parser.skipWhitespace({treatNewlinesAsNonWhitespace: true});\n\tcite = this.parser.parseInlineRun(/(\\r?\\n)/mg);\n\t// If we got a cite, push it\n\tif(cite.length > 0) {\n\t\ttree.push({\n\t\t\ttype: \"element\",\n\t\t\ttag: \"cite\",\n\t\t\tchildren: cite\n\t\t});\n\t}\n\t// Return the blockquote element\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"blockquote\",\n\t\tattributes: {\n\t\t\tclass: { type: \"string\", value: classes.join(\" \") },\n\t\t},\n\t\tchildren: tree\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/rules.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/rules.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/rules.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki pragma rule for rules specifications\n\n```\n\\rules except ruleone ruletwo rulethree\n\\rules only ruleone ruletwo rulethree\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"rules\";\nexports.types = {pragma: true};\n\n/*\nInstantiate parse rule\n*/\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /^\\\\rules[^\\S\\n]/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Move past the pragma invocation\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse whitespace delimited tokens terminated by a line break\n\tvar reMatch = /[^\\S\\n]*(\\S+)|(\\r?\\n)/mg,\n\t\ttokens = [];\n\treMatch.lastIndex = this.parser.pos;\n\tvar match = reMatch.exec(this.parser.source);\n\twhile(match && match.index === this.parser.pos) {\n\t\tthis.parser.pos = reMatch.lastIndex;\n\t\t// Exit if we've got the line break\n\t\tif(match[2]) {\n\t\t\tbreak;\n\t\t}\n\t\t// Process the token\n\t\tif(match[1]) {\n\t\t\ttokens.push(match[1]);\n\t\t}\n\t\t// Match the next token\n\t\tmatch = reMatch.exec(this.parser.source);\n\t}\n\t// Process the tokens\n\tif(tokens.length > 0) {\n\t\tthis.parser.amendRules(tokens[0],tokens.slice(1));\n\t}\n\t// No parse tree nodes to return\n\treturn [];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/styleblock.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/styleblock.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/styleblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for assigning styles and classes to paragraphs and other blocks. For example:\n\n```\n@@.myClass\n@@background-color:red;\nThis paragraph will have the CSS class `myClass`.\n\n* The `<ul>` around this list will also have the class `myClass`\n* List item 2\n\n@@\n```\n\nNote that classes and styles can be mixed subject to the rule that styles must precede classes. For example\n\n```\n@@.myFirstClass.mySecondClass\n@@width:100px;.myThirdClass\nThis is a paragraph\n@@\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"styleblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /@@((?:[^\\.\\r\\n\\s:]+:[^\\r\\n;]+;)+)?(?:\\.([^\\r\\n\\s]+))?\\r?\\n/mg;\n};\n\nexports.parse = function() {\n\tvar reEndString = \"^@@(?:\\\\r?\\\\n)?\";\n\tvar classes = [], styles = [];\n\tdo {\n\t\t// Get the class and style\n\t\tif(this.match[1]) {\n\t\t\tstyles.push(this.match[1]);\n\t\t}\n\t\tif(this.match[2]) {\n\t\t\tclasses.push(this.match[2].split(\".\").join(\" \"));\n\t\t}\n\t\t// Move past the match\n\t\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t\t// Look for another line of classes and styles\n\t\tthis.match = this.matchRegExp.exec(this.parser.source);\n\t} while(this.match && this.match.index === this.parser.pos);\n\t// Parse the body\n\tvar tree = this.parser.parseBlocks(reEndString);\n\tfor(var t=0; t<tree.length; t++) {\n\t\tif(classes.length > 0) {\n\t\t\t$tw.utils.addClassToParseTreeNode(tree[t],classes.join(\" \"));\n\t\t}\n\t\tif(styles.length > 0) {\n\t\t\t$tw.utils.addAttributeToParseTreeNode(tree[t],\"style\",styles.join(\"\"));\n\t\t}\n\t}\n\treturn tree;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/styleinline.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/styleinline.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/styleinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for assigning styles and classes to inline runs. For example:\n\n```\n@@.myClass This is some text with a class@@\n@@background-color:red;This is some text with a background colour@@\n@@width:100px;.myClass This is some text with a class and a width@@\n```\n\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"styleinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /@@((?:[^\\.\\r\\n\\s:]+:[^\\r\\n;]+;)+)?(\\.(?:[^\\r\\n\\s]+)\\s+)?/mg;\n};\n\nexports.parse = function() {\n\tvar reEnd = /@@/g;\n\t// Get the styles and class\n\tvar stylesString = this.match[1],\n\t\tclassString = this.match[2] ? this.match[2].split(\".\").join(\" \") : undefined;\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse the run up to the terminator\n\tvar tree = this.parser.parseInlineRun(reEnd,{eatTerminator: true});\n\t// Return the classed span\n\tvar node = {\n\t\ttype: \"element\",\n\t\ttag: \"span\",\n\t\tattributes: {\n\t\t\t\"class\": {type: \"string\", value: \"tc-inline-style\"}\n\t\t},\n\t\tchildren: tree\n\t};\n\tif(classString) {\n\t\t$tw.utils.addClassToParseTreeNode(node,classString);\n\t}\n\tif(stylesString) {\n\t\t$tw.utils.addAttributeToParseTreeNode(node,\"style\",stylesString);\n\t}\n\treturn [node];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/syslink.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/syslink.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/syslink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for system tiddler links.\nCan be suppressed preceding them with `~`.\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"syslink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = new RegExp(\n\t\t\"~?\\\\$:\\\\/[\" +\n\t\t$tw.config.textPrimitives.anyLetter.substr(1,$tw.config.textPrimitives.anyLetter.length - 2) +\n\t\t\"\\/._-]+\",\n\t\t\"mg\"\n\t);\n};\n\nexports.parse = function() {\n\tvar match = this.match[0];\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Create the link unless it is suppressed\n\tif(match.substr(0,1) === \"~\") {\n\t\treturn [{type: \"text\", text: match.substr(1)}];\n\t} else {\n\t\treturn [{\n\t\t\ttype: \"link\",\n\t\t\tattributes: {\n\t\t\t\tto: {type: \"string\", value: match}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\",\n\t\t\t\ttext: match\n\t\t\t}]\n\t\t}];\n\t}\n};\n\n})();",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/table.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/table.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/table.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for tables.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"table\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /^\\|(?:[^\\n]*)\\|(?:[fhck]?)\\r?(?:\\n|$)/mg;\n};\n\nvar processRow = function(prevColumns) {\n\tvar cellRegExp = /(?:\\|([^\\n\\|]*)\\|)|(\\|[fhck]?\\r?(?:\\n|$))/mg,\n\t\tcellTermRegExp = /((?:\\x20*)\\|)/mg,\n\t\ttree = [],\n\t\tcol = 0,\n\t\tcolSpanCount = 1,\n\t\tprevCell,\n\t\tvAlign;\n\t// Match a single cell\n\tcellRegExp.lastIndex = this.parser.pos;\n\tvar cellMatch = cellRegExp.exec(this.parser.source);\n\twhile(cellMatch && cellMatch.index === this.parser.pos) {\n\t\tif(cellMatch[1] === \"~\") {\n\t\t\t// Rowspan\n\t\t\tvar last = prevColumns[col];\n\t\t\tif(last) {\n\t\t\t\tlast.rowSpanCount++;\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(last.element,\"rowspan\",last.rowSpanCount);\n\t\t\t\tvAlign = $tw.utils.getAttributeValueFromParseTreeNode(last.element,\"valign\",\"center\");\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(last.element,\"valign\",vAlign);\n\t\t\t\tif(colSpanCount > 1) {\n\t\t\t\t\t$tw.utils.addAttributeToParseTreeNode(last.element,\"colspan\",colSpanCount);\n\t\t\t\t\tcolSpanCount = 1;\n\t\t\t\t}\n\t\t\t}\n\t\t\t// Move to just before the `|` terminating the cell\n\t\t\tthis.parser.pos = cellRegExp.lastIndex - 1;\n\t\t} else if(cellMatch[1] === \">\") {\n\t\t\t// Colspan\n\t\t\tcolSpanCount++;\n\t\t\t// Move to just before the `|` terminating the cell\n\t\t\tthis.parser.pos = cellRegExp.lastIndex - 1;\n\t\t} else if(cellMatch[1] === \"<\" && prevCell) {\n\t\t\tcolSpanCount = 1 + $tw.utils.getAttributeValueFromParseTreeNode(prevCell,\"colspan\",1);\n\t\t\t$tw.utils.addAttributeToParseTreeNode(prevCell,\"colspan\",colSpanCount);\n\t\t\tcolSpanCount = 1;\n\t\t\t// Move to just before the `|` terminating the cell\n\t\t\tthis.parser.pos = cellRegExp.lastIndex - 1;\n\t\t} else if(cellMatch[2]) {\n\t\t\t// End of row\n\t\t\tif(prevCell && colSpanCount > 1) {\n\t\t\t\tif(prevCell.attributes && prevCell.attributes && prevCell.attributes.colspan) {\n\t\t\t\t\t\tcolSpanCount += prevCell.attributes.colspan.value;\n\t\t\t\t} else {\n\t\t\t\t\tcolSpanCount -= 1;\n\t\t\t\t}\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(prevCell,\"colspan\",colSpanCount);\n\t\t\t}\n\t\t\tthis.parser.pos = cellRegExp.lastIndex - 1;\n\t\t\tbreak;\n\t\t} else {\n\t\t\t// For ordinary cells, step beyond the opening `|`\n\t\t\tthis.parser.pos++;\n\t\t\t// Look for a space at the start of the cell\n\t\t\tvar spaceLeft = false;\n\t\t\tvAlign = null;\n\t\t\tif(this.parser.source.substr(this.parser.pos).search(/^\\^([^\\^]|\\^\\^)/) === 0) {\n\t\t\t\tvAlign = \"top\";\n\t\t\t} else if(this.parser.source.substr(this.parser.pos).search(/^,([^,]|,,)/) === 0) {\n\t\t\t\tvAlign = \"bottom\";\n\t\t\t}\n\t\t\tif(vAlign) {\n\t\t\t\tthis.parser.pos++;\n\t\t\t}\n\t\t\tvar chr = this.parser.source.substr(this.parser.pos,1);\n\t\t\twhile(chr === \" \") {\n\t\t\t\tspaceLeft = true;\n\t\t\t\tthis.parser.pos++;\n\t\t\t\tchr = this.parser.source.substr(this.parser.pos,1);\n\t\t\t}\n\t\t\t// Check whether this is a heading cell\n\t\t\tvar cell;\n\t\t\tif(chr === \"!\") {\n\t\t\t\tthis.parser.pos++;\n\t\t\t\tcell = {type: \"element\", tag: \"th\", children: []};\n\t\t\t} else {\n\t\t\t\tcell = {type: \"element\", tag: \"td\", children: []};\n\t\t\t}\n\t\t\ttree.push(cell);\n\t\t\t// Record information about this cell\n\t\t\tprevCell = cell;\n\t\t\tprevColumns[col] = {rowSpanCount:1,element:cell};\n\t\t\t// Check for a colspan\n\t\t\tif(colSpanCount > 1) {\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(cell,\"colspan\",colSpanCount);\n\t\t\t\tcolSpanCount = 1;\n\t\t\t}\n\t\t\t// Parse the cell\n\t\t\tcell.children = this.parser.parseInlineRun(cellTermRegExp,{eatTerminator: true});\n\t\t\t// Set the alignment for the cell\n\t\t\tif(vAlign) {\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(cell,\"valign\",vAlign);\n\t\t\t}\n\t\t\tif(this.parser.source.substr(this.parser.pos - 2,1) === \" \") { // spaceRight\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(cell,\"align\",spaceLeft ? \"center\" : \"left\");\n\t\t\t} else if(spaceLeft) {\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(cell,\"align\",\"right\");\n\t\t\t}\n\t\t\t// Move back to the closing `|`\n\t\t\tthis.parser.pos--;\n\t\t}\n\t\tcol++;\n\t\tcellRegExp.lastIndex = this.parser.pos;\n\t\tcellMatch = cellRegExp.exec(this.parser.source);\n\t}\n\treturn tree;\n};\n\nexports.parse = function() {\n\tvar rowContainerTypes = {\"c\":\"caption\", \"h\":\"thead\", \"\":\"tbody\", \"f\":\"tfoot\"},\n\t\ttable = {type: \"element\", tag: \"table\", children: []},\n\t\trowRegExp = /^\\|([^\\n]*)\\|([fhck]?)\\r?(?:\\n|$)/mg,\n\t\trowTermRegExp = /(\\|(?:[fhck]?)\\r?(?:\\n|$))/mg,\n\t\tprevColumns = [],\n\t\tcurrRowType,\n\t\trowContainer,\n\t\trowCount = 0;\n\t// Match the row\n\trowRegExp.lastIndex = this.parser.pos;\n\tvar rowMatch = rowRegExp.exec(this.parser.source);\n\twhile(rowMatch && rowMatch.index === this.parser.pos) {\n\t\tvar rowType = rowMatch[2];\n\t\t// Check if it is a class assignment\n\t\tif(rowType === \"k\") {\n\t\t\t$tw.utils.addClassToParseTreeNode(table,rowMatch[1]);\n\t\t\tthis.parser.pos = rowMatch.index + rowMatch[0].length;\n\t\t} else {\n\t\t\t// Otherwise, create a new row if this one is of a different type\n\t\t\tif(rowType !== currRowType) {\n\t\t\t\trowContainer = {type: \"element\", tag: rowContainerTypes[rowType], children: []};\n\t\t\t\ttable.children.push(rowContainer);\n\t\t\t\tcurrRowType = rowType;\n\t\t\t}\n\t\t\t// Is this a caption row?\n\t\t\tif(currRowType === \"c\") {\n\t\t\t\t// If so, move past the opening `|` of the row\n\t\t\t\tthis.parser.pos++;\n\t\t\t\t// Move the caption to the first row if it isn't already\n\t\t\t\tif(table.children.length !== 1) {\n\t\t\t\t\ttable.children.pop(); // Take rowContainer out of the children array\n\t\t\t\t\ttable.children.splice(0,0,rowContainer); // Insert it at the bottom\t\t\t\t\t\t\n\t\t\t\t}\n\t\t\t\t// Set the alignment - TODO: figure out why TW did this\n//\t\t\t\trowContainer.attributes.align = rowCount === 0 ? \"top\" : \"bottom\";\n\t\t\t\t// Parse the caption\n\t\t\t\trowContainer.children = this.parser.parseInlineRun(rowTermRegExp,{eatTerminator: true});\n\t\t\t} else {\n\t\t\t\t// Create the row\n\t\t\t\tvar theRow = {type: \"element\", tag: \"tr\", children: []};\n\t\t\t\t$tw.utils.addClassToParseTreeNode(theRow,rowCount%2 ? \"oddRow\" : \"evenRow\");\n\t\t\t\trowContainer.children.push(theRow);\n\t\t\t\t// Process the row\n\t\t\t\ttheRow.children = processRow.call(this,prevColumns);\n\t\t\t\tthis.parser.pos = rowMatch.index + rowMatch[0].length;\n\t\t\t\t// Increment the row count\n\t\t\t\trowCount++;\n\t\t\t}\n\t\t}\n\t\trowMatch = rowRegExp.exec(this.parser.source);\n\t}\n\treturn [table];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/transcludeblock.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/transcludeblock.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/transcludeblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for block-level transclusion. For example:\n\n```\n{{MyTiddler}}\n{{MyTiddler||TemplateTitle}}\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"transcludeblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\{\\{([^\\{\\}\\|]*)(?:\\|\\|([^\\|\\{\\}]+))?\\}\\}(?:\\r?\\n|$)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Get the match details\n\tvar template = $tw.utils.trim(this.match[2]),\n\t\ttextRef = $tw.utils.trim(this.match[1]);\n\t// Prepare the transclude widget\n\tvar transcludeNode = {\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {},\n\t\t\tisBlock: true\n\t\t};\n\t// Prepare the tiddler widget\n\tvar tr, targetTitle, targetField, targetIndex, tiddlerNode;\n\tif(textRef) {\n\t\ttr = $tw.utils.parseTextReference(textRef);\n\t\ttargetTitle = tr.title;\n\t\ttargetField = tr.field;\n\t\ttargetIndex = tr.index;\n\t\ttiddlerNode = {\n\t\t\ttype: \"tiddler\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {type: \"string\", value: targetTitle}\n\t\t\t},\n\t\t\tisBlock: true,\n\t\t\tchildren: [transcludeNode]\n\t\t};\n\t}\n\tif(template) {\n\t\ttranscludeNode.attributes.tiddler = {type: \"string\", value: template};\n\t\tif(textRef) {\n\t\t\treturn [tiddlerNode];\n\t\t} else {\n\t\t\treturn [transcludeNode];\n\t\t}\n\t} else {\n\t\tif(textRef) {\n\t\t\ttranscludeNode.attributes.tiddler = {type: \"string\", value: targetTitle};\n\t\t\tif(targetField) {\n\t\t\t\ttranscludeNode.attributes.field = {type: \"string\", value: targetField};\n\t\t\t}\n\t\t\tif(targetIndex) {\n\t\t\t\ttranscludeNode.attributes.index = {type: \"string\", value: targetIndex};\n\t\t\t}\n\t\t\treturn [tiddlerNode];\n\t\t} else {\n\t\t\treturn [transcludeNode];\n\t\t}\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/transcludeinline.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/transcludeinline.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/transcludeinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for inline-level transclusion. For example:\n\n```\n{{MyTiddler}}\n{{MyTiddler||TemplateTitle}}\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"transcludeinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\{\\{([^\\{\\}\\|]*)(?:\\|\\|([^\\|\\{\\}]+))?\\}\\}/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Get the match details\n\tvar template = $tw.utils.trim(this.match[2]),\n\t\ttextRef = $tw.utils.trim(this.match[1]);\n\t// Prepare the transclude widget\n\tvar transcludeNode = {\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {}\n\t\t};\n\t// Prepare the tiddler widget\n\tvar tr, targetTitle, targetField, targetIndex, tiddlerNode;\n\tif(textRef) {\n\t\ttr = $tw.utils.parseTextReference(textRef);\n\t\ttargetTitle = tr.title;\n\t\ttargetField = tr.field;\n\t\ttargetIndex = tr.index;\n\t\ttiddlerNode = {\n\t\t\ttype: \"tiddler\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {type: \"string\", value: targetTitle}\n\t\t\t},\n\t\t\tchildren: [transcludeNode]\n\t\t};\n\t}\n\tif(template) {\n\t\ttranscludeNode.attributes.tiddler = {type: \"string\", value: template};\n\t\tif(textRef) {\n\t\t\treturn [tiddlerNode];\n\t\t} else {\n\t\t\treturn [transcludeNode];\n\t\t}\n\t} else {\n\t\tif(textRef) {\n\t\t\ttranscludeNode.attributes.tiddler = {type: \"string\", value: targetTitle};\n\t\t\tif(targetField) {\n\t\t\t\ttranscludeNode.attributes.field = {type: \"string\", value: targetField};\n\t\t\t}\n\t\t\tif(targetIndex) {\n\t\t\t\ttranscludeNode.attributes.index = {type: \"string\", value: targetIndex};\n\t\t\t}\n\t\t\treturn [tiddlerNode];\n\t\t} else {\n\t\t\treturn [transcludeNode];\n\t\t}\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/typedblock.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/typedblock.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/typedblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for typed blocks. For example:\n\n```\n$$$.js\nThis will be rendered as JavaScript\n$$$\n\n$$$.svg\n<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"150\" height=\"100\">\n <circle cx=\"100\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"2\" fill=\"red\" />\n</svg>\n$$$\n\n$$$text/vnd.tiddlywiki>text/html\nThis will be rendered as an //HTML representation// of WikiText\n$$$\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.name = \"typedblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\$\\$\\$([^ >\\r\\n]*)(?: *> *([^ \\r\\n]+))?\\r?\\n/mg;\n};\n\nexports.parse = function() {\n\tvar reEnd = /\\r?\\n\\$\\$\\$\\r?(?:\\n|$)/mg;\n\t// Save the type\n\tvar parseType = this.match[1],\n\t\trenderType = this.match[2];\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Look for the end of the block\n\treEnd.lastIndex = this.parser.pos;\n\tvar match = reEnd.exec(this.parser.source),\n\t\ttext;\n\t// Process the block\n\tif(match) {\n\t\ttext = this.parser.source.substring(this.parser.pos,match.index);\n\t\tthis.parser.pos = match.index + match[0].length;\n\t} else {\n\t\ttext = this.parser.source.substr(this.parser.pos);\n\t\tthis.parser.pos = this.parser.sourceLength;\n\t}\n\t// Parse the block according to the specified type\n\tvar parser = this.parser.wiki.parseText(parseType,text,{defaultType: \"text/plain\"});\n\t// If there's no render type, just return the parse tree\n\tif(!renderType) {\n\t\treturn parser.tree;\n\t} else {\n\t\t// Otherwise, render to the rendertype and return in a <PRE> tag\n\t\tvar widgetNode = this.parser.wiki.makeWidget(parser),\n\t\t\tcontainer = $tw.fakeDocument.createElement(\"div\");\n\t\twidgetNode.render(container,null);\n\t\ttext = renderType === \"text/html\" ? container.innerHTML : container.textContent;\n\t\treturn [{\n\t\t\ttype: \"element\",\n\t\t\ttag: \"pre\",\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\",\n\t\t\t\ttext: text\n\t\t\t}]\n\t\t}];\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/whitespace.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/whitespace.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/whitespace.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki pragma rule for whitespace specifications\n\n```\n\\whitespace trim\n\\whitespace notrim\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"whitespace\";\nexports.types = {pragma: true};\n\n/*\nInstantiate parse rule\n*/\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /^\\\\whitespace[^\\S\\n]/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\tvar self = this;\n\t// Move past the pragma invocation\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse whitespace delimited tokens terminated by a line break\n\tvar reMatch = /[^\\S\\n]*(\\S+)|(\\r?\\n)/mg,\n\t\ttokens = [];\n\treMatch.lastIndex = this.parser.pos;\n\tvar match = reMatch.exec(this.parser.source);\n\twhile(match && match.index === this.parser.pos) {\n\t\tthis.parser.pos = reMatch.lastIndex;\n\t\t// Exit if we've got the line break\n\t\tif(match[2]) {\n\t\t\tbreak;\n\t\t}\n\t\t// Process the token\n\t\tif(match[1]) {\n\t\t\ttokens.push(match[1]);\n\t\t}\n\t\t// Match the next token\n\t\tmatch = reMatch.exec(this.parser.source);\n\t}\n\t// Process the tokens\n\t$tw.utils.each(tokens,function(token) {\n\t\tswitch(token) {\n\t\t\tcase \"trim\":\n\t\t\t\tself.parser.configTrimWhiteSpace = true;\n\t\t\t\tbreak;\n\t\t\tcase \"notrim\":\n\t\t\t\tself.parser.configTrimWhiteSpace = false;\n\t\t\t\tbreak;\n\t\t}\n\t});\n\t// No parse tree nodes to return\n\treturn [];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/wikilink.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/wikilink.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/wikilink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for wiki links. For example:\n\n```\nAWikiLink\nAnotherLink\n~SuppressedLink\n```\n\nPrecede a camel case word with `~` to prevent it from being recognised as a link.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"wikilink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = new RegExp($tw.config.textPrimitives.unWikiLink + \"?\" + $tw.config.textPrimitives.wikiLink,\"mg\");\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get the details of the match\n\tvar linkText = this.match[0];\n\t// Move past the macro call\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// If the link starts with the unwikilink character then just output it as plain text\n\tif(linkText.substr(0,1) === $tw.config.textPrimitives.unWikiLink) {\n\t\treturn [{type: \"text\", text: linkText.substr(1)}];\n\t}\n\t// If the link has been preceded with a blocked letter then don't treat it as a link\n\tif(this.match.index > 0) {\n\t\tvar preRegExp = new RegExp($tw.config.textPrimitives.blockPrefixLetters,\"mg\");\n\t\tpreRegExp.lastIndex = this.match.index-1;\n\t\tvar preMatch = preRegExp.exec(this.parser.source);\n\t\tif(preMatch && preMatch.index === this.match.index-1) {\n\t\t\treturn [{type: \"text\", text: linkText}];\n\t\t}\n\t}\n\treturn [{\n\t\ttype: \"link\",\n\t\tattributes: {\n\t\t\tto: {type: \"string\", value: linkText}\n\t\t},\n\t\tchildren: [{\n\t\t\ttype: \"text\",\n\t\t\ttext: linkText\n\t\t}]\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/wikiparser.js": {
"title": "$:/core/modules/parsers/wikiparser/wikiparser.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/wikiparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe wiki text parser processes blocks of source text into a parse tree.\n\nThe parse tree is made up of nested arrays of these JavaScript objects:\n\n\t{type: \"element\", tag: <string>, attributes: {}, children: []} - an HTML element\n\t{type: \"text\", text: <string>} - a text node\n\t{type: \"entity\", value: <string>} - an entity\n\t{type: \"raw\", html: <string>} - raw HTML\n\nAttributes are stored as hashmaps of the following objects:\n\n\t{type: \"string\", value: <string>} - literal string\n\t{type: \"indirect\", textReference: <textReference>} - indirect through a text reference\n\t{type: \"macro\", macro: <TBD>} - indirect through a macro invocation\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar WikiParser = function(type,text,options) {\n\tthis.wiki = options.wiki;\n\tvar self = this;\n\t// Check for an externally linked tiddler\n\tif($tw.browser && (text || \"\") === \"\" && options._canonical_uri) {\n\t\tthis.loadRemoteTiddler(options._canonical_uri);\n\t\ttext = $tw.language.getRawString(\"LazyLoadingWarning\");\n\t}\n\t// Initialise the classes if we don't have them already\n\tif(!this.pragmaRuleClasses) {\n\t\tWikiParser.prototype.pragmaRuleClasses = $tw.modules.createClassesFromModules(\"wikirule\",\"pragma\",$tw.WikiRuleBase);\n\t\tthis.setupRules(WikiParser.prototype.pragmaRuleClasses,\"$:/config/WikiParserRules/Pragmas/\");\n\t}\n\tif(!this.blockRuleClasses) {\n\t\tWikiParser.prototype.blockRuleClasses = $tw.modules.createClassesFromModules(\"wikirule\",\"block\",$tw.WikiRuleBase);\n\t\tthis.setupRules(WikiParser.prototype.blockRuleClasses,\"$:/config/WikiParserRules/Block/\");\n\t}\n\tif(!this.inlineRuleClasses) {\n\t\tWikiParser.prototype.inlineRuleClasses = $tw.modules.createClassesFromModules(\"wikirule\",\"inline\",$tw.WikiRuleBase);\n\t\tthis.setupRules(WikiParser.prototype.inlineRuleClasses,\"$:/config/WikiParserRules/Inline/\");\n\t}\n\t// Save the parse text\n\tthis.type = type || \"text/vnd.tiddlywiki\";\n\tthis.source = text || \"\";\n\tthis.sourceLength = this.source.length;\n\t// Flag for ignoring whitespace\n\tthis.configTrimWhiteSpace = false;\n\t// Set current parse position\n\tthis.pos = 0;\n\t// Instantiate the pragma parse rules\n\tthis.pragmaRules = this.instantiateRules(this.pragmaRuleClasses,\"pragma\",0);\n\t// Instantiate the parser block and inline rules\n\tthis.blockRules = this.instantiateRules(this.blockRuleClasses,\"block\",0);\n\tthis.inlineRules = this.instantiateRules(this.inlineRuleClasses,\"inline\",0);\n\t// Parse any pragmas\n\tthis.tree = [];\n\tvar topBranch = this.parsePragmas();\n\t// Parse the text into inline runs or blocks\n\tif(options.parseAsInline) {\n\t\ttopBranch.push.apply(topBranch,this.parseInlineRun());\n\t} else {\n\t\ttopBranch.push.apply(topBranch,this.parseBlocks());\n\t}\n\t// Return the parse tree\n};\n\n/*\n*/\nWikiParser.prototype.loadRemoteTiddler = function(url) {\n\tvar self = this;\n\t$tw.utils.httpRequest({\n\t\turl: url,\n\t\ttype: \"GET\",\n\t\tcallback: function(err,data) {\n\t\t\tif(!err) {\n\t\t\t\tvar tiddlers = self.wiki.deserializeTiddlers(\".tid\",data,self.wiki.getCreationFields());\n\t\t\t\t$tw.utils.each(tiddlers,function(tiddler) {\n\t\t\t\t\ttiddler[\"_canonical_uri\"] = url;\n\t\t\t\t});\n\t\t\t\tif(tiddlers) {\n\t\t\t\t\tself.wiki.addTiddlers(tiddlers);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n};\n\n/*\n*/\nWikiParser.prototype.setupRules = function(proto,configPrefix) {\n\tvar self = this;\n\tif(!$tw.safemode) {\n\t\t$tw.utils.each(proto,function(object,name) {\n\t\t\tif(self.wiki.getTiddlerText(configPrefix + name,\"enable\") !== \"enable\") {\n\t\t\t\tdelete proto[name];\n\t\t\t}\n\t\t});\n\t}\n};\n\n/*\nInstantiate an array of parse rules\n*/\nWikiParser.prototype.instantiateRules = function(classes,type,startPos) {\n\tvar rulesInfo = [],\n\t\tself = this;\n\t$tw.utils.each(classes,function(RuleClass) {\n\t\t// Instantiate the rule\n\t\tvar rule = new RuleClass(self);\n\t\trule.is = {};\n\t\trule.is[type] = true;\n\t\trule.init(self);\n\t\tvar matchIndex = rule.findNextMatch(startPos);\n\t\tif(matchIndex !== undefined) {\n\t\t\trulesInfo.push({\n\t\t\t\trule: rule,\n\t\t\t\tmatchIndex: matchIndex\n\t\t\t});\n\t\t}\n\t});\n\treturn rulesInfo;\n};\n\n/*\nSkip any whitespace at the current position. Options are:\n\ttreatNewlinesAsNonWhitespace: true if newlines are NOT to be treated as whitespace\n*/\nWikiParser.prototype.skipWhitespace = function(options) {\n\toptions = options || {};\n\tvar whitespaceRegExp = options.treatNewlinesAsNonWhitespace ? /([^\\S\\n]+)/mg : /(\\s+)/mg;\n\twhitespaceRegExp.lastIndex = this.pos;\n\tvar whitespaceMatch = whitespaceRegExp.exec(this.source);\n\tif(whitespaceMatch && whitespaceMatch.index === this.pos) {\n\t\tthis.pos = whitespaceRegExp.lastIndex;\n\t}\n};\n\n/*\nGet the next match out of an array of parse rule instances\n*/\nWikiParser.prototype.findNextMatch = function(rules,startPos) {\n\t// Find the best matching rule by finding the closest match position\n\tvar matchingRule,\n\t\tmatchingRulePos = this.sourceLength;\n\t// Step through each rule\n\tfor(var t=0; t<rules.length; t++) {\n\t\tvar ruleInfo = rules[t];\n\t\t// Ask the rule to get the next match if we've moved past the current one\n\t\tif(ruleInfo.matchIndex !== undefined && ruleInfo.matchIndex < startPos) {\n\t\t\truleInfo.matchIndex = ruleInfo.rule.findNextMatch(startPos);\n\t\t}\n\t\t// Adopt this match if it's closer than the current best match\n\t\tif(ruleInfo.matchIndex !== undefined && ruleInfo.matchIndex <= matchingRulePos) {\n\t\t\tmatchingRule = ruleInfo;\n\t\t\tmatchingRulePos = ruleInfo.matchIndex;\n\t\t}\n\t}\n\treturn matchingRule;\n};\n\n/*\nParse any pragmas at the beginning of a block of parse text\n*/\nWikiParser.prototype.parsePragmas = function() {\n\tvar currentTreeBranch = this.tree;\n\twhile(true) {\n\t\t// Skip whitespace\n\t\tthis.skipWhitespace();\n\t\t// Check for the end of the text\n\t\tif(this.pos >= this.sourceLength) {\n\t\t\tbreak;\n\t\t}\n\t\t// Check if we've arrived at a pragma rule match\n\t\tvar nextMatch = this.findNextMatch(this.pragmaRules,this.pos);\n\t\t// If not, just exit\n\t\tif(!nextMatch || nextMatch.matchIndex !== this.pos) {\n\t\t\tbreak;\n\t\t}\n\t\t// Process the pragma rule\n\t\tvar subTree = nextMatch.rule.parse();\n\t\tif(subTree.length > 0) {\n\t\t\t// Quick hack; we only cope with a single parse tree node being returned, which is true at the moment\n\t\t\tcurrentTreeBranch.push.apply(currentTreeBranch,subTree);\n\t\t\tsubTree[0].children = [];\n\t\t\tcurrentTreeBranch = subTree[0].children;\n\t\t}\n\t}\n\treturn currentTreeBranch;\n};\n\n/*\nParse a block from the current position\n\tterminatorRegExpString: optional regular expression string that identifies the end of plain paragraphs. Must not include capturing parenthesis\n*/\nWikiParser.prototype.parseBlock = function(terminatorRegExpString) {\n\tvar terminatorRegExp = terminatorRegExpString ? new RegExp(\"(\" + terminatorRegExpString + \"|\\\\r?\\\\n\\\\r?\\\\n)\",\"mg\") : /(\\r?\\n\\r?\\n)/mg;\n\tthis.skipWhitespace();\n\tif(this.pos >= this.sourceLength) {\n\t\treturn [];\n\t}\n\t// Look for a block rule that applies at the current position\n\tvar nextMatch = this.findNextMatch(this.blockRules,this.pos);\n\tif(nextMatch && nextMatch.matchIndex === this.pos) {\n\t\treturn nextMatch.rule.parse();\n\t}\n\t// Treat it as a paragraph if we didn't find a block rule\n\treturn [{type: \"element\", tag: \"p\", children: this.parseInlineRun(terminatorRegExp)}];\n};\n\n/*\nParse a series of blocks of text until a terminating regexp is encountered or the end of the text\n\tterminatorRegExpString: terminating regular expression\n*/\nWikiParser.prototype.parseBlocks = function(terminatorRegExpString) {\n\tif(terminatorRegExpString) {\n\t\treturn this.parseBlocksTerminated(terminatorRegExpString);\n\t} else {\n\t\treturn this.parseBlocksUnterminated();\n\t}\n};\n\n/*\nParse a block from the current position to the end of the text\n*/\nWikiParser.prototype.parseBlocksUnterminated = function() {\n\tvar tree = [];\n\twhile(this.pos < this.sourceLength) {\n\t\ttree.push.apply(tree,this.parseBlock());\n\t}\n\treturn tree;\n};\n\n/*\nParse blocks of text until a terminating regexp is encountered\n*/\nWikiParser.prototype.parseBlocksTerminated = function(terminatorRegExpString) {\n\tvar terminatorRegExp = new RegExp(\"(\" + terminatorRegExpString + \")\",\"mg\"),\n\t\ttree = [];\n\t// Skip any whitespace\n\tthis.skipWhitespace();\n\t// Check if we've got the end marker\n\tterminatorRegExp.lastIndex = this.pos;\n\tvar match = terminatorRegExp.exec(this.source);\n\t// Parse the text into blocks\n\twhile(this.pos < this.sourceLength && !(match && match.index === this.pos)) {\n\t\tvar blocks = this.parseBlock(terminatorRegExpString);\n\t\ttree.push.apply(tree,blocks);\n\t\t// Skip any whitespace\n\t\tthis.skipWhitespace();\n\t\t// Check if we've got the end marker\n\t\tterminatorRegExp.lastIndex = this.pos;\n\t\tmatch = terminatorRegExp.exec(this.source);\n\t}\n\tif(match && match.index === this.pos) {\n\t\tthis.pos = match.index + match[0].length;\n\t}\n\treturn tree;\n};\n\n/*\nParse a run of text at the current position\n\tterminatorRegExp: a regexp at which to stop the run\n\toptions: see below\nOptions available:\n\teatTerminator: move the parse position past any encountered terminator (default false)\n*/\nWikiParser.prototype.parseInlineRun = function(terminatorRegExp,options) {\n\tif(terminatorRegExp) {\n\t\treturn this.parseInlineRunTerminated(terminatorRegExp,options);\n\t} else {\n\t\treturn this.parseInlineRunUnterminated(options);\n\t}\n};\n\nWikiParser.prototype.parseInlineRunUnterminated = function(options) {\n\tvar tree = [];\n\t// Find the next occurrence of an inline rule\n\tvar nextMatch = this.findNextMatch(this.inlineRules,this.pos);\n\t// Loop around the matches until we've reached the end of the text\n\twhile(this.pos < this.sourceLength && nextMatch) {\n\t\t// Process the text preceding the run rule\n\t\tif(nextMatch.matchIndex > this.pos) {\n\t\t\tthis.pushTextWidget(tree,this.source.substring(this.pos,nextMatch.matchIndex));\n\t\t\tthis.pos = nextMatch.matchIndex;\n\t\t}\n\t\t// Process the run rule\n\t\ttree.push.apply(tree,nextMatch.rule.parse());\n\t\t// Look for the next run rule\n\t\tnextMatch = this.findNextMatch(this.inlineRules,this.pos);\n\t}\n\t// Process the remaining text\n\tif(this.pos < this.sourceLength) {\n\t\tthis.pushTextWidget(tree,this.source.substr(this.pos));\n\t}\n\tthis.pos = this.sourceLength;\n\treturn tree;\n};\n\nWikiParser.prototype.parseInlineRunTerminated = function(terminatorRegExp,options) {\n\toptions = options || {};\n\tvar tree = [];\n\t// Find the next occurrence of the terminator\n\tterminatorRegExp.lastIndex = this.pos;\n\tvar terminatorMatch = terminatorRegExp.exec(this.source);\n\t// Find the next occurrence of a inlinerule\n\tvar inlineRuleMatch = this.findNextMatch(this.inlineRules,this.pos);\n\t// Loop around until we've reached the end of the text\n\twhile(this.pos < this.sourceLength && (terminatorMatch || inlineRuleMatch)) {\n\t\t// Return if we've found the terminator, and it precedes any inline rule match\n\t\tif(terminatorMatch) {\n\t\t\tif(!inlineRuleMatch || inlineRuleMatch.matchIndex >= terminatorMatch.index) {\n\t\t\t\tif(terminatorMatch.index > this.pos) {\n\t\t\t\t\tthis.pushTextWidget(tree,this.source.substring(this.pos,terminatorMatch.index));\n\t\t\t\t}\n\t\t\t\tthis.pos = terminatorMatch.index;\n\t\t\t\tif(options.eatTerminator) {\n\t\t\t\t\tthis.pos += terminatorMatch[0].length;\n\t\t\t\t}\n\t\t\t\treturn tree;\n\t\t\t}\n\t\t}\n\t\t// Process any inline rule, along with the text preceding it\n\t\tif(inlineRuleMatch) {\n\t\t\t// Preceding text\n\t\t\tif(inlineRuleMatch.matchIndex > this.pos) {\n\t\t\t\tthis.pushTextWidget(tree,this.source.substring(this.pos,inlineRuleMatch.matchIndex));\n\t\t\t\tthis.pos = inlineRuleMatch.matchIndex;\n\t\t\t}\n\t\t\t// Process the inline rule\n\t\t\ttree.push.apply(tree,inlineRuleMatch.rule.parse());\n\t\t\t// Look for the next inline rule\n\t\t\tinlineRuleMatch = this.findNextMatch(this.inlineRules,this.pos);\n\t\t\t// Look for the next terminator match\n\t\t\tterminatorRegExp.lastIndex = this.pos;\n\t\t\tterminatorMatch = terminatorRegExp.exec(this.source);\n\t\t}\n\t}\n\t// Process the remaining text\n\tif(this.pos < this.sourceLength) {\n\t\tthis.pushTextWidget(tree,this.source.substr(this.pos));\n\t}\n\tthis.pos = this.sourceLength;\n\treturn tree;\n};\n\n/*\nPush a text widget onto an array, respecting the configTrimWhiteSpace setting\n*/\nWikiParser.prototype.pushTextWidget = function(array,text) {\n\tif(this.configTrimWhiteSpace) {\n\t\ttext = $tw.utils.trim(text);\n\t}\n\tif(text) {\n\t\tarray.push({type: \"text\", text: text});\t\t\n\t}\n};\n\n/*\nParse zero or more class specifiers `.classname`\n*/\nWikiParser.prototype.parseClasses = function() {\n\tvar classRegExp = /\\.([^\\s\\.]+)/mg,\n\t\tclassNames = [];\n\tclassRegExp.lastIndex = this.pos;\n\tvar match = classRegExp.exec(this.source);\n\twhile(match && match.index === this.pos) {\n\t\tthis.pos = match.index + match[0].length;\n\t\tclassNames.push(match[1]);\n\t\tmatch = classRegExp.exec(this.source);\n\t}\n\treturn classNames;\n};\n\n/*\nAmend the rules used by this instance of the parser\n\ttype: `only` keeps just the named rules, `except` keeps all but the named rules\n\tnames: array of rule names\n*/\nWikiParser.prototype.amendRules = function(type,names) {\n\tnames = names || [];\n\t// Define the filter function\n\tvar keepFilter;\n\tif(type === \"only\") {\n\t\tkeepFilter = function(name) {\n\t\t\treturn names.indexOf(name) !== -1;\n\t\t};\n\t} else if(type === \"except\") {\n\t\tkeepFilter = function(name) {\n\t\t\treturn names.indexOf(name) === -1;\n\t\t};\n\t} else {\n\t\treturn;\n\t}\n\t// Define a function to process each of our rule arrays\n\tvar processRuleArray = function(ruleArray) {\n\t\tfor(var t=ruleArray.length-1; t>=0; t--) {\n\t\t\tif(!keepFilter(ruleArray[t].rule.name)) {\n\t\t\t\truleArray.splice(t,1);\n\t\t\t}\n\t\t}\n\t};\n\t// Process each rule array\n\tprocessRuleArray(this.pragmaRules);\n\tprocessRuleArray(this.blockRules);\n\tprocessRuleArray(this.inlineRules);\n};\n\nexports[\"text/vnd.tiddlywiki\"] = WikiParser;\n\n})();\n\n",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/wikiparser/rules/wikirulebase.js": {
"title": "$:/core/modules/parsers/wikiparser/rules/wikirulebase.js",
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/wikirulebase.js\ntype: application/javascript\nmodule-type: global\n\nBase class for wiki parser rules\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nThis constructor is always overridden with a blank constructor, and so shouldn't be used\n*/\nvar WikiRuleBase = function() {\n};\n\n/*\nTo be overridden by individual rules\n*/\nWikiRuleBase.prototype.init = function(parser) {\n\tthis.parser = parser;\n};\n\n/*\nDefault implementation of findNextMatch uses RegExp matching\n*/\nWikiRuleBase.prototype.findNextMatch = function(startPos) {\n\tthis.matchRegExp.lastIndex = startPos;\n\tthis.match = this.matchRegExp.exec(this.parser.source);\n\treturn this.match ? this.match.index : undefined;\n};\n\nexports.WikiRuleBase = WikiRuleBase;\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/pluginswitcher.js": {
"title": "$:/core/modules/pluginswitcher.js",
"text": "/*\\\ntitle: $:/core/modules/pluginswitcher.js\ntype: application/javascript\nmodule-type: global\n\nManages switching plugins for themes and languages.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\noptions:\nwiki: wiki store to be used\npluginType: type of plugin to be switched\ncontrollerTitle: title of tiddler used to control switching of this resource\ndefaultPlugins: array of default plugins to be used if nominated plugin isn't found\nonSwitch: callback when plugin is switched (single parameter is array of plugin titles)\n*/\nfunction PluginSwitcher(options) {\n\tthis.wiki = options.wiki;\n\tthis.pluginType = options.pluginType;\n\tthis.controllerTitle = options.controllerTitle;\n\tthis.defaultPlugins = options.defaultPlugins || [];\n\tthis.onSwitch = options.onSwitch;\n\t// Switch to the current plugin\n\tthis.switchPlugins();\n\t// Listen for changes to the selected plugin\n\tvar self = this;\n\tthis.wiki.addEventListener(\"change\",function(changes) {\n\t\tif($tw.utils.hop(changes,self.controllerTitle)) {\n\t\t\tself.switchPlugins();\n\t\t}\n\t});\n}\n\nPluginSwitcher.prototype.switchPlugins = function() {\n\t// Get the name of the current theme\n\tvar selectedPluginTitle = this.wiki.getTiddlerText(this.controllerTitle);\n\t// If it doesn't exist, then fallback to one of the default themes\n\tvar index = 0;\n\twhile(!this.wiki.getTiddler(selectedPluginTitle) && index < this.defaultPlugins.length) {\n\t\tselectedPluginTitle = this.defaultPlugins[index++];\n\t}\n\t// Accumulate the titles of the plugins that we need to load\n\tvar plugins = [],\n\t\tself = this,\n\t\taccumulatePlugin = function(title) {\n\t\t\tvar tiddler = self.wiki.getTiddler(title);\n\t\t\tif(tiddler && tiddler.isPlugin() && plugins.indexOf(title) === -1) {\n\t\t\t\tplugins.push(title);\n\t\t\t\tvar pluginInfo = JSON.parse(self.wiki.getTiddlerText(title)),\n\t\t\t\t\tdependents = $tw.utils.parseStringArray(tiddler.fields.dependents || \"\");\n\t\t\t\t$tw.utils.each(dependents,function(title) {\n\t\t\t\t\taccumulatePlugin(title);\n\t\t\t\t});\n\t\t\t}\n\t\t};\n\taccumulatePlugin(selectedPluginTitle);\n\t// Read the plugin info for the incoming plugins\n\tvar changes = $tw.wiki.readPluginInfo(plugins);\n\t// Unregister any existing theme tiddlers\n\tvar unregisteredTiddlers = $tw.wiki.unregisterPluginTiddlers(this.pluginType);\n\t// Register any new theme tiddlers\n\tvar registeredTiddlers = $tw.wiki.registerPluginTiddlers(this.pluginType,plugins);\n\t// Unpack the current theme tiddlers\n\t$tw.wiki.unpackPluginTiddlers();\n\t// Call the switch handler\n\tif(this.onSwitch) {\n\t\tthis.onSwitch(plugins);\n\t}\n};\n\nexports.PluginSwitcher = PluginSwitcher;\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/saver-handler.js": {
"title": "$:/core/modules/saver-handler.js",
"text": "/*\\\ntitle: $:/core/modules/saver-handler.js\ntype: application/javascript\nmodule-type: global\n\nThe saver handler tracks changes to the store and handles saving the entire wiki via saver modules.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInstantiate the saver handler with the following options:\nwiki: wiki to be synced\ndirtyTracking: true if dirty tracking should be performed\n*/\nfunction SaverHandler(options) {\n\tvar self = this;\n\tthis.wiki = options.wiki;\n\tthis.dirtyTracking = options.dirtyTracking;\n\tthis.preloadDirty = options.preloadDirty || [];\n\tthis.pendingAutoSave = false;\n\t// Make a logger\n\tthis.logger = new $tw.utils.Logger(\"saver-handler\");\n\t// Initialise our savers\n\tif($tw.browser) {\n\t\tthis.initSavers();\n\t}\n\t// Only do dirty tracking if required\n\tif($tw.browser && this.dirtyTracking) {\n\t\t// Compile the dirty tiddler filter\n\t\tthis.filterFn = this.wiki.compileFilter(this.wiki.getTiddlerText(this.titleSyncFilter));\n\t\t// Count of changes that have not yet been saved\n\t\tvar filteredChanges = self.filterFn.call(self.wiki,function(iterator) {\n\t\t\t\t$tw.utils.each(self.preloadDirty,function(title) {\n\t\t\t\t\tvar tiddler = self.wiki.getTiddler(title);\n\t\t\t\t\titerator(tiddler,title);\n\t\t\t\t});\n\t\t});\n\t\tthis.numChanges = filteredChanges.length;\n\t\t// Listen out for changes to tiddlers\n\t\tthis.wiki.addEventListener(\"change\",function(changes) {\n\t\t\t// Filter the changes so that we only count changes to tiddlers that we care about\n\t\t\tvar filteredChanges = self.filterFn.call(self.wiki,function(iterator) {\n\t\t\t\t$tw.utils.each(changes,function(change,title) {\n\t\t\t\t\tvar tiddler = self.wiki.getTiddler(title);\n\t\t\t\t\titerator(tiddler,title);\n\t\t\t\t});\n\t\t\t});\n\t\t\t// Adjust the number of changes\n\t\t\tself.numChanges += filteredChanges.length;\n\t\t\tself.updateDirtyStatus();\n\t\t\t// Do any autosave if one is pending and there's no more change events\n\t\t\tif(self.pendingAutoSave && self.wiki.getSizeOfTiddlerEventQueue() === 0) {\n\t\t\t\t// Check if we're dirty\n\t\t\t\tif(self.numChanges > 0) {\n\t\t\t\t\tself.saveWiki({\n\t\t\t\t\t\tmethod: \"autosave\",\n\t\t\t\t\t\tdownloadType: \"text/plain\"\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t\tself.pendingAutoSave = false;\n\t\t\t}\n\t\t});\n\t\t// Listen for the autosave event\n\t\t$tw.rootWidget.addEventListener(\"tm-auto-save-wiki\",function(event) {\n\t\t\t// Do the autosave unless there are outstanding tiddler change events\n\t\t\tif(self.wiki.getSizeOfTiddlerEventQueue() === 0) {\n\t\t\t\t// Check if we're dirty\n\t\t\t\tif(self.numChanges > 0) {\n\t\t\t\t\tself.saveWiki({\n\t\t\t\t\t\tmethod: \"autosave\",\n\t\t\t\t\t\tdownloadType: \"text/plain\"\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t// Otherwise put ourselves in the \"pending autosave\" state and wait for the change event before we do the autosave\n\t\t\t\tself.pendingAutoSave = true;\n\t\t\t}\n\t\t});\n\t\t// Set up our beforeunload handler\n\t\t$tw.addUnloadTask(function(event) {\n\t\t\tvar confirmationMessage;\n\t\t\tif(self.isDirty()) {\n\t\t\t\tconfirmationMessage = $tw.language.getString(\"UnsavedChangesWarning\");\n\t\t\t\tevent.returnValue = confirmationMessage; // Gecko\n\t\t\t}\n\t\t\treturn confirmationMessage;\n\t\t});\n\t}\n\t// Install the save action handlers\n\tif($tw.browser) {\n\t\t$tw.rootWidget.addEventListener(\"tm-save-wiki\",function(event) {\n\t\t\tself.saveWiki({\n\t\t\t\ttemplate: event.param,\n\t\t\t\tdownloadType: \"text/plain\",\n\t\t\t\tvariables: event.paramObject\n\t\t\t});\n\t\t});\n\t\t$tw.rootWidget.addEventListener(\"tm-download-file\",function(event) {\n\t\t\tself.saveWiki({\n\t\t\t\tmethod: \"download\",\n\t\t\t\ttemplate: event.param,\n\t\t\t\tdownloadType: \"text/plain\",\n\t\t\t\tvariables: event.paramObject\n\t\t\t});\n\t\t});\n\t}\n}\n\nSaverHandler.prototype.titleSyncFilter = \"$:/config/SaverFilter\";\nSaverHandler.prototype.titleAutoSave = \"$:/config/AutoSave\";\nSaverHandler.prototype.titleSavedNotification = \"$:/language/Notifications/Save/Done\";\n\n/*\nSelect the appropriate saver modules and set them up\n*/\nSaverHandler.prototype.initSavers = function(moduleType) {\n\tmoduleType = moduleType || \"saver\";\n\t// Instantiate the available savers\n\tthis.savers = [];\n\tvar self = this;\n\t$tw.modules.forEachModuleOfType(moduleType,function(title,module) {\n\t\tif(module.canSave(self)) {\n\t\t\tself.savers.push(module.create(self.wiki));\n\t\t}\n\t});\n\t// Sort the savers into priority order\n\tthis.savers.sort(function(a,b) {\n\t\tif(a.info.priority < b.info.priority) {\n\t\t\treturn -1;\n\t\t} else {\n\t\t\tif(a.info.priority > b.info.priority) {\n\t\t\t\treturn +1;\n\t\t\t} else {\n\t\t\t\treturn 0;\n\t\t\t}\n\t\t}\n\t});\n};\n\n/*\nSave the wiki contents. Options are:\n\tmethod: \"save\", \"autosave\" or \"download\"\n\ttemplate: the tiddler containing the template to save\n\tdownloadType: the content type for the saved file\n*/\nSaverHandler.prototype.saveWiki = function(options) {\n\toptions = options || {};\n\tvar self = this,\n\t\tmethod = options.method || \"save\";\n\t// Ignore autosave if disabled\n\tif(method === \"autosave\" && this.wiki.getTiddlerText(this.titleAutoSave,\"yes\") !== \"yes\") {\n\t\treturn false;\n\t}\n\tvar\tvariables = options.variables || {},\n\t\ttemplate = options.template || \"$:/core/save/all\",\n\t\tdownloadType = options.downloadType || \"text/plain\",\n\t\ttext = this.wiki.renderTiddler(downloadType,template,options),\n\t\tcallback = function(err) {\n\t\t\tif(err) {\n\t\t\t\talert($tw.language.getString(\"Error/WhileSaving\") + \":\\n\\n\" + err);\n\t\t\t} else {\n\t\t\t\t// Clear the task queue if we're saving (rather than downloading)\n\t\t\t\tif(method !== \"download\") {\n\t\t\t\t\tself.numChanges = 0;\n\t\t\t\t\tself.updateDirtyStatus();\n\t\t\t\t}\n\t\t\t\t$tw.notifier.display(self.titleSavedNotification);\n\t\t\t\tif(options.callback) {\n\t\t\t\t\toptions.callback();\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t// Call the highest priority saver that supports this method\n\tfor(var t=this.savers.length-1; t>=0; t--) {\n\t\tvar saver = this.savers[t];\n\t\tif(saver.info.capabilities.indexOf(method) !== -1 && saver.save(text,method,callback,{variables: {filename: variables.filename}})) {\n\t\t\tthis.logger.log(\"Saving wiki with method\",method,\"through saver\",saver.info.name);\n\t\t\treturn true;\n\t\t}\n\t}\n\treturn false;\n};\n\n/*\nChecks whether the wiki is dirty (ie the window shouldn't be closed)\n*/\nSaverHandler.prototype.isDirty = function() {\n\treturn this.numChanges > 0;\n};\n\n/*\nUpdate the document body with the class \"tc-dirty\" if the wiki has unsaved/unsynced changes\n*/\nSaverHandler.prototype.updateDirtyStatus = function() {\n\tif($tw.browser) {\n\t\t$tw.utils.toggleClass(document.body,\"tc-dirty\",this.isDirty());\n\t}\n};\n\nexports.SaverHandler = SaverHandler;\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/savers/andtidwiki.js": {
"title": "$:/core/modules/savers/andtidwiki.js",
"text": "/*\\\ntitle: $:/core/modules/savers/andtidwiki.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via the AndTidWiki Android app\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false, netscape: false, Components: false */\n\"use strict\";\n\nvar AndTidWiki = function(wiki) {\n};\n\nAndTidWiki.prototype.save = function(text,method,callback,options) {\n\tvar filename = options && options.variables ? options.variables.filename : null;\n\tif (method === \"download\") {\n\t\t// Support download\n\t\tif (window.twi.saveDownload) {\n\t\t\ttry {\n\t\t\t\twindow.twi.saveDownload(text,filename);\n\t\t\t} catch(err) {\n\t\t\t\tif (err.message === \"Method not found\") {\n\t\t\t\t\twindow.twi.saveDownload(text);\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tvar link = document.createElement(\"a\");\n\t\t\tlink.setAttribute(\"href\",\"data:text/plain,\" + encodeURIComponent(text));\n\t\t\tif (filename) {\n\t\t\t link.setAttribute(\"download\",filename);\n\t\t\t}\n\t\t\tdocument.body.appendChild(link);\n\t\t\tlink.click();\n\t\t\tdocument.body.removeChild(link);\n\t\t}\n\t} else if (window.twi.saveWiki) {\n\t\t// Direct save in Tiddloid\n\t\twindow.twi.saveWiki(text);\n\t} else {\n\t\t// Get the pathname of this document\n\t\tvar pathname = decodeURIComponent(document.location.toString().split(\"#\")[0]);\n\t\t// Strip the file://\n\t\tif(pathname.indexOf(\"file://\") === 0) {\n\t\t\tpathname = pathname.substr(7);\n\t\t}\n\t\t// Strip any query or location part\n\t\tvar p = pathname.indexOf(\"?\");\n\t\tif(p !== -1) {\n\t\t\tpathname = pathname.substr(0,p);\n\t\t}\n\t\tp = pathname.indexOf(\"#\");\n\t\tif(p !== -1) {\n\t\t\tpathname = pathname.substr(0,p);\n\t\t}\n\t\t// Save the file\n\t\twindow.twi.saveFile(pathname,text);\n\t}\n\t// Call the callback\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nAndTidWiki.prototype.info = {\n\tname: \"andtidwiki\",\n\tpriority: 1600,\n\tcapabilities: [\"save\", \"autosave\", \"download\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn !!window.twi && !!window.twi.saveFile;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new AndTidWiki(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/beaker.js": {
"title": "$:/core/modules/savers/beaker.js",
"text": "/*\\\ntitle: $:/core/modules/savers/beaker.js\ntype: application/javascript\nmodule-type: saver\n\nSaves files using the Beaker browser's (https://beakerbrowser.com) Dat protocol (https://datproject.org/)\nCompatible with beaker >= V0.7.2\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSet up the saver\n*/\nvar BeakerSaver = function(wiki) {\n\tthis.wiki = wiki;\n};\n\nBeakerSaver.prototype.save = function(text,method,callback) {\n\tvar dat = new DatArchive(\"\" + window.location),\n\t\tpathname = (\"\" + window.location.pathname).split(\"#\")[0];\n\tdat.stat(pathname).then(function(value) {\n\t\tif(value.isDirectory()) {\n\t\t\tpathname = pathname + \"/index.html\";\n\t\t}\n\t\tdat.writeFile(pathname,text,\"utf8\").then(function(value) {\n\t\t\tcallback(null);\n\t\t},function(reason) {\n\t\t\tcallback(\"Beaker Saver Write Error: \" + reason);\n\t\t});\n\t},function(reason) {\n\t\tcallback(\"Beaker Saver Stat Error: \" + reason);\n\t});\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nBeakerSaver.prototype.info = {\n\tname: \"beaker\",\n\tpriority: 3000,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn !!window.DatArchive && location.protocol===\"dat:\";\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new BeakerSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/download.js": {
"title": "$:/core/modules/savers/download.js",
"text": "/*\\\ntitle: $:/core/modules/savers/download.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via HTML5's download APIs\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar DownloadSaver = function(wiki) {\n};\n\nDownloadSaver.prototype.save = function(text,method,callback,options) {\n\toptions = options || {};\n\t// Get the current filename\n\tvar filename = options.variables.filename;\n\tif(!filename) {\n\t\tvar p = document.location.pathname.lastIndexOf(\"/\");\n\t\tif(p !== -1) {\n\t\t\t// We decode the pathname because document.location is URL encoded by the browser\n\t\t\tfilename = decodeURIComponent(document.location.pathname.substr(p+1));\n\t\t}\n\t}\n\tif(!filename) {\n\t\tfilename = \"tiddlywiki.html\";\n\t}\n\t// Set up the link\n\tvar link = document.createElement(\"a\");\n\tif(Blob !== undefined) {\n\t\tvar blob = new Blob([text], {type: \"text/html\"});\n\t\tlink.setAttribute(\"href\", URL.createObjectURL(blob));\n\t} else {\n\t\tlink.setAttribute(\"href\",\"data:text/html,\" + encodeURIComponent(text));\n\t}\n\tlink.setAttribute(\"download\",filename);\n\tdocument.body.appendChild(link);\n\tlink.click();\n\tdocument.body.removeChild(link);\n\t// Callback that we succeeded\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nDownloadSaver.prototype.info = {\n\tname: \"download\",\n\tpriority: 100\n};\n\nObject.defineProperty(DownloadSaver.prototype.info, \"capabilities\", {\n\tget: function() {\n\t\tvar capabilities = [\"save\", \"download\"];\n\t\tif(($tw.wiki.getTextReference(\"$:/config/DownloadSaver/AutoSave\") || \"\").toLowerCase() === \"yes\") {\n\t\t\tcapabilities.push(\"autosave\");\n\t\t}\n\t\treturn capabilities;\n\t}\n});\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn document.createElement(\"a\").download !== undefined;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new DownloadSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/fsosaver.js": {
"title": "$:/core/modules/savers/fsosaver.js",
"text": "/*\\\ntitle: $:/core/modules/savers/fsosaver.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via MS FileSystemObject ActiveXObject\n\nNote: Since TiddlyWiki's markup contains the MOTW, the FileSystemObject normally won't be available. \nHowever, if the wiki is loaded as an .HTA file (Windows HTML Applications) then the FSO can be used.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar FSOSaver = function(wiki) {\n};\n\nFSOSaver.prototype.save = function(text,method,callback) {\n\t// Get the pathname of this document\n\tvar pathname = unescape(document.location.pathname);\n\t// Test for a Windows path of the form /x:\\blah...\n\tif(/^\\/[A-Z]\\:\\\\[^\\\\]+/i.test(pathname)) {\t// ie: ^/[a-z]:/[^/]+\n\t\t// Remove the leading slash\n\t\tpathname = pathname.substr(1);\n\t} else if(document.location.hostname !== \"\" && /^\\/\\\\[^\\\\]+\\\\[^\\\\]+/i.test(pathname)) {\t// test for \\\\server\\share\\blah... - ^/[^/]+/[^/]+\n\t\t// Remove the leading slash\n\t\tpathname = pathname.substr(1);\n\t\t// reconstruct UNC path\n\t\tpathname = \"\\\\\\\\\" + document.location.hostname + pathname;\n\t} else {\n\t\treturn false;\n\t}\n\t// Save the file (as UTF-16)\n\tvar fso = new ActiveXObject(\"Scripting.FileSystemObject\");\n\tvar file = fso.OpenTextFile(pathname,2,-1,-1);\n\tfile.Write(text);\n\tfile.Close();\n\t// Callback that we succeeded\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nFSOSaver.prototype.info = {\n\tname: \"FSOSaver\",\n\tpriority: 120,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\ttry {\n\t\treturn (window.location.protocol === \"file:\") && !!(new ActiveXObject(\"Scripting.FileSystemObject\"));\n\t} catch(e) { return false; }\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new FSOSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/gitea.js": {
"title": "$:/core/modules/savers/gitea.js",
"text": "/*\\\ntitle: $:/core/modules/savers/gitea.js\ntype: application/javascript\nmodule-type: saver\n\nSaves wiki by pushing a commit to the gitea\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar GiteaSaver = function(wiki) {\n\tthis.wiki = wiki;\n};\n\nGiteaSaver.prototype.save = function(text,method,callback) {\n\tvar self = this,\n\t\tusername = this.wiki.getTiddlerText(\"$:/Gitea/Username\"),\n\t\tpassword = $tw.utils.getPassword(\"Gitea\"),\n\t\trepo = this.wiki.getTiddlerText(\"$:/Gitea/Repo\"),\n\t\tpath = this.wiki.getTiddlerText(\"$:/Gitea/Path\",\"\"),\n\t\tfilename = this.wiki.getTiddlerText(\"$:/Gitea/Filename\"),\n\t\tbranch = this.wiki.getTiddlerText(\"$:/Gitea/Branch\") || \"master\",\n\t\tendpoint = this.wiki.getTiddlerText(\"$:/Gitea/ServerURL\") || \"https://gitea\",\n\t\theaders = {\n\t\t\t\"Accept\": \"application/json\",\n\t\t\t\"Content-Type\": \"application/json;charset=UTF-8\",\n\t\t\t\"Authorization\": \"Basic \" + window.btoa(username + \":\" + password)\n\t\t};\n\t// Bail if we don't have everything we need\n\tif(!username || !password || !repo || !path || !filename) {\n\t\treturn false;\n\t}\n\t// Make sure the path start and ends with a slash\n\tif(path.substring(0,1) !== \"/\") {\n\t\tpath = \"/\" + path;\n\t}\n\tif(path.substring(path.length - 1) !== \"/\") {\n\t\tpath = path + \"/\";\n\t}\n\t// Compose the base URI\n\tvar uri = endpoint + \"/repos/\" + repo + \"/contents\" + path;\n\t// Perform a get request to get the details (inc shas) of files in the same path as our file\n\t$tw.utils.httpRequest({\n\t\turl: uri,\n\t\ttype: \"GET\",\n\t\theaders: headers,\n\t\tdata: {\n\t\t\tref: branch\n\t\t},\n\t\tcallback: function(err,getResponseDataJson,xhr) {\n\t\t\tvar getResponseData,sha = \"\";\n\t\t\tif(err && xhr.status !== 404) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tvar use_put = true;\n\t\t\tif(xhr.status !== 404) {\n\t\t\t\tgetResponseData = JSON.parse(getResponseDataJson);\n\t\t\t\t$tw.utils.each(getResponseData,function(details) {\n\t\t\t\t\tif(details.name === filename) {\n\t\t\t\t\t\tsha = details.sha;\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t\tif(sha === \"\"){\n\t\t\t\t\tuse_put = false;\n\t\t\t\t}\n\t\t\t}\n\t\t\tvar data = {\n\t\t\t\tmessage: $tw.language.getRawString(\"ControlPanel/Saving/GitService/CommitMessage\"),\n\t\t\t\tcontent: $tw.utils.base64Encode(text),\n\t\t\t\tsha: sha\n\t\t\t};\n\t\t\t$tw.utils.httpRequest({\n\t\t\t\turl: endpoint + \"/repos/\" + repo + \"/branches/\" + branch,\n\t\t\t\ttype: \"GET\",\n\t\t\t\theaders: headers,\n\t\t\t\tcallback: function(err,getResponseDataJson,xhr) {\n\t\t\t\t\tif(xhr.status === 404) {\n\t\t\t\t\t\tcallback(\"Please ensure the branch in the Gitea repo exists\");\n\t\t\t\t\t}else{\n\t\t\t\t\t\tdata[\"branch\"] = branch;\n\t\t\t\t\t\tself.upload(uri + filename, use_put?\"PUT\":\"POST\", headers, data, callback);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n\treturn true;\n};\n\nGiteaSaver.prototype.upload = function(uri,method,headers,data,callback) {\n\t$tw.utils.httpRequest({\n\t\turl: uri,\n\t\ttype: method,\n\t\theaders: headers,\n\t\tdata: JSON.stringify(data),\n\t\tcallback: function(err,putResponseDataJson,xhr) {\n\t\t\tif(err) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tvar putResponseData = JSON.parse(putResponseDataJson);\n\t\t\tcallback(null);\n\t\t}\n\t});\n};\n\n/*\nInformation about this saver\n*/\nGiteaSaver.prototype.info = {\n\tname: \"Gitea\",\n\tpriority: 2000,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new GiteaSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/github.js": {
"title": "$:/core/modules/savers/github.js",
"text": "/*\\\ntitle: $:/core/modules/savers/github.js\ntype: application/javascript\nmodule-type: saver\n\nSaves wiki by pushing a commit to the GitHub v3 REST API\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar GitHubSaver = function(wiki) {\n\tthis.wiki = wiki;\n};\n\nGitHubSaver.prototype.save = function(text,method,callback) {\n\tvar self = this,\n\t\tusername = this.wiki.getTiddlerText(\"$:/GitHub/Username\"),\n\t\tpassword = $tw.utils.getPassword(\"github\"),\n\t\trepo = this.wiki.getTiddlerText(\"$:/GitHub/Repo\"),\n\t\tpath = this.wiki.getTiddlerText(\"$:/GitHub/Path\",\"\"),\n\t\tfilename = this.wiki.getTiddlerText(\"$:/GitHub/Filename\"),\n\t\tbranch = this.wiki.getTiddlerText(\"$:/GitHub/Branch\") || \"master\",\n\t\tendpoint = this.wiki.getTiddlerText(\"$:/GitHub/ServerURL\") || \"https://api.github.com\",\n\t\theaders = {\n\t\t\t\"Accept\": \"application/vnd.github.v3+json\",\n\t\t\t\"Content-Type\": \"application/json;charset=UTF-8\",\n\t\t\t\"Authorization\": \"Basic \" + window.btoa(username + \":\" + password)\n\t\t};\n\t// Bail if we don't have everything we need\n\tif(!username || !password || !repo || !path || !filename) {\n\t\treturn false;\n\t}\n\t// Make sure the path start and ends with a slash\n\tif(path.substring(0,1) !== \"/\") {\n\t\tpath = \"/\" + path;\n\t}\n\tif(path.substring(path.length - 1) !== \"/\") {\n\t\tpath = path + \"/\";\n\t}\n\t// Compose the base URI\n\tvar uri = endpoint + \"/repos/\" + repo + \"/contents\" + path;\n\t// Perform a get request to get the details (inc shas) of files in the same path as our file\n\t$tw.utils.httpRequest({\n\t\turl: uri,\n\t\ttype: \"GET\",\n\t\theaders: headers,\n\t\tdata: {\n\t\t\tref: branch\n\t\t},\n\t\tcallback: function(err,getResponseDataJson,xhr) {\n\t\t\tvar getResponseData,sha = \"\";\n\t\t\tif(err && xhr.status !== 404) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tif(xhr.status !== 404) {\n\t\t\t\tgetResponseData = JSON.parse(getResponseDataJson);\n\t\t\t\t$tw.utils.each(getResponseData,function(details) {\n\t\t\t\t\tif(details.name === filename) {\n\t\t\t\t\t\tsha = details.sha;\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t}\n\t\t\tvar data = {\n\t\t\t\tmessage: $tw.language.getRawString(\"ControlPanel/Saving/GitService/CommitMessage\"),\n\t\t\t\tcontent: $tw.utils.base64Encode(text),\n\t\t\t\tbranch: branch,\n\t\t\t\tsha: sha\n\t\t\t};\n\t\t\t// Perform a PUT request to save the file\n\t\t\t$tw.utils.httpRequest({\n\t\t\t\turl: uri + filename,\n\t\t\t\ttype: \"PUT\",\n\t\t\t\theaders: headers,\n\t\t\t\tdata: JSON.stringify(data),\n\t\t\t\tcallback: function(err,putResponseDataJson,xhr) {\n\t\t\t\t\tif(err) {\n\t\t\t\t\t\treturn callback(err);\n\t\t\t\t\t}\n\t\t\t\t\tvar putResponseData = JSON.parse(putResponseDataJson);\n\t\t\t\t\tcallback(null);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nGitHubSaver.prototype.info = {\n\tname: \"github\",\n\tpriority: 2000,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new GitHubSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/gitlab.js": {
"title": "$:/core/modules/savers/gitlab.js",
"text": "/*\\\ntitle: $:/core/modules/savers/gitlab.js\ntype: application/javascript\nmodule-type: saver\n\nSaves wiki by pushing a commit to the GitLab REST API\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: true */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar GitLabSaver = function(wiki) {\n\tthis.wiki = wiki;\n};\n\nGitLabSaver.prototype.save = function(text,method,callback) {\n\t/* See https://docs.gitlab.com/ee/api/repository_files.html */\n\tvar self = this,\n\t\tusername = this.wiki.getTiddlerText(\"$:/GitLab/Username\"),\n\t\tpassword = $tw.utils.getPassword(\"gitlab\"),\n\t\trepo = this.wiki.getTiddlerText(\"$:/GitLab/Repo\"),\n\t\tpath = this.wiki.getTiddlerText(\"$:/GitLab/Path\",\"\"),\n\t\tfilename = this.wiki.getTiddlerText(\"$:/GitLab/Filename\"),\n\t\tbranch = this.wiki.getTiddlerText(\"$:/GitLab/Branch\") || \"master\",\n\t\tendpoint = this.wiki.getTiddlerText(\"$:/GitLab/ServerURL\") || \"https://gitlab.com/api/v4\",\n\t\theaders = {\n\t\t\t\"Content-Type\": \"application/json;charset=UTF-8\",\n\t\t\t\"Private-Token\": password\n\t\t};\n\t// Bail if we don't have everything we need\n\tif(!username || !password || !repo || !path || !filename) {\n\t\treturn false;\n\t}\n\t// Make sure the path start and ends with a slash\n\tif(path.substring(0,1) !== \"/\") {\n\t\tpath = \"/\" + path;\n\t}\n\tif(path.substring(path.length - 1) !== \"/\") {\n\t\tpath = path + \"/\";\n\t}\n\t// Compose the base URI\n\tvar uri = endpoint + \"/projects/\" + encodeURIComponent(repo) + \"/repository/\";\n\t// Perform a get request to get the details (inc shas) of files in the same path as our file\n\t$tw.utils.httpRequest({\n\t\turl: uri + \"tree/?path=\" + encodeURIComponent(path.replace(/^\\/+|\\/$/g, '')) + \"&branch=\" + encodeURIComponent(branch.replace(/^\\/+|\\/$/g, '')),\n\t\ttype: \"GET\",\n\t\theaders: headers,\n\t\tcallback: function(err,getResponseDataJson,xhr) {\n\t\t\tvar getResponseData,sha = \"\";\n\t\t\tif(err && xhr.status !== 404) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tvar requestType = \"POST\";\n\t\t\tif(xhr.status !== 404) {\n\t\t\t\tgetResponseData = JSON.parse(getResponseDataJson);\n\t\t\t\t$tw.utils.each(getResponseData,function(details) {\n\t\t\t\t\tif(details.name === filename) {\n\t\t\t\t\t\trequestType = \"PUT\";\n\t\t\t\t\t\tsha = details.sha;\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t}\n\t\t\tvar data = {\n\t\t\t\tcommit_message: $tw.language.getRawString(\"ControlPanel/Saving/GitService/CommitMessage\"),\n\t\t\t\tcontent: text,\n\t\t\t\tbranch: branch,\n\t\t\t\tsha: sha\n\t\t\t};\n\t\t\t// Perform a request to save the file\n\t\t\t$tw.utils.httpRequest({\n\t\t\t\turl: uri + \"files/\" + encodeURIComponent(path.replace(/^\\/+/, '') + filename),\n\t\t\t\ttype: requestType,\n\t\t\t\theaders: headers,\n\t\t\t\tdata: JSON.stringify(data),\n\t\t\t\tcallback: function(err,putResponseDataJson,xhr) {\n\t\t\t\t\tif(err) {\n\t\t\t\t\t\treturn callback(err);\n\t\t\t\t\t}\n\t\t\t\t\tvar putResponseData = JSON.parse(putResponseDataJson);\n\t\t\t\t\tcallback(null);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nGitLabSaver.prototype.info = {\n\tname: \"gitlab\",\n\tpriority: 2000,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new GitLabSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/manualdownload.js": {
"title": "$:/core/modules/savers/manualdownload.js",
"text": "/*\\\ntitle: $:/core/modules/savers/manualdownload.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via HTML5's download APIs\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Title of the tiddler containing the download message\nvar downloadInstructionsTitle = \"$:/language/Modals/Download\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar ManualDownloadSaver = function(wiki) {\n};\n\nManualDownloadSaver.prototype.save = function(text,method,callback) {\n\t$tw.modal.display(downloadInstructionsTitle,{\n\t\tdownloadLink: \"data:text/html,\" + encodeURIComponent(text)\n\t});\n\t// Callback that we succeeded\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nManualDownloadSaver.prototype.info = {\n\tname: \"manualdownload\",\n\tpriority: 0,\n\tcapabilities: [\"save\", \"download\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new ManualDownloadSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/msdownload.js": {
"title": "$:/core/modules/savers/msdownload.js",
"text": "/*\\\ntitle: $:/core/modules/savers/msdownload.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via window.navigator.msSaveBlob()\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar MsDownloadSaver = function(wiki) {\n};\n\nMsDownloadSaver.prototype.save = function(text,method,callback) {\n\t// Get the current filename\n\tvar filename = \"tiddlywiki.html\",\n\t\tp = document.location.pathname.lastIndexOf(\"/\");\n\tif(p !== -1) {\n\t\tfilename = document.location.pathname.substr(p+1);\n\t}\n\t// Set up the link\n\tvar blob = new Blob([text], {type: \"text/html\"});\n\twindow.navigator.msSaveBlob(blob,filename);\n\t// Callback that we succeeded\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nMsDownloadSaver.prototype.info = {\n\tname: \"msdownload\",\n\tpriority: 110,\n\tcapabilities: [\"save\", \"download\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn !!window.navigator.msSaveBlob;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new MsDownloadSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/put.js": {
"title": "$:/core/modules/savers/put.js",
"text": "/*\\\ntitle: $:/core/modules/savers/put.js\ntype: application/javascript\nmodule-type: saver\n\nSaves wiki by performing a PUT request to the server\n\nWorks with any server which accepts a PUT request\nto the current URL, such as a WebDAV server.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nRetrieve ETag if available\n*/\nvar retrieveETag = function(self) {\n\tvar headers = {\n\t\tAccept: \"*/*;charset=UTF-8\"\n\t};\n\t$tw.utils.httpRequest({\n\t\turl: self.uri(),\n\t\ttype: \"HEAD\",\n\t\theaders: headers,\n\t\tcallback: function(err,data,xhr) {\n\t\t\tif(err) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tvar etag = xhr.getResponseHeader(\"ETag\");\n\t\t\tif(!etag) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tself.etag = etag.replace(/^W\\//,\"\");\n\t\t}\n\t});\n};\n\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar PutSaver = function(wiki) {\n\tthis.wiki = wiki;\n\tvar self = this;\n\tvar uri = this.uri();\n\t// Async server probe. Until probe finishes, save will fail fast\n\t// See also https://github.com/Jermolene/TiddlyWiki5/issues/2276\n\t$tw.utils.httpRequest({\n\t\turl: uri,\n\t\ttype: \"OPTIONS\",\n\t\tcallback: function(err,data,xhr) {\n\t\t\t// Check DAV header http://www.webdav.org/specs/rfc2518.html#rfc.section.9.1\n\t\t\tif(!err) {\n\t\t\t\tself.serverAcceptsPuts = xhr.status === 200 && !!xhr.getResponseHeader(\"dav\");\n\t\t\t}\n\t\t}\n\t});\n\tretrieveETag(this);\n};\n\nPutSaver.prototype.uri = function() {\n\treturn document.location.toString().split(\"#\")[0];\n};\n\n// TODO: in case of edit conflict\n// Prompt: Do you want to save over this? Y/N\n// Merging would be ideal, and may be possible using future generic merge flow\nPutSaver.prototype.save = function(text,method,callback) {\n\tif(!this.serverAcceptsPuts) {\n\t\treturn false;\n\t}\n\tvar self = this;\n\tvar headers = {\n\t\t\"Content-Type\": \"text/html;charset=UTF-8\"\n\t};\n\tif(this.etag) {\n\t\theaders[\"If-Match\"] = this.etag;\n\t}\n\t$tw.utils.httpRequest({\n\t\turl: this.uri(),\n\t\ttype: \"PUT\",\n\t\theaders: headers,\n\t\tdata: text,\n\t\tcallback: function(err,data,xhr) {\n\t\t\tif(err) {\n\t\t\t\t// response is textual: \"XMLHttpRequest error code: 412\"\n\t\t\t\tvar status = Number(err.substring(err.indexOf(':') + 2, err.length))\n\t\t\t\tif(status === 412) { // edit conflict\n\t\t\t\t\tvar message = $tw.language.getString(\"Error/EditConflict\");\n\t\t\t\t\tcallback(message);\n\t\t\t\t} else {\n\t\t\t\t\tcallback(err); // fail\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tself.etag = xhr.getResponseHeader(\"ETag\");\n\t\t\t\tif(self.etag == null) {\n\t\t\t\t\tretrieveETag(self);\n\t\t\t\t}\n\t\t\t\tcallback(null); // success\n\t\t\t}\n\t\t}\n\t});\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nPutSaver.prototype.info = {\n\tname: \"put\",\n\tpriority: 2000,\n\tcapabilities: [\"save\",\"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn /^https?:/.test(location.protocol);\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new PutSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/tiddlyfox.js": {
"title": "$:/core/modules/savers/tiddlyfox.js",
"text": "/*\\\ntitle: $:/core/modules/savers/tiddlyfox.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via the TiddlyFox file extension\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false, netscape: false, Components: false */\n\"use strict\";\n\nvar TiddlyFoxSaver = function(wiki) {\n};\n\nTiddlyFoxSaver.prototype.save = function(text,method,callback) {\n\tvar messageBox = document.getElementById(\"tiddlyfox-message-box\");\n\tif(messageBox) {\n\t\t// Get the pathname of this document\n\t\tvar pathname = document.location.toString().split(\"#\")[0];\n\t\t// Replace file://localhost/ with file:///\n\t\tif(pathname.indexOf(\"file://localhost/\") === 0) {\n\t\t\tpathname = \"file://\" + pathname.substr(16);\n\t\t}\n\t\t// Windows path file:///x:/blah/blah --> x:\\blah\\blah\n\t\tif(/^file\\:\\/\\/\\/[A-Z]\\:\\//i.test(pathname)) {\n\t\t\t// Remove the leading slash and convert slashes to backslashes\n\t\t\tpathname = pathname.substr(8).replace(/\\//g,\"\\\\\");\n\t\t// Firefox Windows network path file://///server/share/blah/blah --> //server/share/blah/blah\n\t\t} else if(pathname.indexOf(\"file://///\") === 0) {\n\t\t\tpathname = \"\\\\\\\\\" + unescape(pathname.substr(10)).replace(/\\//g,\"\\\\\");\n\t\t// Mac/Unix local path file:///path/path --> /path/path\n\t\t} else if(pathname.indexOf(\"file:///\") === 0) {\n\t\t\tpathname = unescape(pathname.substr(7));\n\t\t// Mac/Unix local path file:/path/path --> /path/path\n\t\t} else if(pathname.indexOf(\"file:/\") === 0) {\n\t\t\tpathname = unescape(pathname.substr(5));\n\t\t// Otherwise Windows networth path file://server/share/path/path --> \\\\server\\share\\path\\path\n\t\t} else {\n\t\t\tpathname = \"\\\\\\\\\" + unescape(pathname.substr(7)).replace(new RegExp(\"/\",\"g\"),\"\\\\\");\n\t\t}\n\t\t// Create the message element and put it in the message box\n\t\tvar message = document.createElement(\"div\");\n\t\tmessage.setAttribute(\"data-tiddlyfox-path\",decodeURIComponent(pathname));\n\t\tmessage.setAttribute(\"data-tiddlyfox-content\",text);\n\t\tmessageBox.appendChild(message);\n\t\t// Add an event handler for when the file has been saved\n\t\tmessage.addEventListener(\"tiddlyfox-have-saved-file\",function(event) {\n\t\t\tcallback(null);\n\t\t}, false);\n\t\t// Create and dispatch the custom event to the extension\n\t\tvar event = document.createEvent(\"Events\");\n\t\tevent.initEvent(\"tiddlyfox-save-file\",true,false);\n\t\tmessage.dispatchEvent(event);\n\t\treturn true;\n\t} else {\n\t\treturn false;\n\t}\n};\n\n/*\nInformation about this saver\n*/\nTiddlyFoxSaver.prototype.info = {\n\tname: \"tiddlyfox\",\n\tpriority: 1500,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new TiddlyFoxSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/tiddlyie.js": {
"title": "$:/core/modules/savers/tiddlyie.js",
"text": "/*\\\ntitle: $:/core/modules/savers/tiddlyie.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via Internet Explorer BHO extenion (TiddlyIE)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar TiddlyIESaver = function(wiki) {\n};\n\nTiddlyIESaver.prototype.save = function(text,method,callback) {\n\t// Check existence of TiddlyIE BHO extension (note: only works after document is complete)\n\tif(typeof(window.TiddlyIE) != \"undefined\") {\n\t\t// Get the pathname of this document\n\t\tvar pathname = unescape(document.location.pathname);\n\t\t// Test for a Windows path of the form /x:/blah...\n\t\tif(/^\\/[A-Z]\\:\\/[^\\/]+/i.test(pathname)) {\t// ie: ^/[a-z]:/[^/]+ (is this better?: ^/[a-z]:/[^/]+(/[^/]+)*\\.[^/]+ )\n\t\t\t// Remove the leading slash\n\t\t\tpathname = pathname.substr(1);\n\t\t\t// Convert slashes to backslashes\n\t\t\tpathname = pathname.replace(/\\//g,\"\\\\\");\n\t\t} else if(document.hostname !== \"\" && /^\\/[^\\/]+\\/[^\\/]+/i.test(pathname)) {\t// test for \\\\server\\share\\blah... - ^/[^/]+/[^/]+\n\t\t\t// Convert slashes to backslashes\n\t\t\tpathname = pathname.replace(/\\//g,\"\\\\\");\n\t\t\t// reconstruct UNC path\n\t\t\tpathname = \"\\\\\\\\\" + document.location.hostname + pathname;\n\t\t} else return false;\n\t\t// Prompt the user to save the file\n\t\twindow.TiddlyIE.save(pathname, text);\n\t\t// Callback that we succeeded\n\t\tcallback(null);\n\t\treturn true;\n\t} else {\n\t\treturn false;\n\t}\n};\n\n/*\nInformation about this saver\n*/\nTiddlyIESaver.prototype.info = {\n\tname: \"tiddlyiesaver\",\n\tpriority: 1500,\n\tcapabilities: [\"save\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn (window.location.protocol === \"file:\");\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new TiddlyIESaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/twedit.js": {
"title": "$:/core/modules/savers/twedit.js",
"text": "/*\\\ntitle: $:/core/modules/savers/twedit.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via the TWEdit iOS app\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false, netscape: false, Components: false */\n\"use strict\";\n\nvar TWEditSaver = function(wiki) {\n};\n\nTWEditSaver.prototype.save = function(text,method,callback) {\n\t// Bail if we're not running under TWEdit\n\tif(typeof DeviceInfo !== \"object\") {\n\t\treturn false;\n\t}\n\t// Get the pathname of this document\n\tvar pathname = decodeURIComponent(document.location.pathname);\n\t// Strip any query or location part\n\tvar p = pathname.indexOf(\"?\");\n\tif(p !== -1) {\n\t\tpathname = pathname.substr(0,p);\n\t}\n\tp = pathname.indexOf(\"#\");\n\tif(p !== -1) {\n\t\tpathname = pathname.substr(0,p);\n\t}\n\t// Remove the leading \"/Documents\" from path\n\tvar prefix = \"/Documents\";\n\tif(pathname.indexOf(prefix) === 0) {\n\t\tpathname = pathname.substr(prefix.length);\n\t}\n\t// Error handler\n\tvar errorHandler = function(event) {\n\t\t// Error\n\t\tcallback($tw.language.getString(\"Error/SavingToTWEdit\") + \": \" + event.target.error.code);\n\t};\n\t// Get the file system\n\twindow.requestFileSystem(LocalFileSystem.PERSISTENT,0,function(fileSystem) {\n\t\t// Now we've got the filesystem, get the fileEntry\n\t\tfileSystem.root.getFile(pathname, {create: true}, function(fileEntry) {\n\t\t\t// Now we've got the fileEntry, create the writer\n\t\t\tfileEntry.createWriter(function(writer) {\n\t\t\t\twriter.onerror = errorHandler;\n\t\t\t\twriter.onwrite = function() {\n\t\t\t\t\tcallback(null);\n\t\t\t\t};\n\t\t\t\twriter.position = 0;\n\t\t\t\twriter.write(text);\n\t\t\t},errorHandler);\n\t\t}, errorHandler);\n\t}, errorHandler);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nTWEditSaver.prototype.info = {\n\tname: \"twedit\",\n\tpriority: 1600,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new TWEditSaver(wiki);\n};\n\n/////////////////////////// Hack\n// HACK: This ensures that TWEdit recognises us as a TiddlyWiki document\nif($tw.browser) {\n\twindow.version = {title: \"TiddlyWiki\"};\n}\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/upload.js": {
"title": "$:/core/modules/savers/upload.js",
"text": "/*\\\ntitle: $:/core/modules/savers/upload.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via upload to a server.\n\nDesigned to be compatible with BidiX's UploadPlugin at http://tiddlywiki.bidix.info/#UploadPlugin\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar UploadSaver = function(wiki) {\n\tthis.wiki = wiki;\n};\n\nUploadSaver.prototype.save = function(text,method,callback) {\n\t// Get the various parameters we need\n\tvar backupDir = this.wiki.getTextReference(\"$:/UploadBackupDir\") || \".\",\n\t\tusername = this.wiki.getTextReference(\"$:/UploadName\"),\n\t\tpassword = $tw.utils.getPassword(\"upload\"),\n\t\tuploadDir = this.wiki.getTextReference(\"$:/UploadDir\") || \".\",\n\t\tuploadFilename = this.wiki.getTextReference(\"$:/UploadFilename\") || \"index.html\",\n\t\turl = this.wiki.getTextReference(\"$:/UploadURL\");\n\t// Bail out if we don't have the bits we need\n\tif(!username || username.toString().trim() === \"\" || !password || password.toString().trim() === \"\") {\n\t\treturn false;\n\t}\n\t// Construct the url if not provided\n\tif(!url) {\n\t\turl = \"http://\" + username + \".tiddlyspot.com/store.cgi\";\n\t}\n\t// Assemble the header\n\tvar boundary = \"---------------------------\" + \"AaB03x\";\t\n\tvar uploadFormName = \"UploadPlugin\";\n\tvar head = [];\n\thead.push(\"--\" + boundary + \"\\r\\nContent-disposition: form-data; name=\\\"UploadPlugin\\\"\\r\\n\");\n\thead.push(\"backupDir=\" + backupDir + \";user=\" + username + \";password=\" + password + \";uploaddir=\" + uploadDir + \";;\"); \n\thead.push(\"\\r\\n\" + \"--\" + boundary);\n\thead.push(\"Content-disposition: form-data; name=\\\"userfile\\\"; filename=\\\"\" + uploadFilename + \"\\\"\");\n\thead.push(\"Content-Type: text/html;charset=UTF-8\");\n\thead.push(\"Content-Length: \" + text.length + \"\\r\\n\");\n\thead.push(\"\");\n\t// Assemble the tail and the data itself\n\tvar tail = \"\\r\\n--\" + boundary + \"--\\r\\n\",\n\t\tdata = head.join(\"\\r\\n\") + text + tail;\n\t// Do the HTTP post\n\tvar http = new XMLHttpRequest();\n\thttp.open(\"POST\",url,true,username,password);\n\thttp.setRequestHeader(\"Content-Type\",\"multipart/form-data; charset=UTF-8; boundary=\" + boundary);\n\thttp.onreadystatechange = function() {\n\t\tif(http.readyState == 4 && http.status == 200) {\n\t\t\tif(http.responseText.substr(0,4) === \"0 - \") {\n\t\t\t\tcallback(null);\n\t\t\t} else {\n\t\t\t\tcallback(http.responseText);\n\t\t\t}\n\t\t}\n\t};\n\ttry {\n\t\thttp.send(data);\n\t} catch(ex) {\n\t\treturn callback($tw.language.getString(\"Error/Caption\") + \":\" + ex);\n\t}\n\t$tw.notifier.display(\"$:/language/Notifications/Save/Starting\");\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nUploadSaver.prototype.info = {\n\tname: \"upload\",\n\tpriority: 2000,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new UploadSaver(wiki);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/server/authenticators/basic.js": {
"title": "$:/core/modules/server/authenticators/basic.js",
"text": "/*\\\ntitle: $:/core/modules/server/authenticators/basic.js\ntype: application/javascript\nmodule-type: authenticator\n\nAuthenticator for WWW basic authentication\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nif($tw.node) {\n\tvar util = require(\"util\"),\n\t\tfs = require(\"fs\"),\n\t\turl = require(\"url\"),\n\t\tpath = require(\"path\");\n}\n\nfunction BasicAuthenticator(server) {\n\tthis.server = server;\n\tthis.credentialsData = [];\n}\n\n/*\nReturns true if the authenticator is active, false if it is inactive, or a string if there is an error\n*/\nBasicAuthenticator.prototype.init = function() {\n\t// Read the credentials data\n\tthis.credentialsFilepath = this.server.get(\"credentials\");\n\tif(this.credentialsFilepath) {\n\t\tvar resolveCredentialsFilepath = path.resolve($tw.boot.wikiPath,this.credentialsFilepath);\n\t\tif(fs.existsSync(resolveCredentialsFilepath) && !fs.statSync(resolveCredentialsFilepath).isDirectory()) {\n\t\t\tvar credentialsText = fs.readFileSync(resolveCredentialsFilepath,\"utf8\"),\n\t\t\t\tcredentialsData = $tw.utils.parseCsvStringWithHeader(credentialsText);\n\t\t\tif(typeof credentialsData === \"string\") {\n\t\t\t\treturn \"Error: \" + credentialsData + \" reading credentials from '\" + resolveCredentialsFilepath + \"'\";\n\t\t\t} else {\n\t\t\t\tthis.credentialsData = credentialsData;\n\t\t\t}\n\t\t} else {\n\t\t\treturn \"Error: Unable to load user credentials from '\" + resolveCredentialsFilepath + \"'\";\n\t\t}\n\t}\n\t// Add the hardcoded username and password if specified\n\tif(this.server.get(\"username\") && this.server.get(\"password\")) {\n\t\tthis.credentialsData = this.credentialsData || [];\n\t\tthis.credentialsData.push({\n\t\t\tusername: this.server.get(\"username\"),\n\t\t\tpassword: this.server.get(\"password\")\n\t\t});\n\t}\n\treturn this.credentialsData.length > 0;\n};\n\n/*\nReturns true if the request is authenticated and assigns the \"authenticatedUsername\" state variable.\nReturns false if the request couldn't be authenticated having sent an appropriate response to the browser\n*/\nBasicAuthenticator.prototype.authenticateRequest = function(request,response,state) {\n\t// Extract the incoming username and password from the request\n\tvar header = request.headers.authorization || \"\";\n\tif(!header && state.allowAnon) {\n\t\t// If there's no header and anonymous access is allowed then we don't set authenticatedUsername\n\t\treturn true;\n\t}\n\tvar token = header.split(/\\s+/).pop() || \"\",\n\t\tauth = $tw.utils.base64Decode(token),\n\t\tparts = auth.split(/:/),\n\t\tincomingUsername = parts[0],\n\t\tincomingPassword = parts[1];\n\t// Check that at least one of the credentials matches\n\tvar matchingCredentials = this.credentialsData.find(function(credential) {\n\t\treturn credential.username === incomingUsername && credential.password === incomingPassword;\n\t});\n\tif(matchingCredentials) {\n\t\t// If so, add the authenticated username to the request state\n\t\tstate.authenticatedUsername = incomingUsername;\n\t\treturn true;\n\t} else {\n\t\t// If not, return an authentication challenge\n\t\tresponse.writeHead(401,\"Authentication required\",{\n\t\t\t\"WWW-Authenticate\": 'Basic realm=\"Please provide your username and password to login to ' + state.server.servername + '\"'\n\t\t});\n\t\tresponse.end();\n\t\treturn false;\n\t}\n};\n\nexports.AuthenticatorClass = BasicAuthenticator;\n\n})();\n",
"type": "application/javascript",
"module-type": "authenticator"
},
"$:/core/modules/server/authenticators/header.js": {
"title": "$:/core/modules/server/authenticators/header.js",
"text": "/*\\\ntitle: $:/core/modules/server/authenticators/header.js\ntype: application/javascript\nmodule-type: authenticator\n\nAuthenticator for trusted header authentication\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nfunction HeaderAuthenticator(server) {\n\tthis.server = server;\n\tthis.header = server.get(\"authenticated-user-header\");\n}\n\n/*\nReturns true if the authenticator is active, false if it is inactive, or a string if there is an error\n*/\nHeaderAuthenticator.prototype.init = function() {\n\treturn !!this.header;\n};\n\n/*\nReturns true if the request is authenticated and assigns the \"authenticatedUsername\" state variable.\nReturns false if the request couldn't be authenticated having sent an appropriate response to the browser\n*/\nHeaderAuthenticator.prototype.authenticateRequest = function(request,response,state) {\n\t// Otherwise, authenticate as the username in the specified header\n\tvar username = request.headers[this.header];\n\tif(!username && !state.allowAnon) {\n\t\tresponse.writeHead(401,\"Authorization header required to login to '\" + state.server.servername + \"'\");\n\t\tresponse.end();\n\t\treturn false;\n\t} else {\n\t\t// authenticatedUsername will be undefined for anonymous users\n\t\tstate.authenticatedUsername = username;\n\t\treturn true;\n\t}\n};\n\nexports.AuthenticatorClass = HeaderAuthenticator;\n\n})();\n",
"type": "application/javascript",
"module-type": "authenticator"
},
"$:/core/modules/server/routes/delete-tiddler.js": {
"title": "$:/core/modules/server/routes/delete-tiddler.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/delete-tiddler.js\ntype: application/javascript\nmodule-type: route\n\nDELETE /recipes/default/tiddlers/:title\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.method = \"DELETE\";\n\nexports.path = /^\\/bags\\/default\\/tiddlers\\/(.+)$/;\n\nexports.handler = function(request,response,state) {\n\tvar title = decodeURIComponent(state.params[0]);\n\tstate.wiki.deleteTiddler(title);\n\tresponse.writeHead(204, \"OK\", {\n\t\t\"Content-Type\": \"text/plain\"\n\t});\n\tresponse.end();\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/routes/get-favicon.js": {
"title": "$:/core/modules/server/routes/get-favicon.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/get-favicon.js\ntype: application/javascript\nmodule-type: route\n\nGET /favicon.ico\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.method = \"GET\";\n\nexports.path = /^\\/favicon.ico$/;\n\nexports.handler = function(request,response,state) {\n\tresponse.writeHead(200, {\"Content-Type\": \"image/x-icon\"});\n\tvar buffer = state.wiki.getTiddlerText(\"$:/favicon.ico\",\"\");\n\tresponse.end(buffer,\"base64\");\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/routes/get-file.js": {
"title": "$:/core/modules/server/routes/get-file.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/get-file.js\ntype: application/javascript\nmodule-type: route\n\nGET /files/:filepath\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.method = \"GET\";\n\nexports.path = /^\\/files\\/(.+)$/;\n\nexports.handler = function(request,response,state) {\n\tvar path = require(\"path\"),\n\t\tfs = require(\"fs\"),\n\t\tutil = require(\"util\"),\n\t\tsuppliedFilename = decodeURIComponent(state.params[0]),\n\t\tfilename = path.resolve($tw.boot.wikiPath,\"files\",suppliedFilename),\n\t\textension = path.extname(filename);\n\tfs.readFile(filename,function(err,content) {\n\t\tvar status,content,type = \"text/plain\";\n\t\tif(err) {\n\t\t\tconsole.log(\"Error accessing file \" + filename + \": \" + err.toString());\n\t\t\tstatus = 404;\n\t\t\tcontent = \"File '\" + suppliedFilename + \"' not found\";\n\t\t} else {\n\t\t\tstatus = 200;\n\t\t\tcontent = content;\n\t\t\ttype = ($tw.config.fileExtensionInfo[extension] ? $tw.config.fileExtensionInfo[extension].type : \"application/octet-stream\");\n\t\t}\n\t\tresponse.writeHead(status,{\n\t\t\t\"Content-Type\": type\n\t\t});\n\t\tresponse.end(content);\n\t});\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/routes/get-index.js": {
"title": "$:/core/modules/server/routes/get-index.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/get-index.js\ntype: application/javascript\nmodule-type: route\n\nGET /\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar zlib = require(\"zlib\");\n\nexports.method = \"GET\";\n\nexports.path = /^\\/$/;\n\nexports.handler = function(request,response,state) {\n\tvar acceptEncoding = request.headers[\"accept-encoding\"];\n\tif(!acceptEncoding) {\n\t\tacceptEncoding = \"\";\n\t}\n\tvar text = state.wiki.renderTiddler(state.server.get(\"root-render-type\"),state.server.get(\"root-tiddler\")),\n\t\tresponseHeaders = {\n\t\t\"Content-Type\": state.server.get(\"root-serve-type\")\n\t};\n\t/*\n\tIf the gzip=yes flag for `listen` is set, check if the user agent permits\n\tcompression. If so, compress our response. Note that we use the synchronous\n\tfunctions from zlib to stay in the imperative style. The current `Server`\n\tdoesn't depend on this, and we may just as well use the async versions.\n\t*/\n\tif(state.server.enableGzip) {\n\t\tif (/\\bdeflate\\b/.test(acceptEncoding)) {\n\t\t\tresponseHeaders[\"Content-Encoding\"] = \"deflate\";\n\t\t\ttext = zlib.deflateSync(text);\n\t\t} else if (/\\bgzip\\b/.test(acceptEncoding)) {\n\t\t\tresponseHeaders[\"Content-Encoding\"] = \"gzip\";\n\t\t\ttext = zlib.gzipSync(text);\n\t\t}\n\t}\n\tresponse.writeHead(200,responseHeaders);\n\tresponse.end(text);\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/routes/get-login-basic.js": {
"title": "$:/core/modules/server/routes/get-login-basic.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/get-login-basic.js\ntype: application/javascript\nmodule-type: route\n\nGET /login-basic -- force a Basic Authentication challenge\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.method = \"GET\";\n\nexports.path = /^\\/login-basic$/;\n\nexports.handler = function(request,response,state) {\n\tif(!state.authenticatedUsername) {\n\t\t// Challenge if there's no username\n\t\tresponse.writeHead(401,{\n\t\t\t\"WWW-Authenticate\": 'Basic realm=\"Please provide your username and password to login to ' + state.server.servername + '\"'\n\t\t});\n\t\tresponse.end();\t\t\n\t} else {\n\t\t// Redirect to the root wiki if login worked\n\t\tresponse.writeHead(302,{\n\t\t\tLocation: \"/\"\n\t\t});\n\t\tresponse.end();\n\t}\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/routes/get-status.js": {
"title": "$:/core/modules/server/routes/get-status.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/get-status.js\ntype: application/javascript\nmodule-type: route\n\nGET /status\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.method = \"GET\";\n\nexports.path = /^\\/status$/;\n\nexports.handler = function(request,response,state) {\n\tresponse.writeHead(200, {\"Content-Type\": \"application/json\"});\n\tvar text = JSON.stringify({\n\t\tusername: state.authenticatedUsername || state.server.get(\"anon-username\") || \"\",\n\t\tanonymous: !state.authenticatedUsername,\n\t\tread_only: !state.server.isAuthorized(\"writers\",state.authenticatedUsername),\n\t\tspace: {\n\t\t\trecipe: \"default\"\n\t\t},\n\t\ttiddlywiki_version: $tw.version\n\t});\n\tresponse.end(text,\"utf8\");\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/routes/get-tiddler-html.js": {
"title": "$:/core/modules/server/routes/get-tiddler-html.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/get-tiddler-html.js\ntype: application/javascript\nmodule-type: route\n\nGET /:title\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.method = \"GET\";\n\nexports.path = /^\\/([^\\/]+)$/;\n\nexports.handler = function(request,response,state) {\n\tvar title = decodeURIComponent(state.params[0]),\n\t\ttiddler = state.wiki.getTiddler(title);\n\tif(tiddler) {\n\t\tvar renderType = tiddler.getFieldString(\"_render_type\"),\n\t\t\trenderTemplate = tiddler.getFieldString(\"_render_template\");\n\t\t// Tiddler fields '_render_type' and '_render_template' overwrite\n\t\t// system wide settings for render type and template\n\t\tif(state.wiki.isSystemTiddler(title)) {\n\t\t\trenderType = renderType || state.server.get(\"system-tiddler-render-type\");\n\t\t\trenderTemplate = renderTemplate || state.server.get(\"system-tiddler-render-template\");\n\t\t} else {\n\t\t\trenderType = renderType || state.server.get(\"tiddler-render-type\");\n\t\t\trenderTemplate = renderTemplate || state.server.get(\"tiddler-render-template\");\n\t\t}\n\t\tvar text = state.wiki.renderTiddler(renderType,renderTemplate,{parseAsInline: true, variables: {currentTiddler: title}});\n\t\t// Naughty not to set a content-type, but it's the easiest way to ensure the browser will see HTML pages as HTML, and accept plain text tiddlers as CSS or JS\n\t\tresponse.writeHead(200);\n\t\tresponse.end(text,\"utf8\");\n\t} else {\n\t\tresponse.writeHead(404);\n\t\tresponse.end();\n\t}\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/routes/get-tiddler.js": {
"title": "$:/core/modules/server/routes/get-tiddler.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/get-tiddler.js\ntype: application/javascript\nmodule-type: route\n\nGET /recipes/default/tiddlers/:title\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.method = \"GET\";\n\nexports.path = /^\\/recipes\\/default\\/tiddlers\\/(.+)$/;\n\nexports.handler = function(request,response,state) {\n\tvar title = decodeURIComponent(state.params[0]),\n\t\ttiddler = state.wiki.getTiddler(title),\n\t\ttiddlerFields = {},\n\t\tknownFields = [\n\t\t\t\"bag\", \"created\", \"creator\", \"modified\", \"modifier\", \"permissions\", \"recipe\", \"revision\", \"tags\", \"text\", \"title\", \"type\", \"uri\"\n\t\t];\n\tif(tiddler) {\n\t\t$tw.utils.each(tiddler.fields,function(field,name) {\n\t\t\tvar value = tiddler.getFieldString(name);\n\t\t\tif(knownFields.indexOf(name) !== -1) {\n\t\t\t\ttiddlerFields[name] = value;\n\t\t\t} else {\n\t\t\t\ttiddlerFields.fields = tiddlerFields.fields || {};\n\t\t\t\ttiddlerFields.fields[name] = value;\n\t\t\t}\n\t\t});\n\t\ttiddlerFields.revision = state.wiki.getChangeCount(title);\n\t\ttiddlerFields.bag = \"default\";\n\t\ttiddlerFields.type = tiddlerFields.type || \"text/vnd.tiddlywiki\";\n\t\tresponse.writeHead(200, {\"Content-Type\": \"application/json\"});\n\t\tresponse.end(JSON.stringify(tiddlerFields),\"utf8\");\n\t} else {\n\t\tresponse.writeHead(404);\n\t\tresponse.end();\n\t}\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/routes/get-tiddlers-json.js": {
"title": "$:/core/modules/server/routes/get-tiddlers-json.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/get-tiddlers-json.js\ntype: application/javascript\nmodule-type: route\n\nGET /recipes/default/tiddlers/tiddlers.json?filter=<filter>\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar DEFAULT_FILTER = \"[all[tiddlers]!is[system]sort[title]]\";\n\nexports.method = \"GET\";\n\nexports.path = /^\\/recipes\\/default\\/tiddlers.json$/;\n\nexports.handler = function(request,response,state) {\n\tvar filter = state.queryParameters.filter || DEFAULT_FILTER;\n\tif($tw.wiki.getTiddlerText(\"$:/config/Server/AllowAllExternalFilters\") !== \"yes\") {\n\t\tif($tw.wiki.getTiddlerText(\"$:/config/Server/ExternalFilters/\" + filter) !== \"yes\") {\n\t\t\tconsole.log(\"Blocked attempt to GET /recipes/default/tiddlers/tiddlers.json with filter: \" + filter);\n\t\t\tresponse.writeHead(403);\n\t\t\tresponse.end();\n\t\t\treturn;\n\t\t}\n\t}\n\tvar excludeFields = (state.queryParameters.exclude || \"text\").split(\",\"),\n\t\ttitles = state.wiki.filterTiddlers(filter);\n\tresponse.writeHead(200, {\"Content-Type\": \"application/json\"});\n\tvar tiddlers = [];\n\t$tw.utils.each(titles,function(title) {\n\t\tvar tiddler = state.wiki.getTiddler(title);\n\t\tif(tiddler) {\n\t\t\tvar tiddlerFields = tiddler.getFieldStrings({exclude: excludeFields});\n\t\t\ttiddlerFields.revision = state.wiki.getChangeCount(title);\n\t\t\ttiddlerFields.type = tiddlerFields.type || \"text/vnd.tiddlywiki\";\n\t\t\ttiddlers.push(tiddlerFields);\n\t\t}\n\t});\n\tvar text = JSON.stringify(tiddlers);\n\tresponse.end(text,\"utf8\");\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/routes/put-tiddler.js": {
"title": "$:/core/modules/server/routes/put-tiddler.js",
"text": "/*\\\ntitle: $:/core/modules/server/routes/put-tiddler.js\ntype: application/javascript\nmodule-type: route\n\nPUT /recipes/default/tiddlers/:title\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.method = \"PUT\";\n\nexports.path = /^\\/recipes\\/default\\/tiddlers\\/(.+)$/;\n\nexports.handler = function(request,response,state) {\n\tvar title = decodeURIComponent(state.params[0]),\n\tfields = JSON.parse(state.data);\n\t// Pull up any subfields in the `fields` object\n\tif(fields.fields) {\n\t\t$tw.utils.each(fields.fields,function(field,name) {\n\t\t\tfields[name] = field;\n\t\t});\n\t\tdelete fields.fields;\n\t}\n\t// Remove any revision field\n\tif(fields.revision) {\n\t\tdelete fields.revision;\n\t}\n\tstate.wiki.addTiddler(new $tw.Tiddler(state.wiki.getCreationFields(),fields,{title: title},state.wiki.getModificationFields()));\n\tvar changeCount = state.wiki.getChangeCount(title).toString();\n\tresponse.writeHead(204, \"OK\",{\n\t\tEtag: \"\\\"default/\" + encodeURIComponent(title) + \"/\" + changeCount + \":\\\"\",\n\t\t\"Content-Type\": \"text/plain\"\n\t});\n\tresponse.end();\n};\n\n}());\n",
"type": "application/javascript",
"module-type": "route"
},
"$:/core/modules/server/server.js": {
"title": "$:/core/modules/server/server.js",
"text": "/*\\\ntitle: $:/core/modules/server/server.js\ntype: application/javascript\nmodule-type: library\n\nServe tiddlers over http\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nif($tw.node) {\n\tvar util = require(\"util\"),\n\t\tfs = require(\"fs\"),\n\t\turl = require(\"url\"),\n\t\tpath = require(\"path\"),\n\t\tquerystring = require(\"querystring\");\n}\n\n/*\nA simple HTTP server with regexp-based routes\noptions: variables - optional hashmap of variables to set (a misnomer - they are really constant parameters)\n\t\t routes - optional array of routes to use\n\t\t wiki - reference to wiki object\n*/\nfunction Server(options) {\n\tvar self = this;\n\tthis.routes = options.routes || [];\n\tthis.authenticators = options.authenticators || [];\n\tthis.wiki = options.wiki;\n\tthis.servername = $tw.utils.transliterateToSafeASCII(this.wiki.getTiddlerText(\"$:/SiteTitle\") || \"TiddlyWiki5\");\n\t// Initialise the variables\n\tthis.variables = $tw.utils.extend({},this.defaultVariables);\n\tif(options.variables) {\n\t\tfor(var variable in options.variables) {\n\t\t\tif(options.variables[variable]) {\n\t\t\t\tthis.variables[variable] = options.variables[variable];\n\t\t\t}\n\t\t}\t\t\n\t}\n\t$tw.utils.extend({},this.defaultVariables,options.variables);\n\t// Initialise CSRF\n\tthis.csrfDisable = this.get(\"csrf-disable\") === \"yes\";\n\t// Initialize Gzip compression\n\tthis.enableGzip = this.get(\"gzip\") === \"yes\";\n\t// Initialise authorization\n\tvar authorizedUserName = (this.get(\"username\") && this.get(\"password\")) ? this.get(\"username\") : \"(anon)\";\n\tthis.authorizationPrincipals = {\n\t\treaders: (this.get(\"readers\") || authorizedUserName).split(\",\").map($tw.utils.trim),\n\t\twriters: (this.get(\"writers\") || authorizedUserName).split(\",\").map($tw.utils.trim)\n\t}\n\t// Load and initialise authenticators\n\t$tw.modules.forEachModuleOfType(\"authenticator\", function(title,authenticatorDefinition) {\n\t\t// console.log(\"Loading server route \" + title);\n\t\tself.addAuthenticator(authenticatorDefinition.AuthenticatorClass);\n\t});\n\t// Load route handlers\n\t$tw.modules.forEachModuleOfType(\"route\", function(title,routeDefinition) {\n\t\t// console.log(\"Loading server route \" + title);\n\t\tself.addRoute(routeDefinition);\n\t});\n\t// Initialise the http vs https\n\tthis.listenOptions = null;\n\tthis.protocol = \"http\";\n\tvar tlsKeyFilepath = this.get(\"tls-key\"),\n\t\ttlsCertFilepath = this.get(\"tls-cert\");\n\tif(tlsCertFilepath && tlsKeyFilepath) {\n\t\tthis.listenOptions = {\n\t\t\tkey: fs.readFileSync(path.resolve($tw.boot.wikiPath,tlsKeyFilepath),\"utf8\"),\n\t\t\tcert: fs.readFileSync(path.resolve($tw.boot.wikiPath,tlsCertFilepath),\"utf8\")\n\t\t};\n\t\tthis.protocol = \"https\";\n\t}\n\tthis.transport = require(this.protocol);\n}\n\nServer.prototype.defaultVariables = {\n\tport: \"8080\",\n\thost: \"127.0.0.1\",\n\t\"root-tiddler\": \"$:/core/save/all\",\n\t\"root-render-type\": \"text/plain\",\n\t\"root-serve-type\": \"text/html\",\n\t\"tiddler-render-type\": \"text/html\",\n\t\"tiddler-render-template\": \"$:/core/templates/server/static.tiddler.html\",\n\t\"system-tiddler-render-type\": \"text/plain\",\n\t\"system-tiddler-render-template\": \"$:/core/templates/wikified-tiddler\",\n\t\"debug-level\": \"none\",\n\t\"gzip\": \"no\"\n};\n\nServer.prototype.get = function(name) {\n\treturn this.variables[name];\n};\n\nServer.prototype.addRoute = function(route) {\n\tthis.routes.push(route);\n};\n\nServer.prototype.addAuthenticator = function(AuthenticatorClass) {\n\t// Instantiate and initialise the authenticator\n\tvar authenticator = new AuthenticatorClass(this),\n\t\tresult = authenticator.init();\n\tif(typeof result === \"string\") {\n\t\t$tw.utils.error(\"Error: \" + result);\n\t} else if(result) {\n\t\t// Only use the authenticator if it initialised successfully\n\t\tthis.authenticators.push(authenticator);\n\t}\n};\n\nServer.prototype.findMatchingRoute = function(request,state) {\n\tvar pathprefix = this.get(\"path-prefix\") || \"\";\n\tfor(var t=0; t<this.routes.length; t++) {\n\t\tvar potentialRoute = this.routes[t],\n\t\t\tpathRegExp = potentialRoute.path,\n\t\t\tpathname = state.urlInfo.pathname,\n\t\t\tmatch;\n\t\tif(pathprefix) {\n\t\t\tif(pathname.substr(0,pathprefix.length) === pathprefix) {\n\t\t\t\tpathname = pathname.substr(pathprefix.length) || \"/\";\n\t\t\t\tmatch = potentialRoute.path.exec(pathname);\n\t\t\t} else {\n\t\t\t\tmatch = false;\n\t\t\t}\n\t\t} else {\n\t\t\tmatch = potentialRoute.path.exec(pathname);\n\t\t}\n\t\tif(match && request.method === potentialRoute.method) {\n\t\t\tstate.params = [];\n\t\t\tfor(var p=1; p<match.length; p++) {\n\t\t\t\tstate.params.push(match[p]);\n\t\t\t}\n\t\t\treturn potentialRoute;\n\t\t}\n\t}\n\treturn null;\n};\n\nServer.prototype.methodMappings = {\n\t\"GET\": \"readers\",\n\t\"OPTIONS\": \"readers\",\n\t\"HEAD\": \"readers\",\n\t\"PUT\": \"writers\",\n\t\"POST\": \"writers\",\n\t\"DELETE\": \"writers\"\n};\n\n/*\nCheck whether a given user is authorized for the specified authorizationType (\"readers\" or \"writers\"). Pass null or undefined as the username to check for anonymous access\n*/\nServer.prototype.isAuthorized = function(authorizationType,username) {\n\tvar principals = this.authorizationPrincipals[authorizationType] || [];\n\treturn principals.indexOf(\"(anon)\") !== -1 || (username && (principals.indexOf(\"(authenticated)\") !== -1 || principals.indexOf(username) !== -1));\n}\n\nServer.prototype.requestHandler = function(request,response) {\n\t// Compose the state object\n\tvar self = this;\n\tvar state = {};\n\tstate.wiki = self.wiki;\n\tstate.server = self;\n\tstate.urlInfo = url.parse(request.url);\n\tstate.queryParameters = querystring.parse(state.urlInfo.query);\n\t// Get the principals authorized to access this resource\n\tvar authorizationType = this.methodMappings[request.method] || \"readers\";\n\t// Check for the CSRF header if this is a write\n\tif(!this.csrfDisable && authorizationType === \"writers\" && request.headers[\"x-requested-with\"] !== \"TiddlyWiki\") {\n\t\tresponse.writeHead(403,\"'X-Requested-With' header required to login to '\" + this.servername + \"'\");\n\t\tresponse.end();\n\t\treturn;\t\t\n\t}\n\t// Check whether anonymous access is granted\n\tstate.allowAnon = this.isAuthorized(authorizationType,null);\n\t// Authenticate with the first active authenticator\n\tif(this.authenticators.length > 0) {\n\t\tif(!this.authenticators[0].authenticateRequest(request,response,state)) {\n\t\t\t// Bail if we failed (the authenticator will have sent the response)\n\t\t\treturn;\n\t\t}\t\t\n\t}\n\t// Authorize with the authenticated username\n\tif(!this.isAuthorized(authorizationType,state.authenticatedUsername)) {\n\t\tresponse.writeHead(401,\"'\" + state.authenticatedUsername + \"' is not authorized to access '\" + this.servername + \"'\");\n\t\tresponse.end();\n\t\treturn;\n\t}\n\t// Find the route that matches this path\n\tvar route = self.findMatchingRoute(request,state);\n\t// Optionally output debug info\n\tif(self.get(\"debug-level\") !== \"none\") {\n\t\tconsole.log(\"Request path:\",JSON.stringify(state.urlInfo));\n\t\tconsole.log(\"Request headers:\",JSON.stringify(request.headers));\n\t\tconsole.log(\"authenticatedUsername:\",state.authenticatedUsername);\n\t}\n\t// Return a 404 if we didn't find a route\n\tif(!route) {\n\t\tresponse.writeHead(404);\n\t\tresponse.end();\n\t\treturn;\n\t}\n\t// Receive the request body if necessary and hand off to the route handler\n\tif(route.bodyFormat === \"stream\" || request.method === \"GET\" || request.method === \"HEAD\") {\n\t\t// Let the route handle the request stream itself\n\t\troute.handler(request,response,state);\n\t} else if(route.bodyFormat === \"string\" || !route.bodyFormat) {\n\t\t// Set the encoding for the incoming request\n\t\trequest.setEncoding(\"utf8\");\n\t\tvar data = \"\";\n\t\trequest.on(\"data\",function(chunk) {\n\t\t\tdata += chunk.toString();\n\t\t});\n\t\trequest.on(\"end\",function() {\n\t\t\tstate.data = data;\n\t\t\troute.handler(request,response,state);\n\t\t});\n\t} else if(route.bodyFormat === \"buffer\") {\n\t\tvar data = [];\n\t\trequest.on(\"data\",function(chunk) {\n\t\t\tdata.push(chunk);\n\t\t});\n\t\trequest.on(\"end\",function() {\n\t\t\tstate.data = Buffer.concat(data);\n\t\t\troute.handler(request,response,state);\n\t\t})\n\t} else {\n\t\tresponse.writeHead(400,\"Invalid bodyFormat \" + route.bodyFormat + \" in route \" + route.method + \" \" + route.path.source);\n\t\tresponse.end();\n\t}\n};\n\n/*\nListen for requests\nport: optional port number (falls back to value of \"port\" variable)\nhost: optional host address (falls back to value of \"host\" variable)\nprefix: optional prefix (falls back to value of \"path-prefix\" variable)\n*/\nServer.prototype.listen = function(port,host,prefix) {\n\tvar self = this;\n\t// Handle defaults for port and host\n\tport = port || this.get(\"port\");\n\thost = host || this.get(\"host\");\n\tprefix = prefix || this.get(\"path-prefix\") || \"\";\n\t// Check for the port being a string and look it up as an environment variable\n\tif(parseInt(port,10).toString() !== port) {\n\t\tport = process.env[port] || 8080;\n\t}\n\t// Warn if required plugins are missing\n\tif(!$tw.wiki.getTiddler(\"$:/plugins/tiddlywiki/tiddlyweb\") || !$tw.wiki.getTiddler(\"$:/plugins/tiddlywiki/filesystem\")) {\n\t\t$tw.utils.warning(\"Warning: Plugins required for client-server operation (\\\"tiddlywiki/filesystem\\\" and \\\"tiddlywiki/tiddlyweb\\\") are missing from tiddlywiki.info file\");\n\t}\n\t// Create the server\n\tvar server;\n\tif(this.listenOptions) {\n\t\tserver = this.transport.createServer(this.listenOptions,this.requestHandler.bind(this));\n\t} else {\n\t\tserver = this.transport.createServer(this.requestHandler.bind(this));\n\t}\n\t// Display the port number after we've started listening (the port number might have been specified as zero, in which case we will get an assigned port)\n\tserver.on(\"listening\",function() {\n\t\tvar address = server.address();\n\t\t$tw.utils.log(\"Serving on \" + self.protocol + \"://\" + address.address + \":\" + address.port + prefix,\"brown/orange\");\n\t\t$tw.utils.log(\"(press ctrl-C to exit)\",\"red\");\n\t});\n\t// Listen\n\treturn server.listen(port,host);\n};\n\nexports.Server = Server;\n\n})();\n",
"type": "application/javascript",
"module-type": "library"
},
"$:/core/modules/browser-messaging.js": {
"title": "$:/core/modules/browser-messaging.js",
"text": "/*\\\ntitle: $:/core/modules/browser-messaging.js\ntype: application/javascript\nmodule-type: startup\n\nBrowser message handling\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"browser-messaging\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\n/*\nLoad a specified url as an iframe and call the callback when it is loaded. If the url is already loaded then the existing iframe instance is used\n*/\nfunction loadIFrame(url,callback) {\n\t// Check if iframe already exists\n\tvar iframeInfo = $tw.browserMessaging.iframeInfoMap[url];\n\tif(iframeInfo) {\n\t\t// We've already got the iframe\n\t\tcallback(null,iframeInfo);\n\t} else {\n\t\t// Create the iframe and save it in the list\n\t\tvar iframe = document.createElement(\"iframe\");\n\t\tiframeInfo = {\n\t\t\turl: url,\n\t\t\tstatus: \"loading\",\n\t\t\tdomNode: iframe\n\t\t};\n\t\t$tw.browserMessaging.iframeInfoMap[url] = iframeInfo;\n\t\tsaveIFrameInfoTiddler(iframeInfo);\n\t\t// Add the iframe to the DOM and hide it\n\t\tiframe.style.display = \"none\";\n\t\tiframe.setAttribute(\"library\",\"true\");\n\t\tdocument.body.appendChild(iframe);\n\t\t// Set up onload\n\t\tiframe.onload = function() {\n\t\t\tiframeInfo.status = \"loaded\";\n\t\t\tsaveIFrameInfoTiddler(iframeInfo);\n\t\t\tcallback(null,iframeInfo);\n\t\t};\n\t\tiframe.onerror = function() {\n\t\t\tcallback(\"Cannot load iframe\");\n\t\t};\n\t\ttry {\n\t\t\tiframe.src = url;\n\t\t} catch(ex) {\n\t\t\tcallback(ex);\n\t\t}\n\t}\n}\n\n/*\nUnload library iframe for given url\n*/\nfunction unloadIFrame(url){\n\t$tw.utils.each(document.getElementsByTagName('iframe'), function(iframe) {\n\t\tif(iframe.getAttribute(\"library\") === \"true\" &&\n\t\t iframe.getAttribute(\"src\") === url) {\n\t\t\tiframe.parentNode.removeChild(iframe);\n\t\t}\n\t});\n}\n\nfunction saveIFrameInfoTiddler(iframeInfo) {\n\t$tw.wiki.addTiddler(new $tw.Tiddler($tw.wiki.getCreationFields(),{\n\t\ttitle: \"$:/temp/ServerConnection/\" + iframeInfo.url,\n\t\ttext: iframeInfo.status,\n\t\ttags: [\"$:/tags/ServerConnection\"],\n\t\turl: iframeInfo.url\n\t},$tw.wiki.getModificationFields()));\n}\n\nexports.startup = function() {\n\t// Initialise the store of iframes we've created\n\t$tw.browserMessaging = {\n\t\tiframeInfoMap: {} // Hashmap by URL of {url:,status:\"loading/loaded\",domNode:}\n\t};\n\t// Listen for widget messages to control loading the plugin library\n\t$tw.rootWidget.addEventListener(\"tm-load-plugin-library\",function(event) {\n\t\tvar paramObject = event.paramObject || {},\n\t\t\turl = paramObject.url;\n\t\tif(url) {\n\t\t\tloadIFrame(url,function(err,iframeInfo) {\n\t\t\t\tif(err) {\n\t\t\t\t\talert($tw.language.getString(\"Error/LoadingPluginLibrary\") + \": \" + url);\n\t\t\t\t} else {\n\t\t\t\t\tiframeInfo.domNode.contentWindow.postMessage({\n\t\t\t\t\t\tverb: \"GET\",\n\t\t\t\t\t\turl: \"recipes/library/tiddlers.json\",\n\t\t\t\t\t\tcookies: {\n\t\t\t\t\t\t\ttype: \"save-info\",\n\t\t\t\t\t\t\tinfoTitlePrefix: paramObject.infoTitlePrefix || \"$:/temp/RemoteAssetInfo/\",\n\t\t\t\t\t\t\turl: url\n\t\t\t\t\t\t}\n\t\t\t\t\t},\"*\");\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n\t// Listen for widget messages to control unloading the plugin library\n\t$tw.rootWidget.addEventListener(\"tm-unload-plugin-library\",function(event) {\n\t\tvar paramObject = event.paramObject || {},\n\t\t\turl = paramObject.url;\n\t\t$tw.browserMessaging.iframeInfoMap[url] = undefined;\n\t\tif(url) {\n\t\t\tunloadIFrame(url);\n\t\t\t$tw.utils.each(\n\t\t\t\t$tw.wiki.filterTiddlers(\"[[$:/temp/ServerConnection/\" + url + \"]] [prefix[$:/temp/RemoteAssetInfo/\" + url + \"/]]\"),\n\t\t\t\tfunction(title) {\n\t\t\t\t\t$tw.wiki.deleteTiddler(title);\n\t\t\t\t}\n\t\t\t);\n\t\t}\n\t});\n\t$tw.rootWidget.addEventListener(\"tm-load-plugin-from-library\",function(event) {\n\t\tvar paramObject = event.paramObject || {},\n\t\t\turl = paramObject.url,\n\t\t\ttitle = paramObject.title;\n\t\tif(url && title) {\n\t\t\tloadIFrame(url,function(err,iframeInfo) {\n\t\t\t\tif(err) {\n\t\t\t\t\talert($tw.language.getString(\"Error/LoadingPluginLibrary\") + \": \" + url);\n\t\t\t\t} else {\n\t\t\t\t\tiframeInfo.domNode.contentWindow.postMessage({\n\t\t\t\t\t\tverb: \"GET\",\n\t\t\t\t\t\turl: \"recipes/library/tiddlers/\" + encodeURIComponent(title) + \".json\",\n\t\t\t\t\t\tcookies: {\n\t\t\t\t\t\t\ttype: \"save-tiddler\",\n\t\t\t\t\t\t\turl: url\n\t\t\t\t\t\t}\n\t\t\t\t\t},\"*\");\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n\t// Listen for window messages from other windows\n\twindow.addEventListener(\"message\",function listener(event){\n\t\t// console.log(\"browser-messaging: \",document.location.toString())\n\t\t// console.log(\"browser-messaging: Received message from\",event.origin);\n\t\t// console.log(\"browser-messaging: Message content\",event.data);\n\t\tswitch(event.data.verb) {\n\t\t\tcase \"GET-RESPONSE\":\n\t\t\t\tif(event.data.status.charAt(0) === \"2\") {\n\t\t\t\t\tif(event.data.cookies) {\n\t\t\t\t\t\tif(event.data.cookies.type === \"save-info\") {\n\t\t\t\t\t\t\tvar tiddlers = JSON.parse(event.data.body);\n\t\t\t\t\t\t\t$tw.utils.each(tiddlers,function(tiddler) {\n\t\t\t\t\t\t\t\t$tw.wiki.addTiddler(new $tw.Tiddler($tw.wiki.getCreationFields(),tiddler,{\n\t\t\t\t\t\t\t\t\ttitle: event.data.cookies.infoTitlePrefix + event.data.cookies.url + \"/\" + tiddler.title,\n\t\t\t\t\t\t\t\t\t\"original-title\": tiddler.title,\n\t\t\t\t\t\t\t\t\ttext: \"\",\n\t\t\t\t\t\t\t\t\ttype: \"text/vnd.tiddlywiki\",\n\t\t\t\t\t\t\t\t\t\"original-type\": tiddler.type,\n\t\t\t\t\t\t\t\t\t\"plugin-type\": undefined,\n\t\t\t\t\t\t\t\t\t\"original-plugin-type\": tiddler[\"plugin-type\"],\n\t\t\t\t\t\t\t\t\t\"module-type\": undefined,\n\t\t\t\t\t\t\t\t\t\"original-module-type\": tiddler[\"module-type\"],\n\t\t\t\t\t\t\t\t\ttags: [\"$:/tags/RemoteAssetInfo\"],\n\t\t\t\t\t\t\t\t\t\"original-tags\": $tw.utils.stringifyList(tiddler.tags || []),\n\t\t\t\t\t\t\t\t\t\"server-url\": event.data.cookies.url\n\t\t\t\t\t\t\t\t},$tw.wiki.getModificationFields()));\n\t\t\t\t\t\t\t});\n\t\t\t\t\t\t} else if(event.data.cookies.type === \"save-tiddler\") {\n\t\t\t\t\t\t\tvar tiddler = JSON.parse(event.data.body);\n\t\t\t\t\t\t\t$tw.wiki.addTiddler(new $tw.Tiddler(tiddler));\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t}\n\t},false);\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/commands.js": {
"title": "$:/core/modules/startup/commands.js",
"text": "/*\\\ntitle: $:/core/modules/startup/commands.js\ntype: application/javascript\nmodule-type: startup\n\nCommand processing\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"commands\";\nexports.platforms = [\"node\"];\nexports.after = [\"story\"];\nexports.synchronous = false;\n\nexports.startup = function(callback) {\n\t// On the server, start a commander with the command line arguments\n\tvar commander = new $tw.Commander(\n\t\t$tw.boot.argv,\n\t\tfunction(err) {\n\t\t\tif(err) {\n\t\t\t\treturn $tw.utils.error(\"Error: \" + err);\n\t\t\t}\n\t\t\tcallback();\n\t\t},\n\t\t$tw.wiki,\n\t\t{output: process.stdout, error: process.stderr}\n\t);\n\tcommander.execute();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/CSSescape.js": {
"title": "$:/core/modules/startup/CSSescape.js",
"text": "/*\\\ntitle: $:/core/modules/startup/CSSescape.js\ntype: application/javascript\nmodule-type: startup\n\nPolyfill for CSS.escape()\n\n\\*/\n(function(root,factory){\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"css-escape\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\n/*! https://mths.be/cssescape v1.5.1 by @mathias | MIT license */\n// https://github.com/umdjs/umd/blob/master/returnExports.js\nexports.startup = factory(root);\n}(typeof global != 'undefined' ? global : this, function(root) {\n\n\tif (root.CSS && root.CSS.escape) {\n\t\treturn;\n\t}\n\n\t// https://drafts.csswg.org/cssom/#serialize-an-identifier\n\tvar cssEscape = function(value) {\n\t\tif (arguments.length == 0) {\n\t\t\tthrow new TypeError('`CSS.escape` requires an argument.');\n\t\t}\n\t\tvar string = String(value);\n\t\tvar length = string.length;\n\t\tvar index = -1;\n\t\tvar codeUnit;\n\t\tvar result = '';\n\t\tvar firstCodeUnit = string.charCodeAt(0);\n\t\twhile (++index < length) {\n\t\t\tcodeUnit = string.charCodeAt(index);\n\t\t\t// Note: there’s no need to special-case astral symbols, surrogate\n\t\t\t// pairs, or lone surrogates.\n\n\t\t\t// If the character is NULL (U+0000), then the REPLACEMENT CHARACTER\n\t\t\t// (U+FFFD).\n\t\t\tif (codeUnit == 0x0000) {\n\t\t\t\tresult += '\\uFFFD';\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tif (\n\t\t\t\t// If the character is in the range [\\1-\\1F] (U+0001 to U+001F) or is\n\t\t\t\t// U+007F, […]\n\t\t\t\t(codeUnit >= 0x0001 && codeUnit <= 0x001F) || codeUnit == 0x007F ||\n\t\t\t\t// If the character is the first character and is in the range [0-9]\n\t\t\t\t// (U+0030 to U+0039), […]\n\t\t\t\t(index == 0 && codeUnit >= 0x0030 && codeUnit <= 0x0039) ||\n\t\t\t\t// If the character is the second character and is in the range [0-9]\n\t\t\t\t// (U+0030 to U+0039) and the first character is a `-` (U+002D), […]\n\t\t\t\t(\n\t\t\t\t\tindex == 1 &&\n\t\t\t\t\tcodeUnit >= 0x0030 && codeUnit <= 0x0039 &&\n\t\t\t\t\tfirstCodeUnit == 0x002D\n\t\t\t\t)\n\t\t\t) {\n\t\t\t\t// https://drafts.csswg.org/cssom/#escape-a-character-as-code-point\n\t\t\t\tresult += '\\\\' + codeUnit.toString(16) + ' ';\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tif (\n\t\t\t\t// If the character is the first character and is a `-` (U+002D), and\n\t\t\t\t// there is no second character, […]\n\t\t\t\tindex == 0 &&\n\t\t\t\tlength == 1 &&\n\t\t\t\tcodeUnit == 0x002D\n\t\t\t) {\n\t\t\t\tresult += '\\\\' + string.charAt(index);\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\t// If the character is not handled by one of the above rules and is\n\t\t\t// greater than or equal to U+0080, is `-` (U+002D) or `_` (U+005F), or\n\t\t\t// is in one of the ranges [0-9] (U+0030 to U+0039), [A-Z] (U+0041 to\n\t\t\t// U+005A), or [a-z] (U+0061 to U+007A), […]\n\t\t\tif (\n\t\t\t\tcodeUnit >= 0x0080 ||\n\t\t\t\tcodeUnit == 0x002D ||\n\t\t\t\tcodeUnit == 0x005F ||\n\t\t\t\tcodeUnit >= 0x0030 && codeUnit <= 0x0039 ||\n\t\t\t\tcodeUnit >= 0x0041 && codeUnit <= 0x005A ||\n\t\t\t\tcodeUnit >= 0x0061 && codeUnit <= 0x007A\n\t\t\t) {\n\t\t\t\t// the character itself\n\t\t\t\tresult += string.charAt(index);\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\t// Otherwise, the escaped character.\n\t\t\t// https://drafts.csswg.org/cssom/#escape-a-character\n\t\t\tresult += '\\\\' + string.charAt(index);\n\n\t\t}\n\t\treturn result;\n\t};\n\n\tif (!root.CSS) {\n\t\troot.CSS = {};\n\t}\n\n\troot.CSS.escape = cssEscape;\n\n}));\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/favicon.js": {
"title": "$:/core/modules/startup/favicon.js",
"text": "/*\\\ntitle: $:/core/modules/startup/favicon.js\ntype: application/javascript\nmodule-type: startup\n\nFavicon handling\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"favicon\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\t\t\n// Favicon tiddler\nvar FAVICON_TITLE = \"$:/favicon.ico\";\n\nexports.startup = function() {\n\t// Set up the favicon\n\tsetFavicon();\n\t// Reset the favicon when the tiddler changes\n\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\tif($tw.utils.hop(changes,FAVICON_TITLE)) {\n\t\t\tsetFavicon();\n\t\t}\n\t});\n};\n\nfunction setFavicon() {\n\tvar tiddler = $tw.wiki.getTiddler(FAVICON_TITLE);\n\tif(tiddler) {\n\t\tvar faviconLink = document.getElementById(\"faviconLink\");\n\t\tfaviconLink.setAttribute(\"href\",\"data:\" + tiddler.fields.type + \";base64,\" + tiddler.fields.text);\n\t}\n}\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/info.js": {
"title": "$:/core/modules/startup/info.js",
"text": "/*\\\ntitle: $:/core/modules/startup/info.js\ntype: application/javascript\nmodule-type: startup\n\nInitialise $:/info tiddlers via $:/temp/info-plugin pseudo-plugin\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"info\";\nexports.before = [\"startup\"];\nexports.after = [\"load-modules\"];\nexports.synchronous = true;\n\nvar TITLE_INFO_PLUGIN = \"$:/temp/info-plugin\";\n\nexports.startup = function() {\n\t// Collect up the info tiddlers\n\tvar infoTiddlerFields = {};\n\t// Give each info module a chance to fill in as many info tiddlers as they want\n\t$tw.modules.forEachModuleOfType(\"info\",function(title,moduleExports) {\n\t\tif(moduleExports && moduleExports.getInfoTiddlerFields) {\n\t\t\tvar tiddlerFieldsArray = moduleExports.getInfoTiddlerFields(infoTiddlerFields);\n\t\t\t$tw.utils.each(tiddlerFieldsArray,function(fields) {\n\t\t\t\tif(fields) {\n\t\t\t\t\tinfoTiddlerFields[fields.title] = fields;\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n\t// Bake the info tiddlers into a plugin. We use the non-standard plugin-type \"info\" because ordinary plugins are only registered asynchronously after being loaded dynamically\n\tvar fields = {\n\t\ttitle: TITLE_INFO_PLUGIN,\n\t\ttype: \"application/json\",\n\t\t\"plugin-type\": \"info\",\n\t\ttext: JSON.stringify({tiddlers: infoTiddlerFields},null,$tw.config.preferences.jsonSpaces)\n\t};\n\t$tw.wiki.addTiddler(new $tw.Tiddler(fields));\n\t$tw.wiki.readPluginInfo([TITLE_INFO_PLUGIN]);\n\t$tw.wiki.registerPluginTiddlers(\"info\");\n\t$tw.wiki.unpackPluginTiddlers();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/load-modules.js": {
"title": "$:/core/modules/startup/load-modules.js",
"text": "/*\\\ntitle: $:/core/modules/startup/load-modules.js\ntype: application/javascript\nmodule-type: startup\n\nLoad core modules\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"load-modules\";\nexports.synchronous = true;\n\nexports.startup = function() {\n\t// Load modules\n\t$tw.modules.applyMethods(\"utils\",$tw.utils);\n\tif($tw.node) {\n\t\t$tw.modules.applyMethods(\"utils-node\",$tw.utils);\n\t}\n\t$tw.modules.applyMethods(\"global\",$tw);\n\t$tw.modules.applyMethods(\"config\",$tw.config);\n\t$tw.Tiddler.fieldModules = $tw.modules.getModulesByTypeAsHashmap(\"tiddlerfield\");\n\t$tw.modules.applyMethods(\"tiddlermethod\",$tw.Tiddler.prototype);\n\t$tw.modules.applyMethods(\"wikimethod\",$tw.Wiki.prototype);\n\t$tw.wiki.addIndexersToWiki();\n\t$tw.modules.applyMethods(\"tiddlerdeserializer\",$tw.Wiki.tiddlerDeserializerModules);\n\t$tw.macros = $tw.modules.getModulesByTypeAsHashmap(\"macro\");\n\t$tw.wiki.initParsers();\n\t$tw.Commander.initCommands();\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/password.js": {
"title": "$:/core/modules/startup/password.js",
"text": "/*\\\ntitle: $:/core/modules/startup/password.js\ntype: application/javascript\nmodule-type: startup\n\nPassword handling\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"password\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\nexports.startup = function() {\n\t$tw.rootWidget.addEventListener(\"tm-set-password\",function(event) {\n\t\t$tw.passwordPrompt.createPrompt({\n\t\t\tserviceName: $tw.language.getString(\"Encryption/PromptSetPassword\"),\n\t\t\tnoUserName: true,\n\t\t\tsubmitText: $tw.language.getString(\"Encryption/SetPassword\"),\n\t\t\tcanCancel: true,\n\t\t\trepeatPassword: true,\n\t\t\tcallback: function(data) {\n\t\t\t\tif(data) {\n\t\t\t\t\t$tw.crypto.setPassword(data.password);\n\t\t\t\t}\n\t\t\t\treturn true; // Get rid of the password prompt\n\t\t\t}\n\t\t});\n\t});\n\t$tw.rootWidget.addEventListener(\"tm-clear-password\",function(event) {\n\t\tif($tw.browser) {\n\t\t\tif(!confirm($tw.language.getString(\"Encryption/ConfirmClearPassword\"))) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t}\n\t\t$tw.crypto.setPassword(null);\n\t});\n\t// Ensure that $:/isEncrypted is maintained properly\n\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\tif($tw.utils.hop(changes,\"$:/isEncrypted\")) {\n\t\t\t$tw.crypto.updateCryptoStateTiddler();\n\t\t}\n\t});\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/plugins.js": {
"title": "$:/core/modules/startup/plugins.js",
"text": "/*\\\ntitle: $:/core/modules/startup/plugins.js\ntype: application/javascript\nmodule-type: startup\n\nStartup logic concerned with managing plugins\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"plugins\";\nexports.after = [\"load-modules\"];\nexports.synchronous = true;\n\nvar TITLE_REQUIRE_RELOAD_DUE_TO_PLUGIN_CHANGE = \"$:/status/RequireReloadDueToPluginChange\";\n\nvar PREFIX_CONFIG_REGISTER_PLUGIN_TYPE = \"$:/config/RegisterPluginType/\";\n\nexports.startup = function() {\n\t$tw.wiki.addTiddler({title: TITLE_REQUIRE_RELOAD_DUE_TO_PLUGIN_CHANGE,text: \"no\"});\n\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\tvar changesToProcess = [],\n\t\t\trequireReloadDueToPluginChange = false;\n\t\t$tw.utils.each(Object.keys(changes),function(title) {\n\t\t\tvar tiddler = $tw.wiki.getTiddler(title),\n\t\t\t\trequiresReload = $tw.wiki.doesPluginRequireReload(title);\n\t\t\tif(requiresReload) {\n\t\t\t\trequireReloadDueToPluginChange = true;\n\t\t\t} else if(tiddler) {\n\t\t\t\tvar pluginType = tiddler.fields[\"plugin-type\"];\n\t\t\t\tif($tw.wiki.getTiddlerText(PREFIX_CONFIG_REGISTER_PLUGIN_TYPE + (tiddler.fields[\"plugin-type\"] || \"\"),\"no\") === \"yes\") {\n\t\t\t\t\tchangesToProcess.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t\tif(requireReloadDueToPluginChange) {\n\t\t\t$tw.wiki.addTiddler({title: TITLE_REQUIRE_RELOAD_DUE_TO_PLUGIN_CHANGE,text: \"yes\"});\n\t\t}\n\t\t// Read or delete the plugin info of the changed tiddlers\n\t\tif(changesToProcess.length > 0) {\n\t\t\tvar changes = $tw.wiki.readPluginInfo(changesToProcess);\n\t\t\tif(changes.modifiedPlugins.length > 0 || changes.deletedPlugins.length > 0) {\n\t\t\t\t// (Re-)register any modified plugins\n\t\t\t\t$tw.wiki.registerPluginTiddlers(null,changes.modifiedPlugins);\n\t\t\t\t// Unregister any deleted plugins\n\t\t\t\t$tw.wiki.unregisterPluginTiddlers(null,changes.deletedPlugins);\n\t\t\t\t// Unpack the shadow tiddlers\n\t\t\t\t$tw.wiki.unpackPluginTiddlers();\n\t\t\t}\n\t\t}\n\t});\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/render.js": {
"title": "$:/core/modules/startup/render.js",
"text": "/*\\\ntitle: $:/core/modules/startup/render.js\ntype: application/javascript\nmodule-type: startup\n\nTitle, stylesheet and page rendering\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"render\";\nexports.platforms = [\"browser\"];\nexports.after = [\"story\"];\nexports.synchronous = true;\n\n// Default story and history lists\nvar PAGE_TITLE_TITLE = \"$:/core/wiki/title\";\nvar PAGE_STYLESHEET_TITLE = \"$:/core/ui/PageStylesheet\";\nvar PAGE_TEMPLATE_TITLE = \"$:/core/ui/PageTemplate\";\n\n// Time (in ms) that we defer refreshing changes to draft tiddlers\nvar DRAFT_TIDDLER_TIMEOUT_TITLE = \"$:/config/Drafts/TypingTimeout\";\nvar THROTTLE_REFRESH_TIMEOUT = 400;\n\nexports.startup = function() {\n\t// Set up the title\n\t$tw.titleWidgetNode = $tw.wiki.makeTranscludeWidget(PAGE_TITLE_TITLE,{document: $tw.fakeDocument, parseAsInline: true});\n\t$tw.titleContainer = $tw.fakeDocument.createElement(\"div\");\n\t$tw.titleWidgetNode.render($tw.titleContainer,null);\n\tdocument.title = $tw.titleContainer.textContent;\n\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\tif($tw.titleWidgetNode.refresh(changes,$tw.titleContainer,null)) {\n\t\t\tdocument.title = $tw.titleContainer.textContent;\n\t\t}\n\t});\n\t// Set up the styles\n\t$tw.styleWidgetNode = $tw.wiki.makeTranscludeWidget(PAGE_STYLESHEET_TITLE,{document: $tw.fakeDocument});\n\t$tw.styleContainer = $tw.fakeDocument.createElement(\"style\");\n\t$tw.styleWidgetNode.render($tw.styleContainer,null);\n\t$tw.styleElement = document.createElement(\"style\");\n\t$tw.styleElement.innerHTML = $tw.styleContainer.textContent;\n\tdocument.head.insertBefore($tw.styleElement,document.head.firstChild);\n\t$tw.wiki.addEventListener(\"change\",$tw.perf.report(\"styleRefresh\",function(changes) {\n\t\tif($tw.styleWidgetNode.refresh(changes,$tw.styleContainer,null)) {\n\t\t\t$tw.styleElement.innerHTML = $tw.styleContainer.textContent;\n\t\t}\n\t}));\n\t// Display the $:/core/ui/PageTemplate tiddler to kick off the display\n\t$tw.perf.report(\"mainRender\",function() {\n\t\t$tw.pageWidgetNode = $tw.wiki.makeTranscludeWidget(PAGE_TEMPLATE_TITLE,{document: document, parentWidget: $tw.rootWidget});\n\t\t$tw.pageContainer = document.createElement(\"div\");\n\t\t$tw.utils.addClass($tw.pageContainer,\"tc-page-container-wrapper\");\n\t\tdocument.body.insertBefore($tw.pageContainer,document.body.firstChild);\n\t\t$tw.pageWidgetNode.render($tw.pageContainer,null);\n \t\t$tw.hooks.invokeHook(\"th-page-refreshed\");\n\t})();\n\t// Remove any splash screen elements\n\tvar removeList = document.querySelectorAll(\".tc-remove-when-wiki-loaded\");\n\t$tw.utils.each(removeList,function(removeItem) {\n\t\tif(removeItem.parentNode) {\n\t\t\tremoveItem.parentNode.removeChild(removeItem);\n\t\t}\n\t});\n\t// Prepare refresh mechanism\n\tvar deferredChanges = Object.create(null),\n\t\ttimerId;\n\tfunction refresh() {\n\t\t// Process the refresh\n\t\t$tw.hooks.invokeHook(\"th-page-refreshing\");\n\t\t$tw.pageWidgetNode.refresh(deferredChanges);\n\t\tdeferredChanges = Object.create(null);\n\t\t$tw.hooks.invokeHook(\"th-page-refreshed\");\n\t}\n\t// Add the change event handler\n\t$tw.wiki.addEventListener(\"change\",$tw.perf.report(\"mainRefresh\",function(changes) {\n\t\t// Check if only tiddlers that are throttled have changed\n\t\tvar onlyThrottledTiddlersHaveChanged = true;\n\t\tfor(var title in changes) {\n\t\t\tvar tiddler = $tw.wiki.getTiddler(title);\n\t\t\tif(!tiddler || !(tiddler.hasField(\"draft.of\") || tiddler.hasField(\"throttle.refresh\"))) {\n\t\t\t\tonlyThrottledTiddlersHaveChanged = false;\n\t\t\t}\n\t\t}\n\t\t// Defer the change if only drafts have changed\n\t\tif(timerId) {\n\t\t\tclearTimeout(timerId);\n\t\t}\n\t\ttimerId = null;\n\t\tif(onlyThrottledTiddlersHaveChanged) {\n\t\t\tvar timeout = parseInt($tw.wiki.getTiddlerText(DRAFT_TIDDLER_TIMEOUT_TITLE,\"\"),10);\n\t\t\tif(isNaN(timeout)) {\n\t\t\t\ttimeout = THROTTLE_REFRESH_TIMEOUT;\n\t\t\t}\n\t\t\ttimerId = setTimeout(refresh,timeout);\n\t\t\t$tw.utils.extend(deferredChanges,changes);\n\t\t} else {\n\t\t\t$tw.utils.extend(deferredChanges,changes);\n\t\t\trefresh();\n\t\t}\n\t}));\n\t// Fix up the link between the root widget and the page container\n\t$tw.rootWidget.domNodes = [$tw.pageContainer];\n\t$tw.rootWidget.children = [$tw.pageWidgetNode];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/rootwidget.js": {
"title": "$:/core/modules/startup/rootwidget.js",
"text": "/*\\\ntitle: $:/core/modules/startup/rootwidget.js\ntype: application/javascript\nmodule-type: startup\n\nSetup the root widget and the core root widget handlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"rootwidget\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.before = [\"story\"];\nexports.synchronous = true;\n\nexports.startup = function() {\n\t// Install the modal message mechanism\n\t$tw.modal = new $tw.utils.Modal($tw.wiki);\n\t$tw.rootWidget.addEventListener(\"tm-modal\",function(event) {\n\t\t$tw.modal.display(event.param,{variables: event.paramObject, event: event});\n\t});\n\t// Install the notification mechanism\n\t$tw.notifier = new $tw.utils.Notifier($tw.wiki);\n\t$tw.rootWidget.addEventListener(\"tm-notify\",function(event) {\n\t\t$tw.notifier.display(event.param,{variables: event.paramObject});\n\t});\n\t// Install the copy-to-clipboard mechanism\n\t$tw.rootWidget.addEventListener(\"tm-copy-to-clipboard\",function(event) {\n\t\t$tw.utils.copyToClipboard(event.param);\n\t});\n\t// Install the tm-focus-selector message\n\t$tw.rootWidget.addEventListener(\"tm-focus-selector\",function(event) {\n\t\tvar selector = event.param || \"\",\n\t\t\telement;\n\t\ttry {\n\t\t\telement = document.querySelector(selector);\n\t\t} catch(e) {\n\t\t\tconsole.log(\"Error in selector: \",selector)\n\t\t}\n\t\tif(element && element.focus) {\n\t\t\telement.focus(event.paramObject);\n\t\t}\n\t});\n\t// Install the scroller\n\t$tw.pageScroller = new $tw.utils.PageScroller();\n\t$tw.rootWidget.addEventListener(\"tm-scroll\",function(event) {\n\t\t$tw.pageScroller.handleEvent(event);\n\t});\n\tvar fullscreen = $tw.utils.getFullScreenApis();\n\tif(fullscreen) {\n\t\t$tw.rootWidget.addEventListener(\"tm-full-screen\",function(event) {\n\t\t\tvar fullScreenDocument = event.event ? event.event.target.ownerDocument : document;\n\t\t\tif(event.param === \"enter\") {\n\t\t\t\tfullScreenDocument.documentElement[fullscreen._requestFullscreen](Element.ALLOW_KEYBOARD_INPUT);\n\t\t\t} else if(event.param === \"exit\") {\n\t\t\t\tfullScreenDocument[fullscreen._exitFullscreen]();\n\t\t\t} else {\n\t\t\t\tif(fullScreenDocument[fullscreen._fullscreenElement]) {\n\t\t\t\t\tfullScreenDocument[fullscreen._exitFullscreen]();\n\t\t\t\t} else {\n\t\t\t\t\tfullScreenDocument.documentElement[fullscreen._requestFullscreen](Element.ALLOW_KEYBOARD_INPUT);\n\t\t\t\t}\t\t\t\t\n\t\t\t}\n\t\t});\n\t}\n\t// If we're being viewed on a data: URI then give instructions for how to save\n\tif(document.location.protocol === \"data:\") {\n\t\t$tw.rootWidget.dispatchEvent({\n\t\t\ttype: \"tm-modal\",\n\t\t\tparam: \"$:/language/Modals/SaveInstructions\"\n\t\t});\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup.js": {
"title": "$:/core/modules/startup.js",
"text": "/*\\\ntitle: $:/core/modules/startup.js\ntype: application/javascript\nmodule-type: startup\n\nMiscellaneous startup logic for both the client and server.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"startup\";\nexports.after = [\"load-modules\"];\nexports.synchronous = true;\n\n// Set to `true` to enable performance instrumentation\nvar PERFORMANCE_INSTRUMENTATION_CONFIG_TITLE = \"$:/config/Performance/Instrumentation\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.startup = function() {\n\tvar modules,n,m,f;\n\t// Minimal browser detection\n\tif($tw.browser) {\n\t\t$tw.browser.isIE = (/msie|trident/i.test(navigator.userAgent));\n\t\t$tw.browser.isFirefox = !!document.mozFullScreenEnabled;\n\t}\n\t// Platform detection\n\t$tw.platform = {};\n\tif($tw.browser) {\n\t\t$tw.platform.isMac = /Mac/.test(navigator.platform);\n\t\t$tw.platform.isWindows = /win/i.test(navigator.platform);\n\t\t$tw.platform.isLinux = /Linux/i.test(navigator.platform);\n\t} else {\n\t\tswitch(require(\"os\").platform()) {\n\t\t\tcase \"darwin\":\n\t\t\t\t$tw.platform.isMac = true;\n\t\t\t\tbreak;\n\t\t\tcase \"win32\":\n\t\t\t\t$tw.platform.isWindows = true;\n\t\t\t\tbreak;\n\t\t\tcase \"freebsd\":\n\t\t\t\t$tw.platform.isLinux = true;\n\t\t\t\tbreak;\n\t\t\tcase \"linux\":\n\t\t\t\t$tw.platform.isLinux = true;\n\t\t\t\tbreak;\n\t\t}\n\t}\n\t// Initialise version\n\t$tw.version = $tw.utils.extractVersionInfo();\n\t// Set up the performance framework\n\t$tw.perf = new $tw.Performance($tw.wiki.getTiddlerText(PERFORMANCE_INSTRUMENTATION_CONFIG_TITLE,\"no\") === \"yes\");\n\t// Create a root widget for attaching event handlers. By using it as the parentWidget for another widget tree, one can reuse the event handlers\n\t$tw.rootWidget = new widget.widget({\n\t\ttype: \"widget\",\n\t\tchildren: []\n\t},{\n\t\twiki: $tw.wiki,\n\t\tdocument: $tw.browser ? document : $tw.fakeDocument\n\t});\n\t// Execute any startup actions\n\tvar executeStartupTiddlers = function(tag) {\n\t\t$tw.utils.each($tw.wiki.filterTiddlers(\"[all[shadows+tiddlers]tag[\" + tag + \"]!has[draft.of]]\"),function(title) {\n\t\t\t$tw.rootWidget.invokeActionString($tw.wiki.getTiddlerText(title),$tw.rootWidget);\n\t\t});\n\t};\n\texecuteStartupTiddlers(\"$:/tags/StartupAction\");\n\tif($tw.browser) {\n\t\texecuteStartupTiddlers(\"$:/tags/StartupAction/Browser\");\t\t\n\t}\n\tif($tw.node) {\n\t\texecuteStartupTiddlers(\"$:/tags/StartupAction/Node\");\t\t\n\t}\n\t// Kick off the language manager and switcher\n\t$tw.language = new $tw.Language();\n\t$tw.languageSwitcher = new $tw.PluginSwitcher({\n\t\twiki: $tw.wiki,\n\t\tpluginType: \"language\",\n\t\tcontrollerTitle: \"$:/language\",\n\t\tdefaultPlugins: [\n\t\t\t\"$:/languages/en-GB\"\n\t\t],\n\t\tonSwitch: function(plugins) {\n\t\t\tif($tw.browser) {\n\t\t\t\tvar pluginTiddler = $tw.wiki.getTiddler(plugins[0]);\n\t\t\t\tif(pluginTiddler) {\n\t\t\t\t\tdocument.documentElement.setAttribute(\"dir\",pluginTiddler.getFieldString(\"text-direction\") || \"auto\");\n\t\t\t\t} else {\n\t\t\t\t\tdocument.documentElement.removeAttribute(\"dir\");\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n\t// Kick off the theme manager\n\t$tw.themeManager = new $tw.PluginSwitcher({\n\t\twiki: $tw.wiki,\n\t\tpluginType: \"theme\",\n\t\tcontrollerTitle: \"$:/theme\",\n\t\tdefaultPlugins: [\n\t\t\t\"$:/themes/tiddlywiki/snowwhite\",\n\t\t\t\"$:/themes/tiddlywiki/vanilla\"\n\t\t]\n\t});\n\t// Kick off the keyboard manager\n\t$tw.keyboardManager = new $tw.KeyboardManager();\n\t// Listen for shortcuts\n\tif($tw.browser) {\n\t\t$tw.utils.addEventListeners(document,[{\n\t\t\tname: \"keydown\",\n\t\t\thandlerObject: $tw.keyboardManager,\n\t\t\thandlerMethod: \"handleKeydownEvent\"\n\t\t}]);\n\t}\n\t// Clear outstanding tiddler store change events to avoid an unnecessary refresh cycle at startup\n\t$tw.wiki.clearTiddlerEventQueue();\n\t// Find a working syncadaptor\n\t$tw.syncadaptor = undefined;\n\t$tw.modules.forEachModuleOfType(\"syncadaptor\",function(title,module) {\n\t\tif(!$tw.syncadaptor && module.adaptorClass) {\n\t\t\t$tw.syncadaptor = new module.adaptorClass({wiki: $tw.wiki});\n\t\t}\n\t});\n\t// Set up the syncer object if we've got a syncadaptor\n\tif($tw.syncadaptor) {\n\t\t$tw.syncer = new $tw.Syncer({wiki: $tw.wiki, syncadaptor: $tw.syncadaptor});\n\t}\n\t// Setup the saver handler\n\t$tw.saverHandler = new $tw.SaverHandler({\n\t\twiki: $tw.wiki,\n\t\tdirtyTracking: !$tw.syncadaptor,\n\t\tpreloadDirty: $tw.boot.preloadDirty || []\n\t});\n\t// Host-specific startup\n\tif($tw.browser) {\n\t\t// Install the popup manager\n\t\t$tw.popup = new $tw.utils.Popup();\n\t\t// Install the animator\n\t\t$tw.anim = new $tw.utils.Animator();\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/story.js": {
"title": "$:/core/modules/startup/story.js",
"text": "/*\\\ntitle: $:/core/modules/startup/story.js\ntype: application/javascript\nmodule-type: startup\n\nLoad core modules\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"story\";\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\n// Default story and history lists\nvar DEFAULT_STORY_TITLE = \"$:/StoryList\";\nvar DEFAULT_HISTORY_TITLE = \"$:/HistoryList\";\n\n// Default tiddlers\nvar DEFAULT_TIDDLERS_TITLE = \"$:/DefaultTiddlers\";\n\n// Config\nvar CONFIG_UPDATE_ADDRESS_BAR = \"$:/config/Navigation/UpdateAddressBar\"; // Can be \"no\", \"permalink\", \"permaview\"\nvar CONFIG_UPDATE_HISTORY = \"$:/config/Navigation/UpdateHistory\"; // Can be \"yes\" or \"no\"\nvar CONFIG_PERMALINKVIEW_COPY_TO_CLIPBOARD = \"$:/config/Navigation/Permalinkview/CopyToClipboard\"; // Can be \"yes\" (default) or \"no\"\nvar CONFIG_PERMALINKVIEW_UPDATE_ADDRESS_BAR = \"$:/config/Navigation/Permalinkview/UpdateAddressBar\"; // Can be \"yes\" (default) or \"no\"\n\n\n// Links to help, if there is no param\nvar HELP_OPEN_EXTERNAL_WINDOW = \"http://tiddlywiki.com/#WidgetMessage%3A%20tm-open-external-window\";\n\nexports.startup = function() {\n\t// Open startup tiddlers\n\topenStartupTiddlers({\n\t\tdisableHistory: $tw.boot.disableStartupNavigation\n\t});\n\tif($tw.browser) {\n\t\t// Set up location hash update\n\t\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\t\tif($tw.utils.hop(changes,DEFAULT_STORY_TITLE) || $tw.utils.hop(changes,DEFAULT_HISTORY_TITLE)) {\n\t\t\t\tupdateLocationHash({\n\t\t\t\t\tupdateAddressBar: $tw.wiki.getTiddlerText(CONFIG_UPDATE_ADDRESS_BAR,\"permaview\").trim(),\n\t\t\t\t\tupdateHistory: $tw.wiki.getTiddlerText(CONFIG_UPDATE_HISTORY,\"no\").trim()\n\t\t\t\t});\n\t\t\t}\n\t\t});\n\t\t// Listen for changes to the browser location hash\n\t\twindow.addEventListener(\"hashchange\",function() {\n\t\t\tvar hash = $tw.utils.getLocationHash();\n\t\t\tif(hash !== $tw.locationHash) {\n\t\t\t\t$tw.locationHash = hash;\n\t\t\t\topenStartupTiddlers({defaultToCurrentStory: true});\n\t\t\t}\n\t\t},false);\n\t\t// Listen for the tm-browser-refresh message\n\t\t$tw.rootWidget.addEventListener(\"tm-browser-refresh\",function(event) {\n\t\t\twindow.location.reload(true);\n\t\t});\n\t\t// Listen for tm-open-external-window message\n\t\t$tw.rootWidget.addEventListener(\"tm-open-external-window\",function(event) {\n\t\t\tvar paramObject = event.paramObject || {},\n\t\t\t\tstrUrl = event.param || HELP_OPEN_EXTERNAL_WINDOW,\n\t\t\t\tstrWindowName = paramObject.windowName,\n\t\t\t\tstrWindowFeatures = paramObject.windowFeatures;\n\t\t\twindow.open(strUrl, strWindowName, strWindowFeatures);\n\t\t});\n\t\t// Listen for the tm-print message\n\t\t$tw.rootWidget.addEventListener(\"tm-print\",function(event) {\n\t\t\t(event.event.view || window).print();\n\t\t});\n\t\t// Listen for the tm-home message\n\t\t$tw.rootWidget.addEventListener(\"tm-home\",function(event) {\n\t\t\twindow.location.hash = \"\";\n\t\t\tvar storyFilter = $tw.wiki.getTiddlerText(DEFAULT_TIDDLERS_TITLE),\n\t\t\t\tstoryList = $tw.wiki.filterTiddlers(storyFilter);\n\t\t\t//invoke any hooks that might change the default story list\n\t\t\tstoryList = $tw.hooks.invokeHook(\"th-opening-default-tiddlers-list\",storyList);\n\t\t\t$tw.wiki.addTiddler({title: DEFAULT_STORY_TITLE, text: \"\", list: storyList},$tw.wiki.getModificationFields());\n\t\t\tif(storyList[0]) {\n\t\t\t\t$tw.wiki.addToHistory(storyList[0]);\n\t\t\t}\n\t\t});\n\t\t// Listen for the tm-permalink message\n\t\t$tw.rootWidget.addEventListener(\"tm-permalink\",function(event) {\n\t\t\tupdateLocationHash({\n\t\t\t\tupdateAddressBar: $tw.wiki.getTiddlerText(CONFIG_PERMALINKVIEW_UPDATE_ADDRESS_BAR,\"yes\").trim() === \"yes\" ? \"permalink\" : \"none\",\n\t\t\t\tupdateHistory: $tw.wiki.getTiddlerText(CONFIG_UPDATE_HISTORY,\"no\").trim(),\n\t\t\t\ttargetTiddler: event.param || event.tiddlerTitle,\n\t\t\t\tcopyToClipboard: $tw.wiki.getTiddlerText(CONFIG_PERMALINKVIEW_COPY_TO_CLIPBOARD,\"yes\").trim() === \"yes\" ? \"permalink\" : \"none\"\n\t\t\t});\n\t\t});\n\t\t// Listen for the tm-permaview message\n\t\t$tw.rootWidget.addEventListener(\"tm-permaview\",function(event) {\n\t\t\tupdateLocationHash({\n\t\t\t\tupdateAddressBar: $tw.wiki.getTiddlerText(CONFIG_PERMALINKVIEW_UPDATE_ADDRESS_BAR,\"yes\").trim() === \"yes\" ? \"permaview\" : \"none\",\n\t\t\t\tupdateHistory: $tw.wiki.getTiddlerText(CONFIG_UPDATE_HISTORY,\"no\").trim(),\n\t\t\t\ttargetTiddler: event.param || event.tiddlerTitle,\n\t\t\t\tcopyToClipboard: $tw.wiki.getTiddlerText(CONFIG_PERMALINKVIEW_COPY_TO_CLIPBOARD,\"yes\").trim() === \"yes\" ? \"permaview\" : \"none\"\n\t\t\t});\t\t\t\t\n\t\t});\n\t}\n};\n\n/*\nProcess the location hash to open the specified tiddlers. Options:\ndisableHistory: if true $:/History is NOT updated\ndefaultToCurrentStory: If true, the current story is retained as the default, instead of opening the default tiddlers\n*/\nfunction openStartupTiddlers(options) {\n\toptions = options || {};\n\t// Work out the target tiddler and the story filter. \"null\" means \"unspecified\"\n\tvar target = null,\n\t\tstoryFilter = null;\n\tif($tw.locationHash.length > 1) {\n\t\tvar hash = $tw.locationHash.substr(1),\n\t\t\tsplit = hash.indexOf(\":\");\n\t\tif(split === -1) {\n\t\t\ttarget = decodeURIComponent(hash.trim());\n\t\t} else {\n\t\t\ttarget = decodeURIComponent(hash.substr(0,split).trim());\n\t\t\tstoryFilter = decodeURIComponent(hash.substr(split + 1).trim());\n\t\t}\n\t}\n\t// If the story wasn't specified use the current tiddlers or a blank story\n\tif(storyFilter === null) {\n\t\tif(options.defaultToCurrentStory) {\n\t\t\tvar currStoryList = $tw.wiki.getTiddlerList(DEFAULT_STORY_TITLE);\n\t\t\tstoryFilter = $tw.utils.stringifyList(currStoryList);\n\t\t} else {\n\t\t\tif(target && target !== \"\") {\n\t\t\t\tstoryFilter = \"\";\n\t\t\t} else {\n\t\t\t\tstoryFilter = $tw.wiki.getTiddlerText(DEFAULT_TIDDLERS_TITLE);\n\t\t\t}\n\t\t}\n\t}\n\t// Process the story filter to get the story list\n\tvar storyList = $tw.wiki.filterTiddlers(storyFilter);\n\t// Invoke any hooks that want to change the default story list\n\tstoryList = $tw.hooks.invokeHook(\"th-opening-default-tiddlers-list\",storyList);\n\t// If the target tiddler isn't included then splice it in at the top\n\tif(target && storyList.indexOf(target) === -1) {\n\t\tstoryList.unshift(target);\n\t}\n\t// Save the story list\n\t$tw.wiki.addTiddler({title: DEFAULT_STORY_TITLE, text: \"\", list: storyList},$tw.wiki.getModificationFields());\n\t// Update history\n\tif(!options.disableHistory) {\n\t\t// If a target tiddler was specified add it to the history stack\n\t\tif(target && target !== \"\") {\n\t\t\t// The target tiddler doesn't need double square brackets, but we'll silently remove them if they're present\n\t\t\tif(target.indexOf(\"[[\") === 0 && target.substr(-2) === \"]]\") {\n\t\t\t\ttarget = target.substr(2,target.length - 4);\n\t\t\t}\n\t\t\t$tw.wiki.addToHistory(target);\n\t\t} else if(storyList.length > 0) {\n\t\t\t$tw.wiki.addToHistory(storyList[0]);\n\t\t}\t\t\n\t}\n}\n\n/*\noptions: See below\noptions.updateAddressBar: \"permalink\", \"permaview\" or \"no\" (defaults to \"permaview\")\noptions.updateHistory: \"yes\" or \"no\" (defaults to \"no\")\noptions.copyToClipboard: \"permalink\", \"permaview\" or \"no\" (defaults to \"no\")\noptions.targetTiddler: optional title of target tiddler for permalink\n*/\nfunction updateLocationHash(options) {\n\t// Get the story and the history stack\n\tvar storyList = $tw.wiki.getTiddlerList(DEFAULT_STORY_TITLE),\n\t\thistoryList = $tw.wiki.getTiddlerData(DEFAULT_HISTORY_TITLE,[]),\n\t\ttargetTiddler = \"\";\n\tif(options.targetTiddler) {\n\t\ttargetTiddler = options.targetTiddler;\n\t} else {\n\t\t// The target tiddler is the one at the top of the stack\n\t\tif(historyList.length > 0) {\n\t\t\ttargetTiddler = historyList[historyList.length-1].title;\n\t\t}\n\t\t// Blank the target tiddler if it isn't present in the story\n\t\tif(storyList.indexOf(targetTiddler) === -1) {\n\t\t\ttargetTiddler = \"\";\n\t\t}\n\t}\n\t// Assemble the location hash\n\tswitch(options.updateAddressBar) {\n\t\tcase \"permalink\":\n\t\t\t$tw.locationHash = \"#\" + encodeURIComponent(targetTiddler);\n\t\t\tbreak;\n\t\tcase \"permaview\":\n\t\t\t$tw.locationHash = \"#\" + encodeURIComponent(targetTiddler) + \":\" + encodeURIComponent($tw.utils.stringifyList(storyList));\n\t\t\tbreak;\n\t}\n\t// Copy URL to the clipboard\n\tswitch(options.copyToClipboard) {\n\t\tcase \"permalink\":\n\t\t\t$tw.utils.copyToClipboard($tw.utils.getLocationPath() + \"#\" + encodeURIComponent(targetTiddler));\n\t\t\tbreak;\n\t\tcase \"permaview\":\n\t\t\t$tw.utils.copyToClipboard($tw.utils.getLocationPath() + \"#\" + encodeURIComponent(targetTiddler) + \":\" + encodeURIComponent($tw.utils.stringifyList(storyList)));\n\t\t\tbreak;\n\t}\n\t// Only change the location hash if we must, thus avoiding unnecessary onhashchange events\n\tif($tw.utils.getLocationHash() !== $tw.locationHash) {\n\t\tif(options.updateHistory === \"yes\") {\n\t\t\t// Assign the location hash so that history is updated\n\t\t\twindow.location.hash = $tw.locationHash;\n\t\t} else {\n\t\t\t// We use replace so that browser history isn't affected\n\t\t\twindow.location.replace(window.location.toString().split(\"#\")[0] + $tw.locationHash);\n\t\t}\n\t}\n}\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/windows.js": {
"title": "$:/core/modules/startup/windows.js",
"text": "/*\\\ntitle: $:/core/modules/startup/windows.js\ntype: application/javascript\nmodule-type: startup\n\nSetup root widget handlers for the messages concerned with opening external browser windows\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"windows\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\n// Global to keep track of open windows (hashmap by title)\nvar windows = {};\n\nexports.startup = function() {\n\t// Handle open window message\n\t$tw.rootWidget.addEventListener(\"tm-open-window\",function(event) {\n\t\t// Get the parameters\n\t\tvar refreshHandler,\n\t\t\ttitle = event.param || event.tiddlerTitle,\n\t\t\tparamObject = event.paramObject || {},\n\t\t\twindowTitle = paramObject.windowTitle || title,\n\t\t\ttemplate = paramObject.template || \"$:/core/templates/single.tiddler.window\",\n\t\t\twidth = paramObject.width || \"700\",\n\t\t\theight = paramObject.height || \"600\",\n\t\t\tvariables = $tw.utils.extend({},paramObject,{currentTiddler: title});\n\t\t// Open the window\n\t\tvar srcWindow,\n\t\t srcDocument;\n\t\t// In case that popup blockers deny opening a new window\n\t\ttry {\n\t\t\tsrcWindow = window.open(\"\",\"external-\" + title,\"scrollbars,width=\" + width + \",height=\" + height),\n\t\t\tsrcDocument = srcWindow.document;\n\t\t}\n\t\tcatch(e) {\n\t\t\treturn;\n\t\t}\n\t\twindows[title] = srcWindow;\n\t\t// Check for reopening the same window\n\t\tif(srcWindow.haveInitialisedWindow) {\n\t\t\treturn;\n\t\t}\n\t\t// Initialise the document\n\t\tsrcDocument.write(\"<html><head></head><body class='tc-body tc-single-tiddler-window'></body></html>\");\n\t\tsrcDocument.close();\n\t\tsrcDocument.title = windowTitle;\n\t\tsrcWindow.addEventListener(\"beforeunload\",function(event) {\n\t\t\tdelete windows[title];\n\t\t\t$tw.wiki.removeEventListener(\"change\",refreshHandler);\n\t\t},false);\n\t\t// Set up the styles\n\t\tvar styleWidgetNode = $tw.wiki.makeTranscludeWidget(\"$:/core/ui/PageStylesheet\",{\n\t\t\t\tdocument: $tw.fakeDocument,\n\t\t\t\tvariables: variables,\n\t\t\t\timportPageMacros: true}),\n\t\t\tstyleContainer = $tw.fakeDocument.createElement(\"style\");\n\t\tstyleWidgetNode.render(styleContainer,null);\n\t\tvar styleElement = srcDocument.createElement(\"style\");\n\t\tstyleElement.innerHTML = styleContainer.textContent;\n\t\tsrcDocument.head.insertBefore(styleElement,srcDocument.head.firstChild);\n\t\t// Render the text of the tiddler\n\t\tvar parser = $tw.wiki.parseTiddler(template),\n\t\t\twidgetNode = $tw.wiki.makeWidget(parser,{document: srcDocument, parentWidget: $tw.rootWidget, variables: variables});\n\t\twidgetNode.render(srcDocument.body,srcDocument.body.firstChild);\n\t\t// Function to handle refreshes\n\t\trefreshHandler = function(changes) {\n\t\t\tif(styleWidgetNode.refresh(changes,styleContainer,null)) {\n\t\t\t\tstyleElement.innerHTML = styleContainer.textContent;\n\t\t\t}\n\t\t\twidgetNode.refresh(changes);\n\t\t};\n\t\t$tw.wiki.addEventListener(\"change\",refreshHandler);\n\t\t// Listen for keyboard shortcuts\n\t\t$tw.utils.addEventListeners(srcDocument,[{\n\t\t\tname: \"keydown\",\n\t\t\thandlerObject: $tw.keyboardManager,\n\t\t\thandlerMethod: \"handleKeydownEvent\"\n\t\t},{\n\t\t\tname: \"click\",\n\t\t\thandlerObject: $tw.popup,\n\t\t\thandlerMethod: \"handleEvent\"\n\t\t}]);\n\t\tsrcWindow.haveInitialisedWindow = true;\n\t});\n\t// Close open windows when unloading main window\n\t$tw.addUnloadTask(function() {\n\t\t$tw.utils.each(windows,function(win) {\n\t\t\twin.close();\n\t\t});\n\t});\n\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/story.js": {
"title": "$:/core/modules/story.js",
"text": "/*\\\ntitle: $:/core/modules/story.js\ntype: application/javascript\nmodule-type: global\n\nLightweight object for managing interactions with the story and history lists.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nConstruct Story object with options:\nwiki: reference to wiki object to use to resolve tiddler titles\nstoryTitle: title of story list tiddler\nhistoryTitle: title of history list tiddler\n*/\nfunction Story(options) {\n\toptions = options || {};\n\tthis.wiki = options.wiki || $tw.wiki;\n\tthis.storyTitle = options.storyTitle || \"$:/StoryList\";\n\tthis.historyTitle = options.historyTitle || \"$:/HistoryList\";\n};\n\nStory.prototype.navigateTiddler = function(navigateTo,navigateFromTitle,navigateFromClientRect) {\n\tthis.addToStory(navigateTo,navigateFromTitle);\n\tthis.addToHistory(navigateTo,navigateFromClientRect);\n};\n\nStory.prototype.getStoryList = function() {\n\treturn this.wiki.getTiddlerList(this.storyTitle) || [];\n};\n\nStory.prototype.addToStory = function(navigateTo,navigateFromTitle,options) {\n\toptions = options || {};\n\tvar storyList = this.getStoryList();\n\t// See if the tiddler is already there\n\tvar slot = storyList.indexOf(navigateTo);\n\t// Quit if it already exists in the story river\n\tif(slot >= 0) {\n\t\treturn;\n\t}\n\t// First we try to find the position of the story element we navigated from\n\tvar fromIndex = storyList.indexOf(navigateFromTitle);\n\tif(fromIndex >= 0) {\n\t\t// The tiddler is added from inside the river\n\t\t// Determine where to insert the tiddler; Fallback is \"below\"\n\t\tswitch(options.openLinkFromInsideRiver) {\n\t\t\tcase \"top\":\n\t\t\t\tslot = 0;\n\t\t\t\tbreak;\n\t\t\tcase \"bottom\":\n\t\t\t\tslot = storyList.length;\n\t\t\t\tbreak;\n\t\t\tcase \"above\":\n\t\t\t\tslot = fromIndex;\n\t\t\t\tbreak;\n\t\t\tcase \"below\": // Intentional fall-through\n\t\t\tdefault:\n\t\t\t\tslot = fromIndex + 1;\n\t\t\t\tbreak;\n\t\t}\n\t} else {\n\t\t// The tiddler is opened from outside the river. Determine where to insert the tiddler; default is \"top\"\n\t\tif(options.openLinkFromOutsideRiver === \"bottom\") {\n\t\t\t// Insert at bottom\n\t\t\tslot = storyList.length;\n\t\t} else {\n\t\t\t// Insert at top\n\t\t\tslot = 0;\n\t\t}\n\t}\n\t// Add the tiddler\n\tstoryList.splice(slot,0,navigateTo);\n\t// Save the story\n\tthis.saveStoryList(storyList);\n};\n\nStory.prototype.saveStoryList = function(storyList) {\n\tvar storyTiddler = this.wiki.getTiddler(this.storyTitle);\n\tthis.wiki.addTiddler(new $tw.Tiddler(\n\t\tthis.wiki.getCreationFields(),\n\t\t{title: this.storyTitle},\n\t\tstoryTiddler,\n\t\t{list: storyList},\n\t\tthis.wiki.getModificationFields()\n\t));\n};\n\nStory.prototype.addToHistory = function(navigateTo,navigateFromClientRect) {\n\tvar titles = $tw.utils.isArray(navigateTo) ? navigateTo : [navigateTo];\n\t// Add a new record to the top of the history stack\n\tvar historyList = this.wiki.getTiddlerData(this.historyTitle,[]);\n\t$tw.utils.each(titles,function(title) {\n\t\thistoryList.push({title: title, fromPageRect: navigateFromClientRect});\n\t});\n\tthis.wiki.setTiddlerData(this.historyTitle,historyList,{\"current-tiddler\": titles[titles.length-1]});\n};\n\nStory.prototype.storyCloseTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyCloseAllTiddlers = function() {\n// TBD\n};\n\nStory.prototype.storyCloseOtherTiddlers = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyEditTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyDeleteTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storySaveTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyCancelTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyNewTiddler = function(targetTitle) {\n// TBD\n};\n\nexports.Story = Story;\n\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/storyviews/classic.js": {
"title": "$:/core/modules/storyviews/classic.js",
"text": "/*\\\ntitle: $:/core/modules/storyviews/classic.js\ntype: application/javascript\nmodule-type: storyview\n\nViews the story as a linear sequence\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar easing = \"cubic-bezier(0.645, 0.045, 0.355, 1)\"; // From http://easings.net/#easeInOutCubic\n\nvar ClassicStoryView = function(listWidget) {\n\tthis.listWidget = listWidget;\n};\n\nClassicStoryView.prototype.navigateTo = function(historyInfo) {\n\tvar duration = $tw.utils.getAnimationDuration()\n\tvar listElementIndex = this.listWidget.findListItem(0,historyInfo.title);\n\tif(listElementIndex === undefined) {\n\t\treturn;\n\t}\n\tvar listItemWidget = this.listWidget.children[listElementIndex],\n\t\ttargetElement = listItemWidget.findFirstDomNode();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\tif(duration) {\n\t\t// Scroll the node into view\n\t\tthis.listWidget.dispatchEvent({type: \"tm-scroll\", target: targetElement});\t\n\t} else {\n\t\ttargetElement.scrollIntoView();\n\t}\n};\n\nClassicStoryView.prototype.insert = function(widget) {\n\tvar duration = $tw.utils.getAnimationDuration();\n\tif(duration) {\n\t\tvar targetElement = widget.findFirstDomNode();\n\t\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\t\tif(!(targetElement instanceof Element)) {\n\t\t\treturn;\n\t\t}\n\t\t// Get the current height of the tiddler\n\t\tvar computedStyle = window.getComputedStyle(targetElement),\n\t\t\tcurrMarginBottom = parseInt(computedStyle.marginBottom,10),\n\t\t\tcurrMarginTop = parseInt(computedStyle.marginTop,10),\n\t\t\tcurrHeight = targetElement.offsetHeight + currMarginTop;\n\t\t// Reset the margin once the transition is over\n\t\tsetTimeout(function() {\n\t\t\t$tw.utils.setStyle(targetElement,[\n\t\t\t\t{transition: \"none\"},\n\t\t\t\t{marginBottom: \"\"}\n\t\t\t]);\n\t\t},duration);\n\t\t// Set up the initial position of the element\n\t\t$tw.utils.setStyle(targetElement,[\n\t\t\t{transition: \"none\"},\n\t\t\t{marginBottom: (-currHeight) + \"px\"},\n\t\t\t{opacity: \"0.0\"}\n\t\t]);\n\t\t$tw.utils.forceLayout(targetElement);\n\t\t// Transition to the final position\n\t\t$tw.utils.setStyle(targetElement,[\n\t\t\t{transition: \"opacity \" + duration + \"ms \" + easing + \", \" +\n\t\t\t\t\t\t\"margin-bottom \" + duration + \"ms \" + easing},\n\t\t\t{marginBottom: currMarginBottom + \"px\"},\n\t\t\t{opacity: \"1.0\"}\n\t]);\n\t}\n};\n\nClassicStoryView.prototype.remove = function(widget) {\n\tvar duration = $tw.utils.getAnimationDuration();\n\tif(duration) {\n\t\tvar targetElement = widget.findFirstDomNode(),\n\t\t\tremoveElement = function() {\n\t\t\t\twidget.removeChildDomNodes();\n\t\t\t};\n\t\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\t\tif(!(targetElement instanceof Element)) {\n\t\t\tremoveElement();\n\t\t\treturn;\n\t\t}\n\t\t// Get the current height of the tiddler\n\t\tvar currWidth = targetElement.offsetWidth,\n\t\t\tcomputedStyle = window.getComputedStyle(targetElement),\n\t\t\tcurrMarginBottom = parseInt(computedStyle.marginBottom,10),\n\t\t\tcurrMarginTop = parseInt(computedStyle.marginTop,10),\n\t\t\tcurrHeight = targetElement.offsetHeight + currMarginTop;\n\t\t// Remove the dom nodes of the widget at the end of the transition\n\t\tsetTimeout(removeElement,duration);\n\t\t// Animate the closure\n\t\t$tw.utils.setStyle(targetElement,[\n\t\t\t{transition: \"none\"},\n\t\t\t{transform: \"translateX(0px)\"},\n\t\t\t{marginBottom: currMarginBottom + \"px\"},\n\t\t\t{opacity: \"1.0\"}\n\t\t]);\n\t\t$tw.utils.forceLayout(targetElement);\n\t\t$tw.utils.setStyle(targetElement,[\n\t\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", \" +\n\t\t\t\t\t\t\"opacity \" + duration + \"ms \" + easing + \", \" +\n\t\t\t\t\t\t\"margin-bottom \" + duration + \"ms \" + easing},\n\t\t\t{transform: \"translateX(-\" + currWidth + \"px)\"},\n\t\t\t{marginBottom: (-currHeight) + \"px\"},\n\t\t\t{opacity: \"0.0\"}\n\t\t]);\n\t} else {\n\t\twidget.removeChildDomNodes();\n\t}\n};\n\nexports.classic = ClassicStoryView;\n\n})();",
"type": "application/javascript",
"module-type": "storyview"
},
"$:/core/modules/storyviews/pop.js": {
"title": "$:/core/modules/storyviews/pop.js",
"text": "/*\\\ntitle: $:/core/modules/storyviews/pop.js\ntype: application/javascript\nmodule-type: storyview\n\nAnimates list insertions and removals\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar PopStoryView = function(listWidget) {\n\tthis.listWidget = listWidget;\n};\n\nPopStoryView.prototype.navigateTo = function(historyInfo) {\n\tvar listElementIndex = this.listWidget.findListItem(0,historyInfo.title);\n\tif(listElementIndex === undefined) {\n\t\treturn;\n\t}\n\tvar listItemWidget = this.listWidget.children[listElementIndex],\n\t\ttargetElement = listItemWidget.findFirstDomNode();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Scroll the node into view\n\tthis.listWidget.dispatchEvent({type: \"tm-scroll\", target: targetElement});\n};\n\nPopStoryView.prototype.insert = function(widget) {\n\tvar targetElement = widget.findFirstDomNode(),\n\t\tduration = $tw.utils.getAnimationDuration();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Reset once the transition is over\n\tsetTimeout(function() {\n\t\t$tw.utils.setStyle(targetElement,[\n\t\t\t{transition: \"none\"},\n\t\t\t{transform: \"none\"}\n\t\t]);\n\t\t$tw.utils.setStyle(widget.document.body,[\n\t\t\t{\"overflow-x\": \"\"}\n\t\t]);\n\t},duration);\n\t// Prevent the page from overscrolling due to the zoom factor\n\t$tw.utils.setStyle(widget.document.body,[\n\t\t{\"overflow-x\": \"hidden\"}\n\t]);\n\t// Set up the initial position of the element\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: \"none\"},\n\t\t{transform: \"scale(2)\"},\n\t\t{opacity: \"0.0\"}\n\t]);\n\t$tw.utils.forceLayout(targetElement);\n\t// Transition to the final position\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"opacity \" + duration + \"ms ease-in-out\"},\n\t\t{transform: \"scale(1)\"},\n\t\t{opacity: \"1.0\"}\n\t]);\n};\n\nPopStoryView.prototype.remove = function(widget) {\n\tvar targetElement = widget.findFirstDomNode(),\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t\tremoveElement = function() {\n\t\t\tif(targetElement && targetElement.parentNode) {\n\t\t\t\twidget.removeChildDomNodes();\n\t\t\t}\n\t\t};\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\tremoveElement();\n\t\treturn;\n\t}\n\t// Remove the element at the end of the transition\n\tsetTimeout(removeElement,duration);\n\t// Animate the closure\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: \"none\"},\n\t\t{transform: \"scale(1)\"},\n\t\t{opacity: \"1.0\"}\n\t]);\n\t$tw.utils.forceLayout(targetElement);\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"opacity \" + duration + \"ms ease-in-out\"},\n\t\t{transform: \"scale(0.1)\"},\n\t\t{opacity: \"0.0\"}\n\t]);\n};\n\nexports.pop = PopStoryView;\n\n})();\n",
"type": "application/javascript",
"module-type": "storyview"
},
"$:/core/modules/storyviews/zoomin.js": {
"title": "$:/core/modules/storyviews/zoomin.js",
"text": "/*\\\ntitle: $:/core/modules/storyviews/zoomin.js\ntype: application/javascript\nmodule-type: storyview\n\nZooms between individual tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar easing = \"cubic-bezier(0.645, 0.045, 0.355, 1)\"; // From http://easings.net/#easeInOutCubic\n\nvar ZoominListView = function(listWidget) {\n\tvar self = this;\n\tthis.listWidget = listWidget;\n\t// Get the index of the tiddler that is at the top of the history\n\tvar history = this.listWidget.wiki.getTiddlerDataCached(this.listWidget.historyTitle,[]),\n\t\ttargetTiddler;\n\tif(history.length > 0) {\n\t\ttargetTiddler = history[history.length-1].title;\n\t}\n\t// Make all the tiddlers position absolute, and hide all but the top (or first) one\n\t$tw.utils.each(this.listWidget.children,function(itemWidget,index) {\n\t\tvar domNode = itemWidget.findFirstDomNode();\n\t\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\t\tif(!(domNode instanceof Element)) {\n\t\t\treturn;\n\t\t}\n\t\tif((targetTiddler && targetTiddler !== itemWidget.parseTreeNode.itemTitle) || (!targetTiddler && index)) {\n\t\t\tdomNode.style.display = \"none\";\n\t\t} else {\n\t\t\tself.currentTiddlerDomNode = domNode;\n\t\t}\n\t\t$tw.utils.addClass(domNode,\"tc-storyview-zoomin-tiddler\");\n\t});\n};\n\nZoominListView.prototype.navigateTo = function(historyInfo) {\n\tvar duration = $tw.utils.getAnimationDuration(),\n\t\tlistElementIndex = this.listWidget.findListItem(0,historyInfo.title);\n\tif(listElementIndex === undefined) {\n\t\treturn;\n\t}\n\tvar listItemWidget = this.listWidget.children[listElementIndex],\n\t\ttargetElement = listItemWidget.findFirstDomNode();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Make the new tiddler be position absolute and visible so that we can measure it\n\t$tw.utils.addClass(targetElement,\"tc-storyview-zoomin-tiddler\");\n\t$tw.utils.setStyle(targetElement,[\n\t\t{display: \"block\"},\n\t\t{transformOrigin: \"0 0\"},\n\t\t{transform: \"translateX(0px) translateY(0px) scale(1)\"},\n\t\t{transition: \"none\"},\n\t\t{opacity: \"0.0\"}\n\t]);\n\t// Get the position of the source node, or use the centre of the window as the source position\n\tvar sourceBounds = historyInfo.fromPageRect || {\n\t\t\tleft: window.innerWidth/2 - 2,\n\t\t\ttop: window.innerHeight/2 - 2,\n\t\t\twidth: window.innerWidth/8,\n\t\t\theight: window.innerHeight/8\n\t\t};\n\t// Try to find the title node in the target tiddler\n\tvar titleDomNode = findTitleDomNode(listItemWidget) || listItemWidget.findFirstDomNode(),\n\t\tzoomBounds = titleDomNode.getBoundingClientRect();\n\t// Compute the transform for the target tiddler to make the title lie over the source rectange\n\tvar targetBounds = targetElement.getBoundingClientRect(),\n\t\tscale = sourceBounds.width / zoomBounds.width,\n\t\tx = sourceBounds.left - targetBounds.left - (zoomBounds.left - targetBounds.left) * scale,\n\t\ty = sourceBounds.top - targetBounds.top - (zoomBounds.top - targetBounds.top) * scale;\n\t// Transform the target tiddler to its starting position\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transform: \"translateX(\" + x + \"px) translateY(\" + y + \"px) scale(\" + scale + \")\"}\n\t]);\n\t// Force layout\n\t$tw.utils.forceLayout(targetElement);\n\t// Apply the ending transitions with a timeout to ensure that the previously applied transformations are applied first\n\tvar self = this,\n\t\tprevCurrentTiddler = this.currentTiddlerDomNode;\n\tthis.currentTiddlerDomNode = targetElement;\n\t// Transform the target tiddler to its natural size\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", opacity \" + duration + \"ms \" + easing},\n\t\t{opacity: \"1.0\"},\n\t\t{transform: \"translateX(0px) translateY(0px) scale(1)\"},\n\t\t{zIndex: \"500\"},\n\t]);\n\t// Transform the previous tiddler out of the way and then hide it\n\tif(prevCurrentTiddler && prevCurrentTiddler !== targetElement) {\n\t\tscale = zoomBounds.width / sourceBounds.width;\n\t\tx = zoomBounds.left - targetBounds.left - (sourceBounds.left - targetBounds.left) * scale;\n\t\ty = zoomBounds.top - targetBounds.top - (sourceBounds.top - targetBounds.top) * scale;\n\t\t$tw.utils.setStyle(prevCurrentTiddler,[\n\t\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", opacity \" + duration + \"ms \" + easing},\n\t\t\t{opacity: \"0.0\"},\n\t\t\t{transformOrigin: \"0 0\"},\n\t\t\t{transform: \"translateX(\" + x + \"px) translateY(\" + y + \"px) scale(\" + scale + \")\"},\n\t\t\t{zIndex: \"0\"}\n\t\t]);\n\t\t// Hide the tiddler when the transition has finished\n\t\tsetTimeout(function() {\n\t\t\tif(self.currentTiddlerDomNode !== prevCurrentTiddler) {\n\t\t\t\tprevCurrentTiddler.style.display = \"none\";\n\t\t\t}\n\t\t},duration);\n\t}\n\t// Scroll the target into view\n//\t$tw.pageScroller.scrollIntoView(targetElement);\n};\n\n/*\nFind the first child DOM node of a widget that has the class \"tc-title\"\n*/\nfunction findTitleDomNode(widget,targetClass) {\n\ttargetClass = targetClass || \"tc-title\";\n\tvar domNode = widget.findFirstDomNode();\n\tif(domNode && domNode.querySelector) {\n\t\treturn domNode.querySelector(\".\" + targetClass);\n\t}\n\treturn null;\n}\n\nZoominListView.prototype.insert = function(widget) {\n\tvar targetElement = widget.findFirstDomNode();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Make the newly inserted node position absolute and hidden\n\t$tw.utils.addClass(targetElement,\"tc-storyview-zoomin-tiddler\");\n\t$tw.utils.setStyle(targetElement,[\n\t\t{display: \"none\"}\n\t]);\n};\n\nZoominListView.prototype.remove = function(widget) {\n\tvar targetElement = widget.findFirstDomNode(),\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t\tremoveElement = function() {\n\t\t\twidget.removeChildDomNodes();\n\t\t};\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\tremoveElement();\n\t\treturn;\n\t}\n\t// Abandon if hidden\n\tif(targetElement.style.display != \"block\" ) {\n\t\tremoveElement();\n\t\treturn;\n\t}\n\t// Set up the tiddler that is being closed\n\t$tw.utils.addClass(targetElement,\"tc-storyview-zoomin-tiddler\");\n\t$tw.utils.setStyle(targetElement,[\n\t\t{display: \"block\"},\n\t\t{transformOrigin: \"50% 50%\"},\n\t\t{transform: \"translateX(0px) translateY(0px) scale(1)\"},\n\t\t{transition: \"none\"},\n\t\t{zIndex: \"0\"}\n\t]);\n\t// We'll move back to the previous or next element in the story\n\tvar toWidget = widget.previousSibling();\n\tif(!toWidget) {\n\t\ttoWidget = widget.nextSibling();\n\t}\n\tvar toWidgetDomNode = toWidget && toWidget.findFirstDomNode();\n\t// Set up the tiddler we're moving back in\n\tif(toWidgetDomNode) {\n\t\t$tw.utils.addClass(toWidgetDomNode,\"tc-storyview-zoomin-tiddler\");\n\t\t$tw.utils.setStyle(toWidgetDomNode,[\n\t\t\t{display: \"block\"},\n\t\t\t{transformOrigin: \"50% 50%\"},\n\t\t\t{transform: \"translateX(0px) translateY(0px) scale(10)\"},\n\t\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", opacity \" + duration + \"ms \" + easing},\n\t\t\t{opacity: \"0\"},\n\t\t\t{zIndex: \"500\"}\n\t\t]);\n\t\tthis.currentTiddlerDomNode = toWidgetDomNode;\n\t}\n\t// Animate them both\n\t// Force layout\n\t$tw.utils.forceLayout(this.listWidget.parentDomNode);\n\t// First, the tiddler we're closing\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transformOrigin: \"50% 50%\"},\n\t\t{transform: \"translateX(0px) translateY(0px) scale(0.1)\"},\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", opacity \" + duration + \"ms \" + easing},\n\t\t{opacity: \"0\"},\n\t\t{zIndex: \"0\"}\n\t]);\n\tsetTimeout(removeElement,duration);\n\t// Now the tiddler we're going back to\n\tif(toWidgetDomNode) {\n\t\t$tw.utils.setStyle(toWidgetDomNode,[\n\t\t\t{transform: \"translateX(0px) translateY(0px) scale(1)\"},\n\t\t\t{opacity: \"1\"}\n\t\t]);\n\t}\n\treturn true; // Indicate that we'll delete the DOM node\n};\n\nexports.zoomin = ZoominListView;\n\n})();\n",
"type": "application/javascript",
"module-type": "storyview"
},
"$:/core/modules/syncer.js": {
"title": "$:/core/modules/syncer.js",
"text": "/*\\\ntitle: $:/core/modules/syncer.js\ntype: application/javascript\nmodule-type: global\n\nThe syncer tracks changes to the store and synchronises them to a remote data store represented as a \"sync adaptor\"\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nDefaults\n*/\nSyncer.prototype.titleIsLoggedIn = \"$:/status/IsLoggedIn\";\nSyncer.prototype.titleIsAnonymous = \"$:/status/IsAnonymous\";\nSyncer.prototype.titleIsReadOnly = \"$:/status/IsReadOnly\";\nSyncer.prototype.titleUserName = \"$:/status/UserName\";\nSyncer.prototype.titleSyncFilter = \"$:/config/SyncFilter\";\nSyncer.prototype.titleSyncPollingInterval = \"$:/config/SyncPollingInterval\";\nSyncer.prototype.titleSyncDisableLazyLoading = \"$:/config/SyncDisableLazyLoading\";\nSyncer.prototype.titleSavedNotification = \"$:/language/Notifications/Save/Done\";\nSyncer.prototype.titleSyncThrottleInterval = \"$:/config/SyncThrottleInterval\";\nSyncer.prototype.taskTimerInterval = 1 * 1000; // Interval for sync timer\nSyncer.prototype.throttleInterval = 1 * 1000; // Defer saving tiddlers if they've changed in the last 1s...\nSyncer.prototype.errorRetryInterval = 5 * 1000; // Interval to retry after an error\nSyncer.prototype.fallbackInterval = 10 * 1000; // Unless the task is older than 10s\nSyncer.prototype.pollTimerInterval = 60 * 1000; // Interval for polling for changes from the adaptor\n\n/*\nInstantiate the syncer with the following options:\nsyncadaptor: reference to syncadaptor to be used\nwiki: wiki to be synced\n*/\nfunction Syncer(options) {\n\tvar self = this;\n\tthis.wiki = options.wiki;\n\t// Save parameters\n\tthis.syncadaptor = options.syncadaptor;\n\tthis.disableUI = !!options.disableUI;\n\tthis.titleIsLoggedIn = options.titleIsLoggedIn || this.titleIsLoggedIn;\n\tthis.titleUserName = options.titleUserName || this.titleUserName;\n\tthis.titleSyncFilter = options.titleSyncFilter || this.titleSyncFilter;\n\tthis.titleSavedNotification = options.titleSavedNotification || this.titleSavedNotification;\n\tthis.taskTimerInterval = options.taskTimerInterval || this.taskTimerInterval;\n\tthis.throttleInterval = options.throttleInterval || parseInt(this.wiki.getTiddlerText(this.titleSyncThrottleInterval,\"\"),10) || this.throttleInterval;\n\tthis.errorRetryInterval = options.errorRetryInterval || this.errorRetryInterval;\n\tthis.fallbackInterval = options.fallbackInterval || this.fallbackInterval;\n\tthis.pollTimerInterval = options.pollTimerInterval || parseInt(this.wiki.getTiddlerText(this.titleSyncPollingInterval,\"\"),10) || this.pollTimerInterval;\n\tthis.logging = \"logging\" in options ? options.logging : true;\n\t// Make a logger\n\tthis.logger = new $tw.utils.Logger(\"syncer\" + ($tw.browser ? \"-browser\" : \"\") + ($tw.node ? \"-server\" : \"\") + (this.syncadaptor.name ? (\"-\" + this.syncadaptor.name) : \"\"),{\n\t\tcolour: \"cyan\",\n\t\tenable: this.logging,\n\t\tsaveHistory: true\n\t});\n\t// Make another logger for connection errors\n\tthis.loggerConnection = new $tw.utils.Logger(\"syncer\" + ($tw.browser ? \"-browser\" : \"\") + ($tw.node ? \"-server\" : \"\") + (this.syncadaptor.name ? (\"-\" + this.syncadaptor.name) : \"\") + \"-connection\",{\n\t\tcolour: \"cyan\",\n\t\tenable: this.logging\n\t});\n\t// Ask the syncadaptor to use the main logger\n\tif(this.syncadaptor.setLoggerSaveBuffer) {\n\t\tthis.syncadaptor.setLoggerSaveBuffer(this.logger);\n\t}\n\t// Compile the dirty tiddler filter\n\tthis.filterFn = this.wiki.compileFilter(this.wiki.getTiddlerText(this.titleSyncFilter));\n\t// Record information for known tiddlers\n\tthis.readTiddlerInfo();\n\tthis.titlesToBeLoaded = {}; // Hashmap of titles of tiddlers that need loading from the server\n\tthis.titlesHaveBeenLazyLoaded = {}; // Hashmap of titles of tiddlers that have already been lazily loaded from the server\n\t// Timers\n\tthis.taskTimerId = null; // Timer for task dispatch\n\tthis.pollTimerId = null; // Timer for polling server\n\t// Number of outstanding requests\n\tthis.numTasksInProgress = 0;\n\t// Listen out for changes to tiddlers\n\tthis.wiki.addEventListener(\"change\",function(changes) {\n\t\t// Filter the changes to just include ones that are being synced\n\t\tvar filteredChanges = self.getSyncedTiddlers(function(callback) {\n\t\t\t$tw.utils.each(changes,function(change,title) {\n\t\t\t\tvar tiddler = self.wiki.tiddlerExists(title) && self.wiki.getTiddler(title);\n\t\t\t\tcallback(tiddler,title);\n\t\t\t});\n\t\t});\n\t\tif(filteredChanges.length > 0) {\n\t\t\tself.processTaskQueue();\n\t\t} else {\n\t\t\t// Look for deletions of tiddlers we're already syncing\t\n\t\t\tvar outstandingDeletion = false\n\t\t\t$tw.utils.each(changes,function(change,title,object) {\n\t\t\t\tif(change.deleted && $tw.utils.hop(self.tiddlerInfo,title)) {\n\t\t\t\t\toutstandingDeletion = true;\n\t\t\t\t}\n\t\t\t});\n\t\t\tif(outstandingDeletion) {\n\t\t\t\tself.processTaskQueue();\n\t\t\t}\n\t\t}\n\t});\n\t// Browser event handlers\n\tif($tw.browser && !this.disableUI) {\n\t\t// Set up our beforeunload handler\n\t\t$tw.addUnloadTask(function(event) {\n\t\t\tvar confirmationMessage;\n\t\t\tif(self.isDirty()) {\n\t\t\t\tconfirmationMessage = $tw.language.getString(\"UnsavedChangesWarning\");\n\t\t\t\tevent.returnValue = confirmationMessage; // Gecko\n\t\t\t}\n\t\t\treturn confirmationMessage;\n\t\t});\n\t\t// Listen out for login/logout/refresh events in the browser\n\t\t$tw.rootWidget.addEventListener(\"tm-login\",function() {\n\t\t\tself.handleLoginEvent();\n\t\t});\n\t\t$tw.rootWidget.addEventListener(\"tm-logout\",function() {\n\t\t\tself.handleLogoutEvent();\n\t\t});\n\t\t$tw.rootWidget.addEventListener(\"tm-server-refresh\",function() {\n\t\t\tself.handleRefreshEvent();\n\t\t});\n\t\t$tw.rootWidget.addEventListener(\"tm-copy-syncer-logs-to-clipboard\",function() {\n\t\t\t$tw.utils.copyToClipboard($tw.utils.getSystemInfo() + \"\\n\\nLog:\\n\" + self.logger.getBuffer());\n\t\t});\n\t}\n\t// Listen out for lazyLoad events\n\tif(!this.disableUI && $tw.wiki.getTiddlerText(this.titleSyncDisableLazyLoading) !== \"yes\") {\n\t\tthis.wiki.addEventListener(\"lazyLoad\",function(title) {\n\t\t\tself.handleLazyLoadEvent(title);\n\t\t});\t\t\n\t}\n\t// Get the login status\n\tthis.getStatus(function(err,isLoggedIn) {\n\t\t// Do a sync from the server\n\t\tself.syncFromServer();\n\t});\n}\n\n/*\nShow a generic network error alert\n*/\nSyncer.prototype.displayError = function(msg,err) {\n\tif(err === ($tw.language.getString(\"Error/XMLHttpRequest\") + \": 0\")) {\n\t\tthis.loggerConnection.alert($tw.language.getString(\"Error/NetworkErrorAlert\"));\n\t\tthis.logger.log(msg + \":\",err);\n\t} else {\n\t\tthis.logger.alert(msg + \":\",err);\n\t}\n};\n\n/*\nReturn an array of the tiddler titles that are subjected to syncing\n*/\nSyncer.prototype.getSyncedTiddlers = function(source) {\n\treturn this.filterFn.call(this.wiki,source);\n};\n\n/*\nReturn an array of the tiddler titles that are subjected to syncing\n*/\nSyncer.prototype.getTiddlerRevision = function(title) {\n\tif(this.syncadaptor && this.syncadaptor.getTiddlerRevision) {\n\t\treturn this.syncadaptor.getTiddlerRevision(title);\n\t} else {\n\t\treturn this.wiki.getTiddler(title).fields.revision;\t\n\t} \n};\n\n/*\nRead (or re-read) the latest tiddler info from the store\n*/\nSyncer.prototype.readTiddlerInfo = function() {\n\t// Hashmap by title of {revision:,changeCount:,adaptorInfo:}\n\t// \"revision\" is the revision of the tiddler last seen on the server, and \"changecount\" is the corresponding local changecount\n\tthis.tiddlerInfo = {};\n\t// Record information for known tiddlers\n\tvar self = this,\n\t\ttiddlers = this.getSyncedTiddlers();\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar tiddler = self.wiki.tiddlerExists(title) && self.wiki.getTiddler(title);\n\t\tself.tiddlerInfo[title] = {\n\t\t\trevision: self.getTiddlerRevision(title),\n\t\t\tadaptorInfo: self.syncadaptor && self.syncadaptor.getTiddlerInfo(tiddler),\n\t\t\tchangeCount: self.wiki.getChangeCount(title)\n\t\t};\n\t});\n};\n\n/*\nChecks whether the wiki is dirty (ie the window shouldn't be closed)\n*/\nSyncer.prototype.isDirty = function() {\n\tthis.logger.log(\"Checking dirty status\");\n\t// Check tiddlers that are in the store and included in the filter function\n\tvar titles = this.getSyncedTiddlers();\n\tfor(var index=0; index<titles.length; index++) {\n\t\tvar title = titles[index],\n\t\t\ttiddlerInfo = this.tiddlerInfo[title];\n\t\tif(this.wiki.tiddlerExists(title)) {\n\t\t\tif(tiddlerInfo) {\n\t\t\t\t// If the tiddler is known on the server and has been modified locally then it needs to be saved to the server\n\t\t\t\tif($tw.wiki.getChangeCount(title) > tiddlerInfo.changeCount) {\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t// If the tiddler isn't known on the server then it needs to be saved to the server\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\t}\n\t// Check tiddlers that are known from the server but not currently in the store\n\ttitles = Object.keys(this.tiddlerInfo);\n\tfor(index=0; index<titles.length; index++) {\n\t\tif(!this.wiki.tiddlerExists(titles[index])) {\n\t\t\t// There must be a pending delete\n\t\t\treturn true;\n\t\t}\n\t}\n\treturn false;\n};\n\n/*\nUpdate the document body with the class \"tc-dirty\" if the wiki has unsaved/unsynced changes\n*/\nSyncer.prototype.updateDirtyStatus = function() {\n\tif($tw.browser && !this.disableUI) {\n\t\tvar dirty = this.isDirty();\n\t\t$tw.utils.toggleClass(document.body,\"tc-dirty\",dirty);\n\t\tif(!dirty) {\n\t\t\tthis.loggerConnection.clearAlerts();\n\t\t}\n\t}\n};\n\n/*\nSave an incoming tiddler in the store, and updates the associated tiddlerInfo\n*/\nSyncer.prototype.storeTiddler = function(tiddlerFields) {\n\t// Save the tiddler\n\tvar tiddler = new $tw.Tiddler(tiddlerFields);\n\tthis.wiki.addTiddler(tiddler);\n\t// Save the tiddler revision and changeCount details\n\tthis.tiddlerInfo[tiddlerFields.title] = {\n\t\trevision: this.getTiddlerRevision(tiddlerFields.title),\n\t\tadaptorInfo: this.syncadaptor.getTiddlerInfo(tiddler),\n\t\tchangeCount: this.wiki.getChangeCount(tiddlerFields.title)\n\t};\n};\n\nSyncer.prototype.getStatus = function(callback) {\n\tvar self = this;\n\t// Check if the adaptor supports getStatus()\n\tif(this.syncadaptor && this.syncadaptor.getStatus) {\n\t\t// Mark us as not logged in\n\t\tthis.wiki.addTiddler({title: this.titleIsLoggedIn,text: \"no\"});\n\t\t// Get login status\n\t\tthis.syncadaptor.getStatus(function(err,isLoggedIn,username,isReadOnly,isAnonymous) {\n\t\t\tif(err) {\n\t\t\t\tself.logger.alert(err);\n\t\t\t} else {\n\t\t\t\t// Set the various status tiddlers\n\t\t\t\tself.wiki.addTiddler({title: self.titleIsReadOnly,text: isReadOnly ? \"yes\" : \"no\"});\n\t\t\t\tself.wiki.addTiddler({title: self.titleIsAnonymous,text: isAnonymous ? \"yes\" : \"no\"});\n\t\t\t\tself.wiki.addTiddler({title: self.titleIsLoggedIn,text: isLoggedIn ? \"yes\" : \"no\"});\n\t\t\t\tif(isLoggedIn) {\n\t\t\t\t\tself.wiki.addTiddler({title: self.titleUserName,text: username || \"\"});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// Invoke the callback\n\t\t\tif(callback) {\n\t\t\t\tcallback(err,isLoggedIn,username);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tcallback(null,true,\"UNAUTHENTICATED\");\n\t}\n};\n\n/*\nSynchronise from the server by reading the skinny tiddler list and queuing up loads for any tiddlers that we don't already have up to date\n*/\nSyncer.prototype.syncFromServer = function() {\n\tvar self = this,\n\t\tcancelNextSync = function() {\n\t\t\tif(self.pollTimerId) {\n\t\t\t\tclearTimeout(self.pollTimerId);\n\t\t\t\tself.pollTimerId = null;\n\t\t\t}\n\t\t},\n\t\ttriggerNextSync = function() {\n\t\t\tself.pollTimerId = setTimeout(function() {\n\t\t\t\tself.pollTimerId = null;\n\t\t\t\tself.syncFromServer.call(self);\n\t\t\t},self.pollTimerInterval);\n\t\t};\n\tif(this.syncadaptor && this.syncadaptor.getUpdatedTiddlers) {\n\t\tthis.logger.log(\"Retrieving updated tiddler list\");\n\t\tcancelNextSync();\n\t\tthis.syncadaptor.getUpdatedTiddlers(self,function(err,updates) {\n\t\t\ttriggerNextSync();\n\t\t\tif(err) {\n\t\t\t\tself.displayError($tw.language.getString(\"Error/RetrievingSkinny\"),err);\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tif(updates) {\n\t\t\t\t$tw.utils.each(updates.modifications,function(title) {\n\t\t\t\t\tself.titlesToBeLoaded[title] = true;\n\t\t\t\t});\n\t\t\t\t$tw.utils.each(updates.deletions,function(title) {\n\t\t\t\t\tdelete self.tiddlerInfo[title];\n\t\t\t\t\tself.logger.log(\"Deleting tiddler missing from server:\",title);\n\t\t\t\t\tself.wiki.deleteTiddler(title);\n\t\t\t\t});\n\t\t\t\tif(updates.modifications.length > 0 || updates.deletions.length > 0) {\n\t\t\t\t\tself.processTaskQueue();\n\t\t\t\t}\t\t\t\t\n\t\t\t}\n\t\t});\n\t} else if(this.syncadaptor && this.syncadaptor.getSkinnyTiddlers) {\n\t\tthis.logger.log(\"Retrieving skinny tiddler list\");\n\t\tcancelNextSync();\n\t\tthis.syncadaptor.getSkinnyTiddlers(function(err,tiddlers) {\n\t\t\ttriggerNextSync();\n\t\t\t// Check for errors\n\t\t\tif(err) {\n\t\t\t\tself.displayError($tw.language.getString(\"Error/RetrievingSkinny\"),err);\n\t\t\t\treturn;\n\t\t\t}\n\t\t\t// Keep track of which tiddlers we already know about have been reported this time\n\t\t\tvar previousTitles = Object.keys(self.tiddlerInfo);\n\t\t\t// Process each incoming tiddler\n\t\t\tfor(var t=0; t<tiddlers.length; t++) {\n\t\t\t\t// Get the incoming tiddler fields, and the existing tiddler\n\t\t\t\tvar tiddlerFields = tiddlers[t],\n\t\t\t\t\tincomingRevision = tiddlerFields.revision + \"\",\n\t\t\t\t\ttiddler = self.wiki.tiddlerExists(tiddlerFields.title) && self.wiki.getTiddler(tiddlerFields.title),\n\t\t\t\t\ttiddlerInfo = self.tiddlerInfo[tiddlerFields.title],\n\t\t\t\t\tcurrRevision = tiddlerInfo ? tiddlerInfo.revision : null,\n\t\t\t\t\tindexInPreviousTitles = previousTitles.indexOf(tiddlerFields.title);\n\t\t\t\tif(indexInPreviousTitles !== -1) {\n\t\t\t\t\tpreviousTitles.splice(indexInPreviousTitles,1);\n\t\t\t\t}\n\t\t\t\t// Ignore the incoming tiddler if it's the same as the revision we've already got\n\t\t\t\tif(currRevision !== incomingRevision) {\n\t\t\t\t\t// Only load the skinny version if we don't already have a fat version of the tiddler\n\t\t\t\t\tif(!tiddler || tiddler.fields.text === undefined) {\n\t\t\t\t\t\tself.storeTiddler(tiddlerFields);\n\t\t\t\t\t}\n\t\t\t\t\t// Do a full load of this tiddler\n\t\t\t\t\tself.titlesToBeLoaded[tiddlerFields.title] = true;\n\t\t\t\t}\n\t\t\t}\n\t\t\t// Delete any tiddlers that were previously reported but missing this time\n\t\t\t$tw.utils.each(previousTitles,function(title) {\n\t\t\t\tdelete self.tiddlerInfo[title];\n\t\t\t\tself.logger.log(\"Deleting tiddler missing from server:\",title);\n\t\t\t\tself.wiki.deleteTiddler(title);\n\t\t\t});\n\t\t\tself.processTaskQueue();\n\t\t});\n\t}\n};\n\n/*\nForce load a tiddler from the server\n*/\nSyncer.prototype.enqueueLoadTiddler = function(title) {\n\tthis.titlesToBeLoaded[title] = true;\n\tthis.processTaskQueue();\n};\n\n/*\nLazily load a skinny tiddler if we can\n*/\nSyncer.prototype.handleLazyLoadEvent = function(title) {\n\t// Ignore if the syncadaptor doesn't handle it\n\tif(!this.syncadaptor.supportsLazyLoading) {\n\t\treturn;\n\t}\n\t// Don't lazy load the same tiddler twice\n\tif(!this.titlesHaveBeenLazyLoaded[title]) {\n\t\t// Don't lazy load if the tiddler isn't included in the sync filter\n\t\tif(this.getSyncedTiddlers().indexOf(title) !== -1) {\n\t\t\t// Mark the tiddler as needing loading, and having already been lazily loaded\n\t\t\tthis.titlesToBeLoaded[title] = true;\n\t\t\tthis.titlesHaveBeenLazyLoaded[title] = true;\n\t\t}\n\t}\n};\n\n/*\nDispay a password prompt and allow the user to login\n*/\nSyncer.prototype.handleLoginEvent = function() {\n\tvar self = this;\n\tthis.getStatus(function(err,isLoggedIn,username) {\n\t\tif(!err && !isLoggedIn) {\n\t\t\t$tw.passwordPrompt.createPrompt({\n\t\t\t\tserviceName: $tw.language.getString(\"LoginToTiddlySpace\"),\n\t\t\t\tcallback: function(data) {\n\t\t\t\t\tself.login(data.username,data.password,function(err,isLoggedIn) {\n\t\t\t\t\t\tself.syncFromServer();\n\t\t\t\t\t});\n\t\t\t\t\treturn true; // Get rid of the password prompt\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n};\n\n/*\nAttempt to login to TiddlyWeb.\n\tusername: username\n\tpassword: password\n\tcallback: invoked with arguments (err,isLoggedIn)\n*/\nSyncer.prototype.login = function(username,password,callback) {\n\tthis.logger.log(\"Attempting to login as\",username);\n\tvar self = this;\n\tif(this.syncadaptor.login) {\n\t\tthis.syncadaptor.login(username,password,function(err) {\n\t\t\tif(err) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tself.getStatus(function(err,isLoggedIn,username) {\n\t\t\t\tif(callback) {\n\t\t\t\t\tcallback(err,isLoggedIn);\n\t\t\t\t}\n\t\t\t});\n\t\t});\n\t} else {\n\t\tcallback(null,true);\n\t}\n};\n\n/*\nAttempt to log out of TiddlyWeb\n*/\nSyncer.prototype.handleLogoutEvent = function() {\n\tthis.logger.log(\"Attempting to logout\");\n\tvar self = this;\n\tif(this.syncadaptor.logout) {\n\t\tthis.syncadaptor.logout(function(err) {\n\t\t\tif(err) {\n\t\t\t\tself.logger.alert(err);\n\t\t\t} else {\n\t\t\t\tself.getStatus();\n\t\t\t}\n\t\t});\n\t}\n};\n\n/*\nImmediately refresh from the server\n*/\nSyncer.prototype.handleRefreshEvent = function() {\n\tthis.syncFromServer();\n};\n\n/*\nProcess the next task\n*/\nSyncer.prototype.processTaskQueue = function() {\n\tvar self = this;\n\t// Only process a task if the sync adaptor is fully initialised and we're not already performing\n\t// a task. If we are already performing a task then we'll dispatch the next one when it completes\n\tif((!this.syncadaptor.isReady || this.syncadaptor.isReady()) && this.numTasksInProgress === 0) {\n\t\t// Choose the next task to perform\n\t\tvar task = this.chooseNextTask();\n\t\t// Perform the task if we had one\n\t\tif(typeof task === \"object\" && task !== null) {\n\t\t\tthis.numTasksInProgress += 1;\n\t\t\ttask.run(function(err) {\n\t\t\t\tself.numTasksInProgress -= 1;\n\t\t\t\tif(err) {\n\t\t\t\t\tself.displayError(\"Sync error while processing \" + task.type + \" of '\" + task.title + \"'\",err);\n\t\t\t\t\tself.updateDirtyStatus();\n\t\t\t\t\tself.triggerTimeout(self.errorRetryInterval);\n\t\t\t\t} else {\n\t\t\t\t\tself.updateDirtyStatus();\n\t\t\t\t\t// Process the next task\n\t\t\t\t\tself.processTaskQueue.call(self);\t\t\t\t\t\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\t// No task is ready so update the status\n\t\t\tthis.updateDirtyStatus();\n\t\t\t// And trigger a timeout if there is a pending task\n\t\t\tif(task === true) {\n\t\t\t\tthis.triggerTimeout();\t\t\t\t\n\t\t\t}\n\t\t}\n\t} else {\n\t\tthis.updateDirtyStatus();\t\t\n\t}\n};\n\nSyncer.prototype.triggerTimeout = function(interval) {\n\tvar self = this;\n\tif(!this.taskTimerId) {\n\t\tthis.taskTimerId = setTimeout(function() {\n\t\t\tself.taskTimerId = null;\n\t\t\tself.processTaskQueue.call(self);\n\t\t},interval || self.taskTimerInterval);\n\t}\n};\n\n/*\nChoose the next sync task. We prioritise saves, then deletes, then loads from the server\n\nReturns either a task object, null if there's no upcoming tasks, or the boolean true if there are pending tasks that aren't yet due\n*/\nSyncer.prototype.chooseNextTask = function() {\n\tvar thresholdLastSaved = (new Date()) - this.throttleInterval,\n\t\thavePending = null;\n\t// First we look for tiddlers that have been modified locally and need saving back to the server\n\tvar titles = this.getSyncedTiddlers();\n\tfor(var index=0; index<titles.length; index++) {\n\t\tvar title = titles[index],\n\t\t\ttiddler = this.wiki.tiddlerExists(title) && this.wiki.getTiddler(title),\n\t\t\ttiddlerInfo = this.tiddlerInfo[title];\n\t\tif(tiddler) {\n\t\t\t// If the tiddler is not known on the server, or has been modified locally no more recently than the threshold then it needs to be saved to the server\n\t\t\tvar hasChanged = !tiddlerInfo || $tw.wiki.getChangeCount(title) > tiddlerInfo.changeCount,\n\t\t\t\tisReadyToSave = !tiddlerInfo || !tiddlerInfo.timestampLastSaved || tiddlerInfo.timestampLastSaved < thresholdLastSaved;\n\t\t\tif(hasChanged) {\n\t\t\t\tif(isReadyToSave) {\n\t\t\t\t\treturn new SaveTiddlerTask(this,title); \t\t\t\t\t\n\t\t\t\t} else {\n\t\t\t\t\thavePending = true;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\t// Second, we check tiddlers that are known from the server but not currently in the store, and so need deleting on the server\n\ttitles = Object.keys(this.tiddlerInfo);\n\tfor(index=0; index<titles.length; index++) {\n\t\ttitle = titles[index];\n\t\ttiddlerInfo = this.tiddlerInfo[title];\n\t\ttiddler = this.wiki.tiddlerExists(title) && this.wiki.getTiddler(title);\n\t\tif(!tiddler) {\n\t\t\treturn new DeleteTiddlerTask(this,title);\n\t\t}\n\t}\n\t// Check for tiddlers that need loading\n\ttitle = Object.keys(this.titlesToBeLoaded)[0];\n\tif(title) {\n\t\tdelete this.titlesToBeLoaded[title];\n\t\treturn new LoadTiddlerTask(this,title);\n\t}\n\t// No tasks are ready\n\treturn havePending;\n};\n\nfunction SaveTiddlerTask(syncer,title) {\n\tthis.syncer = syncer;\n\tthis.title = title;\n\tthis.type = \"save\";\n}\n\nSaveTiddlerTask.prototype.run = function(callback) {\n\tvar self = this,\n\t\tchangeCount = this.syncer.wiki.getChangeCount(this.title),\n\t\ttiddler = this.syncer.wiki.tiddlerExists(this.title) && this.syncer.wiki.getTiddler(this.title);\n\tthis.syncer.logger.log(\"Dispatching 'save' task:\",this.title);\n\tif(tiddler) {\n\t\tthis.syncer.syncadaptor.saveTiddler(tiddler,function(err,adaptorInfo,revision) {\n\t\t\t// If there's an error, exit without changing any internal state\n\t\t\tif(err) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\t// Adjust the info stored about this tiddler\n\t\t\tself.syncer.tiddlerInfo[self.title] = {\n\t\t\t\tchangeCount: changeCount,\n\t\t\t\tadaptorInfo: adaptorInfo,\n\t\t\t\trevision: revision,\n\t\t\t\ttimestampLastSaved: new Date()\n\t\t\t};\n\t\t\t// Invoke the callback\n\t\t\tcallback(null);\n\t\t});\n\t} else {\n\t\tthis.syncer.logger.log(\" Not Dispatching 'save' task:\",this.title,\"tiddler does not exist\");\n\t\t$tw.utils.nextTick(callback(null));\n\t}\n};\n\nfunction DeleteTiddlerTask(syncer,title) {\n\tthis.syncer = syncer;\n\tthis.title = title;\n\tthis.type = \"delete\";\n}\n\nDeleteTiddlerTask.prototype.run = function(callback) {\n\tvar self = this;\n\tthis.syncer.logger.log(\"Dispatching 'delete' task:\",this.title);\n\tthis.syncer.syncadaptor.deleteTiddler(this.title,function(err) {\n\t\t// If there's an error, exit without changing any internal state\n\t\tif(err) {\n\t\t\treturn callback(err);\n\t\t}\n\t\t// Remove the info stored about this tiddler\n\t\tdelete self.syncer.tiddlerInfo[self.title];\n\t\t// Invoke the callback\n\t\tcallback(null);\n\t},{\n\t\ttiddlerInfo: self.syncer.tiddlerInfo[this.title]\n\t});\n};\n\nfunction LoadTiddlerTask(syncer,title) {\n\tthis.syncer = syncer;\n\tthis.title = title;\n\tthis.type = \"load\";\n}\n\nLoadTiddlerTask.prototype.run = function(callback) {\n\tvar self = this;\n\tthis.syncer.logger.log(\"Dispatching 'load' task:\",this.title);\n\tthis.syncer.syncadaptor.loadTiddler(this.title,function(err,tiddlerFields) {\n\t\t// If there's an error, exit without changing any internal state\n\t\tif(err) {\n\t\t\treturn callback(err);\n\t\t}\n\t\t// Update the info stored about this tiddler\n\t\tif(tiddlerFields) {\n\t\t\tself.syncer.storeTiddler(tiddlerFields);\n\t\t}\n\t\t// Invoke the callback\n\t\tcallback(null);\n\t});\n};\n\nexports.Syncer = Syncer;\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/tiddler.js": {
"title": "$:/core/modules/tiddler.js",
"text": "/*\\\ntitle: $:/core/modules/tiddler.js\ntype: application/javascript\nmodule-type: tiddlermethod\n\nExtension methods for the $tw.Tiddler object (constructor and methods required at boot time are in boot/boot.js)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.hasTag = function(tag) {\n\treturn this.fields.tags && this.fields.tags.indexOf(tag) !== -1;\n};\n\nexports.isPlugin = function() {\n\treturn this.fields.type === \"application/json\" && this.hasField(\"plugin-type\");\n};\n\nexports.isDraft = function() {\n\treturn this.hasField(\"draft.of\");\n};\n\nexports.getFieldString = function(field) {\n\tvar value = this.fields[field];\n\t// Check for a missing field\n\tif(value === undefined || value === null) {\n\t\treturn \"\";\n\t}\n\t// Parse the field with the associated module (if any)\n\tvar fieldModule = $tw.Tiddler.fieldModules[field];\n\tif(fieldModule && fieldModule.stringify) {\n\t\treturn fieldModule.stringify.call(this,value);\n\t} else {\n\t\treturn value.toString();\n\t}\n};\n\n/*\nGet the value of a field as a list\n*/\nexports.getFieldList = function(field) {\n\tvar value = this.fields[field];\n\t// Check for a missing field\n\tif(value === undefined || value === null) {\n\t\treturn [];\n\t}\n\treturn $tw.utils.parseStringArray(value);\n};\n\n/*\nGet all the fields as a hashmap of strings. Options:\n\texclude: an array of field names to exclude\n*/\nexports.getFieldStrings = function(options) {\n\toptions = options || {};\n\tvar exclude = options.exclude || [];\n\tvar fields = {};\n\tfor(var field in this.fields) {\n\t\tif($tw.utils.hop(this.fields,field)) {\n\t\t\tif(exclude.indexOf(field) === -1) {\n\t\t\t\tfields[field] = this.getFieldString(field);\n\t\t\t}\n\t\t}\n\t}\n\treturn fields;\n};\n\n/*\nGet all the fields as a name:value block. Options:\n\texclude: an array of field names to exclude\n*/\nexports.getFieldStringBlock = function(options) {\n\toptions = options || {};\n\tvar exclude = options.exclude || [],\n\t\tfields = Object.keys(this.fields).sort(),\n\t\tresult = [];\n\tfor(var t=0; t<fields.length; t++) {\n\t\tvar field = fields[t];\n\t\tif(exclude.indexOf(field) === -1) {\n\t\t\tresult.push(field + \": \" + this.getFieldString(field));\n\t\t}\n\t}\n\treturn result.join(\"\\n\");\n};\n\nexports.getFieldDay = function(field) {\n\tif(this.cache && this.cache.day && $tw.utils.hop(this.cache.day,field) ) {\n\t\treturn this.cache.day[field];\n\t}\n\tvar day = \"\";\n\tif(this.fields[field]) {\n\t\tday = (new Date($tw.utils.parseDate(this.fields[field]))).setHours(0,0,0,0);\n\t}\n\tthis.cache.day = this.cache.day || {};\n\tthis.cache.day[field] = day;\n\treturn day;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "tiddlermethod"
},
"$:/core/modules/upgraders/plugins.js": {
"title": "$:/core/modules/upgraders/plugins.js",
"text": "/*\\\ntitle: $:/core/modules/upgraders/plugins.js\ntype: application/javascript\nmodule-type: upgrader\n\nUpgrader module that checks that plugins are newer than any already installed version\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar UPGRADE_LIBRARY_TITLE = \"$:/UpgradeLibrary\";\n\nvar BLOCKED_PLUGINS = {\n\t\"$:/themes/tiddlywiki/stickytitles\": {\n\t\tversions: [\"*\"]\n\t},\n\t\"$:/plugins/tiddlywiki/fullscreen\": {\n\t\tversions: [\"*\"]\n\t}\n};\n\nexports.upgrade = function(wiki,titles,tiddlers) {\n\tvar self = this,\n\t\tmessages = {},\n\t\tupgradeLibrary,\n\t\tgetLibraryTiddler = function(title) {\n\t\t\tif(!upgradeLibrary) {\n\t\t\t\tupgradeLibrary = wiki.getTiddlerData(UPGRADE_LIBRARY_TITLE,{});\n\t\t\t\tupgradeLibrary.tiddlers = upgradeLibrary.tiddlers || {};\n\t\t\t}\n\t\t\treturn upgradeLibrary.tiddlers[title];\n\t\t};\n\n\t// Go through all the incoming tiddlers\n\t$tw.utils.each(titles,function(title) {\n\t\tvar incomingTiddler = tiddlers[title];\n\t\t// Check if we're dealing with a plugin\n\t\tif(incomingTiddler && incomingTiddler[\"plugin-type\"]) {\n\t\t\t// Check whether the plugin contains JS modules\n\t\t\tvar requiresReload = $tw.wiki.doesPluginInfoRequireReload(JSON.parse(incomingTiddler.text)) ? ($tw.wiki.getTiddlerText(\"$:/language/ControlPanel/Plugins/PluginWillRequireReload\") + \" \") : \"\";\n\t\t\tmessages[title] = requiresReload;\n\t\t\tif(incomingTiddler.version) {\n\t\t\t\t// Upgrade the incoming plugin if it is in the upgrade library\n\t\t\t\tvar libraryTiddler = getLibraryTiddler(title);\n\t\t\t\tif(libraryTiddler && libraryTiddler[\"plugin-type\"] && libraryTiddler.version) {\n\t\t\t\t\ttiddlers[title] = libraryTiddler;\n\t\t\t\t\tmessages[title] = requiresReload + $tw.language.getString(\"Import/Upgrader/Plugins/Upgraded\",{variables: {incoming: incomingTiddler.version, upgraded: libraryTiddler.version}});\n\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t\t// Suppress the incoming plugin if it is older than the currently installed one\n\t\t\t\tvar existingTiddler = wiki.getTiddler(title);\n\t\t\t\tif(existingTiddler && existingTiddler.hasField(\"plugin-type\") && existingTiddler.hasField(\"version\")) {\n\t\t\t\t\t// Reject the incoming plugin by blanking all its fields\n\t\t\t\t\tif($tw.utils.checkVersions(existingTiddler.fields.version,incomingTiddler.version)) {\n\t\t\t\t\t\ttiddlers[title] = Object.create(null);\n\t\t\t\t\t\tmessages[title] = requiresReload + $tw.language.getString(\"Import/Upgrader/Plugins/Suppressed/Version\",{variables: {incoming: incomingTiddler.version, existing: existingTiddler.fields.version}});\n\t\t\t\t\t\treturn;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\t// Check whether the plugin is on the blocked list\n\t\t\tvar blockInfo = BLOCKED_PLUGINS[title];\n\t\t\tif(blockInfo) {\n\t\t\t\tif(blockInfo.versions.indexOf(\"*\") !== -1 || (incomingTiddler.version && blockInfo.versions.indexOf(incomingTiddler.version) !== -1)) {\n\t\t\t\t\ttiddlers[title] = Object.create(null);\n\t\t\t\t\tmessages[title] = $tw.language.getString(\"Import/Upgrader/Plugins/Suppressed/Incompatible\");\n\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n\treturn messages;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "upgrader"
},
"$:/core/modules/upgraders/system.js": {
"title": "$:/core/modules/upgraders/system.js",
"text": "/*\\\ntitle: $:/core/modules/upgraders/system.js\ntype: application/javascript\nmodule-type: upgrader\n\nUpgrader module that suppresses certain system tiddlers that shouldn't be imported\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar DONT_IMPORT_LIST = [\"$:/StoryList\",\"$:/HistoryList\"],\n\tDONT_IMPORT_PREFIX_LIST = [\"$:/temp/\",\"$:/state/\",\"$:/Import\"],\n\tWARN_IMPORT_PREFIX_LIST = [\"$:/core/modules/\"];\n\nexports.upgrade = function(wiki,titles,tiddlers) {\n\tvar self = this,\n\t\tmessages = {},\n\t\tshowAlert = false;\n\t// Check for tiddlers on our list\n\t$tw.utils.each(titles,function(title) {\n\t\tif(DONT_IMPORT_LIST.indexOf(title) !== -1) {\n\t\t\ttiddlers[title] = Object.create(null);\n\t\t\tmessages[title] = $tw.language.getString(\"Import/Upgrader/System/Suppressed\");\n\t\t} else {\n\t\t\tfor(var t=0; t<DONT_IMPORT_PREFIX_LIST.length; t++) {\n\t\t\t\tvar prefix = DONT_IMPORT_PREFIX_LIST[t];\n\t\t\t\tif(title.substr(0,prefix.length) === prefix) {\n\t\t\t\t\ttiddlers[title] = Object.create(null);\n\t\t\t\t\tmessages[title] = $tw.language.getString(\"Import/Upgrader/State/Suppressed\");\n\t\t\t\t}\n\t\t\t}\n\t\t\tfor(var t=0; t<WARN_IMPORT_PREFIX_LIST.length; t++) {\n\t\t\t\tvar prefix = WARN_IMPORT_PREFIX_LIST[t];\n\t\t\t\tif(title.substr(0,prefix.length) === prefix && wiki.isShadowTiddler(title)) {\n\t\t\t\t\tshowAlert = true;\n\t\t\t\t\tmessages[title] = $tw.language.getString(\"Import/Upgrader/System/Warning\");\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n\tif(showAlert) {\n\t\tvar logger = new $tw.utils.Logger(\"import\");\n\t\tlogger.alert($tw.language.getString(\"Import/Upgrader/System/Alert\"));\n\t}\n\treturn messages;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "upgrader"
},
"$:/core/modules/upgraders/themetweaks.js": {
"title": "$:/core/modules/upgraders/themetweaks.js",
"text": "/*\\\ntitle: $:/core/modules/upgraders/themetweaks.js\ntype: application/javascript\nmodule-type: upgrader\n\nUpgrader module that handles the change in theme tweak storage introduced in 5.0.14-beta.\n\nPreviously, theme tweaks were stored in two data tiddlers:\n\n* $:/themes/tiddlywiki/vanilla/metrics\n* $:/themes/tiddlywiki/vanilla/settings\n\nNow, each tweak is stored in its own separate tiddler.\n\nThis upgrader copies any values from the old format to the new. The old data tiddlers are not deleted in case they have been used to store additional indexes.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar MAPPINGS = {\n\t\"$:/themes/tiddlywiki/vanilla/metrics\": {\n\t\t\"fontsize\": \"$:/themes/tiddlywiki/vanilla/metrics/fontsize\",\n\t\t\"lineheight\": \"$:/themes/tiddlywiki/vanilla/metrics/lineheight\",\n\t\t\"storyleft\": \"$:/themes/tiddlywiki/vanilla/metrics/storyleft\",\n\t\t\"storytop\": \"$:/themes/tiddlywiki/vanilla/metrics/storytop\",\n\t\t\"storyright\": \"$:/themes/tiddlywiki/vanilla/metrics/storyright\",\n\t\t\"storywidth\": \"$:/themes/tiddlywiki/vanilla/metrics/storywidth\",\n\t\t\"tiddlerwidth\": \"$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth\"\n\t},\n\t\"$:/themes/tiddlywiki/vanilla/settings\": {\n\t\t\"fontfamily\": \"$:/themes/tiddlywiki/vanilla/settings/fontfamily\"\n\t}\n};\n\nexports.upgrade = function(wiki,titles,tiddlers) {\n\tvar self = this,\n\t\tmessages = {};\n\t// Check for tiddlers on our list\n\t$tw.utils.each(titles,function(title) {\n\t\tvar mapping = MAPPINGS[title];\n\t\tif(mapping) {\n\t\t\tvar tiddler = new $tw.Tiddler(tiddlers[title]),\n\t\t\t\ttiddlerData = wiki.getTiddlerDataCached(tiddler,{});\n\t\t\tfor(var index in mapping) {\n\t\t\t\tvar mappedTitle = mapping[index];\n\t\t\t\tif(!tiddlers[mappedTitle] || tiddlers[mappedTitle].title !== mappedTitle) {\n\t\t\t\t\ttiddlers[mappedTitle] = {\n\t\t\t\t\t\ttitle: mappedTitle,\n\t\t\t\t\t\ttext: tiddlerData[index]\n\t\t\t\t\t};\n\t\t\t\t\tmessages[mappedTitle] = $tw.language.getString(\"Import/Upgrader/ThemeTweaks/Created\",{variables: {\n\t\t\t\t\t\tfrom: title + \"##\" + index\n\t\t\t\t\t}});\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n\treturn messages;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "upgrader"
},
"$:/core/modules/utils/base64-utf8/base64-utf8.module.js": {
"text": "(function(){// From https://gist.github.com/Nijikokun/5192472\n//\n// UTF8 Module\n//\n// Cleaner and modularized utf-8 encoding and decoding library for javascript.\n//\n// copyright: MIT\n// author: Nijiko Yonskai, @nijikokun, nijikokun@gmail.com\n!function(r,e,o,t){void 0!==o.module&&o.module.exports?o.module.exports=e.apply(o):void 0!==o.define&&\"function\"===o.define&&o.define.amd?define(\"utf8\",[],e):o.utf8=e.apply(o)}(0,function(){return{encode:function(r){if(\"string\"!=typeof r)return r;r=r.replace(/\\r\\n/g,\"\\n\");for(var e,o=\"\",t=0;t<r.length;t++)(e=r.charCodeAt(t))<128?o+=String.fromCharCode(e):e>127&&e<2048?(o+=String.fromCharCode(e>>6|192),o+=String.fromCharCode(63&e|128)):(o+=String.fromCharCode(e>>12|224),o+=String.fromCharCode(e>>6&63|128),o+=String.fromCharCode(63&e|128));return o},decode:function(r){if(\"string\"!=typeof r)return r;for(var e=\"\",o=0,t=0;o<r.length;)(t=r.charCodeAt(o))<128?(e+=String.fromCharCode(t),o++):t>191&&t<224?(e+=String.fromCharCode((31&t)<<6|63&r.charCodeAt(o+1)),o+=2):(e+=String.fromCharCode((15&t)<<12|(63&r.charCodeAt(o+1))<<6|63&r.charCodeAt(o+2)),o+=3);return e}}},this),function(r,e,o,t){if(void 0!==o.module&&o.module.exports){if(t&&o.require)for(var n=0;n<t.length;n++)o[t[n]]=o.require(t[n]);o.module.exports=e.apply(o)}else void 0!==o.define&&\"function\"===o.define&&o.define.amd?define(\"base64\",t||[],e):o.base64=e.apply(o)}(0,function(r){var e=r||this.utf8,o=\"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=\";return{encode:function(r){if(void 0===e)throw{error:\"MissingMethod\",message:\"UTF8 Module is missing.\"};if(\"string\"!=typeof r)return r;r=e.encode(r);for(var t,n,i,d,f,a,h,c=\"\",u=0;u<r.length;)d=(t=r.charCodeAt(u++))>>2,f=(3&t)<<4|(n=r.charCodeAt(u++))>>4,a=(15&n)<<2|(i=r.charCodeAt(u++))>>6,h=63&i,isNaN(n)?a=h=64:isNaN(i)&&(h=64),c+=o.charAt(d)+o.charAt(f)+o.charAt(a)+o.charAt(h);return c},decode:function(r){if(void 0===e)throw{error:\"MissingMethod\",message:\"UTF8 Module is missing.\"};if(\"string\"!=typeof r)return r;r=r.replace(/[^A-Za-z0-9\\+\\/\\=]/g,\"\");for(var t,n,i,d,f,a,h=\"\",c=0;c<r.length;)t=o.indexOf(r.charAt(c++))<<2|(d=o.indexOf(r.charAt(c++)))>>4,n=(15&d)<<4|(f=o.indexOf(r.charAt(c++)))>>2,i=(3&f)<<6|(a=o.indexOf(r.charAt(c++))),h+=String.fromCharCode(t),64!=f&&(h+=String.fromCharCode(n)),64!=a&&(h+=String.fromCharCode(i));return e.decode(h)}}},this,[\"utf8\"]);}).call(exports);",
"type": "application/javascript",
"title": "$:/core/modules/utils/base64-utf8/base64-utf8.module.js",
"module-type": "library"
},
"$:/core/modules/utils/crypto.js": {
"title": "$:/core/modules/utils/crypto.js",
"text": "/*\\\ntitle: $:/core/modules/utils/crypto.js\ntype: application/javascript\nmodule-type: utils\n\nUtility functions related to crypto.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nLook for an encrypted store area in the text of a TiddlyWiki file\n*/\nexports.extractEncryptedStoreArea = function(text) {\n\tvar encryptedStoreAreaStartMarker = \"<pre id=\\\"encryptedStoreArea\\\" type=\\\"text/plain\\\" style=\\\"display:none;\\\">\",\n\t\tencryptedStoreAreaStart = text.indexOf(encryptedStoreAreaStartMarker);\n\tif(encryptedStoreAreaStart !== -1) {\n\t\tvar encryptedStoreAreaEnd = text.indexOf(\"</pre>\",encryptedStoreAreaStart);\n\t\tif(encryptedStoreAreaEnd !== -1) {\n\t\t\treturn $tw.utils.htmlDecode(text.substring(encryptedStoreAreaStart + encryptedStoreAreaStartMarker.length,encryptedStoreAreaEnd-1));\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nAttempt to extract the tiddlers from an encrypted store area using the current password. If the password is not provided then the password in the password store will be used\n*/\nexports.decryptStoreArea = function(encryptedStoreArea,password) {\n\tvar decryptedText = $tw.crypto.decrypt(encryptedStoreArea,password);\n\tif(decryptedText) {\n\t\tvar json = JSON.parse(decryptedText),\n\t\t\ttiddlers = [];\n\t\tfor(var title in json) {\n\t\t\tif(title !== \"$:/isEncrypted\") {\n\t\t\t\ttiddlers.push(json[title]);\n\t\t\t}\n\t\t}\n\t\treturn tiddlers;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n\n/*\nAttempt to extract the tiddlers from an encrypted store area using the current password. If that fails, the user is prompted for a password.\nencryptedStoreArea: text of the TiddlyWiki encrypted store area\ncallback: function(tiddlers) called with the array of decrypted tiddlers\n\nThe following configuration settings are supported:\n\n$tw.config.usePasswordVault: causes any password entered by the user to also be put into the system password vault\n*/\nexports.decryptStoreAreaInteractive = function(encryptedStoreArea,callback,options) {\n\t// Try to decrypt with the current password\n\tvar tiddlers = $tw.utils.decryptStoreArea(encryptedStoreArea);\n\tif(tiddlers) {\n\t\tcallback(tiddlers);\n\t} else {\n\t\t// Prompt for a new password and keep trying\n\t\t$tw.passwordPrompt.createPrompt({\n\t\t\tserviceName: \"Enter a password to decrypt the imported TiddlyWiki\",\n\t\t\tnoUserName: true,\n\t\t\tcanCancel: true,\n\t\t\tsubmitText: \"Decrypt\",\n\t\t\tcallback: function(data) {\n\t\t\t\t// Exit if the user cancelled\n\t\t\t\tif(!data) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\t// Attempt to decrypt the tiddlers\n\t\t\t\tvar tiddlers = $tw.utils.decryptStoreArea(encryptedStoreArea,data.password);\n\t\t\t\tif(tiddlers) {\n\t\t\t\t\tif($tw.config.usePasswordVault) {\n\t\t\t\t\t\t$tw.crypto.setPassword(data.password);\n\t\t\t\t\t}\n\t\t\t\t\tcallback(tiddlers);\n\t\t\t\t\t// Exit and remove the password prompt\n\t\t\t\t\treturn true;\n\t\t\t\t} else {\n\t\t\t\t\t// We didn't decrypt everything, so continue to prompt for password\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/csv.js": {
"title": "$:/core/modules/utils/csv.js",
"text": "/*\\\ntitle: $:/core/modules/utils/csv.js\ntype: application/javascript\nmodule-type: utils\n\nA barebones CSV parser\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nParse a CSV string with a header row and return an array of hashmaps.\n*/\nexports.parseCsvStringWithHeader = function(text,options) {\n\toptions = options || {};\n\tvar separator = options.separator || \",\",\n\t\trows = text.split(/\\r?\\n/mg).map(function(row) {\n\t\t\treturn $tw.utils.trim(row);\n\t\t}).filter(function(row) {\n\t\t\treturn row !== \"\";\n\t\t});\n\tif(rows.length < 1) {\n\t\treturn \"Missing header row\";\n\t}\n\tvar headings = rows[0].split(separator),\n\t\tresults = [];\n\tfor(var row=1; row<rows.length; row++) {\n\t\tvar columns = rows[row].split(separator),\n\t\t\tcolumnResult = Object.create(null);\n\t\tif(columns.length !== headings.length) {\n\t\t\treturn \"Malformed CSV row '\" + rows[row] + \"'\";\n\t\t}\n\t\tfor(var column=0; column<columns.length; column++) {\n\t\t\tvar columnName = headings[column];\n\t\t\tcolumnResult[columnName] = $tw.utils.trim(columns[column] || \"\");\n\t\t}\n\t\tresults.push(columnResult);\t\t\t\n\t}\n\treturn results;\n}\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/diff-match-patch/diff_match_patch.js": {
"text": "(function(){function diff_match_patch(){this.Diff_Timeout=1;this.Diff_EditCost=4;this.Match_Threshold=.5;this.Match_Distance=1E3;this.Patch_DeleteThreshold=.5;this.Patch_Margin=4;this.Match_MaxBits=32}var DIFF_DELETE=-1,DIFF_INSERT=1,DIFF_EQUAL=0;\ndiff_match_patch.prototype.diff_main=function(a,b,c,d){\"undefined\"==typeof d&&(d=0>=this.Diff_Timeout?Number.MAX_VALUE:(new Date).getTime()+1E3*this.Diff_Timeout);if(null==a||null==b)throw Error(\"Null input. (diff_main)\");if(a==b)return a?[[DIFF_EQUAL,a]]:[];\"undefined\"==typeof c&&(c=!0);var e=c,f=this.diff_commonPrefix(a,b);c=a.substring(0,f);a=a.substring(f);b=b.substring(f);f=this.diff_commonSuffix(a,b);var g=a.substring(a.length-f);a=a.substring(0,a.length-f);b=b.substring(0,b.length-f);a=this.diff_compute_(a,\nb,e,d);c&&a.unshift([DIFF_EQUAL,c]);g&&a.push([DIFF_EQUAL,g]);this.diff_cleanupMerge(a);return a};\ndiff_match_patch.prototype.diff_compute_=function(a,b,c,d){if(!a)return[[DIFF_INSERT,b]];if(!b)return[[DIFF_DELETE,a]];var e=a.length>b.length?a:b,f=a.length>b.length?b:a,g=e.indexOf(f);return-1!=g?(c=[[DIFF_INSERT,e.substring(0,g)],[DIFF_EQUAL,f],[DIFF_INSERT,e.substring(g+f.length)]],a.length>b.length&&(c[0][0]=c[2][0]=DIFF_DELETE),c):1==f.length?[[DIFF_DELETE,a],[DIFF_INSERT,b]]:(e=this.diff_halfMatch_(a,b))?(b=e[1],f=e[3],a=e[4],e=this.diff_main(e[0],e[2],c,d),c=this.diff_main(b,f,c,d),e.concat([[DIFF_EQUAL,\na]],c)):c&&100<a.length&&100<b.length?this.diff_lineMode_(a,b,d):this.diff_bisect_(a,b,d)};\ndiff_match_patch.prototype.diff_lineMode_=function(a,b,c){var d=this.diff_linesToChars_(a,b);a=d.chars1;b=d.chars2;d=d.lineArray;a=this.diff_main(a,b,!1,c);this.diff_charsToLines_(a,d);this.diff_cleanupSemantic(a);a.push([DIFF_EQUAL,\"\"]);for(var e=d=b=0,f=\"\",g=\"\";b<a.length;){switch(a[b][0]){case DIFF_INSERT:e++;g+=a[b][1];break;case DIFF_DELETE:d++;f+=a[b][1];break;case DIFF_EQUAL:if(1<=d&&1<=e){a.splice(b-d-e,d+e);b=b-d-e;d=this.diff_main(f,g,!1,c);for(e=d.length-1;0<=e;e--)a.splice(b,0,d[e]);b+=\nd.length}d=e=0;g=f=\"\"}b++}a.pop();return a};\ndiff_match_patch.prototype.diff_bisect_=function(a,b,c){for(var d=a.length,e=b.length,f=Math.ceil((d+e)/2),g=2*f,h=Array(g),l=Array(g),k=0;k<g;k++)h[k]=-1,l[k]=-1;h[f+1]=0;l[f+1]=0;k=d-e;for(var m=0!=k%2,p=0,x=0,w=0,q=0,t=0;t<f&&!((new Date).getTime()>c);t++){for(var v=-t+p;v<=t-x;v+=2){var n=f+v;var r=v==-t||v!=t&&h[n-1]<h[n+1]?h[n+1]:h[n-1]+1;for(var y=r-v;r<d&&y<e&&a.charAt(r)==b.charAt(y);)r++,y++;h[n]=r;if(r>d)x+=2;else if(y>e)p+=2;else if(m&&(n=f+k-v,0<=n&&n<g&&-1!=l[n])){var u=d-l[n];if(r>=\nu)return this.diff_bisectSplit_(a,b,r,y,c)}}for(v=-t+w;v<=t-q;v+=2){n=f+v;u=v==-t||v!=t&&l[n-1]<l[n+1]?l[n+1]:l[n-1]+1;for(r=u-v;u<d&&r<e&&a.charAt(d-u-1)==b.charAt(e-r-1);)u++,r++;l[n]=u;if(u>d)q+=2;else if(r>e)w+=2;else if(!m&&(n=f+k-v,0<=n&&n<g&&-1!=h[n]&&(r=h[n],y=f+r-n,u=d-u,r>=u)))return this.diff_bisectSplit_(a,b,r,y,c)}}return[[DIFF_DELETE,a],[DIFF_INSERT,b]]};\ndiff_match_patch.prototype.diff_bisectSplit_=function(a,b,c,d,e){var f=a.substring(0,c),g=b.substring(0,d);a=a.substring(c);b=b.substring(d);f=this.diff_main(f,g,!1,e);e=this.diff_main(a,b,!1,e);return f.concat(e)};\ndiff_match_patch.prototype.diff_linesToChars_=function(a,b){function c(a){for(var b=\"\",c=0,f=-1,g=d.length;f<a.length-1;){f=a.indexOf(\"\\n\",c);-1==f&&(f=a.length-1);var h=a.substring(c,f+1);c=f+1;(e.hasOwnProperty?e.hasOwnProperty(h):void 0!==e[h])?b+=String.fromCharCode(e[h]):(b+=String.fromCharCode(g),e[h]=g,d[g++]=h)}return b}var d=[],e={};d[0]=\"\";var f=c(a),g=c(b);return{chars1:f,chars2:g,lineArray:d}};\ndiff_match_patch.prototype.diff_charsToLines_=function(a,b){for(var c=0;c<a.length;c++){for(var d=a[c][1],e=[],f=0;f<d.length;f++)e[f]=b[d.charCodeAt(f)];a[c][1]=e.join(\"\")}};diff_match_patch.prototype.diff_commonPrefix=function(a,b){if(!a||!b||a.charAt(0)!=b.charAt(0))return 0;for(var c=0,d=Math.min(a.length,b.length),e=d,f=0;c<e;)a.substring(f,e)==b.substring(f,e)?f=c=e:d=e,e=Math.floor((d-c)/2+c);return e};\ndiff_match_patch.prototype.diff_commonSuffix=function(a,b){if(!a||!b||a.charAt(a.length-1)!=b.charAt(b.length-1))return 0;for(var c=0,d=Math.min(a.length,b.length),e=d,f=0;c<e;)a.substring(a.length-e,a.length-f)==b.substring(b.length-e,b.length-f)?f=c=e:d=e,e=Math.floor((d-c)/2+c);return e};\ndiff_match_patch.prototype.diff_commonOverlap_=function(a,b){var c=a.length,d=b.length;if(0==c||0==d)return 0;c>d?a=a.substring(c-d):c<d&&(b=b.substring(0,c));c=Math.min(c,d);if(a==b)return c;d=0;for(var e=1;;){var f=a.substring(c-e);f=b.indexOf(f);if(-1==f)return d;e+=f;if(0==f||a.substring(c-e)==b.substring(0,e))d=e,e++}};\ndiff_match_patch.prototype.diff_halfMatch_=function(a,b){function c(a,b,c){for(var d=a.substring(c,c+Math.floor(a.length/4)),e=-1,g=\"\",h,k,l,m;-1!=(e=b.indexOf(d,e+1));){var p=f.diff_commonPrefix(a.substring(c),b.substring(e)),u=f.diff_commonSuffix(a.substring(0,c),b.substring(0,e));g.length<u+p&&(g=b.substring(e-u,e)+b.substring(e,e+p),h=a.substring(0,c-u),k=a.substring(c+p),l=b.substring(0,e-u),m=b.substring(e+p))}return 2*g.length>=a.length?[h,k,l,m,g]:null}if(0>=this.Diff_Timeout)return null;\nvar d=a.length>b.length?a:b,e=a.length>b.length?b:a;if(4>d.length||2*e.length<d.length)return null;var f=this,g=c(d,e,Math.ceil(d.length/4));d=c(d,e,Math.ceil(d.length/2));if(g||d)g=d?g?g[4].length>d[4].length?g:d:d:g;else return null;if(a.length>b.length){d=g[0];e=g[1];var h=g[2];var l=g[3]}else h=g[0],l=g[1],d=g[2],e=g[3];return[d,e,h,l,g[4]]};\ndiff_match_patch.prototype.diff_cleanupSemantic=function(a){for(var b=!1,c=[],d=0,e=null,f=0,g=0,h=0,l=0,k=0;f<a.length;)a[f][0]==DIFF_EQUAL?(c[d++]=f,g=l,h=k,k=l=0,e=a[f][1]):(a[f][0]==DIFF_INSERT?l+=a[f][1].length:k+=a[f][1].length,e&&e.length<=Math.max(g,h)&&e.length<=Math.max(l,k)&&(a.splice(c[d-1],0,[DIFF_DELETE,e]),a[c[d-1]+1][0]=DIFF_INSERT,d--,d--,f=0<d?c[d-1]:-1,k=l=h=g=0,e=null,b=!0)),f++;b&&this.diff_cleanupMerge(a);this.diff_cleanupSemanticLossless(a);for(f=1;f<a.length;){if(a[f-1][0]==\nDIFF_DELETE&&a[f][0]==DIFF_INSERT){b=a[f-1][1];c=a[f][1];d=this.diff_commonOverlap_(b,c);e=this.diff_commonOverlap_(c,b);if(d>=e){if(d>=b.length/2||d>=c.length/2)a.splice(f,0,[DIFF_EQUAL,c.substring(0,d)]),a[f-1][1]=b.substring(0,b.length-d),a[f+1][1]=c.substring(d),f++}else if(e>=b.length/2||e>=c.length/2)a.splice(f,0,[DIFF_EQUAL,b.substring(0,e)]),a[f-1][0]=DIFF_INSERT,a[f-1][1]=c.substring(0,c.length-e),a[f+1][0]=DIFF_DELETE,a[f+1][1]=b.substring(e),f++;f++}f++}};\ndiff_match_patch.prototype.diff_cleanupSemanticLossless=function(a){function b(a,b){if(!a||!b)return 6;var c=a.charAt(a.length-1),d=b.charAt(0),e=c.match(diff_match_patch.nonAlphaNumericRegex_),f=d.match(diff_match_patch.nonAlphaNumericRegex_),g=e&&c.match(diff_match_patch.whitespaceRegex_),h=f&&d.match(diff_match_patch.whitespaceRegex_);c=g&&c.match(diff_match_patch.linebreakRegex_);d=h&&d.match(diff_match_patch.linebreakRegex_);var k=c&&a.match(diff_match_patch.blanklineEndRegex_),l=d&&b.match(diff_match_patch.blanklineStartRegex_);\nreturn k||l?5:c||d?4:e&&!g&&h?3:g||h?2:e||f?1:0}for(var c=1;c<a.length-1;){if(a[c-1][0]==DIFF_EQUAL&&a[c+1][0]==DIFF_EQUAL){var d=a[c-1][1],e=a[c][1],f=a[c+1][1],g=this.diff_commonSuffix(d,e);if(g){var h=e.substring(e.length-g);d=d.substring(0,d.length-g);e=h+e.substring(0,e.length-g);f=h+f}g=d;h=e;for(var l=f,k=b(d,e)+b(e,f);e.charAt(0)===f.charAt(0);){d+=e.charAt(0);e=e.substring(1)+f.charAt(0);f=f.substring(1);var m=b(d,e)+b(e,f);m>=k&&(k=m,g=d,h=e,l=f)}a[c-1][1]!=g&&(g?a[c-1][1]=g:(a.splice(c-\n1,1),c--),a[c][1]=h,l?a[c+1][1]=l:(a.splice(c+1,1),c--))}c++}};diff_match_patch.nonAlphaNumericRegex_=/[^a-zA-Z0-9]/;diff_match_patch.whitespaceRegex_=/\\s/;diff_match_patch.linebreakRegex_=/[\\r\\n]/;diff_match_patch.blanklineEndRegex_=/\\n\\r?\\n$/;diff_match_patch.blanklineStartRegex_=/^\\r?\\n\\r?\\n/;\ndiff_match_patch.prototype.diff_cleanupEfficiency=function(a){for(var b=!1,c=[],d=0,e=null,f=0,g=!1,h=!1,l=!1,k=!1;f<a.length;)a[f][0]==DIFF_EQUAL?(a[f][1].length<this.Diff_EditCost&&(l||k)?(c[d++]=f,g=l,h=k,e=a[f][1]):(d=0,e=null),l=k=!1):(a[f][0]==DIFF_DELETE?k=!0:l=!0,e&&(g&&h&&l&&k||e.length<this.Diff_EditCost/2&&3==g+h+l+k)&&(a.splice(c[d-1],0,[DIFF_DELETE,e]),a[c[d-1]+1][0]=DIFF_INSERT,d--,e=null,g&&h?(l=k=!0,d=0):(d--,f=0<d?c[d-1]:-1,l=k=!1),b=!0)),f++;b&&this.diff_cleanupMerge(a)};\ndiff_match_patch.prototype.diff_cleanupMerge=function(a){a.push([DIFF_EQUAL,\"\"]);for(var b=0,c=0,d=0,e=\"\",f=\"\",g;b<a.length;)switch(a[b][0]){case DIFF_INSERT:d++;f+=a[b][1];b++;break;case DIFF_DELETE:c++;e+=a[b][1];b++;break;case DIFF_EQUAL:1<c+d?(0!==c&&0!==d&&(g=this.diff_commonPrefix(f,e),0!==g&&(0<b-c-d&&a[b-c-d-1][0]==DIFF_EQUAL?a[b-c-d-1][1]+=f.substring(0,g):(a.splice(0,0,[DIFF_EQUAL,f.substring(0,g)]),b++),f=f.substring(g),e=e.substring(g)),g=this.diff_commonSuffix(f,e),0!==g&&(a[b][1]=f.substring(f.length-\ng)+a[b][1],f=f.substring(0,f.length-g),e=e.substring(0,e.length-g))),0===c?a.splice(b-d,c+d,[DIFF_INSERT,f]):0===d?a.splice(b-c,c+d,[DIFF_DELETE,e]):a.splice(b-c-d,c+d,[DIFF_DELETE,e],[DIFF_INSERT,f]),b=b-c-d+(c?1:0)+(d?1:0)+1):0!==b&&a[b-1][0]==DIFF_EQUAL?(a[b-1][1]+=a[b][1],a.splice(b,1)):b++,c=d=0,f=e=\"\"}\"\"===a[a.length-1][1]&&a.pop();c=!1;for(b=1;b<a.length-1;)a[b-1][0]==DIFF_EQUAL&&a[b+1][0]==DIFF_EQUAL&&(a[b][1].substring(a[b][1].length-a[b-1][1].length)==a[b-1][1]?(a[b][1]=a[b-1][1]+a[b][1].substring(0,\na[b][1].length-a[b-1][1].length),a[b+1][1]=a[b-1][1]+a[b+1][1],a.splice(b-1,1),c=!0):a[b][1].substring(0,a[b+1][1].length)==a[b+1][1]&&(a[b-1][1]+=a[b+1][1],a[b][1]=a[b][1].substring(a[b+1][1].length)+a[b+1][1],a.splice(b+1,1),c=!0)),b++;c&&this.diff_cleanupMerge(a)};\ndiff_match_patch.prototype.diff_xIndex=function(a,b){var c=0,d=0,e=0,f=0,g;for(g=0;g<a.length;g++){a[g][0]!==DIFF_INSERT&&(c+=a[g][1].length);a[g][0]!==DIFF_DELETE&&(d+=a[g][1].length);if(c>b)break;e=c;f=d}return a.length!=g&&a[g][0]===DIFF_DELETE?f:f+(b-e)};\ndiff_match_patch.prototype.diff_prettyHtml=function(a){for(var b=[],c=/&/g,d=/</g,e=/>/g,f=/\\n/g,g=0;g<a.length;g++){var h=a[g][0],l=a[g][1].replace(c,\"&\").replace(d,\"<\").replace(e,\">\").replace(f,\"¶<br>\");switch(h){case DIFF_INSERT:b[g]='<ins style=\"background:#e6ffe6;\">'+l+\"</ins>\";break;case DIFF_DELETE:b[g]='<del style=\"background:#ffe6e6;\">'+l+\"</del>\";break;case DIFF_EQUAL:b[g]=\"<span>\"+l+\"</span>\"}}return b.join(\"\")};\ndiff_match_patch.prototype.diff_text1=function(a){for(var b=[],c=0;c<a.length;c++)a[c][0]!==DIFF_INSERT&&(b[c]=a[c][1]);return b.join(\"\")};diff_match_patch.prototype.diff_text2=function(a){for(var b=[],c=0;c<a.length;c++)a[c][0]!==DIFF_DELETE&&(b[c]=a[c][1]);return b.join(\"\")};\ndiff_match_patch.prototype.diff_levenshtein=function(a){for(var b=0,c=0,d=0,e=0;e<a.length;e++){var f=a[e][1];switch(a[e][0]){case DIFF_INSERT:c+=f.length;break;case DIFF_DELETE:d+=f.length;break;case DIFF_EQUAL:b+=Math.max(c,d),d=c=0}}return b+=Math.max(c,d)};\ndiff_match_patch.prototype.diff_toDelta=function(a){for(var b=[],c=0;c<a.length;c++)switch(a[c][0]){case DIFF_INSERT:b[c]=\"+\"+encodeURI(a[c][1]);break;case DIFF_DELETE:b[c]=\"-\"+a[c][1].length;break;case DIFF_EQUAL:b[c]=\"=\"+a[c][1].length}return b.join(\"\\t\").replace(/%20/g,\" \")};\ndiff_match_patch.prototype.diff_fromDelta=function(a,b){for(var c=[],d=0,e=0,f=b.split(/\\t/g),g=0;g<f.length;g++){var h=f[g].substring(1);switch(f[g].charAt(0)){case \"+\":try{c[d++]=[DIFF_INSERT,decodeURI(h)]}catch(k){throw Error(\"Illegal escape in diff_fromDelta: \"+h);}break;case \"-\":case \"=\":var l=parseInt(h,10);if(isNaN(l)||0>l)throw Error(\"Invalid number in diff_fromDelta: \"+h);h=a.substring(e,e+=l);\"=\"==f[g].charAt(0)?c[d++]=[DIFF_EQUAL,h]:c[d++]=[DIFF_DELETE,h];break;default:if(f[g])throw Error(\"Invalid diff operation in diff_fromDelta: \"+\nf[g]);}}if(e!=a.length)throw Error(\"Delta length (\"+e+\") does not equal source text length (\"+a.length+\").\");return c};diff_match_patch.prototype.match_main=function(a,b,c){if(null==a||null==b||null==c)throw Error(\"Null input. (match_main)\");c=Math.max(0,Math.min(c,a.length));return a==b?0:a.length?a.substring(c,c+b.length)==b?c:this.match_bitap_(a,b,c):-1};\ndiff_match_patch.prototype.match_bitap_=function(a,b,c){function d(a,d){var e=a/b.length,g=Math.abs(c-d);return f.Match_Distance?e+g/f.Match_Distance:g?1:e}if(b.length>this.Match_MaxBits)throw Error(\"Pattern too long for this browser.\");var e=this.match_alphabet_(b),f=this,g=this.Match_Threshold,h=a.indexOf(b,c);-1!=h&&(g=Math.min(d(0,h),g),h=a.lastIndexOf(b,c+b.length),-1!=h&&(g=Math.min(d(0,h),g)));var l=1<<b.length-1;h=-1;for(var k,m,p=b.length+a.length,x,w=0;w<b.length;w++){k=0;for(m=p;k<m;)d(w,\nc+m)<=g?k=m:p=m,m=Math.floor((p-k)/2+k);p=m;k=Math.max(1,c-m+1);var q=Math.min(c+m,a.length)+b.length;m=Array(q+2);for(m[q+1]=(1<<w)-1;q>=k;q--){var t=e[a.charAt(q-1)];m[q]=0===w?(m[q+1]<<1|1)&t:(m[q+1]<<1|1)&t|(x[q+1]|x[q])<<1|1|x[q+1];if(m[q]&l&&(t=d(w,q-1),t<=g))if(g=t,h=q-1,h>c)k=Math.max(1,2*c-h);else break}if(d(w+1,c)>g)break;x=m}return h};\ndiff_match_patch.prototype.match_alphabet_=function(a){for(var b={},c=0;c<a.length;c++)b[a.charAt(c)]=0;for(c=0;c<a.length;c++)b[a.charAt(c)]|=1<<a.length-c-1;return b};\ndiff_match_patch.prototype.patch_addContext_=function(a,b){if(0!=b.length){for(var c=b.substring(a.start2,a.start2+a.length1),d=0;b.indexOf(c)!=b.lastIndexOf(c)&&c.length<this.Match_MaxBits-this.Patch_Margin-this.Patch_Margin;)d+=this.Patch_Margin,c=b.substring(a.start2-d,a.start2+a.length1+d);d+=this.Patch_Margin;(c=b.substring(a.start2-d,a.start2))&&a.diffs.unshift([DIFF_EQUAL,c]);(d=b.substring(a.start2+a.length1,a.start2+a.length1+d))&&a.diffs.push([DIFF_EQUAL,d]);a.start1-=c.length;a.start2-=\nc.length;a.length1+=c.length+d.length;a.length2+=c.length+d.length}};\ndiff_match_patch.prototype.patch_make=function(a,b,c){if(\"string\"==typeof a&&\"string\"==typeof b&&\"undefined\"==typeof c){var d=a;b=this.diff_main(d,b,!0);2<b.length&&(this.diff_cleanupSemantic(b),this.diff_cleanupEfficiency(b))}else if(a&&\"object\"==typeof a&&\"undefined\"==typeof b&&\"undefined\"==typeof c)b=a,d=this.diff_text1(b);else if(\"string\"==typeof a&&b&&\"object\"==typeof b&&\"undefined\"==typeof c)d=a;else if(\"string\"==typeof a&&\"string\"==typeof b&&c&&\"object\"==typeof c)d=a,b=c;else throw Error(\"Unknown call format to patch_make.\");\nif(0===b.length)return[];c=[];a=new diff_match_patch.patch_obj;for(var e=0,f=0,g=0,h=d,l=0;l<b.length;l++){var k=b[l][0],m=b[l][1];e||k===DIFF_EQUAL||(a.start1=f,a.start2=g);switch(k){case DIFF_INSERT:a.diffs[e++]=b[l];a.length2+=m.length;d=d.substring(0,g)+m+d.substring(g);break;case DIFF_DELETE:a.length1+=m.length;a.diffs[e++]=b[l];d=d.substring(0,g)+d.substring(g+m.length);break;case DIFF_EQUAL:m.length<=2*this.Patch_Margin&&e&&b.length!=l+1?(a.diffs[e++]=b[l],a.length1+=m.length,a.length2+=m.length):\nm.length>=2*this.Patch_Margin&&e&&(this.patch_addContext_(a,h),c.push(a),a=new diff_match_patch.patch_obj,e=0,h=d,f=g)}k!==DIFF_INSERT&&(f+=m.length);k!==DIFF_DELETE&&(g+=m.length)}e&&(this.patch_addContext_(a,h),c.push(a));return c};\ndiff_match_patch.prototype.patch_deepCopy=function(a){for(var b=[],c=0;c<a.length;c++){var d=a[c],e=new diff_match_patch.patch_obj;e.diffs=[];for(var f=0;f<d.diffs.length;f++)e.diffs[f]=d.diffs[f].slice();e.start1=d.start1;e.start2=d.start2;e.length1=d.length1;e.length2=d.length2;b[c]=e}return b};\ndiff_match_patch.prototype.patch_apply=function(a,b){if(0==a.length)return[b,[]];a=this.patch_deepCopy(a);var c=this.patch_addPadding(a);b=c+b+c;this.patch_splitMax(a);for(var d=0,e=[],f=0;f<a.length;f++){var g=a[f].start2+d,h=this.diff_text1(a[f].diffs),l=-1;if(h.length>this.Match_MaxBits){var k=this.match_main(b,h.substring(0,this.Match_MaxBits),g);-1!=k&&(l=this.match_main(b,h.substring(h.length-this.Match_MaxBits),g+h.length-this.Match_MaxBits),-1==l||k>=l)&&(k=-1)}else k=this.match_main(b,h,\ng);if(-1==k)e[f]=!1,d-=a[f].length2-a[f].length1;else if(e[f]=!0,d=k-g,g=-1==l?b.substring(k,k+h.length):b.substring(k,l+this.Match_MaxBits),h==g)b=b.substring(0,k)+this.diff_text2(a[f].diffs)+b.substring(k+h.length);else if(g=this.diff_main(h,g,!1),h.length>this.Match_MaxBits&&this.diff_levenshtein(g)/h.length>this.Patch_DeleteThreshold)e[f]=!1;else{this.diff_cleanupSemanticLossless(g);h=0;var m;for(l=0;l<a[f].diffs.length;l++){var p=a[f].diffs[l];p[0]!==DIFF_EQUAL&&(m=this.diff_xIndex(g,h));p[0]===\nDIFF_INSERT?b=b.substring(0,k+m)+p[1]+b.substring(k+m):p[0]===DIFF_DELETE&&(b=b.substring(0,k+m)+b.substring(k+this.diff_xIndex(g,h+p[1].length)));p[0]!==DIFF_DELETE&&(h+=p[1].length)}}}b=b.substring(c.length,b.length-c.length);return[b,e]};\ndiff_match_patch.prototype.patch_addPadding=function(a){for(var b=this.Patch_Margin,c=\"\",d=1;d<=b;d++)c+=String.fromCharCode(d);for(d=0;d<a.length;d++)a[d].start1+=b,a[d].start2+=b;d=a[0];var e=d.diffs;if(0==e.length||e[0][0]!=DIFF_EQUAL)e.unshift([DIFF_EQUAL,c]),d.start1-=b,d.start2-=b,d.length1+=b,d.length2+=b;else if(b>e[0][1].length){var f=b-e[0][1].length;e[0][1]=c.substring(e[0][1].length)+e[0][1];d.start1-=f;d.start2-=f;d.length1+=f;d.length2+=f}d=a[a.length-1];e=d.diffs;0==e.length||e[e.length-\n1][0]!=DIFF_EQUAL?(e.push([DIFF_EQUAL,c]),d.length1+=b,d.length2+=b):b>e[e.length-1][1].length&&(f=b-e[e.length-1][1].length,e[e.length-1][1]+=c.substring(0,f),d.length1+=f,d.length2+=f);return c};\ndiff_match_patch.prototype.patch_splitMax=function(a){for(var b=this.Match_MaxBits,c=0;c<a.length;c++)if(!(a[c].length1<=b)){var d=a[c];a.splice(c--,1);for(var e=d.start1,f=d.start2,g=\"\";0!==d.diffs.length;){var h=new diff_match_patch.patch_obj,l=!0;h.start1=e-g.length;h.start2=f-g.length;\"\"!==g&&(h.length1=h.length2=g.length,h.diffs.push([DIFF_EQUAL,g]));for(;0!==d.diffs.length&&h.length1<b-this.Patch_Margin;){g=d.diffs[0][0];var k=d.diffs[0][1];g===DIFF_INSERT?(h.length2+=k.length,f+=k.length,h.diffs.push(d.diffs.shift()),\nl=!1):g===DIFF_DELETE&&1==h.diffs.length&&h.diffs[0][0]==DIFF_EQUAL&&k.length>2*b?(h.length1+=k.length,e+=k.length,l=!1,h.diffs.push([g,k]),d.diffs.shift()):(k=k.substring(0,b-h.length1-this.Patch_Margin),h.length1+=k.length,e+=k.length,g===DIFF_EQUAL?(h.length2+=k.length,f+=k.length):l=!1,h.diffs.push([g,k]),k==d.diffs[0][1]?d.diffs.shift():d.diffs[0][1]=d.diffs[0][1].substring(k.length))}g=this.diff_text2(h.diffs);g=g.substring(g.length-this.Patch_Margin);k=this.diff_text1(d.diffs).substring(0,\nthis.Patch_Margin);\"\"!==k&&(h.length1+=k.length,h.length2+=k.length,0!==h.diffs.length&&h.diffs[h.diffs.length-1][0]===DIFF_EQUAL?h.diffs[h.diffs.length-1][1]+=k:h.diffs.push([DIFF_EQUAL,k]));l||a.splice(++c,0,h)}}};diff_match_patch.prototype.patch_toText=function(a){for(var b=[],c=0;c<a.length;c++)b[c]=a[c];return b.join(\"\")};\ndiff_match_patch.prototype.patch_fromText=function(a){var b=[];if(!a)return b;a=a.split(\"\\n\");for(var c=0,d=/^@@ -(\\d+),?(\\d*) \\+(\\d+),?(\\d*) @@$/;c<a.length;){var e=a[c].match(d);if(!e)throw Error(\"Invalid patch string: \"+a[c]);var f=new diff_match_patch.patch_obj;b.push(f);f.start1=parseInt(e[1],10);\"\"===e[2]?(f.start1--,f.length1=1):\"0\"==e[2]?f.length1=0:(f.start1--,f.length1=parseInt(e[2],10));f.start2=parseInt(e[3],10);\"\"===e[4]?(f.start2--,f.length2=1):\"0\"==e[4]?f.length2=0:(f.start2--,f.length2=\nparseInt(e[4],10));for(c++;c<a.length;){e=a[c].charAt(0);try{var g=decodeURI(a[c].substring(1))}catch(h){throw Error(\"Illegal escape in patch_fromText: \"+g);}if(\"-\"==e)f.diffs.push([DIFF_DELETE,g]);else if(\"+\"==e)f.diffs.push([DIFF_INSERT,g]);else if(\" \"==e)f.diffs.push([DIFF_EQUAL,g]);else if(\"@\"==e)break;else if(\"\"!==e)throw Error('Invalid patch mode \"'+e+'\" in: '+g);c++}}return b};diff_match_patch.patch_obj=function(){this.diffs=[];this.start2=this.start1=null;this.length2=this.length1=0};\ndiff_match_patch.patch_obj.prototype.toString=function(){for(var a=[\"@@ -\"+(0===this.length1?this.start1+\",0\":1==this.length1?this.start1+1:this.start1+1+\",\"+this.length1)+\" +\"+(0===this.length2?this.start2+\",0\":1==this.length2?this.start2+1:this.start2+1+\",\"+this.length2)+\" @@\\n\"],b,c=0;c<this.diffs.length;c++){switch(this.diffs[c][0]){case DIFF_INSERT:b=\"+\";break;case DIFF_DELETE:b=\"-\";break;case DIFF_EQUAL:b=\" \"}a[c+1]=b+encodeURI(this.diffs[c][1])+\"\\n\"}return a.join(\"\").replace(/%20/g,\" \")};\nthis.diff_match_patch=diff_match_patch;this.DIFF_DELETE=DIFF_DELETE;this.DIFF_INSERT=DIFF_INSERT;this.DIFF_EQUAL=DIFF_EQUAL;\n}).call(exports);",
"type": "application/javascript",
"title": "$:/core/modules/utils/diff-match-patch/diff_match_patch.js",
"module-type": "library"
},
"$:/core/modules/utils/dom/animations/slide.js": {
"title": "$:/core/modules/utils/dom/animations/slide.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/animations/slide.js\ntype: application/javascript\nmodule-type: animation\n\nA simple slide animation that varies the height of the element\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nfunction slideOpen(domNode,options) {\n\toptions = options || {};\n\tvar duration = options.duration || $tw.utils.getAnimationDuration();\n\t// Get the current height of the domNode\n\tvar computedStyle = window.getComputedStyle(domNode),\n\t\tcurrMarginBottom = parseInt(computedStyle.marginBottom,10),\n\t\tcurrMarginTop = parseInt(computedStyle.marginTop,10),\n\t\tcurrPaddingBottom = parseInt(computedStyle.paddingBottom,10),\n\t\tcurrPaddingTop = parseInt(computedStyle.paddingTop,10),\n\t\tcurrHeight = domNode.offsetHeight;\n\t// Reset the margin once the transition is over\n\tsetTimeout(function() {\n\t\t$tw.utils.setStyle(domNode,[\n\t\t\t{transition: \"none\"},\n\t\t\t{marginBottom: \"\"},\n\t\t\t{marginTop: \"\"},\n\t\t\t{paddingBottom: \"\"},\n\t\t\t{paddingTop: \"\"},\n\t\t\t{height: \"auto\"},\n\t\t\t{opacity: \"\"}\n\t\t]);\n\t\tif(options.callback) {\n\t\t\toptions.callback();\n\t\t}\n\t},duration);\n\t// Set up the initial position of the element\n\t$tw.utils.setStyle(domNode,[\n\t\t{transition: \"none\"},\n\t\t{marginTop: \"0px\"},\n\t\t{marginBottom: \"0px\"},\n\t\t{paddingTop: \"0px\"},\n\t\t{paddingBottom: \"0px\"},\n\t\t{height: \"0px\"},\n\t\t{opacity: \"0\"}\n\t]);\n\t$tw.utils.forceLayout(domNode);\n\t// Transition to the final position\n\t$tw.utils.setStyle(domNode,[\n\t\t{transition: \"margin-top \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"margin-bottom \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"padding-top \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"padding-bottom \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"height \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"opacity \" + duration + \"ms ease-in-out\"},\n\t\t{marginBottom: currMarginBottom + \"px\"},\n\t\t{marginTop: currMarginTop + \"px\"},\n\t\t{paddingBottom: currPaddingBottom + \"px\"},\n\t\t{paddingTop: currPaddingTop + \"px\"},\n\t\t{height: currHeight + \"px\"},\n\t\t{opacity: \"1\"}\n\t]);\n}\n\nfunction slideClosed(domNode,options) {\n\toptions = options || {};\n\tvar duration = options.duration || $tw.utils.getAnimationDuration(),\n\t\tcurrHeight = domNode.offsetHeight;\n\t// Clear the properties we've set when the animation is over\n\tsetTimeout(function() {\n\t\t$tw.utils.setStyle(domNode,[\n\t\t\t{transition: \"none\"},\n\t\t\t{marginBottom: \"\"},\n\t\t\t{marginTop: \"\"},\n\t\t\t{paddingBottom: \"\"},\n\t\t\t{paddingTop: \"\"},\n\t\t\t{height: \"auto\"},\n\t\t\t{opacity: \"\"}\n\t\t]);\n\t\tif(options.callback) {\n\t\t\toptions.callback();\n\t\t}\n\t},duration);\n\t// Set up the initial position of the element\n\t$tw.utils.setStyle(domNode,[\n\t\t{height: currHeight + \"px\"},\n\t\t{opacity: \"1\"}\n\t]);\n\t$tw.utils.forceLayout(domNode);\n\t// Transition to the final position\n\t$tw.utils.setStyle(domNode,[\n\t\t{transition: \"margin-top \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"margin-bottom \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"padding-top \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"padding-bottom \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"height \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"opacity \" + duration + \"ms ease-in-out\"},\n\t\t{marginTop: \"0px\"},\n\t\t{marginBottom: \"0px\"},\n\t\t{paddingTop: \"0px\"},\n\t\t{paddingBottom: \"0px\"},\n\t\t{height: \"0px\"},\n\t\t{opacity: \"0\"}\n\t]);\n}\n\nexports.slide = {\n\topen: slideOpen,\n\tclose: slideClosed\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "animation"
},
"$:/core/modules/utils/dom/animator.js": {
"title": "$:/core/modules/utils/dom/animator.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/animator.js\ntype: application/javascript\nmodule-type: utils\n\nOrchestrates animations and transitions\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nfunction Animator() {\n\t// Get the registered animation modules\n\tthis.animations = {};\n\t$tw.modules.applyMethods(\"animation\",this.animations);\n}\n\nAnimator.prototype.perform = function(type,domNode,options) {\n\toptions = options || {};\n\t// Find an animation that can handle this type\n\tvar chosenAnimation;\n\t$tw.utils.each(this.animations,function(animation,name) {\n\t\tif($tw.utils.hop(animation,type)) {\n\t\t\tchosenAnimation = animation[type];\n\t\t}\n\t});\n\tif(!chosenAnimation) {\n\t\tchosenAnimation = function(domNode,options) {\n\t\t\tif(options.callback) {\n\t\t\t\toptions.callback();\n\t\t\t}\n\t\t};\n\t}\n\t// Call the animation\n\tchosenAnimation(domNode,options);\n};\n\nexports.Animator = Animator;\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/browser.js": {
"title": "$:/core/modules/utils/dom/browser.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/browser.js\ntype: application/javascript\nmodule-type: utils\n\nBrowser feature detection\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSet style properties of an element\n\telement: dom node\n\tstyles: ordered array of {name: value} pairs\n*/\nexports.setStyle = function(element,styles) {\n\tif(element.nodeType === 1) { // Element.ELEMENT_NODE\n\t\tfor(var t=0; t<styles.length; t++) {\n\t\t\tfor(var styleName in styles[t]) {\n\t\t\t\telement.style[$tw.utils.convertStyleNameToPropertyName(styleName)] = styles[t][styleName];\n\t\t\t}\n\t\t}\n\t}\n};\n\n/*\nConverts a standard CSS property name into the local browser-specific equivalent. For example:\n\t\"background-color\" --> \"backgroundColor\"\n\t\"transition\" --> \"webkitTransition\"\n*/\n\nvar styleNameCache = {}; // We'll cache the style name conversions\n\nexports.convertStyleNameToPropertyName = function(styleName) {\n\t// Return from the cache if we can\n\tif(styleNameCache[styleName]) {\n\t\treturn styleNameCache[styleName];\n\t}\n\t// Convert it by first removing any hyphens\n\tvar propertyName = $tw.utils.unHyphenateCss(styleName);\n\t// Then check if it needs a prefix\n\tif($tw.browser && document.body.style[propertyName] === undefined) {\n\t\tvar prefixes = [\"O\",\"MS\",\"Moz\",\"webkit\"];\n\t\tfor(var t=0; t<prefixes.length; t++) {\n\t\t\tvar prefixedName = prefixes[t] + propertyName.substr(0,1).toUpperCase() + propertyName.substr(1);\n\t\t\tif(document.body.style[prefixedName] !== undefined) {\n\t\t\t\tpropertyName = prefixedName;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\t// Put it in the cache too\n\tstyleNameCache[styleName] = propertyName;\n\treturn propertyName;\n};\n\n/*\nConverts a JS format CSS property name back into the dashed form used in CSS declarations. For example:\n\t\"backgroundColor\" --> \"background-color\"\n\t\"webkitTransform\" --> \"-webkit-transform\"\n*/\nexports.convertPropertyNameToStyleName = function(propertyName) {\n\t// Rehyphenate the name\n\tvar styleName = $tw.utils.hyphenateCss(propertyName);\n\t// If there's a webkit prefix, add a dash (other browsers have uppercase prefixes, and so get the dash automatically)\n\tif(styleName.indexOf(\"webkit\") === 0) {\n\t\tstyleName = \"-\" + styleName;\n\t} else if(styleName.indexOf(\"-m-s\") === 0) {\n\t\tstyleName = \"-ms\" + styleName.substr(4);\n\t}\n\treturn styleName;\n};\n\n/*\nRound trip a stylename to a property name and back again. For example:\n\t\"transform\" --> \"webkitTransform\" --> \"-webkit-transform\"\n*/\nexports.roundTripPropertyName = function(propertyName) {\n\treturn $tw.utils.convertPropertyNameToStyleName($tw.utils.convertStyleNameToPropertyName(propertyName));\n};\n\n/*\nConverts a standard event name into the local browser specific equivalent. For example:\n\t\"animationEnd\" --> \"webkitAnimationEnd\"\n*/\n\nvar eventNameCache = {}; // We'll cache the conversions\n\nvar eventNameMappings = {\n\t\"transitionEnd\": {\n\t\tcorrespondingCssProperty: \"transition\",\n\t\tmappings: {\n\t\t\ttransition: \"transitionend\",\n\t\t\tOTransition: \"oTransitionEnd\",\n\t\t\tMSTransition: \"msTransitionEnd\",\n\t\t\tMozTransition: \"transitionend\",\n\t\t\twebkitTransition: \"webkitTransitionEnd\"\n\t\t}\n\t},\n\t\"animationEnd\": {\n\t\tcorrespondingCssProperty: \"animation\",\n\t\tmappings: {\n\t\t\tanimation: \"animationend\",\n\t\t\tOAnimation: \"oAnimationEnd\",\n\t\t\tMSAnimation: \"msAnimationEnd\",\n\t\t\tMozAnimation: \"animationend\",\n\t\t\twebkitAnimation: \"webkitAnimationEnd\"\n\t\t}\n\t}\n};\n\nexports.convertEventName = function(eventName) {\n\tif(eventNameCache[eventName]) {\n\t\treturn eventNameCache[eventName];\n\t}\n\tvar newEventName = eventName,\n\t\tmappings = eventNameMappings[eventName];\n\tif(mappings) {\n\t\tvar convertedProperty = $tw.utils.convertStyleNameToPropertyName(mappings.correspondingCssProperty);\n\t\tif(mappings.mappings[convertedProperty]) {\n\t\t\tnewEventName = mappings.mappings[convertedProperty];\n\t\t}\n\t}\n\t// Put it in the cache too\n\teventNameCache[eventName] = newEventName;\n\treturn newEventName;\n};\n\n/*\nReturn the names of the fullscreen APIs\n*/\nexports.getFullScreenApis = function() {\n\tvar d = document,\n\t\tdb = d.body,\n\t\tresult = {\n\t\t\"_requestFullscreen\": db.webkitRequestFullscreen !== undefined ? \"webkitRequestFullscreen\" :\n\t\t\t\t\t\t\tdb.mozRequestFullScreen !== undefined ? \"mozRequestFullScreen\" :\n\t\t\t\t\t\t\tdb.msRequestFullscreen !== undefined ? \"msRequestFullscreen\" :\n\t\t\t\t\t\t\tdb.requestFullscreen !== undefined ? \"requestFullscreen\" : \"\",\n\t\t\"_exitFullscreen\": d.webkitExitFullscreen !== undefined ? \"webkitExitFullscreen\" :\n\t\t\t\t\t\t\td.mozCancelFullScreen !== undefined ? \"mozCancelFullScreen\" :\n\t\t\t\t\t\t\td.msExitFullscreen !== undefined ? \"msExitFullscreen\" :\n\t\t\t\t\t\t\td.exitFullscreen !== undefined ? \"exitFullscreen\" : \"\",\n\t\t\"_fullscreenElement\": d.webkitFullscreenElement !== undefined ? \"webkitFullscreenElement\" :\n\t\t\t\t\t\t\td.mozFullScreenElement !== undefined ? \"mozFullScreenElement\" :\n\t\t\t\t\t\t\td.msFullscreenElement !== undefined ? \"msFullscreenElement\" :\n\t\t\t\t\t\t\td.fullscreenElement !== undefined ? \"fullscreenElement\" : \"\",\n\t\t\"_fullscreenChange\": d.webkitFullscreenElement !== undefined ? \"webkitfullscreenchange\" :\n\t\t\t\t\t\t\td.mozFullScreenElement !== undefined ? \"mozfullscreenchange\" :\n\t\t\t\t\t\t\td.msFullscreenElement !== undefined ? \"MSFullscreenChange\" :\n\t\t\t\t\t\t\td.fullscreenElement !== undefined ? \"fullscreenchange\" : \"\"\n\t};\n\tif(!result._requestFullscreen || !result._exitFullscreen || !result._fullscreenElement || !result._fullscreenChange) {\n\t\treturn null;\n\t} else {\n\t\treturn result;\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/csscolorparser.js": {
"title": "$:/core/modules/utils/dom/csscolorparser.js",
"text": "// (c) Dean McNamee <dean@gmail.com>, 2012.\n//\n// https://github.com/deanm/css-color-parser-js\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to\n// deal in the Software without restriction, including without limitation the\n// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n// sell copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n// IN THE SOFTWARE.\n\n// http://www.w3.org/TR/css3-color/\nvar kCSSColorTable = {\n \"transparent\": [0,0,0,0], \"aliceblue\": [240,248,255,1],\n \"antiquewhite\": [250,235,215,1], \"aqua\": [0,255,255,1],\n \"aquamarine\": [127,255,212,1], \"azure\": [240,255,255,1],\n \"beige\": [245,245,220,1], \"bisque\": [255,228,196,1],\n \"black\": [0,0,0,1], \"blanchedalmond\": [255,235,205,1],\n \"blue\": [0,0,255,1], \"blueviolet\": [138,43,226,1],\n \"brown\": [165,42,42,1], \"burlywood\": [222,184,135,1],\n \"cadetblue\": [95,158,160,1], \"chartreuse\": [127,255,0,1],\n \"chocolate\": [210,105,30,1], \"coral\": [255,127,80,1],\n \"cornflowerblue\": [100,149,237,1], \"cornsilk\": [255,248,220,1],\n \"crimson\": [220,20,60,1], \"cyan\": [0,255,255,1],\n \"darkblue\": [0,0,139,1], \"darkcyan\": [0,139,139,1],\n \"darkgoldenrod\": [184,134,11,1], \"darkgray\": [169,169,169,1],\n \"darkgreen\": [0,100,0,1], \"darkgrey\": [169,169,169,1],\n \"darkkhaki\": [189,183,107,1], \"darkmagenta\": [139,0,139,1],\n \"darkolivegreen\": [85,107,47,1], \"darkorange\": [255,140,0,1],\n \"darkorchid\": [153,50,204,1], \"darkred\": [139,0,0,1],\n \"darksalmon\": [233,150,122,1], \"darkseagreen\": [143,188,143,1],\n \"darkslateblue\": [72,61,139,1], \"darkslategray\": [47,79,79,1],\n \"darkslategrey\": [47,79,79,1], \"darkturquoise\": [0,206,209,1],\n \"darkviolet\": [148,0,211,1], \"deeppink\": [255,20,147,1],\n \"deepskyblue\": [0,191,255,1], \"dimgray\": [105,105,105,1],\n \"dimgrey\": [105,105,105,1], \"dodgerblue\": [30,144,255,1],\n \"firebrick\": [178,34,34,1], \"floralwhite\": [255,250,240,1],\n \"forestgreen\": [34,139,34,1], \"fuchsia\": [255,0,255,1],\n \"gainsboro\": [220,220,220,1], \"ghostwhite\": [248,248,255,1],\n \"gold\": [255,215,0,1], \"goldenrod\": [218,165,32,1],\n \"gray\": [128,128,128,1], \"green\": [0,128,0,1],\n \"greenyellow\": [173,255,47,1], \"grey\": [128,128,128,1],\n \"honeydew\": [240,255,240,1], \"hotpink\": [255,105,180,1],\n \"indianred\": [205,92,92,1], \"indigo\": [75,0,130,1],\n \"ivory\": [255,255,240,1], \"khaki\": [240,230,140,1],\n \"lavender\": [230,230,250,1], \"lavenderblush\": [255,240,245,1],\n \"lawngreen\": [124,252,0,1], \"lemonchiffon\": [255,250,205,1],\n \"lightblue\": [173,216,230,1], \"lightcoral\": [240,128,128,1],\n \"lightcyan\": [224,255,255,1], \"lightgoldenrodyellow\": [250,250,210,1],\n \"lightgray\": [211,211,211,1], \"lightgreen\": [144,238,144,1],\n \"lightgrey\": [211,211,211,1], \"lightpink\": [255,182,193,1],\n \"lightsalmon\": [255,160,122,1], \"lightseagreen\": [32,178,170,1],\n \"lightskyblue\": [135,206,250,1], \"lightslategray\": [119,136,153,1],\n \"lightslategrey\": [119,136,153,1], \"lightsteelblue\": [176,196,222,1],\n \"lightyellow\": [255,255,224,1], \"lime\": [0,255,0,1],\n \"limegreen\": [50,205,50,1], \"linen\": [250,240,230,1],\n \"magenta\": [255,0,255,1], \"maroon\": [128,0,0,1],\n \"mediumaquamarine\": [102,205,170,1], \"mediumblue\": [0,0,205,1],\n \"mediumorchid\": [186,85,211,1], \"mediumpurple\": [147,112,219,1],\n \"mediumseagreen\": [60,179,113,1], \"mediumslateblue\": [123,104,238,1],\n \"mediumspringgreen\": [0,250,154,1], \"mediumturquoise\": [72,209,204,1],\n \"mediumvioletred\": [199,21,133,1], \"midnightblue\": [25,25,112,1],\n \"mintcream\": [245,255,250,1], \"mistyrose\": [255,228,225,1],\n \"moccasin\": [255,228,181,1], \"navajowhite\": [255,222,173,1],\n \"navy\": [0,0,128,1], \"oldlace\": [253,245,230,1],\n \"olive\": [128,128,0,1], \"olivedrab\": [107,142,35,1],\n \"orange\": [255,165,0,1], \"orangered\": [255,69,0,1],\n \"orchid\": [218,112,214,1], \"palegoldenrod\": [238,232,170,1],\n \"palegreen\": [152,251,152,1], \"paleturquoise\": [175,238,238,1],\n \"palevioletred\": [219,112,147,1], \"papayawhip\": [255,239,213,1],\n \"peachpuff\": [255,218,185,1], \"peru\": [205,133,63,1],\n \"pink\": [255,192,203,1], \"plum\": [221,160,221,1],\n \"powderblue\": [176,224,230,1], \"purple\": [128,0,128,1],\n \"red\": [255,0,0,1], \"rosybrown\": [188,143,143,1],\n \"royalblue\": [65,105,225,1], \"saddlebrown\": [139,69,19,1],\n \"salmon\": [250,128,114,1], \"sandybrown\": [244,164,96,1],\n \"seagreen\": [46,139,87,1], \"seashell\": [255,245,238,1],\n \"sienna\": [160,82,45,1], \"silver\": [192,192,192,1],\n \"skyblue\": [135,206,235,1], \"slateblue\": [106,90,205,1],\n \"slategray\": [112,128,144,1], \"slategrey\": [112,128,144,1],\n \"snow\": [255,250,250,1], \"springgreen\": [0,255,127,1],\n \"steelblue\": [70,130,180,1], \"tan\": [210,180,140,1],\n \"teal\": [0,128,128,1], \"thistle\": [216,191,216,1],\n \"tomato\": [255,99,71,1], \"turquoise\": [64,224,208,1],\n \"violet\": [238,130,238,1], \"wheat\": [245,222,179,1],\n \"white\": [255,255,255,1], \"whitesmoke\": [245,245,245,1],\n \"yellow\": [255,255,0,1], \"yellowgreen\": [154,205,50,1]}\n\nfunction clamp_css_byte(i) { // Clamp to integer 0 .. 255.\n i = Math.round(i); // Seems to be what Chrome does (vs truncation).\n return i < 0 ? 0 : i > 255 ? 255 : i;\n}\n\nfunction clamp_css_float(f) { // Clamp to float 0.0 .. 1.0.\n return f < 0 ? 0 : f > 1 ? 1 : f;\n}\n\nfunction parse_css_int(str) { // int or percentage.\n if (str[str.length - 1] === '%')\n return clamp_css_byte(parseFloat(str) / 100 * 255);\n return clamp_css_byte(parseInt(str));\n}\n\nfunction parse_css_float(str) { // float or percentage.\n if (str[str.length - 1] === '%')\n return clamp_css_float(parseFloat(str) / 100);\n return clamp_css_float(parseFloat(str));\n}\n\nfunction css_hue_to_rgb(m1, m2, h) {\n if (h < 0) h += 1;\n else if (h > 1) h -= 1;\n\n if (h * 6 < 1) return m1 + (m2 - m1) * h * 6;\n if (h * 2 < 1) return m2;\n if (h * 3 < 2) return m1 + (m2 - m1) * (2/3 - h) * 6;\n return m1;\n}\n\nfunction parseCSSColor(css_str) {\n // Remove all whitespace, not compliant, but should just be more accepting.\n var str = css_str.replace(/ /g, '').toLowerCase();\n\n // Color keywords (and transparent) lookup.\n if (str in kCSSColorTable) return kCSSColorTable[str].slice(); // dup.\n\n // #abc and #abc123 syntax.\n if (str[0] === '#') {\n if (str.length === 4) {\n var iv = parseInt(str.substr(1), 16); // TODO(deanm): Stricter parsing.\n if (!(iv >= 0 && iv <= 0xfff)) return null; // Covers NaN.\n return [((iv & 0xf00) >> 4) | ((iv & 0xf00) >> 8),\n (iv & 0xf0) | ((iv & 0xf0) >> 4),\n (iv & 0xf) | ((iv & 0xf) << 4),\n 1];\n } else if (str.length === 7) {\n var iv = parseInt(str.substr(1), 16); // TODO(deanm): Stricter parsing.\n if (!(iv >= 0 && iv <= 0xffffff)) return null; // Covers NaN.\n return [(iv & 0xff0000) >> 16,\n (iv & 0xff00) >> 8,\n iv & 0xff,\n 1];\n }\n\n return null;\n }\n\n var op = str.indexOf('('), ep = str.indexOf(')');\n if (op !== -1 && ep + 1 === str.length) {\n var fname = str.substr(0, op);\n var params = str.substr(op+1, ep-(op+1)).split(',');\n var alpha = 1; // To allow case fallthrough.\n switch (fname) {\n case 'rgba':\n if (params.length !== 4) return null;\n alpha = parse_css_float(params.pop());\n // Fall through.\n case 'rgb':\n if (params.length !== 3) return null;\n return [parse_css_int(params[0]),\n parse_css_int(params[1]),\n parse_css_int(params[2]),\n alpha];\n case 'hsla':\n if (params.length !== 4) return null;\n alpha = parse_css_float(params.pop());\n // Fall through.\n case 'hsl':\n if (params.length !== 3) return null;\n var h = (((parseFloat(params[0]) % 360) + 360) % 360) / 360; // 0 .. 1\n // NOTE(deanm): According to the CSS spec s/l should only be\n // percentages, but we don't bother and let float or percentage.\n var s = parse_css_float(params[1]);\n var l = parse_css_float(params[2]);\n var m2 = l <= 0.5 ? l * (s + 1) : l + s - l * s;\n var m1 = l * 2 - m2;\n return [clamp_css_byte(css_hue_to_rgb(m1, m2, h+1/3) * 255),\n clamp_css_byte(css_hue_to_rgb(m1, m2, h) * 255),\n clamp_css_byte(css_hue_to_rgb(m1, m2, h-1/3) * 255),\n alpha];\n default:\n return null;\n }\n }\n\n return null;\n}\n\ntry { exports.parseCSSColor = parseCSSColor } catch(e) { }\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom.js": {
"title": "$:/core/modules/utils/dom.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom.js\ntype: application/javascript\nmodule-type: utils\n\nVarious static DOM-related utility functions.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nDetermines whether element 'a' contains element 'b'\nCode thanks to John Resig, http://ejohn.org/blog/comparing-document-position/\n*/\nexports.domContains = function(a,b) {\n\treturn a.contains ?\n\t\ta !== b && a.contains(b) :\n\t\t!!(a.compareDocumentPosition(b) & 16);\n};\n\nexports.removeChildren = function(node) {\n\twhile(node.hasChildNodes()) {\n\t\tnode.removeChild(node.firstChild);\n\t}\n};\n\nexports.hasClass = function(el,className) {\n\treturn el && el.className && el.className.toString().split(\" \").indexOf(className) !== -1;\n};\n\nexports.addClass = function(el,className) {\n\tvar c = el.className.split(\" \");\n\tif(c.indexOf(className) === -1) {\n\t\tc.push(className);\n\t\tel.className = c.join(\" \");\n\t}\n};\n\nexports.removeClass = function(el,className) {\n\tvar c = el.className.split(\" \"),\n\t\tp = c.indexOf(className);\n\tif(p !== -1) {\n\t\tc.splice(p,1);\n\t\tel.className = c.join(\" \");\n\t}\n};\n\nexports.toggleClass = function(el,className,status) {\n\tif(status === undefined) {\n\t\tstatus = !exports.hasClass(el,className);\n\t}\n\tif(status) {\n\t\texports.addClass(el,className);\n\t} else {\n\t\texports.removeClass(el,className);\n\t}\n};\n\n/*\nGet the first parent element that has scrollbars or use the body as fallback.\n*/\nexports.getScrollContainer = function(el) {\n\tvar doc = el.ownerDocument;\n\twhile(el.parentNode) {\t\n\t\tel = el.parentNode;\n\t\tif(el.scrollTop) {\n\t\t\treturn el;\n\t\t}\n\t}\n\treturn doc.body;\n};\n\n/*\nGet the scroll position of the viewport\nReturns:\n\t{\n\t\tx: horizontal scroll position in pixels,\n\t\ty: vertical scroll position in pixels\n\t}\n*/\nexports.getScrollPosition = function(srcWindow) {\n\tvar scrollWindow = srcWindow || window;\n\tif(\"scrollX\" in scrollWindow) {\n\t\treturn {x: scrollWindow.scrollX, y: scrollWindow.scrollY};\n\t} else {\n\t\treturn {x: scrollWindow.document.documentElement.scrollLeft, y: scrollWindow.document.documentElement.scrollTop};\n\t}\n};\n\n/*\nAdjust the height of a textarea to fit its content, preserving scroll position, and return the height\n*/\nexports.resizeTextAreaToFit = function(domNode,minHeight) {\n\t// Get the scroll container and register the current scroll position\n\tvar container = $tw.utils.getScrollContainer(domNode),\n\t\tscrollTop = container.scrollTop;\n // Measure the specified minimum height\n\tdomNode.style.height = minHeight;\n\tvar measuredHeight = domNode.offsetHeight || parseInt(minHeight,10);\n\t// Set its height to auto so that it snaps to the correct height\n\tdomNode.style.height = \"auto\";\n\t// Calculate the revised height\n\tvar newHeight = Math.max(domNode.scrollHeight + domNode.offsetHeight - domNode.clientHeight,measuredHeight);\n\t// Only try to change the height if it has changed\n\tif(newHeight !== domNode.offsetHeight) {\n\t\tdomNode.style.height = newHeight + \"px\";\n\t\t// Make sure that the dimensions of the textarea are recalculated\n\t\t$tw.utils.forceLayout(domNode);\n\t\t// Set the container to the position we registered at the beginning\n\t\tcontainer.scrollTop = scrollTop;\n\t}\n\treturn newHeight;\n};\n\n/*\nGets the bounding rectangle of an element in absolute page coordinates\n*/\nexports.getBoundingPageRect = function(element) {\n\tvar scrollPos = $tw.utils.getScrollPosition(element.ownerDocument.defaultView),\n\t\tclientRect = element.getBoundingClientRect();\n\treturn {\n\t\tleft: clientRect.left + scrollPos.x,\n\t\twidth: clientRect.width,\n\t\tright: clientRect.right + scrollPos.x,\n\t\ttop: clientRect.top + scrollPos.y,\n\t\theight: clientRect.height,\n\t\tbottom: clientRect.bottom + scrollPos.y\n\t};\n};\n\n/*\nSaves a named password in the browser\n*/\nexports.savePassword = function(name,password) {\n\tvar done = false;\n\ttry {\n\t\twindow.localStorage.setItem(\"tw5-password-\" + name,password);\n\t\tdone = true;\n\t} catch(e) {\n\t}\n\tif(!done) {\n\t\t$tw.savedPasswords = $tw.savedPasswords || Object.create(null);\n\t\t$tw.savedPasswords[name] = password;\n\t}\n};\n\n/*\nRetrieve a named password from the browser\n*/\nexports.getPassword = function(name) {\n\tvar value;\n\ttry {\n\t\tvalue = window.localStorage.getItem(\"tw5-password-\" + name);\n\t} catch(e) {\n\t}\n\tif(value !== undefined) {\n\t\treturn value;\n\t} else {\n\t\treturn ($tw.savedPasswords || Object.create(null))[name] || \"\";\n\t}\n};\n\n/*\nForce layout of a dom node and its descendents\n*/\nexports.forceLayout = function(element) {\n\tvar dummy = element.offsetWidth;\n};\n\n/*\nPulse an element for debugging purposes\n*/\nexports.pulseElement = function(element) {\n\t// Event handler to remove the class at the end\n\telement.addEventListener($tw.browser.animationEnd,function handler(event) {\n\t\telement.removeEventListener($tw.browser.animationEnd,handler,false);\n\t\t$tw.utils.removeClass(element,\"pulse\");\n\t},false);\n\t// Apply the pulse class\n\t$tw.utils.removeClass(element,\"pulse\");\n\t$tw.utils.forceLayout(element);\n\t$tw.utils.addClass(element,\"pulse\");\n};\n\n/*\nAttach specified event handlers to a DOM node\ndomNode: where to attach the event handlers\nevents: array of event handlers to be added (see below)\nEach entry in the events array is an object with these properties:\nhandlerFunction: optional event handler function\nhandlerObject: optional event handler object\nhandlerMethod: optionally specifies object handler method name (defaults to `handleEvent`)\n*/\nexports.addEventListeners = function(domNode,events) {\n\t$tw.utils.each(events,function(eventInfo) {\n\t\tvar handler;\n\t\tif(eventInfo.handlerFunction) {\n\t\t\thandler = eventInfo.handlerFunction;\n\t\t} else if(eventInfo.handlerObject) {\n\t\t\tif(eventInfo.handlerMethod) {\n\t\t\t\thandler = function(event) {\n\t\t\t\t\teventInfo.handlerObject[eventInfo.handlerMethod].call(eventInfo.handlerObject,event);\n\t\t\t\t};\t\n\t\t\t} else {\n\t\t\t\thandler = eventInfo.handlerObject;\n\t\t\t}\n\t\t}\n\t\tdomNode.addEventListener(eventInfo.name,handler,false);\n\t});\n};\n\n/*\nGet the computed styles applied to an element as an array of strings of individual CSS properties\n*/\nexports.getComputedStyles = function(domNode) {\n\tvar textAreaStyles = window.getComputedStyle(domNode,null),\n\t\tstyleDefs = [],\n\t\tname;\n\tfor(var t=0; t<textAreaStyles.length; t++) {\n\t\tname = textAreaStyles[t];\n\t\tstyleDefs.push(name + \": \" + textAreaStyles.getPropertyValue(name) + \";\");\n\t}\n\treturn styleDefs;\n};\n\n/*\nApply a set of styles passed as an array of strings of individual CSS properties\n*/\nexports.setStyles = function(domNode,styleDefs) {\n\tdomNode.style.cssText = styleDefs.join(\"\");\n};\n\n/*\nCopy the computed styles from a source element to a destination element\n*/\nexports.copyStyles = function(srcDomNode,dstDomNode) {\n\t$tw.utils.setStyles(dstDomNode,$tw.utils.getComputedStyles(srcDomNode));\n};\n\n/*\nCopy plain text to the clipboard on browsers that support it\n*/\nexports.copyToClipboard = function(text,options) {\n\toptions = options || {};\n\tvar textArea = document.createElement(\"textarea\");\n\ttextArea.style.position = \"fixed\";\n\ttextArea.style.top = 0;\n\ttextArea.style.left = 0;\n\ttextArea.style.fontSize = \"12pt\";\n\ttextArea.style.width = \"2em\";\n\ttextArea.style.height = \"2em\";\n\ttextArea.style.padding = 0;\n\ttextArea.style.border = \"none\";\n\ttextArea.style.outline = \"none\";\n\ttextArea.style.boxShadow = \"none\";\n\ttextArea.style.background = \"transparent\";\n\ttextArea.value = text;\n\tdocument.body.appendChild(textArea);\n\ttextArea.select();\n\ttextArea.setSelectionRange(0,text.length);\n\tvar succeeded = false;\n\ttry {\n\t\tsucceeded = document.execCommand(\"copy\");\n\t} catch (err) {\n\t}\n\tif(!options.doNotNotify) {\n\t\t$tw.notifier.display(succeeded ? \"$:/language/Notifications/CopiedToClipboard/Succeeded\" : \"$:/language/Notifications/CopiedToClipboard/Failed\");\n\t}\n\tdocument.body.removeChild(textArea);\n};\n\nexports.getLocationPath = function() {\n\treturn window.location.toString().split(\"#\")[0];\n};\n\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/dragndrop.js": {
"title": "$:/core/modules/utils/dom/dragndrop.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/dragndrop.js\ntype: application/javascript\nmodule-type: utils\n\nBrowser data transfer utilities, used with the clipboard and drag and drop\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nOptions:\n\ndomNode: dom node to make draggable\ndragImageType: \"pill\" or \"dom\"\ndragTiddlerFn: optional function to retrieve the title of tiddler to drag\ndragFilterFn: optional function to retreive the filter defining a list of tiddlers to drag\nwidget: widget to use as the contect for the filter\n*/\nexports.makeDraggable = function(options) {\n\tvar dragImageType = options.dragImageType || \"dom\",\n\t\tdragImage,\n\t\tdomNode = options.domNode;\n\t// Make the dom node draggable (not necessary for anchor tags)\n\tif((domNode.tagName || \"\").toLowerCase() !== \"a\") {\n\t\tdomNode.setAttribute(\"draggable\",\"true\");\t\t\n\t}\n\t// Add event handlers\n\t$tw.utils.addEventListeners(domNode,[\n\t\t{name: \"dragstart\", handlerFunction: function(event) {\n\t\t\tif(event.dataTransfer === undefined) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t\t// Collect the tiddlers being dragged\n\t\t\tvar dragTiddler = options.dragTiddlerFn && options.dragTiddlerFn(),\n\t\t\t\tdragFilter = options.dragFilterFn && options.dragFilterFn(),\n\t\t\t\ttitles = dragTiddler ? [dragTiddler] : [],\n\t\t\t \tstartActions = options.startActions;\n\t\t\tif(dragFilter) {\n\t\t\t\ttitles.push.apply(titles,options.widget.wiki.filterTiddlers(dragFilter,options.widget));\n\t\t\t}\n\t\t\tvar titleString = $tw.utils.stringifyList(titles);\n\t\t\t// Check that we've something to drag\n\t\t\tif(titles.length > 0 && event.target === domNode) {\n\t\t\t\t// Mark the drag in progress\n\t\t\t\t$tw.dragInProgress = domNode;\n\t\t\t\t// Set the dragging class on the element being dragged\n\t\t\t\t$tw.utils.addClass(event.target,\"tc-dragging\");\n\t\t\t\t// Invoke drag-start actions if given\n\t\t\t\tif(startActions !== undefined) {\n\t\t\t\t\toptions.widget.invokeActionString(startActions,options.widget,event,{actionTiddler: titleString});\n\t\t\t\t}\n\t\t\t\t// Create the drag image elements\n\t\t\t\tdragImage = options.widget.document.createElement(\"div\");\n\t\t\t\tdragImage.className = \"tc-tiddler-dragger\";\n\t\t\t\tvar inner = options.widget.document.createElement(\"div\");\n\t\t\t\tinner.className = \"tc-tiddler-dragger-inner\";\n\t\t\t\tinner.appendChild(options.widget.document.createTextNode(\n\t\t\t\t\ttitles.length === 1 ? \n\t\t\t\t\t\ttitles[0] :\n\t\t\t\t\t\ttitles.length + \" tiddlers\"\n\t\t\t\t));\n\t\t\t\tdragImage.appendChild(inner);\n\t\t\t\toptions.widget.document.body.appendChild(dragImage);\n\t\t\t\t// Set the data transfer properties\n\t\t\t\tvar dataTransfer = event.dataTransfer;\n\t\t\t\t// Set up the image\n\t\t\t\tdataTransfer.effectAllowed = \"all\";\n\t\t\t\tif(dataTransfer.setDragImage) {\n\t\t\t\t\tif(dragImageType === \"pill\") {\n\t\t\t\t\t\tdataTransfer.setDragImage(dragImage.firstChild,-16,-16);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tvar r = domNode.getBoundingClientRect();\n\t\t\t\t\t\tdataTransfer.setDragImage(domNode,event.clientX-r.left,event.clientY-r.top);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// Set up the data transfer\n\t\t\t\tif(dataTransfer.clearData) {\n\t\t\t\t\tdataTransfer.clearData();\t\t\t\t\t\n\t\t\t\t}\n\t\t\t\tvar jsonData = [];\n\t\t\t\tif(titles.length > 1) {\n\t\t\t\t\ttitles.forEach(function(title) {\n\t\t\t\t\t\tjsonData.push(options.widget.wiki.getTiddlerAsJson(title));\n\t\t\t\t\t});\n\t\t\t\t\tjsonData = \"[\" + jsonData.join(\",\") + \"]\";\n\t\t\t\t} else {\n\t\t\t\t\tjsonData = options.widget.wiki.getTiddlerAsJson(titles[0]);\n\t\t\t\t}\n\t\t\t\t// IE doesn't like these content types\n\t\t\t\tif(!$tw.browser.isIE) {\n\t\t\t\t\tdataTransfer.setData(\"text/vnd.tiddler\",jsonData);\n\t\t\t\t\tdataTransfer.setData(\"text/plain\",titleString);\n\t\t\t\t\tdataTransfer.setData(\"text/x-moz-url\",\"data:text/vnd.tiddler,\" + encodeURIComponent(jsonData));\n\t\t\t\t}\n\t\t\t\tdataTransfer.setData(\"URL\",\"data:text/vnd.tiddler,\" + encodeURIComponent(jsonData));\n\t\t\t\tdataTransfer.setData(\"Text\",titleString);\n\t\t\t\tevent.stopPropagation();\n\t\t\t}\n\t\t\treturn false;\n\t\t}},\n\t\t{name: \"dragend\", handlerFunction: function(event) {\n\t\t\tif(event.target === domNode) {\n\t\t\t\t// Collect the tiddlers being dragged\n\t\t\t\tvar dragTiddler = options.dragTiddlerFn && options.dragTiddlerFn(),\n\t\t\t\t\tdragFilter = options.dragFilterFn && options.dragFilterFn(),\n\t\t\t\t\ttitles = dragTiddler ? [dragTiddler] : [],\n\t\t\t \t\tendActions = options.endActions;\n\t\t\t\tif(dragFilter) {\n\t\t\t\t\ttitles.push.apply(titles,options.widget.wiki.filterTiddlers(dragFilter,options.widget));\n\t\t\t\t}\n\t\t\t\tvar titleString = $tw.utils.stringifyList(titles);\n\t\t\t\t$tw.dragInProgress = null;\n\t\t\t\t// Invoke drag-end actions if given\n\t\t\t\tif(endActions !== undefined) {\n\t\t\t\t\toptions.widget.invokeActionString(endActions,options.widget,event,{actionTiddler: titleString});\n\t\t\t\t}\n\t\t\t\t// Remove the dragging class on the element being dragged\n\t\t\t\t$tw.utils.removeClass(event.target,\"tc-dragging\");\n\t\t\t\t// Delete the drag image element\n\t\t\t\tif(dragImage) {\n\t\t\t\t\tdragImage.parentNode.removeChild(dragImage);\n\t\t\t\t\tdragImage = null;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t}}\n\t]);\n};\n\nexports.importDataTransfer = function(dataTransfer,fallbackTitle,callback) {\n\t// Try each provided data type in turn\n\tif($tw.log.IMPORT) {\n\t\tconsole.log(\"Available data types:\");\n\t\tfor(var type=0; type<dataTransfer.types.length; type++) {\n\t\t\tconsole.log(\"type\",dataTransfer.types[type],dataTransfer.getData(dataTransfer.types[type]))\n\t\t}\n\t}\n\tfor(var t=0; t<importDataTypes.length; t++) {\n\t\tif(!$tw.browser.isIE || importDataTypes[t].IECompatible) {\n\t\t\t// Get the data\n\t\t\tvar dataType = importDataTypes[t];\n\t\t\t\tvar data = dataTransfer.getData(dataType.type);\n\t\t\t// Import the tiddlers in the data\n\t\t\tif(data !== \"\" && data !== null) {\n\t\t\t\tif($tw.log.IMPORT) {\n\t\t\t\t\tconsole.log(\"Importing data type '\" + dataType.type + \"', data: '\" + data + \"'\")\n\t\t\t\t}\n\t\t\t\tvar tiddlerFields = dataType.toTiddlerFieldsArray(data,fallbackTitle);\n\t\t\t\tcallback(tiddlerFields);\n\t\t\t\treturn;\n\t\t\t}\n\t\t}\n\t}\n};\n\nvar importDataTypes = [\n\t{type: \"text/vnd.tiddler\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn parseJSONTiddlers(data,fallbackTitle);\n\t}},\n\t{type: \"URL\", IECompatible: true, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\t// Check for tiddler data URI\n\t\tvar match = decodeURIComponent(data).match(/^data\\:text\\/vnd\\.tiddler,(.*)/i);\n\t\tif(match) {\n\t\t\treturn parseJSONTiddlers(match[1],fallbackTitle);\n\t\t} else {\n\t\t\treturn [{title: fallbackTitle, text: data}]; // As URL string\n\t\t}\n\t}},\n\t{type: \"text/x-moz-url\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\t// Check for tiddler data URI\n\t\tvar match = decodeURIComponent(data).match(/^data\\:text\\/vnd\\.tiddler,(.*)/i);\n\t\tif(match) {\n\t\t\treturn parseJSONTiddlers(match[1],fallbackTitle);\n\t\t} else {\n\t\t\treturn [{title: fallbackTitle, text: data}]; // As URL string\n\t\t}\n\t}},\n\t{type: \"text/html\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn [{title: fallbackTitle, text: data}];\n\t}},\n\t{type: \"text/plain\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn [{title: fallbackTitle, text: data}];\n\t}},\n\t{type: \"Text\", IECompatible: true, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn [{title: fallbackTitle, text: data}];\n\t}},\n\t{type: \"text/uri-list\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn [{title: fallbackTitle, text: data}];\n\t}}\n];\n\nfunction parseJSONTiddlers(json,fallbackTitle) {\n\tvar data = JSON.parse(json);\n\tif(!$tw.utils.isArray(data)) {\n\t\tdata = [data];\n\t}\n\tdata.forEach(function(fields) {\n\t\tfields.title = fields.title || fallbackTitle;\n\t});\n\treturn data;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/http.js": {
"title": "$:/core/modules/utils/dom/http.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/http.js\ntype: application/javascript\nmodule-type: utils\n\nBrowser HTTP support\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nA quick and dirty HTTP function; to be refactored later. Options are:\n\turl: URL to retrieve\n\theaders: hashmap of headers to send\n\ttype: GET, PUT, POST etc\n\tcallback: function invoked with (err,data,xhr)\n\treturnProp: string name of the property to return as first argument of callback\n*/\nexports.httpRequest = function(options) {\n\tvar type = options.type || \"GET\",\n\t\turl = options.url,\n\t\theaders = options.headers || {accept: \"application/json\"},\n\t\treturnProp = options.returnProp || \"responseText\",\n\t\trequest = new XMLHttpRequest(),\n\t\tdata = \"\",\n\t\tf,results;\n\t// Massage the data hashmap into a string\n\tif(options.data) {\n\t\tif(typeof options.data === \"string\") { // Already a string\n\t\t\tdata = options.data;\n\t\t} else { // A hashmap of strings\n\t\t\tresults = [];\n\t\t\t$tw.utils.each(options.data,function(dataItem,dataItemTitle) {\n\t\t\t\tresults.push(dataItemTitle + \"=\" + encodeURIComponent(dataItem));\n\t\t\t});\n\t\t\tif(type === \"GET\" || type === \"HEAD\") {\n\t\t\t\turl += \"?\" + results.join(\"&\");\n\t\t\t} else {\n\t\t\t\tdata = results.join(\"&\");\n\t\t\t}\n\t\t}\n\t}\n\t// Set up the state change handler\n\trequest.onreadystatechange = function() {\n\t\tif(this.readyState === 4) {\n\t\t\tif(this.status === 200 || this.status === 201 || this.status === 204) {\n\t\t\t\t// Success!\n\t\t\t\toptions.callback(null,this[returnProp],this);\n\t\t\t\treturn;\n\t\t\t}\n\t\t// Something went wrong\n\t\toptions.callback($tw.language.getString(\"Error/XMLHttpRequest\") + \": \" + this.status,null,this);\n\t\t}\n\t};\n\t// Make the request\n\trequest.open(type,url,true);\n\tif(headers) {\n\t\t$tw.utils.each(headers,function(header,headerTitle,object) {\n\t\t\trequest.setRequestHeader(headerTitle,header);\n\t\t});\n\t}\n\tif(data && !$tw.utils.hop(headers,\"Content-type\")) {\n\t\trequest.setRequestHeader(\"Content-type\",\"application/x-www-form-urlencoded; charset=UTF-8\");\n\t}\n\tif(!$tw.utils.hop(headers,\"X-Requested-With\")) {\n\t\trequest.setRequestHeader(\"X-Requested-With\",\"TiddlyWiki\");\n\t}\n\ttry {\n\t\trequest.send(data);\n\t} catch(e) {\n\t\toptions.callback(e,null,this);\n\t}\n\treturn request;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/keyboard.js": {
"title": "$:/core/modules/utils/dom/keyboard.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/keyboard.js\ntype: application/javascript\nmodule-type: utils\n\nKeyboard utilities; now deprecated. Instead, use $tw.keyboardManager\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n[\"parseKeyDescriptor\",\"checkKeyDescriptor\"].forEach(function(method) {\n\texports[method] = function() {\n\t\tif($tw.keyboardManager) {\n\t\t\treturn $tw.keyboardManager[method].apply($tw.keyboardManager,Array.prototype.slice.call(arguments,0));\n\t\t} else {\n\t\t\treturn null\n\t\t}\n\t};\n});\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/modal.js": {
"title": "$:/core/modules/utils/dom/modal.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/modal.js\ntype: application/javascript\nmodule-type: utils\n\nModal message mechanism\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nvar Modal = function(wiki) {\n\tthis.wiki = wiki;\n\tthis.modalCount = 0;\n};\n\n/*\nDisplay a modal dialogue\n\ttitle: Title of tiddler to display\n\toptions: see below\nOptions include:\n\tdownloadLink: Text of a big download link to include\n*/\nModal.prototype.display = function(title,options) {\n\toptions = options || {};\n\tthis.srcDocument = options.variables && (options.variables.rootwindow === \"true\" ||\n\t\t\t\toptions.variables.rootwindow === \"yes\") ? document :\n\t\t\t\t(options.event.event && options.event.event.target ? options.event.event.target.ownerDocument : document);\n\tthis.srcWindow = this.srcDocument.defaultView;\n\tvar self = this,\n\t\trefreshHandler,\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t\ttiddler = this.wiki.getTiddler(title);\n\t// Don't do anything if the tiddler doesn't exist\n\tif(!tiddler) {\n\t\treturn;\n\t}\n\t// Create the variables\n\tvar variables = $tw.utils.extend({currentTiddler: title},options.variables);\n\t// Create the wrapper divs\n\tvar wrapper = this.srcDocument.createElement(\"div\"),\n\t\tmodalBackdrop = this.srcDocument.createElement(\"div\"),\n\t\tmodalWrapper = this.srcDocument.createElement(\"div\"),\n\t\tmodalHeader = this.srcDocument.createElement(\"div\"),\n\t\theaderTitle = this.srcDocument.createElement(\"h3\"),\n\t\tmodalBody = this.srcDocument.createElement(\"div\"),\n\t\tmodalLink = this.srcDocument.createElement(\"a\"),\n\t\tmodalFooter = this.srcDocument.createElement(\"div\"),\n\t\tmodalFooterHelp = this.srcDocument.createElement(\"span\"),\n\t\tmodalFooterButtons = this.srcDocument.createElement(\"span\");\n\t// Up the modal count and adjust the body class\n\tthis.modalCount++;\n\tthis.adjustPageClass();\n\t// Add classes\n\t$tw.utils.addClass(wrapper,\"tc-modal-wrapper\");\n\tif(tiddler.fields && tiddler.fields.class) {\n\t\t$tw.utils.addClass(wrapper,tiddler.fields.class);\n\t}\n\t$tw.utils.addClass(modalBackdrop,\"tc-modal-backdrop\");\n\t$tw.utils.addClass(modalWrapper,\"tc-modal\");\n\t$tw.utils.addClass(modalHeader,\"tc-modal-header\");\n\t$tw.utils.addClass(modalBody,\"tc-modal-body\");\n\t$tw.utils.addClass(modalFooter,\"tc-modal-footer\");\n\t// Join them together\n\twrapper.appendChild(modalBackdrop);\n\twrapper.appendChild(modalWrapper);\n\tmodalHeader.appendChild(headerTitle);\n\tmodalWrapper.appendChild(modalHeader);\n\tmodalWrapper.appendChild(modalBody);\n\tmodalFooter.appendChild(modalFooterHelp);\n\tmodalFooter.appendChild(modalFooterButtons);\n\tmodalWrapper.appendChild(modalFooter);\n\t// Render the title of the message\n\tvar headerWidgetNode = this.wiki.makeTranscludeWidget(title,{\n\t\tfield: \"subtitle\",\n\t\tmode: \"inline\",\n\t\tchildren: [{\n\t\t\ttype: \"text\",\n\t\t\tattributes: {\n\t\t\t\ttext: {\n\t\t\t\t\ttype: \"string\",\n\t\t\t\t\tvalue: title\n\t\t}}}],\n\t\tparentWidget: $tw.rootWidget,\n\t\tdocument: this.srcDocument,\n\t\tvariables: variables,\n\t\timportPageMacros: true\n\t});\n\theaderWidgetNode.render(headerTitle,null);\n\t// Render the body of the message\n\tvar bodyWidgetNode = this.wiki.makeTranscludeWidget(title,{\n\t\tparentWidget: $tw.rootWidget,\n\t\tdocument: this.srcDocument,\n\t\tvariables: variables,\n\t\timportPageMacros: true\n\t});\n\tbodyWidgetNode.render(modalBody,null);\n\t// Setup the link if present\n\tif(options.downloadLink) {\n\t\tmodalLink.href = options.downloadLink;\n\t\tmodalLink.appendChild(this.srcDocument.createTextNode(\"Right-click to save changes\"));\n\t\tmodalBody.appendChild(modalLink);\n\t}\n\t// Render the footer of the message\n\tif(tiddler.fields && tiddler.fields.help) {\n\t\tvar link = this.srcDocument.createElement(\"a\");\n\t\tlink.setAttribute(\"href\",tiddler.fields.help);\n\t\tlink.setAttribute(\"target\",\"_blank\");\n\t\tlink.setAttribute(\"rel\",\"noopener noreferrer\");\n\t\tlink.appendChild(this.srcDocument.createTextNode(\"Help\"));\n\t\tmodalFooterHelp.appendChild(link);\n\t\tmodalFooterHelp.style.float = \"left\";\n\t}\n\tvar footerWidgetNode = this.wiki.makeTranscludeWidget(title,{\n\t\tfield: \"footer\",\n\t\tmode: \"inline\",\n\t\tchildren: [{\n\t\t\ttype: \"button\",\n\t\t\tattributes: {\n\t\t\t\tmessage: {\n\t\t\t\t\ttype: \"string\",\n\t\t\t\t\tvalue: \"tm-close-tiddler\"\n\t\t\t\t}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\",\n\t\t\t\tattributes: {\n\t\t\t\t\ttext: {\n\t\t\t\t\t\ttype: \"string\",\n\t\t\t\t\t\tvalue: $tw.language.getString(\"Buttons/Close/Caption\")\n\t\t\t}}}\n\t\t]}],\n\t\tparentWidget: $tw.rootWidget,\n\t\tdocument: this.srcDocument,\n\t\tvariables: variables,\n\t\timportPageMacros: true\n\t});\n\tfooterWidgetNode.render(modalFooterButtons,null);\n\t// Set up the refresh handler\n\trefreshHandler = function(changes) {\n\t\theaderWidgetNode.refresh(changes,modalHeader,null);\n\t\tbodyWidgetNode.refresh(changes,modalBody,null);\n\t\tfooterWidgetNode.refresh(changes,modalFooterButtons,null);\n\t};\n\tthis.wiki.addEventListener(\"change\",refreshHandler);\n\t// Add the close event handler\n\tvar closeHandler = function(event) {\n\t\t// Remove our refresh handler\n\t\tself.wiki.removeEventListener(\"change\",refreshHandler);\n\t\t// Decrease the modal count and adjust the body class\n\t\tself.modalCount--;\n\t\tself.adjustPageClass();\n\t\t// Force layout and animate the modal message away\n\t\t$tw.utils.forceLayout(modalBackdrop);\n\t\t$tw.utils.forceLayout(modalWrapper);\n\t\t$tw.utils.setStyle(modalBackdrop,[\n\t\t\t{opacity: \"0\"}\n\t\t]);\n\t\t$tw.utils.setStyle(modalWrapper,[\n\t\t\t{transform: \"translateY(\" + self.srcWindow.innerHeight + \"px)\"}\n\t\t]);\n\t\t// Set up an event for the transition end\n\t\tself.srcWindow.setTimeout(function() {\n\t\t\tif(wrapper.parentNode) {\n\t\t\t\t// Remove the modal message from the DOM\n\t\t\t\tself.srcDocument.body.removeChild(wrapper);\n\t\t\t}\n\t\t},duration);\n\t\t// Don't let anyone else handle the tm-close-tiddler message\n\t\treturn false;\n\t};\n\theaderWidgetNode.addEventListener(\"tm-close-tiddler\",closeHandler,false);\n\tbodyWidgetNode.addEventListener(\"tm-close-tiddler\",closeHandler,false);\n\tfooterWidgetNode.addEventListener(\"tm-close-tiddler\",closeHandler,false);\n\t// Set the initial styles for the message\n\t$tw.utils.setStyle(modalBackdrop,[\n\t\t{opacity: \"0\"}\n\t]);\n\t$tw.utils.setStyle(modalWrapper,[\n\t\t{transformOrigin: \"0% 0%\"},\n\t\t{transform: \"translateY(\" + (-this.srcWindow.innerHeight) + \"px)\"}\n\t]);\n\t// Put the message into the document\n\tthis.srcDocument.body.appendChild(wrapper);\n\t// Set up animation for the styles\n\t$tw.utils.setStyle(modalBackdrop,[\n\t\t{transition: \"opacity \" + duration + \"ms ease-out\"}\n\t]);\n\t$tw.utils.setStyle(modalWrapper,[\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms ease-in-out\"}\n\t]);\n\t// Force layout\n\t$tw.utils.forceLayout(modalBackdrop);\n\t$tw.utils.forceLayout(modalWrapper);\n\t// Set final animated styles\n\t$tw.utils.setStyle(modalBackdrop,[\n\t\t{opacity: \"0.7\"}\n\t]);\n\t$tw.utils.setStyle(modalWrapper,[\n\t\t{transform: \"translateY(0px)\"}\n\t]);\n};\n\nModal.prototype.adjustPageClass = function() {\n\tvar windowContainer = $tw.pageContainer ? ($tw.pageContainer === this.srcDocument.body.firstChild ? $tw.pageContainer : this.srcDocument.body.firstChild) : null;\n\tif(windowContainer) {\n\t\t$tw.utils.toggleClass(windowContainer,\"tc-modal-displayed\",this.modalCount > 0);\n\t}\n};\n\nexports.Modal = Modal;\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/notifier.js": {
"title": "$:/core/modules/utils/dom/notifier.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/notifier.js\ntype: application/javascript\nmodule-type: utils\n\nNotifier mechanism\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nvar Notifier = function(wiki) {\n\tthis.wiki = wiki;\n};\n\n/*\nDisplay a notification\n\ttitle: Title of tiddler containing the notification text\n\toptions: see below\nOptions include:\n*/\nNotifier.prototype.display = function(title,options) {\n\toptions = options || {};\n\t// Create the wrapper divs\n\tvar self = this,\n\t\tnotification = document.createElement(\"div\"),\n\t\ttiddler = this.wiki.getTiddler(title),\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t\trefreshHandler;\n\t// Don't do anything if the tiddler doesn't exist\n\tif(!tiddler) {\n\t\treturn;\n\t}\n\t// Add classes\n\t$tw.utils.addClass(notification,\"tc-notification\");\n\t// Create the variables\n\tvar variables = $tw.utils.extend({currentTiddler: title},options.variables);\n\t// Render the body of the notification\n\tvar widgetNode = this.wiki.makeTranscludeWidget(title,{\n\t\tparentWidget: $tw.rootWidget,\n\t\tdocument: document,\n\t\tvariables: variables,\n\t\timportPageMacros: true});\n\twidgetNode.render(notification,null);\n\trefreshHandler = function(changes) {\n\t\twidgetNode.refresh(changes,notification,null);\n\t};\n\tthis.wiki.addEventListener(\"change\",refreshHandler);\n\t// Set the initial styles for the notification\n\t$tw.utils.setStyle(notification,[\n\t\t{opacity: \"0\"},\n\t\t{transformOrigin: \"0% 0%\"},\n\t\t{transform: \"translateY(\" + (-window.innerHeight) + \"px)\"},\n\t\t{transition: \"opacity \" + duration + \"ms ease-out, \" + $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms ease-in-out\"}\n\t]);\n\t// Add the notification to the DOM\n\tdocument.body.appendChild(notification);\n\t// Force layout\n\t$tw.utils.forceLayout(notification);\n\t// Set final animated styles\n\t$tw.utils.setStyle(notification,[\n\t\t{opacity: \"1.0\"},\n\t\t{transform: \"translateY(0px)\"}\n\t]);\n\t// Set a timer to remove the notification\n\twindow.setTimeout(function() {\n\t\t// Remove our change event handler\n\t\tself.wiki.removeEventListener(\"change\",refreshHandler);\n\t\t// Force layout and animate the notification away\n\t\t$tw.utils.forceLayout(notification);\n\t\t$tw.utils.setStyle(notification,[\n\t\t\t{opacity: \"0.0\"},\n\t\t\t{transform: \"translateX(\" + (notification.offsetWidth) + \"px)\"}\n\t\t]);\n\t\t// Remove the modal message from the DOM once the transition ends\n\t\tsetTimeout(function() {\n\t\t\tif(notification.parentNode) {\n\t\t\t\tdocument.body.removeChild(notification);\n\t\t\t}\n\t\t},duration);\n\t},$tw.config.preferences.notificationDuration);\n};\n\nexports.Notifier = Notifier;\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/popup.js": {
"title": "$:/core/modules/utils/dom/popup.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/popup.js\ntype: application/javascript\nmodule-type: utils\n\nModule that creates a $tw.utils.Popup object prototype that manages popups in the browser\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nCreates a Popup object with these options:\n\trootElement: the DOM element to which the popup zapper should be attached\n*/\nvar Popup = function(options) {\n\toptions = options || {};\n\tthis.rootElement = options.rootElement || document.documentElement;\n\tthis.popups = []; // Array of {title:,wiki:,domNode:} objects\n};\n\n/*\nTrigger a popup open or closed. Parameters are in a hashmap:\n\ttitle: title of the tiddler where the popup details are stored\n\tdomNode: dom node to which the popup will be positioned (one of domNode or domNodeRect is required)\n\tdomNodeRect: rectangle to which the popup will be positioned\n\twiki: wiki\n\tforce: if specified, forces the popup state to true or false (instead of toggling it)\n\tfloating: if true, skips registering the popup, meaning that it will need manually clearing\n*/\nPopup.prototype.triggerPopup = function(options) {\n\t// Check if this popup is already active\n\tvar index = this.findPopup(options.title);\n\t// Compute the new state\n\tvar state = index === -1;\n\tif(options.force !== undefined) {\n\t\tstate = options.force;\n\t}\n\t// Show or cancel the popup according to the new state\n\tif(state) {\n\t\tthis.show(options);\n\t} else {\n\t\tthis.cancel(index);\n\t}\n};\n\nPopup.prototype.findPopup = function(title) {\n\tvar index = -1;\n\tfor(var t=0; t<this.popups.length; t++) {\n\t\tif(this.popups[t].title === title) {\n\t\t\tindex = t;\n\t\t}\n\t}\n\treturn index;\n};\n\nPopup.prototype.handleEvent = function(event) {\n\tif(event.type === \"click\") {\n\t\t// Find out what was clicked on\n\t\tvar info = this.popupInfo(event.target),\n\t\t\tcancelLevel = info.popupLevel - 1;\n\t\t// Don't remove the level that was clicked on if we clicked on a handle\n\t\tif(info.isHandle) {\n\t\t\tcancelLevel++;\n\t\t}\n\t\t// Cancel\n\t\tthis.cancel(cancelLevel);\n\t}\n};\n\n/*\nFind the popup level containing a DOM node. Returns:\npopupLevel: count of the number of nested popups containing the specified element\nisHandle: true if the specified element is within a popup handle\n*/\nPopup.prototype.popupInfo = function(domNode) {\n\tvar isHandle = false,\n\t\tpopupCount = 0,\n\t\tnode = domNode;\n\t// First check ancestors to see if we're within a popup handle\n\twhile(node) {\n\t\tif($tw.utils.hasClass(node,\"tc-popup-handle\")) {\n\t\t\tisHandle = true;\n\t\t\tpopupCount++;\n\t\t}\n\t\tif($tw.utils.hasClass(node,\"tc-popup-keep\")) {\n\t\t\tisHandle = true;\n\t\t}\n\t\tnode = node.parentNode;\n\t}\n\t// Then count the number of ancestor popups\n\tnode = domNode;\n\twhile(node) {\n\t\tif($tw.utils.hasClass(node,\"tc-popup\")) {\n\t\t\tpopupCount++;\n\t\t}\n\t\tnode = node.parentNode;\n\t}\n\tvar info = {\n\t\tpopupLevel: popupCount,\n\t\tisHandle: isHandle\n\t};\n\treturn info;\n};\n\n/*\nDisplay a popup by adding it to the stack\n*/\nPopup.prototype.show = function(options) {\n\t// Find out what was clicked on\n\tvar info = this.popupInfo(options.domNode);\n\t// Cancel any higher level popups\n\tthis.cancel(info.popupLevel);\n\n\t// Store the popup details if not already there\n\tif(!options.floating && this.findPopup(options.title) === -1) {\n\t\tthis.popups.push({\n\t\t\ttitle: options.title,\n\t\t\twiki: options.wiki,\n\t\t\tdomNode: options.domNode,\n\t\t\tnoStateReference: options.noStateReference\n\t\t});\n\t}\n\t// Set the state tiddler\n\tvar rect;\n\tif(options.domNodeRect) {\n\t\trect = options.domNodeRect;\n\t} else {\n\t\trect = {\n\t\t\tleft: options.domNode.offsetLeft,\n\t\t\ttop: options.domNode.offsetTop,\n\t\t\twidth: options.domNode.offsetWidth,\n\t\t\theight: options.domNode.offsetHeight\n\t\t};\n\t}\n\tvar popupRect = \"(\" + rect.left + \",\" + rect.top + \",\" + \n\t\t\t\trect.width + \",\" + rect.height + \")\";\n\tif(options.noStateReference) {\n\t\toptions.wiki.setText(options.title,\"text\",undefined,popupRect);\n\t} else {\n\t\toptions.wiki.setTextReference(options.title,popupRect);\n\t}\n\t// Add the click handler if we have any popups\n\tif(this.popups.length > 0) {\n\t\tthis.rootElement.addEventListener(\"click\",this,true);\t\t\n\t}\n};\n\n/*\nCancel all popups at or above a specified level or DOM node\nlevel: popup level to cancel (0 cancels all popups)\n*/\nPopup.prototype.cancel = function(level) {\n\tvar numPopups = this.popups.length;\n\tlevel = Math.max(0,Math.min(level,numPopups));\n\tfor(var t=level; t<numPopups; t++) {\n\t\tvar popup = this.popups.pop();\n\t\tif(popup.title) {\n\t\t\tif(popup.noStateReference) {\n\t\t\t\tpopup.wiki.deleteTiddler(popup.title);\n\t\t\t} else {\n\t\t\t\tpopup.wiki.deleteTiddler($tw.utils.parseTextReference(popup.title).title);\n \t\t}\n\t\t}\n\t}\n\tif(this.popups.length === 0) {\n\t\tthis.rootElement.removeEventListener(\"click\",this,false);\n\t}\n};\n\n/*\nReturns true if the specified title and text identifies an active popup\n*/\nPopup.prototype.readPopupState = function(text) {\n\tvar popupLocationRegExp = /^\\((-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+)\\)$/;\n\treturn popupLocationRegExp.test(text);\n};\n\nexports.Popup = Popup;\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/scroller.js": {
"title": "$:/core/modules/utils/dom/scroller.js",
"text": "/*\\\ntitle: $:/core/modules/utils/dom/scroller.js\ntype: application/javascript\nmodule-type: utils\n\nModule that creates a $tw.utils.Scroller object prototype that manages scrolling in the browser\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nEvent handler for when the `tm-scroll` event hits the document body\n*/\nvar PageScroller = function() {\n\tthis.idRequestFrame = null;\n\tthis.requestAnimationFrame = window.requestAnimationFrame ||\n\t\twindow.webkitRequestAnimationFrame ||\n\t\twindow.mozRequestAnimationFrame ||\n\t\tfunction(callback) {\n\t\t\treturn window.setTimeout(callback, 1000/60);\n\t\t};\n\tthis.cancelAnimationFrame = window.cancelAnimationFrame ||\n\t\twindow.webkitCancelAnimationFrame ||\n\t\twindow.webkitCancelRequestAnimationFrame ||\n\t\twindow.mozCancelAnimationFrame ||\n\t\twindow.mozCancelRequestAnimationFrame ||\n\t\tfunction(id) {\n\t\t\twindow.clearTimeout(id);\n\t\t};\n};\n\nPageScroller.prototype.isScrolling = function() {\n\treturn this.idRequestFrame !== null;\n}\n\nPageScroller.prototype.cancelScroll = function(srcWindow) {\n\tif(this.idRequestFrame) {\n\t\tthis.cancelAnimationFrame.call(srcWindow,this.idRequestFrame);\n\t\tthis.idRequestFrame = null;\n\t}\n};\n\n/*\nHandle an event\n*/\nPageScroller.prototype.handleEvent = function(event) {\n\tif(event.type === \"tm-scroll\") {\n\t\treturn this.scrollIntoView(event.target);\n\t}\n\treturn true;\n};\n\n/*\nHandle a scroll event hitting the page document\n*/\nPageScroller.prototype.scrollIntoView = function(element,callback) {\n\tvar self = this,\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t srcWindow = element ? element.ownerDocument.defaultView : window;\n\t// Now get ready to scroll the body\n\tthis.cancelScroll(srcWindow);\n\tthis.startTime = Date.now();\n\t// Get the height of any position:fixed toolbars\n\tvar toolbar = srcWindow.document.querySelector(\".tc-adjust-top-of-scroll\"),\n\t\toffset = 0;\n\tif(toolbar) {\n\t\toffset = toolbar.offsetHeight;\n\t}\n\t// Get the client bounds of the element and adjust by the scroll position\n\tvar getBounds = function() {\n\t\t\tvar clientBounds = typeof callback === 'function' ? callback() : element.getBoundingClientRect(),\n\t\t\t\tscrollPosition = $tw.utils.getScrollPosition(srcWindow);\n\t\t\treturn {\n\t\t\t\tleft: clientBounds.left + scrollPosition.x,\n\t\t\t\ttop: clientBounds.top + scrollPosition.y - offset,\n\t\t\t\twidth: clientBounds.width,\n\t\t\t\theight: clientBounds.height\n\t\t\t};\n\t\t},\n\t\t// We'll consider the horizontal and vertical scroll directions separately via this function\n\t\t// targetPos/targetSize - position and size of the target element\n\t\t// currentPos/currentSize - position and size of the current scroll viewport\n\t\t// returns: new position of the scroll viewport\n\t\tgetEndPos = function(targetPos,targetSize,currentPos,currentSize) {\n\t\t\tvar newPos = targetPos;\n\t\t\t// If we are scrolling within 50 pixels of the top/left then snap to zero\n\t\t\tif(newPos < 50) {\n\t\t\t\tnewPos = 0;\n\t\t\t}\n\t\t\treturn newPos;\n\t\t},\n\t\tdrawFrame = function drawFrame() {\n\t\t\tvar t;\n\t\t\tif(duration <= 0) {\n\t\t\t\tt = 1;\n\t\t\t} else {\n\t\t\t\tt = ((Date.now()) - self.startTime) / duration;\t\n\t\t\t}\n\t\t\tif(t >= 1) {\n\t\t\t\tself.cancelScroll(srcWindow);\n\t\t\t\tt = 1;\n\t\t\t}\n\t\t\tt = $tw.utils.slowInSlowOut(t);\n\t\t\tvar scrollPosition = $tw.utils.getScrollPosition(srcWindow),\n\t\t\t\tbounds = getBounds(),\n\t\t\t\tendX = getEndPos(bounds.left,bounds.width,scrollPosition.x,srcWindow.innerWidth),\n\t\t\t\tendY = getEndPos(bounds.top,bounds.height,scrollPosition.y,srcWindow.innerHeight);\n\t\t\tsrcWindow.scrollTo(scrollPosition.x + (endX - scrollPosition.x) * t,scrollPosition.y + (endY - scrollPosition.y) * t);\n\t\t\tif(t < 1) {\n\t\t\t\tself.idRequestFrame = self.requestAnimationFrame.call(srcWindow,drawFrame);\n\t\t\t}\n\t\t};\n\tdrawFrame();\n};\n\nexports.PageScroller = PageScroller;\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/edition-info.js": {
"title": "$:/core/modules/utils/edition-info.js",
"text": "/*\\\ntitle: $:/core/modules/utils/edition-info.js\ntype: application/javascript\nmodule-type: utils-node\n\nInformation about the available editions\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar fs = require(\"fs\"),\n\tpath = require(\"path\");\n\nvar editionInfo;\n\nexports.getEditionInfo = function() {\n\tif(!editionInfo) {\n\t\t// Enumerate the edition paths\n\t\tvar editionPaths = $tw.getLibraryItemSearchPaths($tw.config.editionsPath,$tw.config.editionsEnvVar);\n\t\teditionInfo = {};\n\t\tfor(var editionIndex=0; editionIndex<editionPaths.length; editionIndex++) {\n\t\t\tvar editionPath = editionPaths[editionIndex];\n\t\t\t// Enumerate the folders\n\t\t\tvar entries = fs.readdirSync(editionPath);\n\t\t\tfor(var entryIndex=0; entryIndex<entries.length; entryIndex++) {\n\t\t\t\tvar entry = entries[entryIndex];\n\t\t\t\t// Check if directories have a valid tiddlywiki.info\n\t\t\t\tif(!editionInfo[entry] && $tw.utils.isDirectory(path.resolve(editionPath,entry))) {\n\t\t\t\t\tvar info;\n\t\t\t\t\ttry {\n\t\t\t\t\t\tinfo = JSON.parse(fs.readFileSync(path.resolve(editionPath,entry,\"tiddlywiki.info\"),\"utf8\"));\n\t\t\t\t\t} catch(ex) {\n\t\t\t\t\t}\n\t\t\t\t\tif(info) {\n\t\t\t\t\t\teditionInfo[entry] = info;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\treturn editionInfo;\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils-node"
},
"$:/core/modules/utils/fakedom.js": {
"title": "$:/core/modules/utils/fakedom.js",
"text": "/*\\\ntitle: $:/core/modules/utils/fakedom.js\ntype: application/javascript\nmodule-type: global\n\nA barebones implementation of DOM interfaces needed by the rendering mechanism.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Sequence number used to enable us to track objects for testing\nvar sequenceNumber = null;\n\nvar bumpSequenceNumber = function(object) {\n\tif(sequenceNumber !== null) {\n\t\tobject.sequenceNumber = sequenceNumber++;\n\t}\n};\n\nvar TW_TextNode = function(text) {\n\tbumpSequenceNumber(this);\n\tthis.textContent = text + \"\";\n};\n\nObject.defineProperty(TW_TextNode.prototype, \"nodeType\", {\n\tget: function() {\n\t\treturn 3;\n\t}\n});\n\nObject.defineProperty(TW_TextNode.prototype, \"formattedTextContent\", {\n\tget: function() {\n\t\treturn this.textContent.replace(/(\\r?\\n)/g,\"\");\n\t}\n});\n\nvar TW_Element = function(tag,namespace) {\n\tbumpSequenceNumber(this);\n\tthis.isTiddlyWikiFakeDom = true;\n\tthis.tag = tag;\n\tthis.attributes = {};\n\tthis.isRaw = false;\n\tthis.children = [];\n\tthis._style = {};\n\tthis.namespaceURI = namespace || \"http://www.w3.org/1999/xhtml\";\n};\n\nObject.defineProperty(TW_Element.prototype, \"style\", {\n\tget: function() {\n\t\treturn this._style;\n\t},\n\tset: function(str) {\n\t\tvar self = this;\n\t\tstr = str || \"\";\n\t\t$tw.utils.each(str.split(\";\"),function(declaration) {\n\t\t\tvar parts = declaration.split(\":\"),\n\t\t\t\tname = $tw.utils.trim(parts[0]),\n\t\t\t\tvalue = $tw.utils.trim(parts[1]);\n\t\t\tif(name && value) {\n\t\t\t\tself._style[$tw.utils.convertStyleNameToPropertyName(name)] = value;\n\t\t\t}\n\t\t});\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"nodeType\", {\n\tget: function() {\n\t\treturn 1;\n\t}\n});\n\nTW_Element.prototype.getAttribute = function(name) {\n\tif(this.isRaw) {\n\t\tthrow \"Cannot getAttribute on a raw TW_Element\";\n\t}\n\treturn this.attributes[name];\n};\n\nTW_Element.prototype.setAttribute = function(name,value) {\n\tif(this.isRaw) {\n\t\tthrow \"Cannot setAttribute on a raw TW_Element\";\n\t}\n\tthis.attributes[name] = value + \"\";\n};\n\nTW_Element.prototype.setAttributeNS = function(namespace,name,value) {\n\tthis.setAttribute(name,value);\n};\n\nTW_Element.prototype.removeAttribute = function(name) {\n\tif(this.isRaw) {\n\t\tthrow \"Cannot removeAttribute on a raw TW_Element\";\n\t}\n\tif($tw.utils.hop(this.attributes,name)) {\n\t\tdelete this.attributes[name];\n\t}\n};\n\nTW_Element.prototype.appendChild = function(node) {\n\tthis.children.push(node);\n\tnode.parentNode = this;\n};\n\nTW_Element.prototype.insertBefore = function(node,nextSibling) {\n\tif(nextSibling) {\n\t\tvar p = this.children.indexOf(nextSibling);\n\t\tif(p !== -1) {\n\t\t\tthis.children.splice(p,0,node);\n\t\t\tnode.parentNode = this;\n\t\t} else {\n\t\t\tthis.appendChild(node);\n\t\t}\n\t} else {\n\t\tthis.appendChild(node);\n\t}\n};\n\nTW_Element.prototype.removeChild = function(node) {\n\tvar p = this.children.indexOf(node);\n\tif(p !== -1) {\n\t\tthis.children.splice(p,1);\n\t}\n};\n\nTW_Element.prototype.hasChildNodes = function() {\n\treturn !!this.children.length;\n};\n\nObject.defineProperty(TW_Element.prototype, \"childNodes\", {\n\tget: function() {\n\t\treturn this.children;\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"firstChild\", {\n\tget: function() {\n\t\treturn this.children[0];\n\t}\n});\n\nTW_Element.prototype.addEventListener = function(type,listener,useCapture) {\n\t// Do nothing\n};\n\nObject.defineProperty(TW_Element.prototype, \"tagName\", {\n\tget: function() {\n\t\treturn this.tag || \"\";\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"className\", {\n\tget: function() {\n\t\treturn this.attributes[\"class\"] || \"\";\n\t},\n\tset: function(value) {\n\t\tthis.attributes[\"class\"] = value + \"\";\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"value\", {\n\tget: function() {\n\t\treturn this.attributes.value || \"\";\n\t},\n\tset: function(value) {\n\t\tthis.attributes.value = value + \"\";\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"outerHTML\", {\n\tget: function() {\n\t\tvar output = [],attr,a,v;\n\t\toutput.push(\"<\",this.tag);\n\t\tif(this.attributes) {\n\t\t\tattr = [];\n\t\t\tfor(a in this.attributes) {\n\t\t\t\tattr.push(a);\n\t\t\t}\n\t\t\tattr.sort();\n\t\t\tfor(a=0; a<attr.length; a++) {\n\t\t\t\tv = this.attributes[attr[a]];\n\t\t\t\tif(v !== undefined) {\n\t\t\t\t\toutput.push(\" \",attr[a],\"=\\\"\",$tw.utils.htmlEncode(v),\"\\\"\");\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tif(this._style) {\n\t\t\tvar style = [];\n\t\t\tfor(var s in this._style) {\n\t\t\t\tstyle.push($tw.utils.convertPropertyNameToStyleName(s) + \":\" + this._style[s] + \";\");\n\t\t\t}\n\t\t\tif(style.length > 0) {\n\t\t\t\toutput.push(\" style=\\\"\",style.join(\"\"),\"\\\"\");\n\t\t\t}\n\t\t}\n\t\toutput.push(\">\");\n\t\tif($tw.config.htmlVoidElements.indexOf(this.tag) === -1) {\n\t\t\toutput.push(this.innerHTML);\n\t\t\toutput.push(\"</\",this.tag,\">\");\n\t\t}\n\t\treturn output.join(\"\");\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"innerHTML\", {\n\tget: function() {\n\t\tif(this.isRaw) {\n\t\t\treturn this.rawHTML;\n\t\t} else {\n\t\t\tvar b = [];\n\t\t\t$tw.utils.each(this.children,function(node) {\n\t\t\t\tif(node instanceof TW_Element) {\n\t\t\t\t\tb.push(node.outerHTML);\n\t\t\t\t} else if(node instanceof TW_TextNode) {\n\t\t\t\t\tb.push($tw.utils.htmlEncode(node.textContent));\n\t\t\t\t}\n\t\t\t});\n\t\t\treturn b.join(\"\");\n\t\t}\n\t},\n\tset: function(value) {\n\t\tthis.isRaw = true;\n\t\tthis.rawHTML = value;\n\t\tthis.rawTextContent = null;\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"textInnerHTML\", {\n\tset: function(value) {\n\t\tif(this.isRaw) {\n\t\t\tthis.rawTextContent = value;\n\t\t} else {\n\t\t\tthrow \"Cannot set textInnerHTML of a non-raw TW_Element\";\n\t\t}\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"textContent\", {\n\tget: function() {\n\t\tif(this.isRaw) {\n\t\t\tif(this.rawTextContent === null) {\n\t\t\t\treturn \"\";\n\t\t\t} else {\n\t\t\t\treturn this.rawTextContent;\n\t\t\t}\n\t\t} else {\n\t\t\tvar b = [];\n\t\t\t$tw.utils.each(this.children,function(node) {\n\t\t\t\tb.push(node.textContent);\n\t\t\t});\n\t\t\treturn b.join(\"\");\n\t\t}\n\t},\n\tset: function(value) {\n\t\tthis.children = [new TW_TextNode(value)];\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"formattedTextContent\", {\n\tget: function() {\n\t\tif(this.isRaw) {\n\t\t\treturn \"\";\n\t\t} else {\n\t\t\tvar b = [],\n\t\t\t\tisBlock = $tw.config.htmlBlockElements.indexOf(this.tag) !== -1;\n\t\t\tif(isBlock) {\n\t\t\t\tb.push(\"\\n\");\n\t\t\t}\n\t\t\tif(this.tag === \"li\") {\n\t\t\t\tb.push(\"* \");\n\t\t\t}\n\t\t\t$tw.utils.each(this.children,function(node) {\n\t\t\t\tb.push(node.formattedTextContent);\n\t\t\t});\n\t\t\tif(isBlock) {\n\t\t\t\tb.push(\"\\n\");\n\t\t\t}\n\t\t\treturn b.join(\"\");\n\t\t}\n\t}\n});\n\nvar document = {\n\tsetSequenceNumber: function(value) {\n\t\tsequenceNumber = value;\n\t},\n\tcreateElementNS: function(namespace,tag) {\n\t\treturn new TW_Element(tag,namespace);\n\t},\n\tcreateElement: function(tag) {\n\t\treturn new TW_Element(tag);\n\t},\n\tcreateTextNode: function(text) {\n\t\treturn new TW_TextNode(text);\n\t},\n\tcompatMode: \"CSS1Compat\", // For KaTeX to know that we're not a browser in quirks mode\n\tisTiddlyWikiFakeDom: true\n};\n\nexports.fakeDocument = document;\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/utils/filesystem.js": {
"title": "$:/core/modules/utils/filesystem.js",
"text": "/*\\\ntitle: $:/core/modules/utils/filesystem.js\ntype: application/javascript\nmodule-type: utils-node\n\nFile system utilities\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar fs = require(\"fs\"),\n\tpath = require(\"path\");\n\n/*\nRecursively (and synchronously) copy a directory and all its content\n*/\nexports.copyDirectory = function(srcPath,dstPath) {\n\t// Remove any trailing path separators\n\tsrcPath = $tw.utils.removeTrailingSeparator(srcPath);\n\tdstPath = $tw.utils.removeTrailingSeparator(dstPath);\n\t// Create the destination directory\n\tvar err = $tw.utils.createDirectory(dstPath);\n\tif(err) {\n\t\treturn err;\n\t}\n\t// Function to copy a folder full of files\n\tvar copy = function(srcPath,dstPath) {\n\t\tvar srcStats = fs.lstatSync(srcPath),\n\t\t\tdstExists = fs.existsSync(dstPath);\n\t\tif(srcStats.isFile()) {\n\t\t\t$tw.utils.copyFile(srcPath,dstPath);\n\t\t} else if(srcStats.isDirectory()) {\n\t\t\tvar items = fs.readdirSync(srcPath);\n\t\t\tfor(var t=0; t<items.length; t++) {\n\t\t\t\tvar item = items[t],\n\t\t\t\t\terr = copy(srcPath + path.sep + item,dstPath + path.sep + item);\n\t\t\t\tif(err) {\n\t\t\t\t\treturn err;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t};\n\tcopy(srcPath,dstPath);\n\treturn null;\n};\n\n/*\nCopy a file\n*/\nvar FILE_BUFFER_LENGTH = 64 * 1024,\n\tfileBuffer;\n\nexports.copyFile = function(srcPath,dstPath) {\n\t// Create buffer if required\n\tif(!fileBuffer) {\n\t\tfileBuffer = Buffer.alloc(FILE_BUFFER_LENGTH);\n\t}\n\t// Create any directories in the destination\n\t$tw.utils.createDirectory(path.dirname(dstPath));\n\t// Copy the file\n\tvar srcFile = fs.openSync(srcPath,\"r\"),\n\t\tdstFile = fs.openSync(dstPath,\"w\"),\n\t\tbytesRead = 1,\n\t\tpos = 0;\n\twhile (bytesRead > 0) {\n\t\tbytesRead = fs.readSync(srcFile,fileBuffer,0,FILE_BUFFER_LENGTH,pos);\n\t\tfs.writeSync(dstFile,fileBuffer,0,bytesRead);\n\t\tpos += bytesRead;\n\t}\n\tfs.closeSync(srcFile);\n\tfs.closeSync(dstFile);\n\treturn null;\n};\n\n/*\nRemove trailing path separator\n*/\nexports.removeTrailingSeparator = function(dirPath) {\n\tvar len = dirPath.length;\n\tif(dirPath.charAt(len-1) === path.sep) {\n\t\tdirPath = dirPath.substr(0,len-1);\n\t}\n\treturn dirPath;\n};\n\n/*\nRecursively create a directory\n*/\nexports.createDirectory = function(dirPath) {\n\tif(dirPath.substr(dirPath.length-1,1) !== path.sep) {\n\t\tdirPath = dirPath + path.sep;\n\t}\n\tvar pos = 1;\n\tpos = dirPath.indexOf(path.sep,pos);\n\twhile(pos !== -1) {\n\t\tvar subDirPath = dirPath.substr(0,pos);\n\t\tif(!$tw.utils.isDirectory(subDirPath)) {\n\t\t\ttry {\n\t\t\t\tfs.mkdirSync(subDirPath);\n\t\t\t} catch(e) {\n\t\t\t\treturn \"Error creating directory '\" + subDirPath + \"'\";\n\t\t\t}\n\t\t}\n\t\tpos = dirPath.indexOf(path.sep,pos + 1);\n\t}\n\treturn null;\n};\n\n/*\nRecursively create directories needed to contain a specified file\n*/\nexports.createFileDirectories = function(filePath) {\n\treturn $tw.utils.createDirectory(path.dirname(filePath));\n};\n\n/*\nRecursively delete a directory\n*/\nexports.deleteDirectory = function(dirPath) {\n\tif(fs.existsSync(dirPath)) {\n\t\tvar entries = fs.readdirSync(dirPath);\n\t\tfor(var entryIndex=0; entryIndex<entries.length; entryIndex++) {\n\t\t\tvar currPath = dirPath + path.sep + entries[entryIndex];\n\t\t\tif(fs.lstatSync(currPath).isDirectory()) {\n\t\t\t\t$tw.utils.deleteDirectory(currPath);\n\t\t\t} else {\n\t\t\t\tfs.unlinkSync(currPath);\n\t\t\t}\n\t\t}\n\tfs.rmdirSync(dirPath);\n\t}\n\treturn null;\n};\n\n/*\nCheck if a path identifies a directory\n*/\nexports.isDirectory = function(dirPath) {\n\treturn fs.existsSync(dirPath) && fs.statSync(dirPath).isDirectory();\n};\n\n/*\nCheck if a path identifies a directory that is empty\n*/\nexports.isDirectoryEmpty = function(dirPath) {\n\tif(!$tw.utils.isDirectory(dirPath)) {\n\t\treturn false;\n\t}\n\tvar files = fs.readdirSync(dirPath),\n\t\tempty = true;\n\t$tw.utils.each(files,function(file,index) {\n\t\tif(file.charAt(0) !== \".\") {\n\t\t\tempty = false;\n\t\t}\n\t});\n\treturn empty;\n};\n\n/*\nRecursively delete a tree of empty directories\n*/\nexports.deleteEmptyDirs = function(dirpath,callback) {\n\tvar self = this;\n\tfs.readdir(dirpath,function(err,files) {\n\t\tif(err) {\n\t\t\treturn callback(err);\n\t\t}\n\t\tif(files.length > 0) {\n\t\t\treturn callback(null);\n\t\t}\n\t\tfs.rmdir(dirpath,function(err) {\n\t\t\tif(err) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tself.deleteEmptyDirs(path.dirname(dirpath),callback);\n\t\t});\n\t});\n};\n\n/*\nCreate a fileInfo object for saving a tiddler:\n\tfilepath: the absolute path to the file containing the tiddler\n\ttype: the type of the tiddler file (NOT the type of the tiddler)\n\thasMetaFile: true if the file also has a companion .meta file\nOptions include:\n\tdirectory: absolute path of root directory to which we are saving\n\tpathFilters: optional array of filters to be used to generate the base path\n\twiki: optional wiki for evaluating the pathFilters\n*/\nexports.generateTiddlerFileInfo = function(tiddler,options) {\n\tvar fileInfo = {};\n\t// Check if the tiddler has any unsafe fields that can't be expressed in a .tid or .meta file: containing control characters, or leading/trailing whitespace\n\tvar hasUnsafeFields = false;\n\t$tw.utils.each(tiddler.getFieldStrings(),function(value,fieldName) {\n\t\tif(fieldName !== \"text\") {\n\t\t\thasUnsafeFields = hasUnsafeFields || /[\\x00-\\x1F]/mg.test(value);\n\t\t\thasUnsafeFields = hasUnsafeFields || ($tw.utils.trim(value) !== value);\n\t\t}\n\t});\n\t// Check for field values \n\tif(hasUnsafeFields) {\n\t\t// Save as a JSON file\n\t\tfileInfo.type = \"application/json\";\n\t\tfileInfo.hasMetaFile = false;\n\t} else {\n\t\t// Save as a .tid or a text/binary file plus a .meta file\n\t\tvar tiddlerType = tiddler.fields.type || \"text/vnd.tiddlywiki\";\n\t\tif(tiddlerType === \"text/vnd.tiddlywiki\") {\n\t\t\t// Save as a .tid file\n\t\t\tfileInfo.type = \"application/x-tiddler\";\n\t\t\tfileInfo.hasMetaFile = false;\n\t\t} else {\n\t\t\t// Save as a text/binary file and a .meta file\n\t\t\tfileInfo.type = tiddlerType;\n\t\t\tfileInfo.hasMetaFile = true;\n\t\t}\n\t}\n\t// Take the file extension from the tiddler content type\n\tvar contentTypeInfo = $tw.config.contentTypeInfo[fileInfo.type] || {extension: \"\"};\n\t// Generate the filepath\n\tfileInfo.filepath = $tw.utils.generateTiddlerFilepath(tiddler.fields.title,{\n\t\textension: contentTypeInfo.extension,\n\t\tdirectory: options.directory,\n\t\tpathFilters: options.pathFilters,\n\t\twiki: options.wiki\n\t});\n\treturn fileInfo;\n};\n\n/*\nGenerate the filepath for saving a tiddler\nOptions include:\n\textension: file extension to be added the finished filepath\n\tdirectory: absolute path of root directory to which we are saving\n\tpathFilters: optional array of filters to be used to generate the base path\n\twiki: optional wiki for evaluating the pathFilters\n*/\nexports.generateTiddlerFilepath = function(title,options) {\n\tvar self = this,\n\t\tdirectory = options.directory || \"\",\n\t\textension = options.extension || \"\",\n\t\tfilepath;\n\t// Check if any of the pathFilters applies\n\tif(options.pathFilters && options.wiki) {\n\t\t$tw.utils.each(options.pathFilters,function(filter) {\n\t\t\tif(!filepath) {\n\t\t\t\tvar source = options.wiki.makeTiddlerIterator([title]),\n\t\t\t\t\tresult = options.wiki.filterTiddlers(filter,null,source);\n\t\t\t\tif(result.length > 0) {\n\t\t\t\t\tfilepath = result[0];\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n\t// If not, generate a base pathname\n\tif(!filepath) {\n\t\tfilepath = title;\n\t\t// If the filepath already ends in the extension then remove it\n\t\tif(filepath.substring(filepath.length - extension.length) === extension) {\n\t\t\tfilepath = filepath.substring(0,filepath.length - extension.length);\n\t\t}\n\t\t// Remove any forward or backward slashes so we don't create directories\n\t\tfilepath = filepath.replace(/\\/|\\\\/g,\"_\");\n\t}\n\t// Don't let the filename start with a dot because such files are invisible on *nix\n\tfilepath = filepath.replace(/^\\./g,\"_\");\n\t// Remove any characters that can't be used in cross-platform filenames\n\tfilepath = $tw.utils.transliterate(filepath.replace(/<|>|\\:|\\\"|\\||\\?|\\*|\\^/g,\"_\"));\n\t// Truncate the filename if it is too long\n\tif(filepath.length > 200) {\n\t\tfilepath = filepath.substr(0,200);\n\t}\n\t// If the resulting filename is blank (eg because the title is just punctuation characters)\n\tif(!filepath) {\n\t\t// ...then just use the character codes of the title\n\t\tfilepath = \"\";\t\n\t\t$tw.utils.each(title.split(\"\"),function(char) {\n\t\t\tif(filepath) {\n\t\t\t\tfilepath += \"-\";\n\t\t\t}\n\t\t\tfilepath += char.charCodeAt(0).toString();\n\t\t});\n\t}\n\t// Add a uniquifier if the file already exists\n\tvar fullPath,\n\t\tcount = 0;\n\tdo {\n\t\tfullPath = path.resolve(directory,filepath + (count ? \"_\" + count : \"\") + extension);\n\t\tcount++;\n\t} while(fs.existsSync(fullPath));\n\t// Return the full path to the file\n\treturn fullPath;\n};\n\n/*\nSave a tiddler to a file described by the fileInfo:\n\tfilepath: the absolute path to the file containing the tiddler\n\ttype: the type of the tiddler file (NOT the type of the tiddler)\n\thasMetaFile: true if the file also has a companion .meta file\n*/\nexports.saveTiddlerToFile = function(tiddler,fileInfo,callback) {\n\t$tw.utils.createDirectory(path.dirname(fileInfo.filepath));\n\tif(fileInfo.hasMetaFile) {\n\t\t// Save the tiddler as a separate body and meta file\n\t\tvar typeInfo = $tw.config.contentTypeInfo[tiddler.fields.type || \"text/plain\"] || {encoding: \"utf8\"};\n\t\tfs.writeFile(fileInfo.filepath,tiddler.fields.text,typeInfo.encoding,function(err) {\n\t\t\tif(err) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tfs.writeFile(fileInfo.filepath + \".meta\",tiddler.getFieldStringBlock({exclude: [\"text\",\"bag\"]}),\"utf8\",callback);\n\t\t});\n\t} else {\n\t\t// Save the tiddler as a self contained templated file\n\t\tif(fileInfo.type === \"application/x-tiddler\") {\n\t\t\tfs.writeFile(fileInfo.filepath,tiddler.getFieldStringBlock({exclude: [\"text\",\"bag\"]}) + (!!tiddler.fields.text ? \"\\n\\n\" + tiddler.fields.text : \"\"),\"utf8\",callback);\n\t\t} else {\n\t\t\tfs.writeFile(fileInfo.filepath,JSON.stringify([tiddler.getFieldStrings({exclude: [\"bag\"]})],null,$tw.config.preferences.jsonSpaces),\"utf8\",callback);\n\t\t}\n\t}\n};\n\n/*\nSave a tiddler to a file described by the fileInfo:\n\tfilepath: the absolute path to the file containing the tiddler\n\ttype: the type of the tiddler file (NOT the type of the tiddler)\n\thasMetaFile: true if the file also has a companion .meta file\n*/\nexports.saveTiddlerToFileSync = function(tiddler,fileInfo) {\n\t$tw.utils.createDirectory(path.dirname(fileInfo.filepath));\n\tif(fileInfo.hasMetaFile) {\n\t\t// Save the tiddler as a separate body and meta file\n\t\tvar typeInfo = $tw.config.contentTypeInfo[tiddler.fields.type || \"text/plain\"] || {encoding: \"utf8\"};\n\t\tfs.writeFileSync(fileInfo.filepath,tiddler.fields.text,typeInfo.encoding);\n\t\tfs.writeFileSync(fileInfo.filepath + \".meta\",tiddler.getFieldStringBlock({exclude: [\"text\",\"bag\"]}),\"utf8\");\n\t} else {\n\t\t// Save the tiddler as a self contained templated file\n\t\tif(fileInfo.type === \"application/x-tiddler\") {\n\t\t\tfs.writeFileSync(fileInfo.filepath,tiddler.getFieldStringBlock({exclude: [\"text\",\"bag\"]}) + (!!tiddler.fields.text ? \"\\n\\n\" + tiddler.fields.text : \"\"),\"utf8\");\n\t\t} else {\n\t\t\tfs.writeFileSync(fileInfo.filepath,JSON.stringify([tiddler.getFieldStrings({exclude: [\"bag\"]})],null,$tw.config.preferences.jsonSpaces),\"utf8\");\n\t\t}\n\t}\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils-node"
},
"$:/core/modules/utils/logger.js": {
"title": "$:/core/modules/utils/logger.js",
"text": "/*\\\ntitle: $:/core/modules/utils/logger.js\ntype: application/javascript\nmodule-type: utils\n\nA basic logging implementation\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar ALERT_TAG = \"$:/tags/Alert\";\n\n/*\nMake a new logger\n*/\nfunction Logger(componentName,options) {\n\toptions = options || {};\n\tthis.componentName = componentName || \"\";\n\tthis.colour = options.colour || \"white\";\n\tthis.enable = \"enable\" in options ? options.enable : true;\n\tthis.save = \"save\" in options ? options.save : true;\n\tthis.saveLimit = options.saveLimit || 100 * 1024;\n\tthis.saveBufferLogger = this;\n\tthis.buffer = \"\";\n\tthis.alertCount = 0;\n}\n\nLogger.prototype.setSaveBuffer = function(logger) {\n\tthis.saveBufferLogger = logger;\n};\n\n/*\nLog a message\n*/\nLogger.prototype.log = function(/* args */) {\n\tvar self = this;\n\tif(this.enable) {\n\t\tif(this.saveBufferLogger.save) {\n\t\t\tthis.saveBufferLogger.buffer += $tw.utils.formatDateString(new Date(),\"YYYY MM DD 0hh:0mm:0ss.0XXX\") + \":\";\n\t\t\t$tw.utils.each(Array.prototype.slice.call(arguments,0),function(arg,index) {\n\t\t\t\tself.saveBufferLogger.buffer += \" \" + arg;\n\t\t\t});\n\t\t\tthis.saveBufferLogger.buffer += \"\\n\";\n\t\t\tthis.saveBufferLogger.buffer = this.saveBufferLogger.buffer.slice(-this.saveBufferLogger.saveLimit);\t\t\t\n\t\t}\n\t\tif(console !== undefined && console.log !== undefined) {\n\t\t\treturn Function.apply.call(console.log, console, [$tw.utils.terminalColour(this.colour),this.componentName + \":\"].concat(Array.prototype.slice.call(arguments,0)).concat($tw.utils.terminalColour()));\n\t\t}\n\t} \n};\n\n/*\nRead the message buffer\n*/\nLogger.prototype.getBuffer = function() {\n\treturn this.saveBufferLogger.buffer;\n};\n\n/*\nLog a structure as a table\n*/\nLogger.prototype.table = function(value) {\n\t(console.table || console.log)(value);\n};\n\n/*\nAlert a message\n*/\nLogger.prototype.alert = function(/* args */) {\n\tif(this.enable) {\n\t\t// Prepare the text of the alert\n\t\tvar text = Array.prototype.join.call(arguments,\" \");\n\t\t// Create alert tiddlers in the browser\n\t\tif($tw.browser) {\n\t\t\t// Check if there is an existing alert with the same text and the same component\n\t\t\tvar existingAlerts = $tw.wiki.getTiddlersWithTag(ALERT_TAG),\n\t\t\t\talertFields,\n\t\t\t\texistingCount,\n\t\t\t\tself = this;\n\t\t\t$tw.utils.each(existingAlerts,function(title) {\n\t\t\t\tvar tiddler = $tw.wiki.getTiddler(title);\n\t\t\t\tif(tiddler.fields.text === text && tiddler.fields.component === self.componentName && tiddler.fields.modified && (!alertFields || tiddler.fields.modified < alertFields.modified)) {\n\t\t\t\t\t\talertFields = $tw.utils.extend({},tiddler.fields);\n\t\t\t\t}\n\t\t\t});\n\t\t\tif(alertFields) {\n\t\t\t\texistingCount = alertFields.count || 1;\n\t\t\t} else {\n\t\t\t\talertFields = {\n\t\t\t\t\ttitle: $tw.wiki.generateNewTitle(\"$:/temp/alerts/alert\",{prefix: \"\"}),\n\t\t\t\t\ttext: text,\n\t\t\t\t\ttags: [ALERT_TAG],\n\t\t\t\t\tcomponent: this.componentName\n\t\t\t\t};\n\t\t\t\texistingCount = 0;\n\t\t\t\tthis.alertCount += 1;\n\t\t\t}\n\t\t\talertFields.modified = new Date();\n\t\t\tif(++existingCount > 1) {\n\t\t\t\talertFields.count = existingCount;\n\t\t\t} else {\n\t\t\t\talertFields.count = undefined;\n\t\t\t}\n\t\t\t$tw.wiki.addTiddler(new $tw.Tiddler(alertFields));\n\t\t\t// Log the alert as well\n\t\t\tthis.log.apply(this,Array.prototype.slice.call(arguments,0));\n\t\t} else {\n\t\t\t// Print an orange message to the console if not in the browser\n\t\t\tconsole.error(\"\\x1b[1;33m\" + text + \"\\x1b[0m\");\n\t\t}\t\t\n\t}\n};\n\n/*\nClear outstanding alerts\n*/\nLogger.prototype.clearAlerts = function() {\n\tvar self = this;\n\tif($tw.browser && this.alertCount > 0) {\n\t\t$tw.utils.each($tw.wiki.getTiddlersWithTag(ALERT_TAG),function(title) {\n\t\t\tvar tiddler = $tw.wiki.getTiddler(title);\n\t\t\tif(tiddler.fields.component === self.componentName) {\n\t\t\t\t$tw.wiki.deleteTiddler(title);\n\t\t\t}\n\t\t});\n\t\tthis.alertCount = 0;\n\t}\n};\n\nexports.Logger = Logger;\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/parsetree.js": {
"title": "$:/core/modules/utils/parsetree.js",
"text": "/*\\\ntitle: $:/core/modules/utils/parsetree.js\ntype: application/javascript\nmodule-type: utils\n\nParse tree utility functions.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.addAttributeToParseTreeNode = function(node,name,value) {\n\tnode.attributes = node.attributes || {};\n\tnode.attributes[name] = {type: \"string\", value: value};\n};\n\nexports.getAttributeValueFromParseTreeNode = function(node,name,defaultValue) {\n\tif(node.attributes && node.attributes[name] && node.attributes[name].value !== undefined) {\n\t\treturn node.attributes[name].value;\n\t}\n\treturn defaultValue;\n};\n\nexports.addClassToParseTreeNode = function(node,classString) {\n\tvar classes = [];\n\tnode.attributes = node.attributes || {};\n\tnode.attributes[\"class\"] = node.attributes[\"class\"] || {type: \"string\", value: \"\"};\n\tif(node.attributes[\"class\"].type === \"string\") {\n\t\tif(node.attributes[\"class\"].value !== \"\") {\n\t\t\tclasses = node.attributes[\"class\"].value.split(\" \");\n\t\t}\n\t\tif(classString !== \"\") {\n\t\t\t$tw.utils.pushTop(classes,classString.split(\" \"));\n\t\t}\n\t\tnode.attributes[\"class\"].value = classes.join(\" \");\n\t}\n};\n\nexports.addStyleToParseTreeNode = function(node,name,value) {\n\t\tnode.attributes = node.attributes || {};\n\t\tnode.attributes.style = node.attributes.style || {type: \"string\", value: \"\"};\n\t\tif(node.attributes.style.type === \"string\") {\n\t\t\tnode.attributes.style.value += name + \":\" + value + \";\";\n\t\t}\n};\n\nexports.findParseTreeNode = function(nodeArray,search) {\n\tfor(var t=0; t<nodeArray.length; t++) {\n\t\tif(nodeArray[t].type === search.type && nodeArray[t].tag === search.tag) {\n\t\t\treturn nodeArray[t];\n\t\t}\n\t}\n\treturn undefined;\n};\n\n/*\nHelper to get the text of a parse tree node or array of nodes\n*/\nexports.getParseTreeText = function getParseTreeText(tree) {\n\tvar output = [];\n\tif($tw.utils.isArray(tree)) {\n\t\t$tw.utils.each(tree,function(node) {\n\t\t\toutput.push(getParseTreeText(node));\n\t\t});\n\t} else {\n\t\tif(tree.type === \"text\") {\n\t\t\toutput.push(tree.text);\n\t\t}\n\t\tif(tree.children) {\n\t\t\treturn getParseTreeText(tree.children);\n\t\t}\n\t}\n\treturn output.join(\"\");\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/performance.js": {
"title": "$:/core/modules/utils/performance.js",
"text": "/*\\\ntitle: $:/core/modules/utils/performance.js\ntype: application/javascript\nmodule-type: global\n\nPerformance measurement.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nfunction Performance(enabled) {\n\tthis.enabled = !!enabled;\n\tthis.measures = {}; // Hashmap by measurement name of {time:, invocations:}\n\tthis.logger = new $tw.utils.Logger(\"performance\");\n\tthis.showGreeting();\n}\n\nPerformance.prototype.showGreeting = function() {\n\tif($tw.browser) {\n\t\tthis.logger.log(\"Execute $tw.perf.log(); to see filter execution timings\");\t\t\n\t}\n};\n\n/*\nWrap performance reporting around a top level function\n*/\nPerformance.prototype.report = function(name,fn) {\n\tvar self = this;\n\tif(this.enabled) {\n\t\treturn function() {\n\t\t\tvar startTime = $tw.utils.timer(),\n\t\t\t\tresult = fn.apply(this,arguments);\n\t\t\tself.logger.log(name + \": \" + $tw.utils.timer(startTime).toFixed(2) + \"ms\");\n\t\t\treturn result;\n\t\t};\n\t} else {\n\t\treturn fn;\n\t}\n};\n\nPerformance.prototype.log = function() {\n\tvar self = this,\n\t\ttotalTime = 0,\n\t\torderedMeasures = Object.keys(this.measures).sort(function(a,b) {\n\t\t\tif(self.measures[a].time > self.measures[b].time) {\n\t\t\t\treturn -1;\n\t\t\t} else if (self.measures[a].time < self.measures[b].time) {\n\t\t\t\treturn + 1;\n\t\t\t} else {\n\t\t\t\treturn 0;\n\t\t\t}\n\t\t});\n\t$tw.utils.each(orderedMeasures,function(name) {\n\t\ttotalTime += self.measures[name].time;\n\t});\n\tvar results = []\n\t$tw.utils.each(orderedMeasures,function(name) {\n\t\tvar measure = self.measures[name];\n\t\tresults.push({name: name,invocations: measure.invocations, avgTime: measure.time / measure.invocations, totalTime: measure.time, percentTime: (measure.time / totalTime) * 100})\n\t});\n\tself.logger.table(results);\n};\n\n/*\nWrap performance measurements around a subfunction\n*/\nPerformance.prototype.measure = function(name,fn) {\n\tvar self = this;\n\tif(this.enabled) {\n\t\treturn function() {\n\t\t\tvar startTime = $tw.utils.timer(),\n\t\t\t\tresult = fn.apply(this,arguments);\n\t\t\tif(!(name in self.measures)) {\n\t\t\t\tself.measures[name] = {time: 0, invocations: 0};\n\t\t\t}\n\t\t\tself.measures[name].time += $tw.utils.timer(startTime);\n\t\t\tself.measures[name].invocations++;\n\t\t\treturn result;\n\t\t};\n\t} else {\n\t\treturn fn;\n\t}\n};\n\nexports.Performance = Performance;\n\n})();\n",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/utils/pluginmaker.js": {
"title": "$:/core/modules/utils/pluginmaker.js",
"text": "/*\\\ntitle: $:/core/modules/utils/pluginmaker.js\ntype: application/javascript\nmodule-type: utils\n\nA quick and dirty way to pack up plugins within the browser.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nRepack a plugin, and then delete any non-shadow payload tiddlers\n*/\nexports.repackPlugin = function(title,additionalTiddlers,excludeTiddlers) {\n\tadditionalTiddlers = additionalTiddlers || [];\n\texcludeTiddlers = excludeTiddlers || [];\n\t// Get the plugin tiddler\n\tvar pluginTiddler = $tw.wiki.getTiddler(title);\n\tif(!pluginTiddler) {\n\t\tthrow \"No such tiddler as \" + title;\n\t}\n\t// Extract the JSON\n\tvar jsonPluginTiddler;\n\ttry {\n\t\tjsonPluginTiddler = JSON.parse(pluginTiddler.fields.text);\n\t} catch(e) {\n\t\tthrow \"Cannot parse plugin tiddler \" + title + \"\\n\" + $tw.language.getString(\"Error/Caption\") + \": \" + e;\n\t}\n\t// Get the list of tiddlers\n\tvar tiddlers = Object.keys(jsonPluginTiddler.tiddlers);\n\t// Add the additional tiddlers\n\t$tw.utils.pushTop(tiddlers,additionalTiddlers);\n\t// Remove any excluded tiddlers\n\tfor(var t=tiddlers.length-1; t>=0; t--) {\n\t\tif(excludeTiddlers.indexOf(tiddlers[t]) !== -1) {\n\t\t\ttiddlers.splice(t,1);\n\t\t}\n\t}\n\t// Pack up the tiddlers into a block of JSON\n\tvar plugins = {};\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar tiddler = $tw.wiki.getTiddler(title),\n\t\t\tfields = {};\n\t\t$tw.utils.each(tiddler.fields,function (value,name) {\n\t\t\tfields[name] = tiddler.getFieldString(name);\n\t\t});\n\t\tplugins[title] = fields;\n\t});\n\t// Retrieve and bump the version number\n\tvar pluginVersion = $tw.utils.parseVersion(pluginTiddler.getFieldString(\"version\") || \"0.0.0\") || {\n\t\t\tmajor: \"0\",\n\t\t\tminor: \"0\",\n\t\t\tpatch: \"0\"\n\t\t};\n\tpluginVersion.patch++;\n\tvar version = pluginVersion.major + \".\" + pluginVersion.minor + \".\" + pluginVersion.patch;\n\tif(pluginVersion.prerelease) {\n\t\tversion += \"-\" + pluginVersion.prerelease;\n\t}\n\tif(pluginVersion.build) {\n\t\tversion += \"+\" + pluginVersion.build;\n\t}\n\t// Save the tiddler\n\t$tw.wiki.addTiddler(new $tw.Tiddler(pluginTiddler,{text: JSON.stringify({tiddlers: plugins},null,4), version: version}));\n\t// Delete any non-shadow constituent tiddlers\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tif($tw.wiki.tiddlerExists(title)) {\n\t\t\t$tw.wiki.deleteTiddler(title);\n\t\t}\n\t});\n\t// Trigger an autosave\n\t$tw.rootWidget.dispatchEvent({type: \"tm-auto-save-wiki\"});\n\t// Return a heartwarming confirmation\n\treturn \"Plugin \" + title + \" successfully saved\";\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/transliterate.js": {
"title": "$:/core/modules/utils/transliterate.js",
"text": "/*\\\ntitle: $:/core/modules/utils/transliterate.js\ntype: application/javascript\nmodule-type: utils\n\nTransliteration static utility functions.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nTransliterate string to ASCII\n\n(Some pairs taken from http://semplicewebsites.com/removing-accents-javascript)\n*/\nexports.transliterationPairs = 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:\"o\",\n\t\"ṓ\":\"o\",\n\t\"ṑ\":\"o\",\n\t\"ǫ\":\"o\",\n\t\"ǭ\":\"o\",\n\t\"ø\":\"o\",\n\t\"ǿ\":\"o\",\n\t\"õ\":\"o\",\n\t\"ṍ\":\"o\",\n\t\"ṏ\":\"o\",\n\t\"ȭ\":\"o\",\n\t\"ƣ\":\"oi\",\n\t\"ꝏ\":\"oo\",\n\t\"ɛ\":\"e\",\n\t\"ᶓ\":\"e\",\n\t\"ɔ\":\"o\",\n\t\"ᶗ\":\"o\",\n\t\"ȣ\":\"ou\",\n\t\"ṕ\":\"p\",\n\t\"ṗ\":\"p\",\n\t\"ꝓ\":\"p\",\n\t\"ƥ\":\"p\",\n\t\"ᵱ\":\"p\",\n\t\"ᶈ\":\"p\",\n\t\"ꝕ\":\"p\",\n\t\"ᵽ\":\"p\",\n\t\"ꝑ\":\"p\",\n\t\"ꝙ\":\"q\",\n\t\"ʠ\":\"q\",\n\t\"ɋ\":\"q\",\n\t\"ꝗ\":\"q\",\n\t\"ŕ\":\"r\",\n\t\"ř\":\"r\",\n\t\"ŗ\":\"r\",\n\t\"ṙ\":\"r\",\n\t\"ṛ\":\"r\",\n\t\"ṝ\":\"r\",\n\t\"ȑ\":\"r\",\n\t\"ɾ\":\"r\",\n\t\"ᵳ\":\"r\",\n\t\"ȓ\":\"r\",\n\t\"ṟ\":\"r\",\n\t\"ɼ\":\"r\",\n\t\"ᵲ\":\"r\",\n\t\"ᶉ\":\"r\",\n\t\"ɍ\":\"r\",\n\t\"ɽ\":\"r\",\n\t\"ↄ\":\"c\",\n\t\"ꜿ\":\"c\",\n\t\"ɘ\":\"e\",\n\t\"ɿ\":\"r\",\n\t\"ś\":\"s\",\n\t\"ṥ\":\"s\",\n\t\"š\":\"s\",\n\t\"ṧ\":\"s\",\n\t\"ş\":\"s\",\n\t\"ŝ\":\"s\",\n\t\"ș\":\"s\",\n\t\"ṡ\":\"s\",\n\t\"ṣ\":\"s\",\n\t\"ṩ\":\"s\",\n\t\"ʂ\":\"s\",\n\t\"ᵴ\":\"s\",\n\t\"ᶊ\":\"s\",\n\t\"ȿ\":\"s\",\n\t\"ɡ\":\"g\",\n\t\"ᴑ\":\"o\",\n\t\"ᴓ\":\"o\",\n\t\"ᴝ\":\"u\",\n\t\"ť\":\"t\",\n\t\"ţ\":\"t\",\n\t\"ṱ\":\"t\",\n\t\"ț\":\"t\",\n\t\"ȶ\":\"t\",\n\t\"ẗ\":\"t\",\n\t\"ⱦ\":\"t\",\n\t\"ṫ\":\"t\",\n\t\"ṭ\":\"t\",\n\t\"ƭ\":\"t\",\n\t\"ṯ\":\"t\",\n\t\"ᵵ\":\"t\",\n\t\"ƫ\":\"t\",\n\t\"ʈ\":\"t\",\n\t\"ŧ\":\"t\",\n\t\"ᵺ\":\"th\",\n\t\"ɐ\":\"a\",\n\t\"ᴂ\":\"ae\",\n\t\"ǝ\":\"e\",\n\t\"ᵷ\":\"g\",\n\t\"ɥ\":\"h\",\n\t\"ʮ\":\"h\",\n\t\"ʯ\":\"h\",\n\t\"ᴉ\":\"i\",\n\t\"ʞ\":\"k\",\n\t\"ꞁ\":\"l\",\n\t\"ɯ\":\"m\",\n\t\"ɰ\":\"m\",\n\t\"ᴔ\":\"oe\",\n\t\"ɹ\":\"r\",\n\t\"ɻ\":\"r\",\n\t\"ɺ\":\"r\",\n\t\"ⱹ\":\"r\",\n\t\"ʇ\":\"t\",\n\t\"ʌ\":\"v\",\n\t\"ʍ\":\"w\",\n\t\"ʎ\":\"y\",\n\t\"ꜩ\":\"tz\",\n\t\"ú\":\"u\",\n\t\"ŭ\":\"u\",\n\t\"ǔ\":\"u\",\n\t\"û\":\"u\",\n\t\"ṷ\":\"u\",\n\t\"ü\":\"u\",\n\t\"ǘ\":\"u\",\n\t\"ǚ\":\"u\",\n\t\"ǜ\":\"u\",\n\t\"ǖ\":\"u\",\n\t\"ṳ\":\"u\",\n\t\"ụ\":\"u\",\n\t\"ű\":\"u\",\n\t\"ȕ\":\"u\",\n\t\"ù\":\"u\",\n\t\"ủ\":\"u\",\n\t\"ư\":\"u\",\n\t\"ứ\":\"u\",\n\t\"ự\":\"u\",\n\t\"ừ\":\"u\",\n\t\"ử\":\"u\",\n\t\"ữ\":\"u\",\n\t\"ȗ\":\"u\",\n\t\"ū\":\"u\",\n\t\"ṻ\":\"u\",\n\t\"ų\":\"u\",\n\t\"ᶙ\":\"u\",\n\t\"ů\":\"u\",\n\t\"ũ\":\"u\",\n\t\"ṹ\":\"u\",\n\t\"ṵ\":\"u\",\n\t\"ᵫ\":\"ue\",\n\t\"ꝸ\":\"um\",\n\t\"ⱴ\":\"v\",\n\t\"ꝟ\":\"v\",\n\t\"ṿ\":\"v\",\n\t\"ʋ\":\"v\",\n\t\"ᶌ\":\"v\",\n\t\"ⱱ\":\"v\",\n\t\"ṽ\":\"v\",\n\t\"ꝡ\":\"vy\",\n\t\"ẃ\":\"w\",\n\t\"ŵ\":\"w\",\n\t\"ẅ\":\"w\",\n\t\"ẇ\":\"w\",\n\t\"ẉ\":\"w\",\n\t\"ẁ\":\"w\",\n\t\"ⱳ\":\"w\",\n\t\"ẘ\":\"w\",\n\t\"ẍ\":\"x\",\n\t\"ẋ\":\"x\",\n\t\"ᶍ\":\"x\",\n\t\"ý\":\"y\",\n\t\"ŷ\":\"y\",\n\t\"ÿ\":\"y\",\n\t\"ẏ\":\"y\",\n\t\"ỵ\":\"y\",\n\t\"ỳ\":\"y\",\n\t\"ƴ\":\"y\",\n\t\"ỷ\":\"y\",\n\t\"ỿ\":\"y\",\n\t\"ȳ\":\"y\",\n\t\"ẙ\":\"y\",\n\t\"ɏ\":\"y\",\n\t\"ỹ\":\"y\",\n\t\"ź\":\"z\",\n\t\"ž\":\"z\",\n\t\"ẑ\":\"z\",\n\t\"ʑ\":\"z\",\n\t\"ⱬ\":\"z\",\n\t\"ż\":\"z\",\n\t\"ẓ\":\"z\",\n\t\"ȥ\":\"z\",\n\t\"ẕ\":\"z\",\n\t\"ᵶ\":\"z\",\n\t\"ᶎ\":\"z\",\n\t\"ʐ\":\"z\",\n\t\"ƶ\":\"z\",\n\t\"ɀ\":\"z\",\n\t\"ff\":\"ff\",\n\t\"ffi\":\"ffi\",\n\t\"ffl\":\"ffl\",\n\t\"fi\":\"fi\",\n\t\"fl\":\"fl\",\n\t\"ij\":\"ij\",\n\t\"œ\":\"oe\",\n\t\"st\":\"st\",\n\t\"ₐ\":\"a\",\n\t\"ₑ\":\"e\",\n\t\"ᵢ\":\"i\",\n\t\"ⱼ\":\"j\",\n\t\"ₒ\":\"o\",\n\t\"ᵣ\":\"r\",\n\t\"ᵤ\":\"u\",\n\t\"ᵥ\":\"v\",\n\t\"ₓ\":\"x\",\n\t\"Ё\":\"YO\",\n\t\"Й\":\"I\",\n\t\"Ц\":\"TS\",\n\t\"У\":\"U\",\n\t\"К\":\"K\",\n\t\"Е\":\"E\",\n\t\"Н\":\"N\",\n\t\"Г\":\"G\",\n\t\"Ш\":\"SH\",\n\t\"Щ\":\"SCH\",\n\t\"З\":\"Z\",\n\t\"Х\":\"H\",\n\t\"Ъ\":\"'\",\n\t\"ё\":\"yo\",\n\t\"й\":\"i\",\n\t\"ц\":\"ts\",\n\t\"у\":\"u\",\n\t\"к\":\"k\",\n\t\"е\":\"e\",\n\t\"н\":\"n\",\n\t\"г\":\"g\",\n\t\"ш\":\"sh\",\n\t\"щ\":\"sch\",\n\t\"з\":\"z\",\n\t\"х\":\"h\",\n\t\"ъ\":\"'\",\n\t\"Ф\":\"F\",\n\t\"Ы\":\"I\",\n\t\"В\":\"V\",\n\t\"А\":\"a\",\n\t\"П\":\"P\",\n\t\"Р\":\"R\",\n\t\"О\":\"O\",\n\t\"Л\":\"L\",\n\t\"Д\":\"D\",\n\t\"Ж\":\"ZH\",\n\t\"Э\":\"E\",\n\t\"ф\":\"f\",\n\t\"ы\":\"i\",\n\t\"в\":\"v\",\n\t\"а\":\"a\",\n\t\"п\":\"p\",\n\t\"р\":\"r\",\n\t\"о\":\"o\",\n\t\"л\":\"l\",\n\t\"д\":\"d\",\n\t\"ж\":\"zh\",\n\t\"э\":\"e\",\n\t\"Я\":\"Ya\",\n\t\"Ч\":\"CH\",\n\t\"С\":\"S\",\n\t\"М\":\"M\",\n\t\"И\":\"I\",\n\t\"Т\":\"T\",\n\t\"Ь\":\"'\",\n\t\"Б\":\"B\",\n\t\"Ю\":\"YU\",\n\t\"я\":\"ya\",\n\t\"ч\":\"ch\",\n\t\"с\":\"s\",\n\t\"м\":\"m\",\n\t\"и\":\"i\",\n\t\"т\":\"t\",\n\t\"ь\":\"'\",\n\t\"б\":\"b\",\n\t\"ю\":\"yu\"\n};\n\nexports.transliterate = function(str) {\n\treturn str.replace(/[^A-Za-z0-9\\[\\] ]/g,function(ch) {\n\t\treturn exports.transliterationPairs[ch] || ch\n\t});\n};\n\nexports.transliterateToSafeASCII = function(str) {\n\treturn str.replace(/[^\\x00-\\x7F]/g,function(ch) {\n\t\treturn exports.transliterationPairs[ch] || \"\"\n\t});\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/utils.js": {
"title": "$:/core/modules/utils/utils.js",
"text": "/*\\\ntitle: $:/core/modules/utils/utils.js\ntype: application/javascript\nmodule-type: utils\n\nVarious static utility functions.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar base64utf8 = require(\"$:/core/modules/utils/base64-utf8/base64-utf8.module.js\");\n\n/*\nDisplay a message, in colour if we're on a terminal\n*/\nexports.log = function(text,colour) {\n\tconsole.log($tw.node ? exports.terminalColour(colour) + text + exports.terminalColour() : text);\n};\n\nexports.terminalColour = function(colour) {\n\tif(!$tw.browser && $tw.node && process.stdout.isTTY) {\n\t\tif(colour) {\n\t\t\tvar code = exports.terminalColourLookup[colour];\n\t\t\tif(code) {\n\t\t\t\treturn \"\\x1b[\" + code + \"m\";\n\t\t\t}\n\t\t} else {\n\t\t\treturn \"\\x1b[0m\"; // Cancel colour\n\t\t}\n\t}\n\treturn \"\";\n};\n\nexports.terminalColourLookup = {\n\t\"black\": \"0;30\",\n\t\"red\": \"0;31\",\n\t\"green\": \"0;32\",\n\t\"brown/orange\": \"0;33\",\n\t\"blue\": \"0;34\",\n\t\"purple\": \"0;35\",\n\t\"cyan\": \"0;36\",\n\t\"light gray\": \"0;37\"\n};\n\n/*\nDisplay a warning, in colour if we're on a terminal\n*/\nexports.warning = function(text) {\n\texports.log(text,\"brown/orange\");\n};\n\n/*\nReturn the integer represented by the str (string).\nReturn the dflt (default) parameter if str is not a base-10 number.\n*/\nexports.getInt = function(str,deflt) {\n\tvar i = parseInt(str,10);\n\treturn isNaN(i) ? deflt : i;\n}\n\n/*\nRepeatedly replaces a substring within a string. Like String.prototype.replace, but without any of the default special handling of $ sequences in the replace string\n*/\nexports.replaceString = function(text,search,replace) {\n\treturn text.replace(search,function() {\n\t\treturn replace;\n\t});\n};\n\n/*\nRepeats a string\n*/\nexports.repeat = function(str,count) {\n\tvar result = \"\";\n\tfor(var t=0;t<count;t++) {\n\t\tresult += str;\n\t}\n\treturn result;\n};\n\n/*\nTrim whitespace from the start and end of a string\nThanks to Steven Levithan, http://blog.stevenlevithan.com/archives/faster-trim-javascript\n*/\nexports.trim = function(str) {\n\tif(typeof str === \"string\") {\n\t\treturn str.replace(/^\\s\\s*/, '').replace(/\\s\\s*$/, '');\n\t} else {\n\t\treturn str;\n\t}\n};\n\n/*\nConvert a string to sentence case (ie capitalise first letter)\n*/\nexports.toSentenceCase = function(str) {\n\treturn (str || \"\").replace(/^\\S/, function(c) {return c.toUpperCase();});\n}\n\n/*\nConvert a string to title case (ie capitalise each initial letter)\n*/\nexports.toTitleCase = function(str) {\n\treturn (str || \"\").replace(/(^|\\s)\\S/g, function(c) {return c.toUpperCase();});\n}\n\t\n/*\nFind the line break preceding a given position in a string\nReturns position immediately after that line break, or the start of the string\n*/\nexports.findPrecedingLineBreak = function(text,pos) {\n\tvar result = text.lastIndexOf(\"\\n\",pos - 1);\n\tif(result === -1) {\n\t\tresult = 0;\n\t} else {\n\t\tresult++;\n\t\tif(text.charAt(result) === \"\\r\") {\n\t\t\tresult++;\n\t\t}\n\t}\n\treturn result;\n};\n\n/*\nFind the line break following a given position in a string\n*/\nexports.findFollowingLineBreak = function(text,pos) {\n\t// Cut to just past the following line break, or to the end of the text\n\tvar result = text.indexOf(\"\\n\",pos);\n\tif(result === -1) {\n\t\tresult = text.length;\n\t} else {\n\t\tif(text.charAt(result) === \"\\r\") {\n\t\t\tresult++;\n\t\t}\n\t}\n\treturn result;\n};\n\n/*\nReturn the number of keys in an object\n*/\nexports.count = function(object) {\n\treturn Object.keys(object || {}).length;\n};\n\n/*\nDetermine whether an array-item is an object-property\n*/\nexports.hopArray = function(object,array) {\n\tfor(var i=0; i<array.length; i++) {\n\t\tif($tw.utils.hop(object,array[i])) {\n\t\t\treturn true;\n\t\t}\n\t}\n\treturn false;\n};\n\n/*\nRemove entries from an array\n\tarray: array to modify\n\tvalue: a single value to remove, or an array of values to remove\n*/\nexports.removeArrayEntries = function(array,value) {\n\tvar t,p;\n\tif($tw.utils.isArray(value)) {\n\t\tfor(t=0; t<value.length; t++) {\n\t\t\tp = array.indexOf(value[t]);\n\t\t\tif(p !== -1) {\n\t\t\t\tarray.splice(p,1);\n\t\t\t}\n\t\t}\n\t} else {\n\t\tp = array.indexOf(value);\n\t\tif(p !== -1) {\n\t\t\tarray.splice(p,1);\n\t\t}\n\t}\n};\n\n/*\nCheck whether any members of a hashmap are present in another hashmap\n*/\nexports.checkDependencies = function(dependencies,changes) {\n\tvar hit = false;\n\t$tw.utils.each(changes,function(change,title) {\n\t\tif($tw.utils.hop(dependencies,title)) {\n\t\t\thit = true;\n\t\t}\n\t});\n\treturn hit;\n};\n\nexports.extend = function(object /* [, src] */) {\n\t$tw.utils.each(Array.prototype.slice.call(arguments, 1), function(source) {\n\t\tif(source) {\n\t\t\tfor(var property in source) {\n\t\t\t\tobject[property] = source[property];\n\t\t\t}\n\t\t}\n\t});\n\treturn object;\n};\n\nexports.deepCopy = function(object) {\n\tvar result,t;\n\tif($tw.utils.isArray(object)) {\n\t\t// Copy arrays\n\t\tresult = object.slice(0);\n\t} else if(typeof object === \"object\") {\n\t\tresult = {};\n\t\tfor(t in object) {\n\t\t\tif(object[t] !== undefined) {\n\t\t\t\tresult[t] = $tw.utils.deepCopy(object[t]);\n\t\t\t}\n\t\t}\n\t} else {\n\t\tresult = object;\n\t}\n\treturn result;\n};\n\nexports.extendDeepCopy = function(object,extendedProperties) {\n\tvar result = $tw.utils.deepCopy(object),t;\n\tfor(t in extendedProperties) {\n\t\tif(extendedProperties[t] !== undefined) {\n\t\t\tresult[t] = $tw.utils.deepCopy(extendedProperties[t]);\n\t\t}\n\t}\n\treturn result;\n};\n\nexports.deepFreeze = function deepFreeze(object) {\n\tvar property, key;\n\tif(object) {\n\t\tObject.freeze(object);\n\t\tfor(key in object) {\n\t\t\tproperty = object[key];\n\t\t\tif($tw.utils.hop(object,key) && (typeof property === \"object\") && !Object.isFrozen(property)) {\n\t\t\t\tdeepFreeze(property);\n\t\t\t}\n\t\t}\n\t}\n};\n\nexports.slowInSlowOut = function(t) {\n\treturn (1 - ((Math.cos(t * Math.PI) + 1) / 2));\n};\n\nexports.formatDateString = function(date,template) {\n\tvar result = \"\",\n\t\tt = template,\n\t\tmatches = [\n\t\t\t[/^0hh12/, function() {\n\t\t\t\treturn $tw.utils.pad($tw.utils.getHours12(date));\n\t\t\t}],\n\t\t\t[/^wYYYY/, function() {\n\t\t\t\treturn $tw.utils.getYearForWeekNo(date);\n\t\t\t}],\n\t\t\t[/^hh12/, function() {\n\t\t\t\treturn $tw.utils.getHours12(date);\n\t\t\t}],\n\t\t\t[/^DDth/, function() {\n\t\t\t\treturn date.getDate() + $tw.utils.getDaySuffix(date);\n\t\t\t}],\n\t\t\t[/^YYYY/, function() {\n\t\t\t\treturn date.getFullYear();\n\t\t\t}],\n\t\t\t[/^0hh/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getHours());\n\t\t\t}],\n\t\t\t[/^0mm/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getMinutes());\n\t\t\t}],\n\t\t\t[/^0ss/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getSeconds());\n\t\t\t}],\n\t\t\t[/^0XXX/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getMilliseconds(),3);\n\t\t\t}],\n\t\t\t[/^0DD/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getDate());\n\t\t\t}],\n\t\t\t[/^0MM/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getMonth()+1);\n\t\t\t}],\n\t\t\t[/^0WW/, function() {\n\t\t\t\treturn $tw.utils.pad($tw.utils.getWeek(date));\n\t\t\t}],\n\t\t\t[/^ddd/, function() {\n\t\t\t\treturn $tw.language.getString(\"Date/Short/Day/\" + date.getDay());\n\t\t\t}],\n\t\t\t[/^mmm/, function() {\n\t\t\t\treturn $tw.language.getString(\"Date/Short/Month/\" + (date.getMonth() + 1));\n\t\t\t}],\n\t\t\t[/^DDD/, function() {\n\t\t\t\treturn $tw.language.getString(\"Date/Long/Day/\" + date.getDay());\n\t\t\t}],\n\t\t\t[/^MMM/, function() {\n\t\t\t\treturn $tw.language.getString(\"Date/Long/Month/\" + (date.getMonth() + 1));\n\t\t\t}],\n\t\t\t[/^TZD/, function() {\n\t\t\t\tvar tz = date.getTimezoneOffset(),\n\t\t\t\tatz = Math.abs(tz);\n\t\t\t\treturn (tz < 0 ? '+' : '-') + $tw.utils.pad(Math.floor(atz / 60)) + ':' + $tw.utils.pad(atz % 60);\n\t\t\t}],\n\t\t\t[/^wYY/, function() {\n\t\t\t\treturn $tw.utils.pad($tw.utils.getYearForWeekNo(date) - 2000);\n\t\t\t}],\n\t\t\t[/^[ap]m/, function() {\n\t\t\t\treturn $tw.utils.getAmPm(date).toLowerCase();\n\t\t\t}],\n\t\t\t[/^hh/, function() {\n\t\t\t\treturn date.getHours();\n\t\t\t}],\n\t\t\t[/^mm/, function() {\n\t\t\t\treturn date.getMinutes();\n\t\t\t}],\n\t\t\t[/^ss/, function() {\n\t\t\t\treturn date.getSeconds();\n\t\t\t}],\n\t\t\t[/^XXX/, function() {\n\t\t\t\treturn date.getMilliseconds();\n\t\t\t}],\n\t\t\t[/^[AP]M/, function() {\n\t\t\t\treturn $tw.utils.getAmPm(date).toUpperCase();\n\t\t\t}],\n\t\t\t[/^DD/, function() {\n\t\t\t\treturn date.getDate();\n\t\t\t}],\n\t\t\t[/^MM/, function() {\n\t\t\t\treturn date.getMonth() + 1;\n\t\t\t}],\n\t\t\t[/^WW/, function() {\n\t\t\t\treturn $tw.utils.getWeek(date);\n\t\t\t}],\n\t\t\t[/^YY/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getFullYear() - 2000);\n\t\t\t}]\n\t\t];\n\t// If the user wants everything in UTC, shift the datestamp\n\t// Optimize for format string that essentially means\n\t// 'return raw UTC (tiddlywiki style) date string.'\n\tif(t.indexOf(\"[UTC]\") == 0 ) {\n\t\tif(t == \"[UTC]YYYY0MM0DD0hh0mm0ssXXX\")\n\t\t\treturn $tw.utils.stringifyDate(new Date());\n\t\tvar offset = date.getTimezoneOffset() ; // in minutes\n\t\tdate = new Date(date.getTime()+offset*60*1000) ;\n\t\tt = t.substr(5) ;\n\t}\n\twhile(t.length){\n\t\tvar matchString = \"\";\n\t\t$tw.utils.each(matches, function(m) {\n\t\t\tvar match = m[0].exec(t);\n\t\t\tif(match) {\n\t\t\t\tmatchString = m[1].call();\n\t\t\t\tt = t.substr(match[0].length);\n\t\t\t\treturn false;\n\t\t\t}\n\t\t});\n\t\tif(matchString) {\n\t\t\tresult += matchString;\n\t\t} else {\n\t\t\tresult += t.charAt(0);\n\t\t\tt = t.substr(1);\n\t\t}\n\t}\n\tresult = result.replace(/\\\\(.)/g,\"$1\");\n\treturn result;\n};\n\nexports.getAmPm = function(date) {\n\treturn $tw.language.getString(\"Date/Period/\" + (date.getHours() >= 12 ? \"pm\" : \"am\"));\n};\n\nexports.getDaySuffix = function(date) {\n\treturn $tw.language.getString(\"Date/DaySuffix/\" + date.getDate());\n};\n\nexports.getWeek = function(date) {\n\tvar dt = new Date(date.getTime());\n\tvar d = dt.getDay();\n\tif(d === 0) {\n\t\td = 7; // JavaScript Sun=0, ISO Sun=7\n\t}\n\tdt.setTime(dt.getTime() + (4 - d) * 86400000);// shift day to Thurs of same week to calculate weekNo\n\tvar x = new Date(dt.getFullYear(),0,1);\n\tvar n = Math.floor((dt.getTime() - x.getTime()) / 86400000);\n\treturn Math.floor(n / 7) + 1;\n};\n\nexports.getYearForWeekNo = function(date) {\n\tvar dt = new Date(date.getTime());\n\tvar d = dt.getDay();\n\tif(d === 0) {\n\t\td = 7; // JavaScript Sun=0, ISO Sun=7\n\t}\n\tdt.setTime(dt.getTime() + (4 - d) * 86400000);// shift day to Thurs of same week\n\treturn dt.getFullYear();\n};\n\nexports.getHours12 = function(date) {\n\tvar h = date.getHours();\n\treturn h > 12 ? h-12 : ( h > 0 ? h : 12 );\n};\n\n/*\nConvert a date delta in milliseconds into a string representation of \"23 seconds ago\", \"27 minutes ago\" etc.\n\tdelta: delta in milliseconds\nReturns an object with these members:\n\tdescription: string describing the delta period\n\tupdatePeriod: time in millisecond until the string will be inaccurate\n*/\nexports.getRelativeDate = function(delta) {\n\tvar futurep = false;\n\tif(delta < 0) {\n\t\tdelta = -1 * delta;\n\t\tfuturep = true;\n\t}\n\tvar units = [\n\t\t{name: \"Years\", duration: 365 * 24 * 60 * 60 * 1000},\n\t\t{name: \"Months\", duration: (365/12) * 24 * 60 * 60 * 1000},\n\t\t{name: \"Days\", duration: 24 * 60 * 60 * 1000},\n\t\t{name: \"Hours\", duration: 60 * 60 * 1000},\n\t\t{name: \"Minutes\", duration: 60 * 1000},\n\t\t{name: \"Seconds\", duration: 1000}\n\t];\n\tfor(var t=0; t<units.length; t++) {\n\t\tvar result = Math.floor(delta / units[t].duration);\n\t\tif(result >= 2) {\n\t\t\treturn {\n\t\t\t\tdelta: delta,\n\t\t\t\tdescription: $tw.language.getString(\n\t\t\t\t\t\"RelativeDate/\" + (futurep ? \"Future\" : \"Past\") + \"/\" + units[t].name,\n\t\t\t\t\t{variables:\n\t\t\t\t\t\t{period: result.toString()}\n\t\t\t\t\t}\n\t\t\t\t),\n\t\t\t\tupdatePeriod: units[t].duration\n\t\t\t};\n\t\t}\n\t}\n\treturn {\n\t\tdelta: delta,\n\t\tdescription: $tw.language.getString(\n\t\t\t\"RelativeDate/\" + (futurep ? \"Future\" : \"Past\") + \"/Second\",\n\t\t\t{variables:\n\t\t\t\t{period: \"1\"}\n\t\t\t}\n\t\t),\n\t\tupdatePeriod: 1000\n\t};\n};\n\n// Convert & to \"&\", < to \"<\", > to \">\", \" to \""\"\nexports.htmlEncode = function(s) {\n\tif(s) {\n\t\treturn s.toString().replace(/&/mg,\"&\").replace(/</mg,\"<\").replace(/>/mg,\">\").replace(/\\\"/mg,\""\");\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\n// Converts all HTML entities to their character equivalents\nexports.entityDecode = function(s) {\n\tvar converter = String.fromCodePoint || String.fromCharCode,\n\t\te = s.substr(1,s.length-2), // Strip the & and the ;\n\t\tc;\n\tif(e.charAt(0) === \"#\") {\n\t\tif(e.charAt(1) === \"x\" || e.charAt(1) === \"X\") {\n\t\t\tc = parseInt(e.substr(2),16);\n\t\t} else {\n\t\t\tc = parseInt(e.substr(1),10);\n\t\t}\n\t\tif(isNaN(c)) {\n\t\t\treturn s;\n\t\t} else {\n\t\t\treturn converter(c);\n\t\t}\n\t} else {\n\t\tc = $tw.config.htmlEntities[e];\n\t\tif(c) {\n\t\t\treturn converter(c);\n\t\t} else {\n\t\t\treturn s; // Couldn't convert it as an entity, just return it raw\n\t\t}\n\t}\n};\n\nexports.unescapeLineBreaks = function(s) {\n\treturn s.replace(/\\\\n/mg,\"\\n\").replace(/\\\\b/mg,\" \").replace(/\\\\s/mg,\"\\\\\").replace(/\\r/mg,\"\");\n};\n\n/*\n * Returns an escape sequence for given character. Uses \\x for characters <=\n * 0xFF to save space, \\u for the rest.\n *\n * The code needs to be in sync with th code template in the compilation\n * function for \"action\" nodes.\n */\n// Copied from peg.js, thanks to David Majda\nexports.escape = function(ch) {\n\tvar charCode = ch.charCodeAt(0);\n\tif(charCode <= 0xFF) {\n\t\treturn '\\\\x' + $tw.utils.pad(charCode.toString(16).toUpperCase());\n\t} else {\n\t\treturn '\\\\u' + $tw.utils.pad(charCode.toString(16).toUpperCase(),4);\n\t}\n};\n\n// Turns a string into a legal JavaScript string\n// Copied from peg.js, thanks to David Majda\nexports.stringify = function(s) {\n\t/*\n\t* ECMA-262, 5th ed., 7.8.4: All characters may appear literally in a string\n\t* literal except for the closing quote character, backslash, carriage return,\n\t* line separator, paragraph separator, and line feed. Any character may\n\t* appear in the form of an escape sequence.\n\t*\n\t* For portability, we also escape all non-ASCII characters.\n\t*/\n\treturn (s || \"\")\n\t\t.replace(/\\\\/g, '\\\\\\\\') // backslash\n\t\t.replace(/\"/g, '\\\\\"') // double quote character\n\t\t.replace(/'/g, \"\\\\'\") // single quote character\n\t\t.replace(/\\r/g, '\\\\r') // carriage return\n\t\t.replace(/\\n/g, '\\\\n') // line feed\n\t\t.replace(/[\\x00-\\x1f\\x80-\\uFFFF]/g, exports.escape); // non-ASCII characters\n};\n\n// Turns a string into a legal JSON string\n// Derived from peg.js, thanks to David Majda\nexports.jsonStringify = function(s) {\n\t// See http://www.json.org/\n\treturn (s || \"\")\n\t\t.replace(/\\\\/g, '\\\\\\\\') // backslash\n\t\t.replace(/\"/g, '\\\\\"') // double quote character\n\t\t.replace(/\\r/g, '\\\\r') // carriage return\n\t\t.replace(/\\n/g, '\\\\n') // line feed\n\t\t.replace(/\\x08/g, '\\\\b') // backspace\n\t\t.replace(/\\x0c/g, '\\\\f') // formfeed\n\t\t.replace(/\\t/g, '\\\\t') // tab\n\t\t.replace(/[\\x00-\\x1f\\x80-\\uFFFF]/g,function(s) {\n\t\t\treturn '\\\\u' + $tw.utils.pad(s.charCodeAt(0).toString(16).toUpperCase(),4);\n\t\t}); // non-ASCII characters\n};\n\n/*\nEscape the RegExp special characters with a preceding backslash\n*/\nexports.escapeRegExp = function(s) {\n return s.replace(/[\\-\\/\\\\\\^\\$\\*\\+\\?\\.\\(\\)\\|\\[\\]\\{\\}]/g, '\\\\$&');\n};\n\n// Checks whether a link target is external, i.e. not a tiddler title\nexports.isLinkExternal = function(to) {\n\tvar externalRegExp = /^(?:file|http|https|mailto|ftp|irc|news|data|skype):[^\\s<>{}\\[\\]`|\"\\\\^]+(?:\\/|\\b)/i;\n\treturn externalRegExp.test(to);\n};\n\nexports.nextTick = function(fn) {\n/*global window: false */\n\tif(typeof process === \"undefined\") {\n\t\t// Apparently it would be faster to use postMessage - http://dbaron.org/log/20100309-faster-timeouts\n\t\twindow.setTimeout(fn,4);\n\t} else {\n\t\tprocess.nextTick(fn);\n\t}\n};\n\n/*\nConvert a hyphenated CSS property name into a camel case one\n*/\nexports.unHyphenateCss = function(propName) {\n\treturn propName.replace(/-([a-z])/gi, function(match0,match1) {\n\t\treturn match1.toUpperCase();\n\t});\n};\n\n/*\nConvert a camelcase CSS property name into a dashed one (\"backgroundColor\" --> \"background-color\")\n*/\nexports.hyphenateCss = function(propName) {\n\treturn propName.replace(/([A-Z])/g, function(match0,match1) {\n\t\treturn \"-\" + match1.toLowerCase();\n\t});\n};\n\n/*\nParse a text reference of one of these forms:\n* title\n* !!field\n* title!!field\n* title##index\n* etc\nReturns an object with the following fields, all optional:\n* title: tiddler title\n* field: tiddler field name\n* index: JSON property index\n*/\nexports.parseTextReference = function(textRef) {\n\t// Separate out the title, field name and/or JSON indices\n\tvar reTextRef = /(?:(.*?)!!(.+))|(?:(.*?)##(.+))|(.*)/mg,\n\t\tmatch = reTextRef.exec(textRef),\n\t\tresult = {};\n\tif(match && reTextRef.lastIndex === textRef.length) {\n\t\t// Return the parts\n\t\tif(match[1]) {\n\t\t\tresult.title = match[1];\n\t\t}\n\t\tif(match[2]) {\n\t\t\tresult.field = match[2];\n\t\t}\n\t\tif(match[3]) {\n\t\t\tresult.title = match[3];\n\t\t}\n\t\tif(match[4]) {\n\t\t\tresult.index = match[4];\n\t\t}\n\t\tif(match[5]) {\n\t\t\tresult.title = match[5];\n\t\t}\n\t} else {\n\t\t// If we couldn't parse it\n\t\tresult.title = textRef\n\t}\n\treturn result;\n};\n\n/*\nChecks whether a string is a valid fieldname\n*/\nexports.isValidFieldName = function(name) {\n\tif(!name || typeof name !== \"string\") {\n\t\treturn false;\n\t}\n\tname = name.toLowerCase().trim();\n\tvar fieldValidatorRegEx = /^[a-z0-9\\-\\._]+$/mg;\n\treturn fieldValidatorRegEx.test(name);\n};\n\n/*\nExtract the version number from the meta tag or from the boot file\n*/\n\n// Browser version\nexports.extractVersionInfo = function() {\n\tif($tw.packageInfo) {\n\t\treturn $tw.packageInfo.version;\n\t} else {\n\t\tvar metatags = document.getElementsByTagName(\"meta\");\n\t\tfor(var t=0; t<metatags.length; t++) {\n\t\t\tvar m = metatags[t];\n\t\t\tif(m.name === \"tiddlywiki-version\") {\n\t\t\t\treturn m.content;\n\t\t\t}\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nGet the animation duration in ms\n*/\nexports.getAnimationDuration = function() {\n\treturn parseInt($tw.wiki.getTiddlerText(\"$:/config/AnimationDuration\",\"400\"),10) || 0;\n};\n\n/*\nHash a string to a number\nDerived from http://stackoverflow.com/a/15710692\n*/\nexports.hashString = function(str) {\n\treturn str.split(\"\").reduce(function(a,b) {\n\t\ta = ((a << 5) - a) + b.charCodeAt(0);\n\t\treturn a & a;\n\t},0);\n};\n\n/*\nDecode a base64 string\n*/\nexports.base64Decode = function(string64) {\n\treturn base64utf8.base64.decode.call(base64utf8,string64);\n};\n\n/*\nEncode a string to base64\n*/\nexports.base64Encode = function(string64) {\n\treturn base64utf8.base64.encode.call(base64utf8,string64);\n};\n\n/*\nConvert a hashmap into a tiddler dictionary format sequence of name:value pairs\n*/\nexports.makeTiddlerDictionary = function(data) {\n\tvar output = [];\n\tfor(var name in data) {\n\t\toutput.push(name + \": \" + data[name]);\n\t}\n\treturn output.join(\"\\n\");\n};\n\n/*\nHigh resolution microsecond timer for profiling\n*/\nexports.timer = function(base) {\n\tvar m;\n\tif($tw.node) {\n\t\tvar r = process.hrtime();\n\t\tm = r[0] * 1e3 + (r[1] / 1e6);\n\t} else if(window.performance) {\n\t\tm = performance.now();\n\t} else {\n\t\tm = Date.now();\n\t}\n\tif(typeof base !== \"undefined\") {\n\t\tm = m - base;\n\t}\n\treturn m;\n};\n\n/*\nConvert text and content type to a data URI\n*/\nexports.makeDataUri = function(text,type,_canonical_uri) {\n\ttype = type || \"text/vnd.tiddlywiki\";\n\tvar typeInfo = $tw.config.contentTypeInfo[type] || $tw.config.contentTypeInfo[\"text/plain\"],\n\t\tisBase64 = typeInfo.encoding === \"base64\",\n\t\tparts = [];\n\tif(_canonical_uri) {\n\t\tparts.push(_canonical_uri);\n\t} else {\n\t\tparts.push(\"data:\");\n\t\tparts.push(type);\n\t\tparts.push(isBase64 ? \";base64\" : \"\");\n\t\tparts.push(\",\");\n\t\tparts.push(isBase64 ? text : encodeURIComponent(text));\t\t\n\t}\n\treturn parts.join(\"\");\n};\n\n/*\nUseful for finding out the fully escaped CSS selector equivalent to a given tag. For example:\n\n$tw.utils.tagToCssSelector(\"$:/tags/Stylesheet\") --> tc-tagged-\\%24\\%3A\\%2Ftags\\%2FStylesheet\n*/\nexports.tagToCssSelector = function(tagName) {\n\treturn \"tc-tagged-\" + encodeURIComponent(tagName).replace(/[!\"#$%&'()*+,\\-./:;<=>?@[\\\\\\]^`{\\|}~,]/mg,function(c) {\n\t\treturn \"\\\\\" + c;\n\t});\n};\n\n/*\nIE does not have sign function\n*/\nexports.sign = Math.sign || function(x) {\n\tx = +x; // convert to a number\n\tif (x === 0 || isNaN(x)) {\n\t\treturn x;\n\t}\n\treturn x > 0 ? 1 : -1;\n};\n\n/*\nIE does not have an endsWith function\n*/\nexports.strEndsWith = function(str,ending,position) {\n\tif(str.endsWith) {\n\t\treturn str.endsWith(ending,position);\n\t} else {\n\t\tif (typeof position !== 'number' || !isFinite(position) || Math.floor(position) !== position || position > str.length) {\n\t\t\tposition = str.length;\n\t\t}\n\t\tposition -= ending.length;\n\t\tvar lastIndex = str.indexOf(ending, position);\n\t\treturn lastIndex !== -1 && lastIndex === position;\n\t}\n};\n\n/*\nReturn system information useful for debugging\n*/\nexports.getSystemInfo = function(str,ending,position) {\n\tvar results = [],\n\t\tsave = function(desc,value) {\n\t\t\tresults.push(desc + \": \" + value);\n\t\t};\n\tif($tw.browser) {\n\t\tsave(\"User Agent\",navigator.userAgent);\n\t\tsave(\"Online Status\",window.navigator.onLine);\n\t}\n\tif($tw.node) {\n\t\tsave(\"Node Version\",process.version);\n\t}\n\treturn results.join(\"\\n\");\n};\n\nexports.parseNumber = function(str) {\n\treturn parseFloat(str) || 0;\n};\n\nexports.parseInt = function(str) {\n\treturn parseInt(str,10) || 0;\n};\n\nexports.stringifyNumber = function(num) {\n\treturn num + \"\";\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/widgets/action-createtiddler.js": {
"title": "$:/core/modules/widgets/action-createtiddler.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/action-createtiddler.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to create a new tiddler with a unique name and specified fields.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw:false, require:false, exports:false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar CreateTiddlerWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nCreateTiddlerWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nCreateTiddlerWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nCreateTiddlerWidget.prototype.execute = function() {\n\tthis.actionBaseTitle = this.getAttribute(\"$basetitle\");\n\tthis.hasBase = !!this.actionBaseTitle;\n\tthis.actionSaveTitle = this.getAttribute(\"$savetitle\");\n\tthis.actionSaveDraftTitle = this.getAttribute(\"$savedrafttitle\");\n\tthis.actionTimestamp = this.getAttribute(\"$timestamp\",\"yes\") === \"yes\";\n\t//Following params are new since 5.1.22\n\tthis.actionTemplate = this.getAttribute(\"$template\");\n\tthis.useTemplate = !!this.actionTemplate;\n\tthis.actionOverwrite = this.getAttribute(\"$overwrite\",\"no\");\n\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nCreateTiddlerWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif($tw.utils.count(changedAttributes) > 0) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nCreateTiddlerWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tvar title = this.wiki.getTiddlerText(\"$:/language/DefaultNewTiddlerTitle\"), // Get the initial new-tiddler title\n\t\tfields = {},\n\t\tcreationFields,\n\t\tmodificationFields;\n\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\tif(name.charAt(0) !== \"$\") {\n\t\t\tfields[name] = attribute;\n\t\t}\n\t});\n\tif(this.actionTimestamp) {\n\t\tcreationFields = this.wiki.getCreationFields();\n\t\tmodificationFields = this.wiki.getModificationFields();\n\t}\n\tif(this.hasBase && this.actionOverwrite === \"no\") {\n\t\ttitle = this.wiki.generateNewTitle(this.actionBaseTitle);\n\t} else if (this.hasBase && this.actionOverwrite === \"yes\") {\n\t\ttitle = this.actionBaseTitle\n\t}\n\t// NO $basetitle BUT $template parameter is available\n\t// the title MUST be unique, otherwise the template would be overwritten\n\tif (!this.hasBase && this.useTemplate) {\n\t\ttitle = this.wiki.generateNewTitle(this.actionTemplate);\n\t} else if (!this.hasBase && !this.useTemplate) {\n\t\t// If NO $basetitle AND NO $template use initial title\n\t\t// DON'T overwrite any stuff\n\t\ttitle = this.wiki.generateNewTitle(title);\n\t}\n\tvar templateTiddler = this.wiki.getTiddler(this.actionTemplate) || {};\n\tvar tiddler = this.wiki.addTiddler(new $tw.Tiddler(templateTiddler.fields,creationFields,fields,modificationFields,{title: title}));\n\tif(this.actionSaveTitle) {\n\t\tthis.wiki.setTextReference(this.actionSaveTitle,title,this.getVariable(\"currentTiddler\"));\n\t}\n\tif(this.actionSaveDraftTitle) {\n\t\tthis.wiki.setTextReference(this.actionSaveDraftTitle,this.wiki.generateDraftTitle(title),this.getVariable(\"currentTiddler\"));\n\t}\n\treturn true; // Action was invoked\n};\n\nexports[\"action-createtiddler\"] = CreateTiddlerWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-deletefield.js": {
"title": "$:/core/modules/widgets/action-deletefield.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/action-deletefield.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to delete fields of a tiddler.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DeleteFieldWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDeleteFieldWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDeleteFieldWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nDeleteFieldWidget.prototype.execute = function() {\n\tthis.actionTiddler = this.getAttribute(\"$tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.actionField = this.getAttribute(\"$field\");\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nDeleteFieldWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"$tiddler\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nDeleteFieldWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tvar self = this,\n\t\ttiddler = this.wiki.getTiddler(self.actionTiddler),\n\t\tremoveFields = {},\n\t\thasChanged = false;\n\tif(this.actionField && tiddler) {\n\t\tremoveFields[this.actionField] = undefined;\n\t\tif(this.actionField in tiddler.fields) {\n\t\t\thasChanged = true;\n\t\t}\n\t}\n\tif(tiddler) {\n\t\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\t\tif(name.charAt(0) !== \"$\" && name !== \"title\") {\n\t\t\t\tremoveFields[name] = undefined;\n\t\t\t\thasChanged = true;\n\t\t\t}\n\t\t});\n\t\tif(hasChanged) {\n\t\t\tthis.wiki.addTiddler(new $tw.Tiddler(this.wiki.getCreationFields(),tiddler,removeFields,this.wiki.getModificationFields()));\t\t\t\n\t\t}\n\t}\n\treturn true; // Action was invoked\n};\n\nexports[\"action-deletefield\"] = DeleteFieldWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-deletetiddler.js": {
"title": "$:/core/modules/widgets/action-deletetiddler.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/action-deletetiddler.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to delete a tiddler.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DeleteTiddlerWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDeleteTiddlerWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDeleteTiddlerWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nDeleteTiddlerWidget.prototype.execute = function() {\n\tthis.actionFilter = this.getAttribute(\"$filter\");\n\tthis.actionTiddler = this.getAttribute(\"$tiddler\");\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nDeleteTiddlerWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"$filter\"] || changedAttributes[\"$tiddler\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nDeleteTiddlerWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tvar tiddlers = [];\n\tif(this.actionFilter) {\n\t\ttiddlers = this.wiki.filterTiddlers(this.actionFilter,this);\n\t}\n\tif(this.actionTiddler) {\n\t\ttiddlers.push(this.actionTiddler);\n\t}\n\tfor(var t=0; t<tiddlers.length; t++) {\n\t\tthis.wiki.deleteTiddler(tiddlers[t]);\n\t}\n\treturn true; // Action was invoked\n};\n\nexports[\"action-deletetiddler\"] = DeleteTiddlerWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-listops.js": {
"title": "$:/core/modules/widgets/action-listops.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/action-listops.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to apply list operations to any tiddler field (defaults to the 'list' field of the current tiddler)\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\nvar ActionListopsWidget = function(parseTreeNode, options) {\n\tthis.initialise(parseTreeNode, options);\n};\n/**\n * Inherit from the base widget class\n */\nActionListopsWidget.prototype = new Widget();\n/**\n * Render this widget into the DOM\n */\nActionListopsWidget.prototype.render = function(parent, nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n/**\n * Compute the internal state of the widget\n */\nActionListopsWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.target = this.getAttribute(\"$tiddler\", this.getVariable(\n\t\t\"currentTiddler\"));\n\tthis.filter = this.getAttribute(\"$filter\");\n\tthis.subfilter = this.getAttribute(\"$subfilter\");\n\tthis.listField = this.getAttribute(\"$field\", \"list\");\n\tthis.listIndex = this.getAttribute(\"$index\");\n\tthis.filtertags = this.getAttribute(\"$tags\");\n};\n/**\n * \tRefresh the widget by ensuring our attributes are up to date\n */\nActionListopsWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.$tiddler || changedAttributes.$filter ||\n\t\tchangedAttributes.$subfilter || changedAttributes.$field ||\n\t\tchangedAttributes.$index || changedAttributes.$tags) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n/**\n * \tInvoke the action associated with this widget\n */\nActionListopsWidget.prototype.invokeAction = function(triggeringWidget,\n\tevent) {\n\t//Apply the specified filters to the lists\n\tvar field = this.listField,\n\t\tindex,\n\t\ttype = \"!!\",\n\t\tlist = this.listField;\n\tif(this.listIndex) {\n\t\tfield = undefined;\n\t\tindex = this.listIndex;\n\t\ttype = \"##\";\n\t\tlist = this.listIndex;\n\t}\n\tif(this.filter) {\n\t\tthis.wiki.setText(this.target, field, index, $tw.utils.stringifyList(\n\t\t\tthis.wiki\n\t\t\t.filterTiddlers(this.filter, this)));\n\t}\n\tif(this.subfilter) {\n\t\tvar subfilter = \"[list[\" + this.target + type + list + \"]] \" + this.subfilter;\n\t\tthis.wiki.setText(this.target, field, index, $tw.utils.stringifyList(\n\t\t\tthis.wiki\n\t\t\t.filterTiddlers(subfilter, this)));\n\t}\n\tif(this.filtertags) {\n\t\tvar tiddler = this.wiki.getTiddler(this.target),\n\t\t\toldtags = tiddler ? (tiddler.fields.tags || []).slice(0) : [],\n\t\t\ttagfilter = \"[list[\" + this.target + \"!!tags]] \" + this.filtertags,\n\t\t\tnewtags = this.wiki.filterTiddlers(tagfilter,this);\n\t\tif($tw.utils.stringifyList(oldtags.sort()) !== $tw.utils.stringifyList(newtags.sort())) {\n\t\t\tthis.wiki.setText(this.target,\"tags\",undefined,$tw.utils.stringifyList(newtags));\t\t\t\n\t\t}\n\t}\n\treturn true; // Action was invoked\n};\n\nexports[\"action-listops\"] = ActionListopsWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-navigate.js": {
"title": "$:/core/modules/widgets/action-navigate.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/action-navigate.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to navigate to a tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar NavigateWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nNavigateWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nNavigateWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nNavigateWidget.prototype.execute = function() {\n\tthis.actionTo = this.getAttribute(\"$to\");\n\tthis.actionScroll = this.getAttribute(\"$scroll\");\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nNavigateWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"$to\"] || changedAttributes[\"$scroll\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nNavigateWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tevent = event || {};\n\tvar bounds = triggeringWidget && triggeringWidget.getBoundingClientRect && triggeringWidget.getBoundingClientRect(),\n\t\tsuppressNavigation = event.metaKey || event.ctrlKey || (event.button === 1);\n\tif(this.actionScroll === \"yes\") {\n\t\tsuppressNavigation = false;\n\t} else if(this.actionScroll === \"no\") {\n\t\tsuppressNavigation = true;\n\t}\n\tthis.dispatchEvent({\n\t\ttype: \"tm-navigate\",\n\t\tnavigateTo: this.actionTo === undefined ? this.getVariable(\"currentTiddler\") : this.actionTo,\n\t\tnavigateFromTitle: this.getVariable(\"storyTiddler\"),\n\t\tnavigateFromNode: triggeringWidget,\n\t\tnavigateFromClientRect: bounds && { top: bounds.top, left: bounds.left, width: bounds.width, right: bounds.right, bottom: bounds.bottom, height: bounds.height\n\t\t},\n\t\tnavigateSuppressNavigation: suppressNavigation\n\t});\n\treturn true; // Action was invoked\n};\n\nexports[\"action-navigate\"] = NavigateWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-popup.js": {
"title": "$:/core/modules/widgets/action-popup.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/action-popup.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to trigger a popup.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ActionPopupWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nActionPopupWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nActionPopupWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nActionPopupWidget.prototype.execute = function() {\n\tthis.actionState = this.getAttribute(\"$state\");\n\tthis.actionCoords = this.getAttribute(\"$coords\");\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nActionPopupWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"$state\"] || changedAttributes[\"$coords\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nActionPopupWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\t// Trigger the popup\n\tvar popupLocationRegExp = /^\\((-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+)\\)$/,\n\t\tmatch = popupLocationRegExp.exec(this.actionCoords);\n\tif(match) {\n\t\t$tw.popup.triggerPopup({\n\t\t\tdomNode: null,\n\t\t\tdomNodeRect: {\n\t\t\t\tleft: parseFloat(match[1]),\n\t\t\t\ttop: parseFloat(match[2]),\n\t\t\t\twidth: parseFloat(match[3]),\n\t\t\t\theight: parseFloat(match[4])\n\t\t\t},\n\t\t\ttitle: this.actionState,\n\t\t\twiki: this.wiki\n\t\t});\n\t}\n\treturn true; // Action was invoked\n};\n\nexports[\"action-popup\"] = ActionPopupWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-sendmessage.js": {
"title": "$:/core/modules/widgets/action-sendmessage.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/action-sendmessage.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to send a message\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar SendMessageWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nSendMessageWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nSendMessageWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nSendMessageWidget.prototype.execute = function() {\n\tthis.actionMessage = this.getAttribute(\"$message\");\n\tthis.actionParam = this.getAttribute(\"$param\");\n\tthis.actionName = this.getAttribute(\"$name\");\n\tthis.actionValue = this.getAttribute(\"$value\",\"\");\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nSendMessageWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(Object.keys(changedAttributes).length) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nSendMessageWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\t// Get the string parameter\n\tvar param = this.actionParam;\n\t// Assemble the attributes as a hashmap\n\tvar paramObject = Object.create(null);\n\tvar count = 0;\n\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\tif(name.charAt(0) !== \"$\") {\n\t\t\tparamObject[name] = attribute;\n\t\t\tcount++;\n\t\t}\n\t});\n\t// Add name/value pair if present\n\tif(this.actionName) {\n\t\tparamObject[this.actionName] = this.actionValue;\n\t}\n\t// Dispatch the message\n\tthis.dispatchEvent({\n\t\ttype: this.actionMessage,\n\t\tparam: param,\n\t\tparamObject: paramObject,\n\t\ttiddlerTitle: this.getVariable(\"currentTiddler\"),\n\t\tnavigateFromTitle: this.getVariable(\"storyTiddler\"),\n\t\tevent: event\n\t});\n\treturn true; // Action was invoked\n};\n\nexports[\"action-sendmessage\"] = SendMessageWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-setfield.js": {
"title": "$:/core/modules/widgets/action-setfield.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/action-setfield.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to set a single field or index on a tiddler.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar SetFieldWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nSetFieldWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nSetFieldWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nSetFieldWidget.prototype.execute = function() {\n\tthis.actionTiddler = this.getAttribute(\"$tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.actionField = this.getAttribute(\"$field\");\n\tthis.actionIndex = this.getAttribute(\"$index\");\n\tthis.actionValue = this.getAttribute(\"$value\");\n\tthis.actionTimestamp = this.getAttribute(\"$timestamp\",\"yes\") === \"yes\";\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nSetFieldWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"$tiddler\"] || changedAttributes[\"$field\"] || changedAttributes[\"$index\"] || changedAttributes[\"$value\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nSetFieldWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tvar self = this,\n\t\toptions = {};\n\toptions.suppressTimestamp = !this.actionTimestamp;\n\tif((typeof this.actionField == \"string\") || (typeof this.actionIndex == \"string\") || (typeof this.actionValue == \"string\")) {\n\t\tthis.wiki.setText(this.actionTiddler,this.actionField,this.actionIndex,this.actionValue,options);\n\t}\n\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\tif(name.charAt(0) !== \"$\") {\n\t\t\tself.wiki.setText(self.actionTiddler,name,undefined,attribute,options);\n\t\t}\n\t});\n\treturn true; // Action was invoked\n};\n\nexports[\"action-setfield\"] = SetFieldWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/browse.js": {
"title": "$:/core/modules/widgets/browse.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/browse.js\ntype: application/javascript\nmodule-type: widget\n\nBrowse widget for browsing for files to import\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar BrowseWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nBrowseWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nBrowseWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create element\n\tvar domNode = this.document.createElement(\"input\");\n\tdomNode.setAttribute(\"type\",\"file\");\n\tif(this.browseMultiple) {\n\t\tdomNode.setAttribute(\"multiple\",\"multiple\");\n\t}\n\tif(this.tooltip) {\n\t\tdomNode.setAttribute(\"title\",this.tooltip);\n\t}\n\t// Nw.js supports \"nwsaveas\" to force a \"save as\" dialogue that allows a new or existing file to be selected\n\tif(this.nwsaveas) {\n\t\tdomNode.setAttribute(\"nwsaveas\",this.nwsaveas);\n\t}\n\t// Nw.js supports \"webkitdirectory\" and \"nwdirectory\" to allow a directory to be selected\n\tif(this.webkitdirectory) {\n\t\tdomNode.setAttribute(\"webkitdirectory\",this.webkitdirectory);\n\t}\n\tif(this.nwdirectory) {\n\t\tdomNode.setAttribute(\"nwdirectory\",this.nwdirectory);\n\t}\n\t// Add a click event handler\n\tdomNode.addEventListener(\"change\",function (event) {\n\t\tif(self.message) {\n\t\t\tself.dispatchEvent({type: self.message, param: self.param, files: event.target.files});\n\t\t} else {\n\t\t\tself.wiki.readFiles(event.target.files,{\n\t\t\t\tcallback: function(tiddlerFieldsArray) {\n\t\t\t\t\tself.dispatchEvent({type: \"tm-import-tiddlers\", param: JSON.stringify(tiddlerFieldsArray)});\n\t\t\t\t},\n\t\t\t\tdeserializer: self.deserializer\n\t\t\t});\n\t\t}\n\t\treturn false;\n\t},false);\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nBrowseWidget.prototype.execute = function() {\n\tthis.browseMultiple = this.getAttribute(\"multiple\");\n\tthis.deserializer = this.getAttribute(\"deserializer\");\n\tthis.message = this.getAttribute(\"message\");\n\tthis.param = this.getAttribute(\"param\");\n\tthis.tooltip = this.getAttribute(\"tooltip\");\n\tthis.nwsaveas = this.getAttribute(\"nwsaveas\");\n\tthis.webkitdirectory = this.getAttribute(\"webkitdirectory\");\n\tthis.nwdirectory = this.getAttribute(\"nwdirectory\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nBrowseWidget.prototype.refresh = function(changedTiddlers) {\n\treturn false;\n};\n\nexports.browse = BrowseWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/button.js": {
"title": "$:/core/modules/widgets/button.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/button.js\ntype: application/javascript\nmodule-type: widget\n\nButton widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ButtonWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nButtonWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nButtonWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create element\n\tvar tag = \"button\";\n\tif(this.buttonTag && $tw.config.htmlUnsafeElements.indexOf(this.buttonTag) === -1) {\n\t\ttag = this.buttonTag;\n\t}\n\tvar domNode = this.document.createElement(tag);\n\t// Assign classes\n\tvar classes = this[\"class\"].split(\" \") || [],\n\t\tisPoppedUp = (this.popup || this.popupTitle) && this.isPoppedUp();\n\tif(this.selectedClass) {\n\t\tif((this.set || this.setTitle) && this.setTo && this.isSelected()) {\n\t\t\t$tw.utils.pushTop(classes,this.selectedClass.split(\" \"));\n\t\t}\n\t\tif(isPoppedUp) {\n\t\t\t$tw.utils.pushTop(classes,this.selectedClass.split(\" \"));\n\t\t}\n\t}\n\tif(isPoppedUp) {\n\t\t$tw.utils.pushTop(classes,\"tc-popup-handle\");\n\t}\n\tdomNode.className = classes.join(\" \");\n\t// Assign other attributes\n\tif(this.style) {\n\t\tdomNode.setAttribute(\"style\",this.style);\n\t}\n\tif(this.tooltip) {\n\t\tdomNode.setAttribute(\"title\",this.tooltip);\n\t}\n\tif(this[\"aria-label\"]) {\n\t\tdomNode.setAttribute(\"aria-label\",this[\"aria-label\"]);\n\t}\n\t// Set the tabindex\n\tif(this.tabIndex) {\n\t\tdomNode.setAttribute(\"tabindex\",this.tabIndex);\n\t}\t\n\t// Add a click event handler\n\tdomNode.addEventListener(\"click\",function (event) {\n\t\tvar handled = false;\n\t\tif(self.invokeActions(self,event)) {\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.to) {\n\t\t\tself.navigateTo(event);\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.message) {\n\t\t\tself.dispatchMessage(event);\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.popup || self.popupTitle) {\n\t\t\tself.triggerPopup(event);\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.set || self.setTitle) {\n\t\t\tself.setTiddler();\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.actions) {\n\t\t\tself.invokeActionString(self.actions,self,event);\n\t\t}\n\t\tif(handled) {\n\t\t\tevent.preventDefault();\n\t\t\tevent.stopPropagation();\n\t\t}\n\t\treturn handled;\n\t},false);\n\t// Make it draggable if required\n\tif(this.dragTiddler || this.dragFilter) {\n\t\t$tw.utils.makeDraggable({\n\t\t\tdomNode: domNode,\n\t\t\tdragTiddlerFn: function() {return self.dragTiddler;},\n\t\t\tdragFilterFn: function() {return self.dragFilter;},\n\t\t\twidget: this\n\t\t});\n\t}\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nWe don't allow actions to propagate because we trigger actions ourselves\n*/\nButtonWidget.prototype.allowActionPropagation = function() {\n\treturn false;\n};\n\nButtonWidget.prototype.getBoundingClientRect = function() {\n\treturn this.domNodes[0].getBoundingClientRect();\n};\n\nButtonWidget.prototype.isSelected = function() {\n return this.setTitle ? (this.setField ? this.wiki.getTiddler(this.setTitle).getFieldString(this.setField) === this.setTo :\n\t\t(this.setIndex ? this.wiki.extractTiddlerDataItem(this.setTitle,this.setIndex) === this.setTo :\n\t\t\tthis.wiki.getTiddlerText(this.setTitle))) || this.defaultSetValue || this.getVariable(\"currentTiddler\") :\n\t\tthis.wiki.getTextReference(this.set,this.defaultSetValue,this.getVariable(\"currentTiddler\")) === this.setTo;\n};\n\nButtonWidget.prototype.isPoppedUp = function() {\n\tvar tiddler = this.popupTitle ? this.wiki.getTiddler(this.popupTitle) : this.wiki.getTiddler(this.popup);\n\tvar result = tiddler && tiddler.fields.text ? $tw.popup.readPopupState(tiddler.fields.text) : false;\n\treturn result;\n};\n\nButtonWidget.prototype.navigateTo = function(event) {\n\tvar bounds = this.getBoundingClientRect();\n\tthis.dispatchEvent({\n\t\ttype: \"tm-navigate\",\n\t\tnavigateTo: this.to,\n\t\tnavigateFromTitle: this.getVariable(\"storyTiddler\"),\n\t\tnavigateFromNode: this,\n\t\tnavigateFromClientRect: { top: bounds.top, left: bounds.left, width: bounds.width, right: bounds.right, bottom: bounds.bottom, height: bounds.height\n\t\t},\n\t\tnavigateSuppressNavigation: event.metaKey || event.ctrlKey || (event.button === 1),\n\t\tevent: event\n\t});\n};\n\nButtonWidget.prototype.dispatchMessage = function(event) {\n\tthis.dispatchEvent({type: this.message, param: this.param, tiddlerTitle: this.getVariable(\"currentTiddler\"), event: event});\n};\n\nButtonWidget.prototype.triggerPopup = function(event) {\n\tif(this.popupTitle) {\n\t\t$tw.popup.triggerPopup({\n\t\t\tdomNode: this.domNodes[0],\n\t\t\ttitle: this.popupTitle,\n\t\t\twiki: this.wiki,\n\t\t\tnoStateReference: true\n\t\t});\n\t} else {\n\t\t$tw.popup.triggerPopup({\n\t\t\tdomNode: this.domNodes[0],\n\t\t\ttitle: this.popup,\n\t\t\twiki: this.wiki\n\t\t});\n\t}\n};\n\nButtonWidget.prototype.setTiddler = function() {\n\tif(this.setTitle) {\n\t\tthis.setField ? this.wiki.setText(this.setTitle,this.setField,undefined,this.setTo) :\n\t\t\t\t(this.setIndex ? this.wiki.setText(this.setTitle,undefined,this.setIndex,this.setTo) :\n\t\t\t\tthis.wiki.setText(this.setTitle,\"text\",undefined,this.setTo));\n\t} else {\n\t\tthis.wiki.setTextReference(this.set,this.setTo,this.getVariable(\"currentTiddler\"));\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nButtonWidget.prototype.execute = function() {\n\t// Get attributes\n\tthis.actions = this.getAttribute(\"actions\");\n\tthis.to = this.getAttribute(\"to\");\n\tthis.message = this.getAttribute(\"message\");\n\tthis.param = this.getAttribute(\"param\");\n\tthis.set = this.getAttribute(\"set\");\n\tthis.setTo = this.getAttribute(\"setTo\");\n\tthis.popup = this.getAttribute(\"popup\");\n\tthis.hover = this.getAttribute(\"hover\");\n\tthis[\"class\"] = this.getAttribute(\"class\",\"\");\n\tthis[\"aria-label\"] = this.getAttribute(\"aria-label\");\n\tthis.tooltip = this.getAttribute(\"tooltip\");\n\tthis.style = this.getAttribute(\"style\");\n\tthis.selectedClass = this.getAttribute(\"selectedClass\");\n\tthis.defaultSetValue = this.getAttribute(\"default\",\"\");\n\tthis.buttonTag = this.getAttribute(\"tag\");\n\tthis.dragTiddler = this.getAttribute(\"dragTiddler\");\n\tthis.dragFilter = this.getAttribute(\"dragFilter\");\n\tthis.setTitle = this.getAttribute(\"setTitle\");\n\tthis.setField = this.getAttribute(\"setField\");\n\tthis.setIndex = this.getAttribute(\"setIndex\");\n\tthis.popupTitle = this.getAttribute(\"popupTitle\");\n\tthis.tabIndex = this.getAttribute(\"tabindex\");\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nButtonWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.actions || changedAttributes.to || changedAttributes.message || changedAttributes.param || changedAttributes.set || changedAttributes.setTo || changedAttributes.popup || changedAttributes.hover || changedAttributes[\"class\"] || changedAttributes.selectedClass || changedAttributes.style || changedAttributes.dragFilter || changedAttributes.dragTiddler || (this.set && changedTiddlers[this.set]) || (this.popup && changedTiddlers[this.popup]) || (this.popupTitle && changedTiddlers[this.popupTitle]) || changedAttributes.setTitle || changedAttributes.setField || changedAttributes.setIndex || changedAttributes.popupTitle) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.button = ButtonWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/checkbox.js": {
"title": "$:/core/modules/widgets/checkbox.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/checkbox.js\ntype: application/javascript\nmodule-type: widget\n\nCheckbox widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar CheckboxWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nCheckboxWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nCheckboxWidget.prototype.render = function(parent,nextSibling) {\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Create our elements\n\tthis.labelDomNode = this.document.createElement(\"label\");\n\tthis.labelDomNode.setAttribute(\"class\",this.checkboxClass);\n\tthis.inputDomNode = this.document.createElement(\"input\");\n\tthis.inputDomNode.setAttribute(\"type\",\"checkbox\");\n\tif(this.getValue()) {\n\t\tthis.inputDomNode.setAttribute(\"checked\",\"true\");\n\t}\n\tthis.labelDomNode.appendChild(this.inputDomNode);\n\tthis.spanDomNode = this.document.createElement(\"span\");\n\tthis.labelDomNode.appendChild(this.spanDomNode);\n\t// Add a click event handler\n\t$tw.utils.addEventListeners(this.inputDomNode,[\n\t\t{name: \"change\", handlerObject: this, handlerMethod: \"handleChangeEvent\"}\n\t]);\n\t// Insert the label into the DOM and render any children\n\tparent.insertBefore(this.labelDomNode,nextSibling);\n\tthis.renderChildren(this.spanDomNode,null);\n\tthis.domNodes.push(this.labelDomNode);\n};\n\nCheckboxWidget.prototype.getValue = function() {\n\tvar tiddler = this.wiki.getTiddler(this.checkboxTitle);\n\tif(tiddler) {\n\t\tif(this.checkboxTag) {\n\t\t\tif(this.checkboxInvertTag) {\n\t\t\t\treturn !tiddler.hasTag(this.checkboxTag);\n\t\t\t} else {\n\t\t\t\treturn tiddler.hasTag(this.checkboxTag);\n\t\t\t}\n\t\t}\n\t\tif(this.checkboxField) {\n\t\t\tvar value;\n\t\t\tif($tw.utils.hop(tiddler.fields,this.checkboxField)) {\n\t\t\t\tvalue = tiddler.fields[this.checkboxField] || \"\";\n\t\t\t} else {\n\t\t\t\tvalue = this.checkboxDefault || \"\";\n\t\t\t}\n\t\t\tif(value === this.checkboxChecked) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t\tif(value === this.checkboxUnchecked) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t}\n\t\tif(this.checkboxIndex) {\n\t\t\tvar value = this.wiki.extractTiddlerDataItem(tiddler,this.checkboxIndex,this.checkboxDefault || \"\");\n\t\t\tif(value === this.checkboxChecked) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t\tif(value === this.checkboxUnchecked) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t}\n\t} else {\n\t\tif(this.checkboxTag) {\n\t\t\treturn false;\n\t\t}\n\t\tif(this.checkboxField) {\n\t\t\tif(this.checkboxDefault === this.checkboxChecked) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t\tif(this.checkboxDefault === this.checkboxUnchecked) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t}\n\t}\n\treturn false;\n};\n\nCheckboxWidget.prototype.handleChangeEvent = function(event) {\n\tvar checked = this.inputDomNode.checked,\n\t\ttiddler = this.wiki.getTiddler(this.checkboxTitle),\n\t\tfallbackFields = {text: \"\"},\n\t\tnewFields = {title: this.checkboxTitle},\n\t\thasChanged = false,\n\t\ttagCheck = false,\n\t\thasTag = tiddler && tiddler.hasTag(this.checkboxTag),\n\t\tvalue = checked ? this.checkboxChecked : this.checkboxUnchecked;\n\tif(this.checkboxTag && this.checkboxInvertTag === \"yes\") {\n\t\ttagCheck = hasTag === checked;\n\t} else {\n\t\ttagCheck = hasTag !== checked;\n\t}\n\t// Set the tag if specified\n\tif(this.checkboxTag && (!tiddler || tagCheck)) {\n\t\tnewFields.tags = tiddler ? (tiddler.fields.tags || []).slice(0) : [];\n\t\tvar pos = newFields.tags.indexOf(this.checkboxTag);\n\t\tif(pos !== -1) {\n\t\t\tnewFields.tags.splice(pos,1);\n\t\t}\n\t\tif(this.checkboxInvertTag === \"yes\" && !checked) {\n\t\t\tnewFields.tags.push(this.checkboxTag);\n\t\t} else if(this.checkboxInvertTag !== \"yes\" && checked) {\n\t\t\tnewFields.tags.push(this.checkboxTag);\n\t\t}\n\t\thasChanged = true;\n\t}\n\t// Set the field if specified\n\tif(this.checkboxField) {\n\t\tif(!tiddler || tiddler.fields[this.checkboxField] !== value) {\n\t\t\tnewFields[this.checkboxField] = value;\n\t\t\thasChanged = true;\n\t\t}\n\t}\n\t// Set the index if specified\n\tif(this.checkboxIndex) {\n\t\tvar indexValue = this.wiki.extractTiddlerDataItem(this.checkboxTitle,this.checkboxIndex);\n\t\tif(!tiddler || indexValue !== value) {\n\t\t\thasChanged = true;\n\t\t}\n\t}\n\tif(hasChanged) {\n\t\tif(this.checkboxIndex) {\n\t\t\tthis.wiki.setText(this.checkboxTitle,\"\",this.checkboxIndex,value);\n\t\t} else {\n\t\t\tthis.wiki.addTiddler(new $tw.Tiddler(this.wiki.getCreationFields(),fallbackFields,tiddler,newFields,this.wiki.getModificationFields()));\n\t\t}\n\t}\n\t// Trigger actions\n\tif(this.checkboxActions) {\n\t\tthis.invokeActionString(this.checkboxActions,this,event);\n\t}\n\tif(this.checkboxCheckActions && checked) {\n\t\tthis.invokeActionString(this.checkboxCheckActions,this,event);\n\t}\n\tif(this.checkboxUncheckActions && !checked) {\n\t\tthis.invokeActionString(this.checkboxUncheckActions,this,event);\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nCheckboxWidget.prototype.execute = function() {\n\t// Get the parameters from the attributes\n\tthis.checkboxActions = this.getAttribute(\"actions\");\n\tthis.checkboxCheckActions = this.getAttribute(\"checkactions\");\n\tthis.checkboxUncheckActions = this.getAttribute(\"uncheckactions\");\n\tthis.checkboxTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.checkboxTag = this.getAttribute(\"tag\");\n\tthis.checkboxField = this.getAttribute(\"field\");\n\tthis.checkboxIndex = this.getAttribute(\"index\");\n\tthis.checkboxChecked = this.getAttribute(\"checked\");\n\tthis.checkboxUnchecked = this.getAttribute(\"unchecked\");\n\tthis.checkboxDefault = this.getAttribute(\"default\");\n\tthis.checkboxClass = this.getAttribute(\"class\",\"\");\n\tthis.checkboxInvertTag = this.getAttribute(\"invertTag\",\"\");\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nCheckboxWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.tag || changedAttributes.invertTag || changedAttributes.field || changedAttributes.index || changedAttributes.checked || changedAttributes.unchecked || changedAttributes[\"default\"] || changedAttributes[\"class\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\tvar refreshed = false;\n\t\tif(changedTiddlers[this.checkboxTitle]) {\n\t\t\tthis.inputDomNode.checked = this.getValue();\n\t\t\trefreshed = true;\n\t\t}\n\t\treturn this.refreshChildren(changedTiddlers) || refreshed;\n\t}\n};\n\nexports.checkbox = CheckboxWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/codeblock.js": {
"title": "$:/core/modules/widgets/codeblock.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/codeblock.js\ntype: application/javascript\nmodule-type: widget\n\nCode block node widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar CodeBlockWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nCodeBlockWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nCodeBlockWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar codeNode = this.document.createElement(\"code\"),\n\t\tdomNode = this.document.createElement(\"pre\");\n\tcodeNode.appendChild(this.document.createTextNode(this.getAttribute(\"code\")));\n\tdomNode.appendChild(codeNode);\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.domNodes.push(domNode);\n\tif(this.postRender) {\n\t\tthis.postRender();\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nCodeBlockWidget.prototype.execute = function() {\n\tthis.language = this.getAttribute(\"language\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nCodeBlockWidget.prototype.refresh = function(changedTiddlers) {\n\treturn false;\n};\n\nexports.codeblock = CodeBlockWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/count.js": {
"title": "$:/core/modules/widgets/count.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/count.js\ntype: application/javascript\nmodule-type: widget\n\nCount widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar CountWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nCountWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nCountWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar textNode = this.document.createTextNode(this.currentCount);\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nCountWidget.prototype.execute = function() {\n\t// Get parameters from our attributes\n\tthis.filter = this.getAttribute(\"filter\");\n\t// Execute the filter\n\tif(this.filter) {\n\t\tthis.currentCount = this.wiki.filterTiddlers(this.filter,this).length;\n\t} else {\n\t\tthis.currentCount = \"0\";\n\t}\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nCountWidget.prototype.refresh = function(changedTiddlers) {\n\t// Re-execute the filter to get the count\n\tthis.computeAttributes();\n\tvar oldCount = this.currentCount;\n\tthis.execute();\n\tif(this.currentCount !== oldCount) {\n\t\t// Regenerate and rerender the widget and replace the existing DOM node\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\n\t}\n\n};\n\nexports.count = CountWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/diff-text.js": {
"title": "$:/core/modules/widgets/diff-text.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/diff-text.js\ntype: application/javascript\nmodule-type: widget\n\nWidget to display a diff between two texts\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget,\n\tdmp = require(\"$:/core/modules/utils/diff-match-patch/diff_match_patch.js\");\n\nvar DiffTextWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDiffTextWidget.prototype = new Widget();\n\nDiffTextWidget.prototype.invisibleCharacters = {\n\t\"\\n\": \"↩︎\\n\",\n\t\"\\r\": \"⇠\",\n\t\"\\t\": \"⇥\\t\"\n};\n\n/*\nRender this widget into the DOM\n*/\nDiffTextWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create the diff\n\tvar dmpObject = new dmp.diff_match_patch(),\n\t\tdiffs = dmpObject.diff_main(this.getAttribute(\"source\"),this.getAttribute(\"dest\"));\n\t// Apply required cleanup\n\tswitch(this.getAttribute(\"cleanup\",\"semantic\")) {\n\t\tcase \"none\":\n\t\t\t// No cleanup\n\t\t\tbreak;\n\t\tcase \"efficiency\":\n\t\t\tdmpObject.diff_cleanupEfficiency(diffs);\n\t\t\tbreak;\n\t\tdefault: // case \"semantic\"\n\t\t\tdmpObject.diff_cleanupSemantic(diffs);\n\t\t\tbreak;\n\t}\n\t// Create the elements\n\tvar domContainer = this.document.createElement(\"div\"), \n\t\tdomDiff = this.createDiffDom(diffs);\n\tparent.insertBefore(domContainer,nextSibling);\n\t// Set variables\n\tthis.setVariable(\"diff-count\",diffs.reduce(function(acc,diff) {\n\t\tif(diff[0] !== dmp.DIFF_EQUAL) {\n\t\t\tacc++;\n\t\t}\n\t\treturn acc;\n\t},0).toString());\n\t// Render child widgets\n\tthis.renderChildren(domContainer,null);\n\t// Render the diff\n\tdomContainer.appendChild(domDiff);\n\t// Save our container\n\tthis.domNodes.push(domContainer);\n};\n\n/*\nCreate DOM elements representing a list of diffs\n*/\nDiffTextWidget.prototype.createDiffDom = function(diffs) {\n\tvar self = this;\n\t// Create the element and assign the attributes\n\tvar domPre = this.document.createElement(\"pre\"),\n\t\tdomCode = this.document.createElement(\"code\");\n\t$tw.utils.each(diffs,function(diff) {\n\t\tvar tag = diff[0] === dmp.DIFF_INSERT ? \"ins\" : (diff[0] === dmp.DIFF_DELETE ? \"del\" : \"span\"),\n\t\t\tclassName = diff[0] === dmp.DIFF_INSERT ? \"tc-diff-insert\" : (diff[0] === dmp.DIFF_DELETE ? \"tc-diff-delete\" : \"tc-diff-equal\"),\n\t\t\tdom = self.document.createElement(tag),\n\t\t\ttext = diff[1],\n\t\t\tcurrPos = 0,\n\t\t\tre = /([\\x00-\\x1F])/mg,\n\t\t\tmatch = re.exec(text),\n\t\t\tspan,\n\t\t\tprintable;\n\t\tdom.className = className;\n\t\twhile(match) {\n\t\t\tif(currPos < match.index) {\n\t\t\t\tdom.appendChild(self.document.createTextNode(text.slice(currPos,match.index)));\n\t\t\t}\n\t\t\tspan = self.document.createElement(\"span\");\n\t\t\tspan.className = \"tc-diff-invisible\";\n\t\t\tprintable = self.invisibleCharacters[match[0]] || (\"[0x\" + match[0].charCodeAt(0).toString(16) + \"]\");\n\t\t\tspan.appendChild(self.document.createTextNode(printable));\n\t\t\tdom.appendChild(span);\n\t\t\tcurrPos = match.index + match[0].length;\n\t\t\tmatch = re.exec(text);\n\t\t}\n\t\tif(currPos < text.length) {\n\t\t\tdom.appendChild(self.document.createTextNode(text.slice(currPos)));\n\t\t}\n\t\tdomCode.appendChild(dom);\n\t});\n\tdomPre.appendChild(domCode);\n\treturn domPre;\n};\n\n/*\nCompute the internal state of the widget\n*/\nDiffTextWidget.prototype.execute = function() {\n\t// Make child widgets\n\tvar parseTreeNodes;\n\tif(this.parseTreeNode && this.parseTreeNode.children && this.parseTreeNode.children.length > 0) {\n\t\tparseTreeNodes = this.parseTreeNode.children;\n\t} else {\n\t\tparseTreeNodes = [{\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {type: \"string\", value: \"$:/language/Diffs/CountMessage\"}\n\t\t\t}\n\t\t}];\n\t}\n\tthis.makeChildWidgets(parseTreeNodes);\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nDiffTextWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.source || changedAttributes.dest || changedAttributes.cleanup) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports[\"diff-text\"] = DiffTextWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/draggable.js": {
"title": "$:/core/modules/widgets/draggable.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/draggable.js\ntype: application/javascript\nmodule-type: widget\n\nDraggable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DraggableWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDraggableWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDraggableWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Sanitise the specified tag\n\tvar tag = this.draggableTag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"div\";\n\t}\n\t// Create our element\n\tvar domNode = this.document.createElement(tag);\n\t// Assign classes\n\tvar classes = [\"tc-draggable\"];\n\tif(this.draggableClasses) {\n\t\tclasses.push(this.draggableClasses);\n\t}\n\tdomNode.setAttribute(\"class\",classes.join(\" \"));\n\t// Add event handlers\n\t$tw.utils.makeDraggable({\n\t\tdomNode: domNode,\n\t\tdragTiddlerFn: function() {return self.getAttribute(\"tiddler\");},\n\t\tdragFilterFn: function() {return self.getAttribute(\"filter\");},\n\t\tstartActions: self.startActions,\n\t\tendActions: self.endActions,\n\t\twidget: this\n\t});\n\t// Insert the link into the DOM and render any children\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nDraggableWidget.prototype.execute = function() {\n\t// Pick up our attributes\n\tthis.draggableTag = this.getAttribute(\"tag\",\"div\");\n\tthis.draggableClasses = this.getAttribute(\"class\");\n\tthis.startActions = this.getAttribute(\"startactions\");\n\tthis.endActions = this.getAttribute(\"endactions\");\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nDraggableWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tag || changedAttributes[\"class\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.draggable = DraggableWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/droppable.js": {
"title": "$:/core/modules/widgets/droppable.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/droppable.js\ntype: application/javascript\nmodule-type: widget\n\nDroppable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DroppableWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDroppableWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDroppableWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar tag = this.parseTreeNode.isBlock ? \"div\" : \"span\";\n\tif(this.droppableTag && $tw.config.htmlUnsafeElements.indexOf(this.droppableTag) === -1) {\n\t\ttag = this.droppableTag;\n\t}\n\t// Create element and assign classes\n\tvar domNode = this.document.createElement(tag),\n\t\tclasses = (this[\"class\"] || \"\").split(\" \");\n\tclasses.push(\"tc-droppable\");\n\tdomNode.className = classes.join(\" \");\n\t// Add event handlers\n\tif(this.droppableEnable) {\n\t\t$tw.utils.addEventListeners(domNode,[\n\t\t\t{name: \"dragenter\", handlerObject: this, handlerMethod: \"handleDragEnterEvent\"},\n\t\t\t{name: \"dragover\", handlerObject: this, handlerMethod: \"handleDragOverEvent\"},\n\t\t\t{name: \"dragleave\", handlerObject: this, handlerMethod: \"handleDragLeaveEvent\"},\n\t\t\t{name: \"drop\", handlerObject: this, handlerMethod: \"handleDropEvent\"}\n\t\t]);\t\t\n\t}\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n\t// Stack of outstanding enter/leave events\n\tthis.currentlyEntered = [];\n};\n\nDroppableWidget.prototype.enterDrag = function(event) {\n\tif(this.currentlyEntered.indexOf(event.target) === -1) {\n\t\tthis.currentlyEntered.push(event.target);\n\t}\n\t// If we're entering for the first time we need to apply highlighting\n\t$tw.utils.addClass(this.domNodes[0],\"tc-dragover\");\n};\n\nDroppableWidget.prototype.leaveDrag = function(event) {\n\tvar pos = this.currentlyEntered.indexOf(event.target);\n\tif(pos !== -1) {\n\t\tthis.currentlyEntered.splice(pos,1);\n\t}\n\t// Remove highlighting if we're leaving externally. The hacky second condition is to resolve a problem with Firefox whereby there is an erroneous dragenter event if the node being dragged is within the dropzone\n\tif(this.currentlyEntered.length === 0 || (this.currentlyEntered.length === 1 && this.currentlyEntered[0] === $tw.dragInProgress)) {\n\t\tthis.currentlyEntered = [];\n\t\t$tw.utils.removeClass(this.domNodes[0],\"tc-dragover\");\n\t}\n};\n\nDroppableWidget.prototype.handleDragEnterEvent = function(event) {\n\tthis.enterDrag(event);\n\t// Tell the browser that we're ready to handle the drop\n\tevent.preventDefault();\n\t// Tell the browser not to ripple the drag up to any parent drop handlers\n\tevent.stopPropagation();\n\treturn false;\n};\n\nDroppableWidget.prototype.handleDragOverEvent = function(event) {\n\t// Check for being over a TEXTAREA or INPUT\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) !== -1) {\n\t\treturn false;\n\t}\n\t// Tell the browser that we're still interested in the drop\n\tevent.preventDefault();\n\t// Set the drop effect\n\tevent.dataTransfer.dropEffect = this.droppableEffect;\n\treturn false;\n};\n\nDroppableWidget.prototype.handleDragLeaveEvent = function(event) {\n\tthis.leaveDrag(event);\n\treturn false;\n};\n\nDroppableWidget.prototype.handleDropEvent = function(event) {\n\tvar self = this;\n\tthis.leaveDrag(event);\n\t// Check for being over a TEXTAREA or INPUT\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) !== -1) {\n\t\treturn false;\n\t}\n\tvar dataTransfer = event.dataTransfer;\n\t// Remove highlighting\n\t$tw.utils.removeClass(this.domNodes[0],\"tc-dragover\");\n\t// Try to import the various data types we understand\n\t$tw.utils.importDataTransfer(dataTransfer,null,function(fieldsArray) {\n\t\tfieldsArray.forEach(function(fields) {\n\t\t\tself.performActions(fields.title || fields.text,event);\n\t\t});\n\t});\n\t// Tell the browser that we handled the drop\n\tevent.preventDefault();\n\t// Stop the drop ripple up to any parent handlers\n\tevent.stopPropagation();\n\treturn false;\n};\n\nDroppableWidget.prototype.performActions = function(title,event) {\n\tif(this.droppableActions) {\n\t\tvar modifierKey = event.ctrlKey && ! event.shiftKey ? \"ctrl\" : event.shiftKey && !event.ctrlKey ? \"shift\" : \n\t\t\t\tevent.ctrlKey && event.shiftKey ? \"ctrl-shift\" : \"normal\" ;\n\t\tthis.invokeActionString(this.droppableActions,this,event,{actionTiddler: title, modifier: modifierKey});\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nDroppableWidget.prototype.execute = function() {\n\tthis.droppableActions = this.getAttribute(\"actions\");\n\tthis.droppableEffect = this.getAttribute(\"effect\",\"copy\");\n\tthis.droppableTag = this.getAttribute(\"tag\");\n\tthis.droppableClass = this.getAttribute(\"class\");\n\tthis.droppableEnable = (this.getAttribute(\"enable\") || \"yes\") === \"yes\";\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nDroppableWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"class\"] || changedAttributes.tag || changedAttributes.enable) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.droppable = DroppableWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/dropzone.js": {
"title": "$:/core/modules/widgets/dropzone.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/dropzone.js\ntype: application/javascript\nmodule-type: widget\n\nDropzone widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DropZoneWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDropZoneWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDropZoneWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create element\n\tvar domNode = this.document.createElement(\"div\");\n\tdomNode.className = this.dropzoneClass || \"tc-dropzone\";\n\t// Add event handlers\n\tif(this.dropzoneEnable) {\n\t\t$tw.utils.addEventListeners(domNode,[\n\t\t\t{name: \"dragenter\", handlerObject: this, handlerMethod: \"handleDragEnterEvent\"},\n\t\t\t{name: \"dragover\", handlerObject: this, handlerMethod: \"handleDragOverEvent\"},\n\t\t\t{name: \"dragleave\", handlerObject: this, handlerMethod: \"handleDragLeaveEvent\"},\n\t\t\t{name: \"drop\", handlerObject: this, handlerMethod: \"handleDropEvent\"},\n\t\t\t{name: \"paste\", handlerObject: this, handlerMethod: \"handlePasteEvent\"},\n\t\t\t{name: \"dragend\", handlerObject: this, handlerMethod: \"handleDragEndEvent\"}\n\t\t]);\t\t\n\t}\n\tdomNode.addEventListener(\"click\",function (event) {\n\t},false);\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n\t// Stack of outstanding enter/leave events\n\tthis.currentlyEntered = [];\n};\n\nDropZoneWidget.prototype.enterDrag = function(event) {\n\tif(this.currentlyEntered.indexOf(event.target) === -1) {\n\t\tthis.currentlyEntered.push(event.target);\n\t}\n\t// If we're entering for the first time we need to apply highlighting\n\t$tw.utils.addClass(this.domNodes[0],\"tc-dragover\");\n};\n\nDropZoneWidget.prototype.leaveDrag = function(event) {\n\tvar pos = this.currentlyEntered.indexOf(event.target);\n\tif(pos !== -1) {\n\t\tthis.currentlyEntered.splice(pos,1);\n\t}\n\t// Remove highlighting if we're leaving externally\n\tif(this.currentlyEntered.length === 0) {\n\t\t$tw.utils.removeClass(this.domNodes[0],\"tc-dragover\");\n\t}\n};\n\nDropZoneWidget.prototype.handleDragEnterEvent = function(event) {\n\t// Check for this window being the source of the drag\n\tif($tw.dragInProgress) {\n\t\treturn false;\n\t}\n\tthis.enterDrag(event);\n\t// Tell the browser that we're ready to handle the drop\n\tevent.preventDefault();\n\t// Tell the browser not to ripple the drag up to any parent drop handlers\n\tevent.stopPropagation();\n};\n\nDropZoneWidget.prototype.handleDragOverEvent = function(event) {\n\t// Check for being over a TEXTAREA or INPUT\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) !== -1) {\n\t\treturn false;\n\t}\n\t// Check for this window being the source of the drag\n\tif($tw.dragInProgress) {\n\t\treturn false;\n\t}\n\t// Tell the browser that we're still interested in the drop\n\tevent.preventDefault();\n\tevent.dataTransfer.dropEffect = \"copy\"; // Explicitly show this is a copy\n};\n\nDropZoneWidget.prototype.handleDragLeaveEvent = function(event) {\n\tthis.leaveDrag(event);\n};\n\nDropZoneWidget.prototype.handleDragEndEvent = function(event) {\n\t$tw.utils.removeClass(this.domNodes[0],\"tc-dragover\");\n};\n\nDropZoneWidget.prototype.handleDropEvent = function(event) {\n\tvar self = this,\n\t\treadFileCallback = function(tiddlerFieldsArray) {\n\t\t\tself.dispatchEvent({type: \"tm-import-tiddlers\", param: JSON.stringify(tiddlerFieldsArray)});\n\t\t};\n\tthis.leaveDrag(event);\n\t// Check for being over a TEXTAREA or INPUT\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) !== -1) {\n\t\treturn false;\n\t}\n\t// Check for this window being the source of the drag\n\tif($tw.dragInProgress) {\n\t\treturn false;\n\t}\n\tvar self = this,\n\t\tdataTransfer = event.dataTransfer;\n\t// Remove highlighting\n\t$tw.utils.removeClass(this.domNodes[0],\"tc-dragover\");\n\t// Import any files in the drop\n\tvar numFiles = 0;\n\tif(dataTransfer.files) {\n\t\tnumFiles = this.wiki.readFiles(dataTransfer.files,{\n\t\t\tcallback: readFileCallback,\n\t\t\tdeserializer: this.dropzoneDeserializer\n\t\t});\n\t}\n\t// Try to import the various data types we understand\n\tif(numFiles === 0) {\n\t\t$tw.utils.importDataTransfer(dataTransfer,this.wiki.generateNewTitle(\"Untitled\"),readFileCallback);\n\t}\n\t// Tell the browser that we handled the drop\n\tevent.preventDefault();\n\t// Stop the drop ripple up to any parent handlers\n\tevent.stopPropagation();\n};\n\nDropZoneWidget.prototype.handlePasteEvent = function(event) {\n\tvar self = this,\n\t\treadFileCallback = function(tiddlerFieldsArray) {\n\t\t\tself.dispatchEvent({type: \"tm-import-tiddlers\", param: JSON.stringify(tiddlerFieldsArray)});\n\t\t};\n\t// Let the browser handle it if we're in a textarea or input box\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) == -1 && !event.target.isContentEditable) {\n\t\tvar self = this,\n\t\t\titems = event.clipboardData.items;\n\t\t// Enumerate the clipboard items\n\t\tfor(var t = 0; t<items.length; t++) {\n\t\t\tvar item = items[t];\n\t\t\tif(item.kind === \"file\") {\n\t\t\t\t// Import any files\n\t\t\t\tthis.wiki.readFile(item.getAsFile(),{\n\t\t\t\t\tcallback: readFileCallback,\n\t\t\t\t\tdeserializer: this.dropzoneDeserializer\n\t\t\t\t});\n\t\t\t} else if(item.kind === \"string\") {\n\t\t\t\t// Create tiddlers from string items\n\t\t\t\tvar type = item.type;\n\t\t\t\titem.getAsString(function(str) {\n\t\t\t\t\tvar tiddlerFields = {\n\t\t\t\t\t\ttitle: self.wiki.generateNewTitle(\"Untitled\"),\n\t\t\t\t\t\ttext: str,\n\t\t\t\t\t\ttype: type\n\t\t\t\t\t};\n\t\t\t\t\tif($tw.log.IMPORT) {\n\t\t\t\t\t\tconsole.log(\"Importing string '\" + str + \"', type: '\" + type + \"'\");\n\t\t\t\t\t}\n\t\t\t\t\tself.dispatchEvent({type: \"tm-import-tiddlers\", param: JSON.stringify([tiddlerFields])});\n\t\t\t\t});\n\t\t\t}\n\t\t}\n\t\t// Tell the browser that we've handled the paste\n\t\tevent.stopPropagation();\n\t\tevent.preventDefault();\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nDropZoneWidget.prototype.execute = function() {\n\tthis.dropzoneClass = this.getAttribute(\"class\");\n\tthis.dropzoneDeserializer = this.getAttribute(\"deserializer\");\n\tthis.dropzoneEnable = (this.getAttribute(\"enable\") || \"yes\") === \"yes\";\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nDropZoneWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.enable) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.dropzone = DropZoneWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit-binary.js": {
"title": "$:/core/modules/widgets/edit-binary.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/edit-binary.js\ntype: application/javascript\nmodule-type: widget\n\nEdit-binary widget; placeholder for editing binary tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar BINARY_WARNING_MESSAGE = \"$:/core/ui/BinaryWarning\";\nvar EXPORT_BUTTON_IMAGE = \"$:/core/images/export-button\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EditBinaryWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEditBinaryWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEditBinaryWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nEditBinaryWidget.prototype.execute = function() {\n\t// Get our parameters\n\tvar editTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tvar tiddler = this.wiki.getTiddler(editTitle);\n\tvar type = tiddler.fields.type;\n\tvar text = tiddler.fields.text;\n\t// Transclude the binary data tiddler warning message\n\tvar warn = {\n\t\ttype: \"element\",\n\t\ttag: \"p\",\n\t\tchildren: [{\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {type: \"string\", value: BINARY_WARNING_MESSAGE}\n\t\t\t}\n\t\t}]\n\t};\n\t// Create download link based on draft tiddler title\n\tvar link = {\n\t\ttype: \"element\",\n\t\ttag: \"a\",\n\t\tattributes: {\n\t\t\ttitle: {type: \"indirect\", textReference: \"!!draft.title\"},\n\t\t\tdownload: {type: \"indirect\", textReference: \"!!draft.title\"}\n\t\t},\n\t\tchildren: [{\n\t\ttype: \"transclude\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {type: \"string\", value: EXPORT_BUTTON_IMAGE}\n\t\t\t}\n\t\t}]\n\t};\n\t// Set the link href to internal data URI (no external)\n\tif(text) {\n\t\tlink.attributes.href = {\n\t\t\ttype: \"string\", \n\t\t\tvalue: \"data:\" + type + \";base64,\" + text\n\t\t};\n\t}\n\t// Combine warning message and download link in a div\n\tvar element = {\n\t\ttype: \"element\",\n\t\ttag: \"div\",\n\t\tattributes: {\n\t\t\tclass: {type: \"string\", value: \"tc-binary-warning\"}\n\t\t},\n\t\tchildren: [warn, link]\n\t}\n\t// Construct the child widgets\n\tthis.makeChildWidgets([element]);\n};\n\n/*\nRefresh by refreshing our child widget\n*/\nEditBinaryWidget.prototype.refresh = function(changedTiddlers) {\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports[\"edit-binary\"] = EditBinaryWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit-bitmap.js": {
"title": "$:/core/modules/widgets/edit-bitmap.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/edit-bitmap.js\ntype: application/javascript\nmodule-type: widget\n\nEdit-bitmap widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Default image sizes\nvar DEFAULT_IMAGE_WIDTH = 600,\n\tDEFAULT_IMAGE_HEIGHT = 370,\n\tDEFAULT_IMAGE_TYPE = \"image/png\";\n\n// Configuration tiddlers\nvar LINE_WIDTH_TITLE = \"$:/config/BitmapEditor/LineWidth\",\n\tLINE_COLOUR_TITLE = \"$:/config/BitmapEditor/Colour\",\n\tLINE_OPACITY_TITLE = \"$:/config/BitmapEditor/Opacity\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EditBitmapWidget = function(parseTreeNode,options) {\n\t// Initialise the editor operations if they've not been done already\n\tif(!this.editorOperations) {\n\t\tEditBitmapWidget.prototype.editorOperations = {};\n\t\t$tw.modules.applyMethods(\"bitmapeditoroperation\",this.editorOperations);\n\t}\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEditBitmapWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEditBitmapWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Create the wrapper for the toolbar and render its content\n\tthis.toolbarNode = this.document.createElement(\"div\");\n\tthis.toolbarNode.className = \"tc-editor-toolbar\";\n\tparent.insertBefore(this.toolbarNode,nextSibling);\n\tthis.domNodes.push(this.toolbarNode);\n\t// Create the on-screen canvas\n\tthis.canvasDomNode = $tw.utils.domMaker(\"canvas\",{\n\t\tdocument: this.document,\n\t\t\"class\":\"tc-edit-bitmapeditor\",\n\t\teventListeners: [{\n\t\t\tname: \"touchstart\", handlerObject: this, handlerMethod: \"handleTouchStartEvent\"\n\t\t},{\n\t\t\tname: \"touchmove\", handlerObject: this, handlerMethod: \"handleTouchMoveEvent\"\n\t\t},{\n\t\t\tname: \"touchend\", handlerObject: this, handlerMethod: \"handleTouchEndEvent\"\n\t\t},{\n\t\t\tname: \"mousedown\", handlerObject: this, handlerMethod: \"handleMouseDownEvent\"\n\t\t},{\n\t\t\tname: \"mousemove\", handlerObject: this, handlerMethod: \"handleMouseMoveEvent\"\n\t\t},{\n\t\t\tname: \"mouseup\", handlerObject: this, handlerMethod: \"handleMouseUpEvent\"\n\t\t}]\n\t});\n\t// Set the width and height variables\n\tthis.setVariable(\"tv-bitmap-editor-width\",this.canvasDomNode.width + \"px\");\n\tthis.setVariable(\"tv-bitmap-editor-height\",this.canvasDomNode.height + \"px\");\n\t// Render toolbar child widgets\n\tthis.renderChildren(this.toolbarNode,null);\n\t// // Insert the elements into the DOM\n\tparent.insertBefore(this.canvasDomNode,nextSibling);\n\tthis.domNodes.push(this.canvasDomNode);\n\t// Load the image into the canvas\n\tif($tw.browser) {\n\t\tthis.loadCanvas();\n\t}\n\t// Add widget message listeners\n\tthis.addEventListeners([\n\t\t{type: \"tm-edit-bitmap-operation\", handler: \"handleEditBitmapOperationMessage\"}\n\t]);\n};\n\n/*\nHandle an edit bitmap operation message from the toolbar\n*/\nEditBitmapWidget.prototype.handleEditBitmapOperationMessage = function(event) {\n\t// Invoke the handler\n\tvar handler = this.editorOperations[event.param];\n\tif(handler) {\n\t\thandler.call(this,event);\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nEditBitmapWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.editTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nJust refresh the toolbar\n*/\nEditBitmapWidget.prototype.refresh = function(changedTiddlers) {\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nSet the bitmap size variables and refresh the toolbar\n*/\nEditBitmapWidget.prototype.refreshToolbar = function() {\n\t// Set the width and height variables\n\tthis.setVariable(\"tv-bitmap-editor-width\",this.canvasDomNode.width + \"px\");\n\tthis.setVariable(\"tv-bitmap-editor-height\",this.canvasDomNode.height + \"px\");\n\t// Refresh each of our child widgets\n\t$tw.utils.each(this.children,function(childWidget) {\n\t\tchildWidget.refreshSelf();\n\t});\n};\n\nEditBitmapWidget.prototype.loadCanvas = function() {\n\tvar tiddler = this.wiki.getTiddler(this.editTitle),\n\t\tcurrImage = new Image();\n\t// Set up event handlers for loading the image\n\tvar self = this;\n\tcurrImage.onload = function() {\n\t\t// Copy the image to the on-screen canvas\n\t\tself.initCanvas(self.canvasDomNode,currImage.width,currImage.height,currImage);\n\t\t// And also copy the current bitmap to the off-screen canvas\n\t\tself.currCanvas = self.document.createElement(\"canvas\");\n\t\tself.initCanvas(self.currCanvas,currImage.width,currImage.height,currImage);\n\t\t// Set the width and height input boxes\n\t\tself.refreshToolbar();\n\t};\n\tcurrImage.onerror = function() {\n\t\t// Set the on-screen canvas size and clear it\n\t\tself.initCanvas(self.canvasDomNode,DEFAULT_IMAGE_WIDTH,DEFAULT_IMAGE_HEIGHT);\n\t\t// Set the off-screen canvas size and clear it\n\t\tself.currCanvas = self.document.createElement(\"canvas\");\n\t\tself.initCanvas(self.currCanvas,DEFAULT_IMAGE_WIDTH,DEFAULT_IMAGE_HEIGHT);\n\t\t// Set the width and height input boxes\n\t\tself.refreshToolbar();\n\t};\n\t// Get the current bitmap into an image object\n\tif(tiddler && tiddler.fields.type && tiddler.fields.text) {\n\t\tcurrImage.src = \"data:\" + tiddler.fields.type + \";base64,\" + tiddler.fields.text;\t\t\n\t} else {\n\t\tcurrImage.width = DEFAULT_IMAGE_WIDTH;\n\t\tcurrImage.height = DEFAULT_IMAGE_HEIGHT;\n\t\tcurrImage.onerror();\n\t}\n};\n\nEditBitmapWidget.prototype.initCanvas = function(canvas,width,height,image) {\n\tcanvas.width = width;\n\tcanvas.height = height;\n\tvar ctx = canvas.getContext(\"2d\");\n\tif(image) {\n\t\tctx.drawImage(image,0,0);\n\t} else {\n\t\tctx.fillStyle = \"#fff\";\n\t\tctx.fillRect(0,0,canvas.width,canvas.height);\n\t}\n};\n\n/*\n** Change the size of the canvas, preserving the current image\n*/\nEditBitmapWidget.prototype.changeCanvasSize = function(newWidth,newHeight) {\n\t// Create and size a new canvas\n\tvar newCanvas = this.document.createElement(\"canvas\");\n\tthis.initCanvas(newCanvas,newWidth,newHeight);\n\t// Copy the old image\n\tvar ctx = newCanvas.getContext(\"2d\");\n\tctx.drawImage(this.currCanvas,0,0);\n\t// Set the new canvas as the current one\n\tthis.currCanvas = newCanvas;\n\t// Set the size of the onscreen canvas\n\tthis.canvasDomNode.width = newWidth;\n\tthis.canvasDomNode.height = newHeight;\n\t// Paint the onscreen canvas with the offscreen canvas\n\tctx = this.canvasDomNode.getContext(\"2d\");\n\tctx.drawImage(this.currCanvas,0,0);\n};\n\n/*\n** Rotate the canvas left by 90 degrees\n*/\nEditBitmapWidget.prototype.rotateCanvasLeft = function() {\n\t// Get the current size of the image\n\tvar origWidth = this.currCanvas.width,\n\t\torigHeight = this.currCanvas.height;\n\t// Create and size a new canvas\n\tvar newCanvas = this.document.createElement(\"canvas\"),\n\t\tnewWidth = origHeight,\n\t\tnewHeight = origWidth;\n\tthis.initCanvas(newCanvas,newWidth,newHeight);\n\t// Copy the old image\n\tvar ctx = newCanvas.getContext(\"2d\");\n\tctx.save();\n\tctx.translate(newWidth / 2,newHeight / 2);\n\tctx.rotate(-Math.PI / 2);\n\tctx.drawImage(this.currCanvas,-origWidth / 2,-origHeight / 2);\n\tctx.restore();\n\t// Set the new canvas as the current one\n\tthis.currCanvas = newCanvas;\n\t// Set the size of the onscreen canvas\n\tthis.canvasDomNode.width = newWidth;\n\tthis.canvasDomNode.height = newHeight;\n\t// Paint the onscreen canvas with the offscreen canvas\n\tctx = this.canvasDomNode.getContext(\"2d\");\n\tctx.drawImage(this.currCanvas,0,0);\n};\n\nEditBitmapWidget.prototype.handleTouchStartEvent = function(event) {\n\tthis.brushDown = true;\n\tthis.strokeStart(event.touches[0].clientX,event.touches[0].clientY);\n\tevent.preventDefault();\n\tevent.stopPropagation();\n\treturn false;\n};\n\nEditBitmapWidget.prototype.handleTouchMoveEvent = function(event) {\n\tif(this.brushDown) {\n\t\tthis.strokeMove(event.touches[0].clientX,event.touches[0].clientY);\n\t}\n\tevent.preventDefault();\n\tevent.stopPropagation();\n\treturn false;\n};\n\nEditBitmapWidget.prototype.handleTouchEndEvent = function(event) {\n\tif(this.brushDown) {\n\t\tthis.brushDown = false;\n\t\tthis.strokeEnd();\n\t}\n\tevent.preventDefault();\n\tevent.stopPropagation();\n\treturn false;\n};\n\nEditBitmapWidget.prototype.handleMouseDownEvent = function(event) {\n\tthis.strokeStart(event.clientX,event.clientY);\n\tthis.brushDown = true;\n\tevent.preventDefault();\n\tevent.stopPropagation();\n\treturn false;\n};\n\nEditBitmapWidget.prototype.handleMouseMoveEvent = function(event) {\n\tif(this.brushDown) {\n\t\tthis.strokeMove(event.clientX,event.clientY);\n\t\tevent.preventDefault();\n\t\tevent.stopPropagation();\n\t\treturn false;\n\t}\n\treturn true;\n};\n\nEditBitmapWidget.prototype.handleMouseUpEvent = function(event) {\n\tif(this.brushDown) {\n\t\tthis.brushDown = false;\n\t\tthis.strokeEnd();\n\t\tevent.preventDefault();\n\t\tevent.stopPropagation();\n\t\treturn false;\n\t}\n\treturn true;\n};\n\nEditBitmapWidget.prototype.adjustCoordinates = function(x,y) {\n\tvar canvasRect = this.canvasDomNode.getBoundingClientRect(),\n\t\tscale = this.canvasDomNode.width/canvasRect.width;\n\treturn {x: (x - canvasRect.left) * scale, y: (y - canvasRect.top) * scale};\n};\n\nEditBitmapWidget.prototype.strokeStart = function(x,y) {\n\t// Start off a new stroke\n\tthis.stroke = [this.adjustCoordinates(x,y)];\n};\n\nEditBitmapWidget.prototype.strokeMove = function(x,y) {\n\tvar ctx = this.canvasDomNode.getContext(\"2d\"),\n\t\tt;\n\t// Add the new position to the end of the stroke\n\tthis.stroke.push(this.adjustCoordinates(x,y));\n\t// Redraw the previous image\n\tctx.drawImage(this.currCanvas,0,0);\n\t// Render the stroke\n\tctx.globalAlpha = parseFloat(this.wiki.getTiddlerText(LINE_OPACITY_TITLE,\"1.0\"));\n\tctx.strokeStyle = this.wiki.getTiddlerText(LINE_COLOUR_TITLE,\"#ff0\");\n\tctx.lineWidth = parseFloat(this.wiki.getTiddlerText(LINE_WIDTH_TITLE,\"3\"));\n\tctx.lineCap = \"round\";\n\tctx.lineJoin = \"round\";\n\tctx.beginPath();\n\tctx.moveTo(this.stroke[0].x,this.stroke[0].y);\n\tfor(t=1; t<this.stroke.length-1; t++) {\n\t\tvar s1 = this.stroke[t],\n\t\t\ts2 = this.stroke[t-1],\n\t\t\ttx = (s1.x + s2.x)/2,\n\t\t\tty = (s1.y + s2.y)/2;\n\t\tctx.quadraticCurveTo(s2.x,s2.y,tx,ty);\n\t}\n\tctx.stroke();\n};\n\nEditBitmapWidget.prototype.strokeEnd = function() {\n\t// Copy the bitmap to the off-screen canvas\n\tvar ctx = this.currCanvas.getContext(\"2d\");\n\tctx.drawImage(this.canvasDomNode,0,0);\n\t// Save the image into the tiddler\n\tthis.saveChanges();\n};\n\nEditBitmapWidget.prototype.saveChanges = function() {\n\tvar tiddler = this.wiki.getTiddler(this.editTitle) || new $tw.Tiddler({title: this.editTitle,type: DEFAULT_IMAGE_TYPE});\n\t// data URIs look like \"data:<type>;base64,<text>\"\n\tvar dataURL = this.canvasDomNode.toDataURL(tiddler.fields.type),\n\t\tposColon = dataURL.indexOf(\":\"),\n\t\tposSemiColon = dataURL.indexOf(\";\"),\n\t\tposComma = dataURL.indexOf(\",\"),\n\t\ttype = dataURL.substring(posColon+1,posSemiColon),\n\t\ttext = dataURL.substring(posComma+1);\n\tvar update = {type: type, text: text};\n\tthis.wiki.addTiddler(new $tw.Tiddler(this.wiki.getModificationFields(),tiddler,update,this.wiki.getCreationFields()));\n};\n\nexports[\"edit-bitmap\"] = EditBitmapWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit-shortcut.js": {
"title": "$:/core/modules/widgets/edit-shortcut.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/edit-shortcut.js\ntype: application/javascript\nmodule-type: widget\n\nWidget to display an editable keyboard shortcut\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EditShortcutWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEditShortcutWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEditShortcutWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.inputNode = this.document.createElement(\"input\");\n\t// Assign classes\n\tif(this.shortcutClass) {\n\t\tthis.inputNode.className = this.shortcutClass;\t\t\n\t}\n\t// Assign other attributes\n\tif(this.shortcutStyle) {\n\t\tthis.inputNode.setAttribute(\"style\",this.shortcutStyle);\n\t}\n\tif(this.shortcutTooltip) {\n\t\tthis.inputNode.setAttribute(\"title\",this.shortcutTooltip);\n\t}\n\tif(this.shortcutPlaceholder) {\n\t\tthis.inputNode.setAttribute(\"placeholder\",this.shortcutPlaceholder);\n\t}\n\tif(this.shortcutAriaLabel) {\n\t\tthis.inputNode.setAttribute(\"aria-label\",this.shortcutAriaLabel);\n\t}\n\t// Assign the current shortcut\n\tthis.updateInputNode();\n\t// Add event handlers\n\t$tw.utils.addEventListeners(this.inputNode,[\n\t\t{name: \"keydown\", handlerObject: this, handlerMethod: \"handleKeydownEvent\"}\n\t]);\n\t// Link into the DOM\n\tparent.insertBefore(this.inputNode,nextSibling);\n\tthis.domNodes.push(this.inputNode);\n\t// Focus the input Node if focus === \"yes\" or focus === \"true\"\n\tif(this.shortcutFocus === \"yes\" || this.shortcutFocus === \"true\") {\n\t\tthis.focus();\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nEditShortcutWidget.prototype.execute = function() {\n\tthis.shortcutTiddler = this.getAttribute(\"tiddler\");\n\tthis.shortcutField = this.getAttribute(\"field\");\n\tthis.shortcutIndex = this.getAttribute(\"index\");\n\tthis.shortcutPlaceholder = this.getAttribute(\"placeholder\");\n\tthis.shortcutDefault = this.getAttribute(\"default\",\"\");\n\tthis.shortcutClass = this.getAttribute(\"class\");\n\tthis.shortcutStyle = this.getAttribute(\"style\");\n\tthis.shortcutTooltip = this.getAttribute(\"tooltip\");\n\tthis.shortcutAriaLabel = this.getAttribute(\"aria-label\");\n\tthis.shortcutFocus = this.getAttribute(\"focus\");\n};\n\n/*\nUpdate the value of the input node\n*/\nEditShortcutWidget.prototype.updateInputNode = function() {\n\tif(this.shortcutField) {\n\t\tvar tiddler = this.wiki.getTiddler(this.shortcutTiddler);\n\t\tif(tiddler && $tw.utils.hop(tiddler.fields,this.shortcutField)) {\n\t\t\tthis.inputNode.value = tiddler.getFieldString(this.shortcutField);\n\t\t} else {\n\t\t\tthis.inputNode.value = this.shortcutDefault;\n\t\t}\n\t} else if(this.shortcutIndex) {\n\t\tthis.inputNode.value = this.wiki.extractTiddlerDataItem(this.shortcutTiddler,this.shortcutIndex,this.shortcutDefault);\n\t} else {\n\t\tthis.inputNode.value = this.wiki.getTiddlerText(this.shortcutTiddler,this.shortcutDefault);\n\t}\n};\n\n/*\nHandle a dom \"keydown\" event\n*/\nEditShortcutWidget.prototype.handleKeydownEvent = function(event) {\n\t// Ignore shift, ctrl, meta, alt\n\tif(event.keyCode && $tw.keyboardManager.getModifierKeys().indexOf(event.keyCode) === -1) {\n\t\t// Get the shortcut text representation\n\t\tvar value = $tw.keyboardManager.getPrintableShortcuts([{\n\t\t\tctrlKey: event.ctrlKey,\n\t\t\tshiftKey: event.shiftKey,\n\t\t\taltKey: event.altKey,\n\t\t\tmetaKey: event.metaKey,\n\t\t\tkeyCode: event.keyCode\n\t\t}]);\n\t\tif(value.length > 0) {\n\t\t\tthis.wiki.setText(this.shortcutTiddler,this.shortcutField,this.shortcutIndex,value[0]);\n\t\t}\n\t\t// Ignore the keydown if it was already handled\n\t\tevent.preventDefault();\n\t\tevent.stopPropagation();\n\t\treturn true;\t\t\n\t} else {\n\t\treturn false;\n\t}\n};\n\n/*\nfocus the input node\n*/\nEditShortcutWidget.prototype.focus = function() {\n\tif(this.inputNode.focus && this.inputNode.select) {\n\t\tthis.inputNode.focus();\n\t\tthis.inputNode.select();\n\t}\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget needed re-rendering\n*/\nEditShortcutWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes.placeholder || changedAttributes[\"default\"] || changedAttributes[\"class\"] || changedAttributes.style || changedAttributes.tooltip || changedAttributes[\"aria-label\"] || changedAttributes.focus) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else if(changedTiddlers[this.shortcutTiddler]) {\n\t\tthis.updateInputNode();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports[\"edit-shortcut\"] = EditShortcutWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit-text.js": {
"title": "$:/core/modules/widgets/edit-text.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/edit-text.js\ntype: application/javascript\nmodule-type: widget\n\nEdit-text widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar editTextWidgetFactory = require(\"$:/core/modules/editor/factory.js\").editTextWidgetFactory,\n\tFramedEngine = require(\"$:/core/modules/editor/engines/framed.js\").FramedEngine,\n\tSimpleEngine = require(\"$:/core/modules/editor/engines/simple.js\").SimpleEngine;\n\nexports[\"edit-text\"] = editTextWidgetFactory(FramedEngine,SimpleEngine);\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit.js": {
"title": "$:/core/modules/widgets/edit.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/edit.js\ntype: application/javascript\nmodule-type: widget\n\nEdit widget is a meta-widget chooses the appropriate actual editting widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EditWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEditWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEditWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n// Mappings from content type to editor type are stored in tiddlers with this prefix\nvar EDITOR_MAPPING_PREFIX = \"$:/config/EditorTypeMappings/\";\n\n/*\nCompute the internal state of the widget\n*/\nEditWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.editTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.editField = this.getAttribute(\"field\",\"text\");\n\tthis.editIndex = this.getAttribute(\"index\");\n\tthis.editClass = this.getAttribute(\"class\");\n\tthis.editPlaceholder = this.getAttribute(\"placeholder\");\n\tthis.editTabIndex = this.getAttribute(\"tabindex\");\n\tthis.editFocus = this.getAttribute(\"focus\",\"\");\n\t// Choose the appropriate edit widget\n\tthis.editorType = this.getEditorType();\n\t// Make the child widgets\n\tthis.makeChildWidgets([{\n\t\ttype: \"edit-\" + this.editorType,\n\t\tattributes: {\n\t\t\ttiddler: {type: \"string\", value: this.editTitle},\n\t\t\tfield: {type: \"string\", value: this.editField},\n\t\t\tindex: {type: \"string\", value: this.editIndex},\n\t\t\t\"class\": {type: \"string\", value: this.editClass},\n\t\t\t\"placeholder\": {type: \"string\", value: this.editPlaceholder},\n\t\t\t\"tabindex\": {type: \"string\", value: this.editTabIndex},\n\t\t\t\"focus\": {type: \"string\", value: this.editFocus}\n\t\t},\n\t\tchildren: this.parseTreeNode.children\n\t}]);\n};\n\nEditWidget.prototype.getEditorType = function() {\n\t// Get the content type of the thing we're editing\n\tvar type;\n\tif(this.editField === \"text\") {\n\t\tvar tiddler = this.wiki.getTiddler(this.editTitle);\n\t\tif(tiddler) {\n\t\t\ttype = tiddler.fields.type;\n\t\t}\n\t}\n\ttype = type || \"text/vnd.tiddlywiki\";\n\tvar editorType = this.wiki.getTiddlerText(EDITOR_MAPPING_PREFIX + type);\n\tif(!editorType) {\n\t\tvar typeInfo = $tw.config.contentTypeInfo[type];\n\t\tif(typeInfo && typeInfo.encoding === \"base64\") {\n\t\t\teditorType = \"binary\";\n\t\t} else {\n\t\t\teditorType = \"text\";\n\t\t}\n\t}\n\treturn editorType;\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nEditWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\t// Refresh if an attribute has changed, or the type associated with the target tiddler has changed\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes.tabindex || (changedTiddlers[this.editTitle] && this.getEditorType() !== this.editorType)) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.edit = EditWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/element.js": {
"title": "$:/core/modules/widgets/element.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/element.js\ntype: application/javascript\nmodule-type: widget\n\nElement widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ElementWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nElementWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nElementWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Neuter blacklisted elements\n\tvar tag = this.parseTreeNode.tag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"safe-\" + tag;\n\t}\n\t// Adjust headings by the current base level\n\tvar headingLevel = [\"h1\",\"h2\",\"h3\",\"h4\",\"h5\",\"h6\"].indexOf(tag);\n\tif(headingLevel !== -1) {\n\t\tvar baseLevel = parseInt(this.getVariable(\"tv-adjust-heading-level\",\"0\"),10) || 0;\n\t\theadingLevel = Math.min(Math.max(headingLevel + 1 + baseLevel,1),6);\n\t\ttag = \"h\" + headingLevel;\n\t}\n\t// Create the DOM node\n\tvar domNode = this.document.createElementNS(this.namespace,tag);\n\tthis.assignAttributes(domNode,{excludeEventAttributes: true});\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nElementWidget.prototype.execute = function() {\n\t// Select the namespace for the tag\n\tvar tagNamespaces = {\n\t\t\tsvg: \"http://www.w3.org/2000/svg\",\n\t\t\tmath: \"http://www.w3.org/1998/Math/MathML\",\n\t\t\tbody: \"http://www.w3.org/1999/xhtml\"\n\t\t};\n\tthis.namespace = tagNamespaces[this.parseTreeNode.tag];\n\tif(this.namespace) {\n\t\tthis.setVariable(\"namespace\",this.namespace);\n\t} else {\n\t\tthis.namespace = this.getVariable(\"namespace\",{defaultValue: \"http://www.w3.org/1999/xhtml\"});\n\t}\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nElementWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes(),\n\t\thasChangedAttributes = $tw.utils.count(changedAttributes) > 0;\n\tif(hasChangedAttributes) {\n\t\t// Update our attributes\n\t\tthis.assignAttributes(this.domNodes[0],{excludeEventAttributes: true});\n\t}\n\treturn this.refreshChildren(changedTiddlers) || hasChangedAttributes;\n};\n\nexports.element = ElementWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/encrypt.js": {
"title": "$:/core/modules/widgets/encrypt.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/encrypt.js\ntype: application/javascript\nmodule-type: widget\n\nEncrypt widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EncryptWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEncryptWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEncryptWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar textNode = this.document.createTextNode(this.encryptedText);\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nEncryptWidget.prototype.execute = function() {\n\t// Get parameters from our attributes\n\tthis.filter = this.getAttribute(\"filter\",\"[!is[system]]\");\n\t// Encrypt the filtered tiddlers\n\tvar tiddlers = this.wiki.filterTiddlers(this.filter),\n\t\tjson = {},\n\t\tself = this;\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar tiddler = self.wiki.getTiddler(title),\n\t\t\tjsonTiddler = {};\n\t\tfor(var f in tiddler.fields) {\n\t\t\tjsonTiddler[f] = tiddler.getFieldString(f);\n\t\t}\n\t\tjson[title] = jsonTiddler;\n\t});\n\tthis.encryptedText = $tw.utils.htmlEncode($tw.crypto.encrypt(JSON.stringify(json)));\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nEncryptWidget.prototype.refresh = function(changedTiddlers) {\n\t// We don't need to worry about refreshing because the encrypt widget isn't for interactive use\n\treturn false;\n};\n\nexports.encrypt = EncryptWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/entity.js": {
"title": "$:/core/modules/widgets/entity.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/entity.js\ntype: application/javascript\nmodule-type: widget\n\nHTML entity widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EntityWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEntityWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEntityWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.execute();\n\tvar entityString = this.getAttribute(\"entity\",this.parseTreeNode.entity || \"\"),\n\t\ttextNode = this.document.createTextNode($tw.utils.entityDecode(entityString));\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nEntityWidget.prototype.execute = function() {\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nEntityWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.entity) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports.entity = EntityWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/fieldmangler.js": {
"title": "$:/core/modules/widgets/fieldmangler.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/fieldmangler.js\ntype: application/javascript\nmodule-type: widget\n\nField mangler widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar FieldManglerWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n\tthis.addEventListeners([\n\t\t{type: \"tm-remove-field\", handler: \"handleRemoveFieldEvent\"},\n\t\t{type: \"tm-add-field\", handler: \"handleAddFieldEvent\"},\n\t\t{type: \"tm-remove-tag\", handler: \"handleRemoveTagEvent\"},\n\t\t{type: \"tm-add-tag\", handler: \"handleAddTagEvent\"}\n\t]);\n};\n\n/*\nInherit from the base widget class\n*/\nFieldManglerWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nFieldManglerWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nFieldManglerWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.mangleTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nFieldManglerWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\nFieldManglerWidget.prototype.handleRemoveFieldEvent = function(event) {\n\tvar tiddler = this.wiki.getTiddler(this.mangleTitle),\n\t\tdeletion = {};\n\tdeletion[event.param] = undefined;\n\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,deletion));\n\treturn true;\n};\n\nFieldManglerWidget.prototype.handleAddFieldEvent = function(event) {\n\tvar tiddler = this.wiki.getTiddler(this.mangleTitle),\n\t\taddition = this.wiki.getModificationFields(),\n\t\thadInvalidFieldName = false,\n\t\taddField = function(name,value) {\n\t\t\tvar trimmedName = name.toLowerCase().trim();\n\t\t\tif(!$tw.utils.isValidFieldName(trimmedName)) {\n\t\t\t\tif(!hadInvalidFieldName) {\n\t\t\t\t\talert($tw.language.getString(\n\t\t\t\t\t\t\"InvalidFieldName\",\n\t\t\t\t\t\t{variables:\n\t\t\t\t\t\t\t{fieldName: trimmedName}\n\t\t\t\t\t\t}\n\t\t\t\t\t));\n\t\t\t\t\thadInvalidFieldName = true;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tif(!value && tiddler) {\n\t\t\t\t\tvalue = tiddler.fields[trimmedName];\n\t\t\t\t}\n\t\t\t\taddition[trimmedName] = value || \"\";\n\t\t\t}\n\t\t\treturn;\n\t\t};\n\taddition.title = this.mangleTitle;\n\tif(typeof event.param === \"string\") {\n\t\taddField(event.param,\"\");\n\t}\n\tif(typeof event.paramObject === \"object\") {\n\t\tfor(var name in event.paramObject) {\n\t\t\taddField(name,event.paramObject[name]);\n\t\t}\n\t}\n\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,addition));\n\treturn true;\n};\n\nFieldManglerWidget.prototype.handleRemoveTagEvent = function(event) {\n\tvar tiddler = this.wiki.getTiddler(this.mangleTitle),\n\t\tmodification = this.wiki.getModificationFields();\n\tif(tiddler && tiddler.fields.tags) {\n\t\tvar p = tiddler.fields.tags.indexOf(event.param);\n\t\tif(p !== -1) {\n\t\t\tmodification.tags = (tiddler.fields.tags || []).slice(0);\n\t\t\tmodification.tags.splice(p,1);\n\t\t\tif(modification.tags.length === 0) {\n\t\t\t\tmodification.tags = undefined;\n\t\t\t}\n\t\t\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,modification));\n\t\t}\n\t}\n\treturn true;\n};\n\nFieldManglerWidget.prototype.handleAddTagEvent = function(event) {\n\tvar tiddler = this.wiki.getTiddler(this.mangleTitle),\n\t\tmodification = this.wiki.getModificationFields();\n\tif(tiddler && typeof event.param === \"string\") {\n\t\tvar tag = event.param.trim();\n\t\tif(tag !== \"\") {\n\t\t\tmodification.tags = (tiddler.fields.tags || []).slice(0);\n\t\t\t$tw.utils.pushTop(modification.tags,tag);\n\t\t\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,modification));\t\t\t\n\t\t}\n\t} else if(typeof event.param === \"string\" && event.param.trim() !== \"\" && this.mangleTitle.trim() !== \"\") {\n\t\tvar tag = [];\n\t\ttag.push(event.param.trim());\n\t\tthis.wiki.addTiddler(new $tw.Tiddler({title: this.mangleTitle, tags: tag},modification));\n\t}\n\treturn true;\n};\n\nexports.fieldmangler = FieldManglerWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/fields.js": {
"title": "$:/core/modules/widgets/fields.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/fields.js\ntype: application/javascript\nmodule-type: widget\n\nFields widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar FieldsWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nFieldsWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nFieldsWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar textNode = this.document.createTextNode(this.text);\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nFieldsWidget.prototype.execute = function() {\n\t// Get parameters from our attributes\n\tthis.tiddlerTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.template = this.getAttribute(\"template\");\n\tthis.sort = this.getAttribute(\"sort\",\"yes\") === \"yes\";\n\tthis.sortReverse = this.getAttribute(\"sortReverse\",\"no\") === \"yes\";\n\tthis.exclude = this.getAttribute(\"exclude\");\n\tthis.include = this.getAttribute(\"include\",null);\n\tthis.stripTitlePrefix = this.getAttribute(\"stripTitlePrefix\",\"no\") === \"yes\";\n\t// Get the value to display\n\tvar tiddler = this.wiki.getTiddler(this.tiddlerTitle);\n\n\t// Get the inclusion and exclusion list\n\tvar excludeArr = (this.exclude) ? this.exclude.split(\" \") : [\"text\"];\n\t// Include takes precedence\n\tvar includeArr = (this.include) ? this.include.split(\" \") : null;\n\n\t// Compose the template\n\tvar text = [];\n\tif(this.template && tiddler) {\n\t\tvar fields = [];\n\t\tif (includeArr) { // Include takes precedence\n\t\t\tfor(var i=0; i<includeArr.length; i++) {\n\t\t\t\tif(tiddler.fields[includeArr[i]]) {\n\t\t\t\t\tfields.push(includeArr[i]);\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tfor(var fieldName in tiddler.fields) {\n\t\t\t\tif(excludeArr.indexOf(fieldName) === -1) {\n\t\t\t\t\tfields.push(fieldName);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tif (this.sort) fields.sort();\n\t\tif (this.sortReverse) fields.reverse();\n\t\tfor(var f=0, fmax=fields.length; f<fmax; f++) {\n\t\t\tfieldName = fields[f];\n\t\t\tvar row = this.template,\n\t\t\t\tvalue = tiddler.getFieldString(fieldName);\n\t\t\tif(this.stripTitlePrefix && fieldName === \"title\") {\n\t\t\t\tvar reStrip = /^\\{[^\\}]+\\}(.+)/mg,\n\t\t\t\t\treMatch = reStrip.exec(value);\n\t\t\t\tif(reMatch) {\n\t\t\t\t\tvalue = reMatch[1];\n\t\t\t\t}\n\t\t\t}\n\t\t\trow = $tw.utils.replaceString(row,\"$name$\",fieldName);\n\t\t\trow = $tw.utils.replaceString(row,\"$value$\",value);\n\t\t\trow = $tw.utils.replaceString(row,\"$encoded_value$\",$tw.utils.htmlEncode(value));\n\t\t\ttext.push(row);\n\t\t}\n\t}\n\tthis.text = text.join(\"\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nFieldsWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif( changedAttributes.tiddler || changedAttributes.template || changedAttributes.exclude ||\n\t\tchangedAttributes.include || changedAttributes.sort || changedAttributes.sortReverse ||\n\t\tchangedTiddlers[this.tiddlerTitle] || changedAttributes.stripTitlePrefix) {\n\t\t\tthis.refreshSelf();\n\t\t\treturn true;\n\t} else {\n\t\treturn false;\n\t}\n};\n\nexports.fields = FieldsWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/image.js": {
"title": "$:/core/modules/widgets/image.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/image.js\ntype: application/javascript\nmodule-type: widget\n\nThe image widget displays an image referenced with an external URI or with a local tiddler title.\n\n```\n<$image src=\"TiddlerTitle\" width=\"320\" height=\"400\" class=\"classnames\">\n```\n\nThe image source can be the title of an existing tiddler or the URL of an external image.\n\nExternal images always generate an HTML `<img>` tag.\n\nTiddlers that have a _canonical_uri field generate an HTML `<img>` tag with the src attribute containing the URI.\n\nTiddlers that contain image data generate an HTML `<img>` tag with the src attribute containing a base64 representation of the image.\n\nTiddlers that contain wikitext could be rendered to a DIV of the usual size of a tiddler, and then transformed to the size requested.\n\nThe width and height attributes are interpreted as a number of pixels, and do not need to include the \"px\" suffix.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ImageWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nImageWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nImageWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create element\n\t// Determine what type of image it is\n\tvar tag = \"img\", src = \"\",\n\t\ttiddler = this.wiki.getTiddler(this.imageSource);\n\tif(!tiddler) {\n\t\t// The source isn't the title of a tiddler, so we'll assume it's a URL\n\t\tsrc = this.getVariable(\"tv-get-export-image-link\",{params: [{name: \"src\",value: this.imageSource}],defaultValue: this.imageSource});\n\t} else {\n\t\t// Check if it is an image tiddler\n\t\tif(this.wiki.isImageTiddler(this.imageSource)) {\n\t\t\tvar type = tiddler.fields.type,\n\t\t\t\ttext = tiddler.fields.text,\n\t\t\t\t_canonical_uri = tiddler.fields._canonical_uri;\n\t\t\t// If the tiddler has body text then it doesn't need to be lazily loaded\n\t\t\tif(text) {\n\t\t\t\t// Render the appropriate element for the image type\n\t\t\t\tswitch(type) {\n\t\t\t\t\tcase \"application/pdf\":\n\t\t\t\t\t\ttag = \"embed\";\n\t\t\t\t\t\tsrc = \"data:application/pdf;base64,\" + text;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase \"image/svg+xml\":\n\t\t\t\t\t\tsrc = \"data:image/svg+xml,\" + encodeURIComponent(text);\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tdefault:\n\t\t\t\t\t\tsrc = \"data:\" + type + \";base64,\" + text;\n\t\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t} else if(_canonical_uri) {\n\t\t\t\tswitch(type) {\n\t\t\t\t\tcase \"application/pdf\":\n\t\t\t\t\t\ttag = \"embed\";\n\t\t\t\t\t\tsrc = _canonical_uri;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase \"image/svg+xml\":\n\t\t\t\t\t\tsrc = _canonical_uri;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tdefault:\n\t\t\t\t\t\tsrc = _canonical_uri;\n\t\t\t\t\t\tbreak;\n\t\t\t\t}\t\n\t\t\t} else {\n\t\t\t\t// Just trigger loading of the tiddler\n\t\t\t\tthis.wiki.getTiddlerText(this.imageSource);\n\t\t\t}\n\t\t}\n\t}\n\t// Create the element and assign the attributes\n\tvar domNode = this.document.createElement(tag);\n\tdomNode.setAttribute(\"src\",src);\n\tif(this.imageClass) {\n\t\tdomNode.setAttribute(\"class\",this.imageClass);\t\t\n\t}\n\tif(this.imageWidth) {\n\t\tdomNode.setAttribute(\"width\",this.imageWidth);\n\t}\n\tif(this.imageHeight) {\n\t\tdomNode.setAttribute(\"height\",this.imageHeight);\n\t}\n\tif(this.imageTooltip) {\n\t\tdomNode.setAttribute(\"title\",this.imageTooltip);\t\t\n\t}\n\tif(this.imageAlt) {\n\t\tdomNode.setAttribute(\"alt\",this.imageAlt);\t\t\n\t}\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nImageWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.imageSource = this.getAttribute(\"source\");\n\tthis.imageWidth = this.getAttribute(\"width\");\n\tthis.imageHeight = this.getAttribute(\"height\");\n\tthis.imageClass = this.getAttribute(\"class\");\n\tthis.imageTooltip = this.getAttribute(\"tooltip\");\n\tthis.imageAlt = this.getAttribute(\"alt\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nImageWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.source || changedAttributes.width || changedAttributes.height || changedAttributes[\"class\"] || changedAttributes.tooltip || changedTiddlers[this.imageSource]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\t\n\t}\n};\n\nexports.image = ImageWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/importvariables.js": {
"title": "$:/core/modules/widgets/importvariables.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/importvariables.js\ntype: application/javascript\nmodule-type: widget\n\nImport variable definitions from other tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ImportVariablesWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nImportVariablesWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nImportVariablesWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nImportVariablesWidget.prototype.execute = function(tiddlerList) {\n\tvar widgetPointer = this;\n\t// Get our parameters\n\tthis.filter = this.getAttribute(\"filter\");\n\t// Compute the filter\n\tthis.tiddlerList = tiddlerList || this.wiki.filterTiddlers(this.filter,this);\n\t// Accumulate the <$set> widgets from each tiddler\n\t$tw.utils.each(this.tiddlerList,function(title) {\n\t\tvar parser = widgetPointer.wiki.parseTiddler(title);\n\t\tif(parser) {\n\t\t\tvar parseTreeNode = parser.tree[0];\n\t\t\twhile(parseTreeNode && parseTreeNode.type === \"set\") {\n\t\t\t\tvar node = {\n\t\t\t\t\ttype: \"set\",\n\t\t\t\t\tattributes: parseTreeNode.attributes,\n\t\t\t\t\tparams: parseTreeNode.params,\n\t\t\t\t\tisMacroDefinition: parseTreeNode.isMacroDefinition\n\t\t\t\t};\n\t\t\t\tif (parseTreeNode.isMacroDefinition) {\n\t\t\t\t\t// Macro definitions can be folded into\n\t\t\t\t\t// current widget instead of adding\n\t\t\t\t\t// another link to the chain.\n\t\t\t\t\tvar widget = widgetPointer.makeChildWidget(node);\n\t\t\t\t\twidget.computeAttributes();\n\t\t\t\t\twidget.execute();\n\t\t\t\t\t// We SHALLOW copy over all variables\n\t\t\t\t\t// in widget. We can't use\n\t\t\t\t\t// $tw.utils.assign, because that copies\n\t\t\t\t\t// up the prototype chain, which we\n\t\t\t\t\t// don't want.\n\t\t\t\t\t$tw.utils.each(Object.keys(widget.variables), function(key) {\n\t\t\t\t\t\twidgetPointer.variables[key] = widget.variables[key];\n\t\t\t\t\t});\n\t\t\t\t} else {\n\t\t\t\t\twidgetPointer.makeChildWidgets([node]);\n\t\t\t\t\twidgetPointer = widgetPointer.children[0];\n\t\t\t\t}\n\t\t\t\tparseTreeNode = parseTreeNode.children && parseTreeNode.children[0];\n\t\t\t}\n\t\t} \n\t});\n\n\tif (widgetPointer != this) {\n\t\twidgetPointer.parseTreeNode.children = this.parseTreeNode.children;\n\t} else {\n\t\twidgetPointer.makeChildWidgets();\n\t}\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nImportVariablesWidget.prototype.refresh = function(changedTiddlers) {\n\t// Recompute our attributes and the filter list\n\tvar changedAttributes = this.computeAttributes(),\n\t\ttiddlerList = this.wiki.filterTiddlers(this.getAttribute(\"filter\"),this);\n\t// Refresh if the filter has changed, or the list of tiddlers has changed, or any of the tiddlers in the list has changed\n\tfunction haveListedTiddlersChanged() {\n\t\tvar changed = false;\n\t\ttiddlerList.forEach(function(title) {\n\t\t\tif(changedTiddlers[title]) {\n\t\t\t\tchanged = true;\n\t\t\t}\n\t\t});\n\t\treturn changed;\n\t}\n\tif(changedAttributes.filter || !$tw.utils.isArrayEqual(this.tiddlerList,tiddlerList) || haveListedTiddlersChanged()) {\n\t\t// Compute the filter\n\t\tthis.removeChildDomNodes();\n\t\tthis.execute(tiddlerList);\n\t\tthis.renderChildren(this.parentDomNode,this.findNextSiblingDomNode());\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\nexports.importvariables = ImportVariablesWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/keyboard.js": {
"title": "$:/core/modules/widgets/keyboard.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/keyboard.js\ntype: application/javascript\nmodule-type: widget\n\nKeyboard shortcut widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar KeyboardWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nKeyboardWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nKeyboardWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar tag = this.parseTreeNode.isBlock ? \"div\" : \"span\";\n\tif(this.tag && $tw.config.htmlUnsafeElements.indexOf(this.tag) === -1) {\n\t\ttag = this.tag;\n\t}\n\t// Create element\n\tvar domNode = this.document.createElement(tag);\n\t// Assign classes\n\tvar classes = (this[\"class\"] || \"\").split(\" \");\n\tclasses.push(\"tc-keyboard\");\n\tdomNode.className = classes.join(\" \");\n\t// Add a keyboard event handler\n\tdomNode.addEventListener(\"keydown\",function (event) {\n\t\tif($tw.keyboardManager.checkKeyDescriptors(event,self.keyInfoArray)) {\n\t\t\tself.invokeActions(self,event);\n\t\t\tif(self.actions) {\n\t\t\t\tself.invokeActionString(self.actions,self,event);\n\t\t\t}\n\t\t\tself.dispatchMessage(event);\n\t\t\tevent.preventDefault();\n\t\t\tevent.stopPropagation();\n\t\t\treturn true;\n\t\t}\n\t\treturn false;\n\t},false);\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\nKeyboardWidget.prototype.dispatchMessage = function(event) {\n\tthis.dispatchEvent({type: this.message, param: this.param, tiddlerTitle: this.getVariable(\"currentTiddler\")});\n};\n\n/*\nCompute the internal state of the widget\n*/\nKeyboardWidget.prototype.execute = function() {\n\tvar self = this;\n\t// Get attributes\n\tthis.actions = this.getAttribute(\"actions\",\"\");\n\tthis.message = this.getAttribute(\"message\",\"\");\n\tthis.param = this.getAttribute(\"param\",\"\");\n\tthis.key = this.getAttribute(\"key\",\"\");\n\tthis.tag = this.getAttribute(\"tag\",\"\");\n\tthis.keyInfoArray = $tw.keyboardManager.parseKeyDescriptors(this.key);\n\tthis[\"class\"] = this.getAttribute(\"class\",\"\");\n\tif(this.key.substr(0,2) === \"((\" && this.key.substr(-2,2) === \"))\") {\n\t\tthis.shortcutTiddlers = [];\n\t\tvar name = this.key.substring(2,this.key.length -2);\n\t\t$tw.utils.each($tw.keyboardManager.lookupNames,function(platformDescriptor) {\n\t\t\tself.shortcutTiddlers.push(\"$:/config/\" + platformDescriptor + \"/\" + name);\n\t\t});\n\t}\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nKeyboardWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.message || changedAttributes.param || changedAttributes.key || changedAttributes[\"class\"] || changedAttributes.tag) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\t// Update the keyInfoArray if one of its shortcut-config-tiddlers has changed\n\tif(this.shortcutTiddlers && $tw.utils.hopArray(changedTiddlers,this.shortcutTiddlers)) {\n\t\tthis.keyInfoArray = $tw.keyboardManager.parseKeyDescriptors(this.key);\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.keyboard = KeyboardWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/link.js": {
"title": "$:/core/modules/widgets/link.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/link.js\ntype: application/javascript\nmodule-type: widget\n\nLink widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar LinkWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nLinkWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nLinkWidget.prototype.render = function(parent,nextSibling) {\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Get the value of the tv-wikilinks configuration macro\n\tvar wikiLinksMacro = this.getVariable(\"tv-wikilinks\"),\n\t\tuseWikiLinks = wikiLinksMacro ? (wikiLinksMacro.trim() !== \"no\") : true,\n\t\tmissingLinksEnabled = !(this.hideMissingLinks && this.isMissing && !this.isShadow);\n\t// Render the link if required\n\tif(useWikiLinks && missingLinksEnabled) {\n\t\tthis.renderLink(parent,nextSibling);\n\t} else {\n\t\t// Just insert the link text\n\t\tvar domNode = this.document.createElement(\"span\");\n\t\tparent.insertBefore(domNode,nextSibling);\n\t\tthis.renderChildren(domNode,null);\n\t\tthis.domNodes.push(domNode);\n\t}\n};\n\n/*\nRender this widget into the DOM\n*/\nLinkWidget.prototype.renderLink = function(parent,nextSibling) {\n\tvar self = this;\n\t// Sanitise the specified tag\n\tvar tag = this.linkTag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"a\";\n\t}\n\t// Create our element\n\tvar domNode = this.document.createElement(tag);\n\t// Assign classes\n\tvar classes = [];\n\tif(this.overrideClasses === undefined) {\n\t\tclasses.push(\"tc-tiddlylink\");\n\t\tif(this.isShadow) {\n\t\t\tclasses.push(\"tc-tiddlylink-shadow\");\n\t\t}\n\t\tif(this.isMissing && !this.isShadow) {\n\t\t\tclasses.push(\"tc-tiddlylink-missing\");\n\t\t} else {\n\t\t\tif(!this.isMissing) {\n\t\t\t\tclasses.push(\"tc-tiddlylink-resolves\");\n\t\t\t}\n\t\t}\n\t\tif(this.linkClasses) {\n\t\t\tclasses.push(this.linkClasses);\t\t\t\n\t\t}\n\t} else if(this.overrideClasses !== \"\") {\n\t\tclasses.push(this.overrideClasses)\n\t}\n\tif(classes.length > 0) {\n\t\tdomNode.setAttribute(\"class\",classes.join(\" \"));\n\t}\n\t// Set an href\n\tvar wikilinkTransformFilter = this.getVariable(\"tv-filter-export-link\"),\n\t\twikiLinkText;\n\tif(wikilinkTransformFilter) {\n\t\t// Use the filter to construct the href\n\t\twikiLinkText = this.wiki.filterTiddlers(wikilinkTransformFilter,this,function(iterator) {\n\t\t\titerator(self.wiki.getTiddler(self.to),self.to)\n\t\t})[0];\n\t} else {\n\t\t// Expand the tv-wikilink-template variable to construct the href\n\t\tvar wikiLinkTemplateMacro = this.getVariable(\"tv-wikilink-template\"),\n\t\t\twikiLinkTemplate = wikiLinkTemplateMacro ? wikiLinkTemplateMacro.trim() : \"#$uri_encoded$\";\n\t\twikiLinkText = $tw.utils.replaceString(wikiLinkTemplate,\"$uri_encoded$\",encodeURIComponent(this.to));\n\t\twikiLinkText = $tw.utils.replaceString(wikiLinkText,\"$uri_doubleencoded$\",encodeURIComponent(encodeURIComponent(this.to)));\n\t}\n\t// Override with the value of tv-get-export-link if defined\n\twikiLinkText = this.getVariable(\"tv-get-export-link\",{params: [{name: \"to\",value: this.to}],defaultValue: wikiLinkText});\n\tif(tag === \"a\") {\n\t\tdomNode.setAttribute(\"href\",wikiLinkText);\n\t}\n\t// Set the tabindex\n\tif(this.tabIndex) {\n\t\tdomNode.setAttribute(\"tabindex\",this.tabIndex);\n\t}\n\t// Set the tooltip\n\t// HACK: Performance issues with re-parsing the tooltip prevent us defaulting the tooltip to \"<$transclude field='tooltip'><$transclude field='title'/></$transclude>\"\n\tvar tooltipWikiText = this.tooltip || this.getVariable(\"tv-wikilink-tooltip\");\n\tif(tooltipWikiText) {\n\t\tvar tooltipText = this.wiki.renderText(\"text/plain\",\"text/vnd.tiddlywiki\",tooltipWikiText,{\n\t\t\t\tparseAsInline: true,\n\t\t\t\tvariables: {\n\t\t\t\t\tcurrentTiddler: this.to\n\t\t\t\t},\n\t\t\t\tparentWidget: this\n\t\t\t});\n\t\tdomNode.setAttribute(\"title\",tooltipText);\n\t}\n\tif(this[\"aria-label\"]) {\n\t\tdomNode.setAttribute(\"aria-label\",this[\"aria-label\"]);\n\t}\n\t// Add a click event handler\n\t$tw.utils.addEventListeners(domNode,[\n\t\t{name: \"click\", handlerObject: this, handlerMethod: \"handleClickEvent\"},\n\t]);\n\t// Make the link draggable if required\n\tif(this.draggable === \"yes\") {\n\t\t$tw.utils.makeDraggable({\n\t\t\tdomNode: domNode,\n\t\t\tdragTiddlerFn: function() {return self.to;},\n\t\t\twidget: this\n\t\t});\n\t}\n\t// Insert the link into the DOM and render any children\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\nLinkWidget.prototype.handleClickEvent = function(event) {\n\t// Send the click on its way as a navigate event\n\tvar bounds = this.domNodes[0].getBoundingClientRect();\n\tthis.dispatchEvent({\n\t\ttype: \"tm-navigate\",\n\t\tnavigateTo: this.to,\n\t\tnavigateFromTitle: this.getVariable(\"storyTiddler\"),\n\t\tnavigateFromNode: this,\n\t\tnavigateFromClientRect: { top: bounds.top, left: bounds.left, width: bounds.width, right: bounds.right, bottom: bounds.bottom, height: bounds.height\n\t\t},\n\t\tnavigateSuppressNavigation: event.metaKey || event.ctrlKey || (event.button === 1),\n\t\tmetaKey: event.metaKey,\n\t\tctrlKey: event.ctrlKey,\n\t\taltKey: event.altKey,\n\t\tshiftKey: event.shiftKey\n\t});\n\tif(this.domNodes[0].hasAttribute(\"href\")) {\n\t\tevent.preventDefault();\n\t}\n\tevent.stopPropagation();\n\treturn false;\n};\n\n/*\nCompute the internal state of the widget\n*/\nLinkWidget.prototype.execute = function() {\n\t// Pick up our attributes\n\tthis.to = this.getAttribute(\"to\",this.getVariable(\"currentTiddler\"));\n\tthis.tooltip = this.getAttribute(\"tooltip\");\n\tthis[\"aria-label\"] = this.getAttribute(\"aria-label\");\n\tthis.linkClasses = this.getAttribute(\"class\");\n\tthis.overrideClasses = this.getAttribute(\"overrideClass\");\n\tthis.tabIndex = this.getAttribute(\"tabindex\");\n\tthis.draggable = this.getAttribute(\"draggable\",\"yes\");\n\tthis.linkTag = this.getAttribute(\"tag\",\"a\");\n\t// Determine the link characteristics\n\tthis.isMissing = !this.wiki.tiddlerExists(this.to);\n\tthis.isShadow = this.wiki.isShadowTiddler(this.to);\n\tthis.hideMissingLinks = (this.getVariable(\"tv-show-missing-links\") || \"yes\") === \"no\";\n\t// Make the child widgets\n\tvar templateTree;\n\tif(this.parseTreeNode.children && this.parseTreeNode.children.length > 0) {\n\t\ttemplateTree = this.parseTreeNode.children;\n\t} else {\n\t\t// Default template is a link to the title\n\t\ttemplateTree = [{type: \"text\", text: this.to}];\n\t}\n\tthis.makeChildWidgets(templateTree);\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nLinkWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.to || changedTiddlers[this.to] || changedAttributes[\"aria-label\"] || changedAttributes.tooltip) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.link = LinkWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/linkcatcher.js": {
"title": "$:/core/modules/widgets/linkcatcher.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/linkcatcher.js\ntype: application/javascript\nmodule-type: widget\n\nLinkcatcher widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar LinkCatcherWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n\tthis.addEventListeners([\n\t\t{type: \"tm-navigate\", handler: \"handleNavigateEvent\"}\n\t]);\n};\n\n/*\nInherit from the base widget class\n*/\nLinkCatcherWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nLinkCatcherWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nLinkCatcherWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.catchTo = this.getAttribute(\"to\");\n\tthis.catchMessage = this.getAttribute(\"message\");\n\tthis.catchSet = this.getAttribute(\"set\");\n\tthis.catchSetTo = this.getAttribute(\"setTo\");\n\tthis.catchActions = this.getAttribute(\"actions\");\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n\t// When executing actions we avoid trapping navigate events, so that we don't trigger ourselves recursively\n\tthis.executingActions = false;\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nLinkCatcherWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.to || changedAttributes.message || changedAttributes.set || changedAttributes.setTo) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\n/*\nHandle a tm-navigate event\n*/\nLinkCatcherWidget.prototype.handleNavigateEvent = function(event) {\n\tif(!this.executingActions) {\n\t\t// Execute the actions\n\t\tif(this.catchTo) {\n\t\t\tthis.wiki.setTextReference(this.catchTo,event.navigateTo,this.getVariable(\"currentTiddler\"));\n\t\t}\n\t\tif(this.catchMessage && this.parentWidget) {\n\t\t\tthis.parentWidget.dispatchEvent({\n\t\t\t\ttype: this.catchMessage,\n\t\t\t\tparam: event.navigateTo,\n\t\t\t\tnavigateTo: event.navigateTo\n\t\t\t});\n\t\t}\n\t\tif(this.catchSet) {\n\t\t\tvar tiddler = this.wiki.getTiddler(this.catchSet);\n\t\t\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,{title: this.catchSet, text: this.catchSetTo}));\n\t\t}\n\t\tif(this.catchActions) {\n\t\t\tthis.executingActions = true;\n\t\t\tthis.invokeActionString(this.catchActions,this,event,{navigateTo: event.navigateTo});\n\t\t\tthis.executingActions = false;\n\t\t}\n\t} else {\n\t\t// This is a navigate event generated by the actions of this linkcatcher, so we don't trap it again, but just pass it to the parent\n\t\tthis.parentWidget.dispatchEvent({\n\t\t\ttype: \"tm-navigate\",\n\t\t\tparam: event.navigateTo,\n\t\t\tnavigateTo: event.navigateTo\n\t\t});\n\t}\n\treturn false;\n};\n\nexports.linkcatcher = LinkCatcherWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/list.js": {
"title": "$:/core/modules/widgets/list.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/list.js\ntype: application/javascript\nmodule-type: widget\n\nList and list item widgets\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\n/*\nThe list widget creates list element sub-widgets that reach back into the list widget for their configuration\n*/\n\nvar ListWidget = function(parseTreeNode,options) {\n\t// Initialise the storyviews if they've not been done already\n\tif(!this.storyViews) {\n\t\tListWidget.prototype.storyViews = {};\n\t\t$tw.modules.applyMethods(\"storyview\",this.storyViews);\n\t}\n\t// Main initialisation inherited from widget.js\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nListWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nListWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n\t// Construct the storyview\n\tvar StoryView = this.storyViews[this.storyViewName];\n\tif(this.storyViewName && !StoryView) {\n\t\tStoryView = this.storyViews[\"classic\"];\n\t}\n\tif(StoryView && !this.document.isTiddlyWikiFakeDom) {\n\t\tthis.storyview = new StoryView(this);\n\t} else {\n\t\tthis.storyview = null;\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nListWidget.prototype.execute = function() {\n\t// Get our attributes\n\tthis.template = this.getAttribute(\"template\");\n\tthis.editTemplate = this.getAttribute(\"editTemplate\");\n\tthis.variableName = this.getAttribute(\"variable\",\"currentTiddler\");\n\tthis.storyViewName = this.getAttribute(\"storyview\");\n\tthis.historyTitle = this.getAttribute(\"history\");\n\t// Compose the list elements\n\tthis.list = this.getTiddlerList();\n\tvar members = [],\n\t\tself = this;\n\t// Check for an empty list\n\tif(this.list.length === 0) {\n\t\tmembers = this.getEmptyMessage();\n\t} else {\n\t\t$tw.utils.each(this.list,function(title,index) {\n\t\t\tmembers.push(self.makeItemTemplate(title));\n\t\t});\n\t}\n\t// Construct the child widgets\n\tthis.makeChildWidgets(members);\n\t// Clear the last history\n\tthis.history = [];\n};\n\nListWidget.prototype.getTiddlerList = function() {\n\tvar defaultFilter = \"[!is[system]sort[title]]\";\n\treturn this.wiki.filterTiddlers(this.getAttribute(\"filter\",defaultFilter),this);\n};\n\nListWidget.prototype.getEmptyMessage = function() {\n\tvar emptyMessage = this.getAttribute(\"emptyMessage\",\"\"),\n\t\tparser = this.wiki.parseText(\"text/vnd.tiddlywiki\",emptyMessage,{parseAsInline: true});\n\tif(parser) {\n\t\treturn parser.tree;\n\t} else {\n\t\treturn [];\n\t}\n};\n\n/*\nCompose the template for a list item\n*/\nListWidget.prototype.makeItemTemplate = function(title) {\n\t// Check if the tiddler is a draft\n\tvar tiddler = this.wiki.getTiddler(title),\n\t\tisDraft = tiddler && tiddler.hasField(\"draft.of\"),\n\t\ttemplate = this.template,\n\t\ttemplateTree;\n\tif(isDraft && this.editTemplate) {\n\t\ttemplate = this.editTemplate;\n\t}\n\t// Compose the transclusion of the template\n\tif(template) {\n\t\ttemplateTree = [{type: \"transclude\", attributes: {tiddler: {type: \"string\", value: template}}}];\n\t} else {\n\t\tif(this.parseTreeNode.children && this.parseTreeNode.children.length > 0) {\n\t\t\ttemplateTree = this.parseTreeNode.children;\n\t\t} else {\n\t\t\t// Default template is a link to the title\n\t\t\ttemplateTree = [{type: \"element\", tag: this.parseTreeNode.isBlock ? \"div\" : \"span\", children: [{type: \"link\", attributes: {to: {type: \"string\", value: title}}, children: [\n\t\t\t\t\t{type: \"text\", text: title}\n\t\t\t]}]}];\n\t\t}\n\t}\n\t// Return the list item\n\treturn {type: \"listitem\", itemTitle: title, variableName: this.variableName, children: templateTree};\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nListWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes(),\n\t\tresult;\n\t// Call the storyview\n\tif(this.storyview && this.storyview.refreshStart) {\n\t\tthis.storyview.refreshStart(changedTiddlers,changedAttributes);\n\t}\n\t// Completely refresh if any of our attributes have changed\n\tif(changedAttributes.filter || changedAttributes.template || changedAttributes.editTemplate || changedAttributes.emptyMessage || changedAttributes.storyview || changedAttributes.history) {\n\t\tthis.refreshSelf();\n\t\tresult = true;\n\t} else {\n\t\t// Handle any changes to the list\n\t\tresult = this.handleListChanges(changedTiddlers);\n\t\t// Handle any changes to the history stack\n\t\tif(this.historyTitle && changedTiddlers[this.historyTitle]) {\n\t\t\tthis.handleHistoryChanges();\n\t\t}\n\t}\n\t// Call the storyview\n\tif(this.storyview && this.storyview.refreshEnd) {\n\t\tthis.storyview.refreshEnd(changedTiddlers,changedAttributes);\n\t}\n\treturn result;\n};\n\n/*\nHandle any changes to the history list\n*/\nListWidget.prototype.handleHistoryChanges = function() {\n\t// Get the history data\n\tvar newHistory = this.wiki.getTiddlerDataCached(this.historyTitle,[]);\n\t// Ignore any entries of the history that match the previous history\n\tvar entry = 0;\n\twhile(entry < newHistory.length && entry < this.history.length && newHistory[entry].title === this.history[entry].title) {\n\t\tentry++;\n\t}\n\t// Navigate forwards to each of the new tiddlers\n\twhile(entry < newHistory.length) {\n\t\tif(this.storyview && this.storyview.navigateTo) {\n\t\t\tthis.storyview.navigateTo(newHistory[entry]);\n\t\t}\n\t\tentry++;\n\t}\n\t// Update the history\n\tthis.history = newHistory;\n};\n\n/*\nProcess any changes to the list\n*/\nListWidget.prototype.handleListChanges = function(changedTiddlers) {\n\t// Get the new list\n\tvar prevList = this.list;\n\tthis.list = this.getTiddlerList();\n\t// Check for an empty list\n\tif(this.list.length === 0) {\n\t\t// Check if it was empty before\n\t\tif(prevList.length === 0) {\n\t\t\t// If so, just refresh the empty message\n\t\t\treturn this.refreshChildren(changedTiddlers);\n\t\t} else {\n\t\t\t// Replace the previous content with the empty message\n\t\t\tfor(t=this.children.length-1; t>=0; t--) {\n\t\t\t\tthis.removeListItem(t);\n\t\t\t}\n\t\t\tvar nextSibling = this.findNextSiblingDomNode();\n\t\t\tthis.makeChildWidgets(this.getEmptyMessage());\n\t\t\tthis.renderChildren(this.parentDomNode,nextSibling);\n\t\t\treturn true;\n\t\t}\n\t} else {\n\t\t// If the list was empty then we need to remove the empty message\n\t\tif(prevList.length === 0) {\n\t\t\tthis.removeChildDomNodes();\n\t\t\tthis.children = [];\n\t\t}\n\t\t// Cycle through the list, inserting and removing list items as needed\n\t\tvar hasRefreshed = false;\n\t\tfor(var t=0; t<this.list.length; t++) {\n\t\t\tvar index = this.findListItem(t,this.list[t]);\n\t\t\tif(index === undefined) {\n\t\t\t\t// The list item must be inserted\n\t\t\t\tthis.insertListItem(t,this.list[t]);\n\t\t\t\thasRefreshed = true;\n\t\t\t} else {\n\t\t\t\t// There are intervening list items that must be removed\n\t\t\t\tfor(var n=index-1; n>=t; n--) {\n\t\t\t\t\tthis.removeListItem(n);\n\t\t\t\t\thasRefreshed = true;\n\t\t\t\t}\n\t\t\t\t// Refresh the item we're reusing\n\t\t\t\tvar refreshed = this.children[t].refresh(changedTiddlers);\n\t\t\t\thasRefreshed = hasRefreshed || refreshed;\n\t\t\t}\n\t\t}\n\t\t// Remove any left over items\n\t\tfor(t=this.children.length-1; t>=this.list.length; t--) {\n\t\t\tthis.removeListItem(t);\n\t\t\thasRefreshed = true;\n\t\t}\n\t\treturn hasRefreshed;\n\t}\n};\n\n/*\nFind the list item with a given title, starting from a specified position\n*/\nListWidget.prototype.findListItem = function(startIndex,title) {\n\twhile(startIndex < this.children.length) {\n\t\tif(this.children[startIndex].parseTreeNode.itemTitle === title) {\n\t\t\treturn startIndex;\n\t\t}\n\t\tstartIndex++;\n\t}\n\treturn undefined;\n};\n\n/*\nInsert a new list item at the specified index\n*/\nListWidget.prototype.insertListItem = function(index,title) {\n\t// Create, insert and render the new child widgets\n\tvar widget = this.makeChildWidget(this.makeItemTemplate(title));\n\twidget.parentDomNode = this.parentDomNode; // Hack to enable findNextSiblingDomNode() to work\n\tthis.children.splice(index,0,widget);\n\tvar nextSibling = widget.findNextSiblingDomNode();\n\twidget.render(this.parentDomNode,nextSibling);\n\t// Animate the insertion if required\n\tif(this.storyview && this.storyview.insert) {\n\t\tthis.storyview.insert(widget);\n\t}\n\treturn true;\n};\n\n/*\nRemove the specified list item\n*/\nListWidget.prototype.removeListItem = function(index) {\n\tvar widget = this.children[index];\n\t// Animate the removal if required\n\tif(this.storyview && this.storyview.remove) {\n\t\tthis.storyview.remove(widget);\n\t} else {\n\t\twidget.removeChildDomNodes();\n\t}\n\t// Remove the child widget\n\tthis.children.splice(index,1);\n};\n\nexports.list = ListWidget;\n\nvar ListItemWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nListItemWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nListItemWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nListItemWidget.prototype.execute = function() {\n\t// Set the current list item title\n\tthis.setVariable(this.parseTreeNode.variableName,this.parseTreeNode.itemTitle);\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nListItemWidget.prototype.refresh = function(changedTiddlers) {\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.listitem = ListItemWidget;\n\n})();",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/macrocall.js": {
"title": "$:/core/modules/widgets/macrocall.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/macrocall.js\ntype: application/javascript\nmodule-type: widget\n\nMacrocall widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar MacroCallWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nMacroCallWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nMacroCallWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nMacroCallWidget.prototype.execute = function() {\n\t// Get the parse type if specified\n\tthis.parseType = this.getAttribute(\"$type\",\"text/vnd.tiddlywiki\");\n\tthis.renderOutput = this.getAttribute(\"$output\",\"text/html\");\n\t// Merge together the parameters specified in the parse tree with the specified attributes\n\tvar params = this.parseTreeNode.params ? this.parseTreeNode.params.slice(0) : [];\n\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\tif(name.charAt(0) !== \"$\") {\n\t\t\tparams.push({name: name, value: attribute});\t\t\t\n\t\t}\n\t});\n\t// Get the macro value\n\tvar macroName = this.parseTreeNode.name || this.getAttribute(\"$name\"),\n\t\tvariableInfo = this.getVariableInfo(macroName,{params: params}),\n\t\ttext = variableInfo.text,\n\t\tparseTreeNodes;\n\t// Are we rendering to HTML?\n\tif(this.renderOutput === \"text/html\") {\n\t\t// If so we'll return the parsed macro\n\t\tvar parser = this.wiki.parseText(this.parseType,text,\n\t\t\t\t\t\t\t{parseAsInline: !this.parseTreeNode.isBlock});\n\t\tparseTreeNodes = parser ? parser.tree : [];\n\t\t// Wrap the parse tree in a vars widget assigning the parameters to variables named \"__paramname__\"\n\t\tvar attributes = {};\n\t\t$tw.utils.each(variableInfo.params,function(param) {\n\t\t\tvar name = \"__\" + param.name + \"__\";\n\t\t\tattributes[name] = {\n\t\t\t\tname: name,\n\t\t\t\ttype: \"string\",\n\t\t\t\tvalue: param.value\n\t\t\t};\n\t\t});\n\t\tparseTreeNodes = [{\n\t\t\ttype: \"vars\",\n\t\t\tattributes: attributes,\n\t\t\tchildren: parseTreeNodes\n\t\t}];\n\t} else {\n\t\t// Otherwise, we'll render the text\n\t\tvar plainText = this.wiki.renderText(\"text/plain\",this.parseType,text,{parentWidget: this});\n\t\tparseTreeNodes = [{type: \"text\", text: plainText}];\n\t}\n\t// Construct the child widgets\n\tthis.makeChildWidgets(parseTreeNodes);\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nMacroCallWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif($tw.utils.count(changedAttributes) > 0) {\n\t\t// Rerender ourselves\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.macrocall = MacroCallWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/navigator.js": {
"title": "$:/core/modules/widgets/navigator.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/navigator.js\ntype: application/javascript\nmodule-type: widget\n\nNavigator widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar IMPORT_TITLE = \"$:/Import\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar NavigatorWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n\tthis.addEventListeners([\n\t\t{type: \"tm-navigate\", handler: \"handleNavigateEvent\"},\n\t\t{type: \"tm-edit-tiddler\", handler: \"handleEditTiddlerEvent\"},\n\t\t{type: \"tm-delete-tiddler\", handler: \"handleDeleteTiddlerEvent\"},\n\t\t{type: \"tm-save-tiddler\", handler: \"handleSaveTiddlerEvent\"},\n\t\t{type: \"tm-cancel-tiddler\", handler: \"handleCancelTiddlerEvent\"},\n\t\t{type: \"tm-close-tiddler\", handler: \"handleCloseTiddlerEvent\"},\n\t\t{type: \"tm-close-all-tiddlers\", handler: \"handleCloseAllTiddlersEvent\"},\n\t\t{type: \"tm-close-other-tiddlers\", handler: \"handleCloseOtherTiddlersEvent\"},\n\t\t{type: \"tm-new-tiddler\", handler: \"handleNewTiddlerEvent\"},\n\t\t{type: \"tm-import-tiddlers\", handler: \"handleImportTiddlersEvent\"},\n\t\t{type: \"tm-perform-import\", handler: \"handlePerformImportEvent\"},\n\t\t{type: \"tm-fold-tiddler\", handler: \"handleFoldTiddlerEvent\"},\n\t\t{type: \"tm-fold-other-tiddlers\", handler: \"handleFoldOtherTiddlersEvent\"},\n\t\t{type: \"tm-fold-all-tiddlers\", handler: \"handleFoldAllTiddlersEvent\"},\n\t\t{type: \"tm-unfold-all-tiddlers\", handler: \"handleUnfoldAllTiddlersEvent\"},\n\t\t{type: \"tm-rename-tiddler\", handler: \"handleRenameTiddlerEvent\"}\n\t]);\n};\n\n/*\nInherit from the base widget class\n*/\nNavigatorWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nNavigatorWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nNavigatorWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.storyTitle = this.getAttribute(\"story\");\n\tthis.historyTitle = this.getAttribute(\"history\");\n\tthis.setVariable(\"tv-story-list\",this.storyTitle);\n\tthis.setVariable(\"tv-history-list\",this.historyTitle);\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nNavigatorWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.story || changedAttributes.history) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nNavigatorWidget.prototype.getStoryList = function() {\n\treturn this.storyTitle ? this.wiki.getTiddlerList(this.storyTitle) : null;\n};\n\nNavigatorWidget.prototype.saveStoryList = function(storyList) {\n\tif(this.storyTitle) {\n\t\tvar storyTiddler = this.wiki.getTiddler(this.storyTitle);\n\t\tthis.wiki.addTiddler(new $tw.Tiddler(\n\t\t\t{title: this.storyTitle},\n\t\t\tstoryTiddler,\n\t\t\t{list: storyList}\n\t\t));\t\t\n\t}\n};\n\nNavigatorWidget.prototype.removeTitleFromStory = function(storyList,title) {\n\tif(storyList) {\n\t\tvar p = storyList.indexOf(title);\n\t\twhile(p !== -1) {\n\t\t\tstoryList.splice(p,1);\n\t\t\tp = storyList.indexOf(title);\n\t\t}\t\t\n\t}\n};\n\nNavigatorWidget.prototype.replaceFirstTitleInStory = function(storyList,oldTitle,newTitle) {\n\tif(storyList) {\n\t\tvar pos = storyList.indexOf(oldTitle);\n\t\tif(pos !== -1) {\n\t\t\tstoryList[pos] = newTitle;\n\t\t\tdo {\n\t\t\t\tpos = storyList.indexOf(oldTitle,pos + 1);\n\t\t\t\tif(pos !== -1) {\n\t\t\t\t\tstoryList.splice(pos,1);\n\t\t\t\t}\n\t\t\t} while(pos !== -1);\n\t\t} else {\n\t\t\tstoryList.splice(0,0,newTitle);\n\t\t}\t\t\n\t}\n};\n\nNavigatorWidget.prototype.addToStory = function(title,fromTitle) {\n\tif(this.storyTitle) {\n\t\tthis.wiki.addToStory(title,fromTitle,this.storyTitle,{\n\t\t\topenLinkFromInsideRiver: this.getAttribute(\"openLinkFromInsideRiver\",\"top\"),\n\t\t\topenLinkFromOutsideRiver: this.getAttribute(\"openLinkFromOutsideRiver\",\"top\")\n\t\t});\n\t}\n};\n\n/*\nAdd a new record to the top of the history stack\ntitle: a title string or an array of title strings\nfromPageRect: page coordinates of the origin of the navigation\n*/\nNavigatorWidget.prototype.addToHistory = function(title,fromPageRect) {\n\tthis.wiki.addToHistory(title,fromPageRect,this.historyTitle);\n};\n\n/*\nHandle a tm-navigate event\n*/\nNavigatorWidget.prototype.handleNavigateEvent = function(event) {\n\tevent = $tw.hooks.invokeHook(\"th-navigating\",event);\n\tif(event.navigateTo) {\n\t\tthis.addToStory(event.navigateTo,event.navigateFromTitle);\n\t\tif(!event.navigateSuppressNavigation) {\n\t\t\tthis.addToHistory(event.navigateTo,event.navigateFromClientRect);\n\t\t}\n\t}\n\treturn false;\n};\n\n// Close a specified tiddler\nNavigatorWidget.prototype.handleCloseTiddlerEvent = function(event) {\n\tvar title = event.param || event.tiddlerTitle,\n\t\tstoryList = this.getStoryList();\n\t// Look for tiddlers with this title to close\n\tthis.removeTitleFromStory(storyList,title);\n\tthis.saveStoryList(storyList);\n\treturn false;\n};\n\n// Close all tiddlers\nNavigatorWidget.prototype.handleCloseAllTiddlersEvent = function(event) {\n\tthis.saveStoryList([]);\n\treturn false;\n};\n\n// Close other tiddlers\nNavigatorWidget.prototype.handleCloseOtherTiddlersEvent = function(event) {\n\tvar title = event.param || event.tiddlerTitle;\n\tthis.saveStoryList([title]);\n\treturn false;\n};\n\n// Place a tiddler in edit mode\nNavigatorWidget.prototype.handleEditTiddlerEvent = function(event) {\n\tvar editTiddler = $tw.hooks.invokeHook(\"th-editing-tiddler\",event);\n\tif(!editTiddler) {\n\t\treturn false;\n\t}\n\tvar self = this;\n\tfunction isUnmodifiedShadow(title) {\n\t\treturn self.wiki.isShadowTiddler(title) && !self.wiki.tiddlerExists(title);\n\t}\n\tfunction confirmEditShadow(title) {\n\t\treturn confirm($tw.language.getString(\n\t\t\t\"ConfirmEditShadowTiddler\",\n\t\t\t{variables:\n\t\t\t\t{title: title}\n\t\t\t}\n\t\t));\n\t}\n\tvar title = event.param || event.tiddlerTitle;\n\tif(isUnmodifiedShadow(title) && !confirmEditShadow(title)) {\n\t\treturn false;\n\t}\n\t// Replace the specified tiddler with a draft in edit mode\n\tvar draftTiddler = this.makeDraftTiddler(title);\n\t// Update the story and history if required\n\tif(!event.paramObject || event.paramObject.suppressNavigation !== \"yes\") {\n\t\tvar draftTitle = draftTiddler.fields.title,\n\t\t\tstoryList = this.getStoryList();\n\t\tthis.removeTitleFromStory(storyList,draftTitle);\n\t\tthis.replaceFirstTitleInStory(storyList,title,draftTitle);\n\t\tthis.addToHistory(draftTitle,event.navigateFromClientRect);\n\t\tthis.saveStoryList(storyList);\n\t\treturn false;\n\t}\n};\n\n// Delete a tiddler\nNavigatorWidget.prototype.handleDeleteTiddlerEvent = function(event) {\n\t// Get the tiddler we're deleting\n\tvar title = event.param || event.tiddlerTitle,\n\t\ttiddler = this.wiki.getTiddler(title),\n\t\tstoryList = this.getStoryList(),\n\t\toriginalTitle = tiddler ? tiddler.fields[\"draft.of\"] : \"\",\n\t\toriginalTiddler = originalTitle ? this.wiki.getTiddler(originalTitle) : undefined,\n\t\tconfirmationTitle;\n\tif(!tiddler) {\n\t\treturn false;\n\t}\n\t// Check if the tiddler we're deleting is in draft mode\n\tif(originalTitle) {\n\t\t// If so, we'll prompt for confirmation referencing the original tiddler\n\t\tconfirmationTitle = originalTitle;\n\t} else {\n\t\t// If not a draft, then prompt for confirmation referencing the specified tiddler\n\t\tconfirmationTitle = title;\n\t}\n\t// Seek confirmation\n\tif((this.wiki.getTiddler(originalTitle) || (tiddler.fields.text || \"\") !== \"\") && !confirm($tw.language.getString(\n\t\t\t\t\"ConfirmDeleteTiddler\",\n\t\t\t\t{variables:\n\t\t\t\t\t{title: confirmationTitle}\n\t\t\t\t}\n\t\t\t))) {\n\t\treturn false;\n\t}\n\t// Delete the original tiddler\n\tif(originalTitle) {\n\t\tif(originalTiddler) {\n\t\t\t$tw.hooks.invokeHook(\"th-deleting-tiddler\",originalTiddler);\n\t\t}\n\t\tthis.wiki.deleteTiddler(originalTitle);\n\t\tthis.removeTitleFromStory(storyList,originalTitle);\n\t}\n\t// Invoke the hook function and delete this tiddler\n\t$tw.hooks.invokeHook(\"th-deleting-tiddler\",tiddler);\n\tthis.wiki.deleteTiddler(title);\n\t// Remove the closed tiddler from the story\n\tthis.removeTitleFromStory(storyList,title);\n\tthis.saveStoryList(storyList);\n\t// Trigger an autosave\n\t$tw.rootWidget.dispatchEvent({type: \"tm-auto-save-wiki\"});\n\treturn false;\n};\n\n/*\nCreate/reuse the draft tiddler for a given title\n*/\nNavigatorWidget.prototype.makeDraftTiddler = function(targetTitle) {\n\t// See if there is already a draft tiddler for this tiddler\n\tvar draftTitle = this.wiki.findDraft(targetTitle);\n\tif(draftTitle) {\n\t\treturn this.wiki.getTiddler(draftTitle);\n\t}\n\t// Get the current value of the tiddler we're editing\n\tvar tiddler = this.wiki.getTiddler(targetTitle);\n\t// Save the initial value of the draft tiddler\n\tdraftTitle = this.generateDraftTitle(targetTitle);\n\tvar draftTiddler = new $tw.Tiddler(\n\t\t\ttiddler,\n\t\t\t{\n\t\t\t\ttitle: draftTitle,\n\t\t\t\t\"draft.title\": targetTitle,\n\t\t\t\t\"draft.of\": targetTitle\n\t\t\t},\n\t\t\tthis.wiki.getModificationFields()\n\t\t);\n\tthis.wiki.addTiddler(draftTiddler);\n\treturn draftTiddler;\n};\n\n/*\nGenerate a title for the draft of a given tiddler\n*/\nNavigatorWidget.prototype.generateDraftTitle = function(title) {\n\treturn this.wiki.generateDraftTitle(title);\n};\n\n// Take a tiddler out of edit mode, saving the changes\nNavigatorWidget.prototype.handleSaveTiddlerEvent = function(event) {\n\tvar title = event.param || event.tiddlerTitle,\n\t\ttiddler = this.wiki.getTiddler(title),\n\t\tstoryList = this.getStoryList();\n\t// Replace the original tiddler with the draft\n\tif(tiddler) {\n\t\tvar draftTitle = (tiddler.fields[\"draft.title\"] || \"\").trim(),\n\t\t\tdraftOf = (tiddler.fields[\"draft.of\"] || \"\").trim();\n\t\tif(draftTitle) {\n\t\t\tvar isRename = draftOf !== draftTitle,\n\t\t\t\tisConfirmed = true;\n\t\t\tif(isRename && this.wiki.tiddlerExists(draftTitle)) {\n\t\t\t\tisConfirmed = confirm($tw.language.getString(\n\t\t\t\t\t\"ConfirmOverwriteTiddler\",\n\t\t\t\t\t{variables:\n\t\t\t\t\t\t{title: draftTitle}\n\t\t\t\t\t}\n\t\t\t\t));\n\t\t\t}\n\t\t\tif(isConfirmed) {\n\t\t\t\t// Create the new tiddler and pass it through the th-saving-tiddler hook\n\t\t\t\tvar newTiddler = new $tw.Tiddler(this.wiki.getCreationFields(),tiddler,{\n\t\t\t\t\ttitle: draftTitle,\n\t\t\t\t\t\"draft.title\": undefined,\n\t\t\t\t\t\"draft.of\": undefined\n\t\t\t\t},this.wiki.getModificationFields());\n\t\t\t\tnewTiddler = $tw.hooks.invokeHook(\"th-saving-tiddler\",newTiddler);\n\t\t\t\tthis.wiki.addTiddler(newTiddler);\n\t\t\t\t// If enabled, relink references to renamed tiddler\n\t\t\t\tvar shouldRelink = this.getAttribute(\"relinkOnRename\",\"no\").toLowerCase().trim() === \"yes\";\n\t\t\t\tif(isRename && shouldRelink && this.wiki.tiddlerExists(draftOf)) {\nconsole.log(\"Relinking '\" + draftOf + \"' to '\" + draftTitle + \"'\");\n\t\t\t\t\tthis.wiki.relinkTiddler(draftOf,draftTitle);\n\t\t\t\t}\n\t\t\t\t// Remove the draft tiddler\n\t\t\t\tthis.wiki.deleteTiddler(title);\n\t\t\t\t// Remove the original tiddler if we're renaming it\n\t\t\t\tif(isRename) {\n\t\t\t\t\tthis.wiki.deleteTiddler(draftOf);\n\t\t\t\t}\n\t\t\t\t// #2381 always remove new title & old\n\t\t\t\tthis.removeTitleFromStory(storyList,draftTitle);\n\t\t\t\tthis.removeTitleFromStory(storyList,draftOf);\n\t\t\t\tif(!event.paramObject || event.paramObject.suppressNavigation !== \"yes\") {\n\t\t\t\t\t// Replace the draft in the story with the original\n\t\t\t\t\tthis.replaceFirstTitleInStory(storyList,title,draftTitle);\n\t\t\t\t\tthis.addToHistory(draftTitle,event.navigateFromClientRect);\n\t\t\t\t\tif(draftTitle !== this.storyTitle) {\n\t\t\t\t\t\tthis.saveStoryList(storyList);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// Trigger an autosave\n\t\t\t\t$tw.rootWidget.dispatchEvent({type: \"tm-auto-save-wiki\"});\n\t\t\t}\n\t\t}\n\t}\n\treturn false;\n};\n\n// Take a tiddler out of edit mode without saving the changes\nNavigatorWidget.prototype.handleCancelTiddlerEvent = function(event) {\n\tevent = $tw.hooks.invokeHook(\"th-cancelling-tiddler\", event);\n\t// Flip the specified tiddler from draft back to the original\n\tvar draftTitle = event.param || event.tiddlerTitle,\n\t\tdraftTiddler = this.wiki.getTiddler(draftTitle),\n\t\toriginalTitle = draftTiddler && draftTiddler.fields[\"draft.of\"];\n\tif(draftTiddler && originalTitle) {\n\t\t// Ask for confirmation if the tiddler text has changed\n\t\tvar isConfirmed = true,\n\t\t\toriginalTiddler = this.wiki.getTiddler(originalTitle),\n\t\t\tstoryList = this.getStoryList();\n\t\tif(this.wiki.isDraftModified(draftTitle)) {\n\t\t\tisConfirmed = confirm($tw.language.getString(\n\t\t\t\t\"ConfirmCancelTiddler\",\n\t\t\t\t{variables:\n\t\t\t\t\t{title: draftTitle}\n\t\t\t\t}\n\t\t\t));\n\t\t}\n\t\t// Remove the draft tiddler\n\t\tif(isConfirmed) {\n\t\t\tthis.wiki.deleteTiddler(draftTitle);\n\t\t\tif(!event.paramObject || event.paramObject.suppressNavigation !== \"yes\") {\n\t\t\t\tif(originalTiddler) {\n\t\t\t\t\tthis.replaceFirstTitleInStory(storyList,draftTitle,originalTitle);\n\t\t\t\t\tthis.addToHistory(originalTitle,event.navigateFromClientRect);\n\t\t\t\t} else {\n\t\t\t\t\tthis.removeTitleFromStory(storyList,draftTitle);\n\t\t\t\t}\n\t\t\t\tthis.saveStoryList(storyList);\n\t\t\t}\n\t\t}\n\t}\n\treturn false;\n};\n\n// Create a new draft tiddler\n// event.param can either be the title of a template tiddler, or a hashmap of fields.\n//\n// The title of the newly created tiddler follows these rules:\n// * If a hashmap was used and a title field was specified, use that title\n// * If a hashmap was used without a title field, use a default title, if necessary making it unique with a numeric suffix\n// * If a template tiddler was used, use the title of the template, if necessary making it unique with a numeric suffix\n//\n// If a draft of the target tiddler already exists then it is reused\nNavigatorWidget.prototype.handleNewTiddlerEvent = function(event) {\n\tevent = $tw.hooks.invokeHook(\"th-new-tiddler\", event);\n\t// Get the story details\n\tvar storyList = this.getStoryList(),\n\t\ttemplateTiddler, additionalFields, title, draftTitle, existingTiddler;\n\t// Get the template tiddler (if any)\n\tif(typeof event.param === \"string\") {\n\t\t// Get the template tiddler\n\t\ttemplateTiddler = this.wiki.getTiddler(event.param);\n\t\t// Generate a new title\n\t\ttitle = this.wiki.generateNewTitle(event.param || $tw.language.getString(\"DefaultNewTiddlerTitle\"));\n\t}\n\t// Get the specified additional fields\n\tif(typeof event.paramObject === \"object\") {\n\t\tadditionalFields = event.paramObject;\n\t}\n\tif(typeof event.param === \"object\") { // Backwards compatibility with 5.1.3\n\t\tadditionalFields = event.param;\n\t}\n\tif(additionalFields && additionalFields.title) {\n\t\ttitle = additionalFields.title;\n\t}\n\t// Make a copy of the additional fields excluding any blank ones\n\tvar filteredAdditionalFields = $tw.utils.extend({},additionalFields);\n\tObject.keys(filteredAdditionalFields).forEach(function(fieldName) {\n\t\tif(filteredAdditionalFields[fieldName] === \"\") {\n\t\t\tdelete filteredAdditionalFields[fieldName];\n\t\t}\n\t});\n\t// Generate a title if we don't have one\n\ttitle = title || this.wiki.generateNewTitle($tw.language.getString(\"DefaultNewTiddlerTitle\"));\n\t// Find any existing draft for this tiddler\n\tdraftTitle = this.wiki.findDraft(title);\n\t// Pull in any existing tiddler\n\tif(draftTitle) {\n\t\texistingTiddler = this.wiki.getTiddler(draftTitle);\n\t} else {\n\t\tdraftTitle = this.generateDraftTitle(title);\n\t\texistingTiddler = this.wiki.getTiddler(title);\n\t}\n\t// Merge the tags\n\tvar mergedTags = [];\n\tif(existingTiddler && existingTiddler.fields.tags) {\n\t\t$tw.utils.pushTop(mergedTags,existingTiddler.fields.tags);\n\t}\n\tif(additionalFields && additionalFields.tags) {\n\t\t// Merge tags\n\t\tmergedTags = $tw.utils.pushTop(mergedTags,$tw.utils.parseStringArray(additionalFields.tags));\n\t}\n\tif(templateTiddler && templateTiddler.fields.tags) {\n\t\t// Merge tags\n\t\tmergedTags = $tw.utils.pushTop(mergedTags,templateTiddler.fields.tags);\n\t}\n\t// Save the draft tiddler\n\tvar draftTiddler = new $tw.Tiddler({\n\t\t\ttext: \"\",\n\t\t\t\"draft.title\": title\n\t\t},\n\t\ttemplateTiddler,\n\t\tadditionalFields,\n\t\tthis.wiki.getCreationFields(),\n\t\texistingTiddler,\n\t\tfilteredAdditionalFields,\n\t\t{\n\t\t\ttitle: draftTitle,\n\t\t\t\"draft.of\": title,\n\t\t\ttags: mergedTags\n\t\t},this.wiki.getModificationFields());\n\tthis.wiki.addTiddler(draftTiddler);\n\t// Update the story to insert the new draft at the top and remove any existing tiddler\n\tif(storyList && storyList.indexOf(draftTitle) === -1) {\n\t\tvar slot = storyList.indexOf(event.navigateFromTitle);\n\t\tif(slot === -1) {\n\t\t\tslot = this.getAttribute(\"openLinkFromOutsideRiver\",\"top\") === \"bottom\" ? storyList.length - 1 : slot;\n\t\t}\n\t\tstoryList.splice(slot + 1,0,draftTitle);\n\t}\n\tif(storyList && storyList.indexOf(title) !== -1) {\n\t\tstoryList.splice(storyList.indexOf(title),1);\n\t}\n\tthis.saveStoryList(storyList);\n\t// Add a new record to the top of the history stack\n\tthis.addToHistory(draftTitle);\n\treturn false;\n};\n\n// Import JSON tiddlers into a pending import tiddler\nNavigatorWidget.prototype.handleImportTiddlersEvent = function(event) {\n\t// Get the tiddlers\n\tvar tiddlers = [];\n\ttry {\n\t\ttiddlers = JSON.parse(event.param);\n\t} catch(e) {\n\t}\n\t// Get the current $:/Import tiddler\n\tvar importTiddler = this.wiki.getTiddler(IMPORT_TITLE),\n\t\timportData = this.wiki.getTiddlerData(IMPORT_TITLE,{}),\n\t\tnewFields = new Object({\n\t\t\ttitle: IMPORT_TITLE,\n\t\t\ttype: \"application/json\",\n\t\t\t\"plugin-type\": \"import\",\n\t\t\t\"status\": \"pending\"\n\t\t}),\n\t\tincomingTiddlers = [];\n\t// Process each tiddler\n\timportData.tiddlers = importData.tiddlers || {};\n\t$tw.utils.each(tiddlers,function(tiddlerFields) {\n\t\ttiddlerFields.title = $tw.utils.trim(tiddlerFields.title);\n\t\tvar title = tiddlerFields.title;\n\t\tif(title) {\n\t\t\tincomingTiddlers.push(title);\n\t\t\timportData.tiddlers[title] = tiddlerFields;\n\t\t}\n\t});\n\t// Give the active upgrader modules a chance to process the incoming tiddlers\n\tvar messages = this.wiki.invokeUpgraders(incomingTiddlers,importData.tiddlers);\n\t$tw.utils.each(messages,function(message,title) {\n\t\tnewFields[\"message-\" + title] = message;\n\t});\n\t// Deselect any suppressed tiddlers\n\t$tw.utils.each(importData.tiddlers,function(tiddler,title) {\n\t\tif($tw.utils.count(tiddler) === 0) {\n\t\t\tnewFields[\"selection-\" + title] = \"unchecked\";\n\t\t}\n\t});\n\t// Save the $:/Import tiddler\n\tnewFields.text = JSON.stringify(importData,null,$tw.config.preferences.jsonSpaces);\n\tthis.wiki.addTiddler(new $tw.Tiddler(importTiddler,newFields));\n\t// Update the story and history details\n\tif(this.getVariable(\"tv-auto-open-on-import\") !== \"no\") {\n\t\tvar storyList = this.getStoryList(),\n\t\t\thistory = [];\n\t\t// Add it to the story\n\t\tif(storyList && storyList.indexOf(IMPORT_TITLE) === -1) {\n\t\t\tstoryList.unshift(IMPORT_TITLE);\n\t\t}\n\t\t// And to history\n\t\thistory.push(IMPORT_TITLE);\n\t\t// Save the updated story and history\n\t\tthis.saveStoryList(storyList);\n\t\tthis.addToHistory(history);\n\t}\n\treturn false;\n};\n\n//\nNavigatorWidget.prototype.handlePerformImportEvent = function(event) {\n\tvar self = this,\n\t\timportTiddler = this.wiki.getTiddler(event.param),\n\t\timportData = this.wiki.getTiddlerDataCached(event.param,{tiddlers: {}}),\n\t\timportReport = [];\n\t// Add the tiddlers to the store\n\timportReport.push($tw.language.getString(\"Import/Imported/Hint\") + \"\\n\");\n\t$tw.utils.each(importData.tiddlers,function(tiddlerFields) {\n\t\tvar title = tiddlerFields.title;\n\t\tif(title && importTiddler && importTiddler.fields[\"selection-\" + title] !== \"unchecked\") {\n\t\t\tvar tiddler = new $tw.Tiddler(tiddlerFields);\n\t\t\ttiddler = $tw.hooks.invokeHook(\"th-importing-tiddler\",tiddler);\n\t\t\tself.wiki.addTiddler(tiddler);\n\t\t\timportReport.push(\"# [[\" + tiddlerFields.title + \"]]\");\n\t\t}\n\t});\n\t// Replace the $:/Import tiddler with an import report\n\tthis.wiki.addTiddler(new $tw.Tiddler({\n\t\ttitle: event.param,\n\t\ttext: importReport.join(\"\\n\"),\n\t\t\"status\": \"complete\"\n\t}));\n\t// Navigate to the $:/Import tiddler\n\tthis.addToHistory([event.param]);\n\t// Trigger an autosave\n\t$tw.rootWidget.dispatchEvent({type: \"tm-auto-save-wiki\"});\n};\n\nNavigatorWidget.prototype.handleFoldTiddlerEvent = function(event) {\n\tvar paramObject = event.paramObject || {};\n\tif(paramObject.foldedState) {\n\t\tvar foldedState = this.wiki.getTiddlerText(paramObject.foldedState,\"show\") === \"show\" ? \"hide\" : \"show\";\n\t\tthis.wiki.setText(paramObject.foldedState,\"text\",null,foldedState);\n\t}\n};\n\nNavigatorWidget.prototype.handleFoldOtherTiddlersEvent = function(event) {\n\tvar self = this,\n\t\tparamObject = event.paramObject || {},\n\t\tprefix = paramObject.foldedStatePrefix;\n\t$tw.utils.each(this.getStoryList(),function(title) {\n\t\tself.wiki.setText(prefix + title,\"text\",null,event.param === title ? \"show\" : \"hide\");\n\t});\n};\n\nNavigatorWidget.prototype.handleFoldAllTiddlersEvent = function(event) {\n\tvar self = this,\n\t\tparamObject = event.paramObject || {},\n\t\tprefix = paramObject.foldedStatePrefix || \"$:/state/folded/\";\n\t$tw.utils.each(this.getStoryList(),function(title) {\n\t\tself.wiki.setText(prefix + title,\"text\",null,\"hide\");\n\t});\n};\n\nNavigatorWidget.prototype.handleUnfoldAllTiddlersEvent = function(event) {\n\tvar self = this,\n\t\tparamObject = event.paramObject || {},\n\t\tprefix = paramObject.foldedStatePrefix;\n\t$tw.utils.each(this.getStoryList(),function(title) {\n\t\tself.wiki.setText(prefix + title,\"text\",null,\"show\");\n\t});\n};\n\nNavigatorWidget.prototype.handleRenameTiddlerEvent = function(event) {\n\tvar paramObject = event.paramObject || {},\n\t\tfrom = paramObject.from || event.tiddlerTitle,\n\t\tto = paramObject.to;\n\tthis.wiki.renameTiddler(from,to);\n};\n\nexports.navigator = NavigatorWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/password.js": {
"title": "$:/core/modules/widgets/password.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/password.js\ntype: application/javascript\nmodule-type: widget\n\nPassword widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar PasswordWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nPasswordWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nPasswordWidget.prototype.render = function(parent,nextSibling) {\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Get the current password\n\tvar password = $tw.browser ? $tw.utils.getPassword(this.passwordName) || \"\" : \"\";\n\t// Create our element\n\tvar domNode = this.document.createElement(\"input\");\n\tdomNode.setAttribute(\"type\",\"password\");\n\tdomNode.setAttribute(\"value\",password);\n\t// Add a click event handler\n\t$tw.utils.addEventListeners(domNode,[\n\t\t{name: \"change\", handlerObject: this, handlerMethod: \"handleChangeEvent\"}\n\t]);\n\t// Insert the label into the DOM and render any children\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\nPasswordWidget.prototype.handleChangeEvent = function(event) {\n\tvar password = this.domNodes[0].value;\n\treturn $tw.utils.savePassword(this.passwordName,password);\n};\n\n/*\nCompute the internal state of the widget\n*/\nPasswordWidget.prototype.execute = function() {\n\t// Get the parameters from the attributes\n\tthis.passwordName = this.getAttribute(\"name\",\"\");\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nPasswordWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.name) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.password = PasswordWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/qualify.js": {
"title": "$:/core/modules/widgets/qualify.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/qualify.js\ntype: application/javascript\nmodule-type: widget\n\nQualify text to a variable \n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar QualifyWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nQualifyWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nQualifyWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nQualifyWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.qualifyName = this.getAttribute(\"name\");\n\tthis.qualifyTitle = this.getAttribute(\"title\");\n\t// Set context variable\n\tif(this.qualifyName) {\n\t\tthis.setVariable(this.qualifyName,this.qualifyTitle + \"-\" + this.getStateQualifier());\n\t}\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nQualifyWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.name || changedAttributes.title) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.qualify = QualifyWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/radio.js": {
"title": "$:/core/modules/widgets/radio.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/radio.js\ntype: application/javascript\nmodule-type: widget\n\nSet a field or index at a given tiddler via radio buttons\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar RadioWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nRadioWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nRadioWidget.prototype.render = function(parent,nextSibling) {\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\tvar isChecked = this.getValue() === this.radioValue;\n\t// Create our elements\n\tthis.labelDomNode = this.document.createElement(\"label\");\n\tthis.labelDomNode.setAttribute(\"class\",\n \t\t\"tc-radio \" + this.radioClass + (isChecked ? \" tc-radio-selected\" : \"\")\n \t);\n\tthis.inputDomNode = this.document.createElement(\"input\");\n\tthis.inputDomNode.setAttribute(\"type\",\"radio\");\n\tif(isChecked) {\n\t\tthis.inputDomNode.setAttribute(\"checked\",\"true\");\n\t}\n\tthis.labelDomNode.appendChild(this.inputDomNode);\n\tthis.spanDomNode = this.document.createElement(\"span\");\n\tthis.labelDomNode.appendChild(this.spanDomNode);\n\t// Add a click event handler\n\t$tw.utils.addEventListeners(this.inputDomNode,[\n\t\t{name: \"change\", handlerObject: this, handlerMethod: \"handleChangeEvent\"}\n\t]);\n\t// Insert the label into the DOM and render any children\n\tparent.insertBefore(this.labelDomNode,nextSibling);\n\tthis.renderChildren(this.spanDomNode,null);\n\tthis.domNodes.push(this.labelDomNode);\n};\n\nRadioWidget.prototype.getValue = function() {\n\tvar value,\n\t\ttiddler = this.wiki.getTiddler(this.radioTitle);\n\tif (this.radioIndex) {\n\t\tvalue = this.wiki.extractTiddlerDataItem(this.radioTitle,this.radioIndex);\n\t} else {\n\t\tvalue = tiddler && tiddler.getFieldString(this.radioField);\n\t}\n\treturn value;\n};\n\nRadioWidget.prototype.setValue = function() {\n\tif(this.radioIndex) {\n\t\tthis.wiki.setText(this.radioTitle,\"\",this.radioIndex,this.radioValue);\n\t} else {\n\t\tvar tiddler = this.wiki.getTiddler(this.radioTitle),\n\t\t\taddition = {};\n\t\taddition[this.radioField] = this.radioValue;\n\t\tthis.wiki.addTiddler(new $tw.Tiddler(this.wiki.getCreationFields(),{title: this.radioTitle},tiddler,addition,this.wiki.getModificationFields()));\n\t}\n};\n\nRadioWidget.prototype.handleChangeEvent = function(event) {\n\tif(this.inputDomNode.checked) {\n\t\tthis.setValue();\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nRadioWidget.prototype.execute = function() {\n\t// Get the parameters from the attributes\n\tthis.radioTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.radioField = this.getAttribute(\"field\",\"text\");\n\tthis.radioIndex = this.getAttribute(\"index\");\n\tthis.radioValue = this.getAttribute(\"value\");\n\tthis.radioClass = this.getAttribute(\"class\",\"\");\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nRadioWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes.value || changedAttributes[\"class\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\tvar refreshed = false;\n\t\tif(changedTiddlers[this.radioTitle]) {\n\t\t\tthis.inputDomNode.checked = this.getValue() === this.radioValue;\n\t\t\trefreshed = true;\n\t\t}\n\t\treturn this.refreshChildren(changedTiddlers) || refreshed;\n\t}\n};\n\nexports.radio = RadioWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/range.js": {
"title": "$:/core/modules/widgets/range.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/range.js\ntype: application/javascript\nmodule-type: widget\n\nRange widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar RangeWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nRangeWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nRangeWidget.prototype.render = function(parent,nextSibling) {\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Create our elements\n\tthis.inputDomNode = this.document.createElement(\"input\");\n\tthis.inputDomNode.setAttribute(\"type\",\"range\");\n\tthis.inputDomNode.setAttribute(\"class\",this.elementClass);\n\tif(this.minValue){\n\t\tthis.inputDomNode.setAttribute(\"min\", this.minValue);\n\t}\n\tif(this.maxValue){\n\t\tthis.inputDomNode.setAttribute(\"max\", this.maxValue);\n\t}\n\tif(this.increment){\n\t\tthis.inputDomNode.setAttribute(\"step\", this.increment);\n\t}\n\tthis.inputDomNode.value = this.getValue();\n\t// Add a click event handler\n\t$tw.utils.addEventListeners(this.inputDomNode,[\n\t\t{name: \"input\", handlerObject: this, handlerMethod: \"handleInputEvent\"},\n\t\t{name: \"change\", handlerObject: this, handlerMethod: \"handleInputEvent\"}\t\t\n\t]);\n\t// Insert the label into the DOM and render any children\n\tparent.insertBefore(this.inputDomNode,nextSibling);\n\tthis.domNodes.push(this.inputDomNode);\n};\n\nRangeWidget.prototype.getValue = function() {\n\tvar tiddler = this.wiki.getTiddler(this.tiddlerTitle),\n\t\tfieldName = this.tiddlerField || \"text\",\n\t\tvalue = this.defaultValue;\n\tif(tiddler) {\n\t\tif(this.tiddlerIndex) {\n\t\t\tvalue = this.wiki.extractTiddlerDataItem(tiddler,this.tiddlerIndex,this.defaultValue || \"\");\n\t\t} else {\n\t\t\tif($tw.utils.hop(tiddler.fields,fieldName)) {\n\t\t\t\tvalue = tiddler.fields[fieldName] || \"\";\n\t\t\t} else {\n\t\t\t\tvalue = this.defaultValue || \"\";\n\t\t\t}\n\t\t}\n\t}\n\treturn value;\n};\n\nRangeWidget.prototype.handleInputEvent = function(event) {\n\tif(this.getValue() !== this.inputDomNode.value) {\n\t\tif(this.tiddlerIndex) {\n\t\t\tthis.wiki.setText(this.tiddlerTitle,\"\",this.tiddlerIndex,this.inputDomNode.value);\n\t\t} else {\n\t\t\tthis.wiki.setText(this.tiddlerTitle,this.tiddlerField,null,this.inputDomNode.value);\n\t\t}\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nRangeWidget.prototype.execute = function() {\n\t// Get the parameters from the attributes\n\tthis.tiddlerTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.tiddlerField = this.getAttribute(\"field\");\n\tthis.tiddlerIndex = this.getAttribute(\"index\");\n\tthis.minValue = this.getAttribute(\"min\");\n\tthis.maxValue = this.getAttribute(\"max\");\n\tthis.increment = this.getAttribute(\"increment\");\n\tthis.defaultValue = this.getAttribute(\"default\");\n\tthis.elementClass = this.getAttribute(\"class\",\"\");\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nRangeWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes['min'] || changedAttributes['max'] || changedAttributes['increment'] || changedAttributes[\"default\"] || changedAttributes[\"class\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\tvar refreshed = false;\n\t\tif(changedTiddlers[this.tiddlerTitle]) {\n\t\t\tvar value = this.getValue();\n\t\t\tif(this.inputDomNode.value !== value) {\n\t\t\t\tthis.inputDomNode.value = value;\t\t\t\t\n\t\t\t}\n\t\t\trefreshed = true;\n\t\t}\n\t\treturn this.refreshChildren(changedTiddlers) || refreshed;\n\t}\n};\n\nexports.range = RangeWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/raw.js": {
"title": "$:/core/modules/widgets/raw.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/raw.js\ntype: application/javascript\nmodule-type: widget\n\nRaw widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar RawWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nRawWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nRawWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.execute();\n\tvar div = this.document.createElement(\"div\");\n\tdiv.innerHTML=this.parseTreeNode.html;\n\tparent.insertBefore(div,nextSibling);\n\tthis.domNodes.push(div);\t\n};\n\n/*\nCompute the internal state of the widget\n*/\nRawWidget.prototype.execute = function() {\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nRawWidget.prototype.refresh = function(changedTiddlers) {\n\treturn false;\n};\n\nexports.raw = RawWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/reveal.js": {
"title": "$:/core/modules/widgets/reveal.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/reveal.js\ntype: application/javascript\nmodule-type: widget\n\nReveal widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar RevealWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nRevealWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nRevealWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar tag = this.parseTreeNode.isBlock ? \"div\" : \"span\";\n\tif(this.revealTag && $tw.config.htmlUnsafeElements.indexOf(this.revealTag) === -1) {\n\t\ttag = this.revealTag;\n\t}\n\tvar domNode = this.document.createElement(tag);\n\tvar classes = this[\"class\"].split(\" \") || [];\n\tclasses.push(\"tc-reveal\");\n\tdomNode.className = classes.join(\" \");\n\tif(this.style) {\n\t\tdomNode.setAttribute(\"style\",this.style);\n\t}\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tif(!domNode.isTiddlyWikiFakeDom && this.type === \"popup\" && this.isOpen) {\n\t\tthis.positionPopup(domNode);\n\t\t$tw.utils.addClass(domNode,\"tc-popup\"); // Make sure that clicks don't dismiss popups within the revealed content\n\t}\n\tif(!this.isOpen) {\n\t\tdomNode.setAttribute(\"hidden\",\"true\");\n\t}\n\tthis.domNodes.push(domNode);\n};\n\nRevealWidget.prototype.positionPopup = function(domNode) {\n\tdomNode.style.position = \"absolute\";\n\tdomNode.style.zIndex = \"1000\";\n\tvar left,top;\n\tswitch(this.position) {\n\t\tcase \"left\":\n\t\t\tleft = this.popup.left - domNode.offsetWidth;\n\t\t\ttop = this.popup.top;\n\t\t\tbreak;\n\t\tcase \"above\":\n\t\t\tleft = this.popup.left;\n\t\t\ttop = this.popup.top - domNode.offsetHeight;\n\t\t\tbreak;\n\t\tcase \"aboveright\":\n\t\t\tleft = this.popup.left + this.popup.width;\n\t\t\ttop = this.popup.top + this.popup.height - domNode.offsetHeight;\n\t\t\tbreak;\n\t\tcase \"right\":\n\t\t\tleft = this.popup.left + this.popup.width;\n\t\t\ttop = this.popup.top;\n\t\t\tbreak;\n\t\tcase \"belowleft\":\n\t\t\tleft = this.popup.left + this.popup.width - domNode.offsetWidth;\n\t\t\ttop = this.popup.top + this.popup.height;\n\t\t\tbreak;\n\t\tdefault: // Below\n\t\t\tleft = this.popup.left;\n\t\t\ttop = this.popup.top + this.popup.height;\n\t\t\tbreak;\n\t}\n\tif(!this.positionAllowNegative) {\n\t\tleft = Math.max(0,left);\n\t\ttop = Math.max(0,top);\n\t}\n\tdomNode.style.left = left + \"px\";\n\tdomNode.style.top = top + \"px\";\n};\n\n/*\nCompute the internal state of the widget\n*/\nRevealWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.state = this.getAttribute(\"state\");\n\tthis.revealTag = this.getAttribute(\"tag\");\n\tthis.type = this.getAttribute(\"type\");\n\tthis.text = this.getAttribute(\"text\");\n\tthis.position = this.getAttribute(\"position\");\n\tthis.positionAllowNegative = this.getAttribute(\"positionAllowNegative\") === \"yes\";\n\tthis[\"class\"] = this.getAttribute(\"class\",\"\");\n\tthis.style = this.getAttribute(\"style\",\"\");\n\tthis[\"default\"] = this.getAttribute(\"default\",\"\");\n\tthis.animate = this.getAttribute(\"animate\",\"no\");\n\tthis.retain = this.getAttribute(\"retain\",\"no\");\n\tthis.openAnimation = this.animate === \"no\" ? undefined : \"open\";\n\tthis.closeAnimation = this.animate === \"no\" ? undefined : \"close\";\n\t// Compute the title of the state tiddler and read it\n\tthis.stateTiddlerTitle = this.state;\n\tthis.stateTitle = this.getAttribute(\"stateTitle\");\n\tthis.stateField = this.getAttribute(\"stateField\");\n\tthis.stateIndex = this.getAttribute(\"stateIndex\");\n\tthis.readState();\n\t// Construct the child widgets\n\tvar childNodes = this.isOpen ? this.parseTreeNode.children : [];\n\tthis.hasChildNodes = this.isOpen;\n\tthis.makeChildWidgets(childNodes);\n};\n\n/*\nRead the state tiddler\n*/\nRevealWidget.prototype.readState = function() {\n\t// Read the information from the state tiddler\n\tvar state,\n\t defaultState = this[\"default\"];\n\tif(this.stateTitle) {\n\t\tvar stateTitleTiddler = this.wiki.getTiddler(this.stateTitle);\n\t\tif(this.stateField) {\n\t\t\tstate = stateTitleTiddler ? stateTitleTiddler.getFieldString(this.stateField) || defaultState : defaultState;\n\t\t} else if(this.stateIndex) {\n\t\t\tstate = stateTitleTiddler ? this.wiki.extractTiddlerDataItem(this.stateTitle,this.stateIndex) || defaultState : defaultState;\n\t\t} else if(stateTitleTiddler) {\n\t\t\tstate = this.wiki.getTiddlerText(this.stateTitle) || defaultState;\n\t\t} else {\n\t\t\tstate = defaultState;\n\t\t}\n\t} else {\n\t\tstate = this.stateTiddlerTitle ? this.wiki.getTextReference(this.state,this[\"default\"],this.getVariable(\"currentTiddler\")) : this[\"default\"];\n\t}\n\tif(state === null) {\n\t\tstate = this[\"default\"];\n\t}\n\tswitch(this.type) {\n\t\tcase \"popup\":\n\t\t\tthis.readPopupState(state);\n\t\t\tbreak;\n\t\tcase \"match\":\n\t\t\tthis.isOpen = this.text === state;\n\t\t\tbreak;\n\t\tcase \"nomatch\":\n\t\t\tthis.isOpen = this.text !== state;\n\t\t\tbreak;\n\t\tcase \"lt\":\n\t\t\tthis.isOpen = !!(this.compareStateText(state) < 0);\n\t\t\tbreak;\n\t\tcase \"gt\":\n\t\t\tthis.isOpen = !!(this.compareStateText(state) > 0);\n\t\t\tbreak;\n\t\tcase \"lteq\":\n\t\t\tthis.isOpen = !(this.compareStateText(state) > 0);\n\t\t\tbreak;\n\t\tcase \"gteq\":\n\t\t\tthis.isOpen = !(this.compareStateText(state) < 0);\n\t\t\tbreak;\n\t}\n};\n\nRevealWidget.prototype.compareStateText = function(state) {\n\treturn state.localeCompare(this.text,undefined,{numeric: true,sensitivity: \"case\"});\n};\n\nRevealWidget.prototype.readPopupState = function(state) {\n\tvar popupLocationRegExp = /^\\((-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+)\\)$/,\n\t\tmatch = popupLocationRegExp.exec(state);\n\t// Check if the state matches the location regexp\n\tif(match) {\n\t\t// If so, we're open\n\t\tthis.isOpen = true;\n\t\t// Get the location\n\t\tthis.popup = {\n\t\t\tleft: parseFloat(match[1]),\n\t\t\ttop: parseFloat(match[2]),\n\t\t\twidth: parseFloat(match[3]),\n\t\t\theight: parseFloat(match[4])\n\t\t};\n\t} else {\n\t\t// If not, we're closed\n\t\tthis.isOpen = false;\n\t}\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nRevealWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.state || changedAttributes.type || changedAttributes.text || changedAttributes.position || changedAttributes.positionAllowNegative || changedAttributes[\"default\"] || changedAttributes.animate || changedAttributes.stateTitle || changedAttributes.stateField || changedAttributes.stateIndex) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\tvar currentlyOpen = this.isOpen;\n\t\tthis.readState();\n\t\tif(this.isOpen !== currentlyOpen) {\n\t\t\tif(this.retain === \"yes\") {\n\t\t\t\tthis.updateState();\n\t\t\t} else {\n\t\t\t\tthis.refreshSelf();\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\n/*\nCalled by refresh() to dynamically show or hide the content\n*/\nRevealWidget.prototype.updateState = function() {\n\tvar self = this;\n\t// Read the current state\n\tthis.readState();\n\t// Construct the child nodes if needed\n\tvar domNode = this.domNodes[0];\n\tif(this.isOpen && !this.hasChildNodes) {\n\t\tthis.hasChildNodes = true;\n\t\tthis.makeChildWidgets(this.parseTreeNode.children);\n\t\tthis.renderChildren(domNode,null);\n\t}\n\t// Animate our DOM node\n\tif(!domNode.isTiddlyWikiFakeDom && this.type === \"popup\" && this.isOpen) {\n\t\tthis.positionPopup(domNode);\n\t\t$tw.utils.addClass(domNode,\"tc-popup\"); // Make sure that clicks don't dismiss popups within the revealed content\n\n\t}\n\tif(this.isOpen) {\n\t\tdomNode.removeAttribute(\"hidden\");\n $tw.anim.perform(this.openAnimation,domNode);\n\t} else {\n\t\t$tw.anim.perform(this.closeAnimation,domNode,{callback: function() {\n\t\t\t//make sure that the state hasn't changed during the close animation\n\t\t\tself.readState()\n\t\t\tif(!self.isOpen) {\n\t\t\t\tdomNode.setAttribute(\"hidden\",\"true\");\n\t\t\t}\n\t\t}});\n\t}\n};\n\nexports.reveal = RevealWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/scrollable.js": {
"title": "$:/core/modules/widgets/scrollable.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/scrollable.js\ntype: application/javascript\nmodule-type: widget\n\nScrollable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ScrollableWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n\tthis.scaleFactor = 1;\n\tthis.addEventListeners([\n\t\t{type: \"tm-scroll\", handler: \"handleScrollEvent\"}\n\t]);\n\tif($tw.browser) {\n\t\tthis.requestAnimationFrame = window.requestAnimationFrame ||\n\t\t\twindow.webkitRequestAnimationFrame ||\n\t\t\twindow.mozRequestAnimationFrame ||\n\t\t\tfunction(callback) {\n\t\t\t\treturn window.setTimeout(callback, 1000/60);\n\t\t\t};\n\t\tthis.cancelAnimationFrame = window.cancelAnimationFrame ||\n\t\t\twindow.webkitCancelAnimationFrame ||\n\t\t\twindow.webkitCancelRequestAnimationFrame ||\n\t\t\twindow.mozCancelAnimationFrame ||\n\t\t\twindow.mozCancelRequestAnimationFrame ||\n\t\t\tfunction(id) {\n\t\t\t\twindow.clearTimeout(id);\n\t\t\t};\n\t}\n};\n\n/*\nInherit from the base widget class\n*/\nScrollableWidget.prototype = new Widget();\n\nScrollableWidget.prototype.cancelScroll = function() {\n\tif(this.idRequestFrame) {\n\t\tthis.cancelAnimationFrame.call(window,this.idRequestFrame);\n\t\tthis.idRequestFrame = null;\n\t}\n};\n\n/*\nHandle a scroll event\n*/\nScrollableWidget.prototype.handleScrollEvent = function(event) {\n\t// Pass the scroll event through if our offsetsize is larger than our scrollsize\n\tif(this.outerDomNode.scrollWidth <= this.outerDomNode.offsetWidth && this.outerDomNode.scrollHeight <= this.outerDomNode.offsetHeight && this.fallthrough === \"yes\") {\n\t\treturn true;\n\t}\n\tthis.scrollIntoView(event.target);\n\treturn false; // Handled event\n};\n\n/*\nScroll an element into view\n*/\nScrollableWidget.prototype.scrollIntoView = function(element) {\n\tvar duration = $tw.utils.getAnimationDuration();\n\tthis.cancelScroll();\n\tthis.startTime = Date.now();\n\tvar scrollPosition = {\n\t\tx: this.outerDomNode.scrollLeft,\n\t\ty: this.outerDomNode.scrollTop\n\t};\n\t// Get the client bounds of the element and adjust by the scroll position\n\tvar scrollableBounds = this.outerDomNode.getBoundingClientRect(),\n\t\tclientTargetBounds = element.getBoundingClientRect(),\n\t\tbounds = {\n\t\t\tleft: clientTargetBounds.left + scrollPosition.x - scrollableBounds.left,\n\t\t\ttop: clientTargetBounds.top + scrollPosition.y - scrollableBounds.top,\n\t\t\twidth: clientTargetBounds.width,\n\t\t\theight: clientTargetBounds.height\n\t\t};\n\t// We'll consider the horizontal and vertical scroll directions separately via this function\n\tvar getEndPos = function(targetPos,targetSize,currentPos,currentSize) {\n\t\t\t// If the target is already visible then stay where we are\n\t\t\tif(targetPos >= currentPos && (targetPos + targetSize) <= (currentPos + currentSize)) {\n\t\t\t\treturn currentPos;\n\t\t\t// If the target is above/left of the current view, then scroll to its top/left\n\t\t\t} else if(targetPos <= currentPos) {\n\t\t\t\treturn targetPos;\n\t\t\t// If the target is smaller than the window and the scroll position is too far up, then scroll till the target is at the bottom of the window\n\t\t\t} else if(targetSize < currentSize && currentPos < (targetPos + targetSize - currentSize)) {\n\t\t\t\treturn targetPos + targetSize - currentSize;\n\t\t\t// If the target is big, then just scroll to the top\n\t\t\t} else if(currentPos < targetPos) {\n\t\t\t\treturn targetPos;\n\t\t\t// Otherwise, stay where we are\n\t\t\t} else {\n\t\t\t\treturn currentPos;\n\t\t\t}\n\t\t},\n\t\tendX = getEndPos(bounds.left,bounds.width,scrollPosition.x,this.outerDomNode.offsetWidth),\n\t\tendY = getEndPos(bounds.top,bounds.height,scrollPosition.y,this.outerDomNode.offsetHeight);\n\t// Only scroll if necessary\n\tif(endX !== scrollPosition.x || endY !== scrollPosition.y) {\n\t\tvar self = this,\n\t\t\tdrawFrame;\n\t\tdrawFrame = function () {\n\t\t\tvar t;\n\t\t\tif(duration <= 0) {\n\t\t\t\tt = 1;\n\t\t\t} else {\n\t\t\t\tt = ((Date.now()) - self.startTime) / duration;\t\n\t\t\t}\n\t\t\tif(t >= 1) {\n\t\t\t\tself.cancelScroll();\n\t\t\t\tt = 1;\n\t\t\t}\n\t\t\tt = $tw.utils.slowInSlowOut(t);\n\t\t\tself.outerDomNode.scrollLeft = scrollPosition.x + (endX - scrollPosition.x) * t;\n\t\t\tself.outerDomNode.scrollTop = scrollPosition.y + (endY - scrollPosition.y) * t;\n\t\t\tif(t < 1) {\n\t\t\t\tself.idRequestFrame = self.requestAnimationFrame.call(window,drawFrame);\n\t\t\t}\n\t\t};\n\t\tdrawFrame();\n\t}\n};\n\n/*\nRender this widget into the DOM\n*/\nScrollableWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create elements\n\tthis.outerDomNode = this.document.createElement(\"div\");\n\t$tw.utils.setStyle(this.outerDomNode,[\n\t\t{overflowY: \"auto\"},\n\t\t{overflowX: \"auto\"},\n\t\t{webkitOverflowScrolling: \"touch\"}\n\t]);\n\tthis.innerDomNode = this.document.createElement(\"div\");\n\tthis.outerDomNode.appendChild(this.innerDomNode);\n\t// Assign classes\n\tthis.outerDomNode.className = this[\"class\"] || \"\";\n\t// Insert element\n\tparent.insertBefore(this.outerDomNode,nextSibling);\n\tthis.renderChildren(this.innerDomNode,null);\n\tthis.domNodes.push(this.outerDomNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nScrollableWidget.prototype.execute = function() {\n\t// Get attributes\n\tthis.fallthrough = this.getAttribute(\"fallthrough\",\"yes\");\n\tthis[\"class\"] = this.getAttribute(\"class\");\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nScrollableWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"class\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.scrollable = ScrollableWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/select.js": {
"title": "$:/core/modules/widgets/select.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/select.js\ntype: application/javascript\nmodule-type: widget\n\nSelect widget:\n\n```\n<$select tiddler=\"MyTiddler\" field=\"text\">\n<$list filter=\"[tag[chapter]]\">\n<option value=<<currentTiddler>>>\n<$view field=\"description\"/>\n</option>\n</$list>\n</$select>\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar SelectWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nSelectWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nSelectWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n\tthis.setSelectValue();\n\t$tw.utils.addEventListeners(this.getSelectDomNode(),[\n\t\t{name: \"change\", handlerObject: this, handlerMethod: \"handleChangeEvent\"}\n\t]);\n};\n\n/*\nHandle a change event\n*/\nSelectWidget.prototype.handleChangeEvent = function(event) {\n\t// Get the new value and assign it to the tiddler\n\tif(this.selectMultiple == false) {\n\t\tvar value = this.getSelectDomNode().value;\n\t} else {\n\t\tvar value = this.getSelectValues()\n\t\t\t\tvalue = $tw.utils.stringifyList(value);\n\t}\n\tthis.wiki.setText(this.selectTitle,this.selectField,this.selectIndex,value);\n\t// Trigger actions\n\tif(this.selectActions) {\n\t\tthis.invokeActionString(this.selectActions,this,event);\n\t}\n};\n\n/*\nIf necessary, set the value of the select element to the current value\n*/\nSelectWidget.prototype.setSelectValue = function() {\n\tvar value = this.selectDefault;\n\t// Get the value\n\tif(this.selectIndex) {\n\t\tvalue = this.wiki.extractTiddlerDataItem(this.selectTitle,this.selectIndex,value);\n\t} else {\n\t\tvar tiddler = this.wiki.getTiddler(this.selectTitle);\n\t\tif(tiddler) {\n\t\t\tif(this.selectField === \"text\") {\n\t\t\t\t// Calling getTiddlerText() triggers lazy loading of skinny tiddlers\n\t\t\t\tvalue = this.wiki.getTiddlerText(this.selectTitle);\n\t\t\t} else {\n\t\t\t\tif($tw.utils.hop(tiddler.fields,this.selectField)) {\n\t\t\t\t\tvalue = tiddler.getFieldString(this.selectField);\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tif(this.selectField === \"title\") {\n\t\t\t\tvalue = this.selectTitle;\n\t\t\t}\n\t\t}\n\t}\n\t// Assign it to the select element if it's different than the current value\n\tif (this.selectMultiple) {\n\t\tvalue = value === undefined ? \"\" : value;\n\t\tvar select = this.getSelectDomNode();\n\t\tvar values = Array.isArray(value) ? value : $tw.utils.parseStringArray(value);\n\t\tfor(var i=0; i < select.children.length; i++){\n\t\t\tselect.children[i].selected = values.indexOf(select.children[i].value) !== -1\n\t\t}\n\t} else {\n\t\tvar domNode = this.getSelectDomNode();\n\t\tif(domNode.value !== value) {\n\t\t\tdomNode.value = value;\n\t\t}\n\t}\n};\n\n/*\nGet the DOM node of the select element\n*/\nSelectWidget.prototype.getSelectDomNode = function() {\n\treturn this.children[0].domNodes[0];\n};\n\n// Return an array of the selected opion values\n// select is an HTML select element\nSelectWidget.prototype.getSelectValues = function() {\n\tvar select, result, options, opt;\n\tselect = this.getSelectDomNode();\n\tresult = [];\n\toptions = select && select.options;\n\tfor (var i=0; i<options.length; i++) {\n\t\topt = options[i];\n\t\tif (opt.selected) {\n\t\t\tresult.push(opt.value || opt.text);\n\t\t}\n\t}\n\treturn result;\n}\n\n/*\nCompute the internal state of the widget\n*/\nSelectWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.selectActions = this.getAttribute(\"actions\");\n\tthis.selectTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.selectField = this.getAttribute(\"field\",\"text\");\n\tthis.selectIndex = this.getAttribute(\"index\");\n\tthis.selectClass = this.getAttribute(\"class\");\n\tthis.selectDefault = this.getAttribute(\"default\");\n\tthis.selectMultiple = this.getAttribute(\"multiple\", false);\n\tthis.selectSize = this.getAttribute(\"size\");\n\tthis.selectTooltip = this.getAttribute(\"tooltip\");\n\t// Make the child widgets\n\tvar selectNode = {\n\t\ttype: \"element\",\n\t\ttag: \"select\",\n\t\tchildren: this.parseTreeNode.children\n\t};\n\tif(this.selectClass) {\n\t\t$tw.utils.addAttributeToParseTreeNode(selectNode,\"class\",this.selectClass);\n\t}\n\tif(this.selectMultiple) {\n\t\t$tw.utils.addAttributeToParseTreeNode(selectNode,\"multiple\",\"multiple\");\n\t}\n\tif(this.selectSize) {\n\t\t$tw.utils.addAttributeToParseTreeNode(selectNode,\"size\",this.selectSize);\n\t}\n\tif(this.selectTooltip) {\n\t\t$tw.utils.addAttributeToParseTreeNode(selectNode,\"title\",this.selectTooltip);\n\t}\n\tthis.makeChildWidgets([selectNode]);\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nSelectWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\t// If we're using a different tiddler/field/index then completely refresh ourselves\n\tif(changedAttributes.selectTitle || changedAttributes.selectField || changedAttributes.selectIndex || changedAttributes.selectTooltip) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t// If the target tiddler value has changed, just update setting and refresh the children\n\t} else {\n\t\tvar childrenRefreshed = this.refreshChildren(changedTiddlers);\n\t\tif(changedTiddlers[this.selectTitle] || childrenRefreshed) {\n\t\t\tthis.setSelectValue();\n\t\t} \n\t\treturn childrenRefreshed;\n\t}\n};\n\nexports.select = SelectWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/set.js": {
"title": "$:/core/modules/widgets/set.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/set.js\ntype: application/javascript\nmodule-type: widget\n\nSet variable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar SetWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nSetWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nSetWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nSetWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.setName = this.getAttribute(\"name\",\"currentTiddler\");\n\tthis.setFilter = this.getAttribute(\"filter\");\n\tthis.setSelect = this.getAttribute(\"select\");\n\tthis.setTiddler = this.getAttribute(\"tiddler\");\n\tthis.setSubTiddler = this.getAttribute(\"subtiddler\");\n\tthis.setField = this.getAttribute(\"field\");\n\tthis.setIndex = this.getAttribute(\"index\");\n\tthis.setValue = this.getAttribute(\"value\");\n\tthis.setEmptyValue = this.getAttribute(\"emptyValue\");\n\t// Set context variable\n\tthis.setVariable(this.setName,this.getValue(),this.parseTreeNode.params,!!this.parseTreeNode.isMacroDefinition);\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nGet the value to be assigned\n*/\nSetWidget.prototype.getValue = function() {\n\tvar value = this.setValue;\n\tif(this.setTiddler) {\n\t\tvar tiddler;\n\t\tif(this.setSubTiddler) {\n\t\t\ttiddler = this.wiki.getSubTiddler(this.setTiddler,this.setSubTiddler);\n\t\t} else {\n\t\t\ttiddler = this.wiki.getTiddler(this.setTiddler);\t\t\t\n\t\t}\n\t\tif(!tiddler) {\n\t\t\tvalue = this.setEmptyValue;\n\t\t} else if(this.setField) {\n\t\t\tvalue = tiddler.getFieldString(this.setField) || this.setEmptyValue;\n\t\t} else if(this.setIndex) {\n\t\t\tvalue = this.wiki.extractTiddlerDataItem(this.setTiddler,this.setIndex,this.setEmptyValue);\n\t\t} else {\n\t\t\tvalue = tiddler.fields.text || this.setEmptyValue ;\n\t\t}\n\t} else if(this.setFilter) {\n\t\tvar results = this.wiki.filterTiddlers(this.setFilter,this);\n\t\tif(this.setValue == null) {\n\t\t\tvar select;\n\t\t\tif(this.setSelect) {\n\t\t\t\tselect = parseInt(this.setSelect,10);\n\t\t\t}\n\t\t\tif(select !== undefined) {\n\t\t\t\tvalue = results[select] || \"\";\n\t\t\t} else {\n\t\t\t\tvalue = $tw.utils.stringifyList(results);\t\t\t\n\t\t\t}\n\t\t}\n\t\tif(results.length === 0 && this.setEmptyValue !== undefined) {\n\t\t\tvalue = this.setEmptyValue;\n\t\t}\n\t} else if(!value && this.setEmptyValue) {\n\t\tvalue = this.setEmptyValue;\n\t}\n\treturn value || \"\";\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nSetWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.name || changedAttributes.filter || changedAttributes.select || changedAttributes.tiddler || (this.setTiddler && changedTiddlers[this.setTiddler]) || changedAttributes.field || changedAttributes.index || changedAttributes.value || changedAttributes.emptyValue ||\n\t (this.setFilter && this.getValue() != this.variables[this.setName].value)) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.setvariable = SetWidget;\nexports.set = SetWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/text.js": {
"title": "$:/core/modules/widgets/text.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/text.js\ntype: application/javascript\nmodule-type: widget\n\nText node widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar TextNodeWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nTextNodeWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nTextNodeWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar text = this.getAttribute(\"text\",this.parseTreeNode.text || \"\");\n\ttext = text.replace(/\\r/mg,\"\");\n\tvar textNode = this.document.createTextNode(text);\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nTextNodeWidget.prototype.execute = function() {\n\t// Nothing to do for a text node\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nTextNodeWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.text) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports.text = TextNodeWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/tiddler.js": {
"title": "$:/core/modules/widgets/tiddler.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/tiddler.js\ntype: application/javascript\nmodule-type: widget\n\nTiddler widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar TiddlerWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nTiddlerWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nTiddlerWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nTiddlerWidget.prototype.execute = function() {\n\tthis.tiddlerState = this.computeTiddlerState();\n\tthis.setVariable(\"currentTiddler\",this.tiddlerState.currentTiddler);\n\tthis.setVariable(\"missingTiddlerClass\",this.tiddlerState.missingTiddlerClass);\n\tthis.setVariable(\"shadowTiddlerClass\",this.tiddlerState.shadowTiddlerClass);\n\tthis.setVariable(\"systemTiddlerClass\",this.tiddlerState.systemTiddlerClass);\n\tthis.setVariable(\"tiddlerTagClasses\",this.tiddlerState.tiddlerTagClasses);\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nCompute the tiddler state flags\n*/\nTiddlerWidget.prototype.computeTiddlerState = function() {\n\t// Get our parameters\n\tthis.tiddlerTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\t// Compute the state\n\tvar state = {\n\t\tcurrentTiddler: this.tiddlerTitle || \"\",\n\t\tmissingTiddlerClass: (this.wiki.tiddlerExists(this.tiddlerTitle) || this.wiki.isShadowTiddler(this.tiddlerTitle)) ? \"tc-tiddler-exists\" : \"tc-tiddler-missing\",\n\t\tshadowTiddlerClass: this.wiki.isShadowTiddler(this.tiddlerTitle) ? \"tc-tiddler-shadow\" : \"\",\n\t\tsystemTiddlerClass: this.wiki.isSystemTiddler(this.tiddlerTitle) ? \"tc-tiddler-system\" : \"\",\n\t\ttiddlerTagClasses: this.getTagClasses()\n\t};\n\t// Compute a simple hash to make it easier to detect changes\n\tstate.hash = state.currentTiddler + state.missingTiddlerClass + state.shadowTiddlerClass + state.systemTiddlerClass + state.tiddlerTagClasses;\n\treturn state;\n};\n\n/*\nCreate a string of CSS classes derived from the tags of the current tiddler\n*/\nTiddlerWidget.prototype.getTagClasses = function() {\n\tvar tiddler = this.wiki.getTiddler(this.tiddlerTitle);\n\tif(tiddler) {\n\t\tvar tags = [];\n\t\t$tw.utils.each(tiddler.fields.tags,function(tag) {\n\t\t\ttags.push(\"tc-tagged-\" + encodeURIComponent(tag));\n\t\t});\n\t\treturn tags.join(\" \");\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nTiddlerWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes(),\n\t\tnewTiddlerState = this.computeTiddlerState();\n\tif(changedAttributes.tiddler || newTiddlerState.hash !== this.tiddlerState.hash) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\nexports.tiddler = TiddlerWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/transclude.js": {
"title": "$:/core/modules/widgets/transclude.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/transclude.js\ntype: application/javascript\nmodule-type: widget\n\nTransclude widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar TranscludeWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nTranscludeWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nTranscludeWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nTranscludeWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.transcludeTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.transcludeSubTiddler = this.getAttribute(\"subtiddler\");\n\tthis.transcludeField = this.getAttribute(\"field\");\n\tthis.transcludeIndex = this.getAttribute(\"index\");\n\tthis.transcludeMode = this.getAttribute(\"mode\");\n\t// Parse the text reference\n\tvar parseAsInline = !this.parseTreeNode.isBlock;\n\tif(this.transcludeMode === \"inline\") {\n\t\tparseAsInline = true;\n\t} else if(this.transcludeMode === \"block\") {\n\t\tparseAsInline = false;\n\t}\n\tvar parser = this.wiki.parseTextReference(\n\t\t\t\t\t\tthis.transcludeTitle,\n\t\t\t\t\t\tthis.transcludeField,\n\t\t\t\t\t\tthis.transcludeIndex,\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tparseAsInline: parseAsInline,\n\t\t\t\t\t\t\tsubTiddler: this.transcludeSubTiddler\n\t\t\t\t\t\t}),\n\t\tparseTreeNodes = parser ? parser.tree : this.parseTreeNode.children;\n\t// Set context variables for recursion detection\n\tvar recursionMarker = this.makeRecursionMarker();\n\tthis.setVariable(\"transclusion\",recursionMarker);\n\t// Check for recursion\n\tif(parser) {\n\t\tif(this.parentWidget && this.parentWidget.hasVariable(\"transclusion\",recursionMarker)) {\n\t\t\tparseTreeNodes = [{type: \"element\", tag: \"span\", attributes: {\n\t\t\t\t\"class\": {type: \"string\", value: \"tc-error\"}\n\t\t\t}, children: [\n\t\t\t\t{type: \"text\", text: $tw.language.getString(\"Error/RecursiveTransclusion\")}\n\t\t\t]}];\n\t\t}\n\t}\n\t// Construct the child widgets\n\tthis.makeChildWidgets(parseTreeNodes);\n};\n\n/*\nCompose a string comprising the title, field and/or index to identify this transclusion for recursion detection\n*/\nTranscludeWidget.prototype.makeRecursionMarker = function() {\n\tvar output = [];\n\toutput.push(\"{\");\n\toutput.push(this.getVariable(\"currentTiddler\",{defaultValue: \"\"}));\n\toutput.push(\"|\");\n\toutput.push(this.transcludeTitle || \"\");\n\toutput.push(\"|\");\n\toutput.push(this.transcludeField || \"\");\n\toutput.push(\"|\");\n\toutput.push(this.transcludeIndex || \"\");\n\toutput.push(\"|\");\n\toutput.push(this.transcludeSubTiddler || \"\");\n\toutput.push(\"}\");\n\treturn output.join(\"\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nTranscludeWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedTiddlers[this.transcludeTitle]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\nexports.transclude = TranscludeWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/vars.js": {
"title": "$:/core/modules/widgets/vars.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/vars.js\ntype: application/javascript\nmodule-type: widget\n\nThis widget allows multiple variables to be set in one go:\n\n```\n\\define helloworld() Hello world!\n<$vars greeting=\"Hi\" me={{!!title}} sentence=<<helloworld>>>\n <<greeting>>! I am <<me>> and I say: <<sentence>>\n</$vars>\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar VarsWidget = function(parseTreeNode,options) {\n\t// Call the constructor\n\tWidget.call(this);\n\t// Initialise\t\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nVarsWidget.prototype = Object.create(Widget.prototype);\n\n/*\nRender this widget into the DOM\n*/\nVarsWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nVarsWidget.prototype.execute = function() {\n\t// Parse variables\n\tvar self = this;\n\t$tw.utils.each(this.attributes,function(val,key) {\n\t\tif(key.charAt(0) !== \"$\") {\n\t\t\tself.setVariable(key,val);\n\t\t}\n\t});\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nVarsWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(Object.keys(changedAttributes).length) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports[\"vars\"] = VarsWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/view.js": {
"title": "$:/core/modules/widgets/view.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/view.js\ntype: application/javascript\nmodule-type: widget\n\nView widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ViewWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nViewWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nViewWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tif(this.text) {\n\t\tvar textNode = this.document.createTextNode(this.text);\n\t\tparent.insertBefore(textNode,nextSibling);\n\t\tthis.domNodes.push(textNode);\n\t} else {\n\t\tthis.makeChildWidgets();\n\t\tthis.renderChildren(parent,nextSibling);\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nViewWidget.prototype.execute = function() {\n\t// Get parameters from our attributes\n\tthis.viewTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.viewSubtiddler = this.getAttribute(\"subtiddler\");\n\tthis.viewField = this.getAttribute(\"field\",\"text\");\n\tthis.viewIndex = this.getAttribute(\"index\");\n\tthis.viewFormat = this.getAttribute(\"format\",\"text\");\n\tthis.viewTemplate = this.getAttribute(\"template\",\"\");\n\tthis.viewMode = this.getAttribute(\"mode\",\"block\");\n\tswitch(this.viewFormat) {\n\t\tcase \"htmlwikified\":\n\t\t\tthis.text = this.getValueAsHtmlWikified(this.viewMode);\n\t\t\tbreak;\n\t\tcase \"plainwikified\":\n\t\t\tthis.text = this.getValueAsPlainWikified(this.viewMode);\n\t\t\tbreak;\n\t\tcase \"htmlencodedplainwikified\":\n\t\t\tthis.text = this.getValueAsHtmlEncodedPlainWikified(this.viewMode);\n\t\t\tbreak;\n\t\tcase \"htmlencoded\":\n\t\t\tthis.text = this.getValueAsHtmlEncoded();\n\t\t\tbreak;\n\t\tcase \"urlencoded\":\n\t\t\tthis.text = this.getValueAsUrlEncoded();\n\t\t\tbreak;\n\t\tcase \"doubleurlencoded\":\n\t\t\tthis.text = this.getValueAsDoubleUrlEncoded();\n\t\t\tbreak;\n\t\tcase \"date\":\n\t\t\tthis.text = this.getValueAsDate(this.viewTemplate);\n\t\t\tbreak;\n\t\tcase \"relativedate\":\n\t\t\tthis.text = this.getValueAsRelativeDate();\n\t\t\tbreak;\n\t\tcase \"stripcomments\":\n\t\t\tthis.text = this.getValueAsStrippedComments();\n\t\t\tbreak;\n\t\tcase \"jsencoded\":\n\t\t\tthis.text = this.getValueAsJsEncoded();\n\t\t\tbreak;\n\t\tdefault: // \"text\"\n\t\t\tthis.text = this.getValueAsText();\n\t\t\tbreak;\n\t}\n};\n\n/*\nThe various formatter functions are baked into this widget for the moment. Eventually they will be replaced by macro functions\n*/\n\n/*\nRetrieve the value of the widget. Options are:\nasString: Optionally return the value as a string\n*/\nViewWidget.prototype.getValue = function(options) {\n\toptions = options || {};\n\tvar value = options.asString ? \"\" : undefined;\n\tif(this.viewIndex) {\n\t\tvalue = this.wiki.extractTiddlerDataItem(this.viewTitle,this.viewIndex);\n\t} else {\n\t\tvar tiddler;\n\t\tif(this.viewSubtiddler) {\n\t\t\ttiddler = this.wiki.getSubTiddler(this.viewTitle,this.viewSubtiddler);\t\n\t\t} else {\n\t\t\ttiddler = this.wiki.getTiddler(this.viewTitle);\n\t\t}\n\t\tif(tiddler) {\n\t\t\tif(this.viewField === \"text\" && !this.viewSubtiddler) {\n\t\t\t\t// Calling getTiddlerText() triggers lazy loading of skinny tiddlers\n\t\t\t\tvalue = this.wiki.getTiddlerText(this.viewTitle);\n\t\t\t} else {\n\t\t\t\tif($tw.utils.hop(tiddler.fields,this.viewField)) {\n\t\t\t\t\tif(options.asString) {\n\t\t\t\t\t\tvalue = tiddler.getFieldString(this.viewField);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tvalue = tiddler.fields[this.viewField];\t\t\t\t\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tif(this.viewField === \"title\") {\n\t\t\t\tvalue = this.viewTitle;\n\t\t\t}\n\t\t}\n\t}\n\treturn value;\n};\n\nViewWidget.prototype.getValueAsText = function() {\n\treturn this.getValue({asString: true});\n};\n\nViewWidget.prototype.getValueAsHtmlWikified = function(mode) {\n\treturn this.wiki.renderText(\"text/html\",\"text/vnd.tiddlywiki\",this.getValueAsText(),{\n\t\tparseAsInline: mode !== \"block\",\n\t\tparentWidget: this\n\t});\n};\n\nViewWidget.prototype.getValueAsPlainWikified = function(mode) {\n\treturn this.wiki.renderText(\"text/plain\",\"text/vnd.tiddlywiki\",this.getValueAsText(),{\n\t\tparseAsInline: mode !== \"block\",\n\t\tparentWidget: this\n\t});\n};\n\nViewWidget.prototype.getValueAsHtmlEncodedPlainWikified = function(mode) {\n\treturn $tw.utils.htmlEncode(this.wiki.renderText(\"text/plain\",\"text/vnd.tiddlywiki\",this.getValueAsText(),{\n\t\tparseAsInline: mode !== \"block\",\n\t\tparentWidget: this\n\t}));\n};\n\nViewWidget.prototype.getValueAsHtmlEncoded = function() {\n\treturn $tw.utils.htmlEncode(this.getValueAsText());\n};\n\nViewWidget.prototype.getValueAsUrlEncoded = function() {\n\treturn encodeURIComponent(this.getValueAsText());\n};\n\nViewWidget.prototype.getValueAsDoubleUrlEncoded = function() {\n\treturn encodeURIComponent(encodeURIComponent(this.getValueAsText()));\n};\n\nViewWidget.prototype.getValueAsDate = function(format) {\n\tformat = format || \"YYYY MM DD 0hh:0mm\";\n\tvar value = $tw.utils.parseDate(this.getValue());\n\tif(value && $tw.utils.isDate(value) && value.toString() !== \"Invalid Date\") {\n\t\treturn $tw.utils.formatDateString(value,format);\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\nViewWidget.prototype.getValueAsRelativeDate = function(format) {\n\tvar value = $tw.utils.parseDate(this.getValue());\n\tif(value && $tw.utils.isDate(value) && value.toString() !== \"Invalid Date\") {\n\t\treturn $tw.utils.getRelativeDate((new Date()) - (new Date(value))).description;\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\nViewWidget.prototype.getValueAsStrippedComments = function() {\n\tvar lines = this.getValueAsText().split(\"\\n\"),\n\t\tout = [];\n\tfor(var line=0; line<lines.length; line++) {\n\t\tvar text = lines[line];\n\t\tif(!/^\\s*\\/\\/#/.test(text)) {\n\t\t\tout.push(text);\n\t\t}\n\t}\n\treturn out.join(\"\\n\");\n};\n\nViewWidget.prototype.getValueAsJsEncoded = function() {\n\treturn $tw.utils.stringify(this.getValueAsText());\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nViewWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes.template || changedAttributes.format || changedTiddlers[this.viewTitle]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports.view = ViewWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/widget.js": {
"title": "$:/core/modules/widgets/widget.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/widget.js\ntype: application/javascript\nmodule-type: widget\n\nWidget base class\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nCreate a widget object for a parse tree node\n\tparseTreeNode: reference to the parse tree node to be rendered\n\toptions: see below\nOptions include:\n\twiki: mandatory reference to wiki associated with this render tree\n\tparentWidget: optional reference to a parent renderer node for the context chain\n\tdocument: optional document object to use instead of global document\n*/\nvar Widget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInitialise widget properties. These steps are pulled out of the constructor so that we can reuse them in subclasses\n*/\nWidget.prototype.initialise = function(parseTreeNode,options) {\n\t// Bail if parseTreeNode is undefined, meaning that the widget constructor was called without any arguments so that it can be subclassed\n\tif(parseTreeNode === undefined) {\n\t\treturn;\n\t}\n\toptions = options || {};\n\t// Save widget info\n\tthis.parseTreeNode = parseTreeNode;\n\tthis.wiki = options.wiki;\n\tthis.parentWidget = options.parentWidget;\n\tthis.variablesConstructor = function() {};\n\tthis.variablesConstructor.prototype = this.parentWidget ? this.parentWidget.variables : {};\n\tthis.variables = new this.variablesConstructor();\n\tthis.document = options.document;\n\tthis.attributes = {};\n\tthis.children = [];\n\tthis.domNodes = [];\n\tthis.eventListeners = {};\n\t// Hashmap of the widget classes\n\tif(!this.widgetClasses) {\n\t\t// Get widget classes\n\t\tWidget.prototype.widgetClasses = $tw.modules.applyMethods(\"widget\");\n\t\t// Process any subclasses\n\t\t$tw.modules.forEachModuleOfType(\"widget-subclass\",function(title,module) {\n\t\t\tif(module.baseClass) {\n\t\t\t\tvar baseClass = Widget.prototype.widgetClasses[module.baseClass];\n\t\t\t\tif(!baseClass) {\n\t\t\t\t\tthrow \"Module '\" + title + \"' is attemping to extend a non-existent base class '\" + module.baseClass + \"'\";\n\t\t\t\t}\n\t\t\t\tvar subClass = module.constructor;\n\t\t\t\tsubClass.prototype = new baseClass();\n\t\t\t\t$tw.utils.extend(subClass.prototype,module.prototype);\n\t\t\t\tWidget.prototype.widgetClasses[module.name || module.baseClass] = subClass;\n\t\t\t}\n\t\t});\n\t}\n};\n\n/*\nRender this widget into the DOM\n*/\nWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nWidget.prototype.execute = function() {\n\tthis.makeChildWidgets();\n};\n\n/*\nSet the value of a context variable\nname: name of the variable\nvalue: value of the variable\nparams: array of {name:, default:} for each parameter\nisMacroDefinition: true if the variable is set via a \\define macro pragma (and hence should have variable substitution performed)\n*/\nWidget.prototype.setVariable = function(name,value,params,isMacroDefinition) {\n\tthis.variables[name] = {value: value, params: params, isMacroDefinition: !!isMacroDefinition};\n};\n\n/*\nGet the prevailing value of a context variable\nname: name of variable\noptions: see below\nOptions include\nparams: array of {name:, value:} for each parameter\ndefaultValue: default value if the variable is not defined\n\nReturns an object with the following fields:\n\nparams: array of {name:,value:} of parameters passed to wikitext variables\ntext: text of variable, with parameters properly substituted\n*/\nWidget.prototype.getVariableInfo = function(name,options) {\n\toptions = options || {};\n\tvar actualParams = options.params || [],\n\t\tparentWidget = this.parentWidget;\n\t// Check for the variable defined in the parent widget (or an ancestor in the prototype chain)\n\tif(parentWidget && name in parentWidget.variables) {\n\t\tvar variable = parentWidget.variables[name],\n\t\t\tvalue = variable.value,\n\t\t\tparams = this.resolveVariableParameters(variable.params,actualParams);\n\t\t// Substitute any parameters specified in the definition\n\t\t$tw.utils.each(params,function(param) {\n\t\t\tvalue = $tw.utils.replaceString(value,new RegExp(\"\\\\$\" + $tw.utils.escapeRegExp(param.name) + \"\\\\$\",\"mg\"),param.value);\n\t\t});\n\t\t// Only substitute variable references if this variable was defined with the \\define pragma\n\t\tif(variable.isMacroDefinition) {\n\t\t\tvalue = this.substituteVariableReferences(value);\t\t\t\n\t\t}\n\t\treturn {\n\t\t\ttext: value,\n\t\t\tparams: params\n\t\t};\n\t}\n\t// If the variable doesn't exist in the parent widget then look for a macro module\n\treturn {\n\t\ttext: this.evaluateMacroModule(name,actualParams,options.defaultValue)\n\t};\n};\n\n/*\nSimplified version of getVariableInfo() that just returns the text\n*/\nWidget.prototype.getVariable = function(name,options) {\n\treturn this.getVariableInfo(name,options).text;\n};\n\nWidget.prototype.resolveVariableParameters = function(formalParams,actualParams) {\n\tformalParams = formalParams || [];\n\tactualParams = actualParams || [];\n\tvar nextAnonParameter = 0, // Next candidate anonymous parameter in macro call\n\t\tparamInfo, paramValue,\n\t\tresults = [];\n\t// Step through each of the parameters in the macro definition\n\tfor(var p=0; p<formalParams.length; p++) {\n\t\t// Check if we've got a macro call parameter with the same name\n\t\tparamInfo = formalParams[p];\n\t\tparamValue = undefined;\n\t\tfor(var m=0; m<actualParams.length; m++) {\n\t\t\tif(actualParams[m].name === paramInfo.name) {\n\t\t\t\tparamValue = actualParams[m].value;\n\t\t\t}\n\t\t}\n\t\t// If not, use the next available anonymous macro call parameter\n\t\twhile(nextAnonParameter < actualParams.length && actualParams[nextAnonParameter].name) {\n\t\t\tnextAnonParameter++;\n\t\t}\n\t\tif(paramValue === undefined && nextAnonParameter < actualParams.length) {\n\t\t\tparamValue = actualParams[nextAnonParameter++].value;\n\t\t}\n\t\t// If we've still not got a value, use the default, if any\n\t\tparamValue = paramValue || paramInfo[\"default\"] || \"\";\n\t\t// Store the parameter name and value\n\t\tresults.push({name: paramInfo.name, value: paramValue});\n\t}\n\treturn results;\n};\n\nWidget.prototype.substituteVariableReferences = function(text) {\n\tvar self = this;\n\treturn (text || \"\").replace(/\\$\\(([^\\)\\$]+)\\)\\$/g,function(match,p1,offset,string) {\n\t\treturn self.getVariable(p1,{defaultValue: \"\"});\n\t});\n};\n\nWidget.prototype.evaluateMacroModule = function(name,actualParams,defaultValue) {\n\tif($tw.utils.hop($tw.macros,name)) {\n\t\tvar macro = $tw.macros[name],\n\t\t\targs = [];\n\t\tif(macro.params.length > 0) {\n\t\t\tvar nextAnonParameter = 0, // Next candidate anonymous parameter in macro call\n\t\t\t\tparamInfo, paramValue;\n\t\t\t// Step through each of the parameters in the macro definition\n\t\t\tfor(var p=0; p<macro.params.length; p++) {\n\t\t\t\t// Check if we've got a macro call parameter with the same name\n\t\t\t\tparamInfo = macro.params[p];\n\t\t\t\tparamValue = undefined;\n\t\t\t\tfor(var m=0; m<actualParams.length; m++) {\n\t\t\t\t\tif(actualParams[m].name === paramInfo.name) {\n\t\t\t\t\t\tparamValue = actualParams[m].value;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// If not, use the next available anonymous macro call parameter\n\t\t\t\twhile(nextAnonParameter < actualParams.length && actualParams[nextAnonParameter].name) {\n\t\t\t\t\tnextAnonParameter++;\n\t\t\t\t}\n\t\t\t\tif(paramValue === undefined && nextAnonParameter < actualParams.length) {\n\t\t\t\t\tparamValue = actualParams[nextAnonParameter++].value;\n\t\t\t\t}\n\t\t\t\t// If we've still not got a value, use the default, if any\n\t\t\t\tparamValue = paramValue || paramInfo[\"default\"] || \"\";\n\t\t\t\t// Save the parameter\n\t\t\t\targs.push(paramValue);\n\t\t\t}\n\t\t}\n\t\telse for(var i=0; i<actualParams.length; ++i) {\n\t\t\targs.push(actualParams[i].value);\n\t\t}\n\t\treturn (macro.run.apply(this,args) || \"\").toString();\n\t} else {\n\t\treturn defaultValue;\n\t}\n};\n\n/*\nCheck whether a given context variable value exists in the parent chain\n*/\nWidget.prototype.hasVariable = function(name,value) {\n\tvar node = this;\n\twhile(node) {\n\t\tif($tw.utils.hop(node.variables,name) && node.variables[name].value === value) {\n\t\t\treturn true;\n\t\t}\n\t\tnode = node.parentWidget;\n\t}\n\treturn false;\n};\n\n/*\nConstruct a qualifying string based on a hash of concatenating the values of a given variable in the parent chain\n*/\nWidget.prototype.getStateQualifier = function(name) {\n\tthis.qualifiers = this.qualifiers || Object.create(null);\n\tname = name || \"transclusion\";\n\tif(this.qualifiers[name]) {\n\t\treturn this.qualifiers[name];\n\t} else {\n\t\tvar output = [],\n\t\t\tnode = this;\n\t\twhile(node && node.parentWidget) {\n\t\t\tif($tw.utils.hop(node.parentWidget.variables,name)) {\n\t\t\t\toutput.push(node.getVariable(name));\n\t\t\t}\n\t\t\tnode = node.parentWidget;\n\t\t}\n\t\tvar value = $tw.utils.hashString(output.join(\"\"));\n\t\tthis.qualifiers[name] = value;\n\t\treturn value;\n\t}\n};\n\n/*\nCompute the current values of the attributes of the widget. Returns a hashmap of the names of the attributes that have changed\n*/\nWidget.prototype.computeAttributes = function() {\n\tvar changedAttributes = {},\n\t\tself = this,\n\t\tvalue;\n\t$tw.utils.each(this.parseTreeNode.attributes,function(attribute,name) {\n\t\tif(attribute.type === \"filtered\") {\n\t\t\tvalue = self.wiki.filterTiddlers(attribute.filter,self)[0] || \"\";\n\t\t} else if(attribute.type === \"indirect\") {\n\t\t\tvalue = self.wiki.getTextReference(attribute.textReference,\"\",self.getVariable(\"currentTiddler\"));\n\t\t} else if(attribute.type === \"macro\") {\n\t\t\tvalue = self.getVariable(attribute.value.name,{params: attribute.value.params});\n\t\t} else { // String attribute\n\t\t\tvalue = attribute.value;\n\t\t}\n\t\t// Check whether the attribute has changed\n\t\tif(self.attributes[name] !== value) {\n\t\t\tself.attributes[name] = value;\n\t\t\tchangedAttributes[name] = true;\n\t\t}\n\t});\n\treturn changedAttributes;\n};\n\n/*\nCheck for the presence of an attribute\n*/\nWidget.prototype.hasAttribute = function(name) {\n\treturn $tw.utils.hop(this.attributes,name);\n};\n\n/*\nGet the value of an attribute\n*/\nWidget.prototype.getAttribute = function(name,defaultText) {\n\tif($tw.utils.hop(this.attributes,name)) {\n\t\treturn this.attributes[name];\n\t} else {\n\t\treturn defaultText;\n\t}\n};\n\n/*\nAssign the computed attributes of the widget to a domNode\noptions include:\nexcludeEventAttributes: ignores attributes whose name begins with \"on\"\n*/\nWidget.prototype.assignAttributes = function(domNode,options) {\n\toptions = options || {};\n\tvar self = this;\n\t$tw.utils.each(this.attributes,function(v,a) {\n\t\t// Check exclusions\n\t\tif(options.excludeEventAttributes && a.substr(0,2) === \"on\") {\n\t\t\tv = undefined;\n\t\t}\n\t\tif(v !== undefined) {\n\t\t\tvar b = a.split(\":\");\n\t\t\t// Setting certain attributes can cause a DOM error (eg xmlns on the svg element)\n\t\t\ttry {\n\t\t\t\tif (b.length == 2 && b[0] == \"xlink\"){\n\t\t\t\t\tdomNode.setAttributeNS(\"http://www.w3.org/1999/xlink\",b[1],v);\n\t\t\t\t} else {\n\t\t\t\t\tdomNode.setAttributeNS(null,a,v);\n\t\t\t\t}\n\t\t\t} catch(e) {\n\t\t\t}\n\t\t}\n\t});\n};\n\n/*\nMake child widgets correspondng to specified parseTreeNodes\n*/\nWidget.prototype.makeChildWidgets = function(parseTreeNodes) {\n\tthis.children = [];\n\tvar self = this;\n\t$tw.utils.each(parseTreeNodes || (this.parseTreeNode && this.parseTreeNode.children),function(childNode) {\n\t\tself.children.push(self.makeChildWidget(childNode));\n\t});\n};\n\n/*\nConstruct the widget object for a parse tree node\n*/\nWidget.prototype.makeChildWidget = function(parseTreeNode) {\n\tvar WidgetClass = this.widgetClasses[parseTreeNode.type];\n\tif(!WidgetClass) {\n\t\tWidgetClass = this.widgetClasses.text;\n\t\tparseTreeNode = {type: \"text\", text: \"Undefined widget '\" + parseTreeNode.type + \"'\"};\n\t}\n\treturn new WidgetClass(parseTreeNode,{\n\t\twiki: this.wiki,\n\t\tvariables: {},\n\t\tparentWidget: this,\n\t\tdocument: this.document\n\t});\n};\n\n/*\nGet the next sibling of this widget\n*/\nWidget.prototype.nextSibling = function() {\n\tif(this.parentWidget) {\n\t\tvar index = this.parentWidget.children.indexOf(this);\n\t\tif(index !== -1 && index < this.parentWidget.children.length-1) {\n\t\t\treturn this.parentWidget.children[index+1];\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nGet the previous sibling of this widget\n*/\nWidget.prototype.previousSibling = function() {\n\tif(this.parentWidget) {\n\t\tvar index = this.parentWidget.children.indexOf(this);\n\t\tif(index !== -1 && index > 0) {\n\t\t\treturn this.parentWidget.children[index-1];\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nRender the children of this widget into the DOM\n*/\nWidget.prototype.renderChildren = function(parent,nextSibling) {\n\tvar children = this.children;\n\tfor(var i = 0; i < children.length; i++) {\n\t\tchildren[i].render(parent,nextSibling);\n\t};\n};\n\n/*\nAdd a list of event listeners from an array [{type:,handler:},...]\n*/\nWidget.prototype.addEventListeners = function(listeners) {\n\tvar self = this;\n\t$tw.utils.each(listeners,function(listenerInfo) {\n\t\tself.addEventListener(listenerInfo.type,listenerInfo.handler);\n\t});\n};\n\n/*\nAdd an event listener\n*/\nWidget.prototype.addEventListener = function(type,handler) {\n\tvar self = this;\n\tif(typeof handler === \"string\") { // The handler is a method name on this widget\n\t\tthis.eventListeners[type] = function(event) {\n\t\t\treturn self[handler].call(self,event);\n\t\t};\n\t} else { // The handler is a function\n\t\tthis.eventListeners[type] = function(event) {\n\t\t\treturn handler.call(self,event);\n\t\t};\n\t}\n};\n\n/*\nDispatch an event to a widget. If the widget doesn't handle the event then it is also dispatched to the parent widget\n*/\nWidget.prototype.dispatchEvent = function(event) {\n\t// Dispatch the event if this widget handles it\n\tvar listener = this.eventListeners[event.type];\n\tif(listener) {\n\t\t// Don't propagate the event if the listener returned false\n\t\tif(!listener(event)) {\n\t\t\treturn false;\n\t\t}\n\t}\n\t// Dispatch the event to the parent widget\n\tif(this.parentWidget) {\n\t\treturn this.parentWidget.dispatchEvent(event);\n\t}\n\treturn true;\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nWidget.prototype.refresh = function(changedTiddlers) {\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nRebuild a previously rendered widget\n*/\nWidget.prototype.refreshSelf = function() {\n\tvar nextSibling = this.findNextSiblingDomNode();\n\tthis.removeChildDomNodes();\n\tthis.render(this.parentDomNode,nextSibling);\n};\n\n/*\nRefresh all the children of a widget\n*/\nWidget.prototype.refreshChildren = function(changedTiddlers) {\n\tvar children = this.children,\n\t\trefreshed = false;\n\tfor (var i = 0; i < children.length; i++) {\n\t\trefreshed = children[i].refresh(changedTiddlers) || refreshed;\n\t}\n\treturn refreshed;\n};\n\n/*\nFind the next sibling in the DOM to this widget. This is done by scanning the widget tree through all next siblings and their descendents that share the same parent DOM node\n*/\nWidget.prototype.findNextSiblingDomNode = function(startIndex) {\n\t// Refer to this widget by its index within its parents children\n\tvar parent = this.parentWidget,\n\t\tindex = startIndex !== undefined ? startIndex : parent.children.indexOf(this);\nif(index === -1) {\n\tthrow \"node not found in parents children\";\n}\n\t// Look for a DOM node in the later siblings\n\twhile(++index < parent.children.length) {\n\t\tvar domNode = parent.children[index].findFirstDomNode();\n\t\tif(domNode) {\n\t\t\treturn domNode;\n\t\t}\n\t}\n\t// Go back and look for later siblings of our parent if it has the same parent dom node\n\tvar grandParent = parent.parentWidget;\n\tif(grandParent && parent.parentDomNode === this.parentDomNode) {\n\t\tindex = grandParent.children.indexOf(parent);\n\t\tif(index !== -1) {\n\t\t\treturn parent.findNextSiblingDomNode(index);\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nFind the first DOM node generated by a widget or its children\n*/\nWidget.prototype.findFirstDomNode = function() {\n\t// Return the first dom node of this widget, if we've got one\n\tif(this.domNodes.length > 0) {\n\t\treturn this.domNodes[0];\n\t}\n\t// Otherwise, recursively call our children\n\tfor(var t=0; t<this.children.length; t++) {\n\t\tvar domNode = this.children[t].findFirstDomNode();\n\t\tif(domNode) {\n\t\t\treturn domNode;\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nRemove any DOM nodes created by this widget or its children\n*/\nWidget.prototype.removeChildDomNodes = function() {\n\t// If this widget has directly created DOM nodes, delete them and exit. This assumes that any child widgets are contained within the created DOM nodes, which would normally be the case\n\tif(this.domNodes.length > 0) {\n\t\t$tw.utils.each(this.domNodes,function(domNode) {\n\t\t\tdomNode.parentNode.removeChild(domNode);\n\t\t});\n\t\tthis.domNodes = [];\n\t} else {\n\t\t// Otherwise, ask the child widgets to delete their DOM nodes\n\t\t$tw.utils.each(this.children,function(childWidget) {\n\t\t\tchildWidget.removeChildDomNodes();\n\t\t});\n\t}\n};\n\n/*\nInvoke the action widgets that are descendents of the current widget.\n*/\nWidget.prototype.invokeActions = function(triggeringWidget,event) {\n\tvar handled = false;\n\t// For each child widget\n\tfor(var t=0; t<this.children.length; t++) {\n\t\tvar child = this.children[t];\n\t\t// Invoke the child if it is an action widget\n\t\tif(child.invokeAction) {\n\t\t\tchild.refreshSelf();\n\t\t\tif(child.invokeAction(triggeringWidget,event)) {\n\t\t\t\thandled = true;\n\t\t\t}\n\t\t}\n\t\t// Propagate through through the child if it permits it\n\t\tif(child.allowActionPropagation() && child.invokeActions(triggeringWidget,event)) {\n\t\t\thandled = true;\n\t\t}\n\t}\n\treturn handled;\n};\n\n/*\nInvoke the action widgets defined in a string\n*/\nWidget.prototype.invokeActionString = function(actions,triggeringWidget,event,variables) {\n\tactions = actions || \"\";\n\tvar parser = this.wiki.parseText(\"text/vnd.tiddlywiki\",actions,{\n\t\t\tparentWidget: this,\n\t\t\tdocument: this.document\n\t\t}),\n\t\twidgetNode = this.wiki.makeWidget(parser,{\n\t\t\tparentWidget: this,\n\t\t\tdocument: this.document,\n\t\t\tvariables: variables\n\t\t});\n\tvar container = this.document.createElement(\"div\");\n\twidgetNode.render(container,null);\n\treturn widgetNode.invokeActions(this,event);\n};\n\nWidget.prototype.allowActionPropagation = function() {\n\treturn true;\n};\n\nexports.widget = Widget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/wikify.js": {
"title": "$:/core/modules/widgets/wikify.js",
"text": "/*\\\ntitle: $:/core/modules/widgets/wikify.js\ntype: application/javascript\nmodule-type: widget\n\nWidget to wikify text into a variable\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar WikifyWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nWikifyWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nWikifyWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nWikifyWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.wikifyName = this.getAttribute(\"name\");\n\tthis.wikifyText = this.getAttribute(\"text\");\n\tthis.wikifyType = this.getAttribute(\"type\");\n\tthis.wikifyMode = this.getAttribute(\"mode\",\"block\");\n\tthis.wikifyOutput = this.getAttribute(\"output\",\"text\");\n\t// Create the parse tree\n\tthis.wikifyParser = this.wiki.parseText(this.wikifyType,this.wikifyText,{\n\t\t\tparseAsInline: this.wikifyMode === \"inline\"\n\t\t});\n\t// Create the widget tree \n\tthis.wikifyWidgetNode = this.wiki.makeWidget(this.wikifyParser,{\n\t\t\tdocument: $tw.fakeDocument,\n\t\t\tparentWidget: this\n\t\t});\n\t// Render the widget tree to the container\n\tthis.wikifyContainer = $tw.fakeDocument.createElement(\"div\");\n\tthis.wikifyWidgetNode.render(this.wikifyContainer,null);\n\tthis.wikifyResult = this.getResult();\n\t// Set context variable\n\tthis.setVariable(this.wikifyName,this.wikifyResult);\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nReturn the result string\n*/\nWikifyWidget.prototype.getResult = function() {\n\tvar result;\n\tswitch(this.wikifyOutput) {\n\t\tcase \"text\":\n\t\t\tresult = this.wikifyContainer.textContent;\n\t\t\tbreak;\n\t\tcase \"formattedtext\":\n\t\t\tresult = this.wikifyContainer.formattedTextContent;\n\t\t\tbreak;\n\t\tcase \"html\":\n\t\t\tresult = this.wikifyContainer.innerHTML;\n\t\t\tbreak;\n\t\tcase \"parsetree\":\n\t\t\tresult = JSON.stringify(this.wikifyParser.tree,0,$tw.config.preferences.jsonSpaces);\n\t\t\tbreak;\n\t\tcase \"widgettree\":\n\t\t\tresult = JSON.stringify(this.getWidgetTree(),0,$tw.config.preferences.jsonSpaces);\n\t\t\tbreak;\n\t}\n\treturn result;\n};\n\n/*\nReturn a string of the widget tree\n*/\nWikifyWidget.prototype.getWidgetTree = function() {\n\tvar copyNode = function(widgetNode,resultNode) {\n\t\t\tvar type = widgetNode.parseTreeNode.type;\n\t\t\tresultNode.type = type;\n\t\t\tswitch(type) {\n\t\t\t\tcase \"element\":\n\t\t\t\t\tresultNode.tag = widgetNode.parseTreeNode.tag;\n\t\t\t\t\tbreak;\n\t\t\t\tcase \"text\":\n\t\t\t\t\tresultNode.text = widgetNode.parseTreeNode.text;\n\t\t\t\t\tbreak;\t\n\t\t\t}\n\t\t\tif(Object.keys(widgetNode.attributes || {}).length > 0) {\n\t\t\t\tresultNode.attributes = {};\n\t\t\t\t$tw.utils.each(widgetNode.attributes,function(attr,attrName) {\n\t\t\t\t\tresultNode.attributes[attrName] = widgetNode.getAttribute(attrName);\n\t\t\t\t});\n\t\t\t}\n\t\t\tif(Object.keys(widgetNode.children || {}).length > 0) {\n\t\t\t\tresultNode.children = [];\n\t\t\t\t$tw.utils.each(widgetNode.children,function(widgetChildNode) {\n\t\t\t\t\tvar node = {};\n\t\t\t\t\tresultNode.children.push(node);\n\t\t\t\t\tcopyNode(widgetChildNode,node);\n\t\t\t\t});\n\t\t\t}\n\t\t},\n\t\tresults = {};\n\tcopyNode(this.wikifyWidgetNode,results);\n\treturn results;\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nWikifyWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\t// Refresh ourselves entirely if any of our attributes have changed\n\tif(changedAttributes.name || changedAttributes.text || changedAttributes.type || changedAttributes.mode || changedAttributes.output) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\t// Refresh the widget tree\n\t\tif(this.wikifyWidgetNode.refresh(changedTiddlers)) {\n\t\t\t// Check if there was any change\n\t\t\tvar result = this.getResult();\n\t\t\tif(result !== this.wikifyResult) {\n\t\t\t\t// If so, save the change\n\t\t\t\tthis.wikifyResult = result;\n\t\t\t\tthis.setVariable(this.wikifyName,this.wikifyResult);\n\t\t\t\t// Refresh each of our child widgets\n\t\t\t\t$tw.utils.each(this.children,function(childWidget) {\n\t\t\t\t\tchildWidget.refreshSelf();\n\t\t\t\t});\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\t\t// Just refresh the children\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.wikify = WikifyWidget;\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/wiki-bulkops.js": {
"title": "$:/core/modules/wiki-bulkops.js",
"text": "/*\\\ntitle: $:/core/modules/wiki-bulkops.js\ntype: application/javascript\nmodule-type: wikimethod\n\nBulk tiddler operations such as rename.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nRename a tiddler, and relink any tags or lists that reference it.\n*/\nfunction renameTiddler(fromTitle,toTitle,options) {\n\tfromTitle = (fromTitle || \"\").trim();\n\ttoTitle = (toTitle || \"\").trim();\n\toptions = options || {};\n\tif(fromTitle && toTitle && fromTitle !== toTitle) {\n\t\t// Rename the tiddler itself\n\t\tvar oldTiddler = this.getTiddler(fromTitle),\n\t\t\tnewTiddler = new $tw.Tiddler(oldTiddler,{title: toTitle},this.getModificationFields());\n\t\tnewTiddler = $tw.hooks.invokeHook(\"th-renaming-tiddler\",newTiddler,oldTiddler);\n\t\tthis.addTiddler(newTiddler);\n\t\tthis.deleteTiddler(fromTitle);\n\t\t// Rename any tags or lists that reference it\n\t\tthis.relinkTiddler(fromTitle,toTitle,options)\n\t}\n}\n\n/*\nRelink any tags or lists that reference a given tiddler\n*/\nfunction relinkTiddler(fromTitle,toTitle,options) {\n\tvar self = this;\n\tfromTitle = (fromTitle || \"\").trim();\n\ttoTitle = (toTitle || \"\").trim();\n\toptions = options || {};\n\tif(fromTitle && toTitle && fromTitle !== toTitle) {\n\t\tthis.each(function(tiddler,title) {\n\t\t\tvar type = tiddler.fields.type || \"\";\n\t\t\t// Don't touch plugins or JavaScript modules\n\t\t\tif(!tiddler.fields[\"plugin-type\"] && type !== \"application/javascript\") {\n\t\t\t\tvar tags = tiddler.fields.tags ? tiddler.fields.tags.slice(0) : undefined,\n\t\t\t\t\tlist = tiddler.fields.list ? tiddler.fields.list.slice(0) : undefined,\n\t\t\t\t\tisModified = false;\n\t\t\t\tif(!options.dontRenameInTags) {\n\t\t\t\t\t// Rename tags\n\t\t\t\t\t$tw.utils.each(tags,function (title,index) {\n\t\t\t\t\t\tif(title === fromTitle) {\nconsole.log(\"Renaming tag '\" + tags[index] + \"' to '\" + toTitle + \"' of tiddler '\" + tiddler.fields.title + \"'\");\n\t\t\t\t\t\t\ttags[index] = toTitle;\n\t\t\t\t\t\t\tisModified = true;\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t\tif(!options.dontRenameInLists) {\n\t\t\t\t\t// Rename lists\n\t\t\t\t\t$tw.utils.each(list,function (title,index) {\n\t\t\t\t\t\tif(title === fromTitle) {\nconsole.log(\"Renaming list item '\" + list[index] + \"' to '\" + toTitle + \"' of tiddler '\" + tiddler.fields.title + \"'\");\n\t\t\t\t\t\t\tlist[index] = toTitle;\n\t\t\t\t\t\t\tisModified = true;\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t\tif(isModified) {\n\t\t\t\t\tvar newTiddler = new $tw.Tiddler(tiddler,{tags: tags, list: list},self.getModificationFields())\n\t\t\t\t\tnewTiddler = $tw.hooks.invokeHook(\"th-relinking-tiddler\",newTiddler,tiddler);\n\t\t\t\t\tself.addTiddler(newTiddler);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n};\n\nexports.renameTiddler = renameTiddler;\nexports.relinkTiddler = relinkTiddler;\n\n})();\n",
"type": "application/javascript",
"module-type": "wikimethod"
},
"$:/core/modules/wiki.js": {
"title": "$:/core/modules/wiki.js",
"text": "/*\\\ntitle: $:/core/modules/wiki.js\ntype: application/javascript\nmodule-type: wikimethod\n\nExtension methods for the $tw.Wiki object\n\nAdds the following properties to the wiki object:\n\n* `eventListeners` is a hashmap by type of arrays of listener functions\n* `changedTiddlers` is a hashmap describing changes to named tiddlers since wiki change events were last dispatched. Each entry is a hashmap containing two fields:\n\tmodified: true/false\n\tdeleted: true/false\n* `changeCount` is a hashmap by tiddler title containing a numerical index that starts at zero and is incremented each time a tiddler is created changed or deleted\n* `caches` is a hashmap by tiddler title containing a further hashmap of named cache objects. Caches are automatically cleared when a tiddler is modified or deleted\n* `globalCache` is a hashmap by cache name of cache objects that are cleared whenever any tiddler change occurs\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nvar USER_NAME_TITLE = \"$:/status/UserName\",\n\tTIMESTAMP_DISABLE_TITLE = \"$:/config/TimestampDisable\";\n\n/*\nAdd available indexers to this wiki\n*/\nexports.addIndexersToWiki = function() {\n\tvar self = this;\n\t$tw.utils.each($tw.modules.applyMethods(\"indexer\"),function(Indexer,name) {\n\t\tself.addIndexer(new Indexer(self),name);\n\t});\n};\n\n/*\nGet the value of a text reference. Text references can have any of these forms:\n\t<tiddlertitle>\n\t<tiddlertitle>!!<fieldname>\n\t!!<fieldname> - specifies a field of the current tiddlers\n\t<tiddlertitle>##<index>\n*/\nexports.getTextReference = function(textRef,defaultText,currTiddlerTitle) {\n\tvar tr = $tw.utils.parseTextReference(textRef),\n\t\ttitle = tr.title || currTiddlerTitle;\n\tif(tr.field) {\n\t\tvar tiddler = this.getTiddler(title);\n\t\tif(tr.field === \"title\") { // Special case so we can return the title of a non-existent tiddler\n\t\t\treturn title;\n\t\t} else if(tiddler && $tw.utils.hop(tiddler.fields,tr.field)) {\n\t\t\treturn tiddler.getFieldString(tr.field);\n\t\t} else {\n\t\t\treturn defaultText;\n\t\t}\n\t} else if(tr.index) {\n\t\treturn this.extractTiddlerDataItem(title,tr.index,defaultText);\n\t} else {\n\t\treturn this.getTiddlerText(title,defaultText);\n\t}\n};\n\nexports.setTextReference = function(textRef,value,currTiddlerTitle) {\n\tvar tr = $tw.utils.parseTextReference(textRef),\n\t\ttitle = tr.title || currTiddlerTitle;\n\tthis.setText(title,tr.field,tr.index,value);\n};\n\nexports.setText = function(title,field,index,value,options) {\n\toptions = options || {};\n\tvar creationFields = options.suppressTimestamp ? {} : this.getCreationFields(),\n\t\tmodificationFields = options.suppressTimestamp ? {} : this.getModificationFields();\n\t// Check if it is a reference to a tiddler field\n\tif(index) {\n\t\tvar data = this.getTiddlerData(title,Object.create(null));\n\t\tif(value !== undefined) {\n\t\t\tdata[index] = value;\n\t\t} else {\n\t\t\tdelete data[index];\n\t\t}\n\t\tthis.setTiddlerData(title,data,modificationFields);\n\t} else {\n\t\tvar tiddler = this.getTiddler(title),\n\t\t\tfields = {title: title};\n\t\tfields[field || \"text\"] = value;\n\t\tthis.addTiddler(new $tw.Tiddler(creationFields,tiddler,fields,modificationFields));\n\t}\n};\n\nexports.deleteTextReference = function(textRef,currTiddlerTitle) {\n\tvar tr = $tw.utils.parseTextReference(textRef),\n\t\ttitle,tiddler,fields;\n\t// Check if it is a reference to a tiddler\n\tif(tr.title && !tr.field) {\n\t\tthis.deleteTiddler(tr.title);\n\t// Else check for a field reference\n\t} else if(tr.field) {\n\t\ttitle = tr.title || currTiddlerTitle;\n\t\ttiddler = this.getTiddler(title);\n\t\tif(tiddler && $tw.utils.hop(tiddler.fields,tr.field)) {\n\t\t\tfields = Object.create(null);\n\t\t\tfields[tr.field] = undefined;\n\t\t\tthis.addTiddler(new $tw.Tiddler(tiddler,fields,this.getModificationFields()));\n\t\t}\n\t}\n};\n\nexports.addEventListener = function(type,listener) {\n\tthis.eventListeners = this.eventListeners || {};\n\tthis.eventListeners[type] = this.eventListeners[type] || [];\n\tthis.eventListeners[type].push(listener);\t\n};\n\nexports.removeEventListener = function(type,listener) {\n\tvar listeners = this.eventListeners[type];\n\tif(listeners) {\n\t\tvar p = listeners.indexOf(listener);\n\t\tif(p !== -1) {\n\t\t\tlisteners.splice(p,1);\n\t\t}\n\t}\n};\n\nexports.dispatchEvent = function(type /*, args */) {\n\tvar args = Array.prototype.slice.call(arguments,1),\n\t\tlisteners = this.eventListeners[type];\n\tif(listeners) {\n\t\tfor(var p=0; p<listeners.length; p++) {\n\t\t\tvar listener = listeners[p];\n\t\t\tlistener.apply(listener,args);\n\t\t}\n\t}\n};\n\n/*\nCauses a tiddler to be marked as changed, incrementing the change count, and triggers event handlers.\nThis method should be called after the changes it describes have been made to the wiki.tiddlers[] array.\n\ttitle: Title of tiddler\n\tisDeleted: defaults to false (meaning the tiddler has been created or modified),\n\t\ttrue if the tiddler has been deleted\n*/\nexports.enqueueTiddlerEvent = function(title,isDeleted) {\n\t// Record the touch in the list of changed tiddlers\n\tthis.changedTiddlers = this.changedTiddlers || Object.create(null);\n\tthis.changedTiddlers[title] = this.changedTiddlers[title] || Object.create(null);\n\tthis.changedTiddlers[title][isDeleted ? \"deleted\" : \"modified\"] = true;\n\t// Increment the change count\n\tthis.changeCount = this.changeCount || Object.create(null);\n\tif($tw.utils.hop(this.changeCount,title)) {\n\t\tthis.changeCount[title]++;\n\t} else {\n\t\tthis.changeCount[title] = 1;\n\t}\n\t// Trigger events\n\tthis.eventListeners = this.eventListeners || {};\n\tif(!this.eventsTriggered) {\n\t\tvar self = this;\n\t\t$tw.utils.nextTick(function() {\n\t\t\tvar changes = self.changedTiddlers;\n\t\t\tself.changedTiddlers = Object.create(null);\n\t\t\tself.eventsTriggered = false;\n\t\t\tif($tw.utils.count(changes) > 0) {\n\t\t\t\tself.dispatchEvent(\"change\",changes);\n\t\t\t}\n\t\t});\n\t\tthis.eventsTriggered = true;\n\t}\n};\n\nexports.getSizeOfTiddlerEventQueue = function() {\n\treturn $tw.utils.count(this.changedTiddlers);\n};\n\nexports.clearTiddlerEventQueue = function() {\n\tthis.changedTiddlers = Object.create(null);\n\tthis.changeCount = Object.create(null);\n};\n\nexports.getChangeCount = function(title) {\n\tthis.changeCount = this.changeCount || Object.create(null);\n\tif($tw.utils.hop(this.changeCount,title)) {\n\t\treturn this.changeCount[title];\n\t} else {\n\t\treturn 0;\n\t}\n};\n\n/*\nGenerate an unused title from the specified base\n*/\nexports.generateNewTitle = function(baseTitle,options) {\n\toptions = options || {};\n\tvar c = 0,\n\t\ttitle = baseTitle;\n\twhile(this.tiddlerExists(title) || this.isShadowTiddler(title) || this.findDraft(title)) {\n\t\ttitle = baseTitle + \n\t\t\t(options.prefix || \" \") + \n\t\t\t(++c);\n\t}\n\treturn title;\n};\n\nexports.isSystemTiddler = function(title) {\n\treturn title && title.indexOf(\"$:/\") === 0;\n};\n\nexports.isTemporaryTiddler = function(title) {\n\treturn title && title.indexOf(\"$:/temp/\") === 0;\n};\n\nexports.isImageTiddler = function(title) {\n\tvar tiddler = this.getTiddler(title);\n\tif(tiddler) {\t\t\n\t\tvar contentTypeInfo = $tw.config.contentTypeInfo[tiddler.fields.type || \"text/vnd.tiddlywiki\"];\n\t\treturn !!contentTypeInfo && contentTypeInfo.flags.indexOf(\"image\") !== -1;\n\t} else {\n\t\treturn null;\n\t}\n};\n\nexports.isBinaryTiddler = function(title) {\n\tvar tiddler = this.getTiddler(title);\n\tif(tiddler) {\t\t\n\t\tvar contentTypeInfo = $tw.config.contentTypeInfo[tiddler.fields.type || \"text/vnd.tiddlywiki\"];\n\t\treturn !!contentTypeInfo && contentTypeInfo.encoding === \"base64\";\n\t} else {\n\t\treturn null;\n\t}\n};\n\n/*\nLike addTiddler() except it will silently reject any plugin tiddlers that are older than the currently loaded version. Returns true if the tiddler was imported\n*/\nexports.importTiddler = function(tiddler) {\n\tvar existingTiddler = this.getTiddler(tiddler.fields.title);\n\t// Check if we're dealing with a plugin\n\tif(tiddler && tiddler.hasField(\"plugin-type\") && tiddler.hasField(\"version\") && existingTiddler && existingTiddler.hasField(\"plugin-type\") && existingTiddler.hasField(\"version\")) {\n\t\t// Reject the incoming plugin if it is older\n\t\tif(!$tw.utils.checkVersions(tiddler.fields.version,existingTiddler.fields.version)) {\n\t\t\treturn false;\n\t\t}\n\t}\n\t// Fall through to adding the tiddler\n\tthis.addTiddler(tiddler);\n\treturn true;\n};\n\n/*\nReturn a hashmap of the fields that should be set when a tiddler is created\n*/\nexports.getCreationFields = function() {\n\tif(this.getTiddlerText(TIMESTAMP_DISABLE_TITLE,\"\").toLowerCase() !== \"yes\") {\n\t\tvar fields = {\n\t\t\t\tcreated: new Date()\n\t\t\t},\n\t\t\tcreator = this.getTiddlerText(USER_NAME_TITLE);\n\t\tif(creator) {\n\t\t\tfields.creator = creator;\n\t\t}\n\t\treturn fields;\n\t} else {\n\t\treturn {};\n\t}\n};\n\n/*\nReturn a hashmap of the fields that should be set when a tiddler is modified\n*/\nexports.getModificationFields = function() {\n\tif(this.getTiddlerText(TIMESTAMP_DISABLE_TITLE,\"\").toLowerCase() !== \"yes\") {\n\t\tvar fields = Object.create(null),\n\t\t\tmodifier = this.getTiddlerText(USER_NAME_TITLE);\n\t\tfields.modified = new Date();\n\t\tif(modifier) {\n\t\t\tfields.modifier = modifier;\n\t\t}\n\t\treturn fields;\n\t} else {\n\t\treturn {};\n\t}\n};\n\n/*\nReturn a sorted array of tiddler titles. Options include:\nsortField: field to sort by\nexcludeTag: tag to exclude\nincludeSystem: whether to include system tiddlers (defaults to false)\n*/\nexports.getTiddlers = function(options) {\n\toptions = options || Object.create(null);\n\tvar self = this,\n\t\tsortField = options.sortField || \"title\",\n\t\ttiddlers = [], t, titles = [];\n\tthis.each(function(tiddler,title) {\n\t\tif(options.includeSystem || !self.isSystemTiddler(title)) {\n\t\t\tif(!options.excludeTag || !tiddler.hasTag(options.excludeTag)) {\n\t\t\t\ttiddlers.push(tiddler);\n\t\t\t}\n\t\t}\n\t});\n\ttiddlers.sort(function(a,b) {\n\t\tvar aa = a.fields[sortField].toLowerCase() || \"\",\n\t\t\tbb = b.fields[sortField].toLowerCase() || \"\";\n\t\tif(aa < bb) {\n\t\t\treturn -1;\n\t\t} else {\n\t\t\tif(aa > bb) {\n\t\t\t\treturn 1;\n\t\t\t} else {\n\t\t\t\treturn 0;\n\t\t\t}\n\t\t}\n\t});\n\tfor(t=0; t<tiddlers.length; t++) {\n\t\ttitles.push(tiddlers[t].fields.title);\n\t}\n\treturn titles;\n};\n\nexports.countTiddlers = function(excludeTag) {\n\tvar tiddlers = this.getTiddlers({excludeTag: excludeTag});\n\treturn $tw.utils.count(tiddlers);\n};\n\n/*\nReturns a function iterator(callback) that iterates through the specified titles, and invokes the callback with callback(tiddler,title)\n*/\nexports.makeTiddlerIterator = function(titles) {\n\tvar self = this;\n\tif(!$tw.utils.isArray(titles)) {\n\t\ttitles = Object.keys(titles);\n\t} else {\n\t\ttitles = titles.slice(0);\n\t}\n\treturn function(callback) {\n\t\ttitles.forEach(function(title) {\n\t\t\tcallback(self.getTiddler(title),title);\n\t\t});\n\t};\n};\n\n/*\nSort an array of tiddler titles by a specified field\n\ttitles: array of titles (sorted in place)\n\tsortField: name of field to sort by\n\tisDescending: true if the sort should be descending\n\tisCaseSensitive: true if the sort should consider upper and lower case letters to be different\n*/\nexports.sortTiddlers = function(titles,sortField,isDescending,isCaseSensitive,isNumeric,isAlphaNumeric) {\n\tvar self = this;\n\ttitles.sort(function(a,b) {\n\t\tvar x,y,\n\t\t\tcompareNumbers = function(x,y) {\n\t\t\t\tvar result = \n\t\t\t\t\tisNaN(x) && !isNaN(y) ? (isDescending ? -1 : 1) :\n\t\t\t\t\t!isNaN(x) && isNaN(y) ? (isDescending ? 1 : -1) :\n\t\t\t\t\t\t\t\t\t\t\t(isDescending ? y - x : x - y);\n\t\t\t\treturn result;\n\t\t\t};\n\t\tif(sortField !== \"title\") {\n\t\t\tvar tiddlerA = self.getTiddler(a),\n\t\t\t\ttiddlerB = self.getTiddler(b);\n\t\t\tif(tiddlerA) {\n\t\t\t\ta = tiddlerA.fields[sortField] || \"\";\n\t\t\t} else {\n\t\t\t\ta = \"\";\n\t\t\t}\n\t\t\tif(tiddlerB) {\n\t\t\t\tb = tiddlerB.fields[sortField] || \"\";\n\t\t\t} else {\n\t\t\t\tb = \"\";\n\t\t\t}\n\t\t}\n\t\tx = Number(a);\n\t\ty = Number(b);\n\t\tif(isNumeric && (!isNaN(x) || !isNaN(y))) {\n\t\t\treturn compareNumbers(x,y);\n\t\t} else if(isAlphaNumeric) {\n\t\t\treturn isDescending ? b.localeCompare(a,undefined,{numeric: true,sensitivity: \"base\"}) : a.localeCompare(b,undefined,{numeric: true,sensitivity: \"base\"});\n\t\t} else if($tw.utils.isDate(a) && $tw.utils.isDate(b)) {\n\t\t\treturn isDescending ? b - a : a - b;\n\t\t} else {\n\t\t\ta = String(a);\n\t\t\tb = String(b);\n\t\t\tif(!isCaseSensitive) {\n\t\t\t\ta = a.toLowerCase();\n\t\t\t\tb = b.toLowerCase();\n\t\t\t}\n\t\t\treturn isDescending ? b.localeCompare(a) : a.localeCompare(b);\n\t\t}\n\t});\n};\n\n/*\nFor every tiddler invoke a callback(title,tiddler) with `this` set to the wiki object. Options include:\nsortField: field to sort by\nexcludeTag: tag to exclude\nincludeSystem: whether to include system tiddlers (defaults to false)\n*/\nexports.forEachTiddler = function(/* [options,]callback */) {\n\tvar arg = 0,\n\t\toptions = arguments.length >= 2 ? arguments[arg++] : {},\n\t\tcallback = arguments[arg++],\n\t\ttitles = this.getTiddlers(options),\n\t\tt, tiddler;\n\tfor(t=0; t<titles.length; t++) {\n\t\ttiddler = this.getTiddler(titles[t]);\n\t\tif(tiddler) {\n\t\t\tcallback.call(this,tiddler.fields.title,tiddler);\n\t\t}\n\t}\n};\n\n/*\nReturn an array of tiddler titles that are directly linked within the given parse tree\n */\nexports.extractLinks = function(parseTreeRoot) {\n\t// Count up the links\n\tvar links = [],\n\t\tcheckParseTree = function(parseTree) {\n\t\t\tfor(var t=0; t<parseTree.length; t++) {\n\t\t\t\tvar parseTreeNode = parseTree[t];\n\t\t\t\tif(parseTreeNode.type === \"link\" && parseTreeNode.attributes.to && parseTreeNode.attributes.to.type === \"string\") {\n\t\t\t\t\tvar value = parseTreeNode.attributes.to.value;\n\t\t\t\t\tif(links.indexOf(value) === -1) {\n\t\t\t\t\t\tlinks.push(value);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif(parseTreeNode.children) {\n\t\t\t\t\tcheckParseTree(parseTreeNode.children);\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\tcheckParseTree(parseTreeRoot);\n\treturn links;\n};\n\n/*\nReturn an array of tiddler titles that are directly linked from the specified tiddler\n*/\nexports.getTiddlerLinks = function(title) {\n\tvar self = this;\n\t// We'll cache the links so they only get computed if the tiddler changes\n\treturn this.getCacheForTiddler(title,\"links\",function() {\n\t\t// Parse the tiddler\n\t\tvar parser = self.parseTiddler(title);\n\t\tif(parser) {\n\t\t\treturn self.extractLinks(parser.tree);\n\t\t}\n\t\treturn [];\n\t});\n};\n\n/*\nReturn an array of tiddler titles that link to the specified tiddler\n*/\nexports.getTiddlerBacklinks = function(targetTitle) {\n\tvar self = this,\n\t\tbacklinksIndexer = this.getIndexer(\"BacklinksIndexer\"),\n\t\tbacklinks = backlinksIndexer && backlinksIndexer.lookup(targetTitle);\n\n\tif(!backlinks) {\n\t\tbacklinks = [];\n\t\tthis.forEachTiddler(function(title,tiddler) {\n\t\t\tvar links = self.getTiddlerLinks(title);\n\t\t\tif(links.indexOf(targetTitle) !== -1) {\n\t\t\t\tbacklinks.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn backlinks;\n};\n\n/*\nReturn a hashmap of tiddler titles that are referenced but not defined. Each value is the number of times the missing tiddler is referenced\n*/\nexports.getMissingTitles = function() {\n\tvar self = this,\n\t\tmissing = [];\n// We should cache the missing tiddler list, even if we recreate it every time any tiddler is modified\n\tthis.forEachTiddler(function(title,tiddler) {\n\t\tvar links = self.getTiddlerLinks(title);\n\t\t$tw.utils.each(links,function(link) {\n\t\t\tif((!self.tiddlerExists(link) && !self.isShadowTiddler(link)) && missing.indexOf(link) === -1) {\n\t\t\t\tmissing.push(link);\n\t\t\t}\n\t\t});\n\t});\n\treturn missing;\n};\n\nexports.getOrphanTitles = function() {\n\tvar self = this,\n\t\torphans = this.getTiddlers();\n\tthis.forEachTiddler(function(title,tiddler) {\n\t\tvar links = self.getTiddlerLinks(title);\n\t\t$tw.utils.each(links,function(link) {\n\t\t\tvar p = orphans.indexOf(link);\n\t\t\tif(p !== -1) {\n\t\t\t\torphans.splice(p,1);\n\t\t\t}\n\t\t});\n\t});\n\treturn orphans; // Todo\n};\n\n/*\nRetrieves a list of the tiddler titles that are tagged with a given tag\n*/\nexports.getTiddlersWithTag = function(tag) {\n\t// Try to use the indexer\n\tvar self = this,\n\t\ttagIndexer = this.getIndexer(\"TagIndexer\"),\n\t\tresults = tagIndexer && tagIndexer.subIndexers[3].lookup(tag);\n\tif(!results) {\n\t\t// If not available, perform a manual scan\n\t\tresults = this.getGlobalCache(\"taglist-\" + tag,function() {\n\t\t\tvar tagmap = self.getTagMap();\n\t\t\treturn self.sortByList(tagmap[tag],tag);\n\t\t});\n\t}\n\treturn results;\n};\n\n/*\nGet a hashmap by tag of arrays of tiddler titles\n*/\nexports.getTagMap = function() {\n\tvar self = this;\n\treturn this.getGlobalCache(\"tagmap\",function() {\n\t\tvar tags = Object.create(null),\n\t\t\tstoreTags = function(tagArray,title) {\n\t\t\t\tif(tagArray) {\n\t\t\t\t\tfor(var index=0; index<tagArray.length; index++) {\n\t\t\t\t\t\tvar tag = tagArray[index];\n\t\t\t\t\t\tif($tw.utils.hop(tags,tag)) {\n\t\t\t\t\t\t\ttags[tag].push(title);\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\ttags[tag] = [title];\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t},\n\t\t\ttitle, tiddler;\n\t\t// Collect up all the tags\n\t\tself.eachShadow(function(tiddler,title) {\n\t\t\tif(!self.tiddlerExists(title)) {\n\t\t\t\ttiddler = self.getTiddler(title);\n\t\t\t\tstoreTags(tiddler.fields.tags,title);\n\t\t\t}\n\t\t});\n\t\tself.each(function(tiddler,title) {\n\t\t\tstoreTags(tiddler.fields.tags,title);\n\t\t});\n\t\treturn tags;\n\t});\n};\n\n/*\nLookup a given tiddler and return a list of all the tiddlers that include it in the specified list field\n*/\nexports.findListingsOfTiddler = function(targetTitle,fieldName) {\n\tfieldName = fieldName || \"list\";\n\tvar titles = [];\n\tthis.each(function(tiddler,title) {\n\t\tvar list = $tw.utils.parseStringArray(tiddler.fields[fieldName]);\n\t\tif(list && list.indexOf(targetTitle) !== -1) {\n\t\t\ttitles.push(title);\n\t\t}\n\t});\n\treturn titles;\n};\n\n/*\nSorts an array of tiddler titles according to an ordered list\n*/\nexports.sortByList = function(array,listTitle) {\n\tvar self = this,\n\t\treplacedTitles = Object.create(null);\n\t// Given a title, this function will place it in the correct location\n\t// within titles.\n\tfunction moveItemInList(title) {\n\t\tif(!$tw.utils.hop(replacedTitles, title)) {\n\t\t\treplacedTitles[title] = true;\n\t\t\tvar newPos = -1,\n\t\t\t\ttiddler = self.getTiddler(title);\n\t\t\tif(tiddler) {\n\t\t\t\tvar beforeTitle = tiddler.fields[\"list-before\"],\n\t\t\t\t\tafterTitle = tiddler.fields[\"list-after\"];\n\t\t\t\tif(beforeTitle === \"\") {\n\t\t\t\t\tnewPos = 0;\n\t\t\t\t} else if(afterTitle === \"\") {\n\t\t\t\t\tnewPos = titles.length;\n\t\t\t\t} else if(beforeTitle) {\n\t\t\t\t\t// if this title is placed relative\n\t\t\t\t\t// to another title, make sure that\n\t\t\t\t\t// title is placed before we place\n\t\t\t\t\t// this one.\n\t\t\t\t\tmoveItemInList(beforeTitle);\n\t\t\t\t\tnewPos = titles.indexOf(beforeTitle);\n\t\t\t\t} else if(afterTitle) {\n\t\t\t\t\t// Same deal\n\t\t\t\t\tmoveItemInList(afterTitle);\n\t\t\t\t\tnewPos = titles.indexOf(afterTitle);\n\t\t\t\t\tif(newPos >= 0) {\n\t\t\t\t\t\t++newPos;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// If a new position is specified, let's move it\n\t\t\t\tif (newPos !== -1) {\n\t\t\t\t\t// get its current Pos, and make sure\n\t\t\t\t\t// sure that it's _actually_ in the list\n\t\t\t\t\t// and that it would _actually_ move\n\t\t\t\t\t// (#4275) We don't bother calling\n\t\t\t\t\t// indexOf unless we have a new\n\t\t\t\t\t// position to work with\n\t\t\t\t\tvar currPos = titles.indexOf(title);\n\t\t\t\t\tif(currPos >= 0 && newPos !== currPos) {\n\t\t\t\t\t\t// move it!\n\t\t\t\t\t\ttitles.splice(currPos,1);\n\t\t\t\t\t\tif(newPos >= currPos) {\n\t\t\t\t\t\t\tnewPos--;\n\t\t\t\t\t\t}\n\t\t\t\t\t\ttitles.splice(newPos,0,title);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\tvar list = this.getTiddlerList(listTitle);\n\tif(!array || array.length === 0) {\n\t\treturn [];\n\t} else {\n\t\tvar titles = [], t, title;\n\t\t// First place any entries that are present in the list\n\t\tfor(t=0; t<list.length; t++) {\n\t\t\ttitle = list[t];\n\t\t\tif(array.indexOf(title) !== -1) {\n\t\t\t\ttitles.push(title);\n\t\t\t}\n\t\t}\n\t\t// Then place any remaining entries\n\t\tfor(t=0; t<array.length; t++) {\n\t\t\ttitle = array[t];\n\t\t\tif(list.indexOf(title) === -1) {\n\t\t\t\ttitles.push(title);\n\t\t\t}\n\t\t}\n\t\t// Finally obey the list-before and list-after fields of each tiddler in turn\n\t\tvar sortedTitles = titles.slice(0);\n\t\tfor(t=0; t<sortedTitles.length; t++) {\n\t\t\ttitle = sortedTitles[t];\n\t\t\tmoveItemInList(title);\n\t\t}\n\t\treturn titles;\n\t}\n};\n\nexports.getSubTiddler = function(title,subTiddlerTitle) {\n\tvar bundleInfo = this.getPluginInfo(title) || this.getTiddlerDataCached(title);\n\tif(bundleInfo && bundleInfo.tiddlers) {\n\t\tvar subTiddler = bundleInfo.tiddlers[subTiddlerTitle];\n\t\tif(subTiddler) {\n\t\t\treturn new $tw.Tiddler(subTiddler);\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nRetrieve a tiddler as a JSON string of the fields\n*/\nexports.getTiddlerAsJson = function(title) {\n\tvar tiddler = this.getTiddler(title);\n\tif(tiddler) {\n\t\tvar fields = Object.create(null);\n\t\t$tw.utils.each(tiddler.fields,function(value,name) {\n\t\t\tfields[name] = tiddler.getFieldString(name);\n\t\t});\n\t\treturn JSON.stringify(fields);\n\t} else {\n\t\treturn JSON.stringify({title: title});\n\t}\n};\n\nexports.getTiddlersAsJson = function(filter,spaces) {\n\tvar tiddlers = this.filterTiddlers(filter),\n\t\tspaces = (spaces === undefined) ? $tw.config.preferences.jsonSpaces : spaces,\n\t\tdata = [];\n\tfor(var t=0;t<tiddlers.length; t++) {\n\t\tvar tiddler = this.getTiddler(tiddlers[t]);\n\t\tif(tiddler) {\n\t\t\tvar fields = new Object();\n\t\t\tfor(var field in tiddler.fields) {\n\t\t\t\tfields[field] = tiddler.getFieldString(field);\n\t\t\t}\n\t\t\tdata.push(fields);\n\t\t}\n\t}\n\treturn JSON.stringify(data,null,spaces);\n};\n\n/*\nGet the content of a tiddler as a JavaScript object. How this is done depends on the type of the tiddler:\n\napplication/json: the tiddler JSON is parsed into an object\napplication/x-tiddler-dictionary: the tiddler is parsed as sequence of name:value pairs\n\nOther types currently just return null.\n\ntitleOrTiddler: string tiddler title or a tiddler object\ndefaultData: default data to be returned if the tiddler is missing or doesn't contain data\n\nNote that the same value is returned for repeated calls for the same tiddler data. The value is frozen to prevent modification; otherwise modifications would be visible to all callers\n*/\nexports.getTiddlerDataCached = function(titleOrTiddler,defaultData) {\n\tvar self = this,\n\t\ttiddler = titleOrTiddler;\n\tif(!(tiddler instanceof $tw.Tiddler)) {\n\t\ttiddler = this.getTiddler(tiddler);\t\n\t}\n\tif(tiddler) {\n\t\treturn this.getCacheForTiddler(tiddler.fields.title,\"data\",function() {\n\t\t\t// Return the frozen value\n\t\t\tvar value = self.getTiddlerData(tiddler.fields.title,undefined);\n\t\t\t$tw.utils.deepFreeze(value);\n\t\t\treturn value;\n\t\t}) || defaultData;\n\t} else {\n\t\treturn defaultData;\n\t}\n};\n\n/*\nAlternative, uncached version of getTiddlerDataCached(). The return value can be mutated freely and reused\n*/\nexports.getTiddlerData = function(titleOrTiddler,defaultData) {\n\tvar tiddler = titleOrTiddler,\n\t\tdata;\n\tif(!(tiddler instanceof $tw.Tiddler)) {\n\t\ttiddler = this.getTiddler(tiddler);\t\n\t}\n\tif(tiddler && tiddler.fields.text) {\n\t\tswitch(tiddler.fields.type) {\n\t\t\tcase \"application/json\":\n\t\t\t\t// JSON tiddler\n\t\t\t\ttry {\n\t\t\t\t\tdata = JSON.parse(tiddler.fields.text);\n\t\t\t\t} catch(ex) {\n\t\t\t\t\treturn defaultData;\n\t\t\t\t}\n\t\t\t\treturn data;\n\t\t\tcase \"application/x-tiddler-dictionary\":\n\t\t\t\treturn $tw.utils.parseFields(tiddler.fields.text);\n\t\t}\n\t}\n\treturn defaultData;\n};\n\n/*\nExtract an indexed field from within a data tiddler\n*/\nexports.extractTiddlerDataItem = function(titleOrTiddler,index,defaultText) {\n\tvar data = this.getTiddlerDataCached(titleOrTiddler,Object.create(null)),\n\t\ttext;\n\tif(data && $tw.utils.hop(data,index)) {\n\t\ttext = data[index];\n\t}\n\tif(typeof text === \"string\" || typeof text === \"number\") {\n\t\treturn text.toString();\n\t} else {\n\t\treturn defaultText;\n\t}\n};\n\n/*\nSet a tiddlers content to a JavaScript object. Currently this is done by setting the tiddler's type to \"application/json\" and setting the text to the JSON text of the data.\ntitle: title of tiddler\ndata: object that can be serialised to JSON\nfields: optional hashmap of additional tiddler fields to be set\n*/\nexports.setTiddlerData = function(title,data,fields) {\n\tvar existingTiddler = this.getTiddler(title),\n\t\tnewFields = {\n\t\t\ttitle: title\n\t};\n\tif(existingTiddler && existingTiddler.fields.type === \"application/x-tiddler-dictionary\") {\n\t\tnewFields.text = $tw.utils.makeTiddlerDictionary(data);\n\t} else {\n\t\tnewFields.type = \"application/json\";\n\t\tnewFields.text = JSON.stringify(data,null,$tw.config.preferences.jsonSpaces);\n\t}\n\tthis.addTiddler(new $tw.Tiddler(this.getCreationFields(),existingTiddler,fields,newFields,this.getModificationFields()));\n};\n\n/*\nReturn the content of a tiddler as an array containing each line\n*/\nexports.getTiddlerList = function(title,field,index) {\n\tif(index) {\n\t\treturn $tw.utils.parseStringArray(this.extractTiddlerDataItem(title,index,\"\"));\n\t}\n\tfield = field || \"list\";\n\tvar tiddler = this.getTiddler(title);\n\tif(tiddler) {\n\t\treturn ($tw.utils.parseStringArray(tiddler.fields[field]) || []).slice(0);\n\t}\n\treturn [];\n};\n\n// Return a named global cache object. Global cache objects are cleared whenever a tiddler change occurs\nexports.getGlobalCache = function(cacheName,initializer) {\n\tthis.globalCache = this.globalCache || Object.create(null);\n\tif($tw.utils.hop(this.globalCache,cacheName)) {\n\t\treturn this.globalCache[cacheName];\n\t} else {\n\t\tthis.globalCache[cacheName] = initializer();\n\t\treturn this.globalCache[cacheName];\n\t}\n};\n\nexports.clearGlobalCache = function() {\n\tthis.globalCache = Object.create(null);\n};\n\n// Return the named cache object for a tiddler. If the cache doesn't exist then the initializer function is invoked to create it\nexports.getCacheForTiddler = function(title,cacheName,initializer) {\n\tthis.caches = this.caches || Object.create(null);\n\tvar caches = this.caches[title];\n\tif(caches && caches[cacheName]) {\n\t\treturn caches[cacheName];\n\t} else {\n\t\tif(!caches) {\n\t\t\tcaches = Object.create(null);\n\t\t\tthis.caches[title] = caches;\n\t\t}\n\t\tcaches[cacheName] = initializer();\n\t\treturn caches[cacheName];\n\t}\n};\n\n// Clear all caches associated with a particular tiddler, or, if the title is null, clear all the caches for all the tiddlers\nexports.clearCache = function(title) {\n\tif(title) {\n\t\tthis.caches = this.caches || Object.create(null);\n\t\tif($tw.utils.hop(this.caches,title)) {\n\t\t\tdelete this.caches[title];\n\t\t}\n\t} else {\n\t\tthis.caches = Object.create(null);\n\t}\n};\n\nexports.initParsers = function(moduleType) {\n\t// Install the parser modules\n\t$tw.Wiki.parsers = {};\n\tvar self = this;\n\t$tw.modules.forEachModuleOfType(\"parser\",function(title,module) {\n\t\tfor(var f in module) {\n\t\t\tif($tw.utils.hop(module,f)) {\n\t\t\t\t$tw.Wiki.parsers[f] = module[f]; // Store the parser class\n\t\t\t}\n\t\t}\n\t});\n\t// Use the generic binary parser for any binary types not registered so far\n\tif($tw.Wiki.parsers[\"application/octet-stream\"]) {\n\t\tObject.keys($tw.config.contentTypeInfo).forEach(function(type) {\n\t\t\tif(!$tw.utils.hop($tw.Wiki.parsers,type) && $tw.config.contentTypeInfo[type].encoding === \"base64\") {\n\t\t\t\t$tw.Wiki.parsers[type] = $tw.Wiki.parsers[\"application/octet-stream\"];\n\t\t\t}\n\t\t});\t\t\n\t}\n};\n\n/*\nParse a block of text of a specified MIME type\n\ttype: content type of text to be parsed\n\ttext: text\n\toptions: see below\nOptions include:\n\tparseAsInline: if true, the text of the tiddler will be parsed as an inline run\n\t_canonical_uri: optional string of the canonical URI of this content\n*/\nexports.parseText = function(type,text,options) {\n\ttext = text || \"\";\n\toptions = options || {};\n\t// Select a parser\n\tvar Parser = $tw.Wiki.parsers[type];\n\tif(!Parser && $tw.utils.getFileExtensionInfo(type)) {\n\t\tParser = $tw.Wiki.parsers[$tw.utils.getFileExtensionInfo(type).type];\n\t}\n\tif(!Parser) {\n\t\tParser = $tw.Wiki.parsers[options.defaultType || \"text/vnd.tiddlywiki\"];\n\t}\n\tif(!Parser) {\n\t\treturn null;\n\t}\n\t// Return the parser instance\n\treturn new Parser(type,text,{\n\t\tparseAsInline: options.parseAsInline,\n\t\twiki: this,\n\t\t_canonical_uri: options._canonical_uri\n\t});\n};\n\n/*\nParse a tiddler according to its MIME type\n*/\nexports.parseTiddler = function(title,options) {\n\toptions = $tw.utils.extend({},options);\n\tvar cacheType = options.parseAsInline ? \"inlineParseTree\" : \"blockParseTree\",\n\t\ttiddler = this.getTiddler(title),\n\t\tself = this;\n\treturn tiddler ? this.getCacheForTiddler(title,cacheType,function() {\n\t\t\tif(tiddler.hasField(\"_canonical_uri\")) {\n\t\t\t\toptions._canonical_uri = tiddler.fields._canonical_uri;\n\t\t\t}\n\t\t\treturn self.parseText(tiddler.fields.type,tiddler.fields.text,options);\n\t\t}) : null;\n};\n\nexports.parseTextReference = function(title,field,index,options) {\n\tvar tiddler,text;\n\tif(options.subTiddler) {\n\t\ttiddler = this.getSubTiddler(title,options.subTiddler);\n\t} else {\n\t\ttiddler = this.getTiddler(title);\n\t\tif(field === \"text\" || (!field && !index)) {\n\t\t\tthis.getTiddlerText(title); // Force the tiddler to be lazily loaded\n\t\t\treturn this.parseTiddler(title,options);\n\t\t}\n\t}\n\tif(field === \"text\" || (!field && !index)) {\n\t\tif(tiddler && tiddler.fields) {\n\t\t\treturn this.parseText(tiddler.fields.type,tiddler.fields.text,options);\t\t\t\n\t\t} else {\n\t\t\treturn null;\n\t\t}\n\t} else if(field) {\n\t\tif(field === \"title\") {\n\t\t\ttext = title;\n\t\t} else {\n\t\t\tif(!tiddler || !tiddler.hasField(field)) {\n\t\t\t\treturn null;\n\t\t\t}\n\t\t\ttext = tiddler.fields[field];\n\t\t}\n\t\treturn this.parseText(\"text/vnd.tiddlywiki\",text.toString(),options);\n\t} else if(index) {\n\t\tthis.getTiddlerText(title); // Force the tiddler to be lazily loaded\n\t\ttext = this.extractTiddlerDataItem(tiddler,index,undefined);\n\t\tif(text === undefined) {\n\t\t\treturn null;\n\t\t}\n\t\treturn this.parseText(\"text/vnd.tiddlywiki\",text,options);\n\t}\n};\n\n/*\nMake a widget tree for a parse tree\nparser: parser object\noptions: see below\nOptions include:\ndocument: optional document to use\nvariables: hashmap of variables to set\nparentWidget: optional parent widget for the root node\n*/\nexports.makeWidget = function(parser,options) {\n\toptions = options || {};\n\tvar widgetNode = {\n\t\t\ttype: \"widget\",\n\t\t\tchildren: []\n\t\t},\n\t\tcurrWidgetNode = widgetNode;\n\t// Create set variable widgets for each variable\n\t$tw.utils.each(options.variables,function(value,name) {\n\t\tvar setVariableWidget = {\n\t\t\ttype: \"set\",\n\t\t\tattributes: {\n\t\t\t\tname: {type: \"string\", value: name},\n\t\t\t\tvalue: {type: \"string\", value: value}\n\t\t\t},\n\t\t\tchildren: []\n\t\t};\n\t\tcurrWidgetNode.children = [setVariableWidget];\n\t\tcurrWidgetNode = setVariableWidget;\n\t});\n\t// Add in the supplied parse tree nodes\n\tcurrWidgetNode.children = parser ? parser.tree : [];\n\t// Create the widget\n\treturn new widget.widget(widgetNode,{\n\t\twiki: this,\n\t\tdocument: options.document || $tw.fakeDocument,\n\t\tparentWidget: options.parentWidget\n\t});\n};\n\n/*\nMake a widget tree for transclusion\ntitle: target tiddler title\noptions: as for wiki.makeWidget() plus:\noptions.field: optional field to transclude (defaults to \"text\")\noptions.mode: transclusion mode \"inline\" or \"block\"\noptions.children: optional array of children for the transclude widget\noptions.importVariables: optional importvariables filter string for macros to be included\noptions.importPageMacros: optional boolean; if true, equivalent to passing \"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\" to options.importVariables\n*/\nexports.makeTranscludeWidget = function(title,options) {\n\toptions = options || {};\n\tvar parseTreeDiv = {tree: [{\n\t\t\ttype: \"element\",\n\t\t\ttag: \"div\",\n\t\t\tchildren: []}]},\n\t\tparseTreeImportVariables = {\n\t\t\ttype: \"importvariables\",\n\t\t\tattributes: {\n\t\t\t\tfilter: {\n\t\t\t\t\tname: \"filter\",\n\t\t\t\t\ttype: \"string\"\n\t\t\t\t}\n\t\t\t},\n\t\t\tisBlock: false,\n\t\t\tchildren: []},\n\t\tparseTreeTransclude = {\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {\n\t\t\t\t\tname: \"tiddler\",\n\t\t\t\t\ttype: \"string\",\n\t\t\t\t\tvalue: title}},\n\t\t\tisBlock: !options.parseAsInline};\n\tif(options.importVariables || options.importPageMacros) {\n\t\tif(options.importVariables) {\n\t\t\tparseTreeImportVariables.attributes.filter.value = options.importVariables;\n\t\t} else if(options.importPageMacros) {\n\t\t\tparseTreeImportVariables.attributes.filter.value = \"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\";\n\t\t}\n\t\tparseTreeDiv.tree[0].children.push(parseTreeImportVariables);\n\t\tparseTreeImportVariables.children.push(parseTreeTransclude);\n\t} else {\n\t\tparseTreeDiv.tree[0].children.push(parseTreeTransclude);\n\t}\n\tif(options.field) {\n\t\tparseTreeTransclude.attributes.field = {type: \"string\", value: options.field};\n\t}\n\tif(options.mode) {\n\t\tparseTreeTransclude.attributes.mode = {type: \"string\", value: options.mode};\n\t}\n\tif(options.children) {\n\t\tparseTreeTransclude.children = options.children;\n\t}\n\treturn $tw.wiki.makeWidget(parseTreeDiv,options);\n};\n\n/*\nParse text in a specified format and render it into another format\n\toutputType: content type for the output\n\ttextType: content type of the input text\n\ttext: input text\n\toptions: see below\nOptions include:\nvariables: hashmap of variables to set\nparentWidget: optional parent widget for the root node\n*/\nexports.renderText = function(outputType,textType,text,options) {\n\toptions = options || {};\n\tvar parser = this.parseText(textType,text,options),\n\t\twidgetNode = this.makeWidget(parser,options);\n\tvar container = $tw.fakeDocument.createElement(\"div\");\n\twidgetNode.render(container,null);\n\treturn outputType === \"text/html\" ? container.innerHTML : container.textContent;\n};\n\n/*\nParse text from a tiddler and render it into another format\n\toutputType: content type for the output\n\ttitle: title of the tiddler to be rendered\n\toptions: see below\nOptions include:\nvariables: hashmap of variables to set\nparentWidget: optional parent widget for the root node\n*/\nexports.renderTiddler = function(outputType,title,options) {\n\toptions = options || {};\n\tvar parser = this.parseTiddler(title,options),\n\t\twidgetNode = this.makeWidget(parser,options);\n\tvar container = $tw.fakeDocument.createElement(\"div\");\n\twidgetNode.render(container,null);\n\treturn outputType === \"text/html\" ? container.innerHTML : (outputType === \"text/plain-formatted\" ? container.formattedTextContent : container.textContent);\n};\n\n/*\nReturn an array of tiddler titles that match a search string\n\ttext: The text string to search for\n\toptions: see below\nOptions available:\n\tsource: an iterator function for the source tiddlers, called source(iterator), where iterator is called as iterator(tiddler,title)\n\texclude: An array of tiddler titles to exclude from the search\n\tinvert: If true returns tiddlers that do not contain the specified string\n\tcaseSensitive: If true forces a case sensitive search\n\tfield: If specified, restricts the search to the specified field, or an array of field names\n\tanchored: If true, forces all but regexp searches to be anchored to the start of text\n\texcludeField: If true, the field options are inverted to specify the fields that are not to be searched\n\tThe search mode is determined by the first of these boolean flags to be true\n\t\tliteral: searches for literal string\n\t\twhitespace: same as literal except runs of whitespace are treated as a single space\n\t\tregexp: treats the search term as a regular expression\n\t\twords: (default) treats search string as a list of tokens, and matches if all tokens are found, regardless of adjacency or ordering\n*/\nexports.search = function(text,options) {\n\toptions = options || {};\n\tvar self = this,\n\t\tt,\n\t\tinvert = !!options.invert;\n\t// Convert the search string into a regexp for each term\n\tvar terms, searchTermsRegExps,\n\t\tflags = options.caseSensitive ? \"\" : \"i\",\n\t\tanchor = options.anchored ? \"^\" : \"\";\n\tif(options.literal) {\n\t\tif(text.length === 0) {\n\t\t\tsearchTermsRegExps = null;\n\t\t} else {\n\t\t\tsearchTermsRegExps = [new RegExp(\"(\" + anchor + $tw.utils.escapeRegExp(text) + \")\",flags)];\n\t\t}\n\t} else if(options.whitespace) {\n\t\tterms = [];\n\t\t$tw.utils.each(text.split(/\\s+/g),function(term) {\n\t\t\tif(term) {\n\t\t\t\tterms.push($tw.utils.escapeRegExp(term));\n\t\t\t}\n\t\t});\n\t\tsearchTermsRegExps = [new RegExp(\"(\" + anchor + terms.join(\"\\\\s+\") + \")\",flags)];\n\t} else if(options.regexp) {\n\t\ttry {\n\t\t\tsearchTermsRegExps = [new RegExp(\"(\" + text + \")\",flags)];\t\t\t\n\t\t} catch(e) {\n\t\t\tsearchTermsRegExps = null;\n\t\t\tconsole.log(\"Regexp error parsing /(\" + text + \")/\" + flags + \": \",e);\n\t\t}\n\t} else {\n\t\tterms = text.split(/ +/);\n\t\tif(terms.length === 1 && terms[0] === \"\") {\n\t\t\tsearchTermsRegExps = null;\n\t\t} else {\n\t\t\tsearchTermsRegExps = [];\n\t\t\tfor(t=0; t<terms.length; t++) {\n\t\t\t\tsearchTermsRegExps.push(new RegExp(\"(\" + anchor + $tw.utils.escapeRegExp(terms[t]) + \")\",flags));\n\t\t\t}\n\t\t}\n\t}\n\t// Accumulate the array of fields to be searched or excluded from the search\n\tvar fields = [];\n\tif(options.field) {\n\t\tif($tw.utils.isArray(options.field)) {\n\t\t\t$tw.utils.each(options.field,function(fieldName) {\n\t\t\t\tif(fieldName) {\n\t\t\t\t\tfields.push(fieldName);\t\t\t\t\t\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tfields.push(options.field);\n\t\t}\n\t}\n\t// Use default fields if none specified and we're not excluding fields (excluding fields with an empty field array is the same as searching all fields)\n\tif(fields.length === 0 && !options.excludeField) {\n\t\tfields.push(\"title\");\n\t\tfields.push(\"tags\");\n\t\tfields.push(\"text\");\n\t}\n\t// Function to check a given tiddler for the search term\n\tvar searchTiddler = function(title) {\n\t\tif(!searchTermsRegExps) {\n\t\t\treturn true;\n\t\t}\n\t\tvar notYetFound = searchTermsRegExps.slice();\n\n\t\tvar tiddler = self.getTiddler(title);\n\t\tif(!tiddler) {\n\t\t\ttiddler = new $tw.Tiddler({title: title, text: \"\", type: \"text/vnd.tiddlywiki\"});\n\t\t}\n\t\tvar contentTypeInfo = $tw.config.contentTypeInfo[tiddler.fields.type] || $tw.config.contentTypeInfo[\"text/vnd.tiddlywiki\"],\n\t\t\tsearchFields;\n\t\t// Get the list of fields we're searching\n\t\tif(options.excludeField) {\n\t\t\tsearchFields = Object.keys(tiddler.fields);\n\t\t\t$tw.utils.each(fields,function(fieldName) {\n\t\t\t\tvar p = searchFields.indexOf(fieldName);\n\t\t\t\tif(p !== -1) {\n\t\t\t\t\tsearchFields.splice(p,1);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsearchFields = fields;\n\t\t}\n\t\tfor(var fieldIndex=0; notYetFound.length>0 && fieldIndex<searchFields.length; fieldIndex++) {\n\t\t\t// Don't search the text field if the content type is binary\n\t\t\tvar fieldName = searchFields[fieldIndex];\n\t\t\tif(fieldName === \"text\" && contentTypeInfo.encoding !== \"utf8\") {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tvar str = tiddler.fields[fieldName],\n\t\t\t\tt;\n\t\t\tif(str) {\n\t\t\t\tif($tw.utils.isArray(str)) {\n\t\t\t\t\t// If the field value is an array, test each regexp against each field array entry and fail if each regexp doesn't match at least one field array entry\n\t\t\t\t\tfor(var s=0; s<str.length; s++) {\n\t\t\t\t\t\tfor(t=0; t<notYetFound.length;) {\n\t\t\t\t\t\t\tif(notYetFound[t].test(str[s])) {\n\t\t\t\t\t\t\t\tnotYetFound.splice(t, 1);\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tt++;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\t// If the field isn't an array, force it to a string and test each regexp against it and fail if any do not match\n\t\t\t\t\tstr = tiddler.getFieldString(fieldName);\n\t\t\t\t\tfor(t=0; t<notYetFound.length;) {\n\t\t\t\t\t\tif(notYetFound[t].test(str)) {\n\t\t\t\t\t\t\tnotYetFound.splice(t, 1);\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tt++;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t\treturn notYetFound.length == 0;\n\t};\n\t// Loop through all the tiddlers doing the search\n\tvar results = [],\n\t\tsource = options.source || this.each;\n\tsource(function(tiddler,title) {\n\t\tif(searchTiddler(title) !== options.invert) {\n\t\t\tresults.push(title);\n\t\t}\n\t});\n\t// Remove any of the results we have to exclude\n\tif(options.exclude) {\n\t\tfor(t=0; t<options.exclude.length; t++) {\n\t\t\tvar p = results.indexOf(options.exclude[t]);\n\t\t\tif(p !== -1) {\n\t\t\t\tresults.splice(p,1);\n\t\t\t}\n\t\t}\n\t}\n\treturn results;\n};\n\n/*\nTrigger a load for a tiddler if it is skinny. Returns the text, or undefined if the tiddler is missing, null if the tiddler is being lazily loaded.\n*/\nexports.getTiddlerText = function(title,defaultText) {\n\tvar tiddler = this.getTiddler(title);\n\t// Return undefined if the tiddler isn't found\n\tif(!tiddler) {\n\t\treturn defaultText;\n\t}\n\tif(!tiddler.hasField(\"_is_skinny\")) {\n\t\t// Just return the text if we've got it\n\t\treturn tiddler.fields.text || \"\";\n\t} else {\n\t\t// Tell any listeners about the need to lazily load this tiddler\n\t\tthis.dispatchEvent(\"lazyLoad\",title);\n\t\t// Indicate that the text is being loaded\n\t\treturn null;\n\t}\n};\n\n/*\nCheck whether the text of a tiddler matches a given value. By default, the comparison is case insensitive, and any spaces at either end of the tiddler text is trimmed\n*/\nexports.checkTiddlerText = function(title,targetText,options) {\n\toptions = options || {};\n\tvar text = this.getTiddlerText(title,\"\");\n\tif(!options.noTrim) {\n\t\ttext = text.trim();\n\t}\n\tif(!options.caseSensitive) {\n\t\ttext = text.toLowerCase();\n\t\ttargetText = targetText.toLowerCase();\n\t}\n\treturn text === targetText;\n}\n\n/*\nRead an array of browser File objects, invoking callback(tiddlerFieldsArray) once they're all read\n*/\nexports.readFiles = function(files,options) {\n\tvar callback;\n\tif(typeof options === \"function\") {\n\t\tcallback = options;\n\t\toptions = {};\n\t} else {\n\t\tcallback = options.callback;\n\t}\n\tvar result = [],\n\t\toutstanding = files.length,\n\t\treadFileCallback = function(tiddlerFieldsArray) {\n\t\t\tresult.push.apply(result,tiddlerFieldsArray);\n\t\t\tif(--outstanding === 0) {\n\t\t\t\tcallback(result);\n\t\t\t}\n\t\t};\n\tfor(var f=0; f<files.length; f++) {\n\t\tthis.readFile(files[f],$tw.utils.extend({},options,{callback: readFileCallback}));\n\t}\n\treturn files.length;\n};\n\n/*\nRead a browser File object, invoking callback(tiddlerFieldsArray) with an array of tiddler fields objects\n*/\nexports.readFile = function(file,options) {\n\tvar callback;\n\tif(typeof options === \"function\") {\n\t\tcallback = options;\n\t\toptions = {};\n\t} else {\n\t\tcallback = options.callback;\n\t}\n\t// Get the type, falling back to the filename extension\n\tvar self = this,\n\t\ttype = file.type;\n\tif(type === \"\" || !type) {\n\t\tvar dotPos = file.name.lastIndexOf(\".\");\n\t\tif(dotPos !== -1) {\n\t\t\tvar fileExtensionInfo = $tw.utils.getFileExtensionInfo(file.name.substr(dotPos));\n\t\t\tif(fileExtensionInfo) {\n\t\t\t\ttype = fileExtensionInfo.type;\n\t\t\t}\n\t\t}\n\t}\n\t// Figure out if we're reading a binary file\n\tvar contentTypeInfo = $tw.config.contentTypeInfo[type],\n\t\tisBinary = contentTypeInfo ? contentTypeInfo.encoding === \"base64\" : false;\n\t// Log some debugging information\n\tif($tw.log.IMPORT) {\n\t\tconsole.log(\"Importing file '\" + file.name + \"', type: '\" + type + \"', isBinary: \" + isBinary);\n\t}\n\t// Give the hook a chance to process the drag\n\tif($tw.hooks.invokeHook(\"th-importing-file\",{\n\t\tfile: file,\n\t\ttype: type,\n\t\tisBinary: isBinary,\n\t\tcallback: callback\n\t}) !== true) {\n\t\tthis.readFileContent(file,type,isBinary,options.deserializer,callback);\n\t}\n};\n\n/*\nLower level utility to read the content of a browser File object, invoking callback(tiddlerFieldsArray) with an array of tiddler fields objects\n*/\nexports.readFileContent = function(file,type,isBinary,deserializer,callback) {\n\tvar self = this;\n\t// Create the FileReader\n\tvar reader = new FileReader();\n\t// Onload\n\treader.onload = function(event) {\n\t\tvar text = event.target.result,\n\t\t\ttiddlerFields = {title: file.name || \"Untitled\", type: type};\n\t\tif(isBinary) {\n\t\t\tvar commaPos = text.indexOf(\",\");\n\t\t\tif(commaPos !== -1) {\n\t\t\t\ttext = text.substr(commaPos + 1);\n\t\t\t}\n\t\t}\n\t\t// Check whether this is an encrypted TiddlyWiki file\n\t\tvar encryptedJson = $tw.utils.extractEncryptedStoreArea(text);\n\t\tif(encryptedJson) {\n\t\t\t// If so, attempt to decrypt it with the current password\n\t\t\t$tw.utils.decryptStoreAreaInteractive(encryptedJson,function(tiddlers) {\n\t\t\t\tcallback(tiddlers);\n\t\t\t});\n\t\t} else {\n\t\t\t// Otherwise, just try to deserialise any tiddlers in the file\n\t\t\tcallback(self.deserializeTiddlers(type,text,tiddlerFields,{deserializer: deserializer}));\n\t\t}\n\t};\n\t// Kick off the read\n\tif(isBinary) {\n\t\treader.readAsDataURL(file);\n\t} else {\n\t\treader.readAsText(file);\n\t}\n};\n\n/*\nFind any existing draft of a specified tiddler\n*/\nexports.findDraft = function(targetTitle) {\n\tvar draftTitle = undefined;\n\tthis.forEachTiddler({includeSystem: true},function(title,tiddler) {\n\t\tif(tiddler.fields[\"draft.title\"] && tiddler.fields[\"draft.of\"] === targetTitle) {\n\t\t\tdraftTitle = title;\n\t\t}\n\t});\n\treturn draftTitle;\n}\n\n/*\nCheck whether the specified draft tiddler has been modified.\nIf the original tiddler doesn't exist, create a vanilla tiddler variable,\nto check if additional fields have been added.\n*/\nexports.isDraftModified = function(title) {\n\tvar tiddler = this.getTiddler(title);\n\tif(!tiddler.isDraft()) {\n\t\treturn false;\n\t}\n\tvar ignoredFields = [\"created\", \"modified\", \"title\", \"draft.title\", \"draft.of\"],\n\t\torigTiddler = this.getTiddler(tiddler.fields[\"draft.of\"]) || new $tw.Tiddler({text:\"\", tags:[]}),\n\t\ttitleModified = tiddler.fields[\"draft.title\"] !== tiddler.fields[\"draft.of\"];\n\treturn titleModified || !tiddler.isEqual(origTiddler,ignoredFields);\n};\n\n/*\nAdd a new record to the top of the history stack\ntitle: a title string or an array of title strings\nfromPageRect: page coordinates of the origin of the navigation\nhistoryTitle: title of history tiddler (defaults to $:/HistoryList)\n*/\nexports.addToHistory = function(title,fromPageRect,historyTitle) {\n\tvar story = new $tw.Story({wiki: this, historyTitle: historyTitle});\n\tstory.addToHistory(title,fromPageRect);\t\t\n};\n\n/*\nAdd a new tiddler to the story river\ntitle: a title string or an array of title strings\nfromTitle: the title of the tiddler from which the navigation originated\nstoryTitle: title of story tiddler (defaults to $:/StoryList)\noptions: see story.js\n*/\nexports.addToStory = function(title,fromTitle,storyTitle,options) {\n\tvar story = new $tw.Story({wiki: this, storyTitle: storyTitle});\n\tstory.addToStory(title,fromTitle,options);\t\t\n};\n\n/*\nGenerate a title for the draft of a given tiddler\n*/\nexports.generateDraftTitle = function(title) {\n\tvar c = 0,\n\t\tdraftTitle,\n\t\tusername = this.getTiddlerText(\"$:/status/UserName\"),\n\t\tattribution = username ? \" by \" + username : \"\";\n\tdo {\n\t\tdraftTitle = \"Draft \" + (c ? (c + 1) + \" \" : \"\") + \"of '\" + title + \"'\" + attribution;\n\t\tc++;\n\t} while(this.tiddlerExists(draftTitle));\n\treturn draftTitle;\n};\n\n/*\nInvoke the available upgrader modules\ntitles: array of tiddler titles to be processed\ntiddlers: hashmap by title of tiddler fields of pending import tiddlers. These can be modified by the upgraders. An entry with no fields indicates a tiddler that was pending import has been suppressed. When entries are added to the pending import the tiddlers hashmap may have entries that are not present in the titles array\nReturns a hashmap of messages keyed by tiddler title.\n*/\nexports.invokeUpgraders = function(titles,tiddlers) {\n\t// Collect up the available upgrader modules\n\tvar self = this;\n\tif(!this.upgraderModules) {\n\t\tthis.upgraderModules = [];\n\t\t$tw.modules.forEachModuleOfType(\"upgrader\",function(title,module) {\n\t\t\tif(module.upgrade) {\n\t\t\t\tself.upgraderModules.push(module);\n\t\t\t}\n\t\t});\n\t}\n\t// Invoke each upgrader in turn\n\tvar messages = {};\n\tfor(var t=0; t<this.upgraderModules.length; t++) {\n\t\tvar upgrader = this.upgraderModules[t],\n\t\t\tupgraderMessages = upgrader.upgrade(this,titles,tiddlers);\n\t\t$tw.utils.extend(messages,upgraderMessages);\n\t}\n\treturn messages;\n};\n\n// Determine whether a plugin by title is dynamically loadable\nexports.doesPluginRequireReload = function(title) {\n\treturn this.doesPluginInfoRequireReload(this.getPluginInfo(title) || this.getTiddlerDataCached(title));\n};\n\n// Determine whether a plugin info structure is dynamically loadable\nexports.doesPluginInfoRequireReload = function(pluginInfo) {\n\tif(pluginInfo) {\n\t\tvar foundModule = false;\n\t\t$tw.utils.each(pluginInfo.tiddlers,function(tiddler) {\n\t\t\tif(tiddler.type === \"application/javascript\" && $tw.utils.hop(tiddler,\"module-type\")) {\n\t\t\t\tfoundModule = true;\n\t\t\t}\n\t\t});\n\t\treturn foundModule;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n})();\n\n",
"type": "application/javascript",
"module-type": "wikimethod"
},
"$:/palettes/Blanca": {
"title": "$:/palettes/Blanca",
"name": "Blanca",
"description": "A clean white palette to let you focus",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #66cccc\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #999999\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #ffffff\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #7897f3\nselect-tag-background:\nselect-tag-foreground:\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #ccc\nsidebar-foreground-shadow: rgba(255,255,255, 0.8)\nsidebar-foreground: #acacac\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: #ffffff\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #444444\nsidebar-tiddler-link-foreground: #7897f3\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #eeeeee\ntab-border-selected: #cccccc\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #ffeedd\ntag-foreground: #000\ntiddler-background: <<colour background>>\ntiddler-border: #eee\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #ff9900\ntoolbar-new-button:\ntoolbar-options-button:\ntoolbar-save-button:\ntoolbar-info-button:\ntoolbar-edit-button:\ntoolbar-close-button:\ntoolbar-delete-button:\ntoolbar-cancel-button:\ntoolbar-done-button:\nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/Blue": {
"title": "$:/palettes/Blue",
"name": "Blue",
"description": "A blue theme",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #fff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour foreground>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333353\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #999999\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #ddddff\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #5778d8\nselect-tag-background:\nselect-tag-foreground:\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #ffffff\nsidebar-foreground-shadow: rgba(255,255,255, 0.8)\nsidebar-foreground: #acacac\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: <<colour page-background>>\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #444444\nsidebar-tiddler-link-foreground: #5959c0\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: <<colour background>>\ntab-background: #ccccdd\ntab-border-selected: #ccccdd\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #eeeeff\ntag-foreground: #000\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #666666\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #ffffff\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #ffffff\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #5959c0\ntoolbar-new-button: #5eb95e\ntoolbar-options-button: rgb(128, 88, 165)\ntoolbar-save-button: #0e90d2\ntoolbar-info-button: #0e90d2\ntoolbar-edit-button: rgb(243, 123, 29)\ntoolbar-close-button: #dd514c\ntoolbar-delete-button: #dd514c\ntoolbar-cancel-button: rgb(243, 123, 29)\ntoolbar-done-button: #5eb95e\nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/Muted": {
"title": "$:/palettes/Muted",
"name": "Muted",
"description": "Bright tiddlers on a muted background",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #bbb\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #6f6f70\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #29a6ee\nselect-tag-background:\nselect-tag-foreground:\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #c2c1c2\nsidebar-foreground-shadow: rgba(255,255,255,0)\nsidebar-foreground: #d3d2d4\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: #6f6f70\nsidebar-tab-background: #666667\nsidebar-tab-border-selected: #999\nsidebar-tab-border: #515151\nsidebar-tab-divider: #999\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: #999\nsidebar-tiddler-link-foreground-hover: #444444\nsidebar-tiddler-link-foreground: #d1d0d2\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #d8d8d8\ntab-border-selected: #d8d8d8\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #d5ad34\ntag-foreground: #ffffff\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #182955\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/ContrastLight": {
"title": "$:/palettes/ContrastLight",
"name": "Contrast (Light)",
"description": "High contrast and unambiguous (light version)",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #f00\nalert-border: <<colour background>>\nalert-highlight: <<colour foreground>>\nalert-muted-foreground: #800\nbackground: #fff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background: <<colour background>>\nbutton-foreground: <<colour foreground>>\nbutton-border: <<colour foreground>>\ncode-background: <<colour background>>\ncode-border: <<colour foreground>>\ncode-foreground: <<colour foreground>>\ndirty-indicator: #f00\ndownload-background: #080\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: <<colour foreground>>\ndropdown-tab-background: <<colour foreground>>\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #00a\nexternal-link-foreground: #00e\nforeground: #000\nmessage-background: <<colour foreground>>\nmessage-border: <<colour background>>\nmessage-foreground: <<colour background>>\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: <<colour foreground>>\nmodal-footer-background: <<colour background>>\nmodal-footer-border: <<colour foreground>>\nmodal-header-border: <<colour foreground>>\nmuted-foreground: <<colour foreground>>\nnotification-background: <<colour background>>\nnotification-border: <<colour foreground>>\npage-background: <<colour background>>\npre-background: <<colour background>>\npre-border: <<colour foreground>>\nprimary: #00f\nselect-tag-background:\nselect-tag-foreground:\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: <<colour background>>\nsidebar-controls-foreground: <<colour foreground>>\nsidebar-foreground-shadow: rgba(0,0,0, 0)\nsidebar-foreground: <<colour foreground>>\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: <<colour foreground>>\nsidebar-tab-background-selected: <<colour background>>\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: <<colour foreground>>\nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: <<colour foreground>>\nsidebar-tiddler-link-foreground: <<colour primary>>\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: <<colour background>>\ntab-background: <<colour foreground>>\ntab-border-selected: <<colour foreground>>\ntab-border: <<colour foreground>>\ntab-divider: <<colour foreground>>\ntab-foreground-selected: <<colour foreground>>\ntab-foreground: <<colour background>>\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #000\ntag-foreground: #fff\ntiddler-background: <<colour background>>\ntiddler-border: <<colour foreground>>\ntiddler-controls-foreground-hover: #ddd\ntiddler-controls-foreground-selected: #fdd\ntiddler-controls-foreground: <<colour foreground>>\ntiddler-editor-background: <<colour background>>\ntiddler-editor-border-image: <<colour foreground>>\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: <<colour background>>\ntiddler-editor-fields-odd: <<colour background>>\ntiddler-info-background: <<colour background>>\ntiddler-info-border: <<colour foreground>>\ntiddler-info-tab-background: <<colour background>>\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: <<colour foreground>>\ntiddler-title-foreground: <<colour foreground>>\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: <<colour foreground>>\nvery-muted-foreground: #888888\n"
},
"$:/palettes/ContrastDark": {
"title": "$:/palettes/ContrastDark",
"name": "Contrast (Dark)",
"description": "High contrast and unambiguous (dark version)",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #f00\nalert-border: <<colour background>>\nalert-highlight: <<colour foreground>>\nalert-muted-foreground: #800\nbackground: #000\nblockquote-bar: <<colour muted-foreground>>\nbutton-background: <<colour background>>\nbutton-foreground: <<colour foreground>>\nbutton-border: <<colour foreground>>\ncode-background: <<colour background>>\ncode-border: <<colour foreground>>\ncode-foreground: <<colour foreground>>\ndirty-indicator: #f00\ndownload-background: #080\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: <<colour foreground>>\ndropdown-tab-background: <<colour foreground>>\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #00a\nexternal-link-foreground: #00e\nforeground: #fff\nmessage-background: <<colour foreground>>\nmessage-border: <<colour background>>\nmessage-foreground: <<colour background>>\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: <<colour foreground>>\nmodal-footer-background: <<colour background>>\nmodal-footer-border: <<colour foreground>>\nmodal-header-border: <<colour foreground>>\nmuted-foreground: <<colour foreground>>\nnotification-background: <<colour background>>\nnotification-border: <<colour foreground>>\npage-background: <<colour background>>\npre-background: <<colour background>>\npre-border: <<colour foreground>>\nprimary: #00f\nselect-tag-background:\nselect-tag-foreground:\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: <<colour background>>\nsidebar-controls-foreground: <<colour foreground>>\nsidebar-foreground-shadow: rgba(0,0,0, 0)\nsidebar-foreground: <<colour foreground>>\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: <<colour foreground>>\nsidebar-tab-background-selected: <<colour background>>\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: <<colour foreground>>\nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: <<colour foreground>>\nsidebar-tiddler-link-foreground: <<colour primary>>\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: <<colour background>>\ntab-background: <<colour foreground>>\ntab-border-selected: <<colour foreground>>\ntab-border: <<colour foreground>>\ntab-divider: <<colour foreground>>\ntab-foreground-selected: <<colour foreground>>\ntab-foreground: <<colour background>>\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #fff\ntag-foreground: #000\ntiddler-background: <<colour background>>\ntiddler-border: <<colour foreground>>\ntiddler-controls-foreground-hover: #ddd\ntiddler-controls-foreground-selected: #fdd\ntiddler-controls-foreground: <<colour foreground>>\ntiddler-editor-background: <<colour background>>\ntiddler-editor-border-image: <<colour foreground>>\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: <<colour background>>\ntiddler-editor-fields-odd: <<colour background>>\ntiddler-info-background: <<colour background>>\ntiddler-info-border: <<colour foreground>>\ntiddler-info-tab-background: <<colour background>>\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: <<colour foreground>>\ntiddler-title-foreground: <<colour foreground>>\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: <<colour foreground>>\nvery-muted-foreground: #888888\n"
},
"$:/palettes/DarkPhotos": {
"title": "$:/palettes/DarkPhotos",
"created": "20150402111612188",
"description": "Good with dark photo backgrounds",
"modified": "20150402112344080",
"name": "DarkPhotos",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background: \nbutton-foreground: \nbutton-border: \ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #ddd\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #336438\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #5778d8\nselect-tag-background:\nselect-tag-foreground:\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #ccf\nsidebar-controls-foreground: #fff\nsidebar-foreground-shadow: rgba(0,0,0, 0.5)\nsidebar-foreground: #fff\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #eee\nsidebar-tab-background-selected: rgba(255,255,255, 0.8)\nsidebar-tab-background: rgba(255,255,255, 0.4)\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: rgba(255,255,255, 0.2)\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #aaf\nsidebar-tiddler-link-foreground: #ddf\nsite-title-foreground: #fff\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #d8d8d8\ntab-border-selected: #d8d8d8\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #ec6\ntag-foreground: #ffffff\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #182955\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/GruvboxDark": {
"title": "$:/palettes/GruvboxDark",
"name": "Gruvbox Dark",
"description": "Retro groove color scheme",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"license": "https://github.com/morhetz/gruvbox",
"text": "alert-background: #cc241d\nalert-border: #cc241d\nalert-highlight: #d79921\nalert-muted-foreground: #504945\nbackground: #3c3836\nblockquote-bar: <<colour muted-foreground>>\nbutton-background: #504945\nbutton-foreground: #fbf1c7\nbutton-border: transparent\ncode-background: #504945\ncode-border: #504945\ncode-foreground: #fb4934\ndiff-delete-background: #fb4934\ndiff-delete-foreground: <<colour foreground>>\ndiff-equal-background: \ndiff-equal-foreground: <<colour foreground>>\ndiff-insert-background: #b8bb26\ndiff-insert-foreground: <<colour foreground>>\ndiff-invisible-background: \ndiff-invisible-foreground: <<colour muted-foreground>>\ndirty-indicator: #fb4934\ndownload-background: #b8bb26\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: #665c54\ndropdown-border: <<colour background>>\ndropdown-tab-background-selected: #ebdbb2\ndropdown-tab-background: #665c54\ndropzone-background: #98971a\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #d3869b\nexternal-link-foreground: #8ec07c\nforeground: #fbf1c7\nmenubar-background: #504945\nmenubar-foreground: <<colour foreground>>\nmessage-background: #83a598\nmessage-border: #83a598\nmessage-foreground: #3c3836\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #504945\nmodal-footer-background: #3c3836\nmodal-footer-border: #3c3836\nmodal-header-border: #3c3836\nmuted-foreground: #d5c4a1\nnotification-background: <<colour primary>>\nnotification-border: <<colour primary>>\npage-background: #282828\npre-background: #504945\npre-border: #504945\nprimary: #d79921\nselect-tag-background: #665c54\nselect-tag-foreground: <<colour foreground>>\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #7c6f64\nsidebar-controls-foreground: #504945\nsidebar-foreground-shadow: transparent\nsidebar-foreground: #fbf1c7\nsidebar-muted-foreground-hover: #7c6f64\nsidebar-muted-foreground: #504945\nsidebar-tab-background-selected: #bdae93\nsidebar-tab-background: #3c3836\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: #bdae93\nsidebar-tab-divider: <<colour page-background>>\nsidebar-tab-foreground-selected: #282828\nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #458588\nsidebar-tiddler-link-foreground: #98971a\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #B48EAD\ntab-background-selected: #ebdbb2\ntab-background: #665c54\ntab-border-selected: #665c54\ntab-border: #665c54\ntab-divider: #bdae93\ntab-foreground-selected: #282828\ntab-foreground: #ebdbb2\ntable-border: #7c6f64\ntable-footer-background: #665c54\ntable-header-background: #504945\ntag-background: #d3869b\ntag-foreground: #282828\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #7c6f64\ntiddler-controls-foreground-selected: #7c6f64\ntiddler-controls-foreground: #665c54\ntiddler-editor-background: #282828\ntiddler-editor-border-image: #282828\ntiddler-editor-border: #282828\ntiddler-editor-fields-even: #504945\ntiddler-editor-fields-odd: #7c6f64\ntiddler-info-background: #32302f\ntiddler-info-border: #ebdbb2\ntiddler-info-tab-background: #ebdbb2\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #7c6f64\ntiddler-title-foreground: #a89984\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: #504945\nvery-muted-foreground: #bdae93\nwikilist-background: <<colour page-background>>\nwikilist-button-background: <<colour button-background>>\nwikilist-button-foreground: <<colour button-foreground>>\nwikilist-item: <<colour background>>\nwikilist-toolbar-background: <<colour background>>\nwikilist-toolbar-foreground: <<colour foreground>>\nwikilist-title: <<colour foreground>>\nwikilist-title-svg: <<colour wikilist-title>>\nwikilist-url: <<colour muted-foreground>>\nwikilist-button-open-hover: <<colour primary>>\nwikilist-button-open: <<colour dropzone-background>>\nwikilist-button-remove: <<colour dirty-indicator>>\nwikilist-button-remove-hover: <<colour alert-background>>\nwikilist-droplink-dragover: <<colour dropzone-background>>\nwikilist-button-reveal: <<colour sidebar-tiddler-link-foreground-hover>>\nwikilist-button-reveal-hover: <<colour message-background>>"
},
"$:/palettes/Nord": {
"title": "$:/palettes/Nord",
"name": "Nord",
"description": "An arctic, north-bluish color palette.",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"license": "MIT, arcticicestudio, https://github.com/arcticicestudio/nord/blob/develop/LICENSE.md",
"text": "alert-background: #D08770\nalert-border: #D08770\nalert-highlight: #B48EAD\nalert-muted-foreground: #4C566A\nbackground: #3b4252\nblockquote-bar: <<colour muted-foreground>>\nbutton-background: #4C566A\nbutton-foreground: #D8DEE9\nbutton-border: transparent\ncode-background: #2E3440\ncode-border: #2E3440\ncode-foreground: #BF616A\ndiff-delete-background: #BF616A\ndiff-delete-foreground: <<colour foreground>>\ndiff-equal-background: \ndiff-equal-foreground: <<colour foreground>>\ndiff-insert-background: #A3BE8C\ndiff-insert-foreground: <<colour foreground>>\ndiff-invisible-background: \ndiff-invisible-foreground: <<colour muted-foreground>>\ndirty-indicator: #BF616A\ndownload-background: #A3BE8C\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour background>>\ndropdown-tab-background-selected: #ECEFF4\ndropdown-tab-background: #4C566A\ndropzone-background: #A3BE8C\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #5E81AC\nexternal-link-foreground: #8FBCBB\nforeground: #d8dee9\nmenubar-background: #2E3440\nmenubar-foreground: #d8dee9\nmessage-background: #2E3440\nmessage-border: #2E3440\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #3b4252\nmodal-footer-background: #3b4252\nmodal-footer-border: #3b4252\nmodal-header-border: #3b4252\nmuted-foreground: #4C566A\nnotification-background: <<colour primary>>\nnotification-border: #EBCB8B\npage-background: #2e3440\npre-background: #2E3440\npre-border: #2E3440\nprimary: #5E81AC\nselect-tag-background: #3b4252\nselect-tag-foreground: <<colour foreground>>\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #D8DEE9\nsidebar-controls-foreground: #4C566A\nsidebar-foreground-shadow: transparent\nsidebar-foreground: #D8DEE9\nsidebar-muted-foreground-hover: #4C566A\nsidebar-muted-foreground: #4C566A\nsidebar-tab-background-selected: #ECEFF4\nsidebar-tab-background: #4C566A\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: #4C566A\nsidebar-tab-divider: <<colour page-background>>\nsidebar-tab-foreground-selected: #4C566A\nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #A3BE8C\nsidebar-tiddler-link-foreground: #81A1C1\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #B48EAD\ntab-background-selected: #ECEFF4\ntab-background: #4C566A\ntab-border-selected: #4C566A\ntab-border: #4C566A\ntab-divider: #4C566A\ntab-foreground-selected: #4C566A\ntab-foreground: #D8DEE9\ntable-border: #4C566A\ntable-footer-background: #2e3440\ntable-header-background: #2e3440\ntag-background: #A3BE8C\ntag-foreground: #4C566A\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: \ntiddler-controls-foreground-selected: #EBCB8B\ntiddler-controls-foreground: #4C566A\ntiddler-editor-background: #2e3440\ntiddler-editor-border-image: #2e3440\ntiddler-editor-border: #2e3440\ntiddler-editor-fields-even: #2e3440\ntiddler-editor-fields-odd: #2e3440\ntiddler-info-background: #2e3440\ntiddler-info-border: #2e3440\ntiddler-info-tab-background: #2e3440\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #4C566A\ntiddler-title-foreground: #81A1C1\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: #2d3038\nvery-muted-foreground: #2d3038\n"
},
"$:/palettes/Rocker": {
"title": "$:/palettes/Rocker",
"name": "Rocker",
"description": "A dark theme",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #999999\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #000\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #cc0000\nselect-tag-background:\nselect-tag-foreground:\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #ffffff\nsidebar-foreground-shadow: rgba(255,255,255, 0.0)\nsidebar-foreground: #acacac\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: #000\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #ffbb99\nsidebar-tiddler-link-foreground: #cc0000\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #d8d8d8\ntab-border-selected: #d8d8d8\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #ffbb99\ntag-foreground: #000\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #cc0000\ntoolbar-new-button:\ntoolbar-options-button:\ntoolbar-save-button:\ntoolbar-info-button:\ntoolbar-edit-button:\ntoolbar-close-button:\ntoolbar-delete-button:\ntoolbar-cancel-button:\ntoolbar-done-button:\nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/SolarFlare": {
"title": "$:/palettes/SolarFlare",
"name": "Solar Flare",
"description": "Warm, relaxing earth colours",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": ": Background Tones\n\nbase03: #002b36\nbase02: #073642\n\n: Content Tones\n\nbase01: #586e75\nbase00: #657b83\nbase0: #839496\nbase1: #93a1a1\n\n: Background Tones\n\nbase2: #eee8d5\nbase3: #fdf6e3\n\n: Accent Colors\n\nyellow: #b58900\norange: #cb4b16\nred: #dc322f\nmagenta: #d33682\nviolet: #6c71c4\nblue: #268bd2\ncyan: #2aa198\ngreen: #859900\n\n: Additional Tones (RA)\n\nbase10: #c0c4bb\nviolet-muted: #7c81b0\nblue-muted: #4e7baa\n\nyellow-hot: #ffcc44\norange-hot: #eb6d20\nred-hot: #ff2222\nblue-hot: #2298ee\ngreen-hot: #98ee22\n\n: Palette\n\n: Do not use colour macro for background and foreground\nbackground: #fdf6e3\n download-foreground: <<colour background>>\n dragger-foreground: <<colour background>>\n dropdown-background: <<colour background>>\n modal-background: <<colour background>>\n sidebar-foreground-shadow: <<colour background>>\n tiddler-background: <<colour background>>\n tiddler-border: <<colour background>>\n tiddler-link-background: <<colour background>>\n tab-background-selected: <<colour background>>\n dropdown-tab-background-selected: <<colour tab-background-selected>>\nforeground: #657b83\n dragger-background: <<colour foreground>>\n tab-foreground: <<colour foreground>>\n tab-foreground-selected: <<colour tab-foreground>>\n sidebar-tab-foreground-selected: <<colour tab-foreground-selected>>\n sidebar-tab-foreground: <<colour tab-foreground>>\n sidebar-button-foreground: <<colour foreground>>\n sidebar-controls-foreground: <<colour foreground>>\n sidebar-foreground: <<colour foreground>>\n: base03\n: base02\n: base01\n alert-muted-foreground: <<colour base01>>\n: base00\n code-foreground: <<colour base00>>\n message-foreground: <<colour base00>>\n tag-foreground: <<colour base00>>\n: base0\n sidebar-tiddler-link-foreground: <<colour base0>>\n: base1\n muted-foreground: <<colour base1>>\n blockquote-bar: <<colour muted-foreground>>\n dropdown-border: <<colour muted-foreground>>\n sidebar-muted-foreground: <<colour muted-foreground>>\n tiddler-title-foreground: <<colour muted-foreground>>\n site-title-foreground: <<colour tiddler-title-foreground>>\n: base2\n modal-footer-background: <<colour base2>>\n page-background: <<colour base2>>\n modal-backdrop: <<colour page-background>>\n notification-background: <<colour page-background>>\n code-background: <<colour page-background>>\n code-border: <<colour code-background>>\n pre-background: <<colour page-background>>\n pre-border: <<colour pre-background>>\n sidebar-tab-background-selected: <<colour page-background>>\n table-header-background: <<colour base2>>\n tag-background: <<colour base2>>\n tiddler-editor-background: <<colour base2>>\n tiddler-info-background: <<colour base2>>\n tiddler-info-tab-background: <<colour base2>>\n tab-background: <<colour base2>>\n dropdown-tab-background: <<colour tab-background>>\n: base3\n alert-background: <<colour base3>>\n message-background: <<colour base3>>\n: yellow\n: orange\n: red\n: magenta\n alert-highlight: <<colour magenta>>\n: violet\n external-link-foreground: <<colour violet>>\n: blue\n: cyan\n: green\n: base10\n tiddler-controls-foreground: <<colour base10>>\n: violet-muted\n external-link-foreground-visited: <<colour violet-muted>>\n: blue-muted\n primary: <<colour blue-muted>>\n download-background: <<colour primary>>\n tiddler-link-foreground: <<colour primary>>\n\nalert-border: #b99e2f\ndirty-indicator: #ff0000\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nmessage-border: #cfd6e6\nmodal-border: #999999\nselect-tag-background:\nselect-tag-foreground:\nsidebar-controls-foreground-hover:\nsidebar-muted-foreground-hover:\nsidebar-tab-background: #ded8c5\nsidebar-tiddler-link-foreground-hover:\nstatic-alert-foreground: #aaaaaa\ntab-border: #cccccc\n modal-footer-border: <<colour tab-border>>\n modal-header-border: <<colour tab-border>>\n notification-border: <<colour tab-border>>\n sidebar-tab-border: <<colour tab-border>>\n tab-border-selected: <<colour tab-border>>\n sidebar-tab-border-selected: <<colour tab-border-selected>>\ntab-divider: #d8d8d8\n sidebar-tab-divider: <<colour tab-divider>>\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-border: #dddddd\ntiddler-subtitle-foreground: #c0c0c0\ntoolbar-new-button:\ntoolbar-options-button:\ntoolbar-save-button:\ntoolbar-info-button:\ntoolbar-edit-button:\ntoolbar-close-button:\ntoolbar-delete-button:\ntoolbar-cancel-button:\ntoolbar-done-button:\nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/SolarizedLight": {
"title": "$:/palettes/SolarizedLight",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"description": "Precision colors for machines and people",
"license": "MIT, Ethan Schoonover, https://github.com/altercation/solarized/blob/master/LICENSE",
"name": "SolarizedLight",
"text": "alert-background: #eee8d5\nalert-border: #073642\nalert-highlight: #cb4b16\nalert-muted-foreground: #586e75\nbackground: #fdf6e3\nblockquote-bar: <<colour muted-foreground>>\nbutton-background: #cb4b16\nbutton-foreground: #fdf6e3\nbutton-border: transparent\ncode-background: #eee8d5\ncode-border: #93a1a1\ncode-foreground: #d33682\ndiff-delete-background: #BF616A\ndiff-delete-foreground: <<colour foreground>>\ndiff-equal-background: \ndiff-equal-foreground: <<colour foreground>>\ndiff-insert-background: #859900\ndiff-insert-foreground: <<colour foreground>>\ndiff-invisible-background: \ndiff-invisible-foreground: <<colour muted-foreground>>\ndirty-indicator: #D08770\ndownload-background: #859900\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour background>>\ndropdown-tab-background-selected: #fdf6e3\ndropdown-tab-background: #93a1a1\ndropzone-background: #859900\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: #d33682\nexternal-link-foreground-visited: #b58900\nexternal-link-foreground: #cb4b16\nforeground: #839496\nmessage-background: #586e75\nmessage-border: #586e75\nmessage-foreground: #eee8d5\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #eee8d5\nmodal-footer-background: #eee8d5\nmodal-footer-border: #eee8d5\nmodal-header-border: #eee8d5\nmuted-foreground: #93a1a1\nnotification-background: #EBCB8B\nnotification-border: #D08770\npage-background: #eee8d5\npre-background: #eee8d5\npre-border: #93a1a1\nprimary: #2aa198\nselect-tag-background: #eee8d5\nselect-tag-foreground: <<colour foreground>>\nsidebar-button-foreground: #eee8d5\nsidebar-controls-foreground-hover: #268bd2\nsidebar-controls-foreground: #586e75\nsidebar-foreground-shadow: transparent\nsidebar-foreground: #839496\nsidebar-muted-foreground-hover: #657b83\nsidebar-muted-foreground: #93a1a1\nsidebar-tab-background-selected: #eee8d5\nsidebar-tab-background: #839496\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: #657b83\nsidebar-tab-divider: <<colour page-background>>\nsidebar-tab-foreground-selected: #839496\nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #859900\nsidebar-tiddler-link-foreground: #268bd2\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #dc322f\ntab-background-selected: #fdf6e3\ntab-background: #839496\ntab-border-selected: #93a1a1\ntab-border: #93a1a1\ntab-divider: #fdf6e3\ntab-foreground-selected: #839496\ntab-foreground: #eee8d5\ntable-border: #657b83\ntable-footer-background: #657b83\ntable-header-background: #93a1a1\ntag-background: #6c71c4\ntag-foreground: #eee8d5\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #b58900\ntiddler-controls-foreground-selected: #b58900\ntiddler-controls-foreground: #073642\ntiddler-editor-background: #eee8d5\ntiddler-editor-border-image: #eee8d5\ntiddler-editor-border: #eee8d5\ntiddler-editor-fields-even: #eee8d5\ntiddler-editor-fields-odd: #fdf6e3\ntiddler-info-background: #eee8d5\ntiddler-info-border: #eee8d5\ntiddler-info-tab-background: #586e75\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #586e75\ntiddler-title-foreground: #073642\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: #839496\nvery-muted-foreground: #93a1a1\n"
},
"$:/palettes/SpartanDay": {
"title": "$:/palettes/SpartanDay",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"description": "Cold, spartan day colors",
"name": "Spartan Day",
"text": "alert-background: <<colour background>>\nalert-border: <<colour very-muted-foreground>>\nalert-highlight: <<colour very-muted-foreground>>\nalert-muted-foreground: <<colour muted-foreground>>\nbackground: #FAFAFA\nblockquote-bar: <<colour page-background>>\nbutton-background: transparent\nbutton-foreground: inherit\nbutton-border: <<colour tag-background>>\ncode-background: #ececec\ncode-border: #ececec\ncode-foreground: \ndirty-indicator: #c80000\ndownload-background: <<colour primary>>\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: #FFFFFF\ndropdown-border: <<colour dropdown-background>>\ndropdown-tab-background-selected: <<colour dropdown-background>>\ndropdown-tab-background: #F5F5F5\ndropzone-background: <<colour tag-background>>\nexternal-link-background-hover: transparent\nexternal-link-background-visited: transparent\nexternal-link-background: transparent\nexternal-link-foreground-hover: \nexternal-link-foreground-visited: \nexternal-link-foreground: \nforeground: rgba(0, 0, 0, 0.87)\nmessage-background: <<colour background>>\nmessage-border: <<colour very-muted-foreground>>\nmessage-foreground: rgba(0, 0, 0, 0.54)\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: <<colour very-muted-foreground>>\nmodal-footer-background: <<colour background>>\nmodal-footer-border: <<colour very-muted-foreground>>\nmodal-header-border: <<colour very-muted-foreground>>\nmuted-foreground: rgba(0, 0, 0, 0.54)\nnotification-background: <<colour dropdown-background>>\nnotification-border: <<colour dropdown-background>>\npage-background: #f4f4f4\npre-background: #ececec\npre-border: #ececec\nprimary: #3949ab\nselect-tag-background: <<colour background>>\nselect-tag-foreground: <<colour foreground>>\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #aeaeae\nsidebar-controls-foreground: #c6c6c6\nsidebar-foreground-shadow: transparent\nsidebar-foreground: rgba(0, 0, 0, 0.54)\nsidebar-muted-foreground-hover: rgba(0, 0, 0, 0.54)\nsidebar-muted-foreground: rgba(0, 0, 0, 0.38)\nsidebar-tab-background-selected: <<colour page-background>>\nsidebar-tab-background: transparent\nsidebar-tab-border-selected: <<colour table-border>>\nsidebar-tab-border: transparent\nsidebar-tab-divider: <<colour table-border>>\nsidebar-tab-foreground-selected: rgba(0, 0, 0, 0.87)\nsidebar-tab-foreground: rgba(0, 0, 0, 0.54)\nsidebar-tiddler-link-foreground-hover: rgba(0, 0, 0, 0.87)\nsidebar-tiddler-link-foreground: rgba(0, 0, 0, 0.54)\nsite-title-foreground: rgba(0, 0, 0, 0.87)\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: <<colour background>>\ntab-background: transparent\ntab-border-selected: <<colour table-border>>\ntab-border: transparent\ntab-divider: <<colour table-border>>\ntab-foreground-selected: rgba(0, 0, 0, 0.87)\ntab-foreground: rgba(0, 0, 0, 0.54)\ntable-border: #d8d8d8\ntable-footer-background: <<colour tiddler-editor-fields-odd>>\ntable-header-background: <<colour tiddler-editor-fields-even>>\ntag-background: #ec6\ntag-foreground: <<colour button-foreground>>\ntiddler-background: <<colour background>>\ntiddler-border: #f9f9f9\ntiddler-controls-foreground-hover: <<colour sidebar-controls-foreground-hover>>\ntiddler-controls-foreground-selected: <<colour sidebar-controls-foreground-hover>>\ntiddler-controls-foreground: <<colour sidebar-controls-foreground>>\ntiddler-editor-background: transparent\ntiddler-editor-border-image: \ntiddler-editor-border: #e8e7e7\ntiddler-editor-fields-even: rgba(0, 0, 0, 0.1)\ntiddler-editor-fields-odd: rgba(0, 0, 0, 0.04)\ntiddler-info-background: #F5F5F5\ntiddler-info-border: #F5F5F5\ntiddler-info-tab-background: <<colour tiddler-editor-fields-odd>>\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: <<colour muted-foreground>>\ntiddler-title-foreground: #000000\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: <<colour very-muted-foreground>>\nvery-muted-foreground: rgba(0, 0, 0, 0.12)\n"
},
"$:/palettes/SpartanNight": {
"title": "$:/palettes/SpartanNight",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"description": "Dark spartan colors",
"name": "Spartan Night",
"text": "alert-background: <<colour background>>\nalert-border: <<colour very-muted-foreground>>\nalert-highlight: <<colour very-muted-foreground>>\nalert-muted-foreground: <<colour muted-foreground>>\nbackground: #303030\nblockquote-bar: <<colour page-background>>\nbutton-background: transparent\nbutton-foreground: inherit\nbutton-border: <<colour tag-background>>\ncode-background: <<colour pre-background>>\ncode-border: <<colour pre-border>>\ncode-foreground: rgba(255, 255, 255, 0.54)\ndirty-indicator: #c80000\ndownload-background: <<colour primary>>\ndownload-foreground: <<colour foreground>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: #424242\ndropdown-border: <<colour dropdown-background>>\ndropdown-tab-background-selected: <<colour dropdown-background>>\ndropdown-tab-background: #050505\ndropzone-background: <<colour tag-background>>\nexternal-link-background-hover: transparent\nexternal-link-background-visited: transparent\nexternal-link-background: transparent\nexternal-link-foreground-hover: \nexternal-link-foreground-visited: #7c318c\nexternal-link-foreground: #9e3eb3\nforeground: rgba(255, 255, 255, 0.7)\nmessage-background: <<colour background>>\nmessage-border: <<colour very-muted-foreground>>\nmessage-foreground: rgba(255, 255, 255, 0.54)\nmodal-backdrop: <<colour page-background>>\nmodal-background: <<colour background>>\nmodal-border: <<colour very-muted-foreground>>\nmodal-footer-background: <<colour background>>\nmodal-footer-border: <<colour background>>\nmodal-header-border: <<colour very-muted-foreground>>\nmuted-foreground: rgba(255, 255, 255, 0.54)\nnotification-background: <<colour dropdown-background>>\nnotification-border: <<colour dropdown-background>>\npage-background: #212121\npre-background: #2a2a2a\npre-border: transparent\nprimary: #5656f3\nselect-tag-background: <<colour background>>\nselect-tag-foreground: <<colour foreground>>\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #494949\nsidebar-controls-foreground: #5d5d5d\nsidebar-foreground-shadow: transparent\nsidebar-foreground: rgba(255, 255, 255, 0.54)\nsidebar-muted-foreground-hover: rgba(255, 255, 255, 0.54)\nsidebar-muted-foreground: rgba(255, 255, 255, 0.38)\nsidebar-tab-background-selected: <<colour page-background>>\nsidebar-tab-background: transparent\nsidebar-tab-border-selected: <<colour table-border>>\nsidebar-tab-border: transparent\nsidebar-tab-divider: <<colour table-border>>\nsidebar-tab-foreground-selected: rgba(255, 255, 255, 0.87)\nsidebar-tab-foreground: rgba(255, 255, 255, 0.54)\nsidebar-tiddler-link-foreground-hover: rgba(255, 255, 255, 0.7)\nsidebar-tiddler-link-foreground: rgba(255, 255, 255, 0.54)\nsite-title-foreground: rgba(255, 255, 255, 0.7)\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: <<colour background>>\ntab-background: transparent\ntab-border-selected: <<colour table-border>>\ntab-border: transparent\ntab-divider: <<colour table-border>>\ntab-foreground-selected: rgba(255, 255, 255, 0.87)\ntab-foreground: rgba(255, 255, 255, 0.54)\ntable-border: #3a3a3a\ntable-footer-background: <<colour tiddler-editor-fields-odd>>\ntable-header-background: <<colour tiddler-editor-fields-even>>\ntag-background: #ec6\ntag-foreground: <<colour button-foreground>>\ntiddler-background: <<colour background>>\ntiddler-border: rgb(55,55,55)\ntiddler-controls-foreground-hover: <<colour sidebar-controls-foreground-hover>>\ntiddler-controls-foreground-selected: <<colour sidebar-controls-foreground-hover>>\ntiddler-controls-foreground: <<colour sidebar-controls-foreground>>\ntiddler-editor-background: transparent\ntiddler-editor-border-image: \ntiddler-editor-border: rgba(255, 255, 255, 0.08)\ntiddler-editor-fields-even: rgba(255, 255, 255, 0.1)\ntiddler-editor-fields-odd: rgba(255, 255, 255, 0.04)\ntiddler-info-background: #454545\ntiddler-info-border: #454545\ntiddler-info-tab-background: <<colour tiddler-editor-fields-odd>>\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: <<colour muted-foreground>>\ntiddler-title-foreground: #FFFFFF\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: <<colour very-muted-foreground>>\nvery-muted-foreground: rgba(255, 255, 255, 0.12)\n"
},
"$:/palettes/Twilight": {
"title": "$:/palettes/Twilight",
"tags": "$:/tags/Palette",
"author": "Thomas Elmiger",
"type": "application/x-tiddler-dictionary",
"name": "Twilight",
"description": "Delightful, soft darkness.",
"text": "alert-background: rgb(255, 255, 102)\nalert-border: rgb(232, 232, 125)\nalert-highlight: rgb(255, 51, 51)\nalert-muted-foreground: rgb(224, 82, 82)\nbackground: rgb(38, 38, 38)\nblockquote-bar: rgba(240, 196, 117, 0.7)\nbutton-background: rgb(63, 63, 63)\nbutton-border: rgb(127, 127, 127)\nbutton-foreground: rgb(179, 179, 179)\ncode-background: rgba(0,0,0,0.03)\ncode-border: rgba(0,0,0,0.08)\ncode-foreground: rgb(255, 94, 94)\ndiff-delete-background: #ffc9c9\ndiff-delete-foreground: <<colour foreground>>\ndiff-equal-background: \ndiff-equal-foreground: <<colour foreground>>\ndiff-insert-background: #aaefad\ndiff-insert-foreground: <<colour foreground>>\ndiff-invisible-background: \ndiff-invisible-foreground: <<colour muted-foreground>>\ndirty-indicator: rgb(255, 94, 94)\ndownload-background: #19a974\ndownload-foreground: rgb(38, 38, 38)\ndragger-background: rgb(179, 179, 179)\ndragger-foreground: rgb(38, 38, 38)\ndropdown-background: rgb(38, 38, 38)\ndropdown-border: rgb(255, 255, 255)\ndropdown-tab-background: rgba(0,0,0,.1)\ndropdown-tab-background-selected: rgba(255,255,255,1)\ndropzone-background: #9eebcf\nexternal-link-background: inherit\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-foreground: rgb(179, 179, 255)\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: rgb(153, 153, 255)\nforeground: rgb(179, 179, 179)\nmessage-background: <<colour tag-foreground>>\nmessage-border: #96ccff\nmessage-foreground: <<colour tag-background>>\nmodal-backdrop: rgb(179, 179, 179)\nmodal-background: rgb(38, 38, 38)\nmodal-border: rgba(0,0,0,.5)\nmodal-footer-background: #f4f4f4\nmodal-footer-border: rgba(0,0,0,.1)\nmodal-header-border: rgba(0,0,0,.2)\nmuted-foreground: rgb(255, 255, 255)\nnotification-background: <<colour tag-foreground>>\nnotification-border: <<colour tag-background>>\npage-background: rgb(26, 26, 26)\npre-background: rgb(25, 25, 25)\npre-border: rgba(0,0,0,.2)\nprimary: rgb(255, 201, 102)\nselect-tag-background: \nselect-tag-foreground: \nsidebar-button-foreground: rgb(179, 179, 179)\nsidebar-controls-foreground: rgb(153, 153, 153)\nsidebar-controls-foreground-hover: <<colour tiddler-controls-foreground-hover>>\nsidebar-foreground: rgb(141, 141, 141)\nsidebar-foreground-shadow: transparent\nsidebar-muted-foreground: rgba(0, 0, 0, 0.5)\nsidebar-muted-foreground-hover: rgb(141, 141, 141)\nsidebar-tab-background: rgba(141, 141, 141, 0.2)\nsidebar-tab-background-selected: rgb(26, 26, 26)\nsidebar-tab-border: rgb(127, 127, 127)\nsidebar-tab-border-selected: rgb(127, 127, 127)\nsidebar-tab-divider: rgb(127, 127, 127)\nsidebar-tab-foreground: rgb(179, 179, 179)\nsidebar-tab-foreground-selected: rgb(179, 179, 179)\nsidebar-tiddler-link-foreground: rgb(179, 179, 179)\nsidebar-tiddler-link-foreground-hover: rgb(115, 115, 115)\nsite-title-foreground: rgb(255, 201, 102)\nstatic-alert-foreground: rgba(0,0,0,.3)\ntab-background: rgba(0,0,0,0.125)\ntab-background-selected: rgb(38, 38, 38)\ntab-border: rgb(255, 201, 102)\ntab-border-selected: rgb(255, 201, 102)\ntab-divider: rgb(255, 201, 102)\ntab-foreground: rgb(179, 179, 179)\ntab-foreground-selected: rgb(179, 179, 179)\ntable-border: rgba(255,255,255,.3)\ntable-footer-background: rgba(0,0,0,.4)\ntable-header-background: rgba(0,0,0,.1)\ntag-background: rgb(255, 201, 102)\ntag-foreground: rgb(25, 25, 25)\ntiddler-background: rgb(38, 38, 38)\ntiddler-border: rgba(240, 196, 117, 0.7)\ntiddler-controls-foreground: rgb(128, 128, 128)\ntiddler-controls-foreground-hover: rgba(255, 255, 255, 0.8)\ntiddler-controls-foreground-selected: rgba(255, 255, 255, 0.9)\ntiddler-editor-background: rgb(33, 33, 33)\ntiddler-editor-border: rgb(63, 63, 63)\ntiddler-editor-border-image: rgb(25, 25, 25)\ntiddler-editor-fields-even: rgb(33, 33, 33)\ntiddler-editor-fields-odd: rgb(28, 28, 28)\ntiddler-info-background: rgb(43, 43, 43)\ntiddler-info-border: rgb(25, 25, 25)\ntiddler-info-tab-background: rgb(43, 43, 43)\ntiddler-link-background: rgb(38, 38, 38)\ntiddler-link-foreground: rgb(204, 204, 255)\ntiddler-subtitle-foreground: rgb(255, 255, 255)\ntiddler-title-foreground: rgb(255, 192, 76)\ntoolbar-cancel-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-done-button: \ntoolbar-edit-button: \ntoolbar-info-button: \ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \nuntagged-background: rgb(255, 255, 255)\nvery-muted-foreground: rgba(240, 196, 117, 0.7)\n"
},
"$:/palettes/Vanilla": {
"title": "$:/palettes/Vanilla",
"name": "Vanilla",
"description": "Pale and unobtrusive",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndiff-delete-background: #ffc9c9\ndiff-delete-foreground: <<colour foreground>>\ndiff-equal-background: \ndiff-equal-foreground: <<colour foreground>>\ndiff-insert-background: #aaefad\ndiff-insert-foreground: <<colour foreground>>\ndiff-invisible-background: \ndiff-invisible-foreground: <<colour muted-foreground>>\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #bbb\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #f4f4f4\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #5778d8\nselect-tag-background:\nselect-tag-foreground:\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #aaaaaa\nsidebar-foreground-shadow: rgba(255,255,255, 0.8)\nsidebar-foreground: #acacac\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: #f4f4f4\nsidebar-tab-background: #e0e0e0\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: #e4e4e4\nsidebar-tab-foreground-selected:\nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #444444\nsidebar-tiddler-link-foreground: #999999\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #d8d8d8\ntab-border-selected: #d8d8d8\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #ec6\ntag-foreground: #ffffff\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #182955\ntoolbar-new-button:\ntoolbar-options-button:\ntoolbar-save-button:\ntoolbar-info-button:\ntoolbar-edit-button:\ntoolbar-close-button:\ntoolbar-delete-button:\ntoolbar-cancel-button:\ntoolbar-done-button:\nuntagged-background: #999999\nvery-muted-foreground: #888888\nwikilist-background: #e5e5e5\nwikilist-item: #fff\nwikilist-info: #000\nwikilist-title: #666\nwikilist-title-svg: <<colour wikilist-title>>\nwikilist-url: #aaa\nwikilist-button-open: #4fb82b\nwikilist-button-open-hover: green\nwikilist-button-reveal: #5778d8\nwikilist-button-reveal-hover: blue\nwikilist-button-remove: #d85778\nwikilist-button-remove-hover: red\nwikilist-toolbar-background: #d3d3d3\nwikilist-toolbar-foreground: #888\nwikilist-droplink-dragover: rgba(255,192,192,0.5)\nwikilist-button-background: #acacac\nwikilist-button-foreground: #000\n"
},
"$:/core/readme": {
"title": "$:/core/readme",
"text": "This plugin contains TiddlyWiki's core components, comprising:\n\n* JavaScript code modules\n* Icons\n* Templates needed to create TiddlyWiki's user interface\n* British English (''en-GB'') translations of the localisable strings used by the core\n"
},
"$:/library/sjcl.js/license": {
"title": "$:/library/sjcl.js/license",
"type": "text/plain",
"text": "SJCL is open. You can use, modify and redistribute it under a BSD\nlicense or under the GNU GPL, version 2.0.\n\n---------------------------------------------------------------------\n\nhttp://opensource.org/licenses/BSD-2-Clause\n\nCopyright (c) 2009-2015, Emily Stark, Mike Hamburg and Dan Boneh at\nStanford University. All rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are\nmet:\n\n1. Redistributions of source code must retain the above copyright\nnotice, this list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright\nnotice, this list of conditions and the following disclaimer in the\ndocumentation and/or other materials provided with the distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS\nIS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED\nTO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\nPARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\nHOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\nSPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\nTO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\nPROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\nLIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\nNEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n---------------------------------------------------------------------\n\nhttp://opensource.org/licenses/GPL-2.0\n\nThe Stanford Javascript Crypto Library (hosted here on GitHub) is a\nproject by the Stanford Computer Security Lab to build a secure,\npowerful, fast, small, easy-to-use, cross-browser library for\ncryptography in Javascript.\n\nCopyright (c) 2009-2015, Emily Stark, Mike Hamburg and Dan Boneh at\nStanford University.\n\nThis program is free software; you can redistribute it and/or modify it\nunder the terms of the GNU General Public License as published by the\nFree Software Foundation; either version 2 of the License, or (at your\noption) any later version.\n\nThis program is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General\nPublic License for more details.\n\nYou should have received a copy of the GNU General Public License along\nwith this program; if not, write to the Free Software Foundation, Inc.,\n59 Temple Place, Suite 330, Boston, MA 02111-1307 USA"
},
"$:/core/templates/MOTW.html": {
"title": "$:/core/templates/MOTW.html",
"text": "\\rules only filteredtranscludeinline transcludeinline entity\n<!-- The following comment is called a MOTW comment and is necessary for the TiddlyIE Internet Explorer extension -->\n<!-- saved from url=(0021)https://tiddlywiki.com --> "
},
"$:/core/templates/alltiddlers.template.html": {
"title": "$:/core/templates/alltiddlers.template.html",
"type": "text/vnd.tiddlywiki-html",
"text": "<!-- This template is provided for backwards compatibility with older versions of TiddlyWiki -->\n\n<$set name=\"exportFilter\" value=\"[!is[system]sort[title]]\">\n\n{{$:/core/templates/exporters/StaticRiver}}\n\n</$set>\n"
},
"$:/core/templates/canonical-uri-external-image": {
"title": "$:/core/templates/canonical-uri-external-image",
"text": "<!--\n\nThis template is used to assign the ''_canonical_uri'' field to external images.\n\nChange the `./images/` part to a different base URI. The URI can be relative or absolute.\n\n-->\n./images/<$view field=\"title\" format=\"doubleurlencoded\"/>"
},
"$:/core/templates/canonical-uri-external-raw": {
"title": "$:/core/templates/canonical-uri-external-raw",
"text": "<!--\n\nThis template is used to assign the ''_canonical_uri'' field to external raw files that are stored in the same directory\n\n-->\n<$view field=\"title\" format=\"doubleurlencoded\"/>"
},
"$:/core/templates/canonical-uri-external-text": {
"title": "$:/core/templates/canonical-uri-external-text",
"text": "<!--\n\nThis template is used to assign the ''_canonical_uri'' field to external text files.\n\nChange the `./text/` part to a different base URI. The URI can be relative or absolute.\n\n-->\n./text/<$view field=\"title\" format=\"doubleurlencoded\"/>.tid"
},
"$:/core/templates/css-tiddler": {
"title": "$:/core/templates/css-tiddler",
"text": "<!--\n\nThis template is used for saving CSS tiddlers as a style tag with data attributes representing the tiddler fields.\n\n-->`<style`<$fields template=' data-tiddler-$name$=\"$encoded_value$\"'></$fields>` type=\"text/css\">`<$view field=\"text\" format=\"text\" />`</style>`"
},
"$:/core/templates/exporters/CsvFile": {
"title": "$:/core/templates/exporters/CsvFile",
"tags": "$:/tags/Exporter",
"description": "{{$:/language/Exporters/CsvFile}}",
"extension": ".csv",
"text": "\\define renderContent()\n<$text text=<<csvtiddlers filter:\"\"\"$(exportFilter)$\"\"\" format:\"quoted-comma-sep\">>/>\n\\end\n<<renderContent>>\n"
},
"$:/core/templates/exporters/JsonFile": {
"title": "$:/core/templates/exporters/JsonFile",
"tags": "$:/tags/Exporter",
"description": "{{$:/language/Exporters/JsonFile}}",
"extension": ".json",
"text": "\\define renderContent()\n<$text text=<<jsontiddlers filter:\"\"\"$(exportFilter)$\"\"\">>/>\n\\end\n<<renderContent>>\n"
},
"$:/core/templates/exporters/StaticRiver": {
"title": "$:/core/templates/exporters/StaticRiver",
"tags": "$:/tags/Exporter",
"description": "{{$:/language/Exporters/StaticRiver}}",
"extension": ".html",
"text": "\\define tv-wikilink-template() #$uri_encoded$\n\\define tv-config-toolbar-icons() no\n\\define tv-config-toolbar-text() no\n\\define tv-config-toolbar-class() tc-btn-invisible\n\\rules only filteredtranscludeinline transcludeinline\n<!doctype html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"{{$:/core/templates/version}}\" />\n<meta name=\"format-detection\" content=\"telephone=no\">\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<title>{{$:/core/wiki/title}}</title>\n<div id=\"styleArea\">\n{{$:/boot/boot.css||$:/core/templates/css-tiddler}}\n</div>\n<style type=\"text/css\">\n{{$:/core/ui/PageStylesheet||$:/core/templates/wikified-tiddler}}\n</style>\n</head>\n<body class=\"tc-body\">\n{{$:/StaticBanner||$:/core/templates/html-tiddler}}\n<section class=\"tc-story-river\">\n{{$:/core/templates/exporters/StaticRiver/Content||$:/core/templates/html-tiddler}}\n</section>\n</body>\n</html>\n"
},
"$:/core/templates/exporters/StaticRiver/Content": {
"title": "$:/core/templates/exporters/StaticRiver/Content",
"text": "\\define renderContent()\n{{{ $(exportFilter)$ ||$:/core/templates/static-tiddler}}}\n\\end\n\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n<<renderContent>>\n"
},
"$:/core/templates/exporters/TidFile": {
"title": "$:/core/templates/exporters/TidFile",
"tags": "$:/tags/Exporter",
"description": "{{$:/language/Exporters/TidFile}}",
"extension": ".tid",
"text": "\\define renderContent()\n{{{ $(exportFilter)$ +[limit[1]] ||$:/core/templates/tid-tiddler}}}\n\\end\n\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n<<renderContent>>"
},
"$:/core/save/all-external-js": {
"title": "$:/core/save/all-external-js",
"text": "\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n\\define saveTiddlerFilter()\n[is[tiddler]] -[prefix[$:/state/popup/]] -[[$:/HistoryList]] -[[$:/core]] -[[$:/boot/boot.css]] -[type[application/javascript]library[yes]] -[[$:/boot/boot.js]] -[[$:/boot/bootprefix.js]] +[sort[title]] $(publishFilter)$\n\\end\n{{$:/core/templates/tiddlywiki5-external-js.html}}\n"
},
"$:/core/templates/tiddlywiki5.js": {
"title": "$:/core/templates/tiddlywiki5.js",
"text": "\\rules only filteredtranscludeinline transcludeinline codeinline\n\n/*\n{{ $:/core/copyright.txt ||$:/core/templates/plain-text-tiddler}}\n`*/\n`<!--~~ Library modules ~~-->\n{{{ [is[system]type[application/javascript]library[yes]] ||$:/core/templates/plain-text-tiddler}}}\n<!--~~ Boot prefix ~~-->\n{{ $:/boot/bootprefix.js ||$:/core/templates/plain-text-tiddler}}\n<!--~~ Core plugin ~~-->\n{{$:/core/templates/tiddlywiki5.js/tiddlers}}\n<!--~~ Boot kernel ~~-->\n{{ $:/boot/boot.js ||$:/core/templates/plain-text-tiddler}}\n"
},
"$:/core/templates/tiddlywiki5.js/tiddlers": {
"title": "$:/core/templates/tiddlywiki5.js/tiddlers",
"text": "`\n$tw.preloadTiddlerArray(`<$text text=<<jsontiddlers \"[[$:/core]]\">>/>`);\n$tw.preloadTiddlerArray([{\n\ttitle: \"$:/config/SaveWikiButton/Template\",\n\ttext: \"$:/core/save/all-external-js\"\n}]);\n`\n"
},
"$:/core/templates/tiddlywiki5-external-js.html": {
"title": "$:/core/templates/tiddlywiki5-external-js.html",
"text": "\\rules only filteredtranscludeinline transcludeinline\n<!doctype html>\n{{$:/core/templates/MOTW.html}}<html lang=\"`<$text text={{{ [{$:/language}get[name]] }}}/>`\">\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<!--~~ Raw markup for the top of the head section ~~-->\n{{{ [all[shadows+tiddlers]tag[$:/tags/RawMarkupWikified/TopHead]] ||$:/core/templates/raw-static-tiddler}}}\n<meta http-equiv=\"X-UA-Compatible\" content=\"IE=Edge\"/>\n<meta name=\"application-name\" content=\"TiddlyWiki\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"{{$:/core/templates/version}}\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\" />\n<meta name=\"copyright\" content=\"{{$:/core/copyright.txt}}\" />\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<title>{{$:/core/wiki/title}}</title>\n<!--~~ This is a Tiddlywiki file. The points of interest in the file are marked with this pattern ~~-->\n\n<!--~~ Raw markup ~~-->\n{{{ [all[shadows+tiddlers]tag[$:/core/wiki/rawmarkup]] [all[shadows+tiddlers]tag[$:/tags/RawMarkup]] ||$:/core/templates/plain-text-tiddler}}}\n{{{ [all[shadows+tiddlers]tag[$:/tags/RawMarkupWikified]] ||$:/core/templates/raw-static-tiddler}}}\n</head>\n<body class=\"tc-body\">\n<!--~~ Raw markup for the top of the body section ~~-->\n{{{ [all[shadows+tiddlers]tag[$:/tags/RawMarkupWikified/TopBody]] ||$:/core/templates/raw-static-tiddler}}}\n<!--~~ Static styles ~~-->\n<div id=\"styleArea\">\n{{$:/boot/boot.css||$:/core/templates/css-tiddler}}\n</div>\n<!--~~ Static content for Google and browsers without JavaScript ~~-->\n<noscript>\n<div id=\"splashArea\">\n{{$:/core/templates/static.area}}\n</div>\n</noscript>\n<!--~~ Ordinary tiddlers ~~-->\n{{$:/core/templates/store.area.template.html}}\n<!--~~ Raw markup for the bottom of the body section ~~-->\n{{{ [all[shadows+tiddlers]tag[$:/tags/RawMarkupWikified/BottomBody]] ||$:/core/templates/raw-static-tiddler}}}\n</body>\n<script src=\"%24%3A%2Fcore%2Ftemplates%2Ftiddlywiki5.js\" onerror=\"alert('Error: Cannot load tiddlywiki.js');\"></script>\n</html>\n"
},
"$:/core/templates/html-div-skinny-tiddler": {
"title": "$:/core/templates/html-div-skinny-tiddler",
"text": "<!--\n\nThis template is a variant of $:/core/templates/html-div-tiddler used for saving skinny tiddlers (with no text field)\n\n-->`<div`<$fields template=' $name$=\"$encoded_value$\"'></$fields>`>\n<pre></pre>\n</div>`\n"
},
"$:/core/templates/html-div-tiddler": {
"title": "$:/core/templates/html-div-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers as an HTML DIV tag with attributes representing the tiddler fields.\n\n-->`<div`<$fields template=' $name$=\"$encoded_value$\"'></$fields>`>\n<pre>`<$view field=\"text\" format=\"htmlencoded\" />`</pre>\n</div>`\n"
},
"$:/core/templates/html-tiddler": {
"title": "$:/core/templates/html-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers as raw HTML\n\n--><$view field=\"text\" format=\"htmlwikified\" />"
},
"$:/core/templates/javascript-tiddler": {
"title": "$:/core/templates/javascript-tiddler",
"text": "<!--\n\nThis template is used for saving JavaScript tiddlers as a script tag with data attributes representing the tiddler fields.\n\n-->`<script`<$fields template=' data-tiddler-$name$=\"$encoded_value$\"'></$fields>` type=\"text/javascript\">`<$view field=\"text\" format=\"text\" />`</script>`"
},
"$:/core/templates/json-tiddler": {
"title": "$:/core/templates/json-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers as raw JSON\n\n--><$text text=<<jsontiddler>>/>"
},
"$:/core/templates/module-tiddler": {
"title": "$:/core/templates/module-tiddler",
"text": "<!--\n\nThis template is used for saving JavaScript tiddlers as a script tag with data attributes representing the tiddler fields. The body of the tiddler is wrapped in a call to the `$tw.modules.define` function in order to define the body of the tiddler as a module\n\n-->`<script`<$fields template=' data-tiddler-$name$=\"$encoded_value$\"'></$fields>` type=\"text/javascript\" data-module=\"yes\">$tw.modules.define(\"`<$view field=\"title\" format=\"jsencoded\" />`\",\"`<$view field=\"module-type\" format=\"jsencoded\" />`\",function(module,exports,require) {`<$view field=\"text\" format=\"text\" />`});\n</script>`"
},
"$:/core/templates/plain-text-tiddler": {
"title": "$:/core/templates/plain-text-tiddler",
"text": "<$view field=\"text\" format=\"text\" />"
},
"$:/core/templates/raw-static-tiddler": {
"title": "$:/core/templates/raw-static-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers as static HTML\n\n--><$view field=\"text\" format=\"plainwikified\" />"
},
"$:/core/save/all": {
"title": "$:/core/save/all",
"text": "\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n\\define saveTiddlerFilter()\n[is[tiddler]] -[prefix[$:/state/popup/]] -[[$:/HistoryList]] -[[$:/boot/boot.css]] -[type[application/javascript]library[yes]] -[[$:/boot/boot.js]] -[[$:/boot/bootprefix.js]] +[sort[title]] $(publishFilter)$\n\\end\n{{$:/core/templates/tiddlywiki5.html}}\n"
},
"$:/core/save/empty": {
"title": "$:/core/save/empty",
"text": "\\define saveTiddlerFilter()\n[is[system]] -[prefix[$:/state/popup/]] -[[$:/boot/boot.css]] -[type[application/javascript]library[yes]] -[[$:/boot/boot.js]] -[[$:/boot/bootprefix.js]] +[sort[title]]\n\\end\n{{$:/core/templates/tiddlywiki5.html}}\n"
},
"$:/core/save/lazy-all": {
"title": "$:/core/save/lazy-all",
"text": "\\define saveTiddlerFilter()\n[is[system]] -[prefix[$:/state/popup/]] -[[$:/HistoryList]] -[[$:/boot/boot.css]] -[type[application/javascript]library[yes]] -[[$:/boot/boot.js]] -[[$:/boot/bootprefix.js]] +[sort[title]] \n\\end\n\\define skinnySaveTiddlerFilter()\n[!is[system]]\n\\end\n{{$:/core/templates/tiddlywiki5.html}}\n"
},
"$:/core/save/lazy-images": {
"title": "$:/core/save/lazy-images",
"text": "\\define saveTiddlerFilter()\n[is[tiddler]] -[prefix[$:/state/popup/]] -[[$:/HistoryList]] -[[$:/boot/boot.css]] -[type[application/javascript]library[yes]] -[[$:/boot/boot.js]] -[[$:/boot/bootprefix.js]] -[!is[system]is[image]] +[sort[title]] \n\\end\n\\define skinnySaveTiddlerFilter()\n[is[image]]\n\\end\n{{$:/core/templates/tiddlywiki5.html}}\n"
},
"$:/core/templates/server/static.sidebar.wikitext": {
"title": "$:/core/templates/server/static.sidebar.wikitext",
"text": "\\whitespace trim\n<div class=\"tc-sidebar-scrollable\" style=\"overflow: auto;\">\n<div class=\"tc-sidebar-header\">\n<h1 class=\"tc-site-title\">\n<$transclude tiddler=\"$:/SiteTitle\"/>\n</h1>\n<div class=\"tc-site-subtitle\">\n<$transclude tiddler=\"$:/SiteSubtitle\"/>\n</div>\n<h2>\n</h2>\n<div class=\"tc-sidebar-lists\">\n<$list filter={{$:/DefaultTiddlers}}>\n<div class=\"tc-menu-list-subitem\">\n<$link><$text text=<<currentTiddler>>/></$link>\n</div>\n</$list>\n</div>\n<!-- Currently disabled the recent list as it is unweildy when the responsive narrow view kicks in\n<h2>\n{{$:/language/SideBar/Recent/Caption}}\n</h2>\n<div class=\"tc-sidebar-lists\">\n<$macrocall $name=\"timeline\" format={{$:/language/RecentChanges/DateFormat}}/>\n</div>\n</div>\n</div>\n-->\n"
},
"$:/core/templates/server/static.tiddler.html": {
"title": "$:/core/templates/server/static.tiddler.html",
"text": "\\whitespace trim\n\\define tv-wikilink-template() $uri_encoded$\n\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content={{$:/core/templates/version}} />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\">\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<link rel=\"stylesheet\" href=\"%24%3A%2Fcore%2Ftemplates%2Fstatic.template.css\">\n<title><$view field=\"caption\" format=\"plainwikified\"><$view field=\"title\"/></$view>: <$view tiddler=\"$:/core/wiki/title\" format=\"plainwikified\"/></title>\n</head>\n<body class=\"tc-body\">\n<$transclude tiddler=\"$:/core/templates/server/static.sidebar.wikitext\" mode=\"inline\"/>\n<section class=\"tc-story-river\">\n<div class=\"tc-tiddler-frame\">\n<$transclude tiddler=\"$:/core/templates/server/static.tiddler.wikitext\" mode=\"inline\"/>\n</div>\n</section>\n</body>\n</html>"
},
"$:/core/templates/server/static.tiddler.wikitext": {
"title": "$:/core/templates/server/static.tiddler.wikitext",
"text": "\\whitespace trim\n<div class=\"tc-tiddler-title\">\n<div class=\"tc-titlebar\">\n<h2><$text text=<<currentTiddler>>/></h2>\n</div>\n</div>\n<div class=\"tc-subtitle\">\n<$link to={{!!modifier}}>\n<$view field=\"modifier\"/>\n</$link> <$view field=\"modified\" format=\"date\" template={{$:/language/Tiddler/DateFormat}}/>\n</div>\n<div class=\"tc-tags-wrapper\">\n<$list filter=\"[all[current]tags[]sort[title]]\">\n<a href={{{ [<currentTiddler>encodeuricomponent[]] }}}>\n<$macrocall $name=\"tag-pill\" tag=<<currentTiddler>>/>\n</a>\n</$list>\n</div>\n<div class=\"tc-tiddler-body\">\n<$transclude mode=\"block\"/>\n</div>\n"
},
"$:/core/templates/single.tiddler.window": {
"title": "$:/core/templates/single.tiddler.window",
"text": "\\whitespace trim\n\\define containerClasses()\ntc-page-container tc-page-view-$(storyviewTitle)$ tc-language-$(languageTitle)$\n\\end\n\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n\n<$set name=\"tv-config-toolbar-icons\" value={{$:/config/Toolbar/Icons}}>\n\n<$set name=\"tv-config-toolbar-text\" value={{$:/config/Toolbar/Text}}>\n\n<$set name=\"tv-config-toolbar-class\" value={{$:/config/Toolbar/ButtonClass}}>\n\n<$set name=\"tv-show-missing-links\" value={{$:/config/MissingLinks}}>\n\n<$set name=\"storyviewTitle\" value={{$:/view}}>\n\n<$set name=\"languageTitle\" value={{{ [{$:/language}get[name]] }}}>\n\n<div class=<<containerClasses>>>\n\n<$navigator story=\"$:/StoryList\" history=\"$:/HistoryList\">\n\n<$transclude mode=\"block\"/>\n\n</$navigator>\n\n</div>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n"
},
"$:/core/templates/split-recipe": {
"title": "$:/core/templates/split-recipe",
"text": "<$list filter=\"[!is[system]]\">\ntiddler: <$view field=\"title\" format=\"urlencoded\"/>.tid\n</$list>\n"
},
"$:/core/templates/static-tiddler": {
"title": "$:/core/templates/static-tiddler",
"text": "<a name=<<currentTiddler>>>\n<$transclude tiddler=\"$:/core/ui/ViewTemplate\"/>\n</a>"
},
"$:/core/templates/static.area": {
"title": "$:/core/templates/static.area",
"text": "<$reveal type=\"nomatch\" state=\"$:/isEncrypted\" text=\"yes\">\n{{{ [all[shadows+tiddlers]tag[$:/tags/RawStaticContent]!has[draft.of]] ||$:/core/templates/raw-static-tiddler}}}\n{{$:/core/templates/static.content||$:/core/templates/html-tiddler}}\n</$reveal>\n<$reveal type=\"match\" state=\"$:/isEncrypted\" text=\"yes\">\nThis file contains an encrypted ~TiddlyWiki. Enable ~JavaScript and enter the decryption password when prompted.\n</$reveal>\n<!-- ensure splash screen isn't shown when JS is disabled -->\n`<style>\n.tc-remove-when-wiki-loaded {display: none;}\n</style>`\n"
},
"$:/core/templates/static.content": {
"title": "$:/core/templates/static.content",
"text": "<!-- For Google, and people without JavaScript-->\nThis [[TiddlyWiki|https://tiddlywiki.com]] contains the following tiddlers:\n\n<ul>\n<$list filter=<<saveTiddlerFilter>>>\n<li><$view field=\"title\" format=\"text\"></$view></li>\n</$list>\n</ul>\n"
},
"$:/core/templates/static.template.css": {
"title": "$:/core/templates/static.template.css",
"text": "{{$:/boot/boot.css||$:/core/templates/plain-text-tiddler}}\n\n{{$:/core/ui/PageStylesheet||$:/core/templates/wikified-tiddler}}\n"
},
"$:/core/templates/static.template.html": {
"title": "$:/core/templates/static.template.html",
"type": "text/vnd.tiddlywiki-html",
"text": "\\define tv-wikilink-template() static/$uri_doubleencoded$.html\n\\define tv-config-toolbar-icons() no\n\\define tv-config-toolbar-text() no\n\\define tv-config-toolbar-class() tc-btn-invisible\n\\rules only filteredtranscludeinline transcludeinline\n<!doctype html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"{{$:/core/templates/version}}\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\">\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<title>{{$:/core/wiki/title}}</title>\n<div id=\"styleArea\">\n{{$:/boot/boot.css||$:/core/templates/css-tiddler}}\n</div>\n<style type=\"text/css\">\n{{$:/core/ui/PageStylesheet||$:/core/templates/wikified-tiddler}}\n</style>\n</head>\n<body class=\"tc-body\">\n{{$:/StaticBanner||$:/core/templates/html-tiddler}}\n{{$:/core/ui/PageTemplate||$:/core/templates/html-tiddler}}\n</body>\n</html>\n"
},
"$:/core/templates/static.tiddler.html": {
"title": "$:/core/templates/static.tiddler.html",
"text": "\\define tv-wikilink-template() $uri_doubleencoded$.html\n\\define tv-config-toolbar-icons() no\n\\define tv-config-toolbar-text() no\n\\define tv-config-toolbar-class() tc-btn-invisible\n\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n`<!doctype html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"`{{$:/core/templates/version}}`\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\">\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<link rel=\"stylesheet\" href=\"static.css\">\n<title>`<$view field=\"caption\"><$view field=\"title\"/></$view>: {{$:/core/wiki/title}}`</title>\n</head>\n<body class=\"tc-body\">\n`{{$:/StaticBanner||$:/core/templates/html-tiddler}}`\n<section class=\"tc-story-river\">\n`<$view tiddler=\"$:/core/ui/ViewTemplate\" format=\"htmlwikified\"/>`\n</section>\n</body>\n</html>\n`"
},
"$:/core/templates/store.area.template.html": {
"title": "$:/core/templates/store.area.template.html",
"text": "<$reveal type=\"nomatch\" state=\"$:/isEncrypted\" text=\"yes\">\n`<div id=\"storeArea\" style=\"display:none;\">`\n<$list filter=<<saveTiddlerFilter>> template=\"$:/core/templates/html-div-tiddler\"/>\n<$list filter={{{ [<skinnySaveTiddlerFilter>] }}} template=\"$:/core/templates/html-div-skinny-tiddler\"/>\n`</div>`\n</$reveal>\n<$reveal type=\"match\" state=\"$:/isEncrypted\" text=\"yes\">\n`<!--~~ Encrypted tiddlers ~~-->`\n`<pre id=\"encryptedStoreArea\" type=\"text/plain\" style=\"display:none;\">`\n<$encrypt filter=<<saveTiddlerFilter>>/>\n`</pre>`\n</$reveal>"
},
"$:/core/templates/tid-tiddler": {
"title": "$:/core/templates/tid-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers in TiddlyWeb *.tid format\n\n--><$fields exclude='text bag' template='$name$: $value$\n'></$fields>`\n`<$view field=\"text\" format=\"text\" />"
},
"$:/core/templates/tiddler-metadata": {
"title": "$:/core/templates/tiddler-metadata",
"text": "<!--\n\nThis template is used for saving tiddler metadata *.meta files\n\n--><$fields exclude='text bag' template='$name$: $value$\n'></$fields>"
},
"$:/core/templates/tiddlywiki5.html": {
"title": "$:/core/templates/tiddlywiki5.html",
"text": "<$set name=\"saveTiddlerAndShadowsFilter\" filter=\"[subfilter<saveTiddlerFilter>] [subfilter<saveTiddlerFilter>plugintiddlers[]]\">\n`<!doctype html>\n`{{$:/core/templates/MOTW.html}}`<html lang=\"`<$text text={{{ [{$:/language}get[name]] }}}/>`\">\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<!--~~ Raw markup for the top of the head section ~~-->\n`{{{ [<saveTiddlerAndShadowsFilter>tag[$:/tags/RawMarkupWikified/TopHead]] ||$:/core/templates/raw-static-tiddler}}}`\n<meta http-equiv=\"X-UA-Compatible\" content=\"IE=Edge\"/>\n<meta name=\"application-name\" content=\"TiddlyWiki\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"`{{$:/core/templates/version}}`\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\" />\n<meta name=\"copyright\" content=\"`{{$:/core/copyright.txt}}`\" />\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<title>`{{$:/core/wiki/title}}`</title>\n<!--~~ This is a Tiddlywiki file. The points of interest in the file are marked with this pattern ~~-->\n\n<!--~~ Raw markup ~~-->\n`{{{ [enlist<saveTiddlerAndShadowsFilter>tag[$:/core/wiki/rawmarkup]] ||$:/core/templates/plain-text-tiddler}}}\n{{{ [enlist<saveTiddlerAndShadowsFilter>tag[$:/tags/RawMarkup]] ||$:/core/templates/plain-text-tiddler}}}\n{{{ [enlist<saveTiddlerAndShadowsFilter>tag[$:/tags/RawMarkupWikified]] ||$:/core/templates/raw-static-tiddler}}}`\n</head>\n<body class=\"tc-body\">\n<!--~~ Raw markup for the top of the body section ~~-->\n`{{{ [enlist<saveTiddlerAndShadowsFilter>tag[$:/tags/RawMarkupWikified/TopBody]] ||$:/core/templates/raw-static-tiddler}}}`\n<!--~~ Static styles ~~-->\n<div id=\"styleArea\">\n`{{$:/boot/boot.css||$:/core/templates/css-tiddler}}`\n</div>\n<!--~~ Static content for Google and browsers without JavaScript ~~-->\n<noscript>\n<div id=\"splashArea\">\n`{{$:/core/templates/static.area}}`\n</div>\n</noscript>\n<!--~~ Ordinary tiddlers ~~-->\n`{{$:/core/templates/store.area.template.html}}`\n<!--~~ Library modules ~~-->\n<div id=\"libraryModules\" style=\"display:none;\">\n`{{{ [is[system]type[application/javascript]library[yes]] ||$:/core/templates/javascript-tiddler}}}`\n</div>\n<!--~~ Boot kernel prologue ~~-->\n<div id=\"bootKernelPrefix\" style=\"display:none;\">\n`{{ $:/boot/bootprefix.js ||$:/core/templates/javascript-tiddler}}`\n</div>\n<!--~~ Boot kernel ~~-->\n<div id=\"bootKernel\" style=\"display:none;\">\n`{{ $:/boot/boot.js ||$:/core/templates/javascript-tiddler}}`\n</div>\n<!--~~ Raw markup for the bottom of the body section ~~-->\n`{{{ [enlist<saveTiddlerAndShadowsFilter>tag[$:/tags/RawMarkupWikified/BottomBody]] ||$:/core/templates/raw-static-tiddler}}}`\n</body>\n</html>`\n"
},
"$:/core/templates/version": {
"title": "$:/core/templates/version",
"text": "<<version>>"
},
"$:/core/templates/wikified-tiddler": {
"title": "$:/core/templates/wikified-tiddler",
"text": "<$transclude />"
},
"$:/core/ui/AboveStory/tw2-plugin-check": {
"title": "$:/core/ui/AboveStory/tw2-plugin-check",
"tags": "$:/tags/AboveStory",
"text": "\\define lingo-base() $:/language/AboveStory/ClassicPlugin/\n<$list filter=\"[all[system+tiddlers]tag[systemConfig]limit[1]]\">\n\n<div class=\"tc-message-box\">\n\n<<lingo Warning>>\n\n<ul>\n\n<$list filter=\"[all[system+tiddlers]tag[systemConfig]]\">\n\n<li>\n\n<$link><$view field=\"title\"/></$link>\n\n</li>\n\n</$list>\n\n</ul>\n\n</div>\n\n</$list>\n"
},
"$:/core/ui/Actions/new-image": {
"title": "$:/core/ui/Actions/new-image",
"tags": "$:/tags/Actions",
"description": "create a new image tiddler",
"text": "\\define get-type()\nimage/$(imageType)$\n\\end\n<$vars imageType={{$:/config/NewImageType}}>\n<$action-sendmessage $message=\"tm-new-tiddler\" type=<<get-type>> tags={{$:/config/NewTiddler/Tags!!tags}}/>\n</$vars>\n"
},
"$:/core/ui/Actions/new-journal": {
"title": "$:/core/ui/Actions/new-journal",
"tags": "$:/tags/Actions",
"description": "create a new journal tiddler",
"text": "<$vars journalTitleTemplate={{$:/config/NewJournal/Title}} journalTags={{$:/config/NewJournal/Tags!!tags}} journalText={{$:/config/NewJournal/Text}}>\n<$wikify name=\"journalTitle\" text=\"\"\"<$macrocall $name=\"now\" format=<<journalTitleTemplate>>/>\"\"\">\n<$reveal type=\"nomatch\" state=<<journalTitle>> text=\"\">\n<$action-sendmessage $message=\"tm-new-tiddler\" title=<<journalTitle>> tags=<<journalTags>> text={{{ [<journalTitle>get[]] }}}/>\n</$reveal>\n<$reveal type=\"match\" state=<<journalTitle>> text=\"\">\n<$action-sendmessage $message=\"tm-new-tiddler\" title=<<journalTitle>> tags=<<journalTags>> text=<<journalText>>/>\n</$reveal>\n</$wikify>\n</$vars>\n"
},
"$:/core/ui/Actions/new-tiddler": {
"title": "$:/core/ui/Actions/new-tiddler",
"tags": "$:/tags/Actions",
"description": "create a new empty tiddler",
"text": "<$action-sendmessage $message=\"tm-new-tiddler\" tags={{$:/config/NewTiddler/Tags!!tags}}/>\n"
},
"$:/core/ui/AdvancedSearch/Filter": {
"title": "$:/core/ui/AdvancedSearch/Filter",
"tags": "$:/tags/AdvancedSearch",
"caption": "{{$:/language/Search/Filter/Caption}}",
"text": "\\define lingo-base() $:/language/Search/\n<<lingo Filter/Hint>>\n\n<div class=\"tc-search tc-advanced-search\">\n<$edit-text tiddler=\"$:/temp/advancedsearch\" type=\"search\" tag=\"input\" focus={{$:/config/Search/AutoFocus}}/>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/AdvancedSearch/FilterButton]!has[draft.of]]\"><$transclude/></$list>\n</div>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$set name=\"resultCount\" value=\"\"\"<$count filter={{$:/temp/advancedsearch}}/>\"\"\">\n<div class=\"tc-search-results\">\n<<lingo Filter/Matches>>\n<$list filter={{$:/temp/advancedsearch}} template=\"$:/core/ui/ListItemTemplate\"/>\n</div>\n</$set>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Filter/FilterButtons/clear": {
"title": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/clear",
"tags": "$:/tags/AdvancedSearch/FilterButton",
"text": "<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Filter/FilterButtons/delete": {
"title": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/delete",
"tags": "$:/tags/AdvancedSearch/FilterButton",
"text": "<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button popup=<<qualify \"$:/state/filterDeleteDropdown\">> class=\"tc-btn-invisible\">\n{{$:/core/images/delete-button}}\n</$button>\n</$reveal>\n\n<$reveal state=<<qualify \"$:/state/filterDeleteDropdown\">> type=\"popup\" position=\"belowleft\" animate=\"yes\">\n<div class=\"tc-block-dropdown-wrapper\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown\">\n<div class=\"tc-dropdown-item-plain\">\n<$set name=\"resultCount\" value=\"\"\"<$count filter={{$:/temp/advancedsearch}}/>\"\"\">\nAre you sure you wish to delete <<resultCount>> tiddler(s)?\n</$set>\n</div>\n<div class=\"tc-dropdown-item-plain\">\n<$button class=\"tc-btn\">\n<$action-deletetiddler $filter={{$:/temp/advancedsearch}}/>\nDelete these tiddlers\n</$button>\n</div>\n</div>\n</div>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Filter/FilterButtons/dropdown": {
"title": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/dropdown",
"tags": "$:/tags/AdvancedSearch/FilterButton",
"text": "<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/filterDropdown\">> class=\"tc-btn-invisible\">\n{{$:/core/images/down-arrow}}\n</$button>\n</span>\n\n<$reveal state=<<qualify \"$:/state/filterDropdown\">> type=\"popup\" position=\"belowleft\" animate=\"yes\">\n<$set name=\"tv-show-missing-links\" value=\"yes\">\n<$linkcatcher to=\"$:/temp/advancedsearch\">\n<div class=\"tc-block-dropdown-wrapper\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Filter]]\"><$link to={{!!filter}}><$transclude field=\"description\"/></$link>\n</$list>\n</div>\n</div>\n</$linkcatcher>\n</$set>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Filter/FilterButtons/export": {
"title": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/export",
"tags": "$:/tags/AdvancedSearch/FilterButton",
"text": "<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$macrocall $name=\"exportButton\" exportFilter={{$:/temp/advancedsearch}} lingoBase=\"$:/language/Buttons/ExportTiddlers/\"/>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Shadows": {
"title": "$:/core/ui/AdvancedSearch/Shadows",
"tags": "$:/tags/AdvancedSearch",
"caption": "{{$:/language/Search/Shadows/Caption}}",
"text": "\\define lingo-base() $:/language/Search/\n<$linkcatcher to=\"$:/temp/advancedsearch\">\n\n<<lingo Shadows/Hint>>\n\n<div class=\"tc-search\">\n<$edit-text tiddler=\"$:/temp/advancedsearch\" type=\"search\" tag=\"input\" focus={{$:/config/Search/AutoFocus}}/>\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n</div>\n\n</$linkcatcher>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n\n<$list filter=\"[{$:/temp/advancedsearch}minlength{$:/config/Search/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n\n<$set name=\"resultCount\" value=\"\"\"<$count filter=\"[all[shadows]search{$:/temp/advancedsearch}] -[[$:/temp/advancedsearch]]\"/>\"\"\">\n\n<div class=\"tc-search-results\">\n\n<<lingo Shadows/Matches>>\n\n<$list filter=\"[all[shadows]search{$:/temp/advancedsearch}sort[title]limit[250]] -[[$:/temp/advancedsearch]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n\n</div>\n\n</$set>\n\n</$list>\n\n</$reveal>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"match\" text=\"\">\n\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Standard": {
"title": "$:/core/ui/AdvancedSearch/Standard",
"tags": "$:/tags/AdvancedSearch",
"caption": "{{$:/language/Search/Standard/Caption}}",
"text": "\\define lingo-base() $:/language/Search/\n<$linkcatcher to=\"$:/temp/advancedsearch\">\n\n<<lingo Standard/Hint>>\n\n<div class=\"tc-search\">\n<$edit-text tiddler=\"$:/temp/advancedsearch\" type=\"search\" tag=\"input\" focus={{$:/config/Search/AutoFocus}}/>\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n</div>\n\n</$linkcatcher>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$list filter=\"[{$:/temp/advancedsearch}minlength{$:/config/Search/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n<$set name=\"searchTiddler\" value=\"$:/temp/advancedsearch\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]butfirst[]limit[1]]\" emptyMessage=\"\"\"\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]]\">\n<$transclude/>\n</$list>\n\"\"\">\n<$macrocall $name=\"tabs\" tabsList=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]]\" default={{$:/config/SearchResults/Default}}/>\n</$list>\n</$set>\n</$list>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/System": {
"title": "$:/core/ui/AdvancedSearch/System",
"tags": "$:/tags/AdvancedSearch",
"caption": "{{$:/language/Search/System/Caption}}",
"text": "\\define lingo-base() $:/language/Search/\n<$linkcatcher to=\"$:/temp/advancedsearch\">\n\n<<lingo System/Hint>>\n\n<div class=\"tc-search\">\n<$edit-text tiddler=\"$:/temp/advancedsearch\" type=\"search\" tag=\"input\" focus={{$:/config/Search/AutoFocus}}/>\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n</div>\n\n</$linkcatcher>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n\n<$list filter=\"[{$:/temp/advancedsearch}minlength{$:/config/Search/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n\n<$set name=\"resultCount\" value=\"\"\"<$count filter=\"[is[system]search{$:/temp/advancedsearch}] -[[$:/temp/advancedsearch]]\"/>\"\"\">\n\n<div class=\"tc-search-results\">\n\n<<lingo System/Matches>>\n\n<$list filter=\"[is[system]search{$:/temp/advancedsearch}sort[title]limit[250]] -[[$:/temp/advancedsearch]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n\n</div>\n\n</$set>\n\n</$list>\n\n</$reveal>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"match\" text=\"\">\n\n</$reveal>\n"
},
"$:/AdvancedSearch": {
"title": "$:/AdvancedSearch",
"icon": "$:/core/images/advanced-search-button",
"color": "#bbb",
"text": "<div class=\"tc-advanced-search\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/AdvancedSearch]!has[draft.of]]\" \"$:/core/ui/AdvancedSearch/System\">>\n</div>\n"
},
"$:/core/ui/AlertTemplate": {
"title": "$:/core/ui/AlertTemplate",
"text": "<div class=\"tc-alert\">\n<div class=\"tc-alert-toolbar\">\n<$button class=\"tc-btn-invisible\"><$action-deletetiddler $tiddler=<<currentTiddler>>/>{{$:/core/images/cancel-button}}</$button>\n</div>\n<div class=\"tc-alert-subtitle\">\n<$wikify name=\"format\" text=<<lingo Tiddler/DateFormat>>>\n<$view field=\"component\"/> - <$view field=\"modified\" format=\"date\" template=<<format>>/> <$reveal type=\"nomatch\" state=\"!!count\" text=\"\"><span class=\"tc-alert-highlight\">({{$:/language/Count}}: <$view field=\"count\"/>)</span></$reveal>\n</$wikify>\n</div>\n<div class=\"tc-alert-body\">\n\n<$transclude/>\n\n</div>\n</div>\n"
},
"$:/core/ui/BinaryWarning": {
"title": "$:/core/ui/BinaryWarning",
"text": "\\define lingo-base() $:/language/BinaryWarning/\n<<lingo Prompt>>\n"
},
"$:/core/ui/Components/plugin-info": {
"title": "$:/core/ui/Components/plugin-info",
"text": "\\define lingo-base() $:/language/ControlPanel/Plugins/\n\n\\define popup-state-macro()\n$(qualified-state)$-$(currentTiddler)$\n\\end\n\n\\define tabs-state-macro()\n$(popup-state)$-$(pluginInfoType)$\n\\end\n\n\\define plugin-icon-title()\n$(currentTiddler)$/icon\n\\end\n\n\\define plugin-disable-title()\n$:/config/Plugins/Disabled/$(currentTiddler)$\n\\end\n\n\\define plugin-table-body(type,disabledMessage,default-popup-state)\n<div class=\"tc-plugin-info-chunk tc-plugin-info-toggle\">\n<$reveal type=\"nomatch\" state=<<popup-state>> text=\"yes\" default=\"\"\"$default-popup-state$\"\"\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<popup-state>> setTo=\"yes\">\n{{$:/core/images/chevron-right}}\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<popup-state>> text=\"yes\" default=\"\"\"$default-popup-state$\"\"\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<popup-state>> setTo=\"no\">\n{{$:/core/images/chevron-down}}\n</$button>\n</$reveal>\n</div>\n<div class=\"tc-plugin-info-chunk tc-plugin-info-icon\">\n<$transclude tiddler=<<currentTiddler>> subtiddler=<<plugin-icon-title>>>\n<$transclude tiddler=\"$:/core/images/plugin-generic-$type$\"/>\n</$transclude>\n</div>\n<div class=\"tc-plugin-info-chunk tc-plugin-info-description\">\n<h1>\n''<$text text={{{ [<currentTiddler>get[name]] ~[<currentTiddler>split[/]last[1]] }}}/>'': <$view field=\"description\"><$view field=\"title\"/></$view> $disabledMessage$\n</h1>\n<h2>\n<$view field=\"title\"/>\n</h2>\n<h2>\n<div><em><$view field=\"version\"/></em></div>\n</h2>\n</div>\n\\end\n\n\\define plugin-info(type,default-popup-state)\n<$set name=\"popup-state\" value=<<popup-state-macro>>>\n<$reveal type=\"nomatch\" state=<<plugin-disable-title>> text=\"yes\">\n<$link to={{!!title}} class=\"tc-plugin-info\">\n<<plugin-table-body type:\"$type$\" default-popup-state:\"\"\"$default-popup-state$\"\"\">>\n</$link>\n</$reveal>\n<$reveal type=\"match\" state=<<plugin-disable-title>> text=\"yes\">\n<$link to={{!!title}} class=\"tc-plugin-info tc-plugin-info-disabled\">\n<<plugin-table-body type:\"$type$\" default-popup-state:\"\"\"$default-popup-state$\"\"\" disabledMessage:\"<$macrocall $name='lingo' title='Disabled/Status'/>\">>\n</$link>\n</$reveal>\n<$reveal type=\"match\" text=\"yes\" state=<<popup-state>> default=\"\"\"$default-popup-state$\"\"\">\n<div class=\"tc-plugin-info-dropdown\">\n<div class=\"tc-plugin-info-dropdown-body\">\n<$list filter=\"[all[current]] -[[$:/core]]\">\n<div style=\"float:right;\">\n<$reveal type=\"nomatch\" state=<<plugin-disable-title>> text=\"yes\">\n<$button set=<<plugin-disable-title>> setTo=\"yes\" tooltip={{$:/language/ControlPanel/Plugins/Disable/Hint}} aria-label={{$:/language/ControlPanel/Plugins/Disable/Caption}}>\n<<lingo Disable/Caption>>\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<plugin-disable-title>> text=\"yes\">\n<$button set=<<plugin-disable-title>> setTo=\"no\" tooltip={{$:/language/ControlPanel/Plugins/Enable/Hint}} aria-label={{$:/language/ControlPanel/Plugins/Enable/Caption}}>\n<<lingo Enable/Caption>>\n</$button>\n</$reveal>\n</div>\n</$list>\n<$set name=\"tabsList\" filter=\"[<currentTiddler>list[]] contents\">\n<$macrocall $name=\"tabs\" state=<<tabs-state-macro>> tabsList=<<tabsList>> default={{{ [enlist<tabsList>] }}} template=\"$:/core/ui/PluginInfo\"/>\n</$set>\n</div>\n</div>\n</$reveal>\n</$set>\n\\end\n\n<$macrocall $name=\"plugin-info\" type=<<plugin-type>> default-popup-state=<<default-popup-state>>/>\n"
},
"$:/core/ui/Components/tag-link": {
"title": "$:/core/ui/Components/tag-link",
"text": "<$link>\n<$set name=\"backgroundColor\" value={{!!color}}>\n<span style=<<tag-styles>> class=\"tc-tag-label\">\n<$view field=\"title\" format=\"text\"/>\n</span>\n</$set>\n</$link>"
},
"$:/core/ui/ControlPanel/Advanced": {
"title": "$:/core/ui/ControlPanel/Advanced",
"tags": "$:/tags/ControlPanel/Info",
"caption": "{{$:/language/ControlPanel/Advanced/Caption}}",
"text": "{{$:/language/ControlPanel/Advanced/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Advanced]!has[draft.of]]\" \"$:/core/ui/ControlPanel/TiddlerFields\">>\n</div>\n"
},
"$:/core/ui/ControlPanel/Appearance": {
"title": "$:/core/ui/ControlPanel/Appearance",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Appearance/Caption}}",
"text": "{{$:/language/ControlPanel/Appearance/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Appearance]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Theme\">>\n</div>\n"
},
"$:/core/ui/ControlPanel/Basics": {
"title": "$:/core/ui/ControlPanel/Basics",
"tags": "$:/tags/ControlPanel/Info",
"caption": "{{$:/language/ControlPanel/Basics/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Basics/\n\n\\define show-filter-count(filter)\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $value=\"\"\"$filter$\"\"\"/>\n<$action-setfield $tiddler=\"$:/state/tab--1498284803\" $value=\"$:/core/ui/AdvancedSearch/Filter\"/>\n<$action-navigate $to=\"$:/AdvancedSearch\"/>\n''<$count filter=\"\"\"$filter$\"\"\"/>''\n{{$:/core/images/advanced-search-button}}\n</$button>\n\\end\n\n|<<lingo Version/Prompt>> |''<<version>>'' |\n|<$link to=\"$:/SiteTitle\"><<lingo Title/Prompt>></$link> |<$edit-text tiddler=\"$:/SiteTitle\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/SiteSubtitle\"><<lingo Subtitle/Prompt>></$link> |<$edit-text tiddler=\"$:/SiteSubtitle\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/status/UserName\"><<lingo Username/Prompt>></$link> |<$edit-text tiddler=\"$:/status/UserName\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/config/AnimationDuration\"><<lingo AnimDuration/Prompt>></$link> |<$edit-text tiddler=\"$:/config/AnimationDuration\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/DefaultTiddlers\"><<lingo DefaultTiddlers/Prompt>></$link> |<<lingo DefaultTiddlers/TopHint>><br> <$edit tag=\"textarea\" tiddler=\"$:/DefaultTiddlers\" class=\"tc-edit-texteditor\"/><br>//<<lingo DefaultTiddlers/BottomHint>>// |\n|<$link to=\"$:/language/DefaultNewTiddlerTitle\"><<lingo NewTiddler/Title/Prompt>></$link> |<$edit-text tiddler=\"$:/language/DefaultNewTiddlerTitle\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/config/NewJournal/Title\"><<lingo NewJournal/Title/Prompt>></$link> |<$edit-text tiddler=\"$:/config/NewJournal/Title\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/config/NewJournal/Text\"><<lingo NewJournal/Text/Prompt>></$link> |<$edit tiddler=\"$:/config/NewJournal/Text\" tag=\"textarea\" class=\"tc-edit-texteditor\" default=\"\"/> |\n|<$link to=\"$:/config/NewTiddler/Tags\"><<lingo NewTiddler/Tags/Prompt>></$link> |<$list filter=\"[[$:/config/NewTiddler/Tags]]\" template=\"$:/core/ui/EditTemplate/tags\"/> |\n|<$link to=\"$:/config/NewJournal/Tags\"><<lingo NewJournal/Tags/Prompt>></$link> |<$list filter=\"[[$:/config/NewJournal/Tags]]\" template=\"$:/core/ui/EditTemplate/tags\"/> |\n|<$link to=\"$:/config/AutoFocus\"><<lingo AutoFocus/Prompt>></$link> |{{$:/snippets/minifocusswitcher}} |\n|<<lingo Language/Prompt>> |{{$:/snippets/minilanguageswitcher}} |\n|<<lingo Tiddlers/Prompt>> |<<show-filter-count \"[!is[system]sort[title]]\">> |\n|<<lingo Tags/Prompt>> |<<show-filter-count \"[tags[]sort[title]]\">> |\n|<<lingo SystemTiddlers/Prompt>> |<<show-filter-count \"[is[system]sort[title]]\">> |\n|<<lingo ShadowTiddlers/Prompt>> |<<show-filter-count \"[all[shadows]sort[title]]\">> |\n|<<lingo OverriddenShadowTiddlers/Prompt>> |<<show-filter-count \"[is[tiddler]is[shadow]sort[title]]\">> |\n"
},
"$:/core/ui/ControlPanel/EditorTypes": {
"title": "$:/core/ui/ControlPanel/EditorTypes",
"tags": "$:/tags/ControlPanel/Advanced",
"caption": "{{$:/language/ControlPanel/EditorTypes/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/EditorTypes/\n\n<<lingo Hint>>\n\n<table>\n<tbody>\n<tr>\n<th><<lingo Type/Caption>></th>\n<th><<lingo Editor/Caption>></th>\n</tr>\n<$list filter=\"[all[shadows+tiddlers]prefix[$:/config/EditorTypeMappings/]sort[title]]\">\n<tr>\n<td>\n<$link>\n<$list filter=\"[all[current]removeprefix[$:/config/EditorTypeMappings/]]\">\n<$text text={{!!title}}/>\n</$list>\n</$link>\n</td>\n<td>\n<$view field=\"text\"/>\n</td>\n</tr>\n</$list>\n</tbody>\n</table>\n"
},
"$:/core/ui/ControlPanel/Info": {
"title": "$:/core/ui/ControlPanel/Info",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Info/Caption}}",
"text": "{{$:/language/ControlPanel/Info/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Info]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Basics\">>\n</div>\n"
},
"$:/core/ui/ControlPanel/KeyboardShortcuts": {
"title": "$:/core/ui/ControlPanel/KeyboardShortcuts",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/KeyboardShortcuts/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/KeyboardShortcuts/\n\n\\define new-shortcut(title)\n<div class=\"tc-dropdown-item-plain\">\n<$edit-shortcut tiddler=\"$title$\" placeholder={{$:/language/ControlPanel/KeyboardShortcuts/Add/Prompt}} focus=\"true\" style=\"width:auto;\"/> <$button>\n<<lingo Add/Caption>>\n<$action-listops\n\t$tiddler=\"$(shortcutTitle)$\"\n\t$field=\"text\"\n\t$subfilter=\"[{$title$}]\"\n/>\n<$action-deletetiddler\n\t$tiddler=\"$title$\"\n/>\n</$button>\n</div>\n\\end\n\n\\define shortcut-list-item(caption)\n<td>\n</td>\n<td style=\"text-align:right;font-size:0.7em;\">\n<<lingo Platform/$caption$>>\n</td>\n<td>\n<div style=\"position:relative;\">\n<$button popup=<<qualify \"$:/state/dropdown/$(shortcutTitle)$\">> class=\"tc-btn-invisible\">\n{{$:/core/images/edit-button}}\n</$button>\n<$macrocall $name=\"displayshortcuts\" $output=\"text/html\" shortcuts={{$(shortcutTitle)$}} prefix=\"<kbd>\" separator=\"</kbd> <kbd>\" suffix=\"</kbd>\"/>\n\n<$reveal state=<<qualify \"$:/state/dropdown/$(shortcutTitle)$\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-block-dropdown-wrapper\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown tc-popup-keep\">\n<$list filter=\"[list[$(shortcutTitle)$!!text]sort[title]]\" variable=\"shortcut\" emptyMessage=\"\"\"\n<div class=\"tc-dropdown-item-plain\">\n//<<lingo NoShortcuts/Caption>>//\n</div>\n\"\"\">\n<div class=\"tc-dropdown-item-plain\">\n<$button class=\"tc-btn-invisible\" tooltip={{$:/language/ControlPanel/KeyboardShortcuts/Remove/Hint}}>\n<$action-listops\n\t$tiddler=\"$(shortcutTitle)$\"\n\t$field=\"text\"\n\t$subfilter=\"+[remove<shortcut>]\"\n/>\n<small>{{$:/core/images/close-button}}</small>\n</$button>\n<kbd>\n<$macrocall $name=\"displayshortcuts\" $output=\"text/html\" shortcuts=<<shortcut>>/>\n</kbd>\n</div>\n</$list>\n<hr/>\n<$macrocall $name=\"new-shortcut\" title=<<qualify \"$:/state/new-shortcut/$(shortcutTitle)$\">>/>\n</div>\n</div>\n</$reveal>\n</div>\n</td>\n\\end\n\n\\define shortcut-list(caption,prefix)\n<tr>\n<$list filter=\"[[$prefix$$(shortcutName)$]]\" variable=\"shortcutTitle\">\n<<shortcut-list-item \"$caption$\">>\n</$list>\n</tr>\n\\end\n\n\\define shortcut-editor()\n<<shortcut-list \"All\" \"$:/config/shortcuts/\">>\n<<shortcut-list \"Mac\" \"$:/config/shortcuts-mac/\">>\n<<shortcut-list \"NonMac\" \"$:/config/shortcuts-not-mac/\">>\n<<shortcut-list \"Linux\" \"$:/config/shortcuts-linux/\">>\n<<shortcut-list \"NonLinux\" \"$:/config/shortcuts-not-linux/\">>\n<<shortcut-list \"Windows\" \"$:/config/shortcuts-windows/\">>\n<<shortcut-list \"NonWindows\" \"$:/config/shortcuts-not-windows/\">>\n\\end\n\n\\define shortcut-preview()\n<$macrocall $name=\"displayshortcuts\" $output=\"text/html\" shortcuts={{$(shortcutPrefix)$$(shortcutName)$}} prefix=\"<kbd>\" separator=\"</kbd> <kbd>\" suffix=\"</kbd>\"/>\n\\end\n\n\\define shortcut-item-inner()\n<tr>\n<td>\n<$reveal type=\"nomatch\" state=<<dropdownStateTitle>> text=\"open\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield\n\t$tiddler=<<dropdownStateTitle>>\n\t$value=\"open\"\n/>\n{{$:/core/images/right-arrow}}\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<dropdownStateTitle>> text=\"open\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield\n\t$tiddler=<<dropdownStateTitle>>\n\t$value=\"close\"\n/>\n{{$:/core/images/down-arrow}}\n</$button>\n</$reveal>\n''<$text text=<<shortcutName>>/>''\n</td>\n<td>\n<$transclude tiddler=\"$:/config/ShortcutInfo/$(shortcutName)$\"/>\n</td>\n<td>\n<$list filter=\"$:/config/shortcuts/ $:/config/shortcuts-mac/ $:/config/shortcuts-not-mac/ $:/config/shortcuts-linux/ $:/config/shortcuts-not-linux/ $:/config/shortcuts-windows/ $:/config/shortcuts-not-windows/\" variable=\"shortcutPrefix\">\n<<shortcut-preview>>\n</$list>\n</td>\n</tr>\n<$set name=\"dropdownState\" value={{$(dropdownStateTitle)$}}>\n<$list filter=\"[<dropdownState>match[open]]\" variable=\"listItem\">\n<<shortcut-editor>>\n</$list>\n</$set>\n\\end\n\n\\define shortcut-item()\n<$set name=\"dropdownStateTitle\" value=<<qualify \"$:/state/dropdown/keyboardshortcut/$(shortcutName)$\">>>\n<<shortcut-item-inner>>\n</$set>\n\\end\n\n<table>\n<tbody>\n<$list filter=\"[all[shadows+tiddlers]removeprefix[$:/config/ShortcutInfo/]]\" variable=\"shortcutName\">\n<<shortcut-item>>\n</$list>\n</tbody>\n</table>\n"
},
"$:/core/ui/ControlPanel/LoadedModules": {
"title": "$:/core/ui/ControlPanel/LoadedModules",
"tags": "$:/tags/ControlPanel/Advanced",
"caption": "{{$:/language/ControlPanel/LoadedModules/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/\n<<lingo LoadedModules/Hint>>\n\n{{$:/snippets/modules}}\n"
},
"$:/core/ui/ControlPanel/Modals/AddPlugins": {
"title": "$:/core/ui/ControlPanel/Modals/AddPlugins",
"subtitle": "{{$:/core/images/download-button}} {{$:/language/ControlPanel/Plugins/Add/Caption}}",
"text": "\\define install-plugin-actions()\n<$action-sendmessage $message=\"tm-load-plugin-from-library\" url={{!!url}} title={{$(assetInfo)$!!original-title}}/>\n<$set name=\"url\" value={{!!url}}>\n<$set name=\"currentTiddler\" value=<<assetInfo>>>\n<$list filter=\"[enlist{!!dependents}] [{!!parent-plugin}] +[sort[title]]\" variable=\"dependency\">\n<$action-sendmessage $message=\"tm-load-plugin-from-library\" url=<<url>> title=<<dependency>>/>\n</$list>\n</$set>\n</$set>\n\\end\n\n\\define install-plugin-button()\n<div>\n<$set name=\"libraryVersion\" value={{{ [<assetInfo>get[version]] }}}>\n<$set name=\"installedVersion\" value={{{ [<assetInfo>get[original-title]get[version]] }}}>\n<$set name=\"reinstall-type\" value={{{ [<libraryVersion>compare:version:eq<installedVersion>then[tc-reinstall]] [<libraryVersion>compare:version:gt<installedVersion>then[tc-reinstall-upgrade]] [<libraryVersion>compare:version:lt<installedVersion>then[tc-reinstall-downgrade]] }}}>\n<$button actions=<<install-plugin-actions>> class={{{ [<assetInfo>get[original-title]has[version]then<reinstall-type>] tc-btn-invisible tc-install-plugin +[join[ ]] }}}>\n{{$:/core/images/download-button}}\n<$list filter=\"[<assetInfo>get[original-title]get[version]]\" variable=\"ignore\" emptyMessage=\"{{$:/language/ControlPanel/Plugins/Install/Caption}}\">\n<$list filter=\"[<libraryVersion>compare:version:gt<installedVersion>]\" variable=\"ignore\" emptyMessage=\"\"\"\n<$list filter=\"[<libraryVersion>compare:version:lt<installedVersion>]\" variable=\"ignore\" emptyMessage=\"{{$:/language/ControlPanel/Plugins/Reinstall/Caption}}\">\n{{$:/language/ControlPanel/Plugins/Downgrade/Caption}}\n</$list>\n\"\"\">\n{{$:/language/ControlPanel/Plugins/Update/Caption}}\n</$list>\n</$list>\n</$button>\n<div>\n</div>\n<$reveal stateTitle=<<assetInfo>> stateField=\"requires-reload\" type=\"match\" text=\"yes\">{{$:/language/ControlPanel/Plugins/PluginWillRequireReload}}</$reveal>\n</$set>\n</$set>\n</$set>\n</div>\n\\end\n\n\\define popup-state-macro()\n$:/state/add-plugin-info/$(connectionTiddler)$/$(assetInfo)$\n\\end\n\n\\define display-plugin-info(type)\n<$set name=\"popup-state\" value=<<popup-state-macro>>>\n<div class=\"tc-plugin-info\">\n<div class=\"tc-plugin-info-chunk tc-plugin-info-toggle\">\n<$reveal type=\"nomatch\" state=<<popup-state>> text=\"yes\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<popup-state>> setTo=\"yes\">\n{{$:/core/images/chevron-right}}\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<popup-state>> text=\"yes\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<popup-state>> setTo=\"no\">\n{{$:/core/images/chevron-down}}\n</$button>\n</$reveal>\n</div>\n<div class=\"tc-plugin-info-chunk tc-plugin-info-icon\">\n<$list filter=\"[<assetInfo>has[icon]]\" emptyMessage=\"\"\"<$transclude tiddler=\"$:/core/images/plugin-generic-$type$\"/>\"\"\">\n<img src={{$(assetInfo)$!!icon}}/>\n</$list>\n</div>\n<div class=\"tc-plugin-info-chunk tc-plugin-info-description\">\n<h1><strong><$text text={{{ [<assetInfo>get[name]] ~[<assetInfo>get[original-title]split[/]last[1]] }}}/></strong>: <$view tiddler=<<assetInfo>> field=\"description\"/></h1>\n<h2><$view tiddler=<<assetInfo>> field=\"original-title\"/></h2>\n<div><em><$view tiddler=<<assetInfo>> field=\"version\"/></em></div>\n<$list filter=\"[<assetInfo>get[original-title]get[version]]\" variable=\"installedVersion\"><div><em>{{$:/language/ControlPanel/Plugins/AlreadyInstalled/Hint}}</em></div></$list>\n</div>\n<div class=\"tc-plugin-info-chunk tc-plugin-info-buttons\">\n<<install-plugin-button>>\n</div>\n</div>\n<$set name=\"original-title\" value={{{ [<assetInfo>get[original-title]] }}}>\n<$reveal type=\"match\" text=\"yes\" state=<<popup-state>>>\n<div class=\"tc-plugin-info-dropdown\">\n<$list filter=\"[enlist{!!dependents}] [<currentTiddler>get[parent-plugin]] +[limit[1]] ~[<assetInfo>get[original-title]!is[tiddler]]\" variable=\"ignore\">\n<div class=\"tc-plugin-info-dropdown-message\">\n<$list filter=\"[<assetInfo>get[original-title]!is[tiddler]]\">\n{{$:/language/ControlPanel/Plugins/NotInstalled/Hint}}\n</$list>\n<$set name=\"currentTiddler\" value=<<assetInfo>>>\n<$list filter=\"[enlist{!!dependents}] [<currentTiddler>get[parent-plugin]] +[limit[1]]\" variable=\"ignore\">\n<div>\n{{$:/language/ControlPanel/Plugins/AlsoRequires}}\n<$list filter=\"[enlist{!!dependents}] [{!!parent-plugin}] +[sort[title]]\" variable=\"dependency\">\n<$text text=<<dependency>>/>\n</$list>\n</div>\n</$list>\n</$set>\n</div>\n</$list>\n<div class=\"tc-plugin-info-dropdown-body\">\n<$transclude tiddler=<<assetInfo>> field=\"readme\" mode=\"block\"/>\n</div>\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[$type$]has[parent-plugin]parent-plugin<original-title>limit[1]]\" variable=\"ignore\">\n<div class=\"tc-plugin-info-sub-plugins\">\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[$type$]has[parent-plugin]parent-plugin<original-title>sort[title]]\" variable=\"assetInfo\">\n<<display-plugin-info \"$type$\">>\n</$list>\n</div>\n</$list>\n</div>\n</$reveal>\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[$type$]has[parent-plugin]parent-plugin<original-title>limit[1]]\" variable=\"ignore\">\n<$reveal type=\"nomatch\" text=\"yes\" state=<<popup-state>> tag=\"div\" class=\"tc-plugin-info-sub-plugin-indicator\">\n<$wikify name=\"count\" text=\"\"\"<$count filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[$type$]has[parent-plugin]parent-plugin<original-title>]\"/>\"\"\">\n<$button class=\"tc-btn-invisible\" set=<<popup-state>> setTo=\"yes\">\n{{$:/language/ControlPanel/Plugins/SubPluginPrompt}}\n</$button>\n</$wikify>\n</$reveal>\n</$list>\n</$set>\n</$set>\n\\end\n\n\\define load-plugin-library-button()\n<$button class=\"tc-btn-big-green\">\n<$action-sendmessage $message=\"tm-load-plugin-library\" url={{!!url}} infoTitlePrefix=\"$:/temp/RemoteAssetInfo/\"/>\n{{$:/core/images/chevron-right}} {{$:/language/ControlPanel/Plugins/OpenPluginLibrary}}\n</$button>\n\\end\n\n\\define display-server-assets(type)\n{{$:/language/Search/Search}}: <$edit-text tiddler=\"\"\"$:/temp/RemoteAssetSearch/$(currentTiddler)$\"\"\" default=\"\" type=\"search\" tag=\"input\"/>\n<$reveal state=\"\"\"$:/temp/RemoteAssetSearch/$(currentTiddler)$\"\"\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"\"\"$:/temp/RemoteAssetSearch/$(currentTiddler)$\"\"\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n<div class=\"tc-plugin-library-listing\">\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[$type$]search:author,description,original-title,readme,title{$:/temp/RemoteAssetSearch/$(currentTiddler)$}sort[title]]\" variable=\"assetInfo\">\n<$list filter=\"[[$:/temp/RemoteAssetSearch/$(currentTiddler)$]has[text]] ~[<assetInfo>!has[parent-plugin]]\" variable=\"ignore\"><!-- Hide sub-plugins if we're not searching -->\n<<display-plugin-info \"$type$\">>\n</$list>\n</$list>\n</div>\n\\end\n\n\\define display-server-connection()\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/ServerConnection]suffix{!!url}]\" variable=\"connectionTiddler\" emptyMessage=<<load-plugin-library-button>>>\n\n<$set name=\"transclusion\" value=<<connectionTiddler>>>\n\n<<tabs \"[[$:/core/ui/ControlPanel/Plugins/Add/Updates]] [[$:/core/ui/ControlPanel/Plugins/Add/Plugins]] [[$:/core/ui/ControlPanel/Plugins/Add/Themes]] [[$:/core/ui/ControlPanel/Plugins/Add/Languages]]\" \"$:/core/ui/ControlPanel/Plugins/Add/Plugins\">>\n\n</$set>\n\n</$list>\n\\end\n\n\\define close-library-button()\n<$reveal type='nomatch' state='$:/temp/ServerConnection/$(PluginLibraryURL)$' text=''>\n<$button class='tc-btn-big-green'>\n<$action-sendmessage $message=\"tm-unload-plugin-library\" url={{!!url}}/>\n{{$:/core/images/chevron-left}} {{$:/language/ControlPanel/Plugins/ClosePluginLibrary}}\n<$action-deletetiddler $filter=\"[prefix[$:/temp/ServerConnection/$(PluginLibraryURL)$]][prefix[$:/temp/RemoteAssetInfo/$(PluginLibraryURL)$]]\"/>\n</$button>\n</$reveal>\n\\end\n\n\\define plugin-library-listing()\n<div class=\"tc-tab-set\">\n<$set name=\"defaultTab\" value={{{ [all[tiddlers+shadows]tag[$:/tags/PluginLibrary]] }}}>\n<div class=\"tc-tab-buttons\">\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/PluginLibrary]]\">\n<$button set=<<qualify \"$:/state/addplugins/tab\">> setTo=<<currentTiddler>> default=<<defaultTab>> selectedClass=\"tc-tab-selected\">\n<$set name=\"tv-wikilinks\" value=\"no\">\n<$transclude field=\"caption\"/>\n</$set>\n</$button>\n</$list>\n</div>\n<div class=\"tc-tab-divider\"/>\n<div class=\"tc-tab-content\">\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/PluginLibrary]]\">\n<$reveal type=\"match\" state=<<qualify \"$:/state/addplugins/tab\">> text=<<currentTiddler>> default=<<defaultTab>>>\n<h2><$link><$transclude field=\"caption\"><$view field=\"title\"/></$transclude></$link></h2>\n//<$view field=\"url\"/>//\n<$transclude mode=\"block\"/>\n<$set name=PluginLibraryURL value={{!!url}}>\n<<close-library-button>>\n</$set>\n<<display-server-connection>>\n</$reveal>\n</$list>\n</div>\n</$set>\n</div>\n\\end\n\n\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n\n<div>\n<<plugin-library-listing>>\n</div>\n"
},
"$:/core/ui/ControlPanel/Palette": {
"title": "$:/core/ui/ControlPanel/Palette",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ControlPanel/Palette/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Palette/\n\n{{$:/snippets/paletteswitcher}}\n\n<$reveal type=\"nomatch\" state=\"$:/state/ShowPaletteEditor\" text=\"yes\">\n\n<$button set=\"$:/state/ShowPaletteEditor\" setTo=\"yes\"><<lingo ShowEditor/Caption>></$button>\n\n</$reveal>\n\n<$reveal type=\"match\" state=\"$:/state/ShowPaletteEditor\" text=\"yes\">\n\n<$button set=\"$:/state/ShowPaletteEditor\" setTo=\"no\"><<lingo HideEditor/Caption>></$button>\n{{$:/PaletteManager}}\n\n</$reveal>\n\n"
},
"$:/core/ui/ControlPanel/Parsing": {
"title": "$:/core/ui/ControlPanel/Parsing",
"tags": "$:/tags/ControlPanel/Advanced",
"caption": "{{$:/language/ControlPanel/Parsing/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Parsing/\n\n\\define toggle(Type)\n<$checkbox\ntiddler=\"\"\"$:/config/WikiParserRules/$Type$/$(rule)$\"\"\"\nfield=\"text\"\nchecked=\"enable\"\nunchecked=\"disable\"\ndefault=\"enable\">\n<<rule>>\n</$checkbox>\n\\end\n\n\\define rules(type,Type)\n<$list filter=\"[wikiparserrules[$type$]]\" variable=\"rule\">\n<dd><<toggle $Type$>></dd>\n</$list>\n\\end\n\n<<lingo Hint>>\n\n<dl>\n<dt><<lingo Pragma/Caption>></dt>\n<<rules pragma Pragma>>\n<dt><<lingo Inline/Caption>></dt>\n<<rules inline Inline>>\n<dt><<lingo Block/Caption>></dt>\n<<rules block Block>>\n</dl>"
},
"$:/core/ui/ControlPanel/Plugins/Add/Languages": {
"title": "$:/core/ui/ControlPanel/Plugins/Add/Languages",
"caption": "{{$:/language/ControlPanel/Plugins/Languages/Caption}} (<$count filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[language]]\"/>)",
"text": "<<display-server-assets language>>\n"
},
"$:/core/ui/ControlPanel/Plugins/Add/Plugins": {
"title": "$:/core/ui/ControlPanel/Plugins/Add/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Plugins/Caption}} (<$count filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[plugin]]\"/>)",
"text": "<<display-server-assets plugin>>\n"
},
"$:/core/ui/ControlPanel/Plugins/Add/Themes": {
"title": "$:/core/ui/ControlPanel/Plugins/Add/Themes",
"caption": "{{$:/language/ControlPanel/Plugins/Themes/Caption}} (<$count filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[theme]]\"/>)",
"text": "<<display-server-assets theme>>\n"
},
"$:/core/ui/ControlPanel/Plugins/Add/Updates": {
"title": "$:/core/ui/ControlPanel/Plugins/Add/Updates",
"caption": "<$importvariables filter=\"$:/core/ui/ControlPanel/Plugins/Add/Updates\">{{$:/language/ControlPanel/Plugins/Updates/Caption}} (<<update-count>>)</$importvariables>",
"text": "\\define each-updateable-plugin(body)\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}sort[title]]\" variable=\"assetInfo\">\n<$set name=\"libraryVersion\" value={{{ [<assetInfo>get[version]] }}}>\n<$list filter=\"[<assetInfo>get[original-title]has[version]!version<libraryVersion>]\" variable=\"ignore\">\n<$set name=\"installedVersion\" value={{{ [<assetInfo>get[original-title]get[version]] }}}>\n<$list filter=\"[<installedversion>!match<libraryVersion>]\" variable=\"ignore\">\n$body$\n</$list>\n</$set>\n</$list>\n</$set>\n</$list>\n\\end\n\n\\define update-all-actions()\n<$macrocall $name=\"each-updateable-plugin\" body=\"\"\"\n<<install-plugin-actions>>\n\"\"\"/>\n\\end\n\n\\define update-count()\n<$wikify name=\"count-filter\" text=<<each-updateable-plugin \"[[<$text text=<<assetInfo>>/>]]\">>><$count filter=<<count-filter>>/></$wikify>\n\\end\n\n<$button actions=<<update-all-actions>> class=\"tc-btn-invisible tc-install-plugin tc-reinstall-upgrade\">\n{{$:/core/images/download-button}} {{||$:/language/ControlPanel/Plugins/Updates/UpdateAll/Caption}}\n</$button>\n\n<div class=\"tc-plugin-library-listing\">\n<$macrocall $name=\"each-updateable-plugin\" body=\"\"\"\n<$macrocall $name=\"display-plugin-info\" type={{{ [<assetInfo>get[original-plugin-type]] }}}/>\n\"\"\"/>\n</div>\n"
},
"$:/core/ui/ControlPanel/Plugins/AddPlugins": {
"title": "$:/core/ui/ControlPanel/Plugins/AddPlugins",
"text": "\\define lingo-base() $:/language/ControlPanel/Plugins/\n\n<$button message=\"tm-modal\" param=\"$:/core/ui/ControlPanel/Modals/AddPlugins\" tooltip={{$:/language/ControlPanel/Plugins/Add/Hint}} class=\"tc-btn-big-green tc-primary-btn\">\n{{$:/core/images/download-button}} <<lingo Add/Caption>>\n</$button>\n"
},
"$:/core/ui/ControlPanel/Plugins/Installed/Languages": {
"title": "$:/core/ui/ControlPanel/Plugins/Installed/Languages",
"caption": "{{$:/language/ControlPanel/Plugins/Languages/Caption}} (<$count filter=\"[!has[draft.of]plugin-type[language]]\"/>)",
"text": "<<plugin-table language>>\n"
},
"$:/core/ui/ControlPanel/Plugins/Installed/Plugins": {
"title": "$:/core/ui/ControlPanel/Plugins/Installed/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Plugins/Caption}} (<$count filter=\"[!has[draft.of]plugin-type[plugin]]\"/>)",
"text": "<<plugin-table plugin>>\n"
},
"$:/core/ui/ControlPanel/Plugins/Installed/Themes": {
"title": "$:/core/ui/ControlPanel/Plugins/Installed/Themes",
"caption": "{{$:/language/ControlPanel/Plugins/Themes/Caption}} (<$count filter=\"[!has[draft.of]plugin-type[theme]]\"/>)",
"text": "<<plugin-table theme>>\n"
},
"$:/core/ui/ControlPanel/Plugins": {
"title": "$:/core/ui/ControlPanel/Plugins",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Plugins/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Plugins/\n\n\\define plugin-table(type)\n<$set name=\"plugin-type\" value=\"\"\"$type$\"\"\">\n<$set name=\"qualified-state\" value=<<qualify \"$:/state/plugin-info\">>>\n<$list filter=\"[!has[draft.of]plugin-type[$type$]sort[title]]\" emptyMessage=<<lingo \"Empty/Hint\">> template=\"$:/core/ui/Components/plugin-info\"/>\n</$set>\n</$set>\n\\end\n\n{{$:/core/ui/ControlPanel/Plugins/AddPlugins}}\n\n<<lingo Installed/Hint>>\n\n<<tabs \"[[$:/core/ui/ControlPanel/Plugins/Installed/Plugins]] [[$:/core/ui/ControlPanel/Plugins/Installed/Themes]] [[$:/core/ui/ControlPanel/Plugins/Installed/Languages]]\" \"$:/core/ui/ControlPanel/Plugins/Installed/Plugins\">>\n"
},
"$:/core/ui/ControlPanel/Saving/DownloadSaver": {
"title": "$:/core/ui/ControlPanel/Saving/DownloadSaver",
"tags": "$:/tags/ControlPanel/Saving",
"caption": "{{$:/language/ControlPanel/Saving/DownloadSaver/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Saving/DownloadSaver/\n\n<<lingo Hint>>\n\n!! <$link to=\"$:/config/DownloadSaver/AutoSave\"><<lingo AutoSave/Hint>></$link>\n\n<$checkbox tiddler=\"$:/config/DownloadSaver/AutoSave\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"no\"> <<lingo AutoSave/Description>> </$checkbox>\n"
},
"$:/core/ui/ControlPanel/Saving/General": {
"title": "$:/core/ui/ControlPanel/Saving/General",
"tags": "$:/tags/ControlPanel/Saving",
"caption": "{{$:/language/ControlPanel/Saving/General/Caption}}",
"list-before": "",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/\n\n{{$:/language/ControlPanel/Saving/General/Hint}}\n\n!! <$link to=\"$:/config/AutoSave\"><<lingo AutoSave/Caption>></$link>\n\n<<lingo AutoSave/Hint>>\n\n<$radio tiddler=\"$:/config/AutoSave\" value=\"yes\"> <<lingo AutoSave/Enabled/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/AutoSave\" value=\"no\"> <<lingo AutoSave/Disabled/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Saving/GitHub": {
"title": "$:/core/ui/ControlPanel/Saving/GitHub",
"tags": "$:/tags/ControlPanel/Saving",
"caption": "{{$:/language/ControlPanel/Saving/GitService/GitHub/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Saving/GitService/\n\\define service-name() ~GitHub\n\n<<lingo Description>>\n\n|<<lingo UserName>> |<$edit-text tiddler=\"$:/GitHub/Username\" default=\"\" tag=\"input\"/> |\n|<<lingo GitHub/Password>> |<$password name=\"github\"/> |\n|<<lingo Repo>> |<$edit-text tiddler=\"$:/GitHub/Repo\" default=\"\" tag=\"input\"/> |\n|<<lingo Branch>> |<$edit-text tiddler=\"$:/GitHub/Branch\" default=\"master\" tag=\"input\"/> |\n|<<lingo Path>> |<$edit-text tiddler=\"$:/GitHub/Path\" default=\"\" tag=\"input\"/> |\n|<<lingo Filename>> |<$edit-text tiddler=\"$:/GitHub/Filename\" default=\"\" tag=\"input\"/> |\n|<<lingo ServerURL>> |<$edit-text tiddler=\"$:/GitHub/ServerURL\" default=\"https://api.github.com\" tag=\"input\"/> |"
},
"$:/core/ui/ControlPanel/Saving/GitLab": {
"title": "$:/core/ui/ControlPanel/Saving/GitLab",
"tags": "$:/tags/ControlPanel/Saving",
"caption": "{{$:/language/ControlPanel/Saving/GitService/GitLab/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Saving/GitService/\n\\define service-name() ~GitLab\n\n<<lingo Description>>\n\n|<<lingo UserName>> |<$edit-text tiddler=\"$:/GitLab/Username\" default=\"\" tag=\"input\"/> |\n|<<lingo GitLab/Password>> |<$password name=\"gitlab\"/> |\n|<<lingo Repo>> |<$edit-text tiddler=\"$:/GitLab/Repo\" default=\"\" tag=\"input\"/> |\n|<<lingo Branch>> |<$edit-text tiddler=\"$:/GitLab/Branch\" default=\"master\" tag=\"input\"/> |\n|<<lingo Path>> |<$edit-text tiddler=\"$:/GitLab/Path\" default=\"\" tag=\"input\"/> |\n|<<lingo Filename>> |<$edit-text tiddler=\"$:/GitLab/Filename\" default=\"\" tag=\"input\"/> |\n|<<lingo ServerURL>> |<$edit-text tiddler=\"$:/GitLab/ServerURL\" default=\"https://gitlab.com/api/v4\" tag=\"input\"/> |"
},
"$:/core/ui/ControlPanel/Saving/TiddlySpot": {
"title": "$:/core/ui/ControlPanel/Saving/TiddlySpot",
"tags": "$:/tags/ControlPanel/Saving",
"caption": "{{$:/language/ControlPanel/Saving/TiddlySpot/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Saving/TiddlySpot/\n\n\\define backupURL()\nhttp://$(userName)$.tiddlyspot.com/backup/\n\\end\n\\define backupLink()\n<$reveal type=\"nomatch\" state=\"$:/UploadName\" text=\"\">\n<$set name=\"userName\" value={{$:/UploadName}}>\n<$reveal type=\"match\" state=\"$:/UploadURL\" text=\"\">\n<<backupURL>>\n</$reveal>\n<$reveal type=\"nomatch\" state=\"$:/UploadURL\" text=\"\">\n<$macrocall $name=resolvePath source={{$:/UploadBackupDir}} root={{$:/UploadURL}}>>\n</$reveal>\n</$set>\n</$reveal>\n\\end\n\n<<lingo Description>>\n\n|<<lingo UserName>> |<$edit-text tiddler=\"$:/UploadName\" default=\"\" tag=\"input\"/> |\n|<<lingo Password>> |<$password name=\"upload\"/> |\n|<<lingo Backups>> |<<backupLink>> |\n\n''<<lingo Advanced/Heading>>''\n\n|<<lingo ServerURL>> |<$edit-text tiddler=\"$:/UploadURL\" default=\"\" tag=\"input\"/> |\n|<<lingo Filename>> |<$edit-text tiddler=\"$:/UploadFilename\" default=\"index.html\" tag=\"input\"/> |\n|<<lingo UploadDir>> |<$edit-text tiddler=\"$:/UploadDir\" default=\".\" tag=\"input\"/> |\n|<<lingo BackupDir>> |<$edit-text tiddler=\"$:/UploadBackupDir\" default=\".\" tag=\"input\"/> |\n\n<<lingo TiddlySpot/Hint>>"
},
"$:/core/ui/ControlPanel/Saving/Gitea": {
"title": "$:/core/ui/ControlPanel/Saving/Gitea",
"tags": "$:/tags/ControlPanel/Saving",
"caption": "{{$:/language/ControlPanel/Saving/GitService/Gitea/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Saving/GitService/\n\\define service-name() ~Gitea\n\n<<lingo Description>>\n\n|<<lingo UserName>> |<$edit-text tiddler=\"$:/Gitea/Username\" default=\"\" tag=\"input\"/> |\n|<<lingo Gitea/Password>> |<$password name=\"Gitea\"/> |\n|<<lingo Repo>> |<$edit-text tiddler=\"$:/Gitea/Repo\" default=\"\" tag=\"input\"/> |\n|<<lingo Branch>> |<$edit-text tiddler=\"$:/Gitea/Branch\" default=\"master\" tag=\"input\"/> |\n|<<lingo Path>> |<$edit-text tiddler=\"$:/Gitea/Path\" default=\"\" tag=\"input\"/> |\n|<<lingo Filename>> |<$edit-text tiddler=\"$:/Gitea/Filename\" default=\"\" tag=\"input\"/> |\n|<<lingo ServerURL>> |<$edit-text tiddler=\"$:/Gitea/ServerURL\" default=\"https://gitea/api/v1\" tag=\"input\"/> |\n"
},
"$:/core/ui/ControlPanel/Saving": {
"title": "$:/core/ui/ControlPanel/Saving",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Saving/Caption}}",
"text": "{{$:/language/ControlPanel/Saving/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Saving]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Saving/General\">>\n</div>\n"
},
"$:/core/buttonstyles/Borderless": {
"title": "$:/core/buttonstyles/Borderless",
"tags": "$:/tags/ToolbarButtonStyle",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Borderless}}",
"text": "tc-btn-invisible"
},
"$:/core/buttonstyles/Boxed": {
"title": "$:/core/buttonstyles/Boxed",
"tags": "$:/tags/ToolbarButtonStyle",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Boxed}}",
"text": "tc-btn-boxed"
},
"$:/core/buttonstyles/Rounded": {
"title": "$:/core/buttonstyles/Rounded",
"tags": "$:/tags/ToolbarButtonStyle",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Rounded}}",
"text": "tc-btn-rounded"
},
"$:/core/ui/ControlPanel/Settings/CamelCase": {
"title": "$:/core/ui/ControlPanel/Settings/CamelCase",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/CamelCase/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/CamelCase/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/WikiParserRules/Inline/wikilink\" field=\"text\" checked=\"enable\" unchecked=\"disable\" default=\"enable\"> <$link to=\"$:/config/WikiParserRules/Inline/wikilink\"><<lingo Description>></$link> </$checkbox>\n"
},
"$:/core/ui/ControlPanel/Settings/DefaultMoreSidebarTab": {
"title": "$:/core/ui/ControlPanel/Settings/DefaultMoreSidebarTab",
"caption": "{{$:/language/ControlPanel/Settings/DefaultMoreSidebarTab/Caption}}",
"tags": "$:/tags/ControlPanel/Settings",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/DefaultMoreSidebarTab/\n\n<$link to=\"$:/config/DefaultMoreSidebarTab\"><<lingo Hint>></$link>\n\n<$select tiddler=\"$:/config/DefaultMoreSidebarTab\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/MoreSideBar]!has[draft.of]]\">\n<option value=<<currentTiddler>>><$transclude field=\"caption\"><$text text=<<currentTiddler>>/></$transclude></option>\n</$list>\n</$select>\n"
},
"$:/core/ui/ControlPanel/Settings/DefaultSidebarTab": {
"title": "$:/core/ui/ControlPanel/Settings/DefaultSidebarTab",
"caption": "{{$:/language/ControlPanel/Settings/DefaultSidebarTab/Caption}}",
"tags": "$:/tags/ControlPanel/Settings",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/DefaultSidebarTab/\n\n<$link to=\"$:/config/DefaultSidebarTab\"><<lingo Hint>></$link>\n\n<$select tiddler=\"$:/config/DefaultSidebarTab\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SideBar]!has[draft.of]]\">\n<option value=<<currentTiddler>>><$transclude field=\"caption\"><$text text=<<currentTiddler>>/></$transclude></option>\n</$list>\n</$select>\n"
},
"$:/core/ui/ControlPanel/Settings/EditorToolbar": {
"title": "$:/core/ui/ControlPanel/Settings/EditorToolbar",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/EditorToolbar/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/EditorToolbar/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/TextEditor/EnableToolbar\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"yes\"> <$link to=\"$:/config/TextEditor/EnableToolbar\"><<lingo Description>></$link> </$checkbox>\n\n"
},
"$:/core/ui/ControlPanel/Settings/InfoPanelMode": {
"title": "$:/core/ui/ControlPanel/Settings/InfoPanelMode",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/InfoPanelMode/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/InfoPanelMode/\n<$link to=\"$:/config/TiddlerInfo/Mode\"><<lingo Hint>></$link>\n\n<$radio tiddler=\"$:/config/TiddlerInfo/Mode\" value=\"popup\"> <<lingo Popup/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/TiddlerInfo/Mode\" value=\"sticky\"> <<lingo Sticky/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Settings/LinkToBehaviour": {
"title": "$:/core/ui/ControlPanel/Settings/LinkToBehaviour",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/LinkToBehaviour/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/LinkToBehaviour/\n\n<$link to=\"$:/config/Navigation/openLinkFromInsideRiver\"><<lingo \"InsideRiver/Hint\">></$link>\n\n<$select tiddler=\"$:/config/Navigation/openLinkFromInsideRiver\">\n <option value=\"above\"><<lingo \"OpenAbove\">></option>\n <option value=\"below\"><<lingo \"OpenBelow\">></option>\n <option value=\"top\"><<lingo \"OpenAtTop\">></option>\n <option value=\"bottom\"><<lingo \"OpenAtBottom\">></option>\n</$select>\n\n<$link to=\"$:/config/Navigation/openLinkFromOutsideRiver\"><<lingo \"OutsideRiver/Hint\">></$link>\n\n<$select tiddler=\"$:/config/Navigation/openLinkFromOutsideRiver\">\n <option value=\"top\"><<lingo \"OpenAtTop\">></option>\n <option value=\"bottom\"><<lingo \"OpenAtBottom\">></option>\n</$select>\n"
},
"$:/core/ui/ControlPanel/Settings/MissingLinks": {
"title": "$:/core/ui/ControlPanel/Settings/MissingLinks",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/MissingLinks/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/MissingLinks/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/MissingLinks\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"yes\"> <$link to=\"$:/config/MissingLinks\"><<lingo Description>></$link> </$checkbox>\n\n"
},
"$:/core/ui/ControlPanel/Settings/NavigationAddressBar": {
"title": "$:/core/ui/ControlPanel/Settings/NavigationAddressBar",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/NavigationAddressBar/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/NavigationAddressBar/\n\n<$link to=\"$:/config/Navigation/UpdateAddressBar\"><<lingo Hint>></$link>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateAddressBar\" value=\"permaview\"> <<lingo Permaview/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateAddressBar\" value=\"permalink\"> <<lingo Permalink/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateAddressBar\" value=\"no\"> <<lingo No/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Settings/NavigationHistory": {
"title": "$:/core/ui/ControlPanel/Settings/NavigationHistory",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/NavigationHistory/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/NavigationHistory/\n<$link to=\"$:/config/Navigation/UpdateHistory\"><<lingo Hint>></$link>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateHistory\" value=\"yes\"> <<lingo Yes/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateHistory\" value=\"no\"> <<lingo No/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Settings/NavigationPermalinkviewMode": {
"title": "$:/core/ui/ControlPanel/Settings/NavigationPermalinkviewMode",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/NavigationPermalinkviewMode/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/NavigationPermalinkviewMode/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/Navigation/Permalinkview/CopyToClipboard\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"yes\"> <$link to=\"$:/config/Navigation/Permalinkview/CopyToClipboard\"><<lingo CopyToClipboard/Description>></$link> </$checkbox>\n\n<$checkbox tiddler=\"$:/config/Navigation/Permalinkview/UpdateAddressBar\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"yes\"> <$link to=\"$:/config/Navigation/Permalinkview/UpdateAddressBar\"><<lingo UpdateAddressBar/Description>></$link> </$checkbox>\n"
},
"$:/core/ui/ControlPanel/Settings/PerformanceInstrumentation": {
"title": "$:/core/ui/ControlPanel/Settings/PerformanceInstrumentation",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/PerformanceInstrumentation/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/PerformanceInstrumentation/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/Performance/Instrumentation\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"no\"> <$link to=\"$:/config/Performance/Instrumentation\"><<lingo Description>></$link> </$checkbox>\n"
},
"$:/core/ui/ControlPanel/Settings/TitleLinks": {
"title": "$:/core/ui/ControlPanel/Settings/TitleLinks",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/TitleLinks/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/TitleLinks/\n<$link to=\"$:/config/Tiddlers/TitleLinks\"><<lingo Hint>></$link>\n\n<$radio tiddler=\"$:/config/Tiddlers/TitleLinks\" value=\"yes\"> <<lingo Yes/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/Tiddlers/TitleLinks\" value=\"no\"> <<lingo No/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Settings/ToolbarButtonStyle": {
"title": "$:/core/ui/ControlPanel/Settings/ToolbarButtonStyle",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtonStyle/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/ToolbarButtonStyle/\n<$link to=\"$:/config/Toolbar/ButtonClass\"><<lingo \"Hint\">></$link>\n\n<$select tiddler=\"$:/config/Toolbar/ButtonClass\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ToolbarButtonStyle]]\">\n<option value={{!!text}}>{{!!caption}}</option>\n</$list>\n</$select>\n"
},
"$:/core/ui/ControlPanel/Settings/ToolbarButtons": {
"title": "$:/core/ui/ControlPanel/Settings/ToolbarButtons",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtons/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/ToolbarButtons/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/Toolbar/Icons\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"yes\"> <$link to=\"$:/config/Toolbar/Icons\"><<lingo Icons/Description>></$link> </$checkbox>\n\n<$checkbox tiddler=\"$:/config/Toolbar/Text\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"no\"> <$link to=\"$:/config/Toolbar/Text\"><<lingo Text/Description>></$link> </$checkbox>\n"
},
"$:/core/ui/ControlPanel/Settings": {
"title": "$:/core/ui/ControlPanel/Settings",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Settings/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/\n\n<<lingo Hint>>\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Settings]]\">\n\n<div style=\"border-top:1px solid #eee;\">\n\n!! <$link><$transclude field=\"caption\"/></$link>\n\n<$transclude/>\n\n</div>\n\n</$list>\n"
},
"$:/core/ui/ControlPanel/StoryView": {
"title": "$:/core/ui/ControlPanel/StoryView",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ControlPanel/StoryView/Caption}}",
"text": "{{$:/snippets/viewswitcher}}\n"
},
"$:/core/ui/ControlPanel/Stylesheets": {
"title": "$:/core/ui/ControlPanel/Stylesheets",
"tags": "$:/tags/ControlPanel/Advanced",
"caption": "{{$:/language/ControlPanel/Stylesheets/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/\n\n<<lingo Stylesheets/Hint>>\n\n{{$:/snippets/peek-stylesheets}}\n"
},
"$:/core/ui/ControlPanel/Theme": {
"title": "$:/core/ui/ControlPanel/Theme",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ControlPanel/Theme/Caption}}",
"text": "{{$:/snippets/themeswitcher}}\n"
},
"$:/core/ui/ControlPanel/TiddlerFields": {
"title": "$:/core/ui/ControlPanel/TiddlerFields",
"tags": "$:/tags/ControlPanel/Advanced",
"caption": "{{$:/language/ControlPanel/TiddlerFields/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/\n\n<<lingo TiddlerFields/Hint>>\n\n{{$:/snippets/allfields}}"
},
"$:/core/ui/ControlPanel/Toolbars/EditToolbar": {
"title": "$:/core/ui/ControlPanel/Toolbars/EditToolbar",
"tags": "$:/tags/ControlPanel/Toolbars",
"caption": "{{$:/language/ControlPanel/Toolbars/EditToolbar/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\n\\define config-base() $:/config/EditToolbarButtons/Visibility/\n\n{{$:/language/ControlPanel/Toolbars/EditToolbar/Hint}}\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$macrocall $name=\"list-tagged-draggable\" tag=\"$:/tags/EditToolbar\" itemTemplate=\"$:/core/ui/ControlPanel/Toolbars/ItemTemplate\"/>\n\n</$set>\n\n</$set>"
},
"$:/core/ui/ControlPanel/Toolbars/EditorItemTemplate": {
"title": "$:/core/ui/ControlPanel/Toolbars/EditorItemTemplate",
"text": "\\define config-title()\n$(config-base)$$(currentTiddler)$\n\\end\n\n<$draggable tiddler=<<currentTiddler>>>\n<$checkbox tiddler=<<config-title>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"show\"/> <span class=\"tc-icon-wrapper\"><$transclude tiddler={{!!icon}}/></span> <$transclude field=\"caption\"/> -- <i class=\"tc-muted\"><$transclude field=\"description\"/></i>\n</$draggable>\n"
},
"$:/core/ui/ControlPanel/Toolbars/EditorToolbar": {
"title": "$:/core/ui/ControlPanel/Toolbars/EditorToolbar",
"tags": "$:/tags/ControlPanel/Toolbars",
"caption": "{{$:/language/ControlPanel/Toolbars/EditorToolbar/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\n\\define config-base() $:/config/EditorToolbarButtons/Visibility/\n\n{{$:/language/ControlPanel/Toolbars/EditorToolbar/Hint}}\n\n<$macrocall $name=\"list-tagged-draggable\" tag=\"$:/tags/EditorToolbar\" itemTemplate=\"$:/core/ui/ControlPanel/Toolbars/EditorItemTemplate\"/>\n"
},
"$:/core/ui/ControlPanel/Toolbars/ItemTemplate": {
"title": "$:/core/ui/ControlPanel/Toolbars/ItemTemplate",
"text": "\\define config-title()\n$(config-base)$$(currentTiddler)$\n\\end\n\n<$draggable tiddler=<<currentTiddler>>>\n<$checkbox tiddler=<<config-title>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"show\"/> <span class=\"tc-icon-wrapper\"> <$transclude field=\"caption\"/> <i class=\"tc-muted\">-- <$transclude field=\"description\"/></i></span>\n</$draggable>\n"
},
"$:/core/ui/ControlPanel/Toolbars/PageControls": {
"title": "$:/core/ui/ControlPanel/Toolbars/PageControls",
"tags": "$:/tags/ControlPanel/Toolbars",
"caption": "{{$:/language/ControlPanel/Toolbars/PageControls/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\n\\define config-base() $:/config/PageControlButtons/Visibility/\n\n{{$:/language/ControlPanel/Toolbars/PageControls/Hint}}\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$macrocall $name=\"list-tagged-draggable\" tag=\"$:/tags/PageControls\" itemTemplate=\"$:/core/ui/ControlPanel/Toolbars/ItemTemplate\"/>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/ControlPanel/Toolbars/ViewToolbar": {
"title": "$:/core/ui/ControlPanel/Toolbars/ViewToolbar",
"tags": "$:/tags/ControlPanel/Toolbars",
"caption": "{{$:/language/ControlPanel/Toolbars/ViewToolbar/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\n\\define config-base() $:/config/ViewToolbarButtons/Visibility/\n\n{{$:/language/ControlPanel/Toolbars/ViewToolbar/Hint}}\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$macrocall $name=\"list-tagged-draggable\" tag=\"$:/tags/ViewToolbar\" itemTemplate=\"$:/core/ui/ControlPanel/Toolbars/ItemTemplate\"/>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/ControlPanel/Toolbars": {
"title": "$:/core/ui/ControlPanel/Toolbars",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ControlPanel/Toolbars/Caption}}",
"text": "{{$:/language/ControlPanel/Toolbars/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Toolbars]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Toolbars/ViewToolbar\" \"$:/state/tabs/controlpanel/toolbars\" \"tc-vertical\">>\n</div>\n"
},
"$:/ControlPanel": {
"title": "$:/ControlPanel",
"icon": "$:/core/images/options-button",
"color": "#bbb",
"text": "<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Info\">>\n</div>\n"
},
"$:/core/ui/DefaultSearchResultList": {
"title": "$:/core/ui/DefaultSearchResultList",
"tags": "$:/tags/SearchResults",
"caption": "{{$:/language/Search/DefaultResults/Caption}}",
"text": "\\define searchResultList()\n//<small>{{$:/language/Search/Matches/Title}}</small>//\n\n<$list filter=\"[!is[system]search:title{$(searchTiddler)$}sort[title]limit[250]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n\n//<small>{{$:/language/Search/Matches/All}}</small>//\n\n<$list filter=\"[!is[system]search{$(searchTiddler)$}sort[title]limit[250]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n\n\\end\n<<searchResultList>>\n"
},
"$:/core/ui/EditTemplate/body/preview/diffs-current": {
"title": "$:/core/ui/EditTemplate/body/preview/diffs-current",
"tags": "$:/tags/EditPreview",
"caption": "differences from current",
"list-after": "$:/core/ui/EditTemplate/body/preview/output",
"text": "<$list filter=\"[<currentTiddler>!is[image]]\" emptyMessage={{$:/core/ui/EditTemplate/body/preview/output}}>\n\n<$macrocall $name=\"compareTiddlerText\" sourceTiddlerTitle={{!!draft.of}} destTiddlerTitle=<<currentTiddler>>/>\n\n</$list>\n\n"
},
"$:/core/ui/EditTemplate/body/preview/diffs-shadow": {
"title": "$:/core/ui/EditTemplate/body/preview/diffs-shadow",
"tags": "$:/tags/EditPreview",
"caption": "differences from shadow (if any)",
"list-after": "$:/core/ui/EditTemplate/body/preview/output",
"text": "<$list filter=\"[<currentTiddler>!is[image]]\" emptyMessage={{$:/core/ui/EditTemplate/body/preview/output}}>\n\n<$macrocall $name=\"compareTiddlerText\" sourceTiddlerTitle={{{ [{!!draft.of}shadowsource[]] }}} sourceSubTiddlerTitle={{!!draft.of}} destTiddlerTitle=<<currentTiddler>>/>\n\n</$list>\n\n"
},
"$:/core/ui/EditTemplate/body/preview/output": {
"title": "$:/core/ui/EditTemplate/body/preview/output",
"tags": "$:/tags/EditPreview",
"caption": "{{$:/language/EditTemplate/Body/Preview/Type/Output}}",
"text": "\\import [all[shadows+tiddlers]tag[$:/tags/Macro/View]!has[draft.of]]\n<$set name=\"tv-tiddler-preview\" value=\"yes\">\n\n<$transclude />\n\n</$set>\n"
},
"$:/state/showeditpreview": {
"title": "$:/state/showeditpreview",
"text": "no"
},
"$:/core/ui/EditTemplate/body/editor": {
"title": "$:/core/ui/EditTemplate/body/editor",
"text": "<$edit\n\n field=\"text\"\n class=\"tc-edit-texteditor tc-edit-texteditor-body\"\n placeholder={{$:/language/EditTemplate/Body/Placeholder}}\n tabindex={{$:/config/EditTabIndex}}\n focus={{{ [{$:/config/AutoFocus}match[text]then[true]] ~[[false]] }}}\n\n><$set\n\n name=\"targetTiddler\"\n value=<<currentTiddler>>\n\n><$list\n\n filter=\"[all[shadows+tiddlers]tag[$:/tags/EditorToolbar]!has[draft.of]]\"\n\n><$reveal\n\n type=\"nomatch\"\n state=<<config-visibility-title>>\n text=\"hide\"\n class=\"tc-text-editor-toolbar-item-wrapper\"\n\n><$transclude\n\n tiddler=\"$:/core/ui/EditTemplate/body/toolbar/button\"\n mode=\"inline\"\n\n/></$reveal></$list></$set></$edit>\n"
},
"$:/core/ui/EditTemplate/body/toolbar/button": {
"title": "$:/core/ui/EditTemplate/body/toolbar/button",
"text": "\\define toolbar-button-icon()\n<$list\n\n filter=\"[all[current]!has[custom-icon]]\"\n variable=\"no-custom-icon\"\n\n><$transclude\n\n tiddler={{!!icon}}\n\n/></$list>\n\\end\n\n\\define toolbar-button-tooltip()\n{{!!description}}<$macrocall $name=\"displayshortcuts\" $output=\"text/plain\" shortcuts={{!!shortcuts}} prefix=\"` - [\" separator=\"] [\" suffix=\"]`\"/>\n\\end\n\n\\define toolbar-button()\n<$list\n\n filter={{!!condition}}\n variable=\"list-condition\"\n\n><$wikify\n\n name=\"tooltip-text\"\n text=<<toolbar-button-tooltip>>\n mode=\"inline\"\n output=\"text\"\n\n><$list\n\n filter=\"[all[current]!has[dropdown]]\"\n variable=\"no-dropdown\"\n\n><$button\n\n class=\"tc-btn-invisible $(buttonClasses)$\"\n tooltip=<<tooltip-text>>\n actions={{!!actions}}\n\n><span\n\n data-tw-keyboard-shortcut={{!!shortcuts}}\n\n/><<toolbar-button-icon>><$transclude\n\n tiddler=<<currentTiddler>>\n field=\"text\"\n\n/></$button></$list><$list\n\n filter=\"[all[current]has[dropdown]]\"\n variable=\"dropdown\"\n\n><$set\n\n name=\"dropdown-state\"\n value=<<qualify \"$:/state/EditorToolbarDropdown\">>\n\n><$button\n\n popup=<<dropdown-state>>\n class=\"tc-popup-keep tc-btn-invisible $(buttonClasses)$\"\n selectedClass=\"tc-selected\"\n tooltip=<<tooltip-text>>\n actions={{!!actions}}\n\n><span\n\n data-tw-keyboard-shortcut={{!!shortcuts}}\n\n/><<toolbar-button-icon>><$transclude\n\n tiddler=<<currentTiddler>>\n field=\"text\"\n\n/></$button><$reveal\n\n state=<<dropdown-state>>\n type=\"popup\"\n position=\"below\"\n animate=\"yes\"\n tag=\"span\"\n\n><div\n\n class=\"tc-drop-down tc-popup-keep\"\n\n><$transclude\n\n tiddler={{!!dropdown}}\n mode=\"block\"\n\n/></div></$reveal></$set></$list></$wikify></$list>\n\\end\n\n\\define toolbar-button-outer()\n<$set\n\n name=\"buttonClasses\"\n value={{!!button-classes}}\n\n><<toolbar-button>></$set>\n\\end\n\n<<toolbar-button-outer>>"
},
"$:/core/ui/EditTemplate/body": {
"title": "$:/core/ui/EditTemplate/body",
"tags": "$:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/EditTemplate/Body/\n\\define config-visibility-title()\n$:/config/EditorToolbarButtons/Visibility/$(currentTiddler)$\n\\end\n<$list filter=\"[all[current]has[_canonical_uri]]\">\n\n<div class=\"tc-message-box\">\n\n<<lingo External/Hint>>\n\n<a href={{!!_canonical_uri}}><$text text={{!!_canonical_uri}}/></a>\n\n<$edit-text field=\"_canonical_uri\" class=\"tc-edit-fields\" tabindex={{$:/config/EditTabIndex}}></$edit-text>\n\n</div>\n\n</$list>\n\n<$list filter=\"[all[current]!has[_canonical_uri]]\">\n\n<$reveal state=\"$:/state/showeditpreview\" type=\"match\" text=\"yes\">\n\n<div class=\"tc-tiddler-preview\">\n\n<$transclude tiddler=\"$:/core/ui/EditTemplate/body/editor\" mode=\"inline\"/>\n\n<div class=\"tc-tiddler-preview-preview\">\n\n<$transclude tiddler={{$:/state/editpreviewtype}} mode=\"inline\">\n\n<$transclude tiddler=\"$:/core/ui/EditTemplate/body/preview/output\" mode=\"inline\"/>\n\n</$transclude>\n\n</div>\n\n</div>\n\n</$reveal>\n\n<$reveal state=\"$:/state/showeditpreview\" type=\"nomatch\" text=\"yes\">\n\n<$transclude tiddler=\"$:/core/ui/EditTemplate/body/editor\" mode=\"inline\"/>\n\n</$reveal>\n\n</$list>\n"
},
"$:/core/ui/EditTemplate/controls": {
"title": "$:/core/ui/EditTemplate/controls",
"tags": "$:/tags/EditTemplate",
"text": "\\define config-title()\n$:/config/EditToolbarButtons/Visibility/$(listItem)$\n\\end\n<div class=\"tc-tiddler-title tc-tiddler-edit-title\">\n<$view field=\"title\"/>\n<span class=\"tc-tiddler-controls tc-titlebar\"><$list filter=\"[all[shadows+tiddlers]tag[$:/tags/EditToolbar]!has[draft.of]]\" variable=\"listItem\"><$reveal type=\"nomatch\" state=<<config-title>> text=\"hide\"><$transclude tiddler=<<listItem>>/></$reveal></$list></span>\n<div style=\"clear: both;\"></div>\n</div>\n"
},
"$:/core/ui/EditTemplate/fields": {
"title": "$:/core/ui/EditTemplate/fields",
"tags": "$:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/EditTemplate/\n\\define config-title()\n$:/config/EditTemplateFields/Visibility/$(currentField)$\n\\end\n\n\\define config-filter()\n[[hide]] -[title{$(config-title)$}]\n\\end\n\n\\define current-tiddler-new-field-selector()\n[data-tiddler-title=\"$(currentTiddlerCSSescaped)$\"] .tc-edit-field-add-name input\n\\end\n\n\\define new-field-actions()\n<$action-sendmessage $message=\"tm-add-field\" $name={{{ [<newFieldNameTiddler>get[text]] }}} $value={{{ [<newFieldValueTiddler>get[text]] }}}/>\n<$action-deletetiddler $tiddler=<<newFieldNameTiddler>>/>\n<$action-deletetiddler $tiddler=<<newFieldValueTiddler>>/>\n<$action-sendmessage $message=\"tm-focus-selector\" $param=<<current-tiddler-new-field-selector>>/>\n\\end\n\n\\define new-field()\n<$vars name={{{ [<newFieldNameTiddler>get[text]] }}}>\n<$reveal type=\"nomatch\" text=\"\" default=<<name>>>\n<$button tooltip=<<lingo Fields/Add/Button/Hint>>>\n<$action-sendmessage $message=\"tm-add-field\"\n$name=<<name>>\n$value={{{ [<newFieldValueTiddler>get[text]] }}}/>\n<$action-deletetiddler $tiddler=<<newFieldNameTiddler>>/>\n<$action-deletetiddler $tiddler=<<newFieldValueTiddler>>/>\n<<lingo Fields/Add/Button>>\n</$button>\n</$reveal>\n<$reveal type=\"match\" text=\"\" default=<<name>>>\n<$button>\n<<lingo Fields/Add/Button>>\n</$button>\n</$reveal>\n</$vars>\n\\end\n\\whitespace trim\n\n<div class=\"tc-edit-fields\">\n<table class=\"tc-edit-fields\">\n<tbody>\n<$list filter=\"[all[current]fields[]] +[sort[title]]\" variable=\"currentField\" storyview=\"pop\">\n<$list filter=<<config-filter>> variable=\"temp\">\n<tr class=\"tc-edit-field\">\n<td class=\"tc-edit-field-name\">\n<$text text=<<currentField>>/>:</td>\n<td class=\"tc-edit-field-value\">\n<$edit-text tiddler=<<currentTiddler>> field=<<currentField>> placeholder={{$:/language/EditTemplate/Fields/Add/Value/Placeholder}} tabindex={{$:/config/EditTabIndex}}/>\n</td>\n<td class=\"tc-edit-field-remove\">\n<$button class=\"tc-btn-invisible\" tooltip={{$:/language/EditTemplate/Field/Remove/Hint}} aria-label={{$:/language/EditTemplate/Field/Remove/Caption}}>\n<$action-deletefield $field=<<currentField>>/>\n{{$:/core/images/delete-button}}\n</$button>\n</td>\n</tr>\n</$list>\n</$list>\n</tbody>\n</table>\n</div>\n\n<$fieldmangler>\n<div class=\"tc-edit-field-add\">\n<em class=\"tc-edit\">\n<<lingo Fields/Add/Prompt>> \n</em>\n<span class=\"tc-edit-field-add-name\">\n<$edit-text tiddler=<<newFieldNameTiddler>> tag=\"input\" default=\"\" placeholder={{$:/language/EditTemplate/Fields/Add/Name/Placeholder}} focusPopup=<<qualify \"$:/state/popup/field-dropdown\">> class=\"tc-edit-texteditor tc-popup-handle\" tabindex={{$:/config/EditTabIndex}} focus={{{ [{$:/config/AutoFocus}match[fields]then[true]] ~[[false]] }}}/>\n</span> \n<$button popup=<<qualify \"$:/state/popup/field-dropdown\">> class=\"tc-btn-invisible tc-btn-dropdown\" tooltip={{$:/language/EditTemplate/Field/Dropdown/Hint}} aria-label={{$:/language/EditTemplate/Field/Dropdown/Caption}}>{{$:/core/images/down-arrow}}</$button> \n<$reveal state=<<qualify \"$:/state/popup/field-dropdown\">> type=\"nomatch\" text=\"\" default=\"\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown\">\n<$set name=\"tv-show-missing-links\" value=\"yes\">\n<$linkcatcher to=<<newFieldNameTiddler>>>\n<div class=\"tc-dropdown-item\">\n<<lingo Fields/Add/Dropdown/User>>\n</div>\n<$set name=\"newFieldName\" value={{{ [<newFieldNameTiddler>get[text]] }}}>\n<$list filter=\"[!is[shadow]!is[system]fields[]search:title<newFieldName>sort[]] -created -creator -draft.of -draft.title -modified -modifier -tags -text -title -type\" variable=\"currentField\">\n<$link to=<<currentField>>>\n<$text text=<<currentField>>/>\n</$link>\n</$list>\n<div class=\"tc-dropdown-item\">\n<<lingo Fields/Add/Dropdown/System>>\n</div>\n<$list filter=\"[fields[]search:title<newFieldName>sort[]] -[!is[shadow]!is[system]fields[]]\" variable=\"currentField\">\n<$link to=<<currentField>>>\n<$text text=<<currentField>>/>\n</$link>\n</$list>\n</$set>\n</$linkcatcher>\n</$set>\n</div>\n</$reveal>\n<span class=\"tc-edit-field-add-value\">\n<$set name=\"currentTiddlerCSSescaped\" value={{{ [<currentTiddler>escapecss[]] }}}>\n<$keyboard key=\"((add-field))\" actions=<<new-field-actions>>>\n<$edit-text tiddler=<<newFieldValueTiddler>> tag=\"input\" default=\"\" placeholder={{$:/language/EditTemplate/Fields/Add/Value/Placeholder}} class=\"tc-edit-texteditor\" tabindex={{$:/config/EditTabIndex}}/>\n</$keyboard>\n</$set>\n</span> \n<span class=\"tc-edit-field-add-button\">\n<$macrocall $name=\"new-field\"/>\n</span>\n</div>\n</$fieldmangler>\n"
},
"$:/core/ui/EditTemplate/shadow": {
"title": "$:/core/ui/EditTemplate/shadow",
"tags": "$:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/EditTemplate/Shadow/\n\\define pluginLinkBody()\n<$link to=\"\"\"$(pluginTitle)$\"\"\">\n<$text text=\"\"\"$(pluginTitle)$\"\"\"/>\n</$link>\n\\end\n<$list filter=\"[all[current]get[draft.of]is[shadow]!is[tiddler]]\">\n\n<$list filter=\"[all[current]shadowsource[]]\" variable=\"pluginTitle\">\n\n<$set name=\"pluginLink\" value=<<pluginLinkBody>>>\n<div class=\"tc-message-box\">\n\n<<lingo Warning>>\n\n</div>\n</$set>\n</$list>\n\n</$list>\n\n<$list filter=\"[all[current]get[draft.of]is[shadow]is[tiddler]]\">\n\n<$list filter=\"[all[current]shadowsource[]]\" variable=\"pluginTitle\">\n\n<$set name=\"pluginLink\" value=<<pluginLinkBody>>>\n<div class=\"tc-message-box\">\n\n<<lingo OverriddenWarning>>\n\n</div>\n</$set>\n</$list>\n\n</$list>"
},
"$:/core/ui/EditTemplate/tags": {
"title": "$:/core/ui/EditTemplate/tags",
"tags": "$:/tags/EditTemplate",
"text": "\\whitespace trim\n\n\\define lingo-base() $:/language/EditTemplate/\n\n\\define tag-styles()\nbackground-color:$(backgroundColor)$;\nfill:$(foregroundColor)$;\ncolor:$(foregroundColor)$;\n\\end\n\n\\define tag-body-inner(colour,fallbackTarget,colourA,colourB,icon)\n\\whitespace trim\n<$vars foregroundColor=<<contrastcolour target:\"\"\"$colour$\"\"\" fallbackTarget:\"\"\"$fallbackTarget$\"\"\" colourA:\"\"\"$colourA$\"\"\" colourB:\"\"\"$colourB$\"\"\">> backgroundColor=\"\"\"$colour$\"\"\">\n<span style=<<tag-styles>> class=\"tc-tag-label tc-tag-list-item\">\n<$transclude tiddler=\"\"\"$icon$\"\"\"/><$view field=\"title\" format=\"text\" />\n<$button message=\"tm-remove-tag\" param={{!!title}} class=\"tc-btn-invisible tc-remove-tag-button\">{{$:/core/images/close-button}}</$button>\n</span>\n</$vars>\n\\end\n\n\\define tag-body(colour,palette,icon)\n<$macrocall $name=\"tag-body-inner\" colour=\"\"\"$colour$\"\"\" fallbackTarget={{$palette$##tag-background}} colourA={{$palette$##foreground}} colourB={{$palette$##background}} icon=\"\"\"$icon$\"\"\"/>\n\\end\n\n<div class=\"tc-edit-tags\">\n<$fieldmangler>\n<$list filter=\"[all[current]tags[]sort[title]]\" storyview=\"pop\">\n<$macrocall $name=\"tag-body\" colour={{!!color}} palette={{$:/palette}} icon={{!!icon}}/>\n</$list>\n<$set name=\"tabIndex\" value={{$:/config/EditTabIndex}}>\n<$macrocall $name=\"tag-picker\"/>\n</$set>\n</$fieldmangler>\n</div>\n"
},
"$:/core/ui/EditTemplate/title": {
"title": "$:/core/ui/EditTemplate/title",
"tags": "$:/tags/EditTemplate",
"text": "<$edit-text field=\"draft.title\" class=\"tc-titlebar tc-edit-texteditor\" focus={{{ [{$:/config/AutoFocus}match[title]then[true]] ~[[false]] }}} tabindex={{$:/config/EditTabIndex}}/>\n\n<$vars pattern=\"\"\"[\\|\\[\\]{}]\"\"\" bad-chars=\"\"\"`| [ ] { }`\"\"\">\n\n<$list filter=\"[all[current]regexp:draft.title<pattern>]\" variable=\"listItem\">\n\n<div class=\"tc-message-box\">\n\n{{$:/core/images/warning}} {{$:/language/EditTemplate/Title/BadCharacterWarning}}\n\n</div>\n\n</$list>\n\n</$vars>\n\n<$reveal state=\"!!draft.title\" type=\"nomatch\" text={{!!draft.of}} tag=\"div\">\n\n<$list filter=\"[{!!draft.title}!is[missing]]\" variable=\"listItem\">\n\n<div class=\"tc-message-box\">\n\n{{$:/core/images/warning}} {{$:/language/EditTemplate/Title/Exists/Prompt}}\n\n</div>\n\n</$list>\n\n<$list filter=\"[{!!draft.of}!is[missing]]\" variable=\"listItem\">\n\n<$vars fromTitle={{!!draft.of}} toTitle={{!!draft.title}}>\n\n<$checkbox tiddler=\"$:/config/RelinkOnRename\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"no\"> {{$:/language/EditTemplate/Title/Relink/Prompt}}</$checkbox>\n\n<$list filter=\"[title<fromTitle>backlinks[]limit[1]]\" variable=\"listItem\">\n\n<$vars stateTiddler=<<qualify \"$:/state/edit/references\">> >\n\n<$reveal type=\"nomatch\" state=<<stateTiddler>> text=\"show\">\n<$button set=<<stateTiddler>> setTo=\"show\" class=\"tc-btn-invisible\">{{$:/core/images/right-arrow}} \n<<lingo EditTemplate/Title/References/Prompt>></$button>\n</$reveal>\n<$reveal type=\"match\" state=<<stateTiddler>> text=\"show\">\n<$button set=<<stateTiddler>> setTo=\"hide\" class=\"tc-btn-invisible\">{{$:/core/images/down-arrow}} \n<<lingo EditTemplate/Title/References/Prompt>></$button>\n</$reveal>\n\n<$reveal type=\"match\" state=<<stateTiddler>> text=\"show\">\n<$tiddler tiddler=<<fromTitle>> >\n<$transclude tiddler=\"$:/core/ui/TiddlerInfo/References\"/>\n</$tiddler>\n</$reveal>\n\n</$vars>\n\n</$list>\n\n</$vars>\n\n</$list>\n\n</$reveal>\n"
},
"$:/core/ui/EditTemplate/type": {
"title": "$:/core/ui/EditTemplate/type",
"tags": "$:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/EditTemplate/\n\\whitespace trim\n<div class=\"tc-type-selector\"><$fieldmangler>\n<em class=\"tc-edit\"><<lingo Type/Prompt>></em> <$edit-text field=\"type\" tag=\"input\" default=\"\" placeholder={{$:/language/EditTemplate/Type/Placeholder}} focusPopup=<<qualify \"$:/state/popup/type-dropdown\">> class=\"tc-edit-typeeditor tc-edit-texteditor tc-popup-handle\" tabindex={{$:/config/EditTabIndex}} focus={{{ [{$:/config/AutoFocus}match[type]then[true]] ~[[false]] }}}/> <$button popup=<<qualify \"$:/state/popup/type-dropdown\">> class=\"tc-btn-invisible tc-btn-dropdown\" tooltip={{$:/language/EditTemplate/Type/Dropdown/Hint}} aria-label={{$:/language/EditTemplate/Type/Dropdown/Caption}}>{{$:/core/images/down-arrow}}</$button> <$button message=\"tm-remove-field\" param=\"type\" class=\"tc-btn-invisible tc-btn-icon\" tooltip={{$:/language/EditTemplate/Type/Delete/Hint}} aria-label={{$:/language/EditTemplate/Type/Delete/Caption}}>{{$:/core/images/delete-button}}</$button>\n</$fieldmangler></div>\n\n<div class=\"tc-block-dropdown-wrapper\">\n<$set name=\"tv-show-missing-links\" value=\"yes\">\n<$reveal state=<<qualify \"$:/state/popup/type-dropdown\">> type=\"nomatch\" text=\"\" default=\"\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown\">\n<$linkcatcher to=\"!!type\">\n<$list filter='[all[shadows+tiddlers]prefix[$:/language/Docs/Types/]each[group]sort[group-sort]]'>\n<div class=\"tc-dropdown-item\">\n<$text text={{!!group}}/>\n</div>\n<$list filter=\"[all[shadows+tiddlers]prefix[$:/language/Docs/Types/]group{!!group}] +[sort[description]]\"><$link to={{!!name}}><$view field=\"description\"/> (<$view field=\"name\"/>)</$link>\n</$list>\n</$list>\n</$linkcatcher>\n</div>\n</$reveal>\n</$set>\n</div>\n"
},
"$:/core/ui/EditTemplate": {
"title": "$:/core/ui/EditTemplate",
"text": "\\define save-tiddler-actions()\n<$action-sendmessage $message=\"tm-add-tag\" $param={{{ [<newTagNameTiddler>get[text]] }}}/>\n<$action-deletetiddler $tiddler=<<newTagNameTiddler>>/>\n<$action-sendmessage $message=\"tm-add-field\" $name={{{ [<newFieldNameTiddler>get[text]] }}} $value={{{ [<newFieldValueTiddler>get[text]] }}}/>\n<$action-deletetiddler $tiddler=<<newFieldNameTiddler>>/>\n<$action-deletetiddler $tiddler=<<newFieldValueTiddler>>/>\n<$action-sendmessage $message=\"tm-save-tiddler\"/>\n\\end\n<div data-tiddler-title=<<currentTiddler>> data-tags={{!!tags}} class={{{ tc-tiddler-frame tc-tiddler-edit-frame [<currentTiddler>is[tiddler]then[tc-tiddler-exists]] [<currentTiddler>is[missing]!is[shadow]then[tc-tiddler-missing]] [<currentTiddler>is[shadow]then[tc-tiddler-exists tc-tiddler-shadow]] [<currentTiddler>is[system]then[tc-tiddler-system]] [{!!class}] [<currentTiddler>tags[]encodeuricomponent[]addprefix[tc-tagged-]] +[join[ ]] }}}>\n<$fieldmangler>\n<$vars storyTiddler=<<currentTiddler>> newTagNameTiddler=<<qualify \"$:/temp/NewTagName\">> newFieldNameTiddler=<<qualify \"$:/temp/NewFieldName\">> newFieldValueTiddler=<<qualify \"$:/temp/NewFieldValue\">>>\n<$keyboard key=\"((cancel-edit-tiddler))\" message=\"tm-cancel-tiddler\">\n<$keyboard key=\"((save-tiddler))\" actions=<<save-tiddler-actions>>>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/EditTemplate]!has[draft.of]]\" variable=\"listItem\">\n<$set name=\"tv-config-toolbar-class\" filter=\"[<tv-config-toolbar-class>] [<listItem>encodeuricomponent[]addprefix[tc-btn-]]\">\n<$transclude tiddler=<<listItem>>/>\n</$set>\n</$list>\n</$keyboard>\n</$keyboard>\n</$vars>\n</$fieldmangler>\n</div>\n"
},
"$:/core/ui/Buttons/cancel": {
"title": "$:/core/ui/Buttons/cancel",
"tags": "$:/tags/EditToolbar",
"caption": "{{$:/core/images/cancel-button}} {{$:/language/Buttons/Cancel/Caption}}",
"description": "{{$:/language/Buttons/Cancel/Hint}}",
"text": "<$button message=\"tm-cancel-tiddler\" tooltip={{$:/language/Buttons/Cancel/Hint}} aria-label={{$:/language/Buttons/Cancel/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/cancel-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Cancel/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/delete": {
"title": "$:/core/ui/Buttons/delete",
"tags": "$:/tags/EditToolbar $:/tags/ViewToolbar",
"caption": "{{$:/core/images/delete-button}} {{$:/language/Buttons/Delete/Caption}}",
"description": "{{$:/language/Buttons/Delete/Hint}}",
"text": "<$button message=\"tm-delete-tiddler\" tooltip={{$:/language/Buttons/Delete/Hint}} aria-label={{$:/language/Buttons/Delete/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/delete-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Delete/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/save": {
"title": "$:/core/ui/Buttons/save",
"tags": "$:/tags/EditToolbar",
"caption": "{{$:/core/images/done-button}} {{$:/language/Buttons/Save/Caption}}",
"description": "{{$:/language/Buttons/Save/Hint}}",
"text": "\\define save-tiddler-button()\n<$fieldmangler><$button tooltip={{$:/language/Buttons/Save/Hint}} aria-label={{$:/language/Buttons/Save/Caption}} class=<<tv-config-toolbar-class>>>\n<<save-tiddler-actions>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/done-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Save/Caption}}/></span>\n</$list>\n</$button></$fieldmangler>\n\\end\n<<save-tiddler-button>>\n"
},
"$:/core/ui/EditorToolbar/bold": {
"title": "$:/core/ui/EditorToolbar/bold",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/bold",
"caption": "{{$:/language/Buttons/Bold/Caption}}",
"description": "{{$:/language/Buttons/Bold/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((bold))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"''\"\n\tsuffix=\"''\"\n/>\n"
},
"$:/core/ui/EditorToolbar/clear-dropdown": {
"title": "$:/core/ui/EditorToolbar/clear-dropdown",
"text": "''{{$:/language/Buttons/Clear/Hint}}''\n\n<div class=\"tc-colour-chooser\">\n\n<$macrocall $name=\"colour-picker\" actions=\"\"\"\n\n<$action-sendmessage\n\t$message=\"tm-edit-bitmap-operation\"\n\t$param=\"clear\"\n\tcolour=<<colour-picker-value>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n\"\"\"/>\n\n</div>\n"
},
"$:/core/ui/EditorToolbar/clear": {
"title": "$:/core/ui/EditorToolbar/clear",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/erase",
"caption": "{{$:/language/Buttons/Clear/Caption}}",
"description": "{{$:/language/Buttons/Clear/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/clear-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/editor-height-dropdown": {
"title": "$:/core/ui/EditorToolbar/editor-height-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/EditorHeight/\n''<<lingo Hint>>''\n\n<$radio tiddler=\"$:/config/TextEditor/EditorHeight/Mode\" value=\"auto\"> {{$:/core/images/auto-height}} <<lingo Caption/Auto>></$radio>\n\n<$radio tiddler=\"$:/config/TextEditor/EditorHeight/Mode\" value=\"fixed\"> {{$:/core/images/fixed-height}} <<lingo Caption/Fixed>> <$edit-text tag=\"input\" tiddler=\"$:/config/TextEditor/EditorHeight/Height\" default=\"100px\"/></$radio>\n"
},
"$:/core/ui/EditorToolbar/editor-height": {
"title": "$:/core/ui/EditorToolbar/editor-height",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/fixed-height",
"custom-icon": "yes",
"caption": "{{$:/language/Buttons/EditorHeight/Caption}}",
"description": "{{$:/language/Buttons/EditorHeight/Hint}}",
"condition": "[<targetTiddler>type[]] [<targetTiddler>get[type]prefix[text/]] +[first[]]",
"dropdown": "$:/core/ui/EditorToolbar/editor-height-dropdown",
"text": "<$reveal tag=\"span\" state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"fixed\">\n{{$:/core/images/fixed-height}}\n</$reveal>\n<$reveal tag=\"span\" state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"auto\">\n{{$:/core/images/auto-height}}\n</$reveal>\n"
},
"$:/core/ui/EditorToolbar/excise-dropdown": {
"title": "$:/core/ui/EditorToolbar/excise-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/Excise/\n\n\\define body(config-title)\n''<<lingo Hint>>''\n\n<<lingo Caption/NewTitle>> <$edit-text tag=\"input\" tiddler=\"$config-title$/new-title\" default=\"\" focus=\"true\"/>\n\n<$set name=\"new-title\" value={{$config-title$/new-title}}>\n<$list filter=\"\"\"[<new-title>is[tiddler]]\"\"\">\n<div class=\"tc-error\">\n<<lingo Caption/TiddlerExists>>\n</div>\n</$list>\n</$set>\n\n<$checkbox tiddler=\"\"\"$config-title$/tagnew\"\"\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"false\"> <<lingo Caption/Tag>></$checkbox>\n\n<<lingo Caption/Replace>> <$select tiddler=\"\"\"$config-title$/type\"\"\" default=\"transclude\">\n<option value=\"link\"><<lingo Caption/Replace/Link>></option>\n<option value=\"transclude\"><<lingo Caption/Replace/Transclusion>></option>\n<option value=\"macro\"><<lingo Caption/Replace/Macro>></option>\n</$select>\n\n<$reveal state=\"\"\"$config-title$/type\"\"\" type=\"match\" text=\"macro\">\n<<lingo Caption/MacroName>> <$edit-text tag=\"input\" tiddler=\"\"\"$config-title$/macro-title\"\"\" default=\"translink\"/>\n</$reveal>\n\n<$button>\n<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"excise\"\n\ttitle={{$config-title$/new-title}}\n\ttype={{$config-title$/type}}\n\tmacro={{$config-title$/macro-title}}\n\ttagnew={{$config-title$/tagnew}}\n/>\n<$action-deletetiddler\n\t$tiddler=\"$config-title$/new-title\"\n/>\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n<<lingo Caption/Excise>>\n</$button>\n\\end\n\n<$macrocall $name=\"body\" config-title=<<qualify \"$:/state/Excise/\">>/>\n"
},
"$:/core/ui/EditorToolbar/excise": {
"title": "$:/core/ui/EditorToolbar/excise",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/excise",
"caption": "{{$:/language/Buttons/Excise/Caption}}",
"description": "{{$:/language/Buttons/Excise/Hint}}",
"condition": "[<targetTiddler>type[]] [<targetTiddler>type[text/vnd.tiddlywiki]] +[first[]]",
"shortcuts": "((excise))",
"dropdown": "$:/core/ui/EditorToolbar/excise-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/heading-1": {
"title": "$:/core/ui/EditorToolbar/heading-1",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-1",
"caption": "{{$:/language/Buttons/Heading1/Caption}}",
"description": "{{$:/language/Buttons/Heading1/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"button-classes": "tc-text-editor-toolbar-item-start-group",
"shortcuts": "((heading-1))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"1\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-2": {
"title": "$:/core/ui/EditorToolbar/heading-2",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-2",
"caption": "{{$:/language/Buttons/Heading2/Caption}}",
"description": "{{$:/language/Buttons/Heading2/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-2))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"2\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-3": {
"title": "$:/core/ui/EditorToolbar/heading-3",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-3",
"caption": "{{$:/language/Buttons/Heading3/Caption}}",
"description": "{{$:/language/Buttons/Heading3/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-3))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"3\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-4": {
"title": "$:/core/ui/EditorToolbar/heading-4",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-4",
"caption": "{{$:/language/Buttons/Heading4/Caption}}",
"description": "{{$:/language/Buttons/Heading4/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-4))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"4\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-5": {
"title": "$:/core/ui/EditorToolbar/heading-5",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-5",
"caption": "{{$:/language/Buttons/Heading5/Caption}}",
"description": "{{$:/language/Buttons/Heading5/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-5))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"5\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-6": {
"title": "$:/core/ui/EditorToolbar/heading-6",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-6",
"caption": "{{$:/language/Buttons/Heading6/Caption}}",
"description": "{{$:/language/Buttons/Heading6/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-6))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"6\"\n/>\n"
},
"$:/core/ui/EditorToolbar/italic": {
"title": "$:/core/ui/EditorToolbar/italic",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/italic",
"caption": "{{$:/language/Buttons/Italic/Caption}}",
"description": "{{$:/language/Buttons/Italic/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((italic))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"//\"\n\tsuffix=\"//\"\n/>\n"
},
"$:/core/ui/EditorToolbar/line-width-dropdown": {
"title": "$:/core/ui/EditorToolbar/line-width-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/LineWidth/\n\n\\define toolbar-line-width-inner()\n<$button tag=\"a\" tooltip=\"\"\"$(line-width)$\"\"\">\n\n<$action-setfield\n\t$tiddler=\"$:/config/BitmapEditor/LineWidth\"\n\t$value=\"$(line-width)$\"\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<div style=\"display: inline-block; margin: 4px calc(80px - $(line-width)$); background-color: #000; width: calc(100px + $(line-width)$ * 2); height: $(line-width)$; border-radius: 120px; vertical-align: middle;\"/>\n\n<span style=\"margin-left: 8px;\">\n\n<$text text=\"\"\"$(line-width)$\"\"\"/>\n\n<$reveal state=\"$:/config/BitmapEditor/LineWidth\" type=\"match\" text=\"\"\"$(line-width)$\"\"\" tag=\"span\">\n\n<$entity entity=\" \"/>\n\n<$entity entity=\"✓\"/>\n\n</$reveal>\n\n</span>\n\n</$button>\n\\end\n\n''<<lingo Hint>>''\n\n<$list filter={{$:/config/BitmapEditor/LineWidths}} variable=\"line-width\">\n\n<<toolbar-line-width-inner>>\n\n</$list>\n"
},
"$:/core/ui/EditorToolbar/line-width": {
"title": "$:/core/ui/EditorToolbar/line-width",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/line-width",
"caption": "{{$:/language/Buttons/LineWidth/Caption}}",
"description": "{{$:/language/Buttons/LineWidth/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/line-width-dropdown",
"text": "<$text text={{$:/config/BitmapEditor/LineWidth}}/>"
},
"$:/core/ui/EditorToolbar/link-dropdown": {
"title": "$:/core/ui/EditorToolbar/link-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/Link/\n\n\\define add-link-actions()\n<$action-sendmessage $message=\"tm-edit-text-operation\" $param=\"make-link\" text={{$(linkTiddler)$}} />\n<$action-deletetiddler $tiddler=<<dropdown-state>> />\n<$action-deletetiddler $tiddler=<<searchTiddler>> />\n<$action-deletetiddler $tiddler=<<linkTiddler>> />\n\\end\n\n\\define external-link()\n<$button class=\"tc-btn-invisible\" style=\"width: auto; display: inline-block; background-colour: inherit;\" actions=<<add-link-actions>>>\n{{$:/core/images/chevron-right}}\n</$button>\n\\end\n\n\\define body(config-title)\n''<<lingo Hint>>''\n\n<$vars searchTiddler=\"\"\"$config-title$/search\"\"\" linkTiddler=\"\"\"$config-title$/link\"\"\" linktext=\"\" >\n\n<$vars linkTiddler=<<searchTiddler>>>\n<$keyboard key=\"ENTER\" actions=<<add-link-actions>>>\n<$edit-text tiddler=<<searchTiddler>> type=\"search\" tag=\"input\" focus=\"true\" placeholder={{$:/language/Search/Search}} default=\"\"/>\n<$reveal tag=\"span\" state=<<searchTiddler>> type=\"nomatch\" text=\"\">\n<<external-link>>\n<$button class=\"tc-btn-invisible\" style=\"width: auto; display: inline-block; background-colour: inherit;\">\n<$action-setfield $tiddler=<<searchTiddler>> text=\"\" />\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n</$keyboard>\n</$vars>\n\n<$reveal tag=\"div\" state=<<searchTiddler>> type=\"nomatch\" text=\"\">\n\n<$linkcatcher actions=<<add-link-actions>> to=<<linkTiddler>>>\n\n{{$:/core/ui/SearchResults}}\n\n</$linkcatcher>\n\n</$reveal>\n\n</$vars>\n\n\\end\n\n<$macrocall $name=\"body\" config-title=<<qualify \"$:/state/Link/\">>/>"
},
"$:/core/ui/EditorToolbar/link": {
"title": "$:/core/ui/EditorToolbar/link",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/link",
"caption": "{{$:/language/Buttons/Link/Caption}}",
"description": "{{$:/language/Buttons/Link/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"button-classes": "tc-text-editor-toolbar-item-start-group",
"shortcuts": "((link))",
"dropdown": "$:/core/ui/EditorToolbar/link-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/linkify": {
"title": "$:/core/ui/EditorToolbar/linkify",
"caption": "{{$:/language/Buttons/Linkify/Caption}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"description": "{{$:/language/Buttons/Linkify/Hint}}",
"icon": "$:/core/images/linkify",
"list-before": "$:/core/ui/EditorToolbar/mono-block",
"shortcuts": "((linkify))",
"tags": "$:/tags/EditorToolbar",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"[[\"\n\tsuffix=\"]]\"\n/>\n"
},
"$:/core/ui/EditorToolbar/list-bullet": {
"title": "$:/core/ui/EditorToolbar/list-bullet",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/list-bullet",
"caption": "{{$:/language/Buttons/ListBullet/Caption}}",
"description": "{{$:/language/Buttons/ListBullet/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((list-bullet))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"*\"\n\tcount=\"1\"\n/>\n"
},
"$:/core/ui/EditorToolbar/list-number": {
"title": "$:/core/ui/EditorToolbar/list-number",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/list-number",
"caption": "{{$:/language/Buttons/ListNumber/Caption}}",
"description": "{{$:/language/Buttons/ListNumber/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((list-number))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"#\"\n\tcount=\"1\"\n/>\n"
},
"$:/core/ui/EditorToolbar/mono-block": {
"title": "$:/core/ui/EditorToolbar/mono-block",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/mono-block",
"caption": "{{$:/language/Buttons/MonoBlock/Caption}}",
"description": "{{$:/language/Buttons/MonoBlock/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"button-classes": "tc-text-editor-toolbar-item-start-group",
"shortcuts": "((mono-block))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-lines\"\n\tprefix=\"\n```\"\n\tsuffix=\"```\"\n/>\n"
},
"$:/core/ui/EditorToolbar/mono-line": {
"title": "$:/core/ui/EditorToolbar/mono-line",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/mono-line",
"caption": "{{$:/language/Buttons/MonoLine/Caption}}",
"description": "{{$:/language/Buttons/MonoLine/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((mono-line))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"`\"\n\tsuffix=\"`\"\n/>\n"
},
"$:/core/ui/EditorToolbar/more-dropdown": {
"title": "$:/core/ui/EditorToolbar/more-dropdown",
"text": "\\define config-title()\n$:/config/EditorToolbarButtons/Visibility/$(toolbarItem)$\n\\end\n\n\\define conditional-button()\n<$list filter={{$(toolbarItem)$!!condition}} variable=\"condition\">\n<$transclude tiddler=\"$:/core/ui/EditTemplate/body/toolbar/button\" mode=\"inline\"/> <$transclude tiddler=<<toolbarItem>> field=\"description\"/>\n</$list>\n\\end\n\n<div class=\"tc-text-editor-toolbar-more\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/EditorToolbar]!has[draft.of]] -[[$:/core/ui/EditorToolbar/more]]\">\n<$reveal type=\"match\" state=<<config-visibility-title>> text=\"hide\" tag=\"div\">\n<<conditional-button>>\n</$reveal>\n</$list>\n</div>\n"
},
"$:/core/ui/EditorToolbar/more": {
"title": "$:/core/ui/EditorToolbar/more",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/down-arrow",
"caption": "{{$:/language/Buttons/More/Caption}}",
"description": "{{$:/language/Buttons/More/Hint}}",
"condition": "[<targetTiddler>]",
"dropdown": "$:/core/ui/EditorToolbar/more-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/opacity-dropdown": {
"title": "$:/core/ui/EditorToolbar/opacity-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/Opacity/\n\n\\define toolbar-opacity-inner()\n<$button tag=\"a\" tooltip=\"\"\"$(opacity)$\"\"\">\n\n<$action-setfield\n\t$tiddler=\"$:/config/BitmapEditor/Opacity\"\n\t$value=\"$(opacity)$\"\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<div style=\"display: inline-block; vertical-align: middle; background-color: $(current-paint-colour)$; opacity: $(opacity)$; width: 1em; height: 1em; border-radius: 50%;\"/>\n\n<span style=\"margin-left: 8px;\">\n\n<$text text=\"\"\"$(opacity)$\"\"\"/>\n\n<$reveal state=\"$:/config/BitmapEditor/Opacity\" type=\"match\" text=\"\"\"$(opacity)$\"\"\" tag=\"span\">\n\n<$entity entity=\" \"/>\n\n<$entity entity=\"✓\"/>\n\n</$reveal>\n\n</span>\n\n</$button>\n\\end\n\n\\define toolbar-opacity()\n''<<lingo Hint>>''\n\n<$list filter={{$:/config/BitmapEditor/Opacities}} variable=\"opacity\">\n\n<<toolbar-opacity-inner>>\n\n</$list>\n\\end\n\n<$set name=\"current-paint-colour\" value={{$:/config/BitmapEditor/Colour}}>\n\n<$set name=\"current-opacity\" value={{$:/config/BitmapEditor/Opacity}}>\n\n<<toolbar-opacity>>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/EditorToolbar/opacity": {
"title": "$:/core/ui/EditorToolbar/opacity",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/opacity",
"caption": "{{$:/language/Buttons/Opacity/Caption}}",
"description": "{{$:/language/Buttons/Opacity/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/opacity-dropdown",
"text": "<$text text={{$:/config/BitmapEditor/Opacity}}/>\n"
},
"$:/core/ui/EditorToolbar/paint-dropdown": {
"title": "$:/core/ui/EditorToolbar/paint-dropdown",
"text": "''{{$:/language/Buttons/Paint/Hint}}''\n\n<$macrocall $name=\"colour-picker\" actions=\"\"\"\n\n<$action-setfield\n\t$tiddler=\"$:/config/BitmapEditor/Colour\"\n\t$value=<<colour-picker-value>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n\"\"\"/>\n"
},
"$:/core/ui/EditorToolbar/paint": {
"title": "$:/core/ui/EditorToolbar/paint",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/paint",
"caption": "{{$:/language/Buttons/Paint/Caption}}",
"description": "{{$:/language/Buttons/Paint/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/paint-dropdown",
"text": "\\define toolbar-paint()\n<div style=\"display: inline-block; vertical-align: middle; background-color: $(colour-picker-value)$; width: 1em; height: 1em; border-radius: 50%;\"/>\n\\end\n<$set name=\"colour-picker-value\" value={{$:/config/BitmapEditor/Colour}}>\n<<toolbar-paint>>\n</$set>\n"
},
"$:/core/ui/EditorToolbar/picture-dropdown": {
"title": "$:/core/ui/EditorToolbar/picture-dropdown",
"text": "\\define replacement-text()\n[img[$(imageTitle)$]]\n\\end\n\n''{{$:/language/Buttons/Picture/Hint}}''\n\n<$macrocall $name=\"image-picker\" actions=\"\"\"\n\n<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"replace-selection\"\n\ttext=<<replacement-text>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n\"\"\"/>\n"
},
"$:/core/ui/EditorToolbar/picture": {
"title": "$:/core/ui/EditorToolbar/picture",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/picture",
"caption": "{{$:/language/Buttons/Picture/Caption}}",
"description": "{{$:/language/Buttons/Picture/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((picture))",
"dropdown": "$:/core/ui/EditorToolbar/picture-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/preview-type-dropdown": {
"title": "$:/core/ui/EditorToolbar/preview-type-dropdown",
"text": "\\define preview-type-button()\n<$button tag=\"a\">\n\n<$action-setfield $tiddler=\"$:/state/editpreviewtype\" $value=\"$(previewType)$\"/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<$transclude tiddler=<<previewType>> field=\"caption\" mode=\"inline\">\n\n<$view tiddler=<<previewType>> field=\"title\" mode=\"inline\"/>\n\n</$transclude> \n\n<$reveal tag=\"span\" state=\"$:/state/editpreviewtype\" type=\"match\" text=<<previewType>> default=\"$:/core/ui/EditTemplate/body/preview/output\">\n\n<$entity entity=\" \"/>\n\n<$entity entity=\"✓\"/>\n\n</$reveal>\n\n</$button>\n\\end\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/EditPreview]!has[draft.of]]\" variable=\"previewType\">\n\n<<preview-type-button>>\n\n</$list>\n"
},
"$:/core/ui/EditorToolbar/preview-type": {
"title": "$:/core/ui/EditorToolbar/preview-type",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/chevron-down",
"caption": "{{$:/language/Buttons/PreviewType/Caption}}",
"description": "{{$:/language/Buttons/PreviewType/Hint}}",
"condition": "[all[shadows+tiddlers]tag[$:/tags/EditPreview]!has[draft.of]butfirst[]limit[1]]",
"button-classes": "tc-text-editor-toolbar-item-adjunct",
"dropdown": "$:/core/ui/EditorToolbar/preview-type-dropdown"
},
"$:/core/ui/EditorToolbar/preview": {
"title": "$:/core/ui/EditorToolbar/preview",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/preview-open",
"custom-icon": "yes",
"caption": "{{$:/language/Buttons/Preview/Caption}}",
"description": "{{$:/language/Buttons/Preview/Hint}}",
"condition": "[<targetTiddler>]",
"button-classes": "tc-text-editor-toolbar-item-start-group",
"shortcuts": "((preview))",
"text": "<$reveal state=\"$:/state/showeditpreview\" type=\"match\" text=\"yes\" tag=\"span\">\n{{$:/core/images/preview-open}}\n<$action-setfield $tiddler=\"$:/state/showeditpreview\" $value=\"no\"/>\n</$reveal>\n<$reveal state=\"$:/state/showeditpreview\" type=\"nomatch\" text=\"yes\" tag=\"span\">\n{{$:/core/images/preview-closed}}\n<$action-setfield $tiddler=\"$:/state/showeditpreview\" $value=\"yes\"/>\n</$reveal>\n"
},
"$:/core/ui/EditorToolbar/quote": {
"title": "$:/core/ui/EditorToolbar/quote",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/quote",
"caption": "{{$:/language/Buttons/Quote/Caption}}",
"description": "{{$:/language/Buttons/Quote/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((quote))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-lines\"\n\tprefix=\"\n<<<\"\n\tsuffix=\"<<<\"\n/>\n"
},
"$:/core/ui/EditorToolbar/rotate-left": {
"title": "$:/core/ui/EditorToolbar/rotate-left",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/rotate-left",
"caption": "{{$:/language/Buttons/RotateLeft/Caption}}",
"description": "{{$:/language/Buttons/RotateLeft/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-bitmap-operation\"\n\t$param=\"rotate-left\"\n/>\n"
},
"$:/core/ui/EditorToolbar/size-dropdown": {
"title": "$:/core/ui/EditorToolbar/size-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/Size/\n\n\\define toolbar-button-size-preset(config-title)\n<$set name=\"width\" filter=\"$(sizePair)$ +[first[]]\">\n\n<$set name=\"height\" filter=\"$(sizePair)$ +[last[]]\">\n\n<$button tag=\"a\">\n\n<$action-setfield\n\t$tiddler=\"\"\"$config-title$/new-width\"\"\"\n\t$value=<<width>>\n/>\n\n<$action-setfield\n\t$tiddler=\"\"\"$config-title$/new-height\"\"\"\n\t$value=<<height>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=\"\"\"$config-title$/presets-popup\"\"\"\n/>\n\n<$text text=<<width>>/> × <$text text=<<height>>/>\n\n</$button>\n\n</$set>\n\n</$set>\n\\end\n\n\\define toolbar-button-size(config-title)\n''{{$:/language/Buttons/Size/Hint}}''\n\n<<lingo Caption/Width>> <$edit-text tag=\"input\" tiddler=\"\"\"$config-title$/new-width\"\"\" default=<<tv-bitmap-editor-width>> focus=\"true\" size=\"8\"/> <<lingo Caption/Height>> <$edit-text tag=\"input\" tiddler=\"\"\"$config-title$/new-height\"\"\" default=<<tv-bitmap-editor-height>> size=\"8\"/> <$button popup=\"\"\"$config-title$/presets-popup\"\"\" class=\"tc-btn-invisible tc-popup-keep\" style=\"width: auto; display: inline-block; background-colour: inherit;\" selectedClass=\"tc-selected\">\n{{$:/core/images/down-arrow}}\n</$button>\n\n<$reveal tag=\"span\" state=\"\"\"$config-title$/presets-popup\"\"\" type=\"popup\" position=\"belowleft\" animate=\"yes\">\n\n<div class=\"tc-drop-down tc-popup-keep\">\n\n<$list filter={{$:/config/BitmapEditor/ImageSizes}} variable=\"sizePair\">\n\n<$macrocall $name=\"toolbar-button-size-preset\" config-title=\"$config-title$\"/>\n\n</$list>\n\n</div>\n\n</$reveal>\n\n<$button>\n<$action-sendmessage\n\t$message=\"tm-edit-bitmap-operation\"\n\t$param=\"resize\"\n\twidth={{$config-title$/new-width}}\n\theight={{$config-title$/new-height}}\n/>\n<$action-deletetiddler\n\t$tiddler=\"\"\"$config-title$/new-width\"\"\"\n/>\n<$action-deletetiddler\n\t$tiddler=\"\"\"$config-title$/new-height\"\"\"\n/>\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n<<lingo Caption/Resize>>\n</$button>\n\\end\n\n<$macrocall $name=\"toolbar-button-size\" config-title=<<qualify \"$:/state/Size/\">>/>\n"
},
"$:/core/ui/EditorToolbar/size": {
"title": "$:/core/ui/EditorToolbar/size",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/size",
"caption": "{{$:/language/Buttons/Size/Caption}}",
"description": "{{$:/language/Buttons/Size/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/size-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/stamp-dropdown": {
"title": "$:/core/ui/EditorToolbar/stamp-dropdown",
"text": "\\define toolbar-button-stamp-inner()\n<$button tag=\"a\">\n\n<$list filter=\"[[$(snippetTitle)$]addsuffix[/prefix]is[missing]removesuffix[/prefix]addsuffix[/suffix]is[missing]]\">\n\n<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"replace-selection\"\n\ttext={{$(snippetTitle)$}}\n/>\n\n</$list>\n\n\n<$list filter=\"[[$(snippetTitle)$]addsuffix[/prefix]is[missing]removesuffix[/prefix]addsuffix[/suffix]!is[missing]] [[$(snippetTitle)$]addsuffix[/prefix]!is[missing]removesuffix[/prefix]addsuffix[/suffix]is[missing]] [[$(snippetTitle)$]addsuffix[/prefix]!is[missing]removesuffix[/prefix]addsuffix[/suffix]!is[missing]]\">\n\n<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix={{{ [[$(snippetTitle)$]addsuffix[/prefix]get[text]] }}}\nsuffix={{{ [[$(snippetTitle)$]addsuffix[/suffix]get[text]] }}}\n/>\n\n</$list>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<$transclude tiddler=<<snippetTitle>> field=\"caption\" mode=\"inline\">\n\n<$view tiddler=<<snippetTitle>> field=\"title\" />\n\n</$transclude>\n\n</$button>\n\\end\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TextEditor/Snippet]!has[draft.of]sort[caption]]\" variable=\"snippetTitle\">\n\n<<toolbar-button-stamp-inner>>\n\n</$list>\n\n----\n\n<$button tag=\"a\">\n\n<$action-sendmessage\n\t$message=\"tm-new-tiddler\"\n\ttags=\"$:/tags/TextEditor/Snippet\"\n\tcaption={{$:/language/Buttons/Stamp/New/Title}}\n\ttext={{$:/language/Buttons/Stamp/New/Text}}\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<em>\n\n<$text text={{$:/language/Buttons/Stamp/Caption/New}}/>\n\n</em>\n\n</$button>\n"
},
"$:/core/ui/EditorToolbar/stamp": {
"title": "$:/core/ui/EditorToolbar/stamp",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/stamp",
"caption": "{{$:/language/Buttons/Stamp/Caption}}",
"description": "{{$:/language/Buttons/Stamp/Hint}}",
"condition": "[<targetTiddler>type[]] [<targetTiddler>get[type]prefix[text/]] +[first[]]",
"shortcuts": "((stamp))",
"dropdown": "$:/core/ui/EditorToolbar/stamp-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/strikethrough": {
"title": "$:/core/ui/EditorToolbar/strikethrough",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/strikethrough",
"caption": "{{$:/language/Buttons/Strikethrough/Caption}}",
"description": "{{$:/language/Buttons/Strikethrough/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((strikethrough))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"~~\"\n\tsuffix=\"~~\"\n/>\n"
},
"$:/core/ui/EditorToolbar/subscript": {
"title": "$:/core/ui/EditorToolbar/subscript",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/subscript",
"caption": "{{$:/language/Buttons/Subscript/Caption}}",
"description": "{{$:/language/Buttons/Subscript/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((subscript))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\",,\"\n\tsuffix=\",,\"\n/>\n"
},
"$:/core/ui/EditorToolbar/superscript": {
"title": "$:/core/ui/EditorToolbar/superscript",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/superscript",
"caption": "{{$:/language/Buttons/Superscript/Caption}}",
"description": "{{$:/language/Buttons/Superscript/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((superscript))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"^^\"\n\tsuffix=\"^^\"\n/>\n"
},
"$:/core/ui/EditorToolbar/transcludify": {
"title": "$:/core/ui/EditorToolbar/transcludify",
"caption": "{{$:/language/Buttons/Transcludify/Caption}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"description": "{{$:/language/Buttons/Transcludify/Hint}}",
"icon": "$:/core/images/transcludify",
"list-before": "$:/core/ui/EditorToolbar/mono-block",
"shortcuts": "((transcludify))",
"tags": "$:/tags/EditorToolbar",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"{{\"\n\tsuffix=\"}}\"\n/>\n"
},
"$:/core/ui/EditorToolbar/underline": {
"title": "$:/core/ui/EditorToolbar/underline",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/underline",
"caption": "{{$:/language/Buttons/Underline/Caption}}",
"description": "{{$:/language/Buttons/Underline/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((underline))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"__\"\n\tsuffix=\"__\"\n/>\n"
},
"$:/core/Filters/AllTags": {
"title": "$:/core/Filters/AllTags",
"tags": "$:/tags/Filter",
"filter": "[tags[]!is[system]sort[title]]",
"description": "{{$:/language/Filters/AllTags}}",
"text": ""
},
"$:/core/Filters/AllTiddlers": {
"title": "$:/core/Filters/AllTiddlers",
"tags": "$:/tags/Filter",
"filter": "[!is[system]sort[title]]",
"description": "{{$:/language/Filters/AllTiddlers}}",
"text": ""
},
"$:/core/Filters/Drafts": {
"title": "$:/core/Filters/Drafts",
"tags": "$:/tags/Filter",
"filter": "[has[draft.of]sort[title]]",
"description": "{{$:/language/Filters/Drafts}}",
"text": ""
},
"$:/core/Filters/Missing": {
"title": "$:/core/Filters/Missing",
"tags": "$:/tags/Filter",
"filter": "[all[missing]sort[title]]",
"description": "{{$:/language/Filters/Missing}}",
"text": ""
},
"$:/core/Filters/Orphans": {
"title": "$:/core/Filters/Orphans",
"tags": "$:/tags/Filter",
"filter": "[all[orphans]sort[title]]",
"description": "{{$:/language/Filters/Orphans}}",
"text": ""
},
"$:/core/Filters/OverriddenShadowTiddlers": {
"title": "$:/core/Filters/OverriddenShadowTiddlers",
"tags": "$:/tags/Filter",
"filter": "[is[shadow]]",
"description": "{{$:/language/Filters/OverriddenShadowTiddlers}}",
"text": ""
},
"$:/core/Filters/RecentSystemTiddlers": {
"title": "$:/core/Filters/RecentSystemTiddlers",
"tags": "$:/tags/Filter",
"filter": "[has[modified]!sort[modified]limit[50]]",
"description": "{{$:/language/Filters/RecentSystemTiddlers}}",
"text": ""
},
"$:/core/Filters/RecentTiddlers": {
"title": "$:/core/Filters/RecentTiddlers",
"tags": "$:/tags/Filter",
"filter": "[!is[system]has[modified]!sort[modified]limit[50]]",
"description": "{{$:/language/Filters/RecentTiddlers}}",
"text": ""
},
"$:/core/Filters/SessionTiddlers": {
"title": "$:/core/Filters/SessionTiddlers",
"tags": "$:/tags/Filter",
"filter": "[haschanged[]]",
"description": "{{$:/language/Filters/SessionTiddlers}}",
"text": ""
},
"$:/core/Filters/ShadowTiddlers": {
"title": "$:/core/Filters/ShadowTiddlers",
"tags": "$:/tags/Filter",
"filter": "[all[shadows]sort[title]]",
"description": "{{$:/language/Filters/ShadowTiddlers}}",
"text": ""
},
"$:/core/Filters/StoryList": {
"title": "$:/core/Filters/StoryList",
"tags": "$:/tags/Filter",
"filter": "[list[$:/StoryList]] -$:/AdvancedSearch",
"description": "{{$:/language/Filters/StoryList}}",
"text": ""
},
"$:/core/Filters/SystemTags": {
"title": "$:/core/Filters/SystemTags",
"tags": "$:/tags/Filter",
"filter": "[all[shadows+tiddlers]tags[]is[system]sort[title]]",
"description": "{{$:/language/Filters/SystemTags}}",
"text": ""
},
"$:/core/Filters/SystemTiddlers": {
"title": "$:/core/Filters/SystemTiddlers",
"tags": "$:/tags/Filter",
"filter": "[is[system]sort[title]]",
"description": "{{$:/language/Filters/SystemTiddlers}}",
"text": ""
},
"$:/core/Filters/TypedTiddlers": {
"title": "$:/core/Filters/TypedTiddlers",
"tags": "$:/tags/Filter",
"filter": "[!is[system]has[type]each[type]sort[type]] -[type[text/vnd.tiddlywiki]]",
"description": "{{$:/language/Filters/TypedTiddlers}}",
"text": ""
},
"$:/core/ui/ImportListing": {
"title": "$:/core/ui/ImportListing",
"text": "\\define lingo-base() $:/language/Import/\n\n\\define messageField()\nmessage-$(payloadTiddler)$\n\\end\n\n\\define selectionField()\nselection-$(payloadTiddler)$\n\\end\n\n\\define previewPopupState()\n$(currentTiddler)$!!popup-$(payloadTiddler)$\n\\end\n\n\\define select-all-actions()\n<$list filter=\"[all[current]plugintiddlers[]sort[title]]\" variable=\"payloadTiddler\">\n<$action-setfield $field={{{ [<payloadTiddler>addprefix[selection-]] }}} $value={{$:/state/import/select-all}}/>\n</$list>\n\\end\n\n<table>\n<tbody>\n<tr>\n<th>\n<$checkbox tiddler=\"$:/state/import/select-all\" field=\"text\" checked=\"checked\" unchecked=\"unchecked\" default=\"checked\" actions=<<select-all-actions>>>\n<<lingo Listing/Select/Caption>>\n</$checkbox>\n</th>\n<th>\n<<lingo Listing/Title/Caption>>\n</th>\n<th>\n<<lingo Listing/Status/Caption>>\n</th>\n</tr>\n<$list filter=\"[all[current]plugintiddlers[]sort[title]]\" variable=\"payloadTiddler\">\n<tr>\n<td>\n<$checkbox field=<<selectionField>> checked=\"checked\" unchecked=\"unchecked\" default=\"checked\"/>\n</td>\n<td>\n<$reveal type=\"nomatch\" stateTitle=<<previewPopupState>> text=\"yes\" tag=\"div\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" setTitle=<<previewPopupState>> setTo=\"yes\">\n{{$:/core/images/right-arrow}} <$text text=<<payloadTiddler>>/>\n</$button>\n</$reveal>\n<$reveal type=\"match\" stateTitle=<<previewPopupState>> text=\"yes\" tag=\"div\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" setTitle=<<previewPopupState>> setTo=\"no\">\n{{$:/core/images/down-arrow}} <$text text=<<payloadTiddler>>/>\n</$button>\n</$reveal>\n</td>\n<td>\n<$view field=<<messageField>>/>\n</td>\n</tr>\n<tr>\n<td colspan=\"3\">\n<$reveal type=\"match\" text=\"yes\" stateTitle=<<previewPopupState>> tag=\"div\">\n<$list filter=\"[{$:/state/importpreviewtype}has[text]]\" variable=\"listItem\" emptyMessage={{$:/core/ui/ImportPreviews/Text}}>\n<$transclude tiddler={{$:/state/importpreviewtype}}/>\n</$list>\n</$reveal>\n</td>\n</tr>\n</$list>\n</tbody>\n</table>\n"
},
"$:/core/ui/ImportPreviews/Diff": {
"title": "$:/core/ui/ImportPreviews/Diff",
"tags": "$:/tags/ImportPreview",
"caption": "{{$:/language/Import/Listing/Preview/Diff}}",
"text": "<$macrocall $name=\"compareTiddlerText\" sourceTiddlerTitle=<<payloadTiddler>> destTiddlerTitle=<<currentTiddler>> destSubTiddlerTitle=<<payloadTiddler>>/>\n"
},
"$:/core/ui/ImportPreviews/DiffFields": {
"title": "$:/core/ui/ImportPreviews/DiffFields",
"tags": "$:/tags/ImportPreview",
"caption": "{{$:/language/Import/Listing/Preview/DiffFields}}",
"text": "<$macrocall $name=\"compareTiddlers\" sourceTiddlerTitle=<<payloadTiddler>> destTiddlerTitle=<<currentTiddler>> destSubTiddlerTitle=<<payloadTiddler>> exclude=\"text\"/>\n"
},
"$:/core/ui/ImportPreviews/Fields": {
"title": "$:/core/ui/ImportPreviews/Fields",
"tags": "$:/tags/ImportPreview",
"caption": "{{$:/language/Import/Listing/Preview/Fields}}",
"text": "<table class=\"tc-view-field-table\">\n<tbody>\n<$list filter=\"[<payloadTiddler>subtiddlerfields<currentTiddler>sort[]] -text\" variable=\"fieldName\">\n<tr class=\"tc-view-field\">\n<td class=\"tc-view-field-name\">\n<$text text=<<fieldName>>/>\n</td>\n<td class=\"tc-view-field-value\">\n<$view field=<<fieldName>> tiddler=<<currentTiddler>> subtiddler=<<payloadTiddler>>/>\n</td>\n</tr>\n</$list>\n</tbody>\n</table>\n"
},
"$:/core/ui/ImportPreviews/Text": {
"title": "$:/core/ui/ImportPreviews/Text",
"tags": "$:/tags/ImportPreview",
"caption": "{{$:/language/Import/Listing/Preview/Text}}",
"text": "<$transclude tiddler=<<currentTiddler>> subtiddler=<<payloadTiddler>> mode=\"block\"/>\n"
},
"$:/core/ui/ImportPreviews/TextRaw": {
"title": "$:/core/ui/ImportPreviews/TextRaw",
"tags": "$:/tags/ImportPreview",
"caption": "{{$:/language/Import/Listing/Preview/TextRaw}}",
"text": "<pre><code><$view tiddler=<<currentTiddler>> subtiddler=<<payloadTiddler>> /></code></pre>"
},
"$:/core/ui/KeyboardShortcuts/advanced-search": {
"title": "$:/core/ui/KeyboardShortcuts/advanced-search",
"tags": "$:/tags/KeyboardShortcut",
"key": "((advanced-search))",
"text": "<$navigator story=\"$:/StoryList\" history=\"$:/HistoryList\">\n<$action-navigate $to=\"$:/AdvancedSearch\"/>\n<$action-sendmessage $message=\"tm-focus-selector\" $param=\"\"\"[data-tiddler-title=\"$:/AdvancedSearch\"] .tc-search input\"\"\"/>\n</$navigator>\n"
},
"$:/core/ui/KeyboardShortcuts/new-image": {
"title": "$:/core/ui/KeyboardShortcuts/new-image",
"tags": "$:/tags/KeyboardShortcut",
"key": "((new-image))",
"text": "<$navigator story=\"$:/StoryList\" history=\"$:/HistoryList\" openLinkFromInsideRiver={{$:/config/Navigation/openLinkFromInsideRiver}} openLinkFromOutsideRiver={{$:/config/Navigation/openLinkFromOutsideRiver}} relinkOnRename={{$:/config/RelinkOnRename}}>\n{{$:/core/ui/Actions/new-image}}\n</$navigator>\n"
},
"$:/core/ui/KeyboardShortcuts/new-journal": {
"title": "$:/core/ui/KeyboardShortcuts/new-journal",
"tags": "$:/tags/KeyboardShortcut",
"key": "((new-journal))",
"text": "<$navigator story=\"$:/StoryList\" history=\"$:/HistoryList\" openLinkFromInsideRiver={{$:/config/Navigation/openLinkFromInsideRiver}} openLinkFromOutsideRiver={{$:/config/Navigation/openLinkFromOutsideRiver}} relinkOnRename={{$:/config/RelinkOnRename}}>\n{{$:/core/ui/Actions/new-journal}}\n</$navigator>\n"
},
"$:/core/ui/KeyboardShortcuts/new-tiddler": {
"title": "$:/core/ui/KeyboardShortcuts/new-tiddler",
"tags": "$:/tags/KeyboardShortcut",
"key": "((new-tiddler))",
"text": "<$navigator story=\"$:/StoryList\" history=\"$:/HistoryList\" openLinkFromInsideRiver={{$:/config/Navigation/openLinkFromInsideRiver}} openLinkFromOutsideRiver={{$:/config/Navigation/openLinkFromOutsideRiver}} relinkOnRename={{$:/config/RelinkOnRename}}>\n{{$:/core/ui/Actions/new-tiddler}}\n</$navigator>\n"
},
"$:/core/ui/KeyboardShortcuts/sidebar-search": {
"title": "$:/core/ui/KeyboardShortcuts/sidebar-search",
"tags": "$:/tags/KeyboardShortcut",
"key": "((sidebar-search))",
"text": "<$action-sendmessage $message=\"tm-focus-selector\" $param=\".tc-search input\"/>\n"
},
"$:/core/ui/KeyboardShortcut/toggle-sidebar": {
"title": "$:/core/ui/KeyboardShortcut/toggle-sidebar",
"tags": "$:/tags/KeyboardShortcut",
"key": "((toggle-sidebar))",
"text": "<$list filter=\"[[$:/state/sidebar]is[missing]] [{$:/state/sidebar}removeprefix[yes]]\" emptyMessage=\"\"\"\n<$action-setfield $tiddler=\"$:/state/sidebar\" text=\"yes\"/>\n\"\"\">\n<$action-setfield $tiddler=\"$:/state/sidebar\" text=\"no\"/>\n</$list>\n"
},
"$:/core/ui/ListItemTemplate": {
"title": "$:/core/ui/ListItemTemplate",
"text": "<div class=\"tc-menu-list-item\">\n<$link />\n</div>"
},
"$:/Manager/ItemMain/Fields": {
"title": "$:/Manager/ItemMain/Fields",
"tags": "$:/tags/Manager/ItemMain",
"caption": "{{$:/language/Manager/Item/Fields}}",
"text": "<table>\n<tbody>\n<$list filter=\"[all[current]fields[]sort[title]] -text\" template=\"$:/core/ui/TiddlerFieldTemplate\" variable=\"listItem\"/>\n</tbody>\n</table>\n"
},
"$:/Manager/ItemMain/RawText": {
"title": "$:/Manager/ItemMain/RawText",
"tags": "$:/tags/Manager/ItemMain",
"caption": "{{$:/language/Manager/Item/RawText}}",
"text": "<pre><code><$view/></code></pre>\n"
},
"$:/Manager/ItemMain/WikifiedText": {
"title": "$:/Manager/ItemMain/WikifiedText",
"tags": "$:/tags/Manager/ItemMain",
"caption": "{{$:/language/Manager/Item/WikifiedText}}",
"text": "<$transclude mode=\"block\"/>\n"
},
"$:/Manager/ItemSidebar/Colour": {
"title": "$:/Manager/ItemSidebar/Colour",
"tags": "$:/tags/Manager/ItemSidebar",
"caption": "{{$:/language/Manager/Item/Colour}}",
"text": "\\define swatch-styles()\nheight: 1em;\nbackground-color: $(colour)$\n\\end\n\n<$vars colour={{!!color}}>\n<p style=<<swatch-styles>>/>\n</$vars>\n<p>\n<$edit-text field=\"color\" tag=\"input\" type=\"color\"/> / <$edit-text field=\"color\" tag=\"input\" type=\"text\" size=\"9\"/>\n</p>\n"
},
"$:/Manager/ItemSidebar/Icon": {
"title": "$:/Manager/ItemSidebar/Icon",
"tags": "$:/tags/Manager/ItemSidebar",
"caption": "{{$:/language/Manager/Item/Icon}}",
"text": "<p>\n<div class=\"tc-manager-icon-editor\">\n<$button popup=<<qualify \"$:/state/popup/image-picker\">> class=\"tc-btn-invisible\">\n<$transclude tiddler={{!!icon}}>\n{{$:/language/Manager/Item/Icon/None}}\n</$transclude>\n</$button>\n<div class=\"tc-block-dropdown-wrapper\" style=\"position: static;\">\n<$reveal state=<<qualify \"$:/state/popup/image-picker\">> type=\"nomatch\" text=\"\" default=\"\" tag=\"div\" class=\"tc-popup\">\n<div class=\"tc-block-dropdown tc-popup-keep\" style=\"width: 80%; left: 10%; right: 10%; padding: 0.5em;\">\n<$macrocall $name=\"image-picker-include-tagged-images\" actions=\"\"\"\n<$action-setfield $field=\"icon\" $value=<<imageTitle>>/>\n<$action-deletetiddler $tiddler=<<qualify \"$:/state/popup/image-picker\">>/>\n\"\"\"/>\n</div>\n</$reveal>\n</div>\n</div>\n</p>\n"
},
"$:/Manager/ItemSidebar/Tags": {
"title": "$:/Manager/ItemSidebar/Tags",
"tags": "$:/tags/Manager/ItemSidebar",
"caption": "{{$:/language/Manager/Item/Tags}}",
"text": "\\define tag-checkbox-actions()\n<$action-listops\n\t$tiddler=\"$:/config/Manager/RecentTags\"\n\t$subfilter=\"[<tag>] [list[$:/config/Manager/RecentTags]] +[limit[12]]\"\n/>\n\\end\n\n\\define tag-picker-actions()\n<<tag-checkbox-actions>>\n<$action-listops\n\t$tiddler=<<currentTiddler>>\n\t$field=\"tags\"\n\t$subfilter=\"[<tag>] [all[current]tags[]]\"\n/>\n\\end\n\n<p>\n<$list filter=\"[all[current]tags[]] [list[$:/config/Manager/RecentTags]] +[sort[title]] \" variable=\"tag\">\n<div>\n<$checkbox tiddler=<<currentTiddler>> tag=<<tag>> actions=<<tag-checkbox-actions>>>\n<$macrocall $name=\"tag-pill\" tag=<<tag>>/>\n</$checkbox>\n</div>\n</$list>\n</p>\n<p>\n<$macrocall $name=\"tag-picker\" actions=<<tag-picker-actions>>/>\n</p>\n"
},
"$:/Manager/ItemSidebar/Tools": {
"title": "$:/Manager/ItemSidebar/Tools",
"tags": "$:/tags/Manager/ItemSidebar",
"caption": "{{$:/language/Manager/Item/Tools}}",
"text": "<p>\n<$button to=<<currentTiddler>>>{{$:/core/images/link}} open</$button>\n</p>\n<p>\n<$button message=\"tm-edit-tiddler\" param=<<currentTiddler>>>{{$:/core/images/edit-button}} edit</$button>\n</p>\n"
},
"$:/Manager": {
"title": "$:/Manager",
"icon": "$:/core/images/list",
"color": "#bbb",
"text": "\\define lingo-base() $:/language/Manager/\n\n\\define list-item-content-item()\n<div class=\"tc-manager-list-item-content-item\">\n\t<$vars state-title=\"\"\"$:/state/popup/manager/item/$(listItem)$\"\"\">\n\t\t<$reveal state=<<state-title>> type=\"match\" text=\"show\" default=\"show\" tag=\"div\">\n\t\t\t<$button set=<<state-title>> setTo=\"hide\" class=\"tc-btn-invisible tc-manager-list-item-content-item-heading\">\n\t\t\t\t{{$:/core/images/down-arrow}} <$transclude tiddler=<<listItem>> field=\"caption\"/>\n\t\t\t</$button>\n\t\t</$reveal>\n\t\t<$reveal state=<<state-title>> type=\"nomatch\" text=\"show\" default=\"show\" tag=\"div\">\n\t\t\t<$button set=<<state-title>> setTo=\"show\" class=\"tc-btn-invisible tc-manager-list-item-content-item-heading\">\n\t\t\t\t{{$:/core/images/right-arrow}} <$transclude tiddler=<<listItem>> field=\"caption\"/>\n\t\t\t</$button>\n\t\t</$reveal>\n\t\t<$reveal state=<<state-title>> type=\"match\" text=\"show\" default=\"show\" tag=\"div\" class=\"tc-manager-list-item-content-item-body\">\n\t\t\t<$transclude tiddler=<<listItem>>/>\n\t\t</$reveal>\n\t</$vars>\n</div>\n\\end\n\n<div class=\"tc-manager-wrapper\">\n\t<div class=\"tc-manager-controls\">\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<<lingo Controls/Show/Prompt>> <$select tiddler=\"$:/config/Manager/Show\" default=\"tiddlers\">\n\t\t\t\t<option value=\"tiddlers\"><<lingo Controls/Show/Option/Tiddlers>></option>\n\t\t\t\t<option value=\"tags\"><<lingo Controls/Show/Option/Tags>></option>\n\t\t\t</$select>\n\t\t</div>\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<<lingo Controls/Search/Prompt>> <$edit-text tiddler=\"$:/config/Manager/Filter\" tag=\"input\" default=\"\" placeholder={{$:/language/Manager/Controls/Search/Placeholder}}/>\n\t\t</div>\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<<lingo Controls/FilterByTag/Prompt>> <$select tiddler=\"$:/config/Manager/Tag\" default=\"\">\n\t\t\t\t<option value=\"\"><<lingo Controls/FilterByTag/None>></option>\n\t\t\t\t<$list filter=\"[!is{$:/config/Manager/System}tags[]!is[system]sort[title]]\" variable=\"tag\">\n\t\t\t\t\t<option value=<<tag>>><$text text=<<tag>>/></option>\n\t\t\t\t</$list>\n\t\t\t</$select>\n\t\t</div>\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<<lingo Controls/Sort/Prompt>> <$select tiddler=\"$:/config/Manager/Sort\" default=\"title\">\n\t\t\t\t<optgroup label=\"Common\">\n\t\t\t\t\t<$list filter=\"title modified modifier created creator created\" variable=\"field\">\n\t\t\t\t\t\t<option value=<<field>>><$text text=<<field>>/></option>\n\t\t\t\t\t</$list>\n\t\t\t\t</optgroup>\n\t\t\t\t<optgroup label=\"All\">\n\t\t\t\t\t<$list filter=\"[all{$:/config/Manager/Show}!is{$:/config/Manager/System}fields[]sort[title]] -title -modified -modifier -created -creator -created\" variable=\"field\">\n\t\t\t\t\t\t<option value=<<field>>><$text text=<<field>>/></option>\n\t\t\t\t\t</$list>\n\t\t\t\t</optgroup>\n\t\t\t</$select>\n\t\t\t<$checkbox tiddler=\"$:/config/Manager/Order\" field=\"text\" checked=\"reverse\" unchecked=\"forward\" default=\"forward\">\n\t\t\t\t<<lingo Controls/Order/Prompt>>\n\t\t\t</$checkbox>\n\t\t</div>\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<$checkbox tiddler=\"$:/config/Manager/System\" field=\"text\" checked=\"\" unchecked=\"system\" default=\"system\">\n\t\t\t\t{{$:/language/SystemTiddlers/Include/Prompt}}\n\t\t\t</$checkbox>\n\t\t</div>\n\t</div>\n\t<div class=\"tc-manager-list\">\n\t\t<$list filter=\"[all{$:/config/Manager/Show}!is{$:/config/Manager/System}search{$:/config/Manager/Filter}tag:strict{$:/config/Manager/Tag}sort{$:/config/Manager/Sort}order{$:/config/Manager/Order}]\">\n\t\t\t<$vars transclusion=<<currentTiddler>>>\n\t\t\t\t<div style=\"tc-manager-list-item\">\n\t\t\t\t\t<$button popup=<<qualify \"$:/state/manager/popup\">> class=\"tc-btn-invisible tc-manager-list-item-heading\" selectedClass=\"tc-manager-list-item-heading-selected\">\n\t\t\t\t\t\t<$text text=<<currentTiddler>>/>\n\t\t\t\t\t</$button>\n\t\t\t\t\t<$reveal state=<<qualify \"$:/state/manager/popup\">> type=\"nomatch\" text=\"\" default=\"\" tag=\"div\" class=\"tc-manager-list-item-content tc-popup-handle\">\n\t\t\t\t\t\t<div class=\"tc-manager-list-item-content-tiddler\">\n\t\t\t\t\t\t\t<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Manager/ItemMain]!has[draft.of]]\" variable=\"listItem\">\n\t\t\t\t\t\t\t\t<<list-item-content-item>>\n\t\t\t\t\t\t\t</$list>\n\t\t\t\t\t\t</div>\n\t\t\t\t\t\t<div class=\"tc-manager-list-item-content-sidebar\">\n\t\t\t\t\t\t\t<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Manager/ItemSidebar]!has[draft.of]]\" variable=\"listItem\">\n\t\t\t\t\t\t\t\t<<list-item-content-item>>\n\t\t\t\t\t\t\t</$list>\n\t\t\t\t\t\t</div>\n\t\t\t\t\t</$reveal>\n\t\t\t\t</div>\n\t\t\t</$vars>\n\t\t</$list>\n\t</div>\n</div>\n"
},
"$:/core/ui/MissingTemplate": {
"title": "$:/core/ui/MissingTemplate",
"text": "<div class=\"tc-tiddler-missing\">\n<$button popup=<<qualify \"$:/state/popup/missing\">> class=\"tc-btn-invisible tc-missing-tiddler-label\">\n<$view field=\"title\" format=\"text\" />\n</$button>\n<$reveal state=<<qualify \"$:/state/popup/missing\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\">\n<$transclude tiddler=\"$:/core/ui/ListItemTemplate\"/>\n<hr>\n<$list filter=\"[all[current]backlinks[]sort[title]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n</div>\n</$reveal>\n</div>\n"
},
"$:/core/ui/MoreSideBar/All": {
"title": "$:/core/ui/MoreSideBar/All",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/All/Caption}}",
"text": "<$list filter={{$:/core/Filters/AllTiddlers!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Drafts": {
"title": "$:/core/ui/MoreSideBar/Drafts",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Drafts/Caption}}",
"text": "<$list filter={{$:/core/Filters/Drafts!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Explorer": {
"title": "$:/core/ui/MoreSideBar/Explorer",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Explorer/Caption}}",
"text": "<<tree \"$:/\">>\n"
},
"$:/core/ui/MoreSideBar/Missing": {
"title": "$:/core/ui/MoreSideBar/Missing",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Missing/Caption}}",
"text": "<$list filter={{$:/core/Filters/Missing!!filter}} template=\"$:/core/ui/MissingTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Orphans": {
"title": "$:/core/ui/MoreSideBar/Orphans",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Orphans/Caption}}",
"text": "<$list filter={{$:/core/Filters/Orphans!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Plugins": {
"title": "$:/core/ui/MoreSideBar/Plugins",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/ControlPanel/Plugins/Caption}}",
"text": "\n{{$:/language/ControlPanel/Plugins/Installed/Hint}}\n\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/MoreSideBar/Plugins]!has[draft.of]]\" \"$:/core/ui/MoreSideBar/Plugins/Plugins\">>\n"
},
"$:/core/ui/MoreSideBar/Recent": {
"title": "$:/core/ui/MoreSideBar/Recent",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Recent/Caption}}",
"text": "<$macrocall $name=\"timeline\" format={{$:/language/RecentChanges/DateFormat}}/>\n"
},
"$:/core/ui/MoreSideBar/Shadows": {
"title": "$:/core/ui/MoreSideBar/Shadows",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Shadows/Caption}}",
"text": "<$list filter={{$:/core/Filters/ShadowTiddlers!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/System": {
"title": "$:/core/ui/MoreSideBar/System",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/System/Caption}}",
"text": "<$list filter={{$:/core/Filters/SystemTiddlers!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Tags": {
"title": "$:/core/ui/MoreSideBar/Tags",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Tags/Caption}}",
"text": "<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"\">\n\n{{$:/core/ui/Buttons/tag-manager}}\n\n</$set>\n\n</$set>\n\n</$set>\n\n<$list filter={{$:/core/Filters/AllTags!!filter}}>\n\n<$transclude tiddler=\"$:/core/ui/TagTemplate\"/>\n\n</$list>\n\n<hr class=\"tc-untagged-separator\">\n\n{{$:/core/ui/UntaggedTemplate}}\n"
},
"$:/core/ui/MoreSideBar/Types": {
"title": "$:/core/ui/MoreSideBar/Types",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Types/Caption}}",
"text": "<$list filter={{$:/core/Filters/TypedTiddlers!!filter}}>\n<div class=\"tc-menu-list-item\">\n<$view field=\"type\"/>\n<$list filter=\"[type{!!type}!is[system]sort[title]]\">\n<div class=\"tc-menu-list-subitem\">\n<$link to={{!!title}}><$view field=\"title\"/></$link>\n</div>\n</$list>\n</div>\n</$list>\n"
},
"$:/core/ui/MoreSideBar/Plugins/Languages": {
"title": "$:/core/ui/MoreSideBar/Plugins/Languages",
"tags": "$:/tags/MoreSideBar/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Languages/Caption}}",
"text": "<$list filter=\"[!has[draft.of]plugin-type[language]sort[description]]\" template=\"$:/core/ui/PluginListItemTemplate\" emptyMessage={{$:/language/ControlPanel/Plugins/Empty/Hint}}/>\n"
},
"$:/core/ui/MoreSideBar/Plugins/Plugins": {
"title": "$:/core/ui/MoreSideBar/Plugins/Plugins",
"tags": "$:/tags/MoreSideBar/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Plugins/Caption}}",
"text": "<$list filter=\"[!has[draft.of]plugin-type[plugin]sort[description]]\" template=\"$:/core/ui/PluginListItemTemplate\" emptyMessage={{$:/language/ControlPanel/Plugins/Empty/Hint}}>>/>\n"
},
"$:/core/ui/MoreSideBar/Plugins/Theme": {
"title": "$:/core/ui/MoreSideBar/Plugins/Theme",
"tags": "$:/tags/MoreSideBar/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Themes/Caption}}",
"text": "<$list filter=\"[!has[draft.of]plugin-type[theme]sort[description]]\" template=\"$:/core/ui/PluginListItemTemplate\" emptyMessage={{$:/language/ControlPanel/Plugins/Empty/Hint}}/>\n"
},
"$:/core/ui/Buttons/advanced-search": {
"title": "$:/core/ui/Buttons/advanced-search",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/advanced-search-button}} {{$:/language/Buttons/AdvancedSearch/Caption}}",
"description": "{{$:/language/Buttons/AdvancedSearch/Hint}}",
"text": "\\whitespace trim\n\\define control-panel-button(class)\n<$button to=\"$:/AdvancedSearch\" tooltip={{$:/language/Buttons/AdvancedSearch/Hint}} aria-label={{$:/language/Buttons/AdvancedSearch/Caption}} class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/advanced-search-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/AdvancedSearch/Caption}}/></span>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/AdvancedSearch]]\" emptyMessage=<<control-panel-button>>>\n<<control-panel-button \"tc-selected\">>\n</$list>\n"
},
"$:/core/ui/Buttons/close-all": {
"title": "$:/core/ui/Buttons/close-all",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/close-all-button}} {{$:/language/Buttons/CloseAll/Caption}}",
"description": "{{$:/language/Buttons/CloseAll/Hint}}",
"text": "<$button message=\"tm-close-all-tiddlers\" tooltip={{$:/language/Buttons/CloseAll/Hint}} aria-label={{$:/language/Buttons/CloseAll/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/close-all-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/CloseAll/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/control-panel": {
"title": "$:/core/ui/Buttons/control-panel",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/options-button}} {{$:/language/Buttons/ControlPanel/Caption}}",
"description": "{{$:/language/Buttons/ControlPanel/Hint}}",
"text": "\\whitespace trim\n\\define control-panel-button(class)\n<$button to=\"$:/ControlPanel\" tooltip={{$:/language/Buttons/ControlPanel/Hint}} aria-label={{$:/language/Buttons/ControlPanel/Caption}} class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/options-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/ControlPanel/Caption}}/></span>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/ControlPanel]]\" emptyMessage=<<control-panel-button>>>\n<<control-panel-button \"tc-selected\">>\n</$list>\n"
},
"$:/core/ui/Buttons/encryption": {
"title": "$:/core/ui/Buttons/encryption",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/locked-padlock}} {{$:/language/Buttons/Encryption/Caption}}",
"description": "{{$:/language/Buttons/Encryption/Hint}}",
"text": "\\whitespace trim\n<$reveal type=\"match\" state=\"$:/isEncrypted\" text=\"yes\">\n<$button message=\"tm-clear-password\" tooltip={{$:/language/Buttons/Encryption/ClearPassword/Hint}} aria-label={{$:/language/Buttons/Encryption/ClearPassword/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/locked-padlock}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Encryption/ClearPassword/Caption}}/></span>\n</$list>\n</$button>\n</$reveal>\n<$reveal type=\"nomatch\" state=\"$:/isEncrypted\" text=\"yes\">\n<$button message=\"tm-set-password\" tooltip={{$:/language/Buttons/Encryption/SetPassword/Hint}} aria-label={{$:/language/Buttons/Encryption/SetPassword/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/unlocked-padlock}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Encryption/SetPassword/Caption}}/></span>\n</$list>\n</$button>\n</$reveal>\n"
},
"$:/core/ui/Buttons/export-page": {
"title": "$:/core/ui/Buttons/export-page",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/export-button}} {{$:/language/Buttons/ExportPage/Caption}}",
"description": "{{$:/language/Buttons/ExportPage/Hint}}",
"text": "<$macrocall $name=\"exportButton\" exportFilter=\"[!is[system]sort[title]]\" lingoBase=\"$:/language/Buttons/ExportPage/\"/>"
},
"$:/core/ui/Buttons/fold-all": {
"title": "$:/core/ui/Buttons/fold-all",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/fold-all-button}} {{$:/language/Buttons/FoldAll/Caption}}",
"description": "{{$:/language/Buttons/FoldAll/Hint}}",
"text": "<$button tooltip={{$:/language/Buttons/FoldAll/Hint}} aria-label={{$:/language/Buttons/FoldAll/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-fold-all-tiddlers\" $param=<<currentTiddler>> foldedStatePrefix=\"$:/state/folded/\"/>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\" variable=\"listItem\">\n{{$:/core/images/fold-all-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/FoldAll/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/full-screen": {
"title": "$:/core/ui/Buttons/full-screen",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/full-screen-button}} {{$:/language/Buttons/FullScreen/Caption}}",
"description": "{{$:/language/Buttons/FullScreen/Hint}}",
"text": "<$button message=\"tm-full-screen\" tooltip={{$:/language/Buttons/FullScreen/Hint}} aria-label={{$:/language/Buttons/FullScreen/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/full-screen-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/FullScreen/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/home": {
"title": "$:/core/ui/Buttons/home",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/home-button}} {{$:/language/Buttons/Home/Caption}}",
"description": "{{$:/language/Buttons/Home/Hint}}",
"text": "<$button message=\"tm-home\" tooltip={{$:/language/Buttons/Home/Hint}} aria-label={{$:/language/Buttons/Home/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/home-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Home/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/import": {
"title": "$:/core/ui/Buttons/import",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/import-button}} {{$:/language/Buttons/Import/Caption}}",
"description": "{{$:/language/Buttons/Import/Hint}}",
"text": "<div class=\"tc-file-input-wrapper\">\n<$button tooltip={{$:/language/Buttons/Import/Hint}} aria-label={{$:/language/Buttons/Import/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/import-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Import/Caption}}/></span>\n</$list>\n</$button>\n<$browse tooltip={{$:/language/Buttons/Import/Hint}}/>\n</div>"
},
"$:/core/ui/Buttons/language": {
"title": "$:/core/ui/Buttons/language",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/globe}} {{$:/language/Buttons/Language/Caption}}",
"description": "{{$:/language/Buttons/Language/Hint}}",
"text": "\\whitespace trim\n\\define flag-title()\n$(languagePluginTitle)$/icon\n\\end\n<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/popup/language\">> tooltip={{$:/language/Buttons/Language/Hint}} aria-label={{$:/language/Buttons/Language/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n<span class=\"tc-image-button\">\n<$set name=\"languagePluginTitle\" value={{$:/language}}>\n<$image source=<<flag-title>>/>\n</$set>\n</span>\n</$list>\n<$text text=\" \"/>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Language/Caption}}/></span>\n</$list>\n</$button>\n</span>\n<$reveal state=<<qualify \"$:/state/popup/language\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\">\n{{$:/snippets/languageswitcher}}\n</div>\n</$reveal>\n"
},
"$:/core/ui/Buttons/manager": {
"title": "$:/core/ui/Buttons/manager",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/list}} {{$:/language/Buttons/Manager/Caption}}",
"description": "{{$:/language/Buttons/Manager/Hint}}",
"text": "\\whitespace trim\n\\define manager-button(class)\n<$button to=\"$:/Manager\" tooltip={{$:/language/Buttons/Manager/Hint}} aria-label={{$:/language/Buttons/Manager/Caption}} class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/list}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Manager/Caption}}/></span>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/Manager]]\" emptyMessage=<<manager-button>>>\n<<manager-button \"tc-selected\">>\n</$list>\n"
},
"$:/core/ui/Buttons/more-page-actions": {
"title": "$:/core/ui/Buttons/more-page-actions",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/down-arrow}} {{$:/language/Buttons/More/Caption}}",
"description": "{{$:/language/Buttons/More/Hint}}",
"text": "\\define config-title()\n$:/config/PageControlButtons/Visibility/$(listItem)$\n\\end\n<$button popup=<<qualify \"$:/state/popup/more\">> tooltip={{$:/language/Buttons/More/Hint}} aria-label={{$:/language/Buttons/More/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/down-arrow}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/More/Caption}}/></span>\n</$list>\n</$button><$reveal state=<<qualify \"$:/state/popup/more\">> type=\"popup\" position=\"below\" animate=\"yes\">\n\n<div class=\"tc-drop-down\">\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"tc-btn-invisible\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/PageControls]!has[draft.of]] -[[$:/core/ui/Buttons/more-page-actions]]\" variable=\"listItem\">\n\n<$reveal type=\"match\" state=<<config-title>> text=\"hide\">\n\n<$set name=\"tv-config-toolbar-class\" filter=\"[<tv-config-toolbar-class>] [<listItem>encodeuricomponent[]addprefix[tc-btn-]]\">\n\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n\n</$set>\n\n</$reveal>\n\n</$list>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</div>\n\n</$reveal>"
},
"$:/core/ui/Buttons/new-image": {
"title": "$:/core/ui/Buttons/new-image",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/new-image-button}} {{$:/language/Buttons/NewImage/Caption}}",
"description": "{{$:/language/Buttons/NewImage/Hint}}",
"text": "\\whitespace trim\n<$button tooltip={{$:/language/Buttons/NewImage/Hint}} aria-label={{$:/language/Buttons/NewImage/Caption}} class=<<tv-config-toolbar-class>> actions={{$:/core/ui/Actions/new-image}}>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/new-image-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/NewImage/Caption}}/></span>\n</$list>\n</$button>\n"
},
"$:/core/ui/Buttons/new-journal": {
"title": "$:/core/ui/Buttons/new-journal",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/new-journal-button}} {{$:/language/Buttons/NewJournal/Caption}}",
"description": "{{$:/language/Buttons/NewJournal/Hint}}",
"text": "\\whitespace trim\n\\define journalButton()\n<$button tooltip={{$:/language/Buttons/NewJournal/Hint}} aria-label={{$:/language/Buttons/NewJournal/Caption}} class=<<tv-config-toolbar-class>> actions={{$:/core/ui/Actions/new-journal}}>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/new-journal-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/NewJournal/Caption}}/></span>\n</$list>\n</$button>\n\\end\n<<journalButton>>\n"
},
"$:/core/ui/Buttons/new-tiddler": {
"title": "$:/core/ui/Buttons/new-tiddler",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/new-button}} {{$:/language/Buttons/NewTiddler/Caption}}",
"description": "{{$:/language/Buttons/NewTiddler/Hint}}",
"text": "\\whitespace trim\n<$button actions={{$:/core/ui/Actions/new-tiddler}} tooltip={{$:/language/Buttons/NewTiddler/Hint}} aria-label={{$:/language/Buttons/NewTiddler/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/new-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/NewTiddler/Caption}}/></span>\n</$list>\n</$button>\n"
},
"$:/core/ui/Buttons/palette": {
"title": "$:/core/ui/Buttons/palette",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/palette}} {{$:/language/Buttons/Palette/Caption}}",
"description": "{{$:/language/Buttons/Palette/Hint}}",
"text": "\\whitespace trim\n<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/popup/palette\">> tooltip={{$:/language/Buttons/Palette/Hint}} aria-label={{$:/language/Buttons/Palette/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/palette}}\n</$list>\n<$text text=\" \"/>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Palette/Caption}}/></span>\n</$list>\n</$button>\n</span>\n<$reveal state=<<qualify \"$:/state/popup/palette\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\" style=\"font-size:0.7em;\">\n{{$:/snippets/paletteswitcher}}\n</div>\n</$reveal>\n"
},
"$:/core/ui/Buttons/print": {
"title": "$:/core/ui/Buttons/print",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/print-button}} {{$:/language/Buttons/Print/Caption}}",
"description": "{{$:/language/Buttons/Print/Hint}}",
"text": "<$button message=\"tm-print\" tooltip={{$:/language/Buttons/Print/Hint}} aria-label={{$:/language/Buttons/Print/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/print-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Print/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/refresh": {
"title": "$:/core/ui/Buttons/refresh",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/refresh-button}} {{$:/language/Buttons/Refresh/Caption}}",
"description": "{{$:/language/Buttons/Refresh/Hint}}",
"text": "<$button message=\"tm-browser-refresh\" tooltip={{$:/language/Buttons/Refresh/Hint}} aria-label={{$:/language/Buttons/Refresh/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/refresh-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Refresh/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/save-wiki": {
"title": "$:/core/ui/Buttons/save-wiki",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/save-button}} {{$:/language/Buttons/SaveWiki/Caption}}",
"description": "{{$:/language/Buttons/SaveWiki/Hint}}",
"text": "<$button tooltip={{$:/language/Buttons/SaveWiki/Hint}} aria-label={{$:/language/Buttons/SaveWiki/Caption}} class=<<tv-config-toolbar-class>>>\n<$wikify name=\"site-title\" text={{$:/config/SaveWikiButton/Filename}}>\n<$action-sendmessage $message=\"tm-save-wiki\" $param={{$:/config/SaveWikiButton/Template}} filename=<<site-title>>/>\n</$wikify>\n<span class=\"tc-dirty-indicator\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/save-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/SaveWiki/Caption}}/></span>\n</$list>\n</span>\n</$button>"
},
"$:/core/ui/Buttons/storyview": {
"title": "$:/core/ui/Buttons/storyview",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/storyview-classic}} {{$:/language/Buttons/StoryView/Caption}}",
"description": "{{$:/language/Buttons/StoryView/Hint}}",
"text": "\\whitespace trim\n\\define icon()\n$:/core/images/storyview-$(storyview)$\n\\end\n<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/popup/storyview\">> tooltip={{$:/language/Buttons/StoryView/Hint}} aria-label={{$:/language/Buttons/StoryView/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n<$set name=\"storyview\" value={{$:/view}}>\n<$transclude tiddler=<<icon>>/>\n</$set>\n</$list>\n<$text text=\" \"/>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/StoryView/Caption}}/></span>\n</$list>\n</$button>\n</span>\n<$reveal state=<<qualify \"$:/state/popup/storyview\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\">\n{{$:/snippets/viewswitcher}}\n</div>\n</$reveal>\n"
},
"$:/core/ui/Buttons/tag-manager": {
"title": "$:/core/ui/Buttons/tag-manager",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/tag-button}} {{$:/language/Buttons/TagManager/Caption}}",
"description": "{{$:/language/Buttons/TagManager/Hint}}",
"text": "\\whitespace trim\n\\define control-panel-button(class)\n<$button to=\"$:/TagManager\" tooltip={{$:/language/Buttons/TagManager/Hint}} aria-label={{$:/language/Buttons/TagManager/Caption}} class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/tag-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/TagManager/Caption}}/></span>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/TagManager]]\" emptyMessage=<<control-panel-button>>>\n<<control-panel-button \"tc-selected\">>\n</$list>\n"
},
"$:/core/ui/Buttons/theme": {
"title": "$:/core/ui/Buttons/theme",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/theme-button}} {{$:/language/Buttons/Theme/Caption}}",
"description": "{{$:/language/Buttons/Theme/Hint}}",
"text": "\\whitespace trim\n<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/popup/theme\">> tooltip={{$:/language/Buttons/Theme/Hint}} aria-label={{$:/language/Buttons/Theme/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/theme-button}}\n</$list>\n<$text text=\" \"/>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Theme/Caption}}/></span>\n</$list>\n</$button>\n</span>\n<$reveal state=<<qualify \"$:/state/popup/theme\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\">\n<$linkcatcher to=\"$:/theme\">\n{{$:/snippets/themeswitcher}}\n</$linkcatcher>\n</div>\n</$reveal>\n"
},
"$:/core/ui/Buttons/timestamp": {
"title": "$:/core/ui/Buttons/timestamp",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/timestamp-on}} {{$:/language/Buttons/Timestamp/Caption}}",
"description": "{{$:/language/Buttons/Timestamp/Hint}}",
"text": "\\whitespace trim\n<$reveal type=\"nomatch\" state=\"$:/config/TimestampDisable\" text=\"yes\">\n<$button tooltip={{$:/language/Buttons/Timestamp/On/Hint}} aria-label={{$:/language/Buttons/Timestamp/On/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-setfield $tiddler=\"$:/config/TimestampDisable\" $value=\"yes\"/>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/timestamp-on}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Timestamp/On/Caption}}/></span>\n</$list>\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=\"$:/config/TimestampDisable\" text=\"yes\">\n<$button tooltip={{$:/language/Buttons/Timestamp/Off/Hint}} aria-label={{$:/language/Buttons/Timestamp/Off/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-setfield $tiddler=\"$:/config/TimestampDisable\" $value=\"no\"/>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/timestamp-off}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Timestamp/Off/Caption}}/></span>\n</$list>\n</$button>\n</$reveal>\n"
},
"$:/core/ui/Buttons/unfold-all": {
"title": "$:/core/ui/Buttons/unfold-all",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/unfold-all-button}} {{$:/language/Buttons/UnfoldAll/Caption}}",
"description": "{{$:/language/Buttons/UnfoldAll/Hint}}",
"text": "<$button tooltip={{$:/language/Buttons/UnfoldAll/Hint}} aria-label={{$:/language/Buttons/UnfoldAll/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-unfold-all-tiddlers\" $param=<<currentTiddler>> foldedStatePrefix=\"$:/state/folded/\"/>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\" variable=\"listItem\">\n{{$:/core/images/unfold-all-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/UnfoldAll/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/PageTemplate/pagecontrols": {
"title": "$:/core/ui/PageTemplate/pagecontrols",
"text": "\\whitespace trim\n\\define config-title()\n$:/config/PageControlButtons/Visibility/$(listItem)$\n\\end\n<div class=\"tc-page-controls\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/PageControls]!has[draft.of]]\" variable=\"listItem\">\n<$set name=\"hidden\" value=<<config-title>>>\n<$list filter=\"[<hidden>!text[hide]]\" storyview=\"pop\" variable=\"ignore\">\n<$set name=\"tv-config-toolbar-class\" filter=\"[<tv-config-toolbar-class>] [<listItem>encodeuricomponent[]addprefix[tc-btn-]]\">\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n</$set>\n</$list>\n</$set>\n</$list>\n</div>\n"
},
"$:/core/ui/PageStylesheet": {
"title": "$:/core/ui/PageStylesheet",
"text": "\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n\n<$set name=\"currentTiddler\" value={{$:/language}}>\n\n<$set name=\"languageTitle\" value={{!!name}}>\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Stylesheet]!has[draft.of]]\">\n<$transclude mode=\"block\"/>\n</$list>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/PageTemplate/alerts": {
"title": "$:/core/ui/PageTemplate/alerts",
"tags": "$:/tags/PageTemplate",
"text": "<div class=\"tc-alerts\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Alert]!has[draft.of]]\" template=\"$:/core/ui/AlertTemplate\" storyview=\"pop\"/>\n\n</div>\n"
},
"$:/core/ui/PageTemplate/drafts": {
"title": "$:/core/ui/PageTemplate/drafts",
"tags": "$:/tags/PageTemplate",
"text": "\\whitespace trim\n<$reveal state=\"$:/status/IsReadOnly\" type=\"nomatch\" text=\"yes\" tag=\"div\" class=\"tc-drafts-list\">\n<$list filter=\"[has[draft.of]!sort[modified]] -[list[$:/StoryList]]\">\n<$link>\n{{$:/core/images/edit-button}} <$text text=<<currentTiddler>>/>\n</$link>\n</$list>\n</$reveal>\n"
},
"$:/core/ui/PageTemplate/pluginreloadwarning": {
"title": "$:/core/ui/PageTemplate/pluginreloadwarning",
"tags": "$:/tags/PageTemplate",
"text": "\\define lingo-base() $:/language/\n\n<$list filter=\"[{$:/status/RequireReloadDueToPluginChange}match[yes]]\">\n\n<$reveal type=\"nomatch\" state=\"$:/temp/HidePluginWarning\" text=\"yes\">\n\n<div class=\"tc-plugin-reload-warning\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"\">\n\n<<lingo PluginReloadWarning>> <$button set=\"$:/temp/HidePluginWarning\" setTo=\"yes\" class=\"tc-btn-invisible\">{{$:/core/images/close-button}}</$button>\n\n</$set>\n\n</div>\n\n</$reveal>\n\n</$list>\n"
},
"$:/core/ui/PageTemplate/sidebar": {
"title": "$:/core/ui/PageTemplate/sidebar",
"tags": "$:/tags/PageTemplate",
"text": "\\whitespace trim\n\\define config-title()\n$:/config/SideBarSegments/Visibility/$(listItem)$\n\\end\n\n<$scrollable fallthrough=\"no\" class=\"tc-sidebar-scrollable\">\n\n<div class=\"tc-sidebar-header\">\n\n<$reveal state=\"$:/state/sidebar\" type=\"match\" text=\"yes\" default=\"yes\" retain=\"yes\" animate=\"yes\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SideBarSegment]!has[draft.of]]\" variable=\"listItem\">\n\n<$reveal type=\"nomatch\" state=<<config-title>> text=\"hide\" tag=\"div\">\n\n<$transclude tiddler=<<listItem>> mode=\"block\"/>\n\n</$reveal>\n\n</$list>\n\n</$reveal>\n\n</div>\n\n</$scrollable>\n"
},
"$:/core/ui/PageTemplate/story": {
"title": "$:/core/ui/PageTemplate/story",
"tags": "$:/tags/PageTemplate",
"text": "\\whitespace trim\n<section class=\"tc-story-river\">\n\n<section class=\"story-backdrop\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/AboveStory]!has[draft.of]]\">\n\n<$transclude/>\n\n</$list>\n\n</section>\n\n<$list filter=\"[list[$:/StoryList]]\" history=\"$:/HistoryList\" template={{$:/config/ui/ViewTemplate}} editTemplate={{$:/config/ui/EditTemplate}} storyview={{$:/view}} emptyMessage={{$:/config/EmptyStoryMessage}}/>\n\n<section class=\"story-frontdrop\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/BelowStory]!has[draft.of]]\">\n\n<$transclude/>\n\n</$list>\n\n</section>\n\n</section>\n"
},
"$:/core/ui/PageTemplate/topleftbar": {
"title": "$:/core/ui/PageTemplate/topleftbar",
"tags": "$:/tags/PageTemplate",
"text": "<span class=\"tc-topbar tc-topbar-left\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TopLeftBar]!has[draft.of]]\" variable=\"listItem\" storyview=\"pop\">\n\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n\n</$list>\n\n</span>\n"
},
"$:/core/ui/PageTemplate/toprightbar": {
"title": "$:/core/ui/PageTemplate/toprightbar",
"tags": "$:/tags/PageTemplate",
"text": "<span class=\"tc-topbar tc-topbar-right\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TopRightBar]!has[draft.of]]\" variable=\"listItem\" storyview=\"pop\">\n\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n\n</$list>\n\n</span>\n"
},
"$:/core/ui/PageTemplate": {
"title": "$:/core/ui/PageTemplate",
"text": "\\whitespace trim\n\\define containerClasses()\ntc-page-container tc-page-view-$(storyviewTitle)$ tc-language-$(languageTitle)$\n\\end\n\\import [[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\n\n<$set name=\"tv-config-toolbar-icons\" value={{$:/config/Toolbar/Icons}}>\n\n<$set name=\"tv-config-toolbar-text\" value={{$:/config/Toolbar/Text}}>\n\n<$set name=\"tv-config-toolbar-class\" value={{$:/config/Toolbar/ButtonClass}}>\n\n<$set name=\"tv-enable-drag-and-drop\" value={{$:/config/DragAndDrop/Enable}}>\n\n<$set name=\"tv-show-missing-links\" value={{$:/config/MissingLinks}}>\n\n<$set name=\"storyviewTitle\" value={{$:/view}}>\n\n<$set name=\"languageTitle\" value={{{ [{$:/language}get[name]] }}}>\n\n<div class=<<containerClasses>>>\n\n<$navigator story=\"$:/StoryList\" history=\"$:/HistoryList\" openLinkFromInsideRiver={{$:/config/Navigation/openLinkFromInsideRiver}} openLinkFromOutsideRiver={{$:/config/Navigation/openLinkFromOutsideRiver}} relinkOnRename={{$:/config/RelinkOnRename}}>\n\n<$dropzone enable=<<tv-enable-drag-and-drop>>>\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/PageTemplate]!has[draft.of]]\" variable=\"listItem\">\n\n<$transclude tiddler=<<listItem>>/>\n\n</$list>\n\n</$dropzone>\n\n</$navigator>\n\n</div>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n"
},
"$:/PaletteManager": {
"title": "$:/PaletteManager",
"text": "\\define lingo-base() $:/language/ControlPanel/Palette/Editor/\n\\define describePaletteColour(colour)\n<$transclude tiddler=\"$:/language/Docs/PaletteColours/$colour$\"><$text text=\"$colour$\"/></$transclude>\n\\end\n\\define edit-colour-placeholder()\n edit $(colourName)$\n\\end\n\\define colour-tooltip(showhide) $showhide$ editor for $(newColourName)$ \n\\define resolve-colour(macrocall)\n\\import $:/core/macros/utils\n\\whitespace trim\n<$wikify name=\"name\" text=\"\"\"$macrocall$\"\"\">\n<<name>>\n</$wikify>\n\\end\n\\define delete-colour-index-actions() <$action-setfield $index=<<colourName>>/>\n\\define palette-manager-colour-row-segment()\n\\whitespace trim\n<$edit-text index=<<colourName>> tag=\"input\" placeholder=<<edit-colour-placeholder>> default=\"\"/>\n<br>\n<$edit-text index=<<colourName>> type=\"color\" tag=\"input\" class=\"tc-palette-manager-colour-input\"/>\n<$list filter=\"[<currentTiddler>getindex<colourName>removeprefix[<<]removesuffix[>>]] [<currentTiddler>getindex<colourName>removeprefix[<$]removesuffix[/>]]\" variable=\"ignore\">\n<$set name=\"state\" value={{{ [[$:/state/palettemanager/]addsuffix<currentTiddler>addsuffix[/]addsuffix<colourName>] }}}>\n<$wikify name=\"newColourName\" text=\"\"\"<$macrocall $name=\"resolve-colour\" macrocall={{{ [<currentTiddler>getindex<colourName>] }}}/>\"\"\">\n<$reveal state=<<state>> type=\"nomatch\" text=\"show\">\n<$button tooltip=<<colour-tooltip show>> aria-label=<<colour-tooltip show>> class=\"tc-btn-invisible\" set=<<state>> setTo=\"show\">{{$:/core/images/down-arrow}} <$text text=<<newColourName>>/></$button><br>\n</$reveal>\n<$reveal state=<<state>> type=\"match\" text=\"show\">\n<$button tooltip=<<colour-tooltip hide>> aria-label=<<colour-tooltip show>> class=\"tc-btn-invisible\" actions=\"\"\"<$action-deletetiddler $tiddler=<<state>>/>\"\"\">{{$:/core/images/up-arrow}} <$text text=<<newColourName>>/></$button><br>\n</$reveal>\n<$reveal state=<<state>> type=\"match\" text=\"show\">\n<$set name=\"colourName\" value=<<newColourName>>>\n<br>\n<<palette-manager-colour-row-segment>>\n<br><br>\n</$set>\n</$reveal>\n</$wikify>\n</$set>\n</$list>\n\\end\n\\define palette-manager-colour-row()\n\\whitespace trim\n<tr>\n<td>\n<span style=\"float:right;\">\n<$button tooltip=<<lingo Delete/Hint>> aria-label=<<lingo Delete/Hint>> class=\"tc-btn-invisible\" actions=<<delete-colour-index-actions>>>\n{{$:/core/images/delete-button}}</$button>\n</span>\n''<$macrocall $name=\"describePaletteColour\" colour=<<colourName>>/>''<br/>\n<$macrocall $name=\"colourName\" $output=\"text/plain\"/>\n</td>\n<td>\n<<palette-manager-colour-row-segment>>\n</td>\n</tr>\n\\end\n\\define palette-manager-table()\n\\whitespace trim\n<table>\n<tbody>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Palette]indexes[]]\" variable=\"colourName\">\n<$list filter=\"[<currentTiddler>indexes[]removeprefix<colourName>suffix[]]\" variable=\"ignore\" emptyMessage=\"\"\"\n<$list filter=\"[{$:/state/palettemanager/showexternal}removeprefix[yes]suffix[]]\" variable=\"ignore\">\n<<palette-manager-colour-row>>\n</$list>\n\"\"\">\n<<palette-manager-colour-row>>\n</$list>\n</$list>\n</tbody>\n</table>\n\\end\n<$set name=\"currentTiddler\" value={{$:/palette}}>\n\n<<lingo Prompt>> <$link to={{$:/palette}}><$macrocall $name=\"currentTiddler\" $output=\"text/plain\"/></$link>\n\n<$list filter=\"[all[current]is[shadow]is[tiddler]]\" variable=\"listItem\">\n<<lingo Prompt/Modified>>\n<$button message=\"tm-delete-tiddler\" param={{$:/palette}}><<lingo Reset/Caption>></$button>\n</$list>\n\n<$list filter=\"[all[current]is[shadow]!is[tiddler]]\" variable=\"listItem\">\n<<lingo Clone/Prompt>>\n</$list>\n\n<$button message=\"tm-new-tiddler\" param={{$:/palette}}><<lingo Clone/Caption>></$button>\n\n<$checkbox tiddler=\"$:/state/palettemanager/showexternal\" field=\"text\" checked=\"yes\" unchecked=\"no\"> <<lingo Names/External/Show>></$checkbox>\n\n<<palette-manager-table>>\n"
},
"$:/core/ui/PluginInfo": {
"title": "$:/core/ui/PluginInfo",
"text": "\\define localised-info-tiddler-title()\n$(currentTiddler)$/$(languageTitle)$/$(currentTab)$\n\\end\n\\define info-tiddler-title()\n$(currentTiddler)$/$(currentTab)$\n\\end\n\\define default-tiddler-title()\n$:/core/ui/PluginInfo/Default/$(currentTab)$\n\\end\n<$transclude tiddler=<<localised-info-tiddler-title>> mode=\"block\">\n<$transclude tiddler=<<currentTiddler>> subtiddler=<<localised-info-tiddler-title>> mode=\"block\">\n<$transclude tiddler=<<currentTiddler>> subtiddler=<<info-tiddler-title>> mode=\"block\">\n<$transclude tiddler=<<default-tiddler-title>> mode=\"block\">\n{{$:/language/ControlPanel/Plugin/NoInfoFound/Hint}}\n</$transclude>\n</$transclude>\n</$transclude>\n</$transclude>\n"
},
"$:/core/ui/PluginInfo/Default/contents": {
"title": "$:/core/ui/PluginInfo/Default/contents",
"text": "\\define lingo-base() $:/language/TiddlerInfo/Advanced/PluginInfo/\n<<lingo Hint>>\n<ul>\n<$list filter=\"[all[current]plugintiddlers[]sort[title]]\" emptyMessage=<<lingo Empty/Hint>>>\n<li>\n<$link />\n</li>\n</$list>\n</ul>\n"
},
"$:/core/ui/PluginListItemTemplate": {
"title": "$:/core/ui/PluginListItemTemplate",
"text": "<div class=\"tc-menu-list-item\">\n<$link to={{!!title}}><$view field=\"description\"><$view field=\"title\"/></$view></$link>\n</div>"
},
"$:/core/ui/SearchResults": {
"title": "$:/core/ui/SearchResults",
"text": "<div class=\"tc-search-results\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]butfirst[]limit[1]]\" emptyMessage=\"\"\"\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]]\">\n<$transclude mode=\"block\"/>\n</$list>\n\"\"\">\n\n<$macrocall $name=\"tabs\" tabsList=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]]\" default={{$:/config/SearchResults/Default}}/>\n\n</$list>\n\n</div>\n"
},
"$:/core/ui/SideBar/More": {
"title": "$:/core/ui/SideBar/More",
"tags": "$:/tags/SideBar",
"caption": "{{$:/language/SideBar/More/Caption}}",
"text": "<div class=\"tc-more-sidebar\">\n<$macrocall $name=\"tabs\" tabsList=\"[all[shadows+tiddlers]tag[$:/tags/MoreSideBar]!has[draft.of]]\" default={{$:/config/DefaultMoreSidebarTab}} state=\"$:/state/tab/moresidebar\" class=\"tc-vertical tc-sidebar-tabs-more\" />\n</div>"
},
"$:/core/ui/SideBar/Open": {
"title": "$:/core/ui/SideBar/Open",
"tags": "$:/tags/SideBar",
"caption": "{{$:/language/SideBar/Open/Caption}}",
"text": "\\whitespace trim\n\\define lingo-base() $:/language/CloseAll/\n\n\\define drop-actions()\n<$action-listops $tiddler=<<tv-story-list>> $subfilter=\"+[insertbefore:currentTiddler<actionTiddler>]\"/>\n\\end\n\n\\define placeholder()\n<div class=\"tc-droppable-placeholder\"/>\n\\end\n\n\\define droppable-item(button)\n\\whitespace trim\n<$droppable actions=<<drop-actions>> enable=<<tv-allow-drag-and-drop>>>\n<<placeholder>>\n<div>\n$button$\n</div>\n</$droppable>\n\\end\n\n<div class=\"tc-sidebar-tab-open\">\n<$list filter=\"[list<tv-story-list>]\" history=<<tv-history-list>> storyview=\"pop\">\n<div class=\"tc-sidebar-tab-open-item\">\n<$macrocall $name=\"droppable-item\" button=\"\"\"<$button message=\"tm-close-tiddler\" tooltip={{$:/language/Buttons/Close/Hint}} aria-label={{$:/language/Buttons/Close/Caption}} class=\"tc-btn-invisible tc-btn-mini\">{{$:/core/images/close-button}}</$button> <$link to={{!!title}}><$view field=\"title\"/></$link>\"\"\"/>\n</div>\n</$list>\n<$tiddler tiddler=\"\">\n<div>\n<$macrocall $name=\"droppable-item\" button=\"\"\"<$button message=\"tm-close-all-tiddlers\" class=\"tc-btn-invisible tc-btn-mini\"><<lingo Button>></$button>\"\"\"/>\n</div>\n</$tiddler>\n</div>\n"
},
"$:/core/ui/SideBar/Recent": {
"title": "$:/core/ui/SideBar/Recent",
"tags": "$:/tags/SideBar",
"caption": "{{$:/language/SideBar/Recent/Caption}}",
"text": "<$macrocall $name=\"timeline\" format={{$:/language/RecentChanges/DateFormat}}/>\n"
},
"$:/core/ui/SideBar/Tools": {
"title": "$:/core/ui/SideBar/Tools",
"tags": "$:/tags/SideBar",
"caption": "{{$:/language/SideBar/Tools/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/\n\\define config-title()\n$:/config/PageControlButtons/Visibility/$(listItem)$\n\\end\n\n<<lingo Basics/Version/Prompt>> <<version>>\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/PageControls]!has[draft.of]]\" variable=\"listItem\">\n\n<div style=\"position:relative;\" class={{{ [<listItem>encodeuricomponent[]addprefix[tc-btn-]] }}}>\n\n<$checkbox tiddler=<<config-title>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"show\"/> <$transclude tiddler=<<listItem>>/> <i class=\"tc-muted\"><$transclude tiddler=<<listItem>> field=\"description\"/></i>\n\n</div>\n\n</$list>\n\n</$set>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/SideBarLists": {
"title": "$:/core/ui/SideBarLists",
"text": "<$transclude tiddler=\"$:/core/ui/SideBarSegments/search\"/>\n\n<$transclude tiddler=\"$:/core/ui/SideBarSegments/tabs\"/>\n\n"
},
"$:/core/ui/SideBarSegments/page-controls": {
"title": "$:/core/ui/SideBarSegments/page-controls",
"tags": "$:/tags/SideBarSegment",
"text": "{{||$:/core/ui/PageTemplate/pagecontrols}}\n"
},
"$:/core/ui/SideBarSegments/search": {
"title": "$:/core/ui/SideBarSegments/search",
"tags": "$:/tags/SideBarSegment",
"text": "\\whitespace trim\n<div class=\"tc-sidebar-lists tc-sidebar-search\">\n\n<$set name=\"searchTiddler\" value=\"$:/temp/search\">\n<div class=\"tc-search\">\n<$edit-text tiddler=\"$:/temp/search\" type=\"search\" tag=\"input\" focus={{$:/config/Search/AutoFocus}} focusPopup=<<qualify \"$:/state/popup/search-dropdown\">> class=\"tc-popup-handle\"/>\n<$reveal state=\"$:/temp/search\" type=\"nomatch\" text=\"\">\n<$button tooltip={{$:/language/Buttons/AdvancedSearch/Hint}} aria-label={{$:/language/Buttons/AdvancedSearch/Caption}} class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" text={{$:/temp/search}}/>\n<$action-setfield $tiddler=\"$:/temp/search\" text=\"\"/>\n<$action-navigate $to=\"$:/AdvancedSearch\"/>\n{{$:/core/images/advanced-search-button}}\n</$button>\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/search\" text=\"\" />\n{{$:/core/images/close-button}}\n</$button>\n<$button popup=<<qualify \"$:/state/popup/search-dropdown\">> class=\"tc-btn-invisible\">\n{{$:/core/images/down-arrow}}\n<$list filter=\"[{$:/temp/search}minlength{$:/config/Search/MinLength}limit[1]]\" variable=\"listItem\">\n<$set name=\"searchTerm\" value={{{ [<searchTiddler>get[text]] }}}>\n<$set name=\"resultCount\" value=\"\"\"<$count filter=\"[!is[system]search<searchTerm>]\"/>\"\"\">\n{{$:/language/Search/Matches}}\n</$set>\n</$set>\n</$list>\n</$button>\n</$reveal>\n<$reveal state=\"$:/temp/search\" type=\"match\" text=\"\">\n<$button to=\"$:/AdvancedSearch\" tooltip={{$:/language/Buttons/AdvancedSearch/Hint}} aria-label={{$:/language/Buttons/AdvancedSearch/Caption}} class=\"tc-btn-invisible\">\n{{$:/core/images/advanced-search-button}}\n</$button>\n</$reveal>\n</div>\n\n<$reveal tag=\"div\" class=\"tc-block-dropdown-wrapper\" state=\"$:/temp/search\" type=\"nomatch\" text=\"\">\n\n<$reveal tag=\"div\" class=\"tc-block-dropdown tc-search-drop-down tc-popup-handle\" state=<<qualify \"$:/state/popup/search-dropdown\">> type=\"nomatch\" text=\"\" default=\"\">\n\n<$list filter=\"[{$:/temp/search}minlength{$:/config/Search/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n\n{{$:/core/ui/SearchResults}}\n\n</$list>\n\n</$reveal>\n\n</$reveal>\n\n</$set>\n\n</div>\n"
},
"$:/core/ui/SideBarSegments/site-subtitle": {
"title": "$:/core/ui/SideBarSegments/site-subtitle",
"tags": "$:/tags/SideBarSegment",
"text": "<div class=\"tc-site-subtitle\">\n\n<$transclude tiddler=\"$:/SiteSubtitle\" mode=\"inline\"/>\n\n</div>\n"
},
"$:/core/ui/SideBarSegments/site-title": {
"title": "$:/core/ui/SideBarSegments/site-title",
"tags": "$:/tags/SideBarSegment",
"text": "<h1 class=\"tc-site-title\">\n\n<$transclude tiddler=\"$:/SiteTitle\" mode=\"inline\"/>\n\n</h1>\n"
},
"$:/core/ui/SideBarSegments/tabs": {
"title": "$:/core/ui/SideBarSegments/tabs",
"tags": "$:/tags/SideBarSegment",
"text": "<div class=\"tc-sidebar-lists tc-sidebar-tabs\">\n\n<$macrocall $name=\"tabs\" tabsList=\"[all[shadows+tiddlers]tag[$:/tags/SideBar]!has[draft.of]]\" default={{$:/config/DefaultSidebarTab}} state=\"$:/state/tab/sidebar\" class=\"tc-sidebar-tabs-main\"/>\n\n</div>\n"
},
"$:/TagManager": {
"title": "$:/TagManager",
"icon": "$:/core/images/tag-button",
"color": "#bbb",
"text": "\\define lingo-base() $:/language/TagManager/\n\\define iconEditorTab(type)\n\\whitespace trim\n<$link to=\"\"><<lingo Icons/None>></$link>\n<$list filter=\"[all[shadows+tiddlers]is[image]] [all[shadows+tiddlers]tag[$:/tags/Image]] -[type[application/pdf]] +[sort[title]] +[$type$is[system]]\">\n<$link to={{!!title}}>\n<$transclude/> <$view field=\"title\"/>\n</$link>\n</$list>\n\\end\n\\define iconEditor(title)\n\\whitespace trim\n<div class=\"tc-drop-down-wrapper\">\n<$button popupTitle={{{ [[$:/state/popup/icon/]addsuffix<__title__>] }}} class=\"tc-btn-invisible tc-btn-dropdown\">{{$:/core/images/down-arrow}}</$button>\n<$reveal stateTitle={{{ [[$:/state/popup/icon/]addsuffix<__title__>] }}} type=\"popup\" position=\"belowleft\" text=\"\" default=\"\">\n<div class=\"tc-drop-down\">\n<$linkcatcher actions=\"\"\"<$action-setfield $tiddler=<<__title__>> icon=<<navigateTo>>/>\"\"\">\n<<iconEditorTab type:\"!\">>\n<hr/>\n<<iconEditorTab type:\"\">>\n</$linkcatcher>\n</div>\n</$reveal>\n</div>\n\\end\n\\define toggleButton(state)\n\\whitespace trim\n<$reveal stateTitle=<<__state__>> type=\"match\" text=\"closed\" default=\"closed\">\n<$button setTitle=<<__state__>> setTo=\"open\" class=\"tc-btn-invisible tc-btn-dropdown\" selectedClass=\"tc-selected\">\n{{$:/core/images/info-button}}\n</$button>\n</$reveal>\n<$reveal stateTitle=<<__state__>> type=\"match\" text=\"open\" default=\"closed\">\n<$button setTitle=<<__state__>> setTo=\"closed\" class=\"tc-btn-invisible tc-btn-dropdown\" selectedClass=\"tc-selected\">\n{{$:/core/images/info-button}}\n</$button>\n</$reveal>\n\\end\n\\whitespace trim\n<table class=\"tc-tag-manager-table\">\n<tbody>\n<tr>\n<th><<lingo Colour/Heading>></th>\n<th class=\"tc-tag-manager-tag\"><<lingo Tag/Heading>></th>\n<th><<lingo Count/Heading>></th>\n<th><<lingo Icon/Heading>></th>\n<th><<lingo Info/Heading>></th>\n</tr>\n<$list filter=\"[tags[]!is[system]sort[title]]\">\n<tr>\n<td><$edit-text field=\"color\" tag=\"input\" type=\"color\"/></td>\n<td>{{||$:/core/ui/TagTemplate}}</td>\n<td><$count filter=\"[all[current]tagging[]]\"/></td>\n<td>\n<$macrocall $name=\"iconEditor\" title={{!!title}}/>\n</td>\n<td>\n<$macrocall $name=\"toggleButton\" state={{{ [[$:/state/tag-manager/]addsuffix<currentTiddler>] }}} /> \n</td>\n</tr>\n<tr>\n<td></td>\n<td colspan=\"4\">\n<$reveal stateTitle={{{ [[$:/state/tag-manager/]addsuffix<currentTiddler>] }}} type=\"match\" text=\"open\" default=\"\">\n<table>\n<tbody>\n<tr><td><<lingo Colour/Heading>></td><td><$edit-text field=\"color\" tag=\"input\" type=\"text\" size=\"9\"/></td></tr>\n<tr><td><<lingo Icon/Heading>></td><td><$edit-text field=\"icon\" tag=\"input\" size=\"45\"/></td></tr>\n</tbody>\n</table>\n</$reveal>\n</td>\n</tr>\n</$list>\n<tr>\n<td></td>\n<td style=\"position:relative;\">\n{{$:/core/ui/UntaggedTemplate}}\n</td>\n<td>\n<small class=\"tc-menu-list-count\"><$count filter=\"[untagged[]!is[system]] -[tags[]]\"/></small>\n</td>\n<td></td>\n<td></td>\n</tr>\n</tbody>\n</table>\n"
},
"$:/core/ui/TagTemplate": {
"title": "$:/core/ui/TagTemplate",
"text": "\\whitespace trim\n<span class=\"tc-tag-list-item\">\n<$set name=\"transclusion\" value=<<currentTiddler>>>\n<$macrocall $name=\"tag-pill-body\" tag=<<currentTiddler>> icon={{!!icon}} colour={{!!color}} palette={{$:/palette}} element-tag=\"\"\"$button\"\"\" element-attributes=\"\"\"popup=<<qualify \"$:/state/popup/tag\">> dragFilter='[all[current]tagging[]]' tag='span'\"\"\"/>\n<$reveal state=<<qualify \"$:/state/popup/tag\">> type=\"popup\" position=\"below\" animate=\"yes\" class=\"tc-drop-down\">\n<$set name=\"tv-show-missing-links\" value=\"yes\">\n<$transclude tiddler=\"$:/core/ui/ListItemTemplate\"/>\n</$set>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TagDropdown]!has[draft.of]]\" variable=\"listItem\"> \n<$transclude tiddler=<<listItem>>/> \n</$list>\n<hr>\n<$macrocall $name=\"list-tagged-draggable\" tag=<<currentTiddler>>/>\n</$reveal>\n</$set>\n</span>\n"
},
"$:/core/ui/TiddlerFieldTemplate": {
"title": "$:/core/ui/TiddlerFieldTemplate",
"text": "<tr class=\"tc-view-field\">\n<td class=\"tc-view-field-name\">\n<$text text=<<listItem>>/>\n</td>\n<td class=\"tc-view-field-value\">\n<$view field=<<listItem>>/>\n</td>\n</tr>"
},
"$:/core/ui/TiddlerFields": {
"title": "$:/core/ui/TiddlerFields",
"text": "<table class=\"tc-view-field-table\">\n<tbody>\n<$list filter=\"[all[current]fields[]sort[title]] -text\" template=\"$:/core/ui/TiddlerFieldTemplate\" variable=\"listItem\"/>\n</tbody>\n</table>\n"
},
"$:/core/ui/TiddlerInfo/Advanced/PluginInfo": {
"title": "$:/core/ui/TiddlerInfo/Advanced/PluginInfo",
"tags": "$:/tags/TiddlerInfo/Advanced",
"text": "\\define lingo-base() $:/language/TiddlerInfo/Advanced/PluginInfo/\n<$list filter=\"[all[current]has[plugin-type]]\">\n\n! <<lingo Heading>>\n\n<<lingo Hint>>\n<ul>\n<$list filter=\"[all[current]plugintiddlers[]sort[title]]\" emptyMessage=<<lingo Empty/Hint>>>\n<li>\n<$link to={{!!title}}>\n<$view field=\"title\"/>\n</$link>\n</li>\n</$list>\n</ul>\n\n</$list>\n"
},
"$:/core/ui/TiddlerInfo/Advanced/ShadowInfo": {
"title": "$:/core/ui/TiddlerInfo/Advanced/ShadowInfo",
"tags": "$:/tags/TiddlerInfo/Advanced",
"text": "\\define lingo-base() $:/language/TiddlerInfo/Advanced/ShadowInfo/\n<$set name=\"infoTiddler\" value=<<currentTiddler>>>\n\n''<<lingo Heading>>''\n\n<$list filter=\"[all[current]!is[shadow]]\">\n\n<<lingo NotShadow/Hint>>\n\n</$list>\n\n<$list filter=\"[all[current]is[shadow]]\">\n\n<<lingo Shadow/Hint>>\n\n<$list filter=\"[all[current]shadowsource[]]\">\n\n<$set name=\"pluginTiddler\" value=<<currentTiddler>>>\n<<lingo Shadow/Source>>\n</$set>\n\n</$list>\n\n<$list filter=\"[all[current]is[shadow]is[tiddler]]\">\n\n<<lingo OverriddenShadow/Hint>>\n\n</$list>\n\n\n</$list>\n</$set>\n"
},
"$:/core/ui/TiddlerInfo/Advanced": {
"title": "$:/core/ui/TiddlerInfo/Advanced",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Advanced/Caption}}",
"text": "<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TiddlerInfo/Advanced]!has[draft.of]]\" variable=\"listItem\">\n<$transclude tiddler=<<listItem>>/>\n\n</$list>\n"
},
"$:/core/ui/TiddlerInfo/Fields": {
"title": "$:/core/ui/TiddlerInfo/Fields",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Fields/Caption}}",
"text": "<$transclude tiddler=\"$:/core/ui/TiddlerFields\"/>\n"
},
"$:/core/ui/TiddlerInfo/List": {
"title": "$:/core/ui/TiddlerInfo/List",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/List/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n<$list filter=\"[list{!!title}]\" emptyMessage=<<lingo List/Empty>> template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/TiddlerInfo/Listed": {
"title": "$:/core/ui/TiddlerInfo/Listed",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Listed/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n<$list filter=\"[all[current]listed[]!is[system]]\" emptyMessage=<<lingo Listed/Empty>> template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/TiddlerInfo/References": {
"title": "$:/core/ui/TiddlerInfo/References",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/References/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n<$list filter=\"[all[current]backlinks[]sort[title]]\" emptyMessage=<<lingo References/Empty>> template=\"$:/core/ui/ListItemTemplate\">\n</$list>"
},
"$:/core/ui/TiddlerInfo/Tagging": {
"title": "$:/core/ui/TiddlerInfo/Tagging",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Tagging/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n<$list filter=\"[all[current]tagging[]]\" emptyMessage=<<lingo Tagging/Empty>> template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/TiddlerInfo/Tools": {
"title": "$:/core/ui/TiddlerInfo/Tools",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Tools/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\\define config-title()\n$:/config/ViewToolbarButtons/Visibility/$(listItem)$\n\\end\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ViewToolbar]!has[draft.of]]\" variable=\"listItem\">\n\n<$checkbox tiddler=<<config-title>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"show\"/> <$transclude tiddler=<<listItem>>/> <i class=\"tc-muted\"><$transclude tiddler=<<listItem>> field=\"description\"/></i>\n\n</$list>\n\n</$set>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/TiddlerInfo": {
"title": "$:/core/ui/TiddlerInfo",
"text": "<div style=\"position:relative;\">\n<div class=\"tc-tiddler-controls\" style=\"position:absolute;right:0;\">\n<$reveal state=\"$:/config/TiddlerInfo/Mode\" type=\"match\" text=\"sticky\">\n<$button set=<<tiddlerInfoState>> setTo=\"\" tooltip={{$:/language/Buttons/Info/Hint}} aria-label={{$:/language/Buttons/Info/Caption}} class=\"tc-btn-invisible\">\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n</div>\n</div>\n\n<$macrocall $name=\"tabs\" tabsList=\"[all[shadows+tiddlers]tag[$:/tags/TiddlerInfo]!has[draft.of]]\" default={{$:/config/TiddlerInfo/Default}}/>"
},
"$:/core/ui/TopBar/menu": {
"title": "$:/core/ui/TopBar/menu",
"tags": "$:/tags/TopRightBar",
"text": "<$list filter=\"[[$:/state/sidebar]get[text]] +[else[yes]!match[no]]\" variable=\"ignore\">\n<$button set=\"$:/state/sidebar\" setTo=\"no\" tooltip={{$:/language/Buttons/HideSideBar/Hint}} aria-label={{$:/language/Buttons/HideSideBar/Caption}} class=\"tc-btn-invisible\">{{$:/core/images/chevron-right}}</$button>\n</$list>\n<$list filter=\"[[$:/state/sidebar]get[text]] +[else[yes]match[no]]\" variable=\"ignore\">\n<$button set=\"$:/state/sidebar\" setTo=\"yes\" tooltip={{$:/language/Buttons/ShowSideBar/Hint}} aria-label={{$:/language/Buttons/ShowSideBar/Caption}} class=\"tc-btn-invisible\">{{$:/core/images/chevron-left}}</$button>\n</$list>\n"
},
"$:/core/ui/UntaggedTemplate": {
"title": "$:/core/ui/UntaggedTemplate",
"text": "\\define lingo-base() $:/language/SideBar/\n<$button popup=<<qualify \"$:/state/popup/tag\">> class=\"tc-btn-invisible tc-untagged-label tc-tag-label\">\n<<lingo Tags/Untagged/Caption>>\n</$button>\n<$reveal state=<<qualify \"$:/state/popup/tag\">> type=\"popup\" position=\"below\">\n<div class=\"tc-drop-down\">\n<$list filter=\"[untagged[]!is[system]] -[tags[]] +[sort[title]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n</div>\n</$reveal>\n"
},
"$:/core/ui/ViewTemplate/body": {
"title": "$:/core/ui/ViewTemplate/body",
"tags": "$:/tags/ViewTemplate",
"text": "<$reveal tag=\"div\" class=\"tc-tiddler-body\" type=\"nomatch\" stateTitle=<<folded-state>> text=\"hide\" retain=\"yes\" animate=\"yes\">\n\n<$list filter=\"[all[current]!has[plugin-type]!field:hide-body[yes]]\">\n\n<$transclude>\n\n<$transclude tiddler=\"$:/language/MissingTiddler/Hint\"/>\n\n</$transclude>\n\n</$list>\n\n</$reveal>\n"
},
"$:/core/ui/ViewTemplate/classic": {
"title": "$:/core/ui/ViewTemplate/classic",
"tags": "$:/tags/ViewTemplate $:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/ClassicWarning/\n<$list filter=\"[all[current]type[text/x-tiddlywiki]]\">\n<div class=\"tc-message-box\">\n\n<<lingo Hint>>\n\n<$button set=\"!!type\" setTo=\"text/vnd.tiddlywiki\"><<lingo Upgrade/Caption>></$button>\n\n</div>\n</$list>\n"
},
"$:/core/ui/ViewTemplate/import": {
"title": "$:/core/ui/ViewTemplate/import",
"tags": "$:/tags/ViewTemplate",
"text": "\\define lingo-base() $:/language/Import/\n\n\\define buttons()\n<$button message=\"tm-delete-tiddler\" param=<<currentTiddler>>><<lingo Listing/Cancel/Caption>></$button>\n<$button message=\"tm-perform-import\" param=<<currentTiddler>>><<lingo Listing/Import/Caption>></$button>\n<<lingo Listing/Preview>> <$select tiddler=\"$:/state/importpreviewtype\" default=\"$:/core/ui/ImportPreviews/Text\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ImportPreview]!has[draft.of]]\">\n<option value=<<currentTiddler>>>{{!!caption}}</option>\n</$list>\n</$select>\n\\end\n\n<$list filter=\"[all[current]field:plugin-type[import]]\">\n\n<div class=\"tc-import\">\n\n<<lingo Listing/Hint>>\n\n<<buttons>>\n\n{{||$:/core/ui/ImportListing}}\n\n<<buttons>>\n\n</div>\n\n</$list>\n"
},
"$:/core/ui/ViewTemplate/plugin": {
"title": "$:/core/ui/ViewTemplate/plugin",
"tags": "$:/tags/ViewTemplate",
"text": "<$list filter=\"[all[current]has[plugin-type]] -[all[current]field:plugin-type[import]]\">\n<$set name=\"plugin-type\" value={{!!plugin-type}}>\n<$set name=\"default-popup-state\" value=\"yes\">\n<$set name=\"qualified-state\" value=<<qualify \"$:/state/plugin-info\">>>\n{{||$:/core/ui/Components/plugin-info}}\n</$set>\n</$set>\n</$set>\n</$list>\n"
},
"$:/core/ui/ViewTemplate/subtitle": {
"title": "$:/core/ui/ViewTemplate/subtitle",
"tags": "$:/tags/ViewTemplate",
"text": "\\whitespace trim\n<$reveal type=\"nomatch\" stateTitle=<<folded-state>> text=\"hide\" tag=\"div\" retain=\"yes\" animate=\"yes\">\n<div class=\"tc-subtitle\">\n<$link to={{!!modifier}} />\n<$view field=\"modified\" format=\"date\" template={{$:/language/Tiddler/DateFormat}}/>\n</div>\n</$reveal>\n"
},
"$:/core/ui/ViewTemplate/tags": {
"title": "$:/core/ui/ViewTemplate/tags",
"tags": "$:/tags/ViewTemplate",
"text": "<$reveal type=\"nomatch\" stateTitle=<<folded-state>> text=\"hide\" tag=\"div\" retain=\"yes\" animate=\"yes\">\n<div class=\"tc-tags-wrapper\"><$list filter=\"[all[current]tags[]sort[title]]\" template=\"$:/core/ui/TagTemplate\" storyview=\"pop\"/></div>\n</$reveal>\n"
},
"$:/core/ui/ViewTemplate/title": {
"title": "$:/core/ui/ViewTemplate/title",
"tags": "$:/tags/ViewTemplate",
"text": "\\whitespace trim\n\\define title-styles()\nfill:$(foregroundColor)$;\n\\end\n\\define config-title()\n$:/config/ViewToolbarButtons/Visibility/$(listItem)$\n\\end\n<div class=\"tc-tiddler-title\">\n<div class=\"tc-titlebar\">\n<span class=\"tc-tiddler-controls\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ViewToolbar]!has[draft.of]]\" variable=\"listItem\"><$reveal type=\"nomatch\" state=<<config-title>> text=\"hide\"><$set name=\"tv-config-toolbar-class\" filter=\"[<tv-config-toolbar-class>] [<listItem>encodeuricomponent[]addprefix[tc-btn-]]\"><$transclude tiddler=<<listItem>>/></$set></$reveal></$list>\n</span>\n<$set name=\"tv-wikilinks\" value={{$:/config/Tiddlers/TitleLinks}}>\n<$link>\n<$set name=\"foregroundColor\" value={{!!color}}>\n<span class=\"tc-tiddler-title-icon\" style=<<title-styles>>>\n<$transclude tiddler={{!!icon}}>\n<$transclude tiddler={{$:/config/DefaultTiddlerIcon}}/>\n</$transclude>\n</span>\n</$set>\n<$list filter=\"[all[current]removeprefix[$:/]]\">\n<h2 class=\"tc-title\" title={{$:/language/SystemTiddler/Tooltip}}>\n<span class=\"tc-system-title-prefix\">$:/</span><$text text=<<currentTiddler>>/>\n</h2>\n</$list>\n<$list filter=\"[all[current]!prefix[$:/]]\">\n<h2 class=\"tc-title\">\n<$view field=\"title\"/>\n</h2>\n</$list>\n</$link>\n</$set>\n</div>\n\n<$reveal type=\"nomatch\" text=\"\" default=\"\" state=<<tiddlerInfoState>> class=\"tc-tiddler-info tc-popup-handle\" animate=\"yes\" retain=\"yes\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TiddlerInfoSegment]!has[draft.of]] [[$:/core/ui/TiddlerInfo]]\" variable=\"listItem\"><$transclude tiddler=<<listItem>> mode=\"block\"/></$list>\n\n</$reveal>\n</div>"
},
"$:/core/ui/ViewTemplate/unfold": {
"title": "$:/core/ui/ViewTemplate/unfold",
"tags": "$:/tags/ViewTemplate",
"text": "<$reveal tag=\"div\" type=\"nomatch\" state=\"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-bar\" text=\"hide\">\n<$reveal tag=\"div\" type=\"nomatch\" stateTitle=<<folded-state>> text=\"hide\" default=\"show\" retain=\"yes\" animate=\"yes\">\n<$button tooltip={{$:/language/Buttons/Fold/Hint}} aria-label={{$:/language/Buttons/Fold/Caption}} class=\"tc-fold-banner\">\n<$action-sendmessage $message=\"tm-fold-tiddler\" $param=<<currentTiddler>> foldedState=<<folded-state>>/>\n{{$:/core/images/chevron-up}}\n</$button>\n</$reveal>\n<$reveal tag=\"div\" type=\"nomatch\" stateTitle=<<folded-state>> text=\"show\" default=\"show\" retain=\"yes\" animate=\"yes\">\n<$button tooltip={{$:/language/Buttons/Unfold/Hint}} aria-label={{$:/language/Buttons/Unfold/Caption}} class=\"tc-unfold-banner\">\n<$action-sendmessage $message=\"tm-fold-tiddler\" $param=<<currentTiddler>> foldedState=<<folded-state>>/>\n{{$:/core/images/chevron-down}}\n</$button>\n</$reveal>\n</$reveal>\n"
},
"$:/core/ui/ViewTemplate": {
"title": "$:/core/ui/ViewTemplate",
"text": "\\define folded-state()\n$:/state/folded/$(currentTiddler)$\n\\end\n\\import [all[shadows+tiddlers]tag[$:/tags/Macro/View]!has[draft.of]]\n<$vars storyTiddler=<<currentTiddler>> tiddlerInfoState=<<qualify \"$:/state/popup/tiddler-info\">>><div data-tiddler-title=<<currentTiddler>> data-tags={{!!tags}} class={{{ tc-tiddler-frame tc-tiddler-view-frame [<currentTiddler>is[tiddler]then[tc-tiddler-exists]] [<currentTiddler>is[missing]!is[shadow]then[tc-tiddler-missing]] [<currentTiddler>is[shadow]then[tc-tiddler-exists tc-tiddler-shadow]] [<currentTiddler>is[shadow]is[tiddler]then[tc-tiddler-overridden-shadow]] [<currentTiddler>is[system]then[tc-tiddler-system]] [{!!class}] [<currentTiddler>tags[]encodeuricomponent[]addprefix[tc-tagged-]] +[join[ ]] }}}><$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ViewTemplate]!has[draft.of]]\" variable=\"listItem\"><$transclude tiddler=<<listItem>>/></$list>\n</div>\n</$vars>\n"
},
"$:/core/ui/Buttons/clone": {
"title": "$:/core/ui/Buttons/clone",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/clone-button}} {{$:/language/Buttons/Clone/Caption}}",
"description": "{{$:/language/Buttons/Clone/Hint}}",
"text": "\\whitespace trim\n<$button message=\"tm-new-tiddler\" param=<<currentTiddler>> tooltip={{$:/language/Buttons/Clone/Hint}} aria-label={{$:/language/Buttons/Clone/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/clone-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/Clone/Caption}}/>\n</span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/close-others": {
"title": "$:/core/ui/Buttons/close-others",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/close-others-button}} {{$:/language/Buttons/CloseOthers/Caption}}",
"description": "{{$:/language/Buttons/CloseOthers/Hint}}",
"text": "\\whitespace trim\n<$button message=\"tm-close-other-tiddlers\" param=<<currentTiddler>> tooltip={{$:/language/Buttons/CloseOthers/Hint}} aria-label={{$:/language/Buttons/CloseOthers/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/close-others-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/CloseOthers/Caption}}/>\n</span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/close": {
"title": "$:/core/ui/Buttons/close",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/close-button}} {{$:/language/Buttons/Close/Caption}}",
"description": "{{$:/language/Buttons/Close/Hint}}",
"text": "\\whitespace trim\n<$button message=\"tm-close-tiddler\" tooltip={{$:/language/Buttons/Close/Hint}} aria-label={{$:/language/Buttons/Close/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/close-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text={{$:/language/Buttons/Close/Caption}}/>\n</span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/edit": {
"title": "$:/core/ui/Buttons/edit",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/edit-button}} {{$:/language/Buttons/Edit/Caption}}",
"description": "{{$:/language/Buttons/Edit/Hint}}",
"text": "\\whitespace trim\n<$button message=\"tm-edit-tiddler\" tooltip={{$:/language/Buttons/Edit/Hint}} aria-label={{$:/language/Buttons/Edit/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/edit-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/Edit/Caption}}/>\n</span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/export-tiddler": {
"title": "$:/core/ui/Buttons/export-tiddler",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/export-button}} {{$:/language/Buttons/ExportTiddler/Caption}}",
"description": "{{$:/language/Buttons/ExportTiddler/Hint}}",
"text": "\\define makeExportFilter()\n[[$(currentTiddler)$]]\n\\end\n<$macrocall $name=\"exportButton\" exportFilter=<<makeExportFilter>> lingoBase=\"$:/language/Buttons/ExportTiddler/\" baseFilename=<<currentTiddler>>/>"
},
"$:/core/ui/Buttons/fold-bar": {
"title": "$:/core/ui/Buttons/fold-bar",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/chevron-up}} {{$:/language/Buttons/Fold/FoldBar/Caption}}",
"description": "{{$:/language/Buttons/Fold/FoldBar/Hint}}",
"text": "<!-- This dummy toolbar button is here to allow visibility of the fold-bar to be controlled as if it were a toolbar button -->"
},
"$:/core/ui/Buttons/fold-others": {
"title": "$:/core/ui/Buttons/fold-others",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/fold-others-button}} {{$:/language/Buttons/FoldOthers/Caption}}",
"description": "{{$:/language/Buttons/FoldOthers/Hint}}",
"text": "\\whitespace trim\n<$button tooltip={{$:/language/Buttons/FoldOthers/Hint}} aria-label={{$:/language/Buttons/FoldOthers/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-fold-other-tiddlers\" $param=<<currentTiddler>> foldedStatePrefix=\"$:/state/folded/\"/>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\" variable=\"listItem\">\n{{$:/core/images/fold-others-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/FoldOthers/Caption}}/>\n</span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/fold": {
"title": "$:/core/ui/Buttons/fold",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/fold-button}} {{$:/language/Buttons/Fold/Caption}}",
"description": "{{$:/language/Buttons/Fold/Hint}}",
"text": "\\whitespace trim\n<$reveal type=\"nomatch\" stateTitle=<<folded-state>> text=\"hide\" default=\"show\">\n<$button tooltip={{$:/language/Buttons/Fold/Hint}} aria-label={{$:/language/Buttons/Fold/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-fold-tiddler\" $param=<<currentTiddler>> foldedState=<<folded-state>>/>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\" variable=\"listItem\">\n{{$:/core/images/fold-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/Fold/Caption}}/>\n</span>\n</$list>\n</$button>\n</$reveal>\n<$reveal type=\"match\" stateTitle=<<folded-state>> text=\"hide\" default=\"show\">\n<$button tooltip={{$:/language/Buttons/Unfold/Hint}} aria-label={{$:/language/Buttons/Unfold/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-fold-tiddler\" $param=<<currentTiddler>> foldedState=<<folded-state>>/>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\" variable=\"listItem\">\n{{$:/core/images/unfold-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/Unfold/Caption}}/>\n</span>\n</$list>\n</$button>\n</$reveal>\n"
},
"$:/core/ui/Buttons/info": {
"title": "$:/core/ui/Buttons/info",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/info-button}} {{$:/language/Buttons/Info/Caption}}",
"description": "{{$:/language/Buttons/Info/Hint}}",
"text": "\\whitespace trim\n\\define button-content()\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/info-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text={{$:/language/Buttons/Info/Caption}}/>\n</span>\n</$list>\n\\end\n<$reveal state=\"$:/config/TiddlerInfo/Mode\" type=\"match\" text=\"popup\">\n<$button popup=<<tiddlerInfoState>> tooltip={{$:/language/Buttons/Info/Hint}} aria-label={{$:/language/Buttons/Info/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$macrocall $name=\"button-content\" mode=\"inline\"/>\n</$button>\n</$reveal>\n<$reveal state=\"$:/config/TiddlerInfo/Mode\" type=\"match\" text=\"sticky\">\n<$reveal state=<<tiddlerInfoState>> type=\"match\" text=\"\" default=\"\">\n<$button set=<<tiddlerInfoState>> setTo=\"yes\" tooltip={{$:/language/Buttons/Info/Hint}} aria-label={{$:/language/Buttons/Info/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$macrocall $name=\"button-content\" mode=\"inline\"/>\n</$button>\n</$reveal>\n<$reveal state=<<tiddlerInfoState>> type=\"nomatch\" text=\"\" default=\"\">\n<$button set=<<tiddlerInfoState>> setTo=\"\" tooltip={{$:/language/Buttons/Info/Hint}} aria-label={{$:/language/Buttons/Info/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$macrocall $name=\"button-content\" mode=\"inline\"/>\n</$button>\n</$reveal>\n</$reveal>"
},
"$:/core/ui/Buttons/more-tiddler-actions": {
"title": "$:/core/ui/Buttons/more-tiddler-actions",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/down-arrow}} {{$:/language/Buttons/More/Caption}}",
"description": "{{$:/language/Buttons/More/Hint}}",
"text": "\\whitespace trim\n\\define config-title()\n$:/config/ViewToolbarButtons/Visibility/$(listItem)$\n\\end\n<$button popup=<<qualify \"$:/state/popup/more\">> tooltip={{$:/language/Buttons/More/Hint}} aria-label={{$:/language/Buttons/More/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/down-arrow}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/More/Caption}}/>\n</span>\n</$list>\n</$button>\n<$reveal state=<<qualify \"$:/state/popup/more\">> type=\"popup\" position=\"belowleft\" animate=\"yes\">\n\n<div class=\"tc-drop-down\">\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"tc-btn-invisible\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ViewToolbar]!has[draft.of]] -[[$:/core/ui/Buttons/more-tiddler-actions]]\" variable=\"listItem\">\n\n<$reveal type=\"match\" state=<<config-title>> text=\"hide\">\n\n<$set name=\"tv-config-toolbar-class\" filter=\"[<tv-config-toolbar-class>] [<listItem>encodeuricomponent[]addprefix[tc-btn-]]\">\n\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n\n</$set>\n\n</$reveal>\n\n</$list>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</div>\n\n</$reveal>"
},
"$:/core/ui/Buttons/new-here": {
"title": "$:/core/ui/Buttons/new-here",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/new-here-button}} {{$:/language/Buttons/NewHere/Caption}}",
"description": "{{$:/language/Buttons/NewHere/Hint}}",
"text": "\\whitespace trim\n\\define newHereActions()\n<$set name=\"tags\" filter=\"[<currentTiddler>] [{$:/config/NewTiddler/Tags!!tags}]\">\n<$action-sendmessage $message=\"tm-new-tiddler\" tags=<<tags>>/>\n</$set>\n\\end\n\\define newHereButton()\n<$button actions=<<newHereActions>> tooltip={{$:/language/Buttons/NewHere/Hint}} aria-label={{$:/language/Buttons/NewHere/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/new-here-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text={{$:/language/Buttons/NewHere/Caption}}/>\n</span>\n</$list>\n</$button>\n\\end\n<<newHereButton>>\n"
},
"$:/core/ui/Buttons/new-journal-here": {
"title": "$:/core/ui/Buttons/new-journal-here",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/new-journal-button}} {{$:/language/Buttons/NewJournalHere/Caption}}",
"description": "{{$:/language/Buttons/NewJournalHere/Hint}}",
"text": "\\whitespace trim\n\\define journalButtonTags()\n[[$(currentTiddlerTag)$]] $(journalTags)$\n\\end\n\\define journalButton()\n<$button tooltip={{$:/language/Buttons/NewJournalHere/Hint}} aria-label={{$:/language/Buttons/NewJournalHere/Caption}} class=<<tv-config-toolbar-class>>>\n<$wikify name=\"journalTitle\" text=\"\"\"<$macrocall $name=\"now\" format=<<journalTitleTemplate>>/>\"\"\">\n<$action-sendmessage $message=\"tm-new-tiddler\" title=<<journalTitle>> tags=<<journalButtonTags>>/>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/new-journal-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text={{$:/language/Buttons/NewJournalHere/Caption}}/>\n</span>\n</$list>\n</$wikify>\n</$button>\n\\end\n<$set name=\"journalTitleTemplate\" value={{$:/config/NewJournal/Title}}>\n<$set name=\"journalTags\" value={{$:/config/NewJournal/Tags!!tags}}>\n<$set name=\"currentTiddlerTag\" value=<<currentTiddler>>>\n<<journalButton>>\n</$set>\n</$set>\n</$set>\n"
},
"$:/core/ui/Buttons/open-window": {
"title": "$:/core/ui/Buttons/open-window",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/open-window}} {{$:/language/Buttons/OpenWindow/Caption}}",
"description": "{{$:/language/Buttons/OpenWindow/Hint}}",
"text": "\\whitespace trim\n<$button message=\"tm-open-window\" tooltip={{$:/language/Buttons/OpenWindow/Hint}} aria-label={{$:/language/Buttons/OpenWindow/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/open-window}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/OpenWindow/Caption}}/>\n</span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/permalink": {
"title": "$:/core/ui/Buttons/permalink",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/permalink-button}} {{$:/language/Buttons/Permalink/Caption}}",
"description": "{{$:/language/Buttons/Permalink/Hint}}",
"text": "\\whitespace trim\n<$button message=\"tm-permalink\" tooltip={{$:/language/Buttons/Permalink/Hint}} aria-label={{$:/language/Buttons/Permalink/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/permalink-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/Permalink/Caption}}/>\n</span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/permaview": {
"title": "$:/core/ui/Buttons/permaview",
"tags": "$:/tags/ViewToolbar $:/tags/PageControls",
"caption": "{{$:/core/images/permaview-button}} {{$:/language/Buttons/Permaview/Caption}}",
"description": "{{$:/language/Buttons/Permaview/Hint}}",
"text": "\\whitespace trim\n<$button message=\"tm-permaview\" tooltip={{$:/language/Buttons/Permaview/Hint}} aria-label={{$:/language/Buttons/Permaview/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/permaview-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text=\" \"/>\n<$text text={{$:/language/Buttons/Permaview/Caption}}/>\n</span>\n</$list>\n</$button>"
},
"$:/DefaultTiddlers": {
"title": "$:/DefaultTiddlers",
"text": "GettingStarted\n"
},
"$:/temp/advancedsearch": {
"title": "$:/temp/advancedsearch",
"text": ""
},
"$:/snippets/allfields": {
"title": "$:/snippets/allfields",
"text": "\\define renderfield(title)\n<tr class=\"tc-view-field\"><td class=\"tc-view-field-name\">''$title$'':</td><td class=\"tc-view-field-value\">//{{$:/language/Docs/Fields/$title$}}//</td></tr>\n\\end\n<table class=\"tc-view-field-table\"><tbody><$list filter=\"[fields[]sort[title]]\" variable=\"listItem\"><$macrocall $name=\"renderfield\" title=<<listItem>>/></$list>\n</tbody></table>\n"
},
"$:/config/AnimationDuration": {
"title": "$:/config/AnimationDuration",
"text": "400"
},
"$:/config/AutoFocus": {
"title": "$:/config/AutoFocus",
"text": "title"
},
"$:/config/AutoSave": {
"title": "$:/config/AutoSave",
"text": "yes"
},
"$:/config/BitmapEditor/Colour": {
"title": "$:/config/BitmapEditor/Colour",
"text": "#444"
},
"$:/config/BitmapEditor/ImageSizes": {
"title": "$:/config/BitmapEditor/ImageSizes",
"text": "[[62px 100px]] [[100px 62px]] [[124px 200px]] [[200px 124px]] [[248px 400px]] [[371px 600px]] [[400px 248px]] [[556px 900px]] [[600px 371px]] [[742px 1200px]] [[900px 556px]] [[1200px 742px]]"
},
"$:/config/BitmapEditor/LineWidth": {
"title": "$:/config/BitmapEditor/LineWidth",
"text": "3px"
},
"$:/config/BitmapEditor/LineWidths": {
"title": "$:/config/BitmapEditor/LineWidths",
"text": "0.25px 0.5px 1px 2px 3px 4px 6px 8px 10px 16px 20px 28px 40px 56px 80px"
},
"$:/config/BitmapEditor/Opacities": {
"title": "$:/config/BitmapEditor/Opacities",
"text": "0.01 0.025 0.05 0.075 0.1 0.15 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0"
},
"$:/config/BitmapEditor/Opacity": {
"title": "$:/config/BitmapEditor/Opacity",
"text": "1.0"
},
"$:/config/DefaultMoreSidebarTab": {
"title": "$:/config/DefaultMoreSidebarTab",
"text": "$:/core/ui/MoreSideBar/Tags"
},
"$:/config/DefaultSidebarTab": {
"title": "$:/config/DefaultSidebarTab",
"text": "$:/core/ui/SideBar/Open"
},
"$:/config/DownloadSaver/AutoSave": {
"title": "$:/config/DownloadSaver/AutoSave",
"text": "no"
},
"$:/config/Drafts/TypingTimeout": {
"title": "$:/config/Drafts/TypingTimeout",
"text": "400"
},
"$:/config/EditTemplateFields/Visibility/title": {
"title": "$:/config/EditTemplateFields/Visibility/title",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/tags": {
"title": "$:/config/EditTemplateFields/Visibility/tags",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/text": {
"title": "$:/config/EditTemplateFields/Visibility/text",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/creator": {
"title": "$:/config/EditTemplateFields/Visibility/creator",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/created": {
"title": "$:/config/EditTemplateFields/Visibility/created",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/modified": {
"title": "$:/config/EditTemplateFields/Visibility/modified",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/modifier": {
"title": "$:/config/EditTemplateFields/Visibility/modifier",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/type": {
"title": "$:/config/EditTemplateFields/Visibility/type",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/draft.title": {
"title": "$:/config/EditTemplateFields/Visibility/draft.title",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/draft.of": {
"title": "$:/config/EditTemplateFields/Visibility/draft.of",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/revision": {
"title": "$:/config/EditTemplateFields/Visibility/revision",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/bag": {
"title": "$:/config/EditTemplateFields/Visibility/bag",
"text": "hide"
},
"$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-4": {
"title": "$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-4",
"text": "hide"
},
"$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-5": {
"title": "$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-5",
"text": "hide"
},
"$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-6": {
"title": "$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-6",
"text": "hide"
},
"$:/config/EditorTypeMappings/image/gif": {
"title": "$:/config/EditorTypeMappings/image/gif",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/webp": {
"title": "$:/config/EditorTypeMappings/image/webp",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/heic": {
"title": "$:/config/EditorTypeMappings/image/heic",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/heif": {
"title": "$:/config/EditorTypeMappings/image/heif",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/jpeg": {
"title": "$:/config/EditorTypeMappings/image/jpeg",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/jpg": {
"title": "$:/config/EditorTypeMappings/image/jpg",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/png": {
"title": "$:/config/EditorTypeMappings/image/png",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/x-icon": {
"title": "$:/config/EditorTypeMappings/image/x-icon",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/text/vnd.tiddlywiki": {
"title": "$:/config/EditorTypeMappings/text/vnd.tiddlywiki",
"text": "text"
},
"$:/config/Manager/Show": {
"title": "$:/config/Manager/Show",
"text": "tiddlers"
},
"$:/config/Manager/Filter": {
"title": "$:/config/Manager/Filter",
"text": ""
},
"$:/config/Manager/Order": {
"title": "$:/config/Manager/Order",
"text": "forward"
},
"$:/config/Manager/Sort": {
"title": "$:/config/Manager/Sort",
"text": "title"
},
"$:/config/Manager/System": {
"title": "$:/config/Manager/System",
"text": "system"
},
"$:/config/Manager/Tag": {
"title": "$:/config/Manager/Tag",
"text": ""
},
"$:/state/popup/manager/item/$:/Manager/ItemMain/RawText": {
"title": "$:/state/popup/manager/item/$:/Manager/ItemMain/RawText",
"text": "hide"
},
"$:/config/MissingLinks": {
"title": "$:/config/MissingLinks",
"text": "yes"
},
"$:/config/Navigation/UpdateAddressBar": {
"title": "$:/config/Navigation/UpdateAddressBar",
"text": "no"
},
"$:/config/Navigation/UpdateHistory": {
"title": "$:/config/Navigation/UpdateHistory",
"text": "no"
},
"$:/config/NewImageType": {
"title": "$:/config/NewImageType",
"text": "jpeg"
},
"$:/config/OfficialPluginLibrary": {
"title": "$:/config/OfficialPluginLibrary",
"tags": "$:/tags/PluginLibrary",
"url": "https://tiddlywiki.com/library/v5.1.22/index.html",
"caption": "{{$:/language/OfficialPluginLibrary}}",
"text": "{{$:/language/OfficialPluginLibrary/Hint}}\n"
},
"$:/config/Navigation/openLinkFromInsideRiver": {
"title": "$:/config/Navigation/openLinkFromInsideRiver",
"text": "below"
},
"$:/config/Navigation/openLinkFromOutsideRiver": {
"title": "$:/config/Navigation/openLinkFromOutsideRiver",
"text": "top"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/advanced-search": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/advanced-search",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/close-all": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/close-all",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/encryption": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/encryption",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/export-page": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/export-page",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/fold-all": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/fold-all",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/full-screen": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/full-screen",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/home": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/home",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/refresh": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/refresh",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/import": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/import",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/language": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/language",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/tag-manager": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/tag-manager",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/manager": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/manager",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/more-page-actions": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/more-page-actions",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/new-journal": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/new-journal",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/new-image": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/new-image",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/palette": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/palette",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/permaview": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/permaview",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/print": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/print",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/storyview": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/storyview",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/timestamp": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/timestamp",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/theme": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/theme",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/unfold-all": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/unfold-all",
"text": "hide"
},
"$:/config/Performance/Instrumentation": {
"title": "$:/config/Performance/Instrumentation",
"text": "no"
},
"$:/config/RegisterPluginType/plugin": {
"title": "$:/config/RegisterPluginType/plugin",
"text": "yes"
},
"$:/config/RegisterPluginType/theme": {
"title": "$:/config/RegisterPluginType/theme",
"text": "no"
},
"$:/config/RegisterPluginType/language": {
"title": "$:/config/RegisterPluginType/language",
"text": "no"
},
"$:/config/RegisterPluginType/info": {
"title": "$:/config/RegisterPluginType/info",
"text": "no"
},
"$:/config/RegisterPluginType/import": {
"title": "$:/config/RegisterPluginType/import",
"text": "no"
},
"$:/config/SaveWikiButton/Template": {
"title": "$:/config/SaveWikiButton/Template",
"text": "$:/core/save/all"
},
"$:/config/SaverFilter": {
"title": "$:/config/SaverFilter",
"text": "[all[]] -[[$:/HistoryList]] -[[$:/StoryList]] -[[$:/Import]] -[[$:/isEncrypted]] -[[$:/UploadName]] -[prefix[$:/state/]] -[prefix[$:/temp/]]"
},
"$:/config/Search/AutoFocus": {
"title": "$:/config/Search/AutoFocus",
"text": "true"
},
"$:/config/Search/MinLength": {
"title": "$:/config/Search/MinLength",
"text": "3"
},
"$:/config/SearchResults/Default": {
"title": "$:/config/SearchResults/Default",
"text": "$:/core/ui/DefaultSearchResultList"
},
"$:/config/Server/ExternalFilters/[all[tiddlers]!is[system]sort[title]]": {
"title": "$:/config/Server/ExternalFilters/[all[tiddlers]!is[system]sort[title]]",
"text": "yes"
},
"$:/config/ShortcutInfo/add-field": {
"title": "$:/config/ShortcutInfo/add-field",
"text": "{{$:/language/EditTemplate/Fields/Add/Button/Hint}}"
},
"$:/config/ShortcutInfo/advanced-search": {
"title": "$:/config/ShortcutInfo/advanced-search",
"text": "{{$:/language/Buttons/AdvancedSearch/Hint}}"
},
"$:/config/ShortcutInfo/bold": {
"title": "$:/config/ShortcutInfo/bold",
"text": "{{$:/language/Buttons/Bold/Hint}}"
},
"$:/config/ShortcutInfo/cancel-edit-tiddler": {
"title": "$:/config/ShortcutInfo/cancel-edit-tiddler",
"text": "{{$:/language/Buttons/Cancel/Hint}}"
},
"$:/config/ShortcutInfo/excise": {
"title": "$:/config/ShortcutInfo/excise",
"text": "{{$:/language/Buttons/Excise/Hint}}"
},
"$:/config/ShortcutInfo/heading-1": {
"title": "$:/config/ShortcutInfo/heading-1",
"text": "{{$:/language/Buttons/Heading1/Hint}}"
},
"$:/config/ShortcutInfo/heading-2": {
"title": "$:/config/ShortcutInfo/heading-2",
"text": "{{$:/language/Buttons/Heading2/Hint}}"
},
"$:/config/ShortcutInfo/heading-3": {
"title": "$:/config/ShortcutInfo/heading-3",
"text": "{{$:/language/Buttons/Heading3/Hint}}"
},
"$:/config/ShortcutInfo/heading-4": {
"title": "$:/config/ShortcutInfo/heading-4",
"text": "{{$:/language/Buttons/Heading4/Hint}}"
},
"$:/config/ShortcutInfo/heading-5": {
"title": "$:/config/ShortcutInfo/heading-5",
"text": "{{$:/language/Buttons/Heading5/Hint}}"
},
"$:/config/ShortcutInfo/heading-6": {
"title": "$:/config/ShortcutInfo/heading-6",
"text": "{{$:/language/Buttons/Heading6/Hint}}"
},
"$:/config/ShortcutInfo/italic": {
"title": "$:/config/ShortcutInfo/italic",
"text": "{{$:/language/Buttons/Italic/Hint}}"
},
"$:/config/ShortcutInfo/link": {
"title": "$:/config/ShortcutInfo/link",
"text": "{{$:/language/Buttons/Link/Hint}}"
},
"$:/config/ShortcutInfo/list-bullet": {
"title": "$:/config/ShortcutInfo/list-bullet",
"text": "{{$:/language/Buttons/ListBullet/Hint}}"
},
"$:/config/ShortcutInfo/list-number": {
"title": "$:/config/ShortcutInfo/list-number",
"text": "{{$:/language/Buttons/ListNumber/Hint}}"
},
"$:/config/ShortcutInfo/mono-block": {
"title": "$:/config/ShortcutInfo/mono-block",
"text": "{{$:/language/Buttons/MonoBlock/Hint}}"
},
"$:/config/ShortcutInfo/mono-line": {
"title": "$:/config/ShortcutInfo/mono-line",
"text": "{{$:/language/Buttons/MonoLine/Hint}}"
},
"$:/config/ShortcutInfo/new-image": {
"title": "$:/config/ShortcutInfo/new-image",
"text": "{{$:/language/Buttons/NewImage/Hint}}"
},
"$:/config/ShortcutInfo/new-journal": {
"title": "$:/config/ShortcutInfo/new-journal",
"text": "{{$:/language/Buttons/NewJournal/Hint}}"
},
"$:/config/ShortcutInfo/new-tiddler": {
"title": "$:/config/ShortcutInfo/new-tiddler",
"text": "{{$:/language/Buttons/NewTiddler/Hint}}"
},
"$:/config/ShortcutInfo/picture": {
"title": "$:/config/ShortcutInfo/picture",
"text": "{{$:/language/Buttons/Picture/Hint}}"
},
"$:/config/ShortcutInfo/preview": {
"title": "$:/config/ShortcutInfo/preview",
"text": "{{$:/language/Buttons/Preview/Hint}}"
},
"$:/config/ShortcutInfo/quote": {
"title": "$:/config/ShortcutInfo/quote",
"text": "{{$:/language/Buttons/Quote/Hint}}"
},
"$:/config/ShortcutInfo/save-tiddler": {
"title": "$:/config/ShortcutInfo/save-tiddler",
"text": "{{$:/language/Buttons/Save/Hint}}"
},
"$:/config/ShortcutInfo/sidebar-search": {
"title": "$:/config/ShortcutInfo/sidebar-search",
"text": "{{$:/language/Buttons/SidebarSearch/Hint}}"
},
"$:/config/ShortcutInfo/stamp": {
"title": "$:/config/ShortcutInfo/stamp",
"text": "{{$:/language/Buttons/Stamp/Hint}}"
},
"$:/config/ShortcutInfo/strikethrough": {
"title": "$:/config/ShortcutInfo/strikethrough",
"text": "{{$:/language/Buttons/Strikethrough/Hint}}"
},
"$:/config/ShortcutInfo/subscript": {
"title": "$:/config/ShortcutInfo/subscript",
"text": "{{$:/language/Buttons/Subscript/Hint}}"
},
"$:/config/ShortcutInfo/superscript": {
"title": "$:/config/ShortcutInfo/superscript",
"text": "{{$:/language/Buttons/Superscript/Hint}}"
},
"$:/config/ShortcutInfo/toggle-sidebar": {
"title": "$:/config/ShortcutInfo/toggle-sidebar",
"text": "{{$:/language/Buttons/ToggleSidebar/Hint}}"
},
"$:/config/ShortcutInfo/underline": {
"title": "$:/config/ShortcutInfo/underline",
"text": "{{$:/language/Buttons/Underline/Hint}}"
},
"$:/config/SyncFilter": {
"title": "$:/config/SyncFilter",
"text": "[is[tiddler]] -[[$:/HistoryList]] -[[$:/Import]] -[[$:/isEncrypted]] -[prefix[$:/status/]] -[prefix[$:/state/]] -[prefix[$:/temp/]]"
},
"$:/config/Tags/MinLength": {
"title": "$:/config/Tags/MinLength",
"text": "0"
},
"$:/config/TextEditor/EditorHeight/Height": {
"title": "$:/config/TextEditor/EditorHeight/Height",
"text": "400px"
},
"$:/config/TextEditor/EditorHeight/Mode": {
"title": "$:/config/TextEditor/EditorHeight/Mode",
"text": "auto"
},
"$:/config/TiddlerInfo/Default": {
"title": "$:/config/TiddlerInfo/Default",
"text": "$:/core/ui/TiddlerInfo/Fields"
},
"$:/config/TiddlerInfo/Mode": {
"title": "$:/config/TiddlerInfo/Mode",
"text": "popup"
},
"$:/config/Tiddlers/TitleLinks": {
"title": "$:/config/Tiddlers/TitleLinks",
"text": "no"
},
"$:/config/Toolbar/ButtonClass": {
"title": "$:/config/Toolbar/ButtonClass",
"text": "tc-btn-invisible"
},
"$:/config/Toolbar/Icons": {
"title": "$:/config/Toolbar/Icons",
"text": "yes"
},
"$:/config/Toolbar/Text": {
"title": "$:/config/Toolbar/Text",
"text": "no"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/clone": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/clone",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/close-others": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/close-others",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/export-tiddler": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/export-tiddler",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/info": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/info",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/more-tiddler-actions": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/more-tiddler-actions",
"text": "show"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/new-here": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/new-here",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/new-journal-here": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/new-journal-here",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/open-window": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/open-window",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/permalink": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/permalink",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/permaview": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/permaview",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/delete": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/delete",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-bar": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-bar",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-others": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-others",
"text": "hide"
},
"$:/config/shortcuts-mac/bold": {
"title": "$:/config/shortcuts-mac/bold",
"text": "meta-B"
},
"$:/config/shortcuts-mac/italic": {
"title": "$:/config/shortcuts-mac/italic",
"text": "meta-I"
},
"$:/config/shortcuts-mac/underline": {
"title": "$:/config/shortcuts-mac/underline",
"text": "meta-U"
},
"$:/config/shortcuts-mac/new-image": {
"title": "$:/config/shortcuts-mac/new-image",
"text": "ctrl-I"
},
"$:/config/shortcuts-mac/new-journal": {
"title": "$:/config/shortcuts-mac/new-journal",
"text": "ctrl-J"
},
"$:/config/shortcuts-mac/new-tiddler": {
"title": "$:/config/shortcuts-mac/new-tiddler",
"text": "ctrl-N"
},
"$:/config/shortcuts-not-mac/bold": {
"title": "$:/config/shortcuts-not-mac/bold",
"text": "ctrl-B"
},
"$:/config/shortcuts-not-mac/italic": {
"title": "$:/config/shortcuts-not-mac/italic",
"text": "ctrl-I"
},
"$:/config/shortcuts-not-mac/underline": {
"title": "$:/config/shortcuts-not-mac/underline",
"text": "ctrl-U"
},
"$:/config/shortcuts-not-mac/new-image": {
"title": "$:/config/shortcuts-not-mac/new-image",
"text": "alt-I"
},
"$:/config/shortcuts-not-mac/new-journal": {
"title": "$:/config/shortcuts-not-mac/new-journal",
"text": "alt-J"
},
"$:/config/shortcuts-not-mac/new-tiddler": {
"title": "$:/config/shortcuts-not-mac/new-tiddler",
"text": "alt-N"
},
"$:/config/shortcuts/add-field": {
"title": "$:/config/shortcuts/add-field",
"text": "enter"
},
"$:/config/shortcuts/advanced-search": {
"title": "$:/config/shortcuts/advanced-search",
"text": "ctrl-shift-A"
},
"$:/config/shortcuts/cancel-edit-tiddler": {
"title": "$:/config/shortcuts/cancel-edit-tiddler",
"text": "escape"
},
"$:/config/shortcuts/excise": {
"title": "$:/config/shortcuts/excise",
"text": "ctrl-E"
},
"$:/config/shortcuts/sidebar-search": {
"title": "$:/config/shortcuts/sidebar-search",
"text": "ctrl-shift-F"
},
"$:/config/shortcuts/heading-1": {
"title": "$:/config/shortcuts/heading-1",
"text": "ctrl-1"
},
"$:/config/shortcuts/heading-2": {
"title": "$:/config/shortcuts/heading-2",
"text": "ctrl-2"
},
"$:/config/shortcuts/heading-3": {
"title": "$:/config/shortcuts/heading-3",
"text": "ctrl-3"
},
"$:/config/shortcuts/heading-4": {
"title": "$:/config/shortcuts/heading-4",
"text": "ctrl-4"
},
"$:/config/shortcuts/heading-5": {
"title": "$:/config/shortcuts/heading-5",
"text": "ctrl-5"
},
"$:/config/shortcuts/heading-6": {
"title": "$:/config/shortcuts/heading-6",
"text": "ctrl-6"
},
"$:/config/shortcuts/link": {
"title": "$:/config/shortcuts/link",
"text": "ctrl-L"
},
"$:/config/shortcuts/linkify": {
"title": "$:/config/shortcuts/linkify",
"text": "alt-shift-L"
},
"$:/config/shortcuts/list-bullet": {
"title": "$:/config/shortcuts/list-bullet",
"text": "ctrl-shift-L"
},
"$:/config/shortcuts/list-number": {
"title": "$:/config/shortcuts/list-number",
"text": "ctrl-shift-N"
},
"$:/config/shortcuts/mono-block": {
"title": "$:/config/shortcuts/mono-block",
"text": "ctrl-shift-M"
},
"$:/config/shortcuts/mono-line": {
"title": "$:/config/shortcuts/mono-line",
"text": "ctrl-M"
},
"$:/config/shortcuts/picture": {
"title": "$:/config/shortcuts/picture",
"text": "ctrl-shift-I"
},
"$:/config/shortcuts/preview": {
"title": "$:/config/shortcuts/preview",
"text": "alt-P"
},
"$:/config/shortcuts/quote": {
"title": "$:/config/shortcuts/quote",
"text": "ctrl-Q"
},
"$:/config/shortcuts/save-tiddler": {
"title": "$:/config/shortcuts/save-tiddler",
"text": "ctrl+enter"
},
"$:/config/shortcuts/stamp": {
"title": "$:/config/shortcuts/stamp",
"text": "ctrl-S"
},
"$:/config/shortcuts/strikethrough": {
"title": "$:/config/shortcuts/strikethrough",
"text": "ctrl-T"
},
"$:/config/shortcuts/subscript": {
"title": "$:/config/shortcuts/subscript",
"text": "ctrl-shift-B"
},
"$:/config/shortcuts/superscript": {
"title": "$:/config/shortcuts/superscript",
"text": "ctrl-shift-P"
},
"$:/config/shortcuts/toggle-sidebar": {
"title": "$:/config/shortcuts/toggle-sidebar",
"text": "alt-shift-S"
},
"$:/config/shortcuts/transcludify": {
"title": "$:/config/shortcuts/transcludify",
"text": "alt-shift-T"
},
"$:/config/ui/EditTemplate": {
"title": "$:/config/ui/EditTemplate",
"text": "$:/core/ui/EditTemplate"
},
"$:/config/ui/ViewTemplate": {
"title": "$:/config/ui/ViewTemplate",
"text": "$:/core/ui/ViewTemplate"
},
"$:/config/WikiParserRules/Inline/wikilink": {
"title": "$:/config/WikiParserRules/Inline/wikilink",
"text": "enable"
},
"$:/snippets/currpalettepreview": {
"title": "$:/snippets/currpalettepreview",
"text": "\\define swatchStyle()\nbackground-color: $(swatchColour)$;\n\\end\n\\define swatch()\n<$set name=\"swatchColour\" value={{##$(colour)$}}\n><div class=\"tc-swatch\" style=<<swatchStyle>> title=<<colour>>/></$set>\n\\end\n<div class=\"tc-swatches-horiz\"><$list filter=\"\nforeground\nbackground\nmuted-foreground\nprimary\npage-background\ntab-background\ntiddler-info-background\n\" variable=\"colour\"><<swatch>></$list></div>"
},
"$:/snippets/download-wiki-button": {
"title": "$:/snippets/download-wiki-button",
"text": "\\define lingo-base() $:/language/ControlPanel/Tools/Download/\n<$button class=\"tc-btn-big-green\">\n<$action-sendmessage $message=\"tm-download-file\" $param=\"$:/core/save/all\" filename=\"index.html\"/>\n<<lingo Full/Caption>> {{$:/core/images/save-button}}\n</$button>"
},
"$:/language": {
"title": "$:/language",
"text": "$:/languages/en-GB"
},
"$:/snippets/languageswitcher": {
"title": "$:/snippets/languageswitcher",
"text": "\\define flag-title()\n$(languagePluginTitle)$/icon\n\\end\n\n<$linkcatcher to=\"$:/language\">\n<div class=\"tc-chooser tc-language-chooser\">\n<$list filter=\"[[$:/languages/en-GB]] [plugin-type[language]sort[description]]\">\n<$set name=\"cls\" filter=\"[all[current]field:title{$:/language}]\" value=\"tc-chooser-item tc-chosen\" emptyValue=\"tc-chooser-item\"><div class=<<cls>>>\n<$link>\n<span class=\"tc-image-button\">\n<$set name=\"languagePluginTitle\" value=<<currentTiddler>>>\n<$transclude subtiddler=<<flag-title>>>\n<$list filter=\"[all[current]field:title[$:/languages/en-GB]]\">\n<$transclude tiddler=\"$:/languages/en-GB/icon\"/>\n</$list>\n</$transclude>\n</$set>\n</span>\n<$view field=\"description\">\n<$view field=\"name\">\n<$view field=\"title\"/>\n</$view>\n</$view>\n</$link>\n</div>\n</$set>\n</$list>\n</div>\n</$linkcatcher>"
},
"$:/core/macros/CSS": {
"title": "$:/core/macros/CSS",
"tags": "$:/tags/Macro",
"text": "\\define colour(name)\n<$transclude tiddler={{$:/palette}} index=\"$name$\"><$transclude tiddler=\"$:/palettes/Vanilla\" index=\"$name$\"><$transclude tiddler=\"$:/config/DefaultColourMappings/$name$\"/></$transclude></$transclude>\n\\end\n\n\\define color(name)\n<<colour $name$>>\n\\end\n\n\\define box-shadow(shadow)\n``\n -webkit-box-shadow: $shadow$;\n -moz-box-shadow: $shadow$;\n box-shadow: $shadow$;\n``\n\\end\n\n\\define filter(filter)\n``\n -webkit-filter: $filter$;\n -moz-filter: $filter$;\n filter: $filter$;\n``\n\\end\n\n\\define transition(transition)\n``\n -webkit-transition: $transition$;\n -moz-transition: $transition$;\n transition: $transition$;\n``\n\\end\n\n\\define transform-origin(origin)\n``\n -webkit-transform-origin: $origin$;\n -moz-transform-origin: $origin$;\n transform-origin: $origin$;\n``\n\\end\n\n\\define background-linear-gradient(gradient)\n``\nbackground-image: linear-gradient($gradient$);\nbackground-image: -o-linear-gradient($gradient$);\nbackground-image: -moz-linear-gradient($gradient$);\nbackground-image: -webkit-linear-gradient($gradient$);\nbackground-image: -ms-linear-gradient($gradient$);\n``\n\\end\n\n\\define column-count(columns)\n``\n-moz-column-count: $columns$;\n-webkit-column-count: $columns$;\ncolumn-count: $columns$;\n``\n\\end\n\n\\define datauri(title)\n<$macrocall $name=\"makedatauri\" type={{$title$!!type}} text={{$title$}} _canonical_uri={{$title$!!_canonical_uri}}/>\n\\end\n\n\\define if-sidebar(text)\n<$reveal state=\"$:/state/sidebar\" type=\"match\" text=\"yes\" default=\"yes\">$text$</$reveal>\n\\end\n\n\\define if-no-sidebar(text)\n<$reveal state=\"$:/state/sidebar\" type=\"nomatch\" text=\"yes\" default=\"yes\">$text$</$reveal>\n\\end\n\n\\define if-background-attachment(text)\n<$reveal state=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimage\" type=\"nomatch\" text=\"\">$text$</$reveal>\n\\end\n"
},
"$:/core/macros/colour-picker": {
"title": "$:/core/macros/colour-picker",
"tags": "$:/tags/Macro",
"text": "\\define colour-picker-update-recent()\n<$action-listops\n\t$tiddler=\"$:/config/ColourPicker/Recent\"\n\t$subfilter=\"$(colour-picker-value)$ [list[$:/config/ColourPicker/Recent]remove[$(colour-picker-value)$]] +[limit[8]]\"\n/>\n\\end\n\n\\define colour-picker-inner(actions)\n<$button tag=\"a\" tooltip=\"\"\"$(colour-picker-value)$\"\"\">\n\n$(colour-picker-update-recent)$\n\n$actions$\n\n<span style=\"display:inline-block; background-color: $(colour-picker-value)$; width: 100%; height: 100%; border-radius: 50%;\"/>\n\n</$button>\n\\end\n\n\\define colour-picker-recent-inner(actions)\n<$set name=\"colour-picker-value\" value=\"$(recentColour)$\">\n<$macrocall $name=\"colour-picker-inner\" actions=\"\"\"$actions$\"\"\"/>\n</$set>\n\\end\n\n\\define colour-picker-recent(actions)\n{{$:/language/ColourPicker/Recent}} <$list filter=\"[list[$:/config/ColourPicker/Recent]]\" variable=\"recentColour\">\n<$macrocall $name=\"colour-picker-recent-inner\" actions=\"\"\"$actions$\"\"\"/></$list>\n\\end\n\n\\define colour-picker(actions)\n<div class=\"tc-colour-chooser\">\n\n<$macrocall $name=\"colour-picker-recent\" actions=\"\"\"$actions$\"\"\"/>\n\n---\n\n<$list filter=\"LightPink Pink Crimson LavenderBlush PaleVioletRed HotPink DeepPink MediumVioletRed Orchid Thistle Plum Violet Magenta Fuchsia DarkMagenta Purple MediumOrchid DarkViolet DarkOrchid Indigo BlueViolet MediumPurple MediumSlateBlue SlateBlue DarkSlateBlue Lavender GhostWhite Blue MediumBlue MidnightBlue DarkBlue Navy RoyalBlue CornflowerBlue LightSteelBlue LightSlateGrey SlateGrey DodgerBlue AliceBlue SteelBlue LightSkyBlue SkyBlue DeepSkyBlue LightBlue PowderBlue CadetBlue Azure LightCyan PaleTurquoise Cyan Aqua DarkTurquoise DarkSlateGrey DarkCyan Teal MediumTurquoise LightSeaGreen Turquoise Aquamarine MediumAquamarine MediumSpringGreen MintCream SpringGreen MediumSeaGreen SeaGreen Honeydew LightGreen PaleGreen DarkSeaGreen LimeGreen Lime ForestGreen Green DarkGreen Chartreuse LawnGreen GreenYellow DarkOliveGreen YellowGreen OliveDrab Beige LightGoldenrodYellow Ivory LightYellow Yellow Olive DarkKhaki LemonChiffon PaleGoldenrod Khaki Gold Cornsilk Goldenrod DarkGoldenrod FloralWhite OldLace Wheat Moccasin Orange PapayaWhip BlanchedAlmond NavajoWhite AntiqueWhite Tan BurlyWood Bisque DarkOrange Linen Peru PeachPuff SandyBrown Chocolate SaddleBrown Seashell Sienna LightSalmon Coral OrangeRed DarkSalmon Tomato MistyRose Salmon Snow LightCoral RosyBrown IndianRed Red Brown FireBrick DarkRed Maroon White WhiteSmoke Gainsboro LightGrey Silver DarkGrey Grey DimGrey Black\" variable=\"colour-picker-value\">\n<$macrocall $name=\"colour-picker-inner\" actions=\"\"\"$actions$\"\"\"/>\n</$list>\n\n---\n\n<$edit-text tiddler=\"$:/config/ColourPicker/New\" tag=\"input\" default=\"\" placeholder=\"\"/>\n<$edit-text tiddler=\"$:/config/ColourPicker/New\" type=\"color\" tag=\"input\"/>\n<$set name=\"colour-picker-value\" value={{$:/config/ColourPicker/New}}>\n<$macrocall $name=\"colour-picker-inner\" actions=\"\"\"$actions$\"\"\"/>\n</$set>\n\n</div>\n\n\\end\n"
},
"$:/core/macros/copy-to-clipboard": {
"title": "$:/core/macros/copy-to-clipboard",
"tags": "$:/tags/Macro",
"text": "\\define copy-to-clipboard(src,class:\"tc-btn-invisible\",style)\n<$button class=<<__class__>> style=<<__style__>> message=\"tm-copy-to-clipboard\" param=<<__src__>> tooltip={{$:/language/Buttons/CopyToClipboard/Hint}}>\n{{$:/core/images/copy-clipboard}} <$text text={{$:/language/Buttons/CopyToClipboard/Caption}}/>\n</$button>\n\\end\n\n\\define copy-to-clipboard-above-right(src,class:\"tc-btn-invisible\",style)\n<div style=\"position: relative;\">\n<div style=\"position: absolute; bottom: 0; right: 0;\">\n<$macrocall $name=\"copy-to-clipboard\" src=<<__src__>> class=<<__class__>> style=<<__style__>>/>\n</div>\n</div>\n\\end\n\n"
},
"$:/core/macros/diff": {
"title": "$:/core/macros/diff",
"tags": "$:/tags/Macro",
"text": "\\define compareTiddlerText(sourceTiddlerTitle,sourceSubTiddlerTitle,destTiddlerTitle,destSubTiddlerTitle)\n<$set name=\"source\" tiddler=<<__sourceTiddlerTitle__>> subtiddler=<<__sourceSubTiddlerTitle__>>>\n<$set name=\"dest\" tiddler=<<__destTiddlerTitle__>> subtiddler=<<__destSubTiddlerTitle__>>>\n<$diff-text source=<<source>> dest=<<dest>>/>\n</$set>\n</$set>\n\\end\n\n\\define compareTiddlers(sourceTiddlerTitle,sourceSubTiddlerTitle,destTiddlerTitle,destSubTiddlerTitle,exclude)\n<table class=\"tc-diff-tiddlers\">\n<tbody>\n<$set name=\"sourceFields\" filter=\"[<__sourceTiddlerTitle__>fields[]sort[]]\">\n<$set name=\"destFields\" filter=\"[<__destSubTiddlerTitle__>subtiddlerfields<__destTiddlerTitle__>sort[]]\">\n<$list filter=\"[enlist<sourceFields>] [enlist<destFields>] -[enlist<__exclude__>] +[sort[]]\" variable=\"fieldName\">\n<tr>\n<th>\n<$text text=<<fieldName>>/> \n</th>\n<td>\n<$set name=\"source\" tiddler=<<__sourceTiddlerTitle__>> subtiddler=<<__sourceSubTiddlerTitle__>> field=<<fieldName>>>\n<$set name=\"dest\" tiddler=<<__destTiddlerTitle__>> subtiddler=<<__destSubTiddlerTitle__>> field=<<fieldName>>>\n<$diff-text source=<<source>> dest=<<dest>>>\n</$diff-text>\n</$set>\n</$set>\n</td>\n</tr>\n</$list>\n</$set>\n</$set>\n</tbody>\n</table>\n\\end\n"
},
"$:/core/macros/dumpvariables": {
"title": "$:/core/macros/dumpvariables",
"tags": "$:/tags/Macro",
"text": "\\define dumpvariables()\n<ul>\n<$list filter=\"[variables[]]\" variable=\"varname\">\n<li>\n<strong><code><$text text=<<varname>>/></code></strong>:<br/>\n<$codeblock code={{{ [<varname>getvariable[]] }}}/>\n</li>\n</$list>\n</ul>\n\\end\n"
},
"$:/core/macros/export": {
"title": "$:/core/macros/export",
"tags": "$:/tags/Macro",
"text": "\\define exportButtonFilename(baseFilename)\n$baseFilename$$(extension)$\n\\end\n\n\\define exportButton(exportFilter:\"[!is[system]sort[title]]\",lingoBase,baseFilename:\"tiddlers\")\n<span class=\"tc-popup-keep\"><$button popup=<<qualify \"$:/state/popup/export\">> tooltip={{$lingoBase$Hint}} aria-label={{$lingoBase$Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>match[yes]]\">\n{{$:/core/images/export-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>match[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$lingoBase$Caption}}/></span>\n</$list>\n</$button></span><$reveal state=<<qualify \"$:/state/popup/export\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Exporter]]\">\n<$set name=\"extension\" value={{!!extension}}>\n<$button class=\"tc-btn-invisible\">\n<$action-sendmessage $message=\"tm-download-file\" $param=<<currentTiddler>> exportFilter=\"\"\"$exportFilter$\"\"\" filename=<<exportButtonFilename \"\"\"$baseFilename$\"\"\">>/>\n<$action-deletetiddler $tiddler=<<qualify \"$:/state/popup/export\">>/>\n<$transclude field=\"description\"/>\n</$button>\n</$set>\n</$list>\n</div>\n</$reveal>\n\\end\n"
},
"$:/core/macros/image-picker": {
"title": "$:/core/macros/image-picker",
"created": "20170715180840889",
"modified": "20170715180914005",
"tags": "$:/tags/Macro",
"type": "text/vnd.tiddlywiki",
"text": "\\define image-picker-thumbnail(actions)\n<$button tag=\"a\" tooltip=\"\"\"$(imageTitle)$\"\"\">\n$actions$\n<$transclude tiddler=<<imageTitle>>/>\n</$button>\n\\end\n\n\\define image-picker-list(filter,actions)\n<$list filter=\"\"\"$filter$\"\"\" variable=\"imageTitle\">\n<$macrocall $name=\"image-picker-thumbnail\" actions=\"\"\"$actions$\"\"\"/>\n</$list>\n\\end\n\n\\define image-picker(actions,filter:\"[all[shadows+tiddlers]is[image]] -[type[application/pdf]] +[!has[draft.of]$subfilter$sort[title]]\",subfilter:\"\")\n<div class=\"tc-image-chooser\">\n<$vars state-system=<<qualify \"$:/state/image-picker/system\">>>\n<$checkbox tiddler=<<state-system>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"hide\">\n{{$:/language/SystemTiddlers/Include/Prompt}}\n</$checkbox>\n<$reveal state=<<state-system>> type=\"match\" text=\"hide\" default=\"hide\" tag=\"div\">\n<$macrocall $name=\"image-picker-list\" filter=\"\"\"$filter$ +[!is[system]]\"\"\" actions=\"\"\"$actions$\"\"\"/>\n</$reveal>\n<$reveal state=<<state-system>> type=\"nomatch\" text=\"hide\" default=\"hide\" tag=\"div\">\n<$macrocall $name=\"image-picker-list\" filter=\"\"\"$filter$\"\"\" actions=\"\"\"$actions$\"\"\"/>\n</$reveal>\n</$vars>\n</div>\n\\end\n\n\\define image-picker-include-tagged-images(actions)\n<$macrocall $name=\"image-picker\" filter=\"[all[shadows+tiddlers]is[image]] [all[shadows+tiddlers]tag[$:/tags/Image]] -[type[application/pdf]] +[!has[draft.of]sort[title]]\" actions=\"\"\"$actions$\"\"\"/>\n\\end\n"
},
"$:/core/macros/lingo": {
"title": "$:/core/macros/lingo",
"tags": "$:/tags/Macro",
"text": "\\define lingo-base()\n$:/language/\n\\end\n\n\\define lingo(title)\n{{$(lingo-base)$$title$}}\n\\end\n"
},
"$:/core/macros/list": {
"title": "$:/core/macros/list",
"tags": "$:/tags/Macro",
"text": "\\define list-links(filter,type:\"ul\",subtype:\"li\",class:\"\",emptyMessage)\n\\whitespace trim\n<$type$ class=\"$class$\">\n<$list filter=\"$filter$\" emptyMessage=<<__emptyMessage__>>>\n<$subtype$>\n<$link to={{!!title}}>\n<$transclude field=\"caption\">\n<$view field=\"title\"/>\n</$transclude>\n</$link>\n</$subtype$>\n</$list>\n</$type$>\n\\end\n\n\\define list-links-draggable-drop-actions()\n<$action-listops $tiddler=<<targetTiddler>> $field=<<targetField>> $subfilter=\"+[insertbefore:currentTiddler<actionTiddler>]\"/>\n\\end\n\n\\define list-links-draggable(tiddler,field:\"list\",type:\"ul\",subtype:\"li\",class:\"\",itemTemplate)\n\\whitespace trim\n<span class=\"tc-links-draggable-list\">\n<$vars targetTiddler=\"\"\"$tiddler$\"\"\" targetField=\"\"\"$field$\"\"\">\n<$type$ class=\"$class$\">\n<$list filter=\"[list[$tiddler$!!$field$]]\">\n<$droppable actions=<<list-links-draggable-drop-actions>> tag=\"\"\"$subtype$\"\"\" enable=<<tv-enable-drag-and-drop>>>\n<div class=\"tc-droppable-placeholder\"/>\n<div>\n<$transclude tiddler=\"\"\"$itemTemplate$\"\"\">\n<$link to={{!!title}}>\n<$transclude field=\"caption\">\n<$view field=\"title\"/>\n</$transclude>\n</$link>\n</$transclude>\n</div>\n</$droppable>\n</$list>\n</$type$>\n<$tiddler tiddler=\"\">\n<$droppable actions=<<list-links-draggable-drop-actions>> tag=\"div\" enable=<<tv-enable-drag-and-drop>>>\n<div class=\"tc-droppable-placeholder\">\n \n</div>\n<div style=\"height:0.5em;\"/>\n</$droppable>\n</$tiddler>\n</$vars>\n</span>\n\\end\n\n\\define list-tagged-draggable-drop-actions(tag)\n<!-- Save the current ordering of the tiddlers with this tag -->\n<$set name=\"order\" filter=\"[<__tag__>tagging[]]\">\n<!-- Remove any list-after or list-before fields from the tiddlers with this tag -->\n<$list filter=\"[<__tag__>tagging[]]\">\n<$action-deletefield $field=\"list-before\"/>\n<$action-deletefield $field=\"list-after\"/>\n</$list>\n<!-- Save the new order to the Tag Tiddler -->\n<$action-listops $tiddler=<<__tag__>> $field=\"list\" $filter=\"+[enlist<order>] +[insertbefore:currentTiddler<actionTiddler>]\"/>\n<!-- Make sure the newly added item has the right tag -->\n<!-- Removing this line makes dragging tags within the dropdown work as intended -->\n<!--<$action-listops $tiddler=<<actionTiddler>> $tags=<<__tag__>>/>-->\n<!-- Using the following 5 lines as replacement makes dragging titles from outside into the dropdown apply the tag -->\n<$list filter=\"[<actionTiddler>!contains:tags<__tag__>]\">\n<$fieldmangler tiddler=<<actionTiddler>>>\n<$action-sendmessage $message=\"tm-add-tag\" $param=<<__tag__>>/>\n</$fieldmangler>\n</$list>\n</$set>\n\\end\n\n\\define list-tagged-draggable(tag,subFilter,emptyMessage,itemTemplate,elementTag:\"div\",storyview:\"\")\n\\whitespace trim\n<span class=\"tc-tagged-draggable-list\">\n<$set name=\"tag\" value=<<__tag__>>>\n<$list filter=\"[<__tag__>tagging[]$subFilter$]\" emptyMessage=<<__emptyMessage__>> storyview=<<__storyview__>>>\n<$elementTag$ class=\"tc-menu-list-item\">\n<$droppable actions=\"\"\"<$macrocall $name=\"list-tagged-draggable-drop-actions\" tag=<<__tag__>>/>\"\"\" enable=<<tv-enable-drag-and-drop>>>\n<$elementTag$ class=\"tc-droppable-placeholder\"/>\n<$elementTag$>\n<$transclude tiddler=\"\"\"$itemTemplate$\"\"\">\n<$link to={{!!title}}>\n<$view field=\"title\"/>\n</$link>\n</$transclude>\n</$elementTag$>\n</$droppable>\n</$elementTag$>\n</$list>\n<$tiddler tiddler=\"\">\n<$droppable actions=\"\"\"<$macrocall $name=\"list-tagged-draggable-drop-actions\" tag=<<__tag__>>/>\"\"\" enable=<<tv-enable-drag-and-drop>>>\n<$elementTag$ class=\"tc-droppable-placeholder\"/>\n<$elementTag$ style=\"height:0.5em;\">\n</$elementTag$>\n</$droppable>\n</$tiddler>\n</$set>\n</span>\n\\end\n"
},
"$:/core/macros/tabs": {
"title": "$:/core/macros/tabs",
"tags": "$:/tags/Macro",
"text": "\\define tabs(tabsList,default,state:\"$:/state/tab\",class,template,buttonTemplate,retain)\n<div class=\"tc-tab-set $class$\">\n<div class=\"tc-tab-buttons $class$\">\n<$list filter=\"$tabsList$\" variable=\"currentTab\" storyview=\"pop\"><$set name=\"save-currentTiddler\" value=<<currentTiddler>>><$tiddler tiddler=<<currentTab>>><$button set=<<qualify \"$state$\">> setTo=<<currentTab>> default=\"$default$\" selectedClass=\"tc-tab-selected\" tooltip={{!!tooltip}}>\n<$tiddler tiddler=<<save-currentTiddler>>>\n<$set name=\"tv-wikilinks\" value=\"no\">\n<$transclude tiddler=\"$buttonTemplate$\" mode=\"inline\">\n<$transclude tiddler=<<currentTab>> field=\"caption\">\n<$macrocall $name=\"currentTab\" $type=\"text/plain\" $output=\"text/plain\"/>\n</$transclude>\n</$transclude>\n</$set></$tiddler></$button></$tiddler></$set></$list>\n</div>\n<div class=\"tc-tab-divider $class$\"/>\n<div class=\"tc-tab-content $class$\">\n<$list filter=\"$tabsList$\" variable=\"currentTab\">\n\n<$reveal type=\"match\" state=<<qualify \"$state$\">> text=<<currentTab>> default=\"$default$\" retain=\"\"\"$retain$\"\"\">\n\n<$transclude tiddler=\"$template$\" mode=\"block\">\n\n<$transclude tiddler=<<currentTab>> mode=\"block\"/>\n\n</$transclude>\n\n</$reveal>\n\n</$list>\n</div>\n</div>\n\\end\n"
},
"$:/core/macros/tag-picker": {
"title": "$:/core/macros/tag-picker",
"tags": "$:/tags/Macro",
"text": "\\define add-tag-actions()\n<$action-sendmessage $message=\"tm-add-tag\" $param={{{ [<newTagNameTiddler>get[text]] }}}/>\n<$action-deletetiddler $tiddler=<<newTagNameTiddler>>/>\n\\end\n\n\\define tag-button()\n<$button class=\"tc-btn-invisible\" tag=\"a\" tooltip={{$:/language/EditTemplate/Tags/Add/Button/Hint}}>\n<$action-sendmessage $message=\"tm-add-tag\" $param=<<tag>>/>\n<$action-deletetiddler $tiddler=<<newTagNameTiddler>>/>\n<$macrocall $name=\"tag-pill\" tag=<<tag>>/>\n</$button>\n\\end\n\n\\define tag-picker-inner()\n\\whitespace trim\n<div class=\"tc-edit-add-tag\">\n<span class=\"tc-add-tag-name\">\n<$keyboard key=\"ENTER\" actions=<<add-tag-actions>>>\n<$edit-text tiddler=<<newTagNameTiddler>> tag=\"input\" default=\"\" placeholder={{$:/language/EditTemplate/Tags/Add/Placeholder}} focusPopup=<<qualify \"$:/state/popup/tags-auto-complete\">> class=\"tc-edit-texteditor tc-popup-handle\" tabindex=<<tabIndex>> focus={{{ [{$:/config/AutoFocus}match[tags]then[true]] ~[[false]] }}}/>\n</$keyboard>\n</span> <$button popup=<<qualify \"$:/state/popup/tags-auto-complete\">> class=\"tc-btn-invisible\" tooltip={{$:/language/EditTemplate/Tags/Dropdown/Hint}} aria-label={{$:/language/EditTemplate/Tags/Dropdown/Caption}}>{{$:/core/images/down-arrow}}</$button> <span class=\"tc-add-tag-button\">\n<$set name=\"tag\" value={{{ [<newTagNameTiddler>get[text]] }}}>\n<$button set=\"$:/temp/NewTagName\" setTo=\"\" class=\"\">\n<<add-tag-actions>>\n<$action-deletetiddler $tiddler=<<newTagNameTiddler>>/>\n{{$:/language/EditTemplate/Tags/Add/Button}}\n</$button>\n</$set>\n</span>\n</div>\n<div class=\"tc-block-dropdown-wrapper\">\n<$reveal state=<<qualify \"$:/state/popup/tags-auto-complete\">> type=\"nomatch\" text=\"\" default=\"\">\n<div class=\"tc-block-dropdown\">\n<$set name=\"newTagName\" value={{{ [<newTagNameTiddler>get[text]] }}}>\n<$list filter=\"[<newTagName>minlength{$:/config/Tags/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n<$list filter=\"[tags[]!is[system]search:title<newTagName>sort[]]\" variable=\"tag\">\n<<tag-button>>\n</$list></$list>\n<hr>\n<$list filter=\"[<newTagName>minlength{$:/config/Tags/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n<$list filter=\"[tags[]is[system]search:title<newTagName>sort[]]\" variable=\"tag\">\n<<tag-button>>\n</$list></$list>\n</$set>\n</div>\n</$reveal>\n</div>\n\\end\n\\define tag-picker()\n\\whitespace trim\n<$list filter=\"[<newTagNameTiddler>match[]]\" emptyMessage=<<tag-picker-inner>>>\n<$set name=\"newTagNameTiddler\" value=<<qualify \"$:/temp/NewTagName\">>>\n<<tag-picker-inner>>\n</$set>\n</$list>\n\\end\n"
},
"$:/core/macros/tag": {
"title": "$:/core/macros/tag",
"tags": "$:/tags/Macro",
"text": "\\define tag-pill-styles()\nbackground-color:$(backgroundColor)$;\nfill:$(foregroundColor)$;\ncolor:$(foregroundColor)$;\n\\end\n\n\\define tag-pill-inner(tag,icon,colour,fallbackTarget,colourA,colourB,element-tag,element-attributes,actions)\n<$vars foregroundColor=<<contrastcolour target:\"\"\"$colour$\"\"\" fallbackTarget:\"\"\"$fallbackTarget$\"\"\" colourA:\"\"\"$colourA$\"\"\" colourB:\"\"\"$colourB$\"\"\">> backgroundColor=\"\"\"$colour$\"\"\">\n<$element-tag$ $element-attributes$ class=\"tc-tag-label tc-btn-invisible\" style=<<tag-pill-styles>>>\n$actions$<$transclude tiddler=\"\"\"$icon$\"\"\"/><$view tiddler=<<__tag__>> field=\"title\" format=\"text\" />\n</$element-tag$>\n</$vars>\n\\end\n\n\\define tag-pill-body(tag,icon,colour,palette,element-tag,element-attributes,actions)\n<$macrocall $name=\"tag-pill-inner\" tag=<<__tag__>> icon=\"\"\"$icon$\"\"\" colour=\"\"\"$colour$\"\"\" fallbackTarget={{$palette$##tag-background}} colourA={{$palette$##foreground}} colourB={{$palette$##background}} element-tag=\"\"\"$element-tag$\"\"\" element-attributes=\"\"\"$element-attributes$\"\"\" actions=\"\"\"$actions$\"\"\"/>\n\\end\n\n\\define tag-pill(tag,element-tag:\"span\",element-attributes:\"\",actions:\"\")\n<span class=\"tc-tag-list-item\">\n<$macrocall $name=\"tag-pill-body\" tag=<<__tag__>> icon={{{ [<__tag__>get[icon]] }}} colour={{{ [<__tag__>get[color]] }}} palette={{$:/palette}} element-tag=\"\"\"$element-tag$\"\"\" element-attributes=\"\"\"$element-attributes$\"\"\" actions=\"\"\"$actions$\"\"\"/>\n</span>\n\\end\n\n\\define tag(tag)\n{{$tag$||$:/core/ui/TagTemplate}}\n\\end\n"
},
"$:/core/macros/thumbnails": {
"title": "$:/core/macros/thumbnails",
"tags": "$:/tags/Macro",
"text": "\\define thumbnail(link,icon,color,background-color,image,caption,width:\"280\",height:\"157\")\n<$link to=\"\"\"$link$\"\"\"><div class=\"tc-thumbnail-wrapper\">\n<div class=\"tc-thumbnail-image\" style=\"width:$width$px;height:$height$px;\"><$reveal type=\"nomatch\" text=\"\" default=\"\"\"$image$\"\"\" tag=\"div\" style=\"width:$width$px;height:$height$px;\">\n[img[$image$]]\n</$reveal><$reveal type=\"match\" text=\"\" default=\"\"\"$image$\"\"\" tag=\"div\" class=\"tc-thumbnail-background\" style=\"width:$width$px;height:$height$px;background-color:$background-color$;\"></$reveal></div><div class=\"tc-thumbnail-icon\" style=\"fill:$color$;color:$color$;\">\n$icon$\n</div><div class=\"tc-thumbnail-caption\">\n$caption$\n</div>\n</div></$link>\n\\end\n\n\\define thumbnail-right(link,icon,color,background-color,image,caption,width:\"280\",height:\"157\")\n<div class=\"tc-thumbnail-right-wrapper\"><<thumbnail \"\"\"$link$\"\"\" \"\"\"$icon$\"\"\" \"\"\"$color$\"\"\" \"\"\"$background-color$\"\"\" \"\"\"$image$\"\"\" \"\"\"$caption$\"\"\" \"\"\"$width$\"\"\" \"\"\"$height$\"\"\">></div>\n\\end\n\n\\define list-thumbnails(filter,width:\"280\",height:\"157\")\n<$list filter=\"\"\"$filter$\"\"\"><$macrocall $name=\"thumbnail\" link={{!!link}} icon={{!!icon}} color={{!!color}} background-color={{!!background-color}} image={{!!image}} caption={{!!caption}} width=\"\"\"$width$\"\"\" height=\"\"\"$height$\"\"\"/></$list>\n\\end\n"
},
"$:/core/macros/timeline": {
"title": "$:/core/macros/timeline",
"created": "20141212105914482",
"modified": "20141212110330815",
"tags": "$:/tags/Macro",
"text": "\\define timeline-title()\n\\whitespace trim\n<!-- Override this macro with a global macro \n of the same name if you need to change \n how titles are displayed on the timeline \n -->\n<$view field=\"title\"/>\n\\end\n\\define timeline(limit:\"100\",format:\"DDth MMM YYYY\",subfilter:\"\",dateField:\"modified\")\n<div class=\"tc-timeline\">\n<$list filter=\"[!is[system]$subfilter$has[$dateField$]!sort[$dateField$]limit[$limit$]eachday[$dateField$]]\">\n<div class=\"tc-menu-list-item\">\n<$view field=\"$dateField$\" format=\"date\" template=\"$format$\"/>\n<$list filter=\"[sameday:$dateField${!!$dateField$}!is[system]$subfilter$!sort[$dateField$]]\">\n<div class=\"tc-menu-list-subitem\">\n<$link to={{!!title}}><<timeline-title>></$link>\n</div>\n</$list>\n</div>\n</$list>\n</div>\n\\end\n"
},
"$:/core/macros/toc": {
"title": "$:/core/macros/toc",
"tags": "$:/tags/Macro",
"text": "\\define toc-caption()\n<$set name=\"tv-wikilinks\" value=\"no\">\n <$transclude field=\"caption\">\n <$view field=\"title\"/>\n </$transclude>\n</$set>\n\\end\n\n\\define toc-body(tag,sort:\"\",itemClassFilter,exclude,path)\n<ol class=\"tc-toc\">\n <$list filter=\"\"\"[all[shadows+tiddlers]tag<__tag__>!has[draft.of]$sort$] -[<__tag__>] -[enlist<__exclude__>]\"\"\">\n <$vars item=<<currentTiddler>> path={{{ [<__path__>addsuffix[/]addsuffix<__tag__>] }}}>\n <$set name=\"excluded\" filter=\"\"\"[enlist<__exclude__>] [<__tag__>]\"\"\">\n <$set name=\"toc-item-class\" filter=<<__itemClassFilter__>> emptyValue=\"toc-item-selected\" value=\"toc-item\">\n <li class=<<toc-item-class>>>\n <$list filter=\"[all[current]toc-link[no]]\" emptyMessage=\"<$link><$view field='caption'><$view field='title'/></$view></$link>\">\n <<toc-caption>>\n </$list>\n <$macrocall $name=\"toc-body\" tag=<<item>> sort=<<__sort__>> itemClassFilter=<<__itemClassFilter__>> exclude=<<excluded>> path=<<path>>/>\n </li>\n </$set>\n </$set>\n </$vars>\n </$list>\n</ol>\n\\end\n\n\\define toc(tag,sort:\"\",itemClassFilter:\"\")\n<$macrocall $name=\"toc-body\" tag=<<__tag__>> sort=<<__sort__>> itemClassFilter=<<__itemClassFilter__>> />\n\\end\n\n\\define toc-linked-expandable-body(tag,sort:\"\",itemClassFilter,exclude,path)\n<!-- helper function -->\n<$qualify name=\"toc-state\" title={{{ [[$:/state/toc]addsuffix<__path__>addsuffix[-]addsuffix<currentTiddler>] }}}>\n <$set name=\"toc-item-class\" filter=<<__itemClassFilter__>> emptyValue=\"toc-item-selected\" value=\"toc-item\">\n <li class=<<toc-item-class>>>\n <$link>\n <$reveal type=\"nomatch\" stateTitle=<<toc-state>> text=\"open\">\n <$button setTitle=<<toc-state>> setTo=\"open\" class=\"tc-btn-invisible tc-popup-keep\">\n {{$:/core/images/right-arrow}}\n </$button>\n </$reveal>\n <$reveal type=\"match\" stateTitle=<<toc-state>> text=\"open\">\n <$button setTitle=<<toc-state>> setTo=\"close\" class=\"tc-btn-invisible tc-popup-keep\">\n {{$:/core/images/down-arrow}}\n </$button>\n </$reveal>\n <<toc-caption>>\n </$link>\n <$reveal type=\"match\" stateTitle=<<toc-state>> text=\"open\">\n <$macrocall $name=\"toc-expandable\" tag=<<currentTiddler>> sort=<<__sort__>> itemClassFilter=<<__itemClassFilter__>> exclude=<<__exclude__>> path=<<__path__>>/>\n </$reveal>\n </li>\n </$set>\n</$qualify>\n\\end\n\n\\define toc-unlinked-expandable-body(tag,sort:\"\",itemClassFilter,exclude,path)\n<!-- helper function -->\n<$qualify name=\"toc-state\" title={{{ [[$:/state/toc]addsuffix<__path__>addsuffix[-]addsuffix<currentTiddler>] }}}>\n <$set name=\"toc-item-class\" filter=<<__itemClassFilter__>> emptyValue=\"toc-item-selected\" value=\"toc-item\">\n <li class=<<toc-item-class>>>\n <$reveal type=\"nomatch\" stateTitle=<<toc-state>> text=\"open\">\n <$button setTitle=<<toc-state>> setTo=\"open\" class=\"tc-btn-invisible tc-popup-keep\">\n {{$:/core/images/right-arrow}}\n <<toc-caption>>\n </$button>\n </$reveal>\n <$reveal type=\"match\" stateTitle=<<toc-state>> text=\"open\">\n <$button setTitle=<<toc-state>> setTo=\"close\" class=\"tc-btn-invisible tc-popup-keep\">\n {{$:/core/images/down-arrow}}\n <<toc-caption>>\n </$button>\n </$reveal>\n <$reveal type=\"match\" stateTitle=<<toc-state>> text=\"open\">\n <$macrocall $name=\"toc-expandable\" tag=<<currentTiddler>> sort=<<__sort__>> itemClassFilter=<<__itemClassFilter__>> exclude=<<__exclude__>> path=<<__path__>>/>\n </$reveal>\n </li>\n </$set>\n</$qualify>\n\\end\n\n\\define toc-expandable-empty-message()\n<$macrocall $name=\"toc-linked-expandable-body\" tag=<<tag>> sort=<<sort>> itemClassFilter=<<itemClassFilter>> exclude=<<excluded>> path=<<path>>/>\n\\end\n\n\\define toc-expandable(tag,sort:\"\",itemClassFilter:\"\",exclude,path)\n<$vars tag=<<__tag__>> sort=<<__sort__>> itemClassFilter=<<__itemClassFilter__>> path={{{ [<__path__>addsuffix[/]addsuffix<__tag__>] }}}>\n <$set name=\"excluded\" filter=\"\"\"[enlist<__exclude__>] [<__tag__>]\"\"\">\n <ol class=\"tc-toc toc-expandable\">\n <$list filter=\"\"\"[all[shadows+tiddlers]tag<__tag__>!has[draft.of]$sort$] -[<__tag__>] -[enlist<__exclude__>]\"\"\">\n <$list filter=\"[all[current]toc-link[no]]\" emptyMessage=<<toc-expandable-empty-message>> >\n <$macrocall $name=\"toc-unlinked-expandable-body\" tag=<<__tag__>> sort=<<__sort__>> itemClassFilter=\"\"\"itemClassFilter\"\"\" exclude=<<excluded>> path=<<path>> />\n </$list>\n </$list>\n </ol>\n </$set>\n</$vars>\n\\end\n\n\\define toc-linked-selective-expandable-body(tag,sort:\"\",itemClassFilter,exclude,path)\n<$qualify name=\"toc-state\" title={{{ [[$:/state/toc]addsuffix<__path__>addsuffix[-]addsuffix<currentTiddler>] }}}>\n <$set name=\"toc-item-class\" filter=<<__itemClassFilter__>> emptyValue=\"toc-item-selected\" value=\"toc-item\" >\n <li class=<<toc-item-class>>>\n <$link>\n <$list filter=\"[all[current]tagging[]$sort$limit[1]]\" variable=\"ignore\" emptyMessage=\"<$button class='tc-btn-invisible'>{{$:/core/images/blank}}</$button>\">\n <$reveal type=\"nomatch\" stateTitle=<<toc-state>> text=\"open\">\n <$button setTitle=<<toc-state>> setTo=\"open\" class=\"tc-btn-invisible tc-popup-keep\">\n {{$:/core/images/right-arrow}}\n </$button>\n </$reveal>\n <$reveal type=\"match\" stateTitle=<<toc-state>> text=\"open\">\n <$button setTitle=<<toc-state>> setTo=\"close\" class=\"tc-btn-invisible tc-popup-keep\">\n {{$:/core/images/down-arrow}}\n </$button>\n </$reveal>\n </$list>\n <<toc-caption>>\n </$link>\n <$reveal type=\"match\" stateTitle=<<toc-state>> text=\"open\">\n <$macrocall $name=\"toc-selective-expandable\" tag=<<currentTiddler>> sort=<<__sort__>> itemClassFilter=<<__itemClassFilter__>> exclude=<<__exclude__>> path=<<__path__>>/>\n </$reveal>\n </li>\n </$set>\n</$qualify>\n\\end\n\n\\define toc-unlinked-selective-expandable-body(tag,sort:\"\",itemClassFilter,exclude,path)\n<$qualify name=\"toc-state\" title={{{ [[$:/state/toc]addsuffix<__path__>addsuffix[-]addsuffix<currentTiddler>] }}}>\n <$set name=\"toc-item-class\" filter=<<__itemClassFilter__>> emptyValue=\"toc-item-selected\" value=\"toc-item\">\n <li class=<<toc-item-class>>>\n <$list filter=\"[all[current]tagging[]$sort$limit[1]]\" variable=\"ignore\" emptyMessage=\"<$button class='tc-btn-invisible'>{{$:/core/images/blank}}</$button> <$view field='caption'><$view field='title'/></$view>\">\n <$reveal type=\"nomatch\" stateTitle=<<toc-state>> text=\"open\">\n <$button setTitle=<<toc-state>> setTo=\"open\" class=\"tc-btn-invisible tc-popup-keep\">\n {{$:/core/images/right-arrow}}\n <<toc-caption>>\n </$button>\n </$reveal>\n <$reveal type=\"match\" stateTitle=<<toc-state>> text=\"open\">\n <$button setTitle=<<toc-state>> setTo=\"close\" class=\"tc-btn-invisible tc-popup-keep\">\n {{$:/core/images/down-arrow}}\n <<toc-caption>>\n </$button>\n </$reveal>\n </$list>\n <$reveal type=\"match\" stateTitle=<<toc-state>> text=\"open\">\n <$macrocall $name=\"toc-selective-expandable\" tag=<<currentTiddler>> sort=<<__sort__>> itemClassFilter=<<__itemClassFilter__>> exclude=<<__exclude__>> path=<<__path__>>/>\n </$reveal>\n </li>\n </$set>\n</$qualify>\n\\end\n\n\\define toc-selective-expandable-empty-message()\n<$macrocall $name=\"toc-linked-selective-expandable-body\" tag=<<tag>> sort=<<sort>> itemClassFilter=<<itemClassFilter>> exclude=<<excluded>> path=<<path>>/>\n\\end\n\n\\define toc-selective-expandable(tag,sort:\"\",itemClassFilter,exclude,path)\n<$vars tag=<<__tag__>> sort=<<__sort__>> itemClassFilter=<<__itemClassFilter__>> path={{{ [<__path__>addsuffix[/]addsuffix<__tag__>] }}}>\n <$set name=\"excluded\" filter=\"\"\"[enlist<__exclude__>] [<__tag__>]\"\"\">\n <ol class=\"tc-toc toc-selective-expandable\">\n <$list filter=\"\"\"[all[shadows+tiddlers]tag<__tag__>!has[draft.of]$sort$] -[<__tag__>] -[enlist<__exclude__>]\"\"\">\n <$list filter=\"[all[current]toc-link[no]]\" variable=\"ignore\" emptyMessage=<<toc-selective-expandable-empty-message>> >\n <$macrocall $name=\"toc-unlinked-selective-expandable-body\" tag=<<__tag__>> sort=<<__sort__>> itemClassFilter=<<__itemClassFilter__>> exclude=<<excluded>> path=<<path>>/>\n </$list>\n </$list>\n </ol>\n </$set>\n</$vars>\n\\end\n\n\\define toc-tabbed-external-nav(tag,sort:\"\",selectedTiddler:\"$:/temp/toc/selectedTiddler\",unselectedText,missingText,template:\"\")\n<$tiddler tiddler={{{ [<__selectedTiddler__>get[text]] }}}>\n <div class=\"tc-tabbed-table-of-contents\">\n <$linkcatcher to=<<__selectedTiddler__>>>\n <div class=\"tc-table-of-contents\">\n <$macrocall $name=\"toc-selective-expandable\" tag=<<__tag__>> sort=<<__sort__>> itemClassFilter=\"[all[current]] -[<__selectedTiddler__>get[text]]\"/>\n </div>\n </$linkcatcher>\n <div class=\"tc-tabbed-table-of-contents-content\">\n <$reveal stateTitle=<<__selectedTiddler__>> type=\"nomatch\" text=\"\">\n <$transclude mode=\"block\" tiddler=<<__template__>>>\n <h1><<toc-caption>></h1>\n <$transclude mode=\"block\">$missingText$</$transclude>\n </$transclude>\n </$reveal>\n <$reveal stateTitle=<<__selectedTiddler__>> type=\"match\" text=\"\">\n $unselectedText$\n </$reveal>\n </div>\n </div>\n</$tiddler>\n\\end\n\n\\define toc-tabbed-internal-nav(tag,sort:\"\",selectedTiddler:\"$:/temp/toc/selectedTiddler\",unselectedText,missingText,template:\"\")\n<$linkcatcher to=<<__selectedTiddler__>>>\n <$macrocall $name=\"toc-tabbed-external-nav\" tag=<<__tag__>> sort=<<__sort__>> selectedTiddler=<<__selectedTiddler__>> unselectedText=<<__unselectedText__>> missingText=<<__missingText__>> template=<<__template__>>/>\n</$linkcatcher>\n\\end\n\n"
},
"$:/core/macros/translink": {
"title": "$:/core/macros/translink",
"tags": "$:/tags/Macro",
"text": "\\define translink(title,mode:\"block\")\n<div style=\"border:1px solid #ccc; padding: 0.5em; background: black; foreground; white;\">\n<$link to=\"\"\"$title$\"\"\">\n<$text text=\"\"\"$title$\"\"\"/>\n</$link>\n<div style=\"border:1px solid #ccc; padding: 0.5em; background: white; foreground; black;\">\n<$transclude tiddler=\"\"\"$title$\"\"\" mode=\"$mode$\">\n\"<$text text=\"\"\"$title$\"\"\"/>\" is missing\n</$transclude>\n</div>\n</div>\n\\end\n"
},
"$:/core/macros/tree": {
"title": "$:/core/macros/tree",
"tags": "$:/tags/Macro",
"text": "\\define leaf-link(full-title,chunk,separator: \"/\")\n<$link to=<<__full-title__>>><$text text=<<__chunk__>>/></$link>\n\\end\n\n\\define leaf-node(prefix,chunk)\n<li>\n<$list filter=\"[<__prefix__>addsuffix<__chunk__>is[shadow]] [<__prefix__>addsuffix<__chunk__>is[tiddler]]\" variable=\"full-title\">\n<$list filter=\"[<full-title>removeprefix<__prefix__>]\" variable=\"chunk\">\n<span>{{$:/core/images/file}}</span> <$macrocall $name=\"leaf-link\" full-title=<<full-title>> chunk=<<chunk>>/>\n</$list>\n</$list>\n</li>\n\\end\n\n\\define branch-node(prefix,chunk,separator: \"/\")\n<li>\n<$set name=\"reveal-state\" value={{{ [[$:/state/tree/]addsuffix<__prefix__>addsuffix<__chunk__>] }}}>\n<$reveal type=\"nomatch\" stateTitle=<<reveal-state>> text=\"show\">\n<$button setTitle=<<reveal-state>> setTo=\"show\" class=\"tc-btn-invisible\">\n{{$:/core/images/folder}} <$text text=<<__chunk__>>/>\n</$button>\n</$reveal>\n<$reveal type=\"match\" stateTitle=<<reveal-state>> text=\"show\">\n<$button setTitle=<<reveal-state>> setTo=\"hide\" class=\"tc-btn-invisible\">\n{{$:/core/images/folder}} <$text text=<<__chunk__>>/>\n</$button>\n</$reveal>\n<span>(<$count filter=\"[all[shadows+tiddlers]removeprefix<__prefix__>removeprefix<__chunk__>] -[<__prefix__>addsuffix<__chunk__>]\"/>)</span>\n<$reveal type=\"match\" stateTitle=<<reveal-state>> text=\"show\">\n<$macrocall $name=\"tree-node\" prefix={{{ [<__prefix__>addsuffix<__chunk__>] }}} separator=<<__separator__>>/>\n</$reveal>\n</$set>\n</li>\n\\end\n\n\\define tree-node(prefix,separator: \"/\")\n<ol>\n<$list filter=\"[all[shadows+tiddlers]removeprefix<__prefix__>splitbefore<__separator__>sort[]!suffix<__separator__>]\" variable=\"chunk\">\n<$macrocall $name=\"leaf-node\" prefix=<<__prefix__>> chunk=<<chunk>> separator=<<__separator__>>/>\n</$list>\n<$list filter=\"[all[shadows+tiddlers]removeprefix<__prefix__>splitbefore<__separator__>sort[]suffix<__separator__>]\" variable=\"chunk\">\n<$macrocall $name=\"branch-node\" prefix=<<__prefix__>> chunk=<<chunk>> separator=<<__separator__>>/>\n</$list>\n</ol>\n\\end\n\n\\define tree(prefix: \"$:/\",separator: \"/\")\n<div class=\"tc-tree\">\n<span><$text text=<<__prefix__>>/></span>\n<div>\n<$macrocall $name=\"tree-node\" prefix=<<__prefix__>> separator=<<__separator__>>/>\n</div>\n</div>\n\\end\n"
},
"$:/core/macros/utils": {
"title": "$:/core/macros/utils",
"text": "\\define colour(colour)\n$colour$\n\\end\n"
},
"$:/snippets/minifocusswitcher": {
"title": "$:/snippets/minifocusswitcher",
"text": "<$select tiddler=\"$:/config/AutoFocus\">\n<$list filter=\"title tags text type fields\">\n<option value=<<currentTiddler>>><<currentTiddler>></option>\n</$list>\n</$select>\n"
},
"$:/snippets/minilanguageswitcher": {
"title": "$:/snippets/minilanguageswitcher",
"text": "<$select tiddler=\"$:/language\">\n<$list filter=\"[[$:/languages/en-GB]] [plugin-type[language]sort[title]]\">\n<option value=<<currentTiddler>>><$view field=\"description\"><$view field=\"name\"><$view field=\"title\"/></$view></$view></option>\n</$list>\n</$select>"
},
"$:/snippets/minithemeswitcher": {
"title": "$:/snippets/minithemeswitcher",
"text": "\\define lingo-base() $:/language/ControlPanel/Theme/\n<<lingo Prompt>> <$select tiddler=\"$:/theme\">\n<$list filter=\"[plugin-type[theme]sort[title]]\">\n<option value=<<currentTiddler>>><$view field=\"name\"><$view field=\"title\"/></$view></option>\n</$list>\n</$select>"
},
"$:/snippets/modules": {
"title": "$:/snippets/modules",
"text": "\\define describeModuleType(type)\n{{$:/language/Docs/ModuleTypes/$type$}}\n\\end\n<$list filter=\"[moduletypes[]]\">\n\n!! <$macrocall $name=\"currentTiddler\" $type=\"text/plain\" $output=\"text/plain\"/>\n\n<$macrocall $name=\"describeModuleType\" type=<<currentTiddler>>/>\n\n<ul><$list filter=\"[all[current]modules[]]\"><li><$link><<currentTiddler>></$link>\n</li>\n</$list>\n</ul>\n</$list>\n"
},
"$:/palette": {
"title": "$:/palette",
"text": "$:/palettes/Vanilla"
},
"$:/snippets/paletteeditor": {
"title": "$:/snippets/paletteeditor",
"text": "<$transclude tiddler=\"$:/PaletteManager\"/>\n"
},
"$:/snippets/palettepreview": {
"title": "$:/snippets/palettepreview",
"text": "<$set name=\"currentTiddler\" value={{$:/palette}}>\n{{||$:/snippets/currpalettepreview}}\n</$set>\n"
},
"$:/snippets/paletteswitcher": {
"title": "$:/snippets/paletteswitcher",
"text": "<$linkcatcher to=\"$:/palette\">\n<div class=\"tc-chooser\"><$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Palette]sort[name]]\"><$set name=\"cls\" filter=\"[all[current]prefix{$:/palette}]\" value=\"tc-chooser-item tc-chosen\" emptyValue=\"tc-chooser-item\"><div class=<<cls>>><$link to={{!!title}}>''<$view field=\"name\" format=\"text\"/>'' - <$view field=\"description\" format=\"text\"/>{{||$:/snippets/currpalettepreview}}</$link>\n</div></$set>\n</$list>\n</div>\n</$linkcatcher>\n"
},
"$:/snippets/peek-stylesheets": {
"title": "$:/snippets/peek-stylesheets",
"text": "\\define expandable-stylesheets-list()\n<ol>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Stylesheet]!has[draft.of]]\">\n<$vars state=<<qualify \"$:/state/peek-stylesheets/open/\">>>\n<$set name=\"state\" value={{{ [<state>addsuffix<currentTiddler>] }}}>\n<li>\n<$reveal type=\"match\" state=<<state>> text=\"yes\" tag=\"span\">\n<$button set=<<state>> setTo=\"no\" class=\"tc-btn-invisible\">\n{{$:/core/images/down-arrow}}\n</$button>\n</$reveal>\n<$reveal type=\"nomatch\" state=<<state>> text=\"yes\" tag=\"span\">\n<$button set=<<state>> setTo=\"yes\" class=\"tc-btn-invisible\">\n{{$:/core/images/right-arrow}}\n</$button>\n</$reveal>\n<$link>\n<$view field=\"title\"/>\n</$link>\n<$reveal type=\"match\" state=<<state>> text=\"yes\" tag=\"div\">\n<$set name=\"source\" tiddler=<<currentTiddler>>>\n<$wikify name=\"styles\" text=<<source>>>\n<pre>\n<code>\n<$text text=<<styles>>/>\n</code>\n</pre>\n</$wikify>\n</$set>\n</$reveal>\n</li>\n</$set>\n</$vars>\n</$list>\n</ol>\n\\end\n\n\\define stylesheets-list()\n<ol>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Stylesheet]!has[draft.of]]\">\n<li>\n<$link>\n<$view field=\"title\"/>\n</$link>\n<$set name=\"source\" tiddler=<<currentTiddler>>>\n<$wikify name=\"styles\" text=<<source>>>\n<pre>\n<code>\n<$text text=<<styles>>/>\n</code>\n</pre>\n</$wikify>\n</$set>\n</li>\n</$list>\n</ol>\n\\end\n\n<$vars modeState=<<qualify \"$:/state/peek-stylesheets/mode/\">>>\n\n<$reveal type=\"nomatch\" state=<<modeState>> text=\"expanded\" tag=\"div\">\n<$button set=<<modeState>> setTo=\"expanded\" class=\"tc-btn-invisible\">{{$:/core/images/chevron-right}} {{$:/language/ControlPanel/Stylesheets/Expand/Caption}}</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<modeState>> text=\"expanded\" tag=\"div\">\n<$button set=<<modeState>> setTo=\"restored\" class=\"tc-btn-invisible\">{{$:/core/images/chevron-down}} {{$:/language/ControlPanel/Stylesheets/Restore/Caption}}</$button>\n</$reveal>\n\n<$reveal type=\"nomatch\" state=<<modeState>> text=\"expanded\" tag=\"div\">\n<<expandable-stylesheets-list>>\n</$reveal>\n<$reveal type=\"match\" state=<<modeState>> text=\"expanded\" tag=\"div\">\n<<stylesheets-list>>\n</$reveal>\n\n</$vars>\n"
},
"$:/temp/search": {
"title": "$:/temp/search",
"text": ""
},
"$:/tags/AdvancedSearch": {
"title": "$:/tags/AdvancedSearch",
"list": "[[$:/core/ui/AdvancedSearch/Standard]] [[$:/core/ui/AdvancedSearch/System]] [[$:/core/ui/AdvancedSearch/Shadows]] [[$:/core/ui/AdvancedSearch/Filter]]"
},
"$:/tags/AdvancedSearch/FilterButton": {
"title": "$:/tags/AdvancedSearch/FilterButton",
"list": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/dropdown $:/core/ui/AdvancedSearch/Filter/FilterButtons/clear $:/core/ui/AdvancedSearch/Filter/FilterButtons/export $:/core/ui/AdvancedSearch/Filter/FilterButtons/delete"
},
"$:/tags/ControlPanel": {
"title": "$:/tags/ControlPanel",
"list": "$:/core/ui/ControlPanel/Info $:/core/ui/ControlPanel/Appearance $:/core/ui/ControlPanel/Settings $:/core/ui/ControlPanel/Saving $:/core/ui/ControlPanel/Plugins $:/core/ui/ControlPanel/Tools $:/core/ui/ControlPanel/Internals"
},
"$:/tags/ControlPanel/Info": {
"title": "$:/tags/ControlPanel/Info",
"list": "$:/core/ui/ControlPanel/Basics $:/core/ui/ControlPanel/Advanced"
},
"$:/tags/ControlPanel/Plugins": {
"title": "$:/tags/ControlPanel/Plugins",
"list": "[[$:/core/ui/ControlPanel/Plugins/Installed]] [[$:/core/ui/ControlPanel/Plugins/Add]]"
},
"$:/tags/EditTemplate": {
"title": "$:/tags/EditTemplate",
"list": "[[$:/core/ui/EditTemplate/controls]] [[$:/core/ui/EditTemplate/title]] [[$:/core/ui/EditTemplate/tags]] [[$:/core/ui/EditTemplate/shadow]] [[$:/core/ui/ViewTemplate/classic]] [[$:/core/ui/EditTemplate/body]] [[$:/core/ui/EditTemplate/type]] [[$:/core/ui/EditTemplate/fields]]"
},
"$:/tags/EditToolbar": {
"title": "$:/tags/EditToolbar",
"list": "[[$:/core/ui/Buttons/delete]] [[$:/core/ui/Buttons/cancel]] [[$:/core/ui/Buttons/save]]"
},
"$:/tags/EditorToolbar": {
"title": "$:/tags/EditorToolbar",
"list": "$:/core/ui/EditorToolbar/paint $:/core/ui/EditorToolbar/opacity $:/core/ui/EditorToolbar/line-width $:/core/ui/EditorToolbar/rotate-left $:/core/ui/EditorToolbar/clear $:/core/ui/EditorToolbar/bold $:/core/ui/EditorToolbar/italic $:/core/ui/EditorToolbar/strikethrough $:/core/ui/EditorToolbar/underline $:/core/ui/EditorToolbar/superscript $:/core/ui/EditorToolbar/subscript $:/core/ui/EditorToolbar/mono-line $:/core/ui/EditorToolbar/mono-block $:/core/ui/EditorToolbar/quote $:/core/ui/EditorToolbar/list-bullet $:/core/ui/EditorToolbar/list-number $:/core/ui/EditorToolbar/heading-1 $:/core/ui/EditorToolbar/heading-2 $:/core/ui/EditorToolbar/heading-3 $:/core/ui/EditorToolbar/heading-4 $:/core/ui/EditorToolbar/heading-5 $:/core/ui/EditorToolbar/heading-6 $:/core/ui/EditorToolbar/link $:/core/ui/EditorToolbar/excise $:/core/ui/EditorToolbar/picture $:/core/ui/EditorToolbar/stamp $:/core/ui/EditorToolbar/size $:/core/ui/EditorToolbar/editor-height $:/core/ui/EditorToolbar/more $:/core/ui/EditorToolbar/preview $:/core/ui/EditorToolbar/preview-type"
},
"$:/tags/Manager/ItemMain": {
"title": "$:/tags/Manager/ItemMain",
"list": "$:/Manager/ItemMain/WikifiedText $:/Manager/ItemMain/RawText $:/Manager/ItemMain/Fields"
},
"$:/tags/Manager/ItemSidebar": {
"title": "$:/tags/Manager/ItemSidebar",
"list": "$:/Manager/ItemSidebar/Tags $:/Manager/ItemSidebar/Colour $:/Manager/ItemSidebar/Icon $:/Manager/ItemSidebar/Tools"
},
"$:/tags/MoreSideBar": {
"title": "$:/tags/MoreSideBar",
"list": "[[$:/core/ui/MoreSideBar/All]] [[$:/core/ui/MoreSideBar/Recent]] [[$:/core/ui/MoreSideBar/Tags]] [[$:/core/ui/MoreSideBar/Missing]] [[$:/core/ui/MoreSideBar/Drafts]] [[$:/core/ui/MoreSideBar/Orphans]] [[$:/core/ui/MoreSideBar/Types]] [[$:/core/ui/MoreSideBar/System]] [[$:/core/ui/MoreSideBar/Shadows]] [[$:/core/ui/MoreSideBar/Explorer]] [[$:/core/ui/MoreSideBar/Plugins]]",
"text": ""
},
"$:/tags/PageControls": {
"title": "$:/tags/PageControls",
"list": "[[$:/core/ui/Buttons/home]] [[$:/core/ui/Buttons/close-all]] [[$:/core/ui/Buttons/fold-all]] [[$:/core/ui/Buttons/unfold-all]] [[$:/core/ui/Buttons/permaview]] [[$:/core/ui/Buttons/new-tiddler]] [[$:/core/ui/Buttons/new-journal]] [[$:/core/ui/Buttons/new-image]] [[$:/core/ui/Buttons/import]] [[$:/core/ui/Buttons/export-page]] [[$:/core/ui/Buttons/control-panel]] [[$:/core/ui/Buttons/advanced-search]] [[$:/core/ui/Buttons/manager]] [[$:/core/ui/Buttons/tag-manager]] [[$:/core/ui/Buttons/language]] [[$:/core/ui/Buttons/palette]] [[$:/core/ui/Buttons/theme]] [[$:/core/ui/Buttons/storyview]] [[$:/core/ui/Buttons/encryption]] [[$:/core/ui/Buttons/timestamp]] [[$:/core/ui/Buttons/full-screen]] [[$:/core/ui/Buttons/print]] [[$:/core/ui/Buttons/save-wiki]] [[$:/core/ui/Buttons/refresh]] [[$:/core/ui/Buttons/more-page-actions]]"
},
"$:/tags/PageTemplate": {
"title": "$:/tags/PageTemplate",
"list": "[[$:/core/ui/PageTemplate/topleftbar]] [[$:/core/ui/PageTemplate/toprightbar]] [[$:/core/ui/PageTemplate/sidebar]] [[$:/core/ui/PageTemplate/story]] [[$:/core/ui/PageTemplate/alerts]]",
"text": ""
},
"$:/tags/PluginLibrary": {
"title": "$:/tags/PluginLibrary",
"list": "$:/config/OfficialPluginLibrary"
},
"$:/tags/SideBar": {
"title": "$:/tags/SideBar",
"list": "[[$:/core/ui/SideBar/Open]] [[$:/core/ui/SideBar/Recent]] [[$:/core/ui/SideBar/Tools]] [[$:/core/ui/SideBar/More]]",
"text": ""
},
"$:/tags/SideBarSegment": {
"title": "$:/tags/SideBarSegment",
"list": "[[$:/core/ui/SideBarSegments/site-title]] [[$:/core/ui/SideBarSegments/site-subtitle]] [[$:/core/ui/SideBarSegments/page-controls]] [[$:/core/ui/SideBarSegments/search]] [[$:/core/ui/SideBarSegments/tabs]]"
},
"$:/tags/TiddlerInfo": {
"title": "$:/tags/TiddlerInfo",
"list": "[[$:/core/ui/TiddlerInfo/Tools]] [[$:/core/ui/TiddlerInfo/References]] [[$:/core/ui/TiddlerInfo/Tagging]] [[$:/core/ui/TiddlerInfo/List]] [[$:/core/ui/TiddlerInfo/Listed]] [[$:/core/ui/TiddlerInfo/Fields]]",
"text": ""
},
"$:/tags/TiddlerInfo/Advanced": {
"title": "$:/tags/TiddlerInfo/Advanced",
"list": "[[$:/core/ui/TiddlerInfo/Advanced/ShadowInfo]] [[$:/core/ui/TiddlerInfo/Advanced/PluginInfo]]"
},
"$:/tags/ViewTemplate": {
"title": "$:/tags/ViewTemplate",
"list": "[[$:/core/ui/ViewTemplate/title]] [[$:/core/ui/ViewTemplate/unfold]] [[$:/core/ui/ViewTemplate/subtitle]] [[$:/core/ui/ViewTemplate/tags]] [[$:/core/ui/ViewTemplate/classic]] [[$:/core/ui/ViewTemplate/body]]"
},
"$:/tags/ViewToolbar": {
"title": "$:/tags/ViewToolbar",
"list": "[[$:/core/ui/Buttons/more-tiddler-actions]] [[$:/core/ui/Buttons/info]] [[$:/core/ui/Buttons/new-here]] [[$:/core/ui/Buttons/new-journal-here]] [[$:/core/ui/Buttons/clone]] [[$:/core/ui/Buttons/export-tiddler]] [[$:/core/ui/Buttons/edit]] [[$:/core/ui/Buttons/delete]] [[$:/core/ui/Buttons/permalink]] [[$:/core/ui/Buttons/permaview]] [[$:/core/ui/Buttons/open-window]] [[$:/core/ui/Buttons/close-others]] [[$:/core/ui/Buttons/close]] [[$:/core/ui/Buttons/fold-others]] [[$:/core/ui/Buttons/fold]]"
},
"$:/snippets/themeswitcher": {
"title": "$:/snippets/themeswitcher",
"text": "<$linkcatcher to=\"$:/theme\">\n<div class=\"tc-chooser\"><$list filter=\"[plugin-type[theme]sort[title]]\"><$set name=\"cls\" filter=\"[all[current]field:title{$:/theme}] [[$:/theme]!has[text]addsuffix[s/tiddlywiki/vanilla]field:title<currentTiddler>] +[limit[1]]\" value=\"tc-chooser-item tc-chosen\" emptyValue=\"tc-chooser-item\"><div class=<<cls>>><$link to={{!!title}}>''<$view field=\"name\" format=\"text\"/>'' <$view field=\"description\" format=\"text\"/></$link></div>\n</$set>\n</$list>\n</div>\n</$linkcatcher>"
},
"$:/core/wiki/title": {
"title": "$:/core/wiki/title",
"text": "{{$:/SiteTitle}} --- {{$:/SiteSubtitle}}"
},
"$:/view": {
"title": "$:/view",
"text": "classic"
},
"$:/snippets/viewswitcher": {
"title": "$:/snippets/viewswitcher",
"text": "\\define icon()\n$:/core/images/storyview-$(storyview)$\n\\end\n<$linkcatcher to=\"$:/view\">\n<div class=\"tc-chooser tc-viewswitcher\">\n<$list filter=\"[storyviews[]]\" variable=\"storyview\">\n<$set name=\"cls\" filter=\"[<storyview>prefix{$:/view}]\" value=\"tc-chooser-item tc-chosen\" emptyValue=\"tc-chooser-item\"><div class=<<cls>>>\n<$link to=<<storyview>>><$transclude tiddler=<<icon>>/><$text text=<<storyview>>/></$link>\n</div>\n</$set>\n</$list>\n</div>\n</$linkcatcher>"
}
}
}
<svg class="tc-image-asterisk tc-image-button" width="22pt" height="22pt" viewBox="0 0 32 32">
<g id="text3035" style="font-size:100px;font-family:arial">
<path d="M 0,14.355469 2.2460938,7.421875 C 7.4218645,9.2448552 11.181626,10.82363 13.525391,12.158203 12.906885,6.2663426 12.581365,2.2136123 12.548828,0 l 7.080078,0 c -0.09768,3.2227258 -0.472027,7.2591801 -1.123047,12.109375 3.35284,-1.692646 7.193982,-3.2551444 11.523438,-4.6875 l 2.246094,6.933594 c -4.134146,1.367244 -8.186877,2.278702 -12.158204,2.734375 1.985652,1.725314 4.785129,4.801483 8.398438,9.228515 L 22.65625,30.46875 C 20.768205,27.89718 18.53839,24.397835 15.966797,19.970703 13.557926,24.560595 11.442043,28.059941 9.6191406,30.46875 L 3.8574219,26.318359 C 7.6334528,21.663463 10.335273,18.587294 11.962891,17.089844 7.763661,16.276098 3.7760348,15.364641 0,14.355469" id="path3063" style="font-size:100px;font-family:arial"/>
</g>
</svg>
\whitespace trim
\define lingo-base() $:/language/CloseAll/
\define drop-actions()
<$action-listops $tiddler=<<tv-story-list>> $subfilter="+[insertbefore:currentTiddler<actionTiddler>]"/>
\end
\define placeholder()
<div class="tc-droppable-placeholder"/>
\end
\define droppable-item(button)
\whitespace trim
<$droppable actions=<<drop-actions>>>
<<placeholder>>
<div>
$button$
</div>
</$droppable>
\end
<div class="tc-sidebar-tab-open">
<$list filter="[list<tv-story-list>]" history=<<tv-history-list>> storyview="pop">
<div class="tc-sidebar-tab-open-item">
<$macrocall $name="droppable-item" button="""<$button message="tm-close-tiddler" tooltip={{$:/language/Buttons/Close/Hint}} aria-label={{$:/language/Buttons/Close/Caption}} class="tc-btn-invisible tc-btn-mini">{{$:/core/images/close-button}}</$button> <$link to={{!!title}}><$view field="title"/></$link>"""/>
</div>
</$list>
<$tiddler tiddler="">
<div>
<$macrocall $name="droppable-item" button="""<$button message="tm-close-all-tiddlers" class="tc-btn-invisible tc-btn-mini"><<lingo Button>></$button>"""/>
</div>
</$tiddler>
</div>
<$action-sendmessage
$message="tm-edit-text-operation"
$param="wrap-selection"
prefix={{$:/core/ui/TextEditorToolbar/Footnote!!prefix}}
suffix={{$:/core/ui/TextEditorToolbar/Footnote!!suffix}}
/>
[[Welcome Page]]
[[TableOfContents]]
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{
"tiddlers": {
"$:/language/Buttons/Clear/Hint": {
"title": "$:/language/Buttons/Clear/Hint",
"text": "Clear image to solid color"
},
"$:/language/Buttons/Paint/Caption": {
"title": "$:/language/Buttons/Paint/Caption",
"text": "paint color"
},
"$:/language/Buttons/Paint/Hint": {
"title": "$:/language/Buttons/Paint/Hint",
"text": "Set painting color"
},
"$:/language/Buttons/Palette/Hint": {
"title": "$:/language/Buttons/Palette/Hint",
"text": "Choose the color palette"
},
"$:/language/Buttons/StoryView/Hint": {
"title": "$:/language/Buttons/StoryView/Hint",
"text": "Choose the story visualization"
},
"$:/language/Manager/Item/Colour": {
"title": "$:/language/Manager/Item/Colour",
"text": "Color"
},
"$:/language/TagManager/Colour/Heading": {
"title": "$:/language/TagManager/Colour/Heading",
"text": "Color"
},
"$:/language/RecentChanges/DateFormat": {
"title": "$:/language/RecentChanges/DateFormat",
"text": "MMM DD, YYYY"
},
"$:/language/Tiddler/DateFormat": {
"title": "$:/language/Tiddler/DateFormat",
"text": "MMM DD, YYYY at hh12:0mm am"
},
"$:/languages/en-US/icon": {
"title": "$:/languages/en-US/icon",
"type": "image/svg+xml",
"text": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"1235\" height=\"650\" viewBox=\"0 0 7410 3900\">\n<rect width=\"7410\" height=\"3900\" fill=\"#b22234\"/>\n<path d=\"M0,450H7410m0,600H0m0,600H7410m0,600H0m0,600H7410m0,600H0\" stroke=\"#fff\" stroke-width=\"300\"/>\n<rect width=\"2964\" height=\"2100\" fill=\"#3c3b6e\"/>\n<g fill=\"#fff\">\n<g id=\"s18\">\n<g id=\"s9\">\n<g id=\"s5\">\n<g id=\"s4\">\n<path id=\"s\" d=\"M247,90 317.534230,307.082039 132.873218,172.917961H361.126782L176.465770,307.082039z\"/>\n<use xlink:href=\"#s\" y=\"420\"/>\n<use xlink:href=\"#s\" y=\"840\"/>\n<use xlink:href=\"#s\" y=\"1260\"/>\n</g>\n<use xlink:href=\"#s\" y=\"1680\"/>\n</g>\n<use xlink:href=\"#s4\" x=\"247\" y=\"210\"/>\n</g>\n<use xlink:href=\"#s9\" x=\"494\"/>\n</g>\n<use xlink:href=\"#s18\" x=\"988\"/>\n<use xlink:href=\"#s9\" x=\"1976\"/>\n<use xlink:href=\"#s5\" x=\"2470\"/>\n</g>\n</svg>\n"
}
}
}
base03: #002b36
base02: #073642
base01: #383838
base00: #383a3f
base0: #464646
base1: #2d2d2d
base2: #89bdd3
base3: #faeefb
yellow: #b58900
orange: #cb4b16
red: #dc322f
magenta: #d33682
violet: #6c71c4
blue: #268bd2
cyan: #2aa198
green: #859900
base10: #312c32
violet-muted: #7c81b0
blue-muted: #4e7baa
yellow-hot: #ffcc44
orange-hot: #eb6d20
red-hot: #ff2222
blue-hot: #2298ee
green-hot: #98ee22
background: #9ad3de
download-foreground: <<colour background>>
dragger-foreground: <<colour background>>
dropdown-background: <<colour background>>
modal-background: <<colour background>>
sidebar-foreground-shadow: <<colour background>>
tiddler-background: <<colour background>>
tiddler-border: <<colour background>>
tiddler-link-background: <<colour background>>
tab-background-selected: <<colour background>>
dropdown-tab-background-selected: <<colour tab-background-selected>>
foreground: #22264b
dragger-background: <<colour foreground>>
tab-foreground: <<colour foreground>>
tab-foreground-selected: <<colour tab-foreground>>
sidebar-tab-foreground-selected: <<colour tab-foreground-selected>>
sidebar-tab-foreground: <<colour tab-foreground>>
sidebar-button-foreground: <<colour foreground>>
sidebar-controls-foreground: <<colour foreground>>
sidebar-foreground: <<colour foreground>>
alert-muted-foreground: <<colour base01>>
code-foreground: <<colour base00>>
message-foreground: <<colour base00>>
tag-foreground: <<colour base00>>
sidebar-tiddler-link-foreground: <<colour base0>>
muted-foreground: <<colour base1>>
blockquote-bar: <<colour muted-foreground>>
dropdown-border: <<colour muted-foreground>>
sidebar-muted-foreground: <<colour muted-foreground>>
tiddler-title-foreground: <<colour muted-foreground>>
site-title-foreground: <<colour tiddler-title-foreground>>
modal-footer-background: <<colour base2>>
page-background: <<colour base2>>
modal-backdrop: <<colour page-background>>
notification-background: <<colour page-background>>
code-background: <<colour page-background>>
code-border: <<colour code-background>>
pre-background: <<colour page-background>>
pre-border: <<colour pre-background>>
sidebar-tab-background-selected: <<colour page-background>>
table-header-background: <<colour base2>>
tag-background: <<colour base2>>
tiddler-editor-background: <<colour base2>>
tiddler-info-background: <<colour base2>>
tiddler-info-tab-background: <<colour base2>>
tab-background: <<colour base2>>
dropdown-tab-background: <<colour tab-background>>
alert-background: <<colour base3>>
message-background: <<colour base3>>
alert-highlight: <<colour magenta>>
external-link-foreground: <<colour violet>>
tiddler-controls-foreground: <<colour base10>>
external-link-foreground-visited: <<colour violet-muted>>
primary: <<colour blue-muted>>
download-background: <<colour primary>>
tiddler-link-foreground: <<colour primary>>
alert-border: #89bdd3
dirty-indicator: #ff0000
dropzone-background: rgba(0,200,0,0.7)
external-link-background-hover: inherit
external-link-background-visited: inherit
external-link-background: inherit
external-link-foreground-hover: inherit
message-border: #cfd6e6
modal-border: #999999
sidebar-controls-foreground-hover:
sidebar-muted-foreground-hover:
sidebar-tab-background: #ded8c5
sidebar-tiddler-link-foreground-hover:
static-alert-foreground: #aaaaaa
tab-border: #cccccc
modal-footer-border: <<colour tab-border>>
modal-header-border: <<colour tab-border>>
notification-border: <<colour tab-border>>
sidebar-tab-border: <<colour tab-border>>
tab-border-selected: <<colour tab-border>>
sidebar-tab-border-selected: <<colour tab-border-selected>>
tab-divider: #d8d8d8
sidebar-tab-divider: <<colour tab-divider>>
table-border: #dddddd
table-footer-background: #a8a8a8
tiddler-controls-foreground-hover: #888888
tiddler-controls-foreground-selected: #444444
tiddler-editor-border-image: #ffffff
tiddler-editor-border: #cccccc
tiddler-editor-fields-even: #e0e8e0
tiddler-editor-fields-odd: #f0f4f0
tiddler-info-border: #dddddd
tiddler-subtitle-foreground: #c0c0c0
toolbar-new-button:
toolbar-options-button:
toolbar-save-button:
toolbar-info-button:
toolbar-edit-button:
toolbar-close-button:
toolbar-delete-button:
toolbar-cancel-button:
toolbar-done-button:
untagged-background: #999999
very-muted-foreground: #888888
alert-background: #ffe476
alert-border: #b99e2f
alert-highlight: #881122
alert-muted-foreground: #b99e2f
background: #FEFEFE
blockquote-bar: <<colour muted-foreground>>
button-background:
button-foreground:
button-border:
code-background: #f7f7f9
code-border: #e1e1e8
code-foreground: #dd1144
dirty-indicator: #ff0000
download-background: #34c734
download-foreground: <<colour background>>
dragger-background: <<colour foreground>>
dragger-foreground: <<colour background>>
dropdown-background: <<colour background>>
dropdown-border: <<colour muted-foreground>>
dropdown-tab-background-selected: #fff
dropdown-tab-background: #ececec
dropzone-background: rgba(0,200,0,0.7)
external-link-background-hover: inherit
external-link-background-visited: inherit
external-link-background: inherit
external-link-foreground-hover: inherit
external-link-foreground-visited: #aa0000
external-link-foreground: #cc0000
foreground: #333333
message-background: #ecf2ff
message-border: #cfd6e6
message-foreground: #547599
modal-backdrop: <<colour foreground>>
modal-background: <<colour background>>
modal-border: #999999
modal-footer-background: #f5f5f5
modal-footer-border: #dddddd
modal-header-border: #eeeeee
muted-foreground: #bbb
notification-background: #ffffdd
notification-border: #999999
page-background: #fefefe
pre-background: #f5f5f5
pre-border: #cccccc
primary: #cc0000
sidebar-button-foreground: <<colour foreground>>
sidebar-controls-foreground-hover: #000000
sidebar-controls-foreground: #aaaaaa
sidebar-foreground-shadow: rgba(255,255,255, 0.8)
sidebar-foreground: #acacac
sidebar-muted-foreground-hover: #444444
sidebar-muted-foreground: #c0c0c0
sidebar-tab-background-selected: #f4f4f4
sidebar-tab-background: #e0e0e0
sidebar-tab-border-selected: <<colour tab-border-selected>>
sidebar-tab-border: <<colour tab-border>>
sidebar-tab-divider: #e4e4e4
sidebar-tab-foreground-selected:
sidebar-tab-foreground: <<colour tab-foreground>>
sidebar-tiddler-link-foreground-hover: #444444
sidebar-tiddler-link-foreground: #999999
site-title-foreground: <<colour tiddler-title-foreground>>
static-alert-foreground: #aaaaaa
tab-background-selected: #ffffff
tab-background: #d8d8d8
tab-border-selected: #d8d8d8
tab-border: #cccccc
tab-divider: #d8d8d8
tab-foreground-selected: <<colour tab-foreground>>
tab-foreground: #666666
table-border: #dddddd
table-footer-background: #a8a8a8
table-header-background: #f0f0f0
tag-background:
tag-foreground: #f03838
tiddler-background: <<colour background>>
tiddler-border: <<colour background>>
tiddler-controls-foreground-hover: #888888
tiddler-controls-foreground-selected: #444444
tiddler-controls-foreground: #cccccc
tiddler-editor-background: #f8f8f8
tiddler-editor-border-image: #ffffff
tiddler-editor-border: #cccccc
tiddler-editor-fields-even: #e0e8e0
tiddler-editor-fields-odd: #f0f4f0
tiddler-info-background: #f8f8f8
tiddler-info-border: #dddddd
tiddler-info-tab-background: #f8f8f8
tiddler-link-background: <<colour background>>
tiddler-link-foreground: <<colour primary>>
tiddler-subtitle-foreground: #c0c0c0
tiddler-title-foreground: #182955
toolbar-new-button:
toolbar-options-button:
toolbar-save-button:
toolbar-info-button:
toolbar-edit-button:
toolbar-close-button:
toolbar-delete-button:
toolbar-cancel-button:
toolbar-done-button:
untagged-background: #999999
very-muted-foreground: #888888
{
"tiddlers": {
"$:/plugins/telmiger/MyStory/icon": {
"created": "20190106092402457",
"creator": "Thomas Elmiger",
"text": "<svg class=\"tc-image tc-image-button tc-image-my-story-icon\" width=\"22pt\" height=\"22pt\" viewBox=\"0 0 22 22\">\n<path d=\"M5 0v16h17V0H5zm14 13H8V5h11v8zm-2 8v-3H3V5H0v16h17z\" fill-rule=\"nonzero\"/>\n</svg>",
"type": "",
"title": "$:/plugins/telmiger/MyStory/icon",
"modifier": "Thomas Elmiger",
"modified": "20190106092523193",
"tags": "",
"caption": "MyStory icon",
"e-name": "MyStory Icon"
},
"$:/plugins/telmiger/MyStory/readme": {
"text": "!! My Story\n\nA story list on steroids. Adds bookmarking and alternative labels for story-elements in sidebar tabs: \n\n* The [[MyMarks|$:/plugins/telmiger/MyStory/bookmarks/tab]] tab shows links to bookmarked elements.\n* The [[MyStory|$:/plugins/telmiger/MyStory/tab]] tab can replace the //Open// tab: The listing shows the title of a tiddler, if there is no caption field, the caption, if there is no other field as defined below.\n\n<label>Default field name: <$edit-text tiddler=\"$:/plugins/telmiger/MyStory/settings\" index=\"default-label\" size=\"20\" tag=\"input\"/></label> – `title` might be a good fallback :)\n\n!!! [[Usage settings|$:/plugins/telmiger/MyStory/settings]]\n\nControl active sidebar tabs: <br>\n<$checkbox tiddler=\"$:/plugins/telmiger/MyStory/tab\" tag=\"$:/tags/SideBar\"> <$text text={{$:/plugins/telmiger/MyStory/tab!!caption}}/></$checkbox> \n<$checkbox tiddler=\"$:/plugins/telmiger/MyStory/bookmarks/tab\" tag=\"$:/tags/SideBar\"> <$text text={{$:/plugins/telmiger/MyStory/bookmarks/tab!!caption}}/></$checkbox> \n<$checkbox tiddler=\"$:/core/ui/SideBar/Open\" tag=\"$:/tags/SideBar\"> {{$:/core/ui/SideBar/Open!!caption}}</$checkbox><br>\n<$checkbox tiddler=\"$:/core/ui/SideBar/Recent\" tag=\"$:/tags/SideBar\"> {{$:/core/ui/SideBar/Recent!!caption}}</$checkbox> <$checkbox tiddler=\"$:/core/ui/SideBar/Tools\" tag=\"$:/tags/SideBar\"> {{$:/core/ui/SideBar/Tools!!caption}}</$checkbox> <$checkbox tiddler=\"$:/core/ui/SideBar/More\" tag=\"$:/tags/SideBar\"> {{$:/core/ui/SideBar/More!!caption}}</$checkbox>\n\nShow small buttons for every list entry:<br>\n<$checkbox tiddler=\"$:/plugins/telmiger/MyStory/settings\" index=\"show-edit-button\" checked=\"yes\" unchecked=\"no\"> edit button </$checkbox><br>\n<$checkbox tiddler=\"$:/plugins/telmiger/MyStory/settings\" index=\"show-clone-button\" checked=\"yes\" unchecked=\"no\"> clone button </$checkbox>\n\n{{$:/core/ui/ControlPanel/Settings/DefaultSidebarTab}}\n\nClick the tag and arrange tabs via drag and drop:<br>\n<<tag \"$:/tags/SideBar\">>\n\n!! In-Story Controls\n\nElements tagged <<tag $:/tags/InStoryControls>> can be added below every element (tiddler) in the story.<br>\n<$checkbox tiddler=\"$:/plugins/telmiger/MyStory/settings\" index=\"in-story-controls\" checked=\"show\" unchecked=\"hide\"> show in-story controls</$checkbox>",
"title": "$:/plugins/telmiger/MyStory/readme",
"tags": "",
"modifier": "Thomas Elmiger",
"modified": "20190128221647365",
"creator": "Thomas Elmiger",
"created": "20190105223039966",
"caption": "MyStory Readme"
},
"$:/plugins/telmiger/MyStory/settings": {
"text": "{\n \"default-label\": \"e-name\",\n \"show-edit-button\": \"no\",\n \"last-closed\": \" $:/ControlPanel\",\n \"show-clone-button\": \"no\"\n}",
"type": "application/json",
"title": "$:/plugins/telmiger/MyStory/settings",
"modifier": "Thomas Elmiger",
"modified": "20190128225758316",
"creator": "Thomas Elmiger",
"created": "20190105223523663"
},
"$:/plugins/telmiger/MyStory/styles.css": {
"text": ".te-nav-list {\n list-style: none;\n padding-left: 0;\n}\n\n\n/* icons */\n\n.te-nav-list svg {\n height: 1rem;\n width: 1rem;\n}\n\n.te-nav-list .tc-dirty-indicator svg {\n margin: 0 0.25rem -0.25rem 1rem;\n}\n\n\n/* buttons */\n\n.te-nav-list button.tc-btn-mini {\n margin-right: 0.5rem;\n}\n\n.my-story-footer .my-story-close-all-btn {\n margin-right: 1em;\n}\n\n.te-nav-list .my-story-btn {\n margin-left: 0.25rem;\n}\n\n/* bookmark icon */\n\n.my-bookmark-icon {\n margin-right: 0.33em;\n}\n\n\n/* bookmarks tiddler marker */\n\n.my-story-bkmrk {\n position: absolute;\n right: -0.1em;\n font-size: 2.1rem;\n}\n\n.my-story-bkmrk svg {\n height: 1.75em;\n max-width: 1.75em;\n}\n\n\n/* bookmarks buttons */\n\n.te-btn-inactive, .te-btn-active {\n cursor: pointer;\n}\n\n.te-nav-list .te-btn-inactive,\n.te-nav-list .te-btn-active {\n margin-right: .25rem;\n}\n\n.tc-tiddler-view-frame .te-btn-inactive {\n display: none;\n}\n\n.tc-tiddler-view-frame:hover .te-btn-inactive {\n display: initial;\n}\n\n.te-btn-active svg.my-bookmark {\n color: <<colour primary>>\n}\n\n.te-btn-active svg.my-bookmark:hover {\n color: rgba(123,123,123,0.3);\n fill: <<colour foreground>>\n}\n\n.te-btn-active svg.my-bookmark:hover path.vertical-line {\n fill: transparent;\n}\n\n.te-btn-inactive svg.my-bookmark:hover {\n fill: <<colour foreground>>\n}\n\n.te-btn-inactive svg.my-bookmark:hover path{\n color: <<colour foreground>>\n}\n\n.te-btn-inactive input,\n.te-btn-active input {\n position: absolute;\n left: -100vw;\n}\n\n.my-bkmrks .te-btn-active {\n position: relative;\n top: -0.125rem;\n}\n\n/* InStory Controls*/\n\n.te-footerbar {\n text-align: right;\n border-bottom: 1px solid <<colour primary>>;\n padding: 0.25rem 0;\n}\n\n.te-footerbar button {\n font-size: 1.5rem;\n margin-left: 0.5rem;\n}",
"title": "$:/plugins/telmiger/MyStory/styles.css",
"tags": "$:/tags/Stylesheet",
"modifier": "Thomas Elmiger",
"modified": "20190128220445598",
"creator": "Thomas Elmiger",
"created": "20190105221436571",
"caption": "MyStory Styles"
},
"$:/plugins/telmiger/MyStory/tab": {
"created": "20190101145705620",
"creator": "Thomas Elmiger",
"text": "\\whitespace trim\n\n\\define lingo-base() $:/language/CloseAll/\n\n\\define editmode-icon() \n<span class=\"tc-dirty-indicator\" title=\"in edit mode\">{{$:/core/images/edit-button}}</span>\n\\end\n\n\\define my-close-btn() \n<$list filter=\"[all[current]!has[draft.of]]\" variable=\"title\" emptyMessage=<<editmode-icon>>>\n<$button tooltip={{$:/language/Buttons/Close/Hint}} aria-label={{$:/language/Buttons/Close/Caption}} class=\"tc-btn-invisible tc-btn-mini\">\n<$action-listops $tiddler=\"$:/plugins/telmiger/MyStory/settings\" $index=\"last-closed\" $subfilter=\"+[[]] [<title>]\"/>\n<$action-sendmessage $message=\"tm-close-tiddler\"/>×</$button></$list>\n\\end\n\n\\define openMyTiddlers(filter)\n<$list filter=<<__filter__>> >\n<$action-navigate $to=<<currentTiddler>>/>\n</$list>\n\\end\n\n\\define reopenBtn()\n<$list filter=\"[{$:/plugins/telmiger/MyStory/settings##last-closed}minlength[1]]\">\n<$set name=\"tt\" value=\"reopen last closed element(s)\">\n<$button tooltip=<<tt>> class=\"tc-btn-invisible my-story-reopen-btn\">\n<$set name=\"qualstate\" value=\"$:/config/Navigation/openLinkFromOutsideRiver\">\n<$reveal type=\"match\" state=<<qualstate>> text=\"bottom\">\n<<openMyTiddlers \"[enlist{$:/plugins/telmiger/MyStory/settings##last-closed}]\">>\n</$reveal>\n<$reveal type=\"nomatch\" state=<<qualstate>> text=\"bottom\">\n<<openMyTiddlers \"[enlist{$:/plugins/telmiger/MyStory/settings##last-closed}reverse[]]\">>\n</$reveal>\n</$set>\n{{$:/plugins/telmiger/MyStory/left-arrow}}\n</$button>\n</$set>\n</$list>\n\\end\n\n\\define closeAllBtn()\n<$button class=\"tc-btn-invisible my-story-close-all-btn\">\n<$set name=\"myStory\" filter=\"[list[$:/StoryList]]\" >\n<$action-setfield $tiddler=\"$:/plugins/telmiger/MyStory/settings\" $index=\"last-closed\" $value=<<myStory>>/>\n</$set>\n<$action-sendmessage $message=\"tm-close-all-tiddlers\"/>\n<<lingo Button>>\n</$button>\n\\end\n\n\\define edit-btn()\n<$list filter=\"[[$:/plugins/telmiger/MyStory/settings]getindex[show-edit-button]removeprefix[no]] ~[all[current]!has[draft.of]]\">\n<$list filter=[all[current]minlength[1]]>\n<span class=\"my-story-btn\">\n<$button message=\"tm-edit-tiddler\" tooltip={{$:/language/Buttons/Edit/Hint}} aria-label={{$:/language/Buttons/Edit/Caption}} class=<<tv-config-toolbar-class>>>\n{{$:/core/images/edit-button}}\n</$button>\n</span>\n</$list>\n</$list>\n\\end\n\n\\define clone-btn()\n<$list filter=\"[[$:/plugins/telmiger/MyStory/settings]getindex[show-clone-button]removeprefix[no]] ~[all[current]!has[draft.of]]\">\n<$list filter=[all[current]minlength[1]]>\n<span class=\"my-story-btn\">\n<$button message=\"tm-new-tiddler\" param=<<currentTiddler>> tooltip={{$:/language/Buttons/Clone/Hint}} aria-label={{$:/language/Buttons/Clone/Caption}} class=<<tv-config-toolbar-class>>>\n{{$:/core/images/clone-button}}\n</$button>\n</span>\n</$list>\n</$list>\n\\end\n\n\\define drop-actions()\n<$action-listops $tiddler=\"$:/StoryList\" $subfilter=\"+[insertbefore:currentTiddler<actionTiddler>]\"/>\n\\end\n\n<ul class=\"my-story te-nav-list\">\n<$list filter=\"[list[$:/StoryList]]\" history=\"$:/HistoryList\" storyview=\"pop\">\n<li><div style=\"position: relative;\">\n<$droppable actions=<<drop-actions>>>\n<div class=\"tc-droppable-placeholder\">\n \n</div>\n<div>\n<<my-close-btn>><<bookmark-icon>><<my-link>><<edit-btn>> <<clone-btn>>\n</div>\n</$droppable>\n</div>\n</li>\n</$list>\n</ul>\n<$tiddler tiddler=\"\">\n<$droppable actions=<<drop-actions>>>\n<div class=\"tc-droppable-placeholder\">\n \n</div>\n<footer class=\"my-story-footer\">\n<<closeAllBtn>>\n<<reopenBtn>>\n</footer>\n</$droppable>\n</$tiddler>",
"title": "$:/plugins/telmiger/MyStory/tab",
"tags": "$:/tags/SideBar",
"modifier": "Thomas Elmiger",
"modified": "20190118224023717",
"e-name": "MyStory Tab",
"caption": "MyStory"
},
"$:/plugins/telmiger/MyStory/left-arrow": {
"created": "20190106110559867",
"creator": "Thomas Elmiger",
"text": "<svg class=\"tc-image tc-image-button tc-image-my-story-icon\" width=\"22pt\" height=\"22pt\" viewBox=\"0 0 22 22\">\n<circle cx=\"11\" cy=\"11\" r=\"10\" fill-opacity=\".2\"/><path d=\"M9 12.4l1.7 1.5a1.4 1.4 0 1 1-2 2l-3.9-4c-.5-.5-.5-1.4 0-1.9l4-3.9a1.4 1.4 0 1 1 1.9 2L9 9.6h7.1c.8 0 1.4.6 1.4 1.4 0 .7-.6 1.4-1.4 1.4H9z\"/>\n</svg>",
"type": "",
"title": "$:/plugins/telmiger/MyStory/left-arrow",
"modifier": "Thomas Elmiger",
"modified": "20190106111510030",
"tags": "",
"caption": "left arrow in circle",
"e-name": "left arrow in circle"
},
"$:/plugins/telmiger/MyStory/macros": {
"created": "20190118063746061",
"creator": "Thomas Elmiger",
"text": "\\define my-link-text()\n<$transclude field={{$:/plugins/telmiger/MyStory/settings##default-label}}><$transclude field=\"caption\"><$view field=\"title\"/></$transclude></$transclude> \n\\end\n\n\\define my-link()\n<$link to=<<currentTiddler>> tooltip=<<currentTiddler>>><$set name=\"tv-wikilinks\" value=\"no\"><<my-link-text>></$set></$link>\n\\end\n\n\\define bookmark-icon()\n<$list filter=\"[all[current]tag[MyMark]]\"><span class=\"my-bookmark-icon\">{{$:/plugins/telmiger/MyStory/bookmarks/icon}}</span></$list>\n\\end\n\n\\define MyStoryBkmrkBtn()\n<$list filter=\"[all[current]!tag[MyMark]]\">\n<$checkbox tag=\"MyMark\" class=\"te-btn-inactive\">{{$:/plugins/telmiger/MyStory/bookmarks/icon-off}}</$checkbox>\n</$list>\n<$list filter=\"[all[current]tag[MyMark]]\">\n<$checkbox tag=\"MyMark\" class=\"te-btn-active\">{{$:/plugins/telmiger/MyStory/bookmarks/icon}}</$checkbox>\n</$list>\n\\end",
"caption": "MyStory Macros",
"e-name": "MyStory Macro Collection",
"modified": "20190119085246849",
"modifier": "Thomas Elmiger",
"title": "$:/plugins/telmiger/MyStory/macros",
"tags": "$:/tags/Macro"
},
"$:/plugins/telmiger/MyStory/bookmarks/ViewTemplate": {
"created": "20190118064731266",
"creator": "Thomas Elmiger",
"text": "<span class=\"my-story-bkmrk\"><<MyStoryBkmrkBtn>></span>",
"caption": "MyStory Bookmarks ViewTemplate",
"e-name": "MyStory Bookmarks ViewTemplate",
"modified": "20190118170928931",
"modifier": "Thomas Elmiger",
"title": "$:/plugins/telmiger/MyStory/bookmarks/ViewTemplate",
"tags": "$:/tags/ViewTemplate",
"list-before": "$:/core/ui/ViewTemplate/body"
},
"$:/plugins/telmiger/MyStory/bookmarks/tab": {
"text": "\\define drop-actions()\n<$action-listops $tiddler=\"MyMark\" $subfilter=\"+[insertbefore:currentTiddler<actionTiddler>]\"/>\n\\end\n<ul class=\"my-story te-nav-list\">\n<$list filter=\"[tag[MyMark]!has[draft.of]]\" history=\"$:/HistoryList\" storyview=\"pop\">\n<li class=\"my-bkmrks\"><div style=\"position: relative;\">\n<$droppable actions=<<drop-actions>>>\n<div class=\"tc-droppable-placeholder\">\n \n</div>\n<div><<MyStoryBkmrkBtn>><<my-link>></div>\n</$droppable>\n</div>\n</li>\n</$list>\n</ul>\n<$tiddler tiddler=\"\">\n<$droppable actions=<<drop-actions>>>\n<div class=\"tc-droppable-placeholder\">\n \n</div>\n</$droppable>\n</$tiddler>",
"title": "$:/plugins/telmiger/MyStory/bookmarks/tab",
"tags": "$:/tags/SideBar",
"modifier": "Thomas Elmiger",
"modified": "20190121200116793",
"e-name": "MyMarks Tab",
"creator": "Thomas Elmiger",
"created": "20190121194507035",
"caption": "MyMarks"
},
"$:/plugins/telmiger/MyStory/bookmarks/icon-off": {
"created": "20190118211823363",
"creator": "Thomas Elmiger",
"text": "<svg class=\"em-icon my-bookmark\" xmlns=\"http://www.w3.org/2000/svg\" fill=\"rgba(0,0,0,0.3)\" viewBox=\"0 0 22 22\" width=\"1em\" height=\"1em\" role=\"img\" aria-labelledby=\"title desc\"><title id=\"title\">{{$:/language/Bookmarks/Add-title }}</title><desc id=\"desc\">{{$:/language/Bookmarks/Add-desc}}</desc><path d=\"M22 2v18H9A9 9 0 0 1 9 2h13z\"fill=\"rgba(123,123,123,0.3)\"/><path d=\"M9.6 9.6H6.2c-.8 0-1.4.6-1.4 1.4 0 .8.6 1.4 1.4 1.4h3.4v3.4c0 .8.6 1.4 1.4 1.4.8 0 1.4-.6 1.4-1.4v-3.4h3.4c.8 0 1.4-.6 1.4-1.4 0-.8-.6-1.4-1.4-1.4h-3.4V6.2c0-.8-.6-1.4-1.4-1.4-.8 0-1.4.6-1.4 1.4v3.4z\"/></svg>",
"title": "$:/plugins/telmiger/MyStory/bookmarks/icon-off",
"tags": "$:/tags/Image",
"modifier": "Thomas Elmiger",
"modified": "20190120214728468"
},
"$:/plugins/telmiger/MyStory/bookmarks/icon": {
"created": "20190115223227712",
"creator": "Thomas Elmiger",
"text": "<svg class=\"em-icon my-bookmark\" xmlns=\"http://www.w3.org/2000/svg\" fill=\"#eee\" viewBox=\"0 0 22 22\" width=\"1em\" height=\"1em\" role=\"img\" aria-labelledby=\"title desc\"><title id=\"title\">{{$:/language/Bookmarks/Remove-title}}</title><desc id=\"desc\">{{$:/language/Bookmarks/Remove-desc}}</desc>\n<path d=\"M22 2v18H9A9 9 0 0 1 9 2h13z\" fill=\"currentColor\"/><path class=\"vertical-line\" d=\"M9.6 6.2v9.6c0 .8.6 1.4 1.4 1.4.8 0 1.4-.6 1.4-1.4V6.2c0-.8-.6-1.4-1.4-1.4-.8 0-1.4.6-1.4 1.4z\"/><path d=\"M15.8 9.6H6.2c-.8 0-1.4.6-1.4 1.4 0 .8.6 1.4 1.4 1.4h9.6c.8 0 1.4-.6 1.4-1.4 0-.8-.6-1.4-1.4-1.4z\"/></svg>",
"title": "$:/plugins/telmiger/MyStory/bookmarks/icon",
"tags": "$:/tags/Image",
"modifier": "Thomas Elmiger",
"modified": "20190120214437666"
},
"$:/language/Bookmarks/Add-desc": {
"created": "20190120214508103",
"text": "Click to come back later.",
"title": "$:/language/Bookmarks/Add-desc",
"tags": "",
"modified": "20190120214529736"
},
"$:/language/Bookmarks/Add-title": {
"created": "20190120214627892",
"text": "Bookmark",
"title": "$:/language/Bookmarks/Add-title",
"tags": "",
"modified": "20190120214645088"
},
"$:/language/Bookmarks/Remove-desc": {
"created": "20190120214335196",
"text": "Click to remove mark",
"title": "$:/language/Bookmarks/Remove-desc",
"tags": "",
"modified": "20190120214401890"
},
"$:/language/Bookmarks/Remove-title": {
"created": "20190120214125135",
"text": "Remove bookmark",
"title": "$:/language/Bookmarks/Remove-title",
"tags": "",
"modified": "20190120214330881"
},
"$:/plugins/telmiger/MyStory/support": {
"text": "{{$:/plugins/telmiger/support}}",
"title": "$:/plugins/telmiger/MyStory/support",
"tags": "",
"modifier": "Thomas Elmiger",
"modified": "20190121200345037",
"creator": "Thomas Elmiger",
"created": "20190121200320320"
},
"$:/plugins/telmiger/support": {
"created": "20181103150753927",
"text": "!! Support the Author\n\n''Hi!'' I’m Thomas, the author of [[tid.li/tw5/plugins.html|https://tid.li/tw5/plugins.html]]. Feedback is always welcome, as well as funding for maintenance, support and new projects :)\n\n---\n\n!!! One Time Support\n\nIf using my plugins just makes you happy, consider a one time payment via ~PayPal to reward the effort:\n\nhttps://www.paypal.me/telmiger\n\n---\n\n!!! Permanent Support\n\nIf my tools make you more productive or save you time in your job or your everyday life, you can support me as a Patron: \n\nhttps://www.patreon.com/telmiger\n\n---\n\n!!! Thank You\n\nSubstantial parts of my availabe time go to the deveopment of useful plugins for [[TiddlyWiki|https://tiddlywiki.com/]]. – Many others do the same and I would like to thank them all, especially [[Jeremy Ruston|https://tiddlywiki.com/#JeremyRuston]] and all the active members of the community!\n\n//Hereby I promise to share future revenues (if any) with other developers who’s works I use or who inspired me.//\n\nIf you like my work, I would be very happy to hear from you.\n\n''Thank you very much for your support!''<br>\n//Thomas//\n\nhttps://thomas-elmiger.ch",
"title": "$:/plugins/telmiger/support",
"tags": "",
"modifier": "Thomas Elmiger",
"modified": "20181104091650389",
"creator": "Thomas Elmiger"
},
"$:/plugins/telmiger/MyStory/Buttons/InsertAbove": {
"created": "20190128201124494",
"creator": "Thomas Elmiger",
"text": "\\whitespace trim\n<span class=\"my-story-btn\">\n<$button tooltip={{$:/language/Buttons/InsertAbove/Hint}} aria-label={{$:/language/Buttons/InsertAbove/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-createtiddler $basetitle={{$:/language/DefaultNewTiddlerTitle}} $savetitle=\"$:/temp/new-tiddler-above\"/>\n<$action-listops $tiddler=\"$:/StoryList\" $subfilter=\"+[insertbefore:currentTiddler{$:/temp/new-tiddler-above}]\"/>\n<$action-sendmessage $message=\"tm-edit-tiddler\" $param={{$:/temp/new-tiddler-above}}/>\n{{$:/plugins/telmiger/MyStory/ui/icons/insert-above}}\n</$button>\n</span>",
"title": "$:/plugins/telmiger/MyStory/Buttons/InsertAbove",
"tags": "$:/tags/InStoryControls",
"modifier": "Thomas Elmiger",
"modified": "20190128225530290",
"description": "{{$:/language/Buttons/InsertAbove/Hint}}",
"caption": "{{$:/language/Buttons/InsertAbove/Caption}}"
},
"$:/plugins/telmiger/MyStory/Buttons/InsertBelow": {
"created": "20190128171145652",
"creator": "Thomas Elmiger",
"text": "\\whitespace trim\n<span class=\"my-story-btn\">\n<$button tooltip={{$:/language/Buttons/InsertBelow/Hint}} aria-label={{$:/language/Buttons/InsertBelow/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-createtiddler $basetitle={{$:/language/DefaultNewTiddlerTitle}} $savetitle=\"$:/temp/new-tiddler-below\"/>\n<$action-listops $tiddler=\"$:/StoryList\" $subfilter=\"[{$:/temp/new-tiddler-below}] +[putafter<currentTiddler>]\"/>\n<$action-sendmessage $message=\"tm-edit-tiddler\" $param={{$:/temp/new-tiddler-below}}/>\n{{$:/plugins/telmiger/MyStory/ui/icons/insert-below}}\n</$button>\n</span>",
"title": "$:/plugins/telmiger/MyStory/Buttons/InsertBelow",
"tags": "$:/tags/InStoryControls",
"modifier": "Thomas Elmiger",
"modified": "20190128225513527",
"description": "{{$:/language/Buttons/InsertBelow/Hint}}",
"caption": "{{$:/language/Buttons/InsertBelow/Caption}}"
},
"$:/plugins/telmiger/MyStory/InStoryControls/ViewTemplate": {
"created": "20190128212533801",
"creator": "Thomas Elmiger",
"text": "\\define config-title()\n$:/config/InStoryControls/Visibility/$(listItem)$\n\\end\n<$set name=\"setting\" filter=\"[[$:/plugins/telmiger/MyStory/settings]getindex[in-story-controls]removeprefix[hide]]\" value=\"hide\" emptyValue=\"show\">\n<$reveal type=\"match\" text=\"show\" default=<<setting>>>\n<div class=\"te-footerbar\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/InStoryControls]!has[draft.of]]\" variable=\"listItem\"><$reveal type=\"nomatch\" state=<<config-title>> text=\"hide\"><$set name=\"tv-config-toolbar-class\" filter=\"[<tv-config-toolbar-class>] [<listItem>encodeuricomponent[]addprefix[tc-btn-]]\"><$transclude tiddler=<<listItem>>/></$set></$reveal></$list>\n</div>\n</$reveal>\n</$set>",
"title": "$:/plugins/telmiger/MyStory/InStoryControls/ViewTemplate",
"tags": "$:/tags/ViewTemplate",
"modifier": "Thomas Elmiger",
"modified": "20190128224439850"
},
"$:/plugins/telmiger/MyStory/ui/icons/insert-above": {
"created": "20190128170540016",
"creator": "Thomas Elmiger",
"text": "<svg class=\"em-icon insert-above\" viewBox=\"0 0 22 22\" width=\"1em\" height=\"1em\" role=\"img\" aria-labelledby=\"title desc\" xmlns=\"http://www.w3.org/2000/svg\" style=\"fill-rule:evenodd;\"><title id=\"title\">{{$:/language/Buttons/InsertAbove/Caption}}</title><desc id=\"desc\">{{$:/language/Buttons/InsertAbove/Hint}}</desc>\n<path d=\"M16.09 2.1c1.758 1.131 4.391 2.749 4.394 4.559l.016 10.096c.004 2.33-1.894 4.226-4.237 4.23L5.768 21c-2.343.004-4.248-1.885-4.252-4.215L1.5 6.691c.065-1.928 2.717-3.495 4.396-4.57.799-.512 2.84-2.093 5.089-2.12 2.25-.028 4.383 1.635 5.105 2.1z\" fill-opacity=\".792\"/><path d=\"M9.625 9.624H6.188c-.755 0-1.375.621-1.375 1.375s.62 1.375 1.375 1.375h3.437v3.438c0 .755.621 1.376 1.375 1.376s1.375-.621 1.375-1.375v-3.439h3.438c.754 0 1.375-.62 1.375-1.375 0-.754-.621-1.375-1.375-1.375h-3.438V6.188c0-.755-.621-1.375-1.375-1.375s-1.375.62-1.375 1.375v3.438z\" fill=\"#fff\" fill-rule=\"nonzero\"/></svg>\n",
"title": "$:/plugins/telmiger/MyStory/ui/icons/insert-above",
"tags": "$:/tags/Image",
"modifier": "Thomas Elmiger",
"modified": "20190129070853381"
},
"$:/plugins/telmiger/MyStory/ui/icons/insert-below": {
"created": "20190128165452603",
"creator": "Thomas Elmiger",
"text": "<svg class=\"em-icon insert-below\" viewBox=\"0 0 22 22\" width=\"1em\" height=\"1em\" role=\"img\" aria-labelledby=\"title desc\" xmlns=\"http://www.w3.org/2000/svg\" style=\"fill-rule:evenodd;\"><title id=\"title\">{{$:/language/Buttons/InsertBelow/Caption}}</title><desc id=\"desc\">{{$:/language/Buttons/InsertBelow/Hint}}</desc>\n<path d=\"M5.91 19.9c-1.758-1.131-4.391-2.748-4.394-4.559L1.5 5.246c-.004-2.331 1.894-4.227 4.237-4.23L16.232.998c2.343-.004 4.248 1.885 4.252 4.215L20.5 15.31c-.065 1.927-2.717 3.494-4.396 4.569-.799.512-2.84 2.093-5.089 2.12-2.25.029-4.383-1.634-5.105-2.1z\" fill-opacity=\".792\"/><path d=\"M9.625 9.624H6.188c-.755 0-1.375.621-1.375 1.375s.62 1.375 1.375 1.375h3.437v3.438c0 .755.621 1.376 1.375 1.376s1.375-.621 1.375-1.375v-3.439h3.438c.754 0 1.375-.62 1.375-1.375 0-.754-.621-1.375-1.375-1.375h-3.438V6.188c0-.755-.621-1.375-1.375-1.375s-1.375.62-1.375 1.375v3.438z\" fill=\"#fff\" fill-rule=\"nonzero\"/></svg>",
"title": "$:/plugins/telmiger/MyStory/ui/icons/insert-below",
"tags": "$:/tags/Image",
"modifier": "Thomas Elmiger",
"modified": "20190129071007186"
},
"$:/language/Buttons/InsertAbove/Caption": {
"text": "Insert above",
"title": "$:/language/Buttons/InsertAbove/Caption",
"tags": "",
"modifier": "Thomas Elmiger",
"modified": "20190128184500677",
"creator": "Thomas Elmiger",
"created": "20190128184442283"
},
"$:/language/Buttons/InsertAbove/Hint": {
"text": "Insert a new tiddler above this one",
"title": "$:/language/Buttons/InsertAbove/Hint",
"tags": "",
"modifier": "Thomas Elmiger",
"modified": "20190128184429562",
"creator": "Thomas Elmiger",
"created": "20190128184412226"
},
"$:/language/Buttons/InsertBelow/Hint": {
"text": "Insert a new tiddler below this one",
"title": "$:/language/Buttons/InsertBelow/Hint",
"tags": "",
"modifier": "Thomas Elmiger",
"modified": "20190128184406495",
"creator": "Thomas Elmiger",
"created": "20190128184343201"
},
"$:/language/Buttons/InsertBelow/Caption": {
"text": "Insert below",
"title": "$:/language/Buttons/InsertBelow/Caption",
"tags": "",
"modifier": "Thomas Elmiger",
"modified": "20190128184336335",
"creator": "Thomas Elmiger",
"created": "20190128184309875"
}
}
}
\define drop-actions()
<$action-listops $tiddler="MyMark" $subfilter="+[insertbefore:currentTiddler<actionTiddler>]"/>
\end
<ul class="my-story te-nav-list">
<$list filter="[tag[MyMark]!has[draft.of]]" history="$:/HistoryList" storyview="pop">
<li class="my-bkmrks"><div style="position: relative;">
<$droppable actions=<<drop-actions>>>
<div class="tc-droppable-placeholder">
</div>
<div><<MyStoryBkmrkBtn>><<my-link>></div>
</$droppable>
</div>
</li>
</$list>
</ul>
<$tiddler tiddler="">
<$droppable actions=<<drop-actions>>>
<div class="tc-droppable-placeholder">
</div>
</$droppable>
</$tiddler>
{
"default-label": "e-name",
"show-edit-button": "no",
"last-closed": "$:/ControlPanel $:/plugins/telmiger/MyStory [[ICML19 Deep Learning Theory]]",
"show-clone-button": "no",
"in-story-controls": "show"
}
{
"tiddlers": {
"$:/plugins/tiddlywiki/blog/docs": {
"title": "$:/plugins/tiddlywiki/blog/docs",
"text": "Until there's more documentation, see an example of the use of this plugin here:\n\n* Blog: http://jermolene-blog.github.io/\n* Repository: https://github.com/Jermolene-blog/blog\n"
},
"$:/plugins/tiddlywiki/blog/readme": {
"title": "$:/plugins/tiddlywiki/blog/readme",
"text": "This plugin contains tools to help publish blogs:\n\n* Templates and tools for building static HTML pages and posts\n"
},
"$:/plugins/tiddlywiki/blog/templates/html-page/page": {
"title": "$:/plugins/tiddlywiki/blog/templates/html-page/page",
"text": "\\define tv-wikilink-template() posts/$uri_doubleencoded$.html\n\\define tv-config-toolbar-icons() no\n\\define tv-config-toolbar-text() no\n\\define tv-config-toolbar-class() tc-btn-invisible\n`<!doctype html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"`{{$:/core/templates/version}}`\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\">\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<link rel=\"stylesheet\" href=\"static.css\">\n<title>`<$transclude field=\"caption\"><$view field=\"title\"/></$transclude>: {{$:/core/wiki/title}}`</title>\n</head>\n<body class=\"tc-body\">\n`{{$:/StaticBanner||$:/core/templates/html-tiddler}}`\n<section class=\"tc-story-river\">\n`<$importvariables filter=\"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\">\n<$view tiddler=\"$:/plugins/tiddlywiki/blog/templates/tiddler\" format=\"htmlwikified\"/>\n</$importvariables>`\n</section>\n</body>\n</html>\n`\n"
},
"$:/plugins/tiddlywiki/blog/templates/html-page/post": {
"title": "$:/plugins/tiddlywiki/blog/templates/html-page/post",
"text": "\\define tv-wikilink-template() /$uri_doubleencoded$.html\n\\define tv-config-toolbar-icons() no\n\\define tv-config-toolbar-text() no\n\\define tv-config-toolbar-class() tc-btn-invisible\n`<!doctype html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"`{{$:/core/templates/version}}`\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\">\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"../favicon.ico\">\n<link rel=\"stylesheet\" href=\"../static.css\">\n<title>`<$transclude field=\"caption\"><$view field=\"title\"/></$transclude>: {{$:/core/wiki/title}}`</title>\n</head>\n<body class=\"tc-body\">\n`{{$:/StaticBanner||$:/core/templates/html-tiddler}}`\n<section class=\"tc-story-river\">\n`<$importvariables filter=\"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\">\n<$view tiddler=\"$:/plugins/tiddlywiki/blog/templates/tiddler\" format=\"htmlwikified\"/>\n<$view tiddler=\"$:/plugins/tiddlywiki/blog/templates/menu\" format=\"htmlwikified\"/>\n</$importvariables>`\n</section>\n</body>\n</html>\n`\n"
},
"$:/plugins/tiddlywiki/blog/templates/menu": {
"title": "$:/plugins/tiddlywiki/blog/templates/menu",
"text": "<div class=\"tc-blog-menu\">\n\n<div class=\"tc-blog-menu-item\">\n\n<a href=\"../index.html\">\n\n{{$:/core/images/home-button}}\n\n</a>\n\n</div>\n\n</div>\n"
},
"$:/plugins/tiddlywiki/blog/templates/tiddler": {
"title": "$:/plugins/tiddlywiki/blog/templates/tiddler",
"text": "<div class=\"tc-tiddler-frame tc-tiddler-view-frame\">\n\n<div class=\"tc-tiddler-title\">\n\n<div class=\"tc-titlebar\">\n\n<h2 class=\"tc-title\">\n\n<$transclude field=\"caption\" mode=\"inline\">\n\n<$view field=\"title\"/>\n\n</$transclude>\n\n</h2>\n\n</div>\n\n</div>\n\n<div class=\"tc-subtitle\">\n\n<$view field=\"modified\" format=\"date\" template=\"DDth MMM YYYY\"/>\n\n</div>\n\n<div class=\"tc-tiddler-body\">\n\n<$transclude/>\n\n</div>\n\n</div>\n"
}
}
}
{
"tiddlers": {
"$:/config/EditorTypeMappings/application/javascript": {
"title": "$:/config/EditorTypeMappings/application/javascript",
"text": "codemirror"
},
"$:/config/EditorTypeMappings/application/json": {
"title": "$:/config/EditorTypeMappings/application/json",
"text": "codemirror"
},
"$:/config/EditorTypeMappings/application/x-tiddler-dictionary": {
"title": "$:/config/EditorTypeMappings/application/x-tiddler-dictionary",
"text": "codemirror"
},
"$:/config/EditorTypeMappings/text/css": {
"title": "$:/config/EditorTypeMappings/text/css",
"text": "codemirror"
},
"$:/config/EditorTypeMappings/text/html": {
"title": "$:/config/EditorTypeMappings/text/html",
"text": "codemirror"
},
"$:/config/EditorTypeMappings/text/plain": {
"title": "$:/config/EditorTypeMappings/text/plain",
"text": "codemirror"
},
"$:/config/EditorTypeMappings/text/vnd.tiddlywiki": {
"title": "$:/config/EditorTypeMappings/text/vnd.tiddlywiki",
"text": "codemirror"
},
"$:/config/EditorTypeMappings/text/x-markdown": {
"title": "$:/config/EditorTypeMappings/text/x-markdown",
"text": "codemirror"
},
"$:/config/EditorTypeMappings/text/x-tiddlywiki": {
"title": "$:/config/EditorTypeMappings/text/x-tiddlywiki",
"text": "codemirror"
},
"$:/config/codemirror/cursorBlinkRate": {
"title": "$:/config/codemirror/cursorBlinkRate",
"type": "string",
"text": "530\n"
},
"$:/config/codemirror/extraKeysTW": {
"title": "$:/config/codemirror/extraKeysTW",
"extend": "extraKeys",
"type": "json",
"text": "{\n\t\"Ctrl-Esc\": \"singleSelection\",\n\t\"Esc\": \"\",\n\t\"Ctrl-S\": \"\",\n\t\"Ctrl-U\": \"\",\n\t\"Ctrl-T\": \"\",\n\t\"Alt-T\": \"transposeChars\",\n\t\"Alt-U\": \"undoSelection\",\n\t\"Shift-Alt-U\": \"redoSelection\",\n\t\"Cmd-U\": \"\"\n}\n"
},
"$:/config/codemirror/indentUnit": {
"title": "$:/config/codemirror/indentUnit",
"text": "2\n"
},
"$:/config/codemirror/inputStyle": {
"title": "$:/config/codemirror/inputStyle",
"type": "string",
"text": "textarea\n"
},
"$:/config/codemirror/keyMap": {
"title": "$:/config/codemirror/keyMap",
"type": "string",
"text": "default\n"
},
"$:/config/codemirror/lineNumbers": {
"title": "$:/config/codemirror/lineNumbers",
"type": "bool",
"text": "false\n"
},
"$:/config/codemirror/lineWrapping": {
"title": "$:/config/codemirror/lineWrapping",
"type": "bool",
"text": "true"
},
"$:/config/codemirror/showCursorWhenSelecting": {
"title": "$:/config/codemirror/showCursorWhenSelecting",
"type": "bool",
"text": "true\n"
},
"$:/config/codemirror/styleActiveLine": {
"title": "$:/config/codemirror/styleActiveLine",
"type": "bool",
"text": "false\n"
},
"$:/config/codemirror/tabSize": {
"title": "$:/config/codemirror/tabSize",
"text": "4\n"
},
"$:/config/codemirror/theme": {
"title": "$:/config/codemirror/theme",
"type": "string",
"text": "default\n"
},
"$:/language/codemirror/homeUrl": {
"title": "$:/language/codemirror/homeUrl",
"text": "http://codemirror.net"
},
"$:/language/codemirror/addOnUrl": {
"title": "$:/language/codemirror/addOnUrl",
"text": "http://codemirror.net/doc/manual.html#addons"
},
"$:/language/codemirror/configUrl": {
"title": "$:/language/codemirror/configUrl",
"text": "http://codemirror.net/doc/manual.html#config"
},
"$:/language/codemirror/controlPanel/hint": {
"title": "$:/language/codemirror/controlPanel/hint",
"text": "These settings let you customise the behaviour of [[CodeMirror|$:/plugins/tiddlywiki/codemirror]]."
},
"$:/language/codemirror/controlPanel/usage": {
"title": "$:/language/codemirror/controlPanel/usage",
"text": "Usage information"
},
"$:/language/codemirror/editorFont/hint": {
"title": "$:/language/codemirror/editorFont/hint",
"text": "Editor font family"
},
"$:/language/codemirror/editorFont/info": {
"title": "$:/language/codemirror/editorFont/info",
"text": "Set the font family for the ~CodeMirror text-editor"
},
"$:/language/codemirror/controlPanel/keyboard": {
"title": "$:/language/codemirror/controlPanel/keyboard",
"text": "Keyboard shortcuts"
},
"$:/language/codemirror/keyMap/hint": {
"title": "$:/language/codemirror/keyMap/hint",
"text": "~CodeMirror keymap"
},
"$:/language/codemirror/keyMap/info": {
"title": "$:/language/codemirror/keyMap/info",
"text": "~The Keyboard KeyMap used within the ~CodeMirror text-editor"
},
"$:/language/codemirror/lineNumbers/hint": {
"title": "$:/language/codemirror/lineNumbers/hint",
"text": "Enable line numbers"
},
"$:/language/codemirror/lineNumbers/info": {
"title": "$:/language/codemirror/lineNumbers/info",
"text": "Whether to show line numbers to the left of the editor."
},
"$:/language/codemirror/lineWrapping/hint": {
"title": "$:/language/codemirror/lineWrapping/hint",
"text": "Enable line wrapping"
},
"$:/language/codemirror/lineWrapping/info": {
"title": "$:/language/codemirror/lineWrapping/info",
"text": "Whether CodeMirror should scroll or wrap for long lines. Defaults to `false` (scroll)."
},
"$:/language/codemirror/showCursorWhenSelecting/hint": {
"title": "$:/language/codemirror/showCursorWhenSelecting/hint",
"text": "Show cursor, when selecting"
},
"$:/language/codemirror/showCursorWhenSelecting/info": {
"title": "$:/language/codemirror/showCursorWhenSelecting/info",
"text": "Whether the cursor should be drawn when a selection is active."
},
"$:/language/codemirror/styleActiveLine/hint": {
"title": "$:/language/codemirror/styleActiveLine/hint",
"text": "Highlight active line"
},
"$:/language/codemirror/styleActiveLine/info": {
"title": "$:/language/codemirror/styleActiveLine/info",
"text": "Whether or not to highlight the active text-editor line"
},
"$:/language/codemirror/theme/hint": {
"title": "$:/language/codemirror/theme/hint",
"text": "Select a theme"
},
"$:/language/codemirror/theme/info": {
"title": "$:/language/codemirror/theme/info",
"text": "Choose between ~CodeMirror themes"
},
"$:/plugins/tiddlywiki/codemirror/edit-codemirror.js": {
"title": "$:/plugins/tiddlywiki/codemirror/edit-codemirror.js",
"text": "/*\\\ntitle: $:/plugins/tiddlywiki/codemirror/edit-codemirror.js\ntype: application/javascript\nmodule-type: widget\n\nEdit-codemirror widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar editTextWidgetFactory = require(\"$:/core/modules/editor/factory.js\").editTextWidgetFactory,\n\tCodeMirrorEngine = require(\"$:/plugins/tiddlywiki/codemirror/engine.js\").CodeMirrorEngine;\n\nexports[\"edit-codemirror\"] = editTextWidgetFactory(CodeMirrorEngine,CodeMirrorEngine);\n\n})();\n",
"type": "application/javascript",
"module-type": "widget"
},
"$:/plugins/tiddlywiki/codemirror/engine.js": {
"title": "$:/plugins/tiddlywiki/codemirror/engine.js",
"text": "/*\\\ntitle: $:/plugins/tiddlywiki/codemirror/engine.js\ntype: application/javascript\nmodule-type: library\n\nText editor engine based on a CodeMirror instance\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar CODEMIRROR_OPTIONS = \"$:/config/CodeMirror\",\nHEIGHT_VALUE_TITLE = \"$:/config/TextEditor/EditorHeight/Height\",\nCONFIG_FILTER = \"[all[shadows+tiddlers]prefix[$:/config/codemirror/]]\"\n\t\n// Install CodeMirror\nif($tw.browser && !window.CodeMirror) {\n\n\tvar modules = $tw.modules.types[\"codemirror\"];\n\tvar req = Object.getOwnPropertyNames(modules);\n\n\twindow.CodeMirror = require(\"$:/plugins/tiddlywiki/codemirror/lib/codemirror.js\");\n\t// Install required CodeMirror plugins\n\tif(req) {\n\t\tif($tw.utils.isArray(req)) {\n\t\t\tfor(var index=0; index<req.length; index++) {\n\t\t\t\trequire(req[index]);\n\t\t\t}\n\t\t} else {\n\t\t\trequire(req);\n\t\t}\n\t}\n}\n\nfunction getCmConfig() {\n\tvar type,\n\t\ttest,\n\t\tvalue,\n\t\telement,\n\t\textend,\n\t\ttiddler,\n\t\tconfig = {},\n\t\tconfigTiddlers = $tw.wiki.filterTiddlers(CONFIG_FILTER);\n\n\tif ($tw.utils.isArray(configTiddlers)) {\n\t\tfor (var i=0; i<configTiddlers.length; i++) {\n\t\t\ttiddler = $tw.wiki.getTiddler(configTiddlers[i]);\n\t\t\t\tif (tiddler) {\n\t\t\t\telement = configTiddlers[i].replace(/\\$:\\/config\\/codemirror\\//ig,\"\");\n\t\t\t\t\ttype = (tiddler.fields.type) ? tiddler.fields.type.trim().toLocaleLowerCase() : \"string\";\n\t\t\t\tswitch (type) {\n\t\t\t\t\tcase \"bool\":\n\t\t\t\t\ttest = tiddler.fields.text.trim().toLowerCase();\n\t\t\t\t\tvalue = (test === \"true\") ? true : false;\n\t\t\t\t\tconfig[element] = value;\n\t\t\t\t\tbreak;\n\t\t\t\t\tcase \"string\":\n\t\t\t\t\tvalue = tiddler.fields.text.trim();\n\t\t\t\t\tconfig[element] = value;\n\t\t\t\t\tbreak;\n\t\t\t\t\tcase \"integer\":\n\t\t\t\t\tvalue = parseInt(tiddler.fields.text.trim(), 10);\n\t\t\t\t\tconfig[element] = value;\n\t\t\t\t\tbreak;\n\t\t\t\t\tcase \"json\":\n\t\t\t\t\tvalue = JSON.parse(tiddler.fields.text.trim());\n\t\t\t\t\t\textend = (tiddler.fields.extend) ? tiddler.fields.extend : element;\n\n\t\t\t\t\tif (config[extend]) {\n\t\t\t\t\t\t$tw.utils.extend(config[extend], value);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tconfig[extend] = value;\n\t\t\t\t\t}\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\treturn config;\n}\n\nfunction CodeMirrorEngine(options) {\n\n\t// Save our options\n\tvar self = this;\n\toptions = options || {};\n\tthis.widget = options.widget;\n\tthis.value = options.value;\n\tthis.parentNode = options.parentNode;\n\tthis.nextSibling = options.nextSibling;\n\t// Create the wrapper DIV\n\tthis.domNode = this.widget.document.createElement(\"div\");\n\tif(this.widget.editClass) {\n\t\tthis.domNode.className = this.widget.editClass;\n\t}\n\tthis.domNode.style.display = \"inline-block\";\n\tthis.parentNode.insertBefore(this.domNode,this.nextSibling);\n\tthis.widget.domNodes.push(this.domNode);\n\t\n\t// Set all cm-plugin defaults\n\t// Get the configuration options for the CodeMirror object\n\tvar config = getCmConfig();\n\n\tconfig.mode = options.type;\n\tconfig.value = options.value;\n\t// Create the CodeMirror instance\n\tthis.cm = window.CodeMirror(function(cmDomNode) {\n\t\t// Note that this is a synchronous callback that is called before the constructor returns\n\t\tself.domNode.appendChild(cmDomNode);\n\t},config);\n\n\t// Set up a change event handler\n\tthis.cm.on(\"change\",function() {\n\t\tself.widget.saveChanges(self.getText());\n\t});\n\tthis.cm.on(\"drop\",function(cm,event) {\n\t\tevent.stopPropagation(); // Otherwise TW's dropzone widget sees the drop event\n\t\treturn false;\n\t});\n\tthis.cm.on(\"keydown\",function(cm,event) {\n\t\treturn self.widget.handleKeydownEvent.call(self.widget,event);\n\t});\n}\n\n/*\nSet the text of the engine if it doesn't currently have focus\n*/\nCodeMirrorEngine.prototype.setText = function(text,type) {\n\tvar self = this;\n\tself.cm.setOption(\"mode\",type);\n\tif(!this.cm.hasFocus()) {\n\t\tthis.cm.setValue(text);\n\t}\n};\n\n/*\nGet the text of the engine\n*/\nCodeMirrorEngine.prototype.getText = function() {\n\treturn this.cm.getValue();\n};\n\n/*\nFix the height of textarea to fit content\n*/\nCodeMirrorEngine.prototype.fixHeight = function() {\n\tif(this.widget.editAutoHeight) {\n\t\t// Resize to fit\n\t\tthis.cm.setSize(null,null);\n\t} else {\n\t\tvar fixedHeight = parseInt(this.widget.wiki.getTiddlerText(HEIGHT_VALUE_TITLE,\"400px\"),10);\n\t\tfixedHeight = Math.max(fixedHeight,20);\n\t\tthis.cm.setSize(null,fixedHeight);\n\t}\n};\n\n/*\nFocus the engine node\n*/\nCodeMirrorEngine.prototype.focus = function() {\n\tthis.cm.focus();\n}\n\n/*\nCreate a blank structure representing a text operation\n*/\nCodeMirrorEngine.prototype.createTextOperation = function() {\n\tvar selections = this.cm.listSelections();\n\tif(selections.length > 0) {\n\t\tvar anchorPos = this.cm.indexFromPos(selections[0].anchor),\n\t\theadPos = this.cm.indexFromPos(selections[0].head);\n\t}\n\tvar operation = {\n\t\ttext: this.cm.getValue(),\n\t\tselStart: Math.min(anchorPos,headPos),\n\t\tselEnd: Math.max(anchorPos,headPos),\n\t\tcutStart: null,\n\t\tcutEnd: null,\n\t\treplacement: null,\n\t\tnewSelStart: null,\n\t\tnewSelEnd: null\n\t};\n\toperation.selection = operation.text.substring(operation.selStart,operation.selEnd);\n\treturn operation;\n};\n\n/*\nExecute a text operation\n*/\nCodeMirrorEngine.prototype.executeTextOperation = function(operation) {\n\t// Perform the required changes to the text area and the underlying tiddler\n\tvar newText = operation.text;\n\tif(operation.replacement !== null) {\n\t\tthis.cm.replaceRange(operation.replacement,this.cm.posFromIndex(operation.cutStart),this.cm.posFromIndex(operation.cutEnd));\n\t\tthis.cm.setSelection(this.cm.posFromIndex(operation.newSelStart),this.cm.posFromIndex(operation.newSelEnd));\n\t\tnewText = operation.text.substring(0,operation.cutStart) + operation.replacement + operation.text.substring(operation.cutEnd);\n\t}\n\tthis.cm.focus();\n\treturn newText;\n};\n\nexports.CodeMirrorEngine = CodeMirrorEngine;\n\n})();\n",
"type": "application/javascript",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/lib/codemirror.js": {
"text": "!function(e,t){\"object\"==typeof exports&&\"undefined\"!=typeof module?module.exports=t():\"function\"==typeof define&&define.amd?define(t):e.CodeMirror=t()}(this,function(){\"use strict\";var e=navigator.userAgent,t=navigator.platform,r=/gecko\\/\\d/i.test(e),n=/MSIE \\d/.test(e),i=/Trident\\/(?:[7-9]|\\d{2,})\\..*rv:(\\d+)/.exec(e),o=/Edge\\/(\\d+)/.exec(e),l=n||i||o,s=l&&(n?document.documentMode||6:+(o||i)[1]),a=!o&&/WebKit\\//.test(e),u=a&&/Qt\\/\\d+\\.\\d+/.test(e),c=!o&&/Chrome\\//.test(e),h=/Opera\\//.test(e),f=/Apple Computer/.test(navigator.vendor),d=/Mac OS X 1\\d\\D([8-9]|\\d\\d)\\D/.test(e),p=/PhantomJS/.test(e),g=!o&&/AppleWebKit/.test(e)&&/Mobile\\/\\w+/.test(e),v=/Android/.test(e),m=g||v||/webOS|BlackBerry|Opera Mini|Opera Mobi|IEMobile/i.test(e),y=g||/Mac/.test(t),b=/\\bCrOS\\b/.test(e),w=/win/i.test(t),x=h&&e.match(/Version\\/(\\d*\\.\\d*)/);x&&(x=Number(x[1])),x&&x>=15&&(h=!1,a=!0);var C=y&&(u||h&&(null==x||x<12.11)),S=r||l&&s>=9;function L(e){return new 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l=new tr(e.doc,se(e.doc,o),o);n=o+l.size,i.push(l)}return i}var nr=null;var ir=null;function or(e,t){var r=et(e,t);if(r.length){var n,i=Array.prototype.slice.call(arguments,2);nr?n=nr.delayedCallbacks:ir?n=ir:(n=ir=[],setTimeout(lr,0));for(var o=function(e){n.push(function(){return r[e].apply(null,i)})},l=0;l<r.length;++l)o(l)}}function lr(){var e=ir;ir=null;for(var t=0;t<e.length;++t)e[t]()}function sr(e,t,r,n){for(var i=0;i<t.changes.length;i++){var o=t.changes[i];\"text\"==o?cr(e,t):\"gutter\"==o?fr(e,t,r,n):\"class\"==o?hr(e,t):\"widget\"==o&&dr(e,t,n)}t.changes=null}function ar(e){return e.node==e.text&&(e.node=O(\"div\",null,null,\"position: relative\"),e.text.parentNode&&e.text.parentNode.replaceChild(e.node,e.text),e.node.appendChild(e.text),l&&s<8&&(e.node.style.zIndex=2)),e.node}function ur(e,t){var r=e.display.externalMeasured;return r&&r.line==t.line?(e.display.externalMeasured=null,t.measure=r.measure,r.built):qt(e,t)}function cr(e,t){var r=t.text.className,n=ur(e,t);t.text==t.node&&(t.node=n.pre),t.text.parentNode.replaceChild(n.pre,t.text),t.text=n.pre,n.bgClass!=t.bgClass||n.textClass!=t.textClass?(t.bgClass=n.bgClass,t.textClass=n.textClass,hr(e,t)):r&&(t.text.className=r)}function hr(e,t){!function(e,t){var r=t.bgClass?t.bgClass+\" \"+(t.line.bgClass||\"\"):t.line.bgClass;if(r&&(r+=\" CodeMirror-linebackground\"),t.background)r?t.background.className=r:(t.background.parentNode.removeChild(t.background),t.background=null);else if(r){var n=ar(t);t.background=n.insertBefore(O(\"div\",null,r),n.firstChild),e.display.input.setUneditable(t.background)}}(e,t),t.line.wrapClass?ar(t).className=t.line.wrapClass:t.node!=t.text&&(t.node.className=\"\");var r=t.textClass?t.textClass+\" \"+(t.line.textClass||\"\"):t.line.textClass;t.text.className=r||\"\"}function fr(e,t,r,n){if(t.gutter&&(t.node.removeChild(t.gutter),t.gutter=null),t.gutterBackground&&(t.node.removeChild(t.gutterBackground),t.gutterBackground=null),t.line.gutterClass){var i=ar(t);t.gutterBackground=O(\"div\",null,\"CodeMirror-gutter-background \"+t.line.gutterClass,\"left: \"+(e.options.fixedGutter?n.fixedPos:-n.gutterTotalWidth)+\"px; width: \"+n.gutterTotalWidth+\"px\"),e.display.input.setUneditable(t.gutterBackground),i.insertBefore(t.gutterBackground,t.text)}var o=t.line.gutterMarkers;if(e.options.lineNumbers||o){var l=ar(t),s=t.gutter=O(\"div\",null,\"CodeMirror-gutter-wrapper\",\"left: \"+(e.options.fixedGutter?n.fixedPos:-n.gutterTotalWidth)+\"px\");if(e.display.input.setUneditable(s),l.insertBefore(s,t.text),t.line.gutterClass&&(s.className+=\" \"+t.line.gutterClass),!e.options.lineNumbers||o&&o[\"CodeMirror-linenumbers\"]||(t.lineNumber=s.appendChild(O(\"div\",pe(e.options,r),\"CodeMirror-linenumber CodeMirror-gutter-elt\",\"left: 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vr(e,t,r,n){if(e.noHScroll){(r.alignable||(r.alignable=[])).push(t);var i=n.wrapperWidth;t.style.left=n.fixedPos+\"px\",e.coverGutter||(i-=n.gutterTotalWidth,t.style.paddingLeft=n.gutterTotalWidth+\"px\"),t.style.width=i+\"px\"}e.coverGutter&&(t.style.zIndex=5,t.style.position=\"relative\",e.noHScroll||(t.style.marginLeft=-n.gutterTotalWidth+\"px\"))}function mr(e){if(null!=e.height)return e.height;var t=e.doc.cm;if(!t)return 0;if(!D(document.body,e.node)){var r=\"position: relative;\";e.coverGutter&&(r+=\"margin-left: -\"+t.display.gutters.offsetWidth+\"px;\"),e.noHScroll&&(r+=\"width: \"+t.display.wrapper.clientWidth+\"px;\"),N(t.display.measure,O(\"div\",[e.node],null,r))}return e.height=e.node.parentNode.offsetHeight}function yr(e,t){for(var r=ht(t);r!=e.wrapper;r=r.parentNode)if(!r||1==r.nodeType&&\"true\"==r.getAttribute(\"cm-ignore-events\")||r.parentNode==e.sizer&&r!=e.mover)return!0}function br(e){return e.lineSpace.offsetTop}function wr(e){return e.mover.offsetHeight-e.lineSpace.offsetHeight}function xr(e){if(e.cachedPaddingH)return e.cachedPaddingH;var t=N(e.measure,O(\"pre\",\"x\")),r=window.getComputedStyle?window.getComputedStyle(t):t.currentStyle,n={left:parseInt(r.paddingLeft),right:parseInt(r.paddingRight)};return isNaN(n.left)||isNaN(n.right)||(e.cachedPaddingH=n),n}function Cr(e){return G-e.display.nativeBarWidth}function Sr(e){return e.display.scroller.clientWidth-Cr(e)-e.display.barWidth}function Lr(e){return e.display.scroller.clientHeight-Cr(e)-e.display.barHeight}function kr(e,t,r){if(e.line==t)return{map:e.measure.map,cache:e.measure.cache};for(var n=0;n<e.rest.length;n++)if(e.rest[n]==t)return{map:e.measure.maps[n],cache:e.measure.caches[n]};for(var i=0;i<e.rest.length;i++)if(he(e.rest[i])>r)return{map:e.measure.maps[i],cache:e.measure.caches[i],before:!0}}function Tr(e,t,r,n){return Or(e,Nr(e,t),r,n)}function Mr(e,t){if(t>=e.display.viewFrom&&t<e.display.viewTo)return e.display.view[on(e,t)];var 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r=screen.logicalXDPI/screen.deviceXDPI,n=screen.logicalYDPI/screen.deviceYDPI;return{left:t.left*r,right:t.right*r,top:t.top*n,bottom:t.bottom*n}}(e.display.measure,i))}else{var d;u>0&&(h=n=\"right\"),i=e.options.lineWrapping&&(d=a.getClientRects()).length>1?d[\"right\"==n?d.length-1:0]:a.getBoundingClientRect()}if(l&&s<9&&!u&&(!i||!i.left&&!i.right)){var p=a.parentNode.getClientRects()[0];i=p?{left:p.left,right:p.left+Qr(e.display),top:p.top,bottom:p.bottom}:Dr}for(var g=i.top-t.rect.top,v=i.bottom-t.rect.top,m=(g+v)/2,y=t.view.measure.heights,b=0;b<y.length-1&&!(m<y[b]);b++);var w=b?y[b-1]:0,x=y[b],C={left:(\"right\"==h?i.right:i.left)-t.rect.left,right:(\"left\"==h?i.left:i.right)-t.rect.left,top:w,bottom:x};i.left||i.right||(C.bogus=!0);e.options.singleCursorHeightPerLine||(C.rtop=g,C.rbottom=v);return C}(e,t,r,n)).bogus||(t.cache[a]=o)),{left:o.left,right:o.right,top:i?o.rtop:o.top,bottom:i?o.rbottom:o.bottom}}var Ar,Dr={left:0,right:0,top:0,bottom:0};function Wr(e,t,r){for(var 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r=0;t=Ce(e.doc,t),e.options.lineWrapping||(r=Qr(e.display)*t.ch);var n=se(e.doc,t.line),i=je(n)+br(e.display);return{left:r,right:r,top:i,bottom:i+n.height}}function jr(e,t,r,n,i){var o=ge(e,t,r);return o.xRel=i,n&&(o.outside=!0),o}function Xr(e,t,r){var n=e.doc;if((r+=e.display.viewOffset)<0)return jr(n.first,0,null,!0,-1);var i=fe(n,r),o=n.first+n.size-1;if(i>o)return jr(n.first+n.size-1,se(n,o).text.length,null,!0,1);t<0&&(t=0);for(var l=se(n,i);;){var s=$r(e,l,i,t,r),a=Ie(l),u=a&&a.find(0,!0);if(!a||!(s.ch>u.from.ch||s.ch==u.from.ch&&s.xRel>0))return s;i=he(l=u.to.line)}}function Yr(e,t,r,n){n-=Rr(t);var i=t.text.length,o=le(function(t){return Or(e,r,t-1).bottom<=n},i,0);return{begin:o,end:i=le(function(t){return Or(e,r,t).top>n},o,i)}}function _r(e,t,r,n){return r||(r=Nr(e,t)),Yr(e,t,r,Br(e,t,Or(e,r,n),\"line\").top)}function qr(e,t,r,n){return!(e.bottom<=r)&&(e.top>r||(n?e.left:e.right)>t)}function $r(e,t,r,n,i){i-=je(t);var o=Nr(e,t),l=Rr(t),s=0,a=t.text.length,u=!0,c=Ze(t,e.doc.direction);if(c){var h=(e.options.lineWrapping?function(e,t,r,n,i,o,l){var s=Yr(e,t,n,l),a=s.begin,u=s.end;/\\s/.test(t.text.charAt(u-1))&&u--;for(var c=null,h=null,f=0;f<i.length;f++){var d=i[f];if(!(d.from>=u||d.to<=a)){var p=1!=d.level,g=Or(e,n,p?Math.min(u,d.to)-1:Math.max(a,d.from)).right,v=g<o?o-g+1e9:g-o;(!c||h>v)&&(c=d,h=v)}}c||(c=i[i.length-1]);c.from<a&&(c={from:a,to:c.to,level:c.level});c.to>u&&(c={from:c.from,to:u,level:c.level});return c}:function(e,t,r,n,i,o,l){var s=le(function(s){var a=i[s],u=1!=a.level;return qr(Vr(e,ge(r,u?a.to:a.from,u?\"before\":\"after\"),\"line\",t,n),o,l,!0)},0,i.length-1),a=i[s];if(s>0){var u=1!=a.level,c=Vr(e,ge(r,u?a.from:a.to,u?\"after\":\"before\"),\"line\",t,n);qr(c,o,l,!0)&&c.top>l&&(a=i[s-1])}return a})(e,t,r,o,c,n,i);s=(u=1!=h.level)?h.from:h.to-1,a=u?h.to:h.from-1}var f,d,p=null,g=null,v=le(function(t){var r=Or(e,o,t);return r.top+=l,r.bottom+=l,!!qr(r,n,i,!1)&&(r.top<=i&&r.left<=n&&(p=t,g=r),!0)},s,a),m=!1;if(g){var y=n-g.left<g.right-n,b=y==u;v=p+(b?0:1),d=b?\"after\":\"before\",f=y?g.left:g.right}else{u||v!=a&&v!=s||v++,d=0==v?\"after\":v==t.text.length?\"before\":Or(e,o,v-(u?1:0)).bottom+l<=i==u?\"after\":\"before\";var w=Vr(e,ge(r,v,d),\"line\",t,o);f=w.left,m=i<w.top||i>=w.bottom}return jr(r,v=oe(t.text,v,1),d,m,n-f)}function Zr(e){if(null!=e.cachedTextHeight)return e.cachedTextHeight;if(null==Ar){Ar=O(\"pre\");for(var t=0;t<49;++t)Ar.appendChild(document.createTextNode(\"x\")),Ar.appendChild(O(\"br\"));Ar.appendChild(document.createTextNode(\"x\"))}N(e.measure,Ar);var r=Ar.offsetHeight/50;return r>3&&(e.cachedTextHeight=r),M(e.measure),r||1}function Qr(e){if(null!=e.cachedCharWidth)return e.cachedCharWidth;var t=O(\"span\",\"xxxxxxxxxx\"),r=O(\"pre\",[t]);N(e.measure,r);var n=t.getBoundingClientRect(),i=(n.right-n.left)/10;return i>2&&(e.cachedCharWidth=i),i||10}function Jr(e){for(var t=e.display,r={},n={},i=t.gutters.clientLeft,o=t.gutters.firstChild,l=0;o;o=o.nextSibling,++l)r[e.options.gutters[l]]=o.offsetLeft+o.clientLeft+i,n[e.options.gutters[l]]=o.clientWidth;return{fixedPos:en(t),gutterTotalWidth:t.gutters.offsetWidth,gutterLeft:r,gutterWidth:n,wrapperWidth:t.wrapper.clientWidth}}function en(e){return e.scroller.getBoundingClientRect().left-e.sizer.getBoundingClientRect().left}function tn(e){var t=Zr(e.display),r=e.options.lineWrapping,n=r&&Math.max(5,e.display.scroller.clientWidth/Qr(e.display)-3);return function(i){if(Ve(e.doc,i))return 0;var o=0;if(i.widgets)for(var l=0;l<i.widgets.length;l++)i.widgets[l].height&&(o+=i.widgets[l].height);return r?o+(Math.ceil(i.text.length/n)||1)*t:o+t}}function rn(e){var t=e.doc,r=tn(e);t.iter(function(e){var t=r(e);t!=e.height&&ce(e,t)})}function nn(e,t,r,n){var i=e.display;if(!r&&\"true\"==ht(t).getAttribute(\"cm-not-content\"))return null;var o,l,s=i.lineSpace.getBoundingClientRect();try{o=t.clientX-s.left,l=t.clientY-s.top}catch(t){return null}var a,u=Xr(e,o,l);if(n&&1==u.xRel&&(a=se(e.doc,u.line).text).length==u.ch){var c=I(a,a.length,e.options.tabSize)-a.length;u=ge(u.line,Math.max(0,Math.round((o-xr(e.display).left)/Qr(e.display))-c))}return u}function on(e,t){if(t>=e.display.viewTo)return null;if((t-=e.display.viewFrom)<0)return null;for(var r=e.display.view,n=0;n<r.length;n++)if((t-=r[n].size)<0)return n}function ln(e){e.display.input.showSelection(e.display.input.prepareSelection())}function sn(e,t){void 0===t&&(t=!0);for(var r=e.doc,n={},i=n.cursors=document.createDocumentFragment(),o=n.selection=document.createDocumentFragment(),l=0;l<r.sel.ranges.length;l++)if(t||l!=r.sel.primIndex){var s=r.sel.ranges[l];if(!(s.from().line>=e.display.viewTo||s.to().line<e.display.viewFrom)){var a=s.empty();(a||e.options.showCursorWhenSelecting)&&an(e,s.head,i),a||cn(e,s,o)}}return n}function an(e,t,r){var n=Vr(e,t,\"div\",null,null,!e.options.singleCursorHeightPerLine),i=r.appendChild(O(\"div\",\" \",\"CodeMirror-cursor\"));if(i.style.left=n.left+\"px\",i.style.top=n.top+\"px\",i.style.height=Math.max(0,n.bottom-n.top)*e.options.cursorHeight+\"px\",n.other){var o=r.appendChild(O(\"div\",\" \",\"CodeMirror-cursor CodeMirror-secondarycursor\"));o.style.display=\"\",o.style.left=n.other.left+\"px\",o.style.top=n.other.top+\"px\",o.style.height=.85*(n.other.bottom-n.other.top)+\"px\"}}function un(e,t){return e.top-t.top||e.left-t.left}function cn(e,t,r){var n=e.display,i=e.doc,o=document.createDocumentFragment(),l=xr(e.display),s=l.left,a=Math.max(n.sizerWidth,Sr(e)-n.sizer.offsetLeft)-l.right,u=\"ltr\"==i.direction;function c(e,t,r,n){t<0&&(t=0),t=Math.round(t),n=Math.round(n),o.appendChild(O(\"div\",null,\"CodeMirror-selected\",\"position: absolute; left: \"+e+\"px;\\n top: \"+t+\"px; width: \"+(null==r?a-e:r)+\"px;\\n height: \"+(n-t)+\"px\"))}function h(t,r,n){var o,l,h=se(i,t),f=h.text.length;function d(r,n){return Ur(e,ge(t,r),\"div\",h,n)}function p(t,r,n){var i=_r(e,h,null,t),o=\"ltr\"==r==(\"after\"==n)?\"left\":\"right\";return d(\"after\"==n?i.begin:i.end-(/\\s/.test(h.text.charAt(i.end-1))?2:1),o)[o]}var g=Ze(h,i.direction);return function(e,t,r,n){if(!e)return n(t,r,\"ltr\",0);for(var i=!1,o=0;o<e.length;++o){var l=e[o];(l.from<r&&l.to>t||t==r&&l.to==t)&&(n(Math.max(l.from,t),Math.min(l.to,r),1==l.level?\"rtl\":\"ltr\",o),i=!0)}i||n(t,r,\"ltr\")}(g,r||0,null==n?f:n,function(e,t,i,h){var v=\"ltr\"==i,m=d(e,v?\"left\":\"right\"),y=d(t-1,v?\"right\":\"left\"),b=null==r&&0==e,w=null==n&&t==f,x=0==h,C=!g||h==g.length-1;if(y.top-m.top<=3){var S=(u?w:b)&&C,L=(u?b:w)&&x?s:(v?m:y).left,k=S?a:(v?y:m).right;c(L,m.top,k-L,m.bottom)}else{var T,M,N,O;v?(T=u&&b&&x?s:m.left,M=u?a:p(e,i,\"before\"),N=u?s:p(t,i,\"after\"),O=u&&w&&C?a:y.right):(T=u?p(e,i,\"before\"):s,M=!u&&b&&x?a:m.right,N=!u&&w&&C?s:y.left,O=u?p(t,i,\"after\"):a),c(T,m.top,M-T,m.bottom),m.bottom<y.top&&c(s,m.bottom,null,y.top),c(N,y.top,O-N,y.bottom)}(!o||un(m,o)<0)&&(o=m),un(y,o)<0&&(o=y),(!l||un(m,l)<0)&&(l=m),un(y,l)<0&&(l=y)}),{start:o,end:l}}var f=t.from(),d=t.to();if(f.line==d.line)h(f.line,f.ch,d.ch);else{var p=se(i,f.line),g=se(i,d.line),v=Be(p)==Be(g),m=h(f.line,f.ch,v?p.text.length+1:null).end,y=h(d.line,v?0:null,d.ch).start;v&&(m.top<y.top-2?(c(m.right,m.top,null,m.bottom),c(s,y.top,y.left,y.bottom)):c(m.right,m.top,y.left-m.right,m.bottom)),m.bottom<y.top&&c(s,m.bottom,null,y.top)}r.appendChild(o)}function hn(e){if(e.state.focused){var t=e.display;clearInterval(t.blinker);var r=!0;t.cursorDiv.style.visibility=\"\",e.options.cursorBlinkRate>0?t.blinker=setInterval(function(){return t.cursorDiv.style.visibility=(r=!r)?\"\":\"hidden\"},e.options.cursorBlinkRate):e.options.cursorBlinkRate<0&&(t.cursorDiv.style.visibility=\"hidden\")}}function fn(e){e.state.focused||(e.display.input.focus(),pn(e))}function dn(e){e.state.delayingBlurEvent=!0,setTimeout(function(){e.state.delayingBlurEvent&&(e.state.delayingBlurEvent=!1,gn(e))},100)}function pn(e,t){e.state.delayingBlurEvent&&(e.state.delayingBlurEvent=!1),\"nocursor\"!=e.options.readOnly&&(e.state.focused||(rt(e,\"focus\",e,t),e.state.focused=!0,H(e.display.wrapper,\"CodeMirror-focused\"),e.curOp||e.display.selForContextMenu==e.doc.sel||(e.display.input.reset(),a&&setTimeout(function(){return e.display.input.reset(!0)},20)),e.display.input.receivedFocus()),hn(e))}function gn(e,t){e.state.delayingBlurEvent||(e.state.focused&&(rt(e,\"blur\",e,t),e.state.focused=!1,T(e.display.wrapper,\"CodeMirror-focused\")),clearInterval(e.display.blinker),setTimeout(function(){e.state.focused||(e.display.shift=!1)},150))}function vn(e){for(var t=e.display,r=t.lineDiv.offsetTop,n=0;n<t.view.length;n++){var i=t.view[n],o=void 0;if(!i.hidden){if(l&&s<8){var a=i.node.offsetTop+i.node.offsetHeight;o=a-r,r=a}else{var u=i.node.getBoundingClientRect();o=u.bottom-u.top}var c=i.line.height-o;if(o<2&&(o=Zr(t)),(c>.005||c<-.005)&&(ce(i.line,o),mn(i.line),i.rest))for(var h=0;h<i.rest.length;h++)mn(i.rest[h])}}}function mn(e){if(e.widgets)for(var t=0;t<e.widgets.length;++t){var r=e.widgets[t],n=r.node.parentNode;n&&(r.height=n.offsetHeight)}}function yn(e,t,r){var n=r&&null!=r.top?Math.max(0,r.top):e.scroller.scrollTop;n=Math.floor(n-br(e));var i=r&&null!=r.bottom?r.bottom:n+e.wrapper.clientHeight,o=fe(t,n),l=fe(t,i);if(r&&r.ensure){var s=r.ensure.from.line,a=r.ensure.to.line;s<o?(o=s,l=fe(t,je(se(t,s))+e.wrapper.clientHeight)):Math.min(a,t.lastLine())>=l&&(o=fe(t,je(se(t,a))-e.wrapper.clientHeight),l=a)}return{from:o,to:Math.max(l,o+1)}}function bn(e){var t=e.display,r=t.view;if(t.alignWidgets||t.gutters.firstChild&&e.options.fixedGutter){for(var n=en(t)-t.scroller.scrollLeft+e.doc.scrollLeft,i=t.gutters.offsetWidth,o=n+\"px\",l=0;l<r.length;l++)if(!r[l].hidden){e.options.fixedGutter&&(r[l].gutter&&(r[l].gutter.style.left=o),r[l].gutterBackground&&(r[l].gutterBackground.style.left=o));var s=r[l].alignable;if(s)for(var a=0;a<s.length;a++)s[a].style.left=o}e.options.fixedGutter&&(t.gutters.style.left=n+i+\"px\")}}function wn(e){if(!e.options.lineNumbers)return!1;var t=e.doc,r=pe(e.options,t.first+t.size-1),n=e.display;if(r.length!=n.lineNumChars){var i=n.measure.appendChild(O(\"div\",[O(\"div\",r)],\"CodeMirror-linenumber CodeMirror-gutter-elt\")),o=i.firstChild.offsetWidth,l=i.offsetWidth-o;return n.lineGutter.style.width=\"\",n.lineNumInnerWidth=Math.max(o,n.lineGutter.offsetWidth-l)+1,n.lineNumWidth=n.lineNumInnerWidth+l,n.lineNumChars=n.lineNumInnerWidth?r.length:-1,n.lineGutter.style.width=n.lineNumWidth+\"px\",oi(e),!0}return!1}function xn(e,t){var r=e.display,n=Zr(e.display);t.top<0&&(t.top=0);var i=e.curOp&&null!=e.curOp.scrollTop?e.curOp.scrollTop:r.scroller.scrollTop,o=Lr(e),l={};t.bottom-t.top>o&&(t.bottom=t.top+o);var s=e.doc.height+wr(r),a=t.top<n,u=t.bottom>s-n;if(t.top<i)l.scrollTop=a?0:t.top;else if(t.bottom>i+o){var c=Math.min(t.top,(u?s:t.bottom)-o);c!=i&&(l.scrollTop=c)}var h=e.curOp&&null!=e.curOp.scrollLeft?e.curOp.scrollLeft:r.scroller.scrollLeft,f=Sr(e)-(e.options.fixedGutter?r.gutters.offsetWidth:0),d=t.right-t.left>f;return d&&(t.right=t.left+f),t.left<10?l.scrollLeft=0:t.left<h?l.scrollLeft=Math.max(0,t.left-(d?0:10)):t.right>f+h-3&&(l.scrollLeft=t.right+(d?0:10)-f),l}function Cn(e,t){null!=t&&(kn(e),e.curOp.scrollTop=(null==e.curOp.scrollTop?e.doc.scrollTop:e.curOp.scrollTop)+t)}function Sn(e){kn(e);var t=e.getCursor();e.curOp.scrollToPos={from:t,to:t,margin:e.options.cursorScrollMargin}}function Ln(e,t,r){null==t&&null==r||kn(e),null!=t&&(e.curOp.scrollLeft=t),null!=r&&(e.curOp.scrollTop=r)}function kn(e){var t=e.curOp.scrollToPos;t&&(e.curOp.scrollToPos=null,Tn(e,Kr(e,t.from),Kr(e,t.to),t.margin))}function Tn(e,t,r,n){var i=xn(e,{left:Math.min(t.left,r.left),top:Math.min(t.top,r.top)-n,right:Math.max(t.right,r.right),bottom:Math.max(t.bottom,r.bottom)+n});Ln(e,i.scrollLeft,i.scrollTop)}function Mn(e,t){Math.abs(e.doc.scrollTop-t)<2||(r||ii(e,{top:t}),Nn(e,t,!0),r&&ii(e),Jn(e,100))}function Nn(e,t,r){t=Math.min(e.display.scroller.scrollHeight-e.display.scroller.clientHeight,t),(e.display.scroller.scrollTop!=t||r)&&(e.doc.scrollTop=t,e.display.scrollbars.setScrollTop(t),e.display.scroller.scrollTop!=t&&(e.display.scroller.scrollTop=t))}function On(e,t,r,n){t=Math.min(t,e.display.scroller.scrollWidth-e.display.scroller.clientWidth),(r?t==e.doc.scrollLeft:Math.abs(e.doc.scrollLeft-t)<2)&&!n||(e.doc.scrollLeft=t,bn(e),e.display.scroller.scrollLeft!=t&&(e.display.scroller.scrollLeft=t),e.display.scrollbars.setScrollLeft(t))}function An(e){var t=e.display,r=t.gutters.offsetWidth,n=Math.round(e.doc.height+wr(e.display));return{clientHeight:t.scroller.clientHeight,viewHeight:t.wrapper.clientHeight,scrollWidth:t.scroller.scrollWidth,clientWidth:t.scroller.clientWidth,viewWidth:t.wrapper.clientWidth,barLeft:e.options.fixedGutter?r:0,docHeight:n,scrollHeight:n+Cr(e)+t.barHeight,nativeBarWidth:t.nativeBarWidth,gutterWidth:r}}var Dn=function(e,t,r){this.cm=r;var n=this.vert=O(\"div\",[O(\"div\",null,null,\"min-width: 1px\")],\"CodeMirror-vscrollbar\"),i=this.horiz=O(\"div\",[O(\"div\",null,null,\"height: 100%; min-height: 1px\")],\"CodeMirror-hscrollbar\");e(n),e(i),Je(n,\"scroll\",function(){n.clientHeight&&t(n.scrollTop,\"vertical\")}),Je(i,\"scroll\",function(){i.clientWidth&&t(i.scrollLeft,\"horizontal\")}),this.checkedZeroWidth=!1,l&&s<8&&(this.horiz.style.minHeight=this.vert.style.minWidth=\"18px\")};Dn.prototype.update=function(e){var t=e.scrollWidth>e.clientWidth+1,r=e.scrollHeight>e.clientHeight+1,n=e.nativeBarWidth;if(r){this.vert.style.display=\"block\",this.vert.style.bottom=t?n+\"px\":\"0\";var i=e.viewHeight-(t?n:0);this.vert.firstChild.style.height=Math.max(0,e.scrollHeight-e.clientHeight+i)+\"px\"}else this.vert.style.display=\"\",this.vert.firstChild.style.height=\"0\";if(t){this.horiz.style.display=\"block\",this.horiz.style.right=r?n+\"px\":\"0\",this.horiz.style.left=e.barLeft+\"px\";var o=e.viewWidth-e.barLeft-(r?n:0);this.horiz.firstChild.style.width=Math.max(0,e.scrollWidth-e.clientWidth+o)+\"px\"}else this.horiz.style.display=\"\",this.horiz.firstChild.style.width=\"0\";return!this.checkedZeroWidth&&e.clientHeight>0&&(0==n&&this.zeroWidthHack(),this.checkedZeroWidth=!0),{right:r?n:0,bottom:t?n:0}},Dn.prototype.setScrollLeft=function(e){this.horiz.scrollLeft!=e&&(this.horiz.scrollLeft=e),this.disableHoriz&&this.enableZeroWidthBar(this.horiz,this.disableHoriz,\"horiz\")},Dn.prototype.setScrollTop=function(e){this.vert.scrollTop!=e&&(this.vert.scrollTop=e),this.disableVert&&this.enableZeroWidthBar(this.vert,this.disableVert,\"vert\")},Dn.prototype.zeroWidthHack=function(){var e=y&&!d?\"12px\":\"18px\";this.horiz.style.height=this.vert.style.width=e,this.horiz.style.pointerEvents=this.vert.style.pointerEvents=\"none\",this.disableHoriz=new R,this.disableVert=new R},Dn.prototype.enableZeroWidthBar=function(e,t,r){e.style.pointerEvents=\"auto\",t.set(1e3,function n(){var i=e.getBoundingClientRect();(\"vert\"==r?document.elementFromPoint(i.right-1,(i.top+i.bottom)/2):document.elementFromPoint((i.right+i.left)/2,i.bottom-1))!=e?e.style.pointerEvents=\"none\":t.set(1e3,n)})},Dn.prototype.clear=function(){var e=this.horiz.parentNode;e.removeChild(this.horiz),e.removeChild(this.vert)};var Wn=function(){};function Hn(e,t){t||(t=An(e));var r=e.display.barWidth,n=e.display.barHeight;Fn(e,t);for(var i=0;i<4&&r!=e.display.barWidth||n!=e.display.barHeight;i++)r!=e.display.barWidth&&e.options.lineWrapping&&vn(e),Fn(e,An(e)),r=e.display.barWidth,n=e.display.barHeight}function Fn(e,t){var r=e.display,n=r.scrollbars.update(t);r.sizer.style.paddingRight=(r.barWidth=n.right)+\"px\",r.sizer.style.paddingBottom=(r.barHeight=n.bottom)+\"px\",r.heightForcer.style.borderBottom=n.bottom+\"px solid transparent\",n.right&&n.bottom?(r.scrollbarFiller.style.display=\"block\",r.scrollbarFiller.style.height=n.bottom+\"px\",r.scrollbarFiller.style.width=n.right+\"px\"):r.scrollbarFiller.style.display=\"\",n.bottom&&e.options.coverGutterNextToScrollbar&&e.options.fixedGutter?(r.gutterFiller.style.display=\"block\",r.gutterFiller.style.height=n.bottom+\"px\",r.gutterFiller.style.width=t.gutterWidth+\"px\"):r.gutterFiller.style.display=\"\"}Wn.prototype.update=function(){return{bottom:0,right:0}},Wn.prototype.setScrollLeft=function(){},Wn.prototype.setScrollTop=function(){},Wn.prototype.clear=function(){};var Pn={native:Dn,null:Wn};function En(e){e.display.scrollbars&&(e.display.scrollbars.clear(),e.display.scrollbars.addClass&&T(e.display.wrapper,e.display.scrollbars.addClass)),e.display.scrollbars=new Pn[e.options.scrollbarStyle](function(t){e.display.wrapper.insertBefore(t,e.display.scrollbarFiller),Je(t,\"mousedown\",function(){e.state.focused&&setTimeout(function(){return e.display.input.focus()},0)}),t.setAttribute(\"cm-not-content\",\"true\")},function(t,r){\"horizontal\"==r?On(e,t):Mn(e,t)},e),e.display.scrollbars.addClass&&H(e.display.wrapper,e.display.scrollbars.addClass)}var zn=0;function In(e){var t;e.curOp={cm:e,viewChanged:!1,startHeight:e.doc.height,forceUpdate:!1,updateInput:null,typing:!1,changeObjs:null,cursorActivityHandlers:null,cursorActivityCalled:0,selectionChanged:!1,updateMaxLine:!1,scrollLeft:null,scrollTop:null,scrollToPos:null,focus:!1,id:++zn},t=e.curOp,nr?nr.ops.push(t):t.ownsGroup=nr={ops:[t],delayedCallbacks:[]}}function Rn(e){!function(e,t){var r=e.ownsGroup;if(r)try{!function(e){var t=e.delayedCallbacks,r=0;do{for(;r<t.length;r++)t[r].call(null);for(var n=0;n<e.ops.length;n++){var i=e.ops[n];if(i.cursorActivityHandlers)for(;i.cursorActivityCalled<i.cursorActivityHandlers.length;)i.cursorActivityHandlers[i.cursorActivityCalled++].call(null,i.cm)}}while(r<t.length)}(r)}finally{nr=null,t(r)}}(e.curOp,function(e){for(var t=0;t<e.ops.length;t++)e.ops[t].cm.curOp=null;!function(e){for(var t=e.ops,r=0;r<t.length;r++)Bn(t[r]);for(var n=0;n<t.length;n++)(i=t[n]).updatedDisplay=i.mustUpdate&&ri(i.cm,i.update);var i;for(var o=0;o<t.length;o++)Gn(t[o]);for(var l=0;l<t.length;l++)Un(t[l]);for(var s=0;s<t.length;s++)Vn(t[s])}(e)})}function Bn(e){var t,r,n=e.cm,i=n.display;!(r=(t=n).display).scrollbarsClipped&&r.scroller.offsetWidth&&(r.nativeBarWidth=r.scroller.offsetWidth-r.scroller.clientWidth,r.heightForcer.style.height=Cr(t)+\"px\",r.sizer.style.marginBottom=-r.nativeBarWidth+\"px\",r.sizer.style.borderRightWidth=Cr(t)+\"px\",r.scrollbarsClipped=!0),e.updateMaxLine&&Ye(n),e.mustUpdate=e.viewChanged||e.forceUpdate||null!=e.scrollTop||e.scrollToPos&&(e.scrollToPos.from.line<i.viewFrom||e.scrollToPos.to.line>=i.viewTo)||i.maxLineChanged&&n.options.lineWrapping,e.update=e.mustUpdate&&new ti(n,e.mustUpdate&&{top:e.scrollTop,ensure:e.scrollToPos},e.forceUpdate)}function Gn(e){var t=e.cm,r=t.display;e.updatedDisplay&&vn(t),e.barMeasure=An(t),r.maxLineChanged&&!t.options.lineWrapping&&(e.adjustWidthTo=Tr(t,r.maxLine,r.maxLine.text.length).left+3,t.display.sizerWidth=e.adjustWidthTo,e.barMeasure.scrollWidth=Math.max(r.scroller.clientWidth,r.sizer.offsetLeft+e.adjustWidthTo+Cr(t)+t.display.barWidth),e.maxScrollLeft=Math.max(0,r.sizer.offsetLeft+e.adjustWidthTo-Sr(t))),(e.updatedDisplay||e.selectionChanged)&&(e.preparedSelection=r.input.prepareSelection())}function Un(e){var t=e.cm;null!=e.adjustWidthTo&&(t.display.sizer.style.minWidth=e.adjustWidthTo+\"px\",e.maxScrollLeft<t.doc.scrollLeft&&On(t,Math.min(t.display.scroller.scrollLeft,e.maxScrollLeft),!0),t.display.maxLineChanged=!1);var r=e.focus&&e.focus==W();e.preparedSelection&&t.display.input.showSelection(e.preparedSelection,r),(e.updatedDisplay||e.startHeight!=t.doc.height)&&Hn(t,e.barMeasure),e.updatedDisplay&&li(t,e.barMeasure),e.selectionChanged&&hn(t),t.state.focused&&e.updateInput&&t.display.input.reset(e.typing),r&&fn(e.cm)}function Vn(e){var t=e.cm,r=t.display,n=t.doc;(e.updatedDisplay&&ni(t,e.update),null==r.wheelStartX||null==e.scrollTop&&null==e.scrollLeft&&!e.scrollToPos||(r.wheelStartX=r.wheelStartY=null),null!=e.scrollTop&&Nn(t,e.scrollTop,e.forceScroll),null!=e.scrollLeft&&On(t,e.scrollLeft,!0,!0),e.scrollToPos)&&function(e,t){if(!nt(e,\"scrollCursorIntoView\")){var r=e.display,n=r.sizer.getBoundingClientRect(),i=null;if(t.top+n.top<0?i=!0:t.bottom+n.top>(window.innerHeight||document.documentElement.clientHeight)&&(i=!1),null!=i&&!p){var o=O(\"div\",\"\",null,\"position: absolute;\\n top: \"+(t.top-r.viewOffset-br(e.display))+\"px;\\n height: \"+(t.bottom-t.top+Cr(e)+r.barHeight)+\"px;\\n left: \"+t.left+\"px; width: \"+Math.max(2,t.right-t.left)+\"px;\");e.display.lineSpace.appendChild(o),o.scrollIntoView(i),e.display.lineSpace.removeChild(o)}}}(t,function(e,t,r,n){var i;null==n&&(n=0),e.options.lineWrapping||t!=r||(r=\"before\"==(t=t.ch?ge(t.line,\"before\"==t.sticky?t.ch-1:t.ch,\"after\"):t).sticky?ge(t.line,t.ch+1,\"before\"):t);for(var o=0;o<5;o++){var l=!1,s=Vr(e,t),a=r&&r!=t?Vr(e,r):s,u=xn(e,i={left:Math.min(s.left,a.left),top:Math.min(s.top,a.top)-n,right:Math.max(s.left,a.left),bottom:Math.max(s.bottom,a.bottom)+n}),c=e.doc.scrollTop,h=e.doc.scrollLeft;if(null!=u.scrollTop&&(Mn(e,u.scrollTop),Math.abs(e.doc.scrollTop-c)>1&&(l=!0)),null!=u.scrollLeft&&(On(e,u.scrollLeft),Math.abs(e.doc.scrollLeft-h)>1&&(l=!0)),!l)break}return i}(t,Ce(n,e.scrollToPos.from),Ce(n,e.scrollToPos.to),e.scrollToPos.margin));var i=e.maybeHiddenMarkers,o=e.maybeUnhiddenMarkers;if(i)for(var l=0;l<i.length;++l)i[l].lines.length||rt(i[l],\"hide\");if(o)for(var s=0;s<o.length;++s)o[s].lines.length&&rt(o[s],\"unhide\");r.wrapper.offsetHeight&&(n.scrollTop=t.display.scroller.scrollTop),e.changeObjs&&rt(t,\"changes\",t,e.changeObjs),e.update&&e.update.finish()}function Kn(e,t){if(e.curOp)return t();In(e);try{return t()}finally{Rn(e)}}function jn(e,t){return function(){if(e.curOp)return t.apply(e,arguments);In(e);try{return t.apply(e,arguments)}finally{Rn(e)}}}function Xn(e){return function(){if(this.curOp)return e.apply(this,arguments);In(this);try{return e.apply(this,arguments)}finally{Rn(this)}}}function Yn(e){return function(){var t=this.cm;if(!t||t.curOp)return e.apply(this,arguments);In(t);try{return e.apply(this,arguments)}finally{Rn(t)}}}function _n(e,t,r,n){null==t&&(t=e.doc.first),null==r&&(r=e.doc.first+e.doc.size),n||(n=0);var i=e.display;if(n&&r<i.viewTo&&(null==i.updateLineNumbers||i.updateLineNumbers>t)&&(i.updateLineNumbers=t),e.curOp.viewChanged=!0,t>=i.viewTo)ke&&Ge(e.doc,t)<i.viewTo&&$n(e);else if(r<=i.viewFrom)ke&&Ue(e.doc,r+n)>i.viewFrom?$n(e):(i.viewFrom+=n,i.viewTo+=n);else if(t<=i.viewFrom&&r>=i.viewTo)$n(e);else if(t<=i.viewFrom){var o=Zn(e,r,r+n,1);o?(i.view=i.view.slice(o.index),i.viewFrom=o.lineN,i.viewTo+=n):$n(e)}else if(r>=i.viewTo){var l=Zn(e,t,t,-1);l?(i.view=i.view.slice(0,l.index),i.viewTo=l.lineN):$n(e)}else{var s=Zn(e,t,t,-1),a=Zn(e,r,r+n,1);s&&a?(i.view=i.view.slice(0,s.index).concat(rr(e,s.lineN,a.lineN)).concat(i.view.slice(a.index)),i.viewTo+=n):$n(e)}var u=i.externalMeasured;u&&(r<u.lineN?u.lineN+=n:t<u.lineN+u.size&&(i.externalMeasured=null))}function qn(e,t,r){e.curOp.viewChanged=!0;var n=e.display,i=e.display.externalMeasured;if(i&&t>=i.lineN&&t<i.lineN+i.size&&(n.externalMeasured=null),!(t<n.viewFrom||t>=n.viewTo)){var o=n.view[on(e,t)];if(null!=o.node){var l=o.changes||(o.changes=[]);-1==B(l,r)&&l.push(r)}}}function $n(e){e.display.viewFrom=e.display.viewTo=e.doc.first,e.display.view=[],e.display.viewOffset=0}function Zn(e,t,r,n){var i,o=on(e,t),l=e.display.view;if(!ke||r==e.doc.first+e.doc.size)return{index:o,lineN:r};for(var s=e.display.viewFrom,a=0;a<o;a++)s+=l[a].size;if(s!=t){if(n>0){if(o==l.length-1)return null;i=s+l[o].size-t,o++}else i=s-t;t+=i,r+=i}for(;Ge(e.doc,r)!=r;){if(o==(n<0?0:l.length-1))return null;r+=n*l[o-(n<0?1:0)].size,o+=n}return{index:o,lineN:r}}function Qn(e){for(var t=e.display.view,r=0,n=0;n<t.length;n++){var i=t[n];i.hidden||i.node&&!i.changes||++r}return r}function Jn(e,t){e.doc.highlightFrontier<e.display.viewTo&&e.state.highlight.set(t,E(ei,e))}function ei(e){var t=e.doc;if(!(t.highlightFrontier>=e.display.viewTo)){var r=+new Date+e.options.workTime,n=zt(e,t.highlightFrontier),i=[];t.iter(n.line,Math.min(t.first+t.size,e.display.viewTo+500),function(o){if(n.line>=e.display.viewFrom){var l=o.styles,s=o.text.length>e.options.maxHighlightLength?Ot(t.mode,n.state):null,a=Pt(e,o,n,!0);s&&(n.state=s),o.styles=a.styles;var u=o.styleClasses,c=a.classes;c?o.styleClasses=c:u&&(o.styleClasses=null);for(var h=!l||l.length!=o.styles.length||u!=c&&(!u||!c||u.bgClass!=c.bgClass||u.textClass!=c.textClass),f=0;!h&&f<l.length;++f)h=l[f]!=o.styles[f];h&&i.push(n.line),o.stateAfter=n.save(),n.nextLine()}else o.text.length<=e.options.maxHighlightLength&&It(e,o.text,n),o.stateAfter=n.line%5==0?n.save():null,n.nextLine();if(+new Date>r)return Jn(e,e.options.workDelay),!0}),t.highlightFrontier=n.line,t.modeFrontier=Math.max(t.modeFrontier,n.line),i.length&&Kn(e,function(){for(var t=0;t<i.length;t++)qn(e,i[t],\"text\")})}}var ti=function(e,t,r){var n=e.display;this.viewport=t,this.visible=yn(n,e.doc,t),this.editorIsHidden=!n.wrapper.offsetWidth,this.wrapperHeight=n.wrapper.clientHeight,this.wrapperWidth=n.wrapper.clientWidth,this.oldDisplayWidth=Sr(e),this.force=r,this.dims=Jr(e),this.events=[]};function ri(e,t){var r=e.display,n=e.doc;if(t.editorIsHidden)return $n(e),!1;if(!t.force&&t.visible.from>=r.viewFrom&&t.visible.to<=r.viewTo&&(null==r.updateLineNumbers||r.updateLineNumbers>=r.viewTo)&&r.renderedView==r.view&&0==Qn(e))return!1;wn(e)&&($n(e),t.dims=Jr(e));var i=n.first+n.size,o=Math.max(t.visible.from-e.options.viewportMargin,n.first),l=Math.min(i,t.visible.to+e.options.viewportMargin);r.viewFrom<o&&o-r.viewFrom<20&&(o=Math.max(n.first,r.viewFrom)),r.viewTo>l&&r.viewTo-l<20&&(l=Math.min(i,r.viewTo)),ke&&(o=Ge(e.doc,o),l=Ue(e.doc,l));var s,u,c,h,f=o!=r.viewFrom||l!=r.viewTo||r.lastWrapHeight!=t.wrapperHeight||r.lastWrapWidth!=t.wrapperWidth;u=o,c=l,0==(h=(s=e).display).view.length||u>=h.viewTo||c<=h.viewFrom?(h.view=rr(s,u,c),h.viewFrom=u):(h.viewFrom>u?h.view=rr(s,u,h.viewFrom).concat(h.view):h.viewFrom<u&&(h.view=h.view.slice(on(s,u))),h.viewFrom=u,h.viewTo<c?h.view=h.view.concat(rr(s,h.viewTo,c)):h.viewTo>c&&(h.view=h.view.slice(0,on(s,c)))),h.viewTo=c,r.viewOffset=je(se(e.doc,r.viewFrom)),e.display.mover.style.top=r.viewOffset+\"px\";var d=Qn(e);if(!f&&0==d&&!t.force&&r.renderedView==r.view&&(null==r.updateLineNumbers||r.updateLineNumbers>=r.viewTo))return!1;var p=function(e){if(e.hasFocus())return null;var t=W();if(!t||!D(e.display.lineDiv,t))return null;var r={activeElt:t};if(window.getSelection){var n=window.getSelection();n.anchorNode&&n.extend&&D(e.display.lineDiv,n.anchorNode)&&(r.anchorNode=n.anchorNode,r.anchorOffset=n.anchorOffset,r.focusNode=n.focusNode,r.focusOffset=n.focusOffset)}return r}(e);return d>4&&(r.lineDiv.style.display=\"none\"),function(e,t,r){var n=e.display,i=e.options.lineNumbers,o=n.lineDiv,l=o.firstChild;function s(t){var r=t.nextSibling;return a&&y&&e.display.currentWheelTarget==t?t.style.display=\"none\":t.parentNode.removeChild(t),r}for(var u=n.view,c=n.viewFrom,h=0;h<u.length;h++){var f=u[h];if(f.hidden);else if(f.node&&f.node.parentNode==o){for(;l!=f.node;)l=s(l);var d=i&&null!=t&&t<=c&&f.lineNumber;f.changes&&(B(f.changes,\"gutter\")>-1&&(d=!1),sr(e,f,c,r)),d&&(M(f.lineNumber),f.lineNumber.appendChild(document.createTextNode(pe(e.options,c)))),l=f.node.nextSibling}else{var p=(m=c,b=r,void 0,w=ur(g=e,v=f),v.text=v.node=w.pre,w.bgClass&&(v.bgClass=w.bgClass),w.textClass&&(v.textClass=w.textClass),hr(g,v),fr(g,v,m,b),pr(g,v,b),v.node);o.insertBefore(p,l)}c+=f.size}var g,v,m,b,w;for(;l;)l=s(l)}(e,r.updateLineNumbers,t.dims),d>4&&(r.lineDiv.style.display=\"\"),r.renderedView=r.view,function(e){if(e&&e.activeElt&&e.activeElt!=W()&&(e.activeElt.focus(),e.anchorNode&&D(document.body,e.anchorNode)&&D(document.body,e.focusNode))){var t=window.getSelection(),r=document.createRange();r.setEnd(e.anchorNode,e.anchorOffset),r.collapse(!1),t.removeAllRanges(),t.addRange(r),t.extend(e.focusNode,e.focusOffset)}}(p),M(r.cursorDiv),M(r.selectionDiv),r.gutters.style.height=r.sizer.style.minHeight=0,f&&(r.lastWrapHeight=t.wrapperHeight,r.lastWrapWidth=t.wrapperWidth,Jn(e,400)),r.updateLineNumbers=null,!0}function ni(e,t){for(var r=t.viewport,n=!0;(n&&e.options.lineWrapping&&t.oldDisplayWidth!=Sr(e)||(r&&null!=r.top&&(r={top:Math.min(e.doc.height+wr(e.display)-Lr(e),r.top)}),t.visible=yn(e.display,e.doc,r),!(t.visible.from>=e.display.viewFrom&&t.visible.to<=e.display.viewTo)))&&ri(e,t);n=!1){vn(e);var i=An(e);ln(e),Hn(e,i),li(e,i),t.force=!1}t.signal(e,\"update\",e),e.display.viewFrom==e.display.reportedViewFrom&&e.display.viewTo==e.display.reportedViewTo||(t.signal(e,\"viewportChange\",e,e.display.viewFrom,e.display.viewTo),e.display.reportedViewFrom=e.display.viewFrom,e.display.reportedViewTo=e.display.viewTo)}function ii(e,t){var r=new ti(e,t);if(ri(e,r)){vn(e),ni(e,r);var n=An(e);ln(e),Hn(e,n),li(e,n),r.finish()}}function oi(e){var t=e.display.gutters.offsetWidth;e.display.sizer.style.marginLeft=t+\"px\"}function li(e,t){e.display.sizer.style.minHeight=t.docHeight+\"px\",e.display.heightForcer.style.top=t.docHeight+\"px\",e.display.gutters.style.height=t.docHeight+e.display.barHeight+Cr(e)+\"px\"}function si(e){var t=e.display.gutters,r=e.options.gutters;M(t);for(var n=0;n<r.length;++n){var i=r[n],o=t.appendChild(O(\"div\",null,\"CodeMirror-gutter \"+i));\"CodeMirror-linenumbers\"==i&&(e.display.lineGutter=o,o.style.width=(e.display.lineNumWidth||1)+\"px\")}t.style.display=n?\"\":\"none\",oi(e)}function ai(e){var t=B(e.gutters,\"CodeMirror-linenumbers\");-1==t&&e.lineNumbers?e.gutters=e.gutters.concat([\"CodeMirror-linenumbers\"]):t>-1&&!e.lineNumbers&&(e.gutters=e.gutters.slice(0),e.gutters.splice(t,1))}ti.prototype.signal=function(e,t){ot(e,t)&&this.events.push(arguments)},ti.prototype.finish=function(){for(var e=0;e<this.events.length;e++)rt.apply(null,this.events[e])};var ui=0,ci=null;function hi(e){var t=e.wheelDeltaX,r=e.wheelDeltaY;return null==t&&e.detail&&e.axis==e.HORIZONTAL_AXIS&&(t=e.detail),null==r&&e.detail&&e.axis==e.VERTICAL_AXIS?r=e.detail:null==r&&(r=e.wheelDelta),{x:t,y:r}}function fi(e){var t=hi(e);return t.x*=ci,t.y*=ci,t}function di(e,t){var n=hi(t),i=n.x,o=n.y,l=e.display,s=l.scroller,u=s.scrollWidth>s.clientWidth,c=s.scrollHeight>s.clientHeight;if(i&&u||o&&c){if(o&&y&&a)e:for(var f=t.target,d=l.view;f!=s;f=f.parentNode)for(var p=0;p<d.length;p++)if(d[p].node==f){e.display.currentWheelTarget=f;break e}if(i&&!r&&!h&&null!=ci)return o&&c&&Mn(e,Math.max(0,s.scrollTop+o*ci)),On(e,Math.max(0,s.scrollLeft+i*ci)),(!o||o&&c)&&st(t),void(l.wheelStartX=null);if(o&&null!=ci){var g=o*ci,v=e.doc.scrollTop,m=v+l.wrapper.clientHeight;g<0?v=Math.max(0,v+g-50):m=Math.min(e.doc.height,m+g+50),ii(e,{top:v,bottom:m})}ui<20&&(null==l.wheelStartX?(l.wheelStartX=s.scrollLeft,l.wheelStartY=s.scrollTop,l.wheelDX=i,l.wheelDY=o,setTimeout(function(){if(null!=l.wheelStartX){var e=s.scrollLeft-l.wheelStartX,t=s.scrollTop-l.wheelStartY,r=t&&l.wheelDY&&t/l.wheelDY||e&&l.wheelDX&&e/l.wheelDX;l.wheelStartX=l.wheelStartY=null,r&&(ci=(ci*ui+r)/(ui+1),++ui)}},200)):(l.wheelDX+=i,l.wheelDY+=o))}}l?ci=-.53:r?ci=15:c?ci=-.7:f&&(ci=-1/3);var pi=function(e,t){this.ranges=e,this.primIndex=t};pi.prototype.primary=function(){return this.ranges[this.primIndex]},pi.prototype.equals=function(e){if(e==this)return!0;if(e.primIndex!=this.primIndex||e.ranges.length!=this.ranges.length)return!1;for(var t=0;t<this.ranges.length;t++){var r=this.ranges[t],n=e.ranges[t];if(!me(r.anchor,n.anchor)||!me(r.head,n.head))return!1}return!0},pi.prototype.deepCopy=function(){for(var e=[],t=0;t<this.ranges.length;t++)e[t]=new gi(ye(this.ranges[t].anchor),ye(this.ranges[t].head));return new pi(e,this.primIndex)},pi.prototype.somethingSelected=function(){for(var e=0;e<this.ranges.length;e++)if(!this.ranges[e].empty())return!0;return!1},pi.prototype.contains=function(e,t){t||(t=e);for(var r=0;r<this.ranges.length;r++){var n=this.ranges[r];if(ve(t,n.from())>=0&&ve(e,n.to())<=0)return r}return-1};var gi=function(e,t){this.anchor=e,this.head=t};function vi(e,t){var r=e[t];e.sort(function(e,t){return ve(e.from(),t.from())}),t=B(e,r);for(var n=1;n<e.length;n++){var i=e[n],o=e[n-1];if(ve(o.to(),i.from())>=0){var l=we(o.from(),i.from()),s=be(o.to(),i.to()),a=o.empty()?i.from()==i.head:o.from()==o.head;n<=t&&--t,e.splice(--n,2,new gi(a?s:l,a?l:s))}}return new pi(e,t)}function mi(e,t){return new pi([new gi(e,t||e)],0)}function yi(e){return e.text?ge(e.from.line+e.text.length-1,q(e.text).length+(1==e.text.length?e.from.ch:0)):e.to}function bi(e,t){if(ve(e,t.from)<0)return e;if(ve(e,t.to)<=0)return yi(t);var r=e.line+t.text.length-(t.to.line-t.from.line)-1,n=e.ch;return e.line==t.to.line&&(n+=yi(t).ch-t.to.ch),ge(r,n)}function wi(e,t){for(var r=[],n=0;n<e.sel.ranges.length;n++){var i=e.sel.ranges[n];r.push(new gi(bi(i.anchor,t),bi(i.head,t)))}return vi(r,e.sel.primIndex)}function xi(e,t,r){return e.line==t.line?ge(r.line,e.ch-t.ch+r.ch):ge(r.line+(e.line-t.line),e.ch)}function Ci(e){e.doc.mode=Tt(e.options,e.doc.modeOption),Si(e)}function 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u=o.changes[a];if(r<u.from.line)u.from=ge(u.from.line+n,u.from.ch),u.to=ge(u.to.line+n,u.to.ch);else if(t<=u.to.line){l=!1;break}}l||(e.splice(0,i+1),i=0)}}}function uo(e,t){var r=t.from.line,n=t.to.line,i=t.text.length-(n-r)-1;ao(e.done,r,n,i),ao(e.undone,r,n,i)}function co(e,t,r,n){var i=t,o=t;return\"number\"==typeof t?o=se(e,xe(e,t)):i=he(t),null==i?null:(n(o,i)&&e.cm&&qn(e.cm,i,r),o)}function ho(e){this.lines=e,this.parent=null;for(var t=0,r=0;r<e.length;++r)e[r].parent=this,t+=e[r].height;this.height=t}function fo(e){this.children=e;for(var t=0,r=0,n=0;n<e.length;++n){var i=e[n];t+=i.chunkSize(),r+=i.height,i.parent=this}this.size=t,this.height=r,this.parent=null}gi.prototype.from=function(){return we(this.anchor,this.head)},gi.prototype.to=function(){return be(this.anchor,this.head)},gi.prototype.empty=function(){return this.head.line==this.anchor.line&&this.head.ch==this.anchor.ch},ho.prototype={chunkSize:function(){return this.lines.length},removeInner:function(e,t){for(var r,n=e,i=e+t;n<i;++n){var o=this.lines[n];this.height-=o.height,(r=o).parent=null,De(r),or(o,\"delete\")}this.lines.splice(e,t)},collapse:function(e){e.push.apply(e,this.lines)},insertInner:function(e,t,r){this.height+=r,this.lines=this.lines.slice(0,e).concat(t).concat(this.lines.slice(e));for(var n=0;n<t.length;++n)t[n].parent=this},iterN:function(e,t,r){for(var n=e+t;e<n;++e)if(r(this.lines[e]))return!0}},fo.prototype={chunkSize:function(){return this.size},removeInner:function(e,t){this.size-=t;for(var r=0;r<this.children.length;++r){var n=this.children[r],i=n.chunkSize();if(e<i){var o=Math.min(t,i-e),l=n.height;if(n.removeInner(e,o),this.height-=l-n.height,i==o&&(this.children.splice(r--,1),n.parent=null),0==(t-=o))break;e=0}else e-=i}if(this.size-t<25&&(this.children.length>1||!(this.children[0]instanceof ho))){var s=[];this.collapse(s),this.children=[new ho(s)],this.children[0].parent=this}},collapse:function(e){for(var t=0;t<this.children.length;++t)this.children[t].collapse(e)},insertInner:function(e,t,r){this.size+=t.length,this.height+=r;for(var n=0;n<this.children.length;++n){var i=this.children[n],o=i.chunkSize();if(e<=o){if(i.insertInner(e,t,r),i.lines&&i.lines.length>50){for(var l=i.lines.length%25+25,s=l;s<i.lines.length;){var a=new ho(i.lines.slice(s,s+=25));i.height-=a.height,this.children.splice(++n,0,a),a.parent=this}i.lines=i.lines.slice(0,l),this.maybeSpill()}break}e-=o}},maybeSpill:function(){if(!(this.children.length<=10)){var e=this;do{var t=new fo(e.children.splice(e.children.length-5,5));if(e.parent){e.size-=t.size,e.height-=t.height;var r=B(e.parent.children,e);e.parent.children.splice(r+1,0,t)}else{var n=new fo(e.children);n.parent=e,e.children=[n,t],e=n}t.parent=e.parent}while(e.children.length>10);e.parent.maybeSpill()}},iterN:function(e,t,r){for(var n=0;n<this.children.length;++n){var i=this.children[n],o=i.chunkSize();if(e<o){var l=Math.min(t,o-e);if(i.iterN(e,l,r))return!0;if(0==(t-=l))break;e=0}else e-=o}}};var po=function(e,t,r){if(r)for(var n in r)r.hasOwnProperty(n)&&(this[n]=r[n]);this.doc=e,this.node=t};function go(e,t,r){je(t)<(e.curOp&&e.curOp.scrollTop||e.doc.scrollTop)&&Cn(e,r)}po.prototype.clear=function(){var e=this.doc.cm,t=this.line.widgets,r=this.line,n=he(r);if(null!=n&&t){for(var i=0;i<t.length;++i)t[i]==this&&t.splice(i--,1);t.length||(r.widgets=null);var o=mr(this);ce(r,Math.max(0,r.height-o)),e&&(Kn(e,function(){go(e,r,-o),qn(e,n,\"widget\")}),or(e,\"lineWidgetCleared\",e,this,n))}},po.prototype.changed=function(){var e=this,t=this.height,r=this.doc.cm,n=this.line;this.height=null;var i=mr(this)-t;i&&(ce(n,n.height+i),r&&Kn(r,function(){r.curOp.forceUpdate=!0,go(r,n,i),or(r,\"lineWidgetChanged\",r,e,he(n))}))},lt(po);var vo=0,mo=function(e,t){this.lines=[],this.type=t,this.doc=e,this.id=++vo};function yo(e,t,r,n,i){if(n&&n.shared)return function(e,t,r,n,i){(n=z(n)).shared=!1;var o=[yo(e,t,r,n,i)],l=o[0],s=n.widgetNode;return Ti(e,function(e){s&&(n.widgetNode=s.cloneNode(!0)),o.push(yo(e,Ce(e,t),Ce(e,r),n,i));for(var a=0;a<e.linked.length;++a)if(e.linked[a].isParent)return;l=q(o)}),new bo(o,l)}(e,t,r,n,i);if(e.cm&&!e.cm.curOp)return jn(e.cm,yo)(e,t,r,n,i);var o=new mo(e,i),l=ve(t,r);if(n&&z(n,o,!1),l>0||0==l&&!1!==o.clearWhenEmpty)return o;if(o.replacedWith&&(o.collapsed=!0,o.widgetNode=A(\"span\",[o.replacedWith],\"CodeMirror-widget\"),n.handleMouseEvents||o.widgetNode.setAttribute(\"cm-ignore-events\",\"true\"),n.insertLeft&&(o.widgetNode.insertLeft=!0)),o.collapsed){if(Re(e,t.line,t,r,o)||t.line!=r.line&&Re(e,r.line,t,r,o))throw new Error(\"Inserting collapsed marker partially overlapping an existing one\");ke=!0}o.addToHistory&&Wi(e,{from:t,to:r,origin:\"markText\"},e.sel,NaN);var s,a=t.line,u=e.cm;if(e.iter(a,r.line+1,function(e){var n,i;u&&o.collapsed&&!u.options.lineWrapping&&Be(e)==u.display.maxLine&&(s=!0),o.collapsed&&a!=t.line&&ce(e,0),n=e,i=new Te(o,a==t.line?t.ch:null,a==r.line?r.ch:null),n.markedSpans=n.markedSpans?n.markedSpans.concat([i]):[i],i.marker.attachLine(n),++a}),o.collapsed&&e.iter(t.line,r.line+1,function(t){Ve(e,t)&&ce(t,0)}),o.clearOnEnter&&Je(o,\"beforeCursorEnter\",function(){return o.clear()}),o.readOnly&&(Le=!0,(e.history.done.length||e.history.undone.length)&&e.clearHistory()),o.collapsed&&(o.id=++vo,o.atomic=!0),u){if(s&&(u.curOp.updateMaxLine=!0),o.collapsed)_n(u,t.line,r.line+1);else if(o.className||o.title||o.startStyle||o.endStyle||o.css)for(var c=t.line;c<=r.line;c++)qn(u,c,\"text\");o.atomic&&_i(u.doc),or(u,\"markerAdded\",u,o)}return o}mo.prototype.clear=function(){var e=this;if(!this.explicitlyCleared){var t=this.doc.cm,r=t&&!t.curOp;if(r&&In(t),ot(this,\"clear\")){var n=this.find();n&&or(this,\"clear\",n.from,n.to)}for(var i=null,o=null,l=0;l<this.lines.length;++l){var s=e.lines[l],a=Me(s.markedSpans,e);t&&!e.collapsed?qn(t,he(s),\"text\"):t&&(null!=a.to&&(o=he(s)),null!=a.from&&(i=he(s))),s.markedSpans=Ne(s.markedSpans,a),null==a.from&&e.collapsed&&!Ve(e.doc,s)&&t&&ce(s,Zr(t.display))}if(t&&this.collapsed&&!t.options.lineWrapping)for(var u=0;u<this.lines.length;++u){var c=Be(e.lines[u]),h=Xe(c);h>t.display.maxLineLength&&(t.display.maxLine=c,t.display.maxLineLength=h,t.display.maxLineChanged=!0)}null!=i&&t&&this.collapsed&&_n(t,i,o+1),this.lines.length=0,this.explicitlyCleared=!0,this.atomic&&this.doc.cantEdit&&(this.doc.cantEdit=!1,t&&_i(t.doc)),t&&or(t,\"markerCleared\",t,this,i,o),r&&Rn(t),this.parent&&this.parent.clear()}},mo.prototype.find=function(e,t){var r,n;null==e&&\"bookmark\"==this.type&&(e=1);for(var i=0;i<this.lines.length;++i){var o=this.lines[i],l=Me(o.markedSpans,this);if(null!=l.from&&(r=ge(t?o:he(o),l.from),-1==e))return r;if(null!=l.to&&(n=ge(t?o:he(o),l.to),1==e))return n}return r&&{from:r,to:n}},mo.prototype.changed=function(){var e=this,t=this.find(-1,!0),r=this,n=this.doc.cm;t&&n&&Kn(n,function(){var i=t.line,o=he(t.line),l=Mr(n,o);if(l&&(Fr(l),n.curOp.selectionChanged=n.curOp.forceUpdate=!0),n.curOp.updateMaxLine=!0,!Ve(r.doc,i)&&null!=r.height){var s=r.height;r.height=null;var a=mr(r)-s;a&&ce(i,i.height+a)}or(n,\"markerChanged\",n,e)})},mo.prototype.attachLine=function(e){if(!this.lines.length&&this.doc.cm){var t=this.doc.cm.curOp;t.maybeHiddenMarkers&&-1!=B(t.maybeHiddenMarkers,this)||(t.maybeUnhiddenMarkers||(t.maybeUnhiddenMarkers=[])).push(this)}this.lines.push(e)},mo.prototype.detachLine=function(e){if(this.lines.splice(B(this.lines,e),1),!this.lines.length&&this.doc.cm){var t=this.doc.cm.curOp;(t.maybeHiddenMarkers||(t.maybeHiddenMarkers=[])).push(this)}},lt(mo);var bo=function(e,t){this.markers=e,this.primary=t;for(var r=0;r<e.length;++r)e[r].parent=this};function wo(e){return e.findMarks(ge(e.first,0),e.clipPos(ge(e.lastLine())),function(e){return e.parent})}function xo(e){for(var t=function(t){var r=e[t],n=[r.primary.doc];Ti(r.primary.doc,function(e){return n.push(e)});for(var i=0;i<r.markers.length;i++){var o=r.markers[i];-1==B(n,o.doc)&&(o.parent=null,r.markers.splice(i--,1))}},r=0;r<e.length;r++)t(r)}bo.prototype.clear=function(){if(!this.explicitlyCleared){this.explicitlyCleared=!0;for(var e=0;e<this.markers.length;++e)this.markers[e].clear();or(this,\"clear\")}},bo.prototype.find=function(e,t){return this.primary.find(e,t)},lt(bo);var Co=0,So=function(e,t,r,n,i){if(!(this instanceof So))return new So(e,t,r,n,i);null==r&&(r=0),fo.call(this,[new ho([new jt(\"\",null)])]),this.first=r,this.scrollTop=this.scrollLeft=0,this.cantEdit=!1,this.cleanGeneration=1,this.modeFrontier=this.highlightFrontier=r;var o=ge(r,0);this.sel=mi(o),this.history=new Oi(null),this.id=++Co,this.modeOption=t,this.lineSep=n,this.direction=\"rtl\"==i?\"rtl\":\"ltr\",this.extend=!1,\"string\"==typeof e&&(e=this.splitLines(e)),ki(this,{from:o,to:o,text:e}),ji(this,mi(o),V)};So.prototype=Q(fo.prototype,{constructor:So,iter:function(e,t,r){r?this.iterN(e-this.first,t-e,r):this.iterN(this.first,this.first+this.size,e)},insert:function(e,t){for(var r=0,n=0;n<t.length;++n)r+=t[n].height;this.insertInner(e-this.first,t,r)},remove:function(e,t){this.removeInner(e-this.first,t)},getValue:function(e){var t=ue(this,this.first,this.first+this.size);return!1===e?t:t.join(e||this.lineSeparator())},setValue:Yn(function(e){var t=ge(this.first,0),r=this.first+this.size-1;to(this,{from:t,to:ge(r,se(this,r).text.length),text:this.splitLines(e),origin:\"setValue\",full:!0},!0),this.cm&&Ln(this.cm,0,0),ji(this,mi(t),V)}),replaceRange:function(e,t,r,n){lo(this,e,t=Ce(this,t),r=r?Ce(this,r):t,n)},getRange:function(e,t,r){var n=ae(this,Ce(this,e),Ce(this,t));return!1===r?n:n.join(r||this.lineSeparator())},getLine:function(e){var t=this.getLineHandle(e);return t&&t.text},getLineHandle:function(e){if(de(this,e))return se(this,e)},getLineNumber:function(e){return he(e)},getLineHandleVisualStart:function(e){return\"number\"==typeof e&&(e=se(this,e)),Be(e)},lineCount:function(){return this.size},firstLine:function(){return this.first},lastLine:function(){return this.first+this.size-1},clipPos:function(e){return Ce(this,e)},getCursor:function(e){var t=this.sel.primary();return null==e||\"head\"==e?t.head:\"anchor\"==e?t.anchor:\"end\"==e||\"to\"==e||!1===e?t.to():t.from()},listSelections:function(){return this.sel.ranges},somethingSelected:function(){return this.sel.somethingSelected()},setCursor:Yn(function(e,t,r){Vi(this,Ce(this,\"number\"==typeof e?ge(e,t||0):e),null,r)}),setSelection:Yn(function(e,t,r){Vi(this,Ce(this,e),Ce(this,t||e),r)}),extendSelection:Yn(function(e,t,r){Bi(this,Ce(this,e),t&&Ce(this,t),r)}),extendSelections:Yn(function(e,t){Gi(this,Se(this,e),t)}),extendSelectionsBy:Yn(function(e,t){Gi(this,Se(this,$(this.sel.ranges,e)),t)}),setSelections:Yn(function(e,t,r){if(e.length){for(var n=[],i=0;i<e.length;i++)n[i]=new gi(Ce(this,e[i].anchor),Ce(this,e[i].head));null==t&&(t=Math.min(e.length-1,this.sel.primIndex)),ji(this,vi(n,t),r)}}),addSelection:Yn(function(e,t,r){var n=this.sel.ranges.slice(0);n.push(new gi(Ce(this,e),Ce(this,t||e))),ji(this,vi(n,n.length-1),r)}),getSelection:function(e){for(var t,r=this.sel.ranges,n=0;n<r.length;n++){var i=ae(this,r[n].from(),r[n].to());t=t?t.concat(i):i}return!1===e?t:t.join(e||this.lineSeparator())},getSelections:function(e){for(var t=[],r=this.sel.ranges,n=0;n<r.length;n++){var i=ae(this,r[n].from(),r[n].to());!1!==e&&(i=i.join(e||this.lineSeparator())),t[n]=i}return t},replaceSelection:function(e,t,r){for(var n=[],i=0;i<this.sel.ranges.length;i++)n[i]=e;this.replaceSelections(n,t,r||\"+input\")},replaceSelections:Yn(function(e,t,r){for(var n=[],i=this.sel,o=0;o<i.ranges.length;o++){var l=i.ranges[o];n[o]={from:l.from(),to:l.to(),text:this.splitLines(e[o]),origin:r}}for(var s=t&&\"end\"!=t&&function(e,t,r){for(var n=[],i=ge(e.first,0),o=i,l=0;l<t.length;l++){var s=t[l],a=xi(s.from,i,o),u=xi(yi(s),i,o);if(i=s.to,o=u,\"around\"==r){var c=e.sel.ranges[l],h=ve(c.head,c.anchor)<0;n[l]=new gi(h?u:a,h?a:u)}else n[l]=new gi(a,a)}return new pi(n,e.sel.primIndex)}(this,n,t),a=n.length-1;a>=0;a--)to(this,n[a]);s?Ki(this,s):this.cm&&Sn(this.cm)}),undo:Yn(function(){no(this,\"undo\")}),redo:Yn(function(){no(this,\"redo\")}),undoSelection:Yn(function(){no(this,\"undo\",!0)}),redoSelection:Yn(function(){no(this,\"redo\",!0)}),setExtending:function(e){this.extend=e},getExtending:function(){return this.extend},historySize:function(){for(var e=this.history,t=0,r=0,n=0;n<e.done.length;n++)e.done[n].ranges||++t;for(var i=0;i<e.undone.length;i++)e.undone[i].ranges||++r;return{undo:t,redo:r}},clearHistory:function(){this.history=new Oi(this.history.maxGeneration)},markClean:function(){this.cleanGeneration=this.changeGeneration(!0)},changeGeneration:function(e){return e&&(this.history.lastOp=this.history.lastSelOp=this.history.lastOrigin=null),this.history.generation},isClean:function(e){return this.history.generation==(e||this.cleanGeneration)},getHistory:function(){return{done:Ii(this.history.done),undone:Ii(this.history.undone)}},setHistory:function(e){var t=this.history=new Oi(this.history.maxGeneration);t.done=Ii(e.done.slice(0),null,!0),t.undone=Ii(e.undone.slice(0),null,!0)},setGutterMarker:Yn(function(e,t,r){return co(this,e,\"gutter\",function(e){var n=e.gutterMarkers||(e.gutterMarkers={});return n[t]=r,!r&&re(n)&&(e.gutterMarkers=null),!0})}),clearGutter:Yn(function(e){var t=this;this.iter(function(r){r.gutterMarkers&&r.gutterMarkers[e]&&co(t,r,\"gutter\",function(){return r.gutterMarkers[e]=null,re(r.gutterMarkers)&&(r.gutterMarkers=null),!0})})}),lineInfo:function(e){var t;if(\"number\"==typeof e){if(!de(this,e))return null;if(t=e,!(e=se(this,e)))return null}else if(null==(t=he(e)))return null;return{line:t,handle:e,text:e.text,gutterMarkers:e.gutterMarkers,textClass:e.textClass,bgClass:e.bgClass,wrapClass:e.wrapClass,widgets:e.widgets}},addLineClass:Yn(function(e,t,r){return co(this,e,\"gutter\"==t?\"gutter\":\"class\",function(e){var n=\"text\"==t?\"textClass\":\"background\"==t?\"bgClass\":\"gutter\"==t?\"gutterClass\":\"wrapClass\";if(e[n]){if(L(r).test(e[n]))return!1;e[n]+=\" \"+r}else e[n]=r;return!0})}),removeLineClass:Yn(function(e,t,r){return co(this,e,\"gutter\"==t?\"gutter\":\"class\",function(e){var n=\"text\"==t?\"textClass\":\"background\"==t?\"bgClass\":\"gutter\"==t?\"gutterClass\":\"wrapClass\",i=e[n];if(!i)return!1;if(null==r)e[n]=null;else{var o=i.match(L(r));if(!o)return!1;var l=o.index+o[0].length;e[n]=i.slice(0,o.index)+(o.index&&l!=i.length?\" \":\"\")+i.slice(l)||null}return!0})}),addLineWidget:Yn(function(e,t,r){return i=e,o=new po(n=this,t,r),(l=n.cm)&&o.noHScroll&&(l.display.alignWidgets=!0),co(n,i,\"widget\",function(e){var t=e.widgets||(e.widgets=[]);if(null==o.insertAt?t.push(o):t.splice(Math.min(t.length-1,Math.max(0,o.insertAt)),0,o),o.line=e,l&&!Ve(n,e)){var r=je(e)<n.scrollTop;ce(e,e.height+mr(o)),r&&Cn(l,o.height),l.curOp.forceUpdate=!0}return!0}),l&&or(l,\"lineWidgetAdded\",l,o,\"number\"==typeof i?i:he(i)),o;var n,i,o,l}),removeLineWidget:function(e){e.clear()},markText:function(e,t,r){return yo(this,Ce(this,e),Ce(this,t),r,r&&r.type||\"range\")},setBookmark:function(e,t){var r={replacedWith:t&&(null==t.nodeType?t.widget:t),insertLeft:t&&t.insertLeft,clearWhenEmpty:!1,shared:t&&t.shared,handleMouseEvents:t&&t.handleMouseEvents};return yo(this,e=Ce(this,e),e,r,\"bookmark\")},findMarksAt:function(e){var t=[],r=se(this,(e=Ce(this,e)).line).markedSpans;if(r)for(var n=0;n<r.length;++n){var i=r[n];(null==i.from||i.from<=e.ch)&&(null==i.to||i.to>=e.ch)&&t.push(i.marker.parent||i.marker)}return t},findMarks:function(e,t,r){e=Ce(this,e),t=Ce(this,t);var n=[],i=e.line;return this.iter(e.line,t.line+1,function(o){var l=o.markedSpans;if(l)for(var s=0;s<l.length;s++){var a=l[s];null!=a.to&&i==e.line&&e.ch>=a.to||null==a.from&&i!=e.line||null!=a.from&&i==t.line&&a.from>=t.ch||r&&!r(a.marker)||n.push(a.marker.parent||a.marker)}++i}),n},getAllMarks:function(){var e=[];return this.iter(function(t){var r=t.markedSpans;if(r)for(var n=0;n<r.length;++n)null!=r[n].from&&e.push(r[n].marker)}),e},posFromIndex:function(e){var t,r=this.first,n=this.lineSeparator().length;return this.iter(function(i){var o=i.text.length+n;if(o>e)return t=e,!0;e-=o,++r}),Ce(this,ge(r,t))},indexFromPos:function(e){var t=(e=Ce(this,e)).ch;if(e.line<this.first||e.ch<0)return 0;var r=this.lineSeparator().length;return this.iter(this.first,e.line,function(e){t+=e.text.length+r}),t},copy:function(e){var t=new So(ue(this,this.first,this.first+this.size),this.modeOption,this.first,this.lineSep,this.direction);return t.scrollTop=this.scrollTop,t.scrollLeft=this.scrollLeft,t.sel=this.sel,t.extend=!1,e&&(t.history.undoDepth=this.history.undoDepth,t.setHistory(this.getHistory())),t},linkedDoc:function(e){e||(e={});var t=this.first,r=this.first+this.size;null!=e.from&&e.from>t&&(t=e.from),null!=e.to&&e.to<r&&(r=e.to);var n=new So(ue(this,t,r),e.mode||this.modeOption,t,this.lineSep,this.direction);return e.sharedHist&&(n.history=this.history),(this.linked||(this.linked=[])).push({doc:n,sharedHist:e.sharedHist}),n.linked=[{doc:this,isParent:!0,sharedHist:e.sharedHist}],function(e,t){for(var r=0;r<t.length;r++){var n=t[r],i=n.find(),o=e.clipPos(i.from),l=e.clipPos(i.to);if(ve(o,l)){var s=yo(e,o,l,n.primary,n.primary.type);n.markers.push(s),s.parent=n}}}(n,wo(this)),n},unlinkDoc:function(e){if(e instanceof wl&&(e=e.doc),this.linked)for(var t=0;t<this.linked.length;++t){if(this.linked[t].doc==e){this.linked.splice(t,1),e.unlinkDoc(this),xo(wo(this));break}}if(e.history==this.history){var r=[e.id];Ti(e,function(e){return r.push(e.id)},!0),e.history=new Oi(null),e.history.done=Ii(this.history.done,r),e.history.undone=Ii(this.history.undone,r)}},iterLinkedDocs:function(e){Ti(this,e)},getMode:function(){return this.mode},getEditor:function(){return this.cm},splitLines:function(e){return this.lineSep?e.split(this.lineSep):bt(e)},lineSeparator:function(){return this.lineSep||\"\\n\"},setDirection:Yn(function(e){var t;(\"rtl\"!=e&&(e=\"ltr\"),e!=this.direction)&&(this.direction=e,this.iter(function(e){return e.order=null}),this.cm&&Kn(t=this.cm,function(){Ni(t),_n(t)}))})}),So.prototype.eachLine=So.prototype.iter;var Lo=0;function ko(e){var t=this;if(To(t),!nt(t,e)&&!yr(t.display,e)){st(e),l&&(Lo=+new Date);var r=nn(t,e,!0),n=e.dataTransfer.files;if(r&&!t.isReadOnly())if(n&&n.length&&window.FileReader&&window.File)for(var i=n.length,o=Array(i),s=0,a=function(e,n){if(!t.options.allowDropFileTypes||-1!=B(t.options.allowDropFileTypes,e.type)){var l=new FileReader;l.onload=jn(t,function(){var e=l.result;if(/[\\x00-\\x08\\x0e-\\x1f]{2}/.test(e)&&(e=\"\"),o[n]=e,++s==i){var a={from:r=Ce(t.doc,r),to:r,text:t.doc.splitLines(o.join(t.doc.lineSeparator())),origin:\"paste\"};to(t.doc,a),Ki(t.doc,mi(r,yi(a)))}}),l.readAsText(e)}},u=0;u<i;++u)a(n[u],u);else{if(t.state.draggingText&&t.doc.sel.contains(r)>-1)return t.state.draggingText(e),void setTimeout(function(){return t.display.input.focus()},20);try{var c=e.dataTransfer.getData(\"Text\");if(c){var h;if(t.state.draggingText&&!t.state.draggingText.copy&&(h=t.listSelections()),Xi(t.doc,mi(r,r)),h)for(var f=0;f<h.length;++f)lo(t.doc,\"\",h[f].anchor,h[f].head,\"drag\");t.replaceSelection(c,\"around\",\"paste\"),t.display.input.focus()}}catch(e){}}}}function To(e){e.display.dragCursor&&(e.display.lineSpace.removeChild(e.display.dragCursor),e.display.dragCursor=null)}function Mo(e){if(document.getElementsByClassName)for(var t=document.getElementsByClassName(\"CodeMirror\"),r=0;r<t.length;r++){var n=t[r].CodeMirror;n&&e(n)}}var No=!1;function Oo(){var e;No||(Je(window,\"resize\",function(){null==e&&(e=setTimeout(function(){e=null,Mo(Ao)},100))}),Je(window,\"blur\",function(){return Mo(gn)}),No=!0)}function Ao(e){var t=e.display;t.lastWrapHeight==t.wrapper.clientHeight&&t.lastWrapWidth==t.wrapper.clientWidth||(t.cachedCharWidth=t.cachedTextHeight=t.cachedPaddingH=null,t.scrollbarsClipped=!1,e.setSize())}for(var Do={3:\"Pause\",8:\"Backspace\",9:\"Tab\",13:\"Enter\",16:\"Shift\",17:\"Ctrl\",18:\"Alt\",19:\"Pause\",20:\"CapsLock\",27:\"Esc\",32:\"Space\",33:\"PageUp\",34:\"PageDown\",35:\"End\",36:\"Home\",37:\"Left\",38:\"Up\",39:\"Right\",40:\"Down\",44:\"PrintScrn\",45:\"Insert\",46:\"Delete\",59:\";\",61:\"=\",91:\"Mod\",92:\"Mod\",93:\"Mod\",106:\"*\",107:\"=\",109:\"-\",110:\".\",111:\"/\",127:\"Delete\",145:\"ScrollLock\",173:\"-\",186:\";\",187:\"=\",188:\",\",189:\"-\",190:\".\",191:\"/\",192:\"`\",219:\"[\",220:\"\\\\\",221:\"]\",222:\"'\",63232:\"Up\",63233:\"Down\",63234:\"Left\",63235:\"Right\",63272:\"Delete\",63273:\"Home\",63275:\"End\",63276:\"PageUp\",63277:\"PageDown\",63302:\"Insert\"},Wo=0;Wo<10;Wo++)Do[Wo+48]=Do[Wo+96]=String(Wo);for(var Ho=65;Ho<=90;Ho++)Do[Ho]=String.fromCharCode(Ho);for(var Fo=1;Fo<=12;Fo++)Do[Fo+111]=Do[Fo+63235]=\"F\"+Fo;var Po={};function Eo(e){var t,r,n,i,o=e.split(/-(?!$)/);e=o[o.length-1];for(var l=0;l<o.length-1;l++){var s=o[l];if(/^(cmd|meta|m)$/i.test(s))i=!0;else if(/^a(lt)?$/i.test(s))t=!0;else if(/^(c|ctrl|control)$/i.test(s))r=!0;else{if(!/^s(hift)?$/i.test(s))throw new Error(\"Unrecognized modifier name: \"+s);n=!0}}return t&&(e=\"Alt-\"+e),r&&(e=\"Ctrl-\"+e),i&&(e=\"Cmd-\"+e),n&&(e=\"Shift-\"+e),e}function zo(e){var t={};for(var r in e)if(e.hasOwnProperty(r)){var n=e[r];if(/^(name|fallthrough|(de|at)tach)$/.test(r))continue;if(\"...\"==n){delete e[r];continue}for(var i=$(r.split(\" \"),Eo),o=0;o<i.length;o++){var l=void 0,s=void 0;o==i.length-1?(s=i.join(\" \"),l=n):(s=i.slice(0,o+1).join(\" \"),l=\"...\");var a=t[s];if(a){if(a!=l)throw new Error(\"Inconsistent bindings for \"+s)}else t[s]=l}delete e[r]}for(var u in t)e[u]=t[u];return e}function Io(e,t,r,n){var i=(t=Uo(t)).call?t.call(e,n):t[e];if(!1===i)return\"nothing\";if(\"...\"===i)return\"multi\";if(null!=i&&r(i))return\"handled\";if(t.fallthrough){if(\"[object Array]\"!=Object.prototype.toString.call(t.fallthrough))return Io(e,t.fallthrough,r,n);for(var o=0;o<t.fallthrough.length;o++){var l=Io(e,t.fallthrough[o],r,n);if(l)return l}}}function Ro(e){var t=\"string\"==typeof e?e:Do[e.keyCode];return\"Ctrl\"==t||\"Alt\"==t||\"Shift\"==t||\"Mod\"==t}function Bo(e,t,r){var n=e;return t.altKey&&\"Alt\"!=n&&(e=\"Alt-\"+e),(C?t.metaKey:t.ctrlKey)&&\"Ctrl\"!=n&&(e=\"Ctrl-\"+e),(C?t.ctrlKey:t.metaKey)&&\"Cmd\"!=n&&(e=\"Cmd-\"+e),!r&&t.shiftKey&&\"Shift\"!=n&&(e=\"Shift-\"+e),e}function Go(e,t){if(h&&34==e.keyCode&&e.char)return!1;var r=Do[e.keyCode];return null!=r&&!e.altGraphKey&&(3==e.keyCode&&e.code&&(r=e.code),Bo(r,e,t))}function Uo(e){return\"string\"==typeof e?Po[e]:e}function Vo(e,t){for(var r=e.doc.sel.ranges,n=[],i=0;i<r.length;i++){for(var o=t(r[i]);n.length&&ve(o.from,q(n).to)<=0;){var l=n.pop();if(ve(l.from,o.from)<0){o.from=l.from;break}}n.push(o)}Kn(e,function(){for(var t=n.length-1;t>=0;t--)lo(e.doc,\"\",n[t].from,n[t].to,\"+delete\");Sn(e)})}function Ko(e,t,r){var n=oe(e.text,t+r,r);return n<0||n>e.text.length?null:n}function jo(e,t,r){var n=Ko(e,t.ch,r);return null==n?null:new ge(t.line,n,r<0?\"after\":\"before\")}function Xo(e,t,r,n,i){if(e){var o=Ze(r,t.doc.direction);if(o){var l,s=i<0?q(o):o[0],a=i<0==(1==s.level)?\"after\":\"before\";if(s.level>0||\"rtl\"==t.doc.direction){var u=Nr(t,r);l=i<0?r.text.length-1:0;var c=Or(t,u,l).top;l=le(function(e){return Or(t,u,e).top==c},i<0==(1==s.level)?s.from:s.to-1,l),\"before\"==a&&(l=Ko(r,l,1))}else l=i<0?s.to:s.from;return new ge(n,l,a)}}return new ge(n,i<0?r.text.length:0,i<0?\"before\":\"after\")}Po.basic={Left:\"goCharLeft\",Right:\"goCharRight\",Up:\"goLineUp\",Down:\"goLineDown\",End:\"goLineEnd\",Home:\"goLineStartSmart\",PageUp:\"goPageUp\",PageDown:\"goPageDown\",Delete:\"delCharAfter\",Backspace:\"delCharBefore\",\"Shift-Backspace\":\"delCharBefore\",Tab:\"defaultTab\",\"Shift-Tab\":\"indentAuto\",Enter:\"newlineAndIndent\",Insert:\"toggleOverwrite\",Esc:\"singleSelection\"},Po.pcDefault={\"Ctrl-A\":\"selectAll\",\"Ctrl-D\":\"deleteLine\",\"Ctrl-Z\":\"undo\",\"Shift-Ctrl-Z\":\"redo\",\"Ctrl-Y\":\"redo\",\"Ctrl-Home\":\"goDocStart\",\"Ctrl-End\":\"goDocEnd\",\"Ctrl-Up\":\"goLineUp\",\"Ctrl-Down\":\"goLineDown\",\"Ctrl-Left\":\"goGroupLeft\",\"Ctrl-Right\":\"goGroupRight\",\"Alt-Left\":\"goLineStart\",\"Alt-Right\":\"goLineEnd\",\"Ctrl-Backspace\":\"delGroupBefore\",\"Ctrl-Delete\":\"delGroupAfter\",\"Ctrl-S\":\"save\",\"Ctrl-F\":\"find\",\"Ctrl-G\":\"findNext\",\"Shift-Ctrl-G\":\"findPrev\",\"Shift-Ctrl-F\":\"replace\",\"Shift-Ctrl-R\":\"replaceAll\",\"Ctrl-[\":\"indentLess\",\"Ctrl-]\":\"indentMore\",\"Ctrl-U\":\"undoSelection\",\"Shift-Ctrl-U\":\"redoSelection\",\"Alt-U\":\"redoSelection\",fallthrough:\"basic\"},Po.emacsy={\"Ctrl-F\":\"goCharRight\",\"Ctrl-B\":\"goCharLeft\",\"Ctrl-P\":\"goLineUp\",\"Ctrl-N\":\"goLineDown\",\"Alt-F\":\"goWordRight\",\"Alt-B\":\"goWordLeft\",\"Ctrl-A\":\"goLineStart\",\"Ctrl-E\":\"goLineEnd\",\"Ctrl-V\":\"goPageDown\",\"Shift-Ctrl-V\":\"goPageUp\",\"Ctrl-D\":\"delCharAfter\",\"Ctrl-H\":\"delCharBefore\",\"Alt-D\":\"delWordAfter\",\"Alt-Backspace\":\"delWordBefore\",\"Ctrl-K\":\"killLine\",\"Ctrl-T\":\"transposeChars\",\"Ctrl-O\":\"openLine\"},Po.macDefault={\"Cmd-A\":\"selectAll\",\"Cmd-D\":\"deleteLine\",\"Cmd-Z\":\"undo\",\"Shift-Cmd-Z\":\"redo\",\"Cmd-Y\":\"redo\",\"Cmd-Home\":\"goDocStart\",\"Cmd-Up\":\"goDocStart\",\"Cmd-End\":\"goDocEnd\",\"Cmd-Down\":\"goDocEnd\",\"Alt-Left\":\"goGroupLeft\",\"Alt-Right\":\"goGroupRight\",\"Cmd-Left\":\"goLineLeft\",\"Cmd-Right\":\"goLineRight\",\"Alt-Backspace\":\"delGroupBefore\",\"Ctrl-Alt-Backspace\":\"delGroupAfter\",\"Alt-Delete\":\"delGroupAfter\",\"Cmd-S\":\"save\",\"Cmd-F\":\"find\",\"Cmd-G\":\"findNext\",\"Shift-Cmd-G\":\"findPrev\",\"Cmd-Alt-F\":\"replace\",\"Shift-Cmd-Alt-F\":\"replaceAll\",\"Cmd-[\":\"indentLess\",\"Cmd-]\":\"indentMore\",\"Cmd-Backspace\":\"delWrappedLineLeft\",\"Cmd-Delete\":\"delWrappedLineRight\",\"Cmd-U\":\"undoSelection\",\"Shift-Cmd-U\":\"redoSelection\",\"Ctrl-Up\":\"goDocStart\",\"Ctrl-Down\":\"goDocEnd\",fallthrough:[\"basic\",\"emacsy\"]},Po.default=y?Po.macDefault:Po.pcDefault;var Yo={selectAll:Ji,singleSelection:function(e){return e.setSelection(e.getCursor(\"anchor\"),e.getCursor(\"head\"),V)},killLine:function(e){return Vo(e,function(t){if(t.empty()){var r=se(e.doc,t.head.line).text.length;return t.head.ch==r&&t.head.line<e.lastLine()?{from:t.head,to:ge(t.head.line+1,0)}:{from:t.head,to:ge(t.head.line,r)}}return{from:t.from(),to:t.to()}})},deleteLine:function(e){return Vo(e,function(t){return{from:ge(t.from().line,0),to:Ce(e.doc,ge(t.to().line+1,0))}})},delLineLeft:function(e){return Vo(e,function(e){return{from:ge(e.from().line,0),to:e.from()}})},delWrappedLineLeft:function(e){return Vo(e,function(t){var r=e.charCoords(t.head,\"div\").top+5;return{from:e.coordsChar({left:0,top:r},\"div\"),to:t.from()}})},delWrappedLineRight:function(e){return Vo(e,function(t){var r=e.charCoords(t.head,\"div\").top+5,n=e.coordsChar({left:e.display.lineDiv.offsetWidth+100,top:r},\"div\");return{from:t.from(),to:n}})},undo:function(e){return e.undo()},redo:function(e){return e.redo()},undoSelection:function(e){return e.undoSelection()},redoSelection:function(e){return e.redoSelection()},goDocStart:function(e){return e.extendSelection(ge(e.firstLine(),0))},goDocEnd:function(e){return e.extendSelection(ge(e.lastLine()))},goLineStart:function(e){return e.extendSelectionsBy(function(t){return _o(e,t.head.line)},{origin:\"+move\",bias:1})},goLineStartSmart:function(e){return e.extendSelectionsBy(function(t){return qo(e,t.head)},{origin:\"+move\",bias:1})},goLineEnd:function(e){return e.extendSelectionsBy(function(t){return function(e,t){var r=se(e.doc,t),n=function(e){for(var t;t=Ie(e);)e=t.find(1,!0).line;return e}(r);n!=r&&(t=he(n));return Xo(!0,e,r,t,-1)}(e,t.head.line)},{origin:\"+move\",bias:-1})},goLineRight:function(e){return e.extendSelectionsBy(function(t){var r=e.cursorCoords(t.head,\"div\").top+5;return e.coordsChar({left:e.display.lineDiv.offsetWidth+100,top:r},\"div\")},j)},goLineLeft:function(e){return e.extendSelectionsBy(function(t){var r=e.cursorCoords(t.head,\"div\").top+5;return e.coordsChar({left:0,top:r},\"div\")},j)},goLineLeftSmart:function(e){return e.extendSelectionsBy(function(t){var r=e.cursorCoords(t.head,\"div\").top+5,n=e.coordsChar({left:0,top:r},\"div\");return n.ch<e.getLine(n.line).search(/\\S/)?qo(e,t.head):n},j)},goLineUp:function(e){return e.moveV(-1,\"line\")},goLineDown:function(e){return e.moveV(1,\"line\")},goPageUp:function(e){return e.moveV(-1,\"page\")},goPageDown:function(e){return e.moveV(1,\"page\")},goCharLeft:function(e){return e.moveH(-1,\"char\")},goCharRight:function(e){return e.moveH(1,\"char\")},goColumnLeft:function(e){return e.moveH(-1,\"column\")},goColumnRight:function(e){return e.moveH(1,\"column\")},goWordLeft:function(e){return e.moveH(-1,\"word\")},goGroupRight:function(e){return e.moveH(1,\"group\")},goGroupLeft:function(e){return e.moveH(-1,\"group\")},goWordRight:function(e){return e.moveH(1,\"word\")},delCharBefore:function(e){return e.deleteH(-1,\"char\")},delCharAfter:function(e){return e.deleteH(1,\"char\")},delWordBefore:function(e){return e.deleteH(-1,\"word\")},delWordAfter:function(e){return e.deleteH(1,\"word\")},delGroupBefore:function(e){return e.deleteH(-1,\"group\")},delGroupAfter:function(e){return e.deleteH(1,\"group\")},indentAuto:function(e){return e.indentSelection(\"smart\")},indentMore:function(e){return e.indentSelection(\"add\")},indentLess:function(e){return e.indentSelection(\"subtract\")},insertTab:function(e){return e.replaceSelection(\"\\t\")},insertSoftTab:function(e){for(var t=[],r=e.listSelections(),n=e.options.tabSize,i=0;i<r.length;i++){var o=r[i].from(),l=I(e.getLine(o.line),o.ch,n);t.push(_(n-l%n))}e.replaceSelections(t)},defaultTab:function(e){e.somethingSelected()?e.indentSelection(\"add\"):e.execCommand(\"insertTab\")},transposeChars:function(e){return Kn(e,function(){for(var t=e.listSelections(),r=[],n=0;n<t.length;n++)if(t[n].empty()){var i=t[n].head,o=se(e.doc,i.line).text;if(o)if(i.ch==o.length&&(i=new ge(i.line,i.ch-1)),i.ch>0)i=new ge(i.line,i.ch+1),e.replaceRange(o.charAt(i.ch-1)+o.charAt(i.ch-2),ge(i.line,i.ch-2),i,\"+transpose\");else if(i.line>e.doc.first){var l=se(e.doc,i.line-1).text;l&&(i=new ge(i.line,1),e.replaceRange(o.charAt(0)+e.doc.lineSeparator()+l.charAt(l.length-1),ge(i.line-1,l.length-1),i,\"+transpose\"))}r.push(new gi(i,i))}e.setSelections(r)})},newlineAndIndent:function(e){return Kn(e,function(){for(var t=e.listSelections(),r=t.length-1;r>=0;r--)e.replaceRange(e.doc.lineSeparator(),t[r].anchor,t[r].head,\"+input\");t=e.listSelections();for(var n=0;n<t.length;n++)e.indentLine(t[n].from().line,null,!0);Sn(e)})},openLine:function(e){return e.replaceSelection(\"\\n\",\"start\")},toggleOverwrite:function(e){return e.toggleOverwrite()}};function _o(e,t){var r=se(e.doc,t),n=Be(r);return n!=r&&(t=he(n)),Xo(!0,e,n,t,1)}function qo(e,t){var r=_o(e,t.line),n=se(e.doc,r.line),i=Ze(n,e.doc.direction);if(!i||0==i[0].level){var o=Math.max(0,n.text.search(/\\S/)),l=t.line==r.line&&t.ch<=o&&t.ch;return ge(r.line,l?0:o,r.sticky)}return r}function $o(e,t,r){if(\"string\"==typeof t&&!(t=Yo[t]))return!1;e.display.input.ensurePolled();var n=e.display.shift,i=!1;try{e.isReadOnly()&&(e.state.suppressEdits=!0),r&&(e.display.shift=!1),i=t(e)!=U}finally{e.display.shift=n,e.state.suppressEdits=!1}return i}var Zo=new R;function Qo(e,t,r,n){var i=e.state.keySeq;if(i){if(Ro(t))return\"handled\";if(/\\'$/.test(t)?e.state.keySeq=null:Zo.set(50,function(){e.state.keySeq==i&&(e.state.keySeq=null,e.display.input.reset())}),Jo(e,i+\" \"+t,r,n))return!0}return Jo(e,t,r,n)}function Jo(e,t,r,n){var i=function(e,t,r){for(var n=0;n<e.state.keyMaps.length;n++){var i=Io(t,e.state.keyMaps[n],r,e);if(i)return i}return e.options.extraKeys&&Io(t,e.options.extraKeys,r,e)||Io(t,e.options.keyMap,r,e)}(e,t,n);return\"multi\"==i&&(e.state.keySeq=t),\"handled\"==i&&or(e,\"keyHandled\",e,t,r),\"handled\"!=i&&\"multi\"!=i||(st(r),hn(e)),!!i}function el(e,t){var r=Go(t,!0);return!!r&&(t.shiftKey&&!e.state.keySeq?Qo(e,\"Shift-\"+r,t,function(t){return $o(e,t,!0)})||Qo(e,r,t,function(t){if(\"string\"==typeof t?/^go[A-Z]/.test(t):t.motion)return $o(e,t)}):Qo(e,r,t,function(t){return $o(e,t)}))}var tl=null;function rl(e){var t=this;if(t.curOp.focus=W(),!nt(t,e)){l&&s<11&&27==e.keyCode&&(e.returnValue=!1);var r=e.keyCode;t.display.shift=16==r||e.shiftKey;var n=el(t,e);h&&(tl=n?r:null,!n&&88==r&&!xt&&(y?e.metaKey:e.ctrlKey)&&t.replaceSelection(\"\",null,\"cut\")),18!=r||/\\bCodeMirror-crosshair\\b/.test(t.display.lineDiv.className)||function(e){var t=e.display.lineDiv;function r(e){18!=e.keyCode&&e.altKey||(T(t,\"CodeMirror-crosshair\"),tt(document,\"keyup\",r),tt(document,\"mouseover\",r))}H(t,\"CodeMirror-crosshair\"),Je(document,\"keyup\",r),Je(document,\"mouseover\",r)}(t)}}function nl(e){16==e.keyCode&&(this.doc.sel.shift=!1),nt(this,e)}function il(e){var t=this;if(!(yr(t.display,e)||nt(t,e)||e.ctrlKey&&!e.altKey||y&&e.metaKey)){var r=e.keyCode,n=e.charCode;if(h&&r==tl)return tl=null,void st(e);if(!h||e.which&&!(e.which<10)||!el(t,e)){var i,o=String.fromCharCode(null==n?r:n);if(\"\\b\"!=o)if(!Qo(i=t,\"'\"+o+\"'\",e,function(e){return $o(i,e,!0)}))t.display.input.onKeyPress(e)}}}var ol,ll,sl=function(e,t,r){this.time=e,this.pos=t,this.button=r};function al(e){var t=this,r=t.display;if(!(nt(t,e)||r.activeTouch&&r.input.supportsTouch()))if(r.input.ensurePolled(),r.shift=e.shiftKey,yr(r,e))a||(r.scroller.draggable=!1,setTimeout(function(){return r.scroller.draggable=!0},100));else if(!hl(t,e)){var n,i,o,u=nn(t,e),c=ft(e),h=u?(n=u,i=c,o=+new 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o=b?r.shiftKey&&r.metaKey:r.altKey;i.unit=o?\"rectangle\":\"single\"==t?\"char\":\"double\"==t?\"word\":\"line\"}(null==i.extend||e.doc.extend)&&(i.extend=e.doc.extend||r.shiftKey);null==i.addNew&&(i.addNew=y?r.metaKey:r.ctrlKey);null==i.moveOnDrag&&(i.moveOnDrag=!(y?r.altKey:r.ctrlKey));return i}(e,r,n),u=e.doc.sel;e.options.dragDrop&>&&!e.isReadOnly()&&\"single\"==r&&(i=u.contains(t))>-1&&(ve((i=u.ranges[i]).from(),t)<0||t.xRel>0)&&(ve(i.to(),t)>0||t.xRel<0)?function(e,t,r,n){var i=e.display,o=!1,u=jn(e,function(t){a&&(i.scroller.draggable=!1),e.state.draggingText=!1,tt(i.wrapper.ownerDocument,\"mouseup\",u),tt(i.wrapper.ownerDocument,\"mousemove\",c),tt(i.scroller,\"dragstart\",h),tt(i.scroller,\"drop\",u),o||(st(t),n.addNew||Bi(e.doc,r,null,null,n.extend),a||l&&9==s?setTimeout(function(){i.wrapper.ownerDocument.body.focus(),i.input.focus()},20):i.input.focus())}),c=function(e){o=o||Math.abs(t.clientX-e.clientX)+Math.abs(t.clientY-e.clientY)>=10},h=function(){return 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i=[],u=e.options.tabSize,c=I(se(o,r.line).text,r.ch,u),f=I(se(o,t.line).text,t.ch,u),d=Math.min(c,f),p=Math.max(c,f),g=Math.min(r.line,t.line),v=Math.min(e.lastLine(),Math.max(r.line,t.line));g<=v;g++){var m=se(o,g).text,y=X(m,d,u);d==p?i.push(new gi(ge(g,y),ge(g,y))):m.length>y&&i.push(new gi(ge(g,y),ge(g,X(m,p,u))))}i.length||i.push(new gi(r,r)),ji(o,vi(a.ranges.slice(0,s).concat(i),s),{origin:\"*mouse\",scroll:!1}),e.scrollIntoView(t)}else{var b,w=l,x=ul(e,t,n.unit),C=w.anchor;ve(x.anchor,C)>0?(b=x.head,C=we(w.from(),x.anchor)):(b=x.anchor,C=be(w.to(),x.head));var S=a.ranges.slice(0);S[s]=function(e,t){var r=t.anchor,n=t.head,i=se(e.doc,r.line);if(0==ve(r,n)&&r.sticky==n.sticky)return t;var o=Ze(i);if(!o)return t;var l=qe(o,r.ch,r.sticky),s=o[l];if(s.from!=r.ch&&s.to!=r.ch)return t;var a,u=l+(s.from==r.ch==(1!=s.level)?0:1);if(0==u||u==o.length)return t;if(n.line!=r.line)a=(n.line-r.line)*(\"ltr\"==e.doc.direction?1:-1)>0;else{var c=qe(o,n.ch,n.sticky),h=c-l||(n.ch-r.ch)*(1==s.level?-1:1);a=c==u-1||c==u?h<0:h>0}var f=o[u+(a?-1:0)],d=a==(1==f.level),p=d?f.from:f.to,g=d?\"after\":\"before\";return r.ch==p&&r.sticky==g?t:new gi(new ge(r.line,p,g),n)}(e,new gi(Ce(o,C),b)),ji(o,vi(S,s),K)}}var d=i.wrapper.getBoundingClientRect(),p=0;function g(t){e.state.selectingText=!1,p=1/0,st(t),i.input.focus(),tt(i.wrapper.ownerDocument,\"mousemove\",v),tt(i.wrapper.ownerDocument,\"mouseup\",m),o.history.lastSelOrigin=null}var v=jn(e,function(t){ft(t)?function t(r){var l=++p;var s=nn(e,r,!0,\"rectangle\"==n.unit);if(!s)return;if(0!=ve(s,h)){e.curOp.focus=W(),f(s);var a=yn(i,o);(s.line>=a.to||s.line<a.from)&&setTimeout(jn(e,function(){p==l&&t(r)}),150)}else{var u=r.clientY<d.top?-20:r.clientY>d.bottom?20:0;u&&setTimeout(jn(e,function(){p==l&&(i.scroller.scrollTop+=u,t(r))}),50)}}(t):g(t)}),m=jn(e,g);e.state.selectingText=m,Je(i.wrapper.ownerDocument,\"mousemove\",v),Je(i.wrapper.ownerDocument,\"mouseup\",m)}(e,n,t,o)}(t,u,h,e):ht(e)==r.scroller&&st(e):2==c?(u&&Bi(t.doc,u),setTimeout(function(){return r.input.focus()},20)):3==c&&(S?fl(t,e):dn(t)))}}function ul(e,t,r){if(\"char\"==r)return new gi(t,t);if(\"word\"==r)return e.findWordAt(t);if(\"line\"==r)return new gi(ge(t.line,0),Ce(e.doc,ge(t.line+1,0)));var n=r(e,t);return new gi(n.from,n.to)}function cl(e,t,r,n){var i,o;if(t.touches)i=t.touches[0].clientX,o=t.touches[0].clientY;else try{i=t.clientX,o=t.clientY}catch(t){return!1}if(i>=Math.floor(e.display.gutters.getBoundingClientRect().right))return!1;n&&st(t);var l=e.display,s=l.lineDiv.getBoundingClientRect();if(o>s.bottom||!ot(e,r))return ut(t);o-=s.top-l.viewOffset;for(var a=0;a<e.options.gutters.length;++a){var u=l.gutters.childNodes[a];if(u&&u.getBoundingClientRect().right>=i)return rt(e,r,e,fe(e.doc,o),e.options.gutters[a],t),ut(t)}}function hl(e,t){return cl(e,t,\"gutterClick\",!0)}function fl(e,t){yr(e.display,t)||function(e,t){if(!ot(e,\"gutterContextMenu\"))return!1;return cl(e,t,\"gutterContextMenu\",!1)}(e,t)||nt(e,t,\"contextmenu\")||e.display.input.onContextMenu(t)}function dl(e){e.display.wrapper.className=e.display.wrapper.className.replace(/\\s*cm-s-\\S+/g,\"\")+e.options.theme.replace(/(^|\\s)\\s*/g,\" cm-s-\"),Er(e)}sl.prototype.compare=function(e,t,r){return this.time+400>e&&0==ve(t,this.pos)&&r==this.button};var pl={toString:function(){return\"CodeMirror.Init\"}},gl={},vl={};function ml(e){si(e),_n(e),bn(e)}function yl(e,t,r){if(!t!=!(r&&r!=pl)){var n=e.display.dragFunctions,i=t?Je:tt;i(e.display.scroller,\"dragstart\",n.start),i(e.display.scroller,\"dragenter\",n.enter),i(e.display.scroller,\"dragover\",n.over),i(e.display.scroller,\"dragleave\",n.leave),i(e.display.scroller,\"drop\",n.drop)}}function bl(e){e.options.lineWrapping?(H(e.display.wrapper,\"CodeMirror-wrap\"),e.display.sizer.style.minWidth=\"\",e.display.sizerWidth=null):(T(e.display.wrapper,\"CodeMirror-wrap\"),Ye(e)),rn(e),_n(e),Er(e),setTimeout(function(){return Hn(e)},100)}function wl(e,t){var n=this;if(!(this instanceof wl))return new wl(e,t);this.options=t=t?z(t):{},z(gl,t,!1),ai(t);var i=t.value;\"string\"==typeof i&&(i=new So(i,t.mode,null,t.lineSeparator,t.direction)),this.doc=i;var o=new wl.inputStyles[t.inputStyle](this),u=this.display=new function(e,t,n){var i=this;this.input=n,i.scrollbarFiller=O(\"div\",null,\"CodeMirror-scrollbar-filler\"),i.scrollbarFiller.setAttribute(\"cm-not-content\",\"true\"),i.gutterFiller=O(\"div\",null,\"CodeMirror-gutter-filler\"),i.gutterFiller.setAttribute(\"cm-not-content\",\"true\"),i.lineDiv=A(\"div\",null,\"CodeMirror-code\"),i.selectionDiv=O(\"div\",null,null,\"position: relative; z-index: 1\"),i.cursorDiv=O(\"div\",null,\"CodeMirror-cursors\"),i.measure=O(\"div\",null,\"CodeMirror-measure\"),i.lineMeasure=O(\"div\",null,\"CodeMirror-measure\"),i.lineSpace=A(\"div\",[i.measure,i.lineMeasure,i.selectionDiv,i.cursorDiv,i.lineDiv],null,\"position: relative; outline: none\");var o=A(\"div\",[i.lineSpace],\"CodeMirror-lines\");i.mover=O(\"div\",[o],null,\"position: relative\"),i.sizer=O(\"div\",[i.mover],\"CodeMirror-sizer\"),i.sizerWidth=null,i.heightForcer=O(\"div\",null,null,\"position: absolute; height: \"+G+\"px; width: 1px;\"),i.gutters=O(\"div\",null,\"CodeMirror-gutters\"),i.lineGutter=null,i.scroller=O(\"div\",[i.sizer,i.heightForcer,i.gutters],\"CodeMirror-scroll\"),i.scroller.setAttribute(\"tabIndex\",\"-1\"),i.wrapper=O(\"div\",[i.scrollbarFiller,i.gutterFiller,i.scroller],\"CodeMirror\"),l&&s<8&&(i.gutters.style.zIndex=-1,i.scroller.style.paddingRight=0),a||r&&m||(i.scroller.draggable=!0),e&&(e.appendChild?e.appendChild(i.wrapper):e(i.wrapper)),i.viewFrom=i.viewTo=t.first,i.reportedViewFrom=i.reportedViewTo=t.first,i.view=[],i.renderedView=null,i.externalMeasured=null,i.viewOffset=0,i.lastWrapHeight=i.lastWrapWidth=0,i.updateLineNumbers=null,i.nativeBarWidth=i.barHeight=i.barWidth=0,i.scrollbarsClipped=!1,i.lineNumWidth=i.lineNumInnerWidth=i.lineNumChars=null,i.alignWidgets=!1,i.cachedCharWidth=i.cachedTextHeight=i.cachedPaddingH=null,i.maxLine=null,i.maxLineLength=0,i.maxLineChanged=!1,i.wheelDX=i.wheelDY=i.wheelStartX=i.wheelStartY=null,i.shift=!1,i.selForContextMenu=null,i.activeTouch=null,n.init(i)}(e,i,o);for(var c in u.wrapper.CodeMirror=this,si(this),dl(this),t.lineWrapping&&(this.display.wrapper.className+=\" CodeMirror-wrap\"),En(this),this.state={keyMaps:[],overlays:[],modeGen:0,overwrite:!1,delayingBlurEvent:!1,focused:!1,suppressEdits:!1,pasteIncoming:!1,cutIncoming:!1,selectingText:!1,draggingText:!1,highlight:new R,keySeq:null,specialChars:null},t.autofocus&&!m&&u.input.focus(),l&&s<11&&setTimeout(function(){return n.display.input.reset(!0)},20),function(e){var t=e.display;Je(t.scroller,\"mousedown\",jn(e,al)),Je(t.scroller,\"dblclick\",l&&s<11?jn(e,function(t){if(!nt(e,t)){var r=nn(e,t);if(r&&!hl(e,t)&&!yr(e.display,t)){st(t);var n=e.findWordAt(r);Bi(e.doc,n.anchor,n.head)}}}):function(t){return nt(e,t)||st(t)});S||Je(t.scroller,\"contextmenu\",function(t){return fl(e,t)});var r,n={end:0};function i(){t.activeTouch&&(r=setTimeout(function(){return t.activeTouch=null},1e3),(n=t.activeTouch).end=+new Date)}function o(e,t){if(null==t.left)return!0;var r=t.left-e.left,n=t.top-e.top;return r*r+n*n>400}Je(t.scroller,\"touchstart\",function(i){if(!nt(e,i)&&!function(e){if(1!=e.touches.length)return!1;var t=e.touches[0];return t.radiusX<=1&&t.radiusY<=1}(i)&&!hl(e,i)){t.input.ensurePolled(),clearTimeout(r);var o=+new Date;t.activeTouch={start:o,moved:!1,prev:o-n.end<=300?n:null},1==i.touches.length&&(t.activeTouch.left=i.touches[0].pageX,t.activeTouch.top=i.touches[0].pageY)}}),Je(t.scroller,\"touchmove\",function(){t.activeTouch&&(t.activeTouch.moved=!0)}),Je(t.scroller,\"touchend\",function(r){var n=t.activeTouch;if(n&&!yr(t,r)&&null!=n.left&&!n.moved&&new Date-n.start<300){var l,s=e.coordsChar(t.activeTouch,\"page\");l=!n.prev||o(n,n.prev)?new gi(s,s):!n.prev.prev||o(n,n.prev.prev)?e.findWordAt(s):new gi(ge(s.line,0),Ce(e.doc,ge(s.line+1,0))),e.setSelection(l.anchor,l.head),e.focus(),st(r)}i()}),Je(t.scroller,\"touchcancel\",i),Je(t.scroller,\"scroll\",function(){t.scroller.clientHeight&&(Mn(e,t.scroller.scrollTop),On(e,t.scroller.scrollLeft,!0),rt(e,\"scroll\",e))}),Je(t.scroller,\"mousewheel\",function(t){return di(e,t)}),Je(t.scroller,\"DOMMouseScroll\",function(t){return di(e,t)}),Je(t.wrapper,\"scroll\",function(){return t.wrapper.scrollTop=t.wrapper.scrollLeft=0}),t.dragFunctions={enter:function(t){nt(e,t)||ct(t)},over:function(t){nt(e,t)||(!function(e,t){var r=nn(e,t);if(r){var n=document.createDocumentFragment();an(e,r,n),e.display.dragCursor||(e.display.dragCursor=O(\"div\",null,\"CodeMirror-cursors CodeMirror-dragcursors\"),e.display.lineSpace.insertBefore(e.display.dragCursor,e.display.cursorDiv)),N(e.display.dragCursor,n)}}(e,t),ct(t))},start:function(t){return function(e,t){if(t.preventDefault&&t.preventDefault(),l&&(!e.state.draggingText||+new Date-Lo<100))ct(t);else if(!nt(e,t)&&!yr(e.display,t)&&(t.dataTransfer.setData(\"Text\",e.getSelection()),t.dataTransfer.effectAllowed=\"copyMove\",t.dataTransfer.setDragImage&&!f)){var r=O(\"img\",null,null,\"position: fixed; left: 0; top: 0;\");r.src=\"data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\",h&&(r.width=r.height=1,e.display.wrapper.appendChild(r),r._top=r.offsetTop),t.dataTransfer.setDragImage(r,0,0),h&&r.parentNode.removeChild(r)}}(e,t)},drop:jn(e,ko),leave:function(t){nt(e,t)||To(e)}};var a=t.input.getField();Je(a,\"keyup\",function(t){return nl.call(e,t)}),Je(a,\"keydown\",jn(e,rl)),Je(a,\"keypress\",jn(e,il)),Je(a,\"focus\",function(t){return pn(e,t)}),Je(a,\"blur\",function(t){return gn(e,t)})}(this),Oo(),In(this),this.curOp.forceUpdate=!0,Mi(this,i),t.autofocus&&!m||this.hasFocus()?setTimeout(E(pn,this),20):gn(this),vl)vl.hasOwnProperty(c)&&vl[c](n,t[c],pl);wn(this),t.finishInit&&t.finishInit(this);for(var d=0;d<xl.length;++d)xl[d](n);Rn(this),a&&t.lineWrapping&&\"optimizelegibility\"==getComputedStyle(u.lineDiv).textRendering&&(u.lineDiv.style.textRendering=\"auto\")}wl.defaults=gl,wl.optionHandlers=vl;var xl=[];function Cl(e,t,r,n){var i,o=e.doc;null==r&&(r=\"add\"),\"smart\"==r&&(o.mode.indent?i=zt(e,t).state:r=\"prev\");var l=e.options.tabSize,s=se(o,t),a=I(s.text,null,l);s.stateAfter&&(s.stateAfter=null);var u,c=s.text.match(/^\\s*/)[0];if(n||/\\S/.test(s.text)){if(\"smart\"==r&&((u=o.mode.indent(i,s.text.slice(c.length),s.text))==U||u>150)){if(!n)return;r=\"prev\"}}else u=0,r=\"not\";\"prev\"==r?u=t>o.first?I(se(o,t-1).text,null,l):0:\"add\"==r?u=a+e.options.indentUnit:\"subtract\"==r?u=a-e.options.indentUnit:\"number\"==typeof r&&(u=a+r),u=Math.max(0,u);var h=\"\",f=0;if(e.options.indentWithTabs)for(var d=Math.floor(u/l);d;--d)f+=l,h+=\"\\t\";if(f<u&&(h+=_(u-f)),h!=c)return lo(o,h,ge(t,0),ge(t,c.length),\"+input\"),s.stateAfter=null,!0;for(var p=0;p<o.sel.ranges.length;p++){var g=o.sel.ranges[p];if(g.head.line==t&&g.head.ch<c.length){var v=ge(t,c.length);Ui(o,p,new gi(v,v));break}}}wl.defineInitHook=function(e){return xl.push(e)};var Sl=null;function Ll(e){Sl=e}function kl(e,t,r,n,i){var o=e.doc;e.display.shift=!1,n||(n=o.sel);var l,s=e.state.pasteIncoming||\"paste\"==i,a=bt(t),u=null;if(s&&n.ranges.length>1)if(Sl&&Sl.text.join(\"\\n\")==t){if(n.ranges.length%Sl.text.length==0){u=[];for(var c=0;c<Sl.text.length;c++)u.push(o.splitLines(Sl.text[c]))}}else a.length==n.ranges.length&&e.options.pasteLinesPerSelection&&(u=$(a,function(e){return[e]}));for(var h=n.ranges.length-1;h>=0;h--){var f=n.ranges[h],d=f.from(),p=f.to();f.empty()&&(r&&r>0?d=ge(d.line,d.ch-r):e.state.overwrite&&!s?p=ge(p.line,Math.min(se(o,p.line).text.length,p.ch+q(a).length)):Sl&&Sl.lineWise&&Sl.text.join(\"\\n\")==t&&(d=p=ge(d.line,0))),l=e.curOp.updateInput;var g={from:d,to:p,text:u?u[h%u.length]:a,origin:i||(s?\"paste\":e.state.cutIncoming?\"cut\":\"+input\")};to(e.doc,g),or(e,\"inputRead\",e,g)}t&&!s&&Ml(e,t),Sn(e),e.curOp.updateInput=l,e.curOp.typing=!0,e.state.pasteIncoming=e.state.cutIncoming=!1}function Tl(e,t){var r=e.clipboardData&&e.clipboardData.getData(\"Text\");if(r)return e.preventDefault(),t.isReadOnly()||t.options.disableInput||Kn(t,function(){return kl(t,r,0,null,\"paste\")}),!0}function Ml(e,t){if(e.options.electricChars&&e.options.smartIndent)for(var r=e.doc.sel,n=r.ranges.length-1;n>=0;n--){var i=r.ranges[n];if(!(i.head.ch>100||n&&r.ranges[n-1].head.line==i.head.line)){var o=e.getModeAt(i.head),l=!1;if(o.electricChars){for(var s=0;s<o.electricChars.length;s++)if(t.indexOf(o.electricChars.charAt(s))>-1){l=Cl(e,i.head.line,\"smart\");break}}else o.electricInput&&o.electricInput.test(se(e.doc,i.head.line).text.slice(0,i.head.ch))&&(l=Cl(e,i.head.line,\"smart\"));l&&or(e,\"electricInput\",e,i.head.line)}}}function Nl(e){for(var t=[],r=[],n=0;n<e.doc.sel.ranges.length;n++){var i=e.doc.sel.ranges[n].head.line,o={anchor:ge(i,0),head:ge(i+1,0)};r.push(o),t.push(e.getRange(o.anchor,o.head))}return{text:t,ranges:r}}function Ol(e,t){e.setAttribute(\"autocorrect\",\"off\"),e.setAttribute(\"autocapitalize\",\"off\"),e.setAttribute(\"spellcheck\",!!t)}function Al(){var e=O(\"textarea\",null,null,\"position: absolute; bottom: -1em; padding: 0; width: 1px; height: 1em; outline: none\"),t=O(\"div\",[e],null,\"overflow: hidden; position: relative; width: 3px; height: 0px;\");return a?e.style.width=\"1000px\":e.setAttribute(\"wrap\",\"off\"),g&&(e.style.border=\"1px solid black\"),Ol(e),t}function Dl(e,t,r,n,i){var o=t,l=r,s=se(e,t.line);function a(n){var o,l;if(null==(o=i?function(e,t,r,n){var i=Ze(t,e.doc.direction);if(!i)return jo(t,r,n);r.ch>=t.text.length?(r.ch=t.text.length,r.sticky=\"before\"):r.ch<=0&&(r.ch=0,r.sticky=\"after\");var o=qe(i,r.ch,r.sticky),l=i[o];if(\"ltr\"==e.doc.direction&&l.level%2==0&&(n>0?l.to>r.ch:l.from<r.ch))return jo(t,r,n);var s,a=function(e,r){return Ko(t,e instanceof ge?e.ch:e,r)},u=function(r){return e.options.lineWrapping?(s=s||Nr(e,t),_r(e,t,s,r)):{begin:0,end:t.text.length}},c=u(\"before\"==r.sticky?a(r,-1):r.ch);if(\"rtl\"==e.doc.direction||1==l.level){var h=1==l.level==n<0,f=a(r,h?1:-1);if(null!=f&&(h?f<=l.to&&f<=c.end:f>=l.from&&f>=c.begin)){var d=h?\"before\":\"after\";return new ge(r.line,f,d)}}var p=function(e,t,n){for(var o=function(e,t){return t?new ge(r.line,a(e,1),\"before\"):new ge(r.line,e,\"after\")};e>=0&&e<i.length;e+=t){var l=i[e],s=t>0==(1!=l.level),u=s?n.begin:a(n.end,-1);if(l.from<=u&&u<l.to)return o(u,s);if(u=s?l.from:a(l.to,-1),n.begin<=u&&u<n.end)return o(u,s)}},g=p(o+n,n,c);if(g)return g;var v=n>0?c.end:a(c.begin,-1);return null==v||n>0&&v==t.text.length||!(g=p(n>0?0:i.length-1,n,u(v)))?null:g}(e.cm,s,t,r):jo(s,t,r))){if(n||(l=t.line+r)<e.first||l>=e.first+e.size||(t=new ge(l,t.ch,t.sticky),!(s=se(e,l))))return!1;t=Xo(i,e.cm,s,t.line,r)}else t=o;return!0}if(\"char\"==n)a();else if(\"column\"==n)a(!0);else if(\"word\"==n||\"group\"==n)for(var u=null,c=\"group\"==n,h=e.cm&&e.cm.getHelper(t,\"wordChars\"),f=!0;!(r<0)||a(!f);f=!1){var d=s.text.charAt(t.ch)||\"\\n\",p=te(d,h)?\"w\":c&&\"\\n\"==d?\"n\":!c||/\\s/.test(d)?null:\"p\";if(!c||f||p||(p=\"s\"),u&&u!=p){r<0&&(r=1,a(),t.sticky=\"after\");break}if(p&&(u=p),r>0&&!a(!f))break}var g=Zi(e,t,o,l,!0);return me(o,g)&&(g.hitSide=!0),g}function Wl(e,t,r,n){var i,o,l=e.doc,s=t.left;if(\"page\"==n){var a=Math.min(e.display.wrapper.clientHeight,window.innerHeight||document.documentElement.clientHeight),u=Math.max(a-.5*Zr(e.display),3);i=(r>0?t.bottom:t.top)+r*u}else\"line\"==n&&(i=r>0?t.bottom+3:t.top-3);for(;(o=Xr(e,s,i)).outside;){if(r<0?i<=0:i>=l.height){o.hitSide=!0;break}i+=5*r}return o}var Hl=function(e){this.cm=e,this.lastAnchorNode=this.lastAnchorOffset=this.lastFocusNode=this.lastFocusOffset=null,this.polling=new R,this.composing=null,this.gracePeriod=!1,this.readDOMTimeout=null};function Fl(e,t){var r=Mr(e,t.line);if(!r||r.hidden)return null;var n=se(e.doc,t.line),i=kr(r,n,t.line),o=Ze(n,e.doc.direction),l=\"left\";o&&(l=qe(o,t.ch)%2?\"right\":\"left\");var s=Wr(i.map,t.ch,l);return s.offset=\"right\"==s.collapse?s.end:s.start,s}function Pl(e,t){return t&&(e.bad=!0),e}function El(e,t,r){var n;if(t==e.display.lineDiv){if(!(n=e.display.lineDiv.childNodes[r]))return Pl(e.clipPos(ge(e.display.viewTo-1)),!0);t=null,r=0}else for(n=t;;n=n.parentNode){if(!n||n==e.display.lineDiv)return null;if(n.parentNode&&n.parentNode==e.display.lineDiv)break}for(var i=0;i<e.display.view.length;i++){var o=e.display.view[i];if(o.node==n)return zl(o,t,r)}}function zl(e,t,r){var n=e.text.firstChild,i=!1;if(!t||!D(n,t))return Pl(ge(he(e.line),0),!0);if(t==n&&(i=!0,t=n.childNodes[r],r=0,!t)){var o=e.rest?q(e.rest):e.line;return Pl(ge(he(o),o.text.length),i)}var l=3==t.nodeType?t:null,s=t;for(l||1!=t.childNodes.length||3!=t.firstChild.nodeType||(l=t.firstChild,r&&(r=l.nodeValue.length));s.parentNode!=n;)s=s.parentNode;var a=e.measure,u=a.maps;function c(t,r,n){for(var i=-1;i<(u?u.length:0);i++)for(var o=i<0?a.map:u[i],l=0;l<o.length;l+=3){var s=o[l+2];if(s==t||s==r){var c=he(i<0?e.line:e.rest[i]),h=o[l]+n;return(n<0||s!=t)&&(h=o[l+(n?1:0)]),ge(c,h)}}}var h=c(l,s,r);if(h)return Pl(h,i);for(var f=s.nextSibling,d=l?l.nodeValue.length-r:0;f;f=f.nextSibling){if(h=c(f,f.firstChild,0))return Pl(ge(h.line,h.ch-d),i);d+=f.textContent.length}for(var p=s.previousSibling,g=r;p;p=p.previousSibling){if(h=c(p,p.firstChild,-1))return Pl(ge(h.line,h.ch+g),i);g+=p.textContent.length}}Hl.prototype.init=function(e){var t=this,r=this,n=r.cm,i=r.div=e.lineDiv;function o(e){if(!nt(n,e)){if(n.somethingSelected())Ll({lineWise:!1,text:n.getSelections()}),\"cut\"==e.type&&n.replaceSelection(\"\",null,\"cut\");else{if(!n.options.lineWiseCopyCut)return;var t=Nl(n);Ll({lineWise:!0,text:t.text}),\"cut\"==e.type&&n.operation(function(){n.setSelections(t.ranges,0,V),n.replaceSelection(\"\",null,\"cut\")})}if(e.clipboardData){e.clipboardData.clearData();var o=Sl.text.join(\"\\n\");if(e.clipboardData.setData(\"Text\",o),e.clipboardData.getData(\"Text\")==o)return void e.preventDefault()}var l=Al(),s=l.firstChild;n.display.lineSpace.insertBefore(l,n.display.lineSpace.firstChild),s.value=Sl.text.join(\"\\n\");var a=document.activeElement;P(s),setTimeout(function(){n.display.lineSpace.removeChild(l),a.focus(),a==i&&r.showPrimarySelection()},50)}}Ol(i,n.options.spellcheck),Je(i,\"paste\",function(e){nt(n,e)||Tl(e,n)||s<=11&&setTimeout(jn(n,function(){return t.updateFromDOM()}),20)}),Je(i,\"compositionstart\",function(e){t.composing={data:e.data,done:!1}}),Je(i,\"compositionupdate\",function(e){t.composing||(t.composing={data:e.data,done:!1})}),Je(i,\"compositionend\",function(e){t.composing&&(e.data!=t.composing.data&&t.readFromDOMSoon(),t.composing.done=!0)}),Je(i,\"touchstart\",function(){return r.forceCompositionEnd()}),Je(i,\"input\",function(){t.composing||t.readFromDOMSoon()}),Je(i,\"copy\",o),Je(i,\"cut\",o)},Hl.prototype.prepareSelection=function(){var e=sn(this.cm,!1);return e.focus=this.cm.state.focused,e},Hl.prototype.showSelection=function(e,t){e&&this.cm.display.view.length&&((e.focus||t)&&this.showPrimarySelection(),this.showMultipleSelections(e))},Hl.prototype.showPrimarySelection=function(){var e=window.getSelection(),t=this.cm,n=t.doc.sel.primary(),i=n.from(),o=n.to();if(t.display.viewTo==t.display.viewFrom||i.line>=t.display.viewTo||o.line<t.display.viewFrom)e.removeAllRanges();else{var l=El(t,e.anchorNode,e.anchorOffset),s=El(t,e.focusNode,e.focusOffset);if(!l||l.bad||!s||s.bad||0!=ve(we(l,s),i)||0!=ve(be(l,s),o)){var a=t.display.view,u=i.line>=t.display.viewFrom&&Fl(t,i)||{node:a[0].measure.map[2],offset:0},c=o.line<t.display.viewTo&&Fl(t,o);if(!c){var h=a[a.length-1].measure,f=h.maps?h.maps[h.maps.length-1]:h.map;c={node:f[f.length-1],offset:f[f.length-2]-f[f.length-3]}}if(u&&c){var d,p=e.rangeCount&&e.getRangeAt(0);try{d=k(u.node,u.offset,c.offset,c.node)}catch(e){}d&&(!r&&t.state.focused?(e.collapse(u.node,u.offset),d.collapsed||(e.removeAllRanges(),e.addRange(d))):(e.removeAllRanges(),e.addRange(d)),p&&null==e.anchorNode?e.addRange(p):r&&this.startGracePeriod()),this.rememberSelection()}else e.removeAllRanges()}}},Hl.prototype.startGracePeriod=function(){var e=this;clearTimeout(this.gracePeriod),this.gracePeriod=setTimeout(function(){e.gracePeriod=!1,e.selectionChanged()&&e.cm.operation(function(){return e.cm.curOp.selectionChanged=!0})},20)},Hl.prototype.showMultipleSelections=function(e){N(this.cm.display.cursorDiv,e.cursors),N(this.cm.display.selectionDiv,e.selection)},Hl.prototype.rememberSelection=function(){var e=window.getSelection();this.lastAnchorNode=e.anchorNode,this.lastAnchorOffset=e.anchorOffset,this.lastFocusNode=e.focusNode,this.lastFocusOffset=e.focusOffset},Hl.prototype.selectionInEditor=function(){var e=window.getSelection();if(!e.rangeCount)return!1;var t=e.getRangeAt(0).commonAncestorContainer;return D(this.div,t)},Hl.prototype.focus=function(){\"nocursor\"!=this.cm.options.readOnly&&(this.selectionInEditor()||this.showSelection(this.prepareSelection(),!0),this.div.focus())},Hl.prototype.blur=function(){this.div.blur()},Hl.prototype.getField=function(){return this.div},Hl.prototype.supportsTouch=function(){return!0},Hl.prototype.receivedFocus=function(){var e=this;this.selectionInEditor()?this.pollSelection():Kn(this.cm,function(){return e.cm.curOp.selectionChanged=!0}),this.polling.set(this.cm.options.pollInterval,function t(){e.cm.state.focused&&(e.pollSelection(),e.polling.set(e.cm.options.pollInterval,t))})},Hl.prototype.selectionChanged=function(){var e=window.getSelection();return e.anchorNode!=this.lastAnchorNode||e.anchorOffset!=this.lastAnchorOffset||e.focusNode!=this.lastFocusNode||e.focusOffset!=this.lastFocusOffset},Hl.prototype.pollSelection=function(){if(null==this.readDOMTimeout&&!this.gracePeriod&&this.selectionChanged()){var e=window.getSelection(),t=this.cm;if(v&&c&&this.cm.options.gutters.length&&function(e){for(var t=e;t;t=t.parentNode)if(/CodeMirror-gutter-wrapper/.test(t.className))return!0;return!1}(e.anchorNode))return this.cm.triggerOnKeyDown({type:\"keydown\",keyCode:8,preventDefault:Math.abs}),this.blur(),void this.focus();if(!this.composing){this.rememberSelection();var r=El(t,e.anchorNode,e.anchorOffset),n=El(t,e.focusNode,e.focusOffset);r&&n&&Kn(t,function(){ji(t.doc,mi(r,n),V),(r.bad||n.bad)&&(t.curOp.selectionChanged=!0)})}}},Hl.prototype.pollContent=function(){null!=this.readDOMTimeout&&(clearTimeout(this.readDOMTimeout),this.readDOMTimeout=null);var e,t,r,n=this.cm,i=n.display,o=n.doc.sel.primary(),l=o.from(),s=o.to();if(0==l.ch&&l.line>n.firstLine()&&(l=ge(l.line-1,se(n.doc,l.line-1).length)),s.ch==se(n.doc,s.line).text.length&&s.line<n.lastLine()&&(s=ge(s.line+1,0)),l.line<i.viewFrom||s.line>i.viewTo-1)return!1;l.line==i.viewFrom||0==(e=on(n,l.line))?(t=he(i.view[0].line),r=i.view[0].node):(t=he(i.view[e].line),r=i.view[e-1].node.nextSibling);var a,u,c=on(n,s.line);if(c==i.view.length-1?(a=i.viewTo-1,u=i.lineDiv.lastChild):(a=he(i.view[c+1].line)-1,u=i.view[c+1].node.previousSibling),!r)return!1;for(var h=n.doc.splitLines(function(e,t,r,n,i){var o=\"\",l=!1,s=e.doc.lineSeparator();function a(){l&&(o+=s,l=!1)}function u(e){e&&(a(),o+=e)}function c(t){if(1==t.nodeType){var r=t.getAttribute(\"cm-text\");if(null!=r)return void u(r||t.textContent.replace(/\\u200b/g,\"\"));var o,h=t.getAttribute(\"cm-marker\");if(h){var f=e.findMarks(ge(n,0),ge(i+1,0),(g=+h,function(e){return e.id==g}));return void(f.length&&(o=f[0].find(0))&&u(ae(e.doc,o.from,o.to).join(s)))}if(\"false\"==t.getAttribute(\"contenteditable\"))return;var d=/^(pre|div|p)$/i.test(t.nodeName);d&&a();for(var p=0;p<t.childNodes.length;p++)c(t.childNodes[p]);d&&(l=!0)}else 3==t.nodeType&&u(t.nodeValue);var g}for(;c(t),t!=r;)t=t.nextSibling;return o}(n,r,u,t,a)),f=ae(n.doc,ge(t,0),ge(a,se(n.doc,a).text.length));h.length>1&&f.length>1;)if(q(h)==q(f))h.pop(),f.pop(),a--;else{if(h[0]!=f[0])break;h.shift(),f.shift(),t++}for(var d=0,p=0,g=h[0],v=f[0],m=Math.min(g.length,v.length);d<m&&g.charCodeAt(d)==v.charCodeAt(d);)++d;for(var y=q(h),b=q(f),w=Math.min(y.length-(1==h.length?d:0),b.length-(1==f.length?d:0));p<w&&y.charCodeAt(y.length-p-1)==b.charCodeAt(b.length-p-1);)++p;if(1==h.length&&1==f.length&&t==l.line)for(;d&&d>l.ch&&y.charCodeAt(y.length-p-1)==b.charCodeAt(b.length-p-1);)d--,p++;h[h.length-1]=y.slice(0,y.length-p).replace(/^\\u200b+/,\"\"),h[0]=h[0].slice(d).replace(/\\u200b+$/,\"\");var x=ge(t,d),C=ge(a,f.length?q(f).length-p:0);return h.length>1||h[0]||ve(x,C)?(lo(n.doc,h,x,C,\"+input\"),!0):void 0},Hl.prototype.ensurePolled=function(){this.forceCompositionEnd()},Hl.prototype.reset=function(){this.forceCompositionEnd()},Hl.prototype.forceCompositionEnd=function(){this.composing&&(clearTimeout(this.readDOMTimeout),this.composing=null,this.updateFromDOM(),this.div.blur(),this.div.focus())},Hl.prototype.readFromDOMSoon=function(){var e=this;null==this.readDOMTimeout&&(this.readDOMTimeout=setTimeout(function(){if(e.readDOMTimeout=null,e.composing){if(!e.composing.done)return;e.composing=null}e.updateFromDOM()},80))},Hl.prototype.updateFromDOM=function(){var e=this;!this.cm.isReadOnly()&&this.pollContent()||Kn(this.cm,function(){return _n(e.cm)})},Hl.prototype.setUneditable=function(e){e.contentEditable=\"false\"},Hl.prototype.onKeyPress=function(e){0==e.charCode||this.composing||(e.preventDefault(),this.cm.isReadOnly()||jn(this.cm,kl)(this.cm,String.fromCharCode(null==e.charCode?e.keyCode:e.charCode),0))},Hl.prototype.readOnlyChanged=function(e){this.div.contentEditable=String(\"nocursor\"!=e)},Hl.prototype.onContextMenu=function(){},Hl.prototype.resetPosition=function(){},Hl.prototype.needsContentAttribute=!0;var Il,Rl,Bl,Gl=function(e){this.cm=e,this.prevInput=\"\",this.pollingFast=!1,this.polling=new R,this.hasSelection=!1,this.composing=null};Gl.prototype.init=function(e){var t=this,r=this,n=this.cm;this.createField(e);var i=this.textarea;function o(e){if(!nt(n,e)){if(n.somethingSelected())Ll({lineWise:!1,text:n.getSelections()});else{if(!n.options.lineWiseCopyCut)return;var t=Nl(n);Ll({lineWise:!0,text:t.text}),\"cut\"==e.type?n.setSelections(t.ranges,null,V):(r.prevInput=\"\",i.value=t.text.join(\"\\n\"),P(i))}\"cut\"==e.type&&(n.state.cutIncoming=!0)}}e.wrapper.insertBefore(this.wrapper,e.wrapper.firstChild),g&&(i.style.width=\"0px\"),Je(i,\"input\",function(){l&&s>=9&&t.hasSelection&&(t.hasSelection=null),r.poll()}),Je(i,\"paste\",function(e){nt(n,e)||Tl(e,n)||(n.state.pasteIncoming=!0,r.fastPoll())}),Je(i,\"cut\",o),Je(i,\"copy\",o),Je(e.scroller,\"paste\",function(t){yr(e,t)||nt(n,t)||(n.state.pasteIncoming=!0,r.focus())}),Je(e.lineSpace,\"selectstart\",function(t){yr(e,t)||st(t)}),Je(i,\"compositionstart\",function(){var e=n.getCursor(\"from\");r.composing&&r.composing.range.clear(),r.composing={start:e,range:n.markText(e,n.getCursor(\"to\"),{className:\"CodeMirror-composing\"})}}),Je(i,\"compositionend\",function(){r.composing&&(r.poll(),r.composing.range.clear(),r.composing=null)})},Gl.prototype.createField=function(e){this.wrapper=Al(),this.textarea=this.wrapper.firstChild},Gl.prototype.prepareSelection=function(){var e=this.cm,t=e.display,r=e.doc,n=sn(e);if(e.options.moveInputWithCursor){var i=Vr(e,r.sel.primary().head,\"div\"),o=t.wrapper.getBoundingClientRect(),l=t.lineDiv.getBoundingClientRect();n.teTop=Math.max(0,Math.min(t.wrapper.clientHeight-10,i.top+l.top-o.top)),n.teLeft=Math.max(0,Math.min(t.wrapper.clientWidth-10,i.left+l.left-o.left))}return n},Gl.prototype.showSelection=function(e){var t=this.cm.display;N(t.cursorDiv,e.cursors),N(t.selectionDiv,e.selection),null!=e.teTop&&(this.wrapper.style.top=e.teTop+\"px\",this.wrapper.style.left=e.teLeft+\"px\")},Gl.prototype.reset=function(e){if(!this.contextMenuPending&&!this.composing){var t=this.cm;if(t.somethingSelected()){this.prevInput=\"\";var r=t.getSelection();this.textarea.value=r,t.state.focused&&P(this.textarea),l&&s>=9&&(this.hasSelection=r)}else e||(this.prevInput=this.textarea.value=\"\",l&&s>=9&&(this.hasSelection=null))}},Gl.prototype.getField=function(){return this.textarea},Gl.prototype.supportsTouch=function(){return!1},Gl.prototype.focus=function(){if(\"nocursor\"!=this.cm.options.readOnly&&(!m||W()!=this.textarea))try{this.textarea.focus()}catch(e){}},Gl.prototype.blur=function(){this.textarea.blur()},Gl.prototype.resetPosition=function(){this.wrapper.style.top=this.wrapper.style.left=0},Gl.prototype.receivedFocus=function(){this.slowPoll()},Gl.prototype.slowPoll=function(){var e=this;this.pollingFast||this.polling.set(this.cm.options.pollInterval,function(){e.poll(),e.cm.state.focused&&e.slowPoll()})},Gl.prototype.fastPoll=function(){var e=!1,t=this;t.pollingFast=!0,t.polling.set(20,function r(){t.poll()||e?(t.pollingFast=!1,t.slowPoll()):(e=!0,t.polling.set(60,r))})},Gl.prototype.poll=function(){var e=this,t=this.cm,r=this.textarea,n=this.prevInput;if(this.contextMenuPending||!t.state.focused||wt(r)&&!n&&!this.composing||t.isReadOnly()||t.options.disableInput||t.state.keySeq)return!1;var i=r.value;if(i==n&&!t.somethingSelected())return!1;if(l&&s>=9&&this.hasSelection===i||y&&/[\\uf700-\\uf7ff]/.test(i))return t.display.input.reset(),!1;if(t.doc.sel==t.display.selForContextMenu){var o=i.charCodeAt(0);if(8203!=o||n||(n=\"\"),8666==o)return this.reset(),this.cm.execCommand(\"undo\")}for(var a=0,u=Math.min(n.length,i.length);a<u&&n.charCodeAt(a)==i.charCodeAt(a);)++a;return Kn(t,function(){kl(t,i.slice(a),n.length-a,null,e.composing?\"*compose\":null),i.length>1e3||i.indexOf(\"\\n\")>-1?r.value=e.prevInput=\"\":e.prevInput=i,e.composing&&(e.composing.range.clear(),e.composing.range=t.markText(e.composing.start,t.getCursor(\"to\"),{className:\"CodeMirror-composing\"}))}),!0},Gl.prototype.ensurePolled=function(){this.pollingFast&&this.poll()&&(this.pollingFast=!1)},Gl.prototype.onKeyPress=function(){l&&s>=9&&(this.hasSelection=null),this.fastPoll()},Gl.prototype.onContextMenu=function(e){var t=this,r=t.cm,n=r.display,i=t.textarea,o=nn(r,e),u=n.scroller.scrollTop;if(o&&!h){r.options.resetSelectionOnContextMenu&&-1==r.doc.sel.contains(o)&&jn(r,ji)(r.doc,mi(o),V);var c=i.style.cssText,f=t.wrapper.style.cssText;t.wrapper.style.cssText=\"position: absolute\";var d,p=t.wrapper.getBoundingClientRect();if(i.style.cssText=\"position: absolute; width: 30px; height: 30px;\\n top: \"+(e.clientY-p.top-5)+\"px; left: \"+(e.clientX-p.left-5)+\"px;\\n z-index: 1000; background: \"+(l?\"rgba(255, 255, 255, .05)\":\"transparent\")+\";\\n outline: none; border-width: 0; outline: none; overflow: hidden; opacity: .05; filter: alpha(opacity=5);\",a&&(d=window.scrollY),n.input.focus(),a&&window.scrollTo(null,d),n.input.reset(),r.somethingSelected()||(i.value=t.prevInput=\" \"),t.contextMenuPending=!0,n.selForContextMenu=r.doc.sel,clearTimeout(n.detectingSelectAll),l&&s>=9&&v(),S){ct(e);var g=function(){tt(window,\"mouseup\",g),setTimeout(m,20)};Je(window,\"mouseup\",g)}else setTimeout(m,50)}function v(){if(null!=i.selectionStart){var e=r.somethingSelected(),o=\"\"+(e?i.value:\"\");i.value=\"⇚\",i.value=o,t.prevInput=e?\"\":\"\",i.selectionStart=1,i.selectionEnd=o.length,n.selForContextMenu=r.doc.sel}}function m(){if(t.contextMenuPending=!1,t.wrapper.style.cssText=f,i.style.cssText=c,l&&s<9&&n.scrollbars.setScrollTop(n.scroller.scrollTop=u),null!=i.selectionStart){(!l||l&&s<9)&&v();var e=0,o=function(){n.selForContextMenu==r.doc.sel&&0==i.selectionStart&&i.selectionEnd>0&&\"\"==t.prevInput?jn(r,Ji)(r):e++<10?n.detectingSelectAll=setTimeout(o,500):(n.selForContextMenu=null,n.input.reset())};n.detectingSelectAll=setTimeout(o,200)}}},Gl.prototype.readOnlyChanged=function(e){e||this.reset(),this.textarea.disabled=\"nocursor\"==e},Gl.prototype.setUneditable=function(){},Gl.prototype.needsContentAttribute=!1,function(e){var t=e.optionHandlers;function r(r,n,i,o){e.defaults[r]=n,i&&(t[r]=o?function(e,t,r){r!=pl&&i(e,t,r)}:i)}e.defineOption=r,e.Init=pl,r(\"value\",\"\",function(e,t){return e.setValue(t)},!0),r(\"mode\",null,function(e,t){e.doc.modeOption=t,Ci(e)},!0),r(\"indentUnit\",2,Ci,!0),r(\"indentWithTabs\",!1),r(\"smartIndent\",!0),r(\"tabSize\",4,function(e){Si(e),Er(e),_n(e)},!0),r(\"lineSeparator\",null,function(e,t){if(e.doc.lineSep=t,t){var r=[],n=e.doc.first;e.doc.iter(function(e){for(var i=0;;){var o=e.text.indexOf(t,i);if(-1==o)break;i=o+t.length,r.push(ge(n,o))}n++});for(var i=r.length-1;i>=0;i--)lo(e.doc,t,r[i],ge(r[i].line,r[i].ch+t.length))}}),r(\"specialChars\",/[\\u0000-\\u001f\\u007f-\\u009f\\u00ad\\u061c\\u200b-\\u200f\\u2028\\u2029\\ufeff]/g,function(e,t,r){e.state.specialChars=new RegExp(t.source+(t.test(\"\\t\")?\"\":\"|\\t\"),\"g\"),r!=pl&&e.refresh()}),r(\"specialCharPlaceholder\",$t,function(e){return e.refresh()},!0),r(\"electricChars\",!0),r(\"inputStyle\",m?\"contenteditable\":\"textarea\",function(){throw new Error(\"inputStyle can not (yet) be changed in a running editor\")},!0),r(\"spellcheck\",!1,function(e,t){return e.getInputField().spellcheck=t},!0),r(\"rtlMoveVisually\",!w),r(\"wholeLineUpdateBefore\",!0),r(\"theme\",\"default\",function(e){dl(e),ml(e)},!0),r(\"keyMap\",\"default\",function(e,t,r){var n=Uo(t),i=r!=pl&&Uo(r);i&&i.detach&&i.detach(e,n),n.attach&&n.attach(e,i||null)}),r(\"extraKeys\",null),r(\"configureMouse\",null),r(\"lineWrapping\",!1,bl,!0),r(\"gutters\",[],function(e){ai(e.options),ml(e)},!0),r(\"fixedGutter\",!0,function(e,t){e.display.gutters.style.left=t?en(e.display)+\"px\":\"0\",e.refresh()},!0),r(\"coverGutterNextToScrollbar\",!1,function(e){return Hn(e)},!0),r(\"scrollbarStyle\",\"native\",function(e){En(e),Hn(e),e.display.scrollbars.setScrollTop(e.doc.scrollTop),e.display.scrollbars.setScrollLeft(e.doc.scrollLeft)},!0),r(\"lineNumbers\",!1,function(e){ai(e.options),ml(e)},!0),r(\"firstLineNumber\",1,ml,!0),r(\"lineNumberFormatter\",function(e){return e},ml,!0),r(\"showCursorWhenSelecting\",!1,ln,!0),r(\"resetSelectionOnContextMenu\",!0),r(\"lineWiseCopyCut\",!0),r(\"pasteLinesPerSelection\",!0),r(\"readOnly\",!1,function(e,t){\"nocursor\"==t&&(gn(e),e.display.input.blur()),e.display.input.readOnlyChanged(t)}),r(\"disableInput\",!1,function(e,t){t||e.display.input.reset()},!0),r(\"dragDrop\",!0,yl),r(\"allowDropFileTypes\",null),r(\"cursorBlinkRate\",530),r(\"cursorScrollMargin\",0),r(\"cursorHeight\",1,ln,!0),r(\"singleCursorHeightPerLine\",!0,ln,!0),r(\"workTime\",100),r(\"workDelay\",100),r(\"flattenSpans\",!0,Si,!0),r(\"addModeClass\",!1,Si,!0),r(\"pollInterval\",100),r(\"undoDepth\",200,function(e,t){return e.doc.history.undoDepth=t}),r(\"historyEventDelay\",1250),r(\"viewportMargin\",10,function(e){return e.refresh()},!0),r(\"maxHighlightLength\",1e4,Si,!0),r(\"moveInputWithCursor\",!0,function(e,t){t||e.display.input.resetPosition()}),r(\"tabindex\",null,function(e,t){return e.display.input.getField().tabIndex=t||\"\"}),r(\"autofocus\",null),r(\"direction\",\"ltr\",function(e,t){return e.doc.setDirection(t)},!0)}(wl),Rl=(Il=wl).optionHandlers,Bl=Il.helpers={},Il.prototype={constructor:Il,focus:function(){window.focus(),this.display.input.focus()},setOption:function(e,t){var r=this.options,n=r[e];r[e]==t&&\"mode\"!=e||(r[e]=t,Rl.hasOwnProperty(e)&&jn(this,Rl[e])(this,t,n),rt(this,\"optionChange\",this,e))},getOption:function(e){return this.options[e]},getDoc:function(){return this.doc},addKeyMap:function(e,t){this.state.keyMaps[t?\"push\":\"unshift\"](Uo(e))},removeKeyMap:function(e){for(var t=this.state.keyMaps,r=0;r<t.length;++r)if(t[r]==e||t[r].name==e)return t.splice(r,1),!0},addOverlay:Xn(function(e,t){var r=e.token?e:Il.getMode(this.options,e);if(r.startState)throw new Error(\"Overlays may not be stateful.\");!function(e,t,r){for(var n=0,i=r(t);n<e.length&&r(e[n])<=i;)n++;e.splice(n,0,t)}(this.state.overlays,{mode:r,modeSpec:e,opaque:t&&t.opaque,priority:t&&t.priority||0},function(e){return e.priority}),this.state.modeGen++,_n(this)}),removeOverlay:Xn(function(e){for(var t=this.state.overlays,r=0;r<t.length;++r){var n=t[r].modeSpec;if(n==e||\"string\"==typeof e&&n.name==e)return t.splice(r,1),this.state.modeGen++,void _n(this)}}),indentLine:Xn(function(e,t,r){\"string\"!=typeof t&&\"number\"!=typeof t&&(t=null==t?this.options.smartIndent?\"smart\":\"prev\":t?\"add\":\"subtract\"),de(this.doc,e)&&Cl(this,e,t,r)}),indentSelection:Xn(function(e){for(var t=this,r=this.doc.sel.ranges,n=-1,i=0;i<r.length;i++){var o=r[i];if(o.empty())o.head.line>n&&(Cl(t,o.head.line,e,!0),n=o.head.line,i==t.doc.sel.primIndex&&Sn(t));else{var l=o.from(),s=o.to(),a=Math.max(n,l.line);n=Math.min(t.lastLine(),s.line-(s.ch?0:1))+1;for(var u=a;u<n;++u)Cl(t,u,e);var c=t.doc.sel.ranges;0==l.ch&&r.length==c.length&&c[i].from().ch>0&&Ui(t.doc,i,new gi(l,c[i].to()),V)}}}),getTokenAt:function(e,t){return Ut(this,e,t)},getLineTokens:function(e,t){return Ut(this,ge(e),t,!0)},getTokenTypeAt:function(e){e=Ce(this.doc,e);var t,r=Et(this,se(this.doc,e.line)),n=0,i=(r.length-1)/2,o=e.ch;if(0==o)t=r[2];else for(;;){var l=n+i>>1;if((l?r[2*l-1]:0)>=o)i=l;else{if(!(r[2*l+1]<o)){t=r[2*l+2];break}n=l+1}}var s=t?t.indexOf(\"overlay \"):-1;return s<0?t:0==s?null:t.slice(0,s-1)},getModeAt:function(e){var t=this.doc.mode;return t.innerMode?Il.innerMode(t,this.getTokenAt(e).state).mode:t},getHelper:function(e,t){return this.getHelpers(e,t)[0]},getHelpers:function(e,t){var r=[];if(!Bl.hasOwnProperty(t))return r;var n=Bl[t],i=this.getModeAt(e);if(\"string\"==typeof i[t])n[i[t]]&&r.push(n[i[t]]);else if(i[t])for(var o=0;o<i[t].length;o++){var l=n[i[t][o]];l&&r.push(l)}else i.helperType&&n[i.helperType]?r.push(n[i.helperType]):n[i.name]&&r.push(n[i.name]);for(var s=0;s<n._global.length;s++){var a=n._global[s];a.pred(i,this)&&-1==B(r,a.val)&&r.push(a.val)}return r},getStateAfter:function(e,t){var r=this.doc;return zt(this,(e=xe(r,null==e?r.first+r.size-1:e))+1,t).state},cursorCoords:function(e,t){var r=this.doc.sel.primary();return Vr(this,null==e?r.head:\"object\"==typeof e?Ce(this.doc,e):e?r.from():r.to(),t||\"page\")},charCoords:function(e,t){return Ur(this,Ce(this.doc,e),t||\"page\")},coordsChar:function(e,t){return Xr(this,(e=Gr(this,e,t||\"page\")).left,e.top)},lineAtHeight:function(e,t){return e=Gr(this,{top:e,left:0},t||\"page\").top,fe(this.doc,e+this.display.viewOffset)},heightAtLine:function(e,t,r){var n,i=!1;if(\"number\"==typeof e){var o=this.doc.first+this.doc.size-1;e<this.doc.first?e=this.doc.first:e>o&&(e=o,i=!0),n=se(this.doc,e)}else n=e;return Br(this,n,{top:0,left:0},t||\"page\",r||i).top+(i?this.doc.height-je(n):0)},defaultTextHeight:function(){return Zr(this.display)},defaultCharWidth:function(){return Qr(this.display)},getViewport:function(){return{from:this.display.viewFrom,to:this.display.viewTo}},addWidget:function(e,t,r,n,i){var o,l,s,a=this.display,u=(e=Vr(this,Ce(this.doc,e))).bottom,c=e.left;if(t.style.position=\"absolute\",t.setAttribute(\"cm-ignore-events\",\"true\"),this.display.input.setUneditable(t),a.sizer.appendChild(t),\"over\"==n)u=e.top;else if(\"above\"==n||\"near\"==n){var h=Math.max(a.wrapper.clientHeight,this.doc.height),f=Math.max(a.sizer.clientWidth,a.lineSpace.clientWidth);(\"above\"==n||e.bottom+t.offsetHeight>h)&&e.top>t.offsetHeight?u=e.top-t.offsetHeight:e.bottom+t.offsetHeight<=h&&(u=e.bottom),c+t.offsetWidth>f&&(c=f-t.offsetWidth)}t.style.top=u+\"px\",t.style.left=t.style.right=\"\",\"right\"==i?(c=a.sizer.clientWidth-t.offsetWidth,t.style.right=\"0px\"):(\"left\"==i?c=0:\"middle\"==i&&(c=(a.sizer.clientWidth-t.offsetWidth)/2),t.style.left=c+\"px\"),r&&(o=this,l={left:c,top:u,right:c+t.offsetWidth,bottom:u+t.offsetHeight},null!=(s=xn(o,l)).scrollTop&&Mn(o,s.scrollTop),null!=s.scrollLeft&&On(o,s.scrollLeft))},triggerOnKeyDown:Xn(rl),triggerOnKeyPress:Xn(il),triggerOnKeyUp:nl,triggerOnMouseDown:Xn(al),execCommand:function(e){if(Yo.hasOwnProperty(e))return Yo[e].call(null,this)},triggerElectric:Xn(function(e){Ml(this,e)}),findPosH:function(e,t,r,n){var i=1;t<0&&(i=-1,t=-t);for(var o=Ce(this.doc,e),l=0;l<t&&!(o=Dl(this.doc,o,i,r,n)).hitSide;++l);return o},moveH:Xn(function(e,t){var r=this;this.extendSelectionsBy(function(n){return r.display.shift||r.doc.extend||n.empty()?Dl(r.doc,n.head,e,t,r.options.rtlMoveVisually):e<0?n.from():n.to()},j)}),deleteH:Xn(function(e,t){var r=this.doc.sel,n=this.doc;r.somethingSelected()?n.replaceSelection(\"\",null,\"+delete\"):Vo(this,function(r){var i=Dl(n,r.head,e,t,!1);return e<0?{from:i,to:r.head}:{from:r.head,to:i}})}),findPosV:function(e,t,r,n){var i=1,o=n;t<0&&(i=-1,t=-t);for(var l=Ce(this.doc,e),s=0;s<t;++s){var a=Vr(this,l,\"div\");if(null==o?o=a.left:a.left=o,(l=Wl(this,a,i,r)).hitSide)break}return l},moveV:Xn(function(e,t){var r=this,n=this.doc,i=[],o=!this.display.shift&&!n.extend&&n.sel.somethingSelected();if(n.extendSelectionsBy(function(l){if(o)return e<0?l.from():l.to();var s=Vr(r,l.head,\"div\");null!=l.goalColumn&&(s.left=l.goalColumn),i.push(s.left);var a=Wl(r,s,e,t);return\"page\"==t&&l==n.sel.primary()&&Cn(r,Ur(r,a,\"div\").top-s.top),a},j),i.length)for(var l=0;l<n.sel.ranges.length;l++)n.sel.ranges[l].goalColumn=i[l]}),findWordAt:function(e){var t=se(this.doc,e.line).text,r=e.ch,n=e.ch;if(t){var i=this.getHelper(e,\"wordChars\");\"before\"!=e.sticky&&n!=t.length||!r?++n:--r;for(var o=t.charAt(r),l=te(o,i)?function(e){return te(e,i)}:/\\s/.test(o)?function(e){return/\\s/.test(e)}:function(e){return!/\\s/.test(e)&&!te(e)};r>0&&l(t.charAt(r-1));)--r;for(;n<t.length&&l(t.charAt(n));)++n}return new gi(ge(e.line,r),ge(e.line,n))},toggleOverwrite:function(e){null!=e&&e==this.state.overwrite||((this.state.overwrite=!this.state.overwrite)?H(this.display.cursorDiv,\"CodeMirror-overwrite\"):T(this.display.cursorDiv,\"CodeMirror-overwrite\"),rt(this,\"overwriteToggle\",this,this.state.overwrite))},hasFocus:function(){return this.display.input.getField()==W()},isReadOnly:function(){return!(!this.options.readOnly&&!this.doc.cantEdit)},scrollTo:Xn(function(e,t){Ln(this,e,t)}),getScrollInfo:function(){var e=this.display.scroller;return{left:e.scrollLeft,top:e.scrollTop,height:e.scrollHeight-Cr(this)-this.display.barHeight,width:e.scrollWidth-Cr(this)-this.display.barWidth,clientHeight:Lr(this),clientWidth:Sr(this)}},scrollIntoView:Xn(function(e,t){var r,n;null==e?(e={from:this.doc.sel.primary().head,to:null},null==t&&(t=this.options.cursorScrollMargin)):\"number\"==typeof e?e={from:ge(e,0),to:null}:null==e.from&&(e={from:e,to:null}),e.to||(e.to=e.from),e.margin=t||0,null!=e.from.line?(n=e,kn(r=this),r.curOp.scrollToPos=n):Tn(this,e.from,e.to,e.margin)}),setSize:Xn(function(e,t){var r=this,n=function(e){return\"number\"==typeof e||/^\\d+$/.test(String(e))?e+\"px\":e};null!=e&&(this.display.wrapper.style.width=n(e)),null!=t&&(this.display.wrapper.style.height=n(t)),this.options.lineWrapping&&Pr(this);var 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"title": "$:/plugins/tiddlywiki/codemirror/lib/codemirror.css",
"tags": "[[$:/tags/Stylesheet]]"
},
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"text": ".CodeMirror-dialog {\n position: absolute;\n left: 0; right: 0;\n background: inherit;\n z-index: 15;\n padding: .1em .8em;\n overflow: hidden;\n color: inherit;\n}\n\n.CodeMirror-dialog-top {\n border-bottom: 1px solid #eee;\n top: 0;\n}\n\n.CodeMirror-dialog-bottom {\n border-top: 1px solid #eee;\n bottom: 0;\n}\n\n.CodeMirror-dialog input {\n border: none;\n outline: none;\n background: transparent;\n width: 20em;\n color: inherit;\n font-family: monospace;\n}\n\n.CodeMirror-dialog button {\n font-size: 70%;\n}\n",
"type": "text/css",
"title": "$:/plugins/tiddlywiki/codemirror/addon/dialog/dialog.css",
"tags": "[[$:/tags/Stylesheet]]"
},
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"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/dialog/dialog.js",
"module-type": "codemirror"
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"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/selection/activeline.js",
"module-type": "codemirror"
},
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"title": "$:/plugins/tiddlywiki/codemirror/keyboard",
"text": "\n!!Default keyboard shortcuts\n\n!!!Basic shortcuts\n\n|Shortcut |Function |h\n|Left |goCharLeft |\n|Right |goCharRight |\n|Up |goLineUp |\n|Down |goLineDown |\n|End |goLineEnd |\n|Home |goLineStartSmart |\n|~PageUp |goPageUp |\n|~PageDown |goPageDown |\n|Delete |delCharAfter |\n|Backspace |delCharBefore |\n|Shift-Backspace |delCharBefore |\n|Tab |defaultTab |\n|Shift-Tab |indentAuto |\n|Enter |newlineAndIndent |\n|Insert |toggleOverwrite |\n|Ctrl-Esc |singleSelection |\n\n\n!!!Shortcuts on Windows and Linux\n\n|Shortcut |Function |h\n|Ctrl-A |selectAll |\n|Ctrl-D |deleteLine |\n|Ctrl-Z |undo |\n|Shift-Ctrl-Z |redo |\n|Ctrl-Y |redo |\n|Ctrl-Home |goDocStart |\n|Ctrl-End |goDocEnd |\n|Ctrl-Up |goLineUp |\n|Ctrl-Down |goLineDown |\n|Ctrl-Left |goGroupLeft |\n|Ctrl-Right |goGroupRight |\n|Alt-Left |goLineStart |\n|Alt-Right |goLineEnd |\n|Ctrl-Backspace |delGroupBefore |\n|Ctrl-Delete |delGroupAfter |\n|Ctrl-F |find |\n|Ctrl-G |findNext |\n|Shift-Ctrl-G |findPrev |\n|Shift-Ctrl-F |replace |\n|Shift-Ctrl-R |replaceAll |\n|Ctrl-[ |indentLess |\n|Ctrl-] |indentMore |\n|Alt-U |undoSelection |\n|Shift-Ctrl-U |redoSelection |\n|Shift-Alt-U |redoSelection |\n\n\n!!!Shortcuts on ~MacOs\n\n|Shortcut |Function |h\n|Cmd-A |selectAll |\n|Cmd-D |deleteLine |\n|Cmd-Z |undo |\n|Shift-Cmd-Z |redo |\n|Cmd-Y |redo |\n|Cmd-Home |goDocStart |\n|Cmd-Up |goDocStart |\n|Cmd-End |goDocEnd |\n|Cmd-Down |goDocEnd |\n|Alt-Left |goGroupLeft |\n|Alt-Right |goGroupRight |\n|Cmd-Left |goLineLeft |\n|Cmd-Right |goLineRight |\n|Alt-Backspace |delGroupBefore |\n|Ctrl-Alt-Backspace |delGroupAfter |\n|Alt-Delete |delGroupAfter |\n|Cmd-F |find |\n|Cmd-G |findNext |\n|Shift-Cmd-G |findPrev |\n|Cmd-Alt-F |replace |\n|Shift-Cmd-Alt-F |replaceAll |\n|Cmd-[ |indentLess |\n|Cmd-] |indentMore |\n|Cmd-Backspace |delWrappedLineLeft |\n|Cmd-Delete |delWrappedLineRight |\n|Alt-U |undoSelection |\n|Shift-Alt-U |redoSelection |\n|Ctrl-Up |goDocStart |\n|Ctrl-Down |goDocEnd |\n|Ctrl-F |goCharRight |\n|Ctrl-B |goCharLeft |\n|Ctrl-P |goLineUp |\n|Ctrl-N |goLineDown |\n|Alt-F |goWordRight |\n|Alt-B |goWordLeft |\n|Ctrl-A |goLineStart |\n|Ctrl-E |goLineEnd |\n|Ctrl-V |goPageDown |\n|Shift-Ctrl-V |goPageUp |\n|Ctrl-D |delCharAfter |\n|Ctrl-H |delCharBefore |\n|Alt-D |delWordAfter |\n|Alt-Backspace |delWordBefore |\n|Ctrl-K |killLine |\n|Alt-T |transposeChars |\n|Ctrl-O |openLine |\n\n\n"
},
"$:/plugins/tiddlywiki/codemirror/license": {
"title": "$:/plugins/tiddlywiki/codemirror/license",
"text": "\"\"\"\n~CodeMirror, copyright (c) by Marijn Haverbeke and others\nDistributed under an MIT license: http://codemirror.net/LICENSE\n\nCopyright (c) 2004-2007, Jeremy Ruston\nCopyright (c) 2007-2018, UnaMesa Association\nDistributed under an BSD license: https://tiddlywiki.com/#License\n\"\"\"\n"
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"$:/plugins/tiddlywiki/codemirror/readme": {
"title": "$:/plugins/tiddlywiki/codemirror/readme",
"text": "This plugin provides an enhanced text editor component based on [[CodeMirror|http://codemirror.net]].\nThe basic configuration is designed to be as lightweight as possible and is just around 235kb of size.\nAdditional features can be installed with ~CodeMirror ~AddOns from the plugin library:\n\n* Code colouring for many languages (see [[the official documentation here|http://codemirror.net/mode/index.html]])\n* Auto closing brackets and tags\n* Folding brackets, comments, and tags\n* Auto-completion\n* Search and Replace\n* Fullscreen Editing\n* Optional Emacs, Sublime Text or Vim Keymaps\n\n\n[[Source code|https://github.com/Jermolene/TiddlyWiki5/blob/master/plugins/tiddlywiki/codemirror]]\n\nBased on ~CodeMirror version 5.37.0\n"
},
"$:/core/ui/ControlPanel/Settings/codemirror/editorFont": {
"title": "$:/core/ui/ControlPanel/Settings/codemirror/editorFont",
"tags": "$:/tags/ControlPanel/Settings/CodeMirror",
"caption": "{{$:/language/codemirror/editorFont/hint}}",
"text": "\\define lingo-base() $:/language/ThemeTweaks/\n\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/editorfontfamily\"><<lingo Settings/EditorFontFamily>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/settings/editorfontfamily\" default=\"\" tag=\"input\"/> | |\n"
},
"$:/core/ui/ControlPanel/Settings/codemirror/keyMap": {
"title": "$:/core/ui/ControlPanel/Settings/codemirror/keyMap",
"tags": "$:/tags/ControlPanel/Settings/CodeMirror",
"caption": "{{$:/language/codemirror/keyMap/hint}}",
"text": "\\define lingo-base() $:/language/codemirror/keyMap\n\n<$link to=\"$:/config/codemirror/keyMap\"><<lingo hint>></$link>\n\n<$select tiddler=\"$:/config/codemirror/keyMap\" default=\"default\">\n<option value=\"default\">default</option>\n<$list filter=\"[all[shadows+tiddlers]module-type[codemirror-keymap]!has[draft.of]get[text]]\">\n<option value=<<currentTiddler>>><$transclude><$text text=<<currentTiddler>>/></$transclude></option>\n</$list>\n</$select>\n\n"
},
"$:/core/ui/ControlPanel/Settings/codemirror/lineNumbers": {
"title": "$:/core/ui/ControlPanel/Settings/codemirror/lineNumbers",
"tags": "$:/tags/ControlPanel/Settings/CodeMirror",
"caption": "{{$:/language/codemirror/lineNumbers/hint}}",
"text": "\\define lingo-base() $:/language/codemirror/lineNumbers/\n<<lingo hint>>\n\n<$checkbox tiddler=\"$:/config/codemirror/lineNumbers\" field=\"text\" checked=\"true\" unchecked=\"false\" default=\"false\"> <$link to=\"$:/config/codemirror/lineNumbers\"><<lingo info>></$link> </$checkbox>\n\n"
},
"$:/core/ui/ControlPanel/Settings/codemirror/lineWrapping": {
"title": "$:/core/ui/ControlPanel/Settings/codemirror/lineWrapping",
"tags": "$:/tags/ControlPanel/Settings/CodeMirror",
"caption": "{{$:/language/codemirror/lineWrapping/hint}}",
"text": "\\define lingo-base() $:/language/codemirror/lineWrapping/\n<<lingo hint>>\n\n<$checkbox tiddler=\"$:/config/codemirror/lineWrapping\" field=\"text\" checked=\"true\" unchecked=\"false\" default=\"true\"> <$link to=\"$:/config/codemirror/lineWrapping\"><<lingo info>></$link> </$checkbox>\n\n"
},
"$:/core/ui/ControlPanel/Settings/codemirror/showCursorWhenSelecting": {
"title": "$:/core/ui/ControlPanel/Settings/codemirror/showCursorWhenSelecting",
"tags": "$:/tags/ControlPanel/Settings/CodeMirror",
"caption": "{{$:/language/codemirror/showCursorWhenSelecting/hint}}",
"text": "\\define lingo-base() $:/language/codemirror/showCursorWhenSelecting/\n<<lingo hint>>\n\n<$checkbox tiddler=\"$:/config/codemirror/showCursorWhenSelecting\" field=\"text\" checked=\"true\" unchecked=\"false\" default=\"true\"> <$link to=\"$:/config/codemirror/showCursorWhenSelecting\"><<lingo info>></$link> </$checkbox>\n\n"
},
"$:/core/ui/ControlPanel/Settings/codemirror/styleActiveLine": {
"title": "$:/core/ui/ControlPanel/Settings/codemirror/styleActiveLine",
"tags": "$:/tags/ControlPanel/Settings/CodeMirror",
"caption": "{{$:/language/codemirror/styleActiveLine/hint}}",
"text": "\\define lingo-base() $:/language/codemirror/styleActiveLine/\n<<lingo hint>>\n\n<$checkbox tiddler=\"$:/config/codemirror/styleActiveLine\" field=\"text\" checked=\"true\" unchecked=\"false\" default=\"false\"> <$link to=\"$:/config/codemirror/styleActiveLine\"><<lingo info>></$link> </$checkbox>\n\n"
},
"$:/core/ui/ControlPanel/Settings/codemirror/theme": {
"title": "$:/core/ui/ControlPanel/Settings/codemirror/theme",
"tags": "$:/tags/ControlPanel/Settings/CodeMirror",
"caption": "{{$:/language/codemirror/theme/hint}}",
"text": "\\define lingo-base() $:/language/codemirror/\n\n<$link to=\"$:/config/codemirror/theme\"><<lingo hint>></$link>\n\n<$select tiddler=\"$:/config/codemirror/theme\" default=\"default\">\n<option value=\"default\">default</option>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Stylesheet]module-type[codemirror-theme]!has[draft.of]get[name]]\">\n<option value=<<currentTiddler>>><$transclude field=\"name\"><$text text=<<currentTiddler>>/></$transclude></option>\n</$list>\n</$select>\n\n//see the [[CodeMirror Usage|$:/plugins/tiddlywiki/codemirror/usage]] how to add themes//\n"
},
"$:/plugins/tiddlywiki/codemirror/styles": {
"title": "$:/plugins/tiddlywiki/codemirror/styles",
"tags": "[[$:/tags/Stylesheet]]",
"text": "/* Make the editor resize to fit its content */\n\n.CodeMirror {\n\theight: auto;\n\tborder: 1px solid #ddd;\n\tline-height: 1.5;\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/editorfontfamily}};\n}\n\n.CodeMirror-scroll {\n\toverflow-x: auto;\n\toverflow-y: hidden;\t\n}\n"
},
"$:/core/ui/ControlPanel/Settings/CodeMirror": {
"title": "$:/core/ui/ControlPanel/Settings/CodeMirror",
"tags": "$:/tags/ControlPanel/SettingsTab",
"caption": "CodeMirror",
"list-after": "$:/core/ui/ControlPanel/Settings/TiddlyWiki",
"text": "\\define lingo-base() $:/language/codemirror/controlPanel/\n\n<<lingo hint>>\n\n<$link to=\"$:/plugins/tiddlywiki/codemirror/usage\"><<lingo usage>></$link>\n\n<$link to=\"$:/plugins/tiddlywiki/codemirror/keyboard\"><<lingo keyboard>></$link>\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Settings/CodeMirror]]\">\n\n<div style=\"border-top:1px solid #eee;\">\n\n!! <$link><$transclude field=\"caption\"/></$link>\n\n<$transclude/>\n\n</div>\n\n</$list>\n"
},
"$:/core/ui/ControlPanel/Settings": {
"title": "$:/core/ui/ControlPanel/Settings",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Settings/Caption}}",
"text": "<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/SettingsTab]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Settings/TiddlyWiki\">>\n</div>\n"
},
"$:/core/ui/ControlPanel/Settings/TiddlyWiki": {
"title": "$:/core/ui/ControlPanel/Settings/TiddlyWiki",
"tags": "$:/tags/ControlPanel/SettingsTab",
"caption": "TiddlyWiki",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/\n\n<<lingo Hint>>\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Settings]]\">\n\n<div style=\"border-top:1px solid #eee;\">\n\n!! <$link><$transclude field=\"caption\"/></$link>\n\n<$transclude/>\n\n</div>\n\n</$list>\n"
},
"$:/plugins/tiddlywiki/codemirror/usage": {
"title": "$:/plugins/tiddlywiki/codemirror/usage",
"text": "! Configuration\n\nConfiguration for the ~CodeMirror text-editor can be done from within the CodeMirror Settings Tab in the [[ControlPanel|$:/ControlPanel]] (Settings - ~CodeMirror)\n\n\n!!Setting a different Theme\n\n~CodeMirror themes are available in the [ext[official GitHub repository|https://github.com/codemirror/CodeMirror/tree/master/theme]]\n\nMore themes can be found at https://github.com/FarhadG/code-mirror-themes/tree/master/themes and previewed [ext[here|http://farhadg.github.io/code-mirror-themes/]]\n\n\nTo add a theme to your wiki, follow these four steps:\n\n* choose one of the CSS files and copy its content to a new tiddler\n* remove all comments from the top and tag the tiddler with <<tag-pill \"$:/tags/Stylesheet\">>\n* add a field \"module-type\" with the value \"codemirror-theme\". add a field \"name\" with the exact ''name'' of the theme as value\n* save the tiddler and go to the Settings tab in $:/ControlPanel - look for the \"theme\" dropdown to select your newly added theme\n\n\n!!Line Numbers\n\nTo show or hide the Line Numbers at the left, go to ~ControlPanel - Settings - ~CodeMirror and look for the \"Line Numbers\" checkbox\n\n\n!!Line Wrapping\n\nControls if long lines get visually wrapped to a new line if they're too long to fit the editor width or if the editor should scroll horizontally\n\nTo change the line-wrapping behaviour, go to ~ControlPanel - Settings - ~CodeMirror and look for the \"Line Wrapping\" checkbox\n\n\n!!Show Cursor when selecting\n\nDefines whether the Mouse cursor should be visually shown or hidden when making a text-selection\n\nTo change the show-cursor-when-selecting behaviour, go to ~ControlPanel - Settings - ~CodeMirror and look for the \"Show cursor when selecting\" checkbox\n\n\n!!~CodeMirror Font Family\n\nThe Font-Family used within the ~CodeMirror text-editor defaults to \"monospace\" which will choose your configured monospace system-font\n\nThat setting can be overridden entering one or more Font-Families in the \"Font Family\" input field at ~ControlPanel - Settings - ~CodeMirror\n\n* The entries must be separated by semicolons ','\n* Font-Family Names that contain spaces must be quoted like \"My Font\"\n* If a list of Font-Families is specified, the last Font-Family found on the user-system gets used, non-existing fonts get ignored\n* If none of the specified Font-Families is available, ~CodeMirror uses the default \"monospace\"\n\n\n!!\"Hidden\" Settings:\n\n!!!Cursor Blink Rate\n\nThe cursor blink-rate defines how fast (in milliseconds) the cursor blinks inside the textarea\n\nYou can change it by editing $:/config/codemirror/cursorBlinkRate\n\"0\" disables blinking\n\n!!!Tabsize\n\nThe Tabsize defines the width of a tab character. Default is 4.\n\nYou can change it by editing $:/config/codemirror/tabSize\n\n!!!Indent Unit\n\nNot enabled for vnd.tiddlywiki and x-tiddlywiki\n\nDefines how many spaces a text-block should be indented. Defaults to 2.\n\nYou can change it by editing $:/config/codemirror/indentUnit\n\n"
}
}
}
{
"tiddlers": {
"$:/config/codemirror/autocomplete": {
"title": "$:/config/codemirror/autocomplete",
"extend": "extraKeys",
"type": "json",
"text": "{\n\t\"Ctrl-Space\": \"autocomplete\"\n}"
},
"$:/plugins/tiddlywiki/codemirror/addon/hint/anyword-hint.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(e){\"object\"==typeof exports&&\"object\"==typeof module?e(require(\"../../lib/codemirror\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\"],e):e(CodeMirror)}(function(e){\"use strict\";var r=/[\\w$]+/;e.registerHelper(\"hint\",\"anyword\",function(t,o){for(var i=o&&o.word||r,n=o&&o.range||500,f=t.getCursor(),s=t.getLine(f.line),a=f.ch,c=a;c&&i.test(s.charAt(c-1));)--c;for(var l=c!=a&&s.slice(c,a),d=o&&o.list||[],u={},p=new RegExp(i.source,\"g\"),g=-1;g<=1;g+=2)for(var h=f.line,m=Math.min(Math.max(h+g*n,t.firstLine()),t.lastLine())+g;h!=m;h+=g)for(var y,b=t.getLine(h);y=p.exec(b);)h==f.line&&y[0]===l||l&&0!=y[0].lastIndexOf(l,0)||Object.prototype.hasOwnProperty.call(u,y[0])||(u[y[0]]=!0,d.push(y[0]));return{list:d,from:e.Pos(f.line,c),to:e.Pos(f.line,a)}})});",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/hint/anyword-hint.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror/addon/hint/css-hint.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(e){\"object\"==typeof exports&&\"object\"==typeof module?e(require(\"../../lib/codemirror\"),require(\"../../mode/css/css\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\",\"../../mode/css/css\"],e):e(CodeMirror)}(function(e){\"use strict\";var r={link:1,visited:1,active:1,hover:1,focus:1,\"first-letter\":1,\"first-line\":1,\"first-child\":1,before:1,after:1,lang:1};e.registerHelper(\"hint\",\"css\",function(t){var o=t.getCursor(),s=t.getTokenAt(o),i=e.innerMode(t.getMode(),s.state);if(\"css\"==i.mode.name){if(\"keyword\"==s.type&&0==\"!important\".indexOf(s.string))return{list:[\"!important\"],from:e.Pos(o.line,s.start),to:e.Pos(o.line,s.end)};var n=s.start,a=o.ch,d=s.string.slice(0,a-n);/[^\\w$_-]/.test(d)&&(d=\"\",n=a=o.ch);var c=e.resolveMode(\"text/css\"),f=[],l=i.state.state;return\"pseudo\"==l||\"variable-3\"==s.type?p(r):\"block\"==l||\"maybeprop\"==l?p(c.propertyKeywords):\"prop\"==l||\"parens\"==l||\"at\"==l||\"params\"==l?(p(c.valueKeywords),p(c.colorKeywords)):\"media\"!=l&&\"media_parens\"!=l||(p(c.mediaTypes),p(c.mediaFeatures)),f.length?{list:f,from:e.Pos(o.line,n),to:e.Pos(o.line,a)}:void 0}function p(e){for(var r in e)d&&0!=r.lastIndexOf(d,0)||f.push(r)}})});",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/hint/css-hint.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror/addon/hint/html-hint.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(l){\"object\"==typeof exports&&\"object\"==typeof module?l(require(\"../../lib/codemirror\"),require(\"./xml-hint\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\",\"./xml-hint\"],l):l(CodeMirror)}(function(l){\"use strict\";var t=\"ab aa af ak sq am ar an hy as av ae ay az bm ba eu be bn bh bi bs br bg my ca ch ce ny zh cv kw co cr hr cs da dv nl dz en eo et ee fo fj fi fr ff gl ka de el gn gu ht ha he hz hi ho hu ia id ie ga ig ik io is it iu ja jv kl kn kr ks kk km ki rw ky kv kg ko ku kj la lb lg li ln lo lt lu lv gv mk mg ms ml mt mi mr mh mn na nv nb nd ne ng nn no ii nr oc oj cu om or os pa pi fa pl ps pt qu rm rn ro ru sa sc sd se sm sg sr gd sn si sk sl so st es su sw ss sv ta te tg th ti bo tk tl tn to tr ts tt tw ty ug uk ur uz ve vi vo wa cy wo fy xh yi yo za zu\".split(\" 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"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/hint/html-hint.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror/addon/hint/javascript-hint.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(t){\"object\"==typeof exports&&\"object\"==typeof module?t(require(\"../../lib/codemirror\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\"],t):t(CodeMirror)}(function(t){var e=t.Pos;function r(t,e){for(var r=0,n=t.length;r<n;++r)e(t[r])}function n(n,i,l,f){var c=n.getCursor(),p=l(n,c);if(!/\\b(?:string|comment)\\b/.test(p.type)){var u=t.innerMode(n.getMode(),p.state);if(\"json\"!==u.mode.helperType){p.state=u.state,/^[\\w$_]*$/.test(p.string)?p.end>c.ch&&(p.end=c.ch,p.string=p.string.slice(0,c.ch-p.start)):p={start:c.ch,end:c.ch,string:\"\",state:p.state,type:\".\"==p.string?\"property\":null};for(var d=p;\"property\"==d.type;){if(\".\"!=(d=l(n,e(c.line,d.start))).string)return;if(d=l(n,e(c.line,d.start)),!g)var g=[];g.push(d)}return{list:function(t,e,n,i){var l=[],f=t.string,c=i&&i.globalScope||window;function p(t){0!=t.lastIndexOf(f,0)||function(t,e){if(!Array.prototype.indexOf){for(var r=t.length;r--;)if(t[r]===e)return!0;return!1}return-1!=t.indexOf(e)}(l,t)||l.push(t)}function u(t){\"string\"==typeof t?r(o,p):t instanceof Array?r(s,p):t instanceof Function&&r(a,p),function(t,e){if(Object.getOwnPropertyNames&&Object.getPrototypeOf)for(var r=t;r;r=Object.getPrototypeOf(r))Object.getOwnPropertyNames(r).forEach(e);else for(var n in t)e(n)}(t,p)}if(e&&e.length){var d,g=e.pop();for(g.type&&0===g.type.indexOf(\"variable\")?(i&&i.additionalContext&&(d=i.additionalContext[g.string]),i&&!1===i.useGlobalScope||(d=d||c[g.string])):\"string\"==g.type?d=\"\":\"atom\"==g.type?d=1:\"function\"==g.type&&(null==c.jQuery||\"$\"!=g.string&&\"jQuery\"!=g.string||\"function\"!=typeof c.jQuery?null!=c._&&\"_\"==g.string&&\"function\"==typeof c._&&(d=c._()):d=c.jQuery());null!=d&&e.length;)d=d[e.pop().string];null!=d&&u(d)}else{for(var y=t.state.localVars;y;y=y.next)p(y.name);for(var y=t.state.globalVars;y;y=y.next)p(y.name);i&&!1===i.useGlobalScope||u(c),r(n,p)}return l}(p,g,i,f),from:e(c.line,p.start),to:e(c.line,p.end)}}}}function i(t,e){var r=t.getTokenAt(e);return e.ch==r.start+1&&\".\"==r.string.charAt(0)?(r.end=r.start,r.string=\".\",r.type=\"property\"):/^\\.[\\w$_]*$/.test(r.string)&&(r.type=\"property\",r.start++,r.string=r.string.replace(/\\./,\"\")),r}t.registerHelper(\"hint\",\"javascript\",function(t,e){return n(t,l,function(t,e){return t.getTokenAt(e)},e)}),t.registerHelper(\"hint\",\"coffeescript\",function(t,e){return n(t,f,i,e)});var o=\"charAt charCodeAt indexOf lastIndexOf substring substr slice trim trimLeft trimRight toUpperCase toLowerCase split concat match replace search\".split(\" \"),s=\"length concat join splice push pop shift unshift slice reverse sort indexOf lastIndexOf every some filter forEach map reduce reduceRight \".split(\" \"),a=\"prototype apply call bind\".split(\" \"),l=\"break case catch class const continue debugger default delete do else export extends false finally for function if in import instanceof new null return super switch this throw true try typeof var void while with yield\".split(\" \"),f=\"and break catch class continue delete do else extends false finally for if in instanceof isnt new no not null of off on or return switch then throw true try typeof until void while with yes\".split(\" \")});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/hint/javascript-hint.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror/addon/hint/show-hint.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(t){\"object\"==typeof exports&&\"object\"==typeof module?t(require(\"../../lib/codemirror\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\"],t):t(CodeMirror)}(function(t){\"use strict\";var i=\"CodeMirror-hint\",e=\"CodeMirror-hint-active\";function n(t,i){this.cm=t,this.options=i,this.widget=null,this.debounce=0,this.tick=0,this.startPos=this.cm.getCursor(\"start\"),this.startLen=this.cm.getLine(this.startPos.line).length-this.cm.getSelection().length;var e=this;t.on(\"cursorActivity\",this.activityFunc=function(){e.cursorActivity()})}t.showHint=function(t,i,e){if(!i)return t.showHint(e);e&&e.async&&(i.async=!0);var n={hint:i};if(e)for(var o in e)n[o]=e[o];return t.showHint(n)},t.defineExtension(\"showHint\",function(i){i=function(t,i,e){var n=t.options.hintOptions,o={};for(var s in a)o[s]=a[s];if(n)for(var s in n)void 0!==n[s]&&(o[s]=n[s]);if(e)for(var s in e)void 0!==e[s]&&(o[s]=e[s]);o.hint.resolve&&(o.hint=o.hint.resolve(t,i));return o}(this,this.getCursor(\"start\"),i);var e=this.listSelections();if(!(e.length>1)){if(this.somethingSelected()){if(!i.hint.supportsSelection)return;for(var o=0;o<e.length;o++)if(e[o].head.line!=e[o].anchor.line)return}this.state.completionActive&&this.state.completionActive.close();var s=this.state.completionActive=new n(this,i);s.options.hint&&(t.signal(this,\"startCompletion\",this),s.update(!0))}});var o=window.requestAnimationFrame||function(t){return setTimeout(t,1e3/60)},s=window.cancelAnimationFrame||clearTimeout;function c(t){return\"string\"==typeof t?t:t.text}function r(t,i){for(;i&&i!=t;){if(\"LI\"===i.nodeName.toUpperCase()&&i.parentNode==t)return i;i=i.parentNode}}function h(n,o){this.completion=n,this.data=o,this.picked=!1;var s=this,h=n.cm,l=this.hints=document.createElement(\"ul\");l.className=\"CodeMirror-hints\",this.selectedHint=o.selectedHint||0;for(var a=o.list,u=0;u<a.length;++u){var f=l.appendChild(document.createElement(\"li\")),d=a[u],p=i+(u!=this.selectedHint?\"\":\" \"+e);null!=d.className&&(p=d.className+\" \"+p),f.className=p,d.render?d.render(f,o,d):f.appendChild(document.createTextNode(d.displayText||c(d))),f.hintId=u}var m=h.cursorCoords(n.options.alignWithWord?o.from:null),g=m.left,v=m.bottom,y=!0;l.style.left=g+\"px\",l.style.top=v+\"px\";var w=window.innerWidth||Math.max(document.body.offsetWidth,document.documentElement.offsetWidth),H=window.innerHeight||Math.max(document.body.offsetHeight,document.documentElement.offsetHeight);(n.options.container||document.body).appendChild(l);var k=l.getBoundingClientRect(),C=k.bottom-H,b=l.scrollHeight>l.clientHeight+1,x=h.getScrollInfo();if(C>0){var A=k.bottom-k.top;if(m.top-(m.bottom-k.top)-A>0)l.style.top=(v=m.top-A)+\"px\",y=!1;else if(A>H){l.style.height=H-5+\"px\",l.style.top=(v=m.bottom-k.top)+\"px\";var S=h.getCursor();o.from.ch!=S.ch&&(m=h.cursorCoords(S),l.style.left=(g=m.left)+\"px\",k=l.getBoundingClientRect())}}var T,M=k.right-w;if(M>0&&(k.right-k.left>w&&(l.style.width=w-5+\"px\",M-=k.right-k.left-w),l.style.left=(g=m.left-M)+\"px\"),b)for(var N=l.firstChild;N;N=N.nextSibling)N.style.paddingRight=h.display.nativeBarWidth+\"px\";(h.addKeyMap(this.keyMap=function(t,i){var e={Up:function(){i.moveFocus(-1)},Down:function(){i.moveFocus(1)},PageUp:function(){i.moveFocus(1-i.menuSize(),!0)},PageDown:function(){i.moveFocus(i.menuSize()-1,!0)},Home:function(){i.setFocus(0)},End:function(){i.setFocus(i.length-1)},Enter:i.pick,Tab:i.pick,Esc:i.close},n=t.options.customKeys,o=n?{}:e;function s(t,n){var s;s=\"string\"!=typeof n?function(t){return n(t,i)}:e.hasOwnProperty(n)?e[n]:n,o[t]=s}if(n)for(var c in n)n.hasOwnProperty(c)&&s(c,n[c]);var r=t.options.extraKeys;if(r)for(var c in r)r.hasOwnProperty(c)&&s(c,r[c]);return o}(n,{moveFocus:function(t,i){s.changeActive(s.selectedHint+t,i)},setFocus:function(t){s.changeActive(t)},menuSize:function(){return s.screenAmount()},length:a.length,close:function(){n.close()},pick:function(){s.pick()},data:o})),n.options.closeOnUnfocus)&&(h.on(\"blur\",this.onBlur=function(){T=setTimeout(function(){n.close()},100)}),h.on(\"focus\",this.onFocus=function(){clearTimeout(T)}));return h.on(\"scroll\",this.onScroll=function(){var t=h.getScrollInfo(),i=h.getWrapperElement().getBoundingClientRect(),e=v+x.top-t.top,o=e-(window.pageYOffset||(document.documentElement||document.body).scrollTop);if(y||(o+=l.offsetHeight),o<=i.top||o>=i.bottom)return n.close();l.style.top=e+\"px\",l.style.left=g+x.left-t.left+\"px\"}),t.on(l,\"dblclick\",function(t){var i=r(l,t.target||t.srcElement);i&&null!=i.hintId&&(s.changeActive(i.hintId),s.pick())}),t.on(l,\"click\",function(t){var i=r(l,t.target||t.srcElement);i&&null!=i.hintId&&(s.changeActive(i.hintId),n.options.completeOnSingleClick&&s.pick())}),t.on(l,\"mousedown\",function(){setTimeout(function(){h.focus()},20)}),t.signal(o,\"select\",a[this.selectedHint],l.childNodes[this.selectedHint]),!0}function l(t,i,e,n){if(t.async)t(i,n,e);else{var o=t(i,e);o&&o.then?o.then(n):n(o)}}n.prototype={close:function(){this.active()&&(this.cm.state.completionActive=null,this.tick=null,this.cm.off(\"cursorActivity\",this.activityFunc),this.widget&&this.data&&t.signal(this.data,\"close\"),this.widget&&this.widget.close(),t.signal(this.cm,\"endCompletion\",this.cm))},active:function(){return this.cm.state.completionActive==this},pick:function(i,e){var n=i.list[e];n.hint?n.hint(this.cm,i,n):this.cm.replaceRange(c(n),n.from||i.from,n.to||i.to,\"complete\"),t.signal(i,\"pick\",n),this.close()},cursorActivity:function(){this.debounce&&(s(this.debounce),this.debounce=0);var t=this.cm.getCursor(),i=this.cm.getLine(t.line);if(t.line!=this.startPos.line||i.length-t.ch!=this.startLen-this.startPos.ch||t.ch<this.startPos.ch||this.cm.somethingSelected()||t.ch&&this.options.closeCharacters.test(i.charAt(t.ch-1)))this.close();else{var e=this;this.debounce=o(function(){e.update()}),this.widget&&this.widget.disable()}},update:function(t){if(null!=this.tick){var i=this,e=++this.tick;l(this.options.hint,this.cm,this.options,function(n){i.tick==e&&i.finishUpdate(n,t)})}},finishUpdate:function(i,e){this.data&&t.signal(this.data,\"update\");var n=this.widget&&this.widget.picked||e&&this.options.completeSingle;this.widget&&this.widget.close(),this.data=i,i&&i.list.length&&(n&&1==i.list.length?this.pick(i,0):(this.widget=new h(this,i),t.signal(i,\"shown\")))}},h.prototype={close:function(){if(this.completion.widget==this){this.completion.widget=null,this.hints.parentNode.removeChild(this.hints),this.completion.cm.removeKeyMap(this.keyMap);var t=this.completion.cm;this.completion.options.closeOnUnfocus&&(t.off(\"blur\",this.onBlur),t.off(\"focus\",this.onFocus)),t.off(\"scroll\",this.onScroll)}},disable:function(){this.completion.cm.removeKeyMap(this.keyMap);var t=this;this.keyMap={Enter:function(){t.picked=!0}},this.completion.cm.addKeyMap(this.keyMap)},pick:function(){this.completion.pick(this.data,this.selectedHint)},changeActive:function(i,n){if(i>=this.data.list.length?i=n?this.data.list.length-1:0:i<0&&(i=n?0:this.data.list.length-1),this.selectedHint!=i){var o=this.hints.childNodes[this.selectedHint];o.className=o.className.replace(\" \"+e,\"\"),(o=this.hints.childNodes[this.selectedHint=i]).className+=\" \"+e,o.offsetTop<this.hints.scrollTop?this.hints.scrollTop=o.offsetTop-3:o.offsetTop+o.offsetHeight>this.hints.scrollTop+this.hints.clientHeight&&(this.hints.scrollTop=o.offsetTop+o.offsetHeight-this.hints.clientHeight+3),t.signal(this.data,\"select\",this.data.list[this.selectedHint],o)}},screenAmount:function(){return Math.floor(this.hints.clientHeight/this.hints.firstChild.offsetHeight)||1}},t.registerHelper(\"hint\",\"auto\",{resolve:function(i,e){var n,o=i.getHelpers(e,\"hint\");if(o.length){var s=function(t,i,e){var n=function(t,i){if(!t.somethingSelected())return i;for(var e=[],n=0;n<i.length;n++)i[n].supportsSelection&&e.push(i[n]);return e}(t,o);!function o(s){if(s==n.length)return i(null);l(n[s],t,e,function(t){t&&t.list.length>0?i(t):o(s+1)})}(0)};return s.async=!0,s.supportsSelection=!0,s}return(n=i.getHelper(i.getCursor(),\"hintWords\"))?function(i){return t.hint.fromList(i,{words:n})}:t.hint.anyword?function(i,e){return t.hint.anyword(i,e)}:function(){}}}),t.registerHelper(\"hint\",\"fromList\",function(i,e){var n,o=i.getCursor(),s=i.getTokenAt(o),c=t.Pos(o.line,s.start),r=o;s.start<o.ch&&/\\w/.test(s.string.charAt(o.ch-s.start-1))?n=s.string.substr(0,o.ch-s.start):(n=\"\",c=o);for(var h=[],l=0;l<e.words.length;l++){var a=e.words[l];a.slice(0,n.length)==n&&h.push(a)}if(h.length)return{list:h,from:c,to:r}}),t.commands.autocomplete=t.showHint;var a={hint:t.hint.auto,completeSingle:!0,alignWithWord:!0,closeCharacters:/[\\s()\\[\\]{};:>,]/,closeOnUnfocus:!0,completeOnSingleClick:!0,container:null,customKeys:null,extraKeys:null};t.defineOption(\"hintOptions\",null)});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/hint/show-hint.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror/addon/hint/show-hint.css": {
"text": ".CodeMirror-hints {\n position: absolute;\n z-index: 10;\n overflow: hidden;\n list-style: none;\n\n margin: 0;\n padding: 2px;\n\n -webkit-box-shadow: 2px 3px 5px rgba(0,0,0,.2);\n -moz-box-shadow: 2px 3px 5px rgba(0,0,0,.2);\n box-shadow: 2px 3px 5px rgba(0,0,0,.2);\n border-radius: 3px;\n border: 1px solid silver;\n\n background: white;\n font-size: 90%;\n font-family: monospace;\n\n max-height: 20em;\n overflow-y: auto;\n}\n\n.CodeMirror-hint {\n margin: 0;\n padding: 0 4px;\n border-radius: 2px;\n white-space: pre;\n color: black;\n cursor: pointer;\n}\n\nli.CodeMirror-hint-active {\n background: #08f;\n color: white;\n}\n",
"type": "text/css",
"title": "$:/plugins/tiddlywiki/codemirror/addon/hint/show-hint.css",
"tags": "[[$:/tags/Stylesheet]]"
},
"$:/plugins/tiddlywiki/codemirror/addon/hint/xml-hint.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(t){\"object\"==typeof exports&&\"object\"==typeof module?t(require(\"../../lib/codemirror\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\"],t):t(CodeMirror)}(function(t){\"use strict\";var e=t.Pos;t.registerHelper(\"hint\",\"xml\",function(r,s){var n=s&&s.schemaInfo,a=s&&s.quoteChar||'\"';if(n){var i=r.getCursor(),o=r.getTokenAt(i);o.end>i.ch&&(o.end=i.ch,o.string=o.string.slice(0,i.ch-o.start));var l=t.innerMode(r.getMode(),o.state);if(\"xml\"==l.mode.name){var f,g,c=[],h=!1,p=/\\btag\\b/.test(o.type)&&!/>$/.test(o.string),u=p&&/^\\w/.test(o.string);if(u){var d=r.getLine(i.line).slice(Math.max(0,o.start-2),o.start),m=/<\\/$/.test(d)?\"close\":/<$/.test(d)?\"open\":null;m&&(g=o.start-(\"close\"==m?2:1))}else p&&\"<\"==o.string?m=\"open\":p&&\"</\"==o.string&&(m=\"close\");if(!p&&!l.state.tagName||m){u&&(f=o.string),h=m;var v=l.state.context,y=v&&n[v.tagName],x=v?y&&y.children:n[\"!top\"];if(x&&\"close\"!=m)for(var O=0;O<x.length;++O)f&&0!=x[O].lastIndexOf(f,0)||c.push(\"<\"+x[O]);else if(\"close\"!=m)for(var b in n)!n.hasOwnProperty(b)||\"!top\"==b||\"!attrs\"==b||f&&0!=b.lastIndexOf(f,0)||c.push(\"<\"+b);v&&(!f||\"close\"==m&&0==v.tagName.lastIndexOf(f,0))&&c.push(\"</\"+v.tagName+\">\")}else{var w=(y=n[l.state.tagName])&&y.attrs,I=n[\"!attrs\"];if(!w&&!I)return;if(w){if(I){var P={};for(var A in I)I.hasOwnProperty(A)&&(P[A]=I[A]);for(var A in w)w.hasOwnProperty(A)&&(P[A]=w[A]);w=P}}else w=I;if(\"string\"==o.type||\"=\"==o.string){var M,N=(d=r.getRange(e(i.line,Math.max(0,i.ch-60)),e(i.line,\"string\"==o.type?o.start:o.end))).match(/([^\\s\\u00a0=<>\\\"\\']+)=$/);if(!N||!w.hasOwnProperty(N[1])||!(M=w[N[1]]))return;if(\"function\"==typeof M&&(M=M.call(this,r)),\"string\"==o.type){f=o.string;var $=0;/['\"]/.test(o.string.charAt(0))&&(a=o.string.charAt(0),f=o.string.slice(1),$++);var C=o.string.length;/['\"]/.test(o.string.charAt(C-1))&&(a=o.string.charAt(C-1),f=o.string.substr($,C-2)),h=!0}for(O=0;O<M.length;++O)f&&0!=M[O].lastIndexOf(f,0)||c.push(a+M[O]+a)}else for(var j in\"attribute\"==o.type&&(f=o.string,h=!0),w)!w.hasOwnProperty(j)||f&&0!=j.lastIndexOf(f,0)||c.push(j)}return{list:c,from:h?e(i.line,null==g?o.start:g):i,to:h?e(i.line,o.end):i}}}})});",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/hint/xml-hint.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror-autocomplete/readme": {
"title": "$:/plugins/tiddlywiki/codemirror-autocomplete/readme",
"text": "This plugin enhances the [[CodeMirror|http://codemirror.net]] text editor with Autocompletion functionality. It needs the latest [[CodeMirror plugin|$:/plugins/tiddlywiki/codemirror]] to be installed\n\nIt adds Autocompletion for ''html'', ''javascript'' and ''xml'' and also for ''already present words'' within a text-editor instance\n\nThe ''Keyboard Shortcut'' for autocompletion is `Ctrl+Space`\n\n"
}
}
}
{
"tiddlers": {
"$:/config/codemirror/autoCloseBrackets": {
"title": "$:/config/codemirror/autoCloseBrackets",
"type": "bool",
"text": "true"
},
"$:/config/codemirror/matchBrackets": {
"title": "$:/config/codemirror/matchBrackets",
"type": "bool",
"text": "true\n"
},
"$:/plugins/tiddlywiki/codemirror/addon/edit/closebrackets.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(e){\"object\"==typeof exports&&\"object\"==typeof module?e(require(\"../../lib/codemirror\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\"],e):e(CodeMirror)}(function(e){var t={pairs:\"()[]{}''\\\"\\\"\",triples:\"\",explode:\"[]{}\"},r=e.Pos;function n(e,r){return\"pairs\"==r&&\"string\"==typeof e?e:\"object\"==typeof e&&null!=e[r]?e[r]:t[r]}e.defineOption(\"autoCloseBrackets\",!1,function(t,r,o){o&&o!=e.Init&&(t.removeKeyMap(i),t.state.closeBrackets=null),r&&(a(n(r,\"pairs\")),t.state.closeBrackets=r,t.addKeyMap(i))});var i={Backspace:function(t){var i=s(t);if(!i||t.getOption(\"disableInput\"))return e.Pass;for(var a=n(i,\"pairs\"),o=t.listSelections(),c=0;c<o.length;c++){if(!o[c].empty())return e.Pass;var f=l(t,o[c].head);if(!f||a.indexOf(f)%2!=0)return e.Pass}for(var c=o.length-1;c>=0;c--){var h=o[c].head;t.replaceRange(\"\",r(h.line,h.ch-1),r(h.line,h.ch+1),\"+delete\")}},Enter:function(t){var r=s(t),i=r&&n(r,\"explode\");if(!i||t.getOption(\"disableInput\"))return e.Pass;for(var a=t.listSelections(),o=0;o<a.length;o++){if(!a[o].empty())return e.Pass;var c=l(t,a[o].head);if(!c||i.indexOf(c)%2!=0)return e.Pass}t.operation(function(){var e=t.lineSeparator()||\"\\n\";t.replaceSelection(e+e,null),t.execCommand(\"goCharLeft\"),a=t.listSelections();for(var r=0;r<a.length;r++){var n=a[r].head.line;t.indentLine(n,null,!0),t.indentLine(n+1,null,!0)}})}};function a(e){for(var t=0;t<e.length;t++){var r=e.charAt(t),n=\"'\"+r+\"'\";i[n]||(i[n]=o(r))}}function o(t){return function(i){return function(t,i){var a=s(t);if(!a||t.getOption(\"disableInput\"))return e.Pass;var o=n(a,\"pairs\"),l=o.indexOf(i);if(-1==l)return e.Pass;for(var c,f=n(a,\"triples\"),h=o.charAt(l+1)==i,d=t.listSelections(),u=l%2==0,g=0;g<d.length;g++){var p,v=d[g],m=v.head,b=t.getRange(m,r(m.line,m.ch+1));if(u&&!v.empty())p=\"surround\";else if(!h&&u||b!=i)if(h&&m.ch>1&&f.indexOf(i)>=0&&t.getRange(r(m.line,m.ch-2),m)==i+i){if(m.ch>2&&/\\bstring/.test(t.getTokenTypeAt(r(m.line,m.ch-2))))return e.Pass;p=\"addFour\"}else if(h){var C=0==m.ch?\" \":t.getRange(r(m.line,m.ch-1),m);if(e.isWordChar(b)||C==i||e.isWordChar(C))return e.Pass;p=\"both\"}else{if(!u||!(t.getLine(m.line).length==m.ch||(x=b,P=o,void 0,k=P.lastIndexOf(x),k>-1&&k%2==1)||/\\s/.test(b)))return e.Pass;p=\"both\"}else p=!h||(S=m,void 0,O=(y=t).getTokenAt(r(S.line,S.ch+1)),!/\\bstring/.test(O.type)||O.start!=S.ch||0!=S.ch&&/\\bstring/.test(y.getTokenTypeAt(S)))?f.indexOf(i)>=0&&t.getRange(m,r(m.line,m.ch+3))==i+i+i?\"skipThree\":\"skip\":\"both\";if(c){if(c!=p)return e.Pass}else c=p}var x,P,k;var y,S,O;var R=l%2?o.charAt(l-1):i,A=l%2?i:o.charAt(l+1);t.operation(function(){if(\"skip\"==c)t.execCommand(\"goCharRight\");else if(\"skipThree\"==c)for(var n=0;n<3;n++)t.execCommand(\"goCharRight\");else if(\"surround\"==c){for(var i=t.getSelections(),n=0;n<i.length;n++)i[n]=R+i[n]+A;t.replaceSelections(i,\"around\"),i=t.listSelections().slice();for(var n=0;n<i.length;n++)i[n]=(a=i[n],void 0,o=e.cmpPos(a.anchor,a.head)>0,{anchor:new r(a.anchor.line,a.anchor.ch+(o?-1:1)),head:new r(a.head.line,a.head.ch+(o?1:-1))});t.setSelections(i)}else\"both\"==c?(t.replaceSelection(R+A,null),t.triggerElectric(R+A),t.execCommand(\"goCharLeft\")):\"addFour\"==c&&(t.replaceSelection(R+R+R+R,\"before\"),t.execCommand(\"goCharRight\"));var a,o})}(i,t)}}function s(e){var t=e.state.closeBrackets;return!t||t.override?t:e.getModeAt(e.getCursor()).closeBrackets||t}function l(e,t){var n=e.getRange(r(t.line,t.ch-1),r(t.line,t.ch+1));return 2==n.length?n:null}a(t.pairs+\"`\")});",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/edit/closebrackets.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror/addon/edit/matchbrackets.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(t){\"object\"==typeof exports&&\"object\"==typeof module?t(require(\"../../lib/codemirror\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\"],t):t(CodeMirror)}(function(t){var e=/MSIE \\d/.test(navigator.userAgent)&&(null==document.documentMode||document.documentMode<8),n=t.Pos,r={\"(\":\")>\",\")\":\"(<\",\"[\":\"]>\",\"]\":\"[<\",\"{\":\"}>\",\"}\":\"{<\"};function i(t,e,i){var c=t.getLineHandle(e.line),o=e.ch-1,l=i&&i.afterCursor;null==l&&(l=/(^| )cm-fat-cursor($| )/.test(t.getWrapperElement().className));var h=!l&&o>=0&&r[c.text.charAt(o)]||r[c.text.charAt(++o)];if(!h)return null;var s=\">\"==h.charAt(1)?1:-1;if(i&&i.strict&&s>0!=(o==e.ch))return null;var u=t.getTokenTypeAt(n(e.line,o+1)),f=a(t,n(e.line,o+(s>0?1:0)),s,u||null,i);return null==f?null:{from:n(e.line,o),to:f&&f.pos,match:f&&f.ch==h.charAt(0),forward:s>0}}function a(t,e,i,a,c){for(var o=c&&c.maxScanLineLength||1e4,l=c&&c.maxScanLines||1e3,h=[],s=c&&c.bracketRegex?c.bracketRegex:/[(){}[\\]]/,u=i>0?Math.min(e.line+l,t.lastLine()+1):Math.max(t.firstLine()-1,e.line-l),f=e.line;f!=u;f+=i){var m=t.getLine(f);if(m){var g=i>0?0:m.length-1,d=i>0?m.length:-1;if(!(m.length>o))for(f==e.line&&(g=e.ch-(i<0?1:0));g!=d;g+=i){var k=m.charAt(g);if(s.test(k)&&(void 0===a||t.getTokenTypeAt(n(f,g+1))==a))if(\">\"==r[k].charAt(1)==i>0)h.push(k);else{if(!h.length)return{pos:n(f,g),ch:k};h.pop()}}}}return f-i!=(i>0?t.lastLine():t.firstLine())&&null}function c(t,r,a){for(var c=t.state.matchBrackets.maxHighlightLineLength||1e3,o=[],l=t.listSelections(),h=0;h<l.length;h++){var s=l[h].empty()&&i(t,l[h].head,a);if(s&&t.getLine(s.from.line).length<=c){var u=s.match?\"CodeMirror-matchingbracket\":\"CodeMirror-nonmatchingbracket\";o.push(t.markText(s.from,n(s.from.line,s.from.ch+1),{className:u})),s.to&&t.getLine(s.to.line).length<=c&&o.push(t.markText(s.to,n(s.to.line,s.to.ch+1),{className:u}))}}if(o.length){e&&t.state.focused&&t.focus();var f=function(){t.operation(function(){for(var t=0;t<o.length;t++)o[t].clear()})};if(!r)return f;setTimeout(f,800)}}function o(t){t.operation(function(){t.state.matchBrackets.currentlyHighlighted&&(t.state.matchBrackets.currentlyHighlighted(),t.state.matchBrackets.currentlyHighlighted=null),t.state.matchBrackets.currentlyHighlighted=c(t,!1,t.state.matchBrackets)})}t.defineOption(\"matchBrackets\",!1,function(e,n,r){r&&r!=t.Init&&(e.off(\"cursorActivity\",o),e.state.matchBrackets&&e.state.matchBrackets.currentlyHighlighted&&(e.state.matchBrackets.currentlyHighlighted(),e.state.matchBrackets.currentlyHighlighted=null)),n&&(e.state.matchBrackets=\"object\"==typeof n?n:{},e.on(\"cursorActivity\",o))}),t.defineExtension(\"matchBrackets\",function(){c(this,!0)}),t.defineExtension(\"findMatchingBracket\",function(t,e,n){return(n||\"boolean\"==typeof e)&&(n?(n.strict=e,e=n):e=e?{strict:!0}:null),i(this,t,e)}),t.defineExtension(\"scanForBracket\",function(t,e,n,r){return a(this,t,e,n,r)})});",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/edit/matchbrackets.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror-closebrackets/readme": {
"title": "$:/plugins/tiddlywiki/codemirror-closebrackets/readme",
"text": "This plugin adds the ability to automatically insert the closing brackets when you type an opening bracket.\nAlso enables highlighting of matching brackets.\n\nIt needs the latest [[CodeMirror plugin|$:/plugins/tiddlywiki/codemirror]] to be installed\n\n"
}
}
}
{
"tiddlers": {
"$:/config/codemirror/autoCloseTags": {
"title": "$:/config/codemirror/autoCloseTags",
"type": "bool",
"text": "true\n"
},
"$:/language/codemirror/autoCloseTags/hint": {
"title": "$:/language/codemirror/autoCloseTags/hint",
"text": "Auto-close tags"
},
"$:/language/codemirror/autoCloseTags/info": {
"title": "$:/language/codemirror/autoCloseTags/info",
"text": "Whether or not to automatically close tags"
},
"$:/plugins/tiddlywiki/codemirror/addon/fold/xml-fold.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(e){\"object\"==typeof exports&&\"object\"==typeof module?e(require(\"../../lib/codemirror\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\"],e):e(CodeMirror)}(function(e){\"use strict\";var n=e.Pos;function t(e,n){return e.line-n.line||e.ch-n.ch}var i=\"A-Z_a-z\\\\u00C0-\\\\u00D6\\\\u00D8-\\\\u00F6\\\\u00F8-\\\\u02FF\\\\u0370-\\\\u037D\\\\u037F-\\\\u1FFF\\\\u200C-\\\\u200D\\\\u2070-\\\\u218F\\\\u2C00-\\\\u2FEF\\\\u3001-\\\\uD7FF\\\\uF900-\\\\uFDCF\\\\uFDF0-\\\\uFFFD\",r=new RegExp(\"<(/?)([\"+i+\"][A-Z_a-z\\\\u00C0-\\\\u00D6\\\\u00D8-\\\\u00F6\\\\u00F8-\\\\u02FF\\\\u0370-\\\\u037D\\\\u037F-\\\\u1FFF\\\\u200C-\\\\u200D\\\\u2070-\\\\u218F\\\\u2C00-\\\\u2FEF\\\\u3001-\\\\uD7FF\\\\uF900-\\\\uFDCF\\\\uFDF0-\\\\uFFFD-:.0-9\\\\u00B7\\\\u0300-\\\\u036F\\\\u203F-\\\\u2040]*)\",\"g\");function u(e,n,t,i){this.line=n,this.ch=t,this.cm=e,this.text=e.getLine(n),this.min=i?Math.max(i.from,e.firstLine()):e.firstLine(),this.max=i?Math.min(i.to-1,e.lastLine()):e.lastLine()}function f(e,t){var i=e.cm.getTokenTypeAt(n(e.line,t));return i&&/\\btag\\b/.test(i)}function o(e){if(!(e.line>=e.max))return e.ch=0,e.text=e.cm.getLine(++e.line),!0}function l(e){if(!(e.line<=e.min))return e.text=e.cm.getLine(--e.line),e.ch=e.text.length,!0}function c(e){for(;;){var n=e.text.indexOf(\">\",e.ch);if(-1==n){if(o(e))continue;return}if(f(e,n+1)){var t=e.text.lastIndexOf(\"/\",n),i=t>-1&&!/\\S/.test(e.text.slice(t+1,n));return e.ch=n+1,i?\"selfClose\":\"regular\"}e.ch=n+1}}function a(e){for(;;){var n=e.ch?e.text.lastIndexOf(\"<\",e.ch-1):-1;if(-1==n){if(l(e))continue;return}if(f(e,n+1)){r.lastIndex=n,e.ch=n;var t=r.exec(e.text);if(t&&t.index==n)return t}else e.ch=n}}function s(e){for(;;){r.lastIndex=e.ch;var n=r.exec(e.text);if(!n){if(o(e))continue;return}if(f(e,n.index+1))return e.ch=n.index+n[0].length,n;e.ch=n.index+1}}function h(e){for(;;){var n=e.ch?e.text.lastIndexOf(\">\",e.ch-1):-1;if(-1==n){if(l(e))continue;return}if(f(e,n+1)){var t=e.text.lastIndexOf(\"/\",n),i=t>-1&&!/\\S/.test(e.text.slice(t+1,n));return e.ch=n+1,i?\"selfClose\":\"regular\"}e.ch=n}}function F(e,t){for(var i=[];;){var r,u=s(e),f=e.line,o=e.ch-(u?u[0].length:0);if(!u||!(r=c(e)))return;if(\"selfClose\"!=r)if(u[1]){for(var l=i.length-1;l>=0;--l)if(i[l]==u[2]){i.length=l;break}if(l<0&&(!t||t==u[2]))return{tag:u[2],from:n(f,o),to:n(e.line,e.ch)}}else i.push(u[2])}}function x(e,t){for(var i=[];;){var r=h(e);if(!r)return;if(\"selfClose\"!=r){var u=e.line,f=e.ch,o=a(e);if(!o)return;if(o[1])i.push(o[2]);else{for(var l=i.length-1;l>=0;--l)if(i[l]==o[2]){i.length=l;break}if(l<0&&(!t||t==o[2]))return{tag:o[2],from:n(e.line,e.ch),to:n(u,f)}}}else a(e)}}e.registerHelper(\"fold\",\"xml\",function(e,i){for(var r=new u(e,i.line,0);;){var f=s(r);if(!f||r.line!=i.line)return;var o=c(r);if(!o)return;if(!f[1]&&\"selfClose\"!=o){var l=n(r.line,r.ch),a=F(r,f[2]);return a&&t(a.from,l)>0?{from:l,to:a.from}:null}}}),e.findMatchingTag=function(e,i,r){var f=new u(e,i.line,i.ch,r);if(-1!=f.text.indexOf(\">\")||-1!=f.text.indexOf(\"<\")){var o=c(f),l=o&&n(f.line,f.ch),s=o&&a(f);if(o&&s&&!(t(f,i)>0)){var h={from:n(f.line,f.ch),to:l,tag:s[2]};return\"selfClose\"==o?{open:h,close:null,at:\"open\"}:s[1]?{open:x(f,s[2]),close:h,at:\"close\"}:{open:h,close:F(f=new u(e,l.line,l.ch,r),s[2]),at:\"open\"}}}},e.findEnclosingTag=function(e,n,t,i){for(var r=new u(e,n.line,n.ch,t);;){var f=x(r,i);if(!f)break;var o=F(new u(e,n.line,n.ch,t),f.tag);if(o)return{open:f,close:o}}},e.scanForClosingTag=function(e,n,t,i){return F(new u(e,n.line,n.ch,i?{from:0,to:i}:null),t)}});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/fold/xml-fold.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror/addon/edit/closetag.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(e){\"object\"==typeof exports&&\"object\"==typeof module?e(require(\"../../lib/codemirror\"),require(\"../fold/xml-fold\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\",\"../fold/xml-fold\"],e):e(CodeMirror)}(function(e){e.defineOption(\"autoCloseTags\",!1,function(i,s,l){if(l!=e.Init&&l&&i.removeKeyMap(\"autoCloseTags\"),s){var d={name:\"autoCloseTags\"};(\"object\"!=typeof s||s.whenClosing)&&(d[\"'/'\"]=function(t){return(n=t).getOption(\"disableInput\")?e.Pass:o(n,!0);var n}),(\"object\"!=typeof s||s.whenOpening)&&(d[\"'>'\"]=function(o){return function(o){if(o.getOption(\"disableInput\"))return e.Pass;for(var i=o.listSelections(),s=[],l=o.getOption(\"autoCloseTags\"),d=0;d<i.length;d++){if(!i[d].empty())return e.Pass;var c=i[d].head,f=o.getTokenAt(c),g=e.innerMode(o.getMode(),f.state),u=g.state;if(\"xml\"!=g.mode.name||!u.tagName)return e.Pass;var m=\"html\"==g.mode.configuration,h=\"object\"==typeof l&&l.dontCloseTags||m&&t,p=\"object\"==typeof l&&l.indentTags||m&&n,v=u.tagName;f.end>c.ch&&(v=v.slice(0,v.length-f.end+c.ch));var b=v.toLowerCase();if(!v||\"string\"==f.type&&(f.end!=c.ch||!/[\\\"\\']/.test(f.string.charAt(f.string.length-1))||1==f.string.length)||\"tag\"==f.type&&\"closeTag\"==u.type||f.string.indexOf(\"/\")==f.string.length-1||h&&a(h,b)>-1||r(o,v,c,u,!0))return e.Pass;var y=p&&a(p,b)>-1;s[d]={indent:y,text:\">\"+(y?\"\\n\\n\":\"\")+\"</\"+v+\">\",newPos:y?e.Pos(c.line+1,0):e.Pos(c.line,c.ch+1)}}for(var x=\"object\"==typeof l&&l.dontIndentOnAutoClose,d=i.length-1;d>=0;d--){var P=s[d];o.replaceRange(P.text,i[d].head,i[d].anchor,\"+insert\");var T=o.listSelections().slice(0);T[d]={head:P.newPos,anchor:P.newPos},o.setSelections(T),!x&&P.indent&&(o.indentLine(P.newPos.line,null,!0),o.indentLine(P.newPos.line+1,null,!0))}}(o)}),i.addKeyMap(d)}});var t=[\"area\",\"base\",\"br\",\"col\",\"command\",\"embed\",\"hr\",\"img\",\"input\",\"keygen\",\"link\",\"meta\",\"param\",\"source\",\"track\",\"wbr\"],n=[\"applet\",\"blockquote\",\"body\",\"button\",\"div\",\"dl\",\"fieldset\",\"form\",\"frameset\",\"h1\",\"h2\",\"h3\",\"h4\",\"h5\",\"h6\",\"head\",\"html\",\"iframe\",\"layer\",\"legend\",\"object\",\"ol\",\"p\",\"select\",\"table\",\"ul\"];function o(t,n){for(var o=t.listSelections(),a=[],i=n?\"/\":\"</\",s=t.getOption(\"autoCloseTags\"),l=\"object\"==typeof s&&s.dontIndentOnSlash,d=0;d<o.length;d++){if(!o[d].empty())return e.Pass;var c,f=o[d].head,g=t.getTokenAt(f),u=e.innerMode(t.getMode(),g.state),m=u.state;if(n&&(\"string\"==g.type||\"<\"!=g.string.charAt(0)||g.start!=f.ch-1))return e.Pass;if(\"xml\"!=u.mode.name)if(\"htmlmixed\"==t.getMode().name&&\"javascript\"==u.mode.name)c=i+\"script\";else{if(\"htmlmixed\"!=t.getMode().name||\"css\"!=u.mode.name)return e.Pass;c=i+\"style\"}else{if(!m.context||!m.context.tagName||r(t,m.context.tagName,f,m))return e.Pass;c=i+m.context.tagName}\">\"!=t.getLine(f.line).charAt(g.end)&&(c+=\">\"),a[d]=c}if(t.replaceSelections(a),o=t.listSelections(),!l)for(d=0;d<o.length;d++)(d==o.length-1||o[d].head.line<o[d+1].head.line)&&t.indentLine(o[d].head.line)}function a(e,t){if(e.indexOf)return e.indexOf(t);for(var n=0,o=e.length;n<o;++n)if(e[n]==t)return n;return-1}function r(t,n,o,a,r){if(!e.scanForClosingTag)return!1;var i=Math.min(t.lastLine()+1,o.line+500),s=e.scanForClosingTag(t,o,null,i);if(!s||s.tag!=n)return!1;for(var l=a.context,d=r?1:0;l&&l.tagName==n;l=l.prev)++d;o=s.to;for(var c=1;c<d;c++){var f=e.scanForClosingTag(t,o,null,i);if(!f||f.tag!=n)return!1;o=f.to}return!0}e.commands.closeTag=function(e){return o(e)}});",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/edit/closetag.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror-closetag/readme": {
"title": "$:/plugins/tiddlywiki/codemirror-closetag/readme",
"text": "This plugin adds the ability to ''automatically close Tags''. It needs the latest [[CodeMirror plugin|$:/plugins/tiddlywiki/codemirror]] to be installed\n\n\n"
},
"$:/core/ui/ControlPanel/Settings/codemirror/autoCloseTags": {
"title": "$:/core/ui/ControlPanel/Settings/codemirror/autoCloseTags",
"tags": "$:/tags/ControlPanel/Settings/CodeMirror",
"caption": "{{$:/language/codemirror/autoCloseTags/hint}}",
"text": "\\define lingo-base() $:/language/codemirror/autoCloseTags/\n<<lingo hint>>\n\n<$checkbox tiddler=\"$:/config/codemirror/autoCloseTags\" field=\"text\" checked=\"true\" unchecked=\"false\" default=\"true\"> <$link to=\"$:/config/codemirror/autoCloseTags\"><<lingo info>></$link> </$checkbox>\n\n"
}
}
}
{
"tiddlers": {
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"title": "$:/plugins/tiddlywiki/codemirror/mode/css/css.js",
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"title": "$:/plugins/tiddlywiki/codemirror-mode-css/readme",
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t=e.cc[e.cc.length-1];t!=P&&t!=S||e.cc.pop()}}}),e.registerHelper(\"wordChars\",\"javascript\",/[\\w$]/),e.defineMIME(\"text/javascript\",\"javascript\"),e.defineMIME(\"text/ecmascript\",\"javascript\"),e.defineMIME(\"application/javascript\",\"javascript\"),e.defineMIME(\"application/x-javascript\",\"javascript\"),e.defineMIME(\"application/ecmascript\",\"javascript\"),e.defineMIME(\"application/json\",{name:\"javascript\",json:!0}),e.defineMIME(\"application/x-json\",{name:\"javascript\",json:!0}),e.defineMIME(\"application/ld+json\",{name:\"javascript\",jsonld:!0}),e.defineMIME(\"text/typescript\",{name:\"javascript\",typescript:!0}),e.defineMIME(\"application/typescript\",{name:\"javascript\",typescript:!0})});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror-mode-javascript/readme": {
"title": "$:/plugins/tiddlywiki/codemirror-mode-javascript/readme",
"text": "This plugin adds Syntax Highlighting for Javascript tiddlers (application/javascript) to the [[CodeMirror|http://codemirror.net]] text editor. It needs the latest [[CodeMirror plugin|$:/plugins/tiddlywiki/codemirror]] to be installed\n\n"
}
}
}
{
"tiddlers": {
"$:/plugins/tiddlywiki/codemirror/mode/latexembedded/latexembedded.js": {
"text": "\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/latexembedded/latexembedded.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror-mode-latexembedded/readme": {
"title": "$:/plugins/tiddlywiki/codemirror-mode-latexembedded/readme",
"text": "This plugin adds Syntax Highlighting for LaTeX embedded tiddlers (text/vnd.tiddlywiki) to the [[CodeMirror|http://codemirror.net]] text editor. It needs the latest [[CodeMirror plugin|$:/plugins/tiddlywiki/codemirror]] to be installed\n\n"
}
}
}
{
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"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(t){\"object\"==typeof exports&&\"object\"==typeof module?t(require(\"../../lib/codemirror\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\"],t):t(CodeMirror)}(function(t){\"use strict\";var e={autoSelfClosers:{area:!0,base:!0,br:!0,col:!0,command:!0,embed:!0,frame:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0,menuitem:!0},implicitlyClosed:{dd:!0,li:!0,optgroup:!0,option:!0,p:!0,rp:!0,rt:!0,tbody:!0,td:!0,tfoot:!0,th:!0,tr:!0},contextGrabbers:{dd:{dd:!0,dt:!0},dt:{dd:!0,dt:!0},li:{li:!0},option:{option:!0,optgroup:!0},optgroup:{optgroup:!0},p:{address:!0,article:!0,aside:!0,blockquote:!0,dir:!0,div:!0,dl:!0,fieldset:!0,footer:!0,form:!0,h1:!0,h2:!0,h3:!0,h4:!0,h5:!0,h6:!0,header:!0,hgroup:!0,hr:!0,menu:!0,nav:!0,ol:!0,p:!0,pre:!0,section:!0,table:!0,ul:!0},rp:{rp:!0,rt:!0},rt:{rp:!0,rt:!0},tbody:{tbody:!0,tfoot:!0},td:{td:!0,th:!0},tfoot:{tbody:!0},th:{td:!0,th:!0},thead:{tbody:!0,tfoot:!0},tr:{tr:!0}},doNotIndent:{pre:!0},allowUnquoted:!0,allowMissing:!0,caseFold:!0},n={autoSelfClosers:{},implicitlyClosed:{},contextGrabbers:{},doNotIndent:{},allowUnquoted:!1,allowMissing:!1,allowMissingTagName:!1,caseFold:!1};t.defineMode(\"xml\",function(r,o){var 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"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/xml/xml.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror-mode-xml/readme": {
"title": "$:/plugins/tiddlywiki/codemirror-mode-xml/readme",
"text": "This plugin is a requirement for other Syntax-highlighting plugins and adds Highlighting for XML tiddlers (application/xml) to the [[CodeMirror|http://codemirror.net]] text editor. It needs the latest [[CodeMirror plugin|$:/plugins/tiddlywiki/codemirror]] to be installed\n\n"
}
}
}
{
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"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/search/search.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror/addon/search/jump-to-line.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(e){\"object\"==typeof exports&&\"object\"==typeof module?e(require(\"../../lib/codemirror\"),require(\"../dialog/dialog\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\",\"../dialog/dialog\"],e):e(CodeMirror)}(function(e){\"use strict\";function o(e,o){var r=Number(o);return/^[-+]/.test(o)?e.getCursor().line+r:r-1}e.commands.jumpToLine=function(e){var r,i,t,s,n,l=e.getCursor();r=e,i='Jump to line: <input type=\"text\" style=\"width: 10em\" class=\"CodeMirror-search-field\"/> <span style=\"color: #888\" class=\"CodeMirror-search-hint\">(Use line:column or scroll% syntax)</span>',t=\"Jump to line:\",s=l.line+1+\":\"+l.ch,n=function(r){var i;if(r)if(i=/^\\s*([\\+\\-]?\\d+)\\s*\\:\\s*(\\d+)\\s*$/.exec(r))e.setCursor(o(e,i[1]),Number(i[2]));else if(i=/^\\s*([\\+\\-]?\\d+(\\.\\d+)?)\\%\\s*/.exec(r)){var t=Math.round(e.lineCount()*Number(i[1])/100);/^[-+]/.test(i[1])&&(t=l.line+t+1),e.setCursor(t-1,l.ch)}else(i=/^\\s*\\:?\\s*([\\+\\-]?\\d+)\\s*/.exec(r))&&e.setCursor(o(e,i[1]),l.ch)},r.openDialog?r.openDialog(i,n,{value:s,selectValueOnOpen:!0}):n(prompt(t,s))},e.keyMap.default[\"Alt-G\"]=\"jumpToLine\"});",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/search/jump-to-line.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror/addon/search/searchcursor.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n!function(t){\"object\"==typeof exports&&\"object\"==typeof module?t(require(\"../../lib/codemirror\")):\"function\"==typeof define&&define.amd?define([\"../../lib/codemirror\"],t):t(CodeMirror)}(function(t){\"use strict\";var e,n,r=t.Pos;function i(t,e){for(var n,r,i=null!=(r=(n=t).flags)?r:(n.ignoreCase?\"i\":\"\")+(n.global?\"g\":\"\")+(n.multiline?\"m\":\"\"),o=i,l=0;l<e.length;l++)-1==o.indexOf(e.charAt(l))&&(o+=e.charAt(l));return i==o?t:new RegExp(t.source,o)}function o(t,e,n){e=i(e,\"g\");for(var o=n.line,l=n.ch,h=t.lastLine();o<=h;o++,l=0){e.lastIndex=l;var s=t.getLine(o),c=e.exec(s);if(c)return{from:r(o,c.index),to:r(o,c.index+c[0].length),match:c}}}function l(t,e){for(var n,r=0;;){e.lastIndex=r;var i=e.exec(t);if(!i)return n;if((r=(n=i).index+(n[0].length||1))==t.length)return n}}function h(t,e,n,r){if(t.length==e.length)return n;for(var i=0,o=n+Math.max(0,t.length-e.length);;){if(i==o)return i;var l=i+o>>1,h=r(t.slice(0,l)).length;if(h==n)return l;h>n?o=l:i=l+1}}function s(t,s,c,f){var u;this.atOccurrence=!1,this.doc=t,c=c?t.clipPos(c):r(0,0),this.pos={from:c,to:c},\"object\"==typeof f?u=f.caseFold:(u=f,f=null),\"string\"==typeof s?(null==u&&(u=!1),this.matches=function(i,o){return(i?function(t,i,o,l){if(!i.length)return null;var s=l?e:n,c=s(i).split(/\\r|\\n\\r?/);t:for(var f=o.line,u=o.ch,a=t.firstLine()-1+c.length;f>=a;f--,u=-1){var g=t.getLine(f);u>-1&&(g=g.slice(0,u));var m=s(g);if(1==c.length){var d=m.lastIndexOf(c[0]);if(-1==d)continue t;return{from:r(f,h(g,m,d,s)),to:r(f,h(g,m,d+c[0].length,s))}}var v=c[c.length-1];if(m.slice(0,v.length)==v){var p=1;for(o=f-c.length+1;p<c.length-1;p++)if(s(t.getLine(o+p))!=c[p])continue t;var x=t.getLine(f+1-c.length),L=s(x);if(L.slice(L.length-c[0].length)==c[0])return{from:r(f+1-c.length,h(x,L,x.length-c[0].length,s)),to:r(f,h(g,m,v.length,s))}}}}:function(t,i,o,l){if(!i.length)return null;var s=l?e:n,c=s(i).split(/\\r|\\n\\r?/);t:for(var f=o.line,u=o.ch,a=t.lastLine()+1-c.length;f<=a;f++,u=0){var g=t.getLine(f).slice(u),m=s(g);if(1==c.length){var d=m.indexOf(c[0]);if(-1==d)continue t;return o=h(g,m,d,s)+u,{from:r(f,h(g,m,d,s)+u),to:r(f,h(g,m,d+c[0].length,s)+u)}}var v=m.length-c[0].length;if(m.slice(v)==c[0]){for(var p=1;p<c.length-1;p++)if(s(t.getLine(f+p))!=c[p])continue t;var x=t.getLine(f+c.length-1),L=s(x),C=c[c.length-1];if(L.slice(0,C.length)==C)return{from:r(f,h(g,m,v,s)+u),to:r(f+c.length-1,h(x,L,C.length,s))}}}})(t,s,o,u)}):(s=i(s,\"gm\"),f&&!1===f.multiline?this.matches=function(e,n){return(e?function(t,e,n){e=i(e,\"g\");for(var o=n.line,h=n.ch,s=t.firstLine();o>=s;o--,h=-1){var c=t.getLine(o);h>-1&&(c=c.slice(0,h));var f=l(c,e);if(f)return{from:r(o,f.index),to:r(o,f.index+f[0].length),match:f}}}:o)(t,s,n)}:this.matches=function(e,n){return(e?function(t,e,n){e=i(e,\"gm\");for(var o,h=1,s=n.line,c=t.firstLine();s>=c;){for(var f=0;f<h;f++){var u=t.getLine(s--);o=null==o?u.slice(0,n.ch):u+\"\\n\"+o}h*=2;var a=l(o,e);if(a){var g=o.slice(0,a.index).split(\"\\n\"),m=a[0].split(\"\\n\"),d=s+g.length,v=g[g.length-1].length;return{from:r(d,v),to:r(d+m.length-1,1==m.length?v+m[0].length:m[m.length-1].length),match:a}}}}:function(t,e,n){if(!/\\\\s|\\\\n|\\n|\\\\W|\\\\D|\\[\\^/.test(e.source))return o(t,e,n);e=i(e,\"gm\");for(var l,h=1,s=n.line,c=t.lastLine();s<=c;){for(var f=0;f<h&&!(s>c);f++){var u=t.getLine(s++);l=null==l?u:l+\"\\n\"+u}h*=2,e.lastIndex=n.ch;var a=e.exec(l);if(a){var g=l.slice(0,a.index).split(\"\\n\"),m=a[0].split(\"\\n\"),d=n.line+g.length-1,v=g[g.length-1].length;return{from:r(d,v),to:r(d+m.length-1,1==m.length?v+m[0].length:m[m.length-1].length),match:a}}}})(t,s,n)})}String.prototype.normalize?(e=function(t){return t.normalize(\"NFD\").toLowerCase()},n=function(t){return t.normalize(\"NFD\")}):(e=function(t){return t.toLowerCase()},n=function(t){return t}),s.prototype={findNext:function(){return this.find(!1)},findPrevious:function(){return this.find(!0)},find:function(e){for(var n=this.matches(e,this.doc.clipPos(e?this.pos.from:this.pos.to));n&&0==t.cmpPos(n.from,n.to);)e?n.from.ch?n.from=r(n.from.line,n.from.ch-1):n=n.from.line==this.doc.firstLine()?null:this.matches(e,this.doc.clipPos(r(n.from.line-1))):n.to.ch<this.doc.getLine(n.to.line).length?n.to=r(n.to.line,n.to.ch+1):n=n.to.line==this.doc.lastLine()?null:this.matches(e,r(n.to.line+1,0));if(n)return this.pos=n,this.atOccurrence=!0,this.pos.match||!0;var i=r(e?this.doc.firstLine():this.doc.lastLine()+1,0);return this.pos={from:i,to:i},this.atOccurrence=!1},from:function(){if(this.atOccurrence)return this.pos.from},to:function(){if(this.atOccurrence)return this.pos.to},replace:function(e,n){if(this.atOccurrence){var i=t.splitLines(e);this.doc.replaceRange(i,this.pos.from,this.pos.to,n),this.pos.to=r(this.pos.from.line+i.length-1,i[i.length-1].length+(1==i.length?this.pos.from.ch:0))}}},t.defineExtension(\"getSearchCursor\",function(t,e,n){return new s(this.doc,t,e,n)}),t.defineDocExtension(\"getSearchCursor\",function(t,e,n){return new s(this,t,e,n)}),t.defineExtension(\"selectMatches\",function(e,n){for(var r=[],i=this.getSearchCursor(e,this.getCursor(\"from\"),n);i.findNext()&&!(t.cmpPos(i.to(),this.getCursor(\"to\"))>0);)r.push({anchor:i.from(),head:i.to()});r.length&&this.setSelections(r,0)})});",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/search/searchcursor.js",
"module-type": "codemirror"
},
"$:/plugins/tiddlywiki/codemirror-search-replace/readme": {
"title": "$:/plugins/tiddlywiki/codemirror-search-replace/readme",
"text": "This plugin enhances the [[CodeMirror|http://codemirror.net]] text editor with Search and Replace functionality. It needs the latest [[CodeMirror plugin|$:/plugins/tiddlywiki/codemirror]] to be installed\n\nIt adds these Keyboard Shortcuts to ~CodeMirror:\n\n|Shortcut |Function |h\n|Ctrl-F / Cmd-F |Start searching |\n|Ctrl-G / Cmd-G / Shift-F3 |Find next |\n|Shift-Ctrl-G / Shift-Cmd-G / F3 |Find previous |\n|Shift-Ctrl-F / Cmd-Option-F |Replace |\n|Shift-Ctrl-R / Shift-Cmd-Option-F |Replace all |\n|Alt-F |Persistent search (dialog doesn't autoclose, enter to find next, Shift-Enter to find previous) |\n|Alt-G |Jump to line |\n\n"
}
}
}
// CodeMirror, copyright (c) by Marijn Haverbeke and others
// Distributed under an MIT license: http://codemirror.net/LICENSE
!function(e){"object"==typeof exports&&"object"==typeof module?e(require("../../lib/codemirror"),require("../../addon/mode/multiplex")):"function"==typeof define&&define.amd?define(["../../lib/codemirror","../../addon/mode/multiplex"],e):e(CodeMirror)}(function(e){"use strict";e.defineMode("htmlembedded",function(i,t){var d=t.closeComment||"--%>";return e.multiplexingMode(e.getMode(i,"htmlmixed"),{open:t.openComment||"<%--",close:d,delimStyle:"comment",mode:{token:function(e){return e.skipTo(d)||e.skipToEnd(),"comment"}}},{open:t.open||t.scriptStartRegex||"<%",close:t.close||t.scriptEndRegex||"%>",mode:e.getMode(i,t.scriptingModeSpec)})},"htmlmixed"),e.defineMIME("application/x-ejs",{name:"htmlembedded",scriptingModeSpec:"javascript"}),e.defineMIME("application/x-aspx",{name:"htmlembedded",scriptingModeSpec:"text/x-csharp"}),e.defineMIME("application/x-jsp",{name:"htmlembedded",scriptingModeSpec:"text/x-java"}),e.defineMIME("application/x-erb",{name:"htmlembedded",scriptingModeSpec:"ruby"})});
// CodeMirror, copyright (c) by Marijn Haverbeke and others
// Distributed under an MIT license: http://codemirror.net/LICENSE
/***
|''Name''|latexembedded.js|
|''Description''|Enables TiddlyWikiy syntax highlighting using CodeMirror|
|''Author''|PMario|
|''Version''|0.1.7|
|''Status''|''stable''|
|''Source''|[[GitHub|https://github.com/pmario/CodeMirror2/blob/tw-syntax/mode/tiddlywiki]]|
|''Documentation''|http://codemirror.tiddlyspace.com/|
|''License''|[[MIT License|http://www.opensource.org/licenses/mit-license.php]]|
|''CoreVersion''|2.5.0|
|''Requires''|codemirror.js|
|''Keywords''|syntax highlighting color code mirror codemirror|
! Info
CoreVersion parameter is needed for TiddlyWiki only!
! Modified by Stan to support basic LaTeX equation highlighting.
***/
// CodeMirror, copyright (c) by Marijn Haverbeke and others
// Distributed under an MIT license: http://codemirror.net/LICENSE
/*
* Author: Constantin Jucovschi (c.jucovschi@jacobs-university.de)
* Licence: MIT
*/
(function(mod) {
if (typeof exports == "object" && typeof module == "object") // CommonJS
mod(require("../../lib/codemirror"));
else if (typeof define == "function" && define.amd) // AMD
define(["../../lib/codemirror"], mod);
else // Plain browser env
mod(CodeMirror);
})(function(CodeMirror) {
"use strict";
CodeMirror.defineMode("latexembedded", function() {
"use strict";
// TiddlyWiki variables
var keywords = {
"allTags": true, "closeAll": true, "list": true,
"newJournal": true, "newTiddler": true,
"permaview": true, "saveChanges": true,
"search": true, "slider": true, "tabs": true,
"tag": true, "tagging": true, "tags": true,
"tiddler": true, "timeline": true,
"today": true, "version": true, "option": true,
"with": true, "filter": true
};
var isSpaceName = /[\w_\-]/i,
reHR = /^\-\-\-\-+$/, // <hr>
reWikiCommentStart = /^\/\*\*\*$/, // /***
reWikiCommentStop = /^\*\*\*\/$/, // ***/
reBlockQuote = /^<<<$/,
reJsCodeStart = /^\/\/\{\{\{$/, // //{{{ js block start
reJsCodeStop = /^\/\/\}\}\}$/, // //}}} js stop
reXmlCodeStart = /^<!--\{\{\{-->$/, // xml block start
reXmlCodeStop = /^<!--\}\}\}-->$/, // xml stop
reCodeBlockStart = /^\{\{\{$/, // {{{ TW text div block start
reCodeBlockStop = /^\}\}\}$/, // }}} TW text stop
reUntilCodeStop = /.*?\}\}\}/;
// end of TiddlyWiki variables
function pushCommand(state, command) {
state.cmdState.push(command);
}
function peekCommand(state) {
if (state.cmdState.length > 0) {
return state.cmdState[state.cmdState.length - 1];
} else {
return null;
}
}
function popCommand(state) {
var plug = state.cmdState.pop();
if (plug) {
plug.closeBracket();
}
}
// returns the non-default plugin closest to the end of the list
function getMostPowerful(state) {
var context = state.cmdState;
for (var i = context.length - 1; i >= 0; i--) {
var plug = context[i];
if (plug.name == "DEFAULT") {
continue;
}
return plug;
}
return { styleIdentifier: function() { return null; } };
}
function addPluginPattern(pluginName, cmdStyle, styles) {
return function () {
this.name = pluginName;
this.bracketNo = 0;
this.style = cmdStyle;
this.styles = styles;
this.argument = null; // \begin and \end have arguments that follow. These are stored in the plugin
this.styleIdentifier = function() {
return this.styles[this.bracketNo - 1] || null;
};
this.openBracket = function() {
this.bracketNo++;
return "bracket";
};
this.closeBracket = function() {};
};
}
var plugins = {};
plugins["importmodule"] = addPluginPattern("importmodule", "tag", ["string", "builtin"]);
plugins["documentclass"] = addPluginPattern("documentclass", "tag", ["", "atom"]);
plugins["usepackage"] = addPluginPattern("usepackage", "tag", ["atom"]);
plugins["begin"] = addPluginPattern("begin", "tag", ["atom"]);
plugins["end"] = addPluginPattern("end", "tag", ["atom"]);
plugins["DEFAULT"] = function () {
this.name = "DEFAULT";
this.style = "tag";
this.styleIdentifier = this.openBracket = this.closeBracket = function() {};
};
function setState(state, f) {
state.f = f;
}
// called when in a normal (no environment) context
function normal(source, state) {
var plug;
// tiddlywiki formatting
// check start of blocks
var sol = source.sol(), ch = source.peek();
state.block = false; // indicates the start of a code block.
// check start of blocks
if (sol && /[\/\*!#;:>|]/.test(ch)) {
if (ch == "!") { // tw header
source.skipToEnd();
return "header";
}
if (ch == "*") { // tw list
source.eatWhile('*');
return "comment";
}
if (ch == "#") { // tw numbered list
source.eatWhile('#');
return "comment";
}
if (ch == ";") { // definition list, term
source.eatWhile(';');
return "comment";
}
if (ch == ":") { // definition list, description
source.eatWhile(':');
return "comment";
}
if (ch == ">") { // single line quote
source.eatWhile(">");
return "quote";
}
// starting with '|' crashes the system
/*
if (ch == '|')
return 'header';
*/
}
// rudimentary html:// file:// link matching. TW knows much more ...
if (/[hf]/i.test(ch) &&
/[ti]/i.test(source.peek()) &&
stream.match(/\b(ttps?|tp|ile):\/\/[\-A-Z0-9+&@#\/%?=~_|$!:,.;]*[A-Z0-9+&@#\/%=~_|$]/i))
return "link";
// end of tiddlywiki formatting
// Do we look like '\command' ? If so, attempt to apply the plugin 'command'
if (source.match(/^\\[a-zA-Z@]+/)) {
var cmdName = source.current().slice(1);
plug = plugins[cmdName] || plugins["DEFAULT"];
plug = new plug();
pushCommand(state, plug);
setState(state, beginParams);
return plug.style;
}
// escape characters
if (source.match(/^\\[$&%#{}_]/)) {
return "tag";
}
// white space control characters
if (source.match(/^\\[,;!\/\\]/)) {
return "tag";
}
// find if we're starting various math modes
if (source.match("\\[")) {
setState(state, function(source, state){ return inMathMode(source, state, "\\]"); });
return "keyword";
}
if (source.match("$$")) {
setState(state, function(source, state){ return inMathMode(source, state, "$$"); });
return "keyword";
}
if (source.match("$")) {
setState(state, function(source, state){ return inMathMode(source, state, "$"); });
return "keyword";
}
var ch = source.next();
if (ch == "%") {
source.skipToEnd();
return "comment";
} else if (ch == '}' || ch == ']') {
plug = peekCommand(state);
if (plug) {
plug.closeBracket(ch);
setState(state, beginParams);
} else {
return "error";
}
return "bracket";
} else if (ch == '{' || ch == '[') {
plug = plugins["DEFAULT"];
plug = new plug();
pushCommand(state, plug);
return "bracket";
} else if (/\d/.test(ch)) {
source.eatWhile(/[\w.%]/);
return "atom";
} else {
source.eatWhile(/[\w\-_]/);
plug = getMostPowerful(state);
if (plug.name == 'begin') {
plug.argument = source.current();
}
return plug.styleIdentifier();
}
}
function inMathMode(source, state, endModeSeq) {
if (source.eatSpace()) {
return null;
}
if (source.match(endModeSeq)) {
setState(state, normal);
return "keyword";
}
if (source.match(/^\\[a-zA-Z@]+/)) {
return "tag";
}
if (source.match(/^[a-zA-Z]+/)) {
return "variable-2";
}
// escape characters
if (source.match(/^\\[$&%#{}_]/)) {
return "tag";
}
// white space control characters
if (source.match(/^\\[,;!\/]/)) {
return "tag";
}
// special math-mode characters
if (source.match(/^[\^_&]/)) {
return "tag";
}
// non-special characters
if (source.match(/^[+\-<>|=,\/@!*:;'"`~#?]/)) {
return null;
}
if (source.match(/^(\d+\.\d*|\d*\.\d+|\d+)/)) {
return "number";
}
var ch = source.next();
if (ch == "{" || ch == "}" || ch == "[" || ch == "]" || ch == "(" || ch == ")") {
return "bracket";
}
if (ch == "%") {
source.skipToEnd();
return "comment";
}
return "error";
}
function beginParams(source, state) {
var ch = source.peek(), lastPlug;
if (ch == '{' || ch == '[') {
lastPlug = peekCommand(state);
lastPlug.openBracket(ch);
source.eat(ch);
setState(state, normal);
return "bracket";
}
if (/[ \t\r]/.test(ch)) {
source.eat(ch);
return null;
}
setState(state, normal);
popCommand(state);
return normal(source, state);
}
return {
startState: function() {
return {
cmdState: [],
f: normal
};
},
copyState: function(s) {
return {
cmdState: s.cmdState.slice(),
f: s.f
};
},
token: function(source, state) {
return state.f(source, state);
},
blankLine: function(state) {
state.f = normal;
state.cmdState.length = 0;
},
lineComment: "%"
};
});
CodeMirror.defineMIME("text/vnd.tiddlywiki", "latexembedded");
});
!function(e){"object"==typeof exports&&"object"==typeof module?e(require("../lib/codemirror")):"function"==typeof define&&define.amd?define(["../lib/codemirror"],e):e(CodeMirror)}(function(e){"use strict";e.modeInfo=[{name:"CMake",mime:"text/x-cmake",mode:"cmake",ext:["cmake","cmake.in"],file:/^CMakeLists.txt$/},{name:"Cython",mime:"text/x-cython",mode:"python",ext:["pyx","pxd","pxi"]},{name:"CSS",mime:"text/css",mode:"css",ext:["css"]},{name:"diff",mime:"text/x-diff",mode:"diff",ext:["diff","patch"]},{name:"Embedded Javascript",mime:"application/x-ejs",mode:"htmlembedded",ext:["ejs"]},{name:"Embedded Ruby",mime:"application/x-erb",mode:"htmlembedded",ext:["erb"]},{name:"Erlang",mime:"text/x-erlang",mode:"erlang",ext:["erl"]},{name:"GitHub Flavored Markdown",mime:"text/x-gfm",mode:"gfm",file:/^(readme|contributing|history).md$/i},{name:"Go",mime:"text/x-go",mode:"go",ext:["go"]},{name:"ASP.NET",mime:"application/x-aspx",mode:"htmlembedded",ext:["aspx"],alias:["asp","aspx"]},{name:"HTML",mime:"text/html",mode:"htmlmixed",ext:["html","htm","handlebars","hbs"],alias:["xhtml"]},{name:"HTTP",mime:"message/http",mode:"http"},{name:"JavaScript",mimes:["text/javascript","text/ecmascript","application/javascript","application/x-javascript","application/ecmascript"],mode:"javascript",ext:["js"],alias:["ecmascript","js","node"]},{name:"JSON",mimes:["application/json","application/x-json"],mode:"javascript",ext:["json","map"],alias:["json5"]},{name:"JSON-LD",mime:"application/ld+json",mode:"javascript",ext:["jsonld"],alias:["jsonld"]},{name:"Lua",mime:"text/x-lua",mode:"lua",ext:["lua"]},{name:"Markdown",mime:"text/x-markdown",mode:"markdown",ext:["markdown","md","mkd"]},{name:"MySQL",mime:"text/x-mysql",mode:"sql"},{name:"Plain Text",mime:"text/plain",mode:"null",ext:["txt","text","conf","def","list","log"]},{name:"Python",mime:"text/x-python",mode:"python",ext:["BUILD","bzl","py","pyw"],file:/^(BUCK|BUILD)$/},{name:"SCSS",mime:"text/x-scss",mode:"css",ext:["scss"]},{name:"LaTeX",mime:"text/x-latex",mode:"stex",ext:["text","ltx","tex"],alias:["tex"]},{name:"TiddlyWiki",mime:"text/x-tiddlywiki",mode:"latexembedded"},{name:"TW5",mime:"text/vnd.tiddlywiki",mode:"latexembedded"}];for(var t=0;t<e.modeInfo.length;t++){var m=e.modeInfo[t];m.mimes&&(m.mime=m.mimes[0])}e.findModeByMIME=function(t){t=t.toLowerCase();for(var m=0;m<e.modeInfo.length;m++){var i=e.modeInfo[m];if(i.mime==t)return i;if(i.mimes)for(var a=0;a<i.mimes.length;a++)if(i.mimes[a]==t)return i}return/\+xml$/.test(t)?e.findModeByMIME("application/xml"):/\+json$/.test(t)?e.findModeByMIME("application/json"):void 0},e.findModeByExtension=function(t){for(var m=0;m<e.modeInfo.length;m++){var i=e.modeInfo[m];if(i.ext)for(var a=0;a<i.ext.length;a++)if(i.ext[a]==t)return i}},e.findModeByFileName=function(t){for(var m=0;m<e.modeInfo.length;m++){var i=e.modeInfo[m];if(i.file&&i.file.test(t))return i}var a=t.lastIndexOf("."),o=a>-1&&t.substring(a+1,t.length);if(o)return e.findModeByExtension(o)},e.findModeByName=function(t){t=t.toLowerCase();for(var m=0;m<e.modeInfo.length;m++){var i=e.modeInfo[m];if(i.name.toLowerCase()==t)return i;if(i.alias)for(var a=0;a<i.alias.length;a++)if(i.alias[a].toLowerCase()==t)return i}}});
{
"tiddlers": {
"$:/config/HighlightPlugin/TypeMappings/application/javascript": {
"title": "$:/config/HighlightPlugin/TypeMappings/application/javascript",
"text": "javascript"
},
"$:/config/HighlightPlugin/TypeMappings/application/json": {
"title": "$:/config/HighlightPlugin/TypeMappings/application/json",
"text": "json"
},
"$:/config/HighlightPlugin/TypeMappings/text/css": {
"title": "$:/config/HighlightPlugin/TypeMappings/text/css",
"text": "css"
},
"$:/config/HighlightPlugin/TypeMappings/text/html": {
"title": "$:/config/HighlightPlugin/TypeMappings/text/html",
"text": "html"
},
"$:/config/HighlightPlugin/TypeMappings/image/svg+xml": {
"title": "$:/config/HighlightPlugin/TypeMappings/image/svg+xml",
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BooleanVariables BorderDimensions BorelTannerDistribution Bottom BottomHatTransform BoundaryDiscretizeGraphics BoundaryDiscretizeRegion BoundaryMesh BoundaryMeshRegion BoundaryMeshRegionQ BoundaryStyle BoundedRegionQ BoundingRegion Bounds Box BoxBaselineShift BoxData BoxDimensions Boxed Boxes BoxForm BoxFormFormatTypes BoxFrame BoxID BoxMargins BoxMatrix BoxObject BoxRatios BoxRotation BoxRotationPoint BoxStyle BoxWhiskerChart Bra BracketingBar BraKet BrayCurtisDistance BreadthFirstScan Break BridgeData BrightnessEqualize BroadcastStationData Brown BrownForsytheTest BrownianBridgeProcess BrowserCategory BSplineBasis BSplineCurve BSplineCurve3DBox BSplineCurve3DBoxOptions BSplineCurveBox BSplineCurveBoxOptions BSplineFunction BSplineSurface BSplineSurface3DBox BSplineSurface3DBoxOptions BubbleChart BubbleChart3D BubbleScale BubbleSizes BuildingData BulletGauge BusinessDayQ ButterflyGraph ButterworthFilterModel Button ButtonBar ButtonBox ButtonBoxOptions ButtonCell ButtonContents ButtonData ButtonEvaluator ButtonExpandable ButtonFrame ButtonFunction ButtonMargins ButtonMinHeight ButtonNote ButtonNotebook ButtonSource ButtonStyle ButtonStyleMenuListing Byte ByteArray ByteArrayFormat ByteArrayQ ByteArrayToString ByteCount ByteOrderingC CachedValue CacheGraphics CachePersistence CalendarConvert CalendarData CalendarType Callout CalloutMarker CalloutStyle CallPacket CanberraDistance Cancel CancelButton CandlestickChart CanonicalGraph CanonicalizePolygon CanonicalizePolyhedron CanonicalName CanonicalWarpingCorrespondence CanonicalWarpingDistance CantorMesh CantorStaircase Cap CapForm CapitalDifferentialD Capitalize CapsuleShape CaptureRunning CardinalBSplineBasis CarlemanLinearize CarmichaelLambda CaseOrdering Cases CaseSensitive Cashflow Casoratian Catalan CatalanNumber Catch Catenate CatenateLayer CauchyDistribution CauchyWindow CayleyGraph CDF CDFDeploy CDFInformation CDFWavelet Ceiling CelestialSystem Cell CellAutoOverwrite CellBaseline CellBoundingBox CellBracketOptions CellChangeTimes CellContents CellContext CellDingbat CellDynamicExpression CellEditDuplicate CellElementsBoundingBox CellElementSpacings CellEpilog CellEvaluationDuplicate CellEvaluationFunction CellEvaluationLanguage CellEventActions CellFrame CellFrameColor CellFrameLabelMargins CellFrameLabels CellFrameMargins CellGroup CellGroupData CellGrouping CellGroupingRules CellHorizontalScrolling CellID CellLabel CellLabelAutoDelete CellLabelMargins CellLabelPositioning CellLabelStyle CellLabelTemplate CellMargins CellObject CellOpen CellPrint CellProlog Cells CellSize CellStyle CellTags CellularAutomaton CensoredDistribution Censoring Center CenterArray CenterDot CentralFeature CentralMoment CentralMomentGeneratingFunction Cepstrogram CepstrogramArray CepstrumArray CForm ChampernowneNumber ChangeOptions ChannelBase ChannelBrokerAction ChannelDatabin ChannelHistoryLength ChannelListen ChannelListener ChannelListeners ChannelListenerWait ChannelObject ChannelPreSendFunction ChannelReceiverFunction ChannelSend ChannelSubscribers ChanVeseBinarize Character CharacterCounts CharacterEncoding CharacterEncodingsPath CharacteristicFunction CharacteristicPolynomial CharacterName CharacterRange Characters ChartBaseStyle ChartElementData ChartElementDataFunction ChartElementFunction ChartElements ChartLabels ChartLayout ChartLegends ChartStyle Chebyshev1FilterModel Chebyshev2FilterModel ChebyshevDistance ChebyshevT ChebyshevU Check CheckAbort CheckAll Checkbox CheckboxBar CheckboxBox CheckboxBoxOptions ChemicalData ChessboardDistance ChiDistribution ChineseRemainder ChiSquareDistribution ChoiceButtons ChoiceDialog CholeskyDecomposition Chop ChromaticityPlot ChromaticityPlot3D ChromaticPolynomial Circle CircleBox CircleDot CircleMinus CirclePlus CirclePoints CircleThrough CircleTimes CirculantGraph CircularOrthogonalMatrixDistribution CircularQuaternionMatrixDistribution CircularRealMatrixDistribution CircularSymplecticMatrixDistribution CircularUnitaryMatrixDistribution Circumsphere CityData ClassifierFunction ClassifierInformation ClassifierMeasurements ClassifierMeasurementsObject Classify ClassPriors Clear ClearAll ClearAttributes ClearCookies ClearPermissions ClearSystemCache ClebschGordan ClickPane Clip ClipboardNotebook ClipFill ClippingStyle ClipPlanes ClipPlanesStyle ClipRange Clock ClockGauge ClockwiseContourIntegral Close Closed CloseKernels ClosenessCentrality Closing ClosingAutoSave ClosingEvent CloudAccountData CloudBase CloudConnect CloudDeploy CloudDirectory CloudDisconnect CloudEvaluate CloudExport CloudExpression CloudExpressions CloudFunction CloudGet CloudImport CloudLoggingData CloudObject CloudObjectInformation CloudObjectInformationData CloudObjectNameFormat CloudObjects CloudObjectURLType CloudPublish CloudPut CloudRenderingMethod CloudSave CloudShare CloudSubmit CloudSymbol CloudUnshare ClusterClassify ClusterDissimilarityFunction ClusteringComponents ClusteringTree CMYKColor Coarse CodeAssistOptions Coefficient CoefficientArrays CoefficientDomain CoefficientList CoefficientRules CoifletWavelet Collect Colon ColonForm ColorBalance ColorCombine ColorConvert ColorCoverage ColorData ColorDataFunction ColorDetect ColorDistance ColorFunction ColorFunctionScaling Colorize ColorNegate ColorOutput ColorProfileData ColorQ ColorQuantize ColorReplace ColorRules ColorSelectorSettings ColorSeparate ColorSetter ColorSetterBox ColorSetterBoxOptions ColorSlider ColorsNear ColorSpace ColorToneMapping Column ColumnAlignments ColumnBackgrounds ColumnForm ColumnLines ColumnsEqual ColumnSpacings ColumnWidths CombinedEntityClass CombinerFunction CometData CommonDefaultFormatTypes Commonest CommonestFilter CommonName CommonUnits CommunityBoundaryStyle CommunityGraphPlot CommunityLabels CommunityRegionStyle CompanyData CompatibleUnitQ CompilationOptions CompilationTarget Compile Compiled CompiledCodeFunction CompiledFunction CompilerOptions Complement CompleteGraph CompleteGraphQ CompleteKaryTree CompletionsListPacket Complex Complexes ComplexExpand ComplexInfinity ComplexityFunction ComplexListPlot ComplexPlot ComplexPlot3D ComponentMeasurements ComponentwiseContextMenu Compose ComposeList ComposeSeries CompositeQ Composition CompoundElement CompoundExpression CompoundPoissonDistribution CompoundPoissonProcess CompoundRenewalProcess Compress CompressedData ComputeUncertainty Condition ConditionalExpression Conditioned Cone ConeBox ConfidenceLevel ConfidenceRange ConfidenceTransform ConfigurationPath ConformAudio ConformImages Congruent ConicHullRegion ConicHullRegion3DBox ConicHullRegionBox ConicOptimization Conjugate ConjugateTranspose Conjunction Connect ConnectedComponents ConnectedGraphComponents ConnectedGraphQ ConnectedMeshComponents ConnectedMoleculeComponents ConnectedMoleculeQ ConnectionSettings ConnectLibraryCallbackFunction ConnectSystemModelComponents ConnesWindow ConoverTest ConsoleMessage ConsoleMessagePacket ConsolePrint Constant ConstantArray ConstantArrayLayer ConstantImage ConstantPlusLayer ConstantRegionQ Constants ConstantTimesLayer ConstellationData ConstrainedMax ConstrainedMin Construct Containing ContainsAll ContainsAny ContainsExactly ContainsNone ContainsOnly ContentFieldOptions ContentLocationFunction ContentObject ContentPadding ContentsBoundingBox ContentSelectable ContentSize Context ContextMenu Contexts ContextToFileName Continuation Continue ContinuedFraction ContinuedFractionK ContinuousAction ContinuousMarkovProcess ContinuousTask ContinuousTimeModelQ ContinuousWaveletData ContinuousWaveletTransform ContourDetect ContourGraphics ContourIntegral ContourLabels ContourLines ContourPlot ContourPlot3D Contours ContourShading ContourSmoothing ContourStyle ContraharmonicMean ContrastiveLossLayer Control ControlActive ControlAlignment ControlGroupContentsBox ControllabilityGramian ControllabilityMatrix ControllableDecomposition ControllableModelQ ControllerDuration ControllerInformation ControllerInformationData ControllerLinking ControllerManipulate ControllerMethod ControllerPath ControllerState ControlPlacement ControlsRendering ControlType Convergents ConversionOptions ConversionRules ConvertToBitmapPacket ConvertToPostScript ConvertToPostScriptPacket ConvexHullMesh ConvexPolygonQ ConvexPolyhedronQ ConvolutionLayer Convolve ConwayGroupCo1 ConwayGroupCo2 ConwayGroupCo3 CookieFunction Cookies CoordinateBoundingBox CoordinateBoundingBoxArray CoordinateBounds CoordinateBoundsArray CoordinateChartData CoordinatesToolOptions CoordinateTransform CoordinateTransformData CoprimeQ Coproduct CopulaDistribution Copyable CopyDatabin CopyDirectory CopyFile CopyTag CopyToClipboard CornerFilter CornerNeighbors Correlation CorrelationDistance CorrelationFunction CorrelationTest Cos Cosh CoshIntegral CosineDistance CosineWindow CosIntegral Cot Coth Count CountDistinct CountDistinctBy CounterAssignments CounterBox CounterBoxOptions CounterClockwiseContourIntegral CounterEvaluator CounterFunction CounterIncrements CounterStyle CounterStyleMenuListing CountRoots CountryData Counts CountsBy Covariance CovarianceEstimatorFunction CovarianceFunction CoxianDistribution CoxIngersollRossProcess CoxModel CoxModelFit CramerVonMisesTest CreateArchive CreateCellID CreateChannel CreateCloudExpression CreateDatabin CreateDataSystemModel CreateDialog CreateDirectory CreateDocument CreateFile CreateIntermediateDirectories CreateManagedLibraryExpression CreateNotebook CreatePalette CreatePalettePacket CreatePermissionsGroup CreateScheduledTask CreateSearchIndex CreateSystemModel CreateTemporary CreateUUID CreateWindow CriterionFunction CriticalityFailureImportance CriticalitySuccessImportance CriticalSection Cross CrossEntropyLossLayer CrossingCount CrossingDetect CrossingPolygon CrossMatrix Csc Csch CTCLossLayer Cube CubeRoot Cubics Cuboid CuboidBox Cumulant CumulantGeneratingFunction Cup CupCap Curl CurlyDoubleQuote CurlyQuote CurrencyConvert CurrentDate CurrentImage CurrentlySpeakingPacket CurrentNotebookImage CurrentScreenImage CurrentValue Curry CurvatureFlowFilter CurveClosed Cyan CycleGraph CycleIndexPolynomial Cycles CyclicGroup Cyclotomic Cylinder CylinderBox CylindricalDecompositionD DagumDistribution DamData DamerauLevenshteinDistance DampingFactor Darker Dashed Dashing DatabaseConnect DatabaseDisconnect DatabaseReference Databin DatabinAdd DatabinRemove Databins DatabinUpload DataCompression DataDistribution DataRange DataReversed Dataset Date DateBounds Dated DateDelimiters DateDifference DatedUnit DateFormat DateFunction DateHistogram DateList DateListLogPlot DateListPlot DateListStepPlot DateObject DateObjectQ DateOverlapsQ DatePattern DatePlus DateRange DateReduction DateString DateTicksFormat DateValue DateWithinQ DaubechiesWavelet DavisDistribution DawsonF DayCount DayCountConvention DayHemisphere DaylightQ DayMatchQ DayName DayNightTerminator DayPlus DayRange DayRound DeBruijnGraph DeBruijnSequence Debug DebugTag Decapitalize Decimal DecimalForm DeclareKnownSymbols DeclarePackage Decompose DeconvolutionLayer Decrement Decrypt DecryptFile DedekindEta DeepSpaceProbeData Default DefaultAxesStyle DefaultBaseStyle DefaultBoxStyle DefaultButton DefaultColor DefaultControlPlacement DefaultDuplicateCellStyle DefaultDuration DefaultElement DefaultFaceGridsStyle DefaultFieldHintStyle DefaultFont DefaultFontProperties DefaultFormatType DefaultFormatTypeForStyle DefaultFrameStyle DefaultFrameTicksStyle DefaultGridLinesStyle DefaultInlineFormatType DefaultInputFormatType DefaultLabelStyle DefaultMenuStyle DefaultNaturalLanguage DefaultNewCellStyle DefaultNewInlineCellStyle DefaultNotebook DefaultOptions DefaultOutputFormatType DefaultPrintPrecision DefaultStyle DefaultStyleDefinitions DefaultTextFormatType DefaultTextInlineFormatType DefaultTicksStyle DefaultTooltipStyle DefaultValue DefaultValues Defer DefineExternal DefineInputStreamMethod DefineOutputStreamMethod DefineResourceFunction Definition Degree DegreeCentrality DegreeGraphDistribution DegreeLexicographic DegreeReverseLexicographic DEigensystem DEigenvalues Deinitialization Del DelaunayMesh Delayed Deletable Delete DeleteAnomalies DeleteBorderComponents DeleteCases DeleteChannel DeleteCloudExpression DeleteContents DeleteDirectory DeleteDuplicates DeleteDuplicatesBy DeleteFile DeleteMissing DeleteObject DeletePermissionsKey DeleteSearchIndex DeleteSmallComponents DeleteStopwords DeleteWithContents DeletionWarning DelimitedArray DelimitedSequence Delimiter DelimiterFlashTime DelimiterMatching Delimiters DeliveryFunction Dendrogram Denominator DensityGraphics DensityHistogram DensityPlot DensityPlot3D DependentVariables Deploy Deployed Depth DepthFirstScan Derivative DerivativeFilter DerivedKey DescriptorStateSpace DesignMatrix DestroyAfterEvaluation Det DeviceClose DeviceConfigure DeviceExecute DeviceExecuteAsynchronous DeviceObject DeviceOpen DeviceOpenQ DeviceRead DeviceReadBuffer DeviceReadLatest DeviceReadList DeviceReadTimeSeries Devices DeviceStreams DeviceWrite DeviceWriteBuffer DGaussianWavelet DiacriticalPositioning Diagonal DiagonalizableMatrixQ DiagonalMatrix DiagonalMatrixQ Dialog DialogIndent DialogInput DialogLevel DialogNotebook DialogProlog DialogReturn DialogSymbols Diamond DiamondMatrix DiceDissimilarity DictionaryLookup DictionaryWordQ DifferenceDelta DifferenceOrder DifferenceQuotient DifferenceRoot DifferenceRootReduce Differences DifferentialD DifferentialRoot DifferentialRootReduce DifferentiatorFilter DigitalSignature DigitBlock DigitBlockMinimum DigitCharacter DigitCount DigitQ DihedralAngle DihedralGroup Dilation DimensionalCombinations DimensionalMeshComponents DimensionReduce DimensionReducerFunction DimensionReduction Dimensions DiracComb DiracDelta DirectedEdge DirectedEdges DirectedGraph DirectedGraphQ DirectedInfinity Direction Directive Directory DirectoryName DirectoryQ DirectoryStack DirichletBeta DirichletCharacter DirichletCondition DirichletConvolve DirichletDistribution DirichletEta DirichletL DirichletLambda DirichletTransform DirichletWindow DisableConsolePrintPacket DisableFormatting DiscreteChirpZTransform DiscreteConvolve DiscreteDelta DiscreteHadamardTransform DiscreteIndicator DiscreteLimit DiscreteLQEstimatorGains DiscreteLQRegulatorGains DiscreteLyapunovSolve DiscreteMarkovProcess DiscreteMaxLimit DiscreteMinLimit DiscretePlot DiscretePlot3D DiscreteRatio DiscreteRiccatiSolve DiscreteShift DiscreteTimeModelQ DiscreteUniformDistribution DiscreteVariables DiscreteWaveletData DiscreteWaveletPacketTransform DiscreteWaveletTransform DiscretizeGraphics DiscretizeRegion Discriminant DisjointQ Disjunction Disk DiskBox DiskMatrix DiskSegment Dispatch DispatchQ DispersionEstimatorFunction Display DisplayAllSteps DisplayEndPacket DisplayFlushImagePacket DisplayForm DisplayFunction DisplayPacket DisplayRules DisplaySetSizePacket DisplayString DisplayTemporary DisplayWith DisplayWithRef DisplayWithVariable DistanceFunction DistanceMatrix DistanceTransform Distribute Distributed DistributedContexts DistributeDefinitions DistributionChart DistributionDomain DistributionFitTest DistributionParameterAssumptions DistributionParameterQ Dithering Div Divergence Divide DivideBy Dividers DivideSides Divisible Divisors DivisorSigma DivisorSum DMSList DMSString Do DockedCells DocumentGenerator DocumentGeneratorInformation DocumentGeneratorInformationData DocumentGenerators DocumentNotebook DocumentWeightingRules Dodecahedron DomainRegistrationInformation DominantColors DOSTextFormat Dot DotDashed DotEqual DotLayer DotPlusLayer Dotted DoubleBracketingBar DoubleContourIntegral DoubleDownArrow DoubleLeftArrow DoubleLeftRightArrow DoubleLeftTee DoubleLongLeftArrow DoubleLongLeftRightArrow DoubleLongRightArrow DoubleRightArrow DoubleRightTee DoubleUpArrow DoubleUpDownArrow DoubleVerticalBar DoublyInfinite Down DownArrow DownArrowBar DownArrowUpArrow DownLeftRightVector DownLeftTeeVector DownLeftVector DownLeftVectorBar DownRightTeeVector DownRightVector DownRightVectorBar Downsample DownTee DownTeeArrow DownValues DragAndDrop DrawEdges DrawFrontFaces DrawHighlighted Drop DropoutLayer DSolve DSolveValue Dt DualLinearProgramming DualPolyhedron DualSystemsModel DumpGet DumpSave DuplicateFreeQ Duration Dynamic DynamicBox DynamicBoxOptions DynamicEvaluationTimeout DynamicGeoGraphics DynamicImage DynamicLocation DynamicModule DynamicModuleBox DynamicModuleBoxOptions DynamicModuleParent DynamicModuleValues DynamicName DynamicNamespace DynamicReference DynamicSetting DynamicUpdating DynamicWrapper DynamicWrapperBox DynamicWrapperBoxOptionsE EarthImpactData EarthquakeData EccentricityCentrality Echo EchoFunction EclipseType EdgeAdd EdgeBetweennessCentrality EdgeCapacity EdgeCapForm EdgeColor EdgeConnectivity EdgeContract EdgeCost EdgeCount EdgeCoverQ EdgeCycleMatrix EdgeDashing EdgeDelete EdgeDetect EdgeForm EdgeIndex EdgeJoinForm EdgeLabeling EdgeLabels EdgeLabelStyle EdgeList EdgeOpacity EdgeQ EdgeRenderingFunction EdgeRules EdgeShapeFunction EdgeStyle EdgeThickness EdgeWeight EdgeWeightedGraphQ Editable EditButtonSettings EditCellTagsSettings EditDistance EffectiveInterest Eigensystem Eigenvalues EigenvectorCentrality Eigenvectors Element ElementData ElementwiseLayer ElidedForms Eliminate EliminationOrder Ellipsoid EllipticE EllipticExp EllipticExpPrime EllipticF EllipticFilterModel EllipticK EllipticLog EllipticNomeQ EllipticPi EllipticReducedHalfPeriods EllipticTheta EllipticThetaPrime EmbedCode EmbeddedHTML EmbeddedService EmbeddingLayer EmbeddingObject EmitSound EmphasizeSyntaxErrors EmpiricalDistribution Empty EmptyGraphQ EmptyRegion EnableConsolePrintPacket Enabled Encode Encrypt EncryptedObject EncryptFile End EndAdd EndDialogPacket EndFrontEndInteractionPacket EndOfBuffer EndOfFile EndOfLine EndOfString EndPackage EngineEnvironment EngineeringForm Enter EnterExpressionPacket EnterTextPacket Entity EntityClass EntityClassList EntityCopies EntityFunction EntityGroup EntityInstance EntityList EntityPrefetch EntityProperties EntityProperty EntityPropertyClass EntityRegister EntityStore EntityStores EntityTypeName EntityUnregister EntityValue Entropy EntropyFilter Environment Epilog EpilogFunction Equal EqualColumns EqualRows EqualTilde EqualTo EquatedTo Equilibrium EquirippleFilterKernel Equivalent Erf Erfc Erfi ErlangB ErlangC ErlangDistribution Erosion ErrorBox ErrorBoxOptions ErrorNorm ErrorPacket ErrorsDialogSettings EscapeRadius EstimatedBackground EstimatedDistribution EstimatedProcess EstimatorGains EstimatorRegulator EuclideanDistance EulerAngles EulerCharacteristic EulerE EulerGamma EulerianGraphQ EulerMatrix EulerPhi Evaluatable Evaluate Evaluated EvaluatePacket EvaluateScheduledTask EvaluationBox EvaluationCell EvaluationCompletionAction EvaluationData EvaluationElements EvaluationEnvironment EvaluationMode EvaluationMonitor EvaluationNotebook EvaluationObject EvaluationOrder Evaluator EvaluatorNames EvenQ EventData EventEvaluator EventHandler EventHandlerTag EventLabels EventSeries ExactBlackmanWindow ExactNumberQ ExactRootIsolation ExampleData Except ExcludedForms ExcludedLines ExcludedPhysicalQuantities ExcludePods Exclusions ExclusionsStyle Exists Exit ExitDialog ExoplanetData Exp Expand ExpandAll ExpandDenominator ExpandFileName ExpandNumerator Expectation ExpectationE ExpectedValue ExpGammaDistribution ExpIntegralE ExpIntegralEi ExpirationDate Exponent ExponentFunction ExponentialDistribution ExponentialFamily ExponentialGeneratingFunction ExponentialMovingAverage ExponentialPowerDistribution ExponentPosition ExponentStep Export ExportAutoReplacements ExportByteArray ExportForm ExportPacket ExportString Expression ExpressionCell ExpressionPacket ExpressionUUID ExpToTrig ExtendedEntityClass ExtendedGCD Extension ExtentElementFunction ExtentMarkers ExtentSize ExternalBundle ExternalCall ExternalDataCharacterEncoding ExternalEvaluate ExternalFunction ExternalFunctionName ExternalObject ExternalOptions ExternalSessionObject ExternalSessions ExternalTypeSignature ExternalValue Extract ExtractArchive ExtractLayer ExtremeValueDistributionFaceForm FaceGrids FaceGridsStyle FacialFeatures Factor FactorComplete Factorial Factorial2 FactorialMoment FactorialMomentGeneratingFunction FactorialPower FactorInteger FactorList FactorSquareFree FactorSquareFreeList FactorTerms FactorTermsList Fail Failure FailureAction FailureDistribution FailureQ False FareySequence FARIMAProcess FeatureDistance FeatureExtract FeatureExtraction FeatureExtractor FeatureExtractorFunction FeatureNames FeatureNearest FeatureSpacePlot FeatureSpacePlot3D FeatureTypes FEDisableConsolePrintPacket FeedbackLinearize FeedbackSector FeedbackSectorStyle FeedbackType FEEnableConsolePrintPacket FetalGrowthData Fibonacci Fibonorial FieldCompletionFunction FieldHint FieldHintStyle FieldMasked FieldSize File FileBaseName FileByteCount FileConvert FileDate FileExistsQ FileExtension FileFormat FileHandler FileHash FileInformation FileName FileNameDepth FileNameDialogSettings FileNameDrop FileNameForms FileNameJoin FileNames FileNameSetter FileNameSplit FileNameTake FilePrint FileSize FileSystemMap FileSystemScan FileTemplate FileTemplateApply FileType FilledCurve FilledCurveBox FilledCurveBoxOptions Filling FillingStyle FillingTransform FilteredEntityClass FilterRules FinancialBond FinancialData FinancialDerivative FinancialIndicator Find FindAnomalies FindArgMax FindArgMin FindChannels FindClique FindClusters FindCookies FindCurvePath FindCycle FindDevices FindDistribution FindDistributionParameters FindDivisions FindEdgeCover FindEdgeCut FindEdgeIndependentPaths FindEquationalProof FindEulerianCycle FindExternalEvaluators FindFaces FindFile FindFit FindFormula FindFundamentalCycles FindGeneratingFunction FindGeoLocation FindGeometricConjectures FindGeometricTransform FindGraphCommunities FindGraphIsomorphism FindGraphPartition FindHamiltonianCycle FindHamiltonianPath FindHiddenMarkovStates FindIndependentEdgeSet FindIndependentVertexSet FindInstance FindIntegerNullVector FindKClan FindKClique FindKClub FindKPlex FindLibrary FindLinearRecurrence FindList FindMatchingColor FindMaximum FindMaximumFlow FindMaxValue FindMeshDefects FindMinimum FindMinimumCostFlow FindMinimumCut FindMinValue FindMoleculeSubstructure FindPath FindPeaks FindPermutation FindPostmanTour FindProcessParameters FindRepeat FindRoot FindSequenceFunction FindSettings FindShortestPath FindShortestTour FindSpanningTree FindSystemModelEquilibrium FindTextualAnswer FindThreshold FindTransientRepeat FindVertexCover FindVertexCut FindVertexIndependentPaths Fine FinishDynamic FiniteAbelianGroupCount FiniteGroupCount FiniteGroupData First FirstCase FirstPassageTimeDistribution FirstPosition FischerGroupFi22 FischerGroupFi23 FischerGroupFi24Prime FisherHypergeometricDistribution FisherRatioTest FisherZDistribution Fit FitAll FitRegularization FittedModel FixedOrder FixedPoint FixedPointList FlashSelection Flat Flatten FlattenAt FlattenLayer FlatTopWindow FlipView Floor FlowPolynomial FlushPrintOutputPacket Fold FoldList FoldPair FoldPairList FollowRedirects Font FontColor FontFamily FontForm FontName FontOpacity FontPostScriptName FontProperties FontReencoding FontSize FontSlant FontSubstitutions FontTracking FontVariations FontWeight For ForAll Format FormatRules FormatType FormatTypeAutoConvert FormatValues FormBox FormBoxOptions FormControl FormFunction FormLayoutFunction FormObject FormPage FormTheme FormulaData FormulaLookup FortranForm Forward ForwardBackward Fourier FourierCoefficient FourierCosCoefficient FourierCosSeries FourierCosTransform FourierDCT FourierDCTFilter FourierDCTMatrix FourierDST FourierDSTMatrix FourierMatrix FourierParameters FourierSequenceTransform FourierSeries FourierSinCoefficient FourierSinSeries FourierSinTransform FourierTransform FourierTrigSeries FractionalBrownianMotionProcess FractionalGaussianNoiseProcess FractionalPart FractionBox FractionBoxOptions FractionLine Frame FrameBox FrameBoxOptions Framed FrameInset FrameLabel Frameless FrameMargins FrameRate FrameStyle FrameTicks FrameTicksStyle FRatioDistribution FrechetDistribution FreeQ FrenetSerretSystem FrequencySamplingFilterKernel FresnelC FresnelF FresnelG FresnelS Friday FrobeniusNumber FrobeniusSolve FromAbsoluteTime FromCharacterCode FromCoefficientRules FromContinuedFraction FromDate FromDigits FromDMS FromEntity FromJulianDate FromLetterNumber FromPolarCoordinates FromRomanNumeral FromSphericalCoordinates FromUnixTime Front FrontEndDynamicExpression FrontEndEventActions FrontEndExecute FrontEndObject FrontEndResource FrontEndResourceString FrontEndStackSize FrontEndToken FrontEndTokenExecute FrontEndValueCache FrontEndVersion FrontFaceColor FrontFaceOpacity Full FullAxes FullDefinition FullForm FullGraphics FullInformationOutputRegulator FullOptions FullRegion FullSimplify Function FunctionCompile FunctionCompileExport FunctionCompileExportByteArray FunctionCompileExportLibrary FunctionCompileExportString FunctionDomain FunctionExpand FunctionInterpolation FunctionPeriod FunctionRange FunctionSpace FussellVeselyImportanceGaborFilter GaborMatrix GaborWavelet GainMargins GainPhaseMargins GalaxyData GalleryView Gamma GammaDistribution GammaRegularized GapPenalty GARCHProcess GatedRecurrentLayer Gather GatherBy GaugeFaceElementFunction GaugeFaceStyle GaugeFrameElementFunction GaugeFrameSize GaugeFrameStyle GaugeLabels GaugeMarkers GaugeStyle GaussianFilter GaussianIntegers GaussianMatrix GaussianOrthogonalMatrixDistribution GaussianSymplecticMatrixDistribution GaussianUnitaryMatrixDistribution GaussianWindow GCD GegenbauerC General GeneralizedLinearModelFit GenerateAsymmetricKeyPair GenerateConditions GeneratedCell GeneratedDocumentBinding GenerateDerivedKey GenerateDigitalSignature GenerateDocument GeneratedParameters GeneratedQuantityMagnitudes GenerateHTTPResponse GenerateSecuredAuthenticationKey GenerateSymmetricKey GeneratingFunction GeneratorDescription GeneratorHistoryLength GeneratorOutputType Generic GenericCylindricalDecomposition GenomeData GenomeLookup GeoAntipode GeoArea GeoArraySize GeoBackground GeoBoundingBox GeoBounds GeoBoundsRegion GeoBubbleChart GeoCenter GeoCircle GeodesicClosing GeodesicDilation GeodesicErosion GeodesicOpening GeoDestination GeodesyData GeoDirection GeoDisk GeoDisplacement GeoDistance GeoDistanceList GeoElevationData GeoEntities GeoGraphics GeogravityModelData GeoGridDirectionDifference GeoGridLines GeoGridLinesStyle GeoGridPosition GeoGridRange GeoGridRangePadding GeoGridUnitArea GeoGridUnitDistance GeoGridVector GeoGroup GeoHemisphere GeoHemisphereBoundary GeoHistogram GeoIdentify GeoImage GeoLabels GeoLength GeoListPlot GeoLocation GeologicalPeriodData GeomagneticModelData GeoMarker GeometricAssertion GeometricBrownianMotionProcess GeometricDistribution GeometricMean GeometricMeanFilter GeometricScene GeometricTransformation GeometricTransformation3DBox GeometricTransformation3DBoxOptions GeometricTransformationBox GeometricTransformationBoxOptions GeoModel GeoNearest GeoPath GeoPosition GeoPositionENU GeoPositionXYZ GeoProjection GeoProjectionData GeoRange GeoRangePadding GeoRegionValuePlot GeoResolution GeoScaleBar GeoServer GeoSmoothHistogram GeoStreamPlot GeoStyling GeoStylingImageFunction GeoVariant GeoVector GeoVectorENU GeoVectorPlot GeoVectorXYZ GeoVisibleRegion GeoVisibleRegionBoundary GeoWithinQ GeoZoomLevel GestureHandler GestureHandlerTag Get GetBoundingBoxSizePacket GetContext GetEnvironment GetFileName GetFrontEndOptionsDataPacket GetLinebreakInformationPacket GetMenusPacket GetPageBreakInformationPacket Glaisher GlobalClusteringCoefficient GlobalPreferences GlobalSession Glow GoldenAngle GoldenRatio GompertzMakehamDistribution GoodmanKruskalGamma GoodmanKruskalGammaTest Goto Grad Gradient GradientFilter GradientOrientationFilter GrammarApply GrammarRules GrammarToken Graph Graph3D GraphAssortativity GraphAutomorphismGroup GraphCenter GraphComplement GraphData GraphDensity GraphDiameter GraphDifference GraphDisjointUnion GraphDistance GraphDistanceMatrix GraphElementData GraphEmbedding GraphHighlight GraphHighlightStyle GraphHub Graphics Graphics3D Graphics3DBox Graphics3DBoxOptions GraphicsArray GraphicsBaseline GraphicsBox GraphicsBoxOptions GraphicsColor GraphicsColumn GraphicsComplex GraphicsComplex3DBox GraphicsComplex3DBoxOptions GraphicsComplexBox GraphicsComplexBoxOptions GraphicsContents GraphicsData GraphicsGrid GraphicsGridBox GraphicsGroup GraphicsGroup3DBox GraphicsGroup3DBoxOptions GraphicsGroupBox GraphicsGroupBoxOptions GraphicsGrouping GraphicsHighlightColor GraphicsRow GraphicsSpacing GraphicsStyle GraphIntersection GraphLayout GraphLinkEfficiency GraphPeriphery GraphPlot GraphPlot3D GraphPower GraphPropertyDistribution GraphQ GraphRadius GraphReciprocity GraphRoot GraphStyle GraphUnion Gray GrayLevel Greater GreaterEqual GreaterEqualLess GreaterEqualThan GreaterFullEqual GreaterGreater GreaterLess GreaterSlantEqual GreaterThan GreaterTilde Green GreenFunction Grid GridBaseline GridBox GridBoxAlignment GridBoxBackground GridBoxDividers GridBoxFrame GridBoxItemSize GridBoxItemStyle GridBoxOptions GridBoxSpacings GridCreationSettings GridDefaultElement GridElementStyleOptions GridFrame GridFrameMargins GridGraph GridLines GridLinesStyle GroebnerBasis GroupActionBase GroupBy GroupCentralizer GroupElementFromWord GroupElementPosition GroupElementQ GroupElements GroupElementToWord GroupGenerators Groupings GroupMultiplicationTable GroupOrbits GroupOrder GroupPageBreakWithin GroupSetwiseStabilizer GroupStabilizer GroupStabilizerChain GroupTogetherGrouping GroupTogetherNestedGrouping GrowCutComponents Gudermannian GuidedFilter GumbelDistributionHaarWavelet HadamardMatrix HalfLine HalfNormalDistribution HalfPlane HalfSpace HamiltonianGraphQ HammingDistance HammingWindow HandlerFunctions HandlerFunctionsKeys HankelH1 HankelH2 HankelMatrix HankelTransform HannPoissonWindow HannWindow HaradaNortonGroupHN HararyGraph HarmonicMean HarmonicMeanFilter HarmonicNumber Hash Haversine HazardFunction Head HeadCompose HeaderLines Heads HeavisideLambda HeavisidePi HeavisideTheta HeldGroupHe HeldPart HelpBrowserLookup HelpBrowserNotebook HelpBrowserSettings Here HermiteDecomposition HermiteH HermitianMatrixQ HessenbergDecomposition Hessian HexadecimalCharacter Hexahedron HexahedronBox HexahedronBoxOptions HiddenMarkovProcess HiddenSurface Highlighted HighlightGraph HighlightImage HighlightMesh HighpassFilter HigmanSimsGroupHS HilbertCurve HilbertFilter HilbertMatrix Histogram Histogram3D HistogramDistribution HistogramList HistogramTransform HistogramTransformInterpolation HistoricalPeriodData HitMissTransform HITSCentrality HjorthDistribution HodgeDual HoeffdingD HoeffdingDTest Hold HoldAll HoldAllComplete HoldComplete HoldFirst HoldForm HoldPattern HoldRest HolidayCalendar HomeDirectory HomePage Horizontal HorizontalForm HorizontalGauge HorizontalScrollPosition HornerForm HostLookup HotellingTSquareDistribution HoytDistribution HTMLSave HTTPErrorResponse HTTPRedirect HTTPRequest HTTPRequestData HTTPResponse Hue HumanGrowthData HumpDownHump HumpEqual HurwitzLerchPhi HurwitzZeta HyperbolicDistribution HypercubeGraph HyperexponentialDistribution Hyperfactorial Hypergeometric0F1 Hypergeometric0F1Regularized Hypergeometric1F1 Hypergeometric1F1Regularized Hypergeometric2F1 Hypergeometric2F1Regularized HypergeometricDistribution HypergeometricPFQ HypergeometricPFQRegularized HypergeometricU Hyperlink HyperlinkCreationSettings Hyperplane Hyphenation HyphenationOptions HypoexponentialDistribution HypothesisTestDataI IconData Iconize IconizedObject IconRules Icosahedron Identity IdentityMatrix If IgnoreCase IgnoreDiacritics IgnorePunctuation IgnoreSpellCheck IgnoringInactive Im Image Image3D Image3DProjection Image3DSlices ImageAccumulate ImageAdd ImageAdjust ImageAlign ImageApply ImageApplyIndexed ImageAspectRatio ImageAssemble ImageAugmentationLayer ImageBoundingBoxes ImageCache ImageCacheValid ImageCapture ImageCaptureFunction ImageCases ImageChannels ImageClip ImageCollage ImageColorSpace ImageCompose ImageContainsQ ImageContents ImageConvolve ImageCooccurrence ImageCorners ImageCorrelate ImageCorrespondingPoints ImageCrop ImageData ImageDeconvolve ImageDemosaic ImageDifference ImageDimensions ImageDisplacements ImageDistance ImageEffect ImageExposureCombine ImageFeatureTrack ImageFileApply ImageFileFilter ImageFileScan ImageFilter ImageFocusCombine ImageForestingComponents ImageFormattingWidth ImageForwardTransformation ImageGraphics ImageHistogram ImageIdentify ImageInstanceQ ImageKeypoints ImageLevels ImageLines ImageMargins ImageMarker ImageMarkers ImageMeasurements ImageMesh ImageMultiply ImageOffset ImagePad ImagePadding ImagePartition ImagePeriodogram ImagePerspectiveTransformation ImagePosition ImagePreviewFunction ImagePyramid ImagePyramidApply ImageQ ImageRangeCache ImageRecolor ImageReflect ImageRegion ImageResize ImageResolution ImageRestyle ImageRotate ImageRotated ImageSaliencyFilter ImageScaled ImageScan ImageSize ImageSizeAction ImageSizeCache ImageSizeMultipliers ImageSizeRaw ImageSubtract ImageTake ImageTransformation ImageTrim ImageType ImageValue ImageValuePositions ImagingDevice ImplicitRegion Implies Import ImportAutoReplacements ImportByteArray ImportOptions ImportString ImprovementImportance In Inactivate Inactive IncidenceGraph IncidenceList IncidenceMatrix IncludeAromaticBonds IncludeConstantBasis IncludeDefinitions IncludeDirectories IncludeFileExtension IncludeGeneratorTasks IncludeHydrogens IncludeInflections IncludeMetaInformation IncludePods IncludeQuantities IncludeRelatedTables IncludeSingularTerm IncludeWindowTimes Increment IndefiniteMatrixQ Indent IndentingNewlineSpacings IndentMaxFraction IndependenceTest IndependentEdgeSetQ IndependentPhysicalQuantity IndependentUnit IndependentUnitDimension IndependentVertexSetQ Indeterminate IndeterminateThreshold IndexCreationOptions Indexed IndexGraph IndexTag Inequality InexactNumberQ InexactNumbers InfiniteLine InfinitePlane Infinity Infix InflationAdjust InflationMethod Information InformationData InformationDataGrid Inherited InheritScope InhomogeneousPoissonProcess InitialEvaluationHistory Initialization InitializationCell InitializationCellEvaluation InitializationCellWarning InitializationObjects InitializationValue Initialize InitialSeeding InlineCounterAssignments InlineCounterIncrements InlineRules Inner InnerPolygon InnerPolyhedron Inpaint Input InputAliases InputAssumptions InputAutoReplacements InputField InputFieldBox InputFieldBoxOptions InputForm InputGrouping InputNamePacket InputNotebook InputPacket InputSettings InputStream InputString InputStringPacket InputToBoxFormPacket Insert InsertionFunction InsertionPointObject InsertLinebreaks InsertResults Inset Inset3DBox Inset3DBoxOptions InsetBox InsetBoxOptions Insphere Install InstallService InstanceNormalizationLayer InString Integer IntegerDigits IntegerExponent IntegerLength IntegerName IntegerPart IntegerPartitions IntegerQ IntegerReverse Integers IntegerString Integral Integrate Interactive InteractiveTradingChart Interlaced Interleaving InternallyBalancedDecomposition InterpolatingFunction InterpolatingPolynomial Interpolation InterpolationOrder InterpolationPoints InterpolationPrecision Interpretation InterpretationBox InterpretationBoxOptions InterpretationFunction Interpreter InterpretTemplate InterquartileRange Interrupt InterruptSettings IntersectingQ Intersection Interval IntervalIntersection IntervalMarkers IntervalMarkersStyle IntervalMemberQ IntervalSlider IntervalUnion Into Inverse InverseBetaRegularized InverseCDF InverseChiSquareDistribution InverseContinuousWaveletTransform InverseDistanceTransform InverseEllipticNomeQ InverseErf InverseErfc InverseFourier InverseFourierCosTransform InverseFourierSequenceTransform InverseFourierSinTransform InverseFourierTransform InverseFunction InverseFunctions InverseGammaDistribution InverseGammaRegularized InverseGaussianDistribution InverseGudermannian InverseHankelTransform InverseHaversine InverseImagePyramid InverseJacobiCD InverseJacobiCN InverseJacobiCS InverseJacobiDC InverseJacobiDN InverseJacobiDS InverseJacobiNC InverseJacobiND InverseJacobiNS InverseJacobiSC InverseJacobiSD InverseJacobiSN InverseLaplaceTransform InverseMellinTransform InversePermutation InverseRadon InverseRadonTransform InverseSeries InverseShortTimeFourier InverseSpectrogram InverseSurvivalFunction InverseTransformedRegion InverseWaveletTransform InverseWeierstrassP InverseWishartMatrixDistribution InverseZTransform Invisible InvisibleApplication InvisibleTimes IPAddress IrreduciblePolynomialQ IslandData IsolatingInterval IsomorphicGraphQ IsotopeData Italic Item ItemAspectRatio ItemBox ItemBoxOptions ItemSize ItemStyle ItoProcessJaccardDissimilarity JacobiAmplitude Jacobian JacobiCD JacobiCN JacobiCS JacobiDC JacobiDN JacobiDS JacobiNC JacobiND JacobiNS JacobiP JacobiSC JacobiSD JacobiSN JacobiSymbol JacobiZeta JankoGroupJ1 JankoGroupJ2 JankoGroupJ3 JankoGroupJ4 JarqueBeraALMTest JohnsonDistribution Join JoinAcross Joined JoinedCurve JoinedCurveBox JoinedCurveBoxOptions JoinForm JordanDecomposition JordanModelDecomposition JulianDate JuliaSetBoettcher JuliaSetIterationCount JuliaSetPlot JuliaSetPointsK KagiChart KaiserBesselWindow KaiserWindow KalmanEstimator KalmanFilter KarhunenLoeveDecomposition KaryTree KatzCentrality KCoreComponents KDistribution KEdgeConnectedComponents KEdgeConnectedGraphQ KelvinBei KelvinBer KelvinKei KelvinKer KendallTau KendallTauTest KernelExecute KernelFunction KernelMixtureDistribution Kernels Ket Key KeyCollisionFunction KeyComplement KeyDrop KeyDropFrom KeyExistsQ KeyFreeQ KeyIntersection KeyMap KeyMemberQ KeypointStrength Keys KeySelect KeySort KeySortBy KeyTake KeyUnion KeyValueMap KeyValuePattern Khinchin KillProcess KirchhoffGraph KirchhoffMatrix KleinInvariantJ KnapsackSolve KnightTourGraph KnotData KnownUnitQ KochCurve KolmogorovSmirnovTest KroneckerDelta KroneckerModelDecomposition KroneckerProduct KroneckerSymbol KuiperTest KumaraswamyDistribution Kurtosis KuwaharaFilter KVertexConnectedComponents KVertexConnectedGraphQLABColor Label Labeled LabeledSlider LabelingFunction LabelingSize LabelStyle LabelVisibility LaguerreL LakeData LambdaComponents LambertW LaminaData LanczosWindow LandauDistribution Language LanguageCategory LanguageData LanguageIdentify LanguageOptions LaplaceDistribution LaplaceTransform Laplacian LaplacianFilter LaplacianGaussianFilter Large Larger Last Latitude LatitudeLongitude LatticeData LatticeReduce Launch LaunchKernels LayeredGraphPlot LayerSizeFunction LayoutInformation LCHColor LCM LeaderSize LeafCount LeapYearQ LearnDistribution LearnedDistribution LearningRate LearningRateMultipliers LeastSquares LeastSquaresFilterKernel Left LeftArrow LeftArrowBar LeftArrowRightArrow LeftDownTeeVector LeftDownVector LeftDownVectorBar LeftRightArrow LeftRightVector LeftTee LeftTeeArrow LeftTeeVector LeftTriangle LeftTriangleBar LeftTriangleEqual LeftUpDownVector LeftUpTeeVector LeftUpVector LeftUpVectorBar LeftVector LeftVectorBar LegendAppearance Legended LegendFunction LegendLabel LegendLayout LegendMargins LegendMarkers LegendMarkerSize LegendreP LegendreQ LegendreType Length LengthWhile LerchPhi Less LessEqual LessEqualGreater LessEqualThan LessFullEqual LessGreater LessLess LessSlantEqual LessThan LessTilde LetterCharacter LetterCounts LetterNumber LetterQ Level LeveneTest LeviCivitaTensor LevyDistribution Lexicographic LibraryDataType LibraryFunction LibraryFunctionError LibraryFunctionInformation LibraryFunctionLoad LibraryFunctionUnload LibraryLoad LibraryUnload LicenseID LiftingFilterData LiftingWaveletTransform LightBlue LightBrown LightCyan Lighter LightGray LightGreen Lighting LightingAngle LightMagenta LightOrange LightPink LightPurple LightRed LightSources LightYellow Likelihood Limit LimitsPositioning LimitsPositioningTokens LindleyDistribution Line Line3DBox Line3DBoxOptions LinearFilter LinearFractionalOptimization LinearFractionalTransform LinearGradientImage LinearizingTransformationData LinearLayer LinearModelFit LinearOffsetFunction LinearOptimization LinearProgramming LinearRecurrence LinearSolve LinearSolveFunction LineBox LineBoxOptions LineBreak LinebreakAdjustments LineBreakChart LinebreakSemicolonWeighting LineBreakWithin LineColor LineGraph LineIndent LineIndentMaxFraction LineIntegralConvolutionPlot LineIntegralConvolutionScale LineLegend LineOpacity LineSpacing LineWrapParts LinkActivate LinkClose LinkConnect LinkConnectedQ LinkCreate LinkError LinkFlush LinkFunction LinkHost LinkInterrupt LinkLaunch LinkMode LinkObject LinkOpen LinkOptions LinkPatterns LinkProtocol LinkRankCentrality LinkRead LinkReadHeld LinkReadyQ Links LinkService LinkWrite LinkWriteHeld LiouvilleLambda List Listable ListAnimate ListContourPlot ListContourPlot3D ListConvolve ListCorrelate ListCurvePathPlot ListDeconvolve ListDensityPlot ListDensityPlot3D Listen ListFormat ListFourierSequenceTransform ListInterpolation ListLineIntegralConvolutionPlot ListLinePlot ListLogLinearPlot ListLogLogPlot ListLogPlot ListPicker ListPickerBox ListPickerBoxBackground ListPickerBoxOptions ListPlay ListPlot ListPlot3D ListPointPlot3D ListPolarPlot ListQ ListSliceContourPlot3D ListSliceDensityPlot3D ListSliceVectorPlot3D ListStepPlot ListStreamDensityPlot ListStreamPlot ListSurfacePlot3D ListVectorDensityPlot ListVectorPlot ListVectorPlot3D ListZTransform Literal LiteralSearch LocalAdaptiveBinarize LocalCache LocalClusteringCoefficient LocalizeDefinitions LocalizeVariables LocalObject LocalObjects LocalResponseNormalizationLayer LocalSubmit LocalSymbol LocalTime LocalTimeZone LocationEquivalenceTest LocationTest Locator LocatorAutoCreate LocatorBox LocatorBoxOptions LocatorCentering LocatorPane LocatorPaneBox LocatorPaneBoxOptions LocatorRegion Locked Log Log10 Log2 LogBarnesG LogGamma LogGammaDistribution LogicalExpand LogIntegral LogisticDistribution LogisticSigmoid LogitModelFit LogLikelihood LogLinearPlot LogLogisticDistribution LogLogPlot LogMultinormalDistribution LogNormalDistribution LogPlot LogRankTest LogSeriesDistribution LongEqual Longest LongestCommonSequence LongestCommonSequencePositions LongestCommonSubsequence LongestCommonSubsequencePositions LongestMatch LongestOrderedSequence LongForm Longitude LongLeftArrow LongLeftRightArrow LongRightArrow LongShortTermMemoryLayer Lookup Loopback LoopFreeGraphQ LossFunction LowerCaseQ LowerLeftArrow LowerRightArrow LowerTriangularize LowerTriangularMatrixQ LowpassFilter LQEstimatorGains LQGRegulator LQOutputRegulatorGains LQRegulatorGains LUBackSubstitution LucasL LuccioSamiComponents LUDecomposition LunarEclipse LUVColor LyapunovSolve LyonsGroupLyMachineID MachineName MachineNumberQ MachinePrecision MacintoshSystemPageSetup Magenta Magnification Magnify MailAddressValidation MailExecute MailFolder MailItem MailReceiverFunction MailResponseFunction MailSearch MailServerConnect MailServerConnection MailSettings MainSolve MaintainDynamicCaches Majority MakeBoxes MakeExpression MakeRules ManagedLibraryExpressionID ManagedLibraryExpressionQ MandelbrotSetBoettcher MandelbrotSetDistance MandelbrotSetIterationCount MandelbrotSetMemberQ MandelbrotSetPlot MangoldtLambda ManhattanDistance Manipulate Manipulator MannedSpaceMissionData MannWhitneyTest MantissaExponent Manual Map MapAll MapAt MapIndexed MAProcess MapThread MarchenkoPasturDistribution MarcumQ MardiaCombinedTest MardiaKurtosisTest MardiaSkewnessTest MarginalDistribution MarkovProcessProperties Masking MatchingDissimilarity MatchLocalNameQ MatchLocalNames MatchQ Material MathematicalFunctionData MathematicaNotation MathieuC MathieuCharacteristicA MathieuCharacteristicB MathieuCharacteristicExponent MathieuCPrime MathieuGroupM11 MathieuGroupM12 MathieuGroupM22 MathieuGroupM23 MathieuGroupM24 MathieuS MathieuSPrime MathMLForm MathMLText Matrices MatrixExp MatrixForm MatrixFunction MatrixLog MatrixNormalDistribution MatrixPlot MatrixPower MatrixPropertyDistribution MatrixQ MatrixRank MatrixTDistribution Max MaxBend MaxCellMeasure MaxColorDistance MaxDetect MaxDuration MaxExtraBandwidths MaxExtraConditions MaxFeatureDisplacement MaxFeatures MaxFilter MaximalBy Maximize MaxItems MaxIterations MaxLimit MaxMemoryUsed MaxMixtureKernels MaxOverlapFraction MaxPlotPoints MaxPoints MaxRecursion MaxStableDistribution MaxStepFraction MaxSteps MaxStepSize MaxTrainingRounds MaxValue MaxwellDistribution MaxWordGap McLaughlinGroupMcL Mean MeanAbsoluteLossLayer MeanAround MeanClusteringCoefficient MeanDegreeConnectivity MeanDeviation MeanFilter MeanGraphDistance MeanNeighborDegree MeanShift MeanShiftFilter MeanSquaredLossLayer Median MedianDeviation MedianFilter MedicalTestData Medium MeijerG MeijerGReduce MeixnerDistribution MellinConvolve MellinTransform MemberQ MemoryAvailable MemoryConstrained MemoryConstraint MemoryInUse MengerMesh Menu MenuAppearance MenuCommandKey MenuEvaluator MenuItem MenuList MenuPacket MenuSortingValue MenuStyle MenuView Merge MergeDifferences MergingFunction MersennePrimeExponent MersennePrimeExponentQ Mesh MeshCellCentroid MeshCellCount MeshCellHighlight MeshCellIndex MeshCellLabel MeshCellMarker MeshCellMeasure MeshCellQuality MeshCells MeshCellShapeFunction MeshCellStyle MeshCoordinates MeshFunctions MeshPrimitives MeshQualityGoal MeshRange MeshRefinementFunction MeshRegion MeshRegionQ MeshShading MeshStyle Message MessageDialog MessageList MessageName MessageObject MessageOptions MessagePacket Messages MessagesNotebook MetaCharacters MetaInformation MeteorShowerData Method MethodOptions MexicanHatWavelet MeyerWavelet Midpoint Min MinColorDistance MinDetect MineralData MinFilter MinimalBy MinimalPolynomial MinimalStateSpaceModel Minimize MinimumTimeIncrement MinIntervalSize MinkowskiQuestionMark MinLimit MinMax MinorPlanetData Minors MinRecursion MinSize MinStableDistribution Minus MinusPlus MinValue Missing MissingBehavior MissingDataMethod MissingDataRules MissingQ MissingString MissingStyle MissingValuePattern MittagLefflerE MixedFractionParts MixedGraphQ MixedMagnitude MixedRadix MixedRadixQuantity MixedUnit MixtureDistribution Mod Modal Mode Modular ModularInverse ModularLambda Module Modulus MoebiusMu Molecule MoleculeContainsQ MoleculeEquivalentQ MoleculeGraph MoleculeModify MoleculePattern MoleculePlot MoleculePlot3D MoleculeProperty MoleculeQ MoleculeValue Moment Momentary MomentConvert MomentEvaluate MomentGeneratingFunction MomentOfInertia Monday Monitor MonomialList MonomialOrder MonsterGroupM MoonPhase MoonPosition MorletWavelet MorphologicalBinarize MorphologicalBranchPoints MorphologicalComponents MorphologicalEulerNumber MorphologicalGraph MorphologicalPerimeter MorphologicalTransform MortalityData Most MountainData MouseAnnotation MouseAppearance MouseAppearanceTag MouseButtons Mouseover MousePointerNote MousePosition MovieData MovingAverage MovingMap MovingMedian MoyalDistribution Multicolumn MultiedgeStyle MultigraphQ MultilaunchWarning MultiLetterItalics MultiLetterStyle MultilineFunction Multinomial MultinomialDistribution MultinormalDistribution MultiplicativeOrder Multiplicity MultiplySides Multiselection MultivariateHypergeometricDistribution MultivariatePoissonDistribution MultivariateTDistributionN NakagamiDistribution NameQ Names NamespaceBox NamespaceBoxOptions Nand NArgMax NArgMin NBernoulliB NBodySimulation NBodySimulationData NCache NDEigensystem NDEigenvalues NDSolve NDSolveValue Nearest NearestFunction NearestNeighborGraph NearestTo NebulaData NeedCurrentFrontEndPackagePacket NeedCurrentFrontEndSymbolsPacket NeedlemanWunschSimilarity Needs Negative NegativeBinomialDistribution NegativeDefiniteMatrixQ NegativeIntegers NegativeMultinomialDistribution NegativeRationals NegativeReals NegativeSemidefiniteMatrixQ NeighborhoodData NeighborhoodGraph Nest NestedGreaterGreater NestedLessLess NestedScriptRules NestGraph NestList NestWhile NestWhileList NetAppend NetBidirectionalOperator NetChain NetDecoder NetDelete NetDrop NetEncoder NetEvaluationMode NetExtract NetFlatten NetFoldOperator NetGraph NetInformation NetInitialize NetInsert NetInsertSharedArrays NetJoin NetMapOperator NetMapThreadOperator NetMeasurements NetModel NetNestOperator NetPairEmbeddingOperator NetPort NetPortGradient NetPrepend NetRename NetReplace NetReplacePart NetSharedArray NetStateObject NetTake NetTrain NetTrainResultsObject NetworkPacketCapture NetworkPacketRecording NetworkPacketRecordingDuring NetworkPacketTrace NeumannValue NevilleThetaC NevilleThetaD NevilleThetaN NevilleThetaS NewPrimitiveStyle NExpectation Next NextCell NextDate NextPrime NextScheduledTaskTime NHoldAll NHoldFirst NHoldRest NicholsGridLines NicholsPlot NightHemisphere NIntegrate NMaximize NMaxValue NMinimize NMinValue NominalVariables NonAssociative NoncentralBetaDistribution NoncentralChiSquareDistribution NoncentralFRatioDistribution NoncentralStudentTDistribution NonCommutativeMultiply NonConstants NondimensionalizationTransform None NoneTrue NonlinearModelFit NonlinearStateSpaceModel NonlocalMeansFilter NonNegative NonNegativeIntegers NonNegativeRationals NonNegativeReals NonPositive NonPositiveIntegers NonPositiveRationals NonPositiveReals Nor NorlundB Norm Normal NormalDistribution NormalGrouping NormalizationLayer Normalize Normalized NormalizedSquaredEuclideanDistance NormalMatrixQ NormalsFunction NormFunction Not NotCongruent NotCupCap NotDoubleVerticalBar Notebook NotebookApply NotebookAutoSave NotebookClose NotebookConvertSettings NotebookCreate NotebookCreateReturnObject NotebookDefault NotebookDelete NotebookDirectory NotebookDynamicExpression NotebookEvaluate NotebookEventActions NotebookFileName NotebookFind NotebookFindReturnObject NotebookGet NotebookGetLayoutInformationPacket NotebookGetMisspellingsPacket NotebookImport NotebookInformation NotebookInterfaceObject NotebookLocate NotebookObject NotebookOpen NotebookOpenReturnObject NotebookPath NotebookPrint NotebookPut NotebookPutReturnObject NotebookRead NotebookResetGeneratedCells Notebooks NotebookSave NotebookSaveAs NotebookSelection NotebookSetupLayoutInformationPacket NotebooksMenu NotebookTemplate NotebookWrite NotElement NotEqualTilde NotExists NotGreater NotGreaterEqual NotGreaterFullEqual NotGreaterGreater NotGreaterLess NotGreaterSlantEqual NotGreaterTilde Nothing NotHumpDownHump NotHumpEqual NotificationFunction NotLeftTriangle NotLeftTriangleBar NotLeftTriangleEqual NotLess NotLessEqual NotLessFullEqual NotLessGreater NotLessLess NotLessSlantEqual NotLessTilde NotNestedGreaterGreater NotNestedLessLess NotPrecedes NotPrecedesEqual NotPrecedesSlantEqual NotPrecedesTilde NotReverseElement NotRightTriangle NotRightTriangleBar NotRightTriangleEqual NotSquareSubset NotSquareSubsetEqual NotSquareSuperset NotSquareSupersetEqual NotSubset NotSubsetEqual NotSucceeds NotSucceedsEqual NotSucceedsSlantEqual NotSucceedsTilde NotSuperset NotSupersetEqual NotTilde NotTildeEqual NotTildeFullEqual NotTildeTilde NotVerticalBar Now NoWhitespace NProbability NProduct NProductFactors NRoots NSolve NSum NSumTerms NuclearExplosionData NuclearReactorData Null NullRecords NullSpace NullWords Number NumberCompose NumberDecompose NumberExpand NumberFieldClassNumber NumberFieldDiscriminant NumberFieldFundamentalUnits NumberFieldIntegralBasis NumberFieldNormRepresentatives NumberFieldRegulator NumberFieldRootsOfUnity NumberFieldSignature NumberForm NumberFormat NumberLinePlot NumberMarks NumberMultiplier NumberPadding NumberPoint NumberQ NumberSeparator NumberSigns NumberString Numerator NumeratorDenominator NumericalOrder NumericalSort NumericArray NumericArrayQ NumericArrayType NumericFunction NumericQ NuttallWindow NValues NyquistGridLines NyquistPlotO ObservabilityGramian ObservabilityMatrix ObservableDecomposition ObservableModelQ OceanData Octahedron OddQ Off Offset OLEData On ONanGroupON Once OneIdentity Opacity OpacityFunction OpacityFunctionScaling Open OpenAppend Opener OpenerBox OpenerBoxOptions OpenerView OpenFunctionInspectorPacket Opening OpenRead OpenSpecialOptions OpenTemporary OpenWrite Operate OperatingSystem OptimumFlowData Optional OptionalElement OptionInspectorSettings OptionQ Options OptionsPacket OptionsPattern OptionValue OptionValueBox OptionValueBoxOptions Or Orange Order OrderDistribution OrderedQ Ordering OrderingBy OrderingLayer Orderless OrderlessPatternSequence OrnsteinUhlenbeckProcess Orthogonalize OrthogonalMatrixQ Out Outer OuterPolygon OuterPolyhedron OutputAutoOverwrite OutputControllabilityMatrix OutputControllableModelQ OutputForm OutputFormData OutputGrouping OutputMathEditExpression OutputNamePacket OutputResponse OutputSizeLimit OutputStream Over OverBar OverDot Overflow OverHat Overlaps Overlay OverlayBox OverlayBoxOptions Overscript OverscriptBox OverscriptBoxOptions OverTilde OverVector OverwriteTarget OwenT OwnValuesPackage PackingMethod PaddedForm Padding PaddingLayer PaddingSize PadeApproximant PadLeft PadRight PageBreakAbove PageBreakBelow PageBreakWithin PageFooterLines PageFooters PageHeaderLines PageHeaders PageHeight PageRankCentrality PageTheme PageWidth Pagination PairedBarChart PairedHistogram PairedSmoothHistogram PairedTTest PairedZTest PaletteNotebook PalettePath PalindromeQ Pane PaneBox PaneBoxOptions Panel PanelBox PanelBoxOptions Paneled PaneSelector PaneSelectorBox PaneSelectorBoxOptions PaperWidth ParabolicCylinderD ParagraphIndent ParagraphSpacing ParallelArray ParallelCombine ParallelDo Parallelepiped ParallelEvaluate Parallelization Parallelize ParallelMap ParallelNeeds Parallelogram ParallelProduct ParallelSubmit ParallelSum ParallelTable ParallelTry Parameter ParameterEstimator ParameterMixtureDistribution ParameterVariables ParametricFunction ParametricNDSolve ParametricNDSolveValue ParametricPlot ParametricPlot3D ParametricRegion ParentBox ParentCell ParentConnect ParentDirectory ParentForm Parenthesize ParentList ParentNotebook ParetoDistribution ParetoPickandsDistribution ParkData Part PartBehavior PartialCorrelationFunction PartialD ParticleAcceleratorData ParticleData Partition PartitionGranularity PartitionsP PartitionsQ PartLayer PartOfSpeech PartProtection ParzenWindow PascalDistribution PassEventsDown PassEventsUp Paste PasteAutoQuoteCharacters PasteBoxFormInlineCells PasteButton Path PathGraph PathGraphQ Pattern PatternSequence PatternTest PauliMatrix PaulWavelet Pause PausedTime PDF PeakDetect PeanoCurve PearsonChiSquareTest PearsonCorrelationTest PearsonDistribution PercentForm PerfectNumber PerfectNumberQ PerformanceGoal Perimeter PeriodicBoundaryCondition PeriodicInterpolation Periodogram PeriodogramArray Permanent Permissions PermissionsGroup PermissionsGroupMemberQ PermissionsGroups PermissionsKey PermissionsKeys PermutationCycles PermutationCyclesQ PermutationGroup PermutationLength PermutationList PermutationListQ PermutationMax PermutationMin PermutationOrder PermutationPower PermutationProduct PermutationReplace Permutations PermutationSupport Permute PeronaMalikFilter Perpendicular PerpendicularBisector PersistenceLocation PersistenceTime PersistentObject PersistentObjects PersistentValue PersonData PERTDistribution PetersenGraph PhaseMargins PhaseRange PhysicalSystemData Pi Pick PIDData PIDDerivativeFilter PIDFeedforward PIDTune Piecewise PiecewiseExpand PieChart PieChart3D PillaiTrace PillaiTraceTest PingTime Pink PitchRecognize Pivoting PixelConstrained PixelValue PixelValuePositions Placed Placeholder PlaceholderReplace Plain PlanarAngle PlanarGraph PlanarGraphQ PlanckRadiationLaw PlaneCurveData PlanetaryMoonData PlanetData PlantData Play PlayRange Plot Plot3D Plot3Matrix PlotDivision PlotJoined PlotLabel PlotLabels PlotLayout PlotLegends PlotMarkers PlotPoints PlotRange PlotRangeClipping PlotRangeClipPlanesStyle PlotRangePadding PlotRegion PlotStyle PlotTheme Pluralize Plus PlusMinus Pochhammer PodStates PodWidth Point Point3DBox Point3DBoxOptions PointBox PointBoxOptions PointFigureChart PointLegend PointSize PoissonConsulDistribution PoissonDistribution PoissonProcess PoissonWindow PolarAxes PolarAxesOrigin PolarGridLines PolarPlot PolarTicks PoleZeroMarkers PolyaAeppliDistribution PolyGamma Polygon Polygon3DBox Polygon3DBoxOptions PolygonalNumber PolygonAngle PolygonBox PolygonBoxOptions PolygonCoordinates PolygonDecomposition PolygonHoleScale PolygonIntersections PolygonScale Polyhedron PolyhedronAngle PolyhedronCoordinates PolyhedronData PolyhedronDecomposition PolyhedronGenus PolyLog PolynomialExtendedGCD PolynomialForm PolynomialGCD PolynomialLCM PolynomialMod PolynomialQ PolynomialQuotient PolynomialQuotientRemainder PolynomialReduce PolynomialRemainder Polynomials PoolingLayer PopupMenu PopupMenuBox PopupMenuBoxOptions PopupView PopupWindow Position PositionIndex Positive PositiveDefiniteMatrixQ PositiveIntegers PositiveRationals PositiveReals PositiveSemidefiniteMatrixQ PossibleZeroQ Postfix PostScript Power PowerDistribution PowerExpand PowerMod PowerModList PowerRange PowerSpectralDensity PowersRepresentations PowerSymmetricPolynomial Precedence PrecedenceForm Precedes PrecedesEqual PrecedesSlantEqual PrecedesTilde Precision PrecisionGoal PreDecrement Predict PredictionRoot PredictorFunction PredictorInformation PredictorMeasurements PredictorMeasurementsObject PreemptProtect PreferencesPath Prefix PreIncrement Prepend PrependLayer PrependTo PreprocessingRules PreserveColor PreserveImageOptions Previous PreviousCell PreviousDate PriceGraphDistribution PrimaryPlaceholder Prime PrimeNu PrimeOmega PrimePi PrimePowerQ PrimeQ Primes PrimeZetaP PrimitivePolynomialQ PrimitiveRoot PrimitiveRootList PrincipalComponents PrincipalValue Print PrintableASCIIQ PrintAction PrintForm PrintingCopies PrintingOptions PrintingPageRange PrintingStartingPageNumber PrintingStyleEnvironment Printout3D Printout3DPreviewer PrintPrecision PrintTemporary Prism PrismBox PrismBoxOptions PrivateCellOptions PrivateEvaluationOptions PrivateFontOptions PrivateFrontEndOptions PrivateKey PrivateNotebookOptions PrivatePaths Probability ProbabilityDistribution ProbabilityPlot ProbabilityPr ProbabilityScalePlot ProbitModelFit ProcessConnection ProcessDirectory ProcessEnvironment Processes ProcessEstimator ProcessInformation ProcessObject ProcessParameterAssumptions ProcessParameterQ ProcessStateDomain ProcessStatus ProcessTimeDomain Product ProductDistribution ProductLog ProgressIndicator ProgressIndicatorBox ProgressIndicatorBoxOptions Projection Prolog PromptForm ProofObject Properties Property PropertyList PropertyValue Proportion Proportional Protect Protected ProteinData Pruning PseudoInverse PsychrometricPropertyData PublicKey PublisherID PulsarData PunctuationCharacter Purple Put PutAppend Pyramid PyramidBox PyramidBoxOptionsQBinomial QFactorial QGamma QHypergeometricPFQ QnDispersion QPochhammer QPolyGamma QRDecomposition QuadraticIrrationalQ QuadraticOptimization Quantile QuantilePlot Quantity QuantityArray QuantityDistribution QuantityForm QuantityMagnitude QuantityQ QuantityUnit QuantityVariable QuantityVariableCanonicalUnit QuantityVariableDimensions QuantityVariableIdentifier QuantityVariablePhysicalQuantity Quartics QuartileDeviation Quartiles QuartileSkewness Query QueueingNetworkProcess QueueingProcess QueueProperties Quiet Quit Quotient QuotientRemainderRadialGradientImage RadialityCentrality RadicalBox RadicalBoxOptions RadioButton RadioButtonBar RadioButtonBox RadioButtonBoxOptions Radon RadonTransform RamanujanTau RamanujanTauL RamanujanTauTheta RamanujanTauZ Ramp Random RandomChoice RandomColor RandomComplex RandomEntity RandomFunction RandomGeoPosition RandomGraph RandomImage RandomInstance RandomInteger RandomPermutation RandomPoint RandomPolygon RandomPolyhedron RandomPrime RandomReal RandomSample RandomSeed RandomSeeding RandomVariate RandomWalkProcess RandomWord Range RangeFilter RangeSpecification RankedMax RankedMin RarerProbability Raster Raster3D Raster3DBox Raster3DBoxOptions RasterArray RasterBox RasterBoxOptions Rasterize RasterSize Rational RationalFunctions Rationalize Rationals Ratios RawArray RawBoxes RawData RawMedium RayleighDistribution Re Read ReadByteArray ReadLine ReadList ReadProtected ReadString Real RealAbs RealBlockDiagonalForm RealDigits RealExponent Reals RealSign Reap RecognitionPrior RecognitionThreshold Record RecordLists RecordSeparators Rectangle RectangleBox RectangleBoxOptions RectangleChart RectangleChart3D RectangularRepeatingElement RecurrenceFilter RecurrenceTable RecurringDigitsForm Red Reduce RefBox ReferenceLineStyle ReferenceMarkers ReferenceMarkerStyle Refine ReflectionMatrix ReflectionTransform Refresh RefreshRate Region RegionBinarize RegionBoundary RegionBounds RegionCentroid RegionDifference RegionDimension RegionDisjoint RegionDistance RegionDistanceFunction RegionEmbeddingDimension RegionEqual RegionFunction RegionImage RegionIntersection RegionMeasure RegionMember RegionMemberFunction RegionMoment RegionNearest RegionNearestFunction RegionPlot RegionPlot3D RegionProduct RegionQ RegionResize RegionSize RegionSymmetricDifference RegionUnion RegionWithin RegisterExternalEvaluator RegularExpression Regularization RegularlySampledQ RegularPolygon ReIm ReImLabels ReImPlot ReImStyle Reinstall RelationalDatabase RelationGraph Release ReleaseHold ReliabilityDistribution ReliefImage ReliefPlot RemoteAuthorizationCaching RemoteConnect RemoteConnectionObject RemoteFile RemoteRun RemoteRunProcess Remove RemoveAlphaChannel RemoveAsynchronousTask RemoveAudioStream RemoveBackground RemoveChannelListener RemoveChannelSubscribers Removed RemoveDiacritics RemoveInputStreamMethod RemoveOutputStreamMethod RemoveProperty RemoveScheduledTask RemoveUsers RenameDirectory RenameFile RenderAll RenderingOptions RenewalProcess RenkoChart RepairMesh Repeated RepeatedNull RepeatedString RepeatedTiming RepeatingElement Replace ReplaceAll ReplaceHeldPart ReplaceImageValue ReplaceList ReplacePart ReplacePixelValue ReplaceRepeated ReplicateLayer RequiredPhysicalQuantities Resampling ResamplingAlgorithmData ResamplingMethod Rescale RescalingTransform ResetDirectory ResetMenusPacket ResetScheduledTask ReshapeLayer Residue ResizeLayer Resolve ResourceAcquire ResourceData ResourceFunction ResourceObject ResourceRegister ResourceRemove ResourceSearch ResourceSubmissionObject ResourceSubmit ResourceSystemBase ResourceUpdate ResponseForm Rest RestartInterval Restricted Resultant ResumePacket Return ReturnEntersInput ReturnExpressionPacket ReturnInputFormPacket ReturnPacket ReturnReceiptFunction ReturnTextPacket Reverse ReverseBiorthogonalSplineWavelet ReverseElement ReverseEquilibrium ReverseGraph ReverseSort ReverseSortBy ReverseUpEquilibrium RevolutionAxis RevolutionPlot3D RGBColor RiccatiSolve RiceDistribution RidgeFilter RiemannR RiemannSiegelTheta RiemannSiegelZ RiemannXi Riffle Right RightArrow RightArrowBar RightArrowLeftArrow RightComposition RightCosetRepresentative RightDownTeeVector RightDownVector RightDownVectorBar RightTee RightTeeArrow RightTeeVector RightTriangle RightTriangleBar RightTriangleEqual RightUpDownVector RightUpTeeVector RightUpVector RightUpVectorBar RightVector RightVectorBar RiskAchievementImportance RiskReductionImportance RogersTanimotoDissimilarity RollPitchYawAngles RollPitchYawMatrix RomanNumeral Root RootApproximant RootIntervals RootLocusPlot RootMeanSquare RootOfUnityQ RootReduce Roots RootSum Rotate RotateLabel RotateLeft RotateRight RotationAction RotationBox RotationBoxOptions RotationMatrix RotationTransform Round RoundImplies RoundingRadius Row RowAlignments RowBackgrounds RowBox RowHeights RowLines RowMinHeight RowReduce RowsEqual RowSpacings RSolve RSolveValue RudinShapiro RudvalisGroupRu Rule RuleCondition RuleDelayed RuleForm RulePlot RulerUnits Run RunProcess RunScheduledTask RunThrough RuntimeAttributes RuntimeOptions RussellRaoDissimilaritySameQ SameTest SampledEntityClass SampleDepth SampledSoundFunction SampledSoundList SampleRate SamplingPeriod SARIMAProcess SARMAProcess SASTriangle SatelliteData SatisfiabilityCount SatisfiabilityInstances SatisfiableQ Saturday Save Saveable SaveAutoDelete SaveConnection SaveDefinitions SavitzkyGolayMatrix SawtoothWave Scale Scaled ScaleDivisions ScaledMousePosition ScaleOrigin ScalePadding ScaleRanges ScaleRangeStyle ScalingFunctions ScalingMatrix ScalingTransform Scan ScheduledTask ScheduledTaskActiveQ ScheduledTaskInformation ScheduledTaskInformationData ScheduledTaskObject ScheduledTasks SchurDecomposition ScientificForm ScientificNotationThreshold ScorerGi ScorerGiPrime ScorerHi ScorerHiPrime ScreenRectangle ScreenStyleEnvironment ScriptBaselineShifts ScriptForm ScriptLevel ScriptMinSize ScriptRules ScriptSizeMultipliers Scrollbars ScrollingOptions ScrollPosition SearchAdjustment SearchIndexObject SearchIndices SearchQueryString SearchResultObject Sec Sech SechDistribution SecondOrderConeOptimization SectionGrouping SectorChart SectorChart3D SectorOrigin SectorSpacing SecuredAuthenticationKey SecuredAuthenticationKeys SeedRandom Select Selectable SelectComponents SelectedCells SelectedNotebook SelectFirst Selection SelectionAnimate SelectionCell SelectionCellCreateCell SelectionCellDefaultStyle SelectionCellParentStyle SelectionCreateCell SelectionDebuggerTag SelectionDuplicateCell SelectionEvaluate SelectionEvaluateCreateCell SelectionMove SelectionPlaceholder SelectionSetStyle SelectWithContents SelfLoops SelfLoopStyle SemanticImport SemanticImportString SemanticInterpretation SemialgebraicComponentInstances SemidefiniteOptimization SendMail SendMessage Sequence SequenceAlignment SequenceAttentionLayer SequenceCases SequenceCount SequenceFold SequenceFoldList SequenceForm SequenceHold SequenceLastLayer SequenceMostLayer SequencePosition SequencePredict SequencePredictorFunction SequenceReplace SequenceRestLayer SequenceReverseLayer SequenceSplit Series SeriesCoefficient SeriesData ServiceConnect ServiceDisconnect ServiceExecute ServiceObject ServiceRequest ServiceResponse ServiceSubmit SessionSubmit SessionTime Set SetAccuracy SetAlphaChannel SetAttributes Setbacks SetBoxFormNamesPacket SetCloudDirectory SetCookies SetDelayed SetDirectory SetEnvironment SetEvaluationNotebook SetFileDate SetFileLoadingContext SetNotebookStatusLine SetOptions SetOptionsPacket SetPermissions SetPrecision SetProperty SetSecuredAuthenticationKey SetSelectedNotebook SetSharedFunction SetSharedVariable SetSpeechParametersPacket SetStreamPosition SetSystemModel SetSystemOptions Setter SetterBar SetterBox SetterBoxOptions Setting SetUsers SetValue Shading Shallow ShannonWavelet ShapiroWilkTest Share SharingList Sharpen ShearingMatrix ShearingTransform ShellRegion ShenCastanMatrix ShiftedGompertzDistribution ShiftRegisterSequence Short ShortDownArrow Shortest ShortestMatch ShortestPathFunction ShortLeftArrow ShortRightArrow ShortTimeFourier ShortTimeFourierData ShortUpArrow Show ShowAutoConvert ShowAutoSpellCheck ShowAutoStyles ShowCellBracket ShowCellLabel ShowCellTags ShowClosedCellArea ShowCodeAssist ShowContents ShowControls ShowCursorTracker ShowGroupOpenCloseIcon ShowGroupOpener ShowInvisibleCharacters ShowPageBreaks ShowPredictiveInterface ShowSelection ShowShortBoxForm ShowSpecialCharacters ShowStringCharacters ShowSyntaxStyles ShrinkingDelay ShrinkWrapBoundingBox SiderealTime SiegelTheta SiegelTukeyTest SierpinskiCurve SierpinskiMesh Sign Signature SignedRankTest SignedRegionDistance SignificanceLevel SignPadding SignTest SimilarityRules SimpleGraph SimpleGraphQ SimplePolygonQ SimplePolyhedronQ Simplex Simplify Sin Sinc SinghMaddalaDistribution SingleEvaluation SingleLetterItalics SingleLetterStyle SingularValueDecomposition SingularValueList SingularValuePlot SingularValues Sinh SinhIntegral SinIntegral SixJSymbol Skeleton SkeletonTransform SkellamDistribution Skewness SkewNormalDistribution SkinStyle Skip SliceContourPlot3D SliceDensityPlot3D SliceDistribution SliceVectorPlot3D Slider Slider2D Slider2DBox Slider2DBoxOptions SliderBox SliderBoxOptions SlideView Slot SlotSequence Small SmallCircle Smaller SmithDecomposition SmithDelayCompensator SmithWatermanSimilarity SmoothDensityHistogram SmoothHistogram SmoothHistogram3D SmoothKernelDistribution SnDispersion Snippet SnubPolyhedron SocialMediaData Socket SocketConnect SocketListen SocketListener SocketObject SocketOpen SocketReadMessage SocketReadyQ Sockets SocketWaitAll SocketWaitNext SoftmaxLayer SokalSneathDissimilarity SolarEclipse SolarSystemFeatureData SolidAngle SolidData SolidRegionQ Solve SolveAlways SolveDelayed Sort SortBy SortedBy SortedEntityClass Sound SoundAndGraphics SoundNote SoundVolume SourceLink Sow Space SpaceCurveData SpaceForm Spacer Spacings Span SpanAdjustments SpanCharacterRounding SpanFromAbove SpanFromBoth SpanFromLeft SpanLineThickness SpanMaxSize SpanMinSize SpanningCharacters SpanSymmetric SparseArray SpatialGraphDistribution SpatialMedian SpatialTransformationLayer Speak SpeakTextPacket SpearmanRankTest SpearmanRho SpeciesData SpecificityGoal SpectralLineData Spectrogram SpectrogramArray Specularity SpeechRecognize SpeechSynthesize SpellingCorrection SpellingCorrectionList SpellingDictionaries SpellingDictionariesPath SpellingOptions SpellingSuggestionsPacket Sphere SphereBox SpherePoints SphericalBesselJ SphericalBesselY SphericalHankelH1 SphericalHankelH2 SphericalHarmonicY SphericalPlot3D SphericalRegion SphericalShell SpheroidalEigenvalue SpheroidalJoiningFactor SpheroidalPS SpheroidalPSPrime SpheroidalQS SpheroidalQSPrime SpheroidalRadialFactor SpheroidalS1 SpheroidalS1Prime SpheroidalS2 SpheroidalS2Prime Splice SplicedDistribution SplineClosed SplineDegree SplineKnots SplineWeights Split SplitBy SpokenString Sqrt SqrtBox SqrtBoxOptions Square SquaredEuclideanDistance SquareFreeQ SquareIntersection SquareMatrixQ SquareRepeatingElement SquaresR SquareSubset SquareSubsetEqual SquareSuperset SquareSupersetEqual SquareUnion SquareWave SSSTriangle StabilityMargins StabilityMarginsStyle StableDistribution Stack StackBegin StackComplete StackedDateListPlot StackedListPlot StackInhibit StadiumShape StandardAtmosphereData StandardDeviation StandardDeviationFilter StandardForm Standardize Standardized StandardOceanData StandbyDistribution Star StarClusterData StarData StarGraph StartAsynchronousTask StartExternalSession StartingStepSize StartOfLine StartOfString StartProcess StartScheduledTask StartupSound StartWebSession StateDimensions StateFeedbackGains StateOutputEstimator StateResponse StateSpaceModel StateSpaceRealization StateSpaceTransform StateTransformationLinearize StationaryDistribution StationaryWaveletPacketTransform StationaryWaveletTransform StatusArea StatusCentrality StepMonitor StereochemistryElements StieltjesGamma StirlingS1 StirlingS2 StopAsynchronousTask StoppingPowerData StopScheduledTask StrataVariables StratonovichProcess StreamColorFunction StreamColorFunctionScaling StreamDensityPlot StreamMarkers StreamPlot StreamPoints StreamPosition Streams StreamScale StreamStyle String StringBreak StringByteCount StringCases StringContainsQ StringCount StringDelete StringDrop StringEndsQ StringExpression StringExtract StringForm StringFormat StringFreeQ StringInsert StringJoin StringLength StringMatchQ StringPadLeft StringPadRight StringPart StringPartition StringPosition StringQ StringRepeat StringReplace StringReplaceList StringReplacePart StringReverse StringRiffle StringRotateLeft StringRotateRight StringSkeleton StringSplit StringStartsQ StringTake StringTemplate StringToByteArray StringToStream StringTrim StripBoxes StripOnInput StripWrapperBoxes StrokeForm StructuralImportance StructuredArray StructuredSelection StruveH StruveL Stub StudentTDistribution Style StyleBox StyleBoxAutoDelete StyleData StyleDefinitions StyleForm StyleHints StyleKeyMapping StyleMenuListing StyleNameDialogSettings StyleNames StylePrint StyleSheetPath Subdivide Subfactorial Subgraph SubMinus SubPlus SubresultantPolynomialRemainders SubresultantPolynomials Subresultants Subscript SubscriptBox SubscriptBoxOptions Subscripted Subsequences Subset SubsetEqual SubsetMap SubsetQ Subsets SubStar SubstitutionSystem Subsuperscript SubsuperscriptBox SubsuperscriptBoxOptions Subtract SubtractFrom SubtractSides SubValues Succeeds SucceedsEqual SucceedsSlantEqual SucceedsTilde Success SuchThat Sum SumConvergence SummationLayer Sunday SunPosition Sunrise Sunset SuperDagger SuperMinus SupernovaData SuperPlus Superscript SuperscriptBox SuperscriptBoxOptions Superset SupersetEqual SuperStar Surd SurdForm SurfaceArea SurfaceColor SurfaceData SurfaceGraphics SurvivalDistribution SurvivalFunction SurvivalModel SurvivalModelFit SuspendPacket SuzukiDistribution SuzukiGroupSuz SwatchLegend Switch Symbol SymbolName SymletWavelet Symmetric SymmetricGroup SymmetricKey SymmetricMatrixQ SymmetricPolynomial SymmetricReduction Symmetrize SymmetrizedArray SymmetrizedArrayRules SymmetrizedDependentComponents SymmetrizedIndependentComponents SymmetrizedReplacePart SynchronousInitialization SynchronousUpdating Synonyms Syntax SyntaxForm SyntaxInformation SyntaxLength SyntaxPacket SyntaxQ SynthesizeMissingValues SystemDialogInput SystemException SystemGet SystemHelpPath SystemInformation SystemInformationData SystemInstall SystemModel SystemModeler SystemModelExamples SystemModelLinearize SystemModelParametricSimulate SystemModelPlot SystemModelProgressReporting SystemModelReliability SystemModels SystemModelSimulate SystemModelSimulateSensitivity SystemModelSimulationData SystemOpen SystemOptions SystemProcessData SystemProcesses SystemsConnectionsModel SystemsModelDelay SystemsModelDelayApproximate SystemsModelDelete SystemsModelDimensions SystemsModelExtract SystemsModelFeedbackConnect SystemsModelLabels SystemsModelLinearity SystemsModelMerge SystemsModelOrder SystemsModelParallelConnect SystemsModelSeriesConnect 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"text": "/*\\\ntitle: $:/plugins/tiddlywiki/highlight/highlightblock.js\ntype: application/javascript\nmodule-type: widget\n\nWraps up the fenced code blocks parser for highlight and use in TiddlyWiki5\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar TYPE_MAPPINGS_BASE = \"$:/config/HighlightPlugin/TypeMappings/\";\n\nvar CodeBlockWidget = require(\"$:/core/modules/widgets/codeblock.js\").codeblock;\n\nvar hljs = require(\"$:/plugins/tiddlywiki/highlight/highlight.js\");\n\nhljs.configure({tabReplace: \" \"});\t\n\nCodeBlockWidget.prototype.postRender = function() {\n\tvar domNode = this.domNodes[0],\n\t\tlanguage = this.language,\n\t\ttiddler = this.wiki.getTiddler(TYPE_MAPPINGS_BASE + language);\n\tif(tiddler) {\n\t\tlanguage = tiddler.fields.text || \"\";\n\t}\n\tif(language && hljs.getLanguage(language)) {\n\t\tdomNode.className = language.toLowerCase() + \" hljs\";\n\t\tif($tw.browser && !domNode.isTiddlyWikiFakeDom) {\n\t\t\thljs.highlightBlock(domNode);\t\t\t\n\t\t} else {\n\t\t\tvar text = domNode.textContent;\n\t\t\tdomNode.children[0].innerHTML = hljs.fixMarkup(hljs.highlight(language,text).value);\n\t\t\t// If we're using the fakedom then specially save the original raw text\n\t\t\tif(domNode.isTiddlyWikiFakeDom) {\n\t\t\t\tdomNode.children[0].textInnerHTML = text;\n\t\t\t}\n\t\t}\n\t}\t\n};\n\n})();\n",
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"text": "! Supporting Additional Languages\n \nThe [[highlight.js|https://github.com/highlightjs/highlight.js]] project supports many languages. Only a subset of these languages are supported by the plugin. It is possible for users to change the set of languages supported by the plugin by following these steps:\n \n# Go to the highlight.js project [[download page|https://highlightjs.org/download/]], select the language definitions to include, and press the Download button to download a zip archive containing customised support files for a highlight.js syntax highlighting server.\n# Locate the `highlight.pack.js` file in the highlight plugin -- on a stock Debian 8 system running Tiddlywiki5 under node-js it is located at `/usr/local/lib/node_modules/tiddlywiki/plugins/tiddlywiki/highlight/files/highlight.pack.js`.\n# Replace the plugin `highlight.pack.js` file located in step 2 with the one from the downloaded archive obtained in step 1.\n# Restart the Tiddlywiki server.\n"
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"text": "Copyright (c) 2006, Ivan Sagalaev\nAll rights reserved.\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n * Redistributions of source code must retain the above copyright\n notice, this list of conditions and the following disclaimer.\n * Redistributions in binary form must reproduce the above copyright\n notice, this list of conditions and the following disclaimer in the\n documentation and/or other materials provided with the distribution.\n * Neither the name of highlight.js nor the names of its contributors\n may be used to endorse or promote products derived from this software\n without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY\nEXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE REGENTS AND CONTRIBUTORS BE LIABLE FOR ANY\nDIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\nON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n"
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"$:/plugins/tiddlywiki/highlight/readme": {
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"text": "This plugin provides syntax highlighting of code blocks using v9.18.1 of [[highlight.js|https://github.com/isagalaev/highlight.js]] from Ivan Sagalaev.\n\n! Usage\n\nWhen the plugin is installed it automatically applies highlighting to all codeblocks defined with triple backticks or with the CodeBlockWidget.\n\nThe language can optionally be specified after the opening triple braces:\n\n<$codeblock code=\"\"\"```css\n * { margin: 0; padding: 0; } /* micro reset */\n\nhtml { font-size: 62.5%; }\nbody { font-size: 14px; font-size: 1.4rem; } /* =14px */\nh1 { font-size: 24px; font-size: 2.4rem; } /* =24px */\n```\"\"\"/>\n\nIf no language is specified highlight.js will attempt to automatically detect the language.\n\n! Built-in Language Brushes\n\nThe plugin includes support for the following languages (referred to as \"brushes\" by highlight.js):\n\n* apache\n* arduino\n* arm assembly\n* asciidoc\n* autohotkey\n* awk\n* bash\n* cmake\n* coffeescript\n* cpp\n* cs\n* css\n* diff\n* dockerfile\n* erlang\n* elixir\n* fortran\n* go\n* gradle\n* haskell\n* html\n* http\n* ini\n* intel x86 assembly\n* java\n* javascript\n* json\n* kotlin\n* less\n* lua\n* makefile\n* markdown\n* mathematica\n* matlab\n* nginx\n* objectivec\n* perl\n* php\n* plaintext\n* powershell\n* properties\n* python\n* R\n* ruby\n* rust\n* scss\n* shell session\n* sql\n* swift\n* toml\n* typescript\n* vala\n* vim script\n* xml\n* yaml\n\nYou can also specify the language as a MIME content type (eg `text/html` or `text/css`). The mapping is accomplished via mapping tiddlers whose titles start with `$:/config/HighlightPlugin/TypeMappings/`.\n"
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"$:/plugins/tiddlywiki/highlight/usage": {
"title": "$:/plugins/tiddlywiki/highlight/usage",
"text": "! Usage\n\nFenced code blocks can have a language specifier added to trigger highlighting in a specific language. Otherwise heuristics are used to detect the language.\n\n```\n ```js\n var a = b + c; // Highlighted as JavaScript\n ```\n```\n! Adding Themes\n\nYou can add themes from highlight.js by copying the CSS to a new tiddler and tagging it with [[$:/tags/Stylesheet]]. The available themes can be found on GitHub:\n\nhttps://github.com/isagalaev/highlight.js/tree/master/src/styles\n"
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a=this.parseGroup();if(!a){if(this.settings.throwOnError||\"\\\\\"!==this.nextToken.text[0])throw new g.a(\"Expected group after '\"+n+\"'\",r);return this.handleUnsupportedCmd()}var i=x(a);if(\"fn\"===i.type){if(s.a[i.result].greediness>e.SUPSUB_GREEDINESS)return this.parseGivenFunction(a);throw new g.a(\"Got function '\"+i.result+\"' with no arguments as \"+t,r)}return i.result}},{key:\"handleUnsupportedCmd\",value:function(){for(var e=this.nextToken.text,t=[],r=0;r<e.length;r++)t.push(new v.a(\"textord\",e[r],\"text\"));var n=new v.a(\"text\",{body:t,type:\"text\"},this.mode),a=new v.a(\"color\",{color:this.settings.errorColor,value:[n],type:\"color\"},this.mode);return this.consume(),a}},{key:\"parseAtom\",value:function(e){var t=this.parseImplicitGroup(e);if(\"text\"===this.mode)return t;for(var r=void 0,n=void 0;;){this.consumeSpaces();var a=this.nextToken;if(\"\\\\limits\"===a.text||\"\\\\nolimits\"===a.text){if(!t||\"op\"!==t.type)throw new g.a(\"Limit controls must follow a 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n=this.mode;this.switchMode(\"math\"),this.consume();var a=this.parseExpression(!1,\"$\");return this.expect(\"$\",!1),this.switchMode(n),this.consume(),new v.a(\"styling\",{style:\"text\",value:a},\"math\")}if(\"\\\\begin\"===r){var i=this.parseGivenFunction(t),o=i.value.name;if(!l.a.hasOwnProperty(o))throw new g.a(\"No such environment: \"+o,i.value.nameGroup);var s=l.a[o],u=this.parseArguments(\"\\\\begin{\"+o+\"}\",s),c=u.args,h=u.optArgs,p={mode:this.mode,envName:o,parser:this},m=s.handler(p,c,h);this.expect(\"\\\\end\",!1);var d=this.nextToken,f=this.parseFunction();if(!f)throw new g.a(\"failed to parse function after \\\\end\");if(f.value.name!==o)throw new g.a(\"Mismatch: \\\\begin{\"+o+\"} matched by \\\\end{\"+f.value.name+\"}\",d);return m}return this.parseGivenFunction(t,e)}},{key:\"parseFunction\",value:function(){var e=this.parseGroup();return e?this.parseGivenFunction(e):null}},{key:\"parseGivenFunction\",value:function(e,t){if(\"fn\"===(e=x(e)).type){var 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c=this.nextToken,h=l?this.parseGroupOfType(l,u):this.parseGroup(u);if(!h){if(u){i.push(null);continue}if(this.settings.throwOnError||\"\\\\\"!==this.nextToken.text[0])throw new g.a(\"Expected group after '\"+e+\"'\",c);h=b(this.handleUnsupportedCmd(),c)}var p=void 0;if(\"fn\"===(h=x(h)).type){if(!(s.a[h.result].greediness>n))throw new g.a(\"Got function '\"+h.result+\"' as argument to '\"+e+\"'\",c);p=this.parseGivenFunction(h)}else p=h.result;(u?i:a).push(p)}return{args:a,optArgs:i}}},{key:\"parseGroupOfType\",value:function(e,t){return\"original\"===e&&(e=this.mode),\"color\"===e?this.parseColorGroup(t):\"size\"===e?this.parseSizeGroup(t):\"url\"===e?this.parseUrlGroup(t):this.parseGroup(t,e)}},{key:\"consumeSpaces\",value:function(){for(;\" \"===this.nextToken.text;)this.consume()}},{key:\"parseStringGroup\",value:function(e,t){if(t&&\"[\"!==this.nextToken.text)return null;var r=this.mode;this.mode=\"text\",this.expect(t?\"[\":\"{\");for(var n=\"\",a=this.nextToken,i=a;this.nextToken.text!==(t?\"]\":\"}\");){if(\"EOF\"===this.nextToken.text)throw new g.a(\"Unexpected end of input in \"+e,a.range(this.nextToken,n));n+=(i=this.nextToken).text,this.consume()}return this.mode=r,this.expect(t?\"]\":\"}\"),a.range(i,n)}},{key:\"parseStringGroupWithBalancedBraces\",value:function(e,t){if(t&&\"[\"!==this.nextToken.text)return null;var r=this.mode;this.mode=\"text\",this.expect(t?\"[\":\"{\");for(var n=\"\",a=0,i=this.nextToken,o=i;a>0||this.nextToken.text!==(t?\"]\":\"}\");){if(\"EOF\"===this.nextToken.text)throw new g.a(\"Unexpected end of input in \"+e,i.range(this.nextToken,n));if(n+=(o=this.nextToken).text,\"{\"===o.text)a+=1;else if(\"}\"===o.text){if(a<=0)throw new g.a(\"Unbalanced brace of input in \"+e,i.range(this.nextToken,n));a-=1}this.consume()}return this.mode=r,this.expect(t?\"]\":\"}\"),i.range(o,n)}},{key:\"parseRegexGroup\",value:function(e,t){var r=this.mode;this.mode=\"text\";for(var 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"title": "$:/plugins/tiddlywiki/katex/katex.min.js",
"module-type": "library"
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_AMS-Regular.woff",
"type": "application/font-woff"
},
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_Caligraphic-Bold.woff",
"type": "application/font-woff"
},
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_Caligraphic-Regular.woff",
"type": "application/font-woff"
},
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_Fraktur-Bold.woff",
"type": "application/font-woff"
},
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_Fraktur-Regular.woff",
"type": "application/font-woff"
},
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MzhiyGWoZ5hjuMeIyCjHqMDpiLGMcY7zN+ImJh8k8UybTKjMGNChjZmFWY7bLXMm8w0LFks0yxXKK5RmrEGsN6wU2OrZCtmfs+Ozi7NbYfbPPsL/lYOYwzeGTY5DjOic2pxynZU4PnCWcg5z3uKi5THH551rlJuS2yO2Je5j7BA81jxiPTZ42ntM8N3l+8ZLycvLa4fXCm8fbx3ufj5NPi88Cnzu+Ir5+vtN87/jZ+NX4HfDX8p8QIBbQFXAlUC+wKvBCkFRQXdCTYL3gguBdISwhaSFHQo1Cq0J3hT4L4wsrCTsUbhU+I/xFhFCESURaRF/EgUimSIXIishLURpRBVEnoj5Ei0QbRQdFT4g+EsMU4xDTErMnlis2K/ZFXEjcgXiW+KgEhgSdhJiEFQm/EksS3yUlJIsklyVfS9FJaUqZlrIqZQcA1Me5mwAAAHja3L0JdFxXlS58zrnzvXVv1a26t+Z5lFRSaahJY6kky5IsybMdW5Ydx5Ed24ntOLaTECDzRDMTAo9AGgIkTA1ZAR7QDd3AY2houn/ouRkWdPN6oP9u4O9uSDM0Lv37nFulwSTAe+tf6631R4506w5V5+yzz97ft/c+pxBBQwihLxMbcUhC8sdEHiMyUCybZbNQNjNDbz45NETsq/86hD+MCFpGGPPkfyAfiqNcMy1jjPACnEMr8Cb2IiKEW0EcZ3BLVr4nzYvBoq9SKw/5A37bIpIoiZl0IU/ouTi2LTGdXx5MpUsyrhEs8L4QT+p4MJ/p718YILcne1MRTna3XsNzhEg8vnT1+7kB0p/NDfTDhxGUxzvw98nHUQyNoUQzymNM8GUBE4JWOIyQmyz1BzL9gbQohote1grbok2AFlQr9Rr8NHCmQptU+5VX86XcUE0JLWmqpmvucFInPFa8gey3XuQ8GZ8f2j2cyB4flzVdEhUt2jOkWJhXBl/kPPSluTbJ3Q19OY1ehnY2F6I6QXgKyxJZ4KE/hk4EEQk3ul1E1BQiyaJ0gsMYuxdVLMteeenyrbecuWn1yPLBfbuWZrflczO5Sr6S93rEGMi/nqo3SJ31UJSsMcw6t/GSDo0bZyTLwJtfis5ogTB8lSLe8jLDRFSm79rAk5i+Fug97XdwztebAWxxSjFFNEP38rtuOMFJuS5eMumLY9fzXv+Z07zfT+67Hw5f9Uqe685zkqWb+2444Rz7dO++Y8cCga/yWNIEQeO4++4LkCBchqfgd/ssvtnj1qKvuoXTvYbvp0Tzv+RBXvfpcAhv59OwjhdnQStlywvH2gP3c27TJWORdI5V0GesWqauc4G8x51RFII1gziPdc6BvnEwRl/GLfJDtBMdQifQnubOAczxB+a2cQK3siPNYWEpQAgWOYKIsIB4jr+EBA5zAr6IMOEwuSBiguDqBXi322CS2NzS9UfD3kipGxQvXMxZomRnqpl8poSrbYUUpbSUjsOYBPzloQlcyFPJw+CN4WrZLvsDbF6Jmfb5SVyDo4xNH+BE2wr4E/BO3kq+YJeHYMxx66YjDUJcu0/kUolCNCDBpFMEOxOTsXl0MD0/kg1yDaHbe8bi80mvW4mG/QoJJ/vFPadlvHZcCkxy7xjuJpI/7P3Gidu0Spm4Tu1JFQrxUE4ksibYLvnyJd7aMTQ+c8o2KmVRSp0wfakewsu+XDArEeK3pa/o55cE/CZrh2X06/j661vcnlHlb2TNBqnwaGrtyzAPfogG0DTaj06hg819ORgsASOuhGG0F3iQIYhYXIUBQRKHVsFowTzBq0hGqiarJxHVikNI04QVBZ4Qdh8/dujgrqWufK4rnS0UUy6YExg0uR8HLL/NJj3YJCpyOiccSdc75gDRi3DFl8+kJSZRerFaKeQxu3nz0790C3lgaGjfkoaxfCSS0KX5XbWB7d2FgyeswEt3uD26ScjVzwiyK06OuRS/aRhmKBGMFFR808Ds9u5s+5nPbLlGThhJAXSMpAtvKOpYO3twtTYQnXCT63ymzovCVVuQ4rJAvkCCwVM3BYPJrl1HC2rrroGJ2nWPtR8Y2HINxDiw9jw3AzZ9NzqGbkE3No+DB1BWwKbvmAG1XQXdTcEcERZ0zOEdCBQZc+QiUhRxBYmivShhav41mSDkAXtECL8iYJ7380sI3XjDgX3wvrt37Rwfzuay3T4Qf7iIqYjKdkYyiBszgYFKT+AySNPAdGSYpenY4apzBBZlqAYqT/2GJLoxVe46qHymWqgw0TPh12ucRd8d5kEJFySR3Gievc4lHk92pwZTngS4nUFFbBDpSOPGGwLqodU95z0k7JqucFySGIcbxCuGzVSPnT7EC703a+roYnL2k8mIruQ1EvYc+dti70xVOzzBy1iI49/nuEb/RF8j4+HtrpDXpXA8r/Y3B1xibbbh0ruyZrUbS/Lu/RG9sU/BWPHMlHLJ6jyn7b1VU0Yv9Tdekqnz7n45vHPc2Nv6+Nh8Up49K2qclIW5gNEC+DcBfEKUercA6D31tODiEPVwiDm4vkKaEwNbvBt1sBXmvepV5r4WmJsK7HCLgmqoU34Yxc1e6uiYSzEMuf+AaAgCdUbss0+vSfgemIcxFGzaHsfLw+lL4OrPBvwZItpFH3xCZw6A7D3OURGD3GG8zpkvvd7t4SR3f3TXe7HEedzXv9QU8MEH/2YSzIHorpRf/83Wl99LOJUjja8/WKiU2ec24XMPweemULwZIfCxtM8IX4JDdBYjvw+lcIoTrWIg7WiH6XGMHzSlNjSG47jTIOlI2Ij7ztwR1AwFdFLkWAOE8N3Fws/cvDsZfRPorfmWqMvNKwKmTShXCv/XI71h1o4deAg/D+2YBTuvzBRTMjgJvLD4XHTP4abFvAEH0wTMPeg6xjpZijThQcyhQ/CaZ+Z+ld62Qu/cvdxUSl3BUheDIr7OYNm1dTuTSTsNZ6NXoFZJpK62Xu04XXqnI1jHSFXz/5q07IR9Ky/L/njgZZiMN0kgYSsSf6ud7Eq+8AXyOW845LPP4gOEAzeh9lp+4orZaY4cwGdtbzjsfZFr7bG5m5sgd6E0KjcHAH4gGwwBaesFRzjAXlQiB6lEyDIIAZOd2Ww2Z5m027gG7Y7hVBtftC1nfgIzNDGJU9UKfpfcBd53AQDBM88QxfIp5JmnA14B7yAnbUt4+t2OR3/304LPB6+wYtkS9+6n2VxZa8AE+R2URKGmn6LDFZglxiLojxcv9XexWVKv1Db0tTNNYKIQaj1Aygv9+Z5Q0h8ZcQmiYii80WMRERw477Xz/QfzA6nM9NjQdL9CpwsxCmNSwMPJQ8580dF7cAP7YbRBbxEFxAfp+WVCp85OR2FM8MnFejVl63jwPRcvOnMc/n8e2m0iX9NDz6zAswZeKthE9NMW19ttbjd5oRDnlWpAT0UVdzKQOJgA7NxthaJ9CvEF2RiJDKN4kNk01mdsIUXfjbosvySqjnmt1xD+vGwZXozf/2Wv4tV9MGD3kW9alktpnWpFS16voXPPADbtW/tPYpLXIhv1oRhVc0Q4BOrNcay1frzUXexKUqyPHQfaGdtqheLOALXOgAzBOscxwyEVx4OqRl7+sC1kRt78kZLXV9o/LlwJTRZiloJlcTBbC3tAgTjyWhz502L9tbN3PD16ZsfFozUR3zwcs2fuHnpLMVkshc/LC6Fb08O0q3vg1w/J61AIVZtDgKsFsB0CL1wCWMERnlsVKTVYBqwAyMFA4J3gzpCVTmVN01TESDGVT0qZarlayYAVq5aHJll3QuCX8Hf9rY9b+oNkeN40H13SDD9PHgbr/x//YTaGZY1v/W3rbxtsPEdAOALIahIdWXzOBcYihQAECoi7CG0g3E1wi4CwsArayR9BPG/wYDnSm+4ROHLhBW4CC9Lnz/cWHAtitcULRn5D4tQpOuJtUHRITaCdovcQ5kUdJ2nDzX9TzAwEZLf/ZHO2Np4ojkSmoov15mt1ReW5Bna7uNZ+TG685BnwqjvGfWKCE5K4lSt2ucM5q9BvBnPl6W0jkZJvrFfgiiQAkuZuvSDwoTDHrV7x1aux+NgilngAuM7cAHxBDBiXeTTXnJnCVAN5IQW2g1sAfCWIknARwfjwhKNzQpQQgDsBAeLgmY4ddXQs120XunL9aVmMbuKUUrv3FDcU8kwezL6swzBwDoW0g5qH4CqIhgnCsbX5PZd3H7jdQx5y78mlRN8jN9021U1IXy5f6pKj8a4pn+yLm0MFUVQTkkcj2Vyy2Fso96bSRfyTmQPjI6r34hBvD/T2+wPzu6a6R9KlgdxAQOBlbSQwMlw3ZUwEnxdwL3YtVHsLQaua7S4y7pqEX3HyepRAWbS9OZ3yQ2/xgorB1iFe4C8ByxNETNXhhMTUVpXbapuER5PZZAYeTZimvw7KC6AqCtoL3aLqm6oCLCKjbc2Igfpm7LJdMEUpQHzmw2/y6hdb3/vQhziR53UxxIPCLS8vf/PxHRdAoY81vvENwAcELv7lX9JX2P7do5jqNYyhAGO4D93UXB2HMZzARIpBA01A5mDwkbCQx8K8M98oA+LQRQUeBNByAeafKPEiRerAycBwACJeQeCe5CUwgPMz08O1/r5idzQcsFUZ7cP7VPDvdccxsmFsELAX1PsxQ+J4DeoRmWnchNaZXcukY5iCnzaBYuicjf+DqZ4yJxRGKnGXBpZFdvlTe3okxQgIhPpswrnnDuzepQkKr+y80xdT02AvAGn3N07d96bLiWzPlK4U/7EnVTMxn4jrPbmunFaIu6y6wmvcB1YOgNSESJTX3B7X9SbHjYxgEGTktuOrtyn6SDYSSBbpuHfmQj/aht74MRWLAkUVKhiKDIgJIx7IIowzT4kNCFWkVkAUmTfzA8hYfM6GW3O/dCsgN2qKPYubb/6N3nJ5ual253KzXb0pCWwgbkNpsBTtKIHjfByxs1nUOcdwiQT3O7e3iVO1hPEb4/5CvMsyRI7jrEgo8/LRZDSV3Tt51/jwY49gj3AuIeeSsR5sZ0MRw6PIWA7Wmx4JwO6SbvoKyYmeIMeHdf36/p217cl0fOXgE48/58IHgoFUVz5Z/Avb8PpT8Yklq1r037kj62CT50mJPA1yfeBjQBYJlSo1vxFEQSQ5QyVBeHwSDIq+CDcgP6LS7INbos4t6OIL3gOXOZ67dO097csgPZgA6WjAMj0uBfXjfhF0V0i3qQuwEyo6YPU1b2UMU3IYGAIxBRyq4whaEjFKFrB2Xt/Frb5BlMwGTicSijoop4EnP0TIO5WSAnCLeLTujFt5x2PeI6CX4rSX56jWKoLEE6B0nPBxff7u+PaqOOhwOtCz16IcGkJTqK/ZU0oZHEcoine8N4833HelXJ6qTPUVg/604AB7x3YEHKLFGBdVgDYlm2TzSWBOnGoL3OgbqrEp6lBg/4nA9c2i1p3fzQv6uKKVGoL7nuOHXmKpSmBBVuexNnJITt0o44g8ZzQWcvhdAE9KO0mxr4YFT+FAyKzlBCl8tOYLLRycqAczyfBkQZBqB/ouF4eXLNyIWmKhdam3lAvjo/7e8dEG2phbr0W90OPRZl3DggijxgNCBUKLeRg8gkSBMO9C0QED6EdhtP3cUncu21UoZugUqFsS02T4qaY7UHEC1zrhAQfdOEjcz0FvA0OOg6WGhj3wjUIk1Zv2qSEBC3Y4VJgq3iQcq+8syq8ruF/X3D13NJ+Md3kjA15xxMOJUiFW8HtE7aW5sBZ0u7N3+aSw7oqMPixfGs3i4DvG93zzYH5mMtFTwIH+8QqHjVVvcZc3lCRu1QhAnxiGxL8PGDRBmWInGuvZHI0t+AssGuutbMaT0i/jy8HMUJChy67SVqiJt8+U20jzD7aiTip7wN/op6wNJTTQ7OsGzUwCciYLW1rDr1A0swhN8nJLpd5gpgAKd22jRPaKhYfb0dgqhTRO1DJDCe2mNvbnDCWRLffqqmpgQeLypkvAJODKbmru380Pcur28uSVHk0xAP0RLMhcvi+c4USGT25Y+x0uRhZRHAWaVmiDc8LFs31eipwFCxBhLuuofy8udxyOjv0uGmuLmbrc+lTrk5yi8Hg7nsYiZ5gK3/pm61tenMWWoAy7zLc9ySkuXeGffIJ4dZfCv/rV6qseFBjfJqiJznMT+LeQCwVRf7MXzDWmvvMETFaOHKQDuSxQxLRT1xHSg3rQMuFeV0Wk85U1q9thvsL6C0s6FLAEfBqfBj51dQCa2T7GO1WfLTvh0b9TbMDbnJzX0BZZhCmPkX6Jf3vcKIzDDv9mH4TzhazDSv0o0JbJvbrX4sjUXY+3PsNJXmDfj/KKwrU+2fr9g6bucRHsG517Ky+7dBO3vsIrIAvuybeBDKbg075Dfh+VAT3XmmWvE/GgzBIRhhAJf6FDOuxF0CRhBRyfISxli/39eQ8L86c3UHE7DCAyQ+sE6WrORG1DCeazbIvrJCNgUj89XiD6zTteOZSYHk5EFEpkBd5n8QLojLcRJLruTqXtNCaJFEtT5PL9+EeFpCmCKJJ9za5UIZXn8jGVYn1XquTfOxMqBS2fIAhg5bHZnWiNbUphsLj/z4FTfwrY4gkaeTtx3R4OKdBrRUWqAhaLE4GCQu9leCmrq5qLIAm0C4knQQis//b6xArxgKduuH750O6lHXPNRq3c0xUNez2GJvLoID6ow6jlhjo8wPKP4dok3kKIHaDEYkdsBm7YBbiSETdF5ZglHMPsYVAs9pYO5Cb/o/HBN/32gd0ffbP8httC2SwP6Df6wWd3N07fpxPiqvW9fbWqX3lPzBwb490LvRj0XPT3Dl1/u4F7J+72K9f1RQ2Thjhf0d2Ihvr6womRnueeft17VnY//JqnxC6ZCBLhBdeML/rEbUY45DkzO7mA9btODs0evlPDacBcoss+dVCO7h/dSzzzMnffg1wcZwcDg1+A/5nMu0CnxsknwVJV0VSzYWAXUohLuZGG5BdVmWab0LKuATXBIoPcdLD6qwMVeKLU113Ip8yUuS0Iv91ibB13g2S9HcTktS3w9pn1K6AlNKFCT1LnmRFt3KXfecpQKpWfCpwoBGKy8NNKRfO+5m1eva9XdfnX/H7c23fgpsoQOQW4HPDqOVBCovBXH8ekAWdwOD5cr8PxD/C99/yC0ABDATr3b+RZlAKeXqc9c4Pigy6JSAIvc0JQCM8jGtdF+iKwCcD9NEZCgAhDGweK3V15eDSVTMQjZkoFq+errmsBm0cs3bD+CjpmCpReFbFdhn4HKtBJkx4VvvtPysErBsFaPjuLjckBvx7Riv+NX/vZOfxUd1a6/ui+/coNkUNnz964qqyQiw0sLDVlvykOxpMmJlrK8nSXYpmbTYwbZOjW/hGB/Acm1l9d8ePWjzCxO3ivRX4PNSjXp5CMYB7AGeX5FzrxUZs6GjZH/MLSSH2wvyufSgTstLgZ45TrtTZPAtZI3Y3zQxXaE2jjAKrx68F+NgOmfX/1gd3TehAQK4BooA8i5ku8hKVU7141/OrZWm32NVH52EBc4no4aebIq97/Nz6CH3zju7OyR5gE4yCpA6+cglElgtron/OQvVOt70ztxZ75/kEN4BxvHJo58sxjViAI+grjyg3CuFrAFYvNLl4En8A6SFEqDUE6ISOEIqGgH+6yTIrlA3T8Ng9Ye8hgvNgodRDL977P/1vdNW/EFm7T/uV75/CAvDi7+0hstmuQc91ABwfHv14efOCILzjqZgOC8dWvYtHdN/rmyejRG2AsqN79BXkfqqAjzUNIBXehguUCXskJ5KIL1I3GNij3AOBMTmhYlpVlpCj6ooR5XjwKZMQQwXKVB3u6spk0U8Cg33QbLpiBPKrgCrVcmMazy2amSo1UARRtyOlD2RYJjXPQSyma+QKwzWL/VC/xnole0mgQO2oWknOrx9MJU22QIr76wLlz5DwJwzQi6sRM9OAsvo/kR32tr/u6exQBt37hy2d31glHWv83gdnX2+DF7wNfJsaevQr/L+0+vxv63EOztUiQJVmQLgLBVRVJpakohcjKCSDwHL8Mlllf1BiJ110bsace1NPdVcilU75UnkagzJSxyZps9KbS6Ux5kwQoo8fvtz78pO2CnhzjQlxDVsv9yRPN2FgP12hwPWOxR7mNliu9Y5Oa/BNSGLZaX7eGC+QnDdSO6f4ntxPm0T602Jy3gfgPAzKmpJFbAOhBE20Xwd/IWOBF4cQW58s0ECjQjvm5ycbE+Fit0lfMpo+kFLAcOSeO3WZBZUZ5WLrmmqm2zukt/3oWCDpPD4camJpQxwHBfeQVw/e+4/SlAKk0XrKwkzP4/c2wwm+efSKnTPYW+h6YTcdi6dmHuyZF/Hp39EOuwWi3zWl7iMAJ2rS7NLyw761W4MyBk4UIzrsVsmU2ctbI0YBvPnP66dOZ+XiEqw41aJ69cepAELxCTGEyo/PxKMzHfrTSPAxj7SOy74SuSBwdbZemioJAh9zyGsyT+G3T4257Eni+H/WX+nqLdPDzbXWPRWHmwjylSsA0IbBVE8rtfBmo+cYLs7zuYOApUIfXW2990gfq4PI9+VbLdQ5nzrk6Z5y/rW+foyoxRN3H979Pf1/9KnMlcuunDehXCDr3VmZnBpslieku0CXaFdkJQSkb2kutTLu1psZam7ymsbRJr/G1Pmvply7p1lPvoH/XPx4+2NE/Gey4Gz6zFw01+wFkMn8FFkREF3mKvlY4+Gh9Ee4VjjqG3F8w32Da2TZTc+JDeF1typv1pkPdKjWA68/Gtag14+Pxa9yR97sGdAO0Qt2L+diJwdEHB+yh6aFLuTgRmsbUF3/sDPrzXxh3L8X1iVBBThWLqfWxPwDt3YYONQ80Cz4wcnjBJbOxX9FUpT32XlNnY2/5PG5jY+y3oW3TUyPU8g4NDvT3wfAnE+EQYPn2uNsvPO4mS3NunhbQt3ScJEh5M8ggGWDr9i9rAv/0u7Zr3keUpDdsEOnmK7eoWIiUBx4q9avel70prGcz+HMwA3s2tKP1P8nrXuMI4eRRVc5vn/4IZppCsGT6sp9rdGRBHgRZhFGhmaXBdeAoPKHBE5YSXkas2ymncykat8Vc21JvKMmQEycpkwcTVz8PTbaffM42Hh2oioe64xoY/Vjre/y60mBluu+k2P5sPgqfXUX7m3uyWOfcmOjANkHsnH7GMbw0C60vGprEhsLjdgGExlJnLKqoWinDIBSdKQhNjDBtNtkYrBthCoB8NVbnkmpPOkfTOcorCFMxkkmzLMGo8UNDOXcOq4IcWb5YnliOyCKWoVNm669NmJGmZzb2IC/bfvlnGulIWxj9amvtymu+Oiqsz8cAtvO5d023/pyXZbKv9aPWvzU2250e1GxOFGiOcUERCFU8WRK5dS+zotOali1uJgVaBj3zMy0ztnSQxdFRgI2JMySedrCvkNewudGrhx6WgTlfwOcU9ppwKvAizuT+5TvtnvAc15rF+FGmQF9r/SkvASizeBzFrqv/1WBc5z+5OPldsDN9LE6xGaZ1QlFdMNFY1VhtC/bcRE6E9TqLDXj2H/tuGHQ9+jQARvfO0x8oaI8+4yGRPvz52k2nar28eWcqEk7dafLktT3Y/c5H1GxGvXv3Sex58kErtHfb5cLu3dtfU0t+sKCqhQ8ma69BDuYifwayjoJlGmnWkIIEogirFDYzrqUvOtkkdSOblEkl43B/1OwZpPqusYxSZoucN8AzTFMfgzEbSIxO27fBdD331x+fi78qqyYGb42fOn6uX961uHAwUDJ0SSw6kiZi9FwyUu2bB3r/3Qa5gHHre1jUYqHxiVA03K5VmuRyIOs6ug4dR0813btmiCL3Y6RUaRHSwuJzhT2Hm4MA1hSiUHotIVmRZBoZZv2DQVEUx/qGFjvhZS9eijSrv+oZlnMFsk+jTv7Nzy03IxgdP7ayvLQwNlIZyqYDtuXVFFTHdY3xUr/txEMzDi7oxwObR90pNejogxuzsa9Watx65QfAi6E29U9gvyPTPEcjkrZVHuJ8mkRyMTOg+rZndB+wROxOl/KOomR/+5LfUZ/4wHnN73fndmj/PLA4WBwIp7rLAOq1SroxpGFDHeNdl/VktF7t8xpm+aDfJZPt4Tle54F1dtRp5ZzqKNm+HbeUhagmh2WVLORCscr0g6t3ZwRR9wTjkwbW1fk3D/be1/JFIpkU46WFtee5I6BzU2gPWm5eF8Cyi3pDmcgiCNuFZNElr2obGqg4mFJlGkiFLa0gSfJLoIs75ma3wftMNScb40P93V2ZVMpBmKGtOrlJJZ0qs0AHiDmMpx06YVCuwLieSRW3SBjlA89ayONX+p54C/U2n/966oGZDNaKmVEskm1uXpBF74AVopWdvUBeqwXefrpflQTRUzJmRs/dkt6fH5/ZZe8XlbYBMRaLVZ84mItamCOZpj4waVbiNui9KOqFYQk33T6z2fAOeoQ11CCHg2tYMA75B8xgB8s+TxqAZQ9Q7kvLXDDFsMBDBGAjlIRsih4BxkBMjH4aNNm9c3Ki2J1NWx50AB+QmEK26REr7aLqV6OK5cgljq0NTc2kqfZR3dxirvIZGrzsPE1lGcd4uT4+zz/FTa9o1uqsLAs3NqVwhKZhgNoLnG0Bg1RP7hsaLySEJ7nmdRpxF+9V0pfnJFJzbiKy1wa7+pJyrbIQJhnsvWVHKjs2K+B+IRiiHjj8kRBH6ePidYOlRJ9G0th7fNqwuvrP2bhPGIQ7QsvngZcBYzaXdjly6wbl+0/ybpRDOxafS4NZCK37b8fMtf26wcH0DyAksdDk6tYry03N8fRF5ul9tM4BkD4lY3mHsG3A1YxDR+1Mt03ebTdU2xoyh3uf8AQ/+5mg54neYXPIstUGfv+PRfFHWBRvSqd0AV/9BnUspIAFPZW+SaT+EObLTuYPJ5qjHkwrRzrRG1cneqNvmCNu3R1m07Sl/hxMCZ1WAzqObz2LzIpSNkC3v1O2AOCbOM4QK49f+szegojXwTZWlhafC2F8jjwLrfyTP5VPXr65PCF97Y9po7/1bXVq5HXKX/yZg5tCaz/jFqHdOVRvVuKYFhbxGM1TEEnFTkEkcDDa9HZ4Bu7MBWiT7awvpbEWO23jDOzGGaqm3EaDE5ihFWjsHvvWVTAZD5+Ly0JZlOPnHl7VfI8+6dUPH7/B5Rqyjx9mrW398c+mqQJO/wxXaXO//c1I9MrgtxjmCK79jH81tHUPmm9uXxwfjbA6KENXZJ5mfSmq0lSQtUzby7wFa/AetGf3TjBC22emqQ2iAbPgRMi832w3voNjOYPY7bgiNde0QtwgaTYIla29Stu+SQx6tPnEkBeGhHuj7w457Y26ibRyJTKeEGTBH1BkEnvkYp8Ic0ZVDD+vCInxyOWjmveBN4b0vr53NBp+feMVfvspEARFu60/nXv7bqIC69ExGIbjrW/fZXLQVSKoZPfb5/AAw2etG971rlqhfYifwm0uA1ac28bsN3CDsZC6mRss/1puQK31xDo3KOVzjB3Ey2zcU1u5wSShEcXNI16hFSudskrRBpVN4I0ZV88X3JhGH/Fc4IErbnWqubpaGj51XvO++imvvmN07K6+Ps39W1c8yv3797mNRuTKOc37KriUy87u2rmzr0S7+sgrpve8gXb6wL5nGBu47TZsW8v59zJBrF48ezN29NsHctjLON2O5mx3SGZyyGJ+XmvLgmk4FYWLeTBD3+CVvQioMRhPwOR12nOfuTXKWt2sAqyXk3hz38GqlG1yzPeWJ8AlnX/ZLZrv1e+0ABhrJevw4cO5vOq9fFKXbzl393lo9F+961O07R5zNv7QQw+xPj39yb9ket8Em/gw4xe1ZlkED0ZACS4iQovFL3SiHzqtL2MZRINzKEVlNld7WUoCNxvBqSRzBxabpIFRMobbISuWCaZBqyTDNqhccQxiDO9xYRWrvIo/23syGOSI26V79iylg55CImHi1g9bP+Q1gu/mt+0Kv1zH+FmMiBDwAPz7+6k3ZbNYC7iDYyexTKNWMhGCgHjHMBZDjwGGgD7dtybiHPkhwLauZo6O1DUJJp4lmOCChCQwjYIYKAZMh8nf98yZMz/n/nniF49NdHytU2fm3VJn1p2m2TJfvp0pDYNDpL6wgprYNFySwHu//H6Ag7qltO4lX9V9ElFKrWjrlKL7wOW/l9lGEX16o420SYiDRoLLoY3Ey7QCdmsbfQ6WMUPPPHOGfHjiF8EJ7gJr4z1Exp8lHwfNEj8KxmoAnBL13OsFxve8k7wjjbnxyclxDqfJ6MvxS8lgIZ8vDDp9RGufJ421//z1NX02+DrSmCiXnZxOcu3npIS/iMowp/+s6Q5gnpsoElrEDjStXe0xSGPVHH8G0fSOeKETqA7SAg60gmkxNUA6SUJHnWNWrUB985DzINXF3/jJZv+vfkjG7WdYrVUI07KGAEb1Wqk3m45H/ZbpViRUxkMKIKNUukQKnQw46HPZyShl2iUhhXaBahzHMC12oIAoZ/mHak6UhlY7/Hsy02VxWu3owpnB4u77vQO1jE9w51KJHFk9ev+4LQvYyFqKxrUeKjZGDCJz1w92cWrQp1a2j2bGvCnRGqlyAX+ikI2N9DcCESNzw50GVvxBgdyV6IvYvA+flxJxOhYxGIvDoAMR4Jx3Nl0CFnA65eFEgbQpUIpNZoklRa4po2m7switt3mhe2Dar9fbwH3LTZugrnwiFg5aXrcuiyhCIjJIzDdUG8MmDYPGMEMYQGAytG4Z7Batt3Fjex12VytJ/DEszh46dZdw/7lDM7Y/kSDCoI7fEdg1NiiLkbw30DshieRZufX33XefkU7fPhYNYtwH6M5stN5fL0o/ktIuqdQVmd/DMNLP8XdAF4vojqbajQXkpTW6bSXMAiwmSDhDi1vArK1j4iCzaxQ0hTjWfXYbM38vft9y04dRLptKhAJuXZVQERcpkMYOPPaD9duSl20XMIJUOrXOmTwuTPfnh3xRZSwmyJbNSXxovr/3yMQNjW13fCTXzOuCB792/4Gl/W5pykVLX4g6E3V3j997emz7yVQ2lwx5CV0XBmN+AsYccCFdP5LBIg/zD4s+TCRuAQYQLcvOWLaJq4f2wxlvNg3ah2SJQ2Mjfb3dhXQyYHtcqox6uB46BzCtDTU9tFe2BQQgk57A0kZNJksCUsIEA+ysHKFFQBblB7SqFB/HKysiG1XrSQurzfnHhwOSZ3VJiRwcp6cnB+ckKRsI6MQwYv2zJYk8y/Otr7W+JpiJhOj3S7k7Bxv+zOx1Gs4dP07H/blBItjWdLOvq+SJ1pR1O4R/iL+ACmgQ3fGxrnatGa2KyoMuczyH+ItIBCsmgoUVBKbHHhraZwoeomT/xW8Mbr4RVB+jwYFidzoRCZpuUP0CLshtXm9dmzdurxOwrU4Ep8HSAIwSlPHXvLpdv+fco25yaPJIjzxXHFX5J97S2C5q1TRdwjCpHY30ax7ua49Kmju2++yKx3d7s4TVrtGTpkiUXcBYsl6PJrzmnpAVrugwB7rAaH+B/D5gi4c/ntGJKHXE0EvrEiXxDAx5O5WusMChjKlOqNhhEm1wEmn2ObeDLH6T+5dZHTrDM91ddtaeZRFe/ZoIb3USdxa2rBf9M1Gwuk0afqvoLztvKNu3bcPEc2EXCYqjQX/XaJfBdVcEfOncdRR8NRtjY+ropIi5ZCidyOcSEXOoIIzUGq3PMmzKo/haHXzSl9AiQPMb0GX0ZNMTcxMR6C4RhwTCSdQx0Wq4CnhWjpe4Vced4Hblzvohz8srdNGhnxIrJoWwALIZWH8M0Ius8LLzfKfwhylKcPMjy83gTSfO33Li8k2Xjx7Zv2/b9Gh9sD+XKWq0UJ8tCOhoTAJvqjgs5Os1iU6ouNSuMnfMy0amM9mZhLicz7hxPk1luV4O7CukwSR1ihVYkOhUJijJhZ76uR0DrlLdZ2Sjfl+YF2TCESFEU+4S8GXSM3x5uiT5feKhE/J4fld+pvXnM3kMsxQ/RFMl/019pYK9dtd2lXCJZKA/6Q2nBheP9/gCIXdvfymjSoWR0UI1KGZEX7cds0yvCHxDIpwh6JLucssTuWrA5bLiQM2x5O1d7k0ODyd6DcHOSvgRjEdrNSmv1Ty+mpFMtJ7Cogv8Xt6yA5rOsApgJXIr2LxhdKC5F5g56DjSdbRiOIYOXLvjtFyySGtTKB0BksmAK0H1KmUcfSwnlU5GwqFgwLZ8XtOlomEy7O64MDvQzuaBqO0NctEu/HOm7wbJd/xbF/VmWO464ArOzgN23a7oFy+AOmMuECnPq/SEceFWOEHtHji1d75TGTLJwLD2W6+gcPyhV0jJeKNfuXCekQwDG2Dt2jUcOE8+gVRgXY3mmIn5uU6lmZuZ8GWas/IsskwAW49m8Eu6loyzRBswLy2lp6w0TVZuWowsUbN+Tc+gE/PtJcnhI0fEhuw6fV6nHQiFXOTGTjXPA2CgX07b+MY38S6R1SOu/d7aMPayNpbQy5tqF+bFJGUSbe9bQDxlhGc2tRvAxLLE2g2ThBGLMHXAXfROkRcu/rpbwQe7NJqGjYb9llZylay0fE0PgSRZG2t524VsjD3VJnCJQG/PdHr7aRHzgmf89tEDhUSs28Op2giQbMHy+zf1W279UdOw/IuVUrw7n/BFyZL3T/5K4YU2J46BIBZBL8dobUB3ISjQsMHCYG+UE+eQy4VWdKagG7k+z6KmyJwgiCuqRFhlAEGjw5XyQKnYk6Pp0ngkbFse0F9ZQmNkzNgAWPVfypH5A5s4cD9m2bFrFJTBrTOVhOK+WQobtobF68Zco8MX52XXys1exe0eHgnY22X9lpsNuaOkrR0Hik52bEAZmHs9U825edO8sFlJHb/TB33303wqNYfLDoRsr4+CeehgDNvnNnQXdFsSBeTn/A5m3KqK7dYWKXKAOXT+nN6ZMjx/9RVbPxdsAfcs/hzah843bwYGZeEFAyw2WiHY5phBtqyNFz63rkgCdWNe08Oy1zKbLBjt3b24sGN+bvvMtump8dFqpTw0yFLX+exGytpjgLPfh/f5aZupjejUk/7GhuJFzzLz8WEskslbTyc0tX7z7MIme7HJgPwKi4I/KR8k+0cAGGhzL2JUtr56/PF1GZJbQYY1muPnsYQEJ78IvGoFG2ypK7Ov7eMXMKsYVctMXDTRn+sYHpCxG3BkDdfcHYkFav8rsupIBedXfxOTCgIo9kxaL2ZRne4yrPhfJA5Y0QK0SJk39fi84/HbMXOAeQSlkkG/S0MW8QkUBnfg3OZtE9rLvZhXppAf6CFWjC7p7X16vW/kCyMnK6PnxDsj090xv1szondEDU3FX8Deh3tyjdVIPj927/Lkdg0fTNvm2MK2hplOm41tzRHG5RrkMIxJFpj18eZRASuo2ENU2YuJCrheUWA4NGdoqLZ3jlXVYWiLm0oavDx0pr+vK59JgSaHQ7QTPo8BI5MlWde6w3OWljCc0Y9/eRyEDnlzoAQzJUTeduDkHc1t52ZGlC0DcW8kM9sni92ThUwJRqVbyN7zlWYzLb6HjsPzz8M4LESK0r9LlcTQjavO/hbQ3xPQ3zhw19nmNspbZYXWTBDaWQwzmHp0lU1tupjtmg4mE4Vcoi/Zy6Ly/nxaE6PF3MY6DqckB/5usjKFoXp1I6Xhx2/rHi/d2GxeOuFOeOb6gWAPd5Tt5XsHBfdIvjHa1833hAKNsbM33a4S16CS6rr6Sdqjj+x9VssM3Li0/ZgLO34AfxL60kO9dUYAEJ7FSISOiKK0ImNJ8tAaj87hVgtp0RV6KTPmUQA8I6fu2KnMj+FfGpMyOLiAU62HH2/kJE5Wp1R8SNVffr49EtyHBJx9POHBuULM9HCEt+6Rjx5anw1yNjvXY0Wijv8CLonfDvx5jvovC/M4iznehSWuBlyaX2AciI5D0Jkn7UPKD0QsCKFFSjTbxwKYhOnm6HC1TOsQg36fiebwnMLWOjnZNeenzZMmnRrcTqap6qQyA06ajrLOgiixBGmlXcoYx/900vB49Nq26JwL+7teVi55fYrKreeWuErfqbJwSjgVJr+7lIv4VXoNqz6b50VjvNQANySKikvPNAStJ90wFYDAniMelk9KpKthC9BM8IhqPR5164TzXvSKcL2vm8nJXvsvfA95Gfi8i4vPxQHdlJygILB0wnMXaa6Ixu6whyWUWKLSs16eGaIMonvLA0iCuw5temzTvTTVZBXsPPAplmqiW2GwPI21Xg3krK9xt9eK0UwnnH5X41WvwhwxoopkuRpELOf10MuCwgKfSiby5Lzxmcbr3qRgPsIrPd3JdE7KVTXuoPeNv1UOBA4zjL32c+YTJmgupIZ50keXjS1Izo4zzv4HrCqdDjdbe9w+FMDYjI+WB3vy2TR4zrChoQkyQUfeR7fyaI8qjQ9PwoSsVCttXa7TKoctIQYG2/pJ2w1cfz34ASypRtadral02Tmo9x8ya+N7iw9rvb2+5NJ+nQAd6AdPwPMf/phkunixmCSuJhHDnvNU3z/xCRZWSCS0giR0V7SH7pMNFgMPr10lM5jy5u5mvjfMc2heElmOagVQbJvZwfwMmmYmWPA5FT7rWztgVnefdnKom7MVMEhgfmjQHr9B3zsni7Nefswq+2ShxCu+sjXGe2dl1+pFXRmolrnTOBhkOy10yQ/3Xc5RZc5d7ntY7qIT9o7L+B8JZrVra7/gctDWPTQXOFKLczRNtWdbo5dH85oqSyxbtaKIAsM87cje7l1z26ebtPl2eNHa67omOUW7IKTFHmflU8Eg0KM0K1rcnHqBMdtyJm3HcZkVbJGLxgExRDGl0AyuhrCj2oIg8ZYukhOBchCLepcmw7UpyXUDQLqB3l7TlyUaNykph2/0yqEQvh77IwMjIAEKNxs/adB3oWXDGvnRyL3jWBMEFc5SYZw/3zW4LIUllibl/6U6fVcbg9vgTBZANk200JzT6C5JC9mEm+Pmm/0wpjAAAsUuKxLN5HoW6XIMtqGH6kSPJyeobysPlnp9GT+Iyu9jharlDqyubU3LOSGuzKZklOTfnIqq5Gn4E3D53+vHDijSDdFYJjs9u192nTjjVaKZNCbZTE42jsK145UJrnmwu7t9bQ+2gkWfF3qXTvX07D1M+1ns6e2mfyfmub03OSVqq9gfHWI6EVgbZvpbR5eb3oBMFFyvEqSQhVTUxZF5h4WlkAImWjkjCTxdY7dCcRxhzkg+CjAiLLP12XQpq4IuvvhNy02jPJj3ZewUSCitgoAClr9cu1Y6afBN10hnPRbKQKCdORDx17OyZY5cgVlw8lZdCQbOBmOVAcmZK2fO4ex4OrsUFjOxUI6buckOcxVXX+6+Fhv3f6xMXqEH//R9blvMnDLseCYZkh09SMKvr2K6vub6zpJTltc6c01ay7M5rRWhUeIXzH5tuW25qVS7t9Xq2fYOFxProV5ES0VoTJTlvQwcayOQOkWLTuLLBig54PrsZ3kNk9bPi8P+XFYSEglsfulLnEYeeJnWU/WMu+gZkXyAyOCxXYRrvRdjURsciPjACH6FpldsncMffMqDBff2QJA4fZ5dE/FPyQ/RIbTU3HEd3Nkfo2XpC4gAoybCRcrIsIjwKhIRXYmxijie5w4hp8iG43fzaHZmarLYkwXnqynoEH9IZgHgjRVrDOw6w2gHnCIlj1MO0tkHoZ+Mc/nKBO64ePDZtXrnpzqOa+OkWq/hD5C4HOElyVwNgl/WptIujF3pNC2tFGdqpiTxwQcaeRfFGEQ2g3EraoqiGbXiQVOmJwPkB5jwPq+EhUEtffo+cSJtYmx6ezLiGhLiOYIlr48nXcFu9VF3IhzyGoBeZL9pKzLGMt3LTVZs00+jmYY3FE66Hz3NZHj/msRychpKNmOqQrhrV4UBBdSw5qwKK5geBl9M8Sm/QmTLVpWfBfG38Ld4O/SLheDmMbmO2qMlkWCxDxSrBsCKRufBPFFdQ7QMWOysGliFseD4Q6B6NN3Ac7v3790+MzpcHoxFgn6vRBd01BxAG+X8Fs08OT8Ofmr/2FEsRontxhtTjgWf12P21QKr/vC0635qu7jQ/ZPd6ovJm7hyk/fHeE7y3kgHTMymnRGb0uiAVWEcyPdJV6T/UXfy1wo74X5U7Q52ifBCxuIAG75Mj5eOX3qCSiIGwyd7fWyNHvoK/g4+jFJ0pWWc2XNDIWg+hPEcoolegkF+MDaH2huKILw73ZOjW6D4ak6maRKz3HmnVAlYDHPSII9vugxfsjuQzrh8PN2aR0onw/nNr2P4EABCbzDjMjmFI7NSIrBxHKa85QYSwN8hnwB98dMVlXQvPgcZu9FS1pdj61CRtb6vjT/A6rMtWuaP1vqT6dK4bCTP90+X+rOJPvJbM6W+zKhFyK50qZTO9pI+Jz8/QT6NLwNfBbYKOoNcLK/l7I9E2StN1dIFTojszOYHnL53U73wdKrCUzn4/AMC7sIFk24w6Lcl0vrX1r8G8G1El4TWLwTLEi5exKplO/mUp0gQv518GnnAkkabIQTzgMPH18tYyZJ9qLBRvLqxprZaCdAUaZ7RKkqKa+8NHToVkco5LTqSzn42mBU9JOjXtX2aTh6t13VPoigSO5D3eKI69gbCCsBylnP/NjdBvg4Mfbw5YpuEllXQ1ZGXKPDDdNhPgKZIEjpIM3TSMi12lmieuoAKZs7KZFNZus1JwNlux1zfd8cJ2WxszWOyA+kQ23mnvQEP1txBubM1D/uTYbvv4IyzC88zz9i+9Q16Wt9mf+k43bs2iD+z9hMUQZlmspPPX+1kBMJ01RJcC08N72XlBtVynEKvBHayhBmpSuvuGDv6TkjqV7uwduCtPeVRN08Sb9W81fEyQHCOCzR3uXmMy7jfrfk4OlbAJrh/J8+ivegwutS8lcfA4HhZknnpInKpiupSLto+Irmx6mov2IH5eMLrQKFltgOAx9AoEDpqmbqT0dm3D6F9h/ddd2A/vO3ePTtZdGxmqjlco5Eef2owbJu+TMq/KcVTr3nr16xLKonrC7DASZoZ1rmM3V5xDv5gfUVTNeMU7fhMelDG3fpLTrvkSjn/1nyTvNxZvSRHkorbVuNanzKdLZkBXW80Bs1SdmI5VY2kvIQ0cC+++tW97xsAbPlcplqboxjpenzHsQYv/kCgS5oaU35QVIovWz/DougPllytr7sO6iL+gaKnC2ctELCzDuoHd9yBG/gslh58kHL69639A/lr4gU8Md509fekbY8MtA3R7BplgDrNGPkY1ma7UYB3htl4mZ1FS8sfD5e6vesbhDrLg9Z38nDjztaG1H0y4lPE6c7mA4X8+zwa7+KvXLlyO8cpBkwFOLxCiGkoHHf7lSuXwEmJMcJfghvAIvzY5eXIlcuX4djr4mSBPifInMtL8O2Xr9wm4LgsiJcuX7kTXBurnf4szLMfohKaRDvpTgJ+aPi2idEY3SuFLETCYGsxs7UvUMC+MD800F3wenjqDQsVujtmZylUe1UtW3cbWN+5plNJmpYwkDqxHToboqzCB2AZSR1W+40S0AGSHEskB0ofuPOGOfCLWHeHS2XXS/z8gid5+u375k3ChXFfcdpzZcDWvVHy5NtJ7E5frPWHOFKceWLIkJtxXCjgwOD07HVnD+w5FMXYF+iJ80vynreFkvdfb0d2BwTSQ15pug7mju677obc8z/GntZ3l2/OtMKE0/13ZeaduP7af5EK+RTwrc85dSneOcwrYSzzfWCIuIUFmHCRrScXAGfxC8vO7XUkA7qSuYudlCATIlss52HVK8CcVaeumUq5/YJmYcu/8kmWqVQ7NTBsh8KQ6FSfz84M12gq32OwSDsPlmgP3kmrz+sb6ditWyqxQvOAtQmg02RJrb2vUKazzcf67kohwLYl4IhPnDKU7du3i56zS2XtVREw7iVV9p4RLh2txAKpUDBpSv1eOZpLAFMQeG8uGO4qHZCAwCse1XYSuo15c2AY+44p3r5MJgAsb/Z1R08lo6lQMhkKWHwlUNt1KGZrhhcYMeePjfb3zUqtv2v9A63cBR1Orv2ci+Mvomm0iI6j/94MAPhAo1iRD9HN6gAD8xkscEI7FTWCFFmTlTOI1zmdRl80RHSNnEAS0nlJXzVUwiKq8NbBRRemoZh2SCvSHHWe1S7+rz+83AzPbDu2smfX3Oy2xZnFqcl8PpfPZCpZVvLnq9CBWDcJRWyzOtyNIhIalN1USVIvVztLdzvFSLm0ZLPAILbW073VCo2mwHPNz16YIuLksRvPqvxb3iIs7toRFFTTFgR/9dKZeb58fSn+qjRXXviQjQU9HMjkPbRIKZkn1i3yVyPBVncxU80UeIMDvPrmIME3rg4SoXT+lCkSfffO7S5B5BRPYqnGFzX+uMsYSpXFdJeR1VysdKmw+26NH+1Otb6bzPkDSUXOh3isTorM/jzFjZODIMcgyqJKc7BdqwduU6R7anAnEC8IDA3TCcALu12uTCoWcQVdwVqWbshSxJX17Y/ywpbtITZdwIfHi70jk4Xe0daas1PETeDXW0Mjfd0TY8XCJP5/esbp1ca/AASSnP0i/qarMdLX1Rh1dOwxEidFQEMlNErXJCnAilEdXHzEIjDOrKrZ3zGUPBv/TdkEjIYGchkvoCmcZNkEGvNkCGQdO9GUHQMprAuBysYWq/UaRyM11Uo/rogSTg/2ZzI9yZRyst43cuNgzpXJFJOp85W5Pywbh8KGKnKHDz4SLUw23Xw5h7/oHhsNYlKvi817ac5B4cfG2Ottdy9P7psWlYn03t1J+7HXtv7AaF5YSSj44ASrhf4pvhV/AY2gO5tqkm6DinlWR+ZiezIBb8FsqQGPBf5CpzLK46yVpYgshFgx1QvfF9xyHyumKg/0dMWjfp+uoRE8Iq1vuuGQaSc5ZGDwi3Qnw/bOZ9R9iHTFi0Nt2BYN9OQfDc0APMSCS4mKCUOQPKLNSZq1uzo0B5p7zWl7nzzQK6vkKva6DI8YHfCIhPeEy3Ps7C+wpbo3n6V4a//a8/xF8rtoAR1F73FM/DbES3TfL1o6pMiiQg2CrEryCaRpTq7XvciqidqHqsq8KRhul6vjTSPNqV/1LnQDA/YmTIT6C7/HctM6dHDXzm1TzQkzmOqyU8PpaVb2T2FWe+vHjgUZYvHWDlrLFza2N9iK19pLGtqBjTL4aifntV7FB8LoId/5fn1/6NTTAzsKB/82UNpV+4e/5sIAyIiWiXQlTkyXh8W0x+DkRkPmDE9aHC7vOiAtzXq0xtVvKYMXi41tB4cPjlQkCccZ/hLEbY3lR47uep8Js78N3o7mI4r8A4xFj9vwJ9XW19Wk34DRYWAOqzt31QXsG0j7k6888dz+0Ux/4tN/gDr+4Rj4h240jrbT3IaKVakrTeM8wMGBYQv8RaQiTlC5Ey6F0PoRJ7dBCSuoKc2tgZ42JjAtW9/e2F4t9/UUcrT40fYaLllE3YBXN/bCchbP/ApTXhC5LRso16mH3bDf2dtf52/8zq1zsjxz9oUM9su2b79ejBwMqd7E4imfgCVPEGeZnRbvOj/cf8+YJI39kmnevh2/GS/Etx3Jjh4PVsYr4bTuGGUWO1r78VodP40/j+ro9CdCQTeHUadwLENXyCO24wQMxU2d/UavKdvIbroL/l14wduWm0o211uoedkeqWDhNs1qtl+Nw806QYytSIRxBm+9hrtlvZALeiXJLVq8JGRqe1132GRokA/ssePx9O6QVEoHEwFeV4sBciMYrts9Li4TzrgFwquh0jb3IijQH/2hsq2gKlJJNxPlWCASE3zbRPyI4v7/oJb6QSCrnyJvBL4uftTD0ecbJEEmcY2Gy5y4ev51MCx4qtmcwpwo6Jh7C3kLmIaXqLqAg/AfeGE1zJHrrqMone33SaL4h+T4r6/P9gED2wPUPbptG33uB/gn+E/I55BJoxMyAFUKTWwEDgmZ2CTMFbXhHy03Y3McPxrtDqs7lnwWFoZKfsA0nyuFY14p1xUMYaEx4hcIa9MleO+98N4GrYtXKetkb53GtBkVNg3i2M04zKWeiO0bGSaWb2mHGiYvjfZ6ZmZIKNiVk0z2XjfhH+Ovs/qRnmbB5+FY3M2L8TyNqACVOg6v3dSPulSBR37s553IJKWQ7bVMJZYLA9KBE8M7hLLmu2HMNscPlTltlkxPlBQhFh+b442btguKN1+m+9ESHf2YfALpm/ejDaClgIdW9nvbRd/OtP3CwkAhzikDfh/RB3KpXgknfekci5XgMgmSwd8kVoJ/XawEj/5vBEucvaxIF/4OyO//UJyMfOzXxMkexmv4B+QroB8FFGOc+eNtTYz8d3pwdvlj6WQP22q7hAssWOmEnenG9RQmbT7LznxSzQtZYsSwzCnqgIzNyus3zsRLZgWviQEO3y0kZEx8WSLhsDlUXD/VFYGXTHZX02ultePkS8DUFz/GsW3AHKfuYqsnadw3zJYkDDnn2rFgeq6pboSGl5ebGkaagnzYx204A7R5f/2rky7g7zRH6QXpSC0g9H/uc8kCxx3hdFNXBP0XH+SUjf0G3gcaJX4UPmTgBTZLcfb2Wl+purFFygQNl4Bh0NKJami6unlvFB5LLn45k5D4q3/JamGGwXI8TD4JA5FA8sdiYZ2QgWLOkpz1OqjOvn+BrZr356g2iFKgXOceVlqrrRs5Ed/OCy4+HAY2hC9xpPU5/z6leOfNt333NhfbuoojbxUU+oUU/NXzEv/T/Bs9S1//PRhujq2PnwAMFUC90Ab1E6W+bMhNuIH2ZkVbl5fSpuQ6pTab18ILNB0O/wM5sS/2ve+2Ie2+94ZUFh/47dNjiSRp1F8f8hzfp8TuDWfS4XtjSuuexhcbjS8Sc+cRbLzqcvfAAyt2ZPduFiM4eJn3v/m+mb7HkpKUfKxv5vav/0k6Df+QU99GroKcumA89M3jUVvfCCvg924twXNyfzjmve24S1rBf8cJBg75RUXg/g6vaN6Hftur95WWlsjCUqkPxogjx0VDwJzgkq++m2P5vT/5Y/LHX8ENusclfP5XybsZSxoFeeVz4RC8EfVNlaRd8ScwmEMHriVprUgmHQC5pJhsUjZXKDuCC/iT6/BOqinkPp4jiQRAZatVs7yJxM0NPN1otD79jNLAnMZFVfwRLQIHuKHgP/szgO9na1XMKcoIB1hggfvTz6RS8G8NfYdTlVGFrpckPByo3HfW93B4H4q9oA6/+KYuXPXabVx6Gw3QV3Z0dWPNWos8C3/Fj/oJvDfdbiPZj52vBhlaLy7pbiPc9PpGSVQIDHbA6AyVa07esjKGSSvR+t3WRzksZm97DO+deiNgVw7vAA8UpiLC+p4DuduPXL4g8YLyUGrO63W5b349d7zQq9PL7tbzMLl4RUyffOCGpQ8RzgwC3KDAFabMdfsl/tEHJUVWbo81PR5C5HzvIVqtzPoB8+D3gGeJH80Q3NZ/+l0mnS+uCVhShhF5SdwoA0p2qoCcIIBN72kKR6aW7ggDpxm8IZLadpjXStoeS2jmgGw/avhbbw8Yj2I+lGsK1h64RHond11alUg0dHdc3T/qkSUlgKUdtSn5icdZgdAT8lRth4QDisRyqty6zLtRDU2DDgJx7rEQtzGuVdoaFrfctHcPW+/DAIZdtrOVjYK5cq6S78dpi4FjcsJ69kO28ajx6FK1ugR/jGtet75HXMHedKonrmJpoGoA4I/3pKCdN2Ntou9S34SGb25c8/JbP5WNoUp5yFBePlqXuZ8qxtCGzJ9F+0DmXZv1slrYyFq290ELsEob5xtpNm3lUh+qd3bpXE+NrqNWOkTkfqf96kL/HRI2JVWTNe6lUp+CyYDX9NMRcW4gnDrQCy5B6ZNeyquypkomFl7S09Uj4EdZd5TQ6OM1ml9TWfzLNStmzwx4Je1LX2Kbj1zCgu/wQlaadREieAGrYclbf8OEJUnVarsmogz91cDWHqU6xvBsbesOHZRE1zaF5Nl69Y3vhqLlfh3z4VAaxhfhzk7qcai9ZzV94Or0geturx/TwH7UunpDhUFdJCI0q4G9HgKTW8NCLirJrcdkKZoTsMaBx/J4Md2dDG7UBwshYZtr5mwXebcdSYyfEgy/e3rX0HRGAuLHCVrZQzfzPX2a1pdYhjJ137GuUu5Tn8qVuo7dN6UYFi08OX2a3uMpawJHa4YyyjHj1O6L28N0nSP6GU0T/HocPYHpOsefOesc2XP83b/pc/zd68+N4h3oecBlCZoTCjNwAao7T98RH2qXuGO0OxFLU+zjtTa+n6e+8QUOMGEkVmT8/EDW9sXS+4MuIK2bv+AEf25+UDG29dyxPygSVZdk6ZrvOPk/9/0qBF3AO0iFffYIzarTRBN0nmNfpUO3mka7h2upRCSU5kEE+BoRsIgO/AvQY+FXXcTdQxnbG0sfsAFIiCEz1kuLe8J+MDLai155e26Aye2AreCgy7DnuzQX1suZFz6N/n/2XWw0z9qHPw3cZQ868DGjjX3p0ig/CIgXCRIwohlMe7Gz+MmPBCRcvuYqcYJ4EyPF7nw2YCsS2oP30CCebxOJKtcKm77Vpb0peKBdduxwDnED9xVJG/jVKg2+XqObTd8XTwPtd4d74gKWsEKsVKzPTSyiwCsh3hOJpdOxSE9MxKaFieJWBcXtVgTVrbAyCwmaSSNh96SjdkoklgfHuKjcTCUyPZKZaspRLoY9VqaWsTxkbFrypM9d+pwty/bnLp1zeXVDdkt+sSl4rM5etjUmt1n0CroKaTIORP2+64kikgUZixpWVFFZBTDHMRVHvvUVhDbN1agrSFXD6tIrHrnrzlvO3nTy0MGFHduna5WeQiZl+7yuDoGOc9S8OpH8dXNA1xt0dvxnoZNCnlrn9e/JqNGpynKIedA+5sMq1GBv+kaYaCf/2P4Snrxlb6QgmRaOtwswm9mIbLhlnxAUebMtcBO4bF8gmk3HRG/ftee9YiyNM7WeKE+I7mcX/EAq+GgxHMlkouFfvtATjuJvRzJE4BQCToyIveVkIt0jeZLlXhFeu2wY1ozVvtSgw5VoX7EyrdFR+h0ztEAlMdKgTyVGEqxsSLdTo+n1S3E2zPH1a9RggwXkv8i+jyaDqijSDNKvHwRyLNCCSvq9NEH4seks/uXvpsn9Bmeu+f4a/M+/+vXW77fp/hWvYN5mUAovkjLwts17EnSxyIXPMSlkPdrLKMq/FSqc9AWiyvwYzz3Eu3t4Uh5KqS5v6z7RDHIcceEPS2kNO5z4fxK89rfkL1CKxrAoLuUqG6BhY00V2/afqUwq/363y62LnoLl8pRylks3XTzRP/jBQABfssjHVZ+iZ03Na7gkMwcqbmg8MbBieXXqTlHf2k/JA2BTj8PnRSgm87OvX6TLnOo0z9iD8xlmStorji2pTItW3dja5Cc3kEw7G9A2MhsTo7NAWRLv6k3OlQ+doYVKuuqSsUsmvH9qh+HlzE98WTYFXhleyv2/nX0JnBxVmXi/V6/uo7uqu6r6mL5npufsnqu75z6TTCZ3QiYJEMgJJAQEwylHOETkFoggCAvIoQgLKocgKyvitbCiK3911ZWV3UVF/bO6Lngg0/P/XlV1T08IIfwnP5iuqjfV3/ve977rfUdwF5bzAQapVijh904MjODSJkVsShT9w6FIqT0bAqtEFKKmguLfnfIDcTEaG2dokpGfm5xKLzV0Rmgn5rFGYPDcdoPlZRRMFOhxgqBncoMc9kd7go1/V46Gm5IZ9s+cVeUvf2ESwF+mfDeDXb53aT+1y4OO88NN/0zinjr9k9ZI9Layl17sBlpX4/Jq9UmDC2nJXpeDXF2NucVGt6sHU6pOogS2vcR211vt8ua3kcoSPzOzpmM8I0jBMctkMEuLAxvR9WyjdMG4HeuSE+meZRLG0u4lfo5jlK3XajhdOJF8o3RqQdqdABYghWwRIyW23GAzAaMn72/tjffkdm42u7cV48m4n0c8s2qlEu9NZZqsrIz0xjxRA2pHfzCKn0VIyMe6ImBn8axMa5ETKeIHmdwWaOgS7JkeoEmlNE1oLUfC7JwRYkM9MwFU+acSKAWmxdFyN4pfiWF9MBzwh4JStHM0F9ZEVploVBSGhqEx1F8TjTQk/Lad6A2ZiqhbQbvR6W0zPz//hiMHJn1rYJ1WLBkJwzqVuSznaimuMdRXDYBcoFQ/qHuhRPVsgLE8q9TFPHKsPb/DpE8ap6X+LRxN+i1/QC9vymSJNBLAvN7XMRtlRIRY0xrcACgC5BJRxcLo+Hgh2+FHMdMKmbGsvDsmnF0CCyMOz7Ed1YJC59TK7geWxng0IodTkWXrJjdH9WIjq4o4GltKE3XBilcMPtDUGEtNornGtvbt18ofPjkJdLl8Po9/jF7ybfRth/keP9uXpv6inlr7mpJ7XOd2SXFDm2p11fpABBVLzgFnyS2G4GpvTivDcq3LkUOXxT7GqUrW7NAmuq7JBqu5aXlTIKAtP/f05sYYKhSCvDbcKutBYVQB28DmwruXKUyAAfu8sYSFdWfMHjt+XIDnwtfvKQw+aCeZGU7h7s6GgojLLs9MDDWMb0t1ZThULCYag8niNBCPvoGYSpjpHgnkCic9rmCFl6n1cmUvqyh7y+3FQiSVDR9/bik/Vfn6QIPMzw6lI9POfm2b/wPTj7/ju8F3L+Dl9hv3raZ04Ihcehxv07k5UTBODKZTqDXXXM09gmm75UTc3QzXFI3l5qrYr/OGegjVsFNhAfDmqAF+1//tuMCznsngoJz3OB98XRHlnHVxcu+8YNDmoaTAciIiiDERZzHBXp0XAA+iPx5JSsG4prOb442YI220EDcRQdFU4vmVg5v6w7mZj7MJziJsaQvPnSUhA4wOxKViMa1Ry3UnkmL3pITUPYgfDRcaV7TahAiSprI4McoaidkkG0I79iiIyM0NF3EpHoeQxag8mxNg+foTrZHIxvMftHJtcc0fTMaS3Z/qTGo0HSIQwliTzfZkQ7a3s7RqSdif5BBmdt1NeJaMgVrI49P3s00pO2xKrBLLYHlJN3mBJ939woEx22w0Y0jUeJlxzqXRp8/k1TZ5eFU7rZ/HBLBns1E/xwGQRQdAFo1X7WU3F4n00oZuDmHmvPgZrxieu4oO3qmf1KSrRAO96iqtOTWQF1jtAqd1zgzdiI1a1xmqKhfccN29BHNgxzKNBOVBm2lgc5LImSLHgRDJskw4J4A2zFkiz9Oa8FwYi2s1xh2B4a+4yk8RBh4iploEwN5SdvUKhrJUf1HC0SABVtcN7FlgVqmMSIL4CcyJ0kSS/BbIgFwI6pLeHCs06HFewVyaYMkfkDJtcb3Bb8K3GeYVIj9SlhM9MIJjAhNJ9pfJlkJYMRqyDVyUFARYnWCIxfBusyUpBy3NLq0OgCbHDJaUeE8Dlf5ujaXfk2HM+ZoA5w0y9ec5ERxpwBPHV8tne/EAZZrh7Yh7luRMs7KzsnOfxUmWQTg2dN4WTdtyXojlmKAlccHBxgC6C0UUw+bRcR8OagCOsfF8m2gqsc/fqBOMtOAxy3K0kLtPmx/D3bDuXQBDTqfrnqm2iChTG6S3ttS1YD1Gpx8YnUKTlgWWGOsuNmm0BIF/EksiF6zXCSvIWm7nqIQUwxKQNL4jh65T/CaNhjp7LSCe0GrcvLpuf5CwnOlX1OyMUrm3cq9loRPRicpMVnVxNAE4GgEcFQC+RhVR/lJON9VcnW5enENZGuty1mzedVLBKLDV/w7dFUyXLSLjRoZjg+fMGlQ5prXC8ew5QZY3HYTFIoDQXdbfDDB4GydhjMxSpG24wOQETGNGaVNc84INBuugLm4iwcHdqIO7DoAto6Aq74MN4MTI09/BuphHTyi4y5pr1iiGdoxLWLCCPIOk0Z05jWJTX39BhAlIPDEvXmcQdrWLG8DKVi6IA6Tymcq9FD0uMoP711mSsvZsGMjVaIriaxpgaqP7GNUIx06XHbyVykfC2uGIDw3XCOvzaONDwfRIwCO990bo3LHvpk+8QHqA55l1Lmm+N5YPQ7x0jlrlLcD7vG8G5tijHEKzqMc6lGB53T5kGap5XjVpQ9eqjp4vDIM6xx1CyYuXiHAGTyIXrjXcAbCQDpH/qLYuqihoi8j7i5V7vrho5TRBXLwB8HMe4dN1HAXe8EPfEMyxldoirGe2+rKm19Wa55wp9FXD3qrN8npcNpu1QX+lh5sWGdZVjiP/OXcVRiSrcCJHbFDv0r2SzvHTU7vWgIWiaVvPDX8oeuW3plfd29bK36fpDPoGTsGODjZtmonAbxRiiKSNrdo+42cNJj0gabIhLWW2WWc/NDJ5fWNW8OAeA/r7oW8E4LapTVPtI1TyjaGFXn3lutZ+rn3vhjHBnKp7O0NPZU9UdWD0ha2oL35hZMl4wzDBoL/ynLx+99JJntOl3jSPkU1gToopshyaG7uDkK8HVFniEVmLxMipMaVR4NxKL4NpxmD9K09cNaZJhAlRjhXp6LXb8vbc3Vci7PKbFMD/Zd9wFe9e9LLt6831Fasp8Amn/ZIDtZOp2+yGGbmOw95cHjunyM0TtEoY8xo+73B4n9i9TuEFfz3iW9t4/CkH83OvUsw3b1p+GMwPiu/CPMX7AtwWxbsX4oA4Hz3zqR0uLgBb9AjH3eJVSwiMOZggSWiA98oPtla+W8O75hd4ecNh0U4w/vbNEoe/rFO8V95ZW/lzZC9yES/K2nvhvTXdIOLdB2hIeY3PD1I5FPD0D9s8WlafoTFHdHITohnQ0HlHz/UPgh7EgowW0EOWddPRsn90o2XV+O3zAHcR4E5TuJHXTa8dva8gT4+iqvpDOsY2c+q6InMUgv2u1pJijmbQX8mq0bDROnRkGc+0NEX8zSzTkKjJ068DvBMAb5NG8QzK+NFiuWpTgwrocaCLyy3ozg8gYlF+bUDh1A3ldGbYVtCp2Q58tCjvboykbKN9kGmIMWyz39FZf+7oT9RfY7E1/ck5xR9lF6xPt3WuqzeZlSBWqW+U4ZhPrKDM3gQ1kc90jEnoYU9nQiQIqjUGGPCKT+os5gQhWp7pE1BVZ0MR5zs134LOZnvWbLsT6ZTLcxSZaecrZyuz0nh7Br6FNVlNEFZ8Ar6bquIyg97wvlIoLS9HBYHjjE+ucL6Y5YnOejmOEzBPujdoXERcqepALqOkoQqOiyrBhJw1cdWeh9DD0lgHfKcCOw++c/lBhhoyKq6EKqaj5wh9M+WoxiPOuHk1yGWGkhtBSKjqEN4cddcWcGudciENL0SvuY0dqllWv0O/JTIiWGQOLhcEjXXR2j4uwfw32QhMWRJkOaqs49U3G5yD0+UlAQmm5fp+6P7/HsxxEmzJQjquUN/PkefJHgn174sE5lfvuzbfOAKePvn+6zY+/zLMKevstTzFo6/mDAFydJOw2LTbghNTFYTeq86oNh9vazraDXpa0C2WSMlWCc04WVqVWyq3aq1FkG6EYwY6/OhUtI/e/nLlKaE9LbGcxaqCuOragaEE2Db6vieKOEkEsFr1ckFGDG8aMpKTUQ3RdhMNGclpFYjE7mEd7K3AVTNxPycQRuaZ8NUn0DklwF64B+TMEjonSv9sJqdTJcvZY3qId5w4jpjRA14dcK7aib0WNOJITzqvAsroAZBS3w/KUmOCY5hEo0SLgxDCiqZAbYrm0RZJpfZu0lQ/N8kpKrvyuSTYspwqtYw28wgLX7fxhUprn0jYvlalErmTIFryNiigBpRimFQ6hImGGwnxi8LOJzXtu1v9hDRijeBQOsXRlAohuLBew956pR29skp/bkhQj7XgXuS5+o3gbQMY7fSbH3PsWLg1bsMyzUitSRmMHSAMWIriE/sCImIS1+3taRQFlbU4Vkq3C5WnKk/ZNl09rXMAcyBRI8VWrXJr5Va4GfAjuVA2eBGM4sAJV4cZXmaxwGX39owFwHDVh7tFJIV0FUnZGHIc+dEkLKu7XqPOeq2ndn0T5VejPF0DPVA9/faMcjodkskzVYrrc3WCJOpxjRlHPdDdkjIwX/QlWkyYAd2VoetE2I6eNsVdp4MbTInmnnAS8HEWSeaGg+6CKW09HSIsmK0KsEa19cZ3I1rHG4YEhcprFdB8sjHLWTOG9U/f0BxjVI5nGJ5TmVjzDdN+lnFWz4pleZRCDUIIbHY2cFUlUiMC4CcFWMs1sJYbHJ/dCZtaEKa2kCMN/ChdZSp0SbG9uIuaVsfcqrzF24l5tuiczNG+mLDArmcpXSqj05gMUUmgT0a/kC3dFOAiIwqIBQP8CyGMVQbtWTvVxIt0FBsNmkIlLZjBKOuM45um1u5BnGsTc8CjvnCchc8a6pDnvsC2TSlNE3j9YAdOU3u5Z8rvb0imIyoPlMBICnPvGM8SFpRd1LRiYr2fgzGBQiomSbFUIUBH+ddPrGgCHYdhWFgtUCYZNHZvZ+WVTLNImnO8WeqJUP6bAJvqHsemOh7wNbvKw1dvTz16slSr9aypct0BRKausA21OBxTiq8LprCbcy5/gDc5iNXRFsIQAA9te44QDnOyuTOqglWYaBwOynBNyPM7eIPf8XztqaYtPHtuGzerDhLSkJXlhnaOy4ZlPAfcWUJfmgYWbemsgHuD4lWVP1wlmkYSC6xugehZ/pQkPbW8bgBO1T9e9pj6uDJNuJa0PHeBHCtzYoct4ysdXyea/wO+xDnPbwT8ZOOWhqu2AZ1x2jMwnYhHyvxkxGYz1BtcRn5ZAIHYlEdbWhslxVANhCuvsIKqCeTgzej7N92Yzi2fwbcpOiaRjadsWxMxQ4o4dyYrwC5ZgVb8Ea049t77KAzD87/HN+Fvg665EmBYUm5PYEfPt50kBbc5c33v7lpXleo6LcS3MKFqPipooJmcF2eFPlQctpGCRGZ6fKxVYQXF35O0U8TYvdrfKpxytt6y/phLudAZg0U5smXdkuPHC2F/mGfHUWSqWRcCIRN9r6UN9Un5ttHxVkXR7HQhbonK0s088h84vWfJfcdvlYGmptO5vZPDlXuWFuyWkaZQfIuNQVgkjznG0ekQHgU8Z6n96NgBdMN5ycxuXCYYT+lqijNMaD3sGVh4dPuPfmSH2Nd/DQa5XinQpOaHXnqJGOh4ViC0NrdvHjlpTmJg7iKDc5ObfVjwMb78/FM4ALQ/7Fvr2+WTntqxfsVolMZ4Obark5BKDzZKXvMZSr+lajKOH4Vq54MADfAJ2hGcBOsOpCjHDOaamWrflT73WKAIW+JuOSRJmA+2J81kdMd4Lg3cgWX5oBKUAGLMN4ZyQyu7B1uSDIdpfW9a7epgmGsZYCUCGxnMzI1YPquhqbEoSTQkA/7L5Wd0hUsmQd3quvpDu87t6yxKjAo/Or3N+2mtj2Tn2geu+lzWlkDPUUmIkwn+GVPKla7Ztl2KieJZ19JqKHNftNtTsU3G64EOG5R5hPdfT2Nn519naN38IOwE4clwSMKengYo8FGvA/ABXEB9OVDUcj1GuV9khsTKNSDkSiAOnt16Avoff9fJUwj9J4M/w/E8x86dAFo4AU45++CDCJ8X2/KV8xn0aR5oYcf8Q/guvNLXT/08nOtfKw8iIALXUq32/qXNWN07CwW07F7vjmN77fiHj6HGgdiHQDPafy4flledERLefAuu3npTMLaOJ8zIOfvhat8F9p47G+S/XfVVqhrRTHeafR48ZRq0UfcaK/HRHWGZIZ7qRPxX7KCp6ABrHmD9jm8Z0NBWoKEN61ZMh2s0BAq7p8Q54NP2HVUKwrwLKfYAzrp9WGvgO5wzCDuZzs0C+lmY9QFgGXunI7I7OTmQFQQnxhrJttIA0xk/+H/oFPGGxmJ/bYLrxk/G4nZ9qfiwEHp8T/tOzsWGE5KNHhk/rrWLzj42SClGFVkUbAm5U3/L/XXVSFaozV5pfQiLKNBwlh8QM/Xwo9ML+HJ6TbvrN7hgR1TVvxpHcmsUeGczzjmad6caXkc1KbhzNnvaufaHZzdpGGuzJ3xUPONU2OH7z+HDykq6lm/C1ZvVtTx7v41nMPGvmz07ELK0czYeJyIkwlcyDnh7lgF47vXCcrpVIGjtjVeYONg+Cs2DofptuT678jyaRjmNpgGVryzkTLq6Y5wZwRFfK+Vb7lxb0aJ6C373lCpTQMVqZQaXq22s9vD2Ci888AAWW0TkFWaAX9jGi+ouoPvuZ4PW/Q94c8pWYa/2bAjQ2GPP90tFkuT6ncslH/qGENLAPP/8PxmioQbBzrkU/ywE4qZyUqUhbxiayjxwaP8H7nHV8YP9fzd9cN4342tAb6Gf+NbA+1amKWx8z+hCxpijSTmxPVTFoCLLcdHSuOaM5zb3dImqOTSEKGeljNkJHkYbTNQ5pIq8GbcYf+fYAavB5ESjc4BlZZxoTmCZZRcNGOv0M3VjUNwMptAXPprpEJlsKC4jzm93yg2hLFIybQ0NE5IeDuviZEND/QDbz6H6MYGI7fZ6b0AV9DPgkTHgkVFLpbpUsNomtS4VhvQ1Z7iQ1fN4Tx4rzPbB8V2bA+bOkXF06abbNs7ehp4Po8GhvSee6cdDQ3tveuW2TZtu2+TIyOr7YW1qsa2HvPtd71z8Mroma+ZXMT9n+tzzJ3r2U65iGV4V9C34zB3ODpwUU4NuH29x/O0BicXPoNLVJhGlgIHmfs0Ius6iU/eiQKjlMQtMFf5kieDK3Tj5ViMv6hpIkHdWMaASoxdfkOkJGPZNzP+FGUXf8JUcfWaivz1F9Zlaxzcv6aGHJsc5dlahWiCv6rQEk8wxR2DqtO2xVldy3m0PNyrYUfHEpXppdEYL6hySgn7/OVvsgMwNbsh3rTrD4Hj9hCWEVx6clVeekG7J6pXm7nGpZ2qoR7Rsif5lDF3B4MCJNsstndUYJpfDZJVla8KalLFzJjoZnzxWRh/RdkUl7qycsH5obIsfe8NpmV46RzKMvulr8Y36NoJcmBkf6IRd4a5ZtSRGXXDKwiyrGjXb54Wr0GiDWh+JppqCUZsvDHsl1/a5PQF+8+nZxPLxGR1h/6XHn7wh3+beWjEKt/JtzJlrSgGpLbd+gkdFdXqZP9C7OiDOJcRg/zESXpPN66E9s1xZXoGeUj8SFE4K8/nyNsFOiLOn52Nd3o3tvL1upP+50a0i9ieOEfCotlpjii3W/XiywJs78haj7wOLZlRe5+7/Miz4/fhFhz8alK/20skVa/8rIZ/bQMMTioyd9i6mA4j6EyVko4TCCLLEoGVXT8z9HlQXyS/SjphOwTywH8HAGEUzRBYxq3IsWnfXisovkChjQVL5xXWuuMc592zifatZUTq9aV5Aj8Lfab400GkCBB/Qqe0tkWcd99YWkvoznPV7SQqJLPVmCJYh3y/zLCfwEj3Ql1jBlP7DBrOGha/6GRtClsHQJsVSpb3SJoGJY1d9y/gf8VZfEuANigtnKT7XHvfVjriQRnhum6YDivxY9IPZh/w2cGTQLSt/Ku8utgawENBVDndiDYuYx3P/m8esZggYo1OfP3l9HNRg5/v+UPu+sHf2AVNCJaNUl9BVDW1Bfc3jehQkq1S8fhmRJLBr7Mr/gJ2PWFETxMp/IDysGSKamz8ZGR8ngqGxuHPuj5iWsRRE3EdbmFa/83jnfFfx1qTYRwOwilWfC2UEITMURx5no3zIYxE8d6JJIzmi5y2ZNi76YphW2DBDIgCx9rTxC5b38wjU/fALia6Pahht62yYPQa2xGlcoCvxQjjIfv2bXGEiVmgqc9/8OlvzAbk4oGe6EedMvsc2QzQ9wJNIVHmnBTyrgU3V5nFOE3l8jwOO9cj//XsbUaUiSIERP7n7/BUOLPj4KjBrj92yjsLC+wEWYoYoMEunuw6BpROfDzrlGMBSsmh8IF1us9pTo1xVmrwgIsoZqUFaXAj4c/+XqR5zfrc5DmvTYAY1QRBDfQrWprvSkbYWgcklEy2LH2qRVP9MZ5vzqG2wFRlmIBvs3fq1V0tccUNALnX5zVRLa/2DqfFUP1Ps0eB+NabkdGYYfdzJH+N9VflS5wSkBR7cXR+qD+QzuU2iunNZQCwUM2MjiULn5KygiPLeTb099Dqfn5zlT48Ft35MxA2hYq8R2z0TjqjyCde5VyfN2FX8Peqr4Glfex09L+gbC30l3CM8t1pcySvM3Twe05etQmxXUpJSFieYCuk4vlWzUp0sMsWI6I/ibgByTYDtPW44lUi2rlQYpBBibc11NE4NbymwSjGn27Lr255jRtCffAO+VcA/Vk4KPlwna6vRjQnBLS3gIYRfFNoY0khfuXeQKQ3iXmpq8WlAEa+1961mNTSwnG0nCSmmRnTbUorrVDDcBSUxuHvZBr6tMT3aB7cYzAoNNsfZydZ8uX8ihU6xrLmut2NGODHS0oe4PJNgTM7gVJbHQ20tOBxWpdHerUUmETGzg22M7dfFN9sEFNAMxa2h9EvQdw/A2hZ9EyDXhkr5Vp7aO8GjXWN+UdmS2kWIf//Fl91qJlRnnuuyLcb7jHYfkSjuloKm4GrSr1Iu4bi6XX0tBXNRgE6Og/Xp6SA+x69iLgK4Lqi22gCrWhe8FOxZFPH03qMd+ioxOWvfxmikfPzqpqZM73owaRCvEqLuW5uIlk4Ymcry6rJCWpZNSd821RDp27qypfnQcb3bnXGd63RBxUrn2t2cUmxXlXyGxEiYyAnnRjbGolwi5m+ovNkVjxWnOLnYpqn1Y6QyHdOcDMhGxIvBfRVwYYJOSHWy7mbRxYVV50GqRU0kyEJeKvBGz9fXWx7EVs3N7/2Fy7CZhKaLcsv+czs7853n7m+RRV0lLPwTmcsvTzTEE5dfbs+tdMZcdz2YLZjlVNLRes5ZdKQm2ZdfHs5mwjAIm8GwKCdmI4IQmU3IoqoYtFSzgE8LsWzoNGPun53np2igOYj2JhgRDhqnhQiBZ9W8vtNhnj8AOy/sa6O5pXFDZR36fS+yRO+Kdd98eDKsTB0S8776cISHtcXh7Q6/ygNM33HyIuN1sRPBrPMrXSqn3Q+9o7h8iP+AT3O8c3qUxjz3rKJj5t4/UJCM6VjlGgfOrqaTOpDrH2jZkvkDBfSN3DA6l3445UnOS8pGAQfC8zscmEc/P4McNwAz9cl+ZwKTOynwlX9xc/AZj7f91ef3NYAMl57KN5mBRXhMkEV4hItDOVkVjUhiOEwxuYDVyLs4FnpWClkcbASGF1hDfrWuclGNOzk1KucHmDj+CmhsJaDjzozfzfEGQ6KpMbc4SV1j6eEw6Dlu2fw60Ji4rgqVf6g8w4iGgs8iHAO0iaf2XnTxKak0UtEYy88dZ6sSy4XiTW09fUNxNG1ZZcW4405GVAyJQx0IG6oi4nTv0HBXwH/ZbZxQ+bNoirAIikE9B/Wwhn2dAGtjRK2TD53I52SxuNoez6mIfxeYlwBU1FWM5p7mRUMT8ceJKDKVZypfDbwLuEgAwGHvuhCZvBjVUeVFItKC0Hfe8Wo9VJQPXEdG8F4n30t4Mp8KUPwRtyBIs4fBpsYhL8pExcQ+4kP870BF3Kduq7zEiaKGmtAgJv6grlT+WPktiqIorDgdcNun3nPAirDBHLPxYSIqqih95tGAGhK4Wz995x2s8F4PfAvzOHXxPLyYHg+lvpp7oRM3Ojkt7/1wHLRo0OE3y6Ioz32e5QXVEFgKX+W3lT8qOtp25Od4lWIQpvJlkSJdrHyZYXQVdt0dd376Vk4MHuGZW+9i/iGgkxlHN43Wx4Q1L8RvlClrqHOr+KulchlgcFFW7NxV+Y/SXlsQRcG+9sXH2hM8Z/mDnLV59UVBbWTjyGaLG1D01PLS2bTkrUBhERCOrR7vHEwFVKlhz9LhJIuC8fzSPQ0LMK3wNQNMJoXJqAo8XD14IHXumzGvRvJ2XY2xfKL9sRevCwVz39pf+XOYjam6C0dgcBbgwKmwkR7sHF8dw0gNJBATHXwmpSseCNigIHh1QOYfBhhW+vI0doiv+olxqefdjmIvd9djma5zePtXP4bMbEd/7FFgOY89IZjGsn2WyukTU9vEJx6j7PFXm5IbrsikFvuFibFzsrkz2bBhYBTEjOMQ5fRdM0t9tbVqAJg6aS6R56Ox6ko8HM716YpWz935+GP+fXfFZKRtOuas8NMP2Qb7pUNA+xL1ciKsXrWjNZ/gzpldo5Oql5NhKXD5hAec4yBc4DN5J8aiv5Dj3b3wAXAVbF5g3s79o0Ze0bJYLhhrbOnqHoihfqqu7T0qfFbCQgfvDvPm0ABz6AQpKTzZmw94vrUPgNuqZKKjjw7PNhVJ/ajfNPFTR4/xd652hJMCgrSa31S1LaluNdUXkfEHtC9RH23H6pr/cScIi3qVHb5LncrvZ35WCtaNmLdNDt9omsuXq7Q89fLl1tFapZV/M9TpZWwIG+z0MjkI33WjZS1fjgRvXYZhXeK+Lhq/06w4Mqw5pwcXyyo2racPpwRcEuCRoLGcFc+195VGEmiDZVW+VnkOX2qZc4OgBpiJXHuxNJpAm+DG+ggJoQE0APhXDVNAcz/VApZANQD6m+J60qP1mC8H8KQbIn7sxk3p6aY6GvZOE+06kpi08SWV5yrPWRbHLkBDTxXnjrUsyyRoE8BglUEjyCFKmmJg7iaDE14XLLOqgSDfMh/CT8NaW1VffNkJmg/QlWx1DjGdGPr0R0UL1C3MqizKoLRBnMuKEAlbiBZ88hN6XvljuCKuv6T6Xur7FSm/baLwOy+mbqw0bUNr46cB2H8IcMjxaKG/iBaYrAbHBZx3mpG5tzEXps4tFmjyIli380H3SPpafd2+ss8/rhR7aT38aMRgWJeZ1h3b1DKbmpt0JxmR9j/QnYAKRnfjJRBgubdx/Nl7zF551djnIgjpj9wciJ88+2AEo+344lthv5/jxyxKIXXPyMOVXyB507Kz0C5UeZVF+O0HX1CRFD/mZbm5Sf7EIwHceszL1iBKyQcv4lAcwyDu5tWFuetRmv/YilEVRfnKL9KeLlqdSwb2mPRULpuOGQxzpCm4h+Z+lHZ6szjgHx5u/Kzy4LUPb+qDtQLquOYhGc1dBmBvPCy041d8av/S3Vzlntsv5wHIVYvwnAXeNeybpnieHCkW2psy0SPj2fWIO7EEbM71+ZbKTc1ZADuJvBQa1vMEj6Hce8A/vfYj+9ZECoN7X67cfqquZSenr0NdLOoa2Lt3Lx/OZuXKf/8gunbP+eukvYedE0oNdQVw1+Cel2/Za8eWHXfBscuu+2ce3rdnb/fUCSdOWf/1LwwOFIb4vYesRQ44tfRUd2tj9MhrgbwWsXQmoCbYTqW8WsVFmON7TKy4ds/AGelyPDv5m9fNaRbtveTXMI+TbsnCnA4/k45COtYyTKfw+m/8U/yeS39JAPLNK5Y9ttxaDHsjcDPpqbam96WjPi+vDWB1tF7O9YK8B8hfe/TgLQ8/cvDWhyvP9I2P7hwttx4W0IabHn3klhu/8PeIGe7tHpnIt47yXjwowhfjJwE+7nFr4Vw1jtIJptp6lwblTLg13X+LNMwa5K236LEiiIgge/NNSGRkPiTgm29mg5bPi7s4AO/U4Z0y5+hxZa9yvO7Kl/R4BL2OpEBYcnmUjaZb5IMHzfDcRWHr4EH3HcyIU/eDe9wWXH8bcFpcGmR6a7XnXb4HwmpcE+uNO+fg03kzPfG8TQ5gTzWYuwe4Xzf8k7y5w3c86dQZNARH1xul/Cbosj9XV2meEFjRrWkvOUeqc11WUlAZDt7SAwDf47xZ9M4EmFvxf4P1+u4zgarBmjZdy3rhkCCORl0DdRQ/7/xaODWoPO7Cfb9jJz8WBlnkn/8d7sIyyCIaO5SJa9VYNE9l8ehoIUjczjl6e6mMwkhQ2L/SmvV/4YgkMvCZYfDbZP/uzvKuK6amrsASYpg/C0TgifAngjH+K0dEnmX/ij+/q7O0+4rJySvcWii+z/reQuuc+o4yXshV8pwZzSu6Mp7HIlf4W7rP80v0OH8LkgI3oz85uSiDNvWJhyyzVpAW6J5uB+8sIo9pHyVP4fea/Jac4k40h7AHdTW3t2+XeFFC9xpmC1JoD9DQ0q7wVJ7jMK+gltx9kshL29vbmzkL/Y59gmFZWeZk9gSUK8zSwAMZc8ZYhzXSxmEZNC8yu+lEFgbILMs8wcp0/6bnf8p8Bzf4eLBKSrB/O1oiIY6euTX1ZQkfIj3u2U1jjoov+tHp3U6TGkuNdH+jxu6MGeotj3Jl5hpU+TuEduERkeY5VB5anf/hplnMWYIcxCz+/A961qAtPAlLc2/TcYraWhwaKrYqf3vpuwi9NBRm0LfysQu6u5GNLQvt+Ei88G0crvzlpZe+x/PhZCaTDPOuH2XF/H/53mKe8uoyAL9sSVoq8XjOYreTfhR3DinDcKSr8iI/FPrG4suvHOKlcmtIfJZ8y6GluCNnW1NRXcbMu2iq6YiX9RSHXn+vizpazB72I8Bzme8k8hBzyrtxF3wXXtBR3LnsEPfd1458zeiLUFSZO9Kls5+enxfQG8BzbNhPIc7h38AmESikQ6ieTT4vaIRRDTuc0A4Alzn9NMoe8U/8nBd+884ZYYd36/A+2s9Ar8YDsDUmW42ZQyMOaz3tNLBkLjlgkFeBp76AXqxGxPnq4HrD5RE1uaK7BmC2Ofc8Zf4/BUjmdRb/xAy/c4YDB6vXYHjD56d6qcNHXQvMY8wc3wKW1U8cjszqf3Gj715EL8JLVhqcw9dPI8PoFicWQfLVxyIsHCXUzjur/mSe26bqiplsin72X4e7Hrk8kYuwSFf9/rNfy01O5T57akvKf1rArwQaUo3GRUL8tHBnUiCiPxC8PQ4WxeZke9CVfQfICLrKkQFSXe7UIQ5vgCKdorlRSpDholMbbn5iw8QLl0R5Rm/w3wySpetmDV2lqxKRY1PtK9TcFVGFSKquf+Yzumv/LQXZ9RWn5maKniNHRNdGotOMuhU+PK8T42SP8ZyM7HT5eItBL3yfiAFVJOguhK3/yW0DXkLEzz72ONr9GBigAofRCJrmNJGdO5twBITmMSO3ZrOIUypvV16nnWMoT8QJ4IlB2CPCkxEzsBCTssjVlatjfmnK48Dw+BYrimxlXKRsD92ziM/BLYQqf2Opn4itVAiykXwIc8O+JfN55ivoTaB3R/5FAqznd+gteV6EatSsDzTZUiYLN40kChnlJZqI//kn6D5igN6/k9CYWaZyMsswCXTxryt34Ogz09OPHYt+7GgLQgjmPnc2KwgMvlLkwoi0Vn5+5pkMCl/v4j89/zPAQQx0Bxq7GLN0igO2+sVefpaPSgDGExQ44bL9cSLS7HkOfU0EXj/iyQQtTA1/IqoCfURj3ylrp4zf/b6J+U4yDOsddfy6ubgXB5Ty0gcX1EcvP4J1rRHavJ4a/VTr/ddr0DPX9a9WCFFWDdzwb69ckwitMQgx1oSY7taf33fTSVd1W1bbK/fd/+/tFt5wzjmFm9Y1bGlYf2PhnHPs2PYD8PnA9tiXL7i2b+qGpdctveDaay5cep13NnoJ6FHfd/I0O2muA9ucK2ZNayFL0Nl9xawXyb0Qy11242CraWJxROeBP3WJ2MNYvEFaWonBW0wPE0jNRsTjkjISBzs3dkcLSVbRVJWPLx04pbWPWTIi6mSl4J8cC/E0DkEw+NDYpD+jpxmknZfP6/zKZcXxXLK8zK/J/sktHesHlh/DbjxfRFXcvs5Q3Lb51lFfVns1X6hUbXu7qEJOvYrlhf479YdprakkqrX4yLn1Vr2uls0Tw8UlsdD2Xj6aGlk+0LceKyPBVOu6ts4g7SUV7ISPqeCIgp8eHkdsy8xwV39clJLlNbnT25uDiXggP8qzfaw43Sug35022o0DS8SslUsNXZaW1q8Kp1on0v0doVB7f2a8LRVetV762nHL/Hzb+tXF5s4OTevsGMudfvLAej/PykMt8Xj7aHXu4/Mnw9y/D/b6FMx9oBCsxupXW6LVFxOnwcukb5Rxls2mjKWYqbmFe4tVnyx6giOGmirtudImmLGu3FNKKUZQSZX2fhyrNPqYY1T8/MWK0MhIGLSuCx6QCPe58wmSU/uul1nuM/glbA3M3HCqIRmEM069YWbANAdmrj8di4QXaINOEd/x45C4nwWZhvhnr5NY9vqv8kjruf1c+Phxp38h0OQJMC+a191N412CPXa5eXHLFrfpYrUisjPJ9yBcExYy4wVul75d6ohuwPKZ65eUphW0PgJKcyTHBNklu3JtU3bmI8Ji4mUnhrLW6KaV5QA6M9cxuN2vLN3e2LOWtbYPonbY5gI+fcN4sayzhyFhobURs4E9y5aeopHqev0eXQzymUaH0tyBkIpr9SidZDBa9i1QrZYfdIqk+LEXoX9guGN4tYIHx64W9Rgo+AwIYRafbTLysE5zJNVNCkbfjo6XRPvYdVs2skgi+D+JwMjkusrb/RrOcjfdoO/brQEcA/Np9DH8S9gzReecO+qchbhfG3DOFJ3oJY31cpmzbu4WNYdQXxbANHvG0BjOYIUWvT33YlbhaF1nTBie8bP05oUfVp17SEaF/lMQOmUP/iH2S6TyDioPYfi7ymWVS2neEO2tIcjMM0N9HNxFD6D7Ca6M3mrFum+9tbsbYB2bT6ED+Ie+U3xnAawf2rs9T2OuMnmmr+QWC9Fraz3m1mtyLOAyDa2uhurTG3UZd3rd55qyN4KcIP7a+f0I4oJ0rkh3XrWB5Z0Cv5hmcsMMWayoxAhwNP9KzGgTiDApAvd5QXYf1j6w+sIo1h3EfgvFG8edO9cyPK/JIb6f4QQOdRCJ6R9iwW66RRBB1k5MEKLzqRVDIcIEJDI5QUTiPq/8sPZxKirCYNLc5IzN97BMQGbgCl19q19PwUB62+f2aOMBl5T+QN90zkeKzjFRj6tshhb0qku22z1RJLSIeDv8GEpIZHb8b+D18kdGcAg0VenXv2Ze+w2n6iHxV44M4YEc/hvkG/e4vz5XnpaAqSuXRgvxjesqDRVWjt2+/VhFNJTg9u3txe34XxQD9M/ky68HfvNy0jBU9jevMXsu+VWtPgr3EuacM5Qwhbvp/VOqe0K9tXPm3NEkU9OqdwNuRPeRE6mRdwZBz3Z+z70BcPXQWrp6XYz4UaZRu1Hk+LUPkDftRpsfbaq0iztBBRhXfQDcHR34R4NWZuio5/Z+WD+6KVfzN3kFZ30rYc79h8m3rflzDk269SI80kefeYsDaJPrAeo+XAYuCqDN1cdHl4nrUdfiXFx05Gzc+jnTei/DuXpaXJSJmq7ePdps1s1OPAv+wbvzUb0ADfzFo09sPd2JKVmcnerEl7x/WqsrP18W6BzX+/aDLNi9oZ3qO4vX9qjndaQlrmXv9va5WFtEJ4soAB88+tkfgQKQhn+0gGEU9VB8KEktIpT3Rdl70EvlztoCIJWuwCJa82pDOHyNxpt2mLUaHL1279HztvSiQhwsxnEU/0Ccbu5SzNTV40AfqCJHtf7+o05+ek9VJlG3fK01ke6mDbpuyfr7Tqok1QtoH4gR7HhO3KE50Ww9K3FqWOclUiig1kIBcKqHTl5zcsi9VSgQmqFMKmdXzmJkmaF3GBl/wpRo3dcgh+e+h3swMWnx1+o1kRKgqmFQOEHxxPSxUTujfxrm0Ufjllp0L/5uUTSHG7BKfa1ePQ7spiTXBVnhvy1EdbweRXEv2uJaWxBbvLiPuZsXspnw03XBHev/kQjpauiFICqJagDIO5fVJT0txBM0+3oB1o6cRfXJuvgP16VGqpZIffzHoii7umCQXDD0k8prYwmeDwN7wIEdtXAQvQ7YTyxEhiQCKmJWnNA5mAYl/CMra5Eh73yzPj/LLcjwpm+3j6HtxZ5kCaJ47fbaRexGHbvpjzeu43DjaMuN3e6PWxvdB6PoOO5xRH1G3e5z3/8DMLaeIwAAeNqlVNtO20AQHYeQUktAQaiV+kD3rURKnBiQKgJF4qJIiJsgiKK+oMVZ4oXEjuxNDN/Rh1b9gX5Hv4ZP6FOlHq8XhRRaRJtVds+OZ84cz+6YiF5aE2RR9tuhrwZbZNNPg3OUt6YNHqE31geD8zRpfTZ4FPYbgws0mXtr8LhVzn8yeIJej/4w+AXZhTmDpyhfeA9mK/8cuy86S4otmqEbg3M0Zo0ZPEKr1iuD8zRrJQaPwv7N4ALN5p4ZPJ77mHtn8AQtjn43+AXNFKYNnqKxwgJtUEhduqaIJLXIJ0WM5sijItZ5qmIsUVkjF39GmyQo1r4Bdg14SlgCrIJKsGxp7KCWjzG7tAi0jSdcc63hGacmeDqIoo2wex3Jlq/YnFdk89XqUnm+6lbZpohlK2ANT4rAEyW2FXiO/buzu8i2fR6wNY83RQds26A+AvUJndIusERK2uZH4uR0l0vgdahtIzuth23MdWwDSE7XCPKFFu3oEtS07Pt85QFJPQxUPYxags07VVZjg0zljP+R+Hv+x/CNdOFDXSwXWlxaBlIY52DoYQ1RTKl1p6Xua68FFJyORRTLMGCu4y4zpc55T4W+DFCuvussFJ+u52nXpvSEi5PyrFCih4OrwMHt0wXWK+yz01hFnv+9YMN5Lo0XH/K5m6+EjAkypWoYHeq3Sc+kj7kJy+2NYbQHho6+MQ9VNW0NG9b0pOKhuAbQOVACz0jHZx5trJ6uVWxy9YCbOjvTeoSO3sLHjNE+aiL0+w6Yd4YY0mo/fJ+cIWXDeRlU9fU7tDGfYU5tg4pwnXGNDjRW6BJbn4mCnhpVMGKwpWfVhS1Grlhz3da4AuV1KP1T45ce7Hw2t5IkidPhyr/gVw5abrX42NfAxFzCxDNLFleyE6l8dihiEfVFk6VNzPZ4R9xpX8e2j3wZZ88a4blKeCQYDG3piSBGVC9oiogpX7DG1g7b74ogc97JHErsTi86GZmJZbzPZZuftQXTQjirrx0wrmq2r1S3VqnEXiS7KnZi2U4VV/brKNc/1fhvhE/8VP4CpR9o8wB42m2T15IbVRCG/3/XWoXVrnPOOdvSKDuPkrO9zjnMSrPS4NFIHs14bTI2yQQnoIoLqoAqKKjihgzXvARQpOIJ4A1A0mmZG+ZivtPdp7v/6XMGA+g9/9yBhv95uKj7wgAGMQUBDCGIEMKIYBhRjGAUUzEN0zEDMzELszEHczEP87EAC7EIi7EES7EMy7ECK7EKq7EGa7EO67EBG7EJm7EFWxFDvNM7gSRSSCODLHLYhu3YgZ3Yhd3YAx15FFFCGXuxD/txAAdxCIdxBEcxhmM4jhM4iVM4jTM4i3M4jwu4iEu4jCu4CoMDuIePcAdv4Xc8wN94iLv4AN/jQ/yIz/EFB/ET/uQUBjiEX/ArfsPP+INBhvApw4xwmFGOcJRTOQ3vczpncCZncTbncC6+xFf4Ft/ha3yD2/gBn3Ee53MBF3IRF3MJl3IZl3MFV3IVV3MN13Id13MDN3ITN3MLtzLGODUmmGSKaWaYxbvMcRu3cwd3chf+4m7uoc48CyziY5bwiGXu5T7u5wEe5CG8x8M8wqMcwyc8xuO4zxN4G+/wJE/xNM/wLM/xPC/wIi/xMq/wKg2Os8IqTU6wxjotPsFrtNmgwyZbvE6XbXr0eYOTvMlbfJJP8Wk+w2f5HJ/nC7zNO3yRL/FlvsJXeZev8XW+wTd5j/f5gA/5KOg7Viymx3rUEhnFQk6oC/M9JhLZwF6j0TACJ+umZwwdMhrjVWPgrDUwZgVOWLWGETzValt20xkcq1uDY22rm6bFYpowIUwKU8K0MCfUhxqW43umMos9VVo8Vg7tn3CNa57vho0Jy0rHtWxueNK0TLftuUa7HTou4YBhm6162HDd5qRtTnjB3spvRXp0rVrdU8Fqc9JRq/GmJwl+q+qohrm0UATkskKlM65LXO/bumK+ICwqFmLCuLAQVd3H7a66SN/wWyP9ZU/j411dmY+NrlJVp5gKd3ij8/mGPWzetNqe6XiWYYfMRsu71Ta9UM01qlbHGTRts9FhxGl6sgy1/Urdqxter5YWTwhTwnTU6NRzrfa1hiENtXhWmIu23Gar6XpW0zHsgOHUbHVYmiZ1tFTEbtasimEbTjUsy6YbtZxO0bZZ6WYGOjubTrBtNSzbcFVeMiZMhCtNp+b6XfXKo6appWLD5nXfutE5Y6ciXdO6MK+YETuT7wntdPTHO/MY7Rvdl+lFXXPCNm9KrG+omMrPlhRzcaEmlK/MJSMVy610bpvtt8WVHlWuhm97Vsu+JW4ZndwdTZdKulTSkyOdxi3TqVoV//E0dMkqJIPVpvffQRTSQomXRGeprFhWehOxrDAn1IVqTglNEyoVqXyfOaGaeaog/lI50G4ZVTX1dDou1KZUbH9cjKKwJFSSMqWsUJXOlFPCtDAjlH3l/j5dqH4qPaHq6bow37swpXK5KCwJy/8CTlnkRwAAAAABAAH//wAPAAAAAQAAAADMPaLPAAAAAMb5Mk8AAAAA1pgrCg==",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_Main-Bold.woff",
"type": "application/font-woff"
},
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_Main-BoldItalic.woff",
"type": "application/font-woff"
},
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",
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"type": "application/font-woff"
},
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_Math-Italic.woff",
"type": "application/font-woff"
},
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sVIKw8FI5njl0KBTCP4IXr31t1p5TA7ByG3+HJ7dtx7i8fVvoC1FDuSu64PMRrKeSLopXtlHr1esfkM1oQDk1tOT1kZPvVAKw0npDXcmEBxZiar2yJ62P4W8Ippi+leAVzk+Kps59Bf+UPIdW0GVoP5rvzPowwvtX80iSyN5mY0yiEt0BfJcQkU4gicKfW0Gr4TMOw++baGdnoTZRyQ/JzKwEq5grYhsEsgmUTWKujiaQUQhmOVfKl8N8RJAWyM4pW27muQY3BC/43Bw2GkBeFg6ZkXot2Go2yj9ZO1xPjaR8VM5PyMubAlhTVcrc1UkQTpLxpoe2vscfk32hIFEObvHjAKaq7Gotu0GnFI8R9Ep4f6mEM747LjnWTHtcrlJZxVpRl+XJmuoqahj/sYv5I1uOP5mkWkrFgZDLP6ZL0nWEaRN+gt+34DEyqaC3++FQeyKJZNCBr9CngWYulECTaAntRls7m8GAILBv9BgQCzGCjiGGVI2px5EmSdp+pGnSQcVFJE3anUqu7VjesrTYrg+XkpOpyVJxS1ZnQxUcyueAgDWghBIOpXC91ubbL+VzXhwOCilsIUdmGVJY0VpsivecnMGSTTnxm/jDj515H/HLp5+RwmFNyeUULRyWJgJDQLY0cweMqEt66RlZx4fehwPSlspKxhMd65x5xr1ZkWVls1smha9+7TN+/LVgJpeU/Iev9kupXOalR6OGrknkW4rbDPg87KW6rH4VViXH7z8wEfEnsXsya3SWjOxkYGpSyNjEuZ9RTL6BVtE6ugF9vOO/7mAp6FHBxEyUwVA6liOqYElCBxnYv/AakBMTsIWERITxHNm4wBxYEBULqtyrwCCMYHJ4YKUEtiYJr2AII+mYLIzz/t6vY7J7HWytuX0bRq86tm19+3qjNjaaz6JVvKqCUcEWtcFAmJE0BjGd4hRPYVOQvB7hUtpumWHb/E5wC5GzXVJ7gbT5K/gtMyf4Eo5w/Wg2YAEtM84r8QGLuFzCB7ff6E3fE8gXxrVrxxlR8WTZC8Q8rmOcCqZnx3Q30+caQ/5tq7J//1W7bzZY01dQ/JrE0se2+9XkcIBhZSISDPvYPYpEzbBf1fJ1d3iBfOPYjmims9sjj1fH1QPvCjCPS2Zq9vevc2NdDsnaTYz6iatM1FG6Oh+MrdZ8EdnFiOfSXZef1Mj+RrnV9mPKstl0JlFVafcNLiqZ9dSETLw0vMQ9NroUfxY9I3zjoY6He0WJoBDuO0UDCZ+IuIdDgx7Rj7jrGxw/b0j4wtPgByk3MS/n7vb8rp4NnnX3OQXfCvobRwbACrB3J+FrbtgeICxSkUNKniMKYcfEd1m6FfabEXyrsl37k5yq5v4EjEYhr+iHI+pr9eckRXnwualHp55/QMEalb2nvjR/TffL7/FyusyfY/gQfFdKfBcMnEQY3zAyyb8r2JrDNmLxkZ7MtBoWotlCC3nn2/JJnRLP3pjXG9vrIc8SVXzfcw8qmICdlo3rPrT7+d1/doNBFf4VW3ARPwPfuRNNdaoM7NJqVqgZzFHEJV/CBCT/GFfOg6CfdHciXknEcwJoNLh9SYIUh0F8cVtIf44p3MawfBVP4BDMhizitxutJgNPqAjbVGtZwl5P4TQuV0Gkv5eLDJU9ntBQ0dATxSF8Et85VEzoRnEsVlI9ZYOR2EXmMFGohzwVTCZC8twsDkZyHlxKpD0FXPSkEyXsyY3gOzFLe0jKo8Griy1Q1KRn1qL/W2mM3IDyaLIz7gZeJ0EigBRgCvB+wBsyphKiR4Ap0kEYknYXQsVQKcBJ0W60sxZHuIlN4qyNCbhAwObnMQjKfBYXwOe6/CkALwXw6I8+SrQFjTz6aPY3Mf/b38G8zJVi73i75YzhfTIJ77Db0plz70LPoEd/q84A7loHbfCerzMD4+cNGYM60/4X6cxbLob8MHKjz+IpbAD8HuqEYQADWuBInavP7tZmLs7thvBNpoWo+Be5Y9KdV6UUw1CSV90px/4upxAsuWNxNwiggiwawE9OAz+aOeumhMIn2+7ATcBECwvg5XCx/9bgoNAFL/zI06QQCrRhb7YC8b3Wf0gliS41DO6OYYePSvRBSgHf06Ul8ANcJhSBeYIo3Ul4OHjFO4QZAHlAN2CkayiIg7SHKDc63euCJpB6P3HFAlEsdz8muf4pajDK6DslM2bI1P/i7ZIhsHLl3M/wb8jXURRNozc7cQ+3apJwcRDZcPrhCHYcXH/W7M1GxWyNxw6gquDeDvcXccZH+SugG6gz7JHut3+PYuHWAM1VxxJxrxtFcVQW7ixFQZMdd1VuCPe0gE1AGSzPFMsnhcG/cb8GBqAhIAhWFw40ky4s5dsPP9TOS3goax64M5QHL5Xz6xAnEW8oGB0KhryEx1RfL84fv3abf0q+Z/nGG5fvkaf8N725Vhg7mcBq0VzNXGsuq1cshYLB0NIV6rL5qtzlQhZ2wnb/BnBCET3U8fowN1IIp0AcqS0SMYQZx54ArgjhGIFS6UpAql7JQQAbVpj9FQZHATGILsjJ3orBSSBVEKNsJhYNBrweUMUiLipALkBdZWECHDMQxfUUAUnIVvE8oIF6s5Fnd0UCxJQjx2o+TVLGGNmna0q8+xm/pgQPkgSZDESyYB2kKS9wk3W/0/0OU/2erEvxRgKG+otf6HzvVfjxHOx9Ht13drpVAdTt6EGSw21g6AkuUwfFxoHH0vqGfV+wyOwtEluP9+YBnWKOTvuz6+tnZ24aC+VkFq3gRomDb1s4HDxvAZ6IYolHvWZD9XA/1G4KAuG9rYqLLUVllxxRpYzE8puqnclqZyfFU9GlUem2BxObWZrZAfjnnwyqWPqT5UQC/8fpZaxfYSQ0uawS2bWaiPpjt+/YtpCeLkjHwVhAOJ5Ki3ichZMZ9UPbJmqcZqBfvwaazaD/fLY2FaasZzsqMAuenp1ATII/tyJJhj+3DlBQlrnroxHq6N5v+RWz9ytR6hB9+GV+RUZUFuGR9ZvcelVfZqnDDZlSeb/9DTIVqusq5sojpRBwI95nC7fevbDV8sm5wfCpxrHlBIXwawHDH7wQLYTZXE53x2NY33Xi2luOuu983Z0374tAxCvJkm/lQKs8VJoYZZKEZ3GySOLDGewe9ZSKTHbtuvLKY/Py8GtuPzXtk+gVVHE1aHNh05wKbNjOMFM6Vk4gDT8YYL9JsHP7Oi4TfCtoL3JwfYTJBDiHsISOA/08a6DmXsxzPRsnfHzCwDzXMzU1NT3VLr+m1IRdRyvFEKtgJ8bh6GgeW/8H8QyA4IFEzmKx/xT4dyBOuVkOAWQ3CQsXYkQOSuVhnvY5dUrmsvcDnbkk6vYHXVtUpkg4Vgh/Hy9u9659MR9stUEyRf6n3eI/iyUZf1gHvyVTTZn88DeDwY98MPbmt8S4HymC/D0H+55Gf9XxuajIXiisgGXJMVjjKgxI8B0QOsvMYjhTCDuGbF/muYgk/tZf8m2Qxc7oy663BdH5NczNnB9eTaPpYr5UaM4UNRCuYKlsgPxk+irNiV23RSocsiCDbQXqNTskhXW3RCTVALkRRIUfx48BDGDRw83Zl45uKWciTGP+zdXZnKJnm2nNj2NqCB8PKVyd5XgCfpCty2DnmrOnfrlU9NJWzleszl+yL5KeMRQuV45+j4JV/LuO1wC3C7E9SQx4hAwomHCToNLg9DlN/Jw6VuwXQZymxcF15sXWRZFD+xRYSUmm0rGB9RtXcU8c762SYUJGh3uruUfO8qynwNe9VQgJ7RafIyOu3sLftJvVSiYVNf1eNIpHub+Rc1XCab2IayDGQHFw1DlObqaEbQ4IFnkxLYkklEiTtKw8ATaXD9dCLPNjSinR6XxckWQtnZ9ZVpTcjl3VQtznGsruLsthtx6Lqd7ZjDcksUj7+rt3r4z8s4Fx/BDDcmDHDbo2PV5a3702lkuO5PGHsOzZPbW50MF+8/rO5LiF26rnXgDenEUVtKOjZ4PgWDm0clQekCsWxs+zRm3K9cd8zth6x0NQasjvQhUyKnG45aTaQKc5HMn1IzFBDCGgIJ8UzN533cNxrFy7HsCPpJaVDLNkUJp0HR19GvSWUO3zario+7OG/tA/HeewGkYXuHJDzBSU41Gv6792/79yAJhkydnXUQYs2Gb06dM6KLvjSbhAgD7Lx5xwNLwGAFk6D7hdsMrsr+oDuLizCubA6NlIDtlALod6OK63CqKS/c4HSRaiC9SmZqenNtc2j1eK+Qh3D2EraHOU17RTbLbaLvBQrNgolS1pEdmKJoVYrQHjItGWkuavOzauf3RLwAPQjY5FCz++qrn3RkZxtYnjsXLcG/JGq00XW5kaIopUjM+4QIdVX5ywa16Fw588UHaBiwbTnarueKbYftP9w6ANvm1rI2kis2J1djTjrXcvry5uCwcjI+Ft8XjG40KD+j2OOugvO94FMJxJTOUQAD/aAz9gw4hMjyGezxHkd3StT/0LFpm9RRbxLWToLOLJaqC9s5bDowy84B75WH8RCMx++1OIRXnNHC+PlbJ2Or4WjIRDjj9y0CEn7iLOiWEeADcbCwCN5JKT0DStjF4V37HpVV8xpduWr1qe9AWYnNepJCuFsfl0CMgujayO53MNl4qxC1PDnHErHk8+7G5g4iZ/2O6+2Nr0zP560uVV1M06VTLjqZVhZdZvbB8rrGmmT9qvGxPJy24xo5TF7464W+OwZRFn4a8hD0qh2ztej8ThYMzwQ8jFlTbdq8kA0fxrEkBjoav9mDMESs9h85ENcxcZFrFnx+31elPe+HQpIDLDTIF4syFSwxGTnhevPUOlB5kVn1FJ2Ri84UUisaWmn1cJut/cGMlxGRIxtNhXCf1pJ2jtqxQJeyWJ5DAVnli3dA8+97r+FuFJBzB0WkgILL/j5VZwGgxZK3jABTsenH+ZKYGu188ODS1zOkTPp8PLBeYbCPKOi0XpG8jylYsWawh4zatpDL8bYvcYmuiMgYiDUCMMTwesulxU1yAGInSXxwNIJOaJhQOw1l1n3KRwuzuCRawjWynMEVEgUa7OpmS8C+/SXXrmpePZgIe/2ynHM3jZvUkjTDX0hOfv3ElfhFrVCPtZEP4q+QIaRpvQzR09AwHqdJXIkuMxCr0AhedjpEHcLUnyOjhPrwxk/u3LDBn8ijZdqZRzIqFlQ2ehkBaKse2kEuJJCl58sIKXi5UPcS2U3TPhUeE5M/Hte6+7tbEnANT1lkteXqJUwtsDt1xYUZwomM2ARA5Q1Vte3btzeGG7Xsy5gJ8YD9diey8Ldn/vIhVGXlt7gXyA/L9oCq0Cjt6TwAodxzIjOxCDT2XyCV51ValyDKkAuFXC7d0gSNEst4rR4kKzPjpSLmbTsaFQQGVoCk+5BLpwgFzEAXui4gAjaVziMMMIWenoukgBKixoxx3cdkXqbQdofPhTnzl58O01fbEyHfHh6FPfL9Vfn2GuYE6N/Ghm5tT2ee+6oq4eTUouNjRkKhhrZU8go+iZmks//akvXL2CtUTliplbZ92f+ck107sxSbhJ9xdvXN0SkzVJVjcHFfwCCQ01XJe4VgKqSrX8lSdNxmk0Cj8+DZi7gBpo5fQwd6Q7HAMlge5J5KiA1WAlrCBjcNgnhkWIoU1Wm5HpjMzLLhbjA31zPoGzIRHr2pl8iDHMUrPMR3yAgJswiPPS70umi34nQBtNOaJLfn/31zniCrDxko9p35b0qyVC1JQyrB89qmgd8jiwPCS99HtaSAAVZXKKHCOAU0BgVLeLnJRCH/Qy1T18deS+DA7H/hp4fQhczwdETr2EmuiuTgBAJaB4mVYhdgMHJUn21tM2ZDjSS6OvKQI1MIcKF6zwDawQBImMlhFqTJWbo810MhGFLzWbKk+uWdFBPSKgp5UIFioCYUM+LHJEXCRovbZA27yuAaOvhhUh+u735y69Zucm7x+Pjl+T+fgJZdtyfTk63gybr5NHR2cSEdKaVi5Zw5JJ7g+Aq5P1Azvn1zw08+DOyZZXIpdg/CRVGpV4NNxNEy1YiLdmQIm+b+FPritvFroy3CkOgHVKrdyY0INMKh51qVz65b7020lKM+QjNrsBanJcxMs1i7glNsoRNv7I42cyi++rbNq8Vofo2Q2xlAxiMzRyy+Gjt27aMk4ZcFjSQbhxYHonyPV1ux5Ku3RDU5T5jkTVUV259/nXhLNgojGwXKLXSrIrbfcScN7+B+BtGOV5zMw5CxylvZi5z0me0FuXHEZG+gzsTVgxM0KJWNSEDwyeknt8cyJjyegzzai37CKU4BOwNFEMPBnCqQNbX+t///3q2srqZYm7A1KPNSTeffIvD2z2YM6Tbggz88Hh6h0nBR8OwWb2ksfAvt/W8SfC4H/qAONCGnhB2oPQEuLZjiPIRgieNe688bqTB7hgga+/QOwtKKNNM5PVsVzUDPjQvDzPQ6UiT0NFavXBRNR5gWyO5fK5Ks7lhaRuiG9xWP5lQNVlDCrIbfd//k9BFWQW/4VsGXMsuzU2t2tHNKY8+EYnwH3jg/dSzAIs4+SiyH2cPBDJJlNgz4EV+M/4u+gQD25PO/RpAH3G0NGOL58gSIJfwHHudp2ypx0uHpFhz7wIIQmei26YC+cE20VDjMHlOxoxvDJFY3iE8eTuhft8ecoQ1JXthAj8OHWKU6FRt6jwJ30qkE30E/09PfDAxXfe08l3go+fQkudhVGQexn2KVUwAziPuGPE4MKA0VSWjvQDJdgPO4gYi7Kd5WYhnx8VGa+W33JT4KQtPeXBilDTdm0e5ycIj2w4eDIs8DT/xOO3XwNRsUzdAKUlf3F15pajx26ZjJNiEZwyje6/GQAakW+667GP4uOPV1UZ+3MGqKmvkLnvufvURh2z6gSVAUl1nwcpnrJ59xToaBVd2vFEVA5zqxlCe5glzKtIANeP8LqBRySbBdc2jPvEuOCY1owMFyM1K4Bw3HAS20Um8c5vJ/l4iivbZ94hFiKNGmcKTvMo91rJDnfFi+6PHN6R91pcYffBP04i66WHnFePPPzwIzb/OL941v1XsL8AmjobENtzfKluWR5bP3XL3FjK+Ph0zapdOhvwOw95ee8hL3gceATna+287cfge7Noz5kEqF/va/3W14I1P7QhwPNbD2CP85iu47HCuGv4DOLFrtGRSV7swvZTGX67BmWl701h31uGHzBdSZqqcbJh7fieo7u82OWiujuzJTXKE83CTwdY9793X1BWdg0zWW3NUT3QNkMyVgCMBWy5eBKev4k2ddz18WwkrCMF9XrbPNzce9YwtitX4r1PvOelqzPzzU05CXBHMaSIByQ533l6auuwyfsHrFjBblLCEfJkYGaaqfq3ycSkwJyjXGNJZZTnPPA7cjuHse6TNs16CX1Dp4N1avoVQjRcmqgeOujorjDqsl9908Nvy20vY5Vir4aZ/PqFxe5bZdSz6xHYYxytddwRL3gnj0hw9hI8Tm4HX+mIidHL7YgxYbk9GAX9uoriOCoJmw3RzkW2GsXAl5+FfBo3RV/4vNiSJUrRB1/t8qdEzaD/5MCf57r/lccy/DnpXfCcy+iWjnt5ergYdyOFComKwHMGMLZ9qBeC2sF4lrcwEgj2T1grnMmN4z4xbke0mxenJnOZoUhNtB/2np3nAWrNRr9ENKjFUUDRlPXecmiZC/sH1Jqu7vQYinZ8O1k97Ff9nl0rtkWWLhE6HnhTEqt+7yVeQ3HvXwH8w5k+XLZ1iymGv/vCFyOUBr/QzfpVtlHfMfyrb+t+dCeGKVgb+YfP1sm57m8GbIBj45poFuyT6eY16J4gW6ShDmk8FkmoTZKz9cnp8ZokujKcnQfSuMaVbNCcWUFWlQCSVkIbN985wCJe7UeyOrBj2P/awpDmU5VXn8Aq2bBfQkOebsYTAs1n55k3SXFrL/03EiDby+8c2CHHWgvnfkZ+SL6BghA9rDvcH/Q/5+WTLjIpbE5gsABrpe3W18+MFMtHuDoH/YBI5rBdAGdZvuuiyNxaqR/LAol66gIeI0OMvfNf+ySt1n32wT+N+fBvjPdu2ed2+cJjI82RlUAZgN0V+EgIv/st6eZq9dcnb5n+5FNri5VDRxuriXguOXOLXHQ3HCyJyOuBhyYqoj0dF+/y4CVqR1eHAF5hIuFjAhw7JlZw9IIpYWUFc7ONZqkmchYW+rdwQ76vvbhUIQ7+L3Fmvut9uceeiF69z7H++Kr0TGXT3BWxQioouIflzGfPJiV8222OISr6f6W6mlOTkzaf3gWY+BuAiNfQUx3vCkQ5bng6UQmGvWiwlyySKQT611vdDvZDi+CfHEJOrxjfdI4vBKBx4hVXVn+Hlby9rJPZuMgKKY/3FtOdIkF4fbQ4W9kj/DsXhnrGEJ1jgnaWL+INNErOUomikxbktdU5XGqK/M8soJpZnJLSONLuiQq+bGcooazRwPIWWdFVNzMlpawKuXm2+LojYynZvTTv85EZamha0lNSNI2qgIQiLBS2xKj70McORoPytbIM/gAiFldILWACUvXi7fs3PbBa3HapTtd5460CEq5KPjlJJqdP3dSTr98H+RpHM+hwx8frdQGwwuMmwQMoVgaayOSIk1cdCMEvmBuIw73tZmOqMpzPJWMnmWVX+9U8u/Mu4LSUWAZ0QAaRHXDyiOYQD2XCsZzxl5EPlb3XRwLSuxWqMqM67VLopZdcxcWyPgVi+ZbCBNu5bXWnUrVsiv+mYvmOCZXh7gtembkZbyJUpHe8/aGHbTHFH7xmTMH4axgrwjdOgT0pgpwuo0+d9uC+40/x/D0l6IRjNXiLgo3bI1K/VnDeKrO/Ktor6MedVWIOSYd7q6moFTifYTedwiqg7X7ng5AkMtYQItSnSoVsOhRAy3iZCQ/cFP1lIj3t1PUiPaBdwQJoW0IpAn4rRC6DIC4Sy6jjyeuxZ9Q1XxtLXq7jmU33z15WN3rI25vb2rx+QbpTXr3yvlPD+f27gtaUrLo2reWvvcKQRgKyV/cFZg/L3msbc6GoothQ3FtIrdXDOEEi92677Q7mGdMdkK7NBDULlwyDLD5OPgnx1JaOXgHIgxIDps6guF9YtW1cf6xn3M7c2bx7UtjsVjbTz4SBPkJ03HNQ1MgzqwqVr+Jmu7WF4mFcFu5ZzdFKeGcq0TN1idTOcIUqgA/BKZNPOJFRFJPuL7r/6MiRH/sJzxpmUwHhe0GOPgh6NYr2neZFWkeODCuegCF6aENl2LACCmdCVH+9Vjf2NWKKI+EzlfZIzdpcD0lGTKubuN1oDwwmsZN3tuMLDiGxOrdwMiAJnDFWV2WZh7XZ7q/KzEbzfvXv/1GanVw3n/+p6pcJhzfB8K7X/NT17b9K3ix4FAcj+QXYVxHVz5j6AL5322jM5ozbwhM2U05HFybtFk87P5nGrWAPVYVDlHAHxDtnCW9Mb7Yk000vudwlkSO7sduUq+M0cPttkh79w5gu3Xa7QPIR+Ye/GHebeOiHJMzhQkjp/pvul6WgqgYlMLQzCq9fDAGG5HHQDrT57FKA1+CcB/bJEqUbwLw90ofzHZ7f3oG2LU4vTjOeTQRVcuF+q0IZcE/YSibaI2QgBxsIh9oDm+QjZtgZoM8E8dp+RQ+x+S26W5Vqqwr2bJ5k7jCdm2UhnXwkROs5GnZLk7uoujYswcq5Rd2tSfVVZq77ZZcplcgZl1/p/rj7+RQILmNe9YvYU5fwthef0iGkAbTFyCkpxJ1zSP1M97s5V3ARZ97t4Ytln/p095eT7A68Z9ET4rYFA08ROQO02o4WOtq2QhTi5R57ee+XhaXtQNga8IkBKwJe2VqtHhnhWdd2sGFVyGx7D394/wYx7WYrO+ghIcv65y2jJYH79ArqgiSAJXv25luBGZLsNcIuX07XPYocWKhsHYGQTmGdxYBErzv2G4KNlRWPLF3iMwNuQJSaNuzfocj6m3wZvAe/7Z2UEXB7WFKU2L7545t8VMNE10GZ3/EW0v2UxE+pYFmSTkVyccMNX+edj92hSJKuU8smgfyQvwaa1NBMx1Wt5LMJ+XeU+bPlYtSsUVHCtVCWFfi1mi2rCt5q23VdHwmDJ7ROO80L0z3ko4sLimnotxVBQ2ORG5k7rjRb1C9p996PNaM+bqgKJa9/PSHkA37GI4eg+6ObeP1bruTezyDYgj0HGfM8/rnXvTbEMFEVTX7NfU89Kdn7mod9XS9ig/0dd9AFe6pmCSW9HCXPswqfZJ3hOtg7w9Wb8PUmBPt9FI2Wk1GfGzVpU3QjSmB1c5Z57bWZ8e3igi0QvaMNdj0VQmL83u5LYc3DsAFBa4j+/FlZ9cGmcPfDODikeqgUYiKK+NWvrQny8b8g2yiY3VScGzDsU2kww0gXHhOkn+evZCJGQ3km/eoX1t53n2N4gTyHkjy/HMX85BoSvVqAlXn5bL+9MUJ3l7Il25k4RfgKNpyXlspDRHSDtH61ZhhqKZ0uqYahXX3ACvTIs0TmLbHyi09b/+cxDf1zFrBlC55DgedI8OcAbIbEc4Drv7BGnS1Ni7wCF6XB6Mvy8nPY4C+HYtKnnZzf+lX2A2WlGPliXhHh1ItX8HDKehQ6SxR4hptJFD8hcgG108EBP8VbcpFoOvY4HbrWW+6HTq+WNwtz3uhzNgQSXM5NiAI5d7E330xJq0nwqxhV8gWMKxX+h3fWkxJ7M+8puF+VVFDNYqFQ5OgL5Orco6R47u9fqQ85fEEfckz65ZemeRty+0u/lGObrTbkeMxqQyYofe4F/H38QzSFFtF7Ol4dPqoNCg/4WObwNsQrLAwGiCSTEwP1YQgQDtINSYX8xZbBgl7TkXDZL7PKGFwF6M1br9UW64vlcntCdME5lUwmuuwbPDc0ePanSuaxtV1RrW3XlVprnih2+ImX4uV9izp1gXGjIHW7jz4V5NZfGouGTDZ0eWWmPL5pbMsN3iuHa57bgqTAcLNaXtCJutdFZZ6DxqU3ae9+l4i11VDAyIyNjm+tzHkj7Ntzo2aWaHOqazJAebzQOvcrogt5GUZPWHkYcImUhLFMhxOwcQ0wgSTtiF84zGta69avJESKyel0E+RGBwFcOY1YTvvXhjUQcVgLnYYtscS3cYmxYYnVilUuppNR0+dWJBTHcasVq3yxlBy8CoeKkf7ZLAUvLbaUoDwxLtJW4xUBo7T5vf9P9wvp8nzY7wluyoy5KjF5Q+7t1fey4Z//46n0SPqyPZlyTPhWkMOvghxW0Uin5ALR9glo6/Tv+XvGFOSjMjO+mSt72zkClsYh4RkFfvdScbBoDtdE8lMUtEBEit8kprK9XpEpeAYm+9P1SjQf1KmRMoKutSU3BhVWcWDtymd/+rcBHArKjI2W+MmTeNBnjgynFjZ71Ql4fBc9IMlqTpyJhWf+Z+B1DrTnzy1dCZpglzxYxiPDwNQctlh9/ii1OB2yYh2Li33Fso7B9hmdPn8JT+g54aTgc9rhc3+FMbiCx+f50nTZauApsn5XYxqfn4AXc2VRbGjaBfAwPj6/sj7RiE2e2DrjAnAsHXBy8ONVKfaRfG2slBqeGPLJbLjenrvv6vZVBeXuUxwhDySplPhHlhZKL5hDfjOyLGLr1Llf4b/FP0BZVOE9GjmwDGDhZV4uwb14eQPn84VKsTllH1Dpexz7JNkGfpeKVouXEq6nvmdU1I/E5EceiQYSlctWmQy8hsDr0itf+wa8RS5Vk0Mx16d/8mMfNp8qMUILS95QE57YRfdSWSnec7o2HoxskoRPSsKP15IvoGn0qU4ggClOiqYMGTUwkyW7mygpAlYiTnjzE71WWRZ2AhiUOWyNS/xcnsyQfGxgeW9J9WWW+PgSg3GuD8yKcbuCaNoreBSsF5qlQmVzCNQzUQkCrKoLftdadk+hcNMlu6c1PEjRfnt7Ppck/pDOkm/FKvNJo3nup1XqSo6O6wSn8i7DcGWTPGYaH4NwUBvGfxoOkLHZ6K0a5mfCpgDkETWZZgvzlmv98z+3g8KP/YU4I2mcq5EI/g7aJE5JXtbZDbOMF/vZbBwkqIlVKnG5IGCMj8HewBRa5KRUPYhUNbqGFEXkEGLSzv37dq0tb16Yr47l0rGhYFbjBZsG7MzBmFZXadlBKNZh24iNvlivW8Y60AU/IqKvPM/PMNKBg6n8UC8s/taBlrsxp1CJMaJRl7mQ6DQOrzTBhx+8Vl3b6WE37w1M3bklN5xPMU9IGfnwFWZ0ohSpjLWHD6QizD2t7L9MHsJjH82wGNiFQpYphLq0Ume9kjfd0oiHR6UTkwFzaYtCUv7pQnnOrceS+aw6Fq20wHHVW91vpLJa1Ii53TEIZYiVp2nCjxfBNtXR3o5/aoSIZCcaTg6krSJWudVO6Pr7OauIVWvdMMETVmebM6PTWSsv6oCMXmTXLJUdOWIXreh8ZfHoyqqIuqTquCTCbeZxm+lEVhYBd9+H8HrHm3a3F7X3vcdKdPvV935ADipyMDYuv/51Gys5Afaw6KeEJ/0WOYtiaBzd1fGEDECr4/k4tSB70mnxW+9du+B3Cs0DlzKsv9ylDOsXXsogug4qw5kkfGUkJcoiKTkUCVmpYqu/j4d3dXphtfA0cRGFl8Gl0C0h2IzuU9W4Z623e0eXyFkwU6A7MkTASc3tcrnlfC5Mcfe/O6WgO/vJfnLuiXN19FOgQRC10L1nfDKx4GrSOhuE+z0VVn/fIUth4k63Kcc5FHDZYBapt+jCed/g/Pr62cr0yHDGEo5+M5nTxUwjdrZ8INofyMFw0HbNeZVdIt81Gi/rlsSMTamyzM8t4+Abty94eSv8ae5c5AXZKoXwFy++GA6ps//m11xoeGomEN73wAvamzKKQpeIXdtbhB/Pg17MoqMdP2gcadaKPChTUK8X1KrvCTI5qY/Bwp9IfPdyIlbhrzdmiCxUtblpr1Xl9OFRXMHibDQAlFesdFrG14dD3BLh5/2qFtWzMS/pLDCTcYko5ISCFAtCeebceYO4PcpMm7no+3cfUIkZkLCO9+9jEXnT3Pnlzo98bFbP+cGSghH1KNJ78U1367yG2AKqfPV/XR28daEMX7wODvaJToDNX4X4OrB1gXdGoPnaSCrsYqTXCRTm7QPooMIzKf413nJk1ykvHOcZhY5enCnNlKbBOHJvJ+DNBkPF2XChkbrYmNU/JPjz5blbb4nSueu3rbCenQI51CqTCVDFlx+RI6NjAg4/cNmauq/dUd7/HpsA7/+AnM7FUn1TdsEA/mO/zjv1o31bjr8NvHpVx+tSuLC1KkHap1OgJ3/+gUpv7SIzhrhyRcTQ+/koOdgr8p6dGZ0dydlVL6c/o9cx9jvY+qaz4yHv1OaJVjgtvbKxx6ftTZpJfdQdTM68sr236n2Zcy8QBphRR+nB7jf/ed1viVjQUGSkY93qfuMWxlKwcqNKrdpTxD7miQO/uPE2Y1x7+t3EFS7GF30utz88Vgn73fqzr75XxUN/++wvC+WxsHcqtTbX9oXDvrb1LK1zC0QHGc6C9/n3nVCIHyEoFgjjhpfxZJdkV+JTKoBAIDU4IyJQIJddeFKuTwFReh3ixTjOCv8aX+vEaxdZmRZlu/5Cnl+82MpO7uXmgWTr1iKOEKN5iFJGyrlqvhqPmhHDBwKWxQCbRBR4oUL0zqPwKZpTwqYdD5bwlq21vvCPjcthpm3dASh6e3n0jxLzxRGWKEzFzJI+dvSoY54uBxFgKQCc07NveDot4zcMvSDHhou7tu4S8dUC/megbwk1+O0shgpGfHICIqiMaG5nAlnbUNDJHvDtBcjOcrncKNeHh2dKBZ4+sCPFNO6nnw3/4JE661XQvnml3aySfPExoktbplyGOlWR++EOyLLaWq6OXPsP+0KhiWrJ40uGn/2egSc0t53A6v777rcdOT5+nTIWnMSL7jevlkcq3wr4PZbNxcdhXwX0to4vFASCywQVogPpxRBvwwC8e6IHdL29k5cXzhnUUfchZ85SiYH5C6f6SGb9zHRoLGeXTRzNFxcgtKxTWDy27ktCS46MiEBfCXZK2E2ZS3O1dlp6LSIAW6+TCVd6XMbKtQzTmRH5lpsG7T+P/d4KeryENnVmeE9DFIMZ38FbMIiETvT1mqfenfAPo4VNzXo66fOgJbzES3rtmjhS0utvt27d6ScDrPaDlDKQkOzl5H6wVKL75KktpZH9db1VV4lIDHjT7UoqnmU52VQNxau52erJyOih3dbdOkzKLl02lnBjA7x+A34VqyZVrEyBHgv7InEXlTHvpJNlRaruuT8ij6gyv06H5Qt83yHY9ymyC82hY2dCbp667cWM/IC2pPBTsQc54hZn9NghqzuQn2Lm85Sn6gmS+SHv/ux6JwxSBUFJNh01Da9LRXN4TrWOcDftikKzPdB2Nxjo8fKY6BIDyOYldsvKPN4cA64obsUfwODTR4e5CkxNq4ahTgMWc/tKKZekahqlGohQfVKR8B3hqE92M9po2B6/VrPivev/VdLvplFw18W0OPZanG6m/9XtQg4a514A3/Y1VEPv7rgrGQ8AdglbWCxi3RlmQ3V0CIOW2GiMQ1rTmuLy4RW9Kod6Z57yzjQ6eOH04IwxOGNXjYeLiVjApzJUw5NcxHgzS1Ph939YmL5fV7SOPJHGec1PefxHsTBWW8cWV1wSJ151XOAASR0NKw0/Py4Y4JbEtiljgahP20RGJ0J/+X4brP3rT2hu4p11vfVhJ4cCQD/ACyuIJ8v+Bug1iradiTr1xbRTa/GvbTi3xMcIOjlYf7HeWndkzK4VBrPUvJ/LabqzQBPz4d7VPlUaczOpWJbCbryuj2ZklymP+P0eRubTad2UFhYlU/e4C8Uofkzx8KbvEO7+l/ybrneHwSoCqtO+fuSoqEOGh0fuuGu/XTv6JuxlAV3VcQGiCRDZKj8m++XH8zG4NYzIyfOqkvaIwN+iLLmANg1PL7adsiQZqEoKfl68KslTJA4tGlYVqk4+F8Q3UleYzW8GvM2GEuBSU0OSarKRURZ20RtDNFOREtqKuiDptZhkatJaqUxcQ1IRf83lZ91Xs5CoKLqP3Djj8lTvuZpZ9ViGH5LBXBKW7v4q9w9aHIbU7rP49x6RAOBa9ME/BPp00GUdrT0xpEl98ojGyvOJIwYHgpOB9xZhtGZ9tDQxJQF4L4Z4XCJ6crzUOt1hH5okZac6yQWBlyX7NUkWFokS/MNUaULVfMUxQjSXRympimuqRn0Y77kEnhDkjU6m4x6ij8S2Y8XVnjCkdwwPX0aoaVIamlmm1OMP6wu6wq/tAMGWrj+O8ZVXS5T5GMQwvlbmOJWY7paVJ/BU7VoqW7m3c3Uh+y20vaONxg0i4R49Ag64wqKvD/SEN63xQXHtRW9wvcPvn2mhxszIKD+dx3flteqqViTatBpm+VlGO0nqVCZD4VrvNgz8dHDXPhy6ZNIf2ya74PdTpXJY07WrrjLo6Kgc1v2VIpjqloeCGJRADALSz7u0vqpLvqt4xc3jCqfc+cS3v6d6NJ6zMYZuP1Jm2N38WJmJajPfbxp+fBb2O4wOWJsMb6iZ9IqO9vWK9gG8YxvqkReMCo9/prG9LOp2uJcLHuiQJ2HnpFh/Erv/zGMyg6p+infKiuJm+Mef9sRDMfkTH5djMvHtwHcT5kpqmpfxu+dc0bBCuj/AJBgrKISoxbBGgkec/tqn8VfxAcA/Y50RxG8m2w9ugYpmDnGO9RA8sCSObRZQoTCcLwgQx9W4d2pFsSvC4pSvpbAiAYy/yPBizetNFxNeMPRM3B5F2Nz4UMRMWEP4gMpal1A1ERp1MX6JlEK91c2qFgmNairH9QfJXvx1chaFUQW1zhhOHsXWMJ7+EE0q2NIw5z2/YuBMZdOwXQ/l/fkhy4ZalWsgo9m3tPZJ9jw/DyvXpiIQuvunRvyKBiiIfUP3SZMTzIBQWFIVY2TS7yaniceH/bIZ5HcIME2alHxjkQgT/iIYYarCjxiERFzyBInhT4iYPommexHihvZNcdfY+YP8trEzhXJpj1PRjQxWcpuNiJ0GheF/m52mHqkWH96j5hrp4c8kM+lUIpsn70lgb3BswQc/x2O1Sa/PHPIJnn+fRPFXxXmuSds38dE7RNKbE7L/ltNRXHE0hMIrvD3A7gC3kYl1Rr7Gz87iLyoYv4/iXAJ4LLE9/HpHvEzOaPzQ0UOY32pKZFm7m9+lwz/+Lec6eOHcT4EuQdFW74RBw3WezegxR2SB+VnV0luukmkuT9kCxbRRl+jj7G2i25Y+KnJhkpM/AR0NoK0D+ZOknT/xO/mTfkqlf/Sn946nVP5FORX8iYvk/7CV/4NnKaK69Qh+p34iIE4Mi7uybAjgDK2fjlfO6z0ayMsJWvNiWsvO3t16firOrTEms2Razqg8VYe/cn7qLRvXsRSghjrHM3Po/7Ca+s34l/As74AYHkIkXfQY8MYdCjSi9IaVleyS1VbAG1MjtTmc5mXFeu9YQq874+awKbs0sHRjve+k7FWYNFuEXA8W6I1uXCjCl8JXyOr9/ALGBxiXn51kEv8N+QPwz8OdoscFcsxv5OI7R/yONivXitFucfeXt2npAz9Cnod922fI8b/j57ufuuUKIqQSf578Ab+yhkIcAs5IHCdDl5Jh/F1Bc1v/7N65w8KUoX4z3WGnme5MoVTKWpsXWuFY2IZVBrYk5NJyBByYP7hzLejHmjtU2VNe21XcQ95W1jRe5+elfk0rrpq6bgod3Eb2oZ+JPH37dAgP3HgmlEGYA2SZA/utz7nxLIaG0qJbKKWEIv0su4W+v0kUwgQeoCxwc5Cfa/PLTXIW83BDlpiiyQHNzxQfi4RlHy+kgxr8N7CTf/8/ayfxK9pJrPyLDCX4RlIQdrKAVpxufUl4w2t45+NB7iepMJgXGffZZyH/t3hNcuYVvSan562g11+BZ4+gcbRgw3UiDjPyBnKbsxtGBHMDJoiGOW5WMmn41dAQc2w+vxmy7Nw/3Ieg543Wsc7KI4TkC1TmIoDBwy//5sIh2IDENm/lbQWUuaeNvMb9xMXG+meAHkMpxB6LUTwpDLODkMTh+Y0Ns/wQAH6dlScKhGh1aO9oqXcU7I/65wW9Oibd57vPD9Rt0CaE6AFxJqyK1NPwqZhMVrCTeEVmSMmPEjYqlXgtAQJ61GyZ7RpRQtZx7DJ+rbxtNqL7dH5GKuDWR1rL2/ZesnfbcmtEdwcINn0QAnww/PhjePbymGgNVQz5pXd5wvzi77BObiNDb6TdPxgHOj345KJ1N/LCuZ/RKPkGUpAJT+U6W4h6wb5ToIM/mzHgv+LAZUhc1u0rBDakiUE3F/BN+ET3nd1H8a13HB5r3LG/kTbMQ5L0bO/w5V3WK7wP3919uPuHB9/4hj9c2VVNjM3e/Mgj+LCIfkcrzoGXyqhoDsNoBAjHzz6NAX/8GPiD27OU13HFWXb7+BtvQBX9pwPoIQBYVtHduVQugqMu+oTGjEqTDrmk8T0u1ljRiVfWJwtepj1BXeQ0J5Dy0jt1r4BXYeW92NyvzX72pYjsBXsjK16d3KLwY1NVeJ6Pk08CZm+jy4Fea9PjWQMBvUDfxJmHnFMTHGi7VkYxkzdeKGff6d5uUW5Vzjvlmy1aVzO0W+SO8Lc634xobhmHKeGtgDiB4/2Tq90o/e53HBJ/52NT/FJjELfI0/nM9yKap3f0t/sfu/9JXNwAUgoKg1WDlUo2uCgUWYDhB8TrfEHcHiRTpo0XA92fwlImVli3OBRL/JBa/0zVYygDfPFu0Btj8GRYdqPmkJL8Moc6/8hBOl/uPt072hXCwe7Pu88O5hN5v+Z/EPeRscfyPvjeYuEC2ongtukQePBIMPAhX7SQH96Mt0Z1P+tR6N92vxmn5NV3NhZvh1n+sEcdOjPD3X37X6mhlfuso9FZ8URKQmPy7z1MZlbucWmFxIazwvSbmk+P77XioAVQtCj5OtoCz1xVOK3sSwdtIMZDTWGmrdsH8iX71qKNtasmROvNXjlGOeDTy1Pr3qh/az7gJjQwHB4vzE2WW7rvSAzjVDhq5K0Gd9G7z8tW09TllbMEx4iqeIPNckMnvu25OvGtjUaCZmhlNOhVvOFgyMif8n7pCZvo3zhxBDON3u0Bsx4Ney2bYfNgFm1De0EHdq5tmstym4EHEo8p3ONJzovP6zSB/Tk+vcYvmARB9/eu+uLpmVLZ4FkZfg/HlWRqgjOC8ozoPfOzs/P3EEXClO9tapJfr0hcMD4zA+MucALAPCkwkg9GJlT8XaM8xm8H1kDDuUSVSoDJO4tHDi91vBiXSvYmy8N8uHPN0QU+PDwc6A7hMcW/eX4xX+ToNODTu9/2TFS1FVfbkkEag/3vBX4umdwm9S9csf7ND+7WzkVZ8tjqsz1iueXBXKPpJB6AIH9t5hMxlV/4yBLvdZVYb7++LyfA1TGqcotAlMDUrDvvArAre8cM+B9EEUAwnohkm3xg+RX3hERO+pJZDXvNHYu5j45KSrls73x4hOS/lJ7ZbnrBJMg+H78RIDCd2beW2JX2/uQndp7yv/yEJq6Oru3LLPO+UTQF/P8p+Iw9sP+Sl+/fzr87p0zKjvkCuszh3t9eke1d7caUCy/YdDCeVWN66/o+zVUpebwx+d67efJMDsIjDxuXhhpF7wzN4LG3X9aYTYen28alxrAUdC7cvPduQyURPd1QyF5yate+Qn70qn3VyUqW0yOcFneo4dLyZHj/paHgdyZTN26Znx/dvj274f5NLws0FjMrt7Zc2cvXLB1G6HlSxKuv1JNr/JaeXJzY2JTrfK60Bp/rvdjnNnPO52bsA5cZ/qGSV47F5Jf+6aXf2B9KlBzvbOZxzh78OXFHeQQtdzwgzCgU5Mf4eqeHvMi6Pf5q1A8HegMCpvFwNYIiQybPHgbLzQ09C+IqJ6bsZ+Byezc1AcST8OfYg+1273YmiT4IEc7/VXemE/QI/hxRbVx7sBMoegHCVkajQ5yEap+EAdA+RWZc+a6mTlR1/qAgZdhGvOPD5XTKIqr68kStX3z47RfQ+obfifoX48f///e7cBpwBfslv45V/P0+svX3+0w16+Es/Hccjx3n/9jrxi62rs7XWf9YOoxgFV/HHgO1tdbwuf8BMRqSaQAAAHjapVTdShtBFD4bY2pDtQrSQi/sFEpJINlkozdGEaISGgyKRsSbVsbNxF1NdsPOJNGLPkIvS0tfpi/Rmz5BX6Pfzo5oxGqtGbLzzZlzvvPNmR8iemHNkEXJr0nfDbboufXE4BSlrTcGT9Bbq29wmuatHwZP0uvUvMEZmk99NHjaqqR/GTxDrzLrBs/S88wng+confkKZiv9FKNvOkuMLVqg3wanaMp6afAEvbfeGZymnPXF4Elas34anKFcasng6VQ7JQ2eoaXMM4NnaSHzweA5msp8pg0KqU8XFJFPJ+SRIkY5cimPvkJltGUqauTgz2iTBEntG2DUgqcPS4BeUAGWhsY2Ze9ldmgJaAszXHPVMMepDZ4eomgj7F9E/omnWM7Ns0q5vFyslJ0y2xTSPwlYy/VF4IoCawSunb3p7CyxLY8HrObytuiBbQvU+6A+pCOIjhNK9EKL62Ca74vDoxYPZEtEPgzrEN+FGFoPu/jWMQywgriPsBqh12DrilT1Ku6iL17R1cNA1cPoRLCKXWZVdiNxMUn3z3S3hx9o13iXQl1ZB0odWgFSaB0QDtCHqLyvVxXvy1B7LWJ36EBE0g8D5tjOClOqwwcq9PwAtR069mL+0fIeduQKDzh0Mc8qjXSzcYw4uD06RX+OcbJ1a8jz2MM5nufMePExn+v5Csg4QqZYDaM9vZq4XEN827BcHi9G22Do6eN1d5HjC5bFfLyFcoyhBdQBGiEm0kyJRxe9q6smTdYBcFszMq1M6OgGnkRGO6iO0Cu/Ym6OMcR1v/2g2WPKxvMyqBri78PO6Rjf2HZVG64z1mhXY4XLldW7o6CnSiU0Cba4Bn3YJHJJzXVZ7RKU16H0b89H4db3g+VWR6OR3ePKO+XnNu7nWv6+N8XEnMHEE0sSV8iOfOWxPSFFNBRtFt94ts174uZdt7PZfc+XiUMr7KgRjwSDoeu7IpAIHQRtETHlCdZqNNlOXwSJczNxKLBrN9VOyEws40Pud/lxVzCthrN6bZdxVc16SvWrpZJ0I7+vpC39biy7tFNHzf6r0HcRPu7V/QN8pYF9eNptzckuQ2EAxfH/d9vb27k1zyLmWbWomqJ0MBSlLTUtrhC9iZakbG265hVsrY2PgpjiSajq0klOfquTg0Qh3znc/JfzfAUSOvTIGFAwYsKMBSs27DhwUkIpZZRTQSVVVFNDLXXU00AjTTTTQitttNNBJ11000MvffQzgIvB/K+HIYYZwcsoPsYYZ4JJppjGzwwBgoQIM8c8CywSYYllVoiyyhox4iRYZ4Mkm2yxzQ67qELikhwXfPAkdDzzJfS88sY7L3wKmWthEIowCpMwC4uwChtX3HDLA4/ccS/swiGcyllGc7n8roJuj/fPWZ8cVtNpVY6nDk5VQ0RN7+2rUlKTopoc0w7TqpI4yWpHxxldNKXpolntdxYMhQJFg0VDPxndPugAAAAAAQAB//8ADwAAAAEAAAAAzD2izwAAAADG+TJPAAAAANaYKxE=",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_SansSerif-Bold.woff",
"type": "application/font-woff"
},
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_SansSerif-Italic.woff",
"type": "application/font-woff"
},
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"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_SansSerif-Regular.woff",
"type": "application/font-woff"
},
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_Script-Regular.woff",
"type": "application/font-woff"
},
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"type": "application/font-woff"
},
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",
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"type": "application/font-woff"
},
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},
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",
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"type": "application/font-woff"
},
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",
"title": "$:/plugins/tiddlywiki/katex/fonts/KaTeX_Typewriter-Regular.woff",
"type": "application/font-woff"
},
"$:/plugins/tiddlywiki/katex/katex-logo": {
"title": "$:/plugins/tiddlywiki/katex/katex-logo",
"text": "$$\\KaTeX$$\n"
},
"$:/plugins/tiddlywiki/katex/latex-parser.js": {
"title": "$:/plugins/tiddlywiki/katex/latex-parser.js",
"text": "/*\\\ntitle: $:/plugins/tiddlywiki/katex/latex-parser.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for LaTeX. For example:\n\n```\n\t$$latex-goes-here$$\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except latex-parser \n\\rules only latex-parser \n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"latex-parser\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\$\\$(?!\\$)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tvar reEnd = /\\$\\$/mg;\n\t// Look for the end marker\n\treEnd.lastIndex = this.parser.pos;\n\tvar match = reEnd.exec(this.parser.source),\n\t\ttext,\n\t\tdisplayMode;\n\t// Process the text\n\tif(match) {\n\t\ttext = this.parser.source.substring(this.parser.pos,match.index);\n\t\tdisplayMode = text.indexOf('\\n') != -1;\n\t\tthis.parser.pos = match.index + match[0].length;\n\t} else {\n\t\ttext = this.parser.source.substr(this.parser.pos);\n\t\tdisplayMode = false;\n\t\tthis.parser.pos = this.parser.sourceLength;\n\t}\n\treturn [{\n\t\ttype: \"latex\",\n\t\tattributes: {\n\t\t\ttext: {\n\t\t\t\ttype: \"text\",\n\t\t\t\tvalue: text\n\t\t\t},\n\t\t\tdisplayMode: {\n\t\t\t\ttype: \"text\",\n\t\t\t\tvalue: displayMode ? \"true\" : \"false\"\n\t\t\t}\n\t\t}\n\t}];\n};\n\n})();\n",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/plugins/tiddlywiki/katex/readme": {
"title": "$:/plugins/tiddlywiki/katex/readme",
"text": "This is a TiddlyWiki plugin for mathematical typesetting based on [[KaTeX from Khan Academy|http://khan.github.io/KaTeX/]].\n\nIt is completely self-contained, and doesn't need an Internet connection in order to work. It works both in the browser and under Node.js.\n\n[[Source code|https://github.com/Jermolene/TiddlyWiki5/blob/master/plugins/tiddlywiki/katex]]\n"
},
"$:/plugins/tiddlywiki/katex/snippets/logo": {
"title": "$:/plugins/tiddlywiki/katex/snippets/logo",
"tags": "$:/tags/KaTeX/Snippet",
"text": "$$\\KaTeX$$\n"
},
"$:/plugins/tiddlywiki/katex/styles": {
"title": "$:/plugins/tiddlywiki/katex/styles",
"tags": "[[$:/tags/Stylesheet]]",
"text": "\\rules only filteredtranscludeinline transcludeinline macrodef macrocallinline\n\n/* KaTeX styles */\n\n{{$:/plugins/tiddlywiki/katex/katex.min.css}}\n\n/* Force text-rendering (see https://github.com/Jermolene/TiddlyWiki5/issues/2500) */\n\n.katex {\n text-rendering: auto;\n}\n\n/* Override font URLs */\n\n@font-face {\n\tfont-family: KaTeX_AMS;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_AMS-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Caligraphic;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Caligraphic-Bold.woff'>>) format('woff');\n\tfont-weight: 700;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Caligraphic;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Caligraphic-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Fraktur;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Fraktur-Bold.woff'>>) format('woff');\n\tfont-weight: 700;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Fraktur;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Fraktur-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Main;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Main-Bold.woff'>>) format('woff');\n\tfont-weight: 700;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Main;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Main-BoldItalic.woff'>>) format('woff');\n\tfont-weight: 700;\n\tfont-style: italic;\n}\n\n@font-face {\n\tfont-family: KaTeX_Main;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Main-Italic.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: italic;\n}\n\n@font-face {\n\tfont-family: KaTeX_Main;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Main-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Math;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Math-Italic.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: italic;\n}\n\n@font-face {\n\tfont-family: KaTeX_SansSerif;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_SansSerif-Bold.woff'>>) format('woff');\n\tfont-weight: 700;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_SansSerif;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_SansSerif-Italic.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: italic;\n}\n\n@font-face {\n\tfont-family: KaTeX_SansSerif;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_SansSerif-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Script;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Script-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Size1;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Size1-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Size2;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Size2-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Size3;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Size3-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Size4;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Size4-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n@font-face {\n\tfont-family: KaTeX_Typewriter;\n\tsrc: url(<<datauri '$:/plugins/tiddlywiki/katex/fonts/KaTeX_Typewriter-Regular.woff'>>) format('woff');\n\tfont-weight: 400;\n\tfont-style: normal;\n}\n\n"
},
"$:/plugins/tiddlywiki/katex/ui/EditorToolbar/katex-dropdown": {
"title": "$:/plugins/tiddlywiki/katex/ui/EditorToolbar/katex-dropdown",
"text": "\\define toolbar-button-stamp-inner()\n<$button tag=\"a\">\n\n<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"replace-selection\"\n\ttext={{$(snippetTitle)$}}\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<$view tiddler=<<snippetTitle>> field=\"caption\" mode=\"inline\">\n\n<$transclude tiddler=<<snippetTitle>> mode=\"inline\"/>\n\n</$view>\n\n</$button>\n\\end\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/KaTeX/Snippet]!has[draft.of]sort[caption]]\" variable=\"snippetTitle\">\n\n<<toolbar-button-stamp-inner>>\n\n</$list>\n\n----\n\n<$button tag=\"a\">\n\n<$action-sendmessage\n\t$message=\"tm-new-tiddler\"\n\ttags=\"$:/tags/KaTeX/Snippet\"\n\ttext=\"\"\"$$snippet$$\"\"\"\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<em>\n\n<$text text={{$:/language/Buttons/Stamp/Caption/New}}/>\n\n</em>\n\n</$button>\n\n[ext[KaTeX functions catalogue|https://khan.github.io/KaTeX/function-support.html]]\n"
},
"$:/plugins/tiddlywiki/katex/ui/EditorToolbar/katex": {
"title": "$:/plugins/tiddlywiki/katex/ui/EditorToolbar/katex",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/plugins/tiddlywiki/katex/katex-logo",
"caption": "katex",
"description": "create and insert preconfigured KaTeX snippets",
"condition": "[<targetTiddler>!is[image]]",
"dropdown": "$:/plugins/tiddlywiki/katex/ui/EditorToolbar/katex-dropdown",
"text": ""
},
"$:/plugins/tiddlywiki/katex/usage": {
"title": "$:/plugins/tiddlywiki/katex/usage",
"text": "The usual way to include ~LaTeX is to use `$$`. For example:\n\n```\n$$\\displaystyle f(x) = \\int_{-\\infty}^\\infty\\hat f(\\xi)\\,e^{2 \\pi i \\xi x}\\,d\\xi$$\n```\n\nSingle line equations will render in inline mode. If there are newlines between the `$$` delimiters, the equations will be rendered in display mode.\n\nThe underlying widget can also be used directly, giving more flexibility:\n\n```\n<$latex text=\"f(x) = \\int_{-\\infty}^\\infty\\hat f(\\xi)\\,e^{2 \\pi i \\xi x}\\,d\\xi\" displayMode=\"true\"></$latex>\n```\n\nThe KaTeX widget is provided under the name `<$latex>` and is also available under the alias `<$katex>`. It's better to use the generic `<$latex>` name unless you are running multiple ~LaTeX plugins and wish to specifically target KaTeX.\n"
},
"$:/plugins/tiddlywiki/katex/wrapper.js": {
"title": "$:/plugins/tiddlywiki/katex/wrapper.js",
"text": "/*\\\ntitle: $:/plugins/tiddlywiki/katex/wrapper.js\ntype: application/javascript\nmodule-type: widget\n\nWrapper for `katex.min.js` that provides a `<$latex>` widget. It is also available under the alias `<$katex>`\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar katex = require(\"$:/plugins/tiddlywiki/katex/katex.min.js\"),\n\tWidget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar KaTeXWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nKaTeXWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nKaTeXWidget.prototype.render = function(parent,nextSibling) {\n\t// Housekeeping\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Get the source text\n\tvar text = this.getAttribute(\"text\",this.parseTreeNode.text || \"\");\n\tvar displayMode = this.getAttribute(\"displayMode\",this.parseTreeNode.displayMode || \"false\") === \"true\";\n\t// Render it into a span\n\tvar span = this.document.createElement(\"span\"),\n\t\toptions = {throwOnError: false, displayMode: displayMode};\n\ttry {\n\t\tif(!this.document.isTiddlyWikiFakeDom) {\n\t\t\tkatex.render(text,span,options);\n\t\t} else {\n\t\t\tspan.innerHTML = katex.renderToString(text,options);\n\t\t}\n\t} catch(ex) {\n\t\tspan.className = \"tc-error\";\n\t\tspan.textContent = ex;\n\t}\n\t// Insert it into the DOM\n\tparent.insertBefore(span,nextSibling);\n\tthis.domNodes.push(span);\n};\n\n/*\nCompute the internal state of the widget\n*/\nKaTeXWidget.prototype.execute = function() {\n\t// Nothing to do for a katex widget\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nKaTeXWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.text) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports.latex = KaTeXWidget;\nexports.katex = KaTeXWidget;\n\n})();\n\n",
"type": "application/javascript",
"module-type": "widget"
}
}
}
$:/themes/tiddlywiki/vanilla
{
"tiddlers": {
"$:/themes/telmiger/navigator/icon": {
"text": "<svg class=\"em-icon te-navigator tc-image-button\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 22 22\" width=\"22pt\" height=\"22pt\" role=\"img\" fill=\"currentColor\" fill-rule=\"evenodd\" clip-rule=\"evenodd\">\n<path d=\"M9.703 2.088v-.72A1.3 1.3 0 0 1 10.997.074a1.3 1.3 0 0 1 1.294 1.294v.729a8.959 8.959 0 0 1 4.072 1.704l.526-.526a1.3 1.3 0 0 1 1.83 0 1.3 1.3 0 0 1 0 1.83l-.531.53a8.958 8.958 0 0 1 1.683 4.069h.733c.71 0 1.318.585 1.318 1.295 0 .709-.608 1.294-1.318 1.294h-.733a8.957 8.957 0 0 1-1.682 4.072l.513.513c.502.502.519 1.345.017 1.847s-1.345.485-1.847-.017l-.509-.509a8.959 8.959 0 0 1-4.072 1.704v.703c0 .71-.584 1.317-1.294 1.317-.71 0-1.294-.607-1.294-1.317v-.694a8.955 8.955 0 0 1-4.104-1.687l-.484.483c-.502.502-1.344.519-1.846.017s-.486-1.345.016-1.847l.479-.479a8.96 8.96 0 0 1-1.708-4.106h-.69a1.3 1.3 0 0 1-1.294-1.294 1.3 1.3 0 0 1 1.294-1.295h.69a8.962 8.962 0 0 1 1.709-4.103l-.496-.496a1.3 1.3 0 0 1 0-1.83 1.3 1.3 0 0 1 1.83 0l.5.5a8.949 8.949 0 0 1 4.104-1.687zm-.471 12.504l-1.933 1.932a6.58 6.58 0 0 0 2.404.985v-2.726a4.034 4.034 0 0 1-.471-.191zm3.504-.02a4.063 4.063 0 0 1-.445.188v2.736a6.564 6.564 0 0 0 2.373-.996l-1.928-1.928zM4.46 12.293A6.618 6.618 0 0 0 5.463 14.7l1.929-1.928a4.107 4.107 0 0 1-.201-.479H4.46zm10.276 0c-.052.15-.112.297-.181.438l1.933 1.933a6.6 6.6 0 0 0 .979-2.371h-2.731zm-3.772-3.025a1.733 1.733 0 1 1-.003 3.465 1.733 1.733 0 0 1 .003-3.465zm5.524-1.932l-1.933 1.932c.068.141.129.287.18.436h2.731a6.566 6.566 0 0 0-.978-2.368zM5.464 7.3a6.587 6.587 0 0 0-1.003 2.404h2.731c.057-.163.123-.323.2-.476L5.464 7.3zm6.827-2.796V7.24c.152.054.301.117.444.188L14.664 5.5a6.579 6.579 0 0 0-2.373-.996zm-2.588-.013a6.568 6.568 0 0 0-2.403.985l1.932 1.932c.152-.073.309-.137.471-.191V4.491z\"/></svg>",
"title": "$:/themes/telmiger/navigator/icon",
"tags": "$:/tags/Image",
"modifier": "Thomas Elmiger",
"modified": "20190225064911155",
"creator": "Thomas Elmiger",
"created": "20190201062848367",
"caption": "Navigator"
},
"$:/themes/telmiger/navigator/icons/menu-close": {
"text": "<svg class=\"em-icon te-menu-close tc-image-button\" viewBox=\"0 0 22 22\" xmlns=\"http://www.w3.org/2000/svg\" width=\"22pt\" height=\"22pt\" role=\"img\" fill=\"currentColor\" fill-rule=\"evenodd\"><path d=\"M20.624 22H1.376A1.377 1.377 0 0 1 0 20.624V1.376C0 .617.616 0 1.376 0h19.248C21.383 0 22 .616 22 1.376v19.248c0 .759-.616 1.376-1.376 1.376zM8.774 2.75h-4.65c-.759 0-1.374.617-1.374 1.375v13.75c0 .759.615 1.375 1.374 1.375h4.65A1.375 1.375 0 0 1 7.4 17.875V4.125c0-.758.615-1.375 1.374-1.375zm5.924 10.097l-2.772 2.771a1.304 1.304 0 0 1-1.844-1.844l2.77-2.773-2.77-2.774a1.304 1.304 0 0 1 1.844-1.844L14.7 9.149l2.772-2.766a1.302 1.302 0 0 1 1.845 0 1.3 1.3 0 0 1 0 1.844l-2.767 2.772 2.767 2.77a1.3 1.3 0 0 1 0 1.844 1.302 1.302 0 0 1-1.845 0L14.7 12.847h-.002z\"/></svg>",
"title": "$:/themes/telmiger/navigator/icons/menu-close",
"tags": "$:/tags/Image",
"modifier": "Thomas Elmiger",
"modified": "20190203135953402",
"creator": "Thomas Elmiger",
"created": "20190202081526032"
},
"$:/themes/telmiger/navigator/icons/menu-open": {
"text": "<svg class=\"em-icon te-menu-open tc-image-button\" viewBox=\"0 0 22 22\" xmlns=\"http://www.w3.org/2000/svg\" width=\"22pt\" height=\"22pt\" role=\"img\" fill=\"currentColor\"><path d=\"M20.624 22H1.376A1.377 1.377 0 0 1 0 20.624V1.376C0 .617.616 0 1.376 0h19.248C21.383 0 22 .616 22 1.376v19.248c0 .759-.616 1.376-1.376 1.376zM5.876 2.75H4.124c-.759 0-1.374.617-1.374 1.375v13.75c0 .759.615 1.375 1.374 1.375h1.752c.759 0 1.374-.617 1.374-1.375V4.125c0-.759-.615-1.375-1.374-1.375zm11.95 0h-6.452c-.759 0-1.374.617-1.374 1.375v13.75c0 .759.615 1.375 1.374 1.375h6.452c.759 0 1.374-.617 1.374-1.375V4.125c0-.759-.615-1.375-1.374-1.375zm-1.201 11.7a1.376 1.376 0 0 1 0 2.75h-3.95a1.376 1.376 0 0 1 0-2.75h3.95zm0-4.815a1.376 1.376 0 0 1 0 2.75h-3.95a1.376 1.376 0 0 1 0-2.75h3.95zm0-4.836a1.376 1.376 0 0 1 0 2.75h-3.95a1.376 1.376 0 0 1 0-2.75h3.95z\"/></svg>",
"title": "$:/themes/telmiger/navigator/icons/menu-open",
"tags": "$:/tags/Image",
"modifier": "Thomas Elmiger",
"modified": "20190202075643750",
"creator": "Thomas Elmiger",
"created": "20190202075425710"
},
"$:/themes/telmiger/navigator/icons/new": {
"text": "<svg class=\"em-icon te-new\" viewBox=\"0 0 22 22\" xmlns=\"http://www.w3.org/2000/svg\" width=\"22pt\" height=\"22pt\" role=\"img\" fill=\"currentColor\"><path d=\"M17.25 2.462l-.97.972 1.94 1.945.973-.973-1.945-1.944h.002zm.972-.972l.338-.337a1.375 1.375 0 1 1 1.945 1.944l-.338.338-1.945-1.946v.001zm-2.916 2.92L4.84 14.87C3.853 15.853 3 18.651 3 18.651s2.82-.875 3.784-1.84L17.25 6.352l-1.944-1.945v.002z\" fill-rule=\"nonzero\"/><path d=\"M6.292 4.543v2.125a.707.707 0 0 1-1.415 0l-.001-2.125L2.75 4.54a.707.707 0 0 1 0-1.414l2.125-.003.002-2.123c-.001-.391.316-.709.707-.707A.706.706 0 0 1 6.29 1l.002 2.123 2.123.002c.39 0 .708.317.707.707a.706.706 0 0 1-.707.707l-2.124.002zM7.189 21.716c1.167-.099 2.389-.39 4.072-.919.313-.099.613-.195 1.089-.351 2.75-.898 3.76-1.17 4.94-1.228 1.437-.071 2.381.366 3.022 1.495a.687.687 0 1 0 1.196-.678c-.917-1.617-2.363-2.285-4.286-2.19-1.353.067-2.416.352-5.299 1.294-.471.154-.767.25-1.074.346-3.345 1.051-5.289 1.216-6.806.301a.687.687 0 1 0-.711 1.177c1.089.657 2.364.88 3.857.753z\"/></svg>",
"title": "$:/themes/telmiger/navigator/icons/new",
"tags": "$:/tags/Image",
"modifier": "Thomas Elmiger",
"modified": "20190201202658107",
"creator": "Thomas Elmiger",
"created": "20190201135012521"
},
"$:/themes/telmiger/navigator/icons/to-top": {
"text": "<svg class=\"em-icon te-to-top tc-image-button\" viewBox=\"0 0 22 22\" xmlns=\"http://www.w3.org/2000/svg\" width=\"22pt\" height=\"22pt\" role=\"img\" fill=\"currentColor\"><path d=\"M9.625 10.695l-1.541 1.54a1.375 1.375 0 1 1-1.944-1.944l3.887-3.887c.258-.26.608-.404.973-.404h-.001c.353 0 .705.135.974.404l3.888 3.887a1.376 1.376 0 0 1-1.945 1.945l-1.541-1.541v8.93a1.375 1.375 0 1 1-2.75 0v-8.93z\"/><path d=\"M19.625 1H2.375C1.62 1 1 1.621 1 2.375S1.62 3.75 2.375 3.75h17.25C20.38 3.75 21 3.129 21 2.375S20.38 1 19.625 1z\"/></svg>",
"title": "$:/themes/telmiger/navigator/icons/to-top",
"tags": "$:/tags/Image",
"modifier": "Thomas Elmiger",
"modified": "20190202075725196",
"creator": "Thomas Elmiger",
"created": "20190201221606753"
},
"$:/themes/telmiger/navigator/main": {
"text": "!! Edit Main Navigation\n\n!!! Colours\n\n<$edit-text tag=\"input\" size=\"28\" tiddler=\"$:/themes/telmiger/navigator/settings\" index=\"colour-main-nav\" class=\"\"/> – background<br>\n<$edit-text tag=\"input\" size=\"28\" tiddler=\"$:/themes/telmiger/navigator/settings\" index=\"colour-new-button\" class=\"\"/> – new button background\n\n\n!!! Buttons\n\nMake sure to have only a few buttons here so that they are all visible on small mobile screens!\n\n<$edit-text tiddler=\"$:/themes/telmiger/navigator/TopLeftBar\" class=\"te-edit-text\"/> \n\nTo return to the original state, simply delete the tiddler $:/themes/telmiger/navigator/TopLeftBar that stores your changes.\n\n\n!!! Top left bar\n\nTo make sure, this comes first in the HTML code of this page, the so called left top bar is used for the main navigation. It displays all elements tagged <<tag $:/tags/TopLeftBar>> – in case you have other items there, you can click the tag pill and reorder them via drag and drop.\n",
"title": "$:/themes/telmiger/navigator/main",
"modifier": "Thomas Elmiger",
"modified": "20190205223514480",
"creator": "Thomas Elmiger",
"created": "20190202203807601"
},
"$:/themes/telmiger/navigator/readme": {
"created": "20190202200133608",
"creator": "Thomas Elmiger",
"text": "!! Navigator Theme\n\nThis is a so called //theme// for [[TiddlyWiki|https://tiddlywiki.com]] (TW). It is here to make navigation as easy as possible.\n\n\n!!! Main Navigation\n\nAt the bottom of the screen, where they are easy to reach on mobile devices, we have the most important functions. If you want to add or remove buttons, you are able to do that by changing [[$:/themes/telmiger/navigator/TopLeftBar]] or on the tab //main// of the plugin.\n\n\n!!! Sidebar Navigation\n\n{{$:/themes/telmiger/navigator/ui/Buttons/menu}} This button toggles the secondary navigation, known as the //sidebar// in TW. The button is available in the main navigation and as a //page control// tool in the sidebar itself. – On small screens, tap or click anywhere besides the sidebar to close it.\n\n<$checkbox tiddler=\"$:/themes/telmiger/navigator/settings\" index=\"hide-secondary-nav\" checked=\"yes\" unchecked=\"no\"> hide secondary navigation on startup</$checkbox>\n\nYou can [[add your own tabs|https://tiddlywiki.com/#How%20to%20add%20a%20new%20tab%20to%20the%20sidebar]] there, e.g. a [[table of contents|https://tiddlywiki.com/#Adding%20a%20table%20of%20contents%20to%20the%20sidebar]], click the links to learn how.\n\nThe sidebar contains all elements tagged <<tag $:/tags/SideBarSegment>>.\n\n\n!!! Top Navigation\n\nAt the top of the screen is a bar for three icons or buttons. See [[$:/themes/telmiger/navigator/TopRightBar]] or on the tab //top// of the plugin.\n\n\n!!! New Element\n\nThe [[big button|$:/themes/telmiger/navigator/ui/Buttons/new-tiddler-big]] in the lower right of the screen opens a new addition in the story. \n\n<$checkbox tiddler=\"$:/themes/telmiger/navigator/settings\" index=\"show-new-button\" checked=\"yes\" unchecked=\"no\"> show big new element button</$checkbox>\n\n\n!!! Keyboard Shortcut Hints\n\nOn big screens, the main navigation at the bottom can show hints for keyboard shortcuts (TW versions from 5.1.18).\n\n<$checkbox tiddler=\"$:/themes/telmiger/navigator/settings\" index=\"show-kbd-hints\" checked=\"yes\" unchecked=\"no\"> show keyboard hints</$checkbox>\n\n\n!! Recommended Plugins\n\nNavigator works best together with these [[other plugins|https://tid.li/tw5/plugins.html]] from the same author: \n\n* [[Simple Search|https://tid.li/tw5/plugins.html#%24%3A%2Fplugins%2Ftelmiger%2Fsimple-search]]\n* [[MyStory|https://tid.li/tw5/plugins.html#%24%3A%2Fplugins%2Ftelmiger%2FMyStory]]\n\n\n!!! Feedback\nPlease visit the friendly [[TW user group on Google|https://groups.google.com/forum/#!forum/tiddlywiki]] to connect to other users or the author Thomas Elmiger.\n\n\n!! Core overruling\n\nThis plugin overwrites $:/core/ui/TopBar/menu and nothing else.",
"title": "$:/themes/telmiger/navigator/readme",
"modifier": "Thomas Elmiger",
"modified": "20190303104127474",
"tags": "$:/tags/ControlPanel/Appearance",
"description": "Navigator Theme Readme and Settings",
"list-after": "$:/core/ui/ControlPanel/Theme",
"caption": "Theme Tweaks"
},
"$:/themes/telmiger/navigator/settings": {
"created": "20190202213759003",
"creator": "Thomas Elmiger",
"text": "hide-secondary-nav: yes\ncolour-main-nav: rgb(200, 225, 211)\ncolour-new-button: <<colour sidebar-tab-background>>\nshow-new-button: no\nshow-kbd-hints: no",
"type": "application/x-tiddler-dictionary",
"title": "$:/themes/telmiger/navigator/settings",
"tags": "",
"modifier": "Thomas Elmiger",
"modified": "20190310225739679"
},
"$:/themes/telmiger/navigator/StartupAction": {
"text": "<$reveal type=\"match\" state=\"$:/themes/telmiger/navigator/settings##hide-secondary-nav\" text=\"yes\">\n<$action-setfield $tiddler=\"$:/state/sidebar\" $value=\"no\"/>\n</$reveal>",
"title": "$:/themes/telmiger/navigator/StartupAction",
"tags": "$:/tags/StartupAction",
"modifier": "Thomas Elmiger",
"modified": "20190202230238366",
"creator": "Thomas Elmiger",
"created": "20190202225149898"
},
"$:/themes/telmiger/navigator/styles/dynamic.css": {
"created": "20190203140828079",
"creator": "Thomas Elmiger",
"text": "\\rules except bold underline strikethrough subscript superscript italic dash list\n\\define --breakpoint-not-small() min-width: 28rem\n\\define --breakpoint-medium() min-width: 42rem\n\\define --breakpoint-large() min-width: 77rem\n\\define if-editor-height-fixed(then,else)\n<$reveal state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"fixed\">\n$then$\n</$reveal>\n<$reveal state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"auto\">\n$else$\n</$reveal>\n\\end\n\n/*\n** Styles containing Wikilogic from\n** $:/themes/telmiger/bricks/wikilogic-42-77\n** inspired by TW vanilla\n** \"\"\" \n*/\n\nhtml {\n\tfont-size: {{$:/themes/telmiger/bricks/metrics/fontsize}};\n}\n\n/*\n** no-sidebar rules – mostly from .../001-page.css\n*/\n\n@media (<<--breakpoint-medium>>) {\n\n<<if-no-sidebar \"\n\n\tbody .tc-tiddler-view-frame, body .tc-tiddler-frame.tc-tiddler-edit-frame {\n\t\tmargin-right: auto;\n\t}\n\n\tbody .tc-tiddler-frame.tc-tiddler-edit-frame {\n\t\tmargin-right: 2rem;\n\t}\n\n\">>\n}\n\n\n@media (<<--breakpoint-large>>) {\n\n<<if-no-sidebar \"\n\n\tbody .tc-story-river {\n\t\tmargin-right: auto;\n\t\tpadding-right: 2rem;\n\t}\n\n\tbody .tc-tiddler-frame {\n\t\tmargin-right: auto;\n\t}\n\n\">>\n}\n\n\n/*\n** from .../components/tiddler.css\n*/\n\n<$reveal state=\"$:/themes/tiddlywiki/vanilla/options/stickytitles\" type=\"match\" text=\"yes\">\n.tc-tiddler-title {\n\tposition: -webkit-sticky;\n\tposition: -moz-sticky;\n\tposition: -o-sticky;\n\tposition: -ms-sticky;\n\tposition: sticky;\n\ttop: 0px;\n\tmargin-right: 0rem;\n\tmargin-left: -1rem;\n\tpadding-right: 4rem;\n\tpadding-left: 1rem;\n\tmin-width: calc(100% + 2rem);\n\tz-index: 300;\n\tbackground: <<colour tiddler-background>>;\n\tborder-bottom: 1px solid <<colour tab-divider>>;\n}\n\n@media (<<--breakpoint-not-small>>) {\n\t.tc-tiddler-title {\n\t\tmargin-right: 0rem;\n\t\tmargin-left: -1rem;\n\t\tpadding-right: 2rem;\n\t\tpadding-left: 1rem;\n\t\tmin-width: calc(100% + 2rem);\n\t}\n}\n\n@media (<<--breakpoint-medium>>) {\n\t.tc-tiddler-title {\n\t\tpadding-right: 1rem;\n\t}\n}\n</$reveal>\n\n\n/*\n** from .../edit/tiddler.css\n*/\n\ntextarea.tc-edit-texteditor {\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/editorfontfamily}};\n}\n\n<<if-editor-height-fixed then:\"\"\"\n\nbody .tc-tiddler-preview-preview {\n\toverflow-y: scroll;\n\theight: {{$:/config/TextEditor/EditorHeight/Height}};\n}\n\n\"\"\">>\n\n\n/*\n** Navigator Plugin – Dynamic Elements\n** (c) Thomas Elmiger\n*/\n\n\n#navigator-main {\n background-color: {{$:/themes/telmiger/navigator/settings##colour-main-nav}};\n}\n\n#navigator-main button {\n color: <<colour sidebar-tab-foreground>>;\n}\n\n#navigator-main button.te-big-new-btn {\n background-color: {{$:/themes/telmiger/navigator/settings##colour-new-button}};\n}\n\n\n/* Show big New button? */\n\n<$reveal type=\"match\" stateTitle=\"$:/themes/telmiger/navigator/settings\" stateIndex=\"show-new-button\" text=\"no\">\n\n.tc-page-container .tc-topbar-left button.te-big-new-btn {\n display: none;\n}\n\n</$reveal>\n\n/* Show keyboard hints? */\n\n<$reveal type=\"match\" stateTitle=\"$:/themes/telmiger/navigator/settings\" stateIndex=\"show-kbd-hints\" text=\"no\">\n\n#navigator-main .shortcut-code {\n display: none;\n}\n\n</$reveal>\n\n\n/* End \"\"\" */",
"title": "$:/themes/telmiger/navigator/styles/dynamic.css",
"tags": "$:/tags/Stylesheet MyMark",
"modifier": "Thomas Elmiger",
"modified": "20190304205200392"
},
"$:/themes/telmiger/navigator/top": {
"text": "!! Edit Top Navigation\n\n\n!!! Buttons\n\nMake sure to have the exact number of three buttons here so that they are all usable on small mobile screens!\n\n<$edit-text tiddler=\"$:/themes/telmiger/navigator/TopRightBar\" class=\"te-edit-text\"/> \n\nTo return to the original state, simply delete the tiddler $:/themes/telmiger/navigator/TopRightBar that stores your changes.\n\n\n!!! Top right bar\n\nTo make sure, this comes second in the HTML code of this page, the so called right top bar is used for the top navigation. It displays all elements tagged <<tag $:/tags/TopRightBar>> – in case you have other items there, it might be best to place them in the list above or to remove them.\n",
"title": "$:/themes/telmiger/navigator/top",
"tags": "ToDoComponents",
"modifier": "Thomas Elmiger",
"modified": "20190203212744214",
"creator": "Thomas Elmiger",
"created": "20190203203947555"
},
"$:/themes/telmiger/navigator/TopLeftBar": {
"text": "<nav id=\"navigator-main\" class=\"tc-page-controls\" tabindex=\"0\">\n{{$:/themes/telmiger/navigator/ui/Buttons/menu}}\n<span class=\"shortcut-code large-screen-only\"><kbd>{{$:/config/shortcuts/simple-search}}</kbd></span> {{$:/plugins/telmiger/simple-search/ui/Buttons/search}}\n{{$:/themes/telmiger/navigator/ui/Buttons/to-top}}\n{{$:/core/ui/Buttons/save-wiki}}\n{{$:/themes/telmiger/navigator/ui/Buttons/new-tiddler-big}}\n</nav>",
"title": "$:/themes/telmiger/navigator/TopLeftBar",
"tags": "$:/tags/TopLeftBar",
"modifier": "Thomas Elmiger",
"modified": "20190225215458800",
"creator": "Thomas Elmiger",
"created": "20190201203516392"
},
"$:/themes/telmiger/navigator/TopRightBar": {
"created": "20190201201906464",
"creator": "Thomas Elmiger",
"text": "<nav id=\"navigator-top\" class=\"tc-page-controls\" tabindex=\"0\">\n<ul>\n<li>{{$:/themes/telmiger/navigator/ui/Buttons/about}}</li>\n<li>{{$:/core/ui/Buttons/home}}</li>\n<li>{{$:/themes/telmiger/navigator/ui/Buttons/control-panel}}</li>\n</ul>\n</nav>",
"title": "$:/themes/telmiger/navigator/TopRightBar",
"tags": "$:/tags/TopRightBar",
"modifier": "Thomas Elmiger",
"modified": "20190407141132320",
"list-before": ""
},
"$:/themes/telmiger/navigator/ui/Buttons/menu": {
"text": "<$reveal state=\"$:/state/sidebar\" type=\"nomatch\" text=\"no\">\n<$button set=\"$:/state/sidebar\" setTo=\"no\" tooltip={{$:/language/Buttons/HideSideBar/Hint}} aria-label={{$:/language/Buttons/HideSideBar/Caption}} class=\"tc-btn-invisible te-menu-close tc-selected\">\n<div class=\"te-close-sidebar-btn\"></div>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/themes/telmiger/navigator/icons/menu-close}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\">{{$:/language/Buttons/HideSideBar/Caption}}</span>\n</$list>\n</$button>\n</$reveal>\n<$reveal state=\"$:/state/sidebar\" type=\"match\" text=\"no\">\n<$button set=\"$:/state/sidebar\" setTo=\"yes\" tooltip={{$:/language/Buttons/ShowSideBar/Hint}} aria-label={{$:/language/Buttons/ShowSideBar/Caption}} class=\"tc-btn-invisible te-menu-open\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/themes/telmiger/navigator/icons/menu-open}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\">{{$:/language/Buttons/ShowSideBar/Caption}}</span>\n</$list>\n</$button>\n</$reveal>",
"title": "$:/themes/telmiger/navigator/ui/Buttons/menu",
"tags": "$:/tags/PageControls",
"modifier": "Thomas Elmiger",
"modified": "20190225212300875",
"list-after": "",
"description": "Open/close sidebar menu",
"creator": "Thomas Elmiger",
"created": "20190202080028453",
"caption": "{{$:/themes/telmiger/navigator/icons/menu-open}} Sidebar Navigation"
},
"$:/themes/telmiger/navigator/ui/Buttons/to-top": {
"text": "<$button class=\"tc-btn-invisible te-btn-to-top\" tooltip=\"top of the story\">\n<$list variable=\"target\" filter=\"[list[$:/StoryList]first[]]\">\n<$action-navigate $to=<<target>>/>\n</$list>\n{{$:/themes/telmiger/navigator/icons/to-top}}\n</$button>",
"title": "$:/themes/telmiger/navigator/ui/Buttons/to-top",
"modifier": "Thomas Elmiger",
"modified": "20190201222537288",
"creator": "Thomas Elmiger",
"created": "20160923195909375"
},
"$:/core/ui/TopBar/menu": {
"text": "",
"title": "$:/core/ui/TopBar/menu",
"modifier": "Thomas Elmiger",
"modified": "20190203202106927",
"creator": "Thomas Elmiger",
"created": "20190203202056395"
},
"$:/themes/telmiger/navigator/styles/static.css": {
"created": "20190302143847671",
"creator": "Thomas Elmiger",
"text": "\\rules except bold underline strikethrough subscript superscript italic dash list\n\n/* ** $:/plugins/telmiger/bricks/media/screen-sizes.css ** */\n\n.tc-page-container .not-small-screen-only, .tc-page-container .medium-screen-only, .tc-page-container .large-screen-only { display: none; } @media (min-width: 28rem) { .tc-page-container .not-small-screen-only { display: initial; } .tc-page-container .small-screen-only, .tc-page-container .medium-screen-only, .tc-page-container .large-screen-only { display: none; } } @media (min-width: 42rem) { .tc-page-container .medium-screen-only { display: initial; } .tc-page-container .small-screen-only, .tc-page-container .not-small-screen-only, .tc-page-container .large-screen-only { display: none; } } @media (min-width: 77rem) { .tc-page-container .large-screen-only { display: initial; } .tc-page-container .small-screen-only, .tc-page-container .not-small-screen-only, .tc-page-container .medium-screen-only { display: none; } }\n\n/* ** $:/plugins/telmiger/bricks/page-container.css ** */\n\n.tc-page-container-wrapper { max-width: 100vw; overflow: scroll; } .tc-page-container-wrapper > * { max-width: 100vw; overflow: scroll; }\n\n/* ** $:/plugins/telmiger/bricks/simple-search/adapt.css ** */\n\nbody .tc-search-drop-down .tc-search-results a.tc-tiddlylink:hover { color: rgb(232, 232, 232); }\n\n/* ** $:/plugins/telmiger/ColourManager/bricks.css ** */\n\n.css-test { display: flex; } .padding-no { padding: 0; } .listreveal-open table { margin: 0; width: 100%; } .listreveal-open table th, .listreveal-open table td { padding: 0.5rem 0 0.5rem 0.5rem; } .listreveal-open .css-test { padding: 0.25rem 0.5rem; } th.te-60-p, td.te-60-p { width: 60%; } input[type=color] { margin-top: .33rem; vertical-align: bottom; } .tc-tiddler-body .todo-now h2, .tc-tiddler-body .todo-done h2 { display: flex; margin-top: 0; justify-content: space-between; } .tc-tiddler-body .todo-done h2 { margin-top: 3rem; font-weight: 300; }\n\n/* ** Dropdown items ** (c) Thomas Elmiger **\n*/\n\n/* block dropdown */\n\n.tc-block-dropdown a { padding-top: .125rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; margin-bottom: 0; line-height: 1.25; display: block; width: 100%; }\n\n.tc-block-dropdown a:hover { color: rgb(245, 245, 245); background-color: rgb(136, 206, 166); text-decoration: none; }\n\n/* sidebar tagpill */\n\n.tc-sidebar-lists .tc-tag-list-item, .tc-sidebar-lists .tc-drop-down { width: 100%; max-width: 100%; }\n\n.tc-sidebar-lists .tc-tag-list-item a.tc-tiddlylink { padding-top: .125rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; margin-bottom: 0; line-height: 1.25; display: block; width: 100%; font-size: .875rem; color: black; overflow-wrap: break-word; max-width: 100%; }\n\n/* other, tagpill */\n\n.tc-drop-down .tc-dropdown-item, .tc-block-dropdown .tc-dropdown-item { color: rgb(115, 115, 115); }\n\n.tc-drop-down .tc-dropdown-item-plain, .tc-block-dropdown .tc-dropdown-item, .tc-block-dropdown .tc-dropdown-item-plain .tc-drop-down .tc-dropdown-item { padding-top: .25rem; padding-bottom: .25rem; padding-right: 1rem; padding-left: .5rem; }\n\n.tc-drop-down a, .tc-drop-down button, .tc-drop-down a.tc-tiddlylink, .tc-drop-down a.tc-tiddlylink-external { padding-top: .125rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; margin-bottom: 0; line-height: 1.25; display: block; width: 100%; }\n\n/* color, hover-color */\n\n.tc-drop-down a, .tc-drop-down button, .tc-drop-down a.tc-tiddlylink, .tc-drop-down a.tc-tiddlylink-external { color: black; }\n\n.tc-drop-down button svg.tc-image-button, .tc-drop-down a svg.tc-image-button { fill: black; }\n\n.tc-drop-down a:hover, .tc-drop-down button:hover, .tc-drop-down .tc-file-input-wrapper:hover button, .tc-sidebar-lists .tc-tag-list-item a.tc-tiddlylink:hover { color: rgb(104, 104, 104); background-color: rgba(255, 255, 255, 0.5); text-decoration: none; }\n\n.tc-drop-down button.tc-btn-invisible:hover svg.tc-image-button { fill: rgb(168, 168, 168); }\n\n/* sidebar search */\n\n.tc-search-drop-down .tc-search-results a.tc-tiddlylink { padding-top: .125rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; margin-bottom: 0; line-height: 1.25; display: block; width: 100%; }\n\n.tc-search-drop-down .tc-search-results a.tc-tiddlylink:hover { color: rgb(168, 168, 168); }\n\n/* advanced search */\n\n.tc-advanced-search .tc-edit-type-dropdown a.tc-tiddlylink-missing { padding-top: .125rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; margin-bottom: 0; line-height: 1.25; display: block; width: 100%; color: black; }\n\n.tc-advanced-search .tc-edit-type-dropdown a.tc-tiddlylink-missing:hover { color: rgb(168, 168, 168); background-color: rgba(255, 255, 255, 0.5); }\n\n/* ** $:/themes/telmiger/bricks/000-base.css ** */\n\n\\rules only filteredtranscludeinline transcludeinline macrodef macrocallinline macrocallblock html { -ms-text-size-adjust: 100%; -webkit-text-size-adjust: 100%; } html, body, div, article, section, main, footer, header, form, fieldset, legend, pre, code, p, a, h1, h2, h3, h4, h5, h6, ul, ol, li, dl, dt, dd, textarea, input[type=\"email\"], input[type=\"number\"], input[type=\"password\"], input[type=\"search\"], input[type=\"tel\"], input[type=\"text\"], input[type=\"url\"], .border-box { box-sizing: border-box; max-width: 100%; } html { text-rendering: optimizeLegibility; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } html:-webkit-full-screen { background-color: rgb(245, 245, 245); } body { line-height: 1.5; font-family: 'helvetica neue', helvetica, sans-serif; font-family: \"Helvetica Neue\", Helvetica, -apple-system, BlinkMacSystemFont, \"Segoe UI\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; -moz-tab-size: 3; tab-size: 3; } body.tc-body { word-wrap: break-word; overflow-wrap: break-word; color: rgb(13, 13, 13); fill: rgb(13, 13, 13); background-color: rgb(245, 245, 245); } a { overflow-wrap: break-word; word-wrap: break-word; display: inline-block; vertical-align: top; } @media (min-width: 28rem) { a { display: unset; vertical-align: unset; } } pre { overflow-x: auto; overflow-y: hidden; overflow: scroll; } pre, code { font-family: monaco, Consolas, monospace; border-radius: .25rem; white-space: pre-wrap; } pre { display: block; word-break: normal; word-wrap: break-word; padding-top: .25rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; border-style: solid; border-width: 1px; border-color: rgba(0,0,0,.2); background-color: rgba(0,0,0,0.08); } code { padding-left: .25rem; padding-right: .25rem; border-style: solid; border-width: 1px; border-color: rgba(0,0,0,0.08); color: #e7040f; background-color: rgba(0,0,0,0.03); } pre > code { border-style: none; border-width: 0; padding: 0; background-color: transparent; color: inherit; } dl dt { font-weight: bold; margin-top: 6px; } hr { height: 1px; margin-top: 2rem; margin-bottom: 2rem; border: none; background-color: rgb(80, 150, 110); } .tc-drop-down hr { margin-bottom: 0.5em; margin-top: 0.5em; } .tc-muted { color: rgb(115, 115, 115); } .tc-sidebar-scrollable .tc-muted { color: rgb(150, 150, 150); } .tc-icon-wrapper > svg, .tc-image-button, .tc-image-button svg { height: 1em; width: 1em; } .tc-tiddler-frame img, .tc-tiddler-frame svg, .tc-tiddler-frame canvas, .tc-tiddler-frame embed, .tc-tiddler-frame iframe { max-width: 100%; } .tc-tiddler-body > embed, .tc-tiddler-body > iframe { width: 100%; height: 600px; } .tc-file-input-wrapper { position: relative; overflow: hidden; display: inline-block; vertical-align: middle; } .tc-file-input-wrapper input[type=file] { position: absolute; top: 0; left: 0; right: 0; bottom: 0; font-size: 999px; max-width: 100%; max-height: 100%; filter: alpha(opacity=0); opacity: 0; outline: none; background: white; cursor: pointer; display: inline-block; } .tc-droppable > .tc-droppable-placeholder { display: none; }\n\n/* ** $:/themes/telmiger/bricks/001-page.css ** */\n\nbody { margin: 0; } .tc-page-container { } .tc-story-river { position: relative; padding: 0; } @media (min-width: 28rem) { .tc-story-river { padding: 1rem; } } @media (min-width: 42rem) { .tc-story-river { padding: 2rem; } } @media (min-width: 77rem) { .tc-story-river { margin-right: calc(26rem + 1rem); margin-left: auto; } } .tc-sidebar-header .tc-site-title { margin: 0; margin-top: 2rem; } .tc-site-subtitle { margin-bottom: 2rem; } .tc-sidebar-scrollable { position: fixed; top: 0; right: 0; bottom: 0; left: auto; z-index: 1500; width: auto; max-width: calc(100% - 2rem - 1rem); max-height: 100%; animation: inherit; box-shadow: -4px 4px 8px 0px rgba( 0, 0, 0, 0.4 ); } .tc-sidebar-header { margin-top: 0; overflow-y: auto; overflow-x: hidden; -webkit-overflow-scrolling: touch; } .tc-sidebar-header > .tc-reveal { width: calc(26rem - 2rem - .5rem); max-width: 100%; padding-top: .5rem; padding-bottom: 2rem; padding-left: 1rem; margin-bottom: 2rem; } @media (min-width: 28rem) { .tc-sidebar-header > .tc-reveal { width: 100%; padding-top: 1rem; padding-bottom: 2rem; padding-left: 1rem; margin-left: 1rem; } .tc-sidebar-scrollable { max-width: calc(100% - 4rem); } } @media (min-width: 42rem) { html[dir=\"rtl\"] .tc-sidebar-scrollable { right: auto; } .tc-sidebar-header > .tc-reveal { width: 26rem; } .tc-tiddler-frame { margin-right: calc(66vw - 26rem); } } @media (min-width: 77rem) { .tc-sidebar-header > .tc-reveal { padding-bottom: 4rem; margin-bottom: 4rem; } .tc-tiddler-frame { margin-right: auto; margin-left: auto; } }\n\n/* ** $:/themes/telmiger/bricks/010-fonts.css ** */\n\n.tc-site-title, .tc-site-title a.tc-tiddlylink { font-weight: 500; font-size: 2.25rem; font-variant: small-caps; color: rgb(80, 150, 110); } .tc-subtitle { font-size: .875rem; font-weight: 300; color: rgb(110, 110, 110); } .tc-titlebar { font-weight: 300; color: rgb(64, 120, 88); } .tc-tiddler-missing .tc-title { font-style: italic; font-weight: normal; } .tc-titlebar .tc-title, .tc-tiddler-title-icon { font-weight: 300; font-size: 1.5rem; line-height: 1.25; vertical-align: bottom; } .tc-system-title-prefix,.tc-tiddler-title-icon svg { color: rgb(115, 115, 115); fill: rgb(89, 166, 122); } .tc-titlebar.tc-edit-texteditor { font-size: 1.5rem; line-height: 1.25; } h1, h2, h3, h4, h5, h6 { margin-top: 0; line-height: 1.25; } .tc-tiddler-body h1 { font-size: 1.875rem; margin-bottom: 2rem; } .tc-tiddler-body h2 { font-size: 1.5rem; margin-bottom: 2rem; } .tc-tiddler-body h3 { font-size: 1.25rem; margin-bottom: 1rem; } .tc-tiddler-body h4 { font-size: 1rem; margin-bottom: 1rem; } .tc-tiddler-body h5 { font-size: .875rem; margin-bottom: 1rem; font-family: georgia, times, serif; } .tc-tiddler-body h6 { font-size: .75rem; margin-bottom: .5rem; font-family: georgia, times, serif; } .tc-tiddler-body * + h1, section > h1:first-child { margin-top: 4rem; } .tc-tiddler-body *:not(h1) + h2, section > h2:first-child { margin-top: 4rem; } .tc-tiddler-body *:not(h2) + h3, section > h3:first-child { margin-top: 2rem; } .tc-tiddler-body *:not(h3) + h4, section > h4:first-child { margin-top: 2rem; } .tc-tiddler-body *:not(h4) + h5, section > h5:first-child { margin-top: 2rem; } .tc-tiddler-body *:not(h5) + h6, section > h6:first-child { margin-top: 2rem; } p { font-size: 1rem; line-height: 1.5; margin-top: 0; margin-bottom: .5rem; } @media (min-width: 28rem) { .tc-tiddler-body h1 { font-size: 2.25rem; } } @media (min-width: 42rem) { .tc-titlebar .tc-title, .tc-tiddler-title-icon { font-size: 2.25rem; vertical-align: baseline; } .tc-tiddler-title-icon { vertical-align: text-top; } .tc-tiddler-body h1 { font-size: 3rem; } .tc-tiddler-body h2 { font-size: 2.25rem; } .tc-tiddler-body h3 { font-size: 1.5rem; } .tc-tiddler-body h4 { font-size: 1.25rem; } .tc-tiddler-body h5 { font-size: 1rem; } .tc-tiddler-body h6 { font-size: .875rem; } } .tc-menu-list-count { font-weight: bold; } .tc-menu-list-subitem { padding-left: .5rem; } pre, code { font-family: ; }\n\n/* ** $:/themes/telmiger/bricks/components/chooser.css ** */\n\n.tc-chooser { padding: 0; border-bottom-style: solid; border-bottom-width: 1px; border-color: #00449e; } .tc-chooser-item { line-height: 1; border-top-style: solid; border-top-width: 1px; border-color: #00449e; } .tc-drop-down .tc-chooser-item { padding: .25rem; } .tc-page-controls .tc-chooser-item { border: none; } .tc-page-controls .tc-chooser-item a.tc-tiddlylink { background-color: transparent; } .tc-page-controls .tc-chooser-item:not(.tc-chosen) a.tc-tiddlylink:hover { background-color: rgba(255,255,255,.25); } .tc-chosen, .tc-chooser-item:hover { background-color: rgba(0,0,0,.1); border-color: rgba(0,0,0,.4); } .tc-chosen .tc-tiddlylink { cursor:default; } .tc-chosen > .tc-tiddlylink:before { position: relative; content: \" \"; background: url(data:image/svg+xml,%3Csvg%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%20width%3D%22100%22%20height%3D%22100%22%3E%3Ccircle%20cx%3D%2250%22%20cy%3D%2250%22%20r%3D%2245%22%20fill%3D%22%22%2F%3E%3C%2Fsvg%3E); background-size: .66rem; background-repeat: no-repeat; background-position-y: .33rem; } .tc-chooser-item .tc-tiddlylink { display: block; padding-top: .5rem; padding-bottom: .5rem; width: 100%; height: 100%; text-decoration: none; font-weight: 400; color: #00449e; background-color: rgb(245, 245, 245); background-color: transparent; } .tc-chooser-item a.tc-tiddlylink { } .tc-chooser-item a.tc-tiddlylink.tc-tiddlylink-resolves { font-weight: 300; background-color: rgba(0,0,0,.1); } .tc-chooser-item:not(.tc-chosen) a.tc-tiddlylink:hover { text-decoration: none; color: rgb(245, 245, 245); background-color: #00449e; } .tc-chooser-item:hover .tc-tiddlylink:hover { text-decoration: none; } .tc-drop-down .tc-chooser-item .tc-tiddlylink:hover { color: rgb(13, 13, 13); } .tc-chooser-item a.tc-tiddlylink > div { display: inline-block; vertical-align: bottom; } .tc-chooser-item a.tc-tiddlylink > div:first-child { width: 97%; padding-left: 3em; text-indent: -2.5em; padding-bottom: .5rem; } .tc-chooser-item svg, .tc-chooser-item img{ width: 1em; height: 1em; vertical-align: middle; } .tc-language-chooser .tc-image-button img { width: 2em; vertical-align: -0.15em; } .tc-drop-down .tc-chooser { border: none; } .tc-drop-down .tc-chooser .tc-swatches-horiz { font-size: 0.4em; padding-left: 1.2em; }\n\n/* ** $:/themes/telmiger/bricks/components/control-panel.css ** */\n\n.tc-control-panel table { width: 100%; } .tc-control-panel table input, .tc-control-panel table textarea { padding-left: .5rem; padding-right: .5rem; } .tc-control-panel td input + br { position: absolute; } .tc-control-panel td input + br + input[type=color] { margin-top: .25rem; vertical-align: bottom; } kbd { display: inline-block; padding-top: .25rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; font-size: 0.8em; line-height: 1.2; vertical-align: middle; border-radius: .25rem; box-shadow: inset 0 -1px 0 rgb(115, 115, 115); border-style: solid; border-width: 1px; border-color: rgb(115, 115, 115); color: rgb(13, 13, 13); background-color: rgb(245, 245, 245); }\n\n/* ** $:/themes/telmiger/bricks/components/control-panel/palettes.css ** */\n\n.tc-control-panel .tc-chooser-item a { padding-left: .25rem; } .tc-control-panel .tc-chosen > .tc-tiddlylink::before { margin-right: 4px; } .tc-swatches-horiz { width: 97%; text-align: right; } .tc-swatches-horiz .tc-swatch { display: inline-block; } .tc-swatch { width: 2rem; height: 2rem; margin-right: .25rem; border-style: solid; border-width: 1px; border-color: #888; } @media (min-width: 42rem) { .tc-chooser-item a.tc-tiddlylink > div:first-child { margin-right: 1rem; width: calc(100% - 22rem); min-width: 20rem; } .tc-swatches-horiz { width: 20.75rem; } .tc-swatch { margin-right: .5rem; } }\n\n/* ** $:/themes/telmiger/bricks/components/dropdown.css ** */\n\n.tc-btn-dropdown { text-align: left; } .tc-btn-dropdown svg, .tc-btn-dropdown img { height: 1em; width: 1em; } .tc-drop-down-wrapper { position: relative; } .tc-drop-down { width: 22rem; max-width: 66vw; border-style: solid; border-width: 1px; padding: .5rem; border-radius: .125rem; margin-top: .25rem; line-height: 1.5; text-shadow: none; border-color: rgb(115, 115, 115); background-color: rgb(232, 232, 232); } .tc-drop-down .tc-drop-down { margin-left: 1rem; } .tc-drop-down p { padding-left: .25rem; padding-right: .25rem; } .tc-drop-down svg { fill: rgb(115, 115, 115); height: 1em; width: 1em; margin-right: .5rem; } .tc-drop-down img { width: 1em; margin-right: .5rem; } .tc-drop-down .tc-prompt { padding-left: 1rem; padding-right: 1rem; } .tc-drop-down .tc-file-input-wrapper { width: 100%; } .tc-drop-down .tc-file-input-wrapper button { color: rgb(13, 13, 13); } .tc-block-dropdown-wrapper { position: relative; } .tc-block-dropdown { position: absolute; min-width: 220px; border-radius: .25rem; font-size: .875rem; padding-top: .25rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; margin-left: .5rem; z-index: 1000; text-shadow: none; border-style: solid; border-width: 1px; border-color: rgb(115, 115, 115); background-color: rgb(232, 232, 232); } .tc-page-controls .tc-drop-down { font-size: 1rem; max-width: 80%; }\n\n/* ** $:/themes/telmiger/bricks/components/forms.css ** */\n\ntextarea, input[type=text], input[type=search], input[type=\"\"], input:not([type]) { -webkit-appearance: none; -moz-appearance: none; font-size: 1rem; padding-left: .25rem; padding-right: .25rem; padding-bottom: .25rem; color: rgb(13, 13, 13); border-style: solid; border-width: 1px; border-color: #ccc; background-color: #F7F7F7; background-color: rgb(247, 247, 247); } textarea { padding-left: .5rem; padding-right: .5rem; padding-top: .25rem; padding-bottom: .25rem; } .te-edit-text { width: 100%; } textarea:focus, input[type=text]:focus, input[type=search]:focus, input[type=\"\"]:focus, input:not([type]):focus { background-color: #F7F7F7; background-color: rgb(252, 252, 252); } td textarea, td input[type=text], td input[type=search], td input[type=\"\"], td input:not([type]) { width: 100%; max-width: calc(100% - .5rem); } input[type=\"checkbox\"] { vertical-align: middle; }\n\n/* ** $:/themes/telmiger/bricks/components/messages.css ** */\n\nbody.tc-dirty span.tc-dirty-indicator, body.tc-dirty span.tc-dirty-indicator svg { fill: #ff4136; color: #ff4136; } .tc-error { background: #f00; color: #fff; } .tc-body .tc-error-form { color: #fff; text-shadow: 0 1px 0 rgba(0, 0, 0, 0.5); background-color: rgb(255, 75, 75); border: 8px solid rgb(255, 0, 0); margin-left: -33vw; width: 66vw; max-height: 80vh; overflow-y: scroll; } .tc-alerts { position: fixed; top: 0; left: 0; max-width: 500px; z-index: 20000; } .tc-alert { position: relative; margin: 2rem; padding: 1rem; border-style: solid; border-width: 1px; border-width: .25rem; border-color: rgb(232, 232, 125); background-color: rgb(255, 255, 102); } .tc-alert-toolbar { position: absolute; top: .5rem; right: .5rem; } .tc-alert-toolbar svg { fill: rgb(224, 82, 82); } .tc-alert-subtitle { color: rgb(224, 82, 82); font-weight: bold; } .tc-alert-highlight { color: rgb(255, 51, 51); } @media (min-width: 77rem) { .tc-static-alert { position: relative; } .tc-static-alert-inner { position: absolute; z-index: 100; } } .tc-static-alert-inner { padding: 0 .125rem .125rem 4rem; color: rgba(0,0,0,.3); } .tc-message-box { font-size: 1rem; line-height: 1.5; color: #00449e; padding-top: 1rem; padding-left: 1rem; padding-right: 1rem; padding-bottom: .5rem; margin-left: -1rem; margin-right: -1rem; margin-bottom: 1rem; max-width: calc(100% + 2rem); border-style: solid; border-width: 1px; border-color: #96ccff; background: #cdecff; } .tc-message-box svg { height: 1em; width: 1em; vertical-align: text-bottom; } .tc-notification { position: fixed; top: 1rem; right: 3rem; min-width: 12rem; max-width: 24rem; padding-top: .5rem; padding-bottom: .5rem; padding-left: 1rem; padding-right: 1rem; z-index: 10000; border-style: solid; border-width: 1px; border-radius: .25rem; color: rgb(242, 242, 242); border-color: rgb(242, 242, 242); background-color: rgb(150, 80, 120); } .tc-plugin-reload-warning { position: fixed; top: 0; right: 0; left: 0; padding-top: .5rem; background: rgb(255, 255, 102); text-align: center; z-index: 2000; } .tc-plugin-reload-warning button.tc-btn-invisible { position: relative; margin-left: 2rem; }\n\n/* ** $:/themes/telmiger/bricks/components/modal.css ** */\n\n.tc-modal-wrapper { position: fixed; overflow: auto; overflow-y: scroll; top: 0; right: 0; bottom: 0; left: 0; z-index: 1300; } .tc-modal-backdrop { position: fixed; top: 0; right: 0; bottom: 0; left: 0; background-color: rgb(13, 13, 13); } .tc-modal { position: fixed; top: 1rem; left: 1rem; right: 1rem; border-style: solid; border-width: 1px; border-radius: .25rem; border-color: rgba(0,0,0,.5); background-color: rgb(245, 245, 245); } .tc-modal-header { padding-top: .5rem; padding-bottom: .5rem; padding-left: 1rem; padding-right: 1rem; border-bottom-style: solid; border-bottom-width: 1px; border-radius: .25rem; border-bottom-left-radius: 0; border-bottom-right-radius: 0; border-bottom-color: rgba(0,0,0,.1); background-color: #f4f4f4; } .tc-modal-header h3 { margin: 0; line-height: 30px; } .tc-modal-header img, .tc-modal-header svg { height: 1em; width: 1em; } .tc-modal-body { overflow-y: auto; max-height: 60vh; padding: 1rem; } .tc-modal-footer { text-align: right; padding-top: .5rem; padding-bottom: .5rem; padding-left: 1rem; padding-right: 1rem; margin-bottom: 0; border-top-style: solid; border-top-width: 1px; border-radius: .25rem; border-top-left-radius: 0; border-top-right-radius: 0; border-top-color: rgba(0,0,0,.1); background-color: #f4f4f4; } @media (min-width: 42rem) { .tc-modal { top: 4rem; right: 20%; left: auto; width: 60%; } } @media (min-width: 77rem) { .tc-modal { top: 8rem; right: 25%; left: auto; width: 50%; border-radius: .5rem; } .tc-modal-header { padding-left: 2rem; padding-right: 2rem; border-radius: .5rem; border-bottom-left-radius: 0; border-bottom-right-radius: 0; } .tc-modal-body { padding: 2rem; } .tc-modal-footer { padding-left: 2rem; padding-right: 2rem; border-radius: .5rem; border-top-left-radius: 0; border-top-right-radius: 0; } }\n\n/* ** $:/themes/telmiger/bricks/components/page-controls.css ** */\n\n.tc-page-controls { font-size: 1.5rem; line-height: 1.5; margin-bottom: 1rem; } .tc-page-controls button { margin-right: 1rem; } .tc-page-controls button.tc-selected svg { fill: rgb(227, 227, 227); } .tc-page-controls button.tc-selected:hover svg { fill: rgb(150, 150, 150); } .tc-page-controls a.tc-tiddlylink:hover { text-decoration: none; } .tc-page-controls img { width: 1em; } .tc-page-controls svg { fill: rgb(150, 150, 150); } .tc-page-controls button:hover svg, .tc-page-controls a:hover svg { fill: rgb(227, 227, 227); }\n\n/* ** $:/themes/telmiger/bricks/components/plugin-info.css ** */\n\n.tc-plugin-info { display: block; border-style: solid; border-width: 1px; border-color: rgb(115, 115, 115); margin-bottom: 1rem; padding: .25rem; background-colour: rgb(245, 245, 245); } .tc-plugin-info-disabled { background: -webkit-repeating-linear-gradient(45deg, #ff0, #ff0 10px, #eee 10px, #eee 20px); background: repeating-linear-gradient(45deg, #ff0, #ff0 10px, #eee 10px, #eee 20px); } .tc-plugin-info-disabled:hover { background: -webkit-repeating-linear-gradient(45deg, #aa0, #aa0 10px, #888 10px, #888 20px); background: repeating-linear-gradient(45deg, #aa0, #aa0 10px, #888 10px, #888 20px); } a.tc-tiddlylink.tc-plugin-info:hover { text-decoration: none; background-color: rgb(80, 150, 110); color: rgb(245, 245, 245); fill: rgb(13, 13, 13); } a.tc-tiddlylink.tc-plugin-info:hover .tc-plugin-info > .tc-plugin-info-chunk > svg { fill: rgb(13, 13, 13); } .tc-plugin-info-chunk { display: inline-block; vertical-align: middle; } .tc-plugin-info .tc-plugin-info-chunk h1 { font-size: 1rem; margin-top: .25rem; margin-bottom: .25rem; } .tc-plugin-info .tc-plugin-info-chunk h2 { font-size: .875rem; margin-top: 0; margin-bottom: .25rem; } .tc-plugin-info .tc-plugin-info-chunk h2 div { font-size: .875rem; margin-bottom: .25rem; } .tc-plugin-info .tc-btn-dropdown { margin-left: .5rem; margin-right: .5rem; } .tc-plugin-info:hover .tc-btn-dropdown svg { fill: rgb(245, 245, 245); } .tc-plugin-info > .tc-plugin-info-chunk > img, .tc-plugin-info > .tc-plugin-info-chunk > svg { width: 2em; height: 2em; fill: rgb(115, 115, 115); padding-right: 1rem; } .tc-plugin-info:hover > .tc-plugin-info-chunk > img, .tc-plugin-info:hover > .tc-plugin-info-chunk > svg { width: 2em; height: 2em; fill: rgb(13, 13, 13); } .tc-plugin-info.tc-small-icon > .tc-plugin-info-chunk > img, .tc-plugin-info.tc-small-icon > .tc-plugin-info-chunk > svg { height: 1em; width: 1em; } .tc-plugin-info-dropdown { margin-top: -1rem; border-style: solid; border-width: 1px; border-color: rgb(115, 115, 115); } .tc-control-panel .tc-plugin-info-dropdown { margin-bottom: 1rem; } .tc-plugin-info-dropdown-message { background: #cdecff; padding-top: 1rem; padding-bottom: 1rem; padding-left: 2rem; padding-right: 2rem; font-size: 2.25rem; font-weight: bold; } .tc-plugin-info-dropdown-body { padding: 1rem; }\n\n/* ** $:/themes/telmiger/bricks/components/search.css ** */\n\n.tc-search a svg { width: 1.2em; height: 1.2em; vertical-align: middle; } .tc-search button svg, .tc-search a svg { fill: rgb(150, 150, 150); padding-left: .25rem; padding-right: .25rem; } .tc-search button:hover svg, .tc-search a:hover svg { fill: rgb(227, 227, 227); } .tc-search + .tc-block-dropdown-wrapper { margin-left: -1rem; } .tc-block-dropdown.tc-search-drop-down { position: relative; padding-left: .5rem; padding-right: .5rem; margin-right: 1rem; max-width: 100%; } .tc-sidebar-lists .tc-search { margin-bottom: 1rem; } .tc-sidebar-scrollable input[type=search] { width: 12rem; } .tc-sidebar-lists .tc-block-dropdown { background-color: rgb(26, 26, 26); }\n\n/* ** $:/themes/telmiger/bricks/components/search/advanced.css ** */\n\n.tc-advanced-search input { width: calc( 100% / 1.5 ); margin-right: .5rem; } .tc-advanced-search .tc-search button { border-radius: .25rem; border-style: none; border-width: 0; padding-top: .25rem; padding-bottom: .25rem; min-width: 2rem; text-decoration: none; font-weight: 600; } .tc-advanced-search .tc-popup { position: relative !important; left: initial !important; top: initial !important; } .tc-advanced-search .tc-block-dropdown-wrapper { position: absolute; left: -16rem; } @media (min-width: 28rem) { .tc-advanced-search .tc-block-dropdown-wrapper { left: 0; transform: translateX(-19rem); } } .tc-advanced-search .tc-block-dropdown button:hover { color: ; border-color: ; } .tc-advanced-search .tc-block-dropdown button:hover svg, .tc-advanced-search .tc-block-dropdown a:hover svg { fill: #555; } .tc-advanced-search .tc-block-dropdown-wrapper .tc-block-dropdown { min-width: 18rem; } .tc-advanced-search .tc-block-dropdown .tc-btn { margin: .5rem; border-style: solid; border-width: 1px; }\n\n/* ** $:/themes/telmiger/bricks/components/sidebar.css ** */\n\n.tc-sidebar-scrollable { background-color: #181818; background-color: #181818; } .tc-sidebar-header { color: rgb(255, 255, 255); fill: rgb(255, 255, 255); } .tc-sidebar-header .tc-sidebar-lists p { margin-bottom: .25rem; } .a11y-label { color: rgba(0,0,0,.3); } .tc-sidebar-lists .tc-tab-set { margin-right: .5rem; } .tc-sidebar-lists .tc-tab-content { padding-top: 1rem; background-color: #181818; } .tc-sidebar-lists .tc-tab-content p { font-size: .875rem; } .tc-sidebar-lists .tc-tab-buttons button { font-size: .875rem; line-height: 1; margin-top: .125rem; padding-top: .125rem; background-color: rgb(70, 70, 70); color: rgba(255,255,255,.8); border-color: rgba(255, 255, 255, 0.8); max-height: 1.66rem; }\n\n.tc-sidebar-lists .tc-tab-buttons button:not(.tc-tab-selected) { position: relative; }\n\n.tc-sidebar-lists .tc-tab-buttons button:not(.tc-tab-selected)::before { background-color: #181818; content: \"\"; position: absolute; top: 0; left: 0; width: 100%; height: 100%; opacity: 0; border-radius: .25rem; border-bottom-left-radius: 0; border-bottom-right-radius: 0; }\n\n.tc-sidebar-lists .tc-tab-buttons button:not(.tc-tab-selected):hover::before, .tc-sidebar-lists .tc-tab-buttons button:not(.tc-tab-selected):focus::before { opacity: .35; } .tc-sidebar-lists .tc-tab-divider { border-top-style: solid; border-top-width: 1px; border-color: rgba(255, 255, 255, 0.8); margin-right: 4rem; height: 1px; } .tc-sidebar-lists .tc-tab-buttons.tc-vertical button { border-color: rgba(255, 255, 255, 0.8); background-color: rgb(70, 70, 70); } .tc-sidebar-lists .tc-tab-content.tc-vertical { border-color: rgba(255, 255, 255, 0.8); } .tc-sidebar-lists .tc-tab-buttons button.tc-tab-selected { background-color: #181818; color: rgba(255,255,255,.8); color: rgba(255,255,255,.8); border-color: rgba(255, 255, 255, 0.8); border-bottom-color: #181818; } .tc-sidebar-lists .tc-tab-buttons.tc-vertical button.tc-tab-selected { border-color: rgba(255, 255, 255, 0.8); border-right-color: #181818; background-color: #181818; } .tc-menu-list-item { margin: 0; } .tc-menu-list-item a, .tc-menu-list-item .tc-btn-invisible { padding-bottom: .25rem; } .tc-menu-list-item button { font-style: italic; }\n\n/* ** $:/themes/telmiger/bricks/components/sidebar/tab-content.css ** */\n\n.tc-sidebar-lists .tc-timeline { font-weight: bold; } .tc-sidebar-lists .tc-tab-content label { width: 1.25rem; padding-top: .2rem; margin-right: .25rem; margin-left: -.25rem; } .tc-sidebar-lists .tc-tab-content div div p i { line-height: 1.25; display: inline-block; width: calc(100% - 1.5rem); margin-left: 1.5rem; }\n.tc-sidebar-lists .tc-drop-down button { color: black; }\n\n/* ** $:/themes/telmiger/bricks/components/tabs.css ** */\n\n.tc-tab-buttons { padding-top: 1rem; font-size: .875rem; } .tc-tab-buttons button { position: relative; font-size: .875rem; cursor: pointer; color: rgb(13, 13, 13); line-height: 1.5; padding-top: .25rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; margin-bottom: 0; margin-left: 0; margin-right: 0; font-weight: 300; background: inherit; background-color: rgb(225, 225, 225); border-style: solid; border-width: 1px; border-radius: .25rem; border-bottom-left-radius: 0; border-bottom-right-radius: 0; border-color: rgb(80, 150, 110); height: 2rem; }\n\n.tc-tab-buttons button:not(.tc-tab-selected) { position: relative; }\n\n.tc-tab-buttons button:not(.tc-tab-selected)::before { background-color: rgb(245, 245, 245); content: \"\"; position: absolute; top: 0; left: 0; width: 100%; height: 100%; opacity: 0; border-radius: .25rem; border-bottom-left-radius: 0; border-bottom-right-radius: 0; }\n\n.tc-tab-buttons button:not(.tc-tab-selected):hover::before, .tc-tab-buttons button:not(.tc-tab-selected):focus::before { opacity: .35; } .tc-sidebar-lists .tc-tab-buttons button { margin-right: .125rem; } .tc-tab-divider { border-top-style: solid; border-top-width: 1px; height: 1px; margin-top: 1px; border-color: rgb(80, 150, 110); } .tc-tab-content { padding-top: 2rem; } .tc-tab-set:not(.tc-vertical) { margin-left: -.5rem; max-width: calc(100vw - 1rem) } .tc-tab-set:not(.tc-vertical) > .tc-tab-buttons:first-of-type { margin-left: .5rem; } .tc-tab-set:not(.tc-vertical) > .tc-tab-buttons button { margin-top: -2px; margin-bottom: -2px; margin-left: .25rem; } .tc-tab-set:not(.tc-vertical) > .tc-tab-buttons button:first-of-type { margin-left: 0; } .tc-tab-set:not(.tc-vertical) > .tc-tab-content { padding-left: .5rem; } .tc-tab-content.tc-vertical { display: inline-block; vertical-align: top; padding-top: 1rem; padding-left: 1rem; border-left-style: solid; border-left-width: 1px; border-color: rgb(80, 150, 110); -webkit-flex: 1 0 70%; flex: 1 0 70%; max-width: 75%; } .tc-tab-divider.tc-vertical { display: none; } .tc-tab-set.tc-vertical { display: -webkit-flex; display: flex; } .tc-tab-buttons.tc-vertical { z-index: 100; display: block; vertical-align: top; text-align: right; margin-left: 0; margin-right: -1px; max-width: 33%; -webkit-flex: 0 0 auto; flex: 0 0 auto; } .tc-tab-buttons.tc-vertical button, .tc-tab-buttons.tc-vertical button.tc-tab-selected { display: block; width: 100%; margin-top: 0; margin-right: 0; margin-bottom: .25rem; text-align: right; border-style: solid; border-width: 1px; border-radius: .25rem; border-top-right-radius: 0; border-bottom-right-radius: 0; border-color: rgb(80, 150, 110); background-color: rgb(225, 225, 225); } .tc-tab-buttons button.tc-tab-selected { color: rgb(13, 13, 13); border-style: solid; border-width: 1px; border-color: rgb(80, 150, 110); background-color: rgb(245, 245, 245); border-bottom-color: rgb(245, 245, 245); } .tc-tiddler-info .tc-tab-buttons button.tc-tab-selected { border-bottom-style: solid; border-bottom-width: 1px; border-bottom-color: #f8f8f8; background-color: #f8f8f8; } .tc-tab-buttons.tc-vertical button.tc-tab-selected { border-right-color: rgb(245, 245, 245); background-color: rgb(245, 245, 245); } .tc-tab-buttons button.tc-tab-selected { cursor: default; pointer-events: none; } .tc-drop-down-bullet { display: inline-block; width: 0.5em; } .tc-drop-down .tc-tab-contents a { padding: 0 0.5em 0 0.5em; } .tc-drop-down .tc-tab-buttons button { background-color: rgba(0,0,0,.1); } .tc-drop-down .tc-tab-set .tc-tab-buttons button { display: inline-block; width: auto; margin-bottom: 0px; border-bottom-left-radius: 0; border-bottom-right-radius: 0; } .tc-drop-down .tc-tab-buttons button.tc-tab-selected { border-bottom-style: solid; border-bottom-width: 1px; border-bottom-color: rgba(255,255,255,1); background-color: rgba(255,255,255,1); }\n\n/* ** $:/themes/telmiger/bricks/components/tags.css ** */\n\n.tc-tag-manager-table td { padding-top: .25rem; padding-bottom: .25rem; }\n\n.tc-tag-label { position: relative; }\n\n.tc-tag-label::before { background-color: #000; content: \"\"; position: absolute; top: 0; left: 0; width: 100%; height: 100%; opacity: 0; border-radius: 1rem; }\n\n.tc-tag-label:hover::before, .tc-tag-label:focus::before { opacity: .075; }\n\n.tc-edit-tags > .tc-tag-label { position: relative; }\n\n.tc-edit-tags > .tc-tag-label::before { background-color: #000; content: \"\"; position: absolute; top: 0; left: 0; width: calc(100% - 1.25rem); height: 100%; opacity: 0; border-top-right-radius: 0; border-bottom-right-radius: 0; }\n\n.tc-edit-tags > .tc-tag-label:hover::before, .tc-edit-tags > .tc-tag-label:focus::before { opacity: .075; } .tc-tags-wrapper { margin-bottom: .5rem; } .tc-tags-wrapper:empty { height: 33%; width: 4rem; border-bottom-style: solid; border-bottom-width: 1px; border-width: .25rem; border-bottom-color: rgb(80, 150, 110); } .tc-tags-wrapper:empty:before { content: \"\\00a0\"; } button.tc-tag-label, span.tc-tag-label, .tc-tab-content button.tc-tag-label.tc-untagged-label { display: inline-block; padding-top: .25rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; font-size: .75rem; font-weight: 400; color: rgb(242, 242, 242) !important; white-space: nowrap; line-height: 1; vertical-align: baseline; background-color: rgb(80, 150, 110); border-style: none; border-width: 0; border-radius: 1rem; } .tc-tag-list-item { position: relative; display: inline-block; max-width: 100%; } .tc-untagged-separator { width: 10rem; height: 1px; left: 0; margin-left: 0; border-style: none; border-width: 0; background: rgba(255, 255, 255, 0.8); } button.tc-tag-label.tc-untagged-label, .tc-tab-content button.tc-tag-label.tc-untagged-label { background-color: rgb(150, 80, 120); color: rgb(242, 242, 242); font-style: italic; } .tc-tag-label svg, .tc-tag-label img { vertical-align: text-bottom; height: 1em; width: 1em; } .tc-tag-manager-table .tc-tag-label { white-space: normal; } .tc-tag-manager-tag { width: 100%; }\n\n/* ** $:/themes/telmiger/bricks/components/tiddler-content.css ** */\n\n.tc-tiddler-title { margin-bottom: 1rem; } .tc-tiddler-body { padding-top: 2rem; } .tc-titlebar .tc-title { display: inline-block; margin: .5rem; margin-left: 0; } .tc-tiddler-title-icon { vertical-align: middle; } .tc-tiddler-title-icon svg { margin-right: .25rem; } .tc-titlebar img { height: 1em; } .tc-subtitle a { color: #000; display: block; } @media (min-width: 28rem) { .tc-subtitle { float: right; white-space: unset; } .tc-subtitle a { display: inline-block; } } .tc-tiddler-help { color: rgb(115, 115, 115); margin-top: .5rem; } .tc-tiddler-help a.tc-tiddlylink { color: rgb(89, 166, 122); } .tc-titlebar, .tc-tiddler-edit-title { overflow: hidden; } html body.tc-body.tc-single-tiddler-window { margin: 1em; background: rgb(245, 245, 245); } .tc-single-tiddler-window img, .tc-single-tiddler-window svg, .tc-single-tiddler-window canvas, .tc-single-tiddler-window embed, .tc-single-tiddler-window iframe { max-width: 100%; }\n\n/* ** $:/themes/telmiger/bricks/components/tiddler-controls.css ** */\n\n.tc-tiddler-frame .tc-tiddler-controls { display: block; width: 100%; text-align: right; font-size: 1.5rem; } .tc-tiddler-controls .tc-drop-down, .tc-tiddler-controls .tc-drop-down .tc-drop-down { font-size: 1.25rem; } .tc-tiddler-controls > span > button, .tc-tiddler-controls > span > span > button, .tc-tiddler-controls > span > span > span > button { vertical-align: baseline; margin-left: .5rem; } @media (min-width: 28rem) { .tc-tiddler-frame .tc-tiddler-controls { } .tc-tiddler-controls > span > button { margin-left: 1rem; } } @media (min-width: 42rem) { .tc-tiddler-frame .tc-tiddler-controls { font-size: 2.25rem; } .tc-tiddler-controls > span > button { margin-left: .5rem; } } .tc-tiddler-controls button svg, .tc-tiddler-controls button img { fill: rgb(143, 143, 143); } .tc-tiddler-controls button.tc-selected svg { fill: rgb(64, 64, 64); } .tc-tiddler-controls button.tc-btn-invisible:hover svg { fill: rgb(64, 64, 64); } .tc-tiddler-controls button svg.tc-image-info-button { fill: ; } .tc-tiddler-controls button svg.tc-image-edit-button { fill: ; } .tc-tiddler-controls button svg.tc-image-close-button { fill: ; } .tc-tiddler-controls button svg.tc-image-delete-button { fill: ; } .tc-tiddler-controls button svg.tc-image-cancel-button { fill: ; } .tc-tiddler-controls button svg.tc-image-done-button { fill: ; } .tc-tiddler-controls button svg.tc-image-button { height: 0.75em; }\n\n/* ** $:/themes/telmiger/bricks/components/tiddler-frameless.css ** */\n\n.tc-tiddler-frame { position: relative; padding: 1rem; margin-bottom: 2rem; background-color: rgb(245, 245, 245); } @media (min-width: 28rem) { .tc-tiddler-frame { padding: 2rem; } } @media (min-width: 42rem) { .tc-tiddler-frame { padding-left: 2rem; padding-right: 2rem; border-radius: .125rem; } .tc-tiddler-view-frame { max-width: 54rem; } } @media (min-width: 77rem) { .tc-tiddler-frame { padding-left: 4rem; padding-right: 4rem; height: auto; } .tc-tiddler-view-frame { width: 54rem; } }\n\n/* ** $:/themes/telmiger/bricks/components/tiddler-info-panel.css ** */\n\n.tc-tiddler-info { padding-top: 1rem; padding-bottom: 1rem; border-top-style: solid; border-top-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; border-color: #dddddd; background-color: #f8f8f8; } .tc-tiddler-info p { margin-top: .25rem; margin-bottom: .25rem; } .tc-tiddler-frame .tc-tiddler-info .tc-tab-set { margin-left: 0; } .tc-tiddler-info .tc-tab-content { padding-left: .5rem; padding-right: .5rem; } .tc-tiddler-info .tc-tab-content p > label { padding-bottom: 1rem; display: inline-block; } .tc-tiddler-info .tc-tab-content p > label + button, .tc-tiddler-info .tc-tab-content p > label + span > button { position: absolute; margin-left: 1rem; } .tc-tiddler-info .tc-tab-content p > label + button .tc-btn-text, .tc-tiddler-info .tc-tab-content p > label + span > button .tc-btn-text { display: none; } .tc-tiddler-info .tc-tab-content p > i { position: absolute; margin-left: 4rem; line-height: 1; max-width: 20em; } .tc-view-field-table { width: 100%; } .tc-view-field-name { width: 1%; text-align: right; font-style: italic; font-weight: 200; } .tc-view-field-value { } @media (min-width: 28rem) { .tc-tiddler-info { margin-top: 0; margin-bottom: .25rem; padding-right: 1rem; padding-left: 2rem; } .tc-tiddler-frame .tc-tiddler-info .tc-tab-set { margin-left: -1rem; margin-right: 0; } } @media (min-width: 42rem) { .tc-tiddler-info { margin-top: 0; margin-bottom: 1rem; } }\n\n/* ** $:/themes/telmiger/bricks/components/tree-macro.css ** */\n\n.tc-tree div { padding-left: .5rem; } .tc-tree ol { list-style-type: none; padding-left: 0; margin-top: 0; } .tc-tree ol ol { padding-left: 1em; } .tc-tree button { color: #acacac; } .tc-tree svg { fill: #acacac; } .tc-tree span svg { width: 1em; height: 1em; vertical-align: baseline; } .tc-tree li span { color: lightgray; }\n\n/* ** $:/themes/telmiger/bricks/drafts-list.css ** */\n\n.tc-drafts-list { z-index: 2000; position: fixed; font-size: 0.8em; left: 0; bottom: 0; } .tc-drafts-list a.tc-tiddlylink { margin: 0 0.5em; padding: 4px 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; border: 1px solid #ffffff; background: #ff0000; color: #ffffff; fill: #ffffff; } .tc-drafts-list a svg { width: 1em; height: 1em; margin-right: .25rem; vertical-align: text-bottom; }\n\n/* ** $:/themes/telmiger/bricks/edit/diffs.css ** */\n\n.tc-diff-equal { background-color: ; color: rgb(13, 13, 13); } .tc-diff-insert { background-color: #aaefad; color: rgb(13, 13, 13); } .tc-diff-delete { background-color: #ffc9c9; color: rgb(13, 13, 13); } .tc-diff-invisible { background-color: ; color: rgb(115, 115, 115); } .tc-diff-tiddlers th { text-align: right; background: rgb(245, 245, 245); font-weight: normal; font-style: italic; } .tc-diff-tiddlers pre { margin: 0; padding: 0; border: none; background: none; }\n\n/* ** $:/themes/telmiger/bricks/edit/editor-choosers.css ** */\n\n.tc-colour-chooser a { padding: 3px; width: 2em; height: 2em; vertical-align: middle; } .tc-image-chooser, .tc-colour-chooser { white-space: normal; } .tc-image-chooser a, .tc-colour-chooser a { display: inline-block; vertical-align: top; position: relative; } .tc-image-chooser a { padding: 2px; margin: 2px; width: 4em; height: 4em; border-style: solid; border-width: 1px; border-color: rgb(115, 115, 115); } .tc-image-chooser a:hover, .tc-colour-chooser a:hover { padding: 0; border-width: .125rem; border-style: solid; border-color: rgb(80, 150, 110); background: rgb(80, 150, 110); } .tc-image-chooser a svg, .tc-image-chooser a img { display: inline-block; width: auto; height: auto; max-width: 3.5em; max-height: 3.5em; position: absolute; top: 0; bottom: 0; left: 0; right: 0; }\n\n/* ** $:/themes/telmiger/bricks/edit/editor-toolbar.css ** */\n\n.tc-editor-toolbar { margin-top: .5rem; margin-bottom: .5rem; } .tc-editor-toolbar button { vertical-align: middle; background-color: rgba(0,0,0,0.08); fill: rgb(13, 13, 13); border-radius: .25rem; padding: .25rem; margin-top: .125rem; margin-bottom: .125rem; margin-right: 0; margin-left: .25rem; } .tc-editor-toolbar button.tc-text-editor-toolbar-item-adjunct { margin-left: .125rem; border-radius: .5rem; width: 1em; } .tc-editor-toolbar button.tc-text-editor-toolbar-item-start-group { box-shadow: 1px 1px 2px rgb(64, 64, 64); } .tc-editor-toolbar button.tc-selected { background-color: rgb(80, 150, 110); } .tc-editor-toolbar button svg { width: 1.6em; height: 1.2em; } .tc-editor-toolbar button:hover, .tc-editor-toolbar .tc-text-editor-toolbar-more button:hover svg { background-color: rgb(64, 64, 64); fill: rgb(245, 245, 245); } .tc-editor-toolbar .tc-text-editor-toolbar-more { font-size: .875rem; padding-left: 1rem; margin-left: 2rem; } .tc-editor-toolbar .tc-text-editor-toolbar-more .tc-reveal { line-height: 1.25; margin-bottom: .5rem; } .tc-editor-toolbar .tc-text-editor-toolbar-more button { display: inline-block; position: absolute; left: 1rem; padding: .25rem; width: auto; } .tc-editor-toolbar .tc-search-results { padding: 0; }\n\n/* ** $:/themes/telmiger/bricks/edit/editor.css ** */\n\niframe.tc-edit-texteditor { background-color: transparent; } .tc-tiddler-frame .tc-edit-texteditor { width: 100%; margin: 4px 0 0 0; } .tc-tiddler-frame textarea.tc-edit-texteditor, .tc-tiddler-frame iframe.tc-edit-texteditor { padding: .5rem; background-color: #F7F7F7; line-height: 1.3em; -webkit-appearance: none; -moz-appearance: none; } .tc-tiddler-frame input.tc-edit-texteditor { border-style: none; border-width: 0; border-left-style: solid; border-left-width: 1px; border-width: .5rem; border-bottom-style: solid; border-bottom-width: 1px; padding: .25rem; line-height: 1.25; background-color: #F7F7F7; border-color: #ccc; }\n\n/* ** $:/themes/telmiger/bricks/edit/preview-fonts.css ** */\n\n.tc-tiddler-preview-preview h1 { font-size: 1.875rem; margin-bottom: 2rem; } .tc-tiddler-preview-preview h2 { font-size: 1.5rem; margin-bottom: 2rem; } .tc-tiddler-preview-preview h3 { font-size: 1.25rem; margin-bottom: 1rem; } .tc-tiddler-preview-preview h4 { font-size: 1rem; margin-bottom: 1rem; } .tc-tiddler-preview-preview h5 { font-size: .875rem; margin-bottom: 1rem; font-family: georgia, times, serif; } .tc-tiddler-preview-preview h6 { font-size: .75rem; margin-bottom: .5rem; font-family: georgia, times, serif; } .tc-tiddler-preview-preview * + h1, section > h1:first-child { margin-top: 4rem; } .tc-tiddler-preview-preview *:not(h1) + h2, section > h2:first-child { margin-top: 4rem; } .tc-tiddler-preview-preview *:not(h2) + h3, section > h3:first-child { margin-top: 2rem; } .tc-tiddler-preview-preview *:not(h3) + h4, section > h4:first-child { margin-top: 2rem; } .tc-tiddler-preview-preview *:not(h4) + h5, section > h5:first-child { margin-top: 2rem; } .tc-tiddler-preview-preview *:not(h5) + h6, section > h6:first-child { margin-top: 2rem; }\n\n/* ** $:/themes/telmiger/bricks/edit/tiddler-manager.css ** */\n\n.tc-manager-wrapper { } .tc-manager-controls { } .tc-manager-control { margin-top: .5rem; margin-bottom: .5rem; } .tc-manager-list { width: 100%; border-top-style: solid; border-top-width: 1px; border-right-style: solid; border-right-width: 1px; border-left-style: solid; border-left-width: 1px; border-color: rgb(115, 115, 115); } .tc-manager-list-item { } .tc-manager-list-item-heading { display: block; width: 100%; text-align: left; border-bottom-style: solid; border-bottom-width: 1px; border-color: rgb(115, 115, 115); padding: 3px; } .tc-manager-list-item-heading-selected { font-weight: bold; color: rgb(245, 245, 245); fill: rgb(245, 245, 245); background-color: rgb(13, 13, 13); } .tc-manager-list-item-heading:hover { background: rgb(80, 150, 110); color: rgb(245, 245, 245); } .tc-manager-list-item-content { display: flex; } .tc-manager-list-item-content-sidebar { flex: 1 0; background: #F7F7F7; border-right-style: solid; border-right-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; border-width: .25rem; border-color: rgb(115, 115, 115); white-space: nowrap; } .tc-manager-list-item-content-item-heading { display: block; width: 100%; text-align: left; background: rgb(115, 115, 115); text-transform: uppercase; font-size: .875rem; font-weight: bold; padding-top: .5rem; padding-bottom: .5rem; } .tc-manager-list-item-content-item-body { padding-left: .5rem; padding-right: .5rem; } .tc-manager-list-item-content-item-body > pre { margin-top: .5rem; margin-bottom: .5rem; border: none; background: inherit; } .tc-manager-list-item-content-tiddler { flex: 3 1; border-right-style: solid; border-right-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; <.bl> border-width: .25rem; border-color: rgb(115, 115, 115); } .tc-manager-list-item-content-item-body > table { border: none; padding: 0; margin: 0; } .tc-manager-list-item-content-item-body > table td { border: none; } .tc-manager-icon-editor > button { width: 100%; } .tc-manager-icon-editor > button > svg, .tc-manager-icon-editor > button > button { width: 100%; height: auto; }\n\n/* ** $:/themes/telmiger/bricks/edit/tiddler.css ** */\n\n.tc-tiddler-frame.tc-tiddler-edit-frame { border-style: solid; border-width: 1px; margin-left: .25rem; margin-right: .25rem; width: auto; border-color: #ff4136; } @media (min-width: 28rem) { .tc-tiddler-frame.tc-tiddler-edit-frame { margin-right: 0; margin-left: auto; } } @media (min-width: 42rem) { .tc-tiddler-frame.tc-tiddler-edit-frame { margin-right: calc(66vw - 26rem); } } @media (min-width: 77rem) { .tc-tiddler-frame.tc-tiddler-edit-frame { width: calc(100% - 1.5rem); max-width: calc(54rem + 54rem); margin-left: 1rem; margin-right: 1rem; } .tc-keyboard { width: 100%; } } .tc-tiddler-edit-frame em.tc-edit { color: rgb(115, 115, 115); font-style: normal; } .tc-edit-type-dropdown a.tc-tiddlylink-missing { font-style: normal; } input.tc-titlebar.tc-edit-texteditor { margin-bottom: 1rem; } .tc-edit-tags { margin-bottom: 1rem; background-color: #F7F7F7; } .tc-edit-add-tag { display: inline-block; margin-left: .5rem; padding-left: .5rem; } .tc-edit-add-tag .tc-add-tag-name input { width: 50%; } .tc-edit-add-tag .tc-keyboard { display:inline; } .tc-edit-tags .tc-tag-label { display: inline-block; } .tc-edit-tags-list { margin-top: 1rem; margin-bottom: 1rem; margin-left: 0; margin-right: 0; } .tc-tag-label .tc-remove-tag-button { padding-left: .25rem; color: rgb(242, 242, 242); } .tc-tiddler-preview { } .tc-tiddler-preview-preview { float: right; width: 49%; border: 1px solid #ccc; margin: 4px 0 0 3px; padding: .5rem; } .tc-tiddler-frame .tc-tiddler-preview .tc-edit-texteditor { width: 49%; } .tc-tiddler-frame .tc-tiddler-preview canvas.tc-edit-bitmapeditor { max-width: 49%; } .tc-type-selector { margin-bottom: .5rem; fill: rgb(143, 143, 143); } .tc-type-selector button:hover svg { fill: rgb(64, 64, 64); } .tc-edit-fields { width: 100%; } .tc-edit-fields table, .tc-edit-fields tr, .tc-edit-fields td { border-style: none; border-width: 0; padding: .25rem; } .tc-edit-fields > tbody > .tc-edit-field:nth-child(odd) { background-color: #f0f4f0; } .tc-edit-fields > tbody > .tc-edit-field:nth-child(even) { background-color: #e0e8e0; } .tc-edit-field-name { text-align: right; } .tc-edit-field-value input { width: 100%; } .tc-edit-field-remove svg { height: 1em; width: 1em; vertical-align: middle; fill: rgb(143, 143, 143); } .tc-edit-field-remove button:hover svg { fill: rgb(64, 64, 64); } .tc-edit-field-add-name { display: inline-block; min-width: 15%; max-width: 30%; margin-right: 1rem; } .tc-edit-field-add-value { display: inline-block; min-width: 38%; max-width: 50%; margin-right: 1rem; } .tc-edit-field-add-button { display: inline-block; } .tc-tiddler-frame .tc-binary-warning { width: 100%; height: 5em; text-align: center; padding: 3em 3em 6em 3em; background: rgb(255, 255, 102); border: 1px solid rgb(232, 232, 125); } canvas.tc-edit-bitmapeditor { border: 6px solid rgba(255,255,255,1); cursor: crosshair; -moz-user-select: none; -webkit-user-select: none; -ms-user-select: none; margin-top: 6px; margin-bottom: 6px; } .tc-edit-bitmapeditor-width { display: block; } .tc-edit-bitmapeditor-height { display: block; }\n\n/* ** $:/themes/telmiger/bricks/elements/blockquotes.css ** */\n\nblockquote, q { quotes: \"\\201C\"\"\\201D\"\"\\2018\"\"\\2019\"; } blockquote { border-left-style: solid; border-left-width: 1px; border-width: .5rem; border-color: rgb(89, 166, 122); padding-left: 1rem; margin-left: 1rem; } blockquote cite { display: inline-block; padding-top: .5rem; } blockquote.tc-big-quote { font-family: Georgia, serif; position: relative; background: rgba(0,0,0,0.08); border-style: none; border-width: 0; border-radius: 1rem; margin-top: 2rem; margin-bottom: 2rem; margin-left: 1rem; margin-right: 1rem; padding: 1rem; padding-left: 2rem; max-width: 38rem; } blockquote.tc-big-quote p { font-size: 1rem; max-width: 30em; } blockquote.tc-big-quote cite:before { content: \"\\2014 \\2009\"; } blockquote.tc-big-quote:before { position: absolute; left: -1.2rem; top: 2.4rem; color: rgb(89, 166, 122); content: open-quote; font-size: 6rem; line-height: 0.1em; } blockquote.tc-big-quote:after { position: absolute; right: -1.2rem; bottom: -.6rem; font-family: Georgia, serif; color: rgb(89, 166, 122); content: close-quote; font-size: 6rem; line-height: 0.1em; } @media (min-width: 28rem) { blockquote.tc-big-quote { border-style: none; border-width: 0; border-radius: 1rem; margin-top: 2rem; margin-bottom: 2rem; margin-left: 4rem; margin-right: 4rem; padding: 1rem; padding-left: 2rem; } blockquote.tc-big-quote p { font-size: 1.25rem; } blockquote.tc-big-quote:before { font-size: 9.5rem; left: -4.25rem; top: 3.25rem; } blockquote.tc-big-quote:after { font-size: 9.5rem; right: -4.25rem; bottom: -1.75rem; } }\n\n/* ** $:/themes/telmiger/bricks/elements/buttons.css ** */\n\nhtml button { cursor: pointer; font-size: 1em; color: rgb(13, 13, 13); background: rgba(0,0,0,0.08); border-color: rgba(255, 255, 255, 0.8); border-style: solid; border-width: 1px; border-radius: .25rem; } button:not([class]), button[class=\"\"] { line-height: 1.5; padding-left: .5rem; padding-right: .5rem; border-style: solid; border-width: 1px; opacity: 1; -webkit-transition: opacity .15s ease-in; transition: opacity .15s ease-in; transition: color .15s ease-in, text-decoration .15s ease-in, background-color .15s ease-in; } button:not([class]):hover, button:not([class]):focus { opacity: .5; -webkit-transition: opacity .15s ease-in; transition: opacity .15s ease-in; } button:not([class]):active { opacity: .8; -webkit-transition: opacity .15s ease-out; transition: opacity .15s ease-out; } .tc-tiddler-info .tc-tab-content button, .tc-sidebar-lists .tc-tab-content p > button, .tc-sidebar-lists .tc-tab-content p > span:not(.tc-tag-list-item) > button, .tc-sidebar-lists .tc-file-input-wrapper button, .tc-sidebar-lists .tc-popup-keep button, .tc-add-tag-button button, .tc-edit-field-add-button button { border-radius: .25rem; border-style: none; border-width: 0; color: ; background: linear-gradient(to bottom,rgba(99,99,99,.1) 0,rgba(0,0,0,.125) 100%); text-decoration: none; font-weight: 700; padding-top: .25rem; padding-bottom: .25rem; padding-left: 1rem; padding-right: 1rem; margin-right: 1rem; min-width: 5rem; } .tc-tiddler-info .tc-tab-content button:hover, .tc-tab-content p > button:hover, .tc-sidebar-lists .tc-tab-content p > span:not(.tc-tag-list-item) > button:hover, .tc-sidebar-lists .tc-file-input-wrapper:hover button, .tc-sidebar-lists .tc-popup-keep:hover button, .tc-add-tag-button button:hover, .tc-edit-field-add-button button:hover { background: linear-gradient(to bottom,rgba(99,99,99,.3) 0,rgba(99,99,99,.3) 100%); } .tc-add-tag-button button, .tc-edit-field-add-button button { font-size: .875rem; padding-top: .25rem; padding-bottom: .25rem; padding-left: .5rem; padding-right: .5rem; margin-right: .5rem; min-width: 3rem; } .tc-sidebar-lists .tc-tab-content p > button, .tc-sidebar-lists .tc-tab-content p > span:not(.tc-tag-list-item) > button, .tc-sidebar-lists .tc-file-input-wrapper button, .tc-sidebar-lists .tc-popup-keep button, .tc-tiddler-info .tc-tab-content button { border-style: solid; border-width: 1px; border-radius: .25rem; font-size: .75rem; padding-left: .5rem; padding-right: .5rem; margin-right: .25rem; min-width: 2rem; } button svg, button img, label svg, label img { vertical-align: middle; } .tc-btn-invisible { border: none; border-radius: 0; margin: 0; padding: 0; background: none; cursor: pointer; text-align: left; } .tc-btn-boxed { font-size: 0.6em; padding: 0.2em; margin: 1px; border-style: solid; border-width: 1px; border-color: rgb(143, 143, 143); border-radius: 0.25em; } html body.tc-body .tc-btn-boxed svg { font-size: 1.6666em; } .tc-btn-boxed:hover { background: rgb(115, 115, 115); color: rgb(245, 245, 245); } html body.tc-body .tc-btn-boxed:hover svg { fill: rgb(245, 245, 245); } .tc-btn-rounded { font-size: 0.5em; line-height: 2; padding: 0em 0.3em 0.2em 0.4em; margin: 1px; border-style: solid; border-width: 1px; border-color: rgb(115, 115, 115); background: rgb(115, 115, 115); color: rgb(245, 245, 245); border-radius: 2em; } .tc-btn-rounded:hover { border-style: solid; border-width: 1px; border-color: rgb(115, 115, 115); background: rgb(245, 245, 245); color: rgb(115, 115, 115); } .tc-btn-text { padding: 0; margin: 0; } button.tc-btn-big-green, a.tc-btn-big-green { display: inline-block; font-size: 1.25rem; line-height: 1.5; padding-top: .5rem; padding-bottom: .5rem; padding-left: 1.25rem; padding-right: 1.25rem; margin-top: .5rem; margin-bottom: .5rem; margin-right: .5rem; margin-left: 0; border-style: none; border-width: 0; background: #19a974; color: rgb(245, 245, 245); fill: rgb(245, 245, 245); font-weight: 400; text-decoration: none; } body.tc-body button.tc-btn-big-green:hover { background-color: #19a719; } .tc-btn-big-green svg, .tc-btn-big-green img { margin-top: 0; vertical-align: middle; fill: rgb(245, 245, 245); } .tc-sidebar-lists input { color: rgb(13, 13, 13); } .tc-sidebar-lists button { color: rgb(245, 245, 245); fill: rgb(245, 245, 245); } .tc-sidebar-lists .tc-droppable { display: block; padding-left: 1rem; } .tc-sidebar-lists button.tc-btn-mini { font-size: 1rem; position: absolute; width: 1rem; margin-left: -1rem; } .tc-sidebar-lists button.tc-btn-mini + a { display: inline-block; margin-left: .25rem; } .tc-sidebar-lists button.tc-btn-mini { color: rgb(150, 150, 150); } .tc-sidebar-lists button.tc-btn-mini:hover { color: rgb(227, 227, 227); } .tc-sidebar-lists .tc-tab-set .tc-tab-content p .tc-droppable > button.tc-btn-mini { width: auto; font-size: .875rem; margin-top: 1rem; margin-left: -1rem; color: rgb(255, 255, 255); } .tc-unfold-banner svg, .tc-fold-banner svg { height: 0.75em; fill: rgb(143, 143, 143); } .tc-unfold-banner:hover svg, .tc-fold-banner:hover svg { fill: rgb(64, 64, 64); } html body.tc-body .tc-btn-rounded svg { font-size: 1.6666em; fill: rgb(245, 245, 245); } html body.tc-body .tc-btn-rounded:hover svg { fill: rgb(115, 115, 115); } .tc-btn-icon svg { height: 1em; width: 1em; fill: rgb(143, 143, 143); } svg.tc-image-button { padding: 0px 1px 1px 0px; } svg.tc-image-button, .tc-image-button img { height: 1em; width: 1em; max-width: 100%; max-height: 100%; } .tc-page-controls svg.tc-image-new-button { fill: ; } .tc-page-controls svg.tc-image-options-button { fill: ; } .tc-page-controls svg.tc-image-save-button { fill: ; } .tc-fold-banner, .tc-unfold-banner { position: relative; padding: 0; margin: 0; background: none; border: none; right: 0; width: 100%; text-align: right; } .tc-unfold-banner { border-top: 2px solid; border-color: #f8f8f8; background: none; } .tc-fold-banner:hover svg { background: #f8f8f8; } .tc-unfold-banner:hover { border-top: 2px solid; border-color: #dddddd; background: #f8f8f8; }\n\n/* ** $:/themes/telmiger/bricks/elements/buttons/big-new.css ** */\n\n#navigator-main button.te-big-new-btn { position: fixed; z-index: 1201; right: 1rem; bottom: 0; margin-bottom: 1.5rem; font-size: 2rem; text-align: center; line-height:0; color: rgba(255,255,255,.8); border: 1px solid transparent; border-radius: 5rem; height: 3rem; width: 3rem; } #navigator-main button.te-big-new-btn:hover, #navigator-main button.te-big-new-btn:active, #navigator-main button.te-big-new-btn:focus { background-color: #181818; border-color: rgb(70, 70, 70); } #navigator-main button.te-big-new-btn svg.te-new { fill: rgba(255,255,255,.8); } #navigator-main button.te-big-new-btn:hover svg.te-new, #navigator-main button.te-big-new-btn:active svg.te-new, #navigator-main button.te-big-new-btn:focus svg.te-new { fill: rgba(255,255,255,.8); } @media (min-width: 28rem) { #navigator-main button.te-big-new-btn { right: 2rem; margin-bottom: 1rem; height: 4rem; width: 4rem; } } @media (min-width: 42rem) { #navigator-main button.te-big-new-btn { right: 2rem; margin-bottom: 0.5rem; height: 5rem; width: 5rem; } } @media (min-width: 77rem) { #navigator-main button.te-big-new-btn { right: 2rem; } button .large-screen-only { position: absolute; right: 3rem; width: 20rem; bottom: 0.25rem; color: rgb(13, 13, 13); } } @media (max-height: 20rem) { #navigator-main button.te-big-new-btn { height: 3rem; width: 3rem; } } \n\n/* ** $:/themes/telmiger/bricks/elements/details.css ** */\n\ndetails .tc-drop-down[hidden=\"true\"] { display: none; } @media (min-width: 42rem) { details > span { padding-top: 1rem; padding-bottom: 1rem; padding-left: 2rem; padding-right: 2rem; } }\n\n/* ** $:/themes/telmiger/bricks/elements/images.css ** */\n\nimg { max-width: 100%; } .tc-bordered-image { border-style: solid; border-width: 1px; border-color: rgb(115, 115, 115); padding: 5px; margin: 5px; }\n\n/* ** $:/themes/telmiger/bricks/elements/links.css ** */\n\na { overflow-wrap: break-word; } .tc-tiddlylink, .tc-tiddlylink-external { text-decoration: none; transition: color .15s ease-in, text-decoration .15s ease-in, background-color .15s ease-in; } .tc-tiddlylink:link, .tc-tiddlylink-external:link, .tc-tiddlylink:visited, .tc-tiddlylink-external:visited { transition: color .15s ease-in, text-decoration .15s ease-in, background-color .15s ease-in; } .tc-tiddlylink:hover, .tc-tiddlylink-external:hover { transition: color .15s ease-in, text-decoration .15s ease-in, background-color .15s ease-in; } .tc-tiddlylink:active, .tc-tiddlylink-external:active { transition: color .15s ease-in, text-decoration .15s ease-in, background-color .15s ease-in; } .tc-tiddlylink:focus, .tc-tiddlylink-external:focus { transition: color .15s ease-in, text-decoration .15s ease-in, background-color .15s ease-in; } button.tc-tiddlylink, a.tc-tiddlylink { text-decoration: none; font-weight: normal; color: #00449e; -webkit-user-select: inherit; } button.tc-tiddlylink:hover, a.tc-tiddlylink:hover { text-decoration: underline; } a.tc-tiddlylink-resolves { } a.tc-tiddlylink-shadow { font-weight: bold; } a.tc-tiddlylink-shadow.tc-tiddlylink-resolves { font-weight: normal; } a.tc-tiddlylink-missing { font-style: italic; } a.tc-tiddlylink-external { text-decoration: underline; color: #357edd; background-color: inherit; } a.tc-tiddlylink-external:visited { color: #00449e; background-color: inherit; } a.tc-tiddlylink-external:hover { color: inherit; background-color: inherit; } .tc-sidebar-lists a.tc-tiddlylink { font-size: .875rem; line-height: 1.25; color: rgba(255,255,255,.8); max-width: 100% } .tc-sidebar-lists .tc-btn-mini + a.tc-tiddlylink { margin-bottom: .25rem; } .tc-sidebar-lists a.tc-tiddlylink:hover, button.tc-btn-invisible.tc-missing-tiddler-label:hover { color: rgba(191, 191, 191, 0.8); } .tc-sidebar-header .tc-missing-tiddler-label { font-size: .875rem; line-height: 1.25; margin-bottom: .25rem; color: rgb(255, 255, 255); text-align: left; font-style: italic; } button.tc-btn-invisible.tc-missing-tiddler-label { max-width: 100%; }\n\n/* ** $:/themes/telmiger/bricks/elements/lists.css ** */\n\nul, ol { margin-top: 1rem; max-width: 40rem; } ul ul, ol ol, ul ol, ol ul { margin-top: .5rem; } ol li, ul li { line-height: 1.25; } ol { padding-left: 2rem; } ul { padding-left: 1rem; } li:first-child { margin-top: .5rem; } li:not(:last-child) { padding-bottom: .5rem; }\n\n/* ** $:/themes/telmiger/bricks/elements/tables.css ** */\n\ntable { border-collapse: collapse; border-spacing: 0; border-style: solid; border-width: 1px; border-color: rgba(0,0,0,0.125); width: auto; max-width: 100%; caption-side: bottom; margin-bottom: 2rem; } table th { text-align: left; } tr:nth-child(odd) { background-color: rgba(0,0,0,0.08); } tr:nth-child(even) { background-color: rgba(0,0,0,0.03); } table th, table td { padding-top: .5rem; padding-bottom: .5rem; padding-left: 1rem; padding-right: 1rem; } table thead tr td, table th { background-color: rgba(0,0,0,.1); background-color: rgba(0,0,0,0.125); font-weight: bold; } table tfoot tr td { background-color: rgba(0,0,0,.4); } .tc-csv-table { white-space: nowrap; }\n\n/* ** $:/themes/telmiger/bricks/elements/text/two-columns.css ** */\n\n.cf { *zoom: 1; } .cf:before, .cf:after { content: \" \"; display: table; } .cf:after { clear: both; } .fl { float: left; _display: inline; } .w-100 { width: 100%; } @media screen and (min-width: 30em) { .w-50-ns { width: 50%; } }\n\n/* ** $:/themes/telmiger/bricks/functions/drag-drop.css ** */\n\n.tc-tiddler-dragger { position: relative; z-index: -10000; } .tc-tiddler-dragger-inner { position: absolute; top: -1000px; left: -1000px; display: inline-block; padding: 8px 20px; font-size: 16.9px; font-weight: bold; line-height: 20px; color: rgb(245, 245, 245); text-shadow: 0 1px 0 rgba(0, 0, 0, 1); white-space: nowrap; vertical-align: baseline; background-color: rgb(13, 13, 13); border-radius: 20px; } .tc-tiddler-dragger-cover { position: absolute; background-color: rgb(245, 245, 245); } .tc-dropzone { position: relative; } .tc-dropzone.tc-dragover:before { z-index: 10000; display: block; position: fixed; top: 0; left: 0; right: 0; background: #9eebcf; text-align: center; content: \"Drop here (or use the 'Escape' key to cancel)\"; } .tc-droppable.tc-dragover > .tc-droppable-placeholder { display: block; border: 2px dashed #9eebcf; } .tc-draggable { cursor: move; }\n\n/* ** $:/themes/telmiger/bricks/functions/print.css ** */\n\n@media print { .screen-only, .tc-tiddler-frame .tc-tiddler-controls { display: none; } h1, h2, h3, h4, h5, h6 { break-inside: avoid; } body .tc-sidebar-scrollable { position: relative; top: 0; bottom: unset; left: 0; box-shadow: none; } body .tc-sidebar-header > .tc-reveal { width: 100%; padding: 0; margin: 0; margin-bottom: 2rem; border-bottom-style: solid; border-bottom-width: 1px; border-color: rgb(80, 150, 110); } body.tc-body { background-color: transparent; } body .tc-topbar, body .tc-page-controls, body .tc-sidebar-lists { display: none; } body .tc-story-river { margin: 0; padding: 0; } body .tc-story-river .tc-tiddler-frame { margin: 0; border: none; padding: 0; } }\n\n/* ** $:/themes/telmiger/bricks/functions/zoomin.css ** */\n\n.tc-storyview-zoomin-tiddler { position: absolute; display: block; width: 100%; } @media (min-width: 77rem) { body .tc-story-river > .tc-storyview-zoomin-tiddler, body .tc-story-river > .tc-storyview-zoomin-tiddler.tc-tiddler-edit-frame { width: calc(100% - 4rem); margin-left: auto; margin-right: auto; } }\n\n/* ** $:/themes/telmiger/bricks/todo-components/thumbnail.css ** */\n\n.tc-thumbnail-wrapper { position: relative; display: inline-block; margin: 6px; vertical-align: top; } .tc-thumbnail-right-wrapper { float:right; margin: 0.5em 0 0.5em 0.5em; } .tc-thumbnail-image { text-align: center; overflow: hidden; border-radius: 3px; } .tc-thumbnail-image svg, .tc-thumbnail-image img { filter: alpha(opacity=1); opacity: 1; min-width: 100%; min-height: 100%; max-width: 100%; } .tc-thumbnail-wrapper:hover .tc-thumbnail-image svg, .tc-thumbnail-wrapper:hover .tc-thumbnail-image img { filter: alpha(opacity=0.8); opacity: 0.8; } .tc-thumbnail-background { position: absolute; border-radius: 3px; } .tc-thumbnail-icon svg, .tc-thumbnail-icon img { width: 3em; height: 3em;\n -webkit-filter: drop-shadow(2px 2px 4px rgba(0,0,0,0.3));\n -moz-filter: drop-shadow(2px 2px 4px rgba(0,0,0,0.3));\n filter: drop-shadow(2px 2px 4px rgba(0,0,0,0.3));\n} .tc-thumbnail-wrapper:hover .tc-thumbnail-icon svg, .tc-thumbnail-wrapper:hover .tc-thumbnail-icon img { fill: #fff;\n -webkit-filter: drop-shadow(3px 3px 4px rgba(0,0,0,0.6));\n -moz-filter: drop-shadow(3px 3px 4px rgba(0,0,0,0.6));\n filter: drop-shadow(3px 3px 4px rgba(0,0,0,0.6));\n} .tc-thumbnail-icon { position: absolute; top: 0; left: 0; right: 0; bottom: 0; display: -webkit-flex; -webkit-align-items: center; -webkit-justify-content: center; display: flex; align-items: center; justify-content: center; } .tc-thumbnail-caption { position: absolute; background-color: #777; color: #fff; text-align: center; bottom: 0; width: 100%; filter: alpha(opacity=0.9); opacity: 0.9; line-height: 1.4; border-bottom-left-radius: 3px; border-bottom-right-radius: 3px; } .tc-thumbnail-wrapper:hover .tc-thumbnail-caption { filter: alpha(opacity=1); opacity: 1; }\n\n/* ** $:/themes/telmiger/bricks/todo-components/toc.css ** */\n\n.tc-sidebar-lists .tc-table-of-contents { white-space: nowrap; } .tc-table-of-contents button { color: rgb(255, 255, 255); } .tc-table-of-contents svg { width: 0.7em; height: 0.7em; vertical-align: middle; fill: rgb(255, 255, 255); } .tc-table-of-contents ol { list-style-type: none; padding-left: 0; } .tc-table-of-contents ol ol { padding-left: 1em; } .tc-table-of-contents li { font-size: 1.0em; font-weight: bold; } .tc-table-of-contents li a { font-weight: bold; } .tc-table-of-contents li li { font-size: 0.95em; font-weight: normal; line-height: 1.4; } .tc-table-of-contents li li a { font-weight: normal; } .tc-table-of-contents li li li { font-size: 0.95em; font-weight: 200; line-height: 1.5; } .tc-table-of-contents li li li li { font-size: 0.95em; font-weight: 200; } .tc-tabbed-table-of-contents { display: -webkit-flex; display: flex; } .tc-tabbed-table-of-contents .tc-table-of-contents { z-index: 100; display: inline-block; padding-left: 1em; max-width: 50%; -webkit-flex: 0 0 auto; flex: 0 0 auto; background: rgb(225, 225, 225); border-left: 1px solid; border-top: 1px solid; border-bottom: 1px solid; border-color: rgb(80, 150, 110); } .tc-tabbed-table-of-contents .tc-table-of-contents .toc-item > a, .tc-tabbed-table-of-contents .tc-table-of-contents .toc-item-selected > a { display: block; padding: 0.12em 1em 0.12em 0.25em; } .tc-tabbed-table-of-contents .tc-table-of-contents .toc-item > a { border-top: 1px solid; border-left: 1px solid; border-bottom: 1px solid; border-color: rgb(225, 225, 225); } .tc-tabbed-table-of-contents .tc-table-of-contents .toc-item > a:hover { text-decoration: none; border-top: 1px solid; border-left: 1px solid; border-bottom: 1px solid; border-color: rgb(80, 150, 110); background: rgb(80, 150, 110); } .tc-tabbed-table-of-contents .tc-table-of-contents .toc-item-selected > a { border-top: 1px solid; border-left: 1px solid; border-bottom: 1px solid; border-color: rgb(80, 150, 110); background: rgb(245, 245, 245); margin-right: -1px; } .tc-tabbed-table-of-contents .tc-table-of-contents .toc-item-selected > a:hover { text-decoration: none; } .tc-tabbed-table-of-contents .tc-tabbed-table-of-contents-content { display: inline-block; vertical-align: top; padding-left: 1.5em; padding-right: 1.5em; border: 1px solid; border-color: rgb(80, 150, 110); -webkit-flex: 1 0 50%; flex: 1 0 50%; }\n\n/* ** $:/themes/telmiger/bricks/todo-components/tree.css ** */\n\n.tc-tree div { padding-left: 14px; } .tc-tree ol { list-style-type: none; padding-left: 0; margin-top: 0; } .tc-tree ol ol { padding-left: 1em; } .tc-tree button { color: #acacac; } .tc-tree svg { fill: #acacac; } .tc-tree span svg { width: 1em; height: 1em; vertical-align: baseline; } .tc-tree li span { color: lightgray; }\n\n/* ** $:/themes/telmiger/navigator/styles/nav-top.css ** */\n\n.story-backdrop + div { padding-top: 4rem; } .tc-topbar.tc-topbar-right { display: block; position: absolute; width: 100vw; top: 0; left: 0; right: 0; max-height: 3rem; overflow: hidden; background-color: black; color: white; z-index: 1200; box-shadow: 0 1px 5px rgba(125, 200, 200, 0.5); } @media screen and (min-height: 570px) { .tc-topbar.tc-topbar-right { position: fixed; } } #navigator-top ul { height: 3rem; line-height: 1; padding: 0; margin-top: .5rem; } #navigator-top ul li { list-style: none; display: inline; margin: 0; } #navigator-top ul li:first-child { position: absolute; left: 1rem; } #navigator-top ul li+li { position: absolute; left: calc(100vw / 2 - 0.75rem); } #navigator-top ul li:last-child { position: absolute; right: 1rem; left: auto; } @media (min-width: 28rem) { #navigator-top ul li:first-child { left: 2rem; } #navigator-top ul li:last-child { right: 2rem; } } @media (min-width: 42rem) { #navigator-top ul li:first-child { left: 4rem; } #navigator-top ul li:last-child { right: 4rem; } } @media (min-width: 77rem) { #navigator-top ul li:first-child { } } #navigator-top button { margin: 0; background: none; border: none; }\n\n/* ** $:/themes/telmiger/navigator/styles/nav.css ** */\n\n#navigator-main { position: fixed; z-index: 1200; width: 100vw; right: 0; bottom: 0; left: 0; margin: 0; padding-left: 1rem; padding-right: 1rem; height: 3rem; text-align: left; font-size: 1.66rem; } @media (min-width: 28rem) { #navigator-main { padding-left: 2rem; padding-right: 2rem; } } @media (min-width: 42rem) { #navigator-main { padding-left: 4rem; padding-right: 4rem; } } @media (min-width: 77rem) { #navigator-main { padding-left: 8rem; padding-right: 8rem; } } .shortcut-code { display: inline; font-size: .875rem; color: rgb(115, 115, 115); } .shortcut-code kbd { border: none; background-color: transparent; } #navigator-main button { margin: 0; margin-right: 1rem; background: none; border: none; } #navigator-main button:hover, #navigator-main button:focus, #navigator-main button:active { background: none; border: none; } #navigator-main button:hover svg, #navigator-main button:focus svg, #navigator-main button:active svg { fill: rgb(13, 13, 13); } #navigator-main button > svg { fill: rgb(115, 115, 115); color: rgb(115, 115, 115); } #navigator-main button:hover svg { fill: rgb(13, 13, 13); } #navigator-main button.tc-selected svg { fill: rgb(13, 13, 13); } @media (min-width: 42rem) { #navigator-main button { margin-right: 2rem; } } @media (min-width: 77rem) { #navigator-main button { margin-right: 4rem; } } #navigator-main .te-close-sidebar-btn { position: fixed; z-index: 10; top: 0; left: 0; height: 100vh; width: 50vw; border: none; background: none; margin: 0; padding: 0; } @media (min-width: 42rem) { #navigator-main .te-close-sidebar-btn { display: none; } } .tc-story-river .tc-tiddler-frame:last-of-type { margin-bottom: 8rem; } ",
"title": "$:/themes/telmiger/navigator/styles/static.css",
"modifier": "Thomas Elmiger",
"modified": "20190408153010121",
"tags": "$:/tags/Stylesheet"
},
"$:/palettes/navigator/penguin": {
"text": "_dropdown-link-foreground: rgb(232, 232, 232)\n_edit-background: rgb(247, 247, 247)\n_edit-background-focus: rgb(252, 252, 252)\n_sidebar-background: #181818\n_zzz_Bricks-special-above_:\nalert-background: rgb(255, 255, 102)\nalert-border: rgb(232, 232, 125)\nalert-highlight: rgb(255, 51, 51)\nalert-muted-foreground: rgb(224, 82, 82)\nbackground: rgb(245, 245, 245)\nblockquote-bar: rgb(89, 166, 122)\nbutton-background: rgba(0,0,0,0.08)\nbutton-border: rgba(255, 255, 255, 0.8)\nbutton-foreground: rgb(13, 13, 13)\ncode-background: rgba(0,0,0,0.03)\ncode-border: rgba(0,0,0,0.08)\ncode-foreground: #e7040f\ndiff-delete-background: #ffc9c9\ndiff-delete-foreground: rgb(13, 13, 13)\ndiff-equal-background:\ndiff-equal-foreground: rgb(13, 13, 13)\ndiff-insert-background: #aaefad\ndiff-insert-foreground: rgb(13, 13, 13)\ndiff-invisible-background:\ndiff-invisible-foreground: rgb(115, 115, 115)\ndirty-indicator: #ff4136\ndownload-background: #19a974\ndownload-foreground: rgb(245, 245, 245)\ndragger-background: rgb(13, 13, 13)\ndragger-foreground: rgb(245, 245, 245)\ndropdown-background: rgb(232, 232, 232)\ndropdown-border: rgb(115, 115, 115)\ndropdown-tab-background: rgba(0,0,0,.1)\ndropdown-tab-background-selected: rgba(255,255,255,1)\ndropzone-background: #9eebcf\nexternal-link-background: inherit\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-foreground: #357edd\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #00449e\nforeground: rgb(13, 13, 13)\nmessage-background: #cdecff\nmessage-border: #96ccff\nmessage-foreground: #00449e\nmodal-backdrop: rgb(13, 13, 13)\nmodal-background: rgb(245, 245, 245)\nmodal-border: rgba(0,0,0,.5)\nmodal-footer-background: #f4f4f4\nmodal-footer-border: rgba(0,0,0,.1)\nmodal-header-border: rgba(0,0,0,.2)\nmuted-foreground: rgb(115, 115, 115)\nnotification-background: rgb(150, 80, 120)\nnotification-border: rgb(242, 242, 242)\npage-background: rgb(245, 245, 245)\npre-background: rgba(0,0,0,0.08)\npre-border: rgba(0,0,0,.2)\nprimary: rgb(80, 150, 110)\nsidebar-button-foreground: rgb(245, 245, 245)\nsidebar-controls-foreground: rgb(150, 150, 150)\nsidebar-controls-foreground-hover: rgb(227, 227, 227)\nsidebar-foreground: rgb(255, 255, 255)\nsidebar-foreground-shadow: rgba(255,255,255,0.5)\nsidebar-muted-foreground: rgba(255, 255, 255, 0.5)\nsidebar-muted-foreground-hover: rgb(255, 255, 255)\nsidebar-tab-background: rgb(70, 70, 70)\nsidebar-tab-background-selected: #181818\nsidebar-tab-border: rgba(255, 255, 255, 0.8)\nsidebar-tab-border-selected: rgba(255, 255, 255, 0.8)\nsidebar-tab-divider: rgba(255, 255, 255, 0.8)\nsidebar-tab-foreground: rgba(255,255,255,.8)\nsidebar-tab-foreground-selected: rgba(255,255,255,.8)\nsidebar-tiddler-link-foreground: rgba(255,255,255,.8)\nsidebar-tiddler-link-foreground-hover: rgba(191, 191, 191, 0.8)\nsite-title-foreground: rgb(80, 150, 110)\nstatic-alert-foreground: rgba(0,0,0,.3)\ntab-background: rgb(225, 225, 225)\ntab-background-selected: rgb(245, 245, 245)\ntab-border: rgb(80, 150, 110)\ntab-border-selected: rgb(80, 150, 110)\ntab-divider: rgb(80, 150, 110)\ntab-foreground: rgb(13, 13, 13)\ntab-foreground-selected: rgb(13, 13, 13)\ntable-border: rgba(0,0,0,.1)\ntable-footer-background: rgba(0,0,0,.4)\ntable-header-background: rgba(0,0,0,.1)\ntag-background: rgb(80, 150, 110)\ntag-foreground: rgb(242, 242, 242)\ntiddler-background: rgb(245, 245, 245)\ntiddler-border: rgb(89, 166, 122)\ntiddler-controls-foreground: rgb(143, 143, 143)\ntiddler-controls-foreground-hover: rgb(64, 64, 64)\ntiddler-controls-foreground-selected: rgb(64, 64, 64)\ntiddler-editor-background: #F7F7F7\ntiddler-editor-border: #ccc\ntiddler-editor-border-image: rgba(255,255,255,1)\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: rgb(245, 245, 245)\ntiddler-link-foreground: #00449e\ntiddler-subtitle-foreground: rgb(110, 110, 110)\ntiddler-title-foreground: rgb(64, 120, 88)\ntoolbar-cancel-button:\ntoolbar-close-button:\ntoolbar-delete-button:\ntoolbar-done-button:\ntoolbar-edit-button:\ntoolbar-info-button:\ntoolbar-new-button:\ntoolbar-options-button:\ntoolbar-save-button:\nuntagged-background: rgb(150, 80, 120)\nvery-muted-foreground: rgb(89, 166, 122)",
"type": "application/x-tiddler-dictionary",
"title": "$:/palettes/navigator/penguin",
"tags": "$:/tags/Palette",
"name": "Penguin",
"modifier": "Thomas Elmiger",
"modified": "20190302134319465",
"description": "Penguin – Mostly black and white",
"creator": "Thomas Elmiger",
"created": "20190302133253839"
},
"$:/themes/telmiger/navigator/ui/Buttons/new-tiddler-big": {
"text": "<$button actions={{$:/core/ui/Actions/new-tiddler}} tooltip={{$:/language/Buttons/NewTiddler/Hint}} aria-label={{$:/language/Buttons/NewTiddler/Caption}} class=\"te-big-new-btn tc-btn-invisible\">\n<span class=\"shortcut-code large-screen-only\">\n<kbd>{{$:/config/shortcuts-mac/new-tiddler}}</kbd> => <kbd>{{$:/config/shortcuts/save-tiddler}}</kbd> / <kbd>Escape</kbd>\n</span>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/themes/telmiger/navigator/icons/new}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/NewTiddler/Caption}}/></span>\n</$list>\n</$button>",
"title": "$:/themes/telmiger/navigator/ui/Buttons/new-tiddler-big",
"modifier": "Thomas Elmiger",
"modified": "20190217210901494",
"list-after": "$:/themes/telmiger/navigator/TopLeftBar",
"description": "{{$:/language/Buttons/NewTiddler/Hint}}",
"creator": "Thomas Elmiger",
"created": "20190201135607628",
"caption": "{{$:/plugins/telmiger/icons/new}} {{$:/language/Buttons/NewTiddler/Caption}}"
},
"$:/themes/telmiger/navigator/ui/Buttons/about": {
"created": "20190208064547584",
"creator": "Thomas Elmiger",
"text": "\\define control-panel-button(class)\n<$button to=\"$:/themes/telmiger/navigator\" tooltip={{!!description}} aria-label={{!!description}} class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/themes/telmiger/navigator/icon}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\">About Navigator</span>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/themes/telmiger/navigator]]\" emptyMessage=<<control-panel-button>>>\n<<control-panel-button \"tc-selected\">>\n</$list>\n",
"title": "$:/themes/telmiger/navigator/ui/Buttons/about",
"tags": "$:/tags/PageControls",
"modifier": "Thomas Elmiger",
"modified": "20190303095444038",
"description": "Go to Navigator theme",
"caption": "{{$:/themes/telmiger/navigator/icon}} About Navigator",
"list-before": "$:/themes/telmiger/navigator/ui/Buttons/menu"
},
"$:/palette": {
"text": "$:/palettes/navigator/penguin",
"title": "$:/palette",
"modifier": "Thomas Elmiger",
"modified": "20190302155851643",
"creator": "Thomas Elmiger",
"created": "20180317160745662"
},
"$:/config/PageControlButtons/Visibility/$:/themes/telmiger/navigator/ui/Buttons/about": {
"created": "20190303095000040",
"creator": "Thomas Elmiger",
"text": "hide",
"title": "$:/config/PageControlButtons/Visibility/$:/themes/telmiger/navigator/ui/Buttons/about",
"modified": "20190303095000040",
"modifier": "Thomas Elmiger"
},
"$:/themes/telmiger/navigator/ui/Buttons/control-panel": {
"created": "20190407131945966",
"creator": "Thomas Elmiger",
"text": "\\define open-control-panel()\n<$action-navigate $to=\"$:/ControlPanel\"/>\n\\end\n\n\\define control-panel-button(class:\"not-selected\")\n<$button tooltip=\"show/hide control panel\" aria-label=\"show/hide control panel\" class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/options-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/ControlPanel/Caption}}/></span>\n</$list>\n<$list filter=\"[<__class__>prefix[tc-selected]]\" emptyMessage=<<open-control-panel>>>\n<$action-sendmessage $message=\"tm-close-tiddler\" $param=\"$:/ControlPanel\"/>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/ControlPanel]]\" emptyMessage=<<control-panel-button>>>\n<<control-panel-button \"tc-selected\">>\n</$list>",
"title": "$:/themes/telmiger/navigator/ui/Buttons/control-panel",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/options-button}} {{$:/language/Buttons/ControlPanel/Caption}}",
"description": "{{$:/language/Buttons/ControlPanel/Hint}}",
"modified": "20190407141117452",
"modifier": "Thomas Elmiger"
}
}
}
hide-secondary-nav: yes
colour-main-nav: rgb(200, 225, 211)
colour-new-button: <<colour sidebar-tab-background>>
show-new-button: yes
show-kbd-hints: yes
{
"tiddlers": {
"$:/themes/tiddlywiki/snowwhite/base": {
"title": "$:/themes/tiddlywiki/snowwhite/base",
"tags": "[[$:/tags/Stylesheet]]",
"text": "\\rules only filteredtranscludeinline transcludeinline macrodef macrocallinline\n\n.tc-sidebar-header {\n\ttext-shadow: 0 1px 0 <<colour sidebar-foreground-shadow>>;\n}\n\n.tc-tiddler-info {\n\t<<box-shadow \"inset 1px 2px 3px rgba(0,0,0,0.1)\">>\n}\n\n@media screen {\n\t.tc-tiddler-frame {\n\t\t<<box-shadow \"1px 1px 5px rgba(0, 0, 0, 0.3)\">>\n\t}\n}\n\n@media (max-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\t.tc-tiddler-frame {\n\t\t<<box-shadow none>>\n\t}\n}\n\n.tc-page-controls button svg, .tc-tiddler-controls button svg, .tc-topbar button svg {\n\t<<transition \"fill 150ms ease-in-out\">>\n}\n\n.tc-tiddler-controls button.tc-selected,\n.tc-page-controls button.tc-selected {\n\t<<filter \"drop-shadow(0px -1px 2px rgba(0,0,0,0.25))\">>\n}\n\n.tc-tiddler-frame input.tc-edit-texteditor {\n\t<<box-shadow \"inset 0 1px 8px rgba(0, 0, 0, 0.15)\">>\n}\n\n.tc-edit-tags {\n\t<<box-shadow \"inset 0 1px 8px rgba(0, 0, 0, 0.15)\">>\n}\n\n.tc-tiddler-frame .tc-edit-tags input.tc-edit-texteditor {\n\t<<box-shadow \"none\">>\n\tborder: none;\n\toutline: none;\n}\n\ntextarea.tc-edit-texteditor {\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/editorfontfamily}};\n}\n\ncanvas.tc-edit-bitmapeditor {\n\t<<box-shadow \"2px 2px 5px rgba(0, 0, 0, 0.5)\">>\n}\n\n.tc-drop-down {\n\tborder-radius: 4px;\n\t<<box-shadow \"2px 2px 10px rgba(0, 0, 0, 0.5)\">>\n}\n\n.tc-block-dropdown {\n\tborder-radius: 4px;\n\t<<box-shadow \"2px 2px 10px rgba(0, 0, 0, 0.5)\">>\n}\n\n.tc-modal {\n\tborder-radius: 6px;\n\t<<box-shadow \"0 3px 7px rgba(0,0,0,0.3)\">>\n}\n\n.tc-modal-footer {\n\tborder-radius: 0 0 6px 6px;\n\t<<box-shadow \"inset 0 1px 0 #fff\">>;\n}\n\n\n.tc-alert {\n\tborder-radius: 6px;\n\t<<box-shadow \"0 3px 7px rgba(0,0,0,0.6)\">>\n}\n\n.tc-notification {\n\tborder-radius: 6px;\n\t<<box-shadow \"0 3px 7px rgba(0,0,0,0.3)\">>\n\ttext-shadow: 0 1px 0 rgba(255,255,255, 0.8);\n}\n\n.tc-sidebar-lists .tc-tab-set .tc-tab-divider {\n\tborder-top: none;\n\theight: 1px;\n\t<<background-linear-gradient \"left, rgba(0,0,0,0.15) 0%, rgba(0,0,0,0.0) 100%\">>\n}\n\n.tc-more-sidebar > .tc-tab-set > .tc-tab-buttons > button {\n\t<<background-linear-gradient \"left, rgba(0,0,0,0.01) 0%, rgba(0,0,0,0.1) 100%\">>\n}\n\n.tc-more-sidebar > .tc-tab-set > .tc-tab-buttons > button.tc-tab-selected {\n\t<<background-linear-gradient \"left, rgba(0,0,0,0.05) 0%, rgba(255,255,255,0.05) 100%\">>\n}\n\n.tc-message-box img {\n\t<<box-shadow \"1px 1px 3px rgba(0,0,0,0.5)\">>\n}\n\n.tc-plugin-info {\n\t<<box-shadow \"1px 1px 3px rgba(0,0,0,0.5)\">>\n}\n"
}
}
}
{
"tiddlers": {
"$:/themes/tiddlywiki/vanilla/themetweaks": {
"title": "$:/themes/tiddlywiki/vanilla/themetweaks",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ThemeTweaks/ThemeTweaks}}",
"text": "\\define lingo-base() $:/language/ThemeTweaks/\n\n\\define replacement-text()\n[img[$(imageTitle)$]]\n\\end\n\n\\define backgroundimage-dropdown()\n<div class=\"tc-drop-down-wrapper\">\n<$button popup=<<qualify \"$:/state/popup/themetweaks/backgroundimage\">> class=\"tc-btn-invisible tc-btn-dropdown\">{{$:/core/images/down-arrow}}</$button>\n<$reveal state=<<qualify \"$:/state/popup/themetweaks/backgroundimage\">> type=\"popup\" position=\"belowleft\" text=\"\" default=\"\">\n<div class=\"tc-drop-down\">\n<$macrocall $name=\"image-picker\" actions=\"\"\"\n\n<$action-setfield\n\t$tiddler=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimage\"\n\t$value=<<imageTitle>>\n/>\n\n\"\"\"/>\n</div>\n</$reveal>\n</div>\n\\end\n\n\\define backgroundimageattachment-dropdown()\n<$select tiddler=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment\" default=\"scroll\">\n<option value=\"scroll\"><<lingo Settings/BackgroundImageAttachment/Scroll>></option>\n<option value=\"fixed\"><<lingo Settings/BackgroundImageAttachment/Fixed>></option>\n</$select>\n\\end\n\n\\define backgroundimagesize-dropdown()\n<$select tiddler=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize\" default=\"scroll\">\n<option value=\"auto\"><<lingo Settings/BackgroundImageSize/Auto>></option>\n<option value=\"cover\"><<lingo Settings/BackgroundImageSize/Cover>></option>\n<option value=\"contain\"><<lingo Settings/BackgroundImageSize/Contain>></option>\n</$select>\n\\end\n\n<<lingo ThemeTweaks/Hint>>\n\n! <<lingo Options>>\n\n|<$link to=\"$:/themes/tiddlywiki/vanilla/options/sidebarlayout\"><<lingo Options/SidebarLayout>></$link> |<$select tiddler=\"$:/themes/tiddlywiki/vanilla/options/sidebarlayout\"><option value=\"fixed-fluid\"><<lingo Options/SidebarLayout/Fixed-Fluid>></option><option value=\"fluid-fixed\"><<lingo Options/SidebarLayout/Fluid-Fixed>></option></$select> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/options/stickytitles\"><<lingo Options/StickyTitles>></$link><br>//<<lingo Options/StickyTitles/Hint>>// |<$select tiddler=\"$:/themes/tiddlywiki/vanilla/options/stickytitles\"><option value=\"no\">{{$:/language/No}}</option><option value=\"yes\">{{$:/language/Yes}}</option></$select> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/options/codewrapping\"><<lingo Options/CodeWrapping>></$link> |<$select tiddler=\"$:/themes/tiddlywiki/vanilla/options/codewrapping\"><option value=\"pre\">{{$:/language/No}}</option><option value=\"pre-wrap\">{{$:/language/Yes}}</option></$select> |\n\n! <<lingo Settings>>\n\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/fontfamily\"><<lingo Settings/FontFamily>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/settings/fontfamily\" default=\"\" tag=\"input\"/> | |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/codefontfamily\"><<lingo Settings/CodeFontFamily>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/settings/codefontfamily\" default=\"\" tag=\"input\"/> | |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/editorfontfamily\"><<lingo Settings/EditorFontFamily>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/settings/editorfontfamily\" default=\"\" tag=\"input\"/> | |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimage\"><<lingo Settings/BackgroundImage>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimage\" default=\"\" tag=\"input\"/> |<<backgroundimage-dropdown>> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment\"><<lingo Settings/BackgroundImageAttachment>></$link> |<<backgroundimageattachment-dropdown>> | |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize\"><<lingo Settings/BackgroundImageSize>></$link> |<<backgroundimagesize-dropdown>> | |\n\n! <<lingo Metrics>>\n\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/fontsize\"><<lingo Metrics/FontSize>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/fontsize\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/lineheight\"><<lingo Metrics/LineHeight>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/lineheight\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize\"><<lingo Metrics/BodyFontSize>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/bodylineheight\"><<lingo Metrics/BodyLineHeight>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/bodylineheight\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/storyleft\"><<lingo Metrics/StoryLeft>></$link><br>//<<lingo Metrics/StoryLeft/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/storyleft\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/storytop\"><<lingo Metrics/StoryTop>></$link><br>//<<lingo Metrics/StoryTop/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/storytop\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/storyright\"><<lingo Metrics/StoryRight>></$link><br>//<<lingo Metrics/StoryRight/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/storyright\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/storywidth\"><<lingo Metrics/StoryWidth>></$link><br>//<<lingo Metrics/StoryWidth/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/storywidth\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth\"><<lingo Metrics/TiddlerWidth>></$link><br>//<<lingo Metrics/TiddlerWidth/Hint>>//<br> |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint\"><<lingo Metrics/SidebarBreakpoint>></$link><br>//<<lingo Metrics/SidebarBreakpoint/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth\"><<lingo Metrics/SidebarWidth>></$link><br>//<<lingo Metrics/SidebarWidth/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth\" default=\"\" tag=\"input\"/> |\n"
},
"$:/themes/tiddlywiki/vanilla/base": {
"title": "$:/themes/tiddlywiki/vanilla/base",
"tags": "[[$:/tags/Stylesheet]]",
"text": "\\define custom-background-datauri()\n<$set name=\"background\" value={{$:/themes/tiddlywiki/vanilla/settings/backgroundimage}}>\n<$list filter=\"[<background>is[image]]\">\n`background: url(`\n<$list filter=\"[<background>!has[_canonical_uri]]\">\n`\"`<$macrocall $name=\"datauri\" title={{$:/themes/tiddlywiki/vanilla/settings/backgroundimage}}/>`\"`\n</$list>\n<$list filter=\"[<background>has[_canonical_uri]]\">\n`\"`<$view tiddler={{$:/themes/tiddlywiki/vanilla/settings/backgroundimage}} field=\"_canonical_uri\"/>`\"`\n</$list>\n`) center center;`\n`background-attachment: `{{$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment}}`;\n-webkit-background-size:` {{$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize}}`;\n-moz-background-size:` {{$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize}}`;\n-o-background-size:` {{$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize}}`;\nbackground-size:` {{$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize}}`;`\n</$list>\n</$set>\n\\end\n\n\\define if-fluid-fixed(text,hiddenSidebarText)\n<$reveal state=\"$:/themes/tiddlywiki/vanilla/options/sidebarlayout\" type=\"match\" text=\"fluid-fixed\">\n$text$\n<$reveal state=\"$:/state/sidebar\" type=\"nomatch\" text=\"yes\" default=\"yes\">\n$hiddenSidebarText$\n</$reveal>\n</$reveal>\n\\end\n\n\\define if-editor-height-fixed(then,else)\n<$reveal state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"fixed\">\n$then$\n</$reveal>\n<$reveal state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"auto\">\n$else$\n</$reveal>\n\\end\n\n\\rules only filteredtranscludeinline transcludeinline macrodef macrocallinline macrocallblock\n\n/*\n** Start with the normalize CSS reset, and then belay some of its effects\n*/\n\n{{$:/themes/tiddlywiki/vanilla/reset}}\n\n*, input[type=\"search\"] {\n\tbox-sizing: border-box;\n\t-moz-box-sizing: border-box;\n\t-webkit-box-sizing: border-box;\n}\n\nhtml button {\n\tline-height: 1.2;\n\tcolor: <<colour button-foreground>>;\n\tbackground: <<colour button-background>>;\n\tborder-color: <<colour button-border>>;\n}\n\n/*\n** Basic element styles\n*/\n\nhtml {\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/fontfamily}};\n\ttext-rendering: optimizeLegibility; /* Enables kerning and ligatures etc. */\n\t-webkit-font-smoothing: antialiased;\n\t-moz-osx-font-smoothing: grayscale;\n}\n\nhtml:-webkit-full-screen {\n\tbackground-color: <<colour page-background>>;\n}\n\nbody.tc-body {\n\tfont-size: {{$:/themes/tiddlywiki/vanilla/metrics/fontsize}};\n\tline-height: {{$:/themes/tiddlywiki/vanilla/metrics/lineheight}};\n\tword-wrap: break-word;\n\t<<custom-background-datauri>>\n\tcolor: <<colour foreground>>;\n\tbackground-color: <<colour page-background>>;\n\tfill: <<colour foreground>>;\n}\n\n<<if-background-attachment \"\"\"\n\nbody.tc-body {\n background-color: transparent;\n}\n\n\"\"\">>\n\nh1, h2, h3, h4, h5, h6 {\n\tline-height: 1.2;\n\tfont-weight: 300;\n}\n\npre {\n\tdisplay: block;\n\tpadding: 14px;\n\tmargin-top: 1em;\n\tmargin-bottom: 1em;\n\tword-break: normal;\n\tword-wrap: break-word;\n\twhite-space: {{$:/themes/tiddlywiki/vanilla/options/codewrapping}};\n\tbackground-color: <<colour pre-background>>;\n\tborder: 1px solid <<colour pre-border>>;\n\tpadding: 0 3px 2px;\n\tborder-radius: 3px;\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/codefontfamily}};\n}\n\ncode {\n\tcolor: <<colour code-foreground>>;\n\tbackground-color: <<colour code-background>>;\n\tborder: 1px solid <<colour code-border>>;\n\twhite-space: {{$:/themes/tiddlywiki/vanilla/options/codewrapping}};\n\tpadding: 0 3px 2px;\n\tborder-radius: 3px;\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/codefontfamily}};\n}\n\nblockquote {\n\tborder-left: 5px solid <<colour blockquote-bar>>;\n\tmargin-left: 25px;\n\tpadding-left: 10px;\n\tquotes: \"\\201C\"\"\\201D\"\"\\2018\"\"\\2019\";\n}\n\nblockquote > div {\n\tmargin-top: 1em;\n\tmargin-bottom: 1em;\n}\n\nblockquote.tc-big-quote {\n\tfont-family: Georgia, serif;\n\tposition: relative;\n\tbackground: <<colour pre-background>>;\n\tborder-left: none;\n\tmargin-left: 50px;\n\tmargin-right: 50px;\n\tpadding: 10px;\n border-radius: 8px;\n}\n\nblockquote.tc-big-quote cite:before {\n\tcontent: \"\\2014 \\2009\";\n}\n\nblockquote.tc-big-quote:before {\n\tfont-family: Georgia, serif;\n\tcolor: <<colour blockquote-bar>>;\n\tcontent: open-quote;\n\tfont-size: 8em;\n\tline-height: 0.1em;\n\tmargin-right: 0.25em;\n\tvertical-align: -0.4em;\n\tposition: absolute;\n left: -50px;\n top: 42px;\n}\n\nblockquote.tc-big-quote:after {\n\tfont-family: Georgia, serif;\n\tcolor: <<colour blockquote-bar>>;\n\tcontent: close-quote;\n\tfont-size: 8em;\n\tline-height: 0.1em;\n\tmargin-right: 0.25em;\n\tvertical-align: -0.4em;\n\tposition: absolute;\n right: -80px;\n bottom: -20px;\n}\n\ndl dt {\n\tfont-weight: bold;\n\tmargin-top: 6px;\n}\n\nbutton, textarea, input, select {\n\toutline-color: <<colour primary>>;\n}\n\ntextarea,\ninput[type=text],\ninput[type=search],\ninput[type=\"\"],\ninput:not([type]) {\n\tcolor: <<colour foreground>>;\n\tbackground: <<colour background>>;\n}\n\ninput[type=\"checkbox\"] {\n vertical-align: middle;\n}\n\n.tc-muted {\n\tcolor: <<colour muted-foreground>>;\n}\n\nsvg.tc-image-button {\n\tpadding: 0px 1px 1px 0px;\n}\n\n.tc-icon-wrapper > svg {\n\twidth: 1em;\n\theight: 1em;\n}\n\nkbd {\n\tdisplay: inline-block;\n\tpadding: 3px 5px;\n\tfont-size: 0.8em;\n\tline-height: 1.2;\n\tcolor: <<colour foreground>>;\n\tvertical-align: middle;\n\tbackground-color: <<colour background>>;\n\tborder: solid 1px <<colour muted-foreground>>;\n\tborder-bottom-color: <<colour muted-foreground>>;\n\tborder-radius: 3px;\n\tbox-shadow: inset 0 -1px 0 <<colour muted-foreground>>;\n}\n\n/*\nMarkdown likes putting code elements inside pre elements\n*/\npre > code {\n\tpadding: 0;\n\tborder: none;\n\tbackground-color: inherit;\n\tcolor: inherit;\n}\n\ntable {\n\tborder: 1px solid <<colour table-border>>;\n\twidth: auto;\n\tmax-width: 100%;\n\tcaption-side: bottom;\n\tmargin-top: 1em;\n\tmargin-bottom: 1em;\n\t/* next 2 elements needed, since normalize 8.0.1 */\n\tborder-collapse: collapse;\n\tborder-spacing: 0;\n}\n\ntable th, table td {\n\tpadding: 0 7px 0 7px;\n\tborder-top: 1px solid <<colour table-border>>;\n\tborder-left: 1px solid <<colour table-border>>;\n}\n\ntable thead tr td, table th {\n\tbackground-color: <<colour table-header-background>>;\n\tfont-weight: bold;\n}\n\ntable tfoot tr td {\n\tbackground-color: <<colour table-footer-background>>;\n}\n\n.tc-csv-table {\n\twhite-space: nowrap;\n}\n\n.tc-tiddler-frame img,\n.tc-tiddler-frame svg,\n.tc-tiddler-frame canvas,\n.tc-tiddler-frame embed,\n.tc-tiddler-frame iframe {\n\tmax-width: 100%;\n}\n\n.tc-tiddler-body > embed,\n.tc-tiddler-body > iframe {\n\twidth: 100%;\n\theight: 600px;\n}\n\n/*\n** Links\n*/\n\nbutton.tc-tiddlylink,\na.tc-tiddlylink {\n\ttext-decoration: none;\n\tfont-weight: 500;\n\tcolor: <<colour tiddler-link-foreground>>;\n\t-webkit-user-select: inherit; /* Otherwise the draggable attribute makes links impossible to select */\n}\n\n.tc-sidebar-lists a.tc-tiddlylink {\n\tcolor: <<colour sidebar-tiddler-link-foreground>>;\n}\n\n.tc-sidebar-lists a.tc-tiddlylink:hover {\n\tcolor: <<colour sidebar-tiddler-link-foreground-hover>>;\n}\n\nbutton.tc-tiddlylink:hover,\na.tc-tiddlylink:hover {\n\ttext-decoration: underline;\n}\n\na.tc-tiddlylink-resolves {\n}\n\na.tc-tiddlylink-shadow {\n\tfont-weight: bold;\n}\n\na.tc-tiddlylink-shadow.tc-tiddlylink-resolves {\n\tfont-weight: normal;\n}\n\na.tc-tiddlylink-missing {\n\tfont-style: italic;\n}\n\na.tc-tiddlylink-external {\n\ttext-decoration: underline;\n\tcolor: <<colour external-link-foreground>>;\n\tbackground-color: <<colour external-link-background>>;\n}\n\na.tc-tiddlylink-external:visited {\n\tcolor: <<colour external-link-foreground-visited>>;\n\tbackground-color: <<colour external-link-background-visited>>;\n}\n\na.tc-tiddlylink-external:hover {\n\tcolor: <<colour external-link-foreground-hover>>;\n\tbackground-color: <<colour external-link-background-hover>>;\n}\n\n/*\n** Drag and drop styles\n*/\n\n.tc-tiddler-dragger {\n\tposition: relative;\n\tz-index: -10000;\n}\n\n.tc-tiddler-dragger-inner {\n\tposition: absolute;\n\ttop: -1000px;\n\tleft: -1000px;\n\tdisplay: inline-block;\n\tpadding: 8px 20px;\n\tfont-size: 16.9px;\n\tfont-weight: bold;\n\tline-height: 20px;\n\tcolor: <<colour dragger-foreground>>;\n\ttext-shadow: 0 1px 0 rgba(0, 0, 0, 1);\n\twhite-space: nowrap;\n\tvertical-align: baseline;\n\tbackground-color: <<colour dragger-background>>;\n\tborder-radius: 20px;\n}\n\n.tc-tiddler-dragger-cover {\n\tposition: absolute;\n\tbackground-color: <<colour page-background>>;\n}\n\n.tc-dropzone {\n\tposition: relative;\n}\n\n.tc-dropzone.tc-dragover:before {\n\tz-index: 10000;\n\tdisplay: block;\n\tposition: fixed;\n\ttop: 0;\n\tleft: 0;\n\tright: 0;\n\tbackground: <<colour dropzone-background>>;\n\ttext-align: center;\n\tcontent: \"<<lingo DropMessage>>\";\n}\n\n.tc-droppable > .tc-droppable-placeholder {\n\tdisplay: none;\n}\n\n.tc-droppable.tc-dragover > .tc-droppable-placeholder {\n\tdisplay: block;\n\tborder: 2px dashed <<colour dropzone-background>>;\n}\n\n.tc-draggable {\n\tcursor: move;\n}\n\n.tc-sidebar-tab-open .tc-droppable-placeholder, .tc-tagged-draggable-list .tc-droppable-placeholder,\n.tc-links-draggable-list .tc-droppable-placeholder {\n\tline-height: 2em;\n\theight: 2em;\n}\n\n.tc-sidebar-tab-open-item {\n\tposition: relative;\n}\n\n.tc-sidebar-tab-open .tc-btn-invisible.tc-btn-mini svg {\n\tfont-size: 0.7em;\n\tfill: <<colour muted-foreground>>;\n}\n\n/*\n** Plugin reload warning\n*/\n\n.tc-plugin-reload-warning {\n\tz-index: 1000;\n\tdisplay: block;\n\tposition: fixed;\n\ttop: 0;\n\tleft: 0;\n\tright: 0;\n\tbackground: <<colour alert-background>>;\n\ttext-align: center;\n}\n\n/*\n** Buttons\n*/\n\nbutton svg, button img, label svg, label img {\n\tvertical-align: middle;\n}\n\n.tc-btn-invisible {\n\tpadding: 0;\n\tmargin: 0;\n\tbackground: none;\n\tborder: none;\n \tcursor: pointer;\n\tcolor: <<colour foreground>>;\n}\n\n.tc-btn-boxed {\n\tfont-size: 0.6em;\n\tpadding: 0.2em;\n\tmargin: 1px;\n\tbackground: none;\n\tborder: 1px solid <<colour tiddler-controls-foreground>>;\n\tborder-radius: 0.25em;\n}\n\nhtml body.tc-body .tc-btn-boxed svg {\n\tfont-size: 1.6666em;\n}\n\n.tc-btn-boxed:hover {\n\tbackground: <<colour muted-foreground>>;\n\tcolor: <<colour background>>;\n}\n\nhtml body.tc-body .tc-btn-boxed:hover svg {\n\tfill: <<colour background>>;\n}\n\n.tc-btn-rounded {\n\tfont-size: 0.5em;\n\tline-height: 2;\n\tpadding: 0em 0.3em 0.2em 0.4em;\n\tmargin: 1px;\n\tborder: 1px solid <<colour muted-foreground>>;\n\tbackground: <<colour muted-foreground>>;\n\tcolor: <<colour background>>;\n\tborder-radius: 2em;\n}\n\nhtml body.tc-body .tc-btn-rounded svg {\n\tfont-size: 1.6666em;\n\tfill: <<colour background>>;\n}\n\n.tc-btn-rounded:hover {\n\tborder: 1px solid <<colour muted-foreground>>;\n\tbackground: <<colour background>>;\n\tcolor: <<colour muted-foreground>>;\n}\n\nhtml body.tc-body .tc-btn-rounded:hover svg {\n\tfill: <<colour muted-foreground>>;\n}\n\n.tc-btn-icon svg {\n\theight: 1em;\n\twidth: 1em;\n\tfill: <<colour muted-foreground>>;\n}\n\n.tc-btn-text {\n\tpadding: 0;\n\tmargin: 0;\n}\n\n/* used for documentation \"fake\" buttons */\n.tc-btn-standard {\n\tline-height: 1.8;\n\tcolor: #667;\n\tbackground-color: #e0e0e0;\n\tborder: 1px solid #888;\n\tpadding: 2px 1px 2px 1px;\n\tmargin: 1px 4px 1px 4px;\n}\n\n.tc-btn-big-green {\n\tdisplay: inline-block;\n\tpadding: 8px;\n\tmargin: 4px 8px 4px 8px;\n\tbackground: <<colour download-background>>;\n\tcolor: <<colour download-foreground>>;\n\tfill: <<colour download-foreground>>;\n\tborder: none;\n\tborder-radius: 2px;\n\tfont-size: 1.2em;\n\tline-height: 1.4em;\n\ttext-decoration: none;\n}\n\n.tc-btn-big-green svg,\n.tc-btn-big-green img {\n\theight: 2em;\n\twidth: 2em;\n\tvertical-align: middle;\n\tfill: <<colour download-foreground>>;\n}\n\n.tc-primary-btn {\n \tbackground: <<colour primary>>;\n}\n\n.tc-sidebar-lists input {\n\tcolor: <<colour foreground>>;\n}\n\n.tc-sidebar-lists button {\n\tcolor: <<colour sidebar-button-foreground>>;\n\tfill: <<colour sidebar-button-foreground>>;\n}\n\n.tc-sidebar-lists button.tc-btn-mini {\n\tcolor: <<colour sidebar-muted-foreground>>;\n}\n\n.tc-sidebar-lists button.tc-btn-mini:hover {\n\tcolor: <<colour sidebar-muted-foreground-hover>>;\n}\n\nbutton svg.tc-image-button, button .tc-image-button img {\n\theight: 1em;\n\twidth: 1em;\n}\n\n.tc-unfold-banner {\n\tposition: absolute;\n\tpadding: 0;\n\tmargin: 0;\n\tbackground: none;\n\tborder: none;\n\twidth: 100%;\n\twidth: calc(100% + 2px);\n\tmargin-left: -43px;\n\ttext-align: center;\n\tborder-top: 2px solid <<colour tiddler-info-background>>;\n\tmargin-top: 4px;\n}\n\n.tc-unfold-banner:hover {\n\tbackground: <<colour tiddler-info-background>>;\n\tborder-top: 2px solid <<colour tiddler-info-border>>;\n}\n\n.tc-unfold-banner svg, .tc-fold-banner svg {\n\theight: 0.75em;\n\tfill: <<colour tiddler-controls-foreground>>;\n}\n\n.tc-unfold-banner:hover svg, .tc-fold-banner:hover svg {\n\tfill: <<colour tiddler-controls-foreground-hover>>;\n}\n\n.tc-fold-banner {\n\tposition: absolute;\n\tpadding: 0;\n\tmargin: 0;\n\tbackground: none;\n\tborder: none;\n\twidth: 23px;\n\ttext-align: center;\n\tmargin-left: -35px;\n\ttop: 6px;\n\tbottom: 6px;\n}\n\n.tc-fold-banner:hover {\n\tbackground: <<colour tiddler-info-background>>;\n}\n\n@media (max-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-unfold-banner {\n\t\tposition: static;\n\t\twidth: calc(100% + 59px);\n\t}\n\n\t.tc-fold-banner {\n\t\twidth: 16px;\n\t\tmargin-left: -16px;\n\t\tfont-size: 0.75em;\n\t}\n\n}\n\n/*\n** Tags and missing tiddlers\n*/\n\n.tc-tag-list-item {\n\tposition: relative;\n\tdisplay: inline-block;\n\tmargin-right: 7px;\n}\n\n.tc-tags-wrapper {\n\tmargin: 4px 0 14px 0;\n}\n\n.tc-missing-tiddler-label {\n\tfont-style: italic;\n\tfont-weight: normal;\n\tdisplay: inline-block;\n\tfont-size: 11.844px;\n\tline-height: 14px;\n\twhite-space: nowrap;\n\tvertical-align: baseline;\n}\n\nbutton.tc-tag-label, span.tc-tag-label {\n\tdisplay: inline-block;\n\tpadding: 0.16em 0.7em;\n\tfont-size: 0.9em;\n\tfont-weight: 400;\n\tline-height: 1.2em;\n\tcolor: <<colour tag-foreground>>;\n\twhite-space: nowrap;\n\tvertical-align: baseline;\n\tbackground-color: <<colour tag-background>>;\n\tborder-radius: 1em;\n}\n\n.tc-sidebar-scrollable .tc-tag-label {\n\ttext-shadow: none;\n}\n\n.tc-untagged-separator {\n\twidth: 10em;\n\tleft: 0;\n\tmargin-left: 0;\n\tborder: 0;\n\theight: 1px;\n\tbackground: <<colour tab-divider>>;\n}\n\nbutton.tc-untagged-label {\n\tbackground-color: <<colour untagged-background>>;\n}\n\n.tc-tag-label svg, .tc-tag-label img {\n\theight: 1em;\n\twidth: 1em;\n\tmargin-right: 3px; \n\tmargin-bottom: 1px;\n\tvertical-align: text-bottom;\n}\n\n.tc-edit-tags button.tc-remove-tag-button svg {\n\tfont-size: 0.7em;\n\tvertical-align: middle;\n}\n\n.tc-tag-manager-table .tc-tag-label {\n\twhite-space: normal;\n}\n\n.tc-tag-manager-tag {\n\twidth: 100%;\n}\n\nbutton.tc-btn-invisible.tc-remove-tag-button {\n\toutline: none;\n}\n\n/*\n** Page layout\n*/\n\n.tc-topbar {\n\tposition: fixed;\n\tz-index: 1200;\n}\n\n.tc-topbar-left {\n\tleft: 29px;\n\ttop: 5px;\n}\n\n.tc-topbar-right {\n\ttop: 5px;\n\tright: 29px;\n}\n\n.tc-topbar button {\n\tpadding: 8px;\n}\n\n.tc-topbar svg {\n\tfill: <<colour muted-foreground>>;\n}\n\n.tc-topbar button:hover svg {\n\tfill: <<colour foreground>>;\n}\n\n.tc-sidebar-header {\n\tcolor: <<colour sidebar-foreground>>;\n\tfill: <<colour sidebar-foreground>>;\n}\n\n.tc-sidebar-header .tc-title a.tc-tiddlylink-resolves {\n\tfont-weight: 300;\n}\n\n.tc-sidebar-header .tc-sidebar-lists p {\n\tmargin-top: 3px;\n\tmargin-bottom: 3px;\n}\n\n.tc-sidebar-header .tc-missing-tiddler-label {\n\tcolor: <<colour sidebar-foreground>>;\n}\n\n.tc-advanced-search input {\n\twidth: 60%;\n}\n\n.tc-search a svg {\n\twidth: 1.2em;\n\theight: 1.2em;\n\tvertical-align: middle;\n}\n\n.tc-page-controls {\n\tmargin-top: 14px;\n\tfont-size: 1.5em;\n}\n\n.tc-page-controls .tc-drop-down {\n font-size: 1rem;\n}\n\n.tc-page-controls button {\n\tmargin-right: 0.5em;\n}\n\n.tc-page-controls a.tc-tiddlylink:hover {\n\ttext-decoration: none;\n}\n\n.tc-page-controls img {\n\twidth: 1em;\n}\n\n.tc-page-controls svg {\n\tfill: <<colour sidebar-controls-foreground>>;\n}\n\n.tc-page-controls button:hover svg, .tc-page-controls a:hover svg {\n\tfill: <<colour sidebar-controls-foreground-hover>>;\n}\n\n.tc-menu-list-item {\n\twhite-space: nowrap;\n}\n\n.tc-menu-list-count {\n\tfont-weight: bold;\n}\n\n.tc-menu-list-subitem {\n\tpadding-left: 7px;\n}\n\n.tc-story-river {\n\tposition: relative;\n}\n\n@media (max-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-sidebar-header {\n\t\tpadding: 14px;\n\t\tmin-height: 32px;\n\t\tmargin-top: {{$:/themes/tiddlywiki/vanilla/metrics/storytop}};\n\t}\n\n\t.tc-story-river {\n\t\tposition: relative;\n\t\tpadding: 0;\n\t}\n}\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-message-box {\n\t\tmargin: 21px -21px 21px -21px;\n\t}\n\n\t.tc-sidebar-scrollable {\n\t\tposition: fixed;\n\t\ttop: {{$:/themes/tiddlywiki/vanilla/metrics/storytop}};\n\t\tleft: {{$:/themes/tiddlywiki/vanilla/metrics/storyright}};\n\t\tbottom: 0;\n\t\tright: 0;\n\t\toverflow-y: auto;\n\t\toverflow-x: auto;\n\t\t-webkit-overflow-scrolling: touch;\n\t\tmargin: 0 0 0 -42px;\n\t\tpadding: 71px 0 28px 42px;\n\t}\n\n\thtml[dir=\"rtl\"] .tc-sidebar-scrollable {\n\t\tleft: auto;\n\t\tright: {{$:/themes/tiddlywiki/vanilla/metrics/storyright}};\n\t}\n\n\t.tc-story-river {\n\t\tposition: relative;\n\t\tleft: {{$:/themes/tiddlywiki/vanilla/metrics/storyleft}};\n\t\ttop: {{$:/themes/tiddlywiki/vanilla/metrics/storytop}};\n\t\twidth: {{$:/themes/tiddlywiki/vanilla/metrics/storywidth}};\n\t\tpadding: 42px 42px 42px 42px;\n\t}\n\n<<if-no-sidebar \"\n\n\t.tc-story-river {\n\t\twidth: calc(100% - {{$:/themes/tiddlywiki/vanilla/metrics/storyleft}});\n\t}\n\n\">>\n\n}\n\n@media print {\n\n\tbody.tc-body {\n\t\tbackground-color: transparent;\n\t}\n\n\t.tc-sidebar-header, .tc-topbar {\n\t\tdisplay: none;\n\t}\n\n\t.tc-story-river {\n\t\tmargin: 0;\n\t\tpadding: 0;\n\t}\n\n\t.tc-story-river .tc-tiddler-frame {\n\t\tmargin: 0;\n\t\tborder: none;\n\t\tpadding: 0;\n\t}\n}\n\n/*\n** Tiddler styles\n*/\n\n.tc-tiddler-frame {\n\tposition: relative;\n\tmargin-bottom: 28px;\n\tbackground-color: <<colour tiddler-background>>;\n\tborder: 1px solid <<colour tiddler-border>>;\n}\n\n{{$:/themes/tiddlywiki/vanilla/sticky}}\n\n.tc-tiddler-info {\n\tpadding: 14px 42px 14px 42px;\n\tbackground-color: <<colour tiddler-info-background>>;\n\tborder-top: 1px solid <<colour tiddler-info-border>>;\n\tborder-bottom: 1px solid <<colour tiddler-info-border>>;\n}\n\n.tc-tiddler-info p {\n\tmargin-top: 3px;\n\tmargin-bottom: 3px;\n}\n\n.tc-tiddler-info .tc-tab-buttons button.tc-tab-selected {\n\tbackground-color: <<colour tiddler-info-tab-background>>;\n\tborder-bottom: 1px solid <<colour tiddler-info-tab-background>>;\n}\n\n.tc-view-field-table {\n\twidth: 100%;\n}\n\n.tc-view-field-name {\n\twidth: 1%; /* Makes this column be as narrow as possible */\n\ttext-align: right;\n\tfont-style: italic;\n\tfont-weight: 200;\n}\n\n.tc-view-field-value {\n}\n\n@media (max-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\t.tc-tiddler-frame {\n\t\tpadding: 14px 14px 14px 14px;\n\t}\n\n\t.tc-tiddler-info {\n\t\tmargin: 0 -14px 0 -14px;\n\t}\n}\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\t.tc-tiddler-frame {\n\t\tpadding: 28px 42px 42px 42px;\n\t\twidth: {{$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth}};\n\t\tborder-radius: 2px;\n\t}\n\n<<if-no-sidebar \"\n\n\t.tc-tiddler-frame {\n\t\twidth: 100%;\n\t}\n\n\">>\n\n\t.tc-tiddler-info {\n\t\tmargin: 0 -42px 0 -42px;\n\t}\n}\n\n.tc-site-title,\n.tc-titlebar {\n\tfont-weight: 300;\n\tfont-size: 2.35em;\n\tline-height: 1.2em;\n\tcolor: <<colour tiddler-title-foreground>>;\n\tmargin: 0;\n}\n\n.tc-site-title {\n\tcolor: <<colour site-title-foreground>>;\n}\n\n.tc-tiddler-title-icon {\n\tvertical-align: middle;\n\tmargin-right: .1em;\n}\n\n.tc-system-title-prefix {\n\tcolor: <<colour muted-foreground>>;\n}\n\n.tc-titlebar h2 {\n\tfont-size: 1em;\n\tdisplay: inline;\n}\n\n.tc-titlebar img {\n\theight: 1em;\n}\n\n.tc-subtitle {\n\tfont-size: 0.9em;\n\tcolor: <<colour tiddler-subtitle-foreground>>;\n\tfont-weight: 300;\n}\n\n.tc-subtitle .tc-tiddlylink {\n\tmargin-right: .3em;\n}\n\n.tc-tiddler-missing .tc-title {\n font-style: italic;\n font-weight: normal;\n}\n\n.tc-tiddler-frame .tc-tiddler-controls {\n\tfloat: right;\n}\n\n.tc-tiddler-controls .tc-drop-down {\n\tfont-size: 0.6em;\n}\n\n.tc-tiddler-controls .tc-drop-down .tc-drop-down {\n\tfont-size: 1em;\n}\n\n.tc-tiddler-controls > span > button,\n.tc-tiddler-controls > span > span > button,\n.tc-tiddler-controls > span > span > span > button {\n\tvertical-align: baseline;\n\tmargin-left:5px;\n}\n\n.tc-tiddler-controls button svg, .tc-tiddler-controls button img,\n.tc-search button svg, .tc-search a svg {\n\tfill: <<colour tiddler-controls-foreground>>;\n}\n\n.tc-tiddler-controls button svg, .tc-tiddler-controls button img {\n\theight: 0.75em;\n}\n\n.tc-search button svg, .tc-search a svg {\n height: 1.2em;\n width: 1.2em;\n margin: 0 0.25em;\n}\n\n.tc-tiddler-controls button.tc-selected svg,\n.tc-page-controls button.tc-selected svg {\n\tfill: <<colour tiddler-controls-foreground-selected>>;\n}\n\n.tc-tiddler-controls button.tc-btn-invisible:hover svg,\n.tc-search button:hover svg, .tc-search a:hover svg {\n\tfill: <<colour tiddler-controls-foreground-hover>>;\n}\n\n@media print {\n\t.tc-tiddler-controls {\n\t\tdisplay: none;\n\t}\n}\n\n.tc-tiddler-help { /* Help prompts within tiddler template */\n\tcolor: <<colour muted-foreground>>;\n\tmargin-top: 14px;\n}\n\n.tc-tiddler-help a.tc-tiddlylink {\n\tcolor: <<colour very-muted-foreground>>;\n}\n\n.tc-tiddler-frame .tc-edit-texteditor {\n\twidth: 100%;\n\tmargin: 4px 0 4px 0;\n}\n\n.tc-tiddler-frame input.tc-edit-texteditor,\n.tc-tiddler-frame textarea.tc-edit-texteditor,\n.tc-tiddler-frame iframe.tc-edit-texteditor {\n\tpadding: 3px 3px 3px 3px;\n\tborder: 1px solid <<colour tiddler-editor-border>>;\n\tbackground-color: <<colour tiddler-editor-background>>;\n\tline-height: 1.3em;\n\t-webkit-appearance: none;\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/editorfontfamily}};\n}\n\n.tc-tiddler-frame .tc-binary-warning {\n\twidth: 100%;\n\theight: 5em;\n\ttext-align: center;\n\tpadding: 3em 3em 6em 3em;\n\tbackground: <<colour alert-background>>;\n\tborder: 1px solid <<colour alert-border>>;\n}\n\ncanvas.tc-edit-bitmapeditor {\n\tborder: 6px solid <<colour tiddler-editor-border-image>>;\n\tcursor: crosshair;\n\t-moz-user-select: none;\n\t-webkit-user-select: none;\n\t-ms-user-select: none;\n\tmargin-top: 6px;\n\tmargin-bottom: 6px;\n}\n\n.tc-edit-bitmapeditor-width {\n\tdisplay: block;\n}\n\n.tc-edit-bitmapeditor-height {\n\tdisplay: block;\n}\n\n.tc-tiddler-body {\n\tclear: both;\n}\n\n.tc-tiddler-frame .tc-tiddler-body {\n\tfont-size: {{$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize}};\n\tline-height: {{$:/themes/tiddlywiki/vanilla/metrics/bodylineheight}};\n}\n\n.tc-titlebar, .tc-tiddler-edit-title {\n\toverflow: hidden; /* https://github.com/Jermolene/TiddlyWiki5/issues/282 */\n}\n\nhtml body.tc-body.tc-single-tiddler-window {\n\tmargin: 1em;\n\tbackground: <<colour tiddler-background>>;\n}\n\n.tc-single-tiddler-window img,\n.tc-single-tiddler-window svg,\n.tc-single-tiddler-window canvas,\n.tc-single-tiddler-window embed,\n.tc-single-tiddler-window iframe {\n\tmax-width: 100%;\n}\n\n/*\n** Editor\n*/\n\n.tc-editor-toolbar {\n\tmargin-top: 8px;\n}\n\n.tc-editor-toolbar button {\n\tvertical-align: middle;\n\tbackground-color: <<colour tiddler-controls-foreground>>;\n\tcolor: <<colour tiddler-controls-foreground-selected>>;\n\tfill: <<colour tiddler-controls-foreground-selected>>;\n\tborder-radius: 4px;\n\tpadding: 3px;\n\tmargin: 2px 0 2px 4px;\n}\n\n.tc-editor-toolbar button.tc-text-editor-toolbar-item-adjunct {\n\tmargin-left: 1px;\n\twidth: 1em;\n\tborder-radius: 8px;\n}\n\n.tc-editor-toolbar button.tc-text-editor-toolbar-item-start-group {\n\tmargin-left: 11px;\n}\n\n.tc-editor-toolbar button.tc-selected {\n\tbackground-color: <<colour primary>>;\n}\n\n.tc-editor-toolbar button svg {\n\twidth: 1.6em;\n\theight: 1.2em;\n}\n\n.tc-editor-toolbar button:hover {\n\tbackground-color: <<colour tiddler-controls-foreground-selected>>;\n\tfill: <<colour background>>;\n\tcolor: <<colour background>>;\n}\n\n.tc-editor-toolbar .tc-text-editor-toolbar-more {\n\twhite-space: normal;\n}\n\n.tc-editor-toolbar .tc-text-editor-toolbar-more button {\n\tdisplay: inline-block;\n\tpadding: 3px;\n\twidth: auto;\n}\n\n.tc-editor-toolbar .tc-search-results {\n\tpadding: 0;\n}\n\n/*\n** Adjustments for fluid-fixed mode\n*/\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n<<if-fluid-fixed text:\"\"\"\n\n\t.tc-story-river {\n\t\tpadding-right: 0;\n\t\tposition: relative;\n\t\twidth: auto;\n\t\tleft: 0;\n\t\tmargin-left: {{$:/themes/tiddlywiki/vanilla/metrics/storyleft}};\n\t\tmargin-right: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth}};\n\t}\n\n\t.tc-tiddler-frame {\n\t\twidth: 100%;\n\t}\n\n\t.tc-sidebar-scrollable {\n\t\tleft: auto;\n\t\tbottom: 0;\n\t\tright: 0;\n\t\twidth: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth}};\n\t}\n\n\tbody.tc-body .tc-storyview-zoomin-tiddler {\n\t\twidth: 100%;\n\t\twidth: calc(100% - 42px);\n\t}\n\n\"\"\" hiddenSidebarText:\"\"\"\n\n\t.tc-story-river {\n\t\tpadding-right: 3em;\n\t\tmargin-right: 0;\n\t}\n\n\tbody.tc-body .tc-storyview-zoomin-tiddler {\n\t\twidth: 100%;\n\t\twidth: calc(100% - 84px);\n\t}\n\n\"\"\">>\n\n}\n\n/*\n** Toolbar buttons\n*/\n\n.tc-page-controls svg.tc-image-new-button {\n fill: <<colour toolbar-new-button>>;\n}\n\n.tc-page-controls svg.tc-image-options-button {\n fill: <<colour toolbar-options-button>>;\n}\n\n.tc-page-controls svg.tc-image-save-button {\n fill: <<colour toolbar-save-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-info-button {\n fill: <<colour toolbar-info-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-edit-button {\n fill: <<colour toolbar-edit-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-close-button {\n fill: <<colour toolbar-close-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-delete-button {\n fill: <<colour toolbar-delete-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-cancel-button {\n fill: <<colour toolbar-cancel-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-done-button {\n fill: <<colour toolbar-done-button>>;\n}\n\n/*\n** Tiddler edit mode\n*/\n\n.tc-tiddler-edit-frame em.tc-edit {\n\tcolor: <<colour muted-foreground>>;\n\tfont-style: normal;\n}\n\n.tc-edit-type-dropdown a.tc-tiddlylink-missing {\n\tfont-style: normal;\n}\n\n.tc-type-selector .tc-edit-typeeditor {\n\twidth: 20%;\n}\n\n.tc-edit-tags {\n\tborder: 1px solid <<colour tiddler-editor-border>>;\n\tpadding: 4px 8px 4px 8px;\n}\n\n.tc-edit-add-tag {\n\tdisplay: inline-block;\n}\n\n.tc-edit-add-tag .tc-add-tag-name input {\n\twidth: 50%;\n}\n\n.tc-edit-add-tag .tc-keyboard {\n\tdisplay:inline;\n}\n\n.tc-edit-tags .tc-tag-label {\n\tdisplay: inline-block;\n}\n\n.tc-edit-tags-list {\n\tmargin: 14px 0 14px 0;\n}\n\n.tc-remove-tag-button {\n\tpadding-left: 4px;\n}\n\n.tc-tiddler-preview {\n\toverflow: auto;\n}\n\n.tc-tiddler-preview-preview {\n\tfloat: right;\n\twidth: 49%;\n\tborder: 1px solid <<colour tiddler-editor-border>>;\n\tmargin: 4px 0 3px 3px;\n\tpadding: 3px 3px 3px 3px;\n}\n\n<<if-editor-height-fixed then:\"\"\"\n\n.tc-tiddler-preview-preview {\n\toverflow-y: scroll;\n\theight: {{$:/config/TextEditor/EditorHeight/Height}};\n}\n\n\"\"\">>\n\n.tc-tiddler-frame .tc-tiddler-preview .tc-edit-texteditor {\n\twidth: 49%;\n}\n\n.tc-tiddler-frame .tc-tiddler-preview canvas.tc-edit-bitmapeditor {\n\tmax-width: 49%;\n}\n\n.tc-edit-fields {\n\twidth: 100%;\n}\n\n\n.tc-edit-fields table, .tc-edit-fields tr, .tc-edit-fields td {\n\tborder: none;\n\tpadding: 4px;\n}\n\n.tc-edit-fields > tbody > .tc-edit-field:nth-child(odd) {\n\tbackground-color: <<colour tiddler-editor-fields-odd>>;\n}\n\n.tc-edit-fields > tbody > .tc-edit-field:nth-child(even) {\n\tbackground-color: <<colour tiddler-editor-fields-even>>;\n}\n\n.tc-edit-field-name {\n\ttext-align: right;\n}\n\n.tc-edit-field-value input {\n\twidth: 100%;\n}\n\n.tc-edit-field-remove {\n}\n\n.tc-edit-field-remove svg {\n\theight: 1em;\n\twidth: 1em;\n\tfill: <<colour muted-foreground>>;\n\tvertical-align: middle;\n}\n\n.tc-edit-field-add-name {\n\tdisplay: inline-block;\n\twidth: 15%;\n}\n\n.tc-edit-field-add-value {\n\tdisplay: inline-block;\n\twidth: 40%;\n}\n\n.tc-edit-field-add-button {\n\tdisplay: inline-block;\n\twidth: 10%;\n}\n\n/*\n** Storyview Classes\n*/\n\n.tc-viewswitcher .tc-image-button {\n\tmargin-right: .3em;\n}\n\n.tc-storyview-zoomin-tiddler {\n\tposition: absolute;\n\tdisplay: block;\n\twidth: 100%;\n}\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-storyview-zoomin-tiddler {\n\t\twidth: calc(100% - 84px);\n\t}\n\n}\n\n/*\n** Dropdowns\n*/\n\n.tc-btn-dropdown {\n\ttext-align: left;\n}\n\n.tc-btn-dropdown svg, .tc-btn-dropdown img {\n\theight: 1em;\n\twidth: 1em;\n\tfill: <<colour muted-foreground>>;\n}\n\n.tc-drop-down-wrapper {\n\tposition: relative;\n}\n\n.tc-drop-down {\n\tmin-width: 380px;\n\tborder: 1px solid <<colour dropdown-border>>;\n\tbackground-color: <<colour dropdown-background>>;\n\tpadding: 7px 0 7px 0;\n\tmargin: 4px 0 0 0;\n\twhite-space: nowrap;\n\ttext-shadow: none;\n\tline-height: 1.4;\n}\n\n.tc-drop-down .tc-drop-down {\n\tmargin-left: 14px;\n}\n\n.tc-drop-down button svg, .tc-drop-down a svg {\n\tfill: <<colour foreground>>;\n}\n\n.tc-drop-down button.tc-btn-invisible:hover svg {\n\tfill: <<colour foreground>>;\n}\n\n.tc-drop-down .tc-drop-down-info {\n\tpadding-left: 14px;\n}\n\n.tc-drop-down p {\n\tpadding: 0 14px 0 14px;\n}\n\n.tc-drop-down svg {\n\twidth: 1em;\n\theight: 1em;\n}\n\n.tc-drop-down img {\n\twidth: 1em;\n}\n\n.tc-drop-down a, .tc-drop-down button {\n\tdisplay: block;\n\tpadding: 0 14px 0 14px;\n\twidth: 100%;\n\ttext-align: left;\n\tcolor: <<colour foreground>>;\n\tline-height: 1.4;\n}\n\n.tc-drop-down .tc-tab-set .tc-tab-buttons button {\n\tdisplay: inline-block;\n width: auto;\n margin-bottom: 0px;\n border-bottom-left-radius: 0;\n border-bottom-right-radius: 0;\n}\n\n.tc-drop-down .tc-prompt {\n\tpadding: 0 14px;\n}\n\n.tc-drop-down .tc-chooser {\n\tborder: none;\n}\n\n.tc-drop-down .tc-chooser .tc-swatches-horiz {\n\tfont-size: 0.4em;\n\tpadding-left: 1.2em;\n}\n\n.tc-drop-down .tc-file-input-wrapper {\n\twidth: 100%;\n}\n\n.tc-drop-down .tc-file-input-wrapper button {\n\tcolor: <<colour foreground>>;\n}\n\n.tc-drop-down a:hover, .tc-drop-down button:hover, .tc-drop-down .tc-file-input-wrapper:hover button {\n\tcolor: <<colour tiddler-link-background>>;\n\tbackground-color: <<colour tiddler-link-foreground>>;\n\ttext-decoration: none;\n}\n\n.tc-drop-down .tc-tab-buttons button {\n\tbackground-color: <<colour dropdown-tab-background>>;\n}\n\n.tc-drop-down .tc-tab-buttons button.tc-tab-selected {\n\tbackground-color: <<colour dropdown-tab-background-selected>>;\n\tborder-bottom: 1px solid <<colour dropdown-tab-background-selected>>;\n}\n\n.tc-drop-down-bullet {\n\tdisplay: inline-block;\n\twidth: 0.5em;\n}\n\n.tc-drop-down .tc-tab-contents a {\n\tpadding: 0 0.5em 0 0.5em;\n}\n\n.tc-block-dropdown-wrapper {\n\tposition: relative;\n}\n\n.tc-block-dropdown {\n\tposition: absolute;\n\tmin-width: 220px;\n\tborder: 1px solid <<colour dropdown-border>>;\n\tbackground-color: <<colour dropdown-background>>;\n\tpadding: 7px 0;\n\tmargin: 4px 0 0 0;\n\twhite-space: nowrap;\n\tz-index: 1000;\n\ttext-shadow: none;\n}\n\n.tc-block-dropdown.tc-search-drop-down {\n\tmargin-left: -12px;\n}\n\n.tc-block-dropdown a {\n\tdisplay: block;\n\tpadding: 4px 14px 4px 14px;\n}\n\n.tc-block-dropdown.tc-search-drop-down a {\n\tdisplay: block;\n\tpadding: 0px 10px 0px 10px;\n}\n\n.tc-drop-down .tc-dropdown-item-plain,\n.tc-block-dropdown .tc-dropdown-item-plain {\n\tpadding: 4px 14px 4px 7px;\n}\n\n.tc-drop-down .tc-dropdown-item,\n.tc-block-dropdown .tc-dropdown-item {\n\tpadding: 4px 14px 4px 7px;\n\tcolor: <<colour muted-foreground>>;\n}\n\n.tc-block-dropdown a:hover {\n\tcolor: <<colour tiddler-link-background>>;\n\tbackground-color: <<colour tiddler-link-foreground>>;\n\ttext-decoration: none;\n}\n\n.tc-search-results {\n\tpadding: 0 7px 0 7px;\n}\n\n.tc-image-chooser, .tc-colour-chooser {\n\twhite-space: normal;\n}\n\n.tc-image-chooser a,\n.tc-colour-chooser a {\n\tdisplay: inline-block;\n\tvertical-align: top;\n\ttext-align: center;\n\tposition: relative;\n}\n\n.tc-image-chooser a {\n\tborder: 1px solid <<colour muted-foreground>>;\n\tpadding: 2px;\n\tmargin: 2px;\n\twidth: 4em;\n\theight: 4em;\n}\n\n.tc-colour-chooser a {\n\tpadding: 3px;\n\twidth: 2em;\n\theight: 2em;\n\tvertical-align: middle;\n}\n\n.tc-image-chooser a:hover,\n.tc-colour-chooser a:hover {\n\tbackground: <<colour primary>>;\n\tpadding: 0px;\n\tborder: 3px solid <<colour primary>>;\n}\n\n.tc-image-chooser a svg,\n.tc-image-chooser a img {\n\tdisplay: inline-block;\n\twidth: auto;\n\theight: auto;\n\tmax-width: 3.5em;\n\tmax-height: 3.5em;\n\tposition: absolute;\n\ttop: 0;\n\tbottom: 0;\n\tleft: 0;\n\tright: 0;\n\tmargin: auto;\n}\n\n/*\n** Modals\n*/\n\n.tc-modal-wrapper {\n\tposition: fixed;\n\toverflow: auto;\n\toverflow-y: scroll;\n\ttop: 0;\n\tright: 0;\n\tbottom: 0;\n\tleft: 0;\n\tz-index: 900;\n}\n\n.tc-modal-backdrop {\n\tposition: fixed;\n\ttop: 0;\n\tright: 0;\n\tbottom: 0;\n\tleft: 0;\n\tz-index: 1000;\n\tbackground-color: <<colour modal-backdrop>>;\n}\n\n.tc-modal {\n\tz-index: 1100;\n\tbackground-color: <<colour modal-background>>;\n\tborder: 1px solid <<colour modal-border>>;\n}\n\n@media (max-width: 55em) {\n\t.tc-modal {\n\t\tposition: fixed;\n\t\ttop: 1em;\n\t\tleft: 1em;\n\t\tright: 1em;\n\t}\n\n\t.tc-modal-body {\n\t\toverflow-y: auto;\n\t\tmax-height: 400px;\n\t\tmax-height: 60vh;\n\t}\n}\n\n@media (min-width: 55em) {\n\t.tc-modal {\n\t\tposition: fixed;\n\t\ttop: 2em;\n\t\tleft: 25%;\n\t\twidth: 50%;\n\t}\n\n\t.tc-modal-body {\n\t\toverflow-y: auto;\n\t\tmax-height: 400px;\n\t\tmax-height: 60vh;\n\t}\n}\n\n.tc-modal-header {\n\tpadding: 9px 15px;\n\tborder-bottom: 1px solid <<colour modal-header-border>>;\n}\n\n.tc-modal-header h3 {\n\tmargin: 0;\n\tline-height: 30px;\n}\n\n.tc-modal-header img, .tc-modal-header svg {\n\twidth: 1em;\n\theight: 1em;\n}\n\n.tc-modal-body {\n\tpadding: 15px;\n}\n\n.tc-modal-footer {\n\tpadding: 14px 15px 15px;\n\tmargin-bottom: 0;\n\ttext-align: right;\n\tbackground-color: <<colour modal-footer-background>>;\n\tborder-top: 1px solid <<colour modal-footer-border>>;\n}\n\n/*\n** Notifications\n*/\n\n.tc-notification {\n\tposition: fixed;\n\ttop: 14px;\n\tright: 42px;\n\tz-index: 1300;\n\tmax-width: 280px;\n\tpadding: 0 14px 0 14px;\n\tbackground-color: <<colour notification-background>>;\n\tborder: 1px solid <<colour notification-border>>;\n}\n\n/*\n** Tabs\n*/\n\n.tc-tab-set.tc-vertical {\n\tdisplay: -webkit-flex;\n\tdisplay: flex;\n}\n\n.tc-tab-buttons {\n\tfont-size: 0.85em;\n\tpadding-top: 1em;\n\tmargin-bottom: -2px;\n}\n\n.tc-tab-buttons.tc-vertical {\n\tz-index: 100;\n\tdisplay: block;\n\tpadding-top: 14px;\n\tvertical-align: top;\n\ttext-align: right;\n\tmargin-bottom: inherit;\n\tmargin-right: -1px;\n\tmax-width: 33%;\n\t-webkit-flex: 0 0 auto;\n\tflex: 0 0 auto;\n}\n\n.tc-tab-buttons button.tc-tab-selected {\n\tcolor: <<colour tab-foreground-selected>>;\n\tbackground-color: <<colour tab-background-selected>>;\n\tborder-left: 1px solid <<colour tab-border-selected>>;\n\tborder-top: 1px solid <<colour tab-border-selected>>;\n\tborder-right: 1px solid <<colour tab-border-selected>>;\n}\n\n.tc-tab-buttons button {\n\tcolor: <<colour tab-foreground>>;\n\tpadding: 3px 5px 3px 5px;\n\tmargin-right: 0.3em;\n\tfont-weight: 300;\n\tborder: none;\n\tbackground: inherit;\n\tbackground-color: <<colour tab-background>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-top: 1px solid <<colour tab-border>>;\n\tborder-right: 1px solid <<colour tab-border>>;\n\tborder-top-left-radius: 2px;\n\tborder-top-right-radius: 2px;\n\tborder-bottom-left-radius: 0;\n\tborder-bottom-right-radius: 0;\n}\n\n.tc-tab-buttons.tc-vertical button {\n\tdisplay: block;\n\twidth: 100%;\n\tmargin-top: 3px;\n\tmargin-right: 0;\n\ttext-align: right;\n\tbackground-color: <<colour tab-background>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-bottom: 1px solid <<colour tab-border>>;\n\tborder-right: none;\n\tborder-top-left-radius: 2px;\n\tborder-bottom-left-radius: 2px;\n\tborder-top-right-radius: 0;\n\tborder-bottom-right-radius: 0;\n}\n\n.tc-tab-buttons.tc-vertical button.tc-tab-selected {\n\tbackground-color: <<colour tab-background-selected>>;\n\tborder-right: 1px solid <<colour tab-background-selected>>;\n}\n\n.tc-tab-divider {\n\tborder-top: 1px solid <<colour tab-divider>>;\n}\n\n.tc-tab-divider.tc-vertical {\n\tdisplay: none;\n}\n\n.tc-tab-content {\n\tmargin-top: 14px;\n}\n\n.tc-tab-content.tc-vertical {\n\tdisplay: inline-block;\n\tvertical-align: top;\n\tpadding-top: 0;\n\tpadding-left: 14px;\n\tborder-left: 1px solid <<colour tab-border>>;\n\t-webkit-flex: 1 0 70%;\n\tflex: 1 0 70%;\n\toverflow: auto;\n}\n\n.tc-sidebar-lists .tc-tab-buttons {\n\tmargin-bottom: -1px;\n}\n\n.tc-sidebar-lists .tc-tab-buttons button.tc-tab-selected {\n\tbackground-color: <<colour sidebar-tab-background-selected>>;\n\tcolor: <<colour sidebar-tab-foreground-selected>>;\n\tborder-left: 1px solid <<colour sidebar-tab-border-selected>>;\n\tborder-top: 1px solid <<colour sidebar-tab-border-selected>>;\n\tborder-right: 1px solid <<colour sidebar-tab-border-selected>>;\n}\n\n.tc-sidebar-lists .tc-tab-buttons button {\n\tbackground-color: <<colour sidebar-tab-background>>;\n\tcolor: <<colour sidebar-tab-foreground>>;\n\tborder-left: 1px solid <<colour sidebar-tab-border>>;\n\tborder-top: 1px solid <<colour sidebar-tab-border>>;\n\tborder-right: 1px solid <<colour sidebar-tab-border>>;\n}\n\n.tc-sidebar-lists .tc-tab-divider {\n\tborder-top: 1px solid <<colour sidebar-tab-divider>>;\n}\n\n.tc-more-sidebar > .tc-tab-set > .tc-tab-buttons > button {\n\tdisplay: block;\n\twidth: 100%;\n\tbackground-color: <<colour sidebar-tab-background>>;\n\tborder-top: none;\n\tborder-left: none;\n\tborder-bottom: none;\n\tborder-right: 1px solid #ccc;\n\tmargin-bottom: inherit;\n}\n\n.tc-more-sidebar > .tc-tab-set > .tc-tab-buttons > button.tc-tab-selected {\n\tbackground-color: <<colour sidebar-tab-background-selected>>;\n\tborder: none;\n}\n\n/*\n** Manager\n*/\n\n.tc-manager-wrapper {\n\t\n}\n\n.tc-manager-controls {\n\t\n}\n\n.tc-manager-control {\n\tmargin: 0.5em 0;\n}\n\n.tc-manager-list {\n\twidth: 100%;\n\tborder-top: 1px solid <<colour muted-foreground>>;\n\tborder-left: 1px solid <<colour muted-foreground>>;\n\tborder-right: 1px solid <<colour muted-foreground>>;\n}\n\n.tc-manager-list-item {\n\n}\n\n.tc-manager-list-item-heading {\n display: block;\n width: 100%;\n text-align: left;\t\n\tborder-bottom: 1px solid <<colour muted-foreground>>;\n\tpadding: 3px;\n}\n\n.tc-manager-list-item-heading-selected {\n\tfont-weight: bold;\n\tcolor: <<colour background>>;\n\tfill: <<colour background>>;\n\tbackground-color: <<colour foreground>>;\n}\n\n.tc-manager-list-item-heading:hover {\n\tbackground: <<colour primary>>;\n\tcolor: <<colour background>>;\n}\n\n.tc-manager-list-item-content {\n\tdisplay: flex;\n}\n\n.tc-manager-list-item-content-sidebar {\n flex: 1 0;\n background: <<colour tiddler-editor-background>>;\n border-right: 0.5em solid <<colour muted-foreground>>;\n border-bottom: 0.5em solid <<colour muted-foreground>>;\n white-space: nowrap;\n}\n\n.tc-manager-list-item-content-item-heading {\n\tdisplay: block;\n\twidth: 100%;\n\ttext-align: left;\n background: <<colour muted-foreground>>;\n\ttext-transform: uppercase;\n\tfont-size: 0.6em;\n\tfont-weight: bold;\n padding: 0.5em 0 0.5em 0;\n}\n\n.tc-manager-list-item-content-item-body {\n\tpadding: 0 0.5em 0 0.5em;\n}\n\n.tc-manager-list-item-content-item-body > pre {\n\tmargin: 0.5em 0 0.5em 0;\n\tborder: none;\n\tbackground: inherit;\n}\n\n.tc-manager-list-item-content-tiddler {\n flex: 3 1;\n border-left: 0.5em solid <<colour muted-foreground>>;\n border-right: 0.5em solid <<colour muted-foreground>>;\n border-bottom: 0.5em solid <<colour muted-foreground>>;\n}\n\n.tc-manager-list-item-content-item-body > table {\n\tborder: none;\n\tpadding: 0;\n\tmargin: 0;\n}\n\n.tc-manager-list-item-content-item-body > table td {\n\tborder: none;\n}\n\n.tc-manager-icon-editor > button {\n\twidth: 100%;\n}\n\n.tc-manager-icon-editor > button > svg,\n.tc-manager-icon-editor > button > button {\n\twidth: 100%;\n\theight: auto;\n}\n\n/*\n** Alerts\n*/\n\n.tc-alerts {\n\tposition: fixed;\n\ttop: 28px;\n\tleft: 0;\n\tright: 0;\n\tmax-width: 50%;\n\tz-index: 20000;\n}\n\n.tc-alert {\n\tposition: relative;\n\tmargin: 14px;\n\tpadding: 7px;\n\tborder: 1px solid <<colour alert-border>>;\n\tbackground-color: <<colour alert-background>>;\n}\n\n.tc-alert-toolbar {\n\tposition: absolute;\n\ttop: 7px;\n\tright: 7px;\n line-height: 0;\n}\n\n.tc-alert-toolbar svg {\n\tfill: <<colour alert-muted-foreground>>;\n}\n\n.tc-alert-subtitle {\n\tcolor: <<colour alert-muted-foreground>>;\n\tfont-weight: bold;\n font-size: 0.8em;\n margin-bottom: 0.5em;\n}\n\n.tc-alert-body > p {\n\tmargin: 0;\n}\n\n.tc-alert-highlight {\n\tcolor: <<colour alert-highlight>>;\n}\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-static-alert {\n\t\tposition: relative;\n\t}\n\n\t.tc-static-alert-inner {\n\t\tposition: absolute;\n\t\tz-index: 100;\n\t}\n\n}\n\n.tc-static-alert-inner {\n\tpadding: 0 2px 2px 42px;\n\tcolor: <<colour static-alert-foreground>>;\n}\n\n/*\n** Floating drafts list\n*/\n\n.tc-drafts-list {\n\tz-index: 2000;\n\tposition: fixed;\n\tfont-size: 0.8em;\n\tleft: 0;\n\tbottom: 0;\n}\n\n.tc-drafts-list a {\n\tmargin: 0 0.5em;\n\tpadding: 4px 4px;\n\tborder-top-left-radius: 4px;\n\tborder-top-right-radius: 4px;\n\tborder: 1px solid <<colour background>>;\n\tborder-bottom-none;\n\tbackground: <<colour dirty-indicator>>;\n\tcolor: <<colour background>>;\n\tfill: <<colour background>>;\n}\n\n.tc-drafts-list a:hover {\n\ttext-decoration: none;\n\tbackground: <<colour foreground>>;\n\tcolor: <<colour background>>;\n\tfill: <<colour background>>;\n}\n\n.tc-drafts-list a svg {\n\twidth: 1em;\n\theight: 1em;\n\tvertical-align: text-bottom;\n}\n\n/*\n** Control panel\n*/\n\n.tc-control-panel td {\n\tpadding: 4px;\n}\n\n.tc-control-panel table, .tc-control-panel table input, .tc-control-panel table textarea {\n\twidth: 100%;\n}\n\n.tc-plugin-info {\n\tdisplay: flex;\n\tborder: 1px solid <<colour muted-foreground>>;\n\tfill: <<colour muted-foreground>>;\n\tbackground-color: <<colour background>>;\n\tmargin: 0.5em 0 0.5em 0;\n\tpadding: 4px;\n align-items: center;\n}\n\n.tc-plugin-info-sub-plugins .tc-plugin-info {\n margin: 0.5em;\n\tbackground: <<colour background>>;\n}\n\n.tc-plugin-info-sub-plugin-indicator {\n\tmargin: -16px 1em 0 2em;\n}\n\n.tc-plugin-info-sub-plugin-indicator button {\n\tcolor: <<colour background>>;\n\tbackground: <<colour foreground>>;\n\tborder-radius: 8px;\n padding: 2px 7px;\n font-size: 0.75em;\n}\n\n.tc-plugin-info-sub-plugins .tc-plugin-info-dropdown {\n\tmargin-left: 1em;\n\tmargin-right: 1em;\n}\n\n.tc-plugin-info-disabled {\n\tbackground: -webkit-repeating-linear-gradient(45deg, #ff0, #ff0 10px, #eee 10px, #eee 20px);\n\tbackground: repeating-linear-gradient(45deg, #ff0, #ff0 10px, #eee 10px, #eee 20px);\n}\n\n.tc-plugin-info-disabled:hover {\n\tbackground: -webkit-repeating-linear-gradient(45deg, #aa0, #aa0 10px, #888 10px, #888 20px);\n\tbackground: repeating-linear-gradient(45deg, #aa0, #aa0 10px, #888 10px, #888 20px);\n}\n\na.tc-tiddlylink.tc-plugin-info:hover {\n\ttext-decoration: none;\n\tbackground-color: <<colour primary>>;\n\tcolor: <<colour background>>;\n\tfill: <<colour foreground>>;\n}\n\na.tc-tiddlylink.tc-plugin-info:hover .tc-plugin-info > .tc-plugin-info-chunk > svg {\n\tfill: <<colour foreground>>;\n}\n\n.tc-plugin-info-chunk {\n margin: 2px;\n}\n\n.tc-plugin-info-chunk.tc-plugin-info-toggle {\n\tflex-grow: 0;\n\tflex-shrink: 0;\n\tline-height: 1;\n}\n\n.tc-plugin-info-chunk.tc-plugin-info-icon {\n\tflex-grow: 0;\n\tflex-shrink: 0;\n\tline-height: 1;\n}\n\n.tc-plugin-info-chunk.tc-plugin-info-description {\n\tflex-grow: 1;\n}\n\n.tc-plugin-info-chunk.tc-plugin-info-buttons {\n\tfont-size: 0.8em;\n\tline-height: 1.2;\n\tflex-grow: 0;\n\tflex-shrink: 0;\n text-align: right;\n}\n\n.tc-plugin-info-chunk.tc-plugin-info-description h1 {\n\tfont-size: 1em;\n\tline-height: 1.2;\n\tmargin: 2px 0 2px 0;\n}\n\n.tc-plugin-info-chunk.tc-plugin-info-description h2 {\n\tfont-size: 0.8em;\n\tline-height: 1.2;\n\tmargin: 2px 0 2px 0;\n}\n\n.tc-plugin-info-chunk.tc-plugin-info-description div {\n\tfont-size: 0.7em;\n\tline-height: 1.2;\n\tmargin: 2px 0 2px 0;\n}\n\n.tc-plugin-info-chunk.tc-plugin-info-toggle img, .tc-plugin-info-chunk.tc-plugin-info-toggle svg {\n\twidth: 1em;\n\theight: 1em;\n}\n\n.tc-plugin-info-chunk.tc-plugin-info-icon img, .tc-plugin-info-chunk.tc-plugin-info-icon svg {\n\twidth: 2em;\n\theight: 2em;\n}\n\n.tc-plugin-info-dropdown {\n\tborder: 1px solid <<colour muted-foreground>>;\n\tbackground: <<colour background>>;\n\tmargin-top: -8px;\n}\n\n.tc-plugin-info-dropdown-message {\n\tbackground: <<colour message-background>>;\n\tpadding: 0.5em 1em 0.5em 1em;\n\tfont-weight: bold;\n\tfont-size: 0.8em;\n}\n\n.tc-plugin-info-dropdown-body {\n\tpadding: 1em 1em 0 1em;\n\tbackground: <<colour background>>;\n}\n\n.tc-plugin-info-sub-plugins {\n\tpadding: 0.5em;\n margin: 0 1em 1em 1em;\n\tbackground: <<colour notification-background>>;\n}\n\n.tc-install-plugin {\n\tfont-weight: bold;\n\tbackground: green;\n\tcolor: white;\n\tfill: white;\n\tborder-radius: 4px;\n\tpadding: 3px;\n}\n\n.tc-install-plugin.tc-reinstall-downgrade {\n\tbackground: red;\n}\n\n.tc-install-plugin.tc-reinstall {\n\tbackground: blue;\n}\n\n.tc-install-plugin.tc-reinstall-upgrade {\n\tbackground: orange;\n}\n\n.tc-check-list {\n\tline-height: 2em;\n}\n\n.tc-check-list .tc-image-button {\n\theight: 1.5em;\n}\n\n/*\n** Message boxes\n*/\n\n.tc-message-box {\n\tborder: 1px solid <<colour message-border>>;\n\tbackground: <<colour message-background>>;\n\tpadding: 0px 21px 0px 21px;\n\tfont-size: 12px;\n\tline-height: 18px;\n\tcolor: <<colour message-foreground>>;\n}\n\n.tc-message-box svg {\n\twidth: 1em;\n\theight: 1em;\n vertical-align: text-bottom;\n}\n\n/*\n** Pictures\n*/\n\n.tc-bordered-image {\n\tborder: 1px solid <<colour muted-foreground>>;\n\tpadding: 5px;\n\tmargin: 5px;\n}\n\n/*\n** Floats\n*/\n\n.tc-float-right {\n\tfloat: right;\n}\n\n/*\n** Chooser\n*/\n\n.tc-chooser {\n\tborder-right: 1px solid <<colour table-header-background>>;\n\tborder-left: 1px solid <<colour table-header-background>>;\n}\n\n\n.tc-chooser-item {\n\tborder-bottom: 1px solid <<colour table-header-background>>;\n\tborder-top: 1px solid <<colour table-header-background>>;\n\tpadding: 2px 4px 2px 14px;\n}\n\n.tc-drop-down .tc-chooser-item {\n\tpadding: 2px;\n}\n\n.tc-chosen,\n.tc-chooser-item:hover {\n\tbackground-color: <<colour table-header-background>>;\n\tborder-color: <<colour table-footer-background>>;\n}\n\n.tc-chosen .tc-tiddlylink {\n\tcursor:default;\n}\n\n.tc-chooser-item .tc-tiddlylink {\n\tdisplay: block;\n\ttext-decoration: none;\n\tbackground-color: transparent;\n}\n\n.tc-chooser-item:hover .tc-tiddlylink:hover {\n\ttext-decoration: none;\n}\n\n.tc-drop-down .tc-chosen .tc-tiddlylink,\n.tc-drop-down .tc-chooser-item .tc-tiddlylink:hover {\n\tcolor: <<colour foreground>>;\n}\n\n.tc-chosen > .tc-tiddlylink:before {\n\tmargin-left: -10px;\n\tposition: relative;\n\tcontent: \"» \";\n}\n\n.tc-chooser-item svg,\n.tc-chooser-item img{\n\twidth: 1em;\n\theight: 1em;\n\tvertical-align: middle;\n}\n\n.tc-language-chooser .tc-image-button img {\n\twidth: 2em;\n\tvertical-align: -0.15em;\n}\n\n/*\n** Palette swatches\n*/\n\n.tc-swatches-horiz {\n}\n\n.tc-swatches-horiz .tc-swatch {\n\tdisplay: inline-block;\n}\n\n.tc-swatch {\n\twidth: 2em;\n\theight: 2em;\n\tmargin: 0.4em;\n\tborder: 1px solid #888;\n}\n\ninput.tc-palette-manager-colour-input {\n\twidth: 100%;\n\tpadding: 0;\n}\n\n/*\n** Table of contents\n*/\n\n.tc-sidebar-lists .tc-table-of-contents {\n\twhite-space: nowrap;\n}\n\n.tc-table-of-contents button {\n\tcolor: <<colour sidebar-foreground>>;\n}\n\n.tc-table-of-contents svg {\n\twidth: 0.7em;\n\theight: 0.7em;\n\tvertical-align: middle;\n\tfill: <<colour sidebar-foreground>>;\n}\n\n.tc-table-of-contents ol {\n\tlist-style-type: none;\n\tpadding-left: 0;\n}\n\n.tc-table-of-contents ol ol {\n\tpadding-left: 1em;\n}\n\n.tc-table-of-contents li {\n\tfont-size: 1.0em;\n\tfont-weight: bold;\n}\n\n.tc-table-of-contents li a {\n\tfont-weight: bold;\n}\n\n.tc-table-of-contents li li {\n\tfont-size: 0.95em;\n\tfont-weight: normal;\n\tline-height: 1.4;\n}\n\n.tc-table-of-contents li li a {\n\tfont-weight: normal;\n}\n\n.tc-table-of-contents li li li {\n\tfont-size: 0.95em;\n\tfont-weight: 200;\n\tline-height: 1.5;\n}\n\n.tc-table-of-contents li li li li {\n\tfont-size: 0.95em;\n\tfont-weight: 200;\n}\n\n.tc-tabbed-table-of-contents {\n\tdisplay: -webkit-flex;\n\tdisplay: flex;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents {\n\tz-index: 100;\n\tdisplay: inline-block;\n\tpadding-left: 1em;\n\tmax-width: 50%;\n\t-webkit-flex: 0 0 auto;\n\tflex: 0 0 auto;\n\tbackground: <<colour tab-background>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-top: 1px solid <<colour tab-border>>;\n\tborder-bottom: 1px solid <<colour tab-border>>;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item > a,\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item-selected > a {\n\tdisplay: block;\n\tpadding: 0.12em 1em 0.12em 0.25em;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item > a {\n\tborder-top: 1px solid <<colour tab-background>>;\n\tborder-left: 1px solid <<colour tab-background>>;\n\tborder-bottom: 1px solid <<colour tab-background>>;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item > a:hover {\n\ttext-decoration: none;\n\tborder-top: 1px solid <<colour tab-border>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-bottom: 1px solid <<colour tab-border>>;\n\tbackground: <<colour tab-border>>;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item-selected > a {\n\tborder-top: 1px solid <<colour tab-border>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-bottom: 1px solid <<colour tab-border>>;\n\tbackground: <<colour background>>;\n\tmargin-right: -1px;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item-selected > a:hover {\n\ttext-decoration: none;\n}\n\n.tc-tabbed-table-of-contents .tc-tabbed-table-of-contents-content {\n\tdisplay: inline-block;\n\tvertical-align: top;\n\tpadding-left: 1.5em;\n\tpadding-right: 1.5em;\n\tborder: 1px solid <<colour tab-border>>;\n\t-webkit-flex: 1 0 50%;\n\tflex: 1 0 50%;\n}\n\n/*\n** Dirty indicator\n*/\n\nbody.tc-dirty span.tc-dirty-indicator, body.tc-dirty span.tc-dirty-indicator svg {\n\tfill: <<colour dirty-indicator>>;\n\tcolor: <<colour dirty-indicator>>;\n}\n\n/*\n** File inputs\n*/\n\n.tc-file-input-wrapper {\n\tposition: relative;\n\toverflow: hidden;\n\tdisplay: inline-block;\n\tvertical-align: middle;\n}\n\n.tc-file-input-wrapper input[type=file] {\n\tposition: absolute;\n\ttop: 0;\n\tleft: 0;\n\tright: 0;\n\tbottom: 0;\n\tfont-size: 999px;\n\tmax-width: 100%;\n\tmax-height: 100%;\n\tfilter: alpha(opacity=0);\n\topacity: 0;\n\toutline: none;\n\tbackground: white;\n\tcursor: pointer;\n\tdisplay: inline-block;\n}\n\n/*\n** Thumbnail macros\n*/\n\n.tc-thumbnail-wrapper {\n\tposition: relative;\n\tdisplay: inline-block;\n\tmargin: 6px;\n\tvertical-align: top;\n}\n\n.tc-thumbnail-right-wrapper {\n\tfloat:right;\n\tmargin: 0.5em 0 0.5em 0.5em;\n}\n\n.tc-thumbnail-image {\n\ttext-align: center;\n\toverflow: hidden;\n\tborder-radius: 3px;\n}\n\n.tc-thumbnail-image svg,\n.tc-thumbnail-image img {\n\tfilter: alpha(opacity=1);\n\topacity: 1;\n\tmin-width: 100%;\n\tmin-height: 100%;\n\tmax-width: 100%;\n}\n\n.tc-thumbnail-wrapper:hover .tc-thumbnail-image svg,\n.tc-thumbnail-wrapper:hover .tc-thumbnail-image img {\n\tfilter: alpha(opacity=0.8);\n\topacity: 0.8;\n}\n\n.tc-thumbnail-background {\n\tposition: absolute;\n\tborder-radius: 3px;\n}\n\n.tc-thumbnail-icon svg,\n.tc-thumbnail-icon img {\n\twidth: 3em;\n\theight: 3em;\n\t<<filter \"drop-shadow(2px 2px 4px rgba(0,0,0,0.3))\">>\n}\n\n.tc-thumbnail-wrapper:hover .tc-thumbnail-icon svg,\n.tc-thumbnail-wrapper:hover .tc-thumbnail-icon img {\n\tfill: #fff;\n\t<<filter \"drop-shadow(3px 3px 4px rgba(0,0,0,0.6))\">>\n}\n\n.tc-thumbnail-icon {\n\tposition: absolute;\n\ttop: 0;\n\tleft: 0;\n\tright: 0;\n\tbottom: 0;\n\tdisplay: -webkit-flex;\n\t-webkit-align-items: center;\n\t-webkit-justify-content: center;\n\tdisplay: flex;\n\talign-items: center;\n\tjustify-content: center;\n}\n\n.tc-thumbnail-caption {\n\tposition: absolute;\n\tbackground-color: #777;\n\tcolor: #fff;\n\ttext-align: center;\n\tbottom: 0;\n\twidth: 100%;\n\tfilter: alpha(opacity=0.9);\n\topacity: 0.9;\n\tline-height: 1.4;\n\tborder-bottom-left-radius: 3px;\n\tborder-bottom-right-radius: 3px;\n}\n\n.tc-thumbnail-wrapper:hover .tc-thumbnail-caption {\n\tfilter: alpha(opacity=1);\n\topacity: 1;\n}\n\n/*\n** Diffs\n*/\n\n.tc-diff-equal {\n\tbackground-color: <<colour diff-equal-background>>;\n\tcolor: <<colour diff-equal-foreground>>;\n}\n\n.tc-diff-insert {\n\tbackground-color: <<colour diff-insert-background>>;\n\tcolor: <<colour diff-insert-foreground>>;\n}\n\n.tc-diff-delete {\n\tbackground-color: <<colour diff-delete-background>>;\n\tcolor: <<colour diff-delete-foreground>>;\n}\n\n.tc-diff-invisible {\n\tbackground-color: <<colour diff-invisible-background>>;\n\tcolor: <<colour diff-invisible-foreground>>;\n}\n\n.tc-diff-tiddlers th {\n\ttext-align: right;\n\tbackground: <<colour background>>;\n\tfont-weight: normal;\n\tfont-style: italic;\n}\n\n.tc-diff-tiddlers pre {\n margin: 0;\n padding: 0;\n border: none;\n background: none;\n}\n\n/*\n** Errors\n*/\n\n.tc-error {\n\tbackground: #f00;\n\tcolor: #fff;\n}\n\n/*\n** Tree macro\n*/\n\n.tc-tree div {\n \tpadding-left: 14px;\n}\n\n.tc-tree ol {\n \tlist-style-type: none;\n \tpadding-left: 0;\n \tmargin-top: 0;\n}\n\n.tc-tree ol ol {\n \tpadding-left: 1em; \n}\n\n.tc-tree button { \n \tcolor: #acacac;\n}\n\n.tc-tree svg {\n \tfill: #acacac;\n}\n\n.tc-tree span svg {\n \twidth: 1em;\n \theight: 1em;\n \tvertical-align: baseline;\n}\n\n.tc-tree li span {\n \tcolor: lightgray;\n}\n\nselect {\n color: <<colour select-tag-foreground>>;\n background: <<colour select-tag-background>>;\n}\n\n/*\n** Utility classes for SVG icons\n*/\n\n.tc-fill-background {\n\tfill: <<colour background>>;\n}"
},
"$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize",
"text": "15px"
},
"$:/themes/tiddlywiki/vanilla/metrics/bodylineheight": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/bodylineheight",
"text": "22px"
},
"$:/themes/tiddlywiki/vanilla/metrics/fontsize": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/fontsize",
"text": "14px"
},
"$:/themes/tiddlywiki/vanilla/metrics/lineheight": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/lineheight",
"text": "20px"
},
"$:/themes/tiddlywiki/vanilla/metrics/storyleft": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/storyleft",
"text": "0px"
},
"$:/themes/tiddlywiki/vanilla/metrics/storytop": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/storytop",
"text": "0px"
},
"$:/themes/tiddlywiki/vanilla/metrics/storyright": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/storyright",
"text": "770px"
},
"$:/themes/tiddlywiki/vanilla/metrics/storywidth": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/storywidth",
"text": "770px"
},
"$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth",
"text": "686px"
},
"$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint",
"text": "960px"
},
"$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth",
"text": "350px"
},
"$:/themes/tiddlywiki/vanilla/options/stickytitles": {
"title": "$:/themes/tiddlywiki/vanilla/options/stickytitles",
"text": "no"
},
"$:/themes/tiddlywiki/vanilla/options/sidebarlayout": {
"title": "$:/themes/tiddlywiki/vanilla/options/sidebarlayout",
"text": "fixed-fluid"
},
"$:/themes/tiddlywiki/vanilla/options/codewrapping": {
"title": "$:/themes/tiddlywiki/vanilla/options/codewrapping",
"text": "pre-wrap"
},
"$:/themes/tiddlywiki/vanilla/reset": {
"title": "$:/themes/tiddlywiki/vanilla/reset",
"type": "text/plain",
"text": "/*! normalize.css v8.0.1 | MIT License | github.com/necolas/normalize.css */\n\n/* Document\n ========================================================================== */\n\n/**\n * 1. Correct the line height in all browsers.\n * 2. Prevent adjustments of font size after orientation changes in iOS.\n */\n\nhtml {\n line-height: 1.15; /* 1 */\n -webkit-text-size-adjust: 100%; /* 2 */\n}\n\n/* Sections\n ========================================================================== */\n\n/**\n * Remove the margin in all browsers.\n */\n\nbody {\n margin: 0;\n}\n\n/**\n * Render the `main` element consistently in IE.\n */\n\nmain {\n display: block;\n}\n\n/**\n * Correct the font size and margin on `h1` elements within `section` and\n * `article` contexts in Chrome, Firefox, and Safari.\n */\n\nh1 {\n font-size: 2em;\n margin: 0.67em 0;\n}\n\n/* Grouping content\n ========================================================================== */\n\n/**\n * 1. Add the correct box sizing in Firefox.\n * 2. Show the overflow in Edge and IE.\n */\n\nhr {\n box-sizing: content-box; /* 1 */\n height: 0; /* 1 */\n overflow: visible; /* 2 */\n}\n\n/**\n * 1. Correct the inheritance and scaling of font size in all browsers.\n * 2. Correct the odd `em` font sizing in all browsers.\n */\n\npre {\n font-family: monospace, monospace; /* 1 */\n font-size: 1em; /* 2 */\n}\n\n/* Text-level semantics\n ========================================================================== */\n\n/**\n * Remove the gray background on active links in IE 10.\n */\n\na {\n background-color: transparent;\n}\n\n/**\n * 1. Remove the bottom border in Chrome 57-\n * 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n */\n\nabbr[title] {\n border-bottom: none; /* 1 */\n text-decoration: underline; /* 2 */\n text-decoration: underline dotted; /* 2 */\n}\n\n/**\n * Add the correct font weight in Chrome, Edge, and Safari.\n */\n\nb,\nstrong {\n font-weight: bolder;\n}\n\n/**\n * 1. Correct the inheritance and scaling of font size in all browsers.\n * 2. Correct the odd `em` font sizing in all browsers.\n */\n\ncode,\nkbd,\nsamp {\n font-family: monospace, monospace; /* 1 */\n font-size: 1em; /* 2 */\n}\n\n/**\n * Add the correct font size in all browsers.\n */\n\nsmall {\n font-size: 80%;\n}\n\n/**\n * Prevent `sub` and `sup` elements from affecting the line height in\n * all browsers.\n */\n\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\n\nsub {\n bottom: -0.25em;\n}\n\nsup {\n top: -0.5em;\n}\n\n/* Embedded content\n ========================================================================== */\n\n/**\n * Remove the border on images inside links in IE 10.\n */\n\nimg {\n border-style: none;\n}\n\n/* Forms\n ========================================================================== */\n\n/**\n * 1. Change the font styles in all browsers.\n * 2. Remove the margin in Firefox and Safari.\n */\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n font-family: inherit; /* 1 */\n font-size: 100%; /* 1 */\n line-height: 1.15; /* 1 */\n margin: 0; /* 2 */\n}\n\n/**\n * Show the overflow in IE.\n * 1. Show the overflow in Edge.\n */\n\nbutton,\ninput { /* 1 */\n overflow: visible;\n}\n\n/**\n * Remove the inheritance of text transform in Edge, Firefox, and IE.\n * 1. Remove the inheritance of text transform in Firefox.\n */\n\nbutton,\nselect { /* 1 */\n text-transform: none;\n}\n\n/**\n * Correct the inability to style clickable types in iOS and Safari.\n */\n\nbutton,\n[type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\n\n/**\n * Remove the inner border and padding in Firefox.\n */\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n border-style: none;\n padding: 0;\n}\n\n/**\n * Restore the focus styles unset by the previous rule.\n */\n\nbutton:-moz-focusring,\n[type=\"button\"]:-moz-focusring,\n[type=\"reset\"]:-moz-focusring,\n[type=\"submit\"]:-moz-focusring {\n outline: 1px dotted ButtonText;\n}\n\n/**\n * Correct the padding in Firefox.\n */\n\nfieldset {\n padding: 0.35em 0.75em 0.625em;\n}\n\n/**\n * 1. Correct the text wrapping in Edge and IE.\n * 2. Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n"
},
"$:/themes/tiddlywiki/vanilla/settings/fontfamily": {
"title": "$:/themes/tiddlywiki/vanilla/settings/fontfamily",
"text": "-apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\""
},
"$:/themes/tiddlywiki/vanilla/settings/codefontfamily": {
"title": "$:/themes/tiddlywiki/vanilla/settings/codefontfamily",
"text": "\"SFMono-Regular\",Consolas,\"Liberation Mono\",Menlo,Courier,monospace"
},
"$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment": {
"title": "$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment",
"text": "fixed"
},
"$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize": {
"title": "$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize",
"text": "auto"
},
"$:/themes/tiddlywiki/vanilla/sticky": {
"title": "$:/themes/tiddlywiki/vanilla/sticky",
"text": "<$reveal state=\"$:/themes/tiddlywiki/vanilla/options/stickytitles\" type=\"match\" text=\"yes\">\n``\n.tc-tiddler-title {\n\tposition: -webkit-sticky;\n\tposition: -moz-sticky;\n\tposition: -o-sticky;\n\tposition: -ms-sticky;\n\tposition: sticky;\n\ttop: 0px;\n\tbackground: ``<<colour tiddler-background>>``;\n\tz-index: 500;\n}\n\n``\n<$list filter=\"[range[100]]\">\n`.tc-story-river .tc-tiddler-frame:nth-child(100n+`<$text text=<<currentTiddler>>/>`) {\nz-index: `<$text text={{{ [[200]subtract<currentTiddler>] }}}/>`;\n}\n`\n</$list>\n</$reveal>\n"
}
}
}
yanjiamao.tiddlyspot.com/
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
Thm 2.4.8 a special case of the [[Rao-Blackwell Theorem]]
[[link|https://www.4clojure.com/problems]]
I am using the offline-4clojure solution provided by thattommyhall. To run problem #30 by
```
lein run -m offline-4clojure.p30
```
! Progress
[img[4clojure.png]]
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
Given an improper prior $\pi$, a sample $(x_1, \dots, x_n)$ is a training sample if the corresponding posterior $\pi(\cdot\mid x_1, \dots, x_n)$ is proper and is a minimal ''training sample'' if no subsample is a training sample.
The idea is then to use a minimal training sample. $x_{(l)}$ say, to "properize" the improper prior $\pi$ into $\pi(\cdot\mid x_{(l)})$, and then to use the posterior distribution as if it were a regular proper prior for the remainder of the sample, $x_{(-l)}$ say, in order to avoid the data twice, as in the proposal of Aitkin (1991). When facing a hypothesis $H_0$ with a prior distribution $\pi_0$, with a broader alternative $H_1$ with a prior distribution $\pi_1$, if the minimal training sample under $H_1$ is such that $\pi_0(\cdot\mid x_{(l)})$ is also proer, the pseudo-Bayes factor
$$
B_{10}^{(l)} = \frac{\int_{\Theta_1}f_1(x\mid\theta_1)\pi_1(\theta_1)d\theta_1}{\int_{\Theta_0}f_0(x\mid\theta_0)\pi_0(\theta_0)d\theta_0}
$$
is then independent from the normalizaing constants used in both $\pi_0$ and $\pi_1$.
There are enough difficulties to make us question their use in testing and model choice problems:
# Most pseudo-Bayes factors do not, even though the fractional Bayes factor satisfies $B_{01}^F = 1/B_{10}$.
# When the pseudo-Bayes factors can be expressed as true Bayes factors, the corresponding intrinsic priors are not necessarily appealing and these priors do depend on the choice of the improper reference priors $\pi_0$ and $\pi_1$, hence are hardly intrinsic.
# The pseudo-Bayes factors may also exhibit a bias in favor of one of the hypothesis, in the sence that they can be expressed as a true Bayes factor multiplied by a certain factor. in such cases, it can be seen as modifying the probability of both hypothesis from the reference value $1/2$, a feature we will also encounter for [[least favorables bounds in Section 2.5.2|2.5.2 The absolute error loss]].
# Most often, however, the pseudo-Bayes factors do not correspond to any true Bayes factor, and they may give strongly biased solutions.
# Pseudo-Bayes factors may simply not exist for a whole class of models.
# There are many ways of defining pseudo-Bayes factors and, while most are arguably logical, there is no coherent way of ordering them. Pseudo-Bayes factors, as defined here, do agree with the Likelihood Principle, but the multiplication of possible answers, even if those are close, is not a good signal to users.
# The issue of computing pseudo-Bayes factors.
'' Definition 5.3.1'' The power of a testing procedure $\varphi$ is the probability of rejecting $H_0$ under the alternative hypothesis, that is, $\beta(\theta) = 1-\mathbb E_\theta[\varphi(x)]$ when $\theta\in\Theta_1$. The quantity $1-\beta(\theta)$ is called ''type-two error'', while the ''type-one error'' is $E_\theta[\varphi(x)]$ when $\theta\in\Theta_0$.
'' Definition 5.3.2'' If $\alpha\in[0,1]$ and $\mathcal C_\alpha$ is the class of the procedures $\varphi$ satisfiying the following constraint on the type I error:
$$
\sup_{\theta\in\Theta_0}\mathbb E_\theta[L(\theta, \varphi(x))]=\sup_{\theta\in\Theta_0}P_\theta(\varphi(x)=0)\le\alpha,
$$
a test procedure $\varphi$ is said to be uniformly most powerful at level $\alpha$ (''UMP'') if it minimizes the risk $\mathbb E_\theta[L(\theta, \varphi(x))]$ uniformly on $\Theta_1$ in $\mathcal C_\alpha$.
This optimality is much weaker than the notion of admissibility developed in [[Section 2.4|2.4 Two optimalities: minimaxity and admissibility]].
''Proposition 5.3.3'' Consider $f(x|\theta)$ with a monotone likelihood ratio in $T(x)$. For $H_0:\theta\le\theta_0$ and $H_1:\theta>\theta_0$ there exists a UMP test such that
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \mbox{if } T(x) < c,\\
\gamma & \mbox{if } T(x) = c,\\
0 & \mbox{otherwise}.
\end{array}\right.
$$
<<<
A major difficulty with the Neyman-Pearson approach, namely, that arbitrary significance levels are not necessarily attainable unless one calls for randomization. For discrete cases, $\varphi(x)=\gamma$ means that $\varphi(x) = 1$ with probability $\gamma$. Such procedures are incompatible with the [[Likelihood Principle]].
<<<
''Proposition 5.3.5'' Consider an exponential family
$$
f(x|\theta) = e^{\theta T(x)-\psi(\theta)}h(x)
$$
and $H_0:\theta\le\theta_1$ or $\theta\ge\theta_2$, $H_1:\theta_1<\theta<\theta_2$. There exists a UMP test such that
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
0 & \mbox{if } c_1 < T(x) < c_2,\\
\gamma_i & \mbox{if } T(x) = c_i \qquad (i=1,2),\\
1 & \mbox{otherwise}.
\end{array}\right.
$$
with $(i = 1, 2)$
$$
\mathbb E_{theta_i}[\varphi(x)]=\alpha.
$$
<<<
There is no corresponding UMP test for the opposite case, i.e., $H_0:\theta_1<\theta<\theta_2$
<<<
The Neyman-Pearson solution is to propose an additional reduction of the class of test procedures by considering unbiased tests, i.e., those also satisfying
$$
\sup_{\Theta_0}P_\theta(\varphi(x) = 0)\le\inf_{\Theta_1}P_\theta(\varphi(x) = 0).
$$
In other words, $\varphi$ must also satisfy
$$
\inf_{\Theta_0}\mathbb E_\theta[\varphi(x)]\ge\sup_{\Theta_1}\mathbb E_\theta[\varphi(x)].
$$
The notion of ''uniformly most powerful unbiased'' tests (UMPU) then follows.
Consider $H_0:\theta\in\Theta_0$ to be tested against a point alternative $H_1:\theta\in\Theta_1$ with $\pi$ a prior distribution on $\Theta_0$. From a Bayesian point of view, the test problem can be represented as the test of $H_\pi: x\sim m_\pi$ versus $H_1:x\sim f(x|\theta_1)$, where $m$ is the marginal distribution under $H_0$
$$
m_\pi(x)=\int_{\Theta_0}f(x|\theta)\pi(\theta)d\theta.
$$
Since both hypothesis ($H-\pi$ and $H_1$) are point hypotheses, the [[Neyman-Pearson lemma]] ensures the existence of a UMP test $\varphi_\pi$, at significance level $\alpha$, with power $\beta_\pi = P_{\theta_1}(\varphi_\pi(x) = 0)$. This test is of the form
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \mbox{if } m_\pi(x)>kf(x|\theta_1),\\
0 & \mbox{otherwise}.
\end{array}\right.
$$
'' Definition 5.3.7'' A least favorable distribution is any prior distribution $\pi$ which maximizes the power $\beta_\pi$.
''Theorem 5.3.8'' If the UMP test $\varphi_\pi$ at level $\alpha$ for $H_\pi$ versus $H_1$ satisfies
$$
\underset{\theta\in\Theta_0}{\sup}\mathbb E_\theta[L(\theta,\varphi_\pi)]\le\alpha,
$$
then
# $\varphi_\pi$ is UMP at level $\alpha$;
# if $\varphi_\pi$ is the unique $\alpha$-level test of $H_\pi$ versus $H_1$, $\varphi_\pi$ is the unique UMP test at level $\alpha$ to test $H_0$ versus $H_1$; and
# $\pi$ is a least favorable distribution.
<<<
The constraint in the above theorem may seem unnecessary, but notice that $\varphi_\pi$ is defined by
$$
\int_{\{m_\pi(x)>kf(x|\theta_1)\}}m_\pi(x)dx=\alpha.
$$
<<<
Given a loss function $L$ and a prior distribution $\pi$, the Bayes estimate associated with an observation $x$ is the (usually unique) decision $d$ minimizing the posterior loss
$$
L(\pi, d|x) = \int_\Theta L(\theta, d)\pi(\theta|x)d\theta.
$$
Its minimization can be hindered by two difficulties in practice:
# the explicit computation of the posterior distribution, $\pi(\theta|x)$, may be impossible; and
# even if $\pi(\theta|x)$ is known, this does not necessarily imply that minimizing it is an easy task; indeed, when analytic integration is impossible, numerical minimization sometimes calls for a formidable amount of computing time, especially when $\Theta$ and $\mathcal D$ have large dimensions.
! Definition
Define sets $\Theta$, $\mathcal{X}$ and $\mathcal{A}$, where $\Theta$ are the states of nature, $\mathcal{X}$ the possible observations, and $\mathcal{A}$ the actions that may be taken. An observation $x \in \mathcal{X}$ is distributed as $F(x\mid\theta)$ and therefore provides evidence about the state of nature $\theta\in\Theta$. A decision rule is a function $\delta:{\mathcal{X}}\rightarrow {\mathcal{A}}$, where upon observing $x\in \mathcal{X}$, we choose to take action $\delta(x)\in \mathcal{A}$.
Also define a loss function $L: \Theta \times \mathcal{A} \rightarrow \mathbb{R}$, which specifies the loss we would incur by taking action $a \in \mathcal{A}$ when the true state of nature is $\theta \in \Theta$. Usually we will take this action after observing data $x \in \mathcal{X}$, so that the loss will be $L(\theta,\delta(x))$. (It is possible though unconventional to recast the following definitions in terms of a utility function, which is the negative of the loss.)
Define the risk function as the expectation
$$
R(\theta,\delta)=\operatorname{E}_{F(x\mid\theta)}[{L(\theta,\delta(x))]}.\,\!
$$
Whether a decision rule $\delta$, has low risk depends on the true state of nature $\theta$. A decision rule $\delta^*$ dominates a decision rule $\delta$ if and only if $R(\theta,\delta^*)\le R(\theta,\delta)$ for all $\theta$, and the inequality is strict for some $\theta$.
A decision rule is ''admissible'' (with respect to the loss function) if and only if no other rule dominates it; otherwise it is inadmissible. Thus an admissible decision rule is a maximal element with respect to the above partial order. An inadmissible rule is not preferred (except for reasons of simplicity or computational efficiency), since by definition there is some other rule that will achieve equal or lower risk for all $\theta$. But just because a rule $\delta$ is admissible does not mean it is a good rule to use. Being admissible means there is no other single rule that is always better - but other admissible rules might achieve lower risk for most $\theta$ that occur in practice. (The Bayes risk discussed below is a way of explicitly considering which $\theta$ occur in practice.)
Consider training $$L$$ hidden layers, given $$n$$ training samples that are non-degenerate <<ref "non-degenerate: their pairwise distance is at least $$\delta$$, norm 1 and $$\|x_i-x_j\|_2 \ge \delta$$">>. Suppose the network is overparametrized, <<ref "overparametrized: the number of neurons is polynomial in $$n$$, $$L$$ and $$\delta^{-1}$$, $$m \ge poly(n, L, \delta^{-1})$$">> Then, SGD finds training global minima in
$$
T=\frac{poly(n, L)}{\delta^2}\cdot\log\frac{1}{\epsilon}
$$
iterations for $$\ell_2$$-regression.
Two key messages:
* The theorem is obtained by training w.r.t. hidden layers, where previous work essentially trains only the last layer, which is a convex problem
* Polynomial dependency on $$L$$, in contrast, previous work needs exponential time in $$L$$ [Du et al. ICML19], [Daniely, NIPS17]
** Intrinsically the polynomial bound is possible because @@color:#859900;ReLU prevents exponential gradient explosion/vanishing, in a provable sense@@
** Getting $$e^{O(L)}$$ is almost trivial: each hidden weight matrix $$W_\ell$$ has spectral norm 2, so overall $$2^L$$. The hard part is proving $$poly(L)$$
* For a sufficient large neighborhood of the random initialization, the training objective is //''almost convex''//
<<<
''Main Lemma'' If loss is large, then gradient is large:<br>
$$
\|\nabla F(\overrightarrow{W})\|_F^2\ge F(\overrightarrow{W})\cdot(\delta/n^2)
$$
<<<
<<<
''Main Lemma'' Objective is semi-smooth:<br>
$$
F(\overrightarrow{W} | \overrightarrow{W'})=F(\overrightarrow{W})+\langle\nabla F(\overrightarrow{W}), \overrightarrow{W'}\rangle\pm poly(n, L)\cdot\|\overrightarrow{W'}\|_F
$$
<<<
The objective can be sufficiently decreased if move in the gradient direction. Those two lemmas together implies SGD finds global minima in polynormial time.
The authors prove that if $$m\ge poly(n, L)$$, for a sufficiently large neighborhood fo the random initialization, neural networks behave like Neural Tangent Kernel (NTK):
* Gradient behaves like NTK: $$\nabla F(\overrightarrow{W}) = \left(1\pm\frac{1}{\sqrt{m}}\right)\cdot$$ feature space of NTK
* The objective behaves like NTK: $$F(\overrightarrow{W}^*) = F^{NTK}(\overrightarrow{W}^*)\pm\frac{1}{m^{1/6}}$$
! Intro
A CNN architecture with multiple convolution layers, positing latent, dense and low-dimensional word vectors (initialized to random values) as inputs.
One must represent a sentence in terms of features that depend on the words and short //n//-grams that are frequently observed.
@@color:#859900;
In our case, new (and irregular) words are often to appear so perhaps char is a good entity.
@@
The net consists of 1-dim conv and dynamic //k//-max pooling, which pools //k// values (k is dynamically determined). There are multiple feature maps.
components:
* heap $\sigma$ maps address to value
* promise state map $\psi$ maps address to promise value
* a map $f$ that maps address to a list of fulfil reactions
* a map $r$ that maps address to a list of reject reactions
* a queue $\pi$ that holds scheduled reactions
The promise graph captures control and dataflow in a promise-based program to represent the flow of values through promises, the execution of fulfil and reject reactions, and the dependencies between reactions and promises.
The nodes:
* value
* promise
* function
The edges:
* settlement (v to p)
* registration (p to f)
* link (p to p)
* return (f to v)
[[link|http://mlg.eng.cam.ac.uk/zoubin/papers/lds.pdf]]
* Hinton et al. (1995) note that factor analysis and PCA are closely related
* Digalakis et al. (1993) relate the forware-backward algorithm for HMMs to Kalman filtering
From this paper:
* factor analysis and mixtures of Gaussians can be implemented using autoencoder neural networks
* ICA can be seen as a nonlinear version of factor analysis
Papers:
* [[ACDM [Nesterov 2012]|https://pdfs.semanticscholar.org/0059/cfac9c5b7811866f0729d0917b7478148fc5.pdf]]
* [[APCG [Lin-Lu-Xiao 2014]|https://papers.nips.cc/paper/5356-an-accelerated-proximal-coordinate-gradient-method.pdf]]
APCG does momentum acceleration on top of [[SDCA|Stochastic Dual Coordinate Ascent]].
!! Full Gradient Setting
If $$f$$ is convex and $$L$$-smooth:
* GD in rate $$\varepsilon\propto L/T$$
* Accelerated (with momentum) GD in rate $$\varepsilon\propto L/T^2$$
The mathematics behind accelerated method are:
* potential function proof [Nesterov 2005]
* differen equation proof [Su-Boyd-Candes 2014][Wibisono-Wilson-Jordan 2016]
* geometric proof (ellipsoid method) for a special case [Bubeck-Lee-Singh 2014]
* linear coupling proof [AllenZhu-Orecchia 2014]
** momentum is a linear combination of gradient descent and mirror descent
!! General theory of APCG
Given $$g(y)$$ to be $$L$$ coordinate-smooth and $$\sigma$$-SC:
* [[SDCA|Stochastic Dual Coordinate Ascent]] convergenes in $$T=O(\frac{nL}{\sigma}\log\frac1\epsilon)$$
* APCG in $$T=O(\frac{n\sqrt L}{\sqrt\sigma}\log\frac1\epsilon)$$
Plug it in to the primal form of the master problem:
{{primal_dual_of_master_problem}}
if $$\psi(\cdot)$$ is $$\sigma$$-SC, $$\psi^*(\cdot)$$ is $$\frac1\sigma$$-smooth; if $$f_i(\cdot)$$ is $$L$$-smooth, $$f^*_i(\cdot)$$ is $$\frac1L$$-SC
$$
g(y) = \psi^*(-\frac1n A^Ty) + \frac1n\sum_{i=1}^nf^*_i(y_i)
$$
$$g(\cdot)$$ is $$\frac{1}{\sigma n^2}+\frac{1}{nL}$$ coordinate-smooth and $$\frac{1}{nL}$$-SC
Plugin to the convergence rate:
$$
T=O\left(\frac{n\times \sqrt{ \left(\frac{1}{\sigma n^2}+\frac{1}{nL}\right)} }{\sqrt{\frac{1}{nL}}}\log\frac1\varepsilon\right) = O\left(\left(n+\frac{\sqrt{nL}} {\sqrt\sigma}\right)\log\frac1\varepsilon\right)
$$
! Model Selection Approaches
[[Contrasting AIC and BIC]]
! Outlier and Novelty
[[Outlier Detection]]
! KL-Based
[[KL-Based Changepoint]]
! Google
* [[A Polynomial-Time Dynamic Programming Algorithm for Phrase-Based Decoding with a Fixed Distortion Limit|https://www.transacl.org/ojs/index.php/tacl/article/view/1020]]
* [[Cross-Sentence N-ary Relation Extraction with Graph LSTMs|https://www.transacl.org/ojs/index.php/tacl/article/view/1028]]
* [[Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision|https://arxiv.org/abs/1611.00020]]
* [[Coarse-to-Fine Question Answering for Long Documents|https://homes.cs.washington.edu/~eunsol/papers/acl17eunsol.pdf]]
* [[Automatic Compositor Attribution in the First Folio of Shakespeare|https://arxiv.org/abs/1704.07875]]
* [[A Nested Attention Neural Hybrid Model for Grammatical Error Correction|https://arxiv.org/abs/1707.02026]]
* [[Get To The Point: Summarization with Pointer-Generator Networks|https://arxiv.org/abs/1704.04368]]
* [[Identifying 1950s American Jazz Composers: Fine-Grained IsA Extraction via Modifier Composition|https://www.seas.upenn.edu/~epavlick/papers/finegrained-isa.pdf]]
* [[Learning to Skim Text|https://arxiv.org/abs/1704.06877]]
! Facebook
* [[Automatically Generating Rhythmic Verse with Neural Networks|https://research.fb.com/publications/automatically-generating-rhythmic-verse-with-neural-networks/]]
* [[Enriching Word Vectors with Subword Information|https://research.fb.com/publications/enriching-word-vectors-with-subword-information-2]]
* Reading Wikipedia to Answer Open-Domain Questions
Don Knuth opened for this panel!!! Computer science is similar to math in that we can create our own problems. Knuth don't believe in letting an algorithm to compose music. He thinks computers should help people to reduce the search space. He does believe in automatic theorem proving. He mentioned literate programming many times. I think we should refer to it when considering program induction.
Jokes
# I don't know what a //deep theorem// is, but I think it's similar to what found by deep learning.
# By the time I won Turing award, the prize is 1M$ less than today. But I do got a silver plate that my wife and I used to use it to serve strawberries all the time. Strawberries actually taste much better.
Barbara Liskov introduces the impact of Turing Recipients work. John McCarthy's work on non-monotonic reasoning is mentioned. Maybe related to reasoning the correctness of code.
* Visualization for action recognition
* Self-attention for feature bank
** @@color:#859900;Non-local for action@@
* SlowFast
** visial pathways
** two stream video sequences with conv3d
Active learning (AL) aims to ease the data collection process by automatically deciding which instances an annotator should label to train an algorithm as quickly and effectively as possible.
* Uncertainty sampling
* Bandit algorithms (for data selection?)
! Bibs
* [[Learning Active Learning from Data|http://papers.nips.cc/paper/7010-learning-active-learning-from-data]]: [[code|https://github.com/ksenia-konyushkova/LAL]]
** with [[Meta-Learning]]
** medical imaging examples
! Questions
* applications for vision and language?
! Improving the policy estimation
$$
\nabla_\theta J(\theta)\approx \frac{1}{N}\sum_{i=1}^N\sum_{t=1}^T\nabla_\theta\log\pi_\theta(\mathbf a_t|\mathbf s_t)(Q(\mathbf s_{i, t}, \mathbf a_{i, t})-V(\mathbf s_{i, t}))
$$
$$
\nabla_\theta J(\theta)\approx \frac{1}{N}\sum_{i=1}^N\sum_{t=1}^T\nabla_\theta\log\pi_\theta(\mathbf a_t|\mathbf s_t)(Q(\mathbf s_t, \mathbf a_t)-V(\mathbf s_t))
$$
$$
V(\mathbf s_t) = E_{\mathbf a_t\sim\pi_\theta(\mathbf a_t|\mathbf s_t)}[Q(\mathbf s_t, \mathbf a_t)]
$$
* $$Q^\pi(\mathbf s_t, \mathbf a_t)$$: total reward from taking $$\mathbf a_t$$ in $$\mathbf s_t$$
* $$V^\pi(\mathbf s_t)$$: total reward from taking $$\mathbf s_t$$
* $$A^\pi(\mathbf s_t, \mathbf a_t) = Q^\pi(\mathbf s_t, \mathbf a_t) - V^\pi(\mathbf s_t)$$: how much better $$\mathbf a_t$$ is
Only fit $$V^\pi(\mathbf s_t)$$:
* $$Q^\pi(\mathbf s_t, \mathbf a_t)\approx r(\mathbf s_t, \mathbf a_t) + V^\pi(\mathbf s_{t+1})$$
* $$A^\pi(\mathbf s_t, \mathbf a_t)\approx r(\mathbf s_t, \mathbf a_t) + V^\pi(\mathbf s_{t+1}) - V^\pi(\mathbf s_t)$$
Fit V to what?
$$y_{i,t} \approx \sum_{t'=t}^{T}r(\mathbf s_t, \mathbf a_t)$$
$$y_{i,t} \approx r(\mathbf s_t, \mathbf a_t) + \hat V_\phi^\pi(\mathbf s_{t+1})$$
! Actor-Critic update
This is derived from Policy Gradient Theorem. We want to optimize a expected discounted reward
$$
\eta(\pi_\theta) = \mathbb E_{\pi_\theta}\left[\sum_{t=1}^\infty\gamma^{t-1}r_t|s_0\right]
$$
It has 2 networks (or one net with two heads):
* Actor: predict action from state
* Critic: evaluate state (can also evaluate state and action)
The update iteration is:
# take action $$a\sim\pi_\theta(a|s)$$, get $$(s, a, s', r)$$
# update $$\hat V_\phi^\pi$$ using target $$r+\gamma\hat V_\phi^\pi(s')$$
# evaluate $$\hat A^\pi(s, a)=r(s, a)+\gamma V_\phi^\pi(s')-\hat V_\phi^\pi(s)$$
# $$\nabla_\theta J(\theta)\approx\nabla_\theta\log\pi_\theta(a|s)\hat A^\pi(s, a)$$
# $$\theta\leftarrow\theta+\alpha\nabla_\theta J(\theta)$$
Value and policy update work best with a batch. We can design better estimators of advantage: [[Generalized advantage estimation|https://arxiv.org/abs/1506.02438]]
! Bibs
* Classic papers
** Policy gradient methods for reinforcement learning with function approximation: actor-critic algorithms with value function approximation
* Deep RL
** Asynchronous methods for deep reinforcement learning: A3C -- parallel online actor-critic
** High-dimensional continuous control using generalized advantage estimation: batch-mode actor-critic with blended Monte Carlo and function approximator returns
** Q-Prop: sample-efficient policy-gradient with an off-policy critic: policy gradient with Q-function control variate
The benefits of Adadelta are as follows:
* No manual setting of a learning rate.
* Insensitive to hyperparameters.
* Separate dynamic learning rate per-dimension.
* Minimal computation over gradient descent.
* Robust to large gradients, noise and architecture choice.
* Applicable in both local or distributed environments.
! Talks
* [[MSR Adversarial Machine Learning]]
! Reading list
The talk generalizes following works:
* “Intriguing Properties of Neural Networks” Szegedy et al, 2013
* “Explaining and Harnessing Adversarial Examples” Goodfellow et al 2014
* “Adversarial Perturbations of Deep Neural Networks” Warde-Farley and Goodfellow, 2016
* “Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples” Papernot et al 2016
* “Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples” Papernot et al 2016
* “Adversarial Perturbations Against Deep Neural Networks for Malware Classification” Grosse et al 2016 (not my own work)
* “Distributional Smoothing with Virtual Adversarial Training” Miyato et al 2015 (not my own work)
* “Virtual Adversarial Training for Semi-Supervised Text Classification” Miyato et al 2016
* “Adversarial Examples in the Physical World” Kurakin et al 2016
* [[The Space of Transferable Adversarial Examples|https://arxiv.org/abs/1704.03453]]: some conditions
* [[Universal adversarial perturbations|https://arxiv.org/abs/1610.08401]], CVPR 2017
** One mask to fool them all
** Analyzed model robustness
Neural networks (esp. softmax regressions) are easily fooled. We can add noise-like filters to attack a neural net, misleading it to wrong prediction while making no perceptional difference to human. For example, we can perform gradient ascent on the input to change its prediction.
! Explaining the Weakness
First guess is the model is overfitted. Rather than randomly misclassification, there is a systematic behavior.
This can be explained better, if we imagine the decision surface being far too linear, rather than too complex. Actually, modern deep nets are indeed very piecewise linear. By moving the input along the adversarial direction, images always tend to be mistaken as another fixed class, defending this linear assumption.
Deep neural nets cannot make intentional decisions like human, they learn in a wrong way. They don't learn the contour of the input space, but just some general directions. Adversarial examples unveil its limitations.
! Fast Gradient Sign Method
Maximize
$$
J(x, \theta)+(\tilde x-x)^\top\nabla_xJ(x)
$$
subject to
$$
\|\tilde x-x\|_\infty \le \epsilon
$$
! Library
[[cleverhans|https://github.com/openai/cleverhans]]
We want to avoid log-density computation of $$\log q(z|x, w)$$. From the objective:
$$
\begin{aligned}
&\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta)]-D_{KL}(q(z|x, w)\|p(z))\\
=&\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta) + \log p(z)-\log q(z|x, w)]
\end{aligned}
$$
We introduce a real-valued discriminator function $$T(x, z)$$ such that
$$
T(x, z)\approx-\log p(z)+\log q(z|x, w)
$$
Now we optimize $$T$$ with a similar form of $$D$$ from GAN:
$$
\max_{T\in\mathcal T}\mathbb E_{x\sim p_D}[\mathbb E_{z\sim q(z|x, w)}[\log\sigma(T(x, z))]+\mathbb E_{z\sim p(z)}[\log(1-\sigma(T(x, z)))]]
$$
And the ELBO with
$$
\begin{aligned}
&\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta) + \log p(z)-\log q(z|x, w)]\\
=&\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta) - T^*(x, z)]
\end{aligned}
$$
! Discrete and symbolic representation
* Discrete
** Quantized MemNN
** [[VQ-VAE]]
** [[Gumbel Machinery]]
* Symbolic
** N-Gram Machine: extract information from auto-encoding. neural programming interpreter.
** Differentiable Program Induction
** [[Relation Extraction]]
! Data efficient training
* Hierarchical reinforcement learning?
* Relate active learning and meta learning
** [[Sparse Bayesian Learning with RNNs|Multi-Scale RNN]]
** [[Towards learning to learn distributions|https://arxiv.org/pdf/1710.10304.pdf]]
! Model related
* Embedding
** Char embedding for code completion
* Powerful models
** Fast-slow LSTM
** [[Tensorized LSTMs for Sequence Learning]]
! Taks
* Code completion
** [[Language Model with FS-LSTM]]
** generative model and density estimation
* Program synthesis
** [[NL2Code]]
! Researchers
* Christoph. Treude
* David Lo
! Topics and related fields
* ''Program Synthesis'': natural language to program
** @@color:#859900;Good place to start because there are high quality datasets and excellent examples of previous work.@@
** @@color:#ec5300;Generation may get high evaluation score but cannot run or give wrong answers.@@
** @@color:#ec5300;We have to look for applications of translating from NL (or code) to (not accurate) code.@@
* ''Program Induction'': sample to sample
** @@color:#859900;Powerful models that can simulate modern computer architecture and learn programming paradigms like recursion.@@
** @@color:#ec5300;Now limited to simple tasks.@@
* [[Program Analysis]]: gives proof of correctness in formal language
** we may find related techniques to design metrics for code generation evaluation
* ''Grammar Inference'': traditional program analysis problem of extraction FSA from data
** can be reformulated in the language of machine learning, very close to language modelling
* [[Software Analysis]]
! Algorithms and models
* Domain-Specific Language based models
** Can we learn DSL with rule extraction?
** Learning from LM or NTM unless it can be done end2end. An idea is to use NTM for [[SimplePrograms|Program Synthesis for Character Level Language Modeling]]
* [[Natual language processing|NLP]]
** seq2seq model and many [[Neural Machine Translation]] works can be used directly in program analysis
* [[Sequential Models]]
** advanced architecture e.g. [[Memory Networks]] and [[Neural Abstract Machines]] are strong and effective
* [[Reinforcement Learning]]
** automatic discovery of program context, better training framework and many other applications
* [[Probabilistic Models]]
** training with unlabeled data.
* [[Deep Generative Models]]
** Adversarial training in unsupervised setting
* [[Autoregressive Models]]
** another sequence to sequence translation technique.
* [[Unsupervised Learning]]
** On mining knowledge from forums, etc.
! Software applications
||!formal logic way|!deep learning way|
|Code migration|pattern matching|[[Neural Machine Translation]]|
|Code fixing|compiler theory|[[Language Modelling]]|
|Code generation|DSL-based Models|[[Source Code Generating Models]]|
|Security|program analysis|sequence classification and [[Reinforcement Learning]]|
Other applications I can think of
* testing
* debugging
** bug localization: given a bug report, find the program elements related to the bug.
* documentation
* distributed system analysis
* code optimization
! Related
* [[SE Data]]
* [[Writing of SE Taxonomy]]
* [[AI for SE Challenges]]
[[Model Selection]]
AIC estimates the relative K-L distance of the likelihood function specified by a fitted candidate model, from the unknown true likelihood function that generated the data. The fitted model closest to the truth in the KL sense would not necessarily be the model which best fits the observed sample, since the observed sample can often be fit arbitary well by making the model more and more complex. The best KL model is the model that most accurately describes ''the population distribution and hence future samples from it''.
$$
AIC = -2\log(\mathcal L(\hat\theta|y))+2K.
$$
Let the "true" value of $\theta$ for model $g$ be $\theta_0$. If $g$ is the K-L best model, then the MLE $\hat\theta$ would estimate $\theta_0$.
Akaike showed that the critical issue for getting a applied K-L model selection criterion was to estimate
$$
E_yE_x[\log(g(x|\hat\theta(y)))],
$$
where $x$ and $y$ are independent random samples from the same distribution and both statistical expectations are taken w.r.t. truth ($f$), is the ''model selection target'' of all model selection approaches, based on K-L information.
Akaike (1973) showed that the maximized log-likelihood is biased upward as an estimator of it. He also found that under certain conditions, this bias is approximately equal to $K$, the number of estimatable parameters in the approximating model.
An approximately unbiased estimator of this target for large samples and "good" models is
$$
\log(\mathcal L(\hat\theta|data))-K.
$$
This result is equivalent to
$$
\log(\mathcal L(\hat\theta|data))-K = \text{constant}-\hat E_{\hat\theta}[I(f, \hat g)],
$$
where $\hat g=g(\cdot|\hat\theta)$.
An approximately unbiased estimate of $E_t(l(y))$ would be a constant plus $l-\text{tr}(\hat {\mathbf J}^{-1}\hat{\mathbf K})$. $\hat{\mathbf J}$ is an estimator for the corvatiance matrix of the parameters based on the ''second-derivatives matrix of $l$'' in the parameters and $\hat{\mathbf K}$ is an estimator based on the ''cross-products of first derivatives'' [[Claeskens & Hjort, 2008, pp. 26-7]]. They are asymptotically equal for the true model, so that the trace becomes approximately $p$.
!! Criteria related to AIC
It may be that the crucial AIC approximation is too optimistic and the resulting penalty for model complexity is too weak. AIC_c and AIC3 sometimes perform better. The Deviance Information Criterion has some relationship to AIC. Also, some criteria like Mallows' C_p, and leave-one-out cross-validation are asymptotically equivalent to AIC.
! Power metal
* Orden Ogan: High rating. Plain melody, weak guitar sound, but iconic and quite epic
A class of distributions $F(\Theta)$ is called a ''polynomial family'' if
$$
\forall r, E_{X\in F(\Theta)}[X^r] = M_r(\Theta)
$$
where $M_r(\Theta)$ is a polynomial in $\Theta = (\theta_1, \theta_2, \dots, \theta_k)$.
This definition captures a broad class of distributions such as mixture models whose components are uniform, exponential, Poisson, Guassian or gamma functions.
If the ''moment generating function'' of $X$ defined as $\sum E[X^n]\frac{n!}{t^n}$ converges in a neighborhood of zero, it uniquely determines the probability distribution, i.e.
$$
\forall r, M_r(\Theta) = M_r(\hat\Theta)\Rightarrow F(\Theta)=F(\hat\Theta)
$$
Given a ring $R$, an ideal $I$ generated by $g_1, g_2, \dots, g_n\in R$ denoted by $I=\langle g_1, g_2, \dots, g_n\rangle$ is defined as
$$
I=\left\{\sum_i r_ig_i \mbox{ where } r_i\in R\right\}.
$$
A ''Noetherian ring'' is a ring such that for any sequence of ideals
$$
I_1\subseteq I_2\subseteq I_3\subseteq\dots,
$$
there is $N$ such that $I_N = I_{N+1}=I_{N+2}=\dots$.
''Hilbert's Basis Theorem'' If $R$ is a Noetherian ring, then $R[X]$ is also a Noetherian ring.
We will use this to prove that for any polynomial family, a finite number of moments suffice to uniquely identity any distribution in the family:
If the moment generating function convergences in a neighborhood of zero, there exists $N$ such that
$$
F(\Theta) = F(\hat\Theta)\Leftrightarrow M_r(\Theta) = M_r(\hat\Theta)\forall r\in 1, 2, \dots, N
$$
''Proof:'' Let $Q_r(\Theta, \hat\Theta) = M_r(\Theta)-M_r(\hat\Theta)$. Let $I_1 = \langle Q_1\rangle, I_2 = \langle Q_1, Q_2\rangle, \dots$. This is our ascending chain of ideals in $R[\Theta, \hat\Theta]$. We can invoke Hilbert's basis theorem and conclude that $R[X]$ is a Noetherian ring and hence, there is $N$ such that $I_N = I_{N+1}=\dots$, So far all $N+j$, we have
$$
Q_{N+j}(\Theta, \hat\Theta) = \sum_{i=1}^Np_{ij}(\Theta, \hat\Theta)Q_i(\Theta, \hat\Theta)
$$
for some polynomial $p_{ij}\in R[\Theta, \hat\Theta]$. Thus, if $M_r(\Theta) = M_r(\hat\Theta) for all $r\in 1, 2,\dots, N$.
This is a tutorial prepared for the presentation. Outline based on [[CVPR20 tutorial|http://www.allaboutselfdriving.com/]].
! Hardware
* LiDAR
*
Details:
* dataset of late games change slowly: 5%
* Dirichlet Noise at the root
* reflect/rotating the board
* MCTS works as a policy improvement operator
* When MCTS cannot improve the policy, training is done. MCTS is not needed in run time.
! Computing the policy gradient
Computing the improvement gradient $\pi-p$ requires gradient w.r.t. policy $p_\theta$ and policy-value network parameters $\theta$. We instead minimize
$$
\mathcal L(\theta) = KL(\pi_\theta\|p_\theta)=\pi^T\log p_\theta-c
$$
<<<
AlphaGo Zero uses a quite different approach to deep RL than typical (model-free) algorithms such as policy gradient or Q-learning. By using AlphaGo search we massively improve the policy and self-play outcomes - and then we apply simple, gradient based updates to train the next policy + value network. This appears to be much more stable than incremental, gradient-based policy improvements that can potentially forget previous improvements.
<<< David Silver [[Reddit AMA|http://www.reddit.com/r/MachineLearning/comments/76xjb5/ama_we_are_david_silver_and_julian_schrittwieser/]]
The stableness of self-play is likely a direct result of using tree search. An RL agent may train unstably for two reasons:
* It may forget pertinent information about positions that it no longer visits (change in data distribution)
* It learns to exploit a weak opponent (or a weakness of its own), rather than playing the optimal move.
Tree search helps because:
* AlphaGo Zero uses the tree policy in the first 30 moves to explore positions. In our work we use a NN trained to imitate that tree policy. Because MCTS should explore all plausible moves, an opponent that tries to play outside of the data distribution that the NN is trained on will usually have to play some moves that the MCTS has worked out strong responses to, so as you leave the training distribution, the AI will gain an unassailable lead.
* To overfit to a policy weakness, a player needs to learn to visit a state s where the opponent is weak. However, because MCTS will direct resource to exploring towards s, it can discover improvements to the policy at s during search. MCTS finds these improvements will be found before the neural network is trained to try to play to s. In a method with no look-ahead, the neural network learns to reach s to exploit the weakness immediately. Only later does it realise that V^pi(s) is only large because the policy pi is poor at s, rather than because V*(s) is large.
from [[Thinking Fast and Slow with Deep Learning and Tree Search|https://arxiv.org/abs/1705.08439]]
https://openreview.net/forum?id=rkgpy3C5tX
[[link|http://arxiv.org/abs/1410.3831]]
* [[Continuous approximations to arithmetic functions]]
* [[Inequalities]]
* [[Fisher information metric]]
* [[Fourier Transform]]
* [[Wavelet Transform]]
[[link|https://arxiv.org/pdf/1909.11155v1.pdf]]
$$
\mathcal l_{CE}(p, q) = -[p\log(q) + (1-p)log(1-q)]
$$
$$
\mathcal l_{FL}(p, q) = -[p(1-q)^\gamma\log(q) + (1-p)q^\gamma \log(1-q)]
$$
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* [[Real-world Anomaly Detection in Surveillance Videos|http://arxiv.org/abs/1801.04264v1]]
** Multiple instance learning + ranking loss
Most second order methods require a line-search and cannot be used in the stochastic mode. A carefully tuned SGD is hard to beat on large classification problems. CG offers best combination of speed, reliability and simplicity for smaller accurate real-valued outputs.
* [[paper|http://arxiv.org/abs/1508.03387]]
! Applications
* approximations usign subsets
* mixture model
! Comments
* [[blog|https://xianblog.wordpress.com/tag/approximate-mcmc/]]
A major constraint in the paper is Doeblin’s condition, which implies uniform geometric ergodicity. Not only it is a constraint on the Markov kernel but it is also one for the Markov operator in that it may prove impossible to… prove. The second constraint is that the approximate Markov kernel is close enough to the original, which sounds reasonable. Even though one can always worry that the total variation norm is too weak a norm to mean much. For instance, I presume with some confidence that this does not prevent the approximate Markov kernel from not being ergodic, e.g., not irreducible, not absolutely continuous wrt the target, null recurrent or transient. Actually, the assumption is stronger in that there exists a collection of approximations for all small enough values ε of the total variation distance. (Small enough meaning ε is much smaller than the complement α to 1 of the one step distance between the Markov kernel and the target. With poor kernels, the approximation must thus be very good.) This is less realistic than assuming the availability of one single approximation associated with an existing but undetermined distance ε. (For instance, the three examples of Section 3 in the paper show the existence of approximations achieving a certain distance ε, without providing a constructive determination of such approximations.) Under those assumptions, the average of the sequence of Markov moves according to the approximate kernel converges to the target in total variation (and in expectation for bounded functions). With sharp bounds on those distances. I am still a bit worried at the absence of conditions for the approximation to be ergodic.
<<<
“...for relatively short path lengths, there should exist a range of values for which aMCMC offers better performance in the compminimax sense.”
<<<
The paper also includes computational cost into the picture. Introducing the notion of compminimax error, which is the smallest (total variation) distance among all approximations at a given computational budget. Quite an interesting, innovative, and relevant notion that may however end up being too formal for practical use. And that does not include the time required to construct and calibrate the approximations.
[[Kullback-Leibler divergence]] between two GMMs is not analytically tractable, nor does any efficient computational algorithm exist.
The only method that really can estimate $D(f||g)$ for large values of $d$ with arbitrary accuracy is Monte Carlo simulation. The idea is to draw a sample $x_i$ from the pdf $f$ such that $E_f\log(f(x_i)/g(x_i))=D(f||g)$. The variance of the estimation error is $\frac{1}{n}\text{Var}_f\log(f/g)$.
To compute $D_{\text{MC}}(f||g)$, we need to generate the i.i.d. samples from $f$ by drawing discrete samples from $\pi_a$ and then continuous samples from gaussian component $f_{a_i}(x)$.
Permute the spatial location of feature map $F_s$ to reconstruct a new feature map $F_o(p) = F_s(\text{SF}(p))$.
Multi-layer progressive optimization
decoder
1st part of [[Theory and Algorithms for Forecasting Non-Stationary Time Series]]
! Definition
* Stationarity: distribution invariant to time
* Weak Stationairy:
** 1st moments: $\mathbb E[Z_t]$ is independent of $t$
** 2nd momments: $\mathbb E[Z_tZ_{t-j}] = f(j)$
* Lag operator $\mathfrak LY_t = Y_{t-1}$
! Model
* Autoregression: a linear combination of $p$ history
* Moving average
* ARMA: $Y_t = \sum_{i=1}^pa_iY_{t-i}+\epsilon_t+\sum_{j=1}^qb_j\epsilon_{t-j}$
where $\epsilon$ is the noise term. We can rewrite ARMA in terms of the lag operator:
$$
(1-\sum_{i=1}^pa_i\mathfrak L^i)Y_t = (1+\sum_{j=1}^q)b_j\mathfrak L^j)\epsilon_t
$$
L.H.S. is the characteristic polynomial $P(\mathfrak L)$.
<<<
''Theorem'' [Weak Stationarity of ARMA]<br>
An ARMA(p, q) process is weakly stationary if the roots of the characteristic polynomial $P(z)$ are outside the unit circle.
<<<
ARIMA(p, D, q) model is an ARMA(p, q) model for $(1-\mathfrak L)^DY_t$:
$$
(1-\sum_{i=1}^pa_i\mathfrak L^i)(1-\mathfrak L)^DY_t = (1+\sum_{j=1}^q)b_j\mathfrak L^j)\epsilon_t
$$
Other extensions
* seasonal components (SARIMA)
* side information (ARIMAX)
* long-memory (ARFIMA)
* multi-variate time series models (VAR)
* time-varying coefficients
* other non-linear models
! Estimation
* Maximum likelihood
** Requires further parametric assumptions on the noise distribution
* Method of moments
** Yule-Walker estimator
* Conditional/unconditional least square estimation
** Restricted to certain models.
!! Bounds
<<<
''Definition'' [Invertibility of ARMA]<br>
An ARMA(p, q) process is invertible if the roots of the polynomial
$$
Q(z) = 1+\sum_{j=1}^q b_jz^j
$$
are outside the unit circle.
<<<
If the ARMA model is weakly stationary and invertible, the least square estimate will converge
Submission Password: DvQMF9N8BK
The programming assignment environment uses `python2`. So first activate [[Python virtualenv]]
```
source ~/virtualenvs/python2/bin/activate
```
Then enter `ipython2`, under interactive command:
```
In [1]: import A3Part1
In [2]:
```
Finally, submit with `python submitA3.py`.
!! Linearity
$$
DFT(ax_1[n]+bx_2[n]) = aX_1[k]+bX_2[k]
$$
!! Shift
$$
DFT(x[n-n_0]) = e^{-j2\pi kn_0/N}X[k]
$$
!! Symmetry
|!part|!$x[n]$ real|!$x[n]$ real and even|
|$\mathfrak R\{X[k]\}$ real part|even|even|
|$\mathfrak I\{X[k]\}$ imaginary part|odd|0|
|$\lvert X[k]\rvert$ magnitude|even|even|
|$<X[k]$ phase|odd|0|
!! Convolution
$$
DFT(x_1[n]*x_2[n]) = X_1[k]\times X_2[k]
$$
$$
DFT(x_1[n]\times x_2[n]) = X_1[k]*X_2[k]
$$
!! Energy conservation & decibels
The energy of a signal can be measured in time/frequency domain
$$
\sum_{n=-N/2}^{N/2-1}|x[n]|^2=\frac{1}{N}\sum_{k=-N/2}^{N/2-1}|X[k]|^2
$$
Decibel: $20\log(|X|)$
!! Phase unwrapping
For visualizing the phase spectrum, when reaching $2\pi$, let it grow.
!! Zero padding
Zero-padding a signal is done by adding zeros at the end of the signal.
By adding samples of zeros. The spectrum will be smooth. We will get more interpolated samples in between.
!! Fast Fourier Transform
Coley-Tukey algorithm: breaks down recursively the DFT of a power of 2 size into two pieces of size N/2.
!! FFT and zero-phase windowing
Split the signal and add zero to the center. We avoid getting shifting distortion would occur if we had not centered all the samples.
!! Analysis/synthesis
ADF has been independently discovered by the statistics, machine learning and control communities (see [[Expectation Propagation for Approximate Bayesian Inference]]). ADF can be explained as recursive algorithm repeating the following steps:
* The starting point for the algorithm at time $$t$$ is a "temporal prior" distribution $$q_{t-1}(w)$$ over parameters $$w$$. This $$q_{t-1}(w)$$ incorporates all evidence from datapoints observed in previous timesteps, and it is assumed to approximate the posterior $$p(w|x_{1:t-1})$$ conditioned on all data oberved so far. $$q_{t-1}(w)$$ is assumed to take some simple form, like a Gaussian distribution factorized over the elements of $$w$$.
* Then, some new observations $$x_t$$ are observed. The Bayesian way to learn from these new observations would be to update the posterior: $$p_t(w|xt)\approx p(x_t|w)q_{t-1}(w)$$
* However, this posterior might be complicated, and may not be available in a simple form. Therefore, at this point we approximate $$p_t(w|x_t)$$ by a new simple distribution $$q_t$$, which is in the same family as $$q_{t-1}$$. This step can involve a KL divergence-based approximation, e.g. (Opper, 1998, [[Minka, 2001|Expectation Propagation for Approximate Bayesian Inference]]), or Laplace approximation e.g. ([[Kirkpatrick et al, 2017, Huszár, 2017|Elastic Weight Consolidation]]), or a more involved inner loop such as EP in MatchBox (Stern et al, 2009) or probabilistic backpropagation (Hernandez-Lobato and Adams, 2015) as in this paper.
Fundamental challenges in RL:
* Exploration vs exploitation
* Dealing with delays
Functional approximation in RL with NN. (NeuroDynamicProgramming, Tdgammon). Combine DL and RL.
Atari game challenges:
* sequential decision making
* partial observability
* delayed reward (above handled by RL)
* high dimensional observations
* multiple interacting objects (above handled by DL)
Variety of:
* perspection
* rules
* dynamics
! Related work summary
!! Deep Q Network by DeepMind
CNN + Q-Learning.
The system is not in real-time. Game pauses after each frame, Use Monte-Carlo Tree Search (UCT) for computing approximate Q-values.
!!! UCT-based Planning Agent
Action selection after planning:
$$
\pi^{uct}(s) = \mbox{argmax}_aQ(s, a)
$$
To expand the search tree:
$$
\pi(s_1') = \mbox{argmax}_a Q(s_1', a) + c_{ucb}\sqrt{\log(n(s_1'))/n(s_1', a)}
$$
* $Q(s_1', a)$: estimated value of action a
* $n(s_1')$: number of times state $s_1'$ has been visited
* $n(s_1', a)$: number of times action a was selected under $s_1'$
* $c_{ucb}$: exploitation vs exploration
The complexity can be measured by depth (0.3k) and trajectories (10k).
!! Combining with deep learning
Compress UCT policy/value function into a CNN.Converting CNN output to:
* Q-value
* UCT result.
The problem is training player dist is different from real UCT states. Several CNNs are stacked to reduce the error.
!! DeepMind's Nature
With larger model and more training, best real-time performance right now.
! This work
Can we learn a transition model with such high dimensional observations?
With encoder-decoder. The
[[video|https://www.facebook.com/icml.imls/videos/382464939283864/]]
! 55 years old regression algorithm
$$
y = \sum_{i=1}^m\alpha(x, x_i)y_i
$$
$$
f(x) = \sum_{i=1}^m y_i\frac{k(x_i, x)}{\sum_jk(x_j, x)}
$$
* consistency: given enough data, this converges
* simplicity: no free parameters
! Pooling
Deep Sets
* permutation invariance
* all functions are of the form $$f(X) = \rho(\sum_{x\in X}\phi(x))$$
* Outliers in set: learn function $$f(X)$$ such that $$f(\{x\}\cup X)\ge f(\{x'\}\cup X) + \Delta(x, x')$$
Deep Sets with Attention (Multi-Instance Learning)
* $$f(X) = \rho(\sum_{x\in X}\alpha(w, x)\phi(x))$$
* Attention function e.g. $$\alpha(w, x)\propto \exp(w^T\tanh Vx)$$
Other applications
* Hierarchical attention for sentence classification
* Squeeze Excitation Networks
* Graph Attention Networks
Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions.
To prevent leftward information flow, all values in the input of the softmax are masked out.
Positional attention:
$$
a(Q, K, V) = \text{softmax}(\frac{QK^T}{\sqrt{d_{model}}})\cdot V
$$
! Remarks
* TODO: this attention should work with segmentation. (Probably too much computation, how does this compared to CRF?)
* TODO: attention in stock market?
* Multi-level attention for detection and other large scale memories?
! Bibs
* [[Visual Attention]]
Sequential attention
* NIPS 2015 [[Grammar as a foreign language|https://arxiv.org/abs/1412.7449]]: parsing text
** where it allows the model to glance at words as it generates the parse tree
* 2015 [[A Neural Conversational Model|https://arxiv.org/abs/1506.05869]]
** it lets the model focus on previous parts of the conversation as it generates its response
* [[Using Fast Weights to Attend to the Recent Past]]
* [[Attentive Recurrent Comparators]]
* [[Online and Linear-Time Attention by Enforcing Monotonic Alignments|https://arxiv.org/abs/1704.00784]]: [[blog|http://colinraffel.com/blog/online-and-linear-time-attention-by-enforcing-monotonic-alignments.html]]
Other attentions
* Hierarchical attention
** Learning efficient algorithms with hierarchical attentive memory
* Tensored attention
* Multiple heads
* Pyramidal encoder
** Listen attend and spell
* Hard attention
* [[Pointer Networks]]
! Tutorials
* [[Attention and Augmented Recurrent Neural Networks|https://distill.pub/2016/augmented-rnns/#attentional-interfaces]]
* [[Attention in Deep Learning]]
! Computing
We need a weight for every column: this is an $|\mathbf f|$-length vector $\mathbf a_t$.
* Deterministic soft attention by weighted average: A simplified version of [[Bahdanau et al.|Neural Machine Translation by Jointly Learning to Align and Translate]]'s solution is with previous hidden states $$\mathbf s_{t-1}$$, compute the ''expected input embedding'' $$\mathbf r_t = V\mathbf s_{t-1}$$, then we take product with the source matrix to get the ''attention energy'', $$\mathbf u_t = F^T\mathbf r_t$$ and exponentiate and normalize to 1: $$\mathbf a_t=\text{softmax}(\mathbf u_t)$$. Finally the ''input source vector'' $$\mathbf c_t=F\mathbf a_t$$.
* Stochastic hard attention (Xu et al., 2015) by sampling a column: $$\mathcal L=-\log p(\mathbf w|\mathbf x) = -\log\sum_{\mathbf s}p(\mathbf s|\mathbf x)p(\mathbf w|\mathbf x, \mathbf s)\le-\sum p(\mathbf s|\mathbf x)\log p(\mathbf w|\mathbf x, \mathbf s)$$. With Jensen's inequality, we can minimize a upper bound. This can be sampled with MC:
** $$-\sum_sp(s|x)\log p(w|x, s)\approx-\frac 1 N\sum_{i=1}^Np(s^i|x)\log p(w|x, s)$$
** Sample $N$ sequences of attention decisions from the model
** The gradient is the probability of the gradient of the probability of this sequence scaled by the log probability of generating the target words using that sequence of attention decisions
** This is equivalent to using the [[REINFORCE]] algorithm (Williams, 1992) using the log probability of the observed words as a "reward function".
* Location based attention [[Bahdanau et al., 2014|Neural Machine Translation by Jointly Learning to Align and Translate]] 2k+ references. The inner product in attention energy part is replaced by a MLP: $$\mathbf u_t = \mathbf v^T\tanh(WF+\mathbf r_t)$$.
The method described above is called "early binding", means the attention is computed from previous state. An alternative is we comput hidden states without attention but only apply it at the decoding stage. "late binding" is too late but good for computation. At training time, gradient of attention is independent with the hidden states. Thus easier to parallel.
Attention also clipped the gradient thus help dealing with forgetting problem.
! Comments
Attention only cares about content
* No obvious bias in favor of diagonals, short jumps, ferfility, etc.
* Some work has begun to add other "structural" biases (Luong et al., 2015; Cohn et al., 2016), but there are lots more opportunities. @@color:#859900;Seq2tree works here, too. Putting linguistic features into neural networks@@
In practice, attention still can go wild, partly because of the misleading of BLEU score, especially when source sentence is short.
* Attention pooling for action recognition
* A2-Nets Double Attention Networks
* [[Visual Attention]]
! Sequential Generative Models
refs:
* [[DRAW: A Recurrent Neural Network For Image Generation|https://arxiv.org/abs/1502.04623]]
* [[Attend, Infer, Repeat: Fast Scene Understanding with Generative Models|https://deepmind.com/research/publications/attend-infer-repeat-fast-scene-understanding-generative-models/]]
** understanding 3D scenes
** where is the attention is yet to find
! [[External memory for one-shot generalization]]
[[blog|https://medium.com/@sanyamagarwal/understanding-attentive-recurrent-comparators-ea1b741da5c3]] with an excellent implementation.
! Dynamic representations
* [[Julius Orion Smith III|https://ccrma.stanford.edu/~jos/]]
[[ASP Programming]]
!! Lecture 1
Frequency in radians is $2\pi k/N$.
!! Lecture 2
DFT of complex sinusoid
$$
x_1[n]=\exp(-j2\pi k_0n/N) \qquad n=0, \dots, N-1
$$
$$
X_1[k]=\sum_{n=0}^{N-1}x_1[n]\exp(-j2\pi kn/N)=\frac{1-\exp(-j2\pi(k-k_0))}{1-\exp(-j2\pi(k-k_0)/N)}
$$
$X_1[k]=N$ for $k=k_0$ and $X_1[k]=0$ for $k\neq k_0$
Inverse DFT
$$
x[n]=\frac 1 N\sum_{k=0}^{N-1}X[k]\exp(j2\pi kn/N) \qquad n=0,1,\dots,N-1
$$
Why the normalization factor?
!!! Quizes
# DFT of real sinusoids
#* $\frac{A_0}{2}$ for $k=\pm k_0$ and $0$ for rest.
# IDFT
#* Int values of $s_k[n]$ for $N=4$: $[1,1,1,1],[1,j,-1.-j],[1,-1,1,-1],[1,-j,-1,j]$.
!! Lecture 3
[[ASP Theory 3]]
!!! Quizes
# Symmetry
# phase unwrapping
# zero-phase windowing
# IDFT result: exactly the same.
* [[ENAS for SR]]
* [[Neural Architecture Search]]
* [[Video Streaming Optimization]]
* [[WaveNet]]
* [[Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders]]
* [[Pixel-CNN]]
* [[SampleRNN]]
* Video Pixel Network
* [[Parallel Multiscale Autoregresssive Density Estimation]]
* ByteNet
* [[A Neural Parametric Singing Synthesizer|http://arxiv.org/abs/1704.03809v1]]
** What is the Causual conv input?
** How's the model trained
** How's the time sequence represented
** Hints for OCR context?
* [[VQ-VAE]]
Autoregressive models are trained and evaluated by teacher-forcing, meaning the model receives as input the true values of the variables being conditioned on. During generation, however, these variables are not known, and the model conditions on its own best predictions of their values. This difference between training/evaluation and generation is a known source of trouble: the model only ever learns one-step transitions, and never learns to deal with its own errors. At generation time it can easily run off the tracks.
Compiled awesome 4 on Ubuntu. Use `Win`+`s` for the help
! Stochastic vs batch learning
According to chapter 1 of [[Neural Networks: Tricks of the Trade]], stochastic learning is faster and results better solution because less redundant data is used during the training. While the convergence conditions and other theoretical analyses are easier to conduct on batch learning.
* 1947 Andrey Markov and Emil Post independently prove that the word problem for semigroups is undecidable
* 1956 Noam Chomsky publishes "Three Models for the Descrpition of Language" which describes grammars with production rules and what we now call the [[Chomskian hierarchy of grammars]]
A band-pass filter is a device that passes frequencies within a certain range and rejects (attenuates) frequencies outsides that range.
[[link|http://arxiv.org/abs/1502.03167]]
! Bib
* [[Batch Renormalization]]
* [[Recurrent Batch Normalization]]
! Theory
* [[Reducing Gradient Variance|https://arxiv.org/abs/1805.11604]]
* [[Towards a Theoretical Understanding of Batch Normalization|https://arxiv.org/abs/1805.10694]]
** BN can provably accelerate GD in the settings where no "internal covariate" shift is present
** Decoupling the loss minimization problem into optimizing direction and length of the parameters separately
** Reduces cross-dependencies between layers and thus simplifies curvature structure and GD dynamics
! Problem
; Internal covariate shift
: The distribution of each layer's input change as a function of previou layer parameters.
[img[https://qph.is.quoracdn.net/main-qimg-89eac2a45ba389b24a0c29ad84209b8f?convert_to_webp=true]]
This slows down training. To solve this problem, intermediate layers of deep nets should be constraint by whitening or use batch normalization.
In long column analogy, you have two options for constrains:Fixed (Fig-c) and Pinned (Fig-d). If intermittent supports are fixed with column, support have to carry bending as well as lateral load, which usually are used in concrete column and beam monolithic configuration. If intermittent supports are pinned, such supports resist only lateral loads on compromise of column rotation at that location. Such connections are used in steel structure.
Similarly, we have two options to apply whiten constrained in every hidden layers: Fixed and Pinned. For fixed whiten constrains, we have to apply such whitening in every hidden layer such that before proceed to the next layer, it will be whiten. But, zero mean, unit variance with decorrelation for every dimension for every layer and every gradient update with batch gradient descent and computation of correlation matrices is very storage and computation intensive.
So, alternatively for pinned case similar to rotation flexibility of column section, we can introduce some mean (shift) and variance (scale) for every layer and every dimension, which will be trained as well with weights and bias parameters of the networks through backpropagation. Extra flexibility will increase learning capacity. But, computational complexity remains high.
Batch Normalization solves such problem with some additional assumptions. Followings are the properties of Batch Normalization with mean and variance for a mini batch version:
# Learning faster: Learning rate can be increased compare to non-batch-normalized version.
# Increase Accuracy: Flexibility on mean and variance value for every dimension in every hidden layer provides better learning, hence accuracy of the network.
# Normalization or Whitening of the inputs to each layer: Zero means, unit variances and or not decorrelated.
# To remove the ill-effect of Internal Covariate shift:Transformation makes data to big or to small; change of the input distribution away from normalization due to successive transformation.
# Not-Stuck in the saturation mode: Even if ReLU is not used.
# Integrate Whitening within the gradient descent optimization: Decoupled Whitening between training steps, which modifies network directly, reduces the effort of optimization. So, model blows up when normalization parameters are computed outside the gradient descent step.
# Whitening within gradient descent: Requires inverse square root of covariance matrix as well as derivatives for backpropagation
# Normalization of Individual dimension: Individual dimension of hidden layers are normalized independently rather than joint covariances. So, features are not decorrelated.
# Normalization of mini-batch: Estimation of mean and variance are computed after each mini-batch rather than entire training set. Even ignoring the joint covariance as it will create singular co-variance matrices for such small number of training sample per mini-batch compare to high dimension size of the hidden layer.
# Learning of scale and shift for every dimension: Scaled and shifted values are passed to the next layer, whether mean and variances are calculated after getting all mini-batch activation of current layer. So, forward pass of all the samples within the mini-batch should pass layer wise. Backpropagation is required for getting gradient of weights as well as scaling (variance) and shift (mean).
# Inference: During inference moving averaged mean and variance parameters during mini batch training are considered.
# Convolution Neural Network: Whitening of intermediate layers, before or after the nonlinearity creates a lot of new innovation pathways
! Remarks
Besides the reduction of internal covariate shift, in a Gaussian, linear case, the gradients flow through the network, do not explode or vanish.
BN drawbacks:
* not accurate for small minibatches
* non-i.i.d. minibatches can overfit (batch norm is not accurate?)
''remark'': suppose these can be reduced by `global_stats`
Using moving average may cause the model to blow up, because the bias or scale may increase without restrictions.
Batch Renormalization ensures the activations computed in the forward pass of the training step depend only on a single example and are identical to the activations computed in inference.
BR normalize like this:
$$
\frac{x_i-\mu}{\sigma} = \frac{x_i-\mu_B}{\sigma_B}\cdot r+d, \qquad r = \frac{\sigma_B}{\sigma}, d = \frac{\mu_B-\mu}{\sigma}
$$
Gradient does not propagate through $r$ and $d$, @@color:#859900;this correct for the fact that the minibatch statistics differ from the population ones.@@
! Setup
Let us consider discrete (categorical) HMMs of length $T$ (each observation sequence is $T$ observations long). Let the space of observations be $X = \{1, 2, \dots, N\}$, and let the space of underlying states be $Z = \{1, 2, \dots, M\}$. An HMM $\theta = (\pi, A, B)$ is parameterized by the initial state matrix $\pi$, the state transition matrix $A$, and the emission matrix $B$;
$\pi_i = P(z_1 = i)$, $A_{ij} = P(z_{t+1} = j|z_t = 1)$, and $B_i(j) = P(x_t = j|z_t = i)$. See [[PRML]] for a more detailed treatment of HMMs.
We study the probability of learning the parameterization of $\theta$ from a dataset of $D$ observations. Let $\mathcal X = (X^1, \dots, X^D)$, where each $X^i = (x_1^i, x_2^i, \dots, x_T^i)$. We assume each observation is drawn iid. The learning problem is nontrivial because we are not given the latent variables $Z^i$ for each $X^i$, otherwise we could directly compute $\theta^* = \text{argmax}_\theta P(\mathcal X, \mathcal Z;\theta)$. And there are too many values of $z$ to try.
! Baum-Welch
Baum-Welch can be described as repeating following steps until convergence:
# Compute $Q(\theta, \theta^s)=\sum_{z\in\mathcal Z}\log[P(\mathcal X,z;\theta)]P(z|\mathcal X;\theta^s)$.
# Set $\theta^{s+1} = \underset{\theta}{\text{argmax}}Q(\theta, \theta^s)$.
$$
\underset{\theta}{\text{argmax}}Q(\theta, \theta^s) = \underset{\theta}{\text{argmax}}\sum_{z\in\mathcal Z}\log[P(\mathcal X,z;\theta)]P(z, \mathcal X;\theta^s)
$$
since $P(\mathcal X)$ is not affected by the choice of $\theta$.
$$
P(z,\mathcal X;\theta) = \prod_{d = 1}^D\left(\pi_{z_1^d}B_{z_1^d}(x_1^d)\prod_{t=2}^TA_{z_{t-1}^dz_t^d}B_{z_t^d}(x_t^d)\right)
$$
We can optimize with Lagrange multipliers requiring that $\pi, A_i, B_i(\cdot)$ form valid probability distributions.
$$
\hat L(\theta, \theta^s) = \hat Q(\theta, \theta^s)-\lambda_\pi\left(\sum_{i=1}^M\pi_i-1\right)-\sum_i^M\lambda_{A_i}\left(\sum_{j=1}^MA_{ij}-1\right)-\sum_i^M\lambda_{B_i}\left(\sum_{j=1}^NB_{i}(j)-1\right)
$$
$$
\begin{align*}
\frac{\partial\hat L(\theta, \theta^s)}{\partial\pi_i}&=\\
&=
\end{align*}
$$
When your loss function depends on densities of probability distributions you can easily sample from, often you can construct an auxiliary task, whose Bayes optimal solution depends on values of the densities in question. Examples of auxiliary tasks are: binary classification for likelihood ratio estimation, denoising or score matching for estimating the score function.
! Bib
* [[Maximum likelihood approaches to variance component estimation and to related problems|http://www.lce.esalq.usp.br/arquivos/aulas/2012/LCE5872/Harville1977.pdf]]: The distinction between frequentist and Bayesian models is practically none
!! Textbooks
* [[Bayesian Choice]]
* [[Statistical Rethinking]]
! Applications
* [[Bayesian information criterion]]
! Theory
* [[Inference Methods]]
* [[PAC-Bayesian]]
* [[Holes in Bayesian Statistics|http://www.stat.columbia.edu/~gelman/research/unpublished/bayes_holes_2.pdf]]
! Topics
* [[Scalable Bayesian Inference]]
* [[Bayesian Deep Learning]]
! Algorithms
* [[Expectation Propagation for Approximate Bayesian Inference]]
1.3.5 [[Maximum Likelihood Estimation]]
[[5.2.6 Pseudo-Bayes factors]]
[[5.3.2 Least favorable prior distributions]]
[[6 Bayesian Calculations]]
[[8.2 Admissibility of Bayes estimators]]
! History
First Bayesian NNs are training with Laplace's method. Neal uses HMC. In late 90s, VI is used.
Modern VI relied on a fully factorized approximation, assuming independence of each weight scalar in each layer from all other weights.
Expectation propagation is recently studied, relying on various forms of Renyi's $$\alpha$$-divergence. Seems to sacrifice their estimation of the dominant modes of the posterior.
The posterior of modern neural networks if highly non-convex, and mini-batch approaches have largely been intractable for modern neural networks.
! Challenges
* difficulty of reasoning about choice of the prior $$P(\mathbf W)$$
* intractability of posterior inference
! Introduction
Bayesian deep learning is
<<<
Posterior inference of layered representations of high-dimensional data
<<< Ranganath+, AISTATS 2015
Topics
* Deep exponential families: $$z_{n,l,k}\sim \text{Exp-Fam}(g(w^\top_{l,k}z_{n,l+1}))$$
! Bibs
* Recovering tractable distributions of weights
** ICML 2015 Weight uncertainty in neural networks
* Sampling based
** NIPS 2016 DISCO nets: DISsimilarity COefficient networks
! Algorithms
{{Bayesian Deep Learning Algorithms}}
! Evaluation
{{Bayesian Deep Learning Evaluation}}
! Code
* [[Bayesian Networks|https://github.com/JavierAntoran/Bayesian-Neural-Networks]]
* [[SWAG|https://github.com/wjmaddox/swa_gaussian]]
! Resources
* [[NIPS19 Bayesian Deep Learning Workshop]]
* [[Deep Learning with Bayesian Principles]]
! Tasks
* [[Bayesian Object Detection]]
* [[Bayesian Diagnosis]]
* [[Probabilistic Meta-Learning]]
!! [[Deep Learning with Bayesian Principles]]
Bayesian learning rule: $$\lambda\leftarrow\lambda - \rho\nabla_\mu (\mathbb E_q[\mathcal l(\theta)] - \mathcal H(q))$$
Given a loss, we can recover a variety of learning algorithms by choosing an appropriate $$q$$
* Classical algorithms: Least-squares, gradient descent, Newton's method, Kalman filters, Baum-Welch, Forward-backward, etc.
* Bayesian inference: EM, Laplace's method, SVI, variational message passing
* Deep learning: SGD, RMSprop, Adam
* Reinforcement learning: parameter-space exploration, natural policy-search
* Continual learning: [[Elastic Weight Consolidation]]
* Online learning: Exponential-weight average
* Global optimization: Natural evolutionary strategies, Gaussian homotopy, continuation method & smoothed optimizaiton.
!! Variational Inference
Reparametrization trick for training deep latent variable models. Empirically noted to be difficult to train on larger architectures such as deep residual networks. [[Blier and Ollive|NIPS18 The Description Length of Deep Learning Models]] argue that the difficulty of training is explained by variational methods providing insufficient data compression for DNNs despite being designed for data compression.
* VAE
* flow-based models
* noisy optimization: noisy Adam; noisy KFAC
* [[NIPS11 Practical variational inference for neural networks]]
* [[ICML15 Weight uncertainty in neural networks]]
!! Laplace approximation
assume a Gaussian posterior, $$\mathcal N(\theta^*, \mathcal I(\theta^*)^{-1})$$, where $$\theta^*$$ is MAP estimate and $$ \mathcal I(\theta^*)^{-1}$$ is the inverse of the [[Fisher information matrix|Fisher information metric]] (expected value of the Hessian evaluated at $$\theta^*$$).
* [[Assumed Density Filtering]]
** [[Probabilistic Backpropagation]]
** [[Elastic Weight Consolidation]]: using diagonal Laplace approximations to overcome catastrophic forgetting in deep learning.
** proposed the use of either a diagonal or block Kronecker factord (KFAC) approximation to teh Hessian matrix for Laplace approximation.
* [[A scalable laplace approximation for neural networks]]
!! Sampling based
!!! [[Monte Carlo Methods]]
was at one time a gold standard for inference with neural networks, through the [[Hamilton Monte Carlo]] work of Neal. However, HMC requires full gradients, which is computationally intractable. [[Stochastic Gradient MCMC]] extends MC methods and allows for stochastic gradients to be used in Bayesian inference.
Theoretically, both SGHMC and SGLD aymptotically sample from the posterior in the limit of infinitely small step sizes. In practice, using finite learning rates introduces approximation errors, and tuning stochastic gradient MCMC methods can be quite difficult.
* [[Stochastic Gradient Langevin Dynamics]]
* SGHMC
!!! Dropout Variational Inference
* [[MC-dropout|ICML16 Dropout as a Bayesian Approximation]]
* Concrete dropout: optimize the dropout probability as well.
!!! Stochastic Gradient Descent
* [[Stochastic Gradient Descent as Approximate Bayesian Inference]]
* [[SWAG|A simple baseline for Bayesian uncertainty in deep learning]]
Pros: scales well to large problems; cons: not flexible
!! Posterior density
In SWAG, the posterior density along the directions corresponding to the eigenvectors of the SWAG covariance matrix for PreResNet-164 on CIFAR-100.
Random SVD is used to find these eigenvectors.
Bayes procedures do not necessarily automatically improve the prediction or calibration over a point estimation such as the MAP
* [[Yes, but Did It Work?: Evaluating Variational Inference]]
* [[How good is the Bayes posterior for prediction really?]]
Bayesian methods give RNNs another way to express their uncertainty. Using a prior to integrate out the parameters to average across many models acts as a regularizer.
* [[Dropout as a Bayesian Approximation]]
* weight decay as a variational inference scheme
* [[Stochastic Gradient Langevin Dynamics]] to truncated backpropagation in time directly.
* [[Stochastic Gradient Descent as Approximate Bayesian Inference]]
* Bayes by Backprop
** with centralized Gaussian, KL term can be seen as a form of weight decay, tuned by the std fo the prior and posterior.
* [[Bayesian Recurrent Neural Networks]]
Bayesian information criterion (BIC), also known as the Schwarz criterion is derived from an asymptotic expression of the Bayes factor.
! Applications
* [[KL-Based Changepoint]]
* [[VENAS]]
* [[NAS Weight Sharing]]
* Fit a probabilistic model to the function evaluations $$\langle\lambda, f(\lambda)\rangle$$
* Trade off exploration vs exploitation
Our goal is to find the maximizer: $$x^* = \text{argmax}_{x\in\mathcal X} f(x)$$, by minimizing the difference between the maximum and current best value, namely simple regret and bayes simple regret
$$
SR(n) = f(x^*) - \max f(x_j), BSR = \mathbb E[SR(n)]
$$
! Models
!! GP
Problems for GP approach:
* Complex hyperparameter space
** high-dimentional, low effective dimensionality
** mixed continuous/discrete hyperparameters
** conditional hyperparameters
* Noise: sometimes heteroscedastic, large, non-Gaussian
* Robustness
* Model overhead
High dim GPs:
* [[Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features]]
Random forest can be used as SMAC. Uncertanty is not accurate
[[Tree of Parzen Estimators]]
! Deep Aquisition Network
* Should handle complex structures
* Should be able to yield uncertainty, for exploration. (Can fall back to frequentist uncertainty estimate)
Neural networks
* Bayesian linear regression using the features in the output layer [Snoek et al, ICML 2015]
* Fully Bayesian neural networks, trained with SGHMC [Springenberg et al, NIPS 2016]
!! Ideas
TPE like predictions for neural network?
[[paper|http://arxiv.org/abs/1704.02798]]
remark: MCMC solution is given. What abount VI and Evolution Strategies
Bellman equation equates the value of the current state to the immediate reward plus the value of the next state. It assumes there is no uncertainty in the quantities (or alternatively, that the agent is risk-neutral with respect to uncertainty).
[[Bayesian information criterion]]
!!Model Selection
We can use Bayes' Theorem to find the posterior of each model. We assume equal prior probabilities for each model then the ratio of the odds becomes the Bayes factor $B_{ij} = P(M_i|y)/P(M_j|y)$. [[Schwarz 1978]] showed that in many kinds of models $B_{ij}$ can be roughly approximated by $\text{exp}(-\frac{1}{2}BIC_i+\frac{1}{2}BIC_j)$
The derivation of BIC holds both the model set and the data-generating model fixed as sample size goes to infinity. It is also clear that if the model set contains the true (generating) model, them BIC selection converges with probability 1 to that generating model as $n\rightarrow \infty$ (and the posterior probability of that model goes to 1). [[Cavanaugh and Neath (1999)]] make it clear that the derivation of BIC does not require the true model being in the set.
The difference between AIC and BIC is the $\log(n)$ in BIC, which is needed for idealized asymptotic consistency. BIC assumes equal prior probability for each model. The marginal probability is
$$
\int\left[\prod_{i=1}^ng(x_i|\theta)\right]\pi(\theta)d\theta
$$
Under ''general regularity conditions'', as sample size increases the likelihood function "near" the MLE, $\hat\theta$ can be approximated as
$$
\mathcal L(\theta|x, g) = \mathcal L(\hat\theta|x, g)\exp(-\frac{1}{2}(\theta-\hat\theta)'V(\hat\theta)^{-1}(\theta-\hat\theta)).
$$
where $V(\hat\theta)$ is the estimated $K\times K$ variance-covariance matrix of the MLE.
!!Segmentation
The BIC is probably the most extensively used segmentation and clustering metric due to its simplicity and effectiveness. It is likelihood criterion penalized by the model complexity (number of free parameters in the model) introduced by [[Schwarz (1971)]] and [[Schwarz (1978)]] as a model selection criterion. For a given acoustic segment $X_i$, the BIC value of a model $M_i$ applied to it indicates how well the model fits the data, and is determined by:
$$
BIC(M_i) = \log L(X_i, M_i)-\lambda\frac{1}{2}\#(M_i)\log(N_i)
$$
$\log L(X_i,M_i)$ is the log-likelihood of the data given the considered model. $\lambda$ is a free design parameter dependent on the data being modeled, estimated using development data; $N_i$ is the number of frames in the considered segment and $\#(M_i)$ the number of free parameters to estimate in model $M_i$. Such expression is an approximation of the Bayes Factor (BF) ([[Kass and Raftery (1995)]], [[Chickering and Heckerman (1997)]]) where the acoustic models are trained via ML methods and $N_i$ is considered big.
In order to use BIC to evaluate whether a change point occurs between both segments it evaluates the hypothesis that $X$ better models the data versus the hypothesis that $X_i+X_j$ does instead, like in the GLR ([[Generalized Likelihood Ratio]]), by computing:
$$
\Delta BIC(i,j) = -R(i, j)+\lambda P
$$
where $P$ is the penalty term, which is a function of the number of free parameters in the model. For a full covariance matrix it is
$$
P =\frac{1}{2}(p+\frac{1}{2}p(p+1)\log(N)
$$
The penalty term accounts for the likelihood increase of bigger models versus smaller ones. The term $R(i)$ can be written for the case of models composed on a single Gaussian as:
$$
R(i, j) =\frac{N}{2}\log|\sum_X|-\frac{N_i}{2}\log|\sum_{X_i}|-\frac{N_j}{2}\log|\sum_{x_j}|
$$
For cases where GMM models with multiple Gaussian mixures are used, $\Delta BIC$ is written as
$$
\Delta BIC(M_i) = \log L(X, M)-(\log L(X_i, M_i)+\log L(X_j, M_j))-\lambda\Delta\#(i, j)\log(N)
$$
where $\Delta\#(i, j)$ is the difference between two $BIC(i)$ criteria in the combined model versus the two individual models.
Hao Chen received the master's degree from Zhejiang University, China. He is working toward the PhD degree in the Department of Computer Science and Engineering, The University of Adelaide. His current research interests lie in deep learning: in particular, in its applications aimed to tackle computer vision tasks, such as dense per-pixel image classification, and its theoretical understanding.
[[blog|http://nuit-blanche.blogspot.com.au/2017/11/biologically-inspired-random-projections.html]]
[img width=600 [https://2.bp.blogspot.com/-BzPMWR0J1Eo/Wgq4GUVxxfI/AAAAAAAAU9s/StOTjZTn7PMNEC4H1GoF623HwaGfePfKACLcBGAs/s1600/lshfruitfly.png]]
! Bibs
* [[A neural algorithm for a fundamental computing problem|http://science.sciencemag.org/content/358/6364/793.full]]
* [[Scalable and Sustainable Deep Learning via Randomized Hashing|https://arxiv.org/abs/1602.08194]]
Implementation
* [[SacréBLEU|https://github.com/awslabs/sockeye/tree/master/contrib/sacrebleu]]
```
@inproceedings{chan1999training,
title={Training recurrent network with block-diagonal approximated Levenberg-Marquardt algorithm},
author={Chan, Lai-Wan and Szeto, Chi-Cheong},
booktitle={Neural Networks, 1999. IJCNN'99. International Joint Conference on},
volume={3},
pages={1521--1526},
year={1999},
organization={IEEE}
}
```
* [[Variational Inference for Generative Models]]
* [[CNN for Video Classification]]
* [[Generative Adversarial Network Talk]]
* [[Guaranteed Non-convex Learning Algorithms through Tensor Factorization]]
* [[On Optimization in Deep Learning]]
* [[Scattering Representation]]
* [[KL-Based Changepoint]]
! Planned
* [[Real Time Change-Point Detection]]
* [[Dropout as a Bayesian Approximation]]
* [[Bayesian Choice]]
* [[Pattern Recognition and Machine Learning]]
* [[Model selection and multimodel inference]]
For deep learning:
* [[Deep Learning|Deep Learning (textbook)]]
* [[DNN for ASR]]
* [[Neural Networks: Tricks of the Trade]]
$$
X=\left\{
\begin{array}{ll}
0, & \text{if}\ a=1 \\
1, & \text{otherwise}
\end{array}\right.
$$
[[Default Hotkeys|https://github.com/fuhsjr00/bug.n/blob/master/doc/Default_hotkeys.md]]
|Decrease the gap between windows|`Win`+`Shift`+`Left`|
|Set the previous-ly set layout|`Win`+`Tab`|
|Set floating/monocle/tile layout|`Win`+`f`/`m`/`t`|
|Toggle active window floating|`Win`+`Shift`+`f`|
|Rotate the layout axis|`Win`+`Ctrl`+`t`|
|Open the command GUI|`Win`+`y`|
! Workarounds
!! Multilabel
# As comparison, what would be the closest way to implement this functionality with existing layers? I presume a inner product that has num_output: N*K, re-size into (N,K) and then do a normal softmax that compares this to a (N,1) label.
# And how is this PR better than that.
# See [[this example|http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/pascal-multilabel-with-datalayer.ipynb]] for accuracy test
!! Invalid Device Ordinal
! Variations
* [[MS Caffe|https://github.com/Microsoft/caffe/tree/master]]: for Faster-RCNN and R-FCN
* [[mem caffe|https://github.com/yjxiong/caffe/tree/mem#features]]: MPI
!! Merging the 2
move layer files into /layers
* net.hpp
** ForwardPrefilled: deprecated in ms caffe, replaced with Forward
** input_blob is set before calling Forward
** run memoryoptimize
** add learnable parameters
* syncedmem.hpp
** add use_cuda flag to CaffeMallocHost
** cudaMallocHost improves performance and stability in multi GPU training
* _caffe.cpp
** python_layer moved to layers
* common.cpp
** cluster_seedgen(bool sync)
* base_data_layer.cpp
** StartInternalThread replace CreatePrefetchThread
! Bibs
* [[Dynamic Routing between Capsules|https://research.google.com/pubs/pub46351.html]]
! [[What is wrong with CNN|https://www.youtube.com/watch?v=Mqt8fs6ZbHk]]?
* Too few levels of structure
** Neurons, layers, whole nets666
** For example, we want to have a unit that can detect correlations between input
** Vector nonlinearity like softmax, analyse the covariance structure
* We need to group the neurons in each layer into "capsules" that do a lot of internal computation and then output a compact result
** A capsule is inspired by a mini-column
!! Capsule
Connection binding problem done in sequence.
Each capsule represents the presence and the instantiation parameters of a multi-dimensional entity of the type that the capsule detects. In the visual pathway, a capsule detects a particular type of object or object-part. It outputs 2 things:
* The probability that an object of that type is present
* The generalized pose of the object, includes position, orientation, scale, deformation, velocity, color etc.
Convnet problem
* feature extraction layers are interleaved with subsampling
* relationships between objects not trained
* the geometric view of vision is discarded
Map the percept to the initial hidden state of a deep recurrent neural net and train the RNN to output captions (without retraining the convnet)
! Computation
Routing-by-agreement: Each capsule makes a prediction for the parent capsule, which is then compared with the actual output. If the outputs matches, the coupling coefficient is increased. Let $u_i$ be an output of a capsule $i$, and $j$ be the parent capsule, the prediction is calculated as $u_{i|j} = W_{ij}u_i$. The coupling coefficient $c_{ij}$ is computed using softmax. Loss is high when entity is absent and capsule has high instantiation parameters.
$$
L_k = T_k\max(0, m^+-\|v^k\|)^2+\lambda(1-T_k)\max(0, \|v^k\| - m^-)^2
$$
! Remarks
* Capsule is like a convolutional Gaussian process? Can convolutional kernels capture covariance?
* Attention may not be good for high dimensional data
There are several architectures in the field of Convolutional Networks that have a name. The most common are:
# ''LeNet''. The first successful applications of Convolutional Networks were developed by Yann LeCun in 1990's. Of these, the best known is the LeNet architecture that was used to read zip codes, digits, etc.
# [[AlexNet|Doubts on Caffenet]]. The first work that popularized Convolutional Networks in Computer Vision was the AlexNet, developed by Alex Krizhevsky, Ilya Sutskever and Geoff Hinton. The AlexNet was submitted to the ImageNet ILSVRC challenge in 2012 and significantly outperformed the second runner-up (top 5 error of 16% compared to runner-up with 26% error). The Network had a similar architecture basic as LeNet, but was deeper, bigger, and featured Convolutional Layers stacked on top of each other (previously it was common to only have a single CONV layer immediately followed by a POOL layer).
# ''ZF Net''. The ILSVRC 2013 winner was a Convolutional Network from Matthew Zeiler and Rob Fergus. It became known as the ZF Net (short for Zeiler & Fergus Net). It was an improvement on AlexNet by tweaking the architecture hyperparameters, in particular by expanding the size of the middle convolutional layers.
# [[GoogLeNet|GoogLeNet]]. The ILSVRC 2014 winner was a Convolutional Network from Szegedy et al. from Google. Its main contribution was the development of an Inception Module that dramatically reduced the number of parameters in the network (4M, compared to AlexNet with 60M).
# [[VGGNet]]. The runner-up in ILSVRC 2014 was the network from Karen Simonyan and Andrew Zisserman that became known as the VGGNet. Its main contribution was in showing that the depth of the network is a critical component for good performance. Their final best network contains 16 CONV/FC layers and, appealingly, features an extremely homogeneous architecture that only performs 3x3 convolutions and 2x2 pooling from the beginning to the end. It was later found that despite its slightly weaker classification performance, the VGG ConvNet features outperform those of GoogLeNet in multiple transfer learning tasks. Hence, the VGG network is currently the most preferred choice in the community when extracting CNN features from images. In particular, their pretrained model is available for plug and play use in Caffe. A downside of the VGGNet is that it is more expensive to evaluate and uses a lot more memory and parameters (140M).
# [[ResNet]]. The ILSVRC 2015 winner.
# [[DiracNet]]
Prototype networks:
# Maxout
# NIN
# DropConnect
# [[Densely Connected Convolutional Networks|https://github.com/liuzhuang13/DenseNet]]
# [[Interleaved Group Convolution|https://arxiv.org/abs/1707.02725]]
@article{casella1996rao,
title={Rao-Blackwellisation of sampling schemes},
author={Casella, George and Robert, Christian P},
journal={Biometrika},
volume={83},
number={1},
pages={81--94},
year={1996},
publisher={Biometrika Trust}
}
Denote by $\mathbf x=\{x_j\in\mathbb R, j=1, \dots,n\}$ a multivariate time series of length $n$, and assume that some of its characteristics are changing at $k-1$ instants $t_1,t_2,\dots,t_{k-1}$, with the convention $t_0=0$ and $t_k=n$. To reflect this situation, we assume that the distribution of $\mathbf x$ is defined piecewise on the resulting $k$ segments by $\xi$. The model is defined as
$$
\mathcal M_\theta:\left\{ \begin{array}{l}
p(\mathbf x|\mathbf \xi) = \prod_{i=1}^kp(x_{t_{i-1}+1},\dots,x_{t_i}|\xi_i)\\
\pi(\mathbf \xi|\mu) = \prod_{i=1}^k\pi(\xi_i|\mu)
\end{array}\right.
$$
where $\pi(\cdot|\mu)$ denotes an [[informative prior distribution|Prior Classes]]. We follow a model selection strategy that involves choosing the model with the largest posterior probability:
$$
p(\mathcal M_\theta|\mathbf x)\propto\pi(\mathcal M_\theta)\int p(\mathbf x|\mathbf \xi)\pi(\mathbf \xi|\mu)d\mathbf \xi
$$
The Bayesian framework is useful for the general purpose of model selection because it offers the possibility of computing a posterior model probability on the set of all possible models, provided some prior probability can be established on this set. An alternative is to base model selection on the Bayes factor, which asymptotically leads to the same result.
!! [[BIC|Bayesian information criterion]]
[reynolds04] A penalized likelihood ratio test is used to compare whether the data within a fixed window are better modeled via a single Gaussian or two Gaussians. The window gradually grows at each test until a changepoint is inferred, at which point the window is reinitialized at the inferred changepoint.
[[BIC Changepoint]]
!! KL divergence
[siegler97] uses fixed length windows and computes the symmetric KL divergence between a pair of Gaussians each fit by the data in their respective windows. A post-processing step then sets the changepoints equal to the peaks of the computed KL that exceed a predetermined threshold.
[[KL Changepoint]]
!! Generalized Likelihood Ratio (GLR)
[[GLR Changepoint]]
* [[Channel Pruning|https://arxiv.org/abs/1707.06168]]
* [[ICCV17 Network Slimming|http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Learning_Efficient_Convolutional_ICCV_2017_paper.pdf]]
** Take the gamma in BN as the scaling factor
** Requires finetuning
@article{chen2014voice,
title={Voice conversion using deep neural networks with layer-wise generative training},
author={Chen, Ling-Hui and Ling, Zhen-Hua and Liu, Li-Juan and Dai, Li-Rong},
journal={IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)},
volume={22},
number={12},
pages={1859--1872},
year={2014},
publisher={IEEE Press}
}
Since 2008, have used 18 players in center. Dexter Fowler provides the ability to get on base from the leadoff position.
[[wiki|https://en.wikipedia.org/wiki/Chomsky_hierarchy]]
Level 0 or type-3: [[Regular Expressions]]
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font-size: 1em;
line-height: 1.5em;
font-family: inconsolata, monospace;
letter-spacing: 0.3px;
word-spacing: 1px;
background: #FFFFFF;
color: #000000;
}
.cm-s-chrome-devtools .CodeMirror-lines {
padding: 8px 0;
}
.cm-s-chrome-devtools .CodeMirror-gutters {
box-shadow: 1px 0 2px 0 rgba(0, 0, 0, 0.5);
-webkit-box-shadow: 1px 0 2px 0 rgba(0, 0, 0, 0.5);
background-color: #FFFFFF;
padding-right: 10px;
z-index: 3;
border: none;
}
.cm-s-chrome-devtools div.CodeMirror-cursor {
border-left: 3px solid #000000;
}
.cm-s-chrome-devtools .CodeMirror-activeline-background {
background: #0000001A;
}
.cm-s-chrome-devtools .CodeMirror-selected {
background: #BAD6FD;
}
.cm-s-chrome-devtools .cm-string {
color: #C41A16;
}
.cm-s-chrome-devtools .cm-number {
color: null;
}
.cm-s-chrome-devtools .cm-keyword {
color: #AA0D91;
}
.cm-s-chrome-devtools .cm-atom {
color: null;
}
.cm-s-chrome-devtools .cm-variable {
color: #000000;
}
.cm-s-chrome-devtools .cm-def {
font-style: italic;
}
.cm-s-chrome-devtools .cm-comment {
color: #007400;
}
.cm-s-chrome-devtools .cm-variable-2 {
color: #881280;
}
.cm-s-chrome-devtools .cm-property {
color: null;
}
.cm-s-chrome-devtools .cm-operator {
color: #AA0D91;
}
.cm-s-chrome-devtools .CodeMirror-linenumber {
color: #007400;
}
! Learning clojure
[[Learn clojure in Y Minutes]]
!! Websites
[[4clojure]]
[[braveclojure.com|http://www.braveclojure.com]]
! Properties
[[Transducer]]
[[clojure Snippets]]
! Examples
* [[cljs quick start|https://github.com/clojure/clojurescript/wiki/Quick-Start]]
* [[Compojure Address Book]]
* [[Re-Frame]]
! Tools
* [[Om]]
* [[Datomic]]
* [[Overtone]]
* Figwheel
** Figwheel autoreloads and compiles your clojurescript code so that you can live code and see your changes update in the browser. It also comes with a repl for your clojurescript so you can evaluate your code there. This will come in handy.
* [[Reagent]]
** Reagent wraps the React.js library and provides a neat Clojurescript interface to it. This is my first time using it and it’s been very simple to pick up and use.
* Ring
** Ring is a simple abstraction for HTTP in Clojure. It allows you to create a webserver, define middleware etc.
! Engineering Projects
[[Onyx|https://github.com/MichaelDrogalis/onyx]]
Empty seqeuence
```clojure
user=> (every? empty? ["" [] () '() {} #{} nil])
true
;example of recommended idiom for testing if not empty
user=> (every? seq ["1" [1] '(1) {:1 1} #{1}])
true
```
First read this post [[Understanding CNN for NLP|http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/]]
And this course： [[Stanford CNN for NLP]]
[[A Convolutional Neural Network for Modelling Sentences]]: A good paper to read. The model is kind of too complex.
[[Convolutional Neural Networks for Sentence Classification]]: A one-layer CNN approach with simplicity and empirical performance. This has become the standard baseline for new text classification publications.
! NMT
* [[Convolutional Sequence to Sequence Learning]]
! Practices
* [[Daily News for Stock Market Prediction]]
To efficiently perform text spotting, the majority of methods follow the intuitive process of splitting the task in two: text detection followed by word recognition.
! Text Detection
Detecting instances of words in noisy and cluttered images is a highly non-trivial task.
!! Generative Models
* [[Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data|https://arxiv.org/abs/1611.02788]]
!! Character region methods
Segments pixels into characters, and then group characters into words. Previous methods are:
* finding images with constant stroke width (the distance between two parallel edges) with stroke width transform (SWT). Then clustering pixels together should form words.
* Extreme Regions
* Maximally Stable Extremal Regions with CNN classifier to prune Extreme Regions
!! Sliding window methods
* Random fern classifier on HoG features, grouped into words with a pictorial structures framework
* CNNs trained for character classfication can be used as sliding window classifiers
! Papers
# [[Reading Text in the Wild with CNN]]: best performance
# [[Synthetic Data for Text Localisation in Natural Images]]: best precision and efficiency tradeoff
# LSTM attention based models for various curvatures
# [[Handwritten Chinese RNN|http://arxiv.org/abs/1606.06539v1]]
# [[CTPN|https://github.com/tianzhi0549/CTPN]]
## Best line detection
!! RNN Approach
# [[Sequence Recognition Network]]
# [[Seq2Seq|http://arxiv.org/abs/1607.06125]]
# Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online handwritten Chinese Text Recognition: residual multilayerd BLSTM
! 自然图片文字检测预研
!! 数据集
传统英文文字识别公开数据集太小，不足以支持CNN模型训练。现常用的训练数据集为基于VGG组提出的合成算法生成的Synth90K。共九万张数据。一次类推，我们要解决的问题如果变化稍少、图片文字数量较多，根据中文单字结构复杂进行折中，预期需要二十万张高质量合成数据样本。
结合类似方法的数据集和我们的需求，数据集不会限制。
!! 加强问题
自然场景文字识别尚不存在完美的解决方案。尤其传统依赖MSER的方法对背景的限制很大。相比之下，基于CNN回归模型的目标检测的稳定性好很多。我们将数据限定为广告中印刷体合成文字。则根据难易程度可以依次将数据分为以下三类：
# 如果约束背景简单、文字排列整齐、失真变化不大，基于传统分割方法可以达到100字整体正确率（即包含百字的图像没有单字漏判）85%以上。开发需要预期5人天的时间。不过可能适用性不强。
# 对于更加复杂的问题，复杂背景合成文字，基于目前的目标检测方法，预期至少可以达到90%的准确率和80%的召回率。我们需要做的是增加样本样式等比较基础的训练工作。
# 去掉图片文字朝向的约束，对于段落文字不应产生较大的影响。但现在开发过程遇到了问题，预期的工作量未知。
[[OCR with BiLSTM]]
! Applications
* [[CNN for Video Classification]]
* [[Video Object Segmentation]]
* [[Anomaly Detection in Video]]
! Talk
* [[Deep Learning for Video Analysis]]
! Bib
* [[Asynchronous Temporal Fields for Action Recognition|https://arxiv.org/abs/1612.06371]]
** fully-connected temporal CRF model for reasoning
* Audio in video
** [[CNN Architectures for Large-Scale Audio Classification|http://arxiv.org/abs/1609.09430]]
! Video Classification
Hand-crafted local space-time features like 3D-SIFT, ESURF, HOG3D, MBH, local trinary pattern have reasonable performance on simple datasets such as recognizing human actions, etc. They do not require algorithms to detect human body and are robust to background variations.
[[Improved trajectories|https://hal.inria.fr/hal-00873267v2/document]] is the state-of-the-art low level trajectory extraction method. The main idea is to densely sample feature points in each frame, and track them in the video based on optical flow. Improved trajectories boost the recognition performance by taking camera motion into account, using SURF to complement optical flow matching. Then use RANSAC to estimate homography matrix. This technique achieves 91.2% on UCF-50, 87.9% on UCF-101 and 66.8% on HMDB-51, which is unsurpassed by many deep methods.
These local features lack semantics and discriminative capacity.
!! Datasets & Challenges
* ''MED'14'': 20 events, 8k videos for training and 23k as dry-run validation (1.2k hr in total), 200k videos in test.
* ''UCF-101 & THUMOS-2014'': 13,320 video clips with 101 annotated classes, THUMOS adds 5k+ videos.
* ''Sports-1M'': 1m videos in 487 classes, annotation generated automaticly from YouTube, released by Stanford U.
* ''HMDB-15'': 6,766 videos in 51 classes, more challenging because the context is complicated.
* ''Columbia Consumer Videos'': 9,317 videos in 20 classes.
!! Image-Based
[[Zha et al. BMVC 2015|https://arxiv.org/abs/1503.04144]] gets mAP 38.74% on MED'14:
* AlexNet and VGG19 as base model but features from hidden7 yield best performance
* uses Spatio Temporal Pooling to extract features from 8 different regions
* classification with kernel SVM
* fusion with IDT Fisher Vectors improve results
* claims uniform sampling yields same performance as keyframe detection.
[[Xu et al. CVPR 2015|https://arxiv.org/abs/1411.4006]] uses VLAD encoding on VGG19 feature with SPP and gets 36.8% on MED'14.
!! End-to-End CNN Architectures
Due to the limited ability of capturing spatial-temporal patterns, using video directly as input has been considered. The most straight forward attempt is [[Ji et al. ICML 2010|http://www.cs.odu.edu/~sji/papers/pdf/Ji_ICML10.pdf]], where continous frames are stacked and processed with a 3-dim matrix as conv kernel.
[[Karpathy et al. CVPR 2014|http://cs.stanford.edu/people/karpathy/deepvideo/]] experimented with different fusion and transfer learning schemes. Instead of stacking the frames all at once, they introduce a fusion technique called ''Slow Fusion'' to aggregate temperal information stepwisely. Highest accuracy on UCF-101 model is 65.4%, achieved by fine-tuning the top 3 layers of Sports-1M pretrained model.
[img[slow_fusion.PNG]]
[[Tran et al. ICCV 2015|https://arxiv.org/abs/1412.0767v4]] argues that previous models loss temporal information because they perform CONVs and POOLs only spatially. Slow Fusion can handle temporal information better because temporal information are grouped gradually. They propose C3D architecture that uses full frames as input, with 3x3x3 CONVs and 2x2x2 POOLs and achieve 72.26% on UCF101.
''Remark'': it is not CNN's strength to capture temporal information, spatial and temporal pooling works differently in human perception. And the 3D conv architecture is very memory and time consuming.
[[Sun et al. ICCV 2015|https://arxiv.org/abs/1510.00562]] factorized 3D CONV into spatio and temporal operations and propose F,,ST,,CN:
[img[FSTCN.PNG]]
The score of video clips are fusioned with ''Sparsity Concentration Index'', which puts more weights on the score vectors that have higher degrees of sparsity. Predicts 88.1% on UCF-101 and 59.1% on HMDB-51.
[[Simonyan & Zisserman, NIPS 2014|https://arxiv.org/abs/1406.2199]] propose a two-stream approach, static frames and optical flow stack are convolved separately:
[img[2-stream cnn.PNG]]
Authors argue information which hand-crafted local descriptors care about, i.e., histograms of orientations, kinematic features (divergence, curl and shear) and trajactory feature can all be captured by CONV. The scores of the two stream are fused with linear SVM. 88.0% on UCF-101 and 59.4% on HMDB-51.
''Remark'': Still no advantage over IDT. Optical flow probably can be replaced with deep methods.
[[Wang et al. CVPR 2015|http://arxiv.org/abs/1505.04868]] propose Trajectory-Pooled Deep-Convolutional Descriptors. Replacing optical flows with improved trajectories as input of temporal stream. The so-called TDD is extracted by trajectory pooling trajectories and CONV feature maps. 91.5% on UCF-101 and 65.9% on HMDB-51 with early fusion of TDDs and iDT.
[[Feichtenhofer et al. CVPR 2016|https://www.robots.ox.ac.uk/~vgg/publications/2016/Feichtenhofer16/feichtenhofer16.pdf]] use 3D Conv + 3D pooling to fuse spatial and temporal outputs and combine it with temporal outputs. Base model VGG16, 93.5% on UCF101 and 69.2% on HMDB51.
[[Wang et al. ECCV 2016|https://arxiv.org/abs/1608.00859]] adds RGB difference and warped optical flow to input. Base model Inception-BN, 94.2% on UCF-101 and 69.4% on HMDB51.
[[Wang et al. CVPR 2016|https://arxiv.org/abs/1512.00795]] learn feature representation by modeling an action as a transformation between an initial state (condition) to a new state (effect) with two Siamese CNNs:
[img[Siamese.PNG]]
63.4% on HMDB51 and 92.4 on UCF101. The advantage is the action type is learned.
[[Bilen et al. CVPR 2016|http://www.robots.ox.ac.uk/~vgg/publications/2016/Bilen16a/]] introduced the dynamic image to represent motions with rank pooling in videos, upon which a CNN model is trained for recognition. 65.2% on HMDB51 and 89.1% on UCF101.
''Remark'': the dynamic image is hard for human to recognize.
!! Modeling Long-Term Temporal Dynamics
[[Donahue et al. CVPR 2015|http://arxiv.org/abs/1411.4389]] trained a 2-layer LSTM with features from the two-stream approach.
!! Incorporating Visual Attention
# "Delving Deeper into Convolutional Networks for Learning Video Representations", Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, ICLR 2016. [[link|http://arxiv.org/pdf/1511.06432v4.pdf]]
# "Deep Multi Scale Video Prediction Beyond Mean Square Error", Michael Mathieu, camille couprie, Yann Lecun, ICLR 2016. [[link|http://arxiv.org/pdf/1511.05440v6.pdf]]
# Hierarchical Attention Network for Action Recognition in Videos (HAN) [[link|http://arxiv.org/abs/1607.06416]]
! Object Tracking
# Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, arXiv:1502.06796. [[link|http://arxiv.org/pdf/1502.06796]]
# DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, Hanxi Li, Yi Li and Fatih Porikli, BMVC, 2014. [[link|http://www.bmva.org/bmvc/2014/files/paper028.pdf]]
# Learning a Deep Compact Image Representation for Visual Tracking, N Wang, DY Yeung, NIPS, 2013. [[link|http://winsty.net/papers/dlt.pdf]]
# Hierarchical Convolutional Features for Visual Tracking, Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, ICCV 2015 [[link|http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ma_Hierarchical_Convolutional_Features_ICCV_2015_paper.pdf]] [[Code|https://github.com/jbhuang0604/CF2]]
# Visual Tracking with fully Convolutional Networks, Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, ICCV 2015 [[link|http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf]] [[Code|https://github.com/scott89/FCNT]]
# Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, Hyeonseob Namand Bohyung Han, [[link|http://arxiv.org/pdf/1510.07945.pdf]] [[Code|https://github.com/HyeonseobNam/MDNet]] [[Project Page|http://cvlab.postech.ac.kr/research/mdnet/]]
! [[Video Captioning]]
[[blog|http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/]]
! Tweaks
* Learn embeddings from scratch, no pre-trained word2vec, and no static channel.
* L2 norm constraints left out
! Implementation
!! Inputs
```python
# Placeholders for input, output and dropout
self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
```
* `sequence_length` - truncanated to 59.
!! Embedding layer
has input size `[vocabulary_size, embedding_size]`. To utilize `conv2d`, we have to add 1 dim of pure 1s to the embedding output.
```python
with tf.device('/cpu:0'), tf.name_scope("embedding"):
W = tf.Variable(
tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
name="W")
self.embedded_chars = tf.nn.embedding_lookup(W, self.input_x)
self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
```
`W` is learned during training.
* Regard small-loss instances as correct instances
* Drawbacks
** @@color:red;accumulated error caused by sample-selection bias@@
* Solutions
** utilize memorization
** attenuate the error from noisy data by utilizing two networks
[img[https://ai2-s2-public.s3.amazonaws.com/figures/2017-08-08/e061d23b68e7d4aac5aece4794c044c80e638dca/2-Figure1-1.png]]
* Each network samples its small-loss instances based on memorization of neural networks
* Each network teaches such useful instances ot its peer network (cross-update)
This method is based on hard clustering and discrete mapping.
The converted feature vector $\hat y_t$ at frame $t$ is determined by quantizing the source feature vector $x_t$ to the nearest centroid vector of the source codebook and substituting it with a corresponding centroid vector $c_m^{(y)}$ of the mapping codebook. The large quantization error due to hard clustering is effectively reduced by adopting ''fuzzy vector quantization'' (VQ) that realizes ''soft clustering''. Continuous weights $w_{m,t}^{(x)}$ for individual clusters are determined at each frame according to the source feature vector.
$$
\hat y_t = \sum_{m=1}^Mw_{m,t}^{(x)}c_M^{(y)},
$$
where $M$ is the number of centroid vectors. Another way is
$$
\hat y_t = x_t + \sum_{m=1}^Mw_{m,t}^{(x)}(c_M^{(y)}-c_M^{(x)}).
$$
! TODO
* Autocomplete
* Vim bindings
* Switch to markdown?
* [[pdf|http://ml.informatik.uni-freiburg.de/papers/17-BayesOpt-BOHB.pdf]]
* [[For Deep Learning|https://openreview.net/forum?id=HJMudFkDf]]
To minimize a function $f$ given previous observations $D=\{((x_0, y_0), \dots, (x_i, y_i)\}$, where $y(x) = f(x) + \epsilon$
''BO'' uses an aquisition function $a(\mathbf x)$ based on $p(f|D)$ and picks a point that maximizes it. A popular choice for the aquisition function is the expected improvement (EI) over a reference value $\alpha$ under the current model:
$$
a(\mathbf x, \alpha) = \int\max(0, \alpha-f(\mathbf x))\,dp(f(x)|D)
$$
''Tree-of-Parzen-Estimator (TPE)'' is a special instantiation of the general BO, maximizing
$$
p(y>\alpha|\mathbf x)/p(y<\alpha|\mathbf x)
$$
by drawing samples according to $l(\mathbf x)$ and evaluating the ratio. Stochastic optimization can directly be used to parallelize TPE. TPE allows a user-defined prior over the search space; choosing this as the uniform distribution yields a "safe" BO method.
''Hyperband'' is a bandit-based strategy for hyperparameter optimization. HB typically outperforms random search and Bayesian optimization.
[[paper|https://arxiv.org/abs/1905.04062]]
View the transition kernel of MCMC $$\Pi$$ as an improvement operator. Improvement is 0 only when $$\Pi q=q$$ which only holds for the true posterior.
To measure the improvements, first we compare the KL divergence
$$
\mathcal{L}_1(\theta) = \operatorname{KL}\left[q_\theta\middle\|p\right] - \operatorname{KL}\left[\Pi^tq_\theta\middle\|p\right]
$$
Second measure the amount of change. This objective function merely tries to identify fixed points of the improvement operator:
$$
\mathcal{L}_2(\theta) = \operatorname{KL}\left[\Pi^tq_\theta\middle\|q_\theta\right]
$$
By summing these two terms, the most problematic terms to compute, $$\Pi^tq_\theta$$ cancel out:
$$
\mathcal{L}_1(\theta) + \mathcal{L}_2(\theta) = \mathbb{E}_{z\sim \Pi^t q_\theta} f_\theta(z) - \mathbb{E}_{z\sim q_\theta} f_\theta(z),
$$
where
$$
f_\theta(z) = \log p(z,x) - \log q_\theta(z)
$$
There is one more technical hurdle to overcome, which is to calculate or estimate the derivative of this objective with respect to $$\theta$$. The authors propose a REINFORCE-like score function gradient estimator in Eqn. (12), which is somewhat worrying as it is known to have very high variance. The authors propose overcoming this using a control variate. For more details, please refer to the paper.
* Need for more dynamism
** programs with loops and recursions
** sequence, trees, nested data structure
* Relay Virtual Machine (source => stack VM bytecode and runtime)
** Dynamic shape workloads
** More runtime objects: arrays, tuples, trees, ADTs
** Minimum runtime for dynamic models
* $$\mu$$TVM
** Support bare-metal J-TAG devices, no OS is needed
* Better core Infra
** New integer simplification and analysis
** Unified runtime object protocol
* Accelerator support
** different compute primitives
*** ARM: vector-vector, TPU: matrix-matrix
[[link|http://www.jarrodctaylor.com/posts/Compojure-Address-Book-Part-1/]]
Tools involved:
* Compojure
* Ring
* Midje: testing
* hiccup: render html
To run the project
`lein ring server`
Test/auto test
`lein midje (:auto)`
Start postgres:
`systemctl start postgresql`
[[A Survey of Deep Learning Techniques Applied to Trading|https://www.linkedin.com/pulse/survey-deep-learning-techniques-applied-trading-james-melenkevitz-phd]]
* [[Daily News for Stock Market Prediction]]
* [[Daily Stock Forecast]]
[[Portfolio Theory]]
* built-in datatypes: boolean, lists etc
* primitive expressions: logic and math
* user-declared datatypes: record / abstract data type / symbolic expression (BNF = [[Backus-Naur Form]])
* code: inference rules (Gentzen notation)
* conditionals: rule dispatch via nondeterministic pattern-matching
* repetition: overlines and/or ellipsis notations, and sometimes iterators
* capture-free substitution within a symbolic expression (Church)
! Backpropagating the Diagonal Hessian
Assume each layer has the functional form $o_i = f(y_i) = f(\sum_jw_{ij}x_j)$. The iteration is obtained using the Gaussian-Newton approximation (dropping the term that contain $d^2f(y)$). The cost of computing the diagonal second derivatives is essentially the same as the regular backpropagation. This technique is applied in the ''optimal brain damage'' pruning procedure.
!! Diagonal Bayesian Penalty
Chp4 of [[Neural Networks: Tricks of the Trade]]. The hyperparameters have to be updated to make sure the augmented Hessian is p.d.
The ''evidence for a network'' evaluated with cross-entropy error is as follows:
$$
\ln p(D|\alpha) = -\frac{1}{2}\sum_{i=1}^n\alpha_{[i]}w_i^2-E-\frac{1}{2}|H|+\sum_c\frac{n_c}{2}\ln\alpha_c-\frac N 2\ln(2\Pi)
$$
This algorithm is very sensitive to small eigenvalues of Hessian (which can be omitted). So I don't know what is going on here.
! Computing the Product of the Hessian and a Vector
The finite difference method:
$$
Hv\sim\frac{1}{\alpha}\left(\frac{\partial E}{\partial w}(w+\alpha v)-\frac{\partial E}{\partial w}(w)\right)
$$
! [[Hessian-Free Optimization]]
[[MCMC assisted by Belief Propagaion|https://arxiv.org/abs/1605.09042]]
! [[MCMC]]
* pros: exact
* cons: suffer from slow mixing time
! Belief Propagation
Deterministic message-passing algorithm
* pros: empirically fast, efficient
* cons: lack of control over approximation quality
[[link|https://arxiv.org/abs/1603.05201]]
''Hypothesis'': the lower convolution layers learn redundant filters to extract both positive and negative phase information of an input signal.
''Fact'': The first few conv layers of a deep CNN manage to capture both negative and positive phase information through learning pairs or groups of negatively correlated filters. So there exists a redundancy among the filters from the lower conv layers.
''pairing filter'': $\bar\phi_i = \text{argmin}_{\phi_j}\langle\phi_i, \phi_j\rangle$ where $\phi_j\in\Phi$
; CReLU denoted by $\rho_c:\mathbb R\rightarrow \mathbb R^2$
: $\forall x\in \mathbb R, \rho_c(x) \triangleq ([x]_+, [-x]_+)$
Conv layers with CReLU are easier to reconstruct inputs.
* speech recognition
* caption generation
* [[Question Answering]]
* [[Dialog]]
* [[Neural Machine Translation]]
* following instructions
* summarisation: how to prepare parallel dataset
! Algorithmic challenges
Maximum posterior is intractable. We therefore approximate it with:
* a greedy search, generating words one by one.
* a beam search, keep track of top b hypothesis, but not leading us very far
* or with MC, not very common
Improving search/inference is an open research question:
* Search more effectively
* Guaranteed bounds
* Limit the model to make search easier
! [[Attention Mechanism]]
! Evaluation
* Cross-entropy, perplexity: okay to implement, hard to interpret
* Task-specific evaluation: BLEU, METEOR, WER, ROUGE, easy to implement, okay to interpret
* Can we invent a code specific metric which emphasis on syntax and logic correctness
* [[ICML 2016]]
* [[ICLR 2016]]
* [[KDD 2016]]
!! CVPR 2016
[[Learning Deep Features for Discriminative Localization]]
!! ECCV 2016
[[Fully-Convolutional Siamese Networks for Object Tracking]]
! Misc
* [[IFIP IIP]]
! [[Videos & Talk Notes|Talks]]
! Spacemacs
```lisp
(defun dotspacemacs/user-config ()
"Configuration function for user code.
This function is called at the very end of Spacemacs initialization after
layers configuration. You are free to put any user code."
;; Clojure
;; Pretty symbols for anonymous functions, set literals and partial functions
(setq clojure-enable-fancify-symbols t)
;; Use evil-paredit mode in clojure, still got some problems
;; (add-hook 'emacs-lisp-mode-hook 'evil-paredit-mode)
;; huaihai's configuration
;; Make evil-mode up/down operate in screen lines instead of logical lines
(define-key evil-motion-state-map "j" 'evil-next-visual-line)
(define-key evil-motion-state-map "k" 'evil-previous-visual-line)
;; Also in visual mode
(define-key evil-visual-state-map "j" 'evil-next-visual-line)
(define-key evil-visual-state-map "k" 'evil-previous-visual-line)
;; clojure mode config
;(require 'clojure-mode-extra-font-locking)
(add-hook 'clojure-mode-hook #'smartparens-strict-mode)
(add-hook 'clojure-mode-hook #'evil-smartparens-mode)
(add-hook 'clojure-mode-hook #'rainbow-delimiters-mode)
;; remove trailing whitespace when saving
(add-hook 'before-save-hook 'delete-trailing-whitespace)
;; toggle comments
(define-key evil-normal-state-map ",c " " cl")
;; match paredit.vim key-binding
(define-key evil-normal-state-map ",W" " kw") ; wrap with ()
(define-key evil-normal-state-map ",w[" ; wrap with []
(lambda (&optional arg) (interactive "P") (sp-wrap-with-pair "[")))
(define-key evil-normal-state-map ",w{" ; wrap with {}
(lambda (&optional arg) (interactive "P") (sp-wrap-with-pair "{")))
(define-key evil-normal-state-map ",S" " kW") ; splice, i.e unwrap an sexp
(define-key evil-normal-state-map ",J" " kJ") ; join two sexps
(define-key evil-normal-state-map ",O" 'sp-split-sexp) ; split an sexp
(define-key evil-normal-state-map ",I" " kr") ; raise current symbol
(define-key evil-normal-state-map (kbd ", <up>") " kE") ; splice kill backward
(define-key evil-normal-state-map (kbd ", <down>") " ke") ; forward
;; These are different from vim, here cursor should NOT be on delimits
(define-key evil-normal-state-map ",>" " ks") ; forward slurp
(define-key evil-normal-state-map ",<" " kS") ; backward slurp
;; jr0cket: keybindings for cycling buffers
(global-set-key [C-prior] 'previous-buffer)
(global-set-key [C-next] 'next-buffer)
;; Remap undo to overwrite the Emacs GUI window hide key
;; as the backslash character is used to escape in Emacs, we need to escape it with another backslash
(define-key global-map (kbd "C-\\") 'undo-tree-undo)
(define-key global-map (kbd "M-\\") 'undo-tree-redo)
)
;; Do not write anything past this comment. This is where Emacs will
;; auto-generate custom variable definitions.
```
!! The Idea
If the posterior distribution are in the same family as the prior probability distribution, the prior and posterior are then called ''conjugate distributions'', and the prior is called a conjugate prior for the likelihood funtion. The likelihood function is usually well-determined from a statement of the data-generating process. Different choices of the prior distribution may make teh integral more or less difficult to calculate, and the posterior may take one algebraic form or another. For certain choices of the prior, the posterior has the same algebraic for as the prior. Such a choice is a conjugate prior.
For example, the Gaussian family is conjugate to itself w.r.t. a Gaussian likelihood function. If the likelihood function is Gaussian, choosing a Gaussian prior over the mean will ensure that the posterior distribution is also Gaussian.
Another widely used example is [[Dirichlet-multinomial distribution]].
!! How to choose
Experimenters are often in the position of having had collected some data from which they want to make inferences about the process that produced those data. Bayes theorem,
$$
g(\theta|x_1, \dots, x_n)=\frac{\pi(\theta)L(\theta|x_1, \dots, x_n)}{\int \pi(\theta)L(\theta|x_1, \dots, x_n)d\theta}
$$
is can be used. However the integral is always not tractable.
We consider a likelihood funtion which can be factored as:
$$
L(\theta|x_1, \dots, x_n) = u(x_1,\dots,x_n)v(T(x_1, \dots, x_n), \theta),
$$
where the second term is known as the kernel function and $T(\cdot)$ is the sufficient statistics. The conjugate prior is defined propotional to the kernel function,
$$
g(\theta)\propto v(T(x_1, \dots, x_n), \theta).
$$
[[Exponential Family]] all have conjugate prior.
! Resources
* [[Baidu warp-ctc|https://github.com/baidu-research/warp-ctc]]. Has tf and th binding
* tf CTC loss and CTC beam search for CPU
* nv cudnn7 support GPU CTC
* [[Distill blog|https://distill.pub/2017/ctc/]]
Computing the massive number of alignments is too slow. We can use DP to speedup. Modified beam search can generate more accurate examples. Language model probability can be incorporated in speech recognition.
For numerical stability, compute the loss function in log-space with log-sum-exp trick.
! Properties
* Conditionial independence
* Alignment
** alignment free: objective marginalize all the alignments
** monotonic alignment: strictly sequential
** many-to-one
! Remarks
Wonder if we can replace beam search in NMT with this
Is language models incorporated in the character detection framework?
! Experiment
!! Merging CTPN to ms
* skipping LSTM for legacy issue.
* add reverse and transpose
!! RFCN-LSTM
Base net list of stride 2 layers
* conv1
* pool1
* res3a
* res4a
* [[link|https://arxiv.org/abs/1806.09141]]
* [[Learning stochastic inverse|https://papers.nips.cc/paper/4966-learning-stochastic-inverses.pdf]]
* [[Recursive Autonomy Identification for constructing a CPDAG|http://www.jmlr.org/papers/volume10/yehezkel09a/yehezkel09a.pdf]]
Learn a generative structure $$G_{inv}$$ and then reverse the flow by constructing a stochastic inverse $$G$$. Constructing $$G$$ by recursively introduce a new latent layer, $$H^{(n)}$$, after testing $$n$$-th order conditional independence in $$X$$. But why is independence important?
! Questions
* How to do independence check during training?
* Are activations actually independent?
! Local Context
* Non-Local
** visual self-attention
** pixel level not needed
$$
\mathbf z_i = \mathbf x_i + W_z\sum_{j=1}^{N_p}\frac{f(\mathbf x_i, \mathbf x_j)}{\mathcal C(\mathbf x)}(W_v\cdot\mathbf x_j)
$$
where $$f$$ as Embedded Gaussian:
$$
\omega_{ij} = \frac{\exp(\langle W_q\mathbf x_i, W_k\mathbf x_j\rangle)}{\sum_m\exp(\langle W_q\mathbf x_i, W_k\mathbf x_m\rangle)}
$$
* CCNet
**
* GCNet
** query level attention
* [[EMA|Expectation-Maximization Attention Networks for Semantic Segmentation]]
! Global Context
! Bibs
For RL
* [[EWC|Overcoming catastrophic forgetting in NNs]]
* Generative Replay
* [[Gradient Episodic Memory for Continual Learning|http://papers.nips.cc/paper/7225-gradient-episodic-memory-for-continual-learning]]: [[code|https://github.com/facebookresearch/GradientEpisodicMemory]]
For meta-learning
* use fast weight instead of soft attention? for what? prediction vs attention?
! Criteria
!! BIC
[[BIC Changepoint]]
!! A Heuristic Derivation of BIC
!! AIC
[[Akaike's information criterion]]
Each of these simple criteria involves choosing the model with the best penalized log-likelihood (i.e., the highest value of $l−A_np$ where $l$ is the log-likelihood, $A_n$ is some constant
or some function of the sample size $n$, and $p$ is the number of parameters in the model). For
historical reasons, instead of finding the highest value of $l$ minus a penalty, this is sometimes
expressed as finding the lowest value of $−2l$ plus a penalty, i.e.,
$$
-2l+A_np.
$$
For AIC, $A_n = 2$ and for BIC, $A_n = \ln(n)$.
! Comparing
In some simple cases, the comparison of two models using information criteria can be viewed as equivalent to a likelihood ratio test, with different models representing different [[alpha levels|Alpha level]] (i.e., different emphases on sensitivity or specificity; Lin & Dayton 1997). AIC or BIC could be preferable, depending on sample size and on the relative importance one assigns to sensitivity versus specificity.
An alternative view the lack of consistent selection in AIC is that it attempts to find the model with good performance in some predictive sense. If $n$ is small, then we may have to choose a smaller model to get more reliable coeffecient estimates. However, if $n$ is large, then standard errors will be small and one can afford to use a rich model. Thus from an AIC perspective and for large $n$, Type II error (an underfit model) is considered worse than a Type I error (overfit model). In contrast, with BIC we are willing to take a higher risk of choosing too small a model, to improve our probability of choosing the true model. BIC considers Type I and Type II errors to be about equally undesirable while AIC considers Type II errors to be worse unless $n$ is very small.
!! KL based comparison
Both selection criteria can be derived, and applied, without assuming the true model is in the model set. However, the defining characteristic of BIC (i.e., what is it trying to do) is only evident asymptotically in relation to the concept of a ''quasi-true model''. In contrast AIC
seeks to select only a best model at a given sample size; “best” is in relation to an expected estimated K-L criterion which serves to recognize a bias-variance trade-off in model selection. For AIC, “best” varies with $n$.For BIC, its “best,” i.e., the quasi-true model, does not depend on $n$. However, on average the BIC-selected model approaches its target “best” model from below in terms of the model ordering imposed here by the $I(f,g_r)$. How researchers assess AIC and BIC performance depends on the performance criteria they adopt (true model or best model), the assumptions they make (usually only implicitly, as in simulation studies) about the underlying K-L values, $I(f,g_r)$, $r=1,\dots,R$, and the sample sizes considered. Failure to properly recognize all of these factors and issues has led to much confusion in the model selection literature
about AIC versus BIC.
!! Discussion
Most of the simulations shown here illustrated similar principles. AIC and similar criteria often risk choosing too large a model, while BIC and similar criteria often risk choosing too small a model. For small n, the most likely error is underfitting, so the criteria with lower underfitting rates, such as AIC, often seem better. For larger $n$, the most likely error is overfitting, so more parsimonious criteria, such as BIC, often seem better. Unfortunately, the point at which the $n$ becomes “large” depends on numerous aspects of the situation. In simulations, the relative performance of the ICs at a given $n$ depended on the nature of the “true model” (the distribution from which the data came). This finding is unhelpful for real data, where the truth is unknown. It may be more helpful to think about which aspects of performance (e.g. sensitivity or specificity) are most important in a given situation.
Underfitting and overfitting could be defined as underestimating and overestimating the true number of classes or factors. For observed data for which models are only approximations to reality, more care is required in considering what it means for a model to be too small, correct, or too large (Burnham & Anderson, 2002, p. 32) Performance can be expressed in terms of a quantitative criterion such as MSE, avoiding the use of a “correct” size, but this may favor AIC-like over BIC-like criteria.
There is no obvious conclusion about whether or when to use ICs, instead of some other approach. Kadane and Lazar (2004) suggested that ICs might be used to “deselect” very poor models (p. 279), leaving a few good ones for further study, rather than indicating a single best model. One could use the ICs to suggest a range of model sizes to consider; for example, one could use the BIC-preferred model as a minimum size and the AIC-preferred model as a maximum, and make further choices based on other kinds of fit criteria, on theory, or on subjective inspection of the results (Collins & Lanza 2010). If BIC indicates that a model is too small, it may well be too small (or else fit poorly for some other reason). If AIC indicates that a model is too large, it may well be too large for the data to warrant. Beyond this, theory and judgment are needed.
In case your loss function involves convex functional of the densities (such as in $$f$$-divergences), you can transform your problem by re-expressing it in terms of the convex conjugate. The expression for $$f$$ in terms of its convex conjugate $$f^*$$ is:
$$
f(u) = \underset{v\in\text{dom}(f^*)}{\sup}\{\langle u, v\rangle-f^*(v)\}\ge\underset{\psi}{\sup}\{\langle u, v_\psi\rangle-f(v_\psi)\}
$$
If $$u$$ is a density function, then the inner product $$\langle u, v_\psi\rangle$$ is the expectation of $$v_\psi$$, which can be approximated by Monte Carlo sampling.
My $f(\theta)$ is hard to optimize, because it has non-differentiable and non-convex components like the $\mathcal l_0$-norm of a vector in sparse methods, or the Heaviside step function in classification.
Replace teh non-convex component by a convex approximation, turning your objective into a now typically convex $g$.
See [[Random Projection]] for l1 magic
Here we use Caffenet (Alexnet) as an example
```
[INPUT-[CONV-RELU-POOL-NORM]*2-[CONV-RELU]*3-POOL-[FC-RELU-DROP]*2-FC-LOSS]
```
In more detail, [[and even more|ConvNet layer details]]:
* INPUT [3x227x227] holds image of size [227x227] with 3 color channels (RGB).
* CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and the region they are connected to in the input volume. The first results in a [96x55x55] by a stride of 4, but later don't change the blob height and width, only depth (#filters).
* RELU layer will apply an elementwise activation function, such as the $\max(0,x)$ thresholding at zero. This leaves the size of the volume unchanged.
* POOL layer will perform a downsampling operation along the spatial dimensions (width, height). Each will downsample by 2 in height and width.
* NORM layer is used to normalize the channel response.
* FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1xC], where each of the C numbers correspond to a class score, such as among the 1000 categories of ImageNet. As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
* [[Convolutional Layer]]
* [[Pooling Layer]]
* [[Nonlinearity Layer]]
* [[Metric Learning]]
!! [[Normalization Layer]]
!! Local Response Normalization(LRN)
The local response normalization layer performs a kind of ''lateral inhibition'' by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL mode`, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $(1+(\alpha/n)\sum_ix^2_i)^\beta$, where $n$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
!! Fully-connected
This is what is used in traditional neural networks. It gives the network more flexibility to convert a FC to CONV, allowing us to 'slide' the original ConvNet across a larger image.
!! [[Dropout]]
!! Loss
We can use different loss functions for different tasks.
''Sigmoid cross-entropy loss'' is used for predicting K independent probability values in [0,1]. In logistic regression, our hypothesis took the form:
$$
h_\theta(z) = \frac{1}{1+\exp(-\theta\top z)},
$$
and the model parameters $\theta$ were trained to minimize the cost function:
$$
L(\theta) = -\left(\sum_{i=1}^my_i\ln\log h_\theta(z_i)+(1-y_i)\log(1-h_\theta(z_i))\right)
$$
Softmax loss is used for predicting a single class of $K$ mutually exclusive classes. Our hypothesis takes the form:
$$
h(\mathbf z, j) = \frac{e^{z_j}}{\sum_{k=1}^Ke^{z_k}},\qquad j = 1, \dots, K,
$$
and the loss function will be:
$$
J(z) = -\left(\sum_{i=1}^m\sum_{k=1}^K1_{y_i = k}\log h(z, k)\right).
$$
which is a generalized case of logistic regression loss. (When $K=2$, the softmax reduces to logistic regression).
Since the function maps a vector and a specific index i to a real value, the derivative needs to take the index into account. For the cross entropy cost, we have:
$$
\frac{\partial L}{\partial z_k} = -\sum_{i=1}^m(1_{y_i=k}-h(\mathbf z, k))
$$
Euclidean loss is used for regressing to real-valued labels [-inf,inf]
1d discrete convolution generally:
$$
(f * g)[n] = \sum_{m=-M}^M f[n-m]g[m]
$$
Convolution is great to extract features from images.
! RNN
RNN is hard to train due to vanishing and exploding gradients.
! Moving average
Moving average version is [[Quasi-RNN]]
! Autoregressive
[[WaveNet]] and [[Pixel-CNN]] are examples of autoregressive models
[img height=250 [http://cs231n.github.io/assets/cnn/depthcol.jpeg]][img height=250 [http://cs231n.github.io/assets/nn1/neuron_model.jpeg]]
In Caffenet, the 96 neurons of the first conv layer has a reception field of $3\times3$. Since input volume is cropped beforehead, no padding is needed:
```css
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
/* learning rate and decay multipliers for the filters */
param { lr_mult: 1 decay_mult: 1 }
/* learning rate and decay multipliers for the biases */
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 96 /* learn 96 filters */
kernel_size: 11 /* each filter is 11x11 */
stride: 4 /* step 4 pixels between each filter application */
weight_filler {
type: "gaussian" /* initialize the filters from a Gaussian */
std: 0.01 /* distribution with stdev 0.01 (default mean: 0) */
}
bias_filler {
type: "constant" /* initialize the biases to zero (0) */
value: 0
}
}
}
```
This layer learns the Gabor filters which looks like this
[img height=250 [http://cs231n.github.io/assets/cnn/weights.jpeg]]
There are many convolution kernels in each layer, and each kernel is replicated over the entire image with the same parameters. The function of the convolution operators is to extract different features of the input. The capacity of a neural net varies, depending on the number of layers. The first convolution layers will obtain the low-level features, like edges, lines and corners. The more layers the network has, the higher-level features it will get.
! Backpropagation
The parameters of each convolution kernel are trained by the backpropagation algorithm. The backward pass for a convolution opteration (for both the data and the weights) is also a convolution (but with spatially-flipped filters).
$$
\frac{\partial L}{\partial a(p, q)} = \sum_{u, v}\frac{\partial L}{\partial z(p-u, q-v)}\frac{\partial z(p-u, q-v)}{\partial a(p, q)}=\frac{\partial L}{\partial z(u, v)}*\text{flip}(w)
$$
$$
\frac{\partial L}{\partial w(u, v)} = \sum_{p, q}\frac{\partial L}{\partial z(p, q)}\frac{\partial z(p, q)}{\partial w(u, v)}=a(u, v)*\frac{\partial L}{\partial z}
$$
Caffe implements convolution as matrix multiplication. We reorganize the input into $\mathbf A$ so that each column is the patch to be convoluted ($11\times11\times3$) in the previous case, and the total number of columns is the output size of each filter ($55\times55$). So the objective function becomes:
$$
L = f(\mathbf Z) = f(\mathbf W\mathbf A)
$$
by applying matrix derivation:
$$
\frac{\partial L}{\partial \mathbf W} = \frac{\partial L}{\partial \mathbf Z}\mathbf A^\top
$$
! Generalization
* [[Graph Convolution]]
* [[Dilated Convolution]]
* [[Deformable Convolution]]
* [[Deconvolution]]
Many of the materials borrowed form [[this page|http://cs231n.github.io/convolutional-networks/]], a very good place to start.
Convolutional Networks are a specific class of neural networks which obtain invariant signal representations by cascading trainable filter banks with non-linearities and subsampling and pooling operators. They have been successfully applied to a variety of image and audio recognition tasks. Each layer of the network is connected to the previous one by establishing "connections" or filters, whose output is processed with a non-linearity such as sigmoids or rectifications. The spatial localization is progressively lost by successively "pooling", or subsampling, the resulting feature maps with local averages or general $L^p$ norms.
Convolutional network architectures were originally learnt over a collection of labeled examples, using a backpropagation algorithm, which optimizes a classification error loss function using a gradient descent across the network. As opposed to general neural networks, convolutional networks incoporate a translation invariance prior, which greatly reduces the number of parameters to learn and hence its efficiency in a number of recognition tasks.
It can be trained with unsupervised data with [[sparse autoencoders|Sparse Autoencoder]].
! [[Convolution]]
!! [[Architecture|ConvNet architecture]]
* [[ConvNet Layers]]
* [[Design|Designing a ConvNet]]
* [[Visualization|Visualizing a ConvNet]]
* [[Efficient CNN]]
!! [[Case study|Case study of ConvNets]]
! Practices
!! Data augmentation
* ''Color'': multiply the projection of each patch pixel onto the principle components of the set of all pixels by a factor between 0.5 and 2
* ''Contrast'': raise saturation and value (S and V components of the HSV color representation) of all pixels to a power between 0.25 and 4.
!!! Utilizing pretrained models
Empirical results proved the efficiency of adopting a large pretrained network and training with new data for a new smaller task. Related ideas are these:
* ''Fine-tuning''. In [[Flickr Style|https://github.com/BVLC/caffe/tree/master/models/finetune_flickr_style]] setting of finetuning caffenet, the last FC is replaced with a new one and is trained with a 10× larger learning rate, and the general learning rate starts smaller. Other settings are left unchanged.
* ''Transfer learning''. In this setting, the first several layers of the network is kept unchanged while the later ones are retrained from scratch.
It has been shown that transfer learning (even using the same data for the same task) can suffer from co-adaptation performance drops and (otherwise) representation specifity limits. While finetuning can deal all these problems smoothly.
!! Fighting overfitting
* [[Photometric distortions|http://arxiv.org/abs/1312.5402]], e.g. lighting, color etc.
* Crops vary in size, aspect ratio (e.g. 140 per image)
* Multiple models, 7 vs 1
[[ideas|Thoughts about ConvNets]]
[[link|http://arxiv.org/abs/1408.5882]]: A one-layer CNN approach with simplicity and empirical performance. This has become the standard baseline for new text classification publications. Here is a [[tensorflow implementation|cnn-text-classification]].
! Structure
* Input: pretrained word vectors, such as [[Word2Vec]] or [[GloVe|Global Vectors for Word Representation]].
* Components: 1 layer 1-d Conv, 1-Max pooling, Dropout, l2 regularization, softmax
* Loss: categorical cross-entropy loss.
! Datasets
[[Sentense Classification Datasets]]
! Empirical Results
[[A Sensitivity Analysis of CNN for Sentence Classification]]
! Structure
* WeightNorm after each Conv
* Skip connection with each Conv
* The encoder stack will receive gradients //twice// for each attention.
! Code
Lua code is so unintuitive. It brings me headache.
* EncoderStack: EncoderConv with skip connection
* DecoderConvBlock
! Ideas
* More general seq2seq usages
* Cache mechanism useful?
* Multilanguage
On going courses:
* [[Audio|Audio Signal Processing for Music Applications]]
* [[Deep Learning Courses]]
* [[NLP Courses]]
[[Restricted Boltzmann Machines]]
! Algorithms
!! Convolutional deep belief networks
The difference with regular RBMs is that the weights between $H$ and $V$ are shared among all locations in the hidden layer.
[[Probabilistic max-pooling]]
!! Solving CRBM
Since all units in one layer are conditionally independent given the other layer, inference can be performed using block Gibbs sampling.
! Feature learning
First convert time-domain signals into spectrograms (160 channels). Apply [[PCA whitening|Whitening transformation]] to create lower dimensional representations. Feed in $n_c$ channels of one-dimensional vectors of length $n_V$, where $n_c$ is #PCA components.
For TIMIT, spectrogram had a 20ms window size with 10ms overlaps. PCA whitening reduces the dimension to 80. 300 first-layer bases with a ''filter length'' ($n_W$) of 6 and a max-pooling ratio (local neighborhood size) of 3 were trained.
! Code
error and sparsity_hid are criteria in batches.
!! `train_tirbm_audio_v1.m`
```matlab
1 function train_tirbm_audio_v1(ws, num_bases, spacing, pbias, pbias_lb, pbias_lambda, epsilon, l2reg, epsdecay, nPC, sigmaPC)
```
PCA is done with 1000 sampled data
```matlab
14 [X startframe_list] = concatenate_speech_data(Pall, randsample(length(Pall), min(length(Pall), 1000)));
```
Although it's a samping algorithm, it still needs momentum. (A network basic property?)
```matlab
111 initialmomentum = 0.5; % used for first 5 trials
112finalmomentum = 0.9; % used then
```
Contrastive Divergence?
```matlab
188 function [ferr dW_total dh_total dv_total poshidprobs poshidstates negdata stat] = ...
fobj_tirbm_CD_LB_sparse_audio(imdata, W, hbias_vec, vbias_vec, pars, CD_mode, bias_mode, spacing, l2reg)
```
! Paper list
* [[paper list|https://mattdeitke.github.io/CVPR-2019/]]
! Tutorials
* [[Action Classification and Recognition]]
! Workshops
* [[CVPR19 Theory WorkShop]]
* [[Embedding Vision Workshop]]
* [[Efficient Deep Learning for Computer Vision]]
* [[Uncertainty and Robustness in Deep Visual Learning]]
* [[Object Detection in the Wild]]
Gradient Matching Generative Networks for Zero-Shot Learning
* image embdding: bias towards seen classes
* use additional classifiers
* GAN: match the gradient of classes
* is there labels for unseen classes on the classification task?
* not zero-shot actually
Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval
* It this zero-shot? (class wise)
Zero-Shot Task Transfer
* pairwise correlation of all tasks
C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
* select most confident subset
* What does it have to do with convex optim
Weakly Supervised Learning of Instance Segmentation With Inter-Pixel Relations
* boundry to instance
* Trained with Mask-RCNN
Attention-Based Dropout Layer for Weakly Supervised Object Localization
Domain Generalization by Solving Jigsaw Puzzles
* auxiliary task of solving jigsaw
Transferrable Prototypical Networks for Unsupervised Domain Adaptation
* domain-invariant
* prototypical networks
Blending-Target Domain Adaptation by Adversarial Meta-Adaptation Networks
ELASTIC: Improving CNNs With Dynamic Scaling Policies
* feature pyramid
* dynamic routing?
ScratchDet: Training Single-Shot Object Detectors From Scratch
SFNet: Learning Object-Aware Semantic Correspondence
* global similarity matrix
* trained with cycle
Deep Metric Learning Beyond Binary Supervision
* continuous similarity
* can it be used for retrival?
Learning to Cluster Faces on an Affinity Graph
* clustering as detection and segmentation
* what is the bbox? on non-euclidean space
* the clustering results seems to be not well tuned
C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition
* conditional ae
Photon-Flooded Single-Photon 3D Cameras
* SPAD LiDAR with ambien light: peak is blurred
* Extreme Filtering: reduce distortion but add noise
* find optimal filtering:
High Flux Passive Imaging With Single-Photon Sensors
* PF-SPAD: high flux level advantage
* High dynamic range
Acoustic Non-Line-Of-Sight Imaging
* measure at different locations
Steady-State Non-Line-Of-Sight Imaging
A Theory of Fermat Paths for Non-Line-Of-Sight Shape Reconstruction
* LiDAR
* fermat path find the distance of the surface
* Fermat flow: reconstruct normal
* OCT: femtosec-scale
End-To-End Projector Photometric Compensation
* surface compensate with CNN image translation
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera
Bringing Alive Blurred Moments
Blind Visual Motif Removal From a Single Image
* Blurred image generation from 2 images
* And encoder decoder for watermark/image reconstruction
Non-Local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
* Spectral HIS denoise
Neural Rerendering in the Wild
* 3D point to image with GAN+VGG loss
GeoNet: Deep Geodesic Networks for Point Cloud Analysis
* find the geodesic points
MeshAdv: Adversarial Meshes for Visual Recognition
* 3d adversarial with renderers
Fast Spatially-Varying Indoor Lighting Estimation
*
Rating: Borderline
Summary: This paper proposes a new attribution method called relative attributing propagation (RAP), based on Layerwise Relevance Propagation (LRP). The authors argue that the positive and negative attributions in LRP are redundant because there is mismatch between the contribution and relavance. To differentiate positive and negative contributions, relavance is divided and propagated in two flows. The authors also proposed to handle the bias and batchnorm with a heuristic correction term.
Strength: Comparing to methods that only propagate through the target unit of interest, RAP can better identify the pixels which don't have strong inhibitory effect on it, commonly background. I think the idea is intuitive and the result is supportive. The application to medical diagnosis shows clear advantage comparing to other LRP methods.
Weakness:
* Many arguments are not clearly explained or supported by references. For example, in L319, alpha-beta LRP is critized to cause offset of relavance. However, this offset effect is not quantified and it is not clear if it is because of the choice of alpha, beta parameters or the problem for alpha-beta LRP in general. Most importantly, the motivation and method of bias/batchnorm relavance ajustment in section 4.2.3 is not clear to me.
* There lacks experiments proving the effectiveness of bias/batchnorm ajustments.
* Math equations and notations are not very accurate, difficult to understand.
* Some experiment details are missing. For example, how is the test set selected in the quantative assessment part, how many classes are there and what is the perforance exactly.
Overall Opinion: Borderline. Good result comparing to related methods but the delivery and writing on important technical parts is confusing and difficult to understand.
! Fake Media Abuse
Problem: Photo-realistic CG stunt
Intro:
* Dense person part prediction
* 3D people to Explicit Models
** Expression/pose embedding
** Landmark align?
* Fake media detection?
Other entries
* Instance Mask Projection for High Accuracy Semantic Segmentation of Things
* Global sum pooling for patch trained counting
* Style transfer for domain adaptation
* Domain randomization to improve generalization
* Single image depth estimation + segmentatin
* SANE: stochastically activated network ensembles
** This work is not mathematically sound
* Image translation without paired images
** Training mage translation networks in absence of the paired data
* Automaic labeling of data for transfer learning
**
Go to Low Latency and On Device workshops on Monday
! Automatic evaluation of software for deficiencies
* buffer overflow
* double-free
* use-after-free
! Threats
Microsoft [[STRIDE]] model
! Research directions
* [[ML for Security]]
! Bibs
* [[Detecting and Explaining Causes From Text For a Time Series Event|https://arxiv.org/abs/1707.08852]]
! Introduction
Data and task are posted [[here|https://www.kaggle.com/aaron7sun/stocknews]]. I am curious about how to take a whole sentence as an entity in a higher hierarchy, i.e., we have to find a representation for headlines to be the input of each day's stock prediction.
Following is my take on this task:
# Run TF-IDF+SVM baseline: running [[the code|https://www.kaggle.com/hsrobo/d/aaron7sun/stocknews/tf-idf-svm-baseline]] yields ROC-AUC .563. Actually better than bare guesses.
# Replace TF-IDF with pretrained word2vec on Google News
# Replace SVM with LSTM
# Compare with a sequence prediction LSTM with no news input
# If all of above works, things should get interesting
# Use CNN with fixed word embedding for sentence feature extraction
# Fine-tune the word embedding (deeply doubt if this works, too few words in dataset, should generalize poorly)
# There's very little chance this small dataset can support a dilated convolution layer. That is what I am up to in the very end.
! Tasks
* [[reddit headlines|https://www.kaggle.com/hsrobo/d/aaron7sun/stocknews/tf-idf-svm-baseline]]: data probabily not that relavant
* [[SemEval-2017|http://alt.qcri.org/semeval2017/task5/]]
** [[Team UWaterloo report|https://arxiv.org/abs/1707.09448]]
! Results
!! Simple tweaks
Added called nltk tokenizer to preprocess the data. ROC-AUC dropped to .527. Too much new words.
Should try fasttext, got memory error loading the word embeddings.
!! Convnet moves ahead
As in the baseline approach, we can join the 25 headlines, we can carry on classification with CNN. Here are two approaches in Tensorflow:
* [[WildML one|http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/]]
* [[The new one on Github with pretrained word embedding|https://github.com/mangate/ConvNetSent]]
! TODOs
# run baseline on semeval
# n-gram as embedding?
# shallow feedforward or convnet
! Data
RCquant
! Code
* [[boosting sample|https://uqer.io/community/share/57163aa8228e5b82797f5561]]
* [[Recurrent weighted average|https://github.com/jostmey/rwa]]
* [[RCGAN|https://github.com/ratschlab/RGAN]]
! Research Directions
* Statistics
** Good old quant algorithms
** Time Series
*** Anomaly detection
*** Non-stationary stochastic processes
* Deep Learning
** Sequential Models
*** Recurrent neural nets
*** Autoregressive models: e.g. WaveNet
** Temporal Convolution: hierarchical feature extraction
*** Dilated convolution
*** ConvS2S
** Attention Mechanism: more clever way of associating history information
*** Memory networks: key-value, end-to-end, incorporate various form of knowledge
*** Differentiable memory: complex structured deduction
** [[Bayesian Deep Learning]]: measure the uncertainty
** Reinforcement Learning
*** Trading agent
*** Learning to learn
* Natural Language Processing
** News to vector (Find informative news. I made this term up.)
** Question answering: reading comprehension and dialog agent to assist documentation understanding and archiving
* Computing
** Processor level performace optimization: efficient computation of CONV, GEMM, etc.
*** GPU
*** FPGA
** Model Compression
*** Quantization: ternary weight nets
*** Sparsify: Trimming and vector level sparsity
** Parallel Computing
*** Networking: e.g. infiniband
*** Efficient training: async SGD, MPI and other related topics
*** Serving: RPC, docker containers, etc.
! Bibs
* [[Stochastic Portfolio Theory]]
* [[Deep learning bank distress from news and numerical financial data|https://arxiv.org/abs/1706.09627]]
* Learning to Generate Market Comments from Stock Prices: an ordinary seq2seq pointer network
[[Link|https://www.youtube.com/watch?v=ItDroviU_Vs&list=PL-tWvTpyd1VB928jOYmlK7S_A8UUsCJLW]]
! Classifier-Based Two-Sample Tests
Define distance to be the ''Integral Probability Metrics'': $\rho_{\mathcal F}(P, Q) = \sup_{f\in\mathcal F}[\mathbb E_{X\sim P}f(X)-\mathbb E_{Y\sim Q}f(Y)] = 2\text{ accuracy}-1$. Where $\mathcal F$ is a set of classifiers.
* If we make $\mathcal F$ be the family of 1-Lipschitz functions， we get [[WGAN]].
* If $\|f\|_\infty\le 1$, its total variance used in EBGAN (almost)
* If we use the mean error of auto-encoders: BEGAN
In this talk, $\|f\|_{\mathcal H}\le1$ from a unit ball, the metric is [[MMD|Maximum Mean Discrepancy]] used in GMMN.
A optimal classifier can be found with gradient descent of MMD. The theory seems to be broken in the optimization part
! GANs, Actor-Critic and Multilevel Optimization
Related paper [[Connecting Generative Adversarial Networks and Actor-Critic Methods|https://arxiv.org/abs/1610.01945]]
GAN is trained as a multilevel optimization:
$$
\phi^* = \arg\min_\phi F(\theta^*, \phi)
$$
where $F$ is a major goal
$$
\theta^*(\phi) = \arg\min_\theta f(\theta, \phi)
$$
and $f$ is a sub goal against it. This is NP-Hard even for bilevel linear programming.
! Generator-aware Discriminators & Discriminator-aware Generators
When the discriminator can look at the generator parameters, the training process becomes much stable. This take into account how discriminator will adjust, like [[Unrolled GAN]]s. The problem is @@color:#859900;how can we build a network whose inputs are the weights of another network?@@ Ideally we want a parameterization invariant representation.
We can use Hamiltonian Variational Inference. Use gradients of generator w.r.t. $z$ to adjust $z$. The idea is recently used in Bayesian RNNs "posterior sharpening".
A straight forward analog to GAN: generate multiple samples, choose one the discriminator likes best.
[[code|https://github.com/quark0/darts]]
Problems with DARTS:
* Training is not sparse
* Learning rate schedule for different parameters
* Multi-GPU
! Techniques
* Brightness, saturation, contrast
* [[Color augmentation|https://arxiv.org/abs/1312.5402]]
* PCA Jittering: since AlexNet
* Resize: Scale / Aspect Ratio / Random crop
* Flip
* Rotation
* Affine translation: random image interpolation
* Gaussian noise
* Smoothing
* Label Shuffle
! Implementations
* [[Caffe Augmentation|https://github.com/kevinlin311tw/caffe-augmentation]]
* [[FB ResNet Torch|https://github.com/facebook/fb.resnet.torch]]
! In Practice
|Trick |ResNet Torch|Hikvision ImageNet |
|Brightness, saturation, contrast | | |
|[[Color augmentation|https://arxiv.org/abs/1312.5402]] | T | |
|PCA Jittering | T | T |
|Scale / Aspect Ratio / Random crop| T | T |
|Flip| | |
|Rotation| | |
|Affine translation| | T |
|Gaussian noise| | |
|Smoothing| | |
|Label Shuffle| | T |
! Bibs
* [[mixup]]
Hope to relate this with [[An exact mapping between the Variational Renormalization Group and Deep Learning]]
[[Sentense Classification Datasets]]
! Images
[[Open Images|https://github.com/openimages/dataset]]
! Deep Scene Understanding
[[Places2|http://places2.csail.mit.edu/]]
! Video Classification
Youtube-8M
! Audio
* [[TIMIT]]
All infinitely exchangeable (i.e. permutation-invariant) sequences of random variables have distributional representations that are conditionally i.i.d. conditioned on a random probability measure.
<<<
''Definition'' [exchangeable]<br>
An infinite sequence $$X_1, X_2\dots$$ of random variables is said to be exchangeable if for any infinite cardinal number $$n$$ and any two finite sequences $$i_1, \dots, i_n$$ and $$j_i, \dots, j_n$$ (which each of the $$i$$s/$$j$$s distinct), the two sequences
$$X_{i_1},\dots,X_{i_n}$$ and $$X_{j_1},\dots,X_{j_n}$$ both have the same joint probability distribution.
<<<
$$\mathbf X_{\mathbb N}$$ is exchangeable iff $$X_i|Q\overset{iid}{\sim}Q$$ for some random $$Q$$.
de Finetti's theorem may fail for finite exchangeable sequences.
[[read this post|https://www.zhihu.com/question/276842383/answer/394370152]]
! Previous work
* [[Stacked Hourglass Networks for Human Pose Estimation|https://arxiv.org/abs/1603.06937]]
* Hypercolumns for Object Segmentation and Fine-grained Localization
** Above 2 use deconv as upsampling
* [[Zoom Out-and-In Network with Recursive Training for Object Proposal|https://arxiv.org/abs/1702.05711]]
* [[Find Tiny Faces]]
** 2 examples of using above technique in detection
! Hyper ROI
Instead of upsampling the features, we use ROI Pooling layers of multiple scales.
Should try crop and resize instead of roi, see tf-faster-rcnn code.
* [[talk|http://www.nowozin.net/sebastian/blog/debiasing-approximate-inference.html]]
Checkerboard problem: No direct relationship exists among adjacent pixels. The up-sampled feature map generated by deconvolution can be considered as the result of periodical shuffling of multiple intermediate feature maps computed from the input feature map by independent convolutions.
To overcome this, use Pixel Deconvolutional Networks
Deep Networks define a class of "universal approximators":
''Theorem'' [C'89, H'91] Let $\rho()$ be a bounded, non-constant continuous function. Let $I_m$ denote the $m$-dimensional hypercube, and $C(I_m)$ denote the space of continuous functions on $I_m$. Given any $f\in C(I_m)$ and $\epsilon > 0$, there exists $N > 0$ and $v_i, w_i, b_i, i=1, \dots, N$ such that
$$
F(x) = \sum_{i\le N}v_i\rho(w_i^\top x + b_i)\qquad\text{satisfies}
$$
$$
\underset{x\in I_m}{\sup} |f(x)-F(x)|<\epsilon
$$
This guarantees that even a single hidden-layer network can represent any classification problem in which the boundary is locally linear (smooth). But we do not know how to choose a good architecture, or how to optimize it.
! Bibs
* SLP
* RNN
* SGD
! Refs
* [[blog|https://towardsdatascience.com/semantic-code-search-3cd6d244a39c]]
* [[ICSE 2018 code|https://github.com/guxd/deep-code-search]]
! Deep Convolutional Neural Network
The overall architecture of a DCNN consists of one or more layers of convolution followed by pooling followed by densely connected hidden layers and a softmax classifier.
The initial layers in a DCNN use convolutional layers in place of the standard fully-connected layers. [Baidu 15 Arxiv] use convolution and (max) pooling first layer architecture. The hyperparameters introduced are #convolutional layers, ''input region size'' for convolution and pooling layers and pooling fucntion.
! Deep Local United Neural Network
DLUNN differs from a DCNN by using different weights at each location of the first hidden layer. A united neural neural network can be thought of as convolutional neural networks using locally connected computations and without weight-sharing.
! Generative modeling
In real life we always encounter problems where data generator are so complicated that its distribution cannot be evaluated. Probably drawn from super high dimensional space (image/audio). Suppose we have trainig examples $x\sim p_{data}(x)$ and want a model that can draw samples: $x\sim p_{model}(x)$ where $p_{model} = p_{data}$. Of course this is the idealized scenario.
Generally speaking, given high-dimensional data $X$, we want to estimate a low-dimensional model characterizing the population. Or consider [[Low-dimensionality manifold hypotheis]]
When given input of certain conditions, it can generate output with structure as rich as the input. This technique can be applied to speech synthesis, text translation etc. And it can generate data for other models, simulate the experiment environment and support reinforcement learning or simulate the reward of each action and use it in planning. We care this in:
* Simulation environments
* Prediction
* Inverse problems
* Transfer learning
Finally this might help us to leverage the unlabeled data problem. This possibility has not been demonstrated yet but we can assume the intermediate layers of GAN can be used as features for supervised tasks.
This task is challenging because we have to learn a representation from unlabeled data that captures regularity and complexity.
! Previous training methods
[[Survey of earlier work|http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf]]
People always train a generative model that represents the probability distribution. And fit this model using maximum likelihood.
$$
\theta^* = \underset{\theta}{max}\frac 1 m\sum_{i=1}^m\log p(x^{(i)};\theta)
$$
Prior can be encoded in a parametric generatvie model with density estimation. [[GMM]] is a shallow model that assumes density concentrates in a finite number of modes. If data is sequential, we can exploit temporal regularity with [[Word2Vec]].
Until a few years ago the most popular way is to use undirected graphical models. And the flagship under the context of neural network is ''Deep Boltzmann machines'', where we define an energy function over the visible units and several hidden layers. We define an unnormalized probability distribution by taking $e$ to the negative energy fucntion, which has no constraint on it. And we normalize this positive functions to obtain a probability distribution function:
$$
\begin{aligned}
p(h, x) &= \frac 1 Z \tilde p(h, x)\\
\tilde p(h, x) &= \exp(-E(h, x))\\
Z&=\sum_{h, x}\tilde p(h, x)
\end{aligned}
$$
Unfortunately, computing that normalizing constant is NP-complete and our ability to approximate it is limited. In particular, if we train a Boltzmann machine well enough to represent the sharp distribution of widely separated modes, then our ability to approximate it decays. Our best approximation to the partition function is based on drawing samples from the model using ''MCMC''. And this Monte Carlo method works well as long as it is able to move between different modes. As the modes become sharper and better separated, we have a more difficult time transitioning between them. There are a lot of strategies to increase the temperature of the distribution temperarily in order to force the Markov chain to mix. But this problem is not solved to our satisfaction.
Some have been studying directed graphical models, where a similar problem arises. To compute the partition function, we need to sum over all configuartions of hidden states. ''Variational learning'' has been used to solve this. Where we maximize $\log p(x)-\mathcal D_{KL}(q(x)||p(z|x))$. And one of the most popular deep directed model is the [[Variational Autoencoder]]. The basic idea is we have a distribution of the hidden units $q(z)$, and we can draw samples from this distribution with a neural network and some noise. This idea of a differentiable decoder, a.k.a. a generative net is very wide spread and serveral approaches to train the generative decoder nets.
Another popular model is generative stochastic networks. These networks learn the transition probability of the Markov chains. Which are very similar to GANs. The basic strategy is to give up on having an explicit formula for $p(x)$, just learn to sample incrementally. The drawback is it is difficult for Markov chains to mix from one example to another extremely dissimilar example.
! Fast Neural Style
! Image2Vec
# Follow reddit posts on [[VAE vs GAN|https://www.reddit.com/r/MachineLearning/comments/4r3pjy/variational_autoencoders_vae_vs_generative/]] and [[VAE Tutorial|https://www.reddit.com/r/MachineLearning/comments/4paxkq/160605908_tutorial_on_variational_autoencoders/]]
# [[Deformation Metric]]: Can this stability criterion be used as perceptual feature loss for neural style?
# run on chinese char and face
# try (S)GAN as base. @@color:#859900;DoctorTeeth/supergan repo is not there.@@
# Wait for Google Stlye transfer
# GAN super resolution [[in torch|https://github.com/leehomyc/Photo-Realistic-Super-Resoluton]]
! Overview
* [[Deep Generative Model Overview]]
! Models
* Denoising Autoencoder
** [[Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space|https://arxiv.org/abs/1612.00005]]
* [[Variational Autoencoder]]
** an unrestricted architecture to explicitly specify the conditional likelihood $$p(x|z)$$, but can only efficiently provide a lower bound on the marginal likelihood $$p(x)$$.
* [[Generative Adversarial Network]]
** lacking a closed-form likelihood, an auxiliary discriminator model must be trained to estimate various divergences in order to provide the training signal.
* [[Input Convex Neural Network]]
* [[Reversible Generative Models]]
** Use reversible function to perform change of variable.
* [[TCL|Time-Contrastive Learning]]
* [[Attentive Generative Models]]
* [[DISCO Net]]
* [[Convolutional Gaussian Processes|https://arxiv.org/abs/1709.01894]]
* [[Autoregressive Models]]
[[GANs vs VAEs]]
! Bibs
* [[yingzhen's reading list|http://www.yingzhenli.net/home/blog/?p=566]]
* [[Magenta's reviews|https://github.com/tensorflow/magenta/tree/master/magenta/reviews]]
* [[What does it take to generate natural textures?]]
on evaluation
* [[On the Quantitative Analysis of Decoder-Based Generative Models|https://openreview.net/forum?id=B1M8JF9xx¬eId=B1M8JF9xx]]
! Applications
* [[Neural Style]]
* [[High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis|https://arxiv.org/abs/1611.09969]]
** Autoencoder with perceptual loss
* [[Imitation Learning]]
* [[Talking Heads]]
* [[Faceswap]]
<<tabs "[tag[Deep Learning]nsort[order]]" "Deep Learning Theory" "$:/state/tab" "tc-vertical">>
! Categories
!! [[Vision]]
# [[Object Detection With CNNs]]
# [[CNN for Video]]
# [[CNN for NLP]]
# [[Visual Question Answering]]
# [[Image Captioning]]
# [[Deep Representation Learning]]
# [[Deep Generative Models]]
# [[Deep Super-Resolution]]
# [[Deep Physical Simulation]]
!! Speech
* [[Deep Speech Recognition]]
* [[Deep Speaker Adaptation]]
!! Others
* [[Reinforcement Learning]]
* [[Deep Model Compression]]
* Neural Message Passing for Quantum Chemistry
! Resources
# [[Awesome Deep Learning|https://github.com/kjw0612/awesome-deep-vision#video-captioning]]
! Non-Linearities
* [[Neural Net Functions]]
! Connectivity Pattern
* [[Deep Neural Networks Architectures]]
* Dilated
* Recurrent
* Recursive
* Skip / Residual
* Random
! Optimizer
* [[Back Propagation]]
* [[Optimization Algorithms]]
! Loss
* Cross Entropy
* Adversarial
* Variational
* Max. Likelihood
* Sparse
* L2 Reg
* REINFORCE
! Hyper Parameters
* [[Deep Learning Tricks]]
* Learning Rate
* Decay
* Layer Size
* Batch Size
* Dropout Rate
* Weight init
* Data augmentation
* Gradient clipping
* Beta
* Momemtum
! Theory
!! General
* [[Neural Networks: Tricks of the Trade]] Second Edition, Editors: Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller, 2012.
!! RNN
* Martens J, Sutskever I. Learning recurrent neural networks with hessian-free optimization[C]. Proceedings of the 28th International Conference on Machine Learning (ICML-11). 2011: 1033-1040. [[BibTeX|martens2011learning.txt]]
* Hochreiter, Sepp, and Jürgen Schmidhuber. [[Long short-term memory]]. Neural computation 9.8 (1997): 1735-1780.
* Sutskever, Ilya. [[Training recurrent neural networks|Thesis Training RNN]]. Diss. University of Toronto, 2013.
* Chan, Lai-Wan, and Chi-Cheong Szeto. [[Training recurrent network with block-diagonal approximated Levenberg-Marquardt algorithm|Block-diagonal LM]]. Neural Networks, 1999. IJCNN'99. International Joint Conference on. Vol. 3. IEEE, 1999.
! Vision
* [[Fast-RCNN]]
! Speech
!! ASR
* Dahl G E, Yu D, Deng L, et al. [[Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition|DNN ASR]][J]. Audio, Speech, and Language Processing, IEEE Transactions on, 2012, 20(1): 30-42.
* Graves, Alex, and Jürgen Schmidhuber. [[Framewise phoneme classification with bidirectional LSTM and other neural network architectures|LSTM Phoneme Classification]] Neural Networks 18.5 (2005): 602-610. [[BibTeX|graves2005framewise.txt]]
* Graves, Alan, Abdel-rahman Mohamed, and Geoffrey Hinton. [[Speech recognition with deep recurrent neural networks.|Transducer LSTM]] Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013.
* Graves, Alan, Navdeep Jaitly, and Abdel-rahman Mohamed. [[Hybrid speech recognition with deep bidirectional LSTM." Automatic Speech Recognition and Understanding (ASRU)|Hybrid LSTM]], 2013 IEEE Workshop on. IEEE, 2013.
!! Feature learning
* Wang W, Arora R, Livescu K, et al. UNSUPERVISED LEARNING OF ACOUSTIC FEATURES VIA DEEP CANONICAL CORRELATION ANALYSIS[J].
* Lee H, Pham P, Largman Y, et al. [[Unsupervised feature learning for audio classification using convolutional deep belief networks|CRBM for audio]][C]. Advances in neural information processing systems. 2009: 1096-1104. ([[BibTeX|lee09unsupervised.txt]])
!! TTS
* Ze H, Senior A, Schuster M. [[Statistical parametric speech synthesis using deep neural networks|Google DNN TTS]][C]. Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013: 7962-7966. ([[BibTeX|ze13statistical.txt]])
!! Voice conversion
* Chen L H, Ling Z H, Liu L J, et al. [[Voice conversion using deep neural networks with layer-wise generative training|USTC DNN VC]][J]. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 2014, 22(12): 1859-1872. ([[BibTeX|chen14voice.txt]])
* Desai S, Black A W, Yegnanarayana B, et al. Spectral mapping using artificial neural networks for voice conversion[J]. Audio, Speech, and Language Processing, IEEE Transactions on, 2010, 18(5): 954-964. ([[BibTeX|desai10spectral.txt]])
* Nakashika T, Takashima R, Takiguchi T, et al. Voice conversion in high-order eigen space using deep belief nets[C]. INTERSPEECH. 2013: 369-372. ([[BibTeX|nakashika13voice.txt]])
! [[TODO List|Paper Reading Group]]
# [[top 5 arxiv papers|http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html/2]]
[[UCB - Topics in Deep Learning]]
! Implementations
[[GitXiv Projects]]
* [[Deep Learning Tools]]
* [[Low Precision Training]]
* [[Deep Model Compression]]
* [[Model Distillation]]
* [[Visualizing a ConvNet]]
! Performance
* [[CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks|https://arxiv.org/abs/1706.00517]]
* [[RNN Performance Optimization]]
* [[Improving the speed of neural networks on CPUs|https://research.google.com/pubs/pub37631.html]]
! Training Techniques
* [[Sobolev Training for Neural Networks]]
* [[Distributed Training]]
! Hardware
* [[Google TPU]]
* Nervana
* FPGA
* CoreML chips
! Programming Language
* runtime Automatic Differentiation
** PyTorch
** Chainer
** Gluon: combining dynamic AD with code-tracing to produce "static sub-graphs"
* Special-purpose compiler stack
** XLA, ONNX, NNVM, TVM, DLVM
! Serving Frameworks
* [[TensorFlow Serving|https://tensorflow.github.io/serving/]]: Serving tf models
* [[MXNet-Lambda|https://github.com/awslabs/mxnet-lambda]]: Deploy Apache MXNet with AWS Lambda
* [[deepdetect|https://github.com/beniz/deepdetect]]: Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow, XGBoost and TSNE
* [[tf-docker-demo|https://g.hz.netease.com/mi-demo/tf-docker-demo]]: Serving tf models within Docker as Flask application
* [[rancher|https://github.com/rancher/rancher]]
Two ways dealing with time
* video object segm
* video super resolution
! Video Object Segm without Temporal Information
* One-Shot Video Object Segmentation
** Supervised video segm: has the gt of first frame.
** Optical flow methods are bad handling heavy occlusions
** Finetune from the segmentation network (DAVIS) given first mask (Train longer on the first frame)
* Multiple object tracking
** Tracking by detection: infer temperal coherence with a graphical model
** End2end: online tracking with bbox regressor (still no temperal information)
** cons: no notion of identity (Re-ID); large motions (Motion model)
! Video Super Resolution
* Recurrent generation with GAN (Spatial Discriminator)
* TecoGAN (Temporal Discriminator)
* [[STDP and SGD]]
* [[Biologically Inspired Random Projections]]
* Training techniques to deal with
** [[Adversarial Examples]] in classification
** [[Overcoming catastrophic forgetting in NNs]]
** mode collapse in generative modelling
* Making deep networks amenable to (stable!) online updates from weakly supervised data
** True lifelong learning and open up many applications
* "Batch normalization" for GANs or Deep RL
** make everything wants to train by default
* Moving away from supervised learning
** [[Capsules]]
** efficiently use data for more complex problems
** Come up with better exploration strategies as well as active learning approaches to acquire the relevant information while keeping training manageable
* Build single machine learning system that can solve thousands or millions of tasks and can draw from the experience in solving these tasks to learn to automatically solve new tasks.
** [[Jeff's talk|https://www.matroid.com/scaledml/2017/jeff.pdf]]
* [[New Directions for RNNs]]
* Biologically plausible neural net systems
* Exactly solvable DNN models (through symmetry & group theory)
* New/better learning algorithms & design principles
* Predictions on the organization of biological layered networks
! Probabilistic Models
* [[David Duvenaud|https://www.cs.toronto.edu/~duvenaud/]]: PhD@Cambridge, Harvard, AP@TorontoU. Good PyTorch Coder.
** Talks: [[Generator-aware Discriminators & Discriminator-aware Generators|DALI 2017 GAN Workshop]]
* [[David Pfau|http://davidpfau.com/]]: DeepMind
** Working on GAN, RL and Meta Learning
* Bharath K. Sriperumbudur
* Bernhard Schölkopf
* Xi Chen
! Optimization
* Sanjeev Arora
* Rong Ge
* Tengyu Ma
! Modelling
* [[Oriol Vinyals|https://research.google.com/pubs/OriolVinyals.html]]: DeepMind, PhD@UCB
** Sequence modeling: Seq2Seq, Bayesian RNN, LSTM variants.
! RL
* Xi Chen
! NLP
* [[Graham Neubig|http://www.phontron.com/publications.php]]: neulab has some very cool implementations for NMT and [[NL2Code]]
! Introduction
We will walk through Part II of the [[Deep Learning textbook|http://www.deeplearningbook.org/]] by Goodfellow et al. A pdf version can be found [[here|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/textbooks/deep_learning.pdf]]. Some of Goodfellow's most noticeable works are:
# ''Generative Adversarial Network'': ICML 2015 DL Workshop talk [[(transcript)|Generative Adversarial Network]] [[(video)|https://www.youtube.com/watch?v=LXDuuYSNtUY]]
# ''Adversarial Examples'': HORSE 2016 talk [[(slides)|https://goodfeli.github.io/slides/2016_09_19_horse.pdf]] [[(video)|https://www.youtube.com/watch?v=f95EhYFmsj0]] [[transcript|Adversarial Examples]]
Unless otherwise specified the course lectures and meeting times are:
//Tuesday 19:00-20:00//
!! Guidelines
# Suggested reading materials are listed to enhance understanding of the textbook. Extra reading materials are welcomed to be added to the list. The lecturer is not obliged to cover those papers/blog posts.
# The lecturer is suggested to upload the slides to [[this git repo|https://g.hz.netease.com/mi-tools/mi-course/tree/dev/seminar]] before seminar.
# The scribe should keep the notes of the seminar for later reference. A nice script format is `.pdf` or `.html`. Choosing between LaTeX/Markdown as the markup language according to the lecture content is strongly recommended. You may download the LaTeX template from [[here|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/template.zip]]. @@color:#859900;The materials already covered in the book is not necessary to be taken down, a reference of certain position in the book is enough.@@
!! Rotation
|!Lecturer |!Scribe |
|郭贺 |刘丽娟 |
|张晓博 |李一夫 |
|侯章军 |白海 |
|周立峰 |齐狄浩 |
|杨旭东 |曽杨 |
|陈昊 |张鹏 |
|刘丽娟 |郭贺 |
|李一夫 |张晓博 |
|白海 |侯章军 |
|齐狄浩 |周立峰 |
|曽杨 |杨旭东 |
|张鹏 |陈昊 |
! Schedule and Syllabus
|!No. |!Chapter |!Description |!Course Materials |!Lecturer |
|1 |6.{1-4} |XOR example, neural net architecture, loss, activations |Suggested Readings:<br><li>[[PReLU|https://arxiv.org/abs/1502.01852]]</li><li>[[Do Deep Convolutional Nets Really Need to be Deep and Convolutional?|https://arxiv.org/abs/1603.05691]]</li> |曽杨 |
|2 |6.{5-6} |Backprop |Suggested Readings:<br><li>[[Backprop with Tensorflow|http://blog.aloni.org/posts/backprop-with-tensorflow/]]</li> |刘丽娟 |
|3 |7.{1-3} |Parameter regularization |Suggested Readings:<br><li>[[Regularization insights|https://arxiv.org/abs/1207.0580]]</li> |郭贺 |
|4 |7.{4-11} |Data augmentation, label smoothing, early stopping, ensemble methods |Suggested Reading:<br>TBD |张晓博 |
|5 |7.{12-14} |Dropout, adverasrial training, learning on manifold |Suggested Readings:<br><li>[[Dropout|http://jmlr.org/papers/v15/srivastava14a.html]]</li><li>[[Adversarial training|https://arxiv.org/abs/1412.6572]]</li> |李一夫 |
|6 |8.{1-2} |[[On Optimization in Deep Learning]] |Suggested Readings:<br><li>[[Qualitatively characterizing neural network optimization problems|https://arxiv.org/abs/1412.6544]]</li><li>[[Reddit: deep learning without local minima|https://news.ycombinator.com/item?id=11765111]]</li><li>[[Why does Deep Learning work?|https://charlesmartin14.wordpress.com/2015/03/25/why-does-deep-learning-work/]]</li> |陈昊 |
|7 |8.{3-5} |SGD, momentum, adaptive learning rate |Suggested Readings:<br><li>[[Unit tests for stochastic optimization|https://arxiv.org/abs/1312.6055]]</li> |齐狄浩 |
|8 |8.{7} |Batch normalization, running average, other strategies. (skipping chapter 6) |Suggested Readings:<br><li>[[Thesis: 2nd Order RNN|http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]]</li> |杨旭东 |
|9 |9.{1-5} |CONV, POOL, other strategies |Suggested Readings:<br><li>[[Fast Algorithms for Convolutional Neural Networks|https://arxiv.org/abs/1509.09308]]</li><li>[[cs231n lecture notes|http://cs231n.github.io/convolutional-networks/]]</li> |侯章军 |
|10 |9.{6-11} |Structured output, unsupervised training, relation with neuroscience |Suggested Readings:<br><li>[[Fully Convolutional Networks for Semantic Segmentation|https://arxiv.org/abs/1411.4038]]</li><li>[[Super resolution with CONV|https://arxiv.org/abs/1609.07009]]</li> |张鹏 |
|11 |10.{1-3} |RNN, Bi-RNN |Suggested Readings:<br><li>[[Speech recognition with RNN|http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf]]</li><li>[[The Unreasonable Effectiveness of RNN|http://karpathy.github.io/2015/05/21/rnn-effectiveness/]]</li> |周立峰 |
|12 |10.{4-6} |Seq2seq, recursive NN, [[slides|https://docs.google.com/presentation/d/1fiS5J2a7IqgLZhVEoSC-XvreMILKiyZERWLTNzwL7Lo/edit?usp=sharing]] |Suggested Readings:<br><li>[[Sequence to Sequence Learning with Neural Networks|https://arxiv.org/abs/1409.3215]]</li><li>[[Tensorflow Tutorial: Sequence-to-Sequence Models|https://www.tensorflow.org/versions/r0.11/tutorials/seq2seq/index.html]]</li><li>[[Parsing Natural Scenes and Natural Language with Recursive NNs|http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf]]</li> |陈昊 |
|13 |10.{7-12} |Long-term dependency, echo state networks, Gated RNNs, optimization, explicit memory |Suggested Readings:<br><li>[[MemNN|http://arxiv.org/pdf/1410.3916v8.pdf]]</li><li>[[LSTM: A Search Space Odyssey|http://arxiv.org/abs/1503.04069]]</li><li>[[Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks|https://arxiv.org/abs/1502.05698]]</li> |白海 |
|14 |11.{1-6} |Tuning model performance w.r.t. hyperparameters, debugging strategies, SVT example |Suggested Readings:<br><li>[[Google Street View paper|https://arxiv.org/abs/1312.6082v4]]</li> |杨旭东 |
! Dealing non-linearity
Nonlinearity of problems like XOR popularized [[non-linear hidden layers|http://dl.acm.org/citation.cfm?id=104293]] and kernel methods.
Another well-known example is [[the parity problem|https://en.wikipedia.org/wiki/Perceptrons_(book)]]: although a single non-linear hidden layer is sufficient to represent any function, it is not guaranteed to represent it efficiently, can even require exponentially many more parameters than a deeper model.
! [[Deep Approximiation Theory]]
! Estimation Theory
''Theorem'' [Barron'92] The mean integraded square error between the estimated network $\hat F$ and the target function $f$ is bounded by
$$
O\left(\frac{C_f^2}{N}\right) + O\left(\frac{Nm}{K}\log K\right),
$$
where $K$ is the number of training points, $N$ is the number of neurons, $m$ is the input dimension, and $C_f$ measures the global smoothness of $f$.
This formula combines approximation and estimation error. We cannot explain why @@color:#dc322f;online/stochastic optimization works better than batch normalization@@.
! Plausible Optimization
* [[On Optimization in Deep Learning]]
* [[Rethinking Generalization]]
* [[Batch Normalization]]
* [[Depth for Optimization]]
* [[Optimization Landscape of Neural Networks]]
! Statistical Learning Theory
The complexity or capacity of NNs can be computed by measuring how many configurations can be shattered (VC-dimension)
The capacity of the network, if measured by the number of pieces in a piecewise linear approximation, increases exponentially with depth. [Montufar, Pascanu et al, '14]
These results quantify an upper bound on the empirical risk of deep neural networks. But the bounds might be very pessimistic. We still have to figure out @@color:#dc322f;the superior generalization properties of CNNs versus models with similar capacity@@.
There is a functional equivalence between models of different depths at equal capacity [Ba and Caruana '14].
! Information Theory View
* [[Information Theory for Deep Learning]]
* [[Generalization of Deep Nets]]
! Understanding DNN
* [[Opening the Black Box of Deep Neural Networks via Information|https://arxiv.org/abs/1703.00810]]
* [[Energy Propagation in Deep Convolutional Neural Networks|https://arxiv.org/abs/1704.03636]]
* [[Exploring loss function topology with cyclical learning rates|https://arxiv.org/abs/1702.04283]]
! Gradient measures
* [[Nonlinearity Coefficient]]
! Interpretability
* [[Deep learning in biology]]
* DeepMind: [[Interpreting Deep Neural Networks using Cognitive Psychology|https://deepmind.com/blog/cognitive-psychology/]]
* Static
** [[Torch]]
** [[TensorFlow]]
** [[Caffe]]
* Dynamic: write codes that computes predictions, symbolic representation of computation is written down implicitly (based on operator overloading) by toolkit.
** PyTorch
*** [[The Incredible PyTorch|https://github.com/ritchieng/the-incredible-pytorch]]
** [[DyNet|https://github.com/clab/dynet]]: supports on-the-fly batching
! Others
* [[Kaldi]]
* docker-compose: save docker run settings
[[Benchmarking State-of-the-Art Tools|https://github.com/hclhkbu/dlbench]]
The problem with this setting is that when we have a very large model, e.g. MS'es 150-layer one, the unit has to be copied many many times.
! Choosing Hyperparameter
* Random search ([[Bengio 2012|Neural Networks: Tricks of the Trade]])
* Bayesian optimization ([[Yogatama and Smith, 2015|http://arxiv.org/abs/1503.00693]]; [[Bergstra et al., 2013|http://www.jmlr.org/proceedings/papers/v28/bergstra13.pdf]])
!! Examples
* [[Sentense classification|A Sensitivity Analysis of CNN for Sentence Classification]]
* [[Unsupervised feature learning|http://www.jmlr.org/proceedings/papers/v15/coates11a/coates11a.pdf]]
* [[Hyperparameter in SGD|http://arxiv.org/abs/1508.02788]]
However, sophisticated search methods require previous knowledge about which hyperparameters are worth exploring to begin with. And Bayesian optimization is quite far from practical usage.
! Special Layers
[[Batch Normalization]]: addressing the problem of the internal covariate shift
! Topics
* [[Adversarial Examples]]
* [[Data Augmentation]]
* [[Deep Optimization Tricks]]
* [[Deep Model Compression]]
* [[Regularization for Deep Learning]]
* Distributed training
** [[Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability|http://arxiv.org/abs/1609.06870]]
[[link|https://slideslive.com/38923183/deep-learning-with-bayesian-principles]]
[[Exponential Family]] approximations
!! Bayes as Optimization
$$
q_*(\theta)\propto e^{-\mathcal l(\theta)}\propto p(\mathcal D|\theta)p(\theta)\propto p(\theta|\mathcal D)
$$
The optimal solution is propotional to gibbs measure. This holds for a generic loss function.
!! Conjugate Bayesian Inference from Bayesian Principles
The posterior can be described with the same sufficient statistics $$T(\theta)$$.
$$
\mathcal l(\theta):= -\log p(\mathcal D|\theta)p(\theta) = -\lambda_{\mathcal D}^TT(\theta)
$$
$$
\mathbb E_q[\mathcal l(\theta)] = -\lambda_{\mathcal D}\mu
$$
$$
\nabla_\mu\mathbb E_q[\mathcal l(\theta)] = -\lambda_{\mathcal D}
$$
Forward-backward, SVI, Variational message passing etc. are special cases of the same Bayesian principles. [[AISTATS17 Conjuagate-computation variational inference: Converting variational inference in non-conjugate models to inference in conjugate models|https://arxiv.org/abs/1703.04265]]
* [[blog|https://research.googleblog.com/2017/02/announcing-tensorflow-fold-deep.html]]
* [[paper|https://arxiv.org/abs/1702.02181]]
Implements binary Tree-LSTM and graph convolution model
! Bib
!! Survey
* [[A Survey of Model Compression and Acceleration for Deep Neural Networks|https://arxiv.org/abs/1710.09282]]
!! Inference
* [[Accelerating Deep Convolutional Networks using low-precision and sparsity|http://arxiv.org/abs/1610.00324v1]]
* [[High-Dimensional Geometry of Binary Neural Networks]]
!! Training
* [[Training Quantized Nets: A Deeper Understanding]]
* [[BitNet: Bit-Regularized Deep Neural Networks|https://arxiv.org/abs/1708.04788]]
* [[Deep Binary Reconstruction for Cross-modal Hashing|https://arxiv.org/abs/1708.05127]]: for retrieval
!! Quantization
* [[No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference]]
[[Pruning]]
! Old fashioned
* [[RBF Nerwork]]
* [[Restricted Boltzmann Machines]]
! State-of-the-Art
* [[Convolutional Neural Network]]
* [[Recurrent Neural Networks]]
! Neural Architecture Search
* Evolutionary
** hard to scale
* RL
** generally a parameter search with RL
** try some low complexity methods like random forest?
** what those models indicate?
** [[ENAS-pytorch|https://github.com/carpedm20/ENAS-pytorch]]
* Meta-Learner
** [[SMASH: One-Shot Model Architecture Search through HyperNetworks]]
# learning with a curriculum (Bengio et al., 2009)
# training with diffusion (Mobahi, 2016) - a form of continuation method
# noisy activation functions improve performance on a wide variety of tasks (Gulcehre et al., 2016)
# [[Mollifying Networks]]
# [[Low Precision Training]]
Impact of noise injection (Neelakantan et al., 2015)
!! Using mini-batches
Using mini-batchesof examples, as opposedto one example at a time, is helpful in several ways. First, the gradient of the loss over a mini-batch is an estimate of the gradient over the training set, whose quality improves as the batch size increases. Second, computation over a batch can be much more efficient than m computations for individual examples, due to the parallelism afforded by the modern computing platforms.
* [[A Compositional Object-Based Approach to Learning Physical Dynamics|https://arxiv.org/abs/1612.00341]]
** SGD to learn simple physical laws?
* classic: hierarchical clustering
* mainstream: autoencoders
* deep belief networks
! DNN for image retrieval
With a CNN learned on large labeled set, the output of the intermediate layers can be used as image descriptors. Global CNN descriptors lack geometric invariance [[link|http://arxiv.org/abs/1403.1840]].
[[Deep patches|https://hal.inria.fr/hal-01207966/document]] does not require supervision and utilizes outputs from different layers. Images are processed under the three-step pipeline:
# Interest point detection. Use Hessian-Affine detector to extract points at their characteristic scale and estimate for each point an affine-invariant local region (rotate to dominate gradient orientation).
# Interest point description. Map the affine region to a square and learn its representation.
# Patch matching. Given a clustering of the feature space consisting of $k$ centroids $\{c_1, \dots, c_k\}$, VLAD encodes a set of descriptors as the total shift with respect to their assigned centroid.
To extend CNNs for image retrieval, it is possible to use a unsupervicsed net such as convolutional kernel network. The feature representation of CKN is based on a kernel (feature) map and hence data-independent. Let $M$ and $M'$ be two patches of size $m\times m$, and $\Omega=\{1,\dots,m\}^2$ be a set of pixel locations. The sub-patch $p_z$ from $M$ is of fixed size.
!! Bib
* [[End-to-end Learning of Deep Visual Representations for Image Retrieval|https://arxiv.org/abs/1610.07940]]
! Generating natural images
''Non-parametric'': texture synthesis, super-resolution, in-painting
''parametric''
* variational sampling
* iterative forward diffution process
* GANs
** laplacian pyramid extension
* RNN
* deconvolution nets
! Papers
! Linear Transformations
Linear tarnsformations can be applied at
# input features
# activation of hidden layer
# input to the softmax layer
No matter where the linear transformation is applied, it is typically trained from an ''identity weight matrix'' and ''zero bias'' to optimize either the cross-entropy (CE) training criterion.
''Remark'': The activations inside the DNN are not linear, so maybe softmax is where the transformation is resonable.
!! Linear Input Networks
LIN claims that speaker-dependent features can be linearly transformed to match the average behavior which the speaker-independent DNN model described. LIN is impleneted by adding a linear layer $W^{LIN}$ to the input feature.
''Remark'': Lienar transformation (esp. fer frame) is too simple to handle speaker dependent feature.
!! Linear Output Networks
Since the last hidden layer can be considered as the transformed feature, it is (presumably) reasonable to apply a linear transformation on the last hidden layer for a specific speaker so that after the linear transformation it matches better to the average speaker.
This can be added before the last hidden layer or the output layer depending on which has a smaller number of parameters.
! Conservative Training
Surprisingly, adapting all parameters in DNN to optimize the adaptation criterion over the adaptation set can destroy previously learned information.
CT adds regularization to the adaptation criterion, e.g. to adapt only selected weights.
!! $L_2$ Regularization
The basic idea of the $L_2$ regularized CT is to add the $L_2$ norm of the model parameter difference
$$
R_2(W_{SI}-W) = \|vec(W_{SI}-W)\|^2_2
$$
Then the adapation criterion becomes
$$
J_{L_2}(w, b;S) = J(W, b;S)+\lambda R_2(W_{SI}, W),
$$
!! KL-Divergence Regularization
$$
R_{KLD}(W_{SI}, b_{SI}; W, b; S) = \frac 1 M\sum_{m=1}^M\sum_{i=1}^CP_{SI}(i|o_m;W_{SI}, b_{SI})\log P(i|o_m;W, b),
$$
$P_{SI}(i|o_m;W_{SI}, b_{SI})$ and $P(i|o_m;W, b)$ are the probability that the $m$th observation $o_m$ belongs to class $i$, estimated from the speaker-independent and the adapted DNNs,respectively.
KLD regularization constrains the output probabilities rather than the model parameters. The KLD regularized adaptation technique can be easily extended to the ''sequence-discriminative training''. To prevent overfitting the ''frame smoothing'' is often used in the sequence-discriminative training which leads to the interpolated training criterion
''TODO'': consider changing $P_{SI}$ to a distribution independent from speaker features.
!! Reducing Per-Speaker Footprint
Conservative training cannot solve the problem that a huge adapted model needs to be saved for each speaker (model is too large).
! Subspace Methods
!! PCA
PCA subspace construction aims to simplify the adapation weights to speed up the adaptation.
We extend the problem to a general condition, the adaptation aims to estimate a speaker-specific vector $a$ for each speaker. This approach assumes that new speakers can be represented as a point in the space spanned by the $S$ speakers. For each new speaker,
$$
a = \bar a+\tilde U\tilde g_a
$$
where $\tilde U$ and $\tilde g_a$ are reduced eigen matrix and reduced projection of the adaptation parameter vector and $\bar a$ is the mean of the adaptation parameters.
!! Tensor
The speaker and speech subspaces can also be estimated and combined using three-way connections. An example is disjoint factorized DNN. The speaker posterior probability $p(s|x_t)$ is estimated from the acoustic feature $x_t$ using a DNN. The class posterior probability $p(y_t = i|x_t)$ is estimated as
$$
p(y_t = i|x_t) = \sum_s\frac{\exp(s^\top W_iv_t^{L-1})}{\sum_j\exp(s^\top W_jv_t^{L-1})}p(s|x_t)
$$
where $W\in R^{N_L\times S\times N_{L-1}}$ is a tensor, $S$ is the numebr of output classes in the speaker identification DNN.
! Books
* [[DNN for ASR]]
! Refs
* [[curated list|https://github.com/YapengTian/Single-Image-Super-Resolution]]
! Theory
2 ways of doing it with covn:
# upsampling
# deconv
! Bibs
* [[Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network|https://arxiv.org/abs/1607.01977]]
** examples are fairly simple
* [[Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network|https://arxiv.org/abs/1609.05158]]
** [[a supplement|https://arxiv.org/abs/1609.07009]] discussing deconvolution is also worth reading
* [[EDSR|https://github.com/jmiller656/EDSR-Tensorflow]]
* [[Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network]]
* [[RDN|https://arxiv.org/abs/1802.08797]]
* [[The Contextrual Loss]]
* [[Image Inpainting for Irregular Holes Using Partial Convolutions|https://arxiv.org/abs/1804.07723]], similar to [[sparsity-invariant conv|https://arxiv.org/abs/1708.06500]]?
! Challenge
* [[DIV2K|https://data.vision.ee.ethz.ch/cvl/DIV2K/]]
* [[PIRM 2018|http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=75838©ownerid=90126]]
** [[eval|https://github.com/roimehrez/PIRM2018]]
! Experiments
* [[ENAS for SR]]
[[blog|https://medium.com/@phelixlau/notes-on-deformable-convolutional-networks-baaabbc11cf3]] [[paper|https://arxiv.org/abs/1703.06211]] [[mxnet|https://github.com/msracver/Deformable-ConvNets]]
Deformable conv adds a new layer to learn 2D offset for each input. This can be seen as a learnable [[Dilated Convolution]].
! Talk
Handling geometric transformation
* Use sufficient variated data
* Transformation-invariatnt features
** SIFT
** Deformable part-based models
* STN
** parametrized sampling grid with affine transformation
** bilinear integration is differentiable because of no rules
Deformable ConvNet
* no supervision for learning spatial transformation
* gradient for offsets?
* summing efficient?
Modulated Deformable ConvNet
* Error bounded saliency regions
RCNN feature mimicking
Related works
* Relation Networks and Attention Modules
* active convolution, multi-path network
Open questions
* Capture geometric deformation
* How to disentagle different factors in geometric deformation
We consider the following deformatin cost:
$$
\|\tau\|:=2^{-J}\|\tau\|_\infty+\|\nabla\tau\|_\infty.
$$
Scale $J$ controls how much we pay for absolute displacements. Stability criterion: $\forall \|x\|=1, \tau, \|\Phi(x)-\Phi(x_\tau)\|\le C\|\tau\|$. [[Think about 1|TODOs]]
We can define similar metrics for di↵eomorphisms associated with other transformation groups (e.g. rotation).
[[Fourier Transform vs Wavelet Transform]]
[[Learning in Implicit Generative Models|https://arxiv.org/abs/1610.03483]]
We can test the hypothesis that the true data distribution $p^*(x)$ and our model distribution $q(x)$ are equal, using the density ratio function $r(x) = p^*(x)/q(x)$. We can compute this ratio with
# class-probability estimation
# [[divergence minimization|Divergence Classed GANs]
# ratio matching
# moment matching
The original objective can be obtained by add a Bernoulli (logarithmic) loss over the discriminator $\mathcal D(x;\phi) = p(y=+1|x)$.
! Literatures
* Expressive power
** http://proceedings.mlr.press/v49/eldan16.pdf
** http://proceedings.mlr.press/v70/raghu17a/raghu17a.pdf
** http://proceedings.mlr.press/v65/lee17a/lee17a.pdf
** http://proceedings.mlr.press/v49/cohen16.pdf
** http://proceedings.mlr.press/v65/daniely17a/daniely17a.pdf
** https://openreview.net/pdf?id=B1J_rgWRW
* Landscape characterization
** https://papers.nips.cc/paper/6112-deep-learning-without-poor-local-minima.pdf
** https://openreview.net/pdf?id=ryxB0Rtxx
** http://proceedings.mlr.press/v38/choromanska15.pdf
** http://openaccess.thecvf.com/content_cvpr_2017/papers/Haeffele_Global_Optimality_in_CVPR_2017_paper.pdf
** https://arxiv.org/pdf/1605.08361.pdf
** https://arxiv.org/pdf/1712.08968.pdf
! Accelerate optimization
* [[On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization|https://arxiv.org/abs/1802.06509]]
* [[blog|http://www.offconvex.org/2018/03/02/acceleration-overparameterization/]]
By adding a scalar to the simple $$\mathcal l_p$$ regression problem, the ''induced dynamics'' on the overall model contains a memory of past gradients.
$$
\mathbf{w}^{(t+1)}\leftarrow\mathbf{w}^{(t)}-\rho^{(t)}\nabla{L}(\mathbf{w}^{(t)})-\sum_{\tau=1}^{t-1}\mu^{(t,\tau)}\nabla{L}(\mathbf{w}^{(\tau)})
$$
where $$\rho^{(t)}$$ and $$\mu^{(t,\tau)}$$ are appropriately defined (time-dependent) coefficients.
Deeper linear layer although have similar expressiveness, can also benefits by this acceleration.
@article{desai2010spectral,
title={Spectral mapping using artificial neural networks for voice conversion},
author={Desai, Srinivas and Black, Alan W and Yegnanarayana, B and Prahallad, Kishore},
journal={Audio, Speech, and Language Processing, IEEE Transactions on},
volume={18},
number={5},
pages={954--964},
year={2010},
publisher={IEEE}
}
!! Prefer a stack of small filter CONV to one large receptive field CONV
Suppose that you stack three 3x3 CONV layers on top of each other (with non-linearities in between, of course). In this arrangement, each neuron on the first CONV layer has a 3x3 view of the input volume. A neuron on the second CONV layer has a 3x3 view of the first CONV layer, and hence by extension a 5x5 view of the input volume.
Suppose that instead of these three layers of 3x3 CONV, we only wanted to use a single CONV layer with 7x7 receptive fields. These neurons would have a receptive field size of the input volume that is identical in spatial extent (7x7), but with several disadvantages.
# The neurons would be computing a linear function over the input, while the three stacks of CONV layers contain non-linearities that make their features more expressive.
#If we suppose that all the volumes have $C$ channels, then it can be seen that the single 7x7 CONV layer would contain $C\times(7\times7\times C)=49C^2$ parameters, while the three 3x3 CONV layers would only contain $3\times(C\times(3\times3\times C))=27C^2$ parameters.
Intuitively, stacking CONV layers with tiny filters as opposed to having one CONV layer with big filters allows us to express more powerful features of the input, and with fewer parameters. As a practical disadvantage, we might need more memory to hold all the intermediate CONV layer results if we plan to do backpropagation.
!! Layer Sizings
CONV layer should be using small filters (e.g. 3x3 or at most 5x5), using a stride of $S=1$. The most common setting of POOL is to use max-pooling with receptive fields 2x2 and with a stride of 2.
''Reducing sizing headaches.'' The scheme presented above is pleasing because all the CONV layers preserve the spatial size of their input, while the POOL layers alone are in charge of down-sampling the volumes spatially. In an alternative scheme where we use strides greater than 1 or don't zero-pad the input in CONV layers, we would have to very carefully keep track of the input volumes throughout the CNN architecture and make sure that all strides and filters "work out", and that the ConvNet architecture is nicely and symmetrically wired.
''Why use stride of 1 in CONV?'' Smaller strides work better in practice. Additionally, as already mentioned stride 1 allows us to leave all spatial down-sampling to the POOL layers, with the CONV layers only transforming the input volume depth-wise.
''Why use padding?'' In addition to the aforementioned benefit of keeping the spatial sizes constant after CONV, doing this actually improves performance. If the CONV layers were to not zero-pad the inputs and only perform valid convolutions, then the size of the volumes would reduce by a small amount after each CONV, and the information at the borders would be "washed away" too quickly.
''Compromising based on memory constraints.'' In some cases (especially early in the ConvNet architectures), the amount of memory can build up very quickly with the rules of thumb presented above. For example, filtering a 224x224x3 image with three 3x3 CONV layers with 64 filters each and padding 1 would create three activation volumes of size [224x224x64]. This amounts to a total of about 10 million activations, or 72MB of memory (per image, for both activations and gradients). Since GPUs are often bottlenecked by memory, it may be necessary to compromise. In practice, people prefer to make the compromise at only the first CONV layer of the network. For example, one compromise might be to use a first CONV layer with filter sizes of 7x7 and stride of 2 (as seen in a ZF net). As another example, an AlexNet uses filer sizes of 11x11 and stride of 4.
* [[Learning End-to-End Goal-Oriented Dialog|https://arxiv.org/abs/1605.07683]] test on (6) Dialog bAbI task.
* Evaluation
** [[A Review of Evaluation Techniques for Social Dialogue Systems|https://arxiv.org/abs/1709.04409]]: turn-based metrics often ignore the context and do not account for the fact
** Adversarial examples for evaluating reading comprehension systems
Diffeomorphic transformations defines a bijective tranformation between two image domains. They have topology preserving charactersitics.
The transformation is defined as
$$
f = \exp(v)
$$
where $$v$$ is the input vector field. The inverse transformation can be obtained by
$$
f = \exp(-v)
$$
For SGDs:
* We assume the objective is the finite average, $$f(x):=\frac1n\sum_{i=1}^nf_i(x)$$
* We move by estimating the gradient with a subset of data $$x_{k+1} = x_k-\eta\nabla f_i(x_k)$$
For CD:
* We don't have assumptions on $$f(x)$$
* We only change in one direction $$x_{k+1} = x_k-\eta\nabla_if(x_k)$$
There is different stability. For CD, there always exists a step length which we can decrease the objective. It is usually easier to design and analyse CD algorithms.
! Paper
!! Components
* ''controller'': reads environment input $x$ and emits action (RL) or prediction (supervised) $y$, reads and writes ''memory'' with ''interface vectors'', implemented as a LSTM.
* ''memory'': Memory $M$ provides read vectors $r$. The memory is expandable because memory can be reused.
* ''heads'': read or write memory
* ''interface parameters'': is a set of keys $k$, strengths $\beta$, gates $f$ and erase and write vectors $e$ and $v$.
* ''links'': a ''precedence weight'' $p_t$ keeps track of which locations were most recently written to. Temporal order is necessary so iterate through memories in the order they were written. Temporal link matrix $L_t[i,j]$ represents teh degree to which location $i$ was the location written to after location $j$. Facilitates 2 congnitively important fucntions:
** Sequence chunking: don't write at every step
** Recording: iteratively reprocess a sequence, chunking each time
* ''free list'': tracks the usage of each memory location, usage is automatically increased after each write and optionally decreased after each read. The network can choose to write to the most free location.
!! Access
Weights are bounded by a simplex. $a, w, p\in\Delta_N$, where $N$ is the number of locations in memory.
# Update usage: Usage is automatically increased after each write ($w^W_t$) and optionally decreased after each read ($w^{r, i}_t$) by @@color:#859900;free gates@@ ($f^i_t$): $u_{t+1} = (u_t +w^W_t - u_t\circ w_t^W)\circ\prod_{i=1}^R(1-f^i_{t+1}w_t^{r,i})$. $R$ is the number of read vectors.
# Compute write weights: The controller then uses an @@color:#859900;allocation gate@@ ($g_t^a$) to interpolate between writing to a newly allocated ($a_t$) location, or an existing one found by content ($c^w_t$): $w_t^W = g_t^W[g_t^aa_t+(1-g_t^a)c_t^W]$
# Erase and write memory: $M_t = M_{t-1}\circ(E-w^W_te_t^\top)+w^W_tv_t^\top$
# Set linkage states
# Compute read weights: interpolates among backward weighting $b$, forward weighting $f$ and content read weighting $c$: $w_t^{r,i} = \pi_1b_t^i+\pi_2c_t^{r, i}+\pi_3f_t^i$
# Read words: $r_t^i = M_t^\top w_t^{r, i}$
!! Comparing to NTM
In NMT, NTM and [[Memory Networks]], a searching by ''content-based addressing'' memory mechanism is used, just a softmax.
* NTM could only 'allocate' memory in contiguous blocks, leading to memory management problems
* DNC defines a differentiable @@color:#859900;free list@@ tracking the @@color:#859900;usage@@ ($u_t$) of each memory location
!! Tasks DNC is capable of
The field can feel too open. Infer implicit graph structure. The graph traverse is done by associative key-value memory able to be queried by incomplete keys.
* Question answering
* Navigation
* Program learning
! Code
* `access` reads `controller`'s ouput and emits contents read from memory.
* `controller` is a feedforward or LSTM network.
What's the strength of using sonnet?
! Remarks
* DNC fails at bAbI basic induction task (16).
* A problem is how to scale it up.
Let $l$ be a dilated factor and let $*_l$ be defined as
$$
(F*_lk)(\mathbf p) = \sum_{\mathbf s+l\mathbf t=\mathbf p}F(\mathbf s)k(\mathbf t)
$$
The dilated convolution operator has been referred to in the past as “convolution with a dilated filter”. It plays a key role in the //algorithme à trous//, an algorithm for wavelet decomposition.
Dilated convolutions support exponential expanding receptive fields without losing resolution or coverage.
* [[paper|https://arxiv.org/abs/1706.00388]]
* [[Implementation|https://github.com/szagoruyko/diracnets]]
able to outperform ResNet-1000 with plain DiracNet with only 34 layers
Let $H$ be a measure on some parameter space $\Theta$, like the [[conjugate priors|Dirichlet-multinomial distribution]], a Dirichlet process, , is then a distribution over measures on $\Theta$, where the ''scalar concentration parameter'' $\gamma$ controls the similarity of samples $G\sim DP(\gamma, H)$ to the base measure $H$. Analogously to Gaussian processes, DPs may be characterized by the distribution they induce on finite, measurable partitions $(T_1,\dots, T_l)$ of $\Theta$. For such partitions, the random vector $(G(T_1), \dots, G(T_l))$ has a finite-dimensional Dirichlet distribution:
$$
(G(T_1),\dots,G(T_l))\sim Dir(\gamma H(T_1), \dots,\gamma H(T_l))
$$
Samples from DPs are discrete with probability one, a property highlighted by the following ''stick-breaking construction'':
$$
G(\theta)=\sum_{k=1}^\infty\beta_k\delta(\theta, \theta_k) \qquad \beta'_k\sim Beta(1, \gamma) \qquad \beta_k=\beta'_k\prod_{l=1}^{k-1}(1-\beta'_l)
$$
Each parameter $\theta_k\sim H$ is independently sampled from the base measure, while the weights $\beta=(\beta_1, \beta_2,\dots)$ use beta random variables to partition a unit-length "stick" of probability mass. We denote $\beta\sim GEM(\gamma)$ a sample from this stick-breaking process.
<<<
As $\gamma$ becomes large, $E[\beta'_k]=1/(1+\gamma)\rightarrow0$, and $G$ approaches $H$ by uniformly distributing probability mass among a densely sampled set of discrete parameters $\{\theta_k\}^\infty_{k=1}$.
<<<
Given $G\sim DP(\gamma, H)$, each observation $x_i$ is generated by first choosing a parameter $\bar\theta_i\sim G$, and then sampling $x_i\sim F(\bar\theta_i)$.
''Compute with CRP'': we let $z_i\sim\beta$ indicate the unique component of $G(\theta)$ associated with observation $x_i\sim F(\theta_{z_i})$. Marginalizing $G$, these assignments $z$ demonstrate an important clustering behavior. Letting $N_k$ denote the number of observations already assigned to $\theta_k$,
$$
p(z_i|z_{<i},\gamma) = \frac{1}{\gamma+i-1}\left[\sum_kN_k\delta(z_i, k)+\gamma\delta(z_i, \bar k)\right].
$$
Here, $\bar k$ indicates a previously unused mixture component.
! Sampling
The HDP follows an extension of the DP analogy known as the ''Chinese reataurant franchise''. Each object or group defines a separate restaurant in which customers (observed features) $(w_{ji}, v_{ji})$ sit at tables (clusters or parts) $t_{ji}$. Each table shares ''a single dish'' (parameter) $\tilde\theta_{lt}$, which is ordered from a menu $G_0$ shared among restaurants. Let $\mathbf k_l=\{k_{lt}\}$ denote the global parts assigned to all tables (local parts) of category $l$. We may then integrate over $G_0$ and $G_l$ to find the conditional distributions of these assignment variables:
$$
\begin{align*}
p(t_{ji}|t_{j\bar i}, \alpha)&\propto\sum_tN_{jt}\delta(t_{ji}, t) + \alpha\delta(t_{ji}, \bar t)\\
p(k_{lt}|\mathbf k_{\bar l}, k_{l\bar t}, \gamma)&\propto\sum_kM_{k}\delta(k_{lt}, k) + \gamma\delta(k_{lt}, \bar k)
\end{align*}
$$
Here, $M_k$ is the number of tables previously assigned to $\theta_k$, and $N_{jt}$ the number of customers already seated at the $t$th table in group $j$.
! Applications
[[HDP-HMM Diarization]]
It is also called the ''multivariate Polya distribution''.
! Multinomial distribution over category counts
For a random vector of category counts $\mathbf{x}=(n_1,\dots,n_K)$, distributed according to a multinomial distribution, the marginal distribution is obtained by integrating out $\mathbf p$:
$$
\Pr(\mathbf{x}\mid\boldsymbol{\alpha})=\int_{\mathbf{p}}\Pr(\mathbf{x}\mid \mathbf{p})\Pr(\mathbf{p}\mid\boldsymbol{\alpha})\textrm{d}\mathbf{p}
$$
which results in the following explicit formula:
$$
\Pr(\mathbf{x}\mid\boldsymbol{\alpha})=\frac{N!}
{\prod_{k}\left(n_{k}!\right)}\frac{\Gamma\left(A\right)}
{\Gamma\left(N+A\right)}\prod_{k}\frac{\Gamma(n_{k}+\alpha_{k})}{\Gamma(\alpha_{k})}
$$
where $A$ is defined as the sum $A = \sum \alpha_k$. Note that this differs crucially from the above formula in having an extra term at the front that looks like the factor at the front of a multinomial distribution. Another form for this same compound distribution, written more compactly in terms of the beta function, $B$, is as follows:
$$
\Pr(\mathbf{x}\mid\boldsymbol{\alpha})=\frac{N B\left(A,N\right)}
{\prod_{k:n_k>0} n_k B\left(\alpha_k,n_k \right)} .
$$
$$
\min_f\mathbb E_{(x, y)}\mathbb E_{\epsilon}[\mathcal l(y, f(x, \epsilon))-\frac{1}{2}\mathbb E_{\epsilon_1,\epsilon_2}[\mathcal l(f(x,\epsilon_1),f(x, \epsilon_2))]]
$$
[[An illustrated proof of CAP theorem|http://mwhittaker.github.io/2014/08/16/illustrated-proof-cap-theorem/]]
Violations to strict serializability
* stale reads: caused by network partitions. reads historical values.
* dirty reads: caused by promoted update failure. read rolled back txns.
* lost updates: caused by other nodes don't respond. write something but lost.
! In Action
* [[Distributed Training]]
* In TensorFlow
** [[Deep Learning with Dynamic Computation Graphs]]
* Parallel Framework
** [[A Framework for Parallel and Distributed Training of Neural Networks|https://arxiv.org/abs/1610.07448]]
** [[Distributed Training Large-Scale Deep Architectures|https://arxiv.org/abs/1709.06622]]
** [[Probabilistic Synchronous Parallel|https://arxiv.org/abs/1709.07772]]
* SGD
** [[Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent]]
! Barrier methods
* Bulk Synchronous Parallel
** deterministic
** often serializable
* Stale Synchronous Parallel
** fastest worker waits if it is $s$ iterations ahead the slowest
* Asynchronous Parallel
** no restrictions
** causes delayed updates
* Probabilistic Synchronous Parallel
references:
* [[paper|https://arxiv.org/abs/1609.03126]]
* [[blog|http://www.inference.vc/are-energy-based-gans-actually-energy-based/]]
* [[A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models|https://arxiv.org/abs/1611.03852]]
* [[fGAN]]: generalize to $f$-divergence
! Relationship with mutual information
Original GAN objective itself can be derived from mutual information, and in fact, the discriminator $D$ can be thought of as a variational auxillary variable, exactly the same role as the recognition model $q(c|x)$ in the [[InfoGAN]] paper.
$$
\mathcal l_{GAN}(\theta) = I[x, y]
$$
The idea is simple, if $x_{real}$ and $x_{fake}$ come from the same distribution, it should be impossible to guess the value of $y$ better than chance, hence the mutual information would be 0.
! A unifying view on GAN-type algorithms
The loss for $D$, a discrepancy function $s(x)$ usually takes the following separable form:
$$
\mathcal L(s;P,Q)=\mathbb E_{x\sim P}\mathcal l_1(s(x))+E_{x\sim Q}\mathcal l_2(s(x)),
$$
In the original GAN:
$$
D(x) = \frac{1}{1+e^{-s(x)}}
$$
The training criterion for $s$ becomes:
$$
\begin{aligned}
\mathcal L(s) &= & \mathbb E_{x\sim P}\log D(x)+E_{x\sim Q}\log (1-D(x))\\
&=& \mathbb E_{x\sim P}\text{softminus}(s(x))+E_{x\sim Q}\text{softplus}(s(x))
\end{aligned}
$$
The optimal $$s$$ minimizes $$KL[Q\|P]$$. If we choose nonlinearity $$f=\text{softplus}$$ in the outer loop, we recover the GAN variant which minimizes [[Jensen-Shannon-divergence|https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence]], while choosing $f=\text{softminus}$ recovers the version the authors use in the original paper and in DCGAN.
It is also worth noticing that what teh original GAN objective trying to minimize is a lower bound of the GAN loss. Which may explain for the difficulty of training. (See [[this|http://www.inference.vc/infogan-variational-bound-on-mutual-information-twice/]])
And in the ''least-squares importance estimation'' case, we take loss:
$$
\mathcal L(s;P,Q)=\mathbb E_{x\sim P}s^2(x)-2\mathbb E_{x\sim Q}s(x),
$$
and the Bayes-optimal discrepancy function becomes
$$
s^*(x) = \frac{Q(s(x))}{P(x)}
$$
the same as logistic regression but without the logarithm, and this turns out to be a very important difference. For the very simple and intuitive derivation , see [[Kanamori et al, 2009|http://www.jmlr.org/papers/volume10/kanamori09a/kanamori09a.pdf]].
This is similar to the definition of the Rényi $$\alpha$$-divergence I wrote about last week, except for a missing logarithm. If you choose a nonlinearity $$(x)=x^{\alpha-2}$$, you can recover Rényi divergences for different alpha.
! Gradient Estimators
* [[Gradient Estimators for Implicit Models|https://arxiv.org/abs/1705.07107]]
! Related works
Early hybrids of ANN and HMM have been used in ASR systems. Using only backpropagation to train the ANN makes it challenging to exploit more than two hidden layers and the ''context-dependent model'' described above does not take advantage of the numerous effective techniques developed for GMM-HMMs.
Neural networks for producing bottle-neck features are very similar architecturally to autoencoders since both typically have a small code layer. Deeper neural networks are known to be difficult to train with backpropagation alone.
! Problems to Concern
!! Network architecture
* size, #layers.
* increasing model size leads to overfitting
!! About Pretraining
Initializing DNN weights with unsupervised pre-training was initially thought to be important for good performance, but researchers later found that purely supervised training from random initial
weights yields nearly identical final system performance [19 of Baidu].
! Baidu 15 Arxiv
!! NN Acoustic Models
Hybrid HMM systems uses a neural network to approximate the probability of speech feature $x$ conditioned on HMM state label $y$, $p(x|y)$. We can view the neural networks as a classifier of senones given acoustic input.
$$
p(x|y) = \frac{p(y|x)p(x)}{p(y)}
$$
The prior of acoustic features $p(x)$ is usually not tractable, but fixed during decoding. The construction involves 5 steps:
# Label Set. context-dependent triphone senones.
# Forced Alignment. generate a forced alignment with HMM-GMMM system.
# Neural Net Architecture. CNN, RNN
# [[Neural Net Loss Function]]. cross entropy loss function is default for classification tasks, but it ignores ''the DNN as a component of the larger ASR system''.
# Optimization Algorithm. SGD, quasi-Newton
!! DNN Computations
Each layer has a weight matrix $W$ and bias vector $b$. We compute vector $h$ of first layer activations of a DNN using,
$$
h^{(i)}(x) = \sigma(W^{(i)\top}h^{(i-1)}+b^{(i)}).
$$
where $\sigma(z)$ is a point-wise nonlinearity function. Rectified linear units (ReLU) is shown to have better performance for classification tasks.
$$
\sigma(z) = \max(z, 0)
$$
The final layer is a softmax nonlinearity to output a properly formed probability distribution over the possible output categories,
$$
\hat y_j = \frac{\exp(W_j^{(L)\top}h^{(L-1)}+b_j^{(L)})}{\sum_{k=1}^N\exp(W_k^{(L)\top}h^{(L-1)}+b_k^{(L)})}.
$$
[[Deep Convolutional Neural Networks]]
!! Important refs
* Overview of HMM-based speech recognition systems [8-12]
* discriminative loss functions [38-41]
This is a book written by MS researchers.
[[Multitask Learning for Phone label and State]]
[[Connectionist Temporal Classification]]
[[Speech Representation Learning]]
* zsh: https://github.com/denysdovhan/spaceship-prompt
* tmux: https://github.com/gpakosz/.tmux
Since a fully connected layer occupies most of the parameters, it is prone to overfitting. The dropout method is introduced to prevent overfitting. At each training stage, individual nodes are either "dropped out" of the net with probability 1-p or kept with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed. Only the reduced network is trained on the data in that stage. The removed nodes are then reinserted into the network with their original weights.
In the training stages, the probability a hidden node will be retained (i.e. not dropped) is usually 0.5; for input nodes the retention probability should be much higher, intuitively because information is directly lost when input nodes are ignored.
At testing time after training has finished, we would ideally like to find a sample average of all possible 2^n dropped-out networks; unfortunately this is infeasible for large n. However, we can find an approximation by using the full network with each node's output weighted by a factor of p, so the expected value of the output of any node is the same as in the training stages. This is the biggest contribution of the dropout method: although it effectively generates $2^n$ neural nets, and as such allows for model combination, at test time only a single network needs to be tested.
By avoiding training all nodes on all training data, dropout decreases overfitting in neural nets. The method also significantly improves the speed of training. This makes model combination practical, even for deep neural nets.
! Bibs
The dominant perspective today views dropout as either
* an implicit ensemble method
** [[Understanding Dropout]]
* averaging over an approximate Bayesian posterior
** [[Dropout as a Bayesian Approximation]]
** [[Variational Dropout]]
* [[Pusing the bounds of dropout]]
Data:
* question: $q$
* paragraph: $p$
** with 300-dim Glove embedding, fine-tune 1000 most frequent ones
** 3 match indicators
** token features, POS, NER, TF
** alighed question embedding
Architecture:
* Retriever: find 5 wikipedia articles given any question
* Reader: Similar to [[Attentive Reader]]
** encoder: 3-layer biLSTM
!! Model
* VisualBert (Bert pretraining?)
** Layers to dyno
*** BertSelfAttention: num_attn_head
*** BertIntermediate
!! Visualization
* BERTology
** Attention weights
*** gradients magnitude
*** distribution scatter plot
*** attending to specific positions?
!! Papers
* Learning Dynamic Networks
** Supermasks in Superposition: https://arxiv.org/abs/2006.14769
** BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning: https://arxiv.org/abs/2002.06715
** DynaBERT: Dynamic BERT with Adaptive Width and Depth: https://arxiv.org/abs/2004.04037
* Modifying Transformer Structure
** Efficient Transformers: A Survey: https://arxiv.org/abs/2009.06732
* BERTology
** A Primer in BERTology: What we know about how BERT works: https://arxiv.org/abs/2002.12327
** Are Sixteen Heads Really Better than One?: https://arxiv.org/abs/1905.10650
** What Does BERT Look At? An Analysis of BERT’s Attention: https://arxiv.org/abs/1906.04341
''Rating'': Weakly rejection
''Summary'':
Have proposed a one-stage approach to perform sequential analysis of the scene for instance segmentation.
The idea and framework resemble [1] to segment one instance at a time. Different from [1], a categorical label is predicted with instance binary map. This makes the framework capable of multiclass segmentation with only one model. The authors further mined for common object sorting patterns in different aspects.
''Strengths'':
The paper is overall well-written, with detailed description of the method and ablation study of the model structure. The proposed method benefits from multitask learning and greatly simplifies the multiclass instance segmentation procedure of previous recurrent approaches. Analysis of object sorting patterns on multiple datasets demonstrates that the model can capture the object distribution of different scenarios.
''Issues'':
The authors indicate end-to-end training can boost the performance of the task of instance segmentation. However, the proposed method does not show performance or computation advantage against SOTA RNN approach [2] or 2-stage approach [3].<br>
Reviewer believes it is more fair to compare the performance based on similar backbone networks. However, ResNet-101 is used in this paper while FCN is used in [1] and [2].<br>
More experiments are needed to justify the validity of the proposed method on highly multiclass dataset, e.g. MSCOCO.
''Overall Opinion'': Marginally below acceptance threshold. Not enough experiment to support the multitask idea or the ConvLSTM architecture.
* [1] Romera-Paredes, Bernardino, and Philip Hilaire Sean Torr. "Recurrent instance segmentation." European Conference on Computer Vision. Springer, Cham, 2016.
* [2] Ren, Mengye, and Richard S. Zemel. "End-To-End Instance Segmentation With Recurrent Attention." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.
* [3] He, Kaiming, et al. "Mask r-cnn." Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017.
! cider-jack-in
!! cider-mode
|load current buffer|`C-c C-k`|
! Emacs
|delete spaces between 2 words|`M-\`|
|reload current file|`M-x` `load-file`|
! Paredit
[[Paredit Reference Card|http://pub.gajendra.net/src/paredit-refcard.pdf]]
|slurpage|`ctl`+`<left>`/`<right>`|
|split quote|`M-s`|
! Vim
[[ Vim Command Cheat Sheet|http://www.fprintf.net/vimCheatSheet.html]]
|toggle case|`~`|
|change 3 char|`c3l`|
|global replace (confirmation)|`:%s/OLD/NEW/g(c)`|
|open all folds|`zR`|
! Depthwise separable convolution
* MobileNet
* ShuffleNet
* NASNet-A
! Groupwise
* [[PeleeNet|https://arxiv.org/abs/1804.06882]]
* GroupNet
[[link|https://sites.google.com/view/ecv2019/home]]
Provably no-regret polynormial time (in the number of evaluations of the aquisition function) for solving high dimensional BO
! Generalized additive GPs
$$
g(x) = \sum g^{(j)}(x^{(j)})
$$
where each $$x^{(j)}$$ belongs to a low-dimensional subspace. In generalized additive GPs, the kernels are additive.
[[link|http://www.pnas.org/content/early/2017/03/13/1611835114.abstract]]
Elastic weight consolidation is best described as online sequential (diagonalised) Laplace approximation. This is similar to [[Assumed Density Filtering]] the precursor to expectation-propagation. Laplace approximation takes a probability density $$p$$ and approximates it with a Gaussian. By Bernstein-von Misestype convergence theorems, the posterior will converge to the Laplace. EWC use a diagonalised Hessian for variance estimation.
For the first task, the Hessian would be the Fisher information from task $$A$$, $$F^A$$, plus the Hessian of the log prior $$\log p(\theta)$$.
Bayesian inference wouldn't suffer from catastrophic forgetting which haunts optimization-based methods. But the full posterior is intractable.
* EWC approximated Bayesian computation
The problem is begins at the third task, the right approximation
$$
\log p(\theta|\mathcal D_A, \mathcal D_B)\approx -\sum_i(F^A+F^B)_{i, i}(\theta_i-\theta_i^{A, B})^2
$$
The penalty around $$\theta^A$$ has already been taken account.
EWC further required the knowledge of the task it is performing. The tasks here can be different minibatches of the same task.
! UltimateSLAM
* [[paper|Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios]]
* Event Camera: Dinamic Vision Sensor
** capture sensity change, like edge detection, no motion blur?
** Paradigm Shift
* Grayscale + DVS
** Visual Inertial Fusion
DJI
* Framework
** Sensors: ToF, Camera, MNU, GPS, Barometer
** Local coorinate VIO
** Det&Tracking
* Chanllenges Localization
** High altitude: GPS not stable, view changes through window
** Accedental occasions: picture is blurred, VIO should run fast
* Challenges Depth
**
! Other Models
* Speaker Model Synthesis
* Feature Mapping
The idea is different because this is speaker dependent.
<<<
The alignment indicates that the neutral and emotional utterances have the same content
<<< p33 premilinary
Does that mean it's text-dependent?
* How to align Gaussian components
* Emotional vector generation could be different for different person unless Gaussian components are utterances
If the relationship between the utterances (emo/neutral) can be determined before the mapping learning process, then linear regression is likely enough for local translations.
! Approaches
!! Neural Net
The neural net uses a [[rbf|RBF Nerwork]] for activation function.
!!! Questions
* How to match gaussian componets
* How many input samples are there
* Performance relies on #samples
* If the input is different speech samples and the output is one paticular speech sample, lots of text-based knowledge may affect the result
!! Sparse Representation
!!! Questions
* How to get $D_e$
! TODOs
* Extra training data, larger resolution
* RL learning rate
* modify upsampler for smooth generation
* RELAX gradient estimation
* [[paper|https://openreview.net/forum?id=rJxdQ3jeg¬eId=rJxdQ3jeg]]
Generalized divisive normalization. “which has previously been shown to be highly efficient in Gaussianizing the local joint statistics of natural images.” Gaussianizing here means mapping the data onto a normal distribution. The normalisation operation looks like this, where $w_{i}^{(k)}(m,n)$ is the output of the downsampling step,corresponding to spatial location $(m,n)$, and $\beta$, $\gamma$ are learned parameters.
The distortion measure is MSE. Since no reliable perceptual metric for colored image.
Questions
* What is adaptive entropy coder.
* This should be at least useful in generating images.
* Similar to VAE, but approximate the discrete problem along the rate-distortion curve. Study eq 10, the equivalence to VAE holds at least for any norm, and any affine and invertible perceptual transform.
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
! Perturbed SGD
From a series of work by Rong Ge:
* [[Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition|https://arxiv.org/abs/1503.02101]]: [[blog|http://www.offconvex.org/2016/03/22/saddlepoints/]]
* [[Gradient Descent Converges to Minimizers|https://arxiv.org/abs/1602.04915]]: [[blog|http://www.offconvex.org/2016/03/24/saddles-again/]]
* [[How to Escape Saddle Points Efficiently|https://arxiv.org/abs/1703.00887]]: [[blog|http://www.offconvex.org/2017/07/19/saddle-efficiency/]]
Pertubing Gradient Descent can escape saddle points if already there. Two simple methods have been studied:
* ''Intermittent perturbations'': [Ge et al. 2015] considered adding occasional random perturbations to GD, and were able to provide the first polynomial time guarantee for GD to escape saddle points.
* ''Random Initialization'': [Lee et al. 2016] showed that with only random initialization, GD provably avoids saddle points asymptotically.
<<<
''Perturbed gradient descent''
# ''for'' $t = 1, 2, \dots$ ''do''
# $\qquad x_t\rightarrow x_{t-1}-\eta\nabla f(x_{t-1})$
# $\qquad$ ''if'' perturbation condition holds ''then''
# $\qquad \qquad x_t\rightarrow x_t+\xi_t$
<<<
We make the following two assumptions regarding smoothness:
* Assumption 1: $f$ is $l$-gradient-Lipschitz, i.e. $\forall x_1, x_2, |\nabla f(x_1)-\nabla f(x_2)|\le l|x_1-x_2|$.
* Assumption 2: $f$ is $\rho$-Hessian-Lipschitz, i.e. $\forall x_1, x_2, |\nabla^2 f(x_1)-\nabla^2 f(x_2)|\le \rho|x_1-x_2|$.
A point $x$ is an $\epsilon$-first-order stationary point if $|\nabla f(x)|\le\epsilon$. Further more, if $\lambda_{\min}(\nabla^2f(x))\ge-\sqrt{\rho\epsilon}$, $x$ is a $\epsilon$-second-order stationary point. $\rho$ is the Hessian Lipschitz constant introduced above.
''Theorem'': If Assumption 1 holds, then GD, with $\eta = 1/l$, finds an $\epsilon$-first-order stationary point in $2l(f(x_0)-f^*)/\epsilon^2$ iterations.
For second-order stationary points, same property can be found:
''Main Theorem'': If Assumption 1 and 2 holds, then PGD, with $\eta = O(1/l)$, finds an $\epsilon$-second-order stationary point in $\tilde{O}(2l(f(x_0)-f^*)/\epsilon^2)$ iterations.
! Operator Analysis POV
[[On the Importance of Consistency in Training Deep Neural Networks|https://arxiv.org/abs/1708.00631]]claims that @@color:#859900;some layers are harder to train (esp. with GD), thus are the bottleneck of the whole training process. According to ''the law of minimum''@@.
Because gradient as an operator is contractive? The proposed SGD2 is just a perturbed SGD.
! Natasha 2
My $$f(\theta)$$ is easy to evaluate but hard to optimize, perhaps because it contains discrete operations, or the function is piecewhise constant. Backpropagation is not possible.
Observe that for any probability distribution $$p_\psi$$ over $$\theta$$ the following holds:
$$
\min_\theta f(\theta)\le\min_\psi\mathbb E_{\theta\sim p_\psi}f(\theta)
$$
therefore in ES we concentrate of the following optimization problem instead:
$$
\psi^*\rightarrow \arg\min_\psi\mathbb E_{\theta\sim p_\psi}f(\theta)
$$
Often, depending on the function $$f$$ and class of distribution $$p_\psi$$, a local minimum of $$f$$ can be recovered from a local minimum of $$\psi$$.
[[REINFORCE]] gradient estimator relies on the following trick
$$
\frac{\partial}{\partial \psi_i}\mathbb E_{\theta\sim p_\psi}[f(\theta)]=\mathbb E_{\theta\sim p_\psi}[\frac{\partial}{\partial \psi_i}\log p_\psi(\theta)f(\theta)]
$$
! Policy gradient methods for reinforcement learning with function approximation
* [[paper|https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf]]
! Refs
* [[JMLR14 Neural Evolution Strategies]]
For each positive integer $$n$$ let $$(X_{n, i};i=1, 2, \dots)$$ be an infintiely exhangeable process with mean zero, variance one, and finite absolute third moment. Define
$$
S_n = \frac{1}{\sqrt{n}}\sum_{i=1}^nX_{n, i}
$$
Then if the following condition hold:
https://arxiv.org/ftp/arxiv/papers/1301/1301.2294.pdf
The objective of the EM algorithm is to maximize the likelihood of the observed data.
Use EM to find attention bases
* Program synthesis
** [[NL2Code]]
** [[Language Model with FS-LSTM]]
** [[pix2code]]
* [[Graph2Seq]]
* [[Meta-SBL]]
* [[paper|https://arxiv.org/abs/1705.08439]]
* [[code|https://github.com/gbrik/expert-iteration]]
!Definition
$$
q(\theta)\propto\exp[\lambda^TT(\theta)]
$$
$$\lambda$$ is the natural parameter and $$T(\theta)$$ is the sufficient statistics. $$\mu:=\mathbb E_q[T(\theta)]$$ is the expectation parameter.
In the Gaussian case:
$$
\begin{aligned}
\mathcal N(\theta|m, S^{-1})&\propto \exp[-\frac12(\theta-m)^TS(\theta-m)]\\
&\propto[(Sm)^T\theta + \operatorname{Tr}(-\frac{S}{2}\theta\theta^T)]
\end{aligned}
$$
* Gaussian distribution: $$q(\theta):=\mathcal N(\theta|m, S^{-1})$$
* Natural parameters: $$\lambda:=\{Sm, -S/2\}$$
* Expectation parameters: $$\mu:=\mathbb E_q(\theta), \mathbb E_q(\theta\theta^T)\}$$
!! Single-parameter
$$
f_X(x|\theta) = h(x) \exp \left (\eta(\theta) \cdot T(x) -A(\theta)\right )
$$
where $$T(x)$$, $$h(x)$$, $$\eta(\theta)$$, and $$A(\theta)$$ are known functions.
In the definitions above, the functions $$T(x)$$, $$\eta(\theta)$$ and $$A(\eta)$$ were apparently arbitrarily defined. However, these functions play a significant role in the resulting probability distribution.
* $$T(x)$$ is a sufficient statistic of the distribution. For exponential families, the sufficient statistic is a function of the data that fully summarizes the data $$x$$ within the density function. This means that, for any data sets $x$ and $y$, the density value is the same if $$T(x) = T(y)$$. This is true even if $x$ and $y$ are quite different—that is, $d(x,y)>0$. The dimension of $$T(x)$$ equals the number of parameters of $$\theta$$ and encompasses all of the information regarding the data related to the parameter $$\theta$$. The sufficient statistic of a set of independent identically distributed data observations is simply the sum of individual sufficient statistics, and encapsulates all the information needed to describe the posterior distribution of the parameters, given the data (and hence to derive any desired estimate of the parameters). This important property is further discussed below.
* $$\eta$$ is called the natural parameter. The set of values of $\eta$ for which the function $$f_X(x;\theta)$$ is finite is called the natural parameter space. It can be shown that the natural parameter space is always convex.
* $$A(\eta)$$ is called the log-partition function because it is the logarithm of a normalization factor, without which $$f_X(x;\theta)$$ would not be a probability distribution ("partition function" is often used in statistics as a synonym of "normalization factor"):
$$
A(\eta) = \ln\left ( \int_x h(x) \exp (\eta(\theta) \cdot T(x)) \operatorname{d}x \right )
$$
The function $$A$$ is important in its own right, because the mean, variance and other moments of the sufficient statistic $$T(x)$$ can be derived simply by differentiating $$A(\eta)$$. For example, because $$\ln(x)$$ is one of the components of the sufficient statistic of the gamma distribution, $$\mathbb{E}[\ln x]$$ can be easily determined for this distribution using $$A(\eta)$$. Technically, this is true because
$$
K(u|\eta) = A(\eta+u) - A(\eta),
$$
is the cumulant generating function of the sufficient statistic.
! Properties
Exponential families have a large number of properties that make them extremely useful for statistical analysis. In many cases, it can be shown that, except in a few exceptional cases, only exponential families have these properties. Examples:
* Exponential families have sufficient statistics that can summarize arbitrary amounts of independent identically distributed data using a fixed number of values.
* Exponential families have conjugate priors, an important property in Bayesian statistics.
* The posterior predictive distribution of an exponential-family random variable with a conjugate prior can always be written in closed form (provided that the normalizing factor of the exponential-family distribution can itself be written in closed form). Note that these distributions are often not themselves exponential families. Common examples of non-exponential families arising from exponential ones are the Student's t-distribution, beta-binomial distribution and Dirichlet-multinomial distribution.
* In the mean-field approximation in variational Bayes (used for approximating the posterior distribution in large Bayesian networks), the best approximating posterior distribution of an exponential-family node (a node is a random variable in the context of Bayesian networks) with a conjugate prior is in the same family as the node.
[[Geometry of exponential families]]
!! Conjugate Distributions
In the case of a likelihood which belongs to the exponential family there exits a conjugate prior, which is often also in the exponential family. A conjugate prior $\pi$ for the parameter $\eta$ of an exponential family
$$
f(x|\boldsymbol\eta) = h(x) \exp \left ( {\boldsymbol\eta}^{\rm T}\mathbf{T}(x) -A(\boldsymbol\eta)\right )
$$
is given by
$$
p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) = f(\boldsymbol\chi,\nu) \exp \left (\boldsymbol\eta^{\rm T} \boldsymbol\chi - \nu A(\boldsymbol\eta) \right ),
$$
or equivalently
$$
p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) = f(\boldsymbol\chi,\nu) g(\boldsymbol\eta)^\nu \exp \left (\boldsymbol\eta^{\rm T} \boldsymbol\chi \right ), \qquad \boldsymbol\chi \in \mathbb{R}^s
$$
where $s$ is the dimension of $\boldsymbol\eta$ and $\nu > 0$ and $\boldsymbol\chi$ are hyperparameters (parameters controlling parameters). $v$ correspinds to the effective number of observations that the prior distribution contributes, and $\boldsymbol\chi$ corresponds to the total amount that these pseudo-observations contribute to the sufficient statistic over all observations and pseudo-observations.
We can then compute the posterior as follows:
$$
\begin{aligned}
p(\boldsymbol\eta|\mathbf{X},\boldsymbol\chi,\nu)& \propto p(\mathbf{X}|\boldsymbol\eta) p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) \\
&= \left(\prod_{i=1}^n h(x_i) \right) g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T} \sum_{i=1}^n \mathbf{T}(x_i)\right)
f(\boldsymbol\chi,\nu) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \\
&\propto g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T}\sum_{i=1}^n \mathbf{T}(x_i)\right) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \\
&\propto g(\boldsymbol\eta)^{\nu + n} \exp\left(\boldsymbol\eta^{\rm T} \left(\boldsymbol\chi + \sum_{i=1}^n \mathbf{T}(x_i)\right)\right)
\end{aligned}
$$
! Examples
# normal
# exponential
# log-normal
# [[gamma|Gamma Distribution]]
# chi-squared
# beta
# Dirichlet
# Bernoulli
# categorical
# Poisson
# geometric
# inverse Gaussian
# von Mises-Fisher
* One-shot generalization in deep generative models
* [[Variational Memory Addressing in Generative Models]]
** discrete attention by variational inference
! Applications
!! Face Recognition
# Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR, 2014. [[link|https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf]]
# Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015. [[link|http://arxiv.org/abs/1502.00873]]
Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015. [[link|http://arxiv.org/abs/1503.03832]]
!! Facial Landmark Detection
# Yue Wu, Tal Hassner, Kang Geon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015. [[link|http://arxiv.org/abs/1511.04031]] [[Project|http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/]]
!! Others
# Spoofing 2D Face Detection: Machine See People Who Aren't There [[link|http://128.84.21.199/abs/1608.02128]]
! Bibs
* [[An All-In-One Convolutional Neural Network for Face Analysis|https://arxiv.org/abs/1611.00851]]
* Best up to now: [[Finding Tiny Faces]]
! Models
* [[VGG-Face|http://www.robots.ox.ac.uk/~vgg/software/vgg_face/]]
! Datasets
* [[CelebA|http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html]]
* Few-shot Implementation
** Code: [[fewshot-face-translation-GAN|https://github.com/shaoanlu/fewshot-face-translation-GAN]]
** Using [[SPADE|https://arxiv.org/abs/1903.07291]]: normalization based on semantic supervision
** and [[AdaIN|https://arxiv.org/abs/1905.01723]]: incorporate instance information for generation. Probably usable for few-shot instance segmentation
Factor analysis is the most common example of latent variable model, using a linear function as the mapping.
$$
t = Wx + \mu + \epsilon
$$
Conventionally, the latent variables are defined to be independent and Gaussian with unit variance, so $x\sim N(0, I)$. The noise model is also Gaussian such that $\epsilon\sim N(0, \Psi)$. As a result, $t\sim N(\mu, \Psi+WW^\top)$.
The key motivation for this model is that, because of the diagnality of $\Psi$, the observed variables $t$ are conditionally independent given the latent variables, or factors, $x$. The intention is that the dependencies between the data variables $t$ are explained by a smaller number of latent variables $x$, while $\epsilon$ represents variance unique to each observation variable. This is in contrast to conventional PCA, which effectively treats both variance and covariance identically.
There is no closed-form analytic solution for $W$ and $\Psi$, and so their values must be determined by iterative procedures.
[[Billion-scale similarity search with GPUs|https://arxiv.org/abs/1702.08734]]
* near-optimal exact and approximate GPU k-selection
[[code|https://github.com/obachem/kmc2]]
Random seeding can lead to arbitrarily bad clustering result. K-means++ is slow.
A Markov chain sampling process with the stationary distribution exactly the same as k-means++ is proposed. The result is provably good without any assumptions of the data.
The performance is of up to 1,064x speedup and 1.32% relative error comparing to k-means++
Natural-gradient VI methods exploit the Riemannian geometry of $$q(\theta)$$ by scaling the gradient iwth the inverse of its [[Fisher information matrix (FIM)|Fisher information metric]].
For Gaussian mean-field VI, the method of [[Khan & Lin (2017)|https://emtiyaz.github.io/papers/van_poster.pdf]] gives the following update:
NGVI:
$$
\begin{aligned}
\mathbf \mu_{t+1} &= \mathbf \mu_t + \beta_t\mathbf\sigma_{t+1}^2\circ[\hat\nabla_\mu\mathcal L_t],\\
\mathbf \sigma^{-2}_{t+1} &= \mathbf \sigma_t - 2\beta_t[\hat\nabla_{\sigma^2}\mathcal L_t],\\
\end{aligned}
$$
Update components with larger variance more?? What about trust region?
VON:
$$
\begin{aligned}
\mathbf \mu_{t+1} &= \mathbf \mu_t - \beta_t(\hat{\mathbf g}(\theta_{t}) + \tilde\lambda\mathbf\mu_t)/(s_{t+1}+\tilde\lambda),,\\
\mathbf s_{t+1} &= (1-\beta_t)\mathbf s_t + \beta_t\text{diag}[\hat\nabla_{\sigma^2}\mathcal L_t],\\
\end{aligned}
$$
[[ArXiv|http://arxiv.org/abs/1504.08083]]
This is an exciting model for detection allowing fine-tuning from previous networks.
! Training
!! Multi-task loss
$$
L(p, u, t^u, v) = L_{cls}(p, u)+\lambda[u\ge1]L_{loc}(t^u, v)
$$
where $L_{cls}(p, u)=-\log p_u$ and
$$
L_{loc}(t^u, v) = \sum_{i\in\{x, y, w, h\}}\text{smooth}_{L_1}(t_i^u-v_i),
$$
where
$$
\text{smooth}_{L_1} = \left\{\begin{array}{rr}
0.5x^2 & |x| < 1\\
|x|-0.5 & \text{otherwise,}
\end{array}\right.
$$
!! Mini-batch sampling
2 images, 64 RoIs each. 25% of them have at least intersection over union (IoU) with gt, others have $[0.1, 0.5)$.
! Region Pooling SPP-Net
unify the conv feature map dimensions by convolutions.
Fast-RCNN use ROI pooling to unify the dimensions and replaces SVM with softmax. Use bbox regressor to refine the regions.
[[link|http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks]]
! Implementations
* [[Tensorflow|https://github.com/endernewton/tf-faster-rcnn]]
! Anchors
! Loss function
$$
L(\{p_i\}, \{t_i\}) = \frac {1} {N_{cls}} \sum_iL_{cls}(p_i, p_i^*) + \lambda \frac {1} {N_{reg}} \sum_ip_i^*L_{reg}(t_i, t_i^*)
$$
where $p_i^*$ is the probability of this anchor being an object. $L_{cls}$ is the log loss over 2 classes. $L_{reg}(t_i, t_i^*) = R(t_i-t_I^*)$ is the robust loss function (smooth $L_1$).
! Optimization
randomly sample 256 anchors to compute the loss in a mini-batch to keep positive and negative sample ratio to 1:1. However, if there is not enough positive samples, negative samples are padded.
[[RNN for ASR|http://research.microsoft.com/pubs/164627/4085.pdf]]: Using features other than MFCCs has long been a focus of research. A common technique to merge streams is to use a Tandem method, in which sun
How to generate good spectro-temporal modulation features?
!! Deep Learning
!! wu10robust
It is used to deal with noisy environment.
Constrained nonnegative tensor factorization (cNTF) using common tensor decomposition methods such as CANCECOMP/PARAFAC model with nonnegative constraints.
* [[Blog|https://distill.pub/2018/feature-wise-transformations/]]
! Applications
* VQA
** [[FiLM: Visual Reasoning with a General Conditioning Layer]]
* Style Transfering
** FiLM parameters are computed as the spatial mean and standard deviation statistics of the style feature maps.
* Image Recognition
** Highway Network
** Squeeze-and-Excitation networks
* NLP
** [[Gated-Attention Reader]]
* Reinforcement Learning
** Gated-attention architectures for task-oriented language grounding
**
Given a (proper) convex function $$g(x)$$ for $$x\in\mathbb R^d$$:
<<<
''Definition'' [Fenchel dual]<br>
$$g^*(y):=\max_{x\in\mathbb R^d}\{y^Tx-g(x)\}$$, for $$y\in\mathbb R^d$$
<<<
!!! Examples:
* $$g(x)=\frac1p\|x\|^p_p$$, $$g^*(y)=\frac1q\|y\|^q_q$$, where $$\frac1p+\frac1q=1$$ (Euclidean norm is self-dual)
* $$g(x)=\max\{0, 1-x\}$$, $$g^*(y)=\left\{\begin{array}{lr}y, & y\in[-1, 0]\\+\infty,&otherwise\end{array}\right.$$
* $$g(x)=\log(1+e^x)$$, $$g^*(y)=\left\{\begin{array}{lr}y\log y+(1-y)\log(1-y), & y\in[0, 1]\\+\infty,&otherwise\end{array}\right.$$
<<<
''Theorem'' [Duality]<br>
You get the original function if you take the dual of the duality function<br>
$$g^{**}(x):=\max_{y\in\mathbb R^d}\{x^Ty-g^*(y)\}=g(x)$$
<<<
!!! Properties
* $$g^*(y)$$ is (proper) convex
* $$g(x)$$ is $$L$$-smooth, $$g^*(y)$$ is $$\frac 1L$$-strongly convex
<<<
''Definition'' [Smooth, strongly convex]<br>
$$f(x)$$ is $$L$$-smooth if all of the eigenvalues of the Hessian function are upperbounded by L:
$$
\nabla^2f(x)\preceq L\cdot I
$$
$$f(x)$$ is $$\sigma$$-strongly convex if $$\nabla^2f(x)\succeq \sigma\cdot I$$
<<<
* Unpacking all files
* make
* make test
* [[Few-shot segmentation with guided networks|https://github.com/shelhamer/revolver]]
! Continuous Normalizing Flow
Given an ODE defined by the parametric function $$f(z(t), t;\theta)$$, we solve the intitial value problem $$z(t_0) = z_0, \partial z(t)/\partial t= f(z(t), t;\theta)$$ to obtain $z(t_1)$ which constitutes our observable data. The change in log-density follows:
$$
\frac{\partial\log p(z(t))}{\partial t} = -\text{Tr}\left(\frac{\partial f}{\partial z(t)}\right)
$$
The total change in log-density can be computed by integrating across time:
$$
\log p(z(t_1)) = \log p(z(t_0)) - \int_{t_0}^{t_1}\text{Tr}\left(\frac{\partial f}{\partial z(t)}\right)dt.
$$
Given a datapoint x, we can compute both the point $$z_0$$ which generates $$x$$, as well as $$\log p(x)$$ under the model by solving the initial value problem.
''Adjoint method'': For any scalar loss function which operates on the solution to an initial value problem:
$$
L(z(t_1)) = L\left(\int_{t_0}^{t_1}f(z(t),t;\theta)dt\right)
$$
Then its derivative takes the form of another initial value problem:
$$
\frac{dL}{d\theta} = -\int_{t_1}^{t_0}\left(\frac{\partial L}{\partial z(t)}\right)^T\frac{\partial f(z(t),t;\theta)}{\partial \theta}dt
$$
* Gradients are computed by solving another ODE
* Don't backpropagate through the operations of the solver
* No need to store activations, $$O(1)$$ memory gradients
* $$\left(\frac{\partial L}{\partial z(t)}\right)^T\frac{\partial f(z(t),t;\theta)}{\partial \theta}$$ is implicit vector-jacobian product (reverse-AD)
! Density Estimation with Unrestricted Architecture
Estimate of the log-density with $$\mathcal O(D)$$ cost with
* vector-Jacobian products $$v^T\frac{\partial f}{\partial z}$$ can be computed for approximately the same cost as evaluating $$f$$ using ''reverse-mode automatic differentiation''.
* ''Hutchinson's trace estimator'': unbiased estimate of the trace of a matrix by taking a double product of that matrix with a noise vector
$$
\text{Tr}(A) = E_{p(\epsilon)}[\epsilon^TA\epsilon]
$$
[[f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization|https://arxiv.org/abs/1606.00709]]
* generalize GAN objective to arbitrary $f$-divergences
* simplify GAN algorithm + local convergence proof
* demonstrate different divergences
! Objective
Divergence between two distributions
$$
D_f(P\|Q) = \int_{\mathcal X}q(x)f\left(\frac{p(x)}{q(x)}\right)dx
$$
$f$ is convex and $f(1) = 0$.
<<<
Any convex $f$ can be represented as point-wise max of linear functions
<<<
Every convex function $f$ has a convex Fenchel conjugate $f^*$ so that
$$
f(u) = \underset{t\in\text{dom}_{f^*}}{\sup}\{tu-f^*(t)\}
$$
$$
\begin{aligned}
D_f(P\|Q) &= \int_Xq(x)f\left(\frac{p(x)}{q(x)}\right)dx \\
&= \int_Xq(x)\underset{t_x\in\text{dom}_{f^*}}{\sup}\left\{t_x\frac{p(x)}{q(x)}-f^*(t_x)\right\}dx \\
&\ge \underset{T\in\mathcal T}{\sup}\left(\int_Xp(x)T(x)dx-\int_Xq(x)f^*(T(x))dx\right) \\
&= \underset{T\in\mathcal T}{\sup}\left(\mathbb E_{x\sim P}[T(x)]-\mathbb E_{x\sim Q}[f^*(T(x))]\right)
\end{aligned}
$$
$\mathcal T$ is a function family e.g. some neural networks. We can approximate $f$-divergence from sampling from $Q$ and $P$.
This has a structural similarity with GAN.
* The caveat here is $f^*$ only exist in some cases.
* Only need to change some LOC to change any GAN implementation into this.
! Algorithm
Original GAN uses a doule-loop algorithm.
* Inner loop: tighten divergence lower bound
* Outer loop: minimize generator loss
In practice, the inner loop is run for one step
fGAN proposed single-step algorithm. Which simultaneously take
* a positive gradient w.r.t. variational function $T_\omega(x)$
* a negative gradient w.r.t. generator function $P_\theta(x)$
! Convergence
The ''local'' convergence theorem around a saddle point. Assume:
* $F$ is locally (strongly) convex w.r.t. $\theta$
* $F$ is (strongly) concave w.r.t. $\omega$
that is,
$$
\nabla^2F = \left[\begin{array}{cc}
\nabla_\theta^2F & \nabla_\theta\nabla_\omega F\\
\nabla_\omega\nabla_\theta F& \nabla_\theta^2F
\end{array}\right], \nabla_\theta^2F\succ 0, \nabla_\omega^2F\prec 0
$$
The algorithm has geometric rate of convergence. Define $J(\theta, \omega) = \frac 1 2\|\nabla_\theta F\|^2 + \frac 1 2\|\nabla_\omega F\|^2$, then
$$
J(\theta^t, \omega^t)\le\left(1-\frac{\delta^2}{L}\right)^tJ(\theta^0, \omega^0)
$$
where $\delta$ is strong convexity parameter and $L$ is smoothness parameter.
! Remarks
The divergence is so noisy that I wonder if this is useful.
GRU encode NL queries into FiLM generator
A filter bank is an array of [[band-pass filters|Band-pass Filter]] that separates the input signal into multiple componets, each one carrying single frequency sub-band of the original signal.
* [[link|https://arxiv.org/abs/1612.04402]]
* [[demo|https://github.com/peiyunh/tiny]]
Improvements
* multi-task modeling of scales: trained detectors from multiple layer features (hypercolumn features). Help with large objects not small ones. Foveal descriptors.
* process image pyramid to capture large scale variations
* encode context: high-resolution components are crucial
* change resolution: to find large objects, use 2x smaller canonical resolution, to find small objects, use 2x larger canonical resolution.
Fisher information metric is the only Riemannian metric that "makes sense" for [[statistical manifolds|Statistical manifold]]:
$$
I(\theta) = \left[\int\frac{\partial\log p(x;\theta)}{\partial\theta_i}\frac{\partial\log p(x;\theta)}{\partial\theta_j}p(x;\theta)dx\right]=[g_{ij}]
$$
The [[Infinitesimal length element]] is given by
$$
ds^2 = \sum_{i=1}^d\sum_{j=1}^dd\theta_i^T\nabla^2F(\theta)d\theta_j
$$
Cencov proved that for a non-singular transformation of the parameters $\lambda=f(\theta)$, the information matrix
$$
I(\lambda)=\left[\frac{\partial\theta_i}{\partial\lambda_j}\right]I(\theta)\left[\frac{\partial\theta_i}{\partial\lambda_j}\right].
$$
Normalized and de-correlated activation is well known for improving the conditioning of the Fisher information matrix.
!! Appearances
In reinforcement learning, the natural gradient is defined as $$F^{-1}g$$, where $$F$$ is the average Fisher information matrix
$$
\begin{aligned}
F &= \mathbb E_{s, a\sim\pi}[(\nabla_\theta\log\pi_\theta(a|s))^T(\nabla_\theta\log\pi_\theta(a|s))]\\
&= \mathbb E_{s, a\sim\pi}[-\nabla^2_\theta\log\pi_\theta(a|s)]
\end{aligned}
$$
If the empirical distribution over the data is equal to the model distribution $$p_\theta(y|f(x,\theta))$$, then the Fisher, empirical Fisher and the Hessian are all equal.
!! Connections between Fisher, GGN and Hessian
The Fisher capstures paritla curvature information about the problem
!! Limitations of Empirical Fisher Approximation
* [[paper|https://arxiv.org/abs/1905.12558]]
from [[Optimization for Machine Learning I|https://simons.berkeley.edu/talks/elad-hazan-01-23-2017-1]]
This is a generalized form of Gradient descent, like a best-in-hindsight plus regularization.
We want to minimize the regret
$$
\sum_tf_t(x_t) - \underset{x^*\in K}{\min}\sum_tf_t(x^*)
$$
over time. The most natural way of picking $x$ is choosing the best to our knowledge:
$$
x_t = \arg\underset{x\in K}{\min}\sum_{i=1}^{t-1}f_i(x)
$$
But this result is not stable. We consider linear case, where we replace $f_t$ by $\nabla f_t(x_t)^\top x$, we add regularization:
$$
x_t = \arg\underset{x\in K}{\min}\sum_{i=1}^{t-1}\nabla_t^\top x+\frac 1 \eta R(x)
$$
if $R(x)$ is strongly convex, we ensure stability:
$$
\nabla_t^\top(x_t - x_{t+1}) = O(\eta)
$$
! Choosign $R$
!! Euclidean case
$$
\begin{align}
x_t &= \arg\underset{x\in K}{\min}\sum_{i=1}^{t-1}\nabla_t^\top x+\frac 1 \eta \frac 1 2 \|x\|^2\\
&= \prod_K(-\eta\sum_{i=1}^{t-1}\nabla_t)
\end{align}
$$
This is exactly gradient descent.
!! Multiplicative Weights
$$
\begin{align}
x_t &= \arg\underset{x\in K}{\min}\sum_{i=1}^{t-1}c_i^\top x+\frac 1 \eta \sum_ix_i\log x_i\\
&= \exp(-\eta\sum_{i=1}^{t-1}c_i)/Z_t
\end{align}
$$
!! Online Mirror Descent
! Adaptive Regularization: AdaGrad
OGD update: $x_{t+1} = x_t - \eta l(a_t, b_t, x)a_t$. The feature $a_t$ is always sparse the learning is slow.
AdaGrad treats the regularization factor as a learning problem:
$$
R(x) = |x|^2_A \qquad s.t.\qquad A\succcurlyeq0, Trace(A)=d
$$
The regret bound can be $\sqrt{d}$ better than SGD.
<div class="tc-tiddler-frame">
<$transclude tiddler="Annotations" mode=block>
</div>
With model similar to Universal Sentence Encoder
! Definition
The Fourier transform of a function $x\in L^2(\mathbb R)$ is defined as
$$
\hat x(\omega)=\int x(u)e^{-iwu}du
$$
! Properties
* Linear: $z=\alpha x+\beta y\Rightarrow\hat z=\alpha\hat x+\beta y$.
* Parseval identity: $\|\hat x\|=\|x\|, \langle x, y\rangle=\langle\hat x, \hat y\rangle$.
* Inverse Fourier transform: $x(u) = \int\hat x(\omega)e^{-iwu}dw$.
* Translation: $y(u) = x(u-u_0)\Rightarrow\hat y(\omega)=e^{iwu_0}\hat x(\omega)$.
* Dilation: $y(u)=x(su)$ for $s>0\Rightarrow\hat y(\omega)=s^{-1}\hat x(s^{-1}\omega)$.
[[Fourier Transform vs Wavelet Transform]]
Short time Fourier transform (STFT) suffers from resolution problem. We have equal time and frequency resolution at all frequencies in STFT.
Fourier modulus is unstable: high-frequency information spans a large linear subspace as soon as
there is non-rigid deformation. So we want to smooth along the orbits.
! Bibs
* An OpenCL Deep learning on A10
[[Neural Networks on Silicon|https://github.com/fengbintu/Neural-Networks-on-Silicon]]
! Convolution
divided into 6x8 blocks
* Winograd
computation within one layer is done with local cache, no DDR communication (for 224x224 input)
Terrence talking about [[Free Probability|https://terrytao.wordpress.com/2010/02/10/245a-notes-5-free-probability/#more-3466]].
Speicher: solutions to noncommutative functions
* Tensor product: $\varphi_{xy}(X_1Y_1\dots X_nY_n) = \varphi_x(X_1\dots X-n)\varphi_y(Y_1\dots Y_n)$
* Free product: Anytime $\varphi(X_i)=\varphi(Y_i)=0$ $\forall i$, $\varphi(X_1Y_1\dots X_nY_n) = 0$
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
[[code|https://github.com/bertinetto/siamese-fc]]
! Siamese architecture
Given an exampler image $z$ to a candidate image $x$, siamese networks apply an identical transformation $\phi$ to both inputs and then combine their representations using another function $g$ according to $f(z, x) = g(\phi(z), \phi(x))$.
We say that a function is fully-convolutional if it commutes with translation. We can provide a larger search image and convolve the features to compute the similarity:
$$
f(z, x) = \phi(z)\star\phi(x)+b
$$
! Training
On positive and negative pairs and adotping the logistic loss
$$
l(y, v) = \log(1+\exp(-yv))
$$
where $v$ is the real-valued score and $y\in\{+1, -1\}$ is its ground truth label.
|! |!Discrete |!Continuous |
|!Directed |<li>NADE</li><li>EoNADE</li><li>Fully-visible sigmoid belief networks</li><li>[[Pixel-CNN]]</li><li>[[WaveNet]]</li><li>RNN Language Model</li><li>Context tree switching</li> |<li>Normal Means</li><li>Coninuous Markov Models</li><li>N-AR(p)</li><li>RNADE</li> |
|!Undirected |<li>[[Boltzmann Machines|Restricted Boltzmann Machines]]</li><li>Discrete Markov Random Fields</li><li>Ising, Hopfield and Potts Models</li> |<li>Gaussian MRFs</li><li>Log-linear models</li> |
@@color:#859900;
* Can directly encode how observed points are related
* Any data type can be used
* For directied graphical models: parameter learning simple
* Log-likelihood is directly computable, no approximation needed
* Easy to scale-up, many optimisation tools
@@
@@color:red;
* Order sensitive
* For undirected models, parameter learning difficult: need to compute normalising constants (like RBM)
* Generation can be slow: iterate through elements sequentially, or using a Markov chain
@@
! Bib
* [[Recursive Cortical Network]]
! Previous sequential GANs
* For LM
** SeqGAN
** MaskGAN
* For Neural Dialog
** [[Neural Dialogue Generation|https://github.com/jiweil/Neural-Dialogue-Generation]]
* For NMT
** [[ngohoanhkhoa|https://github.com/ngohoanhkhoa/GAN-NMT]]
** [[ZhenYangIACAS|https://github.com/ZhenYangIACAS/NMT_GAN]]
! Theory
[[Conditional generative models|https://t.co/mD8RR7CA4M]]
* Batch normalization
* Historical averaging (Salimans et al 2016): A reinvention of Polyak averaging in RL
* Minibatch discrimination (Salimans et al 2016): Like 2 sample test, try classify dataset
* Instance noise (Sonderby et al 2016)
* Weight clipping or approximate Wasserstein loss (Arjovsky et al 2017)
* [[Unrolling SGD (Metz et al 2017)|https://github.com/poolio/unrolled_gan]]
* [[SN-GAN]]
[[reddit discussion|https://www.reddit.com/r/MachineLearning/comments/4r3pjy/variational_autoencoders_vae_vs_generative/]]
Both use backprop through continuous random number generation
* VAE:
** generator gets direct output target
** need Reinforce to do discrete latent variables
** possible underfitting due to variational approximation
** gets global image composition right but blurs details
* GAN
** generator never sees the data
** need Reinforce to do discrete visible variables
** possible underfitting due to non-convergence
** gets local image features right but not global structure
GAN can train any differentiable generative network by avoiding the maximum likelihood principle altogether. The metrics that measure the diversity in the generated samples are currently intractable.
Looking forward to off the convex path's new post and paper on GAN.
The generator tries to maximize $E_u[f(D(G(h)))]$. The original GAN uses $f(x)=\log(x)$, making the training more sentitive to terrible generations. If generator wins, $P_{real}$ and $P_{synth}$ are close in JS divergence. While [[WGAN]] uses $f(x)=x$, the two distributions are close in Wasserstein or earth-mover distance. In practice, the analysis can be very off.
* [[code|https://github.com/bdhingra/ga-reader]]
* [[paper|https://arxiv.org/abs/1606.01549]]
The original Gauss-Newton algorithm is an approximation to Newton's method for ''nonlinear least squares problems'', $$\mathcal L(\theta)=\frac12\sum_n(f(x_n, \theta)-y_n)^2$$. By the chain rule, the Hessian can be written as
$$
\nabla^2\mathcal L(\theta) = \underbrace{\sum_n\nabla_\theta f(x_n,,\theta)\nabla_\theta f(x_n, \theta)^T}_{:=G(\theta)}+\underbrace{\sum_nr_n\nabla^2_\theta f(x_n, \theta)}_{:=R(\theta)}
$$
where $$r_n=f(x_n, \theta)-y_n$$ are the residuals. The first part $$G(\theta)$$, is the Gauss-Newton matrix.
The objective can be generalized to the form $$\mathcal L(\theta) = \sum_na_n(b_n(\theta))$$. The genrealized Gauss-Newton matrix (GGN) is defined as the part of the Hessian that ignores the second-order information of $$b_n$$,
$$
G(\theta):=\sum_n[J_\theta b_n(\theta)]^T\nabla^2_ba_n(b_n(\theta))[J_\theta b_n(\theta)]
$$
[[Deduction|http://andrew.gibiansky.com/blog/machine-learning/gauss-newton-matrix/]]
The usual view of the Gauss-Newton matrix is that it is simply a positive semi-definite approximation to the Hessian.
Martens 2010 found that using the Gaussian-Newton matrix instead of $H$ was highly preferable because:
# $H$ is general indefinite, the sub-objective $q_{\theta_n}$ may not be bounded below. Infinite reduction will be possible along any directions of negative curvature that have a non-zero inner product with the gradient.
# The parameter updates produced by using $G_f$ performed much better in practice of neural network learning.
Importance of the Gaussian
# The Gaussian density is the only density for real random variables that is "closed" under marginalization and ''multiplication''.
# A linear (or affine) function of a Gaussian random variable is Gaussian; and, a sum of Gaussian variables is Gaussian.
# Some graphical model algorithms will be tracable only for finite random variables or Gaussian random variables.
# Non-Gaussian variables will be approximated using discrete and Gaussian tools
! Related work
* Relational Network
* Self-attention/Graph convolution network
** [[Dual Graph Convolutional Network for Semantic Segmentation|https://arxiv.org/pdf/1909.06121.pdf]]:
** [[Uncertainty-based Graph Convolutional Networks for Organ Segmentation Refinement|https://openreview.net/pdf?id=UUie86nf5B]]: dropout uncertainty added to graph node
! Task
* Panoptic Segmentation
** SOGNet: Scene Overlap Graph Network for Panoptic Segmentation
* C=AB can be parameterized by 3 matrix dimensions: MxN = MxK * KxN.
* Floating point ops = MN(2K-1)~2MNK (K mult, K-1 sum)
* Bytes through memory = sizeof(dataType) * (MK + KN + MN)
2nd part of [[Theory and Algorithms for Forecasting Non-Stationary Time Series]]
! Previous work
With assumption of stationarity and $\beta$-mixing:
* PAC-learning preserved in that setting (Vidyasagar, 1997), argues $\beta$-mixing is nice.
* VC-dimension bounds for binary classification (Yu, 1994)
* covering number bounds for regression (Meir, 2000)
* Rademacher complexity bounds for general loss functions (MM and Rostamizadeh, 2000)
* PAC-Bayesian bounds (Alquier et al., 2014)
And there are algorithm dependent studies. Mixing assumptions are hard to verify. Even if we know the exact form of mixing, estimating the parameters is still a very difficult problem. The hypothesis and loss function sets are not representative enough.
! The bound
We would like more realistic assumptions.
We use weighted discrepancy guided by real numbers $\mathbf q$, this sequence emphasis particular set of samples. The choice of hypothesis set and $q$ are crucial.
We have to use a sequential version of coverting number:
[[Weighted sequential $\alpha$ cover]]
! Algorithms
* Linear hypothesis
! Generalization
* [[Rethinking Generalization]] [[code|https://github.com/rharish101/DLGeneralization]]
** analyzes the Rademacher complexity of a neural network, which measures how much a model fits random noise in the data.
The generalization error is of the order of $\sqrt{N/m}$, $N$ is the number of effective parameters (or complexity measure), $m$ is the number of samples. VC growth fuction $S_{\mathcal F}(m)$
$$
S_{\mathcal F}(m) = \sup_{z_1,\dots,z_m}|\mathcal F_{z_1,\dots,z_m}|
$$
shows how many ways our training data can be classified by functions in $\mathcal F$. VC bound is too loose to be of no practical use.
Following papers try to quantify the number of true parameters:
* NIPS 2017 [[Spectrally-normalized margin bounds for neural networks|https://arxiv.org/abs/1706.08498]]
* ICLR 2018 [[A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks|https://openreview.net/forum?id=Skz_WfbCZ]]
* [[Stronger generalization bounds for deep nets via a compression approach|https://arxiv.org/abs/1802.05296]]
We consider a ''Uniform bound''
! Compression view
[[blog|http://www.offconvex.org/2018/02/17/generalization2/]]
Noise-stable nets are compressible.
$$
(\sigma_{max}(M))^2 \gg \frac{1}{h} \sum_i (\sigma_i(M)^2)
$$
Ratio of left side and right side is called the stable rank and is at most the linear algebraic rank.
remark: much to compress on the channel-wise?
The idea we end up using is to compress the filters using k-wise independence (an idea from theory of hashing schemes), where k is roughly logarithmic in the number of training samples.
! Theory
The GAN framework consists of two players - the //generator// $G_\phi(z)$ whose aim is to create realistic samples and the //discriminator// $D_\theta(x)$ whose aim is to classify an instance as having come from training data or the generator. Their cost functions are defined as:
$$J^{(D)}(\phi, \theta) = -\frac12\mathbb E_{x\sim p_{real}}\log D_\theta(x)-\frac12\mathbb E_z\log(1-D_\theta(G_\phi(z)))$$
$$J^{(G)}(\phi, \theta) = \frac12\mathbb E_z\log(1-D_\theta(G_\phi(z)))$$
The complete game can be specified as:
$$\underset{\phi}{\min}\underset{\theta}{\max} g(\phi, \theta) = \mathbb E_{x\sim p_{real}}\log D_\theta(x) + \mathbb E_z\log(1-D_\theta(G_\phi(z)))$$
At equilibrium, JS divergence between the data and model distributions is minimized meaning G has converged to $P_{real}$ and D outputs 1/2 for all x.
If we plug in the optimal discrinimator
$$
D^* = \frac{p^*(x)}{p^*(x)+q_{\phi}(x)}
$$
The loss $\mathcal L(\theta^*, \phi) = 2\mathrm{JS}(p^*\|q_\phi)-\log(4)$.
!! Understanding GAN
* Ian's [[Generative Adversarial Network Talk]]
* [[OpenAI's survey|https://openai.com/blog/generative-models/]]
* [[What Arora thinks about GANs|GANs, Some Open Questions]]
* [[How to train a GAN]]
* [[The Numerics of GANs]]
!! Research Directions
* [[GAN Stablizing Strategies]]
* Generate HQ images. Image is a good field for generative models, because we are sensible to image plausibility.
** Higher resolution images can be generated in a multiscale fashion.
* Address GAN training difficulties
** [[Amortised MAP Inference for Image Super-resolution|https://arxiv.org/abs/1610.04490]]
** [[Towards Principled Methods for Training Generative Adversarial Networks|https://arxiv.org/abs/1701.04862]]
** When training GAN, we collpase distribution dimensionalities, but when the 2 distributions does not intersect, we get problem training. So we convolve the distribution and later recover the full distribution.
* Leverage Kullback-Leibler divergence
** Do maximum-likelihood estimation in a likelihood free setting. Similar to approximate Bayesian computation.
** The reverse of KL divergence, use GAN in Variational inference.
* More applications
** Text (large discrete ouput spaces)
** Reinforcement learning.
* Reducing variance?
** Many networks like GAN are stochastic, depends on a non-stationary sampling
** Look into RL literatures where value distribution depends on policy.
! Bibs
!! Extensions
* [[Unsupervised representation learning with deep convolutional generative adversarial networks]]
** Laplacian Pyramid of GANs is further suggested to generate high quality image in a coarse-to-fine manner.
* [[DCGAN|Unsupervised representation learning with deep convolutional generative adversarial networks]]: a stable family of architecture in training.
* Sequential
** [[SeqGAN]]
* [[Stacked GAN|https://arxiv.org/abs/1612.04357]]
* [[Super-Resolution GAN|https://arxiv.org/abs/1609.04802]]
* [[InfoGAN]]
* LS-GAN enforces $D_\theta(G_\phi(z)) - D_\theta(x)\ge\delta(x, G_\phi(z))$ and @@color:#859900;impose a Lipschitz constraint on both G and D using weight decay@@. This leads to D having non-vanishing gradients everywhere between all real and fake sample pairs.
* [[WGAN]] uses Earth-Mover distance as the objective. This requires the discriminator to be a Lipschitz function and they use weight clipping to achieve that.
* [[Improved WGAN|https://arxiv.org/abs/1704.00028]] imposes $\|\nabla_{\hat x}D_{\theta}(\hat x)\|_2\approx 1$ where $\hat x = \epsilon x+(1-\epsilon)G_\phi(z)$. This leads to D having norm 1 gradients everywhere between all real and fake sample pairs in the limit.
* [[CycleGAN and pix2pix|https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix]]: pretty good image effects.
* [[Progressive Growing of GANs for Improved Quality, Stability, and Variation|http://research.nvidia.com/publication/2017-10_Progressive-Growing-of]]
** Good results
* [[SN-GAN]] learns ImageNet
* [[StarGAN]]
* [[BigGAN|https://arxiv.org/abs/1809.11096]]
!! Theory
* [[Divergence Classed GANs]]
* [[Density Ratio GANs]]
!! Applications
* Text Generation
** [[MaskGAN]]
* [[Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling|https://arxiv.org/abs/1610.07584]]
* [[Semi-Supervised Learning with Generative Adversarial Networks|http://arxiv.org/abs/1606.01583]]
** Better images than DCGAN are generated adding the classification label
* [[Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network]]
* [[Learning to Pivot with Adversarial Networks|https://arxiv.org/abs/1611.01046]]
** joint training adversarial networks to constrain the predictive model to be robust on nuisance parameter.
!! Talks
* [[How to train a GAN]]
* [[DALI 2017 GAN Workshop]]
GANs are recent approaches to modelling data in high dimensional spaces. Following are notes from [[Ian Goodfellow's ICML DL Workshop 2015 talk|https://www.youtube.com/watch?v=LXDuuYSNtUY]].
Papers relative in this post:
* Generative Adversarial Networks
* Conditional Generative Adversarial Nets
* On Distinguiashability Criteria for Estimating Generative Models
* Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
! Generative adversarial networks
The general idea of GAN is to learn a sampling mechanism. We will learn to generate some input noise from a fixed prior distribution and then transform that noise into data space.
* The advantage over generative stochastic network is there is no Markov chain involved so no issue of mixing.
* The advantage over [[VAE|Variational Autoencoder]] is that there is no variational lower bound.
!! Game theory: the basics
GANs are trained by //playing a game//. In game theory we consider the following senario:
* N>1 players
* Clearly defined set of actions each player can take
* Clearly defined relationship between actions and outcomes
* Clearly defined value of each outcome
* Can't control the other player's actions, which is different from maximization.
Each player tries to maximize his reward during the game. While simulating the game, we are looking for an equilibrium form, where no player can improve his reward by changing their own strategies.
We are going to formulate the simplest GAN as a two-player zero-sum game, where:
* Your winnings + your opponent's winnings = 0
* ''Minimax theorems'': a rational strategy exists for all such finite games
* Strategy: specification of which moves you make in which circumstances.
* Equilibrium: each player's strategy is the best possible for their opponent's strategy.
This setup is simple because players don't have to think about cooperating with other players. In GAN, we are using a continuous game, but there is a special case where minimax does apply. When players take action randomly it is called ''mixed stategy'' and the equilibrium is called ''mixied strategy equilibrium''.
!! Adversarial nets game
The game is between two players: ''Discriminator'' $D$, and ''Generator'' $G$. The generator is the same thing as the decoder. The discriminater tries to discriminate between:
* A sample from the data distribution
* And a sample from the generator $G$
While $G$ tries to "trick" $D$ by generating samples that are hard for $D$ to distinguish from data. This is a game theory model, we try to do gradient descent on one side and ascent on the other.
The value function takes the form of a binary discrimination task:
$$
\underset{G}{\min}\underset{D}{\max} V(D, G) = E_{x\sim p_{data}}(x)[\log D(x)]+E_{z\sim p_z(x)}[\log(1-D(G(z)))]
$$
Together this forms a mathematical saddle-point problem.
At every step, if the discriminator is optimal, it is going to take on this particular form, the optimal strategy for any $p_{model}(x)$ is always
$$
D(x) = \frac{p_{data}(x)}{p_{data}(x)+p_{model}(x)}
$$
!!! Noise contaction estimation
This is the same function used in noise contraction estimation. The different is that NCE is learning a model that used implicitly to define the discriminator, and here we're learning the generator. And in NCE, the generator is fixed. This function has a close connection to likelihood.
MLE is NCE modified such that the discriminators believes the data distribution are copied into the generator network at every step. NCE is computationally cheaper because it does not update the generator. And because of this, it is failing to obtain the same asymptotic error rate as MLE.
!! Theoretical properties
This process has some nice theoretical properties, if we assume we can optimize directly over probability distributions, then there is one unique global optimum, this saddle point corresponds to the true distribution. Assuming infinite data, infinite model capacity, direct updating of generator's distribution, converge to optimum guaranteed. But in practice, there is no proof that SGD will converge, where we are training gradient steps by opposite sign on the parameters of each model.
This is different from the usually analyzed statistical estimator, where we have an optimization problem where we minimize a function, and we show that in function space, the minimum of our objective function corresponds to recovering the true distribution. In this case we are not guaranteed to reach a critical point, that makes it more problematic when it comes to nonconvex optimization. In particular, we can have a network that oscilates forever.
! [[GANs vs VAEs]]
! Open problems
* Is non-convergence a serious problem in practice?
* If so, how can we prevent non-convergence?
* Is there a better loss function for generator?
! [[Learning in Implicit Generative Models|https://arxiv.org/abs/1610.03483]]
The objective is to make the density ratio funtion $r(x) = p^*(x)/q(x)$ to be one, where $p*(x)$ is the true data distribution and $q(x)$ is the model distribution. By estimating this value, we can avoid computing the marginal likelihood. There are four ways of achieving this objective:
!! Class probability estimation
As in the GANs, we specify a discriminator, $\mathcal D(x;\phi) = p(y|x)$, and a scoring rule.
!! Statistical distance minimization
!! Ratio matching
!! Moment matching
Oblate Spheroidal Earth: The radius from 45 latitude is shorter than 45 to 90
$$
a = z + e\sin^2\theta
$$
related to RANSAC, estimate between all samples and discard unlikely ones.
* 3D pose estimation
** with DL
* orb-SLAM
! Consensus maximization NP-Hard
* tractable robust model estimation
If LKOS can be solved efficiently, MAXCON can be solved
Maxcon is APX-hard (what about the amorized complexity?)
! Chebyshev problem
LkOS with $$k$$ equals to N
Combinatorial dimension = d+1 = number of basis
Repeatedly solve Chebyshev problem on subset of size $$d$$:
$$O(N^{d+1}poly(N, d))$$
A^* search on the tree structure (level-d tree is the chebyshev problem with d points exculded)
For an [[exponential family|Exponential Family]], the [[Kullback-Leibler divergence]] is a [[Bregman divergence]] on the natural parameters. Using Taylor approximation with exact remainder, we get $KL(\theta||\theta+d\theta)=\frac{1}{2}d\theta^T\nabla^2F(\theta)d\theta$. Where $F(\theta)$ is the log-normalizer. The [[Fisher information|Fisher information metric]] is the Hessian of the log-normalizer:
$$
I(\theta) = \left[\frac{\partial\eta}{\partial\theta}\right]=\nabla^2F(\theta) = I^{-1}(\eta) = \left[\frac{\partial\theta}{\partial\eta}\right]
$$
To a given Riemannian manifold $\mathcal M$ with tensor $G$, we may associate a probability measure as follows: Let $p(\theta)=\frac{1}{V}\sqrt{\det G(\theta)}$ with overall volume $V=\int_{\theta\in\Theta}\sqrt{G(\theta)}d\theta$. These distributions are built from infinitesimal volume element and bear the names of Jeffreys priors.
In an exponential family manifold, teh geometry is flat and $\theta/eta$ are dual coordinate systems.
Letting $$\theta = (\theta_1, \dots, \theta_p)'$$, we draw subsets of $$\theta$$ from their exact conditional poster distributions fixing the others.
It takes an unnormalized distribution which can be specified by a factor graph, along with a list of variables values that can be arbitrarily initialized. Gibbs sampling iteratively selects a variable and then samples the chosen variable from its conditional distribution.
! Scan order in Gibbs sampling
The scan order in Gibbs sampling determines which variable is selected in each step:
# random scan
# systematic scan selects a fixed permutation and repeatedly samples the variables in that order
In [[Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much|https://arxiv.org/abs/1606.03432]], a theorem bounding the relative mixing times of random and systematic scan is proved. Under mild conditions, this theorem implies that the mixing times differ by only polynomial factors.
! Image
[[Faster R-CNN Features for Instance Search|http://gitxiv.com/posts/WKiQ5mLBat7qZQozX/faster-r-cnn-features-for-instance-search]]
!! Captioning
[[densecap|https://github.com/jcjohnson/densecap]]
!! Applications
[[Age & Gender Classification|http://gitxiv.com/posts/sKTDSvwzoiRaojLG6/age-and-gender-classification-using-convolutional-neural]]
!! Visualization
!!! Weakly localization
[[Learning Deep Features for Discriminative Localization]]
[[ccnn|https://github.com/pathak22/ccnn]]
[[wsd|https://github.com/marcopede/wsd]]
! NLP
!! Classification
[[Convolutional Neural Networks for Sentence Classification|http://gitxiv.com/posts/2SAx7ccSYYDvNXq8Y/convolutional-neural-networks-for-sentence-classification]]
!! Application
[[A Convolutional Attention Network for Extreme Summarization of Source Code|https://github.com/mast-group/convolutional-attention]]
$\mathbin{\rotatebox[origin=c]{180}{\amalg}}$
* [[code|https://github.com/openai/glow]]
* [[paper|https://d4mucfpksywv.cloudfront.net/research-covers/glow/paper/glow.pdf]]
The GLR is a likelihood-based metric that proposes a ratio between two hypotheses: on one hand $H_0$ considers that both segments are uttered by the same speaker, therefore $X=X_i\cup X_j\sim M(\mu,\sigma)$ represents better the data. H_1 considers that each segment has been uttered by a different speaker, therefore $X_i\sim M_i(\mu_i,\sigma_i)$ and $X_j\sim M_j(\mu_j,\sigma_j)$ together suit better the data. The ratio test is computed as a likelihood ratio between the two hypotheses as
$$
GLR(i,j)=\frac{H_0}{H_1}=\frac{\mathcal L(X, M(\mu,\sigma))}{\mathcal L(X_i, M(\mu_i,\sigma_i))\mathcal L(X_j, M(\mu_j,\sigma_j))}
$$
and determining the distance as $D(i,j)=-\log(GLR(i,j))$ which, upon using an appropriate threshold one can decide whether both segments belong to the same speaker or otherwise.
The advantage of GMM-based technique is that it enables the continuous mapping of acoustic features between speakers on the basis of ''soft clustered conversion functions''.
GMM-based statistical mapping methods have improved the performance of VC to a great extent. Sylianou et al proposed a conversion method with Gaussian mixure that realizes continuous mapping based on soft clustering as follows:
$$
\hat y_t = \sum_{m=1}^Mw_{m,t}^{(x)}(A_mx_t+b_m).
$$
GMM models the densities of source cepstral vectors or joint cepstral vectors, but the map is frame-to frame thus cannot take account of the contextual information.
! JDGMM
GMM is employed as the generative model to describe the distribution of the joint spectral feature space. The distribution of the GMM is defined as
$$
P(v_t) = \sum_{m=1}^M\alpha_mN(v_t;\mu_m^{(v)}, \Sigma_m^{(v)}), \sum_{m=1}^M\alpha_m=1,
$$
The covariancea and cross-diagonal covs are diagonal, these matrices are suitable for modeling the mel-cepstra with weak inter-dimensional correlations.
At the conversion stage, the conditional distribution is
$$
P(y_t|x_t,\lambda^{(v)}) = \sum_{m=1}^M\beta_{m, t}N(y_t;\mu_{m, t}^{(y|x)},\Sigma_m^{(y|x)}),
$$
!!! Problems with GMM
GMM-based models suffer from overfitting and oversmoothing.
* overfitting: using relatively small dataset but overly complex model very likely leads to overfitting.
* oversmoothing: the converted spectra tend to be very smooth as a result of the averaging nature of GMM. Some details in the spectra are lost.
* piecewise linear
* local discontinuity [Toda et al. 2007]
* Source feature clustering because of low inter-speaker covariance. Which can make the sound muffled due to lower variance in converted voice.
* delta and delta2 are not good contextual (dynamic) information.
To build the model, parallel utterances are required for training. Recording of the two speakers are first aligned with DTW (or dynamic programming) and then an F0 transformation is used for the excitation features.
! Output
Six softmax outputs, one determines the number of digits, the others specify each one of them.
! Experiments
!! SVHN
!!! Data preprocessing
# find rectangle bounding of all digits
# expand the bounding box by 30%
# resize to 64x64
# crop 54x54 from it
!!! Model architecture
8 CONV5x5, 1 locally connected layer (maxout), 2 FCs.
!! Internal
No ground truth bounding boxes
!!! Data preprocessing
# find door centroid
# crop 128x128
!!! Model architecture
No dropout, 5 CONV5x5
!! CAPTCHA
!!! Model architecture
No dropout, 9 CONV5x5
33,000 utterance, the topologies are similar to HTS (2005). Speech data are downsampled from 48 kHz to 16kHz. 40 melceps, logarithmic fundamental frequency (log $F_0$) values, and 5-band aperiodicities (0-1, 1-2, 2-4, 4-6, 6-8 kHz) were extracted every 5 ms. The feature is consisted of these and their delta, delta-delta feaures ($3\times(40+1+5)=138$). Five-state, left=to-right, no-skip hidden semi-Markov models (HSMMs) were used. A multi-space probability distribution was used to model log $F_0$ sequences consisting of voiced and unvoiced observations. 2554 questions are used for the decision tree-based context clustering.
The input of DNN included 342 binary features for categorical linguistic contexts (phonemes indentities) and 25 numerical features (the number of syllables in a word, position of the current syllable in phrase), 3 numerical features for ''coarse-coded'' position of the current phoneme and 1 numerical feature for duration of the current segment.
The output features are same aas HMM-based systems. To model $\log F_0$ sequences by a DNN, the continuous $F_0$ with explicit voicing modeling approach was used; voiced/unvoiced binary value was added to the output features.
[[In-Datacenter Performance Analysis of a Tensor Processing Unit|https://dl.acm.org/citation.cfm?id=3080246]]
* The Matrix Unit: 256x256 8-bit multiply-accumulate units
* 700MHz clock rate
* Peak: 92T ops/s
** 65,536 * 2 * 700M
* > 25x as many MACs vs GPU
* > 100x as many MACs vs CPU
* 4MB of on-chip Accumulator memory
* 24MB of on-chip Unified Buffer (activation memory)
* 3.5x as much on-chip memory vs GPU
* Two 2133 MHz DDR3 DRAM channels
* 8 GB of off-chip weight DRAM memory
[[link|https://arxiv.org/abs/1409.4842]]
GoogLeNet uses 12$\times$ fewer parameter than most current deep architectures while achieves the best performance.
! Basic idea
!! Inception module
<<<
Neurons that fire together, wire together.
<<< Hebbian principle
[[Arora et al.|http://arxiv.org/abs/1310.6343]] states that if the distribution of the data-set is representatable by a large, very sparse deep network, then the optimal network topology can be constructed layer by layer by analyzing the correlation statistics of the activations of the last layer and clustering neurons with highly correlated outputs.
(A personal interpretation would be as long as the net is sparse enough, the depth does not matter.)
In image, correlations tend to be local. The very local clusters can be covered by 1x1 CONVs, and less local ones by 3x3 and 5x5 ones. This leads to the naive version of Inception:
[img height=250 [naive_inception.png]]
The pooling path is an adoptation of current deep learning interest which is believed to be beneficial. However, the output number of POOL is the same as the total output of the previous Inception module. And CONV 5x5 are expensive when the number of filters are large. The final design deals with this filter explosion and looks like this:
[img height=250 [inception.png]]
Notice that pooling results (192=>32 in `inception_3a`) and CONV 5x5 inputs (16) are all sharply reduced. All of the CONVs and POOLs are ReLUed.
!! Auxiliary classifiers
There are 2 additional classifiers connected to the intermediate layers where discriminative features may already been generated. So that the gradient signal propagated back can get increased and additional regularization is provided.
! Training
Asynchronous stochastic gradient descent with 0.9 momentum and stepwise learning rate (decreasing by 4% every 8 epochs). Polyak averaging was used to create the final model used at inference time. Several interpolation methods (bilinear, area, nearest neighbor and cubic) are used for resize, since it is quite random, no further practices can be assured to be helpful.
! [[General Design Principles|Inception Evolutions]]
This deal with only the initial state. Neural Tagent Kernel deals with more.
Increasing width, the output of neural networks approximate normal distribution.
```
x => f(0) -> g(0) => f(1) -> g(1) => f(2)
```
There is subtleties of convergence in distribution. A serie of Rademacher random variables (-1, 1) convergences to uniform normal. But the event (sum of series) can have probability zero on the distribution.
!! intuition of multiple hidden layers
* for single input data: f(2) is normal because g(1) are independent
* for multiple input data: different g(1) units will still become increasingly independent.
* the problem is that f(1) units are only independent asymptotically.
!! Preliminaries
* linear envelope property: limit the nonlinearity
* a width function $$h_d$$ specifies the number of hidden units at depth $$d$$.
Proof uses [[Exchangeable Central Limit Theorem]]
!! Limitations of kernel methods
Kernel methods (including the GP posterior mean) can be written as affine transform
$$
\bar f^* = \beta(X, x^*)^Ty+c(X, x^*)
$$
This limited the form of the model.
A kernel essentially means:
* CPU launches the kernel to the GPU with some small overhead
* For each pointwise element, launch one thread
* This thread reads the value it is responsible for, does a simple operation and writes its result
Let's say that we have a cost or error function $E(w)$ that we want to minimize. Gradient descent tells us to modify the weights $w$ in the direction of steepest descent in $E$:
$$
w_t = w_{t-1}-\eta\frac{\partial E(w)}{\partial w}
$$
where $\eta$ is the ''learning rate'', and if it's large you will have a correspondingly large modification of the weights $W$ (in general it shouldn't be too large, otherwise you'll overshoot the local minimum in your cost function).
For a quadratic $E$,
$$
\frac{dE(w)}{dw} = \frac{dE(w_c)}{dw} + (w-w_c)\frac{d^2E(w)}{dw^2}
$$
by setting the derivative to $0$ at $w_{min}$
$$
w_{min} = w_c-\left(\frac{d^2E(w)}{dw^2}\right)^{-1}\frac{dE(w_c)}{dw}
$$
So we can reach optima by setting
$$
\eta = \left(\frac{d^2E(w)}{dw^2}\right)^{-1}
$$
!! Weight decay
In order to effectively limit the number of free parameters in your model so as to avoid over-fitting, it is possible to regularize the cost function. An easy way to do that is by introducing a zero mean Gaussian prior over the weights, which is equivalent to changing the cost function to $\tilde E(w)=E(w)+\frac\lambda2w^2$. In practice this penalizes large weights and effectively limits the freedom in your model. The regularization parameter $\lambda$ determines how you trade off the original cost $E$ with the large weights penalization.
Applying gradient descent to this new cost function we obtain:
$$
w_t=w_{t-1}−\eta\frac{\partial E}{\partial w_{t-1}}-\eta\lambda w_{t-1},
$$
The new term $-\eta\lambda w_i$ coming from the regularization causes the weight to decay in proportion to its size. It causes the weights to exponentially decay to zero, if no other update is scheduled.
!! Stochastic gradient descent
[[Theory|Stochastic Gradient Descent]]
Stochastic Gradient Descent (SGD) simply does away with the expectation in the update and computes the gradient of the parameters using only a single or a few training examples. The new update is given by,
$$
w_t = w_{t-1}-\eta\frac{\partial E(w;x_i, y_i)}{\partial w}
$$
with a pair $(x_i, y_i)$ from the training set.
!! Stochastic gradient with classical momentum (CM)
If the objective has the form of a long shallow ravine leading to the optimum and steep walls on the sides, standard SGD will tend to oscillate across the narrow ravine since the negative gradient will point down one of the steep sides rather than along the ravine towards the optimum. The objectives of deep architectures have this form near local optima and thus standard SGD can lead to very slow convergence particularly after the initial steep gains.
To minimize a cost function $f(\theta)$ classical momentum updates amount to,
$$
\begin{align}
v_t& = \mu v_{t-1}-\eta\nabla E(w_{t-1})\\
w_t&=w_{t-1}+v_t
\end{align}
$$
where $v_t$ denotes the accumulated gradient update, or velocity, $\eta>0$ is the learning rate, and the momentum constant $\mu\in[0, 1]$ governs how we accumulate the velocity.
[[Polyak 1964|http://www.mathnet.ru/php/getFT.phtml?jrnid=zvmmf&paperid=7713&what=fullt&option_lang=eng]] showed that CM can considerably accelerate convergence to a local minimum, requiring $\sqrt{R}$-times fewer iterations than steepest descent to reach the same level of accuracy, where $R$ is the condition number of th e curvature at the minimum and $\mu$ is set to $(\sqrt{R}-1)(\sqrt{R}+1)$.
To find more about the optimizers, [[caffe document|http://caffe.berkeleyvision.org/tutorial/solver.html]] is a good place to start.
We can find similarity between SGD and Spike-timing Dependent Plasticity in [[this talks|Deep learning in biology]]
* For general non-convex problems, GD finds a stationary point in polynomial time.
* GD almost always escapes saddle points asymptotically. [[(Lee et al. 2016) Gradient descent converges to minimizers|https://arxiv.org/abs/1602.04915]]
* GD with perturbations is not slowed down by saddle points. [[(Ge et al. 2015) Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition|https://arxiv.org/abs/1503.02101]], [[(Jin et al. 2017) How to escape saddle points efficiently|https://arxiv.org/abs/1703.00887]]
The speed of gradient descent convergence is proportional to the condition number of Hessian
[[link|https://arxiv.org/abs/1502.03492]]
[[talk|http://videolectures.net/icml2015_duvenaud_reversible_learning/?q=icml%202015]]
TL;DR: We can compute the gradient of learning procedures. The only problem is it's memory inefficient. But if we do the reversible learning thing, we can scale to optimize thousands hyperparameters.
This talk is given on ICML 2015 Bayesian optimization workshop. But interestingly, this paper has nothing to do with Bayesian optimization.
Hyperparameter optimization looks like the most important part of machine learning, and we have to do it without gradient. We can find some continuous relaxation of what we are trying to do, take the gradient and optimize it with gradient descent. So we can think SGD as a funtion and take its gradients.
Think of SGD as a function. If we fix the random seed, with twice differentiable loss from e.g. a deterministic loss funtion of all the hyperparameters. Hyperparameter opt like optimizing the output of the function of SGD.
We can differentiate the SGD. In each for-loop, different samples are in it. It seems like optimizing thousand layer net. Now we have automatic differentiation engines. Why this is not done 20yrs ago, because memory consumption would kill you. The naive reverse-mode differentiation is infeasible from a memory perspective.
! Hyper gradient
!! Reversible learning with finite precision arithmetic
The Hessian-vector product of weights w and loss parameter $\theta$ can be computed exactly by applying RMD to the dot product of the gradient with a vector. The time complexity to reverse SGD is the same as forwarding.
However, the naive reverse would fail due to finite numerical precision. Each multiplication by momentem decay $\gamma < 1$ shifts bits to the right, destroying the least significant bits. By repeatedly multiplying $1/\gamma$ accumulates errors exponentially. A $\gamma > 1$ results in unstable dynamics, and $\gamma = 1$ recovers the leap frog integrator, a perfectly reversible set of dynamics, but one that does not converge.
The $\gamma$ term is analogous to a drag term in the simulation of Hamilton dynamics. Having $\gamma < 1$ corresponds to //dissipative// dynamics which generates heat, increases the entropy of the environment and is not therefore not reversible.
!! Optimal storage of discarded entropy
The number of destroyed bits is $-\log_2(\gamma)$
We can take derivation of cross validation loss.
The computation is huge. 10GB memory.
The naive reverse will go astray
Use a buffer to store the destroyed bits.
if $\beta$ is one, it is Hamiltonian dynamics. Hamiltonian Monte Carlo.
What about Bayesian optimization?
Convolutions in Fourier-domain are simple pointwise multiplications of the Fourier-transform of a signal. This, however, is a very impractical view: computing the spectrum of signal over general graphs @@color:#859900;involves matrix diagonalisation@@, which generally has cubic complexity in the number of nodes. Computing exact convolutions over graphs is thus computationally intensive.
! Bib
* [[Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering|https://arxiv.org/abs/1606.09375]]
* [[Semi-Supervised Classification with Graph Convolutional Networks|https://arxiv.org/abs/1609.02907]]
** [[author's blog|http://tkipf.github.io/graph-convolutional-networks/]]
** [[discussion of expressiveness|http://www.inference.vc/how-powerful-are-graph-convolutions-review-of-kipf-welling-2016-2/]]
! refs
* [[Summary|https://arxiv.org/abs/1806.01261]]
Structured representations and computations, and in particular, systems that operate on graphs
! Representations
* edge and node outputs are lists of vectors or tensors
* global outputs correspond to a single vector
The output of a GN block can also be tailored to the demand of tasks
* edge-focused
* node-focused
* graph-focused
! Structure
* input specifies the relation
* relational structure must be inferred
Examples
* Message-passing NN
* Non-local NN
** intra-/self- attention: computes pairwise attention
** vertex-/graph- attention: handle explicit edges by setting to zero the weights between nodes do not share edges
* Graph Traverse
** Graph Convolution
** Memory-bank representation
* Tasks
** London subway
** Program control flow
** DL2doc
** DL generating models
! Overview
Graphical model is a probabilistic model expressing probability distributions via graph.
The graph learning problem is formulated as follows:
* we are given a set of nodes, each with some observed numeric attributes $x_i$.
* For each node we'd like to predict an output or label $y_i$. We observe these labels for some, but not all, of the nodes.
* We are also given a set of weighted edges, summarised by an adjacency matrix $A$. The main assumption is that when predicting the output $y_i$ for node $i$, the attributes and connectivity of nearby nodes provide useful side information or additional context.
! Applications
* in machine learning
** Learning low-level vision
* statistical physics
** Exactly solved models in statistical mechanics
* theoretical computer science
** Probabilistic reasoning in intelligent systems: networks of plausible inference
! Resources
[[The link to the short course|http://ai.stanford.edu/~paskin/gm-short-course/]]
Probability is the basic. There are alternatives to probability theory:
* Dempster-Shafer theory
* Disjunctive uncertainty, etc.
* (Fuzzy logic is about imprecision, not uncertainty.)
The reason probality theory is better is [[de Finetti's theory|De Finetti's Theorem]]:
<<<
if you fo not reasone according to [[Probability theory]], you can be made to act irrationally.
<<<
! Inference
[[Computing Partition Functions of Graphical Models]]
!! Algorithms
* [[Variable Elimination Algorithm]]
* [[Hammersley-Clifford Theorem]]
* [[Junction Tree Algorithm]]
! Deep Learning
* [[Graph Convolution]]
@article{graves2005framewise,
title={Framewise phoneme classification with bidirectional LSTM and other neural network architectures},
author={Graves, Alex and Schmidhuber, J{\"u}rgen},
journal={Neural Networks},
volume={18},
number={5},
pages={602--610},
year={2005},
publisher={Elsevier}
}
Note of [[a talk given by Anandkumar|http://videolectures.net/iclr2016_anandkumar_nonconvex_learning/]] at ICLR 2016 Keynotes.
Mentioned papers:
# Anandkumar et al. 2012, [[Tensor decompositions for learning latent variable models|http://arxiv.org/abs/1210.7559]]
# Agarwal et al. 2014, [[Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization|https://arxiv.org/abs/1310.7991]]
# Anandkumar et al. 2015, [[Convolutional Dictionary Learning through Tensor Factorization|https://arxiv.org/abs/1506.03509]]
! Non-convexity in unsupervised learning
How do we overcome non-convexity and why Tensors are something everybody should think about to solve this problem. Currently, we have enough data to make supervised neural nets work, however, unsupervised learning is not improved that much.
In the case of unsupervised learning, our goal is to learn model parameters $\theta$ from observations $x$ that maximize the likelihood $p(x; \theta)$. It is reasonable to assume that local optima (or saddle points) should be a problem since there are always exponential no. of critical points according to dimensions.
Traditional local search methods such as SGD, EM and Variational Inference could have problem dealing with those critical points and GAN should also have a non-convex architecture.
! Tensor methods
We can always alter the objective of max likelihood to best tensor decomposition. Under infinite sample condition, the global optimum is preserved:
$$
\arg\underset{\theta}{\max} p(x;\theta) = \arg\underset{\theta}{\min}\|\hat T(x) - T(\theta)\|^2_F
$$
We will later show this technique can suceed with many algorithms.
We can extract latent factors with matrix factorization but the solution is not unique. So we always add orthogonal constraints. There are two drawbacks:
# If the matrix is not orthogonal, the solution is hard to get.
# Latent information is constrained by the rank of matrix.
To overcome these, we can collect more information and apply shared matrix decompotion, generalizing it to tensor decomposition.
A simple example similar to this talk's is given by [[Ge|http://www.offconvex.org/2015/12/17/tensor-decompositions/]]. [[Kruskal|http://www.sciencedirect.com/science/article/pii/0024379577900696]] gave sufficient conditions for such decompotisions to be unique, i.e., when rank one pairs are linearly independent. This is much weaker than the matrix case (orthogonal).
!! Implementing tensor decomposition
[[The general tensor decomposition is NP-hard|http://arxiv.org/abs/0911.1393]]. Ge introduced [[a generalized version of Jenrich's method|http://dl.acm.org/citation.cfm?id=173234]] to
We can use power method to iteratively find eigenvectors. For matrices, we take a random vector, muliply and normalize it and repeat. Then we can get the top eigenvector, substract it and repeat this process. We can do the same for tensors
We extends the notion of matrix product with tensor contraction:
$$
T(u, v, \cdot) = \sum_{i,j}u_iv_jT_{i, j, :}
$$
We contract the tensor along two directions, if we repeat this for orthogonal tensors, we get the eigenvectors. For orthogonal tensors, all the vectors of the decomposition are stationary points. But in the general case, tensor decomposition is non-linear and there are exponentially many of stationary points.
For non-orthogonal tensors, we orthogonalize it with a matrix $W$, which can be found with SVD. When $v_i$'s linearly independent, the orthogonalization is invertible. [[Anandkumar et al. 2012|http://arxiv.org/abs/1210.7559]] gives a proof that tensor power method is efficient and robust to noise.
! Applications
!! Finding latent variables
Authors apply tensor decomposition to clustering problems like topic modeling and [[community detection|https://arxiv.org/abs/1302.2684]], vast improvements are observed in both running time and likelihood comparing to variational inference. This method can speed up probability models in a great extend.
!! Learning representations
One of the use of unsupervised learning is to learn representations. A very popular model is sparse coding. There is strong evidence that neuron conducts sparse coding. In its more simplified form, every sample is a sparse linear combination of dictionary elements. When the dictionary components a linear coherent, we can learn this overcomplete representation [[through tensor method|https://arxiv.org/abs/1506.03509]]. This is a natural assumption because we want these elements to be not redundant. This requires analysing composition of the tensor where the number of components can exceed the dimension, which is impossible for matrix to recover.
We want to find shift-invariant features in e.g. vision applications, convolutional models handle this properly. With convolution constraints, we translate the problem to a tensor where the components are tied to one another. When the componets shift, we can resolve structures efficiently through different operations.
[[Huang et al. 2015|https://arxiv.org/abs/1309.0787]] train text corpus for para-phrase detection. With 4k samples on MSR corpus, the performance is very similar to skipthought's, which relies on extra large book corpus.
Similar story with [[holographic embeddings for knowledge bases|http://arxiv.org/abs/1510.04935]].
!! Reinforcement learning
Another application is reinforcement learning. One challenging aspect is to think about partially observable processes, where we model the environment with hidden state. If we incorporate with Markov DP framework, we can solve these hidden variables through tensor methods. In the author's example, they use memoryless policies to reduce complexity of long history information. First regret bounds are given and exploitation-exploration can be done efficiently under this framework. They also show that tensor methods can have a better reward than CNNs on Atari game playing.
An insight on why this is possible is that the tensor methods are first looking into learning latent representations, and planning in the hidden space could make predictions more effective.
!! Tensors in deep learning
In many recent works, tensors have been effectively used in deep learning frameworks. One natural question is whether SGD is the only thing we can do. Few researchers are dare to train their model from scratch, the major concern is how to avoid bad critical points.
[[Local optima are easy to construct|https://arxiv.org/abs/1511.06856]]. There are mechanisms to overcome these local optima. [[Janzamin et al. 2014|https://arxiv.org/abs/1412.2863]] proposed a method using the score functions. By looking are the relationship between the input and output, it's possible to train a one layer NN with one hidden layer with guarantees. The idea is to look at transformations of the input using score funtions. If we look at the predicted label $y$ and the score funtion of the input $\mathcal S_m(x)$, we will get a linear combination of the weight vectors of the first layer. Now we can construct a hierarchy of them. If we go to 2nd order score funtions, we'll get a decomposition where each rank 1 component correspond to the weight vectors. By resolving with the tensor decomposition, it is possible to learn the weight vectors of the first layer of the two layer network.
Here we require the notion of score funtions to do the transformation. The idea is to overcome the nonlinearity of these neurons here, we also need to transform the input by the score funtions. We need the derivatives of the input model. For example, if we have a Gaussian input, we get Hermite polynomials.
[[Novikov et al. 2015|http://arxiv.org/abs/1509.06569]] uses tensor to compress the dense layers of CNNs. The notion of tensor train format compresses the weight. The idea is we can get a compact representation of these weight matrices. There is potential for a huge compression rate.
[[Cohen et al. 2015|https://arxiv.org/abs/1509.05009]] use hierarchical Tucker tensors for representating arithmetic convnets to study the expressive power of deep neural nets. Decomposition described before is called the [[CP decomposition|https://en.wikipedia.org/wiki/Tensor_rank_decomposition]], whereas the Tucker one is different from the fact that there can be an arbitrary core tensor. The idea is to look at the input tensor and decompose it into a core tensor and its transformation with different factor matrices. In the CP decomposition this core tensor will be a diagonal one. We can do a hierarchical Tucker decomposition by progressively decomposing this core into various representations. They have algebraic arguments to show that deep is better than shallow. Transfering a deep net into a shallow one would require exponentially many parameters.
[[Tensors have also been used in memory model|https://arxiv.org/abs/1511.07972]].
Tensor is a useful tool. Improvements can be done in [[scaling up tensor with dimensionality reduction|https://arxiv.org/abs/1506.04448]] and [[speeding up on computation platforms|http://arxiv.org/abs/1606.05696]]
* [[blog|https://cmaddis.github.io/gumbel-machinery]]
$$h$$ is a discrete variable controlled by $$p(z)$$ and hard threshold function $$h = H(z)$$. Gumbel softmax relaxed the hard theshold with tempreture dependent sigmoid function $$\sigma_t(\cdot)$$. Now $$p(h) \approx p(\sigma_t(z))$$ is differentiable. As the temperature goes to zero, bias goes down but variance goes to infinity, which can leads to the wrong solution.
$$
p(K, g_1, \ldots, g_n) = \frac{\alpha_K}{Z}f_{\log Z}(g_K)\prod_{i \neq K} \frac{f_{\log \alpha_i}(g_i) [g_K \geq g_i]}{F_{\log \alpha_i}(g_K)}
$$
has a similar structure as
$$
p(K, g_1, \ldots, g_n) = p(K)p(g_K) \prod_{i \neq K} p(g_i | K, g_K).
$$
! Introduction
HMC makes use of Hamiltonian dynamics in MCMC, following Newton's laws of motion. HMC is based on dynamical simulation that does not rely on any prior assumptions about the form of the posterior distribution. HMC can be difficult to work with, as setting the leapfrog step sizes is hard. This method does not scale to large data. This is simplified by [[Langevin method|Stochastic Gradient Langevin Dynamics]].
! [[A Conceptual Introduction to Hamiltonian Monte Carlo|https://arxiv.org/abs/1701.02434]]
HMC is a procedure for introducing auxiliary momentum with a probabilistic structure such that one can generate efficient exploration.
As the system evolves, any compression or expansion in position space must be compensated with a respective expansion or compression in momentum space to ensure that the volume of any neighborhood in position-momentum phase space is unchanged.
$$
\pi(q, p) = \pi(p|q)\pi(q)
$$
is called the canoical distribution. We write it in terms of an invariant Hamilton function:
$$
\pi(q, p) = e^{-H(q, p)}
$$
The Hamilton
$$
H(q, p) = -\log\pi(q, p) = -\log\pi(p|q) - \log\pi(q) = K(p, q)+V(q)
$$
where $K$ is the kinetic energy and $V$ is the potential energy.
@inproceedings{harati2012applications,
title={Applications of Dirichlet Process Mixtures to speaker adaptation},
author={Harati Nejad Torbati, AH and Picone, Joe and Sobel, Marc},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on},
pages={4321--4324},
year={2012},
organization={IEEE}
}
! Intro
!! Model
HDP-HMM inadequately models the temporal persistence of states, in which the Bayesian bias toward simpler models is insufficient to prevent the HDP-HMM from giving high posterior probability to models with unrealistically rapid switching.
* Transition distribution
* Emmision distribution
** the conditional distribution of observations given states
** high performance HMMs generally use finite Gaussian mixtures as emission distributions
** nonparametric, used Dirichlet
* Computation
** classical: forward-backward
** nonparametric: Gibbs sampler
!! The speaker diarization task
[[Speaker diarization literatures]]
HF is a practical large scale implementation of Newton's method to learn deep neural networks from random initializations.
$$
q_{\theta_n}(\theta) = M_{\theta_n}(\theta)+\lambda R_{\theta_n}(\theta),
$$
where $M_{\theta_n}(\theta)$ is a $\theta_n$-dependent "local" quadratic approximation to $f(\theta)$ given by
$$
M_{\theta_n}(\theta) = f(\theta_n)+f'(\theta_n)^\top\delta_n+\frac{\delta_n^\top C_n\delta_n}{2},
$$
where $C_n$ is an approximation to the curvature of $f$ at $\theta_n$, $\delta_n=\theta-\theta_n$, and $R_{\theta_n}(\theta)$ is a damping function that penalizes the solution according to the difference between $\theta$ and $\theta_n$, thus encouraging $\theta$ to remain close to $\theta_n$.
In standard HF, A.K.A. truncated-Newton, $C_n = d^2f(\theta_n)$ and $R_{\theta_n}(\theta)=\|\delta_n\|^2/2$. Unlike with quasi-Newton approaches like L-BFGS there is no low-rank or diagonal approximation involved, and as a consequence, fully optimizing the sub-objective $q_{\theta_n}$ can require a matrix inversion or some similarly expensive operation. The HF approach circumvents this problem by performing a much cheaper partial optimization of $q_{\theta_n}$ using the ''linear conjugate gradient algorithm'' (CG).
CG is invoked for minimizing quadratic function $\phi(z) = z^\top (C_n+\lambda I)z/2 - f'(\theta_n)^\top z$ starting from $z = \delta_{n-1}$. The Hessian-vector products is computed within a black-box function. The modifications on HF on neural networks include:
# using the positive semi-definite [[Gauss-Newton curvature matrix]] $G_f$ in place of the possibly indefinite Hessian
# using damping function for $R_{\theta_n}(\theta)=\|\delta_n\|^2/2$ with the damping parameter $\lambda$ adjusted by Levenberg-Marquardt style heuristics
# using a criterion based directly on the value of $q_{\theta_n}$ in order to terminate CG (not residual-error $\|Bz-b\|^2/2$)
# computing the curvature-matrix products $Bv$ using mini-batches and the gradients with much larger mini-batches.
!! [[Structral Damping]]
! Expriments
HF algorithm is capable of training RNNs to solve problems with very long-term dependencies.
[[TIMIT HF]]
HMM combine non-linearity with tractable inference by using a posterior that is a discrete distribution over a fixed number of mutually exclusive alternatives, which makes them exponentially inefficient at dealing with componential structure: to allow the history of a sequence to impose N bits of constraint on the future of the sequence, an HMM requires at least $2^N$ nodes.
! Bibs
* Dayan and Hinton, 1993 Feudal RL
* Parr and Russel, 1998 RL with Hierarchies of Machines
* Sutton, Precup, Singh, 1999 Options
* Precup, 2000 Temporal abstraction in reinforcement learning
* Dietterich et al, 2000 MaxQ
* Fox, Moshkovitz, Tishby, 2016 Principled Option Learning
* Heess et al, 2016 Learning and tranfer of modulated locomotor controllers
* Vezhnevets et al 2017 Feudal Networks for HRL
* Bacon, Harb, Precup, 2017 Option-Critic
* Florensa, Duan, Abbeel, 2017 SNNs for HRL
* Andreas, Klein, Levine, 2017 Policy Sketches
[[Meta-Learning for HRL]]
! TODO
* clojure support
* other themes
! Bibs
* [[Training Very Deep Networks|https://arxiv.org/abs/1507.06228]]
* [[Recurrent Highway Networks|http://arxiv.org/abs/1607.03474]]
The homogeneous Poisson process counts events that occur at a constant rate; it is one of the most well-known [[Levy processes|Levy Process]]. This process is characterized by a rate parameter $\lambda$, also known as //intensity//, such that the number of events in time interval $(t, t+\tau]$ follows a [[Poisson distribution|Poisson Distribution]] with associated parameter $\lambda\tau$. This relation is given as
$$
P [N(t+ \tau) - N(t) = k] = \frac{e^{-\lambda \tau} (\lambda \tau)^k}{k!} \qquad k= 0,1,\ldots,
$$
where $N(t + \tau) − N(t) = k$ is the number of events in time interval $(t, t + \tau]$.
The capacity of Hopfield net is limited by the speed of energy decay related to second-order neuron connection weight. This could explain why RNN is inferior: The hidden state is first-order, which means no cross bit connection (such as term $h_t^i*h_t^j$ in hopfield net) is defined in $\mathbf h_t$
* [[paper|https://arxiv.org/abs/2002.02405]]
* [[blog|https://statmodeling.stat.columbia.edu/2020/02/13/how-good-is-the-bayes-posterior-for-prediction-really/]]
''Observation'':
* By lowering the temperature, the posterior concentrates more on the MAP.
* The underdispersion could be serving as an implicit prior for stronger regularization
''Methodology'': The authors use an overdamped Langevin dynamic to sample from the posterior, except for omitting the Metropolis adjustment.
''Critics'':
* The diagnosis is not exhaustive. No more powerful test such as to run a multiple chains and see if they have mixed.
* Using bad priors but not testing priors with hyperparameters but test only fixed priors.
* Monte Carlo error is not the sampling error
* For complicated models such as ResNet, HMC suffers from all multimodality and non-log-convexity, would hardly mix.
From NIPS 2016 GAN Workshop
# Normalize the inputs
#* normalize the images between -1 and 1
#* Tanh as the last layer of the generator output
# Modified loss function
#* change $\min\log(1-D)$ into $\max\log(D)$: first has vanishing gradient early on.
#* $\mathcal L_G(\theta, \phi) = -\mathbb E_{q_\phi(x)}[\log(D_\theta(x))]$
#* Flip labels when training generator
# Use spherical $z$
#* interpolation via great circle from [[Sampling Generative Networks|https://arxiv.org/abs/1609.04468]]
#* probably because of loss?
# Use BatchNorm properly
#* different batches for real and fake
#* when batchnorm is not an option, use instance norm, anyone tried weight norm?
# Avoid Sparse Gradients: ReLU, MaxPool
#* use LeakyReLU both $G$ and $D$
#* downsampling by: ave pooling, conv+stride
#* upsampling by: PixelShuffle, ConvTranspose2d+stride
# Soft and noisy labels
#* label smoothing
#* making the labels noisy a bit for the discriminator, sometimes
# DCGANs / Hybrid models
#* DCGAN when you can
#* if you can't use CDGANs, and no model is stable, use a hybrid model: KL+GAN or VAE+GAN
# Stability tricks from RL
#* experience replay
#* things that work for deep deterministic policy gradients
#* See [[Connecting Generative Adversarial Networks and Actor-Critic Methods|https://arxiv.org/abs/1610.01945]]
# Use Adam
#* at least for generators
# Track failures early
#* $D$ loss = 0
#* check norms of gradients
#* $D$ loss should have low variance and goes down over times
#* if loss of generator steadily decreases, then it's fooling $D$ with garbage
# Don't balance via loss statistics
# If you have labels, use them
# Add noise to inputs, decay over time
#* Add some artificial noise to inputs to D ([[Arjovsky et. al|https://openreview.net/forum?id=Hk4_qw5xe]], Huszar, 2016) [[inFERENCe|http://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/]]
#* Add Gaussian noise to every layer of generator (EBGAN)
#* Improved GANs: OpenAI code also has it (commented out)
# Train discriminator more
Installing all dependencies for HTS demo CMU Arctic slt
* HTS 2.2 patch for HTK 3.4.1, installed
* [[Festival]] (festvox): festival requires a bunch of files to download and comes with a dataset
* hts_engine API, installed
* SPTK: requires csh, installed tcsh on Arch machine
* tcl/tk (already up to date)
! Model
$$
P(O|\lambda) = \sum_{Q}P(O,Q|\lambda)
$$
where $O$ is speech parameter vector sequence, $Q$ is state and $\lambda$ is the continuous mixture HMM. The speech parameter vector $o_t$ consists of the static feature vector (e.g. cepstral coefficients) and dynamic feature vectors (delta, delta2 cepstral coefficients) that is $o_t = [c_t, \Delta c_t, \Delta^2c_t]$.
The model is solved by alternating gradient descend.
```
@inproceedings{graves2013hybrid,
title={Hybrid speech recognition with deep bidirectional LSTM},
author={Graves, Alan and Jaitly, Navdeep and Mohamed, Abdel-rahman},
booktitle={Automatic Speech Recognition and Understanding (ASRU), 2013 IEEE Workshop on},
pages={273--278},
year={2013},
organization={IEEE}
}
```
! Experiments
|Network |Dev PER |Test PER |Dev FER |Test FER |Dev CE |Test CE |
|DBRNN |19.91+-0.22|21.92+-0.35|30.82+-0.31|31.91+-0.47|1.07+-0.010|1.12+-0.014|
|DBLSTM |17.44+-0.16|19.34+-0.15|28.43+-0.14|29.55+-0.31|0.93+-0.011|0.98+-0.019|
|DBLSTM (Noise) |16.11+-0.15|17.99+-0.13|26.64+-0.08|27.88+-0.16|0.88+-0.008|0.93+-0.004|
A phonetic dictionary was used, with three states per phoneme, giving 183 target states in total. A biphone language model was estimated on the training set, and a simple GMM-HMM system was used to provide forced alignments. The posterior state probabilities provided by the network were not divided by the state occupancy priors, as this has been found to make no difference on TIMIT.
''Summary'': This paper proposes an interesting system to train image dense prediction tasks using only web queried data and their keyword labels. Segmentation mask is estimated with DSRG [1]. To reduce the impact of noisy samples, the authors gradually add in the more difficult examples during training in a curriculum strategy based on CurriculumNet [2].
The authors addressed two kinds of difficulties in web datasets, the unreliable annocations and complex images. To rank reliability of the annications, a method similar to [2] is used. To rank complexity of images, the IoU of saliency detector and Web-DSRG results are compared.
The results shows that the model trained with this scheme is generalizable even without the use of examples on target dataset (VOC+sbd).
''Strengths'':
1. The idea of training segmentation model without any human intervention is very intriguing.
2. AFAIK, this is the first to introduce curriculum learning to webly supervised segmentation.
3. The performance is good.
''Weaknesses'': The authors stressed that web images are often noisy. Although intuitive, it will be helpful to
1. quantify these kinds of noises. For example, what is the portion of difficult/crowded/unexpected images in each class
2. and their effect to the segmention quality.
3. In my opinion, Table 2 cannot prove that the training scheme is robust to the noises. It is possible that the data noise does not effect the final performance. To better support the assumptions in L568, "These images may have a negative impact on semantic drift..." and conclusion in 4.4.2, it is better to also provide the results trained w/o this curriculum training scheme.
Furthermore, it is not very clear whether the proposed training scheme is good at
"identifying noisy samples from hard training samples with one-sided understanding of web noises".
There lacks ablation studies/''qualitative'' and ''quantatative'' results comparing the effectiveness of curriculum learning on each of these hard or noisy cases. More specifically:
1. How good can the classification model select more reliable images.
2. Performance of the clustering also needs more detailed justification.
3. How good can the mask complexity measure segmentation complexity
''Rating'': Borderline
Not confident. I am not familiar with curriculum learning.
[1] Huang et al. Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing
[2] Guo et al. CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
! Keynote Talks
[[Guaranteed Non-convex Learning Algorithms through Tensor Factorization]]
! Papers
!! Theory
[[Data-dependent Initializations of Convolutional Neural Networks]]
!! Older ones
!!! Generating images from captions with attention
from reference, a machine translation result is mentioned (Bahdanau et al., 2015)
combines
bidirectional rnn: for script understanding
inference/generative rnn: for latent sequence posterior estimation and image generation
!!! Neural variational inference for text processing
NVDM, neural variational document model: MLP softmax BoW generation
NASM, neural answer selection model: LSTM
model trained with stochastic gradient variational bayes
!!! All you need is a good init
Unsupervised representation learning with deep convolutional generative adversarial networks
Related works
GANs
* generator and discriminator nets
* unstable to train
this paper developed Deep Convolutional GAN
Representation learning:
* classic: hierarchical clustering
* mainstream: autoencoders
* deep belief networks
!!! Delving Deeper into Convolutional Networks for Learning Video Representations
2017 papers
* [[Hunting through the ICLR 2017 submissions|http://smerity.com/articles/2016/iclr_2017_submissions.html]]
!! Oral Papers
* 7: [[End-to-end Optimized Image Compression]]
!! Talks
[[Talk|https://www.facebook.com/iclr.cc/videos/vb.1026262914069441/1713144705381255/?type=2&theater]]
* [[New Directions for RNNs]]
* Best Paper Award: [[NPI via Recursion]]
* 8: [[Optimization as a Model for Few-Shot Learning]]
! Identity Mapping
* [[Identity Matters in Deep Learning]]
! Optimization
* [[On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima]]
* Inefficiency of Stochastic Gradient Descent with Large Mini-batches (and More Learners)
! Network
* [[Understanding Deep Learning Requires Re-thinking Generalization|https://arxiv.org/abs/1611.03530]]
* [[Categorical Reparameterization with Gumbel-Softmax|https://arxiv.org/abs/1611.01144]]
! NLP
* [[Tracking the world state with recurrent entity networks|https://arxiv.org/abs/1612.03969]]
* Deep Biaffine Attention for Neural Dependency Pasrsing
* ReasoNet: Learning to Stop Reading in Machine Comprehension
* Vocabulary Selection Strategies for Neural Machine Translation
* Deep Character-Level Neural Machine Translation by Learning Morphology
* Iterative Refinement for Machine Translation
* A Convolutional Encoder Model for Neural Machine Translation
* Reference-Aware Language Models
# [[ICML 2015]]
#* [[Invariance Principles for Neural Network Training]]
#* [[video recordings|http://dpkingma.com/?page_id=483]]
# [[ICML 2016]]
# [[ICML 2017]]
# [[ICML 2019]]
! DL Workshop
* [[Generative Adversarial Network Talk]]
! Bayesian Optimization Workshop
* [[Gradient-based Hyperparameter Optimization through Reversible Learning]]
[[List of all accepted papers|http://icml.cc/2016/?page_id=1649#]]
!!! A Deep Learning Approach to Unsupervised Ensemble Learning
RBM
!!! Convolutional Rectifier Networks as Generalized Tensor Decompositions
By analogy to convolutional arithmetic circuits, shows ReLU activation, average pooling losing universality; max pooling loses depth efficiency comparing to product pooling with linear activation. Therefore, authors argue convolutional arithmetic circuits if effectively trained, can have better expressive power and depth eff
!!! End-to-End Speech Recognition in English and Mandarin
From Baidu AI lab
Connectionist Temporal Classification loss function to predict transcriptions from audio: coupled with an RNN model temporal information. Trained from scratch without the need of framewise alignments for pre-training.
dataset: 11,940 hours in English, 9,400 hours in Mandarin.
11 layers with many bidirectional recurrent layers and convolutional layers. Exploit long strides between RNN (reduces computation).
remark: multilayer RNN structure does not seem intuitive.
BatchNorm (before non-linear activation) to accelerate training.
GRU better than 1-D RNN, but RNN scales better as data size increases. (optimization issues?)
Language model: Kneser-Ney smoothed character level 5-gram model.
English: 850mil n-grams trained with KenLM on Common Crawl Repository
Chinese: 2bil n-grams
!!! Inverse Optimal Control with Deep Networks via Policy Optimization
paper not available
Why Regularized Auto-Encoders learn Sparse Representation?
Try to explain the effect of activation function and regularization on enforcing the sparsity of AEs.
1. The suff condition on regularization
2. Case study on Denoising AE and Contractive AE
efficiency than convolutional rectifier nets.
!!! Training Neural Networks Without Gradients: A Scalabel ADMM Approach
* [[ICML 2017 Papers]]
! Tutorials
* [[Recent Advances in Stochastic Convex and Non-Convex Optimization]]
* [[Decoupled Neural Interfaces using Synthetic Gradients|https://arxiv.org/abs/1608.05343]]: summarized in [[Synthetic Gradients]], together with another paper [[on DNI|https://arxiv.org/abs/1703.00522]]
* [[Meta Networks|https://arxiv.org/abs/1703.00837]]
* [[On the Expressive Power of Deep Neural Networks]]
* [[Training Quantized Nets: A Deeper Understanding]]
* [[Attentive Recurrent Comparators]]
! Google Brain
* [[Neural Message Passing for Quantum Chemistry|http://proceedings.mlr.press/v70/gilmer17a.html]]
! Facebook
* High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. //Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu//
* An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis. //Yuandong Tian//
* [[Convolutional Sequence to Sequence Learning]]. //Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann Dauphin//
* Efficient softmax approximation for GPUs. //Edouard Grave, Armand Joulin, Moustapha Cisse, David Grangier, Hervé Jégou//
* Gradient Boosted Decision Trees for High Dimensional Sparse Output. //Si Si, Huan Zhang, Sathiya Keerthi, Dhruv Mahajan, Inderjit Dhillon, Cho-Jui Hsieh//
* Language Modeling with Gated Convolutional Networks. //Yann Dauphin, Angela Fan, Michael Auli, David Grangier//
* Parseval Networks: Improving Robustness to Adversarial Examples. //Moustapha Cissem, Piotr Bojanowski, Edouard Grave//
* Unsupervised Learning by Predicting Noise. //Piotr Bojanowski, Armand Joulin//
* [[Wasserstein Generative Adversarial Networks|WGAN]]. //Martin Arjovsky, Soumith Chintala, Leon Bottou//
! DeepMind
* [[Parallel Multiscale Autoregresssive Density Estimation]]
* [[Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders]]
* Reinforcement Learning
** [[Why is Posterior Sampling Better than Optimism for Reinforcement Learning?]]
** [[DARLA: Improving Zero-Shot Transfer in Reinforcement Learning]]
** [[Automated Curriculum Learning for Neural Networks]]
** [[Learning to learn without gradient descent by gradient descent]]
** [[A Distributional Perspective on Reinforcement Learning]]
** [[A Laplacian Framework for Option Discovery in Reinforcement Learning]]
! Tencent Recommendations
* [[FeUdal Networks for Hierarchical Reinforcement Learning]]
! Tutorials
* [[Variational Bayes and Beyond: Bayesian Inference for Big Data]]
Videos
* [[Homepage|https://www.facebook.com/pg/icml.imls/videos/]]
* Tutorials
** [[PAC-Bayesian]]
** [[Attention in Deep Learning]]
** [[Meta Learning Tutorial]]
* Workshops
** [[Joint Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations|ICML19 Compact DL]]
! Oral Sessions
* [[ICML19 Deep Learning Algorithms]]
* [[ICML19 Deep Learning Theory]]
! Papers
* [[Combining VI and MCMC]]
! Background: Propagating Uncertainty
Uncertainty information can guide output and speedup RL.
Neal (1996) has shown that the prior distribution over nonlinear functions implied by the Bayesian neural network falls in a class of probability distributions known as Gaussian processes. Infinitely wide single hidden layer NNs with distributions placed over their weights converge to Gaussian processes. The Bayesian way to optimize it is to integrate these parameters out. This kind of NNs have been studied as [[Bayesian neural networks|Bayesian DL]].
One of the weakness of the model is if we take the hidden layer to infinity, we have to scale down the weight in the output. No single activation function will dominate.
The square loss objective is the negative Gaussian likelihood. By adding the weight decay term, we incorporate prior to the objective.
!! [[Gaussian Process]]
GPs seems to be a different approach from neural networks.
! Dropout as a Bayesian Approximation of Gaussian Process
[[link|http://arxiv.org/abs/1506.02142v6]]
The dropout objective minimises the KL-divergence between an approximate distribution and the posterior of a deep Gaussian process.
!! Deep Gaussian Process
DGP allows us to model distributions over functions. A deep Gaussian process with $$L$$ layers and covariance function
$$
K(x, y) = \int p(w)p(b)\sigma(w^\top x+b)\sigma(w^\top y+b)dwdb
$$
can be approximated by placing a variational distribution over each component of a spectral decomposition fo the GP's covariance functions.
! Dropout for Recurrent Layers
[[link|http://arxiv.org/abs/1512.05287v5]]
! Applications
* [[Structured Bayesian Pruning via Log-Normal Multiplicative Noise]]
* [[Structured Matrix for Efficient Deep Learning]]
* [[Mixed Precision Training & Inference]]
* How Does Disagreement Help Generalization against Label Corruption?
** [[Noisy Labels]]
** [[Co-Teaching]]
[[videos|https://www.facebook.com/icml.imls/videos/606052416553010/]]
* Why do larger models generalize better? A theoretical perspective via the XOR problem
** larger models generalize better because more candidates in initialization
** PAC bound for XOR detection: $$p=0.98$$, to get $$0.5\%$$ test error with probability $$\ge0.95$$, large network needs 2 samples and small network needs $$\ge 129$$ samples.
* On the Spetral Bias of Neural Networks
** Neural networks can learn arbitary labels, but learn simple functions first
** To quantify simplicity, use (Fourier) Spectrum
** learning gets easier with increasing manifold complexity
* Recursive Sketches for Modular Deep Learning
** moduluar networks looks like Bayesian nets
* Zero-Shot Knowledge Distillation in Deep Learning
** Data Impression: Use Dirichlet distribution to generate continuous embeddings
* [[A Convergence Theory for Deep Learning via Over-Parametrization]]
! Linearized ResNet
Refs
* [[Geometry of linearized neural networks|http://blogs.princeton.edu/imabandit/2016/11/13/geometry-of-linearized-neural-networks/]]
* [[Identity Matters in Deep Learning|https://arxiv.org/abs/1611.04231]]
We consider a linearized net, simplified to a product of matrices:
$$
g(A_0,\dots, A_L) = \mathbb{E}_{(x,y) \sim \nu} \|\prod_{i=0}^L A_i x - y\|^2 ,
$$
which, although non-convex, satisfies the second-order optimality condition:
<<<
''Proposition'' [[[Kawaguchi 2016|https://arxiv.org/abs/1605.07110]]]<br>
Assume that $x$ has a full rank covariance matrix and that $y=Rx$ for some deterministic matrix $R$. Then all local minima of $g$ are global minima.
<<<
The residual network looks like this:
$$
f(A_0,\dots, A_L) = \mathbb{E}_{(x,y) \sim \nu} \|\prod_{i=0}^L (A_i+\mathrm{I}) x - y\|^2 .
$$
<<<
''Proposition'' [Hardt and Ma 2016]<br>
Assume that $x$ has a full rank covariance matrix and that $y=Rx$ for some deterministic matrix $R$. Then $f$ has first order optimality on the set $\{(A_0, \dots, A_L) : \|A_i\| \lt 1\}$.
<<<
''Proof'': One has with $E = R - \prod_{i=0}^L (A_i+\mathrm{I})$ and $\Sigma = xx^{\top}$,
$$
f = (E x)^{\top} Ex = \mathrm{Tr}(E \Sigma E^{\top}) =: \|E\|_{\Sigma}^2 ,
$$
so with $E_{\lt i} = \prod_{j\lt i} (A_j + \mathrm{I})$, and $E_{\gt i}=\prod_{j\gt i} (A_j + \mathrm{I})$,
$$
\begin{eqnarray*}
f(A_0,\dots, A_i + V, \dots, A_L) & = & \|R - \prod_{j \lt i} (A_j + \mathrm{I}) \times (A_i + V +\mathrm{I}) \times \prod_{j\gt i} (A_j + \mathrm{I})\|_{\Sigma}^2 \\
& = & \|E + E_{\lt i} V E_{\gt i}\|_{\Sigma}^2 \\
& = & \|E\|_{\Sigma}^2 + 2 \langle \Sigma E, E_{\lt i} V E_{\gt i} \rangle + \|E_{\lt i} V E_{\gt i}\|_{\Sigma}^2 ,
\end{eqnarray*}
$$
which exactly means that the derivative of $f$ with respect to $A_i$ is equal to $E_{\gt i}^{\top} \Sigma E E_{\lt i}^{\top}$. On the set under consideration one has that $E_{\gt i}$ and $E_{\lt i}$ are invertible (and so is $\Sigma$ by assumption), and thus if this derivative is equal to 0 it must be that $E=0$ and thus $f=0$ (which is the global minimum).
! Remark
* Since non-linearity is so important in deep learning, how similar is it with linear system analysis?
* Similar result is given for RNN in [[Optimization Properties of Linearized RNN]].
! Equivalence of ResNet and RNN
Refs
* [[Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex|https://arxiv.org/abs/1604.03640]]
!! Background
[[Neuromorphic Cognitive Computing]]
Karlheinz Meier - Neuromorphic Computing beyond von Neumann
Cons of von Neumann:
# Uniform
# No separation of memory and computing
# Not programmed
# No established theory (in brain science?)
Firing patterns represents the action potential
Mimic this activity in microscopic level
Biology Cell Level Simulation
# 10,000 reconstructed neurons
# 2 mWalt:100 Walt
# 1000 times slower
Timing problem is foundamental: e.g. simulating brain envolvement within 15yrs
!! Definition
Neuromorphic computing simulates neurons with silicon substrates. And it can solve time and energy issues (?)
# massive parallel
# configurability
# asnchronous communication
# SpiNNaker: 18 ARM 6 inter chip connections
# IBM Almaden: 45nm processors, real programming model for AI problems
# 10,000 faster than biology: 50mil synapses
# real time approach analog cells (Boahen, stanford)
# Qualcomm Neural Processing Units
!! Interests
# Cell properties
## Cell firing patterns, synchronisations, stability, order-chaos
# Closed loops reverse engineering
## Functional units, cortical structures
## small brains: insect brain, 3 layer NN
## owl hearing: coincident neurons
# test theories
# Neuromorphic computing outside neural science
!! Questions
Lateral Prefrontal Cortex volume lossing (simulating this?)
How is this related to von Neuman machine?
! Implementations
[[Google|https://github.com/tensorflow/models/tree/master/im2txt]]
! Bibs
* [[Self-Guiding Multimodal LSTM - when we do not have a perfect training dataset for image captioning|https://arxiv.org/abs/1709.05038]]
! Dataset
* COCO
* [[Conceptual Captions|https://ai.googleblog.com/2018/09/conceptual-captions-new-dataset-and.html]]
! Decoding
* [[ARNet|https://arxiv.org/abs/1803.11439]] [[code|https://github.com/chenxinpeng/ARNet]]
** regularize by reconstructing last hidden state
! Applications
* [[Video Captioning]]
! Rigit transformation symmetries
* Translations: $\{\varphi_v;v\in\mathbb R^2\}$, with $\varphi_v(x)(u)=x(u-v)$.
* Dilations: $\{\varphi_s;s\in\mathbb R_+\}$, with $\varphi_s(x)(u)=s^{-1}x(s^{-1}u)$.
* Rotations: $\{\varphi_\theta;\theta\in[0, 2\pi)\}$, with $\varphi_\theta(x)(u)=x(R_\theta u)$.
* Mirror symmetry: $\{e, M\}$, with $Mx(u_1, u_2) = x(-u_1, u_2)$.
We can combine all these transformations into a single group, the Affine Group $Aff(\mathbb R^2)$. It has 6 degrees of freedom, in the representation
$$
\left(\begin{array}{c}u_1\\u_2\end{array}\right)\rightarrow
\left(\begin{array}{c}v_1\\v_2\end{array}\right)
\left(\begin{array}{cc}a_1&a_2\\a_3&a_4\end{array}\right)
\left(\begin{array}{c}u_1\\u_2\end{array}\right)
$$
A particular simple example is given by [[continuous one-parameter unitary transformations|Unitary Groups]].
This is in general a //non-commutative// group. For some groups, we might only observe partial invariance. In speech, the underlying group modeling time-frequency shifts is the ''Heisenberg'' group.
Given a transformation group $G$ and an input $x$, the ''action of $G$'' onto $x$ is called an ''orbit'':
$$
G\cdot x = \{\varphi_g(x), g\in G\}
$$
With a linear $\Phi(x)$, a 6-dim curvy space looks flat in a high-dimensional space. Group symmetries are not sufficient to beat the curse of dimensionality. It is too strict.
!! Limits of Group Diagonalisation
A shallow (1 layer) network is thus sufficient to achieve invariance to commutative group transformations. However, this architecture has a number of shortcomings.
!!! Non-commutative groups
''Proposition:'' If $G = \{\varphi t\}_t$ is non-commutative, then there is no basis $V$ that diagonalises simultaneously all $\varphi_t$.
Roto-translation is a group that is non-commutative: $(v', \theta')\cdot(v,\theta)=(v+R_{-\theta}v', \theta+\theta')$
!!! Losing degrees of freedom
Because of Hermitic symmetry, $\Phi:\mathbb R^n\rightarrow\mathbb R^{\lceil n/2\rceil}$, we “pay” $n/2$ degrees of freedom to remove group variability, independently of the group dimensionality.
If the group has dimension $p$, a G-invariant representation could have up to $n-p$ d.f.: we are losing discriminability when $p$ is small.
Fourier Phases encode most of the relevant signal information.
! Stability
[[Shallow invariants are unstable|Deformation Metric]]
Image and audio recognition is ''stable'' to local deformations. Let $x\in L^2(\mathbb R^m)$ be the data, $\tau:\mathbb R^m\rightarrow\mathbb R^m$ be the diffeomorphism. And $x_\tau = \varphi_\tau(x)$, where $\varphi_\tau$ is a change of variables: (think of $x_\tau$ as adding noise to the pixel //locations// rather than to the pixel values). $x_\tau(u) = x(u=\tau(u))$.
Informally, if $\|\tau\|$ measures the amount of deformation, many recognition tasks satisfy
$$
\forall x, \tau, |f(x)-f(x_\tau)\lesssim\|\tau\|
$$
Thus $F:\tau\mapsto\Phi(x_\tau)$ is Lipschitz w.r.t. the deformation metric $\|\tau\|$ uniformly on $x$. If our representation is stable, then
$$
\forall x, \tau, \|\Phi(x)-\Phi(x_\tau)\|\le C\|\tau\|\Rightarrow|\hat f(x)-\hat f(x_\tau)|\le\tilde C\|\tau\|
$$
The stablity can be represented by sampling from a Ergodic process:
* In statistical physics, a process with an ''Integral Scale'' is ergodic
* In statistics, linear processes are ergodic (provided the moments are finite).
! Bib
!! Deep Identity and Invariance
!!! ICLR 2017 openreview
* [[Identity Matters in Deep Learning|http://openreview.net/pdf?id=ryxB0Rtxx]]
** Discuss the representation power of ResNet. Deviced a not very powerful model though. The performance needs further investigation.
* [[Learning Invariant Representations Of Planar Curves|http://openreview.net/pdf?id=BymIbLKgl]]
** Linking to numerical differential geometry. Using a Siamese architecture to learn (rotation) invariance. Should help generative model.
* [[Warped Convolutions: Efficient Invariance to Spatial Transformations|http://openreview.net/pdf?id=BkmM8Dceg]]
** Handles scale, rotation and spatial translation with warp and generalized conv. Good performance in pose estimation with ''soft argmax''. Hinders general classification.
! Traditional Methods
!! Descriptors
* [[LATCH: Learned Arrangements of Three Patch Codes|https://github.com/csp256/cudaLATCH]]
* Product quantization: IVFADC
! Autoencoders
[[MNIST example|https://github.com/BVLC/caffe/blob/master/examples/mnist/mnist_autoencoder.prototxt]]
and there are several convolutional autoencoders in caffe and pylearn. However, I don't find it convincing that object level infomation can be extracted this way, considering how [[easily ConvNets can be fooled|http://www.evolvingai.org/fooling]].
! Search algorithms for binary codes
The search time on large-scale retrival database should be sub-linear in the number of examples. Hashing is widely used in this case because it's quick and require only binary data. It uses all dimension in query in contrast to tree-based algorithms.
!! Linear scan in Hamming space
Paralleling `xor`s and 1 counting's are trival in machine level. Easy speedup for multiples cores/machines, and gracefully scaling to longer code lengths.
!! Semantic hashing
Binary codes correspond to addresses in memory.
# provided the radius is small, it is quick ($\mu$s)
# constructing the database is also fast.
The deficit is in codelength scaling, which makes the Hamming ball exponentially larger. In addition, a 32-bit code requires 4Gb memory.
! GPU base fast algorithms
* [[Faiss]]
! [[Meta-Learning with Implicit Gradients|https://arxiv.org/abs/1909.04630]]
Anchor the parameter to stay in the vicinity of $$\theta_0$$ by adding quadratic regularizer $$\|\theta-\theta_0\|$$ to their loss.
The gradient can be calculated in closed form. Related to the curvature, or second derivative of $$f$$ around the minimum we find:
$$
\frac{d\theta^\ast}{d\theta_0} = \frac{1}{1+f''(\theta^\ast)}
$$
Since it involves the inverse of the Hessian plus the identity, there is a practical approximation, using a conjugate gradient inner optimization loop.
! [[Optimizing Millions of Hyperparameters by Implicit Differentiation|https://arxiv.org/abs/1911.02590]]
With [[Implicit Function Theorem]] we obtained for iMAML is a special case of this where the $$\frac{\partial^2 \mathcal{L}_T}{\partial \theta \partial \theta}$$ is the identity. This is because there, the hyperparameter controls a quadratic regularizer $$\frac12\|\theta-\lambda\|^2$$, and indeed if you differentiate this with respect to both $$\lambda$$ and $$\theta$$ you are left wiht a constant times identity.
The primary difficulty is approximating the inverse Hessian, or matrix-vector products. iMAML uses a conjugate gradient method. In this work, they use a Neumann series approximation, which, for a matrix $$U$$ looks as follows:
$$
U^{-1} = \sum_{i=0}^{\infty}(I - U)^i
$$
We attempt to solve the MDP by mimicking an expert policy. The learned policy is referred to as the apprentice policy.
* given: state & action space, roll-out from $\pi^*$, dynamics model
* goal: recover reward function, then use reward to get policy
The challenges are:
* underdefined problem
* difficult to evaluate a learned reward
* demonstrations may not be precisely optimal
Imitation learning is very similar to GAN:
* a generator generate policy samples from $\pi$ and update policy in the outer loop
* a discriminator update reward using samples & demos and update reward in the inner loop
DeepMind use GAN and VAE to learn human actions: [[Robust Imitation of Diverse Behaviors|https://arxiv.org/abs/1707.02747]]
! Bibs
* One-Shot Imitation Learning
* Learning a One-Shot Imitator with MAML
* [[Expert Iteration]]
* [[Iterated Amplification]]
!! MFCC
Frame the signal into 20-40 ms frames. 25ms is standard.
Many meta-learning or hyperparameter optimization problems can be stated as nested optimization problems. If we have some hyperparameters $$\lambda$$ and some paramters $$\theta$$ we are interested in
$$
\operatorname{argmin}_\lambda \mathcal{L}_V (\operatorname{argmin}_\theta \mathcal{L}_T(\theta, \lambda)),
$$
where $$\mathcal L_T$$ is some training loss and $$\mathcal L_V$$ a validation loss. The optimal paramter to the training problem, $$\theta^\ast$$ implicitly depends on the hyperparameters $$\lambda$$:
$$
\theta^\ast(\lambda) = \operatorname{argmin} f(\theta, \lambda).
$$
If this implicit function mapping $$\lambda$$ to $$\theta^\ast$$ is differentiable, and subject to some other conditions, the implicit function theorem states that its derivative is
$$
\left.\frac{\partial\theta^{\ast}}{\partial\lambda}\right\vert_{\lambda_0} = \left.-\left[\frac{\partial^2 \mathcal{L}_T}{\partial \theta \partial \theta}\right]^{-1}\frac{\partial^2\mathcal{L}_T}{\partial \theta \partial \lambda}\right\vert_{\lambda_0, \theta^\ast(\lambda_0)}
$$
IS is a technique which enables us to estimate expectations under a distribution which is difficult to sample from (the target distribution), using an approximating proposal distribution. You sample from your proposal distribution, then weight those samples by the ratio of target distribution to the proposal distribution (evaluated at the sample point). These weights are called importance ratios.
In the event that the proposal distribution is a poor fit to the target distribution, these weights can have a very high variance. Pareto smoothing is a way to control this variance. It builds on the idea of simply truncating the importance ratios by instead fitting a Pareto distribution to them.
$$
E_{p(x)}[f(x)] = \int p(x)f(x)dx = \int q(x)\frac{p(x)}{q(x)}f(x)dx=E_{1(x)}[\frac{p(x)}{q(x)}f(x)]
$$
The large variance of importance sampling comes from the summation, which is a result of the large values. This can be reduced by [[PSIS|Pareto Smoothed Importance Sampling]]
! REINFORCE
! Concrete
!! Relationship wiht REINFORCE
!
Normalizing flows are a series of invertible transformations to latent variables with a simple posterior.
! Volume-Preserving Flows
Design series of transformations s.t. the Jacobian-determinant equals 1 while still it allows to obtain flexible posterior distributions.
Examples:
* Non-linear Independent Components Estimation
* Hamiltonian Variational Inference
!! Inverse Autoregressive Flow
IAFs are stochastic generative models whose latent variabels are arranged to that all elements of a high dimensional observable sample can be generated in parallel. IAFs are a special type of normalising flow.
[[link|https://arxiv.org/abs/1606.04934]] [[github|https://github.com/openai/iaf]]
IAF is the lower triangular inverse Cholesky matrix with ones on the diagonal. It improves nomalizing flow to be computationally cheap to:
# compute and differentiate for its probability density $q(z|x)$
# to sample from, parallizable. (sample $z$?)
# parallizable of these operations across dimensions of $z$
The autoregressive whitening operation makes $y$ with complicated distribution into $z$ with i.i.d. elements. The transformation $y\rightarrow z$ can be completely vectorized:
$$
z = (y-\mu(y))/\sigma(y)
$$
where subtraction and division are elementwise. This has a lower triangular jacobian matrix, whose diagonal elements are the elements of $\sigma(y)$, and:
$$
\log\det\left|\frac{dz}{dy}\right| = -\sum_{i=1}^D\log\sigma_i(y)
$$
Initially, a random sample is drawn from $z\sim\text{Logistic}(0, I)$. The following transformation is applied to $z$:
$$
x_t = z_t\cdot s(z_{<t}, \theta)+\mu(z_{<t}, \theta)
$$
$p(x_t|z_{<t})$ follows a logistic distribution:
$$
p(x_t|z_{<t}) = \mathbb L(x_t|\mu(z_{<t}, \theta), s(z_{<t}, \theta)),
$$
while $\mu(z_{<t}, \theta)$ and $s(z_{<t}, \theta)$ can be any autoregressive model.
!! Householder Flow
[[paper|http://arxiv.org/abs/1611.09630]]
The absolute value of Jacobian-determinant of an orthogonal matrix is 1. Any orthonogal matrix can be represented in the following form:
<<<
$\bf U=I-YSY^\top$
<<< [[The Basis-Kernel Representation of Orthogonal Matrices]]
! Implementations
* Tensorflow models
* [[Keras|https://github.com/kentsommer/keras-inceptionV4]]
! Batch Normalizations
[[BN-Inception|Batch Normalization]]
! Guidelines
[[link|http://arxiv.org/abs/1512.00567]]
General rules are:
# Avoid representational bottlenecks, especially early in the network.
# Add filters per tile in CONVs to get higher dimensional representations
# Spatial aggregation (dimension reduction) before CONVs
# Balance the width and depth.
Grid reduction: avoiding representational bottleneck or too much params. Reduce size with pooling and conv together:
[img[inception_reduction.PNG]]
! Inception-v2
# 7x7 to 3 3x3 CONVs, output size is 35x35x288
# 3 traditional Inceptions, keep size
# Change size to 17x17x768 with grid reduction
# 5 factorized Inceptions, with [1x7, 7x1] CONVs replacing the 3x3 ones
# Change size to 8x8x1280 with grid reduction
# 2 factorized Inceptions, with [1x3, 3x1] CONVs replacing the 3x3 ones with 2048 filters
All together 42 layers.
BN auxiliary as regularizers.
!! Remarks
Historically, Inception-v3 had inherited a lot of the baggage of the earlier incarnations. The technical constraints chiefly came from the need for partitioning the model for distributed training using DistBelief.
! Inception-v4 and Inception-ResNet
[[link|http://arxiv.org/abs/1602.07261]]
Has a more uniform simplified architecture and more inception modules than Inception-v3.
Introduces residual connections: Inception-ResNet. Difference is batch-normalization is only on top of the traditional layers, but not on top of the summations because otherwise, the network won't fit into one GPU.
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
! Differentiable ILP
* Learning from Noisy Data
* Read this related work
! CSP
* [[Constraint and Mathematical Programming Models for Integrated Port Container Terminal Operations|https://arxiv.org/abs/1712.05302]]
! Relational inductive biases
* [[Graph Networks]]
!! Log sum
Let $a_1,\ldots,a_n$ and $b_1,\ldots,b_n$ be nonnegative numbers. Denote the sum of all $a_i$s by $a$ and the sum of all $b_i$s by $b$. The log sum inequality states that
$$
\sum_{i=1}^n a_i\log\frac{a_i}{b_i}\geq a\log\frac{a}{b},
$$
with equality if and only if $\frac{a_i}{b_i}$ are equal for all $i$.
Notice that after setting $f(x)=x\log x$ we have
$$
\begin{align}
\sum_{i=1}^n a_i\log\frac{a_i}{b_i} & {} = \sum_{i=1}^n b_i f\left(\frac{a_i}{b_i}\right)
= b\sum_{i=1}^n \frac{b_i}{b} f\left(\frac{a_i}{b_i}\right) \\
& {} \geq b f\left(\sum_{i=1}^n \frac{b_i}{b}\frac{a_i}{b_i}\right) = b f\left(\frac{1}{b}\sum_{i=1}^n a_i\right)
= b f\left(\frac{a}{b}\right) \\
& {} = a\log\frac{a}{b},
\end{align}
$$
where the inequality follows from Jensen's inequality since $\frac{b_i}{b}\geq 0$, $\sum_i\frac{b_i}{b}= 1$, and $f$ is convex.
! Theory
Probabilistic inference is used to handle noisy and imprecise data. Exact inference is difficult, so approximate methods like Gibbs sampling are used.
* Exact methods (conjugacy, enumeration)
* Numerical integration (Quadrature)
* [[Invariant Risk Minimization]]
* Generalised method of moments
* [[Maximum Likelihood Estimation]]
* Maximum a posteriori
* [[Expectation-Maximization]]
* Contastive estimation (NCE)
* Cavity methods (EP)
* [[Bayes optimality in auxiliary tasks]]
* [[Convex Conjugate]]
!! Approximate Inference
* Laplace approximation
* Integrated nested Laplace approximations
* [[Importance sampling]]
* [[Variational Optimization]]
* [[Variational Bayes]]
* Perturbative corrections
* [[Monte Carlo Methods]]
* [[Reparametrization Trick]]: use MC to estimate variational bound
* [[Convex Relaxation]]
* [[Stein Variational Gradient Descent]]
* [[Debiasing Approximate Inference]]
* [[Combining VI and MCMC]]
! Tools
* [[Stan]]
* [[BOPP|https://github.com/probprog/bopp/]]: favorite tool in favorite language, based on [[Anglican|https://github.com/probprog/anglican/blob/master/doc/codemap.md]]
! Objectives
* prediction: $p(x_{t+1, \dots, \infty}|x_{-\infty, \dots, t})$
* planning: $J = \mathbb E_p[\int_0^\infty dtC(x_t)|x_0, u]$
* parameter estimation: $p(\theta|x_{0, \dots, N})$
* experiment design: $EIG = D[p(f(x_{t, \dots, \infty})|u);p(f(x_{-\infty, \dots, t}))]$
* hypothesis testing: $\frac{p(f(x_{-\infty, \dots, t})|H_0)}{p(f(x_{-\infty, \dots, t})|H_1)}$
Influence function is a classic technique from robust statistics that tells us how the model parameters change as we upweight a training point by an infinitesimal amount. We want to estimate the counterfactual:
<<<
How would the model's predictions change if we did not have this training point?
<<<
IFs give us an efficient approximation by computing the parameter change if $x$ were upweighted by some small $\epsilon$:
$$
\hat{\theta}_{-z} = \min_{\theta\in\Theta}\sum_{z_i\neq z}\frac 1 n\sum_{i=1}^nL(z_i, \theta)+\epsilon L(z,\theta)
$$
The influence of upweighting $z$ on the parameters $\hat\theta$ is given by
$$
\mathcal I_{up,params}(z)=\frac{d\hat\theta_{\epsilon, z}}{d\epsilon}|_{\epsilon=0}=-H_{\hat\theta}^{-1}\nabla_\theta L(z, \hat\theta)
$$
where $H_{\hat\theta} = \frac1n\sum_{i=1}^nL(z_i, \hat\theta)$ is the Hessian and is positive definite (PD) by assumption. The computation of Hessian and its reverse is expensive. The Hessian vector product can be approximated by conjugate gradient:
$$
H^{-1}v = \arg\min_t \frac12t^THt-v^Tt
$$
* [[link|https://arxiv.org/abs/1606.03657]]
* [[blog|http://www.inference.vc/infogan-variational-bound-on-mutual-information-twice/]]
In order to maximise the mutual information between generated samples and a small subset of latent variables $c$, the authors make use of a variational lower bound. This, conveniently, results in a recognition model, similar to the one we see in variational autoencoders. The recognition model infers latent representation $c$ from data.
The idealised InfoGAN objective is the weighted difference of two mutual information terms.
$$
\mathcal l_{infoGAN}(\theta)=I[x,y]−\lambda I[x_{fake},c]
$$
To arrive at the algorithm the authors used, one uses the bound on both mutual information terms.
* When you apply the bound on the first term, you get a lower bound, and you introduce an auxillary distribution that ends up being called the discriminator. This application of the bound is wrong because it bounds the loss function from the wrong side.
* When you apply the bound on the second term, you end up upper bounding the loss function, because of the negative sign. This is a good thing. The combination of a lower bound and an upper bound means that you don't even know which direction you're bounding or approximating the loss function from anymore, it's neither an upper or a lower bound.
! To use this on WGAN
! Definitions
* [[Perplexity]]
* [[Mutual Information]]
! Theorems
[[Principle of maximum entropy]]
!! Data Processing Inqeuality (DPI) & Invariance
Let $I(X;Y)$ be the [[Mutual Information]]:
Reparametrization Invariance: for invertible $\phi, \psi$:
$$
I(X;Y) = I(\phi(X);\psi(Y))
$$
! Talks
[[Information Theory for Deep Learning]]
! Bibs
* [[On Unlimited Sampling|http://www.mit.edu/~ayush/MIT/Unlimited_Sampling.html]]: higher bandwidth sampling on ADC.
* [[talk|https://www.youtube.com/watch?v=bLqJHjXihK8]]
* [[paper|https://arxiv.org/abs/1703.00810]]
* [[blog|https://blog.acolyer.org/2017/11/15/opening-the-black-box-of-deep-neural-networks-via-information-part-i/]]
! The Information Bottleneck Method
* Approximate Minimal Sufficient Statistics:
** Markov chain: $Y\rightarrow X\rightarrow S(X)\rightarrow \hat X$: $\hat X=\arg\min_{I(S(X);Y)=I(X;Y)}I(S(X);X)$
** Relaxation given $p(X;Y)$: $\hat X=\arg\min_{p(\hat X;X)}I(\hat X;X)-\beta I(\hat X;Y)$， $\beta>0$
where the Lagrange multiplier $\beta$ determines the level of relevant information captured by the representation $\hat X, I(\hat X;Y)$. The solution to this problem defines an ''information curvature'': a monotonic concave line of optimal representations that separates the achievable and unachievable regions in the information plane.\
* A Rate-Distortion problem with KL-divergence distortion:
** $d_{IB}(x, \hat x) = D[p(y|x)\|p(y|\hat x)]$
* The ONLY distributional quantization measure which satisfy both DPI (f-divergences) and Statistical Consistency
! DNN Layer represent
* A Markov chain of topologically distincy [soft] partitions of the input variable X
* Successive Refinement of Relevant Information
* Individual neurons can be easily "scrambled" within each layer
! Input Compression bounds
We create a generalization bound considering the cardinality of the input. $T_\epsilon$ is the $\epsilon$ partition of the input variable $X$ w.r.t. the distortion
$$
d_{IB}(x, t) = D(p(y|x)\|p(y|t)]\ge\frac{1}{2\ln 2}\|p(y|x)-p(y|t)\|^2_1
$$
when $p(y|t)=\sum_xp(y|x)p(x|t)$:
$$
\langle d_{IB}\rangle=I(X;Y)-I(T;Y)
$$
minimizing IB distorion we maximize $I(T;Y)$
$$
\epsilon^2<\frac{2^{I(T_\epsilon; X)}+\log\frac 1 \delta}{2m}
$$
$K$ bits of compression of X are like a factor of $2^K$ training examples
There is another information loss:
$$
I(T;Y)\le\hat I_{emp}(T;Y)+O\sqrt{\frac{2^{I(T;Y)}|Y|}{m}}
$$
! The benefit of the hidden layers
More layers take much FEWER training epochs for good generalization. The optimization time depend super-linearly (exponentially?) on the compressed information, delta Ix, for each layer
The relaxation time is exponential to the compression rate:
$$
\Delta t_k\sim\exp(\Delta S_k)
$$
Let the layer compression be: $\Delta S_k = I(X;T_k)-I(X;T_{k-1})$, since $\exp(\sum_k\Delta S_k)>>\sum_k\exp(\Delta S_k)$, exponential boost in relaxation time with K layers!
The inference is done by solving a convex optimization problem about the input. Thus the net should be made ''input convex''.
* [[paper|https://arxiv.org/abs/1908.07801]]
* [[code|https://github.com/GothicAi/Instaboost]]
Crop & paste based on local similarity
* Embedding
** [[Discriminative Loss]]
** [[YOLACT]]
* [[Instance Segmentation Fact Sheet]]
! Panoptic
* [[UPSNet: A Unified Panoptic Segmentation Network|https://github.com/uber-research/UPSNet]]
* [[Instance Mask Projection for High Accuracy Semantic Segmentation of Things]]
! Tricks
* Augmenation
** [640, 800] uniform multiscale
** [[InstaBoost]]
|!Method |!Backbone |!COCO |!Cityscapes test |
| Two Stage |<|<|<|
|[[MaskRCNN|https://arxiv.org/abs/1703.06870]] |R50-FPN |33.6 val |26.2 (fine) 32.0 (+coarse) |
|[[MaskRCNN|https://arxiv.org/abs/1703.06870]] |R101-FPN |35.7 | |
|MaskLab |R101 |35.4 | |
|MaskLab (scale augmentation) |R101 |37.3 | |
|[[MS RCNN|https://arxiv.org/abs/1903.00241]] |R101-FPN |38.3 | |
| Single Stage |<|<|<|
|[[DeepMask|https://arxiv.org/abs/1811.10870]] |R101 |12.6 (224 AR@10) | |
|[[InstanceFCN|https://arxiv.org/pdf/1603.08678.pdf]] |VGG16 |16.6 (224 AR@10) | |
|[[FCIS|https://arxiv.org/abs/1611.07709]] |R101 |29.2 (600) | |
|[[YOLACT]] |R101-FPN |29.8 (550) | |
|[[Discriminative Loss|https://arxiv.org/abs/1708.02551]] |R38 | |17.5 (fine) |
|[[Watershed|https://arxiv.org/abs/1611.08303]] |VGG16 | |19.4 (fine) |
|[[Affinity|https://arxiv.org/abs/1811.10870]] |R101 | |27.3 (fine) |
|[[Pixel Offsets|https://arxiv.org/abs/1906.11109]] |ERFNet | |27.6 (fine) |
[[link|https://www.altera.com/en_US/pdfs/literature/solution-sheets/efficient_neural_networks.pdf]]
Data is passed from one layer to the next through a mechanism called OpenCL Channels or Pipes, which allow data to move from one kernel to the next without sending data to the external memory.
KNL based on socket, no PCIE, 72 cores each with 2 512bit VPU 384g ddr4, 80G/S, 16G MCDRAM
CAFFE-MPI
! Deep learning
# What is an auto-encoder? Why do we “auto-encode”? Hint: it’s really a misnomer.
# What is a Boltzmann Machine? Why a Boltzmann Machine?
# Why do we use sigmoid for an output function? Why tanh? Why not cosine? Why any function in particular?
# Why are CNNs used primarily in imaging and not so much other tasks?
# Explain backpropagation. Seriously. To the target audience described above.
# Is it OK to connect from a Layer 4 output back to a Layer 2 input?
# Can they derive the back-propagation and weights update?
# Extend the above question to non-trivial layers such as convolutional layers, pooling layers, etc.
# How to implement dropout?
# Their intuition when and why some tricks such as max pooling, ReLU, maxout, etc. work. There are no right answers but it helps to understand their thoughts and research experience.
# Can they abstract the forward, backward, update operations as matrix operations, to leverage BLAS and GPU?
! Machine learning
# [[What are the best interview questions to evaluate a machine learning researcher?|https://www.quora.com/What-are-the-best-interview-questions-to-evaluate-a-machine-learning-researcher]]
# [[Machine Learning Engineer interview questions|http://resources.workable.com/machine-learning-engineer-interview-questions]]
[[link|http://arxiv.org/abs/1602.08007]]
[[video|https://www.youtube.com/watch?v=5rYiDpVFXV8]]
! examples modeling structured text
# Synthetic music notes: constraints that notes belong to a perfect chord.
# Nesting: Matching quotes and one style of text in each scope.
# Long distance xor
# $a^nb^n$
! Model rewriting
Consider a gated RNN which reads $(x_1, \cdots, x_t)$. The activities are
$$
h^{t+1} = sigm(b_i+\rho_{ix}+\sum_jh^t_jw_{jix})
$$
where $x$ is the latest character read, $w_{jix}$ are the weights when reading $x$ and $\rho_{ix}$ are the input weights.
''remark:'' layer normalization?
Removing bias worsen the performance, so we can try the reverse thing. Substituting $w_{jix}\rightarrow w_{jix} + w_{ji}^{base}$
The bias has to balance $n$ weights $w_{ij}$ which change at each training step, rewrite $b = nb$. For backprop, amounts to multiplying the learning rate of b_i by $n^2$. Not compensated by Adagrad.
Training on data $x$ is not equivalent to training on data $1-x$.
! Avoid model rewritings
# identify the best vars
# Use a learning algorithm that is insensitive to model rewriting
Standard answer is to use natural gradient using the inverse Fisher metric. But its algorithmic cost is $O(\#params)^2 dim(output))$ per data point. This is unsuable for larger than 100 neurons.
Invariance often provides:
# Fewer arbitrary choices, no model rewriting, fewer magic numbers
# Often, better performance
# Performance transfer
!! Gradient descent
$\theta\leftarrow\theta-\eta\frac{\partial L}{\partial \theta}$ is not homogenous unless unit of $\eta = $ unit of $\theta^2$.
For a small learning rate $\eta$, this is equivalent (up to $O(\eta^2)$) to
$$
\theta^{k+1} = \underset{\theta}{arg min} L(\theta)+\frac{1}{2\eta}\|\theta-\theta^k\|^2
$$
minimizing loss and not moving too far. This depends on the numerical representation of $\theta$.
The solution is to use a norm depends on what the network does, rather than how $\theta$ is represented. Riemannian metrics are norms
$$
\|\delta\theta\|^2 = \delta\theta^\top M(\theta)\delta\theta
$$
with $M(\theta)$ a well-chosen positive definite matrix depending on $\theta$.
Riemannian gradient descent:
$$
\theta^{k+1} = \theta^k -\eta M(\theta)^{-1}\frac{\partial L(\theta^k)}{\partial \theta}
$$
For natural gradient which defines $\|\theta-\theta^k\|^2$ as the KL divergence, $M(\theta)$ is Fisher information matrix. Two possible strategies:
# Try to scale down the natural gradient
# Backprop a metric on the output, but keeping only the diagonal of Hessian breaks invariance
[[paper|https://arxiv.org/abs/1907.02893]]
The authors assume that we have access to data sampled from different environments $$e$$. The data distribution in these different enviroments is different, but there is an underlying causal dependence of the variable of interest $$Y$$ on some of the observed features $$X$$ that remains constant, or invariant across all environments.
Usual empirical risk minimisation (ERM) approaches cannot distinguish between statistical associations that correspond to causal connections, and those that are just spurious correlations. Invariant Risk Minimization can, in certain situations. It does this by finding a representation $$\phi$$ of features, such that the optimal predictor is simultaneously Bayes optimal in all environments.
The loss function that tries to capture this property:
$$
\min_\phi \sum_e \mathcal{R}^e(\Phi) + \lambda \|\nabla_{w\vert w=1}\mathcal{R}^e(w \cdot \Phi)\|^2_2
$$
The first term is the usuasl ERM: we're trying to minimize average risk across all environments, using a single predictor $$\phi$$. The second term looks at whether $$\phi$$ is locally optimal, whether it can be improved locally by scaling by a constant $$w$$.
! Information theoretic explanation
The authors treat the environment index $$e$$ as something outside of the structural equation model. $$E$$ can also be treated as a part of the generative process: an observable random variable. This will help to derive IRM through the lens of conditional dependence relationships.
[img[https://www.inference.vc/content/images/2019/07/IRM_graphical_model.png]]
observable variables:
* $$E$$, the environment index
* $$X$$, the features describing the datapoint
* $$Y$$, the label we wish to predict
OpenAI [[paper|https://arxiv.org/abs/1810.08575]]
Using Transformer as controller.
[[Learn javascript in Y Minutes]]
! Introduction
The i-vector extaction could be seen as a probabilistic compression process that reduces the dimensionality of speech-session super-vectors according to a linear-Gaussian model. The speaker- and channel- dependent super-vector $M_{(s, h)}$ of concatenated GMM means is projected in a low dimensionality space, named Total Variability space, as follows
$$
M_{(s, h)} = m + Tw_{(s, h)}
$$
where $m$ is the mean super-vector of a ''gender-dependent'' UBM, $T$ is called Total Variability matrix and $w_{(s, h)}$ is the resulting i-vector.
! Joint Factor Analysis
A given speaker GMM supervector $s$ can be decomposed as follows:
$$
s = m + Vy + Ux +Dz
$$
where $m$ is a speaker-independent supervector from UBM, $V$ $(U)$ is the eigenvoice (eigenchannel) matrix, $y$ $(x)$ is the speaker factors, with prior $N(0, 1)$, $D$ is the residual matrix and $z$ is the speaker-specific residual factors.
For a 512-mixture GMM-UBM, 300 eigenvoice and 100 eigenchannel components are used.
''Remark'': GMM weights makes the variances of different components vary. How does JFA perform on unnormalized supervectors?
!! Training
[[Factor Analysis and PCA]]
First train $V$ assuming $U$ and $D$ are zeroes, then train $U$ given $V$, and so on. Estimating $V$:
# Accumulate the 0th, 1st and 2nd order sufficient statistics for each speaker $s$ and Gaussian mixture component $c$, $N$, $F$ and $S$.
# Center the 1st and 2nd order statistics to get $\tilde F$, $\tilde S$
# Expand the statistics into matrices, $NN(s) = \text{diag}(N_1(s)I, \dots, N_C(s)I)$, $SS(s) = \text{diag}(\tilde S_1(s),\dots, \tilde S_C)$, $FF(s) = [\tilde F_1(s);\dots;\tilde F_C(s)]$.
# Iterate the estimation of $V$ and $y$ according to the distribution.
Estimation of $U$ is similar except the centering of $F$ is done by $\tilde F = F - N(m+Vy)$ and second order is not used. $D$ with no exception.
[[Deduction|JFA Explaination]]
[[Training Eigenemotion]]
! I-Vector Approach
Training thetotal variability vector $T$ is the same as training $V$ but with all recordings regarded as from different speakers.
!! Channel compensation
Perform LDA then WCCN (techniques empirically determined to perform well) on i-vectors.
The most popular post i-vector process is PLDA.
! ALIZE Example
! DET Curve
[[Probabilistic View of JFA]]
! Training JFA
The joint posterior in a general JFA model is hard to compute. brute force [4] or much more efficiently by the Gauss-Seidel method [17].
!! Gauss-Seidel
It provides a very good approximation to the joint posterior and is guaranteed to find the ''mode'' of the posterior when converge.
The posterior dsitribution of $y(s)$ conditioned on the acoustic observations of the speaker is
$$
N(l^{-1}(s)v^*\Sigma^{-1}\tilde F(s), l^{-1}(s))
$$
where
$$
l(s) = I + v^*\Sigma^{-1}N(s)v
$$
!! Updating UBM variance
TODO: Do I need to update it? during eigenvoice or eigenemotion?
!! Variational Bayes EM training
The Gauss-Seidel method is an instance of variational Bayes. These methods are needed when dealing with general cases of JFA when assignments of frames to Gaussians are treated as hidden variables.
! Why i-vector works
When there is no UBM adaptation, the calculation of joint posterior of hidden variables is straightforward.
The feature vector extracted from one recording rather than multiple recordings as in the JFA case seems to be less reliable. I-vector averaging can be used to merge multiple i-vectors into one.
!! PLDA
This gives the state-of-the-art speaker recognition performance.
The success of i-vector/PLDA cascade can be viewed as one way of decomposing JFA into front-end and back-end models.
''Uncertainty propagation'' incorporates
!! Does LDA/WCCN help with emotions
!! Cosine Similarity vs Likelihood Ratio
! Installing
!! With OpenBLAS
Kaldi now supports linking against the OpenBLAS library, which is an implementation of BLAS and parts of LAPACK. OpenBLAS also automatically compiles Netlib's implementation of LAPACK, so that it can explort LAPACK in its entirety. OpenBLAS is a fork of the GotoBLAS project (an assembler-heavy implementation of BLAS) which is no longer being maintained. In order to use GotoBLAS you can cd from "src" to "../tools", type "make openblas", then cd to "../src" and give the correct option to the "configure" script to use OpenBLAS (look at the comments at the top of the configure script to find this option). Thanks to Sola Aina for suggesting this and helping us to get this to work.
!! Bulding
```
./configure --openblas-root=/
```
! Refs
[[kaldi lectures|https://sites.google.com/site/dpovey/kaldi-lectures]]
! Plenary talks
* Is Deep Learning the New 42?
* [[Learning to learn and compositionality with deep recurrent neural networks|https://www.youtube.com/watch?v=x1kf4Zojtb0]]
* A VC view of investing in ML
! Kernels
The kernel function arises as an effortless way to perform an inner product $\langle x, y\rangle$ in a high-dimensional feature space $\mathcal F$. The positive definiteness of the kernel function guarantees the existence of a dot product space $\mathcal F$ and a mapping $\phi:\mathcal X\rightarrow \mathcal F$ such that $k(x, y) = \langle \phi(x), \phi(y)\rangle_{\mathcal F}$. We will refer to $\phi$ and $k$ as a ''feature map'' and a ''kernel function'', respectively. Likewise, we can interpret $k(x, y)$ as a non-linear similarity measure between $x$ and $y$.
For example, let's consider a polynomial feature map $\phi(x) = (x_1^2, x_2^2, x_1x_2, x_2x_1)$ when $x\in\mathbb R^2$. Then, we have
$$
\langle\phi(x), \phi(y)\rangle_{\mathcal F} = x_1^2y_1^2+x_2^2y_2^2+2x_1x_2y_1y_2 = \langle x, y\rangle^2
$$
the new similarity measure is simply the square of the inner product in $\mathcal X$. The equality holds more generally for a $d$-degree polynomial, i.e., $\phi$ maps $x\in\mathbb R^N$ to the vector $\phi(x)$ whose entries are all possible $d$th degree ordered products of the entries of $x$. We have $k(x, y) = \langle x, y\rangle^d$.
For algorithms depend only through the inner product of the data set, we can obtain a non-linear extension of these algorithms by substituting $\langle x, y\rangle$ with $k(x, y)$.
Algorithms capable of operating with kernels include
* the kernel perceptron
* support vector machines (SVM)
* Gaussian processes
* principal components analysis (PCA)
* canonical correlation analysis
* ridge regression
* spectral clustering
* linear adaptive filters
and many others. Any linear model can be turned into a non-linear model by applying the kernel trick to the model: replacing its features (predictors) by a kernel function. The capability of kernel trick also allows easy invention of domain-specific kernel functions.
; p.s.d. kernel
: A function $k: \mathcal X\times\mathcal X\rightarrow \mathbb R$ is a positive semidefinite kernel if it is symmetric, i.e., $k(x, y) = k(y, x)$, and the Gram matrix is p.s.d.
A p.s.d. kernel defines a space of functions from $\mathcal X$ to $\mathbb R$ called a //reproducing kernel Hilbert space// (RKHS) $\mathscr H$.
We may view the kernel evaluation as an inner product in $\mathscr H$ induced by a map from $\mathcal X$ into $\mathscr H$: $x\rightarrow k(x,\cdot)$:
$$
k(x, y) = \langle k(x,\cdot),k(y,\cdot)\rangle_{\mathscr H}
$$
$k(x,\cdot)$ is a high-dimensional representer of $x$.
!! Mercer's theorem
Are wider range of psd kernels.
There is an intrinsic connection between the integral operator $\mathcal T_k$, covariance operator $\mathcal C_{XX}$, and Gram matrix $\mathbf K$. And there is a fundamental connection between Mercer's theorem in functional analysis and Karhunen-Loeve theorem of stochastic processes.
!! Bochner's theorem
It states that a function $f:\mathbf R^n\rightarrow \mathbf C$ is the Fourier tranform of a finite Borel measure iff $f$ is positive definite.
$$
\varphi(x-y) = \int_{\mathbb R^d}e^{\sqrt{-1}\omega^\top(x-y)}d\Lambda(\omega)
$$
The measure $\Lambda$ determines which frequency component occurs in the kernel by putting non-negative power on each frequency $\omega$.
!! Schoenberg's theorem
Radial kernels: $k(x, y) = \varphi (\|x-y\|^2)$ where $\varphi$ is positive definite. Well known radial kernels include Gaussian RBF, mixture-of-Gaussians, inverse multiquadratic and Matern kernel.
; Schoenberg's theoren
: Suppose $f: \mathbf R_+\rightarrow\mathbf R_+$ is continuous. Then, the following are equivalent:
# The function $\mathbf R^n\ni x \rightarrow f(\|x\|_n)$ is positive semi-definite.
# The function $\mathbf R^n\ni t \rightarrow f(\sqrt{t})$ is the Laplace transform of a finite Borel measure on $\mathbf R_+$.
Originally, this theorem was used to describe isometric embeddings of Hilbert spaces.
!! RKHS
A Hilbert space $\mathscr{H}$ of functions is a reproducing kernel Hilbert space if the evaluation functionals are bounded, i.e., $\forall x\in\mathcal X$ there exists some $C>0$ such that
$$
|f(x)|\le C\|f\|_{\mathscr{H}}
$$
This smoothness property ensures that the solution in RKHS obtained from learning algorithms will be well-behaved. For example, we obtain models $\hat f$ in classification and regression problems that is close to the true solution $f$ also generalize well to unseen test data, i.e., avoid overfitting.
A positive definite kernel defines a unique RKHS of functions from $\mathcal X$ to $\mathbb R$, so it is also called reproducing kernels. We define \textbf{canonical feature map} from $\mathcal X$ into $\mathbb R^{\mathcal X}:=\{f:\mathcal X\rightarrow\mathbb R\}$, via
\begin{align}
\phi: \mathcal X&\rightarrow \mathscr H \subset \mathbb R^{\mathcal X}\\
x &\mapsto k(x,\cdot)
\end{align}
An inner product in $\mathscr H$ satisfies the \emph{reproducing property}, i.e., $\forall f\in\mathscr H$ and $x\in\mathcal X$:
$$
f(x) = \langle f, k(x,\cdot)\rangle_{\mathscr H}
$$
Take $k(y,\cdot)$ as $f$, we get $f(x) = k(y, x) = \langle\phi(x), \phi(y)\rangle_{\mathscr H}$. We do not need to know the feature map explicitly, the inner product can be calculated directly from $k(x, y)$.
! Hilbert Space Embedding of Marginal Distributions
The idea of Kernel mean embedding is to extend the feature map $\phi$ to the space of probability distributions by representing each distribution $\mathbb P$ as a mean function
$$
\phi(\mathbb P) = \mu_{\mathbb P} := \int_{\mathcal X}k(x, \cdot)d\mathbb P(x),
$$
where $k: \mathcal X\times\mathcal X\rightarrow \mathbb R$ is a symmetric and positive definite kernel function. We essentially transform the distribution $\mathbb P$ to an element in the feature space $\mathcal F$, which is nothing but a reproducing kernel Hilbert space endowed with the kernel $k$. Most KRHS methods can therefore be extended to probabiity measures.
$$
\int f(t)d\delta_x(t) = \langle f, k(x, \cdot)\rangle_{\mathscr H}
$$
[[Kernel Trick Cheatsheet]]
* Bochner integral:
* Borel measure:
* Characteristic funtion: If a random variable admits a density function, then the characteristic function is its dual, in the sense that each of them is a Fourier transform of the other.
* Gram matrix is positive semidefinite:$\sum_{i, j=1}^nc_ic_jk(x_i, x_j)\ge 0$ for any $n\in\mathbb N$, any choice of $x\in\mathcal X$ and any $c\in\mathbb R$.
* Hilbert space: compelete normed space where the norm is induced by an inner product. Element can be functions.
** standard Euclidean space $\mathbb R^d$ with $\langle x, y\rangle$ being the dot product
** space of square summable sequences $l^2$ with an inner product $\langle x, y\rangle = \sum_{i=1}^\infty x_iy_i$
** space of square-integrable functions $L_2[a, b]$ with inner product $\langle f, y\rangle = \int_a^b f(x)g(x)dx$
* Hausdorff space: where distinct points have disjoint neighbors.
* Moment generating funtion: Essentially the Laplace transformatino of a random variable $X$. $E[e^{tx}]$
* Positive Semidefinite: In linear algebra, a symmetric $n\times n$ real matrix $M$ is said to be positive definite if the scalar $z^{\mathrm {T} }Mz$ is positive for every non-zero column vector $z$ of $n$ real numbers.
[[paper|https://arxiv.org/abs/1606.03126]]
! Access
# Key Hashing: inverted index finds kvs where k shares at least one word with the question (with frequency $< F = 1000$ to ignore stop words). The retrieval scheme can be complicated.
# Key Addressing: should allow approximate association. aka ''content-based addressing'': $p_{h_i} = \text{Softmax}(A\Phi_X(x)\cdot A\Phi_K(k_{h_i}))$, comparing the question to each key.
# Value Reading: weighted sum of mem values using p_{h_i}$.
! Code
why base model is called `mlp`?
|!command |!operations |
|ctrl+shift+enter |new_window |
|ctrl+shift+w |close_window |
|ctrl+shift+] |next_window |
|ctrl+shift+[ |previous_window |
[[Approximating KL between GMMs]]
! Model
We imagine that data points from a training set are presented sequentially, we can consider that the posterior distribution after the $N$-th point becomes the prior for the next iteration.
With the Bayesian approach, the background information of an observer is captured by his //prior// probability distribution $$P_{t-1}(M) = P(M|\mathcal O_{t-1})$$ given previous inputs $$\mathcal O_{t-1}$$ over the models $$M$$. The effect of a new data $$o_t$$ on the observer is to change the prior into the //posterior// distribution $$P_t(M) = P(M|\mathcal O_t)$$ where $$\mathcal O_t = o_t\cup\mathcal O_{t-1}$$.
!! Acoustic Surprise
The surprise $$S(o_t)$$ is measured as the [[Kullback-Leibler divergence]] $$D_{\text{KL}}$$ between $$P_{t-1}$$ and $$P_t$$,
$$
S(o_t) \doteq D_{\text{KL}}(P_{t-1}||P_{t}) = E_{p_{t-1}}\log\frac{P_{t-1}}{P_{t}}.
$$
And the single model surprise of $M$ is given by
$$
S(o_t, M) = \log\frac{P_{t-1}}{P_{t}}.
$$
The unit of surprise, representing a 2-fold variation between $P_t$ and $P_{t-1}$ is called a ''wow''. Under this definition, the surprise can be understood as
$$
H(P_t, P_{t-1}) - H(P_{t-1})
$$
where $$H(P,Q)$$ is the cross entropy of $$P$$ and $$Q$$, and $$H(P)$$ is the entropy of $$P$$.
KL-dviergence is not symmetric, the first parameter is often referred to as ''true'' and the second ''approximate''. [baldi10bits] and [schuerte13wow] use opposite distribution orders which is somehow bad.
Now the problem is how to define these two distributions. A convenient way to represent posteriors of certain distributions (data generating processes) is [[conjugate prior|Conjugate Prior]].
!!! Poisson [itti06bayesian]
Suppose that data $$x_1, \dots, x_n$$ are independent and identically distributed from a Poisson process where $$\mu$$, the mean, is unknown. The value of each $x_i$ is an integer greater than or equal to zero. For data from a Poisson process, the likelihood function, $$L(\mu|x_1,\dots,x_n)$$, with respect to $$\mu$$, is
$$
L(\mu|x_1,\dots,x_n)\propto \mu^{\sum x_i}\exp(-n\mu), \qquad x_i>0.
$$
The sufficient statistics are $$n$$, the number of data points, and $\sum x_i$ the sum of the data. This likelihood factor is proportional to a gamma distribution of $\mu$. The conjugate prior, $g(\cdot)$, is a gamma distribution with hyperparameters $k, \theta > 0$,
$$
g(\mu|k,\theta)=\frac{\mu^{k-1}\exp\frac{-\mu}{\theta}}{\Gamma(k)\theta^k},\qquad \mu>0.
$$
and the posterior is also a gamma $\mu \sim \Gamma(\mu|k',\theta')$ where the updated hyperparameters are
$$
k' = k+\sum x_i, \qquad \theta' = \frac {\theta}{1+n}.
$$
The KL-divergence of $\Gamma(k_p, \theta_p)$ from $\Gamma(k_q,\theta_q)$ is given by
$$
D_{\mathrm{KL}}(k_p,\theta_p; k_q, \theta_q) = (k_p-k_q)\psi(k_p) - \log\Gamma(k_p) + \log\Gamma(k_q) + k_q(\log \theta_q - \log \theta_p) + k_p\frac{\theta_p - \theta_q}{\theta_q}
$$
where $\psi$ is the digamma function
$$
\psi(x) =\frac{d}{dx} \ln{\Gamma(x)}= \frac{\Gamma'(x)}{\Gamma(x)}.
$$
It is shown (applying [[Stirling's formula|Stirling's Formula]]) that as $N\rightarrow\infty$, $S$ grows linearly according to with $n$ the number of data
$$
S(o_n) \approx n\left(k\theta - \bar\mu (1-\log\bar\mu+\psi(k)+\log\theta)\right).
$$
As the input data accumulates, it is believed old data is not as important. So a decay factor $0<\zeta<1$ is used in [schuerte13wow]
$$
k'_n = \zeta k'_{n-1}+ x_n, \qquad \frac{1}{\theta_n'} = \zeta\frac{1}{\theta'_{n-1}}+\frac 1\theta.
$$
!!! Generalization [baldi10bits]
The result can be easily extended onto [[exponential family|Exponential Family]], which all have conjugate prior and thus easier to compute the posterior. The likelihood of a observation from an exponential family with parameter $\boldsymbol \eta$ is
$$
P(x|\boldsymbol \eta) = h(x) g(\boldsymbol\eta) \exp\left(\boldsymbol\eta^{\rm T} \mathbf{T}(x)\right)
$$
Then, for data $\mathbf{X} = (x_1,\ldots,x_n)$, the likelihood is computed as follows:
$$
p(\mathbf{X}|\boldsymbol\eta) =\left(\prod_{i=1}^n h(x_i) \right) g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T}\sum_{i=1}^n \mathbf{T}(x_i) \right)
$$
Then, for the above conjugate prior:
$$
\begin{align}p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) &= f(\boldsymbol\chi,\nu) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \propto g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi)\end{align}
$$
We can then compute the posterior as follows:
$$
\begin{align}
p(\boldsymbol\eta|\mathbf{X},\boldsymbol\chi,\nu)& \propto p(\mathbf{X}|\boldsymbol\eta) p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) \\
&= \left(\prod_{i=1}^n h(x_i) \right) g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T} \sum_{i=1}^n \mathbf{T}(x_i)\right)
f(\boldsymbol\chi,\nu) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \\
&\propto g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T}\sum_{i=1}^n \mathbf{T}(x_i)\right) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \\
&\propto g(\boldsymbol\eta)^{\nu + n} \exp\left(\boldsymbol\eta^{\rm T} \left(\boldsymbol\chi + \sum_{i=1}^n \mathbf{T}(x_i)\right)\right)
\end{align}
$$
Compute the surprise yields
$$
S(o_t|\boldsymbol\eta(M)) = \log g(\boldsymbol\eta)+E_{p_t}\boldsymbol\eta^{\rm T}\mathbf T(o_t)
$$
Examples given in [baldi10bits] are
* Discrete data
** Multinomial
** Poisson
* Gaussian
** Unknown mean/known variance
** Known mean/unknown variance
** Unknown mean/unknown variance
!!! Gaussian [schuerte13wow]
If we model the data as a Gaussian $P\sim\mathcal G(\mu, \Sigma)$, we can get a closed form of saliency.
$$
\begin{align*}
D_{\text{KL}} &= \int \frac 1 2\left[\log\frac{|\Sigma_2|}{|\Sigma_1|} - (x-\mu_1)^T\Sigma_1^{-1}(x-\mu_1)+(x-\mu_2)^T\Sigma_2^{-1}(x-\mu_2)\right]P_t(x)dx\\
&= \frac 1 2\left[\log\frac{|\Sigma_2|}{|\Sigma_1|} - E_{P_t}[(x-\mu_1)^T\Sigma_1^{-1}(x-\mu_1)]+E_{P_t}[(x-\mu_2)^T\Sigma_2^{-1}(x-\mu_2)]\right]\\
&= \frac 1 2\left[\log\frac{|\Sigma_2|}{|\Sigma_1|} - \text{tr}(I_d)+\text{tr}(\Sigma_2^{-1}\Sigma_1)+(\mu_1-\mu_2)^T\Sigma_2^{-1}(mu_1-\mu_2)\right]\\
\end{align*}
$$
!! Bits (Entropy) and Wows (Surprise)
* For a uniform prior model with $P(o_t)\ll 1$
** Entropy: $\underset{p\rightarrow0+}{\text{lim}}p\log p = 0$
** Surprise: $0$, because the posterior does not change.
* For a very unlikely data
** Entropy: cannot exceed 1
** Surprise: can be quite large ($\log N$, where $N$ is #models)
! Neural Network Implementation
* Input: dyadic image pyramids as feature maps
* Layer: 5 chained cascade of surprise detectors at every pixel in every feature map
** Temporal $SL$: Gaussian/Poisson Likelihood
** Addtion $SS$: spatial surprises
** $S \leftarrow S(SL+SS/20)^{1/3}$ (from LMS)
* Application in eye movements: CRCNS eye-tracking
! In Speech
!! Frequency Spectrogram [schuerte13wow]
The squared magnitude spectrum of:
# Short-time Fourier transform (STFT)
# Short-time cosine transform (STCT)
# Modified discrete cosine transform (MDCT)
$G(t,\omega) = |F(t,\omega)|^2$ is used to characterize the audio features.
!! Speaker diarization task
This can be used to employ a segmentation algorithm ([[Changepoint algorithms]])
KL divergence is also known as information divergence, information gain, relative entropy.
! Motivation
The motivation for Kullback and Leibler's work was to provide a rigorious definition of "information" in relation to Fisher's [[sufficient statistics]]. [[[p79 burnham02model|Model selection and multimodel inference]]]
In information theory, the [[Kraft–McMillan theorem]] establishes that any directly decodable coding scheme for coding a message to identify one value $x_i$ out of a set of possibilities $X$ can be seen as representing an implicit probability distribution $q(x_i)=2^{-l_i}$ over $X$, where $l_i$ is the length of the code for $x_i$ in bits. Therefore, KL divergence can be interpreted as the expected extra message-length per datum that must be communicated if a code that is optimal for a given (wrong) distribution Q is used, compared to using a code based on the true distribution $P$.
$$
\begin{matrix}
D_{\mathrm{KL}}(P\|Q) & = -& \sum_x p(x) \log q(x)& + & \sum_x p(x) \log p(x) \\
& = & H(P,Q) & - & H(P)\, \!
\end{matrix}
$$
where $H(P,Q)$ is called the cross entropy of $P$ and $Q$, and $H(P)$ is the entropy of $P$ [[[wikipedia|http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence#Motivation]]]. This notation $D_{\mathrm{KL}}(P\|Q)$ denotes the ''information lost when $Q$ is used to approximate $P$''. It is the logical basis for model selection in conjunction with likelihood inference. [[[p78 burnham02model|Model selection and multimodel inference]]]
A function $$f(x)$$ is $$L$$-smooth if
* the gradient is $$L$$-Lipschitz continuous: $$\|\nabla f(x) - \nabla f(y)\|\le L\|x-y\|$$
* $$f$$ is bounded by quadratic function: $$|f(y)-f(x)-\langle\nabla f(x), y-x\rangle|\le\frac L 2\|y-x\|^2_2$$
* the norm of the slope if $$\nabla f$$ (which is $$\nabla^2 f$$) is bounded above
* the gradient of $$f$$ is monotonic with additional term $$\langle x-y, \nabla f(x)-\nabla f(y)\rangle\ge\frac1L\|\nabla f(x)-\nabla f(y)\|^2_2$$
* if $$f$$ is twice differentiable, $$\nabla^2 f(x)\preceq L\mathbf I$$
[[Proofs|https://angms.science/doc/CVX/CVX_alphabeta.pdf]]
* Distillation
** DistillBert: 95% of Bert performances in a model 40% smaller and 60% faster
** TinyBERT
* Pruning
** Reducing heads in attention
*** [[Analyzing multi-head self-attention|https://www.aclweb.org/anthology/P19-1580.pdf]]
*** [[Are Sixteen Heads Really Better than One|https://arxiv.org/abs/1905.10650]]
* Data
** py ast to character level, character level completion works better when using 1k training examples.
** training char embedding
** joint word and char embedding for value
** end2end training requires integration of PCFG
* Modeling
** Replace slow lstm with AR
*** This idea has been implemented in meta learning (SNAIL), have performance improvement, but the reason of swapping the model is not clear
** Replace slow lstm with meta learning
* Theory
** Argue the relationship between multi-scale RNN and location sensitive attention
* Code
** enable cross line prediction
** train parsing with PCFG end2end
! Implementations
* [[AWD-LSTM|https://github.com/salesforce/awd-lstm-lm]]
* ENAS cell
* Transformer
** openai's [[finetune-transformer-lm|https://github.com/openai/finetune-transformer-lm]]; [[pytorch version|https://github.com/huggingface/pytorch-openai-transformer-lm]]
** [[Character level language modelling with self-attention|https://arxiv.org/abs/1808.04444]]
** [[Transformer XL]]
! Talks
[[Language Modelling - Phil Blunsom DLSS 2017]]
! Datasets
* NIST Hub5 2000
** Switchboard (SWB)
** CallHome (CH)
! Tasks
BPC on Wikipedia
* LayerNorm LSTM is 1.3
* [[Tensorized LSTMs for Sequence Learning]] 1.198, single model about 1.264
! Bibs
* Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning
** An unsupervised RNN-CNN hybrid feature extractor that is not very efficient or powerful, should know someone has tried this
* [[Memory Architectures in Recurrent Neural Network Language Models]]
! [[Part I|http://videolectures.net/deeplearning2017_blunsom_language_understanding/]]
Much of NLP (e.g. Translation, QA, Dialogue) can be structured as (conditional) language modelling. The simple objective of modelling the next word contains much of the complexity of nature language understanding
<<<
@@color:red;Alice@@ went to the @@color:blue;beach@@. @@color:blue;There@@ @@color:red;she@@ build a ...
<<<
Models can be evaluated with cross entropy, a measure of how many bits are needed to encode text with our model：
$$
H(w^N_1) = -\frac1N\log_2p(w^N_1)
$$
or perplexity, a measure of how surprised our model is on seeing each word:
$$
\text{perplexity}(w_1^N) = 2^{H(w_1^N)}
$$
* Count based N-Gram Models
** Back-off to lower interpolation: if never observe the trigrams, try bigrams
** Heap's Law: smoothing techniques match the empirical distribution of languge
** hard to capture long dependencies, semantic correlations and morphological regualrities.
* Neural N-Gram Language Models
* Recurrent Neural Network Language Models
* Encoder-Decoder Models and Machine Translation
Truncated BPTT is efficient for mini-batching as all sequences have length $T$.
! Part II
Deep RNN Models:
* Stack and skip connections
* Deep-in-time: Recurrent Highway Networks
Scaling large vocabularies, when the softmax becomes hard to compute
* use the neural LM for the most frequent words, and a traditional ngram LM for the rest. This nullifies the neural LM's main advantage
* batch local short-lists: choose a subset of the vocabulary for the segment of the data. [[On Using Very Large Target Vocabulary for Neural Machine Translation|https://arxiv.org/abs/1412.2007]]
* approximate the gradient/change the objective: maximize likelihood by making the log partition function an independent parameter $c$: $\hat p_n = \exp(Wh_n+b)\times\exp(c)$. Mnih and Teh use Noise Contrastive Estimation, to distinguish data from k samples from a noise distribution:
$$
p(Data=1|\hat p_n) = \frac{\hat p_n}{\hat p_n+kp_{noise}(w_n)}
$$
Now parametrising the log partition function as $c$ does not degenerate. This is effective for speeding up training, but has no impact on testing time.
* Factorise the output vocabulary
** [[A scalable hierarchical distributed language model|https://papers.nips.cc/paper/3583-a-scalable-hierarchical-distributed-language-model]]
** [[Efficient softmax approximation for GPUs|https://arxiv.org/abs/1609.04309]]
! Theory
* [[Programming category]]
* [[Computer Science Metanotation]]
* [[Formal Langugages]]
! Tutorials
* [[Learn X in Y Minutes]]
! Functional Programming
!! Languages
* [[clojure]]
* Elixir
* Scala
!! Concepts
* Side effects
! Scientific Computing
* [[MATLAB]]
* [[Python]]
! Introduction
AKA Saddle-point approximation, delta-method
Azevedo-Filho & Shachter 1994 reviews Laplace's method results (univariate and multivariate) and presents a detailed example showing the method used in parameter estimation and probabilistic inference under a Bayesian perspective. Laplace's method is applied to a meta-analysis problem from the medical domain, involving experimental data, and compared to other techniques.
!
[[link|http://arxiv.org/abs/1603.06744]]
[[Hugo's review|https://www.evernote.com/shard/s189/sh/1fe4a407-3aa0-4ad2-8340-e58fceeb1b71/baa114c50096b41f341a54d478af8160]]
! Structured Attention
* Attention before LSTM
* Learn word representation with C2W: don't have to deal with OOV
* Choose where to copy from with CRF
* Code compression while maintaining structure
! Model Zoo
|>|!Direct/Linear|<|
|! |!Parametric |!Non-parametric |
|!Discrete |<li>[[Hidden Markov Model]]</li><li>Discrete LVM</li><li>Sparse LVM</li> |<li>Indian Buffet Process</li><li>[[Dirichlet Process Mixtures]]</li> |
|!Continuous |<li>PCA</li><li>[[Factor Analysis]]</li><li>Independent Component Analysis</li><li>Gaussian LDS</li><li>Latent Gauss Field</li> |<li>Gaussian Process LVM</li> |
|>|!Deep|<|
|! |!Parametric |!Non-parametric |
|!Discrete |<li>Sigmoid Belief Net</li><li>Deep Auto-Regressive Networks</li> |<li>Cascaded Indian Buffet Process</li><li>[[Hierarchical Dirichlet Process|HDP-HMM Diarization]]</li> |
|!Continuous |<li>Nonlinear Factor Analysis</li><li>Nonlinear Gaussian Belief Network</li><li>Deep Latent Gaussian ([[VAE|Variational Autoencoder]], [[DRAW|Variational Inference for Generative Models]])</li> |<li>Deep Gaussian Process</li><li>Recurrent Gaussian Process</li><li>GP State Space Model</li> |
!! Conjugate
* Bayesian mixture models
* Times series models (HMM, linear dynamic systems）
* Factorial models
* Matrix factorization (factor analysis, PCA, CCA)
* [[Dirichlet Process Mixtures]], HDPs
* Multilevel regression (linear, probit, Poisson)
* Stochastic block models
* Mixed-membership models (LDA and some variants)
!! Nonconjugate
* Nonlinear Time series models
* Deep latent Gaussian models
* Models with attention (DRAW)
* Generalized linear models (Poisson regression)
* Stochastic volatility models
* Discrete choice models
* Bayesian neural networks
* Deep exponential families (sparse Gamma or Poison)
* Correlated topic models (nonparametric variants)
* Sigmoid belief network
! Remarks
@@color:#859900;
* Easy sampling
* Easy way to include hierarchy and depth
* Easy to encode structure
* Avoids order dependency assumptions: marginalisation induces dependencies.
* Provide compression and representation
* Scoring, model comparison and selection possible using the marginalised likelihood
@@
@@color:red;
* Inversion process to determin latents corresponding to an input is difficult to generalize
* Difficult to compute marginalised likelihood requiring approximations
* Not easy to specify rich approximations for latent posterior distribution
@@
! Learning Principles
! Refs
* [[LaTeX Snippets]]
* [[LaTeX Symbols]]
! Tools
* [[Detexify|http://detexify.kirelabs.org/classify.html]]
* In Browser: [[MathJax]]
Inline separation
```
P [N(t+ \tau) - N(t) = k] = \frac{e^{-\lambda \tau} (\lambda \tau)^k}{k!} \qquad k= 0,1,\ldots,
```
$$
P [N(t+ \tau) - N(t) = k] = \frac{e^{-\lambda \tau} (\lambda \tau)^k}{k!} \qquad k= 0,1,\ldots,
$$
equation array
```
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \text{if } m_\pi(x)>kf(x|\theta_1),\\
0 & \text{otherwise}.
\end{array}\right.
```
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \text{if } m_\pi(x)>kf(x|\theta_1),\\
0 & \text{otherwise}.
\end{array}\right.
$$
!! Relation operators
|Symbol |$$\LaTeX$$ |Symbol |$$\LaTeX$$ |
|$$\preccurlyeq$$|`\preccurlyeq`|$$\succcurlyeq$$|`\succcurlyeq`|
[[Link|https://arxiv.org/abs/1708.03888]]
''Observation'': Linearly scaling up LR is instable with very large batch size.
Separate learning rate for each layer:
$$
\gamma\lambda^l,\qquad \lambda^l = \eta\times\frac{\|w^l\|}{\|\nabla L(w^l)\|}
$$
* [[World Models|https://worldmodels.github.io/]]
** A Vision model (VAE) encodes the high-dimensional observation into a low-dimensional latent vector
** A Memory RNN integrates historical codes to create a representation that can predict future states.
Get the code: [[learnclojure.clj|http://learnxinyminutes.com/docs/files/learnclojure.clj]]
```
; Comments start with semicolons.
; Clojure is written in "forms", which are just
; lists of things inside parentheses, separated by whitespace.
;
; The clojure reader assumes that the first thing is a
; function or macro to call, and the rest are arguments.
; The first call in a file should be ns, to set the namespace
(ns learnclojure)
; More basic examples:
; str will create a string out of all its arguments
(str "Hello" " " "World") ; => "Hello World"
; Math is straightforward
(+ 1 1) ; => 2
(- 2 1) ; => 1
(* 1 2) ; => 2
(/ 2 1) ; => 2
; Equality is =
(= 1 1) ; => true
(= 2 1) ; => false
; You need not for logic, too
(not true) ; => false
; Nesting forms works as you expect
(+ 1 (- 3 2)) ; = 1 + (3 - 2) => 2
; Types
;;;;;;;;;;;;;
; Clojure uses Java's object types for booleans, strings and numbers.
; Use `class` to inspect them.
(class 1) ; Integer literals are java.lang.Long by default
(class 1.); Float literals are java.lang.Double
(class ""); Strings always double-quoted, and are java.lang.String
(class false) ; Booleans are java.lang.Boolean
(class nil); The "null" value is called nil
; If you want to create a literal list of data, use ' to stop it from
; being evaluated
'(+ 1 2) ; => (+ 1 2)
; (shorthand for (quote (+ 1 2)))
; You can eval a quoted list
(eval '(+ 1 2)) ; => 3
; Collections & Sequences
;;;;;;;;;;;;;;;;;;;
; Lists are linked-list data structures, while Vectors are array-backed.
; Vectors and Lists are java classes too!
(class [1 2 3]); => clojure.lang.PersistentVector
(class '(1 2 3)); => clojure.lang.PersistentList
; A list would be written as just (1 2 3), but we have to quote
; it to stop the reader thinking it's a function.
; Also, (list 1 2 3) is the same as '(1 2 3)
; "Collections" are just groups of data
; Both lists and vectors are collections:
(coll? '(1 2 3)) ; => true
(coll? [1 2 3]) ; => true
; "Sequences" (seqs) are abstract descriptions of lists of data.
; Only lists are seqs.
(seq? '(1 2 3)) ; => true
(seq? [1 2 3]) ; => false
; A seq need only provide an entry when it is accessed.
; So, seqs which can be lazy -- they can define infinite series:
(range 4) ; => (0 1 2 3)
(range) ; => (0 1 2 3 4 ...) (an infinite series)
(take 4 (range)) ; (0 1 2 3)
; Use cons to add an item to the beginning of a list or vector
(cons 4 [1 2 3]) ; => (4 1 2 3)
(cons 4 '(1 2 3)) ; => (4 1 2 3)
; Conj will add an item to a collection in the most efficient way.
; For lists, they insert at the beginning. For vectors, they insert at the end.
(conj [1 2 3] 4) ; => [1 2 3 4]
(conj '(1 2 3) 4) ; => (4 1 2 3)
; Use concat to add lists or vectors together
(concat [1 2] '(3 4)) ; => (1 2 3 4)
; Use filter, map to interact with collections
(map inc [1 2 3]) ; => (2 3 4)
(filter even? [1 2 3]) ; => (2)
; Use reduce to reduce them
(reduce + [1 2 3 4])
; = (+ (+ (+ 1 2) 3) 4)
; => 10
; Reduce can take an initial-value argument too
(reduce conj [] '(3 2 1))
; = (conj (conj (conj [] 3) 2) 1)
; => [3 2 1]
; Functions
;;;;;;;;;;;;;;;;;;;;;
; Use fn to create new functions. A function always returns
; its last statement.
(fn [] "Hello World") ; => fn
; (You need extra parens to call it)
((fn [] "Hello World")) ; => "Hello World"
; You can create a var using def
(def x 1)
x ; => 1
; Assign a function to a var
(def hello-world (fn [] "Hello World"))
(hello-world) ; => "Hello World"
; You can shorten this process by using defn
(defn hello-world [] "Hello World")
; The [] is the list of arguments for the function.
(defn hello [name]
(str "Hello " name))
(hello "Steve") ; => "Hello Steve"
; You can also use this shorthand to create functions:
(def hello2 #(str "Hello " %1))
(hello2 "Fanny") ; => "Hello Fanny"
; You can have multi-variadic functions, too
(defn hello3
([] "Hello World")
([name] (str "Hello " name)))
(hello3 "Jake") ; => "Hello Jake"
(hello3) ; => "Hello World"
; Functions can pack extra arguments up in a seq for you
(defn count-args [& args]
(str "You passed " (count args) " args: " args))
(count-args 1 2 3) ; => "You passed 3 args: (1 2 3)"
; You can mix regular and packed arguments
(defn hello-count [name & args]
(str "Hello " name ", you passed " (count args) " extra args"))
(hello-count "Finn" 1 2 3)
; => "Hello Finn, you passed 3 extra args"
; Maps
;;;;;;;;;;
; Hash maps and array maps share an interface. Hash maps have faster lookups
; but don't retain key order.
(class {:a 1 :b 2 :c 3}) ; => clojure.lang.PersistentArrayMap
(class (hash-map :a 1 :b 2 :c 3)) ; => clojure.lang.PersistentHashMap
; Arraymaps will automatically become hashmaps through most operations
; if they get big enough, so you don't need to worry.
; Maps can use any hashable type as a key, but usually keywords are best
; Keywords are like strings with some efficiency bonuses
(class :a) ; => clojure.lang.Keyword
(def stringmap {"a" 1, "b" 2, "c" 3})
stringmap ; => {"a" 1, "b" 2, "c" 3}
(def keymap {:a 1, :b 2, :c 3})
keymap ; => {:a 1, :c 3, :b 2}
; By the way, commas are always treated as whitespace and do nothing.
; Retrieve a value from a map by calling it as a function
(stringmap "a") ; => 1
(keymap :a) ; => 1
; Keywords can be used to retrieve their value from a map, too!
(:b keymap) ; => 2
; Don't try this with strings.
;("a" stringmap)
; => Exception: java.lang.String cannot be cast to clojure.lang.IFn
; Retrieving a non-present key returns nil
(stringmap "d") ; => nil
; Use assoc to add new keys to hash-maps
(def newkeymap (assoc keymap :d 4))
newkeymap ; => {:a 1, :b 2, :c 3, :d 4}
; But remember, clojure types are immutable!
keymap ; => {:a 1, :b 2, :c 3}
; Use dissoc to remove keys
(dissoc keymap :a :b) ; => {:c 3}
; Sets
;;;;;;
(class #{1 2 3}) ; => clojure.lang.PersistentHashSet
(set [1 2 3 1 2 3 3 2 1 3 2 1]) ; => #{1 2 3}
; Add a member with conj
(conj #{1 2 3} 4) ; => #{1 2 3 4}
; Remove one with disj
(disj #{1 2 3} 1) ; => #{2 3}
; Test for existence by using the set as a function:
(#{1 2 3} 1) ; => 1
(#{1 2 3} 4) ; => nil
; There are more functions in the clojure.sets namespace.
; Useful forms
;;;;;;;;;;;;;;;;;
; Logic constructs in clojure are just macros, and look like
; everything else
(if false "a" "b") ; => "b"
(if false "a") ; => nil
; Use let to create temporary bindings
(let [a 1 b 2]
(> a b)) ; => false
; Group statements together with do
(do
(print "Hello")
"World") ; => "World" (prints "Hello")
; Functions have an implicit do
(defn print-and-say-hello [name]
(print "Saying hello to " name)
(str "Hello " name))
(print-and-say-hello "Jeff") ;=> "Hello Jeff" (prints "Saying hello to Jeff")
; So does let
(let [name "Urkel"]
(print "Saying hello to " name)
(str "Hello " name)) ; => "Hello Urkel" (prints "Saying hello to Urkel")
; Modules
;;;;;;;;;;;;;;;
; Use "use" to get all functions from the module
(use 'clojure.set)
; Now we can use set operations
(intersection #{1 2 3} #{2 3 4}) ; => #{2 3}
(difference #{1 2 3} #{2 3 4}) ; => #{1}
; You can choose a subset of functions to import, too
(use '[clojure.set :only [intersection]])
; Use require to import a module
(require 'clojure.string)
; Use / to call functions from a module
; Here, the module is clojure.string and the function is blank?
(clojure.string/blank? "") ; => true
; You can give a module a shorter name on import
(require '[clojure.string :as str])
(str/replace "This is a test." #"[a-o]" str/upper-case) ; => "THIs Is A tEst."
; (#"" denotes a regular expression literal)
; You can use require (and use, but don't) from a namespace using :require.
; You don't need to quote your modules if you do it this way.
(ns test
(:require
[clojure.string :as str]
[clojure.set :as set]))
; Java
;;;;;;;;;;;;;;;;;
; Java has a huge and useful standard library, so
; you'll want to learn how to get at it.
; Use import to load a java module
(import java.util.Date)
; You can import from an ns too.
(ns test
(:import java.util.Date
java.util.Calendar))
; Use the class name with a "." at the end to make a new instance
(Date.) ; <a date object>
; Use . to call methods. Or, use the ".method" shortcut
(. (Date.) getTime) ; <a timestamp>
(.getTime (Date.)) ; exactly the same thing.
; Use / to call static methods
(System/currentTimeMillis) ; <a timestamp> (system is always present)
; Use doto to make dealing with (mutable) classes more tolerable
(import java.util.Calendar)
(doto (Calendar/getInstance)
(.set 2000 1 1 0 0 0)
.getTime) ; => A Date. set to 2000-01-01 00:00:00
; STM
;;;;;;;;;;;;;;;;;
; Software Transactional Memory is the mechanism clojure uses to handle
; persistent state. There are a few constructs in clojure that use this.
; An atom is the simplest. Pass it an initial value
(def my-atom (atom {}))
; Update an atom with swap!.
; swap! takes a function and calls it with the current value of the atom
; as the first argument, and any trailing arguments as the second
(swap! my-atom assoc :a 1) ; Sets my-atom to the result of (assoc {} :a 1)
(swap! my-atom assoc :b 2) ; Sets my-atom to the result of (assoc {:a 1} :b 2)
; Use '@' to dereference the atom and get the value
my-atom ;=> Atom<#...> (Returns the Atom object)
@my-atom ; => {:a 1 :b 2}
; Here's a simple counter using an atom
(def counter (atom 0))
(defn inc-counter []
(swap! counter inc))
(inc-counter)
(inc-counter)
(inc-counter)
(inc-counter)
(inc-counter)
@counter ; => 5
; Other STM constructs are refs and agents.
; Refs: http://clojure.org/refs
; Agents: http://clojure.org/agents
```
Get the code: [[javascript.js|http://learnxinyminutes.com/docs/files/javascript.js]]
```javascript
// Comments are like C. Single-line comments start with two slashes,
/* and multiline comments start with slash-star
and end with star-slash */
// Statements can be terminated by ;
doStuff();
// ... but they don't have to be, as semicolons are automatically inserted
// wherever there's a newline, except in certain cases.
doStuff()
// Because those cases can cause unexpected results, we'll keep on using
// semicolons in this guide.
///////////////////////////////////
// 1. Numbers, Strings and Operators
// JavaScript has one number type (which is a 64-bit IEEE 754 double).
// As with Lua, don't freak out about the lack of ints: doubles have a 52-bit
// mantissa, which is enough to store integers up to about 9✕10¹⁵ precisely.
3; // = 3
1.5; // = 1.5
// All the basic arithmetic works as you'd expect.
1 + 1; // = 2
8 - 1; // = 7
10 * 2; // = 20
35 / 5; // = 7
// Including uneven division.
5 / 2; // = 2.5
// Bitwise operations also work; when you perform a bitwise operation your float
// is converted to a signed int *up to* 32 bits.
1 << 2; // = 4
// Precedence is enforced with parentheses.
(1 + 3) * 2; // = 8
// There are three special not-a-real-number values:
Infinity; // result of e.g. 1/0
-Infinity; // result of e.g. -1/0
NaN; // result of e.g. 0/0
// There's also a boolean type.
true;
false;
// Strings are created with ' or ".
'abc';
"Hello, world";
// Negation uses the ! symbol
!true; // = false
!false; // = true
// Equality is ==
1 == 1; // = true
2 == 1; // = false
// Inequality is !=
1 != 1; // = false
2 != 1; // = true
// More comparisons
1 < 10; // = true
1 > 10; // = false
2 <= 2; // = true
2 >= 2; // = true
// Strings are concatenated with +
"Hello " + "world!"; // = "Hello world!"
// and are compared with < and >
"a" < "b"; // = true
// Type coercion is performed for comparisons...
"5" == 5; // = true
// ...unless you use ===
"5" === 5; // = false
// You can access characters in a string with charAt
"This is a string".charAt(0); // = 'T'
// ...or use substring to get larger pieces
"Hello world".substring(0, 5); // = "Hello"
// length is a property, so don't use ()
"Hello".length; // = 5
// There's also null and undefined
null; // used to indicate a deliberate non-value
undefined; // used to indicate a value is not currently present (although
// undefined is actually a value itself)
// false, null, undefined, NaN, 0 and "" are falsy; everything else is truthy.
// Note that 0 is falsy and "0" is truthy, even though 0 == "0".
///////////////////////////////////
// 2. Variables, Arrays and Objects
// Variables are declared with the var keyword. JavaScript is dynamically typed,
// so you don't need to specify type. Assignment uses a single = character.
var someVar = 5;
// if you leave the var keyword off, you won't get an error...
someOtherVar = 10;
// ...but your variable will be created in the global scope, not in the scope
// you defined it in.
// Variables declared without being assigned to are set to undefined.
var someThirdVar; // = undefined
// There's shorthand for performing math operations on variables:
someVar += 5; // equivalent to someVar = someVar + 5; someVar is 10 now
someVar *= 10; // now someVar is 100
// and an even-shorter-hand for adding or subtracting 1
someVar++; // now someVar is 101
someVar--; // back to 100
// Arrays are ordered lists of values, of any type.
var myArray = ["Hello", 45, true];
// Their members can be accessed using the square-brackets subscript syntax.
// Array indices start at zero.
myArray[1]; // = 45
// Arrays are mutable and of variable length.
myArray.push("World");
myArray.length; // = 4
// Add/Modify at specific index
myArray[3] = "Hello";
// JavaScript's objects are equivalent to 'dictionaries' or 'maps' in other
// languages: an unordered collection of key-value pairs.
var myObj = {key1: "Hello", key2: "World"};
// Keys are strings, but quotes aren't required if they're a valid
// JavaScript identifier. Values can be any type.
var myObj = {myKey: "myValue", "my other key": 4};
// Object attributes can also be accessed using the subscript syntax,
myObj["my other key"]; // = 4
// ... or using the dot syntax, provided the key is a valid identifier.
myObj.myKey; // = "myValue"
// Objects are mutable; values can be changed and new keys added.
myObj.myThirdKey = true;
// If you try to access a value that's not yet set, you'll get undefined.
myObj.myFourthKey; // = undefined
///////////////////////////////////
// 3. Logic and Control Structures
// The syntax for this section is almost identical to Java's.
// The if structure works as you'd expect.
var count = 1;
if (count == 3){
// evaluated if count is 3
} else if (count == 4){
// evaluated if count is 4
} else {
// evaluated if it's not either 3 or 4
}
// As does while.
while (true){
// An infinite loop!
}
// Do-while loops are like while loops, except they always run at least once.
var input
do {
input = getInput();
} while (!isValid(input))
// the for loop is the same as C and Java:
// initialisation; continue condition; iteration.
for (var i = 0; i < 5; i++){
// will run 5 times
}
// && is logical and, || is logical or
if (house.size == "big" && house.colour == "blue"){
house.contains = "bear";
}
if (colour == "red" || colour == "blue"){
// colour is either red or blue
}
// && and || "short circuit", which is useful for setting default values.
var name = otherName || "default";
// switch statement checks for equality with ===
// use 'break' after each case
// or the cases after the correct one will be executed too.
grade = 'B';
switch (grade) {
case 'A':
console.log("Great job");
break;
case 'B':
console.log("OK job");
break;
case 'C':
console.log("You can do better");
break;
default:
console.log("Oy vey");
break;
}
///////////////////////////////////
// 4. Functions, Scope and Closures
// JavaScript functions are declared with the function keyword.
function myFunction(thing){
return thing.toUpperCase();
}
myFunction("foo"); // = "FOO"
// Note that the value to be returned must start on the same line as the
// 'return' keyword, otherwise you'll always return 'undefined' due to
// automatic semicolon insertion. Watch out for this when using Allman style.
function myFunction()
{
return // <- semicolon automatically inserted here
{
thisIsAn: 'object literal'
}
}
myFunction(); // = undefined
// JavaScript functions are first class objects, so they can be reassigned to
// different variable names and passed to other functions as arguments - for
// example, when supplying an event handler:
function myFunction(){
// this code will be called in 5 seconds' time
}
setTimeout(myFunction, 5000);
// Note: setTimeout isn't part of the JS language, but is provided by browsers
// and Node.js.
// Function objects don't even have to be declared with a name - you can write
// an anonymous function definition directly into the arguments of another.
setTimeout(function(){
// this code will be called in 5 seconds' time
}, 5000);
// JavaScript has function scope; functions get their own scope but other blocks
// do not.
if (true){
var i = 5;
}
i; // = 5 - not undefined as you'd expect in a block-scoped language
// This has led to a common pattern of "immediately-executing anonymous
// functions", which prevent temporary variables from leaking into the global
// scope.
(function(){
var temporary = 5;
// We can access the global scope by assiging to the 'global object', which
// in a web browser is always 'window'. The global object may have a
// different name in non-browser environments such as Node.js.
window.permanent = 10;
})();
temporary; // raises ReferenceError
permanent; // = 10
// One of JavaScript's most powerful features is closures. If a function is
// defined inside another function, the inner function has access to all the
// outer function's variables, even after the outer function exits.
function sayHelloInFiveSeconds(name){
var prompt = "Hello, " + name + "!";
// Inner functions are put in the local scope by default, as if they were
// declared with 'var'.
function inner(){
alert(prompt);
}
setTimeout(inner, 5000);
// setTimeout is asynchronous, so the sayHelloInFiveSeconds function will
// exit immediately, and setTimeout will call inner afterwards. However,
// because inner is "closed over" sayHelloInFiveSeconds, inner still has
// access to the 'prompt' variable when it is finally called.
}
sayHelloInFiveSeconds("Adam"); // will open a popup with "Hello, Adam!" in 5s
///////////////////////////////////
// 5. More about Objects; Constructors and Prototypes
// Objects can contain functions.
var myObj = {
myFunc: function(){
return "Hello world!";
}
};
myObj.myFunc(); // = "Hello world!"
// When functions attached to an object are called, they can access the object
// they're attached to using the this keyword.
myObj = {
myString: "Hello world!",
myFunc: function(){
return this.myString;
}
};
myObj.myFunc(); // = "Hello world!"
// What this is set to has to do with how the function is called, not where
// it's defined. So, our function doesn't work if it isn't called in the
// context of the object.
var myFunc = myObj.myFunc;
myFunc(); // = undefined
// Inversely, a function can be assigned to the object and gain access to it
// through this, even if it wasn't attached when it was defined.
var myOtherFunc = function(){
return this.myString.toUpperCase();
}
myObj.myOtherFunc = myOtherFunc;
myObj.myOtherFunc(); // = "HELLO WORLD!"
// We can also specify a context for a function to execute in when we invoke it
// using 'call' or 'apply'.
var anotherFunc = function(s){
return this.myString + s;
}
anotherFunc.call(myObj, " And Hello Moon!"); // = "Hello World! And Hello Moon!"
// The 'apply' function is nearly identical, but takes an array for an argument list.
anotherFunc.apply(myObj, [" And Hello Sun!"]); // = "Hello World! And Hello Sun!"
// This is useful when working with a function that accepts a sequence of arguments
// and you want to pass an array.
Math.min(42, 6, 27); // = 6
Math.min([42, 6, 27]); // = NaN (uh-oh!)
Math.min.apply(Math, [42, 6, 27]); // = 6
// But, 'call' and 'apply' are only temporary. When we want it to stick, we can use
// bind.
var boundFunc = anotherFunc.bind(myObj);
boundFunc(" And Hello Saturn!"); // = "Hello World! And Hello Saturn!"
// Bind can also be used to partially apply (curry) a function.
var product = function(a, b){ return a * b; }
var doubler = product.bind(this, 2);
doubler(8); // = 16
// When you call a function with the new keyword, a new object is created, and
// made available to the function via the this keyword. Functions designed to be
// called like that are called constructors.
var MyConstructor = function(){
this.myNumber = 5;
}
myNewObj = new MyConstructor(); // = {myNumber: 5}
myNewObj.myNumber; // = 5
// Every JavaScript object has a 'prototype'. When you go to access a property
// on an object that doesn't exist on the actual object, the interpreter will
// look at its prototype.
// Some JS implementations let you access an object's prototype on the magic
// property __proto__. While this is useful for explaining prototypes it's not
// part of the standard; we'll get to standard ways of using prototypes later.
var myObj = {
myString: "Hello world!"
};
var myPrototype = {
meaningOfLife: 42,
myFunc: function(){
return this.myString.toLowerCase()
}
};
myObj.__proto__ = myPrototype;
myObj.meaningOfLife; // = 42
// This works for functions, too.
myObj.myFunc(); // = "hello world!"
// Of course, if your property isn't on your prototype, the prototype's
// prototype is searched, and so on.
myPrototype.__proto__ = {
myBoolean: true
};
myObj.myBoolean; // = true
// There's no copying involved here; each object stores a reference to its
// prototype. This means we can alter the prototype and our changes will be
// reflected everywhere.
myPrototype.meaningOfLife = 43;
myObj.meaningOfLife; // = 43
// We mentioned that __proto__ was non-standard, and there's no standard way to
// change the prototype of an existing object. However, there are two ways to
// create a new object with a given prototype.
// The first is Object.create, which is a recent addition to JS, and therefore
// not available in all implementations yet.
var myObj = Object.create(myPrototype);
myObj.meaningOfLife; // = 43
// The second way, which works anywhere, has to do with constructors.
// Constructors have a property called prototype. This is *not* the prototype of
// the constructor function itself; instead, it's the prototype that new objects
// are given when they're created with that constructor and the new keyword.
MyConstructor.prototype = {
myNumber: 5,
getMyNumber: function(){
return this.myNumber;
}
};
var myNewObj2 = new MyConstructor();
myNewObj2.getMyNumber(); // = 5
myNewObj2.myNumber = 6
myNewObj2.getMyNumber(); // = 6
// Built-in types like strings and numbers also have constructors that create
// equivalent wrapper objects.
var myNumber = 12;
var myNumberObj = new Number(12);
myNumber == myNumberObj; // = true
// Except, they aren't exactly equivalent.
typeof myNumber; // = 'number'
typeof myNumberObj; // = 'object'
myNumber === myNumberObj; // = false
if (0){
// This code won't execute, because 0 is falsy.
}
if (Number(0)){
// This code *will* execute, because Number(0) is truthy.
}
// However, the wrapper objects and the regular builtins share a prototype, so
// you can actually add functionality to a string, for instance.
String.prototype.firstCharacter = function(){
return this.charAt(0);
}
"abc".firstCharacter(); // = "a"
// This fact is often used in "polyfilling", which is implementing newer
// features of JavaScript in an older subset of JavaScript, so that they can be
// used in older environments such as outdated browsers.
// For instance, we mentioned that Object.create isn't yet available in all
// implementations, but we can still use it with this polyfill:
if (Object.create === undefined){ // don't overwrite it if it exists
Object.create = function(proto){
// make a temporary constructor with the right prototype
var Constructor = function(){};
Constructor.prototype = proto;
// then use it to create a new, appropriately-prototyped object
return new Constructor();
}
}
```
[[Website|http://learnxinyminutes.com/]]
! Summary
This paper proposes to select Bayesian networks (BN) from the pareto front of two objectives, misclassification and minimum description length (MDL) with particle swarm optimization (PSO).
! Reader Interest
# The topic of the paper loosely connected with model selection with regularization by introducing MDL. But the fact that the methods lacks theoretical support prevents the work from being interesting. Learning Bayesian networks structure with PSO is not new [1,2]. Authors highlight their contributions as showing
## a bi-objective approach that exploits both the advantages of the shortest description of the original data and expected misclassification
## graphically the discrepancy between MDL and misclassification
## the learned models.
However, the first lacks technicall analysis and the last two are poorly demonstrated. Misclassification is already penalized in the MDL term. Its relationship with commonly used scoring functions for Bayesian learning BIC and BDeu [5] is not discussed.
# Although this paper set up to deal with the trade off between simplicity and accuracy, this work does not contain in depth discussion or practical guidelines to directly answer this question. There is no clear criterior on how to evaluate the model, whether to prefer the simpler MDL objective or the more accurate one. This makes it unlikely to be used by other researchers and practitioners. Simply distributing more weight to missclassificatio does not seem to be a good suggestion because it may suffer from overfitting.
To demonstrate the efficienty of the proposed MOPSO algorithm, the running time and results should be compared with other structure learning algorithms.
This paper does not provide suffcient introduction to BN or PSO. To claim L2 "BNs have become a preferred tool for prediction, diagnosis, decision-making, control, and interpretation of phenomena amenable to modeling", the authors should provide more up to date work and provide comparison to other methods, for example graphical networks [4]. And there lacks justification of the choice of using PSO. L41 "The metaheuristic used in this works is based on PSO (Particle Swarm Optimization) due to its good results in multi-objective problems and its simplicity to implement." How does it compare to other evolutionary strategies in BN? What is the reason of its superiority?
The author should pay more attention on the description of their approaches proposed in the paper. Section 3.3.2 does not provide a complete problem setting and the notations are not clearly explained. In section 3.3.3 terms such as hypervolume, non-dominated, particle need more accurate explaination.
The organization of figures and tables is quite messy. There are no captions or units, figure titles are confusing and there is not enough discussion for the illustrations. Not all optimal values are presented in bond fonts and to make the numbers easier to understand, the authors should add information indicating whether higher or lower is better.
For non-specialists, it is very difficult to understand multi-objective PSO solely from this paper.
There lacks detailed analysis for the evaluation results in Table 2.
! Minor problems
# L165 "where $a$ represents the proportion of instances of 165 the database correctly classified", $a$ should be the number of instances not propotion.
# Vectorized figures and embedded fonts should be used in the figures.
# Positions in PSO, $pbest$ and $gbest$ should be replaced with more formal notations for example $$p_{\text{best}}$$
# $C_i$ in equation (3) should be $C_1$
# Figure 5 "Graphs with the smallest values", should be optimal structures for different objectives.
! References
# Particle swarm optimization based method for Bayesian Network structure learning
# A method for learning Bayesian networks by using immune binary particle swarm optimization
# Review of Causal Discovery Methods Based on Graphical Models
# Relational inductive biases, deep learning, and graph networks
# A Theoretical Analysis of the BDeu Scores in Bayesian Network Structure Learning
[[Link|https://openreview.net/forum?id=S11KBYclx]]
* limitaion of current hyperparameter optimization techniques:
* Bayesian NN with SGHMC generates parameters of the learnign curve
[[GitHub link|https://github.com/jazzsaxmafia/Weakly_detector]]
! Running the program
Label names start with 'n' in imagenet.
Importing tf seems to introduce segmentation fault.
The reason we need learning theory is that otherwise we have to do parameter searching and design algorithms without principles.
! Topics
* [[Representation Learning]]
* [[Reproducing Kernel Hilbert Space]]
* PAC-learning
* bounds
** VC-dimension bound
** covering number bound
** Rademacher complexity bound
** PAC-Bayesian bound
!!! "Old" Generalization bounds
$$
\epsilon^2<\frac{\log|H_\epsilon|+\log\frac 1 \delta}{2m}
$$
* $\epsilon$: generalization error
* $\delta$: confidence
* $m$: number of training examples
* $H_\epsilon$: $\epsilon$-cover of the Hypothesis class (or cardinality)
We typically assume: $|H_\epsilon|\sim (\frac 1 \epsilon)^d$ where $d$ is the class (VC, ...) dimensions
! Examples
* [[Kernel Mean Embedding]]
* [[Generalization Bounds for Non-stationary Mixing Processes]]
@inproceedings{lee2009unsupervised,
title={Unsupervised feature learning for audio classification using convolutional deep belief networks},
author={Lee, Honglak and Pham, Peter and Largman, Yan and Ng, Andrew Y},
booktitle={Advances in neural information processing systems},
pages={1096--1104},
year={2009}
}
Copyright (c) 2014, Danielo Rodriguez
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
* [[paper|https://arxiv.org/abs/1611.02854]]
* [[blog|http://lstm.seas.harvard.edu/lantm/]]
* [[code|https://github.com/harvardnlp/lie-access-memory]]
Memory is accessed using a continuous head in a key-space manifold. The head is moved via Lie group actions, such as shifts or rotations, generated by a controller, and memory access is performed by linear smoothing in key space.
! Addresssing Procedure
A shift group $\mathbb R^2$ acting additively on $\mathbb R^2$. This means that $\mathcal A = \mathbb R^2$ so that $a(h)=(\alpha, \beta)$ acts upon a head $q = (x, y)$ by
$$
a(h)\cdot q = (\alpha, \beta) + (x, y) = (x+\alpha, y+\beta)
$$
A rotation group $SO(3)$ acting on the shpere $S^2=\{v\in\mathbb R^3: \|v\|=1\}$. Each rotation can be described by its axis $\xi$ (a unit vector) and angle $\theta$. An action ($\xi, \theta)\cdot q$ is just the appropriate rotation of the point $q$, and is given by Rodrigues' rotation formula
$$
a(h)\cdot q=(\xi, \theta)\cdot q = q\cos\theta+(\xi\times q)\sin\theta+\xi\langle\xi, q\rangle(1-\cos\theta)
$$
Restrict the expectation propagation framework to assumed density filtering (ADF)
We show how to use TensorFlow to fit the linear function with sinusoidal noise:
```python
import numpy as np
import seaborn
# Define input data
X_data = np.arange(100, step=.1)
y_data = X_data + 20 * np.sin(X_data/10)
# Plot input data
plt.scatter(X_data, y_data)
```
First we need placeholders to manage the input
```python
# Define data size and batch size
n_samples = 1000
batch_size = 100
# Tensorflow is finicky about shapes, so resize
X_data = np.reshape(X_data, (n_samples,1))
y_data = np.reshape(y_data, (n_samples,1))
# Define placeholders for input
X = tf.placeholder(tf.float32, shape=(batch_size, 1))
y = tf.placeholder(tf.float32, shape=(batch_size, 1))
```
Implement the algorithm. Define the loss:
$$
J(W, b) = \frac 1 N\sum_{i=1}^N(y_i-(Wx_i+b))^2
$$
```python
# Define variables to be learned
with tf.variable_scope("linear-regression"): # resuse=False so tensors created anew
W = tf.get_variable("weights", (1, 1),
initializer=tf.random_normal_initializer())
b = tf.get_variable("bias", (1),
initializer=tf.constant_initializer(0.0))
y_pred = tf.matmul(X, W) + b
loss = tf.reduce_sum((y - y_pred)**2/n_samples)
```
Run one step of gradient descent
```python
# Sample code to run one step of gradient descent
In [136]: opt = tf.train.AdamOptimizer()
In [137]: opt_operation = opt.minimize(loss)
In [138]: with tf.Session() as sess:
.....: sess.run(tf.initialize_all_variables())
.....: sess.run([opt_operation], feed_dict={X: X_data, y: y_data})
```
The optimizer is a special node in the computational graph. It can figure out what to optimize, computes the gradient and use assign to do the update.
And to learn the model
```python
# Sample code to run full gradient descent:
# Define optimizer operation
opt_operation = tf.train.AdamOptimizer().minimize(loss)
with tf.Session() as sess:
# Initialize Variables in graph
sess.run(tf.initialize_all_variables())
# Gradient descent loop for 500 steps
for _ in range(500):
# Select random minibatch
indices = np.random.choice(n_samples, batch_size) # use np to get random samples, doubting if it's good
X_batch, y_batch = X_data[indices], y_data[indices]
# Do gradient descent step
_, loss_val = sess.run([opt_operation, loss], feed_dict={X: X_batch, y: y_batch})
```
! GPU
!! See process on one device
```
sudo fuser -v /dev/nvidia*
```
!! Kill processes on all devices
```
for i in $(sudo lsof /dev/nvidia0 | grep python | awk '{print $2}' | sort -u); do kill -9 $i; done
```
! Networking
!! Downloading files from a web directory
With empty preceding folders
```
wget --no-parent -ckr http://www.example.com/files
```
hopefully without
```
wget -np -nH --cut-dirs 5 -r http://www.example.com/files
```
!! Multi-thread SCP with RSYNC
```
rsync -avzhe ssh remoteuser@remotehost:/remote/dir /this/dir/
```
!! SSH with port forwarding (for tensorboard)
```
ssh -L 16006:127.0.0.1:6006 haochen@119.3.217.218
```
! Misc
!! Untar multiple files
```
for f in *.tar.gz; do tar -zxvf $f; done
```
! Arch Linux
!! Managing /etc/*.pac* files
Using interactive script [[dotpac|https://wiki.archlinux.org/index.php/Dotpac]].
Usage: `sudo dotpac`.
!! AUR upgrade
```
yaourt -Su --aur
```
!! Algorithm
The LLE algorithm is based on simple geometric intuitions. Suppose the data consist of $N$
real-valued vectors $x_i$, each of dimensionality $D$, sampled from some smooth underlying manifold. Provided there is sufficient data (such that the manifold is well-sampled), we expect each data point and its neighbors to lie on or close to a locally linear patch of the manifold.
We can characterize the local geometry of these patches by linear coefficients that reconstruct each data point from its neighbors. In the simplest formulation of LLE, one identifies $K$ nearest neighbors per data point, as measured by Euclidean distance. (Alternatively, one can identify neighbors by choosing all points within a ball of fixed radius, or by using more sophisticated rules based on local metrics.) Reconstruction errors are then measured by the cost function:
$$
E(W) = \sum_i\left|x_i - \sum_jW_{ij}x_j\right|^2,
$$
which adds up the squared distances between all the data points and their reconstructions.
The weights $W_{ij}$ summarize the contribution of the $j$th data point to the $i$th reconstruction.
To compute the weights $W_{ij}$, we minimize the cost function subject to two constraints: first, that each data point $x_i$ is reconstructed only from its neighbors, enforcing $W_{ij} = 0$ if $ x_j$ does not belong to this set; second, that the rows of the weight matrix sum to one: $\sum_jW_{ij} = 1$. The optimal weights $W_{ij}$ subject to these constraints are found by solving a least squares problem.
Note that the constrained weights that minimize these reconstruction errors obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings, and translations of that data point and its neighbors. the invariance to translations is enforced by the sum-to-one constraint on the rows of the weight matrix. A consequence of this symmetry is that the reconstruction weights characterize intrinsic geometric properties of each neighborhood, as opposed to properties that depend on a particular frame of reference.
Suppose the data lie on or near a smooth nonlinear manifold of dimensionality $d\ll D$. To a good approximation, then, there exists a linear mapping—consisting of a translation, rotation, and rescaling—that maps the high dimensional coordinates of each neighborhood to global internal coordinates on the manifold. By design, the reconstruction weights $W_{ij}$ reflect intrinsic geometric properties of the data that are invariant to exactly such transformations. We therefore expect their characterization of local geometry in the original data space to be equally valid for
local patches on the manifold. In particular, the same weights $W_{ij}$ that reconstruct the $i$th data point in $D$ dimensions should also reconstruct its embedded manifold coordinates in $d$
dimensions.
LLE constructs a neighborhood preserving mapping based on the above idea. In the final step of the algorithm, each high dimensional observation $x_i$ is mapped to a low dimensional vector $y_i$ representing global internal coordinates on the manifold. This is done by choosing $d$-dimensional coordinates $y_i$ to minimize the embedding cost function:
$$
\Phi(Y) = \sum_i\left|y_i - \sum_jW_{ij}y_j\right|^2
$$
This cost function—like the previous one—is based on locally linear reconstruction errors, but here we fix the weights $W_{ij}$ while optimizing the coordinates $y_i$. The embedding cost defines a quadratic form in the vectors $y_i$. Subject to constraints that make the problem well-posed, it can be minimized by solving a sparse $N\times N$ eigenvector problem, whose bottom $d$ non-zero eigenvectors provide an ordered set of orthogonal coordinates centered on the origin.
!! Emotion Adaption
!!! Problem
Given the neutral records of each speaker $\{s\}$ and records under emotions $\{e\}$ for all but speaker $s_0$, we wish to construct the model to represent the distribution of $s_0$'s emotional records.
We assume the neighbors of $s_0$ under emotionless speech is also his neighbors under emotional speech. And the emotional speech models are adapted with linear combination from the speaker's emotionless models.
!!! Proposal
We find the neighbors with statistical distances between same variance Gaussian distributions. Then find the best reconstruction of $s_0$'s emotionless speech model with his neighbors using LLE, $W$.
$$
\tilde\mu_{N0} = \sum_jW_{0j}\mu_{Nj}
$$
Then, we add models of $s_0$'s emotional speech with corresponding adapted means of his neighbor and the same set of linear combination $W$
$$
\tilde\mu_{e0} = \sum_jW_{0j}\mu_{ej}
$$
After linear transformations of the dimension reduction procedures, the spatial information is preserved.
* [[Can Neural Networks Understand Logical Entailment?|https://openreview.net/forum?id=SkZxCk-0Z]]
* [[Compositional Attention Networks for Machine Reasoning|https://openreview.net/forum?id=S1Euwz-Rb]]
! Theory
* [[LSTM Basic]]
* [[RNN Performance Optimization]]
* Gated Recurrent Units
** [[Revised GRU for speech|https://arxiv.org/abs/1710.00641]]: reset gate removed, replaced tanh with ReLU. Probably not very useful for other tasks
! Applications
* Image recognition
** [[Feedback Networks|https://arxiv.org/abs/1612.09508]]
! Bib
* [[Recurrent Highway Networks|http://arxiv.org/abs/1607.03474]]
** Residual connection for RNN
* [[A-LSTM|http://arxiv.org/abs/1610.06258]]
** a decaying recent hidden state weight matrix called fast accociative memory.
** can be used in visual attention
* [[Hybrid LSTM]]
* [[Tensorized LSTMs for Sequence Learning]]
* [[WRPN: Training and Inference using Wide Reduced-Precision Networks|https://arxiv.org/abs/1704.03079]]
* [[Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent]]
* Trainning Quantized Nets: A Deeper Understanding
** Theoretical analysis of BinaryConnect and Stochastic Rounding
** SR cannot exploit greedy local search
* plain sparsity
* block sparsity
* low rankness
[[link|https://www.youtube.com/watch?v=w3kq7vTta7c]]
<<<
''Problem'' [$$C$$-CLIQUE$$_n$$]<br>
Given graph $$g$$ on $$n$$ vertices, accept iff $$g$$ contains a clique of size $$\ge C$$.
<<<
The reason we care about $$C$$-CLIQUE$$_n$$ is that 3-SAT$$_n$$ reduced to $$C$$-CLIQUE$$_n$$. If we can find sufficient small sized circuits, we can solve $$C$$-CLIQUE efficiently.
For any size $$S$$ boolean circuit, $$s>n$$ can be described by $$\sim O(s\log s)$$ bits. #Boolean functions computed by size $$s$$ circuit is $$\exp(s\log s)$$. #Boolean functions of $$n$$ bits $$\exp(2^n)$$
Bibtex
```
@article{hochreiter1997long,
title={Long short-term memory},
author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
journal={Neural computation},
volume={9},
number={8},
pages={1735--1780},
year={1997},
publisher={MIT Press}
}
```
Here is [[a great article|http://colah.github.io/posts/2015-08-Understanding-LSTMs/]] for an introduction to LSTMs in particular.
LSTMs are able to learn long-term dependencies which classic RNNs fail to.
! Comparing to RNN
[img width=600 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-SimpleRNN.png]]
The repeating module in a standard RNN caontains a single layer
[img width=600 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-chain.png]]
The repeating module in an LSTM contains four interacting layers.
! Detailed structure
!! Cell state
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-C-line.png]]
Cell state runs down the entire chain, the information can be added or removed, regulated by structures called gates.
!! Gates
[img width=100 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-gate.png]]
Gates are composed out of a sigmoid neural net layer and a pointwise multiplication operation. The sigmoid output with value of one means "let everything through". A LSTM has three gates.
!!! Forget gate
With forget gate, we decide what information we're going to throw away from the cell state.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-f.png]]
One scenario where forget gate could be useful is in word generation. In such a problem, the cell state might include the gender of the present subject, so that the correct pronouns can be used. When we see a new subject, we want to forget the gender of the old subject.
!!! Input gate joining with new value
The we want to decide what to add to the cell state.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-i.png]]
Then we add the regulated old value with the new one:
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-C.png]]
!!! Output gate
We filter our output with output gate. First, we run a sigmoid layer which decides what parts of the cell state we’re going to output. Then, we put the cell state through ''tanh'' (to push the values to be between −1 and 1) and multiply it by the output of the sigmoid gate.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-o.png]]
! Variants on LSTM
[Gers & Schmidhuber (2000)|ftp://ftp.idsia.ch/pub/juergen/TimeCount-IJCNN2000.pdf] adds "peehole connections" to let the gate layers look at the cell state.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-var-peepholes.png]]
Another variation is to use coupled forget and input gates, by making these two decisions together.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-var-tied.png]]
''GRU'' ([[Cho, et al. (2014)|http://arxiv.org/pdf/1406.1078v3.pdf]]) is a slightly more dramatic variation on the LSTM. It combines the forget and input gates into a single “update gate.” It also merges the cell state and hidden state, and makes some other changes. The resulting model is simpler than standard LSTM models, and has been growing increasingly popular.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-var-GRU.png]]
$$
\sum_i^\infty i
$$
[[graves05framewise|graves2005framewise.txt]] shows that LSTM is well suited to framewise phoneme classification, and that bidirectional networks are able to capture time dependencies in speech that elude unidirectional nets.
! Backward Pass
# reset all partial derivatives to 0. (What exactly does that mean?)
# starting at time $\tau_1$, propagate the output errors backwards through the unfolded net, using the standard BPTT equations:
$$
\begin{align}
\delta_k(\tau) &= \frac{\partial E(r)}{\partial x_k}\\
\epsilon_j(\tau_i) &= e_j(\tau_1)
\end{align}
$$
! Previous Work
!! Traditional Methods
!!! Bilinear Model
[[Style & Content]]
!! Normalizing Speaker Variations
!!! Vocal tract length normalization
VTLN warps the frequency axis of the filter-bank analysis to account for the fact that the locations of vocal-tract resonances vary roughly monotonically with the vocal tract length of the speaker.
The filter bank process with frequency warping can be written as:
$$
O_\alpha(n) = \sum_{w = l_n}^{h_n}T_n(w)X(\varphi_\alpha(w))\quad 0\le n\le N-1
$$
where $\varphi_\alpha(w)$ is the warping function, $\alpha$ is the warping factor, $X$ is the speech signal and $T_n$ is the n-th filter bank.
This is done in both training and testing with 20 quantized warping factors from 0.8 to 1.18. During the training, the optimal warping factor can be found using the expectation–maximization (EM) algorithm by repeatedly selecting the best factor given the current model and then updating the model using the selected factor.
!!! Feature-space maximum likelihood linear regression
fMLLR applies an affine transform to the feature vector so that the transformed feature better matches the model.
It is typically applied to the testing utterance by first generating recognition results using the raw feature and then re-recognizing the speech with the transformed feature.
In GMM-HMM systems, these feature transformed speaker-adaptation techniques are critical (9%), but the effect is very small in CD-DNN-HMM (2%), which means a well-trained DNN should be quite robust to speaker variaions. Its effect on more complex and solely DNN models is unknown. It is reasonable to guess even less. And both adaptation techniques can be implemented by adding a layer between the hidden and input layer.
!! Neural Nets
!!! Adaptation
!!! Transfer Learning
Transfer learning aims at developing a reasonably performed system for a new task, domain, or distribution efficiently and effectively by retaining and leveraging the knowledge learned from one or more similar tasks, domains, or distributions.
!!! Multitask Learning
Adding new tasks that share part of the representation can improve the original task performance. This is referred to as maultitask learning. Each layer of a neural net can be seen as a representation. The general structure of a MTL net can be described as 2 nets dedicated to each task sharing some part of their layers.
! Net Architectures
!! RNN
The standard RNN output, $y_O^{(t)}$, at a time step $t$ is calculated as:
$$
y_H^{(t)} = f_H(W_Hy^{(t-1)}+W_Ix^{(t)}),
$$
$$
y_O^{(t)}=f_O(W_Oy_H^{(t)},
$$
where $W_H$, $W_I$ and $W_O$ are the hidden, input and output weight matrices, $x_t$ is the input vector at time step $t$, vectors $y_H$ represent the hidden neuron activations. Functions $f_H$ and $f_O$ are the nonlinear activation functions.
!! Clockwork RNN
The main difference between CW-RNN and an RNN is that at each CW-RNN time step $t$, only the output of modules $i$ that satisfy $t \mod T_i = 0$ are executed.
!![[LSTM|Long short-term memory]]
LSTM is an RNN architecture that elegantly addresses the vanishing gradients problem using "memory units". Besides hidden units, the network contains input gates and output gates that controls when the memory to be updated and the information to leave the unit.
! Some Tuning on the Problem
can the sparse coding like thing be used in this?
! Training
[[Thesis Training RNN]]
! Dataset
TIMIT is used in [[the original LSTM paper|graves2005framewise.txt]]. They used 184 of the 3696 training set utterances (randomly chosen, constant for experiments) as ''validation set'' and trained on the rest.
* ''Content Analytics'' that organize and optimize content modules
** Machine learning method can help teachers organize massive open resources and teaching materials. Related fields are
*** Knowledge representation: extract relationships and build theasarus
*** Information retrieval: collect online resources
*** Knowledge graph: group and visualize complex content
** Examples
*** [[Gooru's Open Catalog|http://about.gooru.org/catalog]] is a rich content platform covers different subject areas
*** [[IBM Watson Content Analytics|https://www.ibm.com/support/knowledgecenter/en/SS5RWK_3.5.0/com.ibm.discovery.es.nav.doc/iiypofnv_prodover_cont.htm]] is an extensive knowledge graph
* ''Adaptive learning'' that track student knowledge and personalize instructions
** Machine learning can help with better assessment of individual student (esp. web-based) and adjust the teaching strategy. Related fields are
*** Reinforcement learning: optimizing the instruction/questioning policy
*** Statistics: robust and precise evaluation of student performance
*** Decision tree: a useful algorithm for categorizing student within least questions
** Examples: Adaptive learning systems: [[dreambox|http://www.dreambox.com]], [[ALEKS|https://www.aleks.com/about_aleks]], [[Knewton|http://www.knewtonhighered.com/]]
* ''Game-based learning'': idk, maybe just games [[ST Math|https://web.stmath.com/]], [[Mangahigh|https://www.mangahigh.com/en-au/]]. Don't seem very educational
* ''Grading systems'': score essays, detect plagiarism
** Machine learning could be very good at plagiarism detection and can be trained to know language (this is new)
*** Natural Language Processing can help finding mistakes in writing and grade answers
*** Deep learning can recognize writing styles, grade longer answers
** Examples: [[WriteToLearn|https://www.writetolearn.net/]], [[Lightside|http://turnitin.com/en_us/what-we-offer/revision-assistant]]
** Publication: [[The Eras and Trends of Automatic Short Answer Grading|http://www.uni-weimar.de/medien/webis/publications/papers/stein_2015a.pdf]]
* ''Data analysis''
** There are some data beyond education could be useful
*** Statistics can be used wherever there is data
*** Graph mining on structured data (network)
*** Embedding learning to represent complex data
** Examples:
*** predicting enrollment, dropouts, detecting student social connections and learning interests, good teaching behavior, office hour scheduling etc.
* refresh `g` or `G`
* stage `s` or `S` for all
* commit `c`
* push `p`
* pull `F`
[[link|https://www.inference.vc/notes-on-imaml-meta-learning-without-differentiating-through/]]
In meta-learning, we have a series of independent tasks, with associated training and validation loss functions $$f_i$$ and $$g_i$$, respectively. We have a set of model parameters $$\theta$$ which are shared across the tasks, and the loss functions $$f_i(\theta)$$ and $$g_i(\theta)$$ evaluate how well the model with parameters $$\theta$$ does on the training and test cases of task $$i$$. We have an algorithm that has access to the training loss fi and some meta-parameters $$\theta_0$$, and output some optimal or learned parameters $$\theta^*_i=\operatorname{Alg}(f_i,\theta_0)$$. The goal of the meta-learning algorithm is to optimize the meta-objective
$$
\mathcal{M}(\theta_0) = \sum_i g_i(\operatorname{Alg}(f_i, \theta_0))
$$
with respect to the meta-parameters $$\theta_0$$.
In the early version of [[MAML|Model-Agnostic Meta-Learning]], the algorithm was chosen to be SGD, $$f_i$$ and $$g_i$$ being the training and test loss.
! Stochasticity
A more generous formulation of the meta-objective would allow for stochastic algorithms. If we denote by $$\operatorname{Alg}(f_i, \theta_0)$$ the distribution over solutions the algorithm finds, the meta-objective would be
$$
\mathcal{M}_{stochastic}(\theta_0) = \sum_i \mathbb E_{\theta\sim\operatorname{Alg}(f_i, \theta_0)}g_i(\theta)
$$
The meta-learning objective now depends on $$\theta_0$$ in two ways:
* Differentiable
* The probability with which we find the different solutions change.
! Bayesian
One can think of the anchor point $$\theta_0$$ as the mean of a prior distribution over the neural networks's weights. The inner loop of the algorithm, or $$\operatorname{Alg}(f_i,\theta_0)$$ then finds the maximum-a-posteriori (MAP) approximation to the posterior over $$\theta$$.
In Baysian, we seek to optimize $$\theta_0$$ by maximising the marginal likelihood:
$$
\mathcal{M}_{\text{variational}}(\theta_0, Q_i) = \sum_i \left( KL[Q_i\vert \mathcal{N}_{\theta_0}] + \mathbb{E}_{\theta \sim Q_i} f_i(\theta) \right),
$$
where $$Q_i$$ approximates the posterior over model parameters for task $$i$$. A specific choice of $$Q_i$$ is a dirac delta distribution centred at a specific point $$Q_i(\theta) = \delta(\theta - \theta^{\ast}_i)$$. If we generously ignore some constants that blow up to infinitely large, the KL divergence between the Gaussian prior and the degenerate point-posterior is a simple Euclidean distance, and our variational objective reduces to:
$$
\mathcal{M}_{\text{variational}}(\theta_0, \theta_i) = \sum_i \left( \|\theta_i - \theta_0\|^2 + f_i(\theta_i) \right)
$$
Similar idea:
* [[Meta-Learning Probabilistic Inference For Prediction|https://arxiv.org/abs/1805.09921]]
@inproceedings{martens2011learning,
title={Learning recurrent neural networks with hessian-free optimization},
author={Martens, James and Sutskever, Ilya},
booktitle={Proceedings of the 28th International Conference on Machine Learning (ICML-11)},
pages={1033--1040},
year={2011}
}
<<<
''Definition'' [Martingale]<br>
A random process, $$\{X_n: 0\le n\ge\infty\}$$, is a martingale w.r.t. the information filtration, $$\mathcal F_n$$, and probability distribution, $$P$$, if
# $$E^P[|X_n|]<\infty$$ for all $$n\ge0$$
# $$E^P[X_{n+m}|\mathcal F_n]=X_n$$ for all $$n, m\ge 0$$
<<<
$$\mathcal F_n$$ knows all the information of the model at time $$n$$. Can be simply regarded as $$\{X_1, \dots, X_n\}$$.
Martingales are used to modle fair games and have a rich history in the modeling of gambling problems.
We define a submartingale by replacing condition #2 with
$$
E^P[X_{n+m}|\mathcal F_n]\ge X_n,\qquad \forall n, m\ge 0.
$$
And we define a supermartingale by replacing condition #2 with
$$
E^P[X_{n+m}|\mathcal F_n]\le X_n,\qquad \forall n, m\ge 0.
$$
!! A martingale betting strategy
Keep doubling the bet until we eventually win.
Let $$W_n$$ denote total winnings after $$n$$ coin tosses assuming $$W_0 = 0$$, then $$W_n$$ is a martingale.
$$W_n = 1$$ if we win for the first time at $$n$$th bet and $$W_n = -2^n+1$$ if we have not won.
!! Polya's Urn
Start with an urn with a red ball and a green ball.
Let $$X_n$$ denote the number of red balls in the urn after $$n$$ draws. Then
$$
P(X_{n+1}=k+1|X_n=k) = \frac{k}{n+2}
$$
$$
P(x_{n+1}=k|X_n=k) = \frac{n+2-k}{n+2}
$$
$$M_n=X_n/(n+2)$$ is a martingale.
* [[Algebraic Geometry]]
* [[Analysis]]
* [[Statistics]]
* [[Optimization]]
MathJax is a cross-browser JavaScript library that displays mathematical notation in web browsers, using MathML, LaTeX and ASCIIMathML markup. MathJax is released as open-source software under the Apache license.
Using [[kpe's plugin|https://github.com/kpe/tw5-mathjax-plugin/tree/v0.1.0]], [[the previous one|https://gist.github.com/kpe/cc0547b318e6f8d4ddaa]] uses `MutationObserver` and gets error.
! Usage
!! Inline
We can render inline math like $a2+b2=c2$ by surrounding the MathJax markup with single dollar symbols like so: `$a^2 + b^2 = c^2$`.
!! Block
To generate standalone blocks of math, add double dollar symbols on new lines before and after the markup.
```
$$
P(E) = {n \choose k} p^k (1-p)^{ n-k}
$$
```
$$
P(E) = {n \choose k} p^k (1-p)^{ n-k}
$$
! Properties
[[MATLAB Snippets]]
! Vectorization
[[link|http://ufldl.stanford.edu/wiki/index.php/Logistic_Regression_Vectorization_Example]]
!! Logistic regression vectorization
Consider training a logistic regression model using batch gradient ascent. Suppose our hypothesis is
$$
h_\theta(x) = \frac{1}{1+\exp(-\theta^Tx)},
$$
we let $x_0=1$, so that $x \in \Re^{n+1}$ and $\theta \in \Re^{n+1}$, and $\theta_0$ is our intercept term. We have a training set $\{(x^{(1)}, y^{(1)}), \ldots, (x^{(m)}, y^{(m)})\}$ of $m$ examples, and the batch gradient ascent update rule is $\theta := \theta + \alpha \nabla_\theta \ell(\theta)$, where $\ell(\theta)$ is the log likelihood and $\nabla_\theta \ell(\theta)$ is its derivative.
We thus need to compute the gradient:
$$
\nabla_\theta \ell(\theta) = \sum_{i=1}^m \left(y^{(i)} - h_\theta(x^{(i)}) \right) x^{(i)}_j.
$$
```matlab
% Implementation 3
grad = x * (y- sigmoid(theta'*x))';
```
Logical Indexing
Setting values below a threshold to be zero
```matlab
S(S<th) = 0;
```
! Exponential
In finite dimensions, we define the matrix exponential $e^A$, $A\in\mathbb C^{n\times n}$, as
$$
e^A:=\sum_{k\ge0}\frac{A^k}{k!}.
$$
! Identities
$$
M = \begin{bmatrix}
A & B\\
C & D
\end{bmatrix}
$$
where $M$ is an $n\times n$ and $A$ is an $(n-k)\times(n-k)$ matrix.
* If $A$ is invertible, we can use the technique of [[Schur complementation|https://en.wikipedia.org/wiki/Schur_complement]] to express the inverse of $M$ (if it exists).
* [[Inverting the Schur complement|https://terrytao.wordpress.com/2017/09/16/inverting-the-schur-complement-and-large-dimensional-gelfand-tsetlin-patterns/]]
! Others
* [[Matrix Factorization]]
! Deep Factorization
* [[Stable recovery of the factors from a deep matrix product and application to convolution networks|https://arxiv.org/abs/1703.08044]]
MLE can be situated at the fringe of the Bayesian paradigm. Extended coverage can be found in Lehmann and Casella (1998)
The drawbacks of MLE are
# The practical maximization of $l(\theta|x)$ can be quite complex, especially in multidimensional and constrained settings. Numerical procedures like EM for missing data and Robertson et al. (1997) for order-restricted parameter spaces have beem tailored to this approach but unsolved difficulties remain.
# A maximization technique is bound to give estimators that lack smoothness, as opposed to integration for instance. [[Saxena and Alam 1982]] show that, if $x\sim\chi_p^2(\lambda)$, the MLE of $\lambda$ is equal to 0 for $x<p$. MLE can be quite unstable.
# It lacks decision-theoretic and probabilistic supports. For instance, tests are not possible in a purely maximum likelihood context: it is necessary to call for frequentist justifications, even if they are based upon a likelihood ratio [[see Section 5.3]]. Similarly, confidence regions of the form $C=\{\theta;l(\theta)/l(\hat\theta)\ge c\}$, which are asymptotically shortest, will not depend solely on teh likelihood function if the bound $c$ is to be chosen to achieve coverage at a given level $\alpha$.
[[link|http://mi.eng.cam.ac.uk/research/dialogue/papers/yeyo06.pdf]]
! Transform-based
* ''Feature extraction:'' The input speech examples are first analyzed to extract the spectral parameters that represent the speaker identity. Usually these parameters encode the short-term acoustic features, such as the spectrum shape and the formant structure.
* Train conversion
* Transformation: The new spectral parameters are obtained by applying the trained conversion functions to the source parameters.
* Synthesize speech from the parameters. Mapping the [[prosody]] is an equally important and challenging problem. The souce pitch is shifted and scaled to match the mean and variance of the target speaker.
!! Spectral parameters
Estimating the spectral envelope with LPC, cepstral coefficients and [[LSF|Line spectral frequencies]].
! Bibs
* [Gretton et al. 2012]
* [Smola et al. 2007]
* [[Training generative neural networks via Maximum Mean Discrepancy optimization|https://arxiv.org/abs/1505.03906]]
MMD can be thought as difference between feature means. It lets us do the optimization in closed form
* Can get optimum value on each minibatch
* Avoids two-step optimization issue in GAN
! Definition
RKHS $H$, feature map $\varphi: \mathcal X\rightarrow\mathcal H$, $f(x)=\langle f, \varphi(x)\rangle_{\mathcal H}$, we look at the mean feature difference in $\mathcal H$:
$$
\|\mu_P-\mu_Q\|_{\mathcal H} = \|\mathbb E_{X\sim P}\varphi(X)-\mathbb E_{Y\sim Q}\varphi(Y)\|_{\mathcal H}
$$
Here $\mu_P$ is the [[Kernel Mean Embedding]] of a probability measure
$$
\mu_P = \int k(x, \cdot)P(dx)
$$
We take its operator norm and get
$$
\underset{f:\|f\|_{\mathcal H}\le 1}{\sup}|\langle f, E_{X\sim P}\varphi(X)-\mathbb E_{Y\sim Q}\varphi(Y)\rangle_{\mathcal H}|
$$
And with linearity and reproducing property
$$
\underset{f:\|f\|_{\mathcal H}\le 1}{\sup}|E_{X\sim P}f(X)-\mathbb E_{Y\sim Q}f(Y)|
$$
! Computation
Estimator given sample $X = (x_1, \cdots, x_N)$ and $Y = (y_1, \cdots, y_M)$, we just have to take certain sum of kernels.
$$
MMD^2(X, Y) = \frac{1}{N(N-1)}\sum_{n\neq n'}k(x_n, x_{n'})+\frac{1}{M(M-1)}\sum_{m\neq m'}k(y_m, y_{m'})-\frac{2}{MN}\sum_{m=1}^M\sum_{n=1}^Nk(x_n, y_m)
$$
[[link|https://openreview.net/forum?id=SkFqf0lAZ]]
Has a continous stack state: merging different configurations probabily mess up with the information?
This family uses a universal access mechanism (c.f. associative computers).
* End-To-End Memory Network: make the supervision indirect, supervision signal on output
* [[Key-Value Memory Network]]: makes matchings more relavant, easy to do pointers.
* Forward Prediction Memory Network
* [[Recurrent Entity Network]]
! Sparse Memory Access
* [[Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes|https://arxiv.org/abs/1610.09027]]
** restrict read write size to constant
** kNN memory matching
! Code
[[fb MemNN|https://github.com/facebook/MemNN]]
! Extensions
* [[Variational Memory Addressing in Generative Models]]
! Remarks
Maybe not the most convenient for all tasks: e.g. we search real computers using many more mechanisms (text, trees, read/write time, title/tags).
! Bayesian Meta-Learning
* references
** [[Stein Variational Gradient Descent]]
** [[Conditional Neural Processes]]
** [[Amortized Bayesian Meta-Learning]]
[[link|https://arxiv.org/abs/1703.00837]]
Use loss gradients as meta information. 2 types of loss functions:
* representation loss for good representation learner criteria
* main loss for the input task objective
Slow training with [[REINFORCE]].
Generates fast weights at two time-scales by operating in meta space. To integrate fast weights with slow weights, layer augmentation is used.
External memory for rapid learning and generalization.
* [[AMC: AutoML for Model Compression and Acceleration on Mobile Devices|https://arxiv.org/abs/1802.03494]]
** uses RL to decide layerwise sparsity ratio
** can be found in one GPU-hour
** [[Channel Pruning]] is used
! Methods
!! Bayesian Optimization
!! Knowledge Distillation
* [[Model compression via distillation and quantization|https://arxiv.org/abs/1802.05668]]
! Models
* Binary
* Architecture Search
Deep learning hard to generalize given few examples because there is no optimization techniques dedicated to this kind of problems. I will include ''rapid learning'' and ''continual learning'' literatures here.
''Meta-learning'' suggests we learn in 2 steps:
* acquire knowledge within each separate task (fast)
* extract information across all tasks (slow)
The goal of metal-learner is to acquire knowledge of different tasks. Consider a training algorithm
* input: training set $$D_{train} = \{(X_t, Y_t)\}^T_{t=1}$$
* output: parameters $$\theta$$ of model $$M$$
* obejctive: good performance on test set $$D_{test} = (X, Y)$$
Desire a meta-learning algorithm
* input: meta-training set $$\mathcal D_{meta-train} = \{(D_{train}^{n}, D_{test}^n)\}_{n=1}^N$$, where $$D_{train}^{n}$$ is a set of training set. Slow learning can be done by i.e. [[REINFORCE]].
* output: parameters $$\Theta$$ representing a training algorithm
* objective: good performance on meta-test set $$\mathcal D_{meta-test} = \{(D_{train}^{n'}, D_{test}^{n'})\}_{n'=1}^{N'}$$
! Tutorials
* [[Meta Learning Tutorial]]
* [[Meta-Learning: Why It's Hard and What We Can Do]]
! Theories
* [[RNN-Based Meta-Learning Methods]]
* [[Meta-Learning Taxonomy]]
* [[MAML Interpretation]]
! Applications
* [[Neural Architecture Search]]
!! Rapid learning
* [[One-shot Learning]]
* [[Zero-shot Learning]]
* probabilistic programming approach (pen stroke generation)
* memory-based approach and trained NTM
* [[Optimization as a Model for Few-Shot Learning]]
!! Meta optimizer
* Controlling fast weight update rate: [[Overcoming Catastrophic Forgetting]]
* Use fast weights to replace soft attention mechanism
* [[Synthetic Gradients]]
!! [[Continual Learning]]
* [[Meta-Learning for HRL]]
* [[Overcoming Catastrophic Forgetting]]
* OpenAI's [[Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments|https://arxiv.org/abs/1710.03641]]
** [[RoboSumo|https://github.com/openai/robosumo]]
! Resources
* [[Meta learning symposium|http://metalearning-symposium.ml/]]
! Applications
* [[AutoML]]
Agent has to solve a distribution of related long-horizon tasks, with the goal of learning new tasks in the distribution quickly
! Meta-Learning Shared Hierarchies
Find a set of subpolicies that enable fast learning of the master policy
$$
\max_\theta\mathbb E_{\theta_0}\mathbb E_M\mathbb E_{\tau^{(k)}_M}[\sum_{k=1}^KR(\tau^{(k)}_M)|\phi,\text{RLagent}_{\theta_0}]
$$
* [[RL2]]
* Meta-sgd: Learning to learn quickly for few shot learning
* Simple Neural Attentive Meta-Learner
** replace the LSTM with dilated temporal convolution (like wavenet) + attention
* [[Model-Agnostic Meta-Learning]]
* [[Meta Networks]]
!! Model based
$$
P_\theta(y|x, S) = f_\theta(x, S)
$$
* [[Santoro et al. 16|Meta-Learning with Memory Augmented Neural Networks]]
* Duan et al. 17
* Wang et al. 17
* Munkhdalai & Yu 17
* [[Mishra et al. 17 Meta-Learning with Temporal Convolutions|https://arxiv.org/abs/1707.03141]]
!! Metric Based
The meta-learner attempts to learn a metric which can be used to compare two different examples effectively and performs tasks in the learning metric space.
$$
P_\theta(y|x, S) = \sum_{(x_i, y_i)\in S}k_\theta(x, x_i)y_i
$$
* Koch 15
* Vinyals et al. 16
* Snell et al. 17
* Shyam et al. 17
* Sung et al. 17
!! Optimization Based
$$
P_\theta(y|x, S) = f_{\theta(S)}(x)
$$
$$
\theta(S) = g_{\theta_g}(\theta_0, \{\nabla_{\theta_0}L(x_i, y_i)\}_{(x_i, y_i)\in S})
$$
* Schmidhuber 87, 92
* Bengio et al. 90, 92
* Hochreiter et al. 01
* Li & Malik 16
* Andrychowicz et al. 16
* Ravi & Larochelle 17
* Finn et al. 17
[[pdf|http://proceedings.mlr.press/v48/santoro16.pdf]]
|!Task|!Base-learner |!Space of trained base-learners |! Meta-learning|
|Multi-task Learning|$$\hat\mathbf y = f_{\theta, \psi}(x)$$ |$$S = \{f_{\theta, \psi_\theta^*}(\cdot)\vert\theta\in\Theta\},\text{where }\psi_\theta^* = \arg \min_{\psi\in\Psi}\mathcal L(f_{\theta, psi})$$|$$\min_{f\in S}\mathcal L(f) = \min_{\theta\in\Theta}\mathcal L(f_{\theta, \psi_\theta^*})$$|
|Hyperparameter Optimization|$$\hat\mathbf y = f_{\theta, \psi}(x)$$ |$$S = \{f_{\theta, \psi_\theta^*}(\cdot)\vert\theta\in\Theta\},\text{where }\psi_\theta^* = \arg \min_{\psi\in\Psi}\mathcal L^\theta_{base}(f_{\theta, psi})$$ |$$\min_{f\in S}\mathcal L_{meta}(f) = \min_{\theta\in\Theta}\mathcal L_{meta}(f_{\theta, \psi_\theta^*})$$|
|Automatic Model Selection|$$\hat\mathbf y = f_{g_\theta(X, Y), \psi}(x)$$ |$$S = \{f_{g_\theta(X, Y), \psi^*}(\cdot)\vert\theta\in\Theta\},\text{where }\psi^* = \arg \min_{\psi\in\Psi}\mathcal L_{base}(f_\psi)$$ |$$\min_{f\in S}\mathcal L_{meta}(f) = \min_{\theta\in\Theta}\mathcal L_{meta}(f_{g_\theta(X, Y), \psi^*})$$ |
! Learning to Optimize
[[link|https://openreview.net/forum?id=ry4Vrt5gl]]
''Algorithm 1'' General structure of optimization algorithms
LG2 does not work. Counter examples can be constructed.
! Sparse Approximation
! MAML
The
* Contrastive loss
** Learning a similarity metric discriminatively, with application to face verification
* Triplet loss
** Distance metric learning for large margin nearest neighbor classification
** FaceNet: A unified embedding for face recognition and clustering
* N-pair loss
** Improved deep metric learning with multi-class n-pair loss objective
Discriminative loss
https://mirrors.bfsu.edu.cn/
!! Pip
`pip install -i https://mirrors.bfsu.edu.cn/pypi/web/simple some-package`
FP16 computation:
* 125 TFLOPs in FP16 compared to 15.7 TFLOPs in FP32
* Current internal accumulation occurs in FP32 ot maintain accurarcy
* Exposed to CUDA as Warp Matrix Multiply Accumulate (WMMA)
* Speedup
** 8x compute throughput
** 2x memory throughput
** 1/2x memory cost
** 16 bits precision
More than half of the gradients in SSD training underflows in SSD.
How To:
* Keep stored values in FP16
* Use FP16 computation along with specialized hardware to accelerate math
* Scale the loss to avoid underflowing
* Perform gradient updates in FP32 in order to maintain accuracy
[[paper|https://arxiv.org/abs/1710.09412]]
The mixup training loss is
$$
\mathcal L(\theta)=\mathbb E_{x,y}\mathbb E_{\lambda\sim\beta(0.1)}\mathcal l(\lambda x_1+(1-\lambda)x_2,\lambda y_1+(1-\lambda)y_2)
$$
seems plausible if net is linear. I think this will linearize the classification surface in some way.
If we assume linear loss:
$$
\mathcal l(x, py_1+(1-p)y_2) = p\mathcal l(x, y_1)+(1-p)\mathcal l(x, y_2)
$$
we can simply the loss into
$$
\mathcal L(\theta)=\mathbb E_{x,y}\mathbb E_{\lambda\sim\beta(\alpha, \alpha+1)}\mathcal l(\lambda x+(1-\lambda)x',y)
$$
When mixup mixes up the empirical distribution of the two classes, it turns them into continuous distributions with perfectly overlapping support. It works because:
* the training loss becomes better defined such that the Bayes-optimal solution is unique and easier to find consistently. The class-conditional distributions end up having fully overlapping support. But proof of this being good solution?
* data augmentation turns the training distribution closer to the test distribution. The ideal generalization gap can be seen as Bregman divergence between $p_{test}$ and $p_{aug}$, up to a constant.
This works better than instance noise for GAN. Also works against adversarial examples because some samples generated around the neighborhood might be covering the adversarial examples.
! Deep learning related
* [[Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection|https://arxiv.org/abs/1712.01145]]
* [[Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach|https://arxiv.org/abs/1712.05914]]
! Applications
* [[Android Malware Characterization using Metadata and Machine Learning Techniques|https://arxiv.org/abs/1712.04402]]
! Teams
[[Chicago Cubs]]
[[Tampa Bay]]
[[White Sox]]
* [[Fairness|MLSS20 Fairness]]
[[part1|https://www.youtube.com/watch?v=Igq_S_7IfOU]] [[part2|https://www.youtube.com/watch?v=9oNVFQ9llPc]]
If we know the transition dynamics $$p(s_{t+1}|a_t, s_t)$$:
Process Analysis Toolkit (PAT)
OTF model checking
Markov Decision Process
value iteration: decision making or estimating the equilibrium?
* Is the states and goals always discrete?
* First example is quite different from later ones. Is the solution always optimal or heuristic? What are the scope of problems PAT is capable of?
* How is PAT comparing to the tabular method of value iteration in reinforcement learning?
* Kuhn poker data: simulated?
The ground truth function is itself a neural network.
! Notations
Truth or full reality is denoted as $f$, and $f(x)$ is the integration over the variable $x$. $g_i(x|\theta)$ denotes the $i$th approximating model.
! Overview of Model Selection Criteria
There are two different classes of model selection methods: These have been labeled //efficient// and //consistent//.
Under the frequentist paradigm for model selection one generally has three main approaches:
# optimization of some selection criteria
## based on some form of mean squared error (e.g., Mallows' $C_p$, 1973) or mean squared prediction error (e.g., PRESS, Allen 1970)
## estimates of K-L information (e.g., TIC and the special cases AIC, AIC$_c$ and QAIC$_c$)
## consistent estimators of $K$, the dimension of the "true model" (e.g., [[BIC|BIC Changepoint]])
# tests of hypotheses
# as hoc methods.
!! Estimates of K-L Information
AIC, AIC$_c$ and QAIC$_c$ are estimates of the relative K-L distance between truth $f(x)$ and the approximating model $g(x)$.
* Hypothesis testing of [[Outlier]]
* [[Kullback-Leibler divergence]]
* [[Akaike's information criterion]] gives same result as [[GLR Changepoint]]. The relationship between it and KL remains unknown. According to Akaike's result, they are the same if the model is K-L best.
Model selection and multimodel inference: a practical information-theoretic approach / Kenneth P. Burnham, David R. Anderson. 2nd ed.
Train a good initialization for fine-tuning on many tasks.
$$
\min_\theta\sum_{t}\mathcal L_{val}(\theta-\alpha\nabla_\theta\mathcal L_{train}(\theta))
$$
[[Reptile|https://blog.openai.com/reptile/]] is a first-order approximation to MAML
! Extentions
* [[Probabilistic MAML]]
* [[Modular MAML|https://sites.google.com/view/modular-meta-learning]]
* [[Auto-Meta|https://arxiv.org/abs/1806.06927]]
* [[iMAML]]
[[video|https://www.youtube.com/watch?v=zygEcGcRfMQ]]
!! Roadmap
* Structure Learning
** Single estimate
** Probabilistic
* Causal Discovery
** RAI
** B-RAI
* Deep Neural Networks (discriminative)
** B2N
** BRAINet
!! Task
Common neural networks are not designed to model uncertainty or distinguish between the two.
Learn neural connectivity pattern from unlabeled observed data.
$$
p(y^*|x^*, \mathbf x, \mathbf y) = \int p(y^*|x^*, \phi)p(\phi|\mathbf x, \mathbf y)d\phi
$$
A common approach is to assume a prior $$p(\phi)$$ agnostic to $$X$$.
!! Approach
Assume there is a confounding arch between $$phi$$ and $$\mathbf X$$
B2N: feed the same information into multiple routes
BRAINet
* Causal diagram (RAI) -> Probability of causal relations (NIPS18)
* Construct a deep NN structure such that sub-networks are sampled with respect to the posterior of causal relations (NIPS19)
[[link|http://arxiv.org/abs/1608.04980]]
At [[ICML'2016 Workshop on Optimization Methods for the Next Generation of Machine Learning|http://optml.lehigh.edu/events/icml2016/]], Bengio gave a talk on [[From Curriculum Learning to Mollifying Networks|http://www.iro.umontreal.ca/~bengioy/talks/ICML-OptWorkshop-24June2016.pdf]]. I failed to find the videos.
; Mollifier
: is an infinitely differentialble funtiion that behaves as an approximate identity in the group of convolutions of integrable functions.
A mollifier converges to the Dirac fuction if we rescale it appropriately. It controls the loss funtion to make the gradient exists and approximately convex at the beginning.
* [[What is a Monad?-Computerphile|https://www.youtube.com/watch?v=t1e8gqXLbsU]]
** The Maybe Monad
*** define data Expr for safediv
*** To make if more compact, a monad takes a function as input
*** Haskell can further compact it with do notation.
! Background and Motivation
[[Random forest]]
! Mondrian Forests
Mondrian process + Random forests: which can give same result as offline in a batch way
[[Mondrian process]]
!! Mondrian process distribution over $\mathcal T$
Use $\mathcal {MP}(\lambda, [l_1, u_1], \dots, [l_d, u_d])$ as prior over decision trees $p(\mathcal T|X)$, where the range is given by $X$.
Self-consistency: $p(\mathcal T|X)$ is restriction to range of $X$
Online learning: unveil the decision trees on a larger range, conditional MP
!! Online learning
* the size and lifetime of a node control probability of new splits being introduced
* self-consistent hierarchical Bayesian prior (Pitman-Yor) on the leaf parameters
A stochastic process over binary hierarchical axis-aligned partitions of $\mathbb R^d$.
$\mathcal{MP}(\lambda,[l_1, u_1],[l_2, u_2])$
# Draw $\Delta_\epsilon$ from exponential with rate $u_1-l_1+u_2-l_2$
# if $\Delta_\epsilon > \lambda$ stop,
# else, sample a split
#* split dimension: choose dimension $j$ with prob $\propto u_j-l_j$
#* split location: choose cut location uniformly from $[l_j, u_j]$
#* recurse on left and right subtrees with parameter $\lambda-\Delta_\epsilon$
self-consistency of MP:
The restriction has distribution $\mathcal{MP}(\lambda,[l'_1, u'_1],[l'_2, u'_2])$
! Introduction
Comparing to variational Bayes, MCMC is accurate and now quite scalable. It summaries the posterior of any functional $$f(\theta)$$:
$$
\pi_n(\theta|Y^{(n)}) = \frac{\pi(\theta)L(Y^{(n)}|\theta)}{\int(\pi(\theta)L(Y^{(n)}|\theta)d\theta)} = \frac{\pi(\theta)L(Y^{(n)}|\theta)}{L(Y^{(n)})}
$$
MCMC constructs Markov chain with stationary distribution $$\pi_n(\theta|Y^{(n)})$$. Most are types of Metropolis-Hastings.
# $$\theta^*\sim g(\theta^{(t-1)}$$ sample a proposal
# Accept proposal by letting $$\theta^{(t)}=\theta^*$$ with probability $$a(\theta^*|\theta^{(t-1)})$$
Design of efficient MH algorithms involves choosing good proposal $$g$$.
* [[Gibbs Sampling]]
* Random Walk: $$a(q'|q) = \min\left(1, \frac{\pi(q')}{\pi(q)}\right)$$. The guess-and-check strategy of Random Walk Metropolis is doomed to fail in high-dimensional spaces where there are an exponential number of directions in which to guess but only a singular number of directions that stay within the typical set and pass the check.
* [[Hamilton Monte Carlo]]/[[Langevin MCMC|https://arxiv.org/abs/1707.03663]]: Exploit gradient information to generate samples far from $$\theta^{(t-1)}$$ having high posterior density
* [[Stochastic Gradient Descent as Approximate Bayesian Inference]]
* [[Stochastic Gradient MCMC]]
* Adaptive MCMC
* SMC
* Approximate Bayesian Computation
To identify the regions of parameter space that dominate expectations we need to consider the behavior of both the density and the volume.
! Problems
* Draws are auto-correlated; as level of correlation increases, information provided by each sample decreases.
* "Slowly mixing" Markov chains have highly autocorrelated draws
* A well designed MCMC wiht a good proposal should show rapid convergence and mixing
! Controlling variance
* Importance Sampling
* Quasi Monte Carlo
* Rao-Blackwellization
[[Official COCO API|https://github.com/pdollar/coco]]
https://www.youtube.com/watch?v=jZYPo_od-Sk
!! Robust Statistics ($$\epsilon$$-corrupted)
!!! Spectral signature
Check of corruption: if the corruptions move the mean, they also shift the covariance matrix
# If the top eigenvalue of the empirical covariance of your corrupted data is small, then the corruptions aren't too bad
#* Can just output the empirical mea
# If the top eigenvalue is large, then it can only be large because the bad points are too big in this direction
|Given an $$\epsilon$$-corrupted set of samples that is sufficiently large from...|...we can efficiently get an estimate of the true mean to $$\ell_2$$ error|
|a distribution wiht bounded second moment | $$O(\sqrt\epsilon)$$ [LRV16, DKKLMS16, DKKLMS17] |
|a Gaussian (or sub-Gaussian distribution) with identity covariance | $$O(\epsilon\sqrt{\log 1/\epsilon})$$ [DKKLMS17, SCV17] |
|a Gaussian with unknown covariance | $$O(\epsilon\log 1/\epsilon)$$ [DKKLMS16] |
|a "nice" distribution with bounded $$t$$-th moments | $$O(\epsilon^{1-1/t})$$ [HL18, KS18] |
!!! Applications
Europe gene map:
Add immigrants and learn the 2 largest principle components with the presence of noise.
Outlier detection: quantum entropy scoring
!! Supervised learning
These problems can be phrased in the framework of stochastic optimization:
Given a loss function $$\ell(X, w)$$ and a distribution $$\mathcal D$$ over $$X$$, minimize
$$
f(w) = \mathbb E_{X\sim\mathcal D}[\ell(X, w)]
$$
Challenge: Given $$\epsilon$$-corrupted samples from $$\mathcal D$$, minimize $$f$$.
* ICML19 SEVER: Robust stochastic optimization
** First try: just run SGD using robust estimates. For uncorrupted data, SGD is unbiased.
** Find a robust estimator for the gradient.
!!! NN Backdoor attack
* Training time watermark
* Testing time adversarial
* Empirical Defenses
** The working one: adversarial training
* Certified Defenses
** don't scale and perform worse
** randomized smoothing [Lecuyer etal, Li etal, Cohen etal]
!!!! Randomized smoothing
<<<
''Definition'' [smoothed classifier]<br>
Given a soft classifier $$F:\mathbb R^d\rightarrow \mathcal P(\mathcal Y)$$, its associated ''smoothed classifier'' is given by
$$
G(x) = (F\ast\mathcal N(0, \sigma^2I))(x) = \mathbb E_{\sigma\sim\mathcal N(0, \sigma^2I)}[F(x + \sigma)]
$$
<<<
<<<
''Theorem'' [Cohen etal '19]<br>
Let $$G$$ be a soft classifier, and let $$x\in\mathbb R^d$$. Let $$a, b\in \mathcal Y$$ be the most likely and second most likely class for $$x$$ under $$G$$, with probabilities $$p_a, p_b$$ respectively. Then
$$
\arg\max G(x) = \arg\max G(x + \delta)
$$
for all $$\delta$$ satisfying
$$
\|\sigma\|_2\le\frac\sigma2(\Phi^{-1}(p_A) - \Phi^{-1}(p_B))
$$
where $$\Phi^{-1}$$ is the inverse Gaussian cdf.
<<<
Training smoothed networks
* [Cohen etal 19] Gaussian augmentation (sampling from very high dimension?)
** $$\arg\max_{\|x'-x\|<\epsilon}(-\mathbb E_\sigma \log F(x' + \delta)_y)$$
** find adversarial exampel of $$F$$ that is robust to Gaussian noise
* [NIPS20] SmoothAdv: Directly robustify smoothed network via adversarial training on smoothed loss
** $$\arg\max_{\|x'-x\|<\epsilon}(-\log\mathbb E_\sigma F(x' + \delta)_y)$$
** find adversarial example of $$G$$.
Given data and label $$(x, y)$$, want to find $$\hat x$$ that maximizes loss of $$G$$ in an $$\ell_2$$ ball around $$x$$ w.r.t. cross-entropy loss $$L_{CE}$$.
$$
\begin{aligned}
\hat x &= \arg\max_{\|x'-x\|<\epsilon}L_{CE}(G(x'), y) \\
& = \arg\max_{\|x'-x\|<\epsilon}(-\log\mathbb E_\sigma F(x' + \delta)_y)
\end{aligned}
$$
* Multi-Loop Iterative Algorithm and Multiscale RNNs
! Solving maximally sparse estimation
$$
\min_x\|y-\Phi x\|^2_2+\lambda\|x\|_0
$$
where $\Phi$ is over complete. Useful for solving underdetermined inverse problem and removing outliers.
! Improving Speech Recognition
In [30], Seltzer and Droppo proposed to improve the recognition accuracy of the DNN-HMM system on the TIMIT phone recognition task [10] by adding a secondary task to the training of DNNs. For example, the digit recognition can be enhanced by training the network to simultaneously
* classify the digits
* enhance the noisy speech
* recognize the gender of the speaker
The secondary tasks they investigated are:
* Phone label task
* State context task
* Phone context task
Their results indicate that adding phone label classification as the secondary task does not affect the primary task’s result. This is understandable since the phone label does not provide any additional information than the state label already used in the primary task.
! Recognizing both Phonemes and Graphemes
[4] trains DNN with triphone model and trigrapheme model of the same language.
<<<
''Definition'' [Mutual Information]<br>
$I(X;Y)=D[p(x, y)\|p(x)p(y)] = D[p(x|y)\|p(x)]=H(X)-H(X|Y)$
<<<
for any Markov chain $X\rightarrow Y\rightarrow Z$: $I(X;Y)\ge I(X;Z)$. The mutual information quantifies the number of relevant bits that the input $X$ contains about the label $Y$, on average.
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
@inproceedings{nakashika2013voice,
title={Voice conversion in high-order eigen space using deep belief nets.},
author={Nakashika, Toru and Takashima, Ryoichi and Takiguchi, Tetsuya and Ariki, Yasuo},
booktitle={INTERSPEECH},
pages={369--372},
year={2013}
}
[[code|https://github.com/renqianluo/NAO]]
* Improved Proxy
** [[Learning Curve Prediction with Bayesian Neural Networks]]
* Better Gradient Estimator
** Policy Gradient
** PPO
* Better Controller
** [[NAS and Few-shot learning]]
** [[NAO]]
NAS Controller cannot adapt quickly with few samples. The controller should be able to look into history.
* PPO rollouts
* [[Neural HSMM]] learns with state change
* [[Matching Networks]] uses attention and memory
* LSTM itself as a meta-learner: [[Optimization as a Model for Few-Shot Learning]]
|Model |Task |Performance |Cost (GPU-day)|#Params |FLOPS |Space |Strategy |h
|[[NAS with RL|https://arxiv.org/pdf/1611.01578.pdf]] |CIFAR10 |3.65 |24000 |37.4M | |Multi-branch |REINFORCE with RNN controller |
|[[NASNet-A 6@4032|https://arxiv.org/pdf/1707.07012.pdf]] |ImageNet |82.7 |2000 |88.9M |32.8G |Normal+reduction cell |PPO with RNN controller |
|[[NASNet-A 4@1056|https://arxiv.org/pdf/1707.07012.pdf]] |ImageNet |74.0 |2000 |5.3M |564M |Normal+reduction cell |PPO with RNN controller |
|[[AmoebaNet-A|https://arxiv.org/pdf/1802.01548.pdf]] |ImageNet |75.7 |3150 |6.4M |570M |Normal+reduction cell |evolution |
|[[PNAS|https://arxiv.org/pdf/1806.09055.pdf]] |ImageNet |74.2 |~225 |5.1M |588M |Cell |SMBO |
|[[One-Shot|http://proceedings.mlr.press/v80/bender18a/bender18a.pdf]] |ImageNet |75.2 |- |11.9M |- |Cell |Random Search |
|[[DARTS|https://arxiv.org/pdf/1806.09055.pdf]] |ImageNet |73.3 |1 |4.7M |574M |Normal+reduction cell |Supernet |
|[[SNAS|https://arxiv.org/pdf/1812.09926.pdf]] |ImageNet |72.7 |1.5 |4.3M |522M |Normal+reduction cell |Supernet with Concrete |
|[[MNASNet|https://arxiv.org/pdf/1812.00332.pdf]] |ImageNet |74.0 |3800 |- |6.1ms on V100 |Cell |PPO with RNN controller |
|[[FBNet|https://arxiv.org/pdf/1812.03443.pdf]] |ImageNet |74.9 |20 |5.5M |375M 28.1ms on S8 CPU |Cell |Supernet with Concrete |
|[[ProxylessNAS|https://arxiv.org/pdf/1812.00332.pdf]] |ImageNet |75.1 |15 |- |5.1ms on V100 |Sequential |Single path with STE/REINFORCE |
|[[ASAP|https://arxiv.org/pdf/1904.04123.pdf]] |ImageNet |73.3 |0.2 |- |- |Normal+reduction cell |Supernet with pruning |
|[[Uniform|https://arxiv.org/pdf/1904.00420.pdf]] |ImageNet |74.7 |12 |- |328M |Sequential |Single path with evolution |
|[[Single-path|https://arxiv.org/pdf/1904.02877.pdf]] |ImageNet |75.0 |- |- |- |- |Single path alternative optimization|
|[[DetNAS|https://128.84.21.199/pdf/1903.10979.pdf]] |COCO det|mAP: 40.0 vs 39.2 shufflenetv2|44 | |1.3G |Sequential |Single path with evolution |
|[[NAS-FPN|https://arxiv.org/pdf/1904.07392.pdf]] |COCO det|mAP: 48.3 vs 43.4 FPN |333TPU (?) |166.6M |2.63T |Multi-branch |PPO with RNN controller |
|[[DPC|https://arxiv.org/pdf/1809.04184.pdf]] |Cityscapes segm|mIoU: 82.7 vs 82.1 dlv3+|2590 |0.81M (decoder) |6.84G (decoder) |Multi-branch |Random search with Vizier |
|[[Auto-DeepLab|https://arxiv.org/pdf/1901.02985.pdf]] |Cityscapes segm|mIoU: 82.0|1 |44.4M |695G |Cell |One-Shot |
* PPO
** Using gradient estimates to update neural network can be cast into the unified framework of policy gradient method.
* [[Bayesian Optimization]]
* Evolutionary strategies
** cheap functions of millions of samples. competitive on parallel
** CMA-ES works for continuous space
! Beyond black box optimization
* Hyperparameter gradient descent
** Formulation as [[bilevel optimization problem|https://arxiv.org/abs/1806.04910]]
** Derive through the entire optimization process
** [[Gradient-based Hyperparameter Optimization through Reversible Learning]]
** Interleave optimization steps
* Multi-fidelity optimization
** Successive Halving
** [[Hyperband|https://openreview.net/pdf?id=ry18Ww5ee]]
** BOHB: use TPE to pick configurations and HB to allocate the budgets
! For Practitioners
* If have access to multiple fidelities:
** BOHB: [[code|https://github.com/automl/HpBandSter]]
* If not
** Low-dim: GP-based BO, e.g. [[Spearmint|https://github.com/HIPS/Spearmint]]
** High-dim, categorical, conditional: SMAC (random forest) or TPE
** Purely continuous, budget > 10x dimensionality: CMA-ES
! Single Shot methods
[[One Stage NAS]]
* DARTS, softmax
* DSO
* Gumbel
* BinaryConnect
We wish to concurrently estimate the parameters $$\theta$$ of a model and an architecture $$\alpha$$ by minimizing $$f(\theta, \alpha)$$. But $$f$$ is not differentiable w.r.t. $$\alpha$$. NAS uses ES to estimate the gradient of $$\alpha$$. ENAS' estimation of $$\theta$$ is biased.
DARTS approximates $$f$$ with a relaxation $$g$$, similar to an average model. It starts from a complete graph and learn to prune the edges.
$$
\min_{\theta, \alpha}g(\theta, \alpha)
$$
The estimation of $$\alpha$$ is biased. The final configuration is never sparse.
! Recast weight sharing as hierarchical bayes
To actually learn a discrete structure, we apply ES instead of relaxation. We sample a configuration $$\mathbf w\sim p_\alpha(w)$$. The network parameters are shared across all configurations. The upperbound is
$$
f(\theta, \alpha)\le\mathbb E_{w\sim p(w|\alpha)}f(\theta, w) = J(\theta, \alpha, f)
$$
$$J$$ is differentiable w.r.t. $$\theta$$ and $$\nabla_\alpha J$$ is
$$
\frac{\partial}{\partial\alpha}\mathbb E_{w\sim p(w|\alpha)}f(\theta, w) = \mathbb E_{w\sim p(w|\alpha)}\frac{\partial}{\partial\alpha}f(\theta, w) + \mathbb E_{w\sim p(w|\alpha)}f(\theta, w)\frac{\partial}{\partial\alpha}\log p(w|\alpha)
$$
!! Gumbel trick
The first attempt is use Gumbel trick. We approximate $$f$$ with $$g$$ similar to DARTS, but instead of directly estimate $$g(\theta, \alpha)$$, we sample $$w$$ from a Gumbel softmax $$w\sim \text{Gumbel}(\alpha)$$ which is differentiable w.r.t. $$\alpha$$ and the second term will be constant since in Gumbel we sample from uniform. We start with a high temperature and gradually anneal to a discrete configuration.
This slightly overfits the model.
|!connection |!param |!train |!val |!ft train |!ft val |
|softmax |''3.94M'' |82.90 |66.36 |''97.29'' |80.55 |
|Gumbel |5.06M |''89.72'' |''67.73'' |93.41 |''82.15'' |
|Gumbel2 |4.86M | | |94.49 |81.15 |
!! Graphical model
Gumbel sampling is an empirical Bayes procedure that employs a point estimate for the mid-level, architecture-specific parameters in a hierarchical Bayesian model.
The use of point estimate may lead to an inaccurate point approximation of the integral.
This problem can be formulated as a probabilistic inference in a hierarchical model.
We approximate the distribution over hidden variables $$\theta$$ and $$\alpha$$ with some approximate distribution $$q(\theta, \alpha)$$. Gumbel can be seen as modelling the distribution as a product of independent marginals $$q(\theta, \alpha)=q(\theta)q(\alpha)$$.
!! Solving with approximate inference
* [[Debiasing Approximate Inference]]
! Teacher forcing
When forward pass use the current best
When backward, update others to approximate the current highest.
<<<
First off this ain't no diss record
This for some of my homies that were misrepresented
<<< Where Are They Now, Nas
For the past year and a half, I have been working on the field of Neural Architecture Search (NAS). The techniques to automatically design neural networks for specific tasks are intising for both practitioners and theorists. In production, we need efficient network designs to save battery power of mobile devices, etc. In research, NAS extends the .
The steepest depends on a notion of distance and the gradient is defined w.r.t. the Euclidean distance. The natural graident is a concept from information geometry and applies whe teh gradient is taken w.r.t. the parameters $$\theta$$ of a probability distribution $$p_\theta$$. We use KL divergence to measure the distance. The resulting steepest descent direction is the negative gradient preconditioned with the Hessian of the KL divergence, with is exactly the [[Fisher information matrix|Fisher information metric]] of $$p_\theta$$.
Natural gradient descent has a pre-conditioned matrix multiplied to the loss derivative. This pre-condition matrix is [[Fisher information matrix|Fisher information metric]] $$F(\theta)$$:
$$
\hat\nabla_\theta \mathcal L(\theta) = -F(\theta)^{-1}\nabla_\theta\mathcal L(\theta)
$$
This allows the parameters to take gradients in the distribution space into account.
* Desirable properties: faster convergence, invariance under model reparameterization
[[Neural Tangent Kernel]]
[[blog|https://blogs.princeton.edu/imabandit/2014/03/06/nesterovs-accelerated-gradient-descent-for-smooth-and-strongly-convex-optimization/]]
$$
\begin{align}
v_t& = \mu v_{t-1}-\eta\nabla E(w_{t-1}+\mu v_{t-1})\\
w_t&=w_{t-1}+v_t
\end{align}
$$
Nesterov solves the serial problem in time $O(\sqrt{\kappa}\log(1/\epsilon))$.
! Bibs
* NTM
* [[Lie-Access NTM]]
* [[Differentiable Neural Computer]]
* [[Neural Map: Structured Memory for Deep Reinforcement Learning|https://arxiv.org/abs/1702.08360]]: DNC for 3D navigation RL.
! Remarks
* Unified memory is not enough
** NTM: could only 'allocate' memory in contiguous blocks, leading to management problems
** DNC adds links and usage (good enough?)
** Sparse Access Memory works for both NTM and DNC, scale up?
! Applications
The model must interact with a symbolic executor through non-differentiable operations to search over a large program space.
Differentiable communication? [[Gumbel-Softmax|https://arxiv.org/abs/1611.01144]] trick can to approximate discrete communication decisions with a continuous representation during training.
* [[Semantic Parsing]]
* Meta-Learning: [[Meta-Learning with Memory Augmented Neural Networks]]
* [[Program Induction]]
!! Bibs
* [[Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision]]
!! Challenges
* Existign neural program architecutures do not generalize well.
* No Proof of Generalization
[[list|https://www.ml4aad.org/automl/literature-on-neural-architecture-search/]]
! Taxonomy
* Reinforcement Learning
** NAS
** ENAS
** [[EAS and PathLevel|https://github.com/han-cai/PathLevel-EAS]]
** [[Learning Transferable Architectures for Scalable Image Recognition|PPO for NAS]]
** [[Progressive Neural Architecture Search|PPO for NAS]]
*** sequential model-based optimization, increasing complexity progressively
** [[RENA|https://arxiv.org/abs/1806.07912]]
** [[MnasNet|https://arxiv.org/abs/1807.11626]]: mobile
** [[Transfer Automatic Machine Learning|https://arxiv.org/abs/1803.02780]]
*** TRPO and REINFORCE better than PPO and UREX
* Gradient Based
** [[DARTS]]
** [[Auto-Meta|https://arxiv.org/abs/1806.06927]]: Architecture for few-shot learning. The network is narrow and deep.
* Hyperparameter Optimization
** [[Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization|https://arxiv.org/abs/1603.06560]]
** [[Hyperparameter Optimization: A Spectral Approach|https://arxiv.org/abs/1706.00764]]
** [[Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks (Hinz et al. 2018)|https://www.worldscientific.com/doi/abs/10.1142/S1469026818500086]]
*** Both GA and TPE significantly outperform random search
* Bayesian Optimization
** [[Progressive Neural Architecture Search|https://arxiv.org/abs/1712.00559]]
** [[Neural Architecture Search with Bayesian Optimisation and Optimal Transport|https://arxiv.org/abs/1802.07191]]
** [[Constructing Deep Neural Networks by Bayesian Network Structure Learning]]
** [[Combining Hyperband and Bayesian Optimization]]
** [[Learning Curve Prediction with Bayesian Neural Networks]]
** [[Accelerating Neural Architecture Search using Performance Prediction|https://arxiv.org/abs/1705.10823]]
** [[Efficient Neural Architecture Search with Network Morphism]]
* Model based
** [[SMASH: One-Shot Model Architecture Search through HyperNetworks]]
** [[Simple and efficient architecture search for Convolutional Neural Networks|https://arxiv.org/abs/1711.04528]]
** [[PPP-Net: Platform-aware Progressive Search for Pareto-optimal Neural Architectures|https://openreview.net/forum?id=B1NT3TAIM]]
*** Efficiency is considered
* Evolutionary
** [[Hierarchical Representations for Efficient Architecture Search|https://arxiv.org/abs/1711.00436]]
** [[Regularized Evolution for Image Classifier Architecture Search|https://arxiv.org/abs/1802.01548]]
*** natural variant of the standard //tournament selection strategy//
*** Instead of removing the worst performing one, remove the oldest one.
** [[Lamarckian Evolution of Convolutional Neural Networks|https://arxiv.org/abs/1806.08099]]
*** Weight inheritance
*** How OpenAI Five inheritance weights?
*** Simple search space, optimization is not sample efficient.
! Important Facts
[[NAS Fact Sheet]]
! Problems
* How to define computation
* How to evaluate different methods
* [[NAS Acceleration]]
* [[NAS Weight Sharing]]
* [[NAS Search Space]]
* [[NAS Search Strategies]]
! Notes
* [[Bayesian NAS]]
! Segmentation
!! Backbone
* Cell Operations
** [[IGCV|https://github.com/hellozting/InterleavedGroupConvolutions]]
** SepConv, GroupConv
** Kernel Size (1x3)
* Block Structure
** Skip add/concat (SMASH)
** Bottleneck expansion ([[MobileNetV2|https://arxiv.org/abs/1801.04381]])
* Additional
** [[SwitchNorm|https://github.com/switchablenorms/Switchable-Normalization]]
** ReLUs
** Augmentation
* ShuffleNet V2
!! Decoder
* Context Aggregation
** Dilation (ASPP)
** Kernel size ([[Feature Pyramid Pooling|https://arxiv.org/abs/1805.10180]])
** Pyramid scales
* Upsampler
** Bilinear
** [[Dense Upsample Convoultion|https://arxiv.org/abs/1702.08502]]
** [[Global Attention Upsampling|https://arxiv.org/abs/1805.10180]]: a feature-wise transformation
** [[Competition Unpooling|https://arxiv.org/abs/1807.07803]]
** [[Bottleneck Attention Module|http://arxiv.org/abs/1807.06514]]
** [[Guided Upsampling|https://arxiv.org/abs/1807.07466]]
* Aggretation level
!! Methodology
* Start from scratch
* Take pretrained backbone
** search decoder
** aggregate
* Hyperparameter search
** num_epochs
** learning rate
! Lightweight modules
* Reward
** Computation: FLOPS, mult-add
** Energy consumption
*** [[MONAS|http://arxiv.org/abs/1806.10332]]: network initialized every iteration, using nvprof to compute energy. The multi-objective is controlled by weighting with threshold.
*** MnasNet
** Design multi-objective reward
** #params
* Quantization
** Quantization function
** Widths (binary, ternary, 4, 8)
! Other Tasks
* [[Auto-Meta|https://arxiv.org/abs/1806.06927]]
* [[Few-shot Segmentation|https://arxiv.org/abs/1806.07373]]
! Meta Learning Algorithm
* Differentiable (DARTS)
** quantization
** pooling
* Reinforcement learning (ENAS)
** [[PPO]]
* [[Bayesian Optimization|https://arxiv.org/abs/1807.07362]]
* Evolutionary Strategy ([[Guided ES|https://github.com/brain-research/guided-evolutionary-strategies]])
[[link|http://arxiv.org/abs/1808.10122]]
The idea is model a transition distribution within the recurrent cell.
! Bibs
* [[Convolutional Sequence to Sequence Learning]]
* [[Attention Is All You Need]]
* [[Non-Autoregressive Neural Machine Translation]]
! Architectures
''Kalchbrenner and Blunsom 2013'' CSM Encoder, decode with RNN, predict with a linear softmax output.
* Convolutions learn interactions among features in a local context
* By stacking them, longer range dependencies can be learnt
* Deep ConvNets have a branching structure similar to trees, but not parser is required
* To handle variable length, see [[A Convolutional Neural Network for Modelling Sentences|https://arxiv.org/abs/1404.2188]]
''Sutskever et al. 2014'' seq2seq. The hidden state has to remember a lot of information. Following tricks improve performance：
* backwards input
* decoder ensemble
! Improvements
* [[Attention Mechanism]] add attention to seq2seq translation: +11 [[BLEU]]
! Toolkit
* [[OpenNMT|https://github.com/opennmt/opennmt]]
* [[Nematus|https://github.com/EdinburghNLP/nematus]]: Attention-based encoder-decoder model from EdinburghNLP
[[Bahdanau et al. 2014|https://arxiv.org/abs/1409.0473]]
Pseudo code:
```
F = EncodeAsMatrix(f)
e_0 = <s>
s_0 = w (Learned initial state; Original paper uses Uh_1)
t = 0
while e_t != </s>
t = t+1
r_t = Vs_{t-1}
u_t = v^T tahh(WF + r_t) (computing this matrix product independent from states can be moved outside the loop to speedup)
a_t = softmax(u_t)
c_t = Fa_t
s_t = RNN(s_{t-1}, [e_{t-1};c_t]) (e is a learned embedding)
y_t = softmax(Ps_t+b) (P and b are learned parameters)
e_t|e_{<t} ~ Categorical(y_t)
```
! Cross entropy loss function
The cross entropy objective function for a single training pair $(x, y)$ is
$$
-\sum_{k=1}^K1\{y=k\}\log \hat y_k,
$$
where $K$ is the number of output classes, and $\hat y_k$ is the probability that the model assigns to the input example taking on label $k$.
Cross entropy is a convex approximation to the ideal 0-1 loss for classification.
!! The modified cross entropy
$$
E = -\sum_{j=1}^m(t^j\log\frac{y^j}{t^j}+(1-t^j)\log\frac{1-y^j}{1-t^j}).
$$
! Activation functions
; Activation Funtion
: An activation function is a function $h: \mathcal R\rightarrow\mathcal R$ that is differentiable almost everywhere.
!! The sigmoid
* Standard logistic function $f(x) = 1/(1+e^{-x})$
* Hyperbolic tangent $f(x) = \tanh(x)$
Symmetric sigmoids often converge faster than standard logistic function. A recommended sigmoid is $f(x) = 1.7159 \tanh(\frac 2 3 x)$ for centralized and rotated inputs. The variance of the outputs will also be close to 1 because the ''effective gain'' of the sigmoid is roughly 1 over its useful range:
* $f(\pm)=\pm 1$
* the second derivative is a maximum at $x = 1$
* the effective gain is close to 1
It is also said that target value should be chosen at the point of the maximum second derivative on the sigmoid so as to avoid saturating the output units. Otherwise, outputs would be on the tails of the sigmoid and makes the weights extremely large and stop updates. The uncertainty cannot be identified as well.
[[Noisy Activation Fucntions]]
components
* manager: provides weak supervision
* programmer: seq2seq
* computer: symbolic non-differentiable Lisp interpreter
contributions
# augment seq2seq model with a key-variable memory
# generalize the weakly supervised semantic parsing framework
#* execute partial program
#* prune search space
# REINFORCE + iterative maximum likelihood training process
Gradient descent parameter-space training dynamics:
$$
\dot \theta_t = \frac{\partial}{\partial t}\theta_t = -\eta\nabla_\theta\mathcal L(\theta_t) = \eta\nabla_\theta f_{\theta_t}(\mathcal X)^T\nabla_f\mathcal L(f_\theta(\mathcal X))
$$
Gradient descent function-space training dynamics:
$$
\dot f_{\theta_t}(\mathcal X) = \frac{\partial}{\partial t}f_{\theta_t}(\mathcal X) = -\eta\nabla_\theta f_{\theta_t}(\mathcal X) \frac{\partial \theta_t}{\partial t} = \eta\nabla_\theta f_{\theta_t}(\mathcal X)\nabla_\theta f_{\theta_t}(\mathcal X)^T \mathcal L(f_\theta(\mathcal X))
$$
Neural Tangent Kernel of DNN training dynamics:
$$
\Theta_{\theta_t}(\mathcal X, \mathcal X) = \nabla_\theta f_{\theta_t}(\mathcal X)\nabla_\theta f_{\theta_t}(\mathcal X)^T \in \mathbb R^{nk\times nk}
$$
Neural Tangent Kernel of DNN training dynamics on a test point $$x$$:
$$
\Theta_{\theta_t}(x, \mathcal X) = \nabla_\theta f_{\theta_t}(x)\nabla_\theta f_{\theta_t}(\mathcal X)^T \in \mathbb R^{nk\times nk}
$$
!! Library
[[neural-tangent]]
[[link|https://github.com/google/neural-tangents]]
!! Library usage and improvements
* [[Taylorized Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width]]
! Tutorials
* [[Scalable Bayesian Inference]]
# Computing for neuroscience
## Euro Brain Project
# Neuroscience for computing
BBD: interaction with real env
Human brain is parallel: simulating takes 1000 times longer
postsynapc potential: EPSP (+) / IPSP (-)
A single neuron receives potentials from roughly 10,000 synapses. (Signal is binary?)
Neural models with temporal dynamics. Leaky Integrate-and-Fire model (LIF).
!! Neural Information Encoding
Coding Schemes
!!! Rate coding
$$
r=\frac{n_{sp}}{T}
$$
easy to measure firing rates. neglects all the information possibly contained in the exact timing of the spikes. In real life information can be carried within milisecs.
# Temporal coding
$$
s(t) = \sum_i\delta(t-t_i)
$$
Spike train. Temporal patterns.
# Latency encoding: the earlier fire, stronger.
# Phase encoding
# Population coding: joint activites of a number of neurons
!!! Encoding models
Temporal coding by controlling exact time of spike
!! Synapic Learning Algorithms
Pre-synapse-post
# Spike Timing Dependent Plasticity (STDP)
## long-term potentiation
## long-time depression
!! Integrated Model of Coding and Learning
# Latency and phase encoding
## The original image is partitioned into RFs
## Encoding in RF1
## Compressed
!! Learning, Memory and Embodied Cognition
!! Neuro-Cognitive Robot
! External Memory
RNN memory is stored in the vector of hidden activations
* fragile
** tends to be overwritten by new information
** vanishing gradients leverages by gates
* computation grows with memory size
** $O(N^2)$
** read a whole book once into memory and operate on that
* hard-coded locations make @@color:#859900;indirection (and hence variables)@@ hard
External memory
* less fragile, only some memory is touched at each step
* indirection is possible: memory ''content'' is independent of ''location''
* separates ''computation'' from ''memory''
[[Differentiable Neural Computer]] is the new toy. Finding new ways of accessing memory.
2 directions
* [[Memory Networks]]: simple and SOTA
* [[Neural Abstract Machines]]: for complex structured tasks
! Learning When to Halt
* Problem: The number of steps of computation an RNN gets before emitting an output is determined by the length fo the input sequence, not the difficulty of the task.
* Solution: Train the network to learn how long to 'think' before it 'acts'.
** separate computation time from data time.
** [[Adaptive Computation Time with RNNs|https://arxiv.org/abs/1603.08983]]: reveal the difficulty of data.
! Beyond BPTT
* Problems
** Memory cost increases with sequence length
** Weight update frequency decreases
** The better RNNs get, the longer the sequences we train them on
* Solutions
** Truncated backprop (misses long range interactions)
** RTRL (too expensive)
** Approximate/local [[RTRL|Real-Time Recurrent Learning]] (promising)
** [[Synthetic Gradients]] (drastic)
If the null and alternative hypotheses are point hypotheses, the Neyman-Pearson lemma establishes that there exist [[UMP|5.3.1 UMP and UMPU tests]] test procedures and that they are of the form
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \mbox{if } f(x|\theta_1)<kf(x|\theta_0),\\
0 & \mbox{otherwise},
\end{array}\right.
$$
$k$ being related to the selected significance level $\alpha$.
proof: Lehmann (1986, p. 79)
* [[NIPS 2015]]
* [[NIPS 2016]]
! NIPS 2017
* Convolutional Gaussian Process
** replacing matrix mult in conv with gaussian kernel
* [[Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets]]
* [[Gaussian process modulated renewal processes]]
!! Deep Reinforcement Learning Workshop
* [[Atari Games Reinforcement Learning UMich]]
* Faster Deep Reinforcement Learning
** Online Hogwild!-style asynchronous updates
!! Sources
* [[Spotlight Videos|http://nuit-blanche.blogspot.com/2016/12/spotlight-videos-at-nips2016.html]]
* [[Videos|http://nuit-blanche.blogspot.hk/2017/01/the-nips2016-videos-are-out.html]]
!! Deep Generative Models
* [[Divergence Classed GANs]]
!! NLP
* Word Movers Distance
** provided a way of summarizing the difference between documents using their embeddings. For tasks that are supervised (e.g., text classification) this can be taken one step further. The Supervised Word Movers Distance (paper) performs affine transformations and re-weightings to provide class separation, leading to efficient state-of-the-art performance.
!! Deep learning applications
* SURGE: Surface Regularized Geometry Estimation from a Single Image
** CNN outputs depth, normal, plane and edge. Embed surface planar information to regularize the surface with a multitask objective function.
* [[DeepMath — Deep Sequence Models for Premise Selection|https://arxiv.org/abs/1606.04442]]
** Deep automated theorem proving
!! CNN
* Natual-Parameter Networks: A Class of Probabilistic Neural Networks
** Model activation functions with different distributions
* CNNpack: Packing Convolutional Neural Networks in the Frequency Domain.
** prune conv filters after DCT. idea is simple, and sure it will work. But how can this help training and model structure?
* [[Value Iteration Networks|https://arxiv.org/abs/1602.02867]]
** such models include a differentiable “planning module” which allows networks to make plans and better generalize to unseen domains.
!! RNN
* [[Sequential Neural Models with Stochastic Layers|https://arxiv.org/abs/1605.07571]]
** combines ideas from State Space Models (formally best in class for stochastic sequences like audio) and RNNs, leveraging the best of both worlds.
* [[Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences|https://arxiv.org/abs/1610.09513]]
** adds a “time gate” to LSTMs which greatly improves optimization and performance for long sequence data.
!! Bayesian
* Cooperative Graphical Models
** For a large class of high-order probabilistic models:
*** new variational lower and upper bounds
*** efficient inference algorithms
*** thorough empirical evaluation
* Synthesis of MCMC and Belief Propagation
** [[Computing Partition Functions of Graphical Models]]
* @@color:#859900;Attend, Infer, Repeat: Fast Scene Understanding with Generative Models@@
** Presents an inspiring approach to understanding scenes in an image. Using Bayesian and Variational Inference, the authors construct models that understand the number, location, and type of objects in a picture without any supervision. We are intrigued, as their models can reason/infer about distributions outside of training examples. The models do suffer from specification needs, but none-the-less provide interesting avenues for exploration.
!! Algorithms
* On Regularizing Rademacher Observation Losses
** Adversarial network for different measure regularization
* [[Fast and Provably Good Seeding for k-Means]], exciting result, looking forward to the oral.
!! Theory
* [[Scaled Bregman Theorem]]
[[link|https://papers.nips.cc/paper/4329-practical-variational-inference-for-neural-networks]]
Stochastic method for variational inference with a diagonal Gaussian posterior that can be applied to any differentiable log-loss parametric model.
* [[GP Behavior in Wide Deep Neural Networks]]
* [[Natural Neural Tangent Kernel]]
* Evaluating epistemic uncertainty
** On mean-field Gaussian approximation for BNN regression (2+ hidden layers)
*** (theory) can represent any mean & variance function
*** (practice) weight-space VI + optimizaiton is to be blamed
*** increasing depth does not seem to help to close the gap between MFVI and GP-limit reference
* Modes of the landscapes are connected. But it is hard to sample from.
** subspace variational sampling primarily explores a single modde due to the loss landscape geometric stucture
** It trades off accuracy for solution diversity soboptimally
** Combining local weight averaging and global ensembling is the best of both worlds
TODOs
* reimplement CondAttLSTM with normal sequence and attention blocks
** LSTM is worked as a cell. Can we join it?
** figure out what mask do and find a better way of implement it.
* node and action embedding implemented with tensor, change to variables
* mask and length, lstm can handle padding
** tgt masks as input, print `tgt_action_mask_seq`
** use pack_padded_sequence
* attention over history not implemented (can use Transformer)
* other sequence models
* pretrained embeddings
* spectral normalization
[[Stanford NLP Course]]
* [[NLP Data]]
! Topics
* Perceiving and representing text: [[Word Embedding]]
** handling unknown words
** tokenize? spelling? characters? bytes?
** smaller perceptual units need more complex models, but have fewer OOVs (doubt)
** [[Sentence Representation]]
* [[Text Classification]]
* [[Semantic Parsing]]
* Natual language generation
** [[Language Modelling]]
** [[Conditional Language Modelling]]
* Analytic applications
** topic modelling
** linguistic analysis (discourse, semantics, syntax, morphology)
* [[CNN for NLP]]
! Basics
* [[Virterbi decoding]]
! Task & State-of-the-art techniques
* Question answering (babl): Strongly Supervised MemNN (Weston et al. 2015) 93.3%; Dynamic Memory Network 93.6%
* Sentiment Analysis (SST): Tree-LSTMs (Tai et al. 2015); DMN 88.5%
* Part of speech tagging (PTB-WSJ): Bi-directional LSTM-CRP (Huang et al. 2015);
* [[Efficient Language Models|The Future of Natural Language Processing]]
The performance is always dominated by the size of high quality data.
<<<
Probably the most successful concept is to use distributed representation of words. For example, neural network based language models significantly outperform N-gram models.
<<< [[Efficient Estimation of Word Representations in Vector Space|Word2Vec]]
! Sentence model
is to analysis and represent the semantic content of a sentence for purposes of classification or generation.
* sentiment analysis
* paraphrase detection
* entailment recognition
* summarisation
* discourse analysis
* grounded language learning
* image retrieval
!! Reviews
* Composition based methods for
** vector representation of words or
** tied to particular syntactic relations or word types
* automatically extracted logical forms
* neural networks @@color:#d33682;
''advantages'': can be trained to obtain feature by predicting the context. can be powerful classifier/sentence generator
@@
** neural BoW or bag-of-//n//-grams
** recursive nets
** time-delay nets based on convolutional operations
Size of vocabulary: speech 20K, PTB 50K, Google 1T
This is what Baidu used in //End-to-End Speech Recognition in English and Mandarin//
* Language model: Kneser-Ney smoothed character level 5-gram model.
* English: 850mil n-grams trained with KenLM on Common Crawl Repository
* Chinese: 2bil n-grams
! Chinese vs English
Chinese characters are more similar to English syllables than English characters. In speech, there are 14.1 chars/s in English, 3.3 chars/s in Mandarin. Shannon entropy per char 4.9 bits in English, 12.6 bits in Mandarin.
! Datasets
Lee collected [[these datasets|http://www.cs.cornell.edu/home/llee/data/]].
* [[Google Research Datasets|https://github.com/google-research-datasets]]
** [[Online Discussion|https://github.com/google-research-datasets/coarse-discourse]]
!! Language Modelling
* Penn Treebank: from WSJ, very small and has been heavily processd
* Billion Word corpus： Train and test sentences from the same articles and overlap in time
* WikiText: better option
!! QA
* [[Facebook ParlAI|https://github.com/facebookresearch/ParlAI]]
** bAbI and wiki stuff
** SQuAD
!! Summarization
* [[NEWSROOM|https://summari.es/]]
!! [[Sentense Classification Datasets]]
! Bibs
* [[On the naturalness of software|http://earlbarr.com/publications/naturalness_cacm.pdf]]: start with language model
* [[Machine Learning for Big Code and Naturalness|https://ml4code.github.io/papers.html]]
! Sites
* [[Seminars on Applications of Deep Learning in Software Engineering and Programming Languages|https://sites.google.com/view/mlplse-sp18/]]
* [[Deep Coding Baselines|https://github.com/DeepSE/DeepCodingBaselines]]
* [[Awesome Machine Learning On Source Code|https://github.com/src-d/awesome-machine-learning-on-source-code]]
! Introduction
<<<
''The naturalness hypothesis.'' Software is a form of human communication; software corporahave similar statistical properties to natural language corpora; and these properties can be exploited to build better software engineering tools
<<<
* [[SE+NLP proposal]]
! Directions
* Language model
* Learning over execution traces or flows
* [[Source Code Generating Models]]
* Representation Model
* Pattern Mining: find code idioms
* Debugging
* Traceability
* Code completion
** [[Learning Python Code Suggestion with a Sparse Pointer Network|https://github.com/uclmr/pycodesuggest]]
* Inferring code conventions
* Code defects
* Code migration, porting and clones
* Optimizing an algorithm in simulation
* [[Forum Mining]]
; Saturation
: An activation function $h(x)$ with derivative $h'$ is said to be right saturate if its limit as $x\rightarrow\infty$ is zero.
; Hard and Soft Saturation
: Let $c$ be a constant such that $x > c$ implies $h'(x) = 0$, etc.
* Learning with noise transition
** Forward Correction
** S-adaptation
** Masking
* Learning with selected samples
** MentorNet: regard small-loss instances as correct instances,
** Learning to Reweight Examples
** [[Co-Teaching]]
* Learning with implicit regularization
** Virtual Adversarial Training
** Mean Teachers
** Temporal Ensembling
Naive factorization:
$$
p_{NA}(Y|X;\theta) = p_L(T|x_{1:T'};\theta)\cdot\prod_{t=1}^Tp(y_t|x_{1:T'};\theta)
$$
Decoder input:
* copy source with fertility
* mask out attending to itself
* positional attention with positional encoding $p(j, k) = \sin(j/10000^{k/d})$ (for even $j$). Incorporates position into attention process.
Sample $z$ from a prior distribution and then condition on $z$ to non-autoregressively generate a translation.
[[The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions|https://arxiv.org/abs/1712.05577]] introduces a rigorous measurement of the present of pathological exploding gradient: Gradient scale coefficient (GSC)
* A random change to $$F_l$$ of relative size $$\frac{1}{GSC(l, L+1)}$$ leads to a change in the error of quadratic expectation $$e(F_l)$$ according to the local linear approximation induced by the network gradient.
* as the GSC increases, the relative size of the region that satisfies the first Wolfe condition shrinks proportionally.
! ReLU
ReLU is the abbreviation of Rectified Linear Units. This is a layer of neurons that use the non-saturating activation function $f(x) = \max(0, x)$. It increases the nonlinear properties of the decision function and of the overall network without affecting the receptive fields of the convolution layer.
Other functions are used to increase nonlinearity. For example the saturating hyperbolic tangent $f(x) = \tanh(x)$, $f(x) = |\tanh(x)|$, and the sigmoid function $f(x) = (1 + e ^{-x})^{-1}$. Compared to tanh units, the advantage of ReLU is that the neural network trains several times faster.
''advantages'':
# enforces hard zeros in representation.
# avoids vanishing gradient somehow.
! Variations
* [[ELU|https://arxiv.org/abs/1511.07289]]: openai uses this on ResNet VAEs for IAF.
* Casella G, Robert C P. Rao-Blackwellisation of sampling schemes[J]. Biometrika, 1996, 83(1): 81-94. ([[BibTeX|casella1996rao.txt]])
* Harati Nejad Torbati A H, Picone J, Sobel M. Applications of Dirichlet Process Mixtures to speaker adaptation[C]. Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. IEEE, 2012: 4321-4324. ([[BibTeX|harati12applications.txt]])
* Rosen-Zvi M, Griffiths T, Steyvers M, et al. The author-topic model for authors and documents[C]. Proceedings of the 20th conference on Uncertainty in artificial intelligence. AUAI Press, 2004: 487-494. ([[BibTeX|rosen2004author.txt]])
* Sudderth E B, Torralba A, Freeman W T, et al. [[Describing visual scenes using transformed objects and parts[J]|Transformed DP]]. International Journal of Computer Vision, 2008, 77(1-3): 291-330. ([[BibTeX|sudderth08describing.txt]])
! Bibs
* [[L2 Regularization versus Batch and Weight Normalization|https://arxiv.org/abs/1706.05350]]
** $L_2$ regularization, in combination with these normalization layers, has no regularization effect, rather strongly influences the learning rate
** But learning rate in return is a regularization
! Introduction
$$
y_{NN}(X;\mathbf w, b) = g(X\mathbf w + b)
$$
! Norms
* [[Batch Normalization]]: $y_{BN}(X;\mathbf w, \gamma, \beta) = g(\frac{X\mathbf w - \mu(X\mathbf w)}{\sigma(X\mathbf w)}\gamma + \beta)$
* [[Weight Normalization]]: $y_{WN}(X;\mathbf w, \gamma, \beta) = g(\frac{X\mathbf w}{\|\mathbf w\|_2}\gamma + \beta)$
* [[Layer Normalization|https://arxiv.org/abs/1607.06450]]: $y_{BN}(\mathbf x;W, \gamma, \beta) = g(\frac{\mathbf xW - \mu(\mathbf xW)}{\sigma(\mathbf xW)}\gamma + \beta)$
** instead of taking the statistics of a single unit over a whole batch of inputs, they are taken for a single input over all units in a layer
* Spectral normalization
* Self normalization: keep 0 mean and 1 std by solving a fix point equation to the activation function:
** $selu(x) = \lambda \alpha e^x - \alpha; x<0$
* Batch renormalization: correction for the discrepancy between minibatch and full-batch moments
** $\hat x_i\rightarrow \frac{x_i-\mu_B}{\sigma_B}\cdot\frac{\sigma_B}{\sigma}+\frac{\mu_B-\mu}{\sigma}$
** improves on small/biased minibatches
* Switchable Normalization
* [[Instance Batch Normalization|https://github.com/xingangpan/ibn-net]]
! Remarks
Training normalization is a biased estimator of inference normalization. If the minibatch is biased, barch norm fails.
[[Making Neural Programming Architectures Generalize via Recursion|https://arxiv.org/abs/1704.06611]]
Challenges:
* Generalization to more complex inputs
* Proof of generalization
Approach:
* Instantiation: incorporate recursion into Neural Programmer-Interpreter
* Training methods: As a first step, strong supervision with explicit execution traces to learn a recursive neural program
! NPI
* input: environment observation
* list of predefined functions
* NPI controller: A LSTM takes observation and caller func/args as inputs, modifies states and emits next operation
NPI is trained with execution traces divided by caller functions.
! Deformable Modeling in ConvNets
Girshick et al.'s Region-CNN framework is a combination of region proposals and CNN features. It has three broad stages:
# generating object proposals
# extracting CNN feature maps of each proposal
# filtering the proposal through class specific SVMs
The bottleneck of performance of R-CNN is extracting feature maps for each region. In [[Fast R-CNN|Fast-RCNN]], they obtain $100\times$ speedup over R-CNN by computing the CNN features once and pooling them locally for each proposal; they also streamline the last two stages of R-CNN into a single multi-task learning problem.
After that, the bottleneck becomes region-proposal stage. [[Lenc et al.|http://arxiv.org/abs/1506.06981]] drop the region proposal and use a constant set of regions learnt through K-means clustering on the PASCAL VOC data. [[Faster RCNN by Ren et al.|http://arxiv.org/abs/1506.01497]] starts from a fixed set of proposal, but refined them prior to detection by using a Region Proposal Network which shares weights with the later detection network and streamlines the multi-stage R-CNN framework.
! Examples
# [[Google digit recognition]]
# [[CNN for OCR]]
# [[Face Detection]]
# [[Semantic Segmentation]]
! Models
!! Regressor
# [[Faster R-CNN]]
# [[darknet You Only Look Once (YOLO)|http://arxiv.org/abs/1506.02640]]
# [[Single Shot Multibox Detector (SSD)|http://arxiv.org/abs/1512.02325]]
# [[Inside Out Net (ION)|http://arxiv.org/abs/1512.04143]]
# [[Speed/accuracy trade-offs for modern convolutional object detectors|https://arxiv.org/abs/1611.10012]]
## FRCNN accurate, SSD fast.
## SSD does not work well on small objects? or the information is inferred else where?
# [[ARC-FCN|https://arxiv.org/abs/1612.00534]]
# [[R-FCN|http://arxiv.org/abs/1605.06409]]
## [[Implementation|https://github.com/Orpine/py-R-FCN]]
* [[RON]]
!! Fast Ones
* [[Object detection at 200 Frames Per Second|https://arxiv.org/abs/1805.06361]]
!! Iterative
# [[AttentionNet|http://arxiv.org/abs/1506.07704]]
# [[G-CNN|http://arxiv.org/abs/1512.07729]]
!! Other
* [[The More You Know: Using Knowledge Graphs for Image Classification|https://arxiv.org/abs/1612.04844]]
** Graph Gated NN: use propagation like recurrent nets.
** Graph Search Neural Network
!! Implementation
# [[ReInspect|http://arxiv.org/abs/1506.04878]]: [[tf|https://github.com/Russell91/TensorBox]]
! Data
* [[MS COCO]]
! Topics
* [[Dealing with Context in Detection]]
* Hard sampling
** [[OHEM|https://arxiv.org/abs/1604.03540]]
** [[A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection|https://arxiv.org/abs/1704.03414]]
! Dataset
* 10k real internet images
* 15k synthetic images simulating online ads in Chinese
* 20k synthetic images simulating text in the wild in English
! Architecture
* Horizontal BiLSTM after feature map
* Faster RCNN for character feature extraction
* RFCN for bounding box feature extraction
[[link|https://arxiv.org/abs/1609.04836]], [[code|https://github.com/keskarnitish/large-batch-training]], [[reddit discussion|https://www.reddit.com/r/MachineLearning/comments/53lexr/160904836v1_on_largebatch_training_for_deep/]]
According to [[Hybrid Deterministic-Stochastic Methods for Data Fitting|https://arxiv.org/abs/1104.2373]] and [[Tail bounds for stochastic approximation|https://arxiv.org/abs/1304.5586]], there is also a notion of "sharpness" of minima which is appears as the condition number of the problem.
Very relavent to a 1997 Schmidhuber's paper: [[Flat Minima|http://www.bioinf.jku.at/publications/older/3304.pdf]].
Further reading on opposite case: [[Sharp Minima Can Generalize For Deep Nets|https://arxiv.org/abs/1703.04933]]
There are at least exponentially many global minima for a neural net. Since permuating the nodes in one layer does not change the loss. Finding such points is not easy. Before certain techniques such as momentum came out, those nets were considered impossible to learn.
Thanks to the constantly envolving hardwares and libraries, we do not have to worry about training time //that much// at least for convnets. Empirically, the non-convexity of neural nets seems not to be an issue. In practice, SGD works pretty well in optimizing very large networks even though the problem is proved to be NP-hard. However, researchers never stop studying the loss surface of deep neural nets and searching for better optimization strategies.
[[This paper|https://arxiv.org/abs/1605.07110]] has been renewed on ArXiv recently, which leads me to [[this discussion|https://news.ycombinator.com/item?id=11765111]]. Following are what I find interesting.
! Why SGD works?
[Choromaska et al, AISTATS'15] (also [Dauphin et al, ICML'15] use tools from Statistical Physics to explain the behavior of stochastic gradient methods when training deep neural networks. This offers a macroscopic explanation of why SGD "works", and gives a characterization of the network depth. The model is strongly simplified, and convolution is not considered.
!! Saddle points
We start from discussing saddle points, the vast majority of critical points on the error surfaces of neural networks.
<<<
Here we argue, ... that a deeper and more profound difficulty originates from the proliferation of saddle points, not local minima, especially in high dimensional problems of practical interest. Such saddle points are surrounded by high error plateaus that can dramatically slow down learning, and give the illusory impression of the existence of a local minimum.
<<< Dauphin et al, [[Identifying and attacking the saddle point problem in high-dimensional non-convex optimization|http://arxiv.org/abs/1406.2572]]
The authors introduce saddle-free Newton method which requires the estimation of Hessian. They connect the loss function of a deep net to a high-dimensional Gaussian random field. They show that critical points with high training error are exponentially likely to be saddle points with many negative directions, and all local minima are likely to have error that is very close to that of the global minimum. (Described in [[Entropy-SGD: Biasing Gradient Descent Into Wide Valleys|https://arxiv.org/abs/1611.01838]].)
The convergence of gradient descent is affected by the proliferation of saddle points surrounded by high error plateaus --- as opposed to multiple local minima.
<<<
The time spent by diffusion is inversely proportional to the smallest negative eigenvalue of the Hessian at a saddle point
<<< Kramer's law
<<<
It is believed that for many problems including learning deep nets, almost all local minimum have very similar function value to the global optimum, and hence finding a local minimum is good enough.
<<< Rong Ge, [[Escaping from Saddle Points|http://www.offconvex.org/2016/03/22/saddlepoints/]]
As the model grows deeper, local minima have loss closer to global minima. On the other hand, we do not care about global minimum because it often leads to overfitting.
Saddle points exist along the paths between local minima, most objective functions have exponentially many of those. However, first order optimization algorithms may get stuck at saddle points. Strict saddle points can be escaped and global minima can be achieved in polynomial time [[(Ge et al., 2015)|http://arxiv.org/abs/1503.02101]]. Stochastic gradient introduces noise and help to push the current point away from saddle points.
Non-convex problems can have ''degenerate saddle points'', whose Hessian is p.s.d. and have 0 eigenvalues. The performance of SGD on these kind of tasks is still not well studied.
To conclude this part, AFAIK, we should care more about escaping from saddle point. And gradient based methods can do a better job than second-order methods in practice.
!! Spin-glass Hamiltonian
[[Charles Martin: Why Does Deep Learning Works?|https://charlesmartin14.wordpress.com/2015/03/25/why-does-deep-learning-work/]]
Both papers mentioned above use ideas from statistical physics and spin-glass models.
!!! Hamiltonians
Statistical physicists refer to $$H_x(y)\equiv-\ln p(y|x)$$ as the ''Hamiltonian'', quantifying the energy of $$y$$ given the parameter $$x$$. And $$\mu\equiv -\ln p$$ as ''self-information''. We can rewrite Bayes' formula as:
$$
p(y) = \sigma(-H(y)-\mu)
$$
We can see the features yield by a neural net as Hamiltonian and the softmax computes the classification probability.
!!! Spin-glass
<<<
The long-term behavior of certain neural network models are governed by the statistical mechanism of infinite-range Ising spin-glass Hamiltonians
<<< LeCun et. al., [[The Loss Surfaces of Multilayer Networks, 2015|https://arxiv.org/abs/1412.0233]]
In this paper, he tries to explain the optimization paradigm with spin-glass theory.
A bolder hypothesis is that deep networks are spin funnels. And as the net gets larger, the funnel gets sharper. If this is true, our major concern should be to avoid over-training rather than the convexity of the network.
!! Implicit Bias in SGD
* [[Chaudhari|https://arxiv.org/abs/1611.01838]] proposed a surrogate loss that explicitly biases SGD dynamics towards flat local minima. The corresponding algorithm relates closely to stochastic gradient Langevin dynamics.
* [[SGD performs VI]]
! What does the minima look like?
Take for example the concept of mode connectivity ([[Garipov et al, 2018|https://arxiv.org/abs/1802.10026]]): it seems that the modes found by SGD using different random seeds are not just isolated basins, but they are connected by smooth valleys along which the training and test error are low.
!! No poor local minima
<<<
Deep Learning Networks are (@@color:#859900;roughly@@) Spin Funnels
<<< a tweak from Charles' post
[[Research at Google and Stanford|https://arxiv.org/abs/1412.6544]] confirms that the Deep Learning Energy Landscapes appear to be roughly convex.
Finally we arrive at the paper itself. Nets are optimized well by local gradient methods and seems not to be affected by local minima. The author claims that every local minimum is a global minimum and "bad" saddle points (degenerated ones) exists for deeper nets. Thm 2.3 gives clear result on linear networks.
The main result Thm 3.2 generalizes [[Choromanska et al, 2015|https://arxiv.org/abs/1412.0233]]'s idea for nonlinear network relies on 4 (seemingly strong) assumptions:
# The dimensionality of the output is smaller than the input.
# The inputs are random and decorrelated.
# A connection in the network is activated or not is random with the same probability of success across the network. (ReLU thresholding happens randomly.)
# The network activations are independent of the input, the weights and each other.
They relax the majority of the asssumptions, which is very promising, but leave a weaker condition A1u-m and A5u-m [[(from reddit post)|https://www.reddit.com/r/MachineLearning/comments/4ktqeu/160507110_deep_learning_without_poor_local_minima/]].
Recently DeepMind came up with [[another paper|https://arxiv.org/abs/1611.06310]] claiming the assumptions are too strong for real data. And devised counter examples with finite datatets for rectified MLPs. For finite sized models/datasets, one does not have a globally good behavior of learning regardless of the model size.
Even though deep learning energy landscapes appear to be roughly convex, or as this post referred to, local minimal free, a deep model has to include more engineering details to aid its convergence. Problems such as covariance shift and overfitting still have to be handled by engineering techniques.
!! Arriving on flatter minima
<<<
large-batch methods tend to converge to sharp minimizers of the training and testing functions -- and that sharp minima lead to poorer generalization. In contrast, small-batch methods consistently converge to flat minimizers, and our experiments support a commonly held view that this is due to the inherent noise in the gradient estimation.
<<< [[On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima]]
* [[An Alternative View: When Does SGD Escape Local Minima?|https://arxiv.org/abs/1802.06175]]
! Should 2-nd order methods ever work?
Basiclly no. Because the Hessian vector product require very low variance estimation, which leads to batch size larger than 1000. But [[some rare cases|https://www.reddit.com/r/MachineLearning/comments/599wbr/project_i_accidentally_wrote_a_quasinewton_lbfgs/]] happen when 2nd order methods with small batch size works.
! Gradient Starvation
* [[On the Learning Dynamics of Deep Neural Networks|https://arxiv.org/abs/1809.06848]]
** Some features will dominate the gradient and sheding other equally important features.
[[link|https://arxiv.org/abs/1702.00317]]
SGD stalling has often been attributed to its sensitivity to the conditioning of the problem. But the reason should be more complicated.
! Bibs
* [[Exact solutions to the nonlinear dynamics of learning in deep linear neural networks|https://arxiv.org/abs/1312.6120]]
* [[Exponential expressivity in deep neural networks through transient chaos|https://arxiv.org/abs/1606.05340]]
* [[On the Expressive Power of Deep Neural Networks|https://arxiv.org/abs/1606.05336]]
* [[Deep Information Propagation|https://arxiv.org/abs/1611.01232]]
! Summary
We have combined Riemannian geometry with dynamical mean field theory to study the emergent deterministic properties of signal propagation in deep nonlinear nets.
We derived analytic recursion relations for Euclidean length, correlations, curvature, and Grassmannian length as simple input manifolds propagate forward through the network.
We obtain an excellent quantitative match between theory and simulations
Our results reveal the existence of a transient chaotic phase in which the network expands input manifolds without straightening them out, leading to "space filling" curves that explore many dimensions while turning at a constant rate. The number of turns grows exponentially with depth.
Such exponential growth does not happen with width in a shallow net.
Chaotic deep random networks can also take exponentially curved N-1 Dimensional decision boundaries in the input and flatten them into Hyperplane decision boundaries in the final layer: exponential disentangling!
An order to chaos phase transition governs the dynamics of random deep networks, often used for initialization.
Not all networks at the edge of chaos - with neither vanishing nor exploding gradients are created equal.
The entire Jacobian singular value distribution, and not just its second moment impacts learning speed.
We used introduced free probability theory to deep learning to compute this entire distribution.
We found tanh networks with orthogonal weights have well conditioned Jacobians, but ReLU networks with orthogonal weights, or any network with Gaussian weights does not.
Correspondingly, we found that with orthogonal weights, tanh networks learn Faster than ReLU networks.
[[blog|https://terrytao.wordpress.com/2017/07/11/on-the-universality-of-potential-well-dynamics/]]
''Theorem 1'' A smooth compact non-singular dynamics $(M, X)$ can be embedded smoothly in a potential well system iff it admits a strongly adapted 1-form.
The if direction is mainly a comsequence of the Nash embedding theorem. The only if direction
* [[Auto-DeepLab|https://arxiv.org/abs/1901.02985]] achieves pretty high performance on Cityscapes
** softmax relaxation is used
* Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
** model trained each time, search space shrinking
Weights training is more stable by starting arch training late
! Problem Definition
We want to simultaneously optimize the weights $$w$$ and architecture $$a$$. We do so by minimizing the expectation:
$$
\mathbb E_{p_\theta(a)}[\mathcal L(a, w)]
$$
! Searching Space
!! Supernet structure
$$
^sH^l = \text{Cell}(\beta^l_{\frac{s}{2}\rightarrow s}{}^{\frac{s}{2}}H^{l-1} + \beta^l_{s\rightarrow s}{}^sH^{l-1} +\beta^l_{2s\rightarrow s}{}^{2s}H^{l-1}, ^sH^{l-2}; \alpha)
$$
! Gradient Estimators
High variance
* REINFORCE (Williams 1992)
* NVIL (Mnih et al. 2014)
* MuProp (Gu et al. 2016)
Biased
* Straight through (Bengio et al. 2013)
* $$\frac12$$ estimator (Gregor et al. 2013)
* Concrete/Gumbel-Softmax
! Improvements?
* Use natural gradient to control arch steps
* Use a surrogate model to estimate arch
** [[graph network|https://arxiv.org/abs/1803.03324]]?
* combine sampling and amortized methods to predict the speed and performance
* use MAML + droppath to train good init for net instances
* Siamese networks for one-shot classification
* end2end differentiable nearest neighbor method for one-shot learning
* LSTM-based one-shot optimizer
** [[Attentive Recurrent Comparators]]
* DeepMind NIPS 2016: [[Matching Networks for One Shot Learning|https://arxiv.org/abs/1606.04080]]
** This model uses recent advances in attention and memory to achieve state-of-the-art performance classifying ImageNet images using only a single example from a class. However, we do not know what assumptions the network is making to classify these images.
* [[Prototypical Networks for Few-shot Learning|http://papers.nips.cc/paper/6996-prototypical-networks-for-few-shot-learning]]
** A simple clustering algorithm using [[Regular Bregman Divergences]]
! [[External memory for one-shot generalization]]
Different from online learning, the cost is not known.
The online learning setting is by now fairly well-understood. We know that the optimal regret for a finite action set $|X| = n$ and bounded cost functions is $O(\sqrt{T \log(n)})$[[[ref|https://dl.acm.org/citation.cfm?doid=258128.258179]]]
* DéjàVu: a map of code duplicates on GitHub
** [[paper|https://dl.acm.org/citation.cfm?doid=3152284.3133908]]
** [[blog|https://blog.acolyer.org/2017/11/20/dejavu-a-map-of-code-duplicates-on-github/]]
** percentage of duplications are high
** possibility to learn a large project?
* A model for reasoning about JavaScript promises
** extract graphs to find bugs
* Type test scripts for TypeScript testing
** test with auto generated probes
* Fast and precise type checking for JavaScript
** Facebook's Flow type refinement
* [[NIPS 2017 Tutorial|https://vimeo.com/248504509]]
* [[Homepage|https://optimaltransport.github.io/]]
Optimal transport is the natural geometry for probability measures supported on geometric space.
! Intro
!! 2 Examples
* Earth moving distance origin Monge's Problem
* Kantorovich problem as a relaxation that fixed the transportation routes into a table.
<<<
''Definition'' [Kantorovich Problem]<br>
Given $\mu, \nu$ in $\mathcal P(\Omega)$; a cost function $c$ on $\Omega\times\Omega$, the Kantorovich problem is
$$
\inf_{P\in\Pi(\mu, \nu)}\iint c(x, y)P(dx, dy)
$$
<<<
It's dual is
$$
\sup_{\phi\in L_1, \psi\in L_1(\nu), \phi(x)+\psi(y)\le c(x, y)}\int\phi d\mu +\int\psi d\nu
$$
!! How to compute
For univariate:
$$
W(\mu, \nu) = \int_0^1 c(|F^{-1}_\mu(x)-F^{-1}_\nu(x)|)dx
$$
where $F^{-1}$ is the quantile function.
! Theory
* [[Optimization Algorithms]]
* [[On the universality of potential well dynamics]]
* [[Low-Dimensional Structures]]
* [[Primal-Dual Optimization]]
* [[DE with DL]]
! Convex Optimization
!! Bib
* [[Convex Optimization: Algorithms and Complexity|https://arxiv.org/abs/1405.4980]]
* [[Smooth Distributed Convex Optimization]]
! Non-Convex Optimization
!! Some properties to look for in non-convex optimization
References
* [[Geometry of linearized neural networks|http://blogs.princeton.edu/imabandit/2016/11/13/geometry-of-linearized-neural-networks/]]
* [[Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition|https://arxiv.org/abs/1608.04636]]
We say that a function $f$ admits ''first order optimality'' if all critical points of $f$ are global minima. in particular with first order optimality one has that gradient descent converges to the global minimum, and with second order optimality this is also true provided that @@color:#859900;one avoids saddle points@@. To obtain rates of convergence it can be useful to make more quantitive statements, e.g. $\alpha$-Polyak:
$$
\|\nabla f(x)\|^2\ge\alpha(f(x)-f^*).
$$
Clearly $\alpha$-Polyak implies first order optimality, also implies linear convergence rate. A variant of this condition is $\alpha$-weak-quasi-convexity:
$$
\langle\nabla f(x), x-x^*\rangle\ge\alpha(f(x)-f^*)
$$
in which case gradient descent converges at the slow non-smooth rate $1/\sqrt{t}$.
! Deep Learning
* [[On Optimization in Deep Learning]]
* [[Identity Matters in Deep Learning]]
* [[Optimizing RNN]]
* [[Escaping Saddle Points]]
! Tricks
According to chapter 1 of [[Neural Networks: Tricks of the Trade]]
* substract the means from the input variables
* normalize the variances of the input variables
* decorrelate the input variables
* use a separate learning rate for each weight
! Algorithms
* [[Second Order Methods]]
* [[Search Based Methods]]
! First Order Methods
!! Videos
* [[Optimization for Machine Learning|https://simons.berkeley.edu/talks/elad-hazan-01-23-2017-1]]
* [[Theory of accelerated methods]]
!! Bib
* [[Optimization Methods for Large-Scale Machine Learning]]
!! Algorithms
* [[Stochastic Gradient Descent]]
* [[Nesterov's Accelerated Gradient Descent]]
* Convexified Projected Gradient Descent
* Non-Convex [[Iterative Hard Thresholding]]
* Frank-Wolfe
* [[Follow-The-Regularized-Leader]]
* AdaGrad
* [[LARS|Layer-wise Adaptive Rate Scaling]]
* [[Adam: A Method for Stochastic Optimization|https://arxiv.org/abs/1412.6980]]
** When data features are sparse and bounded gradients, the adaptive method can achieve $O(\log d(\sqrt T)$, an improvement over $O(\sqrt{dT})$ for the non-adaptive method.
* [[AdaDelta]]
* [[A Variational Analysis of Stochastic Gradient Algorithms]]
* [[Entropy-SGD|https://arxiv.org/abs/1611.01838]]
** minus free energy from loss and solve with [[Stochastic Gradient Langevin Dynamics]].
!! Remarks
As is tested out by [[Karpathy|http://cs.stanford.edu/people/karpathy/convnetjs/demo/trainers.html]], Adagrad/Adadelta are "safer" because they don't depend so strongly on setting of learning rates (with Adadelta being slightly better), but well-tuned SGD+Momentum almost always converges faster and at better final values.
Adagrad was more stable (and less prone to crappy hyper-parameter choices) than Adam or constant or Barzilai-Borwein
! Reference
* Learning to learn by gradient descent by gradient descent
! Algorithm
Use LSTM to guide the classifier. SGD resembles the update for the cell state in an LSTM
$$
c_t = f_t\odot c_{t-1} + i_t\odot \tilde c_t
$$
we set cell state be the model parameters $$c_{t-1} = \theta_{t-1}$$, input gate asa learning rate, $$i_t=\alpha_t$$, and candidate cell state be the negative gradient $$\tilde c_t = -\nabla_{\theta_{t-1}}\mathcal L_t$$.
* Meta-training: train learner
* Meta-testing: train meta-learner and best initialization
# Use LSTM to model parameter dynamics during training
#* LSTM parameters are shares across $$M$$'s parameters. Learning an update rule applied to each parameter independently, but each parameter has its own history.
#* Learn $$c_0$$, like learning $$M$$'s initialization
# Inputs to meta-learning LSTM are the loss and gradient of learner
#* preprocess by Andrychowicz et al (2016) to ensure loss and gradient are at the same scale
# Train LSTM according to test sets
#* ignore gradients through the inputs of the LSTM.
! Tricks in training
Parameter sharing across all coordinates of the learner gradient, but each has its own hidden and cell states. This is implemented by batching the loss and gradient along there dimensions. @@color:#859900;Batch size should be really large.@@
* Gradients and losses are normalized the same way as in reference.
* BP don't pass meta-learner.
* Init meta-learner's params small
! Remarks
There are works [[handling catastrophic forgetting|Overcoming Catastrophic Forgetting]]. Should think about it.
Resembles a sequential Gradient synthesizer.
Current best mechanism to control generalization gap has two key ingredients
* stochastic optimization
** during training, it adds the sampling noise that corresponds to empirical population mismatch
* make the model convolutional and very large
Overparametrication affects the energy landscape
Refs
* Models from statistical physics have been considered as possible approximations: [[On Optimization in Deep Learning]]
* Tensor factorization models capture some of the non convexity essence [Anandukar et al. 15, Cohen et al. 15, Haeffele et al. 15]
* [Shafran and Shamir 15] studies bassins of attraction in neural networks in the overparametrized regime
* [Soudry 16, Song et al 16] study Empirical Risk Minimization in two-layer ReLU networks, also in the over-parametrized regime
* [Tian 17] studies learning dynamics in a gaussian generative setting
* [Chaudhari et al 17] studies local smoothing of energy landscape using the local entropy method from statistical physics
* [Pennington bahri 17] Hessian analysis using random matrix theory
* [Soltanolkotabi, Javanmard, Lee 17] layer-wise quadratic NNs
refs
* [[Geometry of linearized neural networks|https://blogs.princeton.edu/imabandit/2016/11/13/geometry-of-linearized-neural-networks/]]
* Gradient Descent Learns Linear Dynamical Systems
** [[blog|http://www.offconvex.org/2016/10/13/gradient-descent-learns-dynamical-systems/]]
** [[paper|https://arxiv.org/abs/1609.05191]]
* [[Lecture Notes on Linear System Theory|http://control.ee.ethz.ch/~ifalst/docs/LectureNotes.pdf]]
* A recent paper that analysis identity mapping in gated networks: [[Overcoming the vanishing gradient problem in plain recurrent networks|https://arxiv.org/abs/1801.06105]]
The simplest version of a recurrent neural network is as follows. It is a mapping of the form $(x_1,\dots,x_T) \mapsto (y_1,\dots,y_T)$ (we are thinking of doing sequence to sequence prediction). In these networks the hidden state is updated as $h_{t+1} = \sigma_1(A h_{t} + B x_{t})$ (with $h_1=0$) and the output is $y_t = \sigma_2(C h_t + D x_t)$. With assumption that $(x_t)$ is an i.i.d. isotropic sequence we can decouple $D$ from $A, B, C$ and can be ignored because it's convex. We can consider the idealized risk:
$$
(\hat{A},\hat{B}, \hat{C}) \mapsto \sum_{k=0}^{+\infty} \|\hat{C} \hat{A}^{k} \hat{B} - C A^{k} B\|_F^2 .
$$
Consider the series $r_k = C A^k B$ and its Fourier transform:
$$
G(\theta) = \sum_{k=0}^{+\infty} r_k \exp(i k \theta) = C (\sum_{k=0}^{+\infty} (\exp(i \theta) A)^k) B = C(\mathrm{I} - \exp(i \theta) A)^{-1} B .
$$
By Parseval’s theorem the idealized risk is equal to the $L_2$ distance between $G$ and $\hat{G}$ (i.e. $\int_{[-\pi, \pi]} \|G(\theta)-\hat{G}(\theta)\|_F^2 d\theta$). We will now show that under appropriate further assumptions, for any $\theta$ that $\|G(\theta) - \hat{G}(\theta)\|_F^2$ is weakly-quasi-convex in $(\hat{A},\hat{B},\hat{C})$ (in particular this shows that the idealized risk is weakly-quasi-convex).
<<<
''The big assumption''<br>
the system is a “single-input single-output” model, that is both $x_t$ and $y_t$ are scalar.
<<<
In Section 9.2 of the lecture notes, it is proved that if a single-inupt single-output system $(A,B,C,D)$ satisfies @@color:#859900;$[B, AB, \dots, A^{n-1}B]\in \mathbb{R}^{n\times n}$ is of full rank@@, then there exists an invertible matrix $T$ such that $(TAT^{-1}, TB, CT^{-1}, D)$ is an equivalent system with the canonical controllable form where $B=(0,\dots,0,1)$, $C=(c_1,\dots,c_n)$ and $A$ has zeros everywhere except on the upper diagonal where it has ones and on the last row where it has $a_n,\dots, a_1$. This kind of system is called controllable system (for some other reason).
<<<
''Theorem'' [Hardt, Ma, Recht 2016]<br>
Let $C(a) := \{z^n + a_1 z^{n-1} + \dots + a_n , z \in \mathbb{C}, |z|=1\}$ and assume there is some cone $\mathcal{C} \subset \mathbb{C}^2$ of angle less than $\pi/2-\alpha$ such that $C(a) \subset \mathcal{C}$. Then the idealized risk is $\alpha$-weakly-quasi-convex on the set of $\hat{a}$ such that $C(\hat{a}) \subset \mathcal{C}$.
<<<
The gradients of the RNN are easy to compute with BPTT, RNNs are difficult to train, especially on problems with long-range temporal dependencies due to their nonlinear iterative nature. The derivative of the loss function at one time can be exponentially large w.r.t. the hidden activations at a much earlier time.
! Vanishing gradient problem
Here is a second order approach: [[Thesis Training RNN]]. Not considered a good solution now.
Merity has [[a blog post|https://smerity.com/articles/2016/orthogonal_init.html]] about using orthogonal initialization to avoid explosion and vanishing in propagation, and has referred to some optimization papers.
! [[Optimization Properties of Linearized RNN]]
[[start from here|http://doc.norang.ca/org-mode.html]]
I have created the most simple .org file under `~/Documents`
! Ideas
* tag is for tasks.
* fast data/time
```python
#!/usr/bin/python
#
# peirce_dev.py
# created 16 Jul 2013
# updated 23 Oct 2014
#
#### MODULES ####
import numpy
import scipy.special
#### FUNCTION ####
def peirce_dev(N, n, m):
"""
Name: peirce_dev
Input: - int, total number of observations (N)
- int, number of outliers to be removed (n)
- int, number of model unknowns (m)
Output: float, squared error threshold (x2)
Features: Returns the squared threshold error deviation for outlier
identification using Peirce's criterion based on Gould's
methodology
"""
# Assign floats to input variables:
N = float(N)
n = float(n)
m = float(m)
#
# Check number of observations:
if N > 1:
# Calculate Q (Nth root of Gould's equation B):
Q = (n**(n/N)*(N - n)**((N - n)/N))/N
#
# Initialize R values (as floats)
Rnew = 1.0
Rold = 0.0 # <- Necessary to prompt while loop
#
# Start iteration to converge on R:
while ( abs(Rnew - Rold) > (N*2.0e-16) ):
# Calculate Lamda
# (1/(N-n)th root of Gould's equation A'):
ldiv = Rnew**n
if ldiv == 0:
ldiv = 1.0e-6
Lamda = ((Q**N)/(ldiv))**(1.0/(N - n))
#
# Calculate x-squared (Gould's equation C):
x2 = 1.0 + (N - m - n)/n*(1.0 - Lamda**2.0)
#
# If x2 goes negative, return 0:
if x2 < 0:
x2 = 0.0
Rold = Rnew
else:
# Use x-squared to update R (Gould's equation D):
Rold = Rnew
Rnew = (
numpy.exp((x2 - 1)/2.0)*
scipy.special.erfc(numpy.sqrt(x2)/numpy.sqrt(2.0))
)
#
else:
x2 = 0.0
return x2
```
One question that outlier detection aims to answer can be phrased:
<<<
Given $n$ independent random variables from a common, but unknown, distribution $\mu$, does a new input $X$ belong to the support of $\mu$?
<<<
* [[Rehearsal]]
* [[Reduce representational overlap]]
Done:
* add user to `audio` group
* installed `jack2`
Failed because cannot connect to internal server with `(use 'overtone.live)`
! References
* Some PAC-Bayesian theorems
* [[Tutorial|https://www.facebook.com/icml.imls/videos/318683639013879/]]
! Theory
In all the previous bounds, with an arbitrarily high probability and for any posterior distribution $$Q$$,
$$
R_{out}(Q) \le R_{in}(Q)+F(Q, \cdot)
$$
$$R_{out}$$ is the risk of a hypothesis on the test data, $$R_{in}(h)$$ is the risk on sample and $$F$$ is the complexity term. New learning algorithms can be designed to make this bound tight.
Optimization problem can be solved or approximated by gradient descent-flavored methods, Monte Carlo Markov Chain, Variational Bayes.
For any prior $$P$$, $$\delta\in(0, 1]$$, we have
$$
Pr\left(\forall Q\in\mathcal H: R_{out}(Q)\le R_{in}(Q)+\sqrt{\frac{D_{KL}(Q\|P)+\ln\frac{2\sqrt m}{\delta}}{2m}}\right) \ge 1-\delta
$$
where $$\mathcal H$$ is the hypothesis space, $$m$$ is the number of samples.
! Examples
SVM with a sigmoid loss and KL-regularized Adaboost have been reinterpreted as minizers of PAC-Bayesian bounds.
For any temperature $$\lambda > 0$$, the minimizer of $$\{R_{in}(Q) + \frac{KL(Q, P)}{\lambda}\}$$ is the celebrated Gibbs posterior (exponential weights)
$$
Q_{\lambda}(h)\propto\exp(-\lambda R_{in}(h))P(h), \qquad \forall h\in\mathcal H
$$
Gibbs posterior exponentially penalizes the in sample risk.
Extreme cases
* $$\lambda\rightarrow 0$$: not trusting the samples, posterior goes to prior
* $$\lambda\rightarrow \infty$$: Dirac mass on Empirical Risk Minimizers (ERMs)
! Variational definition of KL-D
Let $$(\mathbf A, \mathcal A)$$ be a measurable space. (i) For any probability $P$ on $$(\mathbf A, \mathcal A)$$ and any measurable function $$\phi: \mathbf A\rightarrow \mathbb R$$ such that $$\int(\exp\circ\phi)dP<\infty$$,
$$
\log\int(\exp\circ\phi)dP=\underset{Q\ll P}{\text{sup}} \int\phi dQ-KL(Q\|P)
$$
(ii) If $$\phi$$ is upper-bounded on the support of $$P$$, the supremum if reached for the Gibbs distribution $$G$$ given by
$$
\frac{dG}{dP}(a) = \frac{\exp\circ\phi(a)}{\int(\exp\circ\phi)dP},\qquad a\in \mathbf A
$$
! Non-iid or heavy-tailed data
For any integer $$p$$,
$$
\mathcal M_p:=\int\mathbb E(|R_{in}(h)-R_{out}(h)|^p)dP(h)
$$
! Localized PAC-Bayes
PAC-Bayesian bounds express a tradeoff between empirical accuracy and a measure of complexity
$$
R_{out}(Q)\le R_{in}(Q)+\sqrt{\frac{KL(Q\|P)+\ln\frac{\xi(m)}{\delta}}{2m}}
$$
To controll the complexity, choose prior distribution $$P$$:
* use part of the data to learn the prior
* defining the prior in terms of the data generating distribution (localized PAC-Bayes)
! Stability and PAC-Bayes
! PAC-Bayes analysis
(30 mins to go)
! MI Team reading schedule
!! Guidelines
# The lecturer is encouraged to pick his own material, even beyond the list or our recent study fields, as long as he finds his topic more helpful to the group. If so, create your own entry in the list beforehand.
# The lecturer is suggested to upload the slides to [[this git repo|https://g.hz.netease.com/mi-tools/mi-course/tree/dev/seminar]] before seminar.
# The scribe should keep the notes of the seminar for later reference. A nice script format is `.pdf` or `.html`. Choosing between LaTeX/Markdown as the markup language according to the lecture content is strongly recommended. You may download the LaTeX template from [[here|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/template.zip]].
!! Rotation
|!Lecturer |!Scribe |
|郭贺 |杨旭东 |
|张晓博 |陈昊 |
|侯章军 |李一夫 |
|周立峰 |郭贺 |
|杨旭东 |张晓博 |
|陈昊 |侯章军 |
|刘丽娟 |周立峰 |
|李一夫 |刘丽娟 |
!! Listed papers
|!Subject |!Title |!Conf |!Lecturer |!(Planned) Date |!Materials |
|Theory |[[Understanding Deep Convolutional Networks|Understanding Deep ConvNets (Scattering Repr)]] | PTA 2016| xiaobo| 7/26/2016||
|Theory |[[Provable Bounds for Learning Some Deep Representations|http://arxiv.org/abs/1310.6343]] | ICML 2014||||
|Theory |[[Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift|Batch Normalization]] | JMLR 2015| 李一夫| 8/2/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/16.8.2Batch_Normalization.pptx]]|
|Theory |[[Rethinking the Inception Architecture for Computer Vision (Inception-v3)|http://arxiv.org/abs/1512.00567]] |ArXiv 12/2015| 刘丽娟| 8/23/2016|[[slides]]|
|Theory |[[Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units|Concatenated Rectified Linear Units]] | ICML 2016||||
|Theory |[[Noisy Activation Functions|http://arxiv.org/abs/1603.00391]] | ArXiv 3/2016||||
|Theory |[[Large-Margin Softmax Loss for Convolutional Neural Networks|http://jmlr.org/proceedings/papers/v48/liud16.pdf]] | ICML 2016||||
|Theory |[[Layer Normalization|https://arxiv.org/abs/1607.06450]]| ArXiv 7/2016||||
|Tricks |[[Efficient BackProp|http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf]]| 1992||||
|Tricks |[[Mollifying Networks]]| ArXiv 8/2016||||
|Others |[[Gradient-based Hyperparameter Optimization through Reversible Learning]] | ICML 2015|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/hypergrad-talk.pdf]]|
|Others |[[Synthesized Classifiers for Zero-Shot Learning|https://arxiv.org/abs/1603.00550]] | CVPR 2016|||[[vgg slides]]|
|Others |[[Kernel Mean Embedding of Distributions: A Review and Beyonds|http://arxiv.org/abs/1605.09522]] | ArXiv 5/2016| 陈昊| 7/19/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/20160719_kernel_tricks.pdf]]|
|Others |[[Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition|http://arxiv.org/abs/1503.02101]] | ICML 2016||||
|Vision |[[Deep Face Recognition|http://www.robots.ox.ac.uk:5000/~vgg/publications/2015/Parkhi15/parkhi15.pdf]] [[FaceNet: A Unified Embedding for Face Recognition and Clustering|http://120.52.73.12/www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf]] [[Deeply learned face representations are sparse, selective, and robust|http://120.52.73.11/www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sun_Deeply_Learned_Face_2015_CVPR_paper.pdf]] | BMVC 2015| 杨旭东| 6/21/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/85ffa6afd1cc7c897e0ab7b07020228a787d3ef8/seminar/20160621_Face_Recognition.pptx]] [[notes]]|
|Vision |[[Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks|Faster R-CNN]] | NIPS 2015| 郭贺| 7/5/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/Object_Detection.pptx]] [[notes]] |
|Vision |[[Going Deeper With Convolutions (GoogLeNet)|GoogLeNet]] | CVPR 2015| xiaobo| 6/7/2016|[[notes|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/lecture-notes/seminar1.pdf]]|
|Vision |[[Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification|http://jmlr.org/proceedings/papers/v48/zhangc16.pdf]] | ICML 2016||||
|Vision |[[Learning End-to-end Video Classification with Rank-Pooling|http://jmlr.org/proceedings/papers/v48/fernando16.pdf]] | ICML 2016||||
|Vision |[[Bottom-Up and Top-Down Reasoning With Hierarchical Rectified Gaussians|https://arxiv.org/abs/1507.05699]] | CVPR 2016|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/RG21Jul16_VGG.pdf]]|
|Vision |[[DenseCap: Fully Convolutional Localization Networks for Dense Captioning|http://cs.stanford.edu/people/karpathy/densecap/]] | CVPR 2016||||
|Vision |[[G-CNN: an Iterative Grid Based Object Detector|http://arxiv.org/abs/1512.07729]] | CVPR 2016|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/GCNN.pdf]]|
|Vision |[[Learning Deep Features for Discriminative Localization|https://github.com/jazzsaxmafia/Weakly_detector]] | CVPR 2016| 郭贺| 5/31/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/16.5.31.guohe.pptx]] [[notes]]|
|Vision |[[NetVLAD: CNN Architecture for Weakly Supervised Place Recognition|http://arxiv.org/abs/1511.07247]] | CVPR 2016|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/NetVLAD.pptx]]|
|Vision |[[Newtonian Scene Understanding: Unfolding the Dynamics of Objects in Static Images|http://arxiv.org/abs/1511.04048]] | CVPR 2016|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/RG21Jul16_VGG.pdf]]|
|Vision |[[Synthetic Data for Text Localisation in Natural Images]]| CVPR 2016| 陈昊| 6/14/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/20160614_Reading_Text_in_Natural_Images.pptx]] [[notes|Synthetic Data for Text Localisation in Natural Images]]|
|Vision |[[WarpNet: Weakly Supervised Matching for Single-View Construction|https://arxiv.org/abs/1604.05592]] | CVPR 2016||||
|Vision |[[Identity Mappings in Deep Residual Networks|https://arxiv.org/abs/1603.05027]] | ArXiv 7/2016||||
|Vision |[[T-CNN: Tubelets with Convolutional Neural Networks|http://gitxiv.com/posts/do59FBaD68smgdpF6/t-cnn-tubelets-with-convolutional-neural-networks]] | ArXiv 5/2016||||
|Video |[[Deep learning for detecting multiple space-time action tubes in videos|http://www.robots.ox.ac.uk/~vgg/rg/papers/saha16bmvc.pdf]] | BMVC 2016||||
|Video |[[Multi-region two-stream R-CNN for action detection|http://www.robots.ox.ac.uk/~vgg/rg/papers/peng16eccv.pdf]] | ECCV 2016||||
|NLP |[[Distributed Representations of Words and Phrases and their Compositionality(word2vec)|Word2Vec]] | NIPS 2013| 周立峰| 9/27/2016||
|NLP |[[Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling|https://arxiv.org/abs/1412.3555]] | NIPS 2014| xiaobo| 8/30/2016||
|NLP |[[Convolutional Neural Networks for Sentence Classification]] | EMNLP 2014| 周立峰| 7/5/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/Convolutional-Neural-networks-for-sentence-classification.pptx]] [[notes]]|
|NLP |[[A Neural Conversational Model|https://github.com/macournoyer/neuralconvo]] | ICML 2015||||
|NLP |[[Virtual Adversarial Training for Semi-Supervised Text Classification|http://arxiv.org/abs/1605.07725]] | ArXiv 5/2016| 周立峰| 8/9/2016||
|Vision |[[Recent Advances in Convolutional Neural Networks|http://arxiv.org/abs/1512.07108]] | ArXiv 12/2015| 郭贺| 8/16/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/blob/dev/seminar/2016_8_16_CNN_survey_gh.pptx]] [[notes]]|
|RNN |[[LSTM: A Search Space Odyssey|http://arxiv.org/abs/1503.04069]]| ArXiv 3/2015| 侯章军| 6/28/2016 (posponded to 7/5)|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/20160705_lstm.pptx]] [[notes|Long short-term memory]]|
|RNN |[[Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations]] | ArXiv 6/2016||||
|RNN |[[Learning to learn by gradient descent by gradient descent|https://arxiv.org/abs/1606.04474]] | ArXiv 6/2016||||
|RL |[[Action-Conditional Video Prediction using Deep Networks in Atari Games|http://arxiv.org/abs/1507.08750]] | NIPS 2015||||
|RL |[[Asynchronous Methods for Deep Reinforcement Learning|https://arxiv.org/abs/1602.01783]] | ICML 2016||||
|RL |[[Value Iteration Networks|http://arxiv.org/abs/1602.02867]] | ICML 2016||||
|DL Applications |[[Unsupervised Deep Embedding for Clustering Analysis|http://jmlr.org/proceedings/papers/v48/xieb16.pdf]] | ICML 2016||||
|DL Applications |[[Learning Convolutional Neural Networks for Graphs|http://jmlr.org/proceedings/papers/v48/niepert16.pdf]] | ICML 2016| |||
|DL Applications |[[Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin |http://jmlr.org/proceedings/papers/v48/amodei16.pdf]] | ICML 2016||||
!! Other talks
# 6/21/2016, Boosting, 李一夫, [[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/lecture-notes/20160621.pptx]] [[notes|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/lecture-notes/seminar4.pdf]]
# 10/9/2016, Survey: CNN for Video Classification, 陈昊, [[slides|https://docs.google.com/presentation/d/1iAWg2vwUe7CI0AcaMVfIPxDfjW-o1KEOZVsVrqsEMis/edit?usp=sharing]]
Music~
<iframe style="border: 0; width: 100%; height: 120px;" src="https://bandcamp.com/EmbeddedPlayer/album=1327228511/size=large/bgcol=ffffff/linkcol=0687f5/tracklist=false/artwork=small/transparent=true/" seamless><a href="http://wormwood-official.bandcamp.com/album/ghostlands-wounds-from-a-bleeding-earth">Ghostlands - Wounds From a Bleeding Earth by Wormwood</a></iframe>
! MI Team reading schedule
!! Guidelines
# The lecturer is encouraged to pick his own material, even beyond the list or our recent study fields, as long as he finds his topic more helpful to the group. If so, create your own entry in the list beforehand. You can find some interesting papers from
#* [[Arxiv Sanity Preserver|http://www.arxiv-sanity.com/]]
#* [[DeepMind Publications|https://deepmind.com/research/publications/]]
#* [[FAIR Publications|https://research.fb.com/publications/]]
#* [[Google Brain Publications|https://research.google.com/pubs/BrainTeam.html]]
# The lecturer is suggested to upload the slides to [[this git repo|https://g.hz.netease.com/mi-tools/mi-course/tree/dev/seminar]] before seminar.
# The scribe should keep the notes of the seminar for later reference. A nice script format is `.pdf` or `.html`. Choosing between LaTeX/Markdown as the markup language according to the lecture content is strongly recommended. You may download the LaTeX template from [[here|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/template.zip]].
!! Rotation
|!Lecturer |!Scribe |
|郭贺 |杨旭东 |
|胡孟 |侯章军 |
|陈昊 |胡孟 |
|汪丰 |魏凯峰 |
|刘智雯 |周立峰 |
|杨旭东 |刘智雯 |
|邸新汉 |陈昊 |
|侯章军 |邸新汉 |
|刘丽娟 |曽杨 |
|周立峰 |郭贺 |
|魏凯峰 |刘丽娟 |
|曽杨 |汪丰 |
!! Listed papers
Let's start from some 2017 conference orals. Each paper has plenty of discussion online to help with understanding.
|!Subject |!Title |!Conference |!Lecturer |!(Planned) Date |!Materials |
|Vision |Learning Transferable Architectures for Scalable Image Recognition | arXiv 2017| | ||
|Vision |Dual Path Networks | arXiv 2017| | ||
|Vision |Attention-based Extraction of Structured Information from Street View Imagery | arXiv 2017|汪丰 |7/18 ||
|Vision |[[Interleaved Group Convolutions for Deep Neural Networks|https://arxiv.org/abs/1707.02725]] | ICCV 2017|侯章军 | ||
|Vision |Feature Pyramid Networks for Object Detection | CVPR 2017|胡孟 |7/4||
|Vision |A-Fast-RCNN: Hard positive generation via adversary for object detection | CVPR 2017|周立峰 |7/18 ||
|Vision |Fully Convolutional Instance-aware Semantic Segmentation | CVPR 2017|胡孟 |8/1 |[[GitHub|https://github.com/msracver/FCIS]]|
|Vision |YOLO9000: Better, Faster, Stronger | CVPR 2017|魏凯峰 |8/15 ||
|Vision |Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition | CVPR 2017|刘丽娟 | ||
|Vision |UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory | CVPR 2017| | ||
|Vision |Network Dissection: Quantifying Interpretability of Deep Visual Representations | CVPR 2017|杨旭东 | ||
|Vision |AGA: Attribute Guided Augmentation | CVPR 2017| | |[[arxiv|https://arxiv.org/pdf/1612.02559.pdf]],[[Github-Torch|https://github.com/rkwitt/GuidedAugmentation]]|
|Vision |Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data | ICLR 2017| | |[[arxiv|https://arxiv.org/abs/1610.05755]][[No Open Source|..]]|
|Vision |End-to-end Optimized Image Compression | ICLR 2017| | ||
|Vision |Amortised MAP Inference for Image Super-resolution | ICLR 2017| | ||
|Modelling |Neural Architecture Search with Reinforcement Learning | ICLR 2017|邸新汉 |10/24 |[[arxiv|https://arxiv.org/abs/1611.01578]],[[github-chainer|https://github.com/nutszebra/neural_architecture_search_with_reinforcement_learning_appendix_a]],[[DenseNet-Compared-Github|https://github.com/liuzhuang13/DenseNet]]|
|GAN |Towards Principled Methods for Training Generative Adversarial Networks | ICLR 2017|邸新汉 |7/25 |[[WGAN|https://arxiv.org/abs/1701.07875]],[[Github-Tensorflow|https://github.com/shekkizh/WassersteinGAN.tensorflow]]|
|GAN |Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields | arXiv 2017| | | |
|NLP |Learning End-to-End Goal-Oriented Dialog | ICLR 2017| | ||
|Theory |Probabilistic Deep Learning | DALI 2017|陈昊 |7/11 |<li>[[slides|https://docs.google.com/presentation/d/1DQpGu7sgnh787Hqjs1cBq485rbuZnylqcWefiCJr8PQ/edit?usp=sharing]]</li><li>[[Statistical Measures]]</li><li>[[fGAN]]</li><li>[[Variational Autoencoder]]</li>|
|Theory |Global Optimality in Neural Network Training | ICLR 2017| | ||
|Theory |[[On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima]] | ICLR 2017| | ||
|RL |Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic | CVPR 2017| | |[[arxiv|https://arxiv.org/abs/1611.02247]],[[Github-Tensorflow-rllab++|https://github.com/shaneshixiang/rllabplusplus]]|
|RL |Learning to Act by Predicting the Future | ICLR 2017| | ||
|RL |Reinforcement Learning with Unsupervised Auxiliary Tasks | ICLR 2017| | |[[arxiv|https://arxiv.org/abs/1611.05397]],[[Github-Tensorflow+DeepMind+Environment|https://github.com/miyosuda/unreal]]|
! Previous Sessions
* [[Deep Learning Seminar]]
* [[Paper Reading Group 2016]]
[[Conferences]]
! Reading List
!! Audio
!!! Speech
* [[Emotional Speaker Recognition Thesis]]
* [[Bayesian GMM]]
* [[Salient Acoustic Event Detection]]
* [[Speech Synthesis]]
* [[Sytle & Content]]
[[Feature Extraction]]
!!! [[Scattering Representation]]
!!!! Unfinished
<$list filter="[!has[draft.of]tag[Scattering Representation]!tag[done]sort[created]]">
<$checkbox tag="done"> <$link to={{!!title}}><$view field="title"/></$link></$checkbox>
</$list>
!!!! Finished
<$list filter="[!has[draft.of]tag[Scattering Representation]tag[done]sort[created]]">
<$checkbox tag="done"> ~~<$link to={{!!title}}><$view field="title"/></$link>~~</$checkbox>
</$list>
!! Machine Learning
[[Nonparametric Bayes]]
!!! [[NIPS]]
!!! [[ICML]]
!!!! Unfinished
<$list filter="[!has[draft.of]tag[NIPS]!tag[done]sort[created]]">
<$checkbox tag="done"> <$link to={{!!title}}><$view field="title"/></$link></$checkbox>
</$list>
!!!! Finished
<$list filter="[!has[draft.of]tag[Scattering Representation]tag[done]sort[created]]">
<$checkbox tag="done"> ~~<$link to={{!!title}}><$view field="title"/></$link>~~</$checkbox>
</$list>
! Bibliographies
* [[Nonparametric Bayes|Nonparametric Bayes Bib]]
* [[Superiorization|http://math.haifa.ac.il/YAIR/bib-superiorization-censor.html]]
* [[Deep Learning|Deep Learning Bib]]
* [[Audio Signal Processing]]
* Density estimation for likelihood-free inference: learn posterior from sampling
** Fast $\epsilon$-free inference of simulation models with Bayesian conditional density estimation
* Density estimation for MC
** Inference networks for sequential MC in graphical models
Density estimators:
* Autoregressive models
** Neural Autoregressive Distribution Estimation
* Normalizing flows
** [[Improving Variational Inference with Normalizing Flows]]: AR models with Gaussian conditionals are flows
Gaussian flow is limited. To adjust the sampling distribution, we can use Masked Autoregressive Flow:
* MADE: Masked Autoencoder for Distribution Estimation: generate random variables from other AR models recurrently
** Like a inverse of IAF, fast to calculate $p(x)$, slow to sample from.
** Real NVP is both fast to calculate $p(x)$ and fast to sample from, but limited capacity
* Applications:
** [[Yes, but Did It Work?: Evaluating Variational Inference]]
The large variance of importance sampling comes from the summation, which is a result of the large values. PSIS stablizes importance ratios
$$
r_s \triangleq p(\theta_s, y)/q(\theta_s)
$$
by fitting a generalized Pareto distribution:
$$
p(y|\mu, \sigma, k) = \left\{
\begin{array}{ll}
\frac1\sigma(1+k(\frac{y-\mu}{\sigma}))^{-\frac1k-1}, & \text{if}\ k\neq 0 \\
\frac1\sigma\exp(\frac{y-\mu}{\sigma}), & k=0
\end{array}\right.
$$
using the largest $$M$$ importance ratios
* estimate the shape parameter $$\hat k$$
* represent the $$M$$ largest $$r_s$$ by their expected value and truncate all weights at the raw weight maximum.
!! Diagnosis
$$\hat k$$ provides a diagnostic tool, it implies a Renyi divergence upper bound with order $$1/k$$.
* 10.1 [[Variational Inference]]
* 10.2 [[Variational Mixture of Gaussians]]
* ECCV14 Context-based pedestrain path prediction
* JRR20 Human Motion Trajectory Prediction: A Survey
[[definition|https://en.wikipedia.org/wiki/Perplexity]]
Perplexity is sometimes used as a measure of how hard a prediction problem is. This is not always accurate. If you have two choices, one with probability 0.9, then your chances of a correct guess are 90 percent using the optimal strategy. The perplexity is $2^{−0.9 \log_2 0.9 - 0.1 \log_2 0.1}= 1.38$. The inverse of the perplexity (which, in the case of the fair k-sided die, represents the probability of guessing correctly), is 1/1.38 = 0.72, not 0.9.
The perplexity is the exponentiation of the entropy, which is a more clearcut quantity. The entropy is a measure of the expected, or "average", number of bits required to encode the outcome of the random variable, using a theoretical optimal variable-length code, cf. the next section. It can equivalently be regarded as the expected information gain from learning the outcome of the random variable, where information is measured in bits.
* [[paper|http://jmlr.org/proceedings/papers/v48/diamos16.pdf]]
* [[reddit|https://www.reddit.com/r/MachineLearning/comments/4pezja/persistent_rnns_stashing_recurrent_weights_onchip/]]
The peak floating point throughput of a TitanX is 6.144 TFLOP/s. A straightforward implementation of a RNN using GEMM operations achieves 0.099 TFLOP/s at a layer size of 1152 using Nervana Systems GEMM kernels at a mini-batch size of 4. Our initial Persistent RNN implementation with the same layer and mini-batch size achieves over 2.8 TFLOP/s resulting in a 30x speedup.
Synchronization between GPU processors cores is typically achieved implicitly between dependent kernel calls in both CUDA and OpenCL development frameworks. However, this mechanism for synchronization between timesteps requires launching a new kernel that forces the weights to be reloaded from off-chip memory. This causes the synchronization latency of dependent kernels to be approximately 6-10x larger than the time spent performing the math operations for a single timestep, and this cannot be overlapped with computation. We address this problem with an optimized implementation of a global barrier that can be completely overlapped with the math operations for a single timestep.
You can synchronize thread blocks using atomic operations in global memory, as long as you take the weak memory model into account.
The hardest part is providing a guarantee of concurrency to more than one thread block. There are multiple ways to solve this problem, but we implemented cooperative multithreading where threads will yield the machine if too many synchronizations fail.
CUDA doesn't expose a way to yield. Our approach saves a continuation in GPU memory and then aborts the thread block. Code on the CPU monitors the continuation memory and restarts the kernel if any thread blocks fail. We size the kernel so that this is unlikely to happen, and in practice it occurs extremely rarely.
[[link|https://arxiv.org/abs/1609.04802]]
* Lower SNR than SRResNet
* The tweak on perceptual ResNet might be useful
! Models
Generator does not downsample
! Bibs
* Omniglot: [[Human-level concept learning through probabilistic program induction|http://science.sciencemag.org/content/sci/350/6266/1332.full.pdf]]
** Bayesian program learning
* [[Learning to Infer Graphics Programs from Hand-Drawn Images|https://arxiv.org/abs/1707.09627]]: very simple DSL, similar to draw
*
! Bibs
* [[Pixel-RNN|https://arxiv.org/abs/1601.06759]]
* [[Conditional Image Generation with PixelCNN Decoders|https://arxiv.org/abs/1606.05328]]
** use horizontal stack with skip connection for current row, vertical stack for previous rows. no blind spot.
** 1xn and nxn masked convolution can be implemented by half-sized convs followed by a shift in pixels by padding and cropping.
** [[tensorflow implementation|https://github.com/anantzoid/Conditional-PixelCNN-decoder]]
* Generating interpretable images with controllable structure
*
! Implementations
* Pixel-CNN++
** OpenAI implementation with python3/tf. quite memory consuming (even though optimized from original implementation by logistic mixture sampling of pixels), 12G takes 12 in a batch.
** [[tensorflow implementation|https://github.com/openai/pixel-cnn]]
* [[fast-pixel-cnn|https://github.com/PrajitR/fast-pixel-cnn]]: speed up with cache trick for [[Dilated Convolution]]
! Gated conv
* [[Conditional Image Generation with PixelCNN Decoders|https://arxiv.org/abs/1606.05328]]
As Instance Segmentation
* Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding
** [[code|https://github.com/svip-lab/PlanarReconstruction]]
* PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image
** [[code|https://github.com/NVlabs/planercnn]]
Adaptation of seq2seq attention for self referential outputs
Bibs
* Pointer Networks
* Pointer Sentinal Mixture Models
* Text understanding with attention sum reader network
* Pointing the unknown words
! Improving the attention
Multihead attention is used for speech recognition in [[Self-Attentional Acoustic Models|https://arxiv.org/abs/1803.09519]].
! Code completion
*
A discrete random variable $X$ is said to have a Poisson distribution with parameter $\lambda > 0$, if, for $k = 0, 1, 2, \ldots$, the probability mass function of $X$ is given by:
$
\!f(k; \lambda)= \Pr(X=k)= \frac{\lambda^k e^{-\lambda}}{k!},
$
where
* $e$ is Euler's number ($e = 2.71828\dots$)
* $k!$ is the factorial of $k$.
The positive real number $\lambda$ is equal to the expected value of $X$ and also to its variance
$$
\lambda=\operatorname{E}(X)=\operatorname{Var}(X).
$$
The Poisson distribution can be applied to systems with a large number of possible events, each of which is rare. How many such events will occur during a fixed time interval? Under the right circumstances, this is a random number with a Poisson distribution.
! Definition
Total loss:
$$
J(\theta)=E_{\tau\sim\pi_\theta(\tau)}[r(\tau)] = \int\pi_\theta(\tau)r(\tau)d\tau
$$
policy function is assumed to be Markovian:
$$
\pi_\theta(\tau) = p(s_1)\prod_{t=1}^T\pi_\theta(a_t|s_t)p(s_{t+1}|s_t, a_t)
$$
When we take the gradient, we ignore $$p(s_1)$$ and $$p(s_{t+1}|s_t, a_t)$$ term. We make the common log trick $$\frac{\nabla x}{x}=\nabla\log x$$ to allow sampling from paths $$\tau$$:
$$
\log\pi_\theta(\tau) = \log p(s_1) + \sum^T_{t=1}\log\pi_\theta(a_t|s_t)+\log p(s_{t+1}|s_t, a_t)
$$
$$
\nabla_\theta J(\theta)=E_{\tau\sim\pi_\theta(\tau)}[(\sum_{t=1}^T\nabla_\theta\log\pi_\theta(a_t|s_t))(\sum_{t=1}^Tr(s_t, a_t))]
$$
with [[REINFORCE]], also the gradient of sum is the sum of gradient. We sample $$\tau^i$$ from $$\pi_\theta(a_t|s_t)$$ and iteratively update $$\theta\leftarrow\theta+\alpha\nabla_\theta J(\theta)$$. This is valid even when:
* $$r$$ is discontinuous and/or unknown.
* Sample space (of paths) is a discrete set.
Policy can be generated by a neural network:
$$
\pi_\theta(a_t|s_t) = \mathcal N(f(s_t); \Sigma)
$$
We can then do back propagation on the model. Policy generated from a Gaussian.
$$
\log \pi_\theta(a_t|s_t) = -\frac12\|f(s_t)-a_t\|^2_\Sigma+\text{const}
$$
This also works for partially observed occasions. Because Markov property is not used.
! Further Topics
* [[Policy Gradient Reducing Variance]]
* [[Policy Gradient Off-Policy]]
! Comparisons
* Often $$\pi$$ can be simpler than $$Q$$ or $$V$$, e.g., robotic grasp
* $$V$$: doesn't precribe actions
** Would need dynamics model (+ compute 1 Bellman back-up)
* $$Q$$: need to be able to efficiently solve $$\arg\max_u Q_\theta(s, u)$$
** Challenge for continuous / high-dimensional action spaces
*** NAF: Gu, Lillicrap, Sutskever, Levine ICML 2016
*** Input Convex NNs: Amos, Xu, Kolter arXiv 2016
*** [[Deep Energy Q|https://arxiv.org/abs/1702.08165]]: Haarnoja, Tang, Abbeel, Levine, ICML 2017
Policy optimization:
* More compatible with rich architectures (including recurrence)
* More versatile
* More compatible with auxiliary objectives
Dynamic programming:
* More compatible with exploration and off-policy learning
* More sample-efficient when they work
! Improvements
* [[AlphaGo Zero]]
* [[PPO]]
! Bibs
* Classic papers
** Simple statistical gradient-following algorithms for connectionist reinforcement learning: introduces REINFORCE algorithm
** Infinite-horizon policy-gradient estimation: temporally decomposed policy gradient
** Reinforcement learning of motor skills with policy gradients: very accessible overview of optimal baselines and [[natural gradient|Natural Gradient Descent]]
* Deep RL
** Guided policy search: deep RL with importance sampled policy gradient
** Trust region policy optimization: deep RL with natural policy gradient and adaptive step size
** Proximal policy optimization algorithms: deep RL with importance sampled policy gradient
Policy gradient is on-policy because the expectation is drawn from current policy. Once the network is modified, new samples have to be drawn. Training is very inefficient.
! Importance sampling
We sample from $$\bar\pi(\tau)$$ instead with [[Importance sampling]]. We don't have to know the dynamics.
$$
\frac{\pi(\tau)}{\bar\pi(\tau)} = \frac{\prod\pi(a_t|s_t)}{\prod\bar\pi(a_t|s_t)}
$$
* The gradient is effected by the choice of reward function. Even if by just adding a constant.
* It is hard to choose learning rate and convergence is slow due to high variance.
* Natural gradient and slow convergence
We should introduce causality because policy at time $$t'$$ cannot effect reward at time $$t$$ when $$t<t'$$.
$$
\nabla_\theta J(\theta)\approx \frac{1}{N}\sum_{i=1}^N\sum_{t=1}^T\nabla_\theta\log\pi_\theta(a_t|s_t)\left(\sum_{t'=t}^Tr(s_{i,t'}, a_{i,t'})\right)
$$
$$\sum_{t'=t}^Tr(s_{i,t'}, a_{i,t'})$$ is the reward to go often notated with $$\hat Q_{i,t}$$.
$$
\nabla_\theta J(\theta)\approx \frac{1}{N}\nabla_\theta\log\pi_\theta(\tau)[r(\tau)-b]
$$
And we apply ''baselines''. Substracting any constants, leave the policy gradient unbiased. (expectation won't change but the variance will). Average reward baseline $$b=\frac1N\sum_{i=1}^N r(\tau)$$ is not the best, but works pretty well.
$$\text{Var} = E[x^2]-E[x]^2$$, only the first part is related to $$b$$: $$\frac{d\text{Var}}{db} = \frac{d}{db}E[g(\tau)^2(r(\tau)-b)^2]$$. One stationary point is $$b = E[g(\tau)^2r(\tau)]/E(g(\tau)^2)$$, the expected reward weighted by the gradient magnitude.
$$
\nabla_\theta J(\theta)\approx \frac{1}{N}\sum_{i=1}^N\sum_{t=1}^T\nabla_\theta\log\pi_\theta(a_t|s_t)(Q(s_{i, t}, a_{i, t})-b)
$$
In order to reduce variance, pooling layers compute the max or average value of a particular feature over a region of the image. This will ensure that the same result will be obtained, even when image features have small translations. This is an important operation for object classification and detection.
```css
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3 /* pool over a 3x3 region */
stride: 2 /* step two pixels (in the bottom blob) between pooling regions */
}
}
```
$$
z_n = \text{subsample}(x, g)[n] = g(x_{(n-1)m+1:nm})
$$
! Basic
!! Max pooling
$$
g(x) = \max(x), \frac{\partial g}{\partial x_i} = 1_{x_i = \max(x)}
$$
In backpropagation, the backward pass for a $\max(x, y)$ operation has a simple interpretation as only routing the gradient to the input that had the highest value in the forward pass.
!! $L^p$ pooling
$$
g(x) = \|x\|_p = \left(\sum_{k=1}^m|x_k|^p\right)^{1/p}, \frac{\partial g}{\partial x_i} = \left(\sum_{k=1}^m|x_k|^p\right)^{1/p-1}|x_i|^{p-1}
$$
Mean pooling is a special case where $p=1$.
But i am confused by [[this post|http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/]].
! Recent developments
* [[Fractional Max-Pooling|http://arxiv.org/abs/1412.6071]] suggests a method for performing the pooling operation with filters smaller than 2x2. This is done by randomly generating pooling regions with a combination of 1x1, 1x2, 2x1 or 2x2 filters to tile the input activation map. The grids are generated randomly on each forward pass, and at test time the predictions can be averaged across several grids.
* [[Striving for Simplicity: The All Convolutional Net|http://arxiv.org/abs/1412.6806]] proposes to discard the pooling layer in favor of architecture that only consists of repeated CONV layers. To reduce the size of the representation they suggest using larger stride in CONV layer once in a while.
* Parts-based pooling: detect ROIs and concat pooled features for each part to form the image representation. Used in fine-grained classification and image retrieval.
Due to the aggressive reduction in the size of the representation (which is helpful only for smaller datasets to control overfitting), the trend in the literature is towards discarding the pooling layer in modern ConvNets.
! Bibs
* [[Stochastic Portfolio Theory]]
* Non-Euclidean metrics: [[Geometric Learning and Filtering in Finance|https://arxiv.org/abs/1710.05829]]
! Theory
Markowitz modern portfolio theory (1959):
* ''Trade-off the return and risk:'' one should focus on the trade-off between expected return and the risk measured by the standard deviation
* This leads to a quadratic optimization with linear constraints.
* The efficient portforlio is an affine function of the expected return.
Capital Asset Pricing Model (CAPM) generalizes Markowitz by involving a riskless bond.
* [[OpenPose|https://github.com/CMU-Perceptual-Computing-Lab/openpose]]
** a fast inference implementation: [[tf-openpose|https://github.com/ildoonet/tf-openpose]]
* [[tf-pose-estimation|https://github.com/ildoonet/tf-pose-estimation]]: nice code base
* [[CPN|https://github.com/chenyilun95/tf-cpn]]: 2017 COCO Keypoints winner
** GlobalNet: FPN
** RefineNet: upsample and concat
** tricks: soft-NMS, ROIAlign
! As segmentation
* [[Mask R-CNN|https://arxiv.org/abs/1703.06870]]: ROIAlign, keypoints as one-hot masks
*
BlendMask utilizes RoI to guide attention of corresponding parts. One downside of this approach is that the
For regression tasks there has been success of using high-frequency information the position encoding.
For segmentation, the details can be well retained by the feature map itself. It is more important to guide the attention to focus on the target relative positions than encoding highly accurate position information. Direct positions are used for CondInst and Solo V2 instead.
! Code examples
* [[https://github.com/ikostrikov/pytorch-a2c-ppo-acktr/blob/master/algo/ppo.py]]
* [[https://github.com/openai/baselines/blob/master/baselines/ppo2/ppo2.py]]
! Applications
* [[PPO for NAS]]
* [[Learning Transferable Architectures for Scalable Image Recognition|https://arxiv.org/abs/1707.07012]]
* [[Faster Discovery of Neural Architectures by Searching for Paths in a Large Model|https://openreview.net/forum?id=ByQZjx-0-]]
* ''Primal'': $$\min_{x\in\mathbb R^d}\{\psi(x)+\frac1n\sum_{i=1}^nf_i(a_i^Tx)\}$$
* ''Dual'': $$-\min_{y\in\mathbb R^n}\{\psi^*(-\frac1n A^Ty) + \frac1n\sum_{i=1}^nf^*_i(y_i)\}$$
* ''Primal'': $$\min_{x\in\mathbb R^d}\{\psi(x)+\frac1n\sum_{i=1}^nf_i(a_i^Tx)\}$$
* substidue with $$f^{**}$$: $$\min_{x\in\mathbb R^d}\max_{y\in\mathbb R^n}\{\psi(x)+\frac1n\sum_{i=1}^n(y_i\cdot a_i^Tx - f^*_i(y_i)\}$$
* ''Primal-dual'': represent with data matrix: $$\min_{x\in\mathbb R^d}\max_{y\in\mathbb R^n}\{\psi(x)-\frac1n\sum_{i=1}^nf^*_i(y_i) + \frac1n y^TAx\}$$
* swap min and max: $$-\min_{y\in\mathbb R^n}\max_{x\in\mathbb R^d}\{-\psi(x) + \frac1n\sum_{i=1}^nf^*_i(y_i) - \frac1n y^TAx\}$$
* ''Dual'': remove maximization with the definition of the dual of the regularizer $$\psi$$: $$-\min_{y\in\mathbb R^n}\{\psi^*(-\frac1n A^Ty) + \frac1n\sum_{i=1}^nf^*_i(y_i)\}$$
if $$\psi(\cdot)$$ is $$\sigma$$-SC, $$\psi^*(\cdot)$$ is $$\frac1\sigma$$-smooth; if $$f_i(\cdot)$$ is $$L$$-smooth, $$f^*_i(\cdot)$$ is $$\frac1L$$-SC
There are four classes for the master problems:
# $$\sigma>0$$, $$L<+\infty$$ (SC and smooth)
# $$\sigma>0$$, $$L=+\infty$$ (SC and non-smooth)
# $$\sigma=0$$, $$L<+\infty$$ (non-SC and smooth)
# $$\sigma=0$$, $$L=+\infty$$ (SC and non-smooth)
|!Problem|!$$\psi(x)$$|!$$f_i(a^T_ix)$$|!$$\sigma$$|!$$L$$|
| ridge regression | $$\frac\sigma2\|x\|^2$$| $$\frac12(a_i^Tx-b_i)^2$$ | $$\sigma$$ | $$\|a_i\|^2$$|
| Lasso regression | $$\lambda\|x\|_1$$| $$\frac12(a_i^Tx-b_i)^2$$ | $$0$$ | $$\|a_i\|^2$$|
| Logistic regression | $$\lambda\|x\|_1$$ | $$\log(1+e^{-b_i\cdot a_i^Tx})$$ | $$0$$ | $$\frac14\|a_i\|^2$$ |
| SVM |$$\frac\sigma2\|x\|^2$$ | $$\max\{0, 1-b_i\cdot a_i^Tx\}$$ |$$\sigma$$ | $$\infty$$ |
| L1-SVM | $$\lambda\|x\|_1$$ | $$\max\{0, 1-b_i\cdot a_i^Tx\}$$ |$$0$$ | $$\infty$$ |
* [[Zhihu post series|https://zhuanlan.zhihu.com/p/34236792]]
* [[ICML17 Tutorial|Recent Advances in Stochastic Convex and Non-Convex Optimization]]
The most common definition of PCA, due to Hotelling (1933), is that, for a set of observed $d$-dimensional data vectors $\{t_n\}$, the $J$ principal axes $w_j$, are those orthonormal axes onto which the retrained variance under projection is maximal. It can be shown that the vectors $w_j$ are given by the $q$ dominant eigenvectors of the sample covariance matrix
$$
S = \sum_n(t_n-\bar t)(t_n-\bar t)^\top/N
$$
such that
$$
Sw_j = \lambda_jw_j.$$
The vector $x_n = W^\top(t_n-\bar t)$ is thus a $q$-dimensional reduced representation of the observed vector $t_n$.
A complementary property of PCA, and that most closely related to the original discussions of Pearson (1901), is that the projection onto the pricipal subspace minimizes the squared reconstruction error $\sum\|t_n-\bar t\|^2$.
The principle states that, subject to precisely stated prior data, the probability distribution which best represents the current state of knowledge is the one with largest entropy.
The principle of maximum entropy expresses a claim of maximum ignorance. The selected distribution is the one that makes the least claim to being informed beyond the stated prior data, i.e., admitting the most ignorance beyond the stated prior data.
[[Pitman-Koopman theorem]] states that the necessary and sufficient condition for a sampling distribution to admit sufficient statistics of bounded dimension is that it have the general form of a maximum entropy distribution.
Priors have traditionally belonged to one of two classes: informative priors and uninformative priors.
! Informative Priors
Ibrahim and Chen indtroduced the power prior. The power prior is a class of informative prior distribution that takes previous data and resuts into account.
! Weakly Informative Priors
Weakly Informative Prior distributions use prior information for regularization and stabilization, providing enough prior information to prevent results that contradict our knowledge or problems such as an algorithmic failure to explore the state-space.
* [[Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks|https://arxiv.org/abs/1502.05336]]
This uses [[Assumed Density Filtering]] multiple times to perform two non-trivial tasks required in a supervised Bayesian deep network: inference and learning
* ''forward propagation'': In Bayesian deep learning, we maintain a distribution $$q(w)$$ over neural network weights, and each value $$w$$ defines a conditional probability $$p(y|x,w)$$. To predict the label $$y$$ from the input $$x$$, we have to average over $$q(w)$$, that is calculate $$p(y|x)=\int q(w)p(y|x,w)dw$$, which is difficult due to the nonlinearities. Probabilistic backprop uses an ADF-like algorithm to approximate this predictive distribution. Starting from the bottom of the network, it approximates the distribution of the first hidden layer's activations given the input $$x$$ with a Gaussian. The first hidden layer will have a distribution of activations because we have a distribution over the weights) It then propagates that distribution to the second layer, and approximates the result with a Gaussian. This process is repeated until the distribution of $$y$$ given $$x$$ is calculated in the last step, which can be done easily if one uses a probit activation at the last layer.
* ''backward propagation'': Forward propagation allows us to make a prediction given an input. The second task we have to be able to perform is to incorporate evidence from a new datapoint $$(x_t,y_t)$$ by updating the distribution over weights $$q_t(w)$$ to $$p_t(w|x_t,y_t)\propto p(y_t|x_t, w)q_{t-1}(w)$$. We approximate this $$p_t(w|x_t,y_t)$$ in an inner loop, by first running probabilistic forward propagation, then a similar ADF-like sweep backwards in the network.
! PLATIPUS
[[paper|https://arxiv.org/abs/1806.02817]]
Train a hierarchical bayesian model over weights. A posterior task-specific parameter distribution is inferred at meta-test time conditioned on a learned weight prior and a (few-shot) training set.
perform approximate inference via MAP on $$$\phi_i$$ to
! SVGD
[[paper|https://arxiv.org/abs/1806.03836]]
* [[Statistical Measures]]
* [[Inference Methods]]
There are 2 types of genrative models:
* [[Fully-observed Models]]: model from data directly
* [[Latent Variable Models]]
** prescribed models: use likelihood and assume observation noise
** Implicit models: likelihood free
! The
The classical MAP assumes the speaker supervector has prior
$$
m_s = m + Dz_s
$$
where $m$ is the UBM supervector, $D$ is a block diagonal matrix (assuming mixture components are independent) with each block $F\times F$ (often set as diagonal, as GMM model has diagonal covariances) and $z_s$ is drawn from $N(0, I)$, assuming supervectors of different speakers are independent.
Provided that $d$ is non-singular, classical MAP adaptation is guaranteed to be asymptotically equivalent to ''speaker-dependent training''. When the number of mixture components is large, a large amount of enrollment data are needed. \cite{Kenny05jointfactor}.
The simplest neighborhood preserving emotional model is to add the emotion supervector to the speaker dependent $s$. Which is the same as JFA. The basic assumption of JFA is that a speaker and channel dependent supervector of means $m_s$ can be decomposed into:
$$
m_s = s + c, \quad s = m + Vy + Dz, \text{ and }c = Ux.
$$
Here $x$ and $y$ are also drawn from standard Gaussian. To implement MAP estimation on $m_s$, we have to know the posterior of hidden variables $x$, $y$ and $z$. To precisely calculate ''the posterior'' of $m_s$, all recordings have to be processed since they share the same $y_s$ and $z_s$. Sequential estimation algorithm also works if we apply ''speaker-dependent hyperparameters'' $m_s$.
''Remark'': The hyperparameters $m$, $v$, $d$ can be seen as sparse matrix thus related techniques can be introduced.
!! Similarity with sparse representation
We rewrite the sparse representation problem with JFA notations:
$$
! Estimation (Training the PCA System)
\cite{Kenny2005Factor} proved MAP point estimate of $y$ and $z$ is sufficient but $x$ is integrated. \cite{Glembek2009Comparison} used even more simplified point estimation of $x$ and linear scoring function and showed comparable results.
''Remark'': Currently the point estimate, integration scoring either work. I guess the emotion matrix learning is problematic. Per-speaker projection of merged emotion factors shows the speaker-emotion factors are very random.
! Scoring
We compute the log-likelihood ratio between the target speaker model s and the UBM.
!! Log-Likelihood
The classical frame-by-frame GMM log-likelihood evaluation is given by:
$$
\log P(\chi|s) = \sum_{t=1}^T\log\sum_{c=1}^Cw_cN(o_t;\mu_c, \Sigma_c),
$$
The conditional likelihood of the test utterance $P(\chi|s, x)$. Lemma 1 in \cite{kenny2005eigenvoice} using the ''Baum-Welch statistics'' extracted from the utterance. Marginal $P(\chi|s)$ can be calculated by integrating out the standard normal distribution. See Proposition 2 in \cite{kenny2005eigenvoice} for the closed form expression. The expression for the likelihood function is (19) in \cite{kenny2007joint}.
In practice the speaker supervector $s$ has to be estimated for the hypothesized speaker. $Cov(s, s)$ in introduced into $tr(\Sigma^{-1}S_s$ term to diminish the likelihood value inversely proportional to the amount of the speaker's enrollment data.
!! Integrating over Channel Distribution
(13) in \cite{kenny2007joint}:
$$
P(\chi|s) = \int P(\chi|s, x)N(x;0, I)dx \quad (1)
$$
Integrating out the channel is computationally infeasible, so $x$ is estimated with MAP. But it seems to be feasible for the emotional case.
By using fixed alignment of fames of Gaussians, the log-likelihood can be approximated using ''the GMM EM auxiliary function'', a lower bound to (1):
$$
\log \tilde P(\chi|s) = \sum_{c=1}^CN_c\log\frac{1}{2\pi^{F/2}|\Sigma_c|^{1/2}}-\frac 1 2\text{tr}(\Sigma^{-1}S_s)-\frac 1 2\log|L|+\frac 1 2\|L^{-1/2}U^*\Sigma^{-1}F_s\|^2.
$$
The definition of $N$, $F_s$ and $S_s$ can be found in (14-19) in \cite{kenny2007joint}. $N_c$, the number of data assigned to component $c$ is equal for UBM and the target model so the first term can be canceled out in LLR computation.
In the second term, $S_s$ is the second order moment of $\chi$ around speaker $s$,
$$
S_s = S - 2\text{diag}(Fs^*)+\text{diag}(Nss^*),
$$
where $S$ is the block diagonal second order stats and independent of speaker, thus along with third term $L = I+U^*\Sigma^{-1}NU$, is also canceled out.
$L^{-1/2}$ in the fourth term is the inverse of the Cholesky decomposition of $L$. The scoring function we care about is
$$
Q_{int}(\chi|s) = \text{tr}(\Sigma^{-1}\text{diag}(Fs^*))-\frac 1 2\text{tr}(\Sigma^{-1}\text{diag}(Nss^*))+\frac 1 2\|L^{-1/2}U^*\Sigma^{-1}F_s\|^2.
$$
!! Channel Point Estimate
If we knew the channel factor $x$, there would be no need for integrating over the whole distribution of x, but only use the point estimate in LLR. The formula is adopted from thm 1 in \cite{Kenny05jointfactor}
The log likelihood ratio formula is adopted from \cite{Kenny05jointfactor}.
$$
\log \tilde P(\chi|s) = \sum_{c=1}^CN_c\log\frac{1}{2\pi^{F/2}|\Sigma_c|^{1/2}}-\frac 1 2\text{tr}(\Sigma^{-1}S) + M^*\Sigma^{-1}F + \frac 1 2M^*N\Sigma^{-1}M,
$$
where $M$ is $m_s$ in (1). Leading to scoring function
$$
Q_x(\chi|s, x) = M^*\Sigma^{-1}F + \frac 1 2M^*N\Sigma^{-1}M
$$
hence
$$
LLR_x(\chi|s) = Q_x(\chi|s, x_s) - Q_s(\chi|UBM, x_{UBM})
$$
$$
LLR_x(\chi|s) = Q_x(\chi|s, x_s) - Q_s(\chi|UBM, x_{UBM})
$$
!! Linear Scoring
\cite{Dalmasso2009Loquendo} assumes the channel shift is identical to test recordings and UBM. The channel factor $x$ for utterance $\chi$ are estimated using UBM:
$$
LLR_{LPT}(\chi|s) = Q_x(\chi|s, x_{UBM}) - Q_s(\chi|UBM, x_{UBM})
$$
[[link|https://www.youtube.com/watch?v=u8Jt1HkWTn4]]
!! Symemtry in Neural Networks
* Encoding symmetry as invariance under a group
$$
y = h(g\cdot x) = h(x) \quad\forall g\in \mathcal G, x\in\mathcal X
$$
* Preserving symmetry with equivariance
$$
h(g\cdot x) = g\cdot h(x) \quad\forall g\in \mathcal G, x\in\mathcal X
$$
Then we stack an invariant function, the whole thing is invariant?
!!! Permutation Invariant Networks
* 3D G-CNNs [Winkels and Cohen]
* Spherical CNNs [Cohen et al.]
* Deep Sets [Zaheer et al.]
* Neural Message Passing for Quantum Chemistry
* Deep Models of Interactions Across Sets
* Relational Pooling for Graph Representations
Encoding symmetry encourages stabler training and better generalization through
* reduction in dimension of parameter space through weight-tying; and
* capturing structure at multiple scales via pooling
(Deep Set): To make a FC layer equivariant:
$$
y_i = \sigma\left(\sum_{j=1}^nw_{i,j}x_j\right) \rightarrow y_i = \sigma\left(w_0x_i + w_1\sum_{j=1}^nx_j\right)
$$
Parameter being so sparse should be a problem.
!! Symmetry in Probability and Statistics
The implication of [[De Finetti's Theorem]] for Bayesian inference:
* Our models for $$\mathbf X_{\mathbb N}$$ need only consist of i.i.d. distributions $$Q$$ on $$\mathcal X$$.
Reason to do it statistically:
* randomness softens/smoothes hard constraints
* established tools for working with invariant distributions
Distributional symmetry decomposes the problem into structure we care about + random noise
The empirical measure of $$\mathbf X_n$$ is a sufficient statistic:
$$
\mathbb M_{\mathbf X_n}(\cdot):= \sum_{i=1}^n \delta_{X_i}(\cdot)
$$
$$P$$ is exchangeable iff
$$
P(\mathbf X_n\in\cdot\vert\mathbb M_{\mathbf X_n}=m) = \mathbb U_m(\cdot) = \frac{1}{n!}\sum_{\pi\in\mathbb S_n}\delta_{\pi\cdot\mathbf x(m)}(\cdot)
$$
where $$\mathbb U_m$$ is the uniform distribution on all sequences $$(x_1, \dots, x_n)$$ with empirical measure $$m$$.
!! Permutation-Invariant Neural Networks as Exchangeable Probability Models
!! Symmetry in Neural Networks as Probabilistic Symmetry
Build FSA from execution traces.
FSA generation (specification miner):
* Automatic mining of specifications from invocation traces and method invariants
* A framework for the evaluation of specification miners based on finite state machines
Use $\lambda$ calculus
* [[A model for reasoning about JavaScript promises]]
Neural Program Synthesis Tasks: Copy, Grade-school addition, Sorting, Shortest Path, word algebra problems.
! Problems
* Program sketch setting of [[diffrentiable Forth|https://arxiv.org/abs/1605.06640]]: filling holes in given programs trained on input-output examples
! Approaches
* [[Neural Abstract Machines]]
* [[Inductive Logic Programming]]
* Neural Turing Machine
* Reinforcement Learning Neural Turing Machines
* Stack Recurrent Nets
* Neural GPU
** Is recurrent but each "timestep" involves a gated convolutional operation.
* Learning Simple Algorithms from Examples
* [[Program Induction by Rational Generation: Learning to Solve and Explain Algebraic Word Problems]]
* [[Learning Neural Programs To Parse Programs|https://github.com/liuchangacm/neuralparser]]
** predict output by learning the compiler and run the program? what is the point? (m)any applications?
Apply explicitly defined transformations and compose to create complex programs
* [[Neural Programmer|https://arxiv.org/abs/1511.04834]]
** Neural Programmer learns to create programs without needing examples of correct programs.
* Neural RAM
** 14 pre-defined atomic modules., com create coherent programs that span a much higher number of timesteps comparing to DNC.
* Neural Programmer-Interpreter
** enables function calls
** [[NPI via Recursion]]
* Neural Program Lattices
** explicitly creates a stack frame hierarchy, and work with less supervision.
Probabilistic Programming
* [[aProbLog|https://pdfs.semanticscholar.org/e865/6f24a63b4b824d147655f669ae54738b3a81.pdf]]
* DeepProbLog
Reinforcement Learning
* [[NIPS18 Programmatically Interpretable Reinforcement Learning|https://arxiv.org/abs/1804.02477]]
Operations:
* Id, Add, Subtract, Multiply, Divide, Power, Log, Sqrt, Sine, Cosine, Tangent, Factorial, nchoosek.
* Radians Degrees convertion
* Str Float convertion
The instruction $z_i$ is a tuple consisting of
* an operation ($o_i$)
* an ordered sequence of its arguments ($\mathbf a_i$)
* a decision about where its results will be placed($r_i$), to output or memory
* the result of applying the operation to its arguments ($v_i$).
Controller generates 3 kind of instructions
* softmax
* copy input
* copy output
TODO: convert to a "scratch paper" input (stack, heap, tape etc like?)
[[link|https://openreview.net/forum?id=ry_sjFqgx]]
* A significantly different approach than the currently accepted NN methods.
* Character level generation requires delicate rules and logic for the generation.
! The DSL
* SimpleProgram
** Move: left/write and find
** Write: write where? context? more like a read
*** Hash: write hash between current pos and last write
*** Dist: dist between current pos and last write
* SwitchProgram: select subprograms (functions?)
** guard
** branch conditions and programs to use
* StateProgram: The memory block, one integer (for one feature)?
** modified at each input character
! DNC as DSL
* Simple
** random access
** links work for PREV_CHAR
** dist not obvious but is this important?
* Switch
**
* [[Languages]]
* [[Tools]]
* [[Snippets]]
* [[Distributed System]]
* [[Software Research]]
Imperative: Fortran
Object-oriented: C++
* [[The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning|http://openreview.net/pdf?id=BkV4VS9ll]]
** A rather theoretical study of pruning. 2-nd order Taylor without retraining.
* [[Meta Pruning]]
* [[Channel Pruning]]
* Hyperbolic space (arithmetic)
* Topology for connections in weights
* Algebraic topology and parameter connections (representation space)
Manifolds in ML (such as related params) are non simply connected spaces, which are very ugly to compute for algebraic topology.
[[code|https://github.com/open-mmlab/OpenPCDet]]
* SPConv on voxelized pointcloud
* Voxel set abstraction module for keypoint feature
** Unit ball aggregation of voxel features
* Predicted keypoint weighting module
** scale features with FG probability
* Keypoint RoI-grid pooling
**
19/05
* add Bayes Modules
** Normal
* Regularizer
** NormalKLDivergence
[[Snippets|Python Snippets]]
Display a number with leading zeros
```python
"%02d" % (1,)
'{num:02d}'.format(num=i)
```
Batch iterator
```python
def batch(iterable, n=1):
l = len(iterable)
for ndx in range(0, l, n):
yield iterable[ndx:min(ndx + n, l)]
for x in batch(range(0, 10), 3):
print x
```
! Machine learning
[[Outlier]]
I have replaced old `virtualenv2` with the new `virtualenv` package on my ArchBox so as to unify the location of pip packages installed by different virtual environments.
To trigger python2 virtualenv
```
source ~/virtualenvs/python2/bin/activate
```
* $$Q^\pi(\mathbf s_t, \mathbf a_t) = \sum_{t'=t}^TE_{\pi_\theta}[r(\mathbf s_{t'}, \mathbf a_{t'})|\mathbf s_t, \mathbf a_t]$$: total reward from taking $$\mathbf a_t$$ in $$\mathbf s_t$$.
* $$\arg\max_{\mathbf a_t}Q^\pi(\mathbf s_t, \mathbf a_t)$$: best action from $$\mathbf s_t$$, if we then follow $$\pi$$
We want to omit policy gradient and choose best action directly. But run into problems like need to know $$p(s'|s, a)$$ and need to represent $$V(s)$$. So we approximate $$E[V(s')]$$ with $$\max_{a'}Q_\phi(s_i', a_i')$$. By this we skip simulation of actions.
* pros: works even for off-policy samples; only one network
* cons: no convergence guarantees
Q-function is the maximum discounted future reward when we perform action $$a$$ in state $$s$$, $$Q(s_t, a_t) = \max R_{t+1}$$
''Bellman equation'': maximum future reword for this state and action is the immediate reward plus maximum future reward for the next state. $$Q(s, a) = r + \gamma\max_{a'}Q(s', a')$$. @@color:#859900;The maximization step leads to en overestimatio bias.@@
In practice, many of the states are very rarely visited. (maybe a good memory management could speed this process up.) By passing the state through a deep neural net, we can get all Q-values for all actions available immediately.
The network can be optimized with simple squared error loss:
$$
L = \frac 1 2[r + \max_{a'}Q(s', a')-Q(s, a)]^2
$$
Approximation of Q-values using non-linear functions is unstable and takes a week on a single GPU:
* sequential states are strongly correlated
* target value is always changing
* Q-learning is not gradient descent
''Experience replay'' draws minibatches from previous input to break the similarity of subsequent training samples. In ''$$\epsilon$$-greedy exploration'', with probability $\epsilon$ we choose a random action, otherwise go with the highest Q-value.
! Deep Q-Networks
At each step, based on the current state, the agent selects an action $$\epsilon$$-greedily w.r.t. the action values, and adds a transition $$(S_t, A_t, R_{t+1}, \gamma_{t+1}, S_{t+1})$$ to a replay memory buffer, that holds the last ''million'' transitions. The parameters of the neural network are optimized by using SGD to minimize the loss
$$
(R_{t+1}+\gamma_{t+1}\max_{a'}q_{\bar\theta}(S_{t+1}, a')-q_\theta(S_t, A_t))^2
$$
where $$t$$ is a time step randomly picked from the replay memory. $$\theta$$ is the parameters of the //online network//, $$\bar\theta$$ is a periodic copy which is not directly optimized. This keeps the target function from changing too quickly.
!! Extensions to DQN
[[Rainbow: Combining Improvements in Deep Reinforcement Learning|https://arxiv.org/abs/1710.02298]]
''[[Double Q-learning|https://arxiv.org/abs/1509.06461]]'': maximization performed for the bootstrap target, $\theta$ for the selection of the action and $\bar\theta$ for its evaluation.
$$
(R_{t+1}+\gamma_{t+1}q_{\bar\theta}(S_{t+1}, \arg \max_{a'}q_{\theta}(S_{t+1}, a'))-q_\theta(S_t, A_t))^2
$$
''Prioritized replay'' samples transitions with probability $p_t$ relative to the last encountered absolute //TD error//:
$$
p_t\approx|R_{t+1}+\gamma_{t+1}\max_{a'}q_{\bar\theta}(S_{t+1}, a')-q_\theta(S_t, A_t)|^\omega
$$
where $\omega$ is a hyper-parameter that determines the shape of the distribution. When $\omega=1$, this is the absolute Bellman error.
''[[Dueling networks|https://arxiv.org/abs/1511.06581]]'' features two streams of computation, the value and advantage streams, sharing a convolutional encoder, and merged by a special aggregator:
$$
q_\theta(s, a) = v_\eta(f_\xi(s)) + a_\psi(f_\xi(s), a) - \frac{\sum_{a'}a_\psi(f_\xi(s), a')}{N_{\text{actions}}}
$$
By factorizing the architecture of the network, we can improve the result ''a lot''.
''Multi-step learning'' can often lead to faster learning. Forward-view //multi-step// targets can be used. We define the trancated n-step return $R_t^{(n)}$ as $\sum_{k=0}^{n-1}\gamma_t^kR_{t+k+1}$. The alternative loss is
$$
(R_t^{(n)}+\gamma_t^{(n)}\max_{a'}q_{\bar\theta}(S_{t+n}, a')-q_\theta(S_t, A_t))^2
$$
''Distributed RL''
''Noisy Nets'' propose a noisy linear layer that combines a deterministic and noisy stream. Sometimes, many actions must be executed to collect the first reward. Adding noise to network parameters is good for exploration. Simple approach is like elementwise linear noise layer.
! Bibs
* Classic
** Learning from delayed rewards: introduces Q-learning
** Neural fitted Q-iteration: batch-mode Q-learning with neural networks
* Deep RL
** Deep auto-encoder neural networks in reinforcement learning: early image-based Q-learning method using autoencoders to construct embeddings
** Human-level control through deep reinforcement learning: Q-learning with convolutional networks for playing Atari.
** Deep reinforcement learning with double Q-learning: a very effective trick to improve performance of deep Q-learning.
** Continuous control with deep reinforcement learning: continuous Q-learning with actor network for approximate maximization.
** Continuous deep Q-learning with model-based acceleration: continuous Q-learning with action-quadratic value functions.
** Dueling network architectures for deep reinforcement learning: separates value and advantage estimation in Q-function.
* [[blog|http://metamind.io/research/new-neural-network-building-block-allows-faster-and-more-accurate-text-understanding/]]
! 2D variance: spatial qrnn
Given an input sequence $\mathbf X\in\mathbb R^{W\times H\times n}$, spatial qrnn performs convolutions with a bank of $m$ filters, producing a sequence $\mathbf Z\in\mathbb R^{W\times H\times m}$.
To adopt the original function to 2-dimensional images, fo:
$$
\begin{align}
c_t &= \frac 1 2 f_t\odot(c_{t-1}+c'_{t+1}) + (1-f_t)z_t\\
h_t &= o_t\odot c_t
\end{align}
$$
f:
$$
h_t = \frac 1 2 f_t\odot(h_{t-1}+h'_{t+1})+(1-f_t)z_t
$$
! Implementations
* [[tensorflow|https://github.com/Kyubyong/quasi-rnn]]
! Open Domain
* [[Reading Wikipedia to Answer Open-Domain Questions|https://arxiv.org/abs/1704.00051]]: Useful datasets and benchmarks provided
! Examples
* Machine Comprehension by Text-to-Text Neural Question Generation
* [[A Unified Query-based Generative Model for Question Generation and Question Answering|https://arxiv.org/abs/1709.01058]]
* [[Attention-based CNN Matching Net|https://arxiv.org/abs/1709.05036]]: a quite naive convolutional attention implementation for making choices
! SOTA
!! Seq2seq
* Encoder
** BiLSTM, read document $D$, generate augmented document sequence $\mathbf h^d$
** BiLSTM, read query $Q$ and subset $\mathbf h^d$, generate encoding $\mathbf h^q$ (original paper used answer as query so that words came from document)
** compute initial state for decoder
* Decoder
** Location, pointer network over document tokens, softmax $\mathbf\alpha$, compute context vector $\mathbf v$
** Shortlist, reads $\mathbf v$, softmax over answer vocabulary $\mathbf o$
** Source switching network (2-layer MLP) enables model to interpolate between these distributions
! Bibs
* [[DrQA]]
* [[R-Net]]
* [[QANet|https://github.com/NLPLearn/QANet]]: SOTA on SQuAD
! Implementation
Attempts to implement based on ParlAI and PyTorch. A torch version of [[match-lstm and pointer network|https://github.com/shuohangwang/SeqMatchSeq]] should be helpful.
!! Encoder
* character-level embedding
!! Attention
* gated attention-based RNN: corner stone but kind of complicated. Wang & Jiang's match-lstm.
!! Output Layer
* [working] Wang & Jiang's adoption of Pointer Network
!! Visualization
* Attention weights $c_t$.
!! Other Ideas
* DeepMind releases a [[Relation network|https://arxiv.org/abs/1706.01427]], which is similar to attention, in a sum fashion, should be considered.
''Goal'': predict label $y_*$ for test data $x_*$
Random forest:
* Ensemble of randomized decision trees
* sota real world prediction: robust and paralizable
$$
p(y_*|x_*) = \frac{1}{M}\sum_m p(y_*|x_*,\mathcal T_m, X, Y)
$$
* Breiman;s RF: Bagging + subsamples
*Extremely RF: random $k$ sample and choose the best split
** no bagging
** labels do not guide ERT-1
Pros: fast, no overfitting
Cons: not possible to train incrementally
Many important quantities are specific to matrix strucure: e.g. eigenvalues and eigenvectors
Types of matrices for
! Theory
Universal compression properties derived from the Johnson-Lindenstrauss lemma.
[[L1 Magic|https://statweb.stanford.edu/~candes/l1magic/]]
! Bibs
* [[Biologically Inspired Random Projections]]
! Definitions
A ''Rao–Blackwell estimator'' $\delta_1(X)$ of an unobservable quantity $\theta$ is the conditional expected value $E(\delta(X)\mid T(X))$ of some estimator $\delta(X)$ given a sufficient statistic $T(X)$. Call $\delta(X)$ the "original estimator" and $\delta_1(X)$ the "improved estimator". It is important that the improved estimator be observable, i.e. that it not depend on $\theta$. Generally, the conditional expected value of one function of these data given another function of these data does depend on $\theta$, but the very definition of sufficiency given above entails that this one does not.
! The theorem
!! Mean-squared-error version
One case of Rao–Blackwell theorem states:
<<<
The mean squared error of the Rao–Blackwell estimator does not exceed that of the original estimator.
<<<
In other words
$$
\operatorname{E}((\delta_1(X)-\theta)^2)\leq
\operatorname{E}((\delta(X)-\theta)^2).\,\!
$$
The essential tools of the proof besides the definition above are the law of total expectation and the fact that for any random variable $Y$, $E(Y^2)$ cannot be less than $[E(Y)]^2$. That inequality is a case of Jensen's inequality, although it may also be shown to follow instantly from the frequently mentioned fact that
$$
0 \leq \operatorname{Var}(Y) = \operatorname{E}((Y-\operatorname{E}(Y))^2) =
\operatorname{E}(Y^2)-(\operatorname{E}(Y))^2.\,\!
$$
!! Convex loss generalization
The more general version of the Rao–Blackwell theorem speaks of the "expected loss" or risk function:
$$
\operatorname{E}(L(\delta_1(X)))\leq \operatorname{E}(L(\delta(X)))\,\!
$$
where the "loss function" $L$ may be any convex function. For the proof of the more general version, Jensen's inequality cannot be dispensed with.
Radial basis function (RBF) networks are feed-forward networks that have universal approximation properties. Their main advantage is the simplicity of computation of the network paramters. They typically have three layers: an input layer, a hidden layer with a non-linear RBF activation function and a linear output layer. The input can be modeled as a vector of real numbers $\mathbf{x} \in \mathbb{R}^n$. The output of the network is then a scalar function of the input vector, $\varphi : \mathbb{R}^n \to \mathbb{R}$, and is given by
$$
\varphi(\mathbf{x}) = \sum_{i=1}^N a_i \rho(||\mathbf{x}-\mathbf{c}_i||)
$$
where $N$ is the number of neurons in the hidden layer, $\mathbf c_i$ is the center vector for neuron $i$, and $a_i$ is the weight of neuron $i$ in the linear output neuron. $\rho$ is a function that depend only on the distance form a center vector radially symmetric about the input, hence the name radial basis function. The norm is typically taken to be Euclidean, but Mahalanobis distance can do better (?) and the radial basis function is commonly taken to be Gaussian
$$
\rho \big ( \left \Vert \mathbf{x} - \mathbf{c}_i \right \Vert \big ) = \exp \left[ -\beta \left \Vert \mathbf{x} - \mathbf{c}_i \right \Vert ^2 \right].
$$
The Gaussian basis functions are local to the center vector in the sense that
$$
\lim_{||x|| \to \infty}\rho(\left \Vert \mathbf{x} - \mathbf{c}_i \right \Vert) = 0
$$
i.e. changing parameters of one neuron has only a small effect for input values that are far away from the center of that neuron.
[[link|http://dhruvp.github.io/2015/03/07/re-frame/]] and [[previous reagent tutorial|Reagent]]
The project is based on [[reagent|http://dhruvp.github.io/2015/02/23/mailchimip-clojure/]] and is in cljs
I came to a problem in step 2 that phones-component requires a for to generate the view but the for takes 5 arguments.
Problem solved by changing the for sentence. But now the page just does not show anything.
[[link|https://arxiv.org/abs/1412.1842]]
! End-to-end text spotting pipeline
[img[end2end_text_spotting_pipeline.png]]
! Text Detection
This operates in a low-precision, high-recall mode – rather than using a single word location proposal, we carry a sufficiently high number of candidates through several stages of our pipeline.
[[Papers]]
[[Books]]
[[Talks]]
[[Courses]]
! HelloThere
{{HelloThere}}
Atoms are one of the few mutable data structure in Clojure. Reagent extends a Clojure Atom by ensuring that whenever an Atom is mutated, any component that uses it is rerendered
! Example
[[link|http://dhruvp.github.io/2015/02/23/mailchimip-clojure/]]
got problem with map??
Papers reviewed in this post:
# A Unifying Framework for Detecting Outliers and Change Points from Non-Stationary Time Series Data
! The problem
We want the outlier to be detected immediately after it appears. And the data is spouted in a non-stationary way. Online, a.k.a, a priori, change-point detection with
!
* [[Online Natural Gradient as a Kalman Filter|https://arxiv.org/abs/1703.00209]]
[[Video|https://www.youtube.com/watch?v=jPjhiaeYruQ&feature=youtu.be]]
The form of master problem:
$$
\min_{x\in\mathbb R^d}\{\psi(x)+\frac1n\sum_{i=1}^nf_i(x)\}
$$
where $$\psi(x)$$ is a convex regularizer, such as $$\ell_1$$-, and a loss of a finite average over a convex loss function $$f_i(x)$$
This covers alot of ML algorithms, such as
* SVM: $$\frac\sigma 2\|x\|^2+\frac 1n\sum_{i=1}^n\max\{0, 1-b_i\cdot a_i^Tx\}$$, (+Hinge loss), where $$a_i$$ is the data sample and $$b_i\in\{\pm1\}$$
* ridge regression: $$\frac\sigma 2\|x\|^2+\frac{1}{2n}\sum_{i=1}^n(a_i^Tx-b_i)^2$$
* Lasso regression: $$\lambda\|x\|_1+\frac{1}{2n}\sum_{i=1}^n(a_i^Tx-b_i)^2$$
* Logistic regression: $$\lambda\|x\|_1+\frac{1}{n}\sum_{i=1}^n\log(1+e^{-b_i\cdot a_i^Tx})$$
* MAXCON: $$-\frac\sigma 2\|x\|^2-\frac 1n\sum_{i=1}^n\max\{0, \|a_i^Tx\|-1\}$$
Since these all includes a inner product between the weight and sample:
$$
\min_{x\in\mathbb R^d}\{\psi(x)+\frac1n\sum_{i=1}^nf_i(a_i^Tx)\}
$$
! [[Solvers under Primal-Dual Settings]]
{{Solvers under Primal-Dual Settings}}
[[paper|https://arxiv.org/abs/1612.03969]]
! Introduction
RNN is a very deep feedforward neural newwork that has a layer for each timestep. Its weights are shared across time.
! Model structure
* [[Sequential Models]]
* Regularisation
** don't put drop on recurrent connections, on non-recurrent ones. [[Recurrent neural network regularization. Zaremba et al.|https://arxiv.org/abs/1409.2329]]
** [[A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. Gal and Ghahramani|https://arxiv.org/abs/1512.05287]] advocate tying the recurrent dropout mask and sampling at evaluation time.
** [[Variational Dropout]]
** Deterministic dropout: [[Pusing the bounds of dropout]]
! Topics
* ICLR 2017 Talk: [[New Directions for RNNs]]
* [[Meta-Learning]]
* [[RNN Performance Optimization]]
! Training
* [[Optimizing RNN]]
* RTRL
** Approximate: [[Training recurrent networks online without backtracking|https://arxiv.org/abs/1507.07680]]
** Local: original LSTM paper
* [[Synthetic Gradients]]
* [[Waybackprop]]
* [[Training Recurrent Networks Online Without Backtracking]]
! Tools
* [[RNNVis|https://github.com/myaooo/RNNVis]] tf 0.12 visualization
! Motivation
Using an undirected model for the interaction between the hidden and visible variables helps to capture many of the regularities that cannot be modeled efficiently by HMMs. This ensures that the contribution of the likelihood term to the posterior over the hidden variables is approximately ''factorial'', which greatly facilitates inference. Properties of this model family:
# Componential hidden state (exponentially large state space)
# Non-linear dynamics and multimodal predictions
# Simple on-line filtering procedure
# Efficient learning algorithm (maximum likelihood is intractable)
# Simple to learn multiple layers of hidden variables
Models that use latent variables to propagate information through time can be divided into two classes depending on whether there is an efficient procedure for inferring the exact posterior distribution:
# tractable models: linear dynamic systems, HMM
# intractable models
[[link|https://www.vicarious.com/2017/10/26/common-sense-cortex-and-captcha/]]
RCN integrates and builds upon various ideas from compositional models:
* heirarchical composition
* gradual building of invariances
* lateral connections for selectivity
* contour surfaces factorization
** surfaces modeled using pairwise CRF, smoothness of variations of surface properties.
** contours modeled using a compositional hierarchy of features
* joint-explanation based parsing
Inferenced with Belief propagation.
This can be done at the output, intermediate and also input levels.
* Structural regularization: seeks to prevent major changes in the weights that were important for previous tasks.
** present in the loss function
** at a separate merging step
* Dedicating specific sub-parts of the network for each task is another way of reducing representational overlap
The main trade-off is to effectively distribute the capacity of the network across tasks while maintaining important weights and reusing prvious knowledge.
! Reference
* [[Clustering with Bregman Divergences|http://www.jmlr.org/papers/v6/banerjee05b.html]]
! Introduction
A regular Bregman divergence $$d_{\phi}$$ is defined as:
$$
d_\phi(z, z') = \phi(z) - \phi(z') - (z-z')^T\nabla\phi(z')
$$
where $$\phi$$ is a differentiable, strictly convex function of the Legendre type.
For Bregman divergences, the cluster representative achieving minimal distance to its assigned points is the cluster mean.
Any regular exponential family distribution $$p_\psi(z|\theta)$$ with parameters $$\theta$$ and cumulant function $$\psi$$ can be written in terms of a uniquely determined regular Bregman divergence
$$
p_\psi(z|\theta) = \exp\{z^T\theta-\psi(\theta)-g_\psi(z)\} = \exp\{-d_\phi(z, \mu(\theta)) - g_\phi(z)\}
$$
Stochasticity of the transformation parameters $\theta$
* [[Regularization for Deep Learning A Taxonomy|https://arxiv.org/abs/1710.10686]]
* [[Regularization for RNNs]]
* Stochastic
** [[Dropout]]
** Swapout
** DropPath
** Stochastic depth
** DropBlock: [[pytorch|https://github.com/Randl/DropBlock-pytorch]]
Finish following list from [[Regularizing and Optimizing LSTM Language Models|https://arxiv.org/abs/1708.02182]]
* Stochastic Hidden States dropout
* Restrictions on recurrent matrix
** Restricting the capacity of matrix
** Elementwise interatcions
* Act upon activations
** Batch norm
** Weight/Layer norm
** Spectral norm
* DropConnect mask on hidden-hidden weights
Generative Replay
Use a GAN to generate samples of previous tasks.
REINFORCE approximates the expectation with independent samples from the policy distribution, yielding the policy gradient
$$
\nabla\mathcal L_{RL}\approx\sum_{t=1}\nabla\log\pi(\hat y_t|\hat y_{<t}, D, A)\frac{R(\hat Y)-\mu_R}{\sigma_R}
$$
Baseline and natural policy gradient:
* [[Policy gradient methods|http://www.scholarpedia.org/article/Policy_gradient_methods#Likelihood_Ratio_Methods_and_REINFORCE]]
[[Reparametrization Trick]] can help backpropagate through the samples. (rewrite samples from another simple distribution) to lower the variance.
! Tutorials
* [[WildML: Learning Reinforcement Learning|http://www.wildml.com/2016/10/learning-reinforcement-learning/]]
* [[Simple Reinforcement Learning with Tensorflow|https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0]]
* [[Deep RL Bootcamp 2017 Berkeley|http://nuit-blanche.blogspot.hk/2017/10/slides-and-videos-deep-rl-bootcamp-26.html]]
* [[An Outsider's Tour of Reinforcement Learning|http://www.argmin.net/2018/03/26/outsider-rl]]
* [[ICML17 Deep RL Tutorial|https://sites.google.com/view/icml17deeprl]]
! Basic
!! Theorems
<<<
''Theorem'' [Value iteration converges]<br>
At convergence, we have found the optimal value function $V^*$ for the discounted infinite horizon problem, which satisfies the [[Bellman equation]]
$$
\forall S\in S:\qquad V^*(s) = \max_a\sum_{s'}P(s'|s, a)[R(s, a, s')+\gamma V^*(s')]
$$
<<<
''Temporal Difference Learning:'' Policy evaluation for the current policy $\pi_k$
$$
V^{\pi_k}_{i+1}(s) \leftarrow \sum_{s'}P(s'|s, \pi_k(s))[R(s, \pi_k(s), s')+\gamma V^{\pi_k}_{i}(s')]
$$
Policy improvements: find the best action according to one-step look-ahead
$$
\pi_{k+1}(s) \leftarrow \arg\max_a\sum_{s'}P(s'|s, a)[R(s, a, s')+\gamma V^{\pi_k}(s')]
$$
Tabular methods cannot scale.
@@color:#859900;How to update it as a network?@@
<<<
''Theorem'' [Policy iteration converges]<br>
Policy iteration is guaranteed to converge and at convergence, the current policy and its value function ar the optimal policy and the optimal value function.
<<<
We sample states $\epsilon$-greedily.
!! RL agents
* value based, has a value function where policy can be inferred.
* policy based, work in decision space, more direct. Policy Graident.
* [[Actor-Critic Method]]: stores policy and value
* [[Model Based RL]]
!! Anatomy of a RL algorithm
Iterate these 3 steps：
# Generate samples (i.e. run the policy)
# Fit a model to estimate return
#* MC policy gradient: compute $\hat Q = \sum_{t'=t}^T\gamma^{t'-t}r_{t'}$
#* [[Actor-Critic Method]], [[Q-Learning]]: fit $Q_\phi(s, a)$
#* Model-based: estimate $p(s'|s, a)$
# Improve the policy
#* [[Policy Gradient]]: $\theta\leftarrow\theta+\alpha\nabla_\theta J(\theta)$
#* Q-learning: $\pi(s) = \arg\max Q_\phi(s, a)$
#* Model-based: optimize $\pi_\theta(a|s)$
!! Tricks
* Freezing target network
* Error clipping, reward clipping
* Experience replay
!! Training steps
On training half-cheetah, each one is 10x easier than previous. (each episodes is 1000 steps)
* gradient-free methods, e.g. evolution strategies, CMA, etc.
* fully online methods, e.g. A3C, 100,000 episodes ~15 days
* policy gradient methods, e.g. TRPO, 10,000 episodes
* relay buffer value estimation, e.g. Q-learning, DDPG, NAF, etc., 1,000 episodes
* model-based deep RL, e.g. guided policy search
* model-based shallow RL, e.g. PILCO
! Bibs
* [[Faster Deep Reinforcement Learning]]
* [[Playing Doom with SLAM-Augmented Deep Reinforcement Learning|https://arxiv.org/abs/1612.00380]]
* [[Learning to Communicate|https://blog.openai.com/learning-to-communicate/]]: differentiable language for RL
* [[Curiosity-driven Exploration by Self-supervised Prediction|https://pathak22.github.io/noreward-rl/]]: training with sparse reward
! Topics
* [[Imitation Learning]]
* [[Meta-Learning]]
* [[Noisy Networks for Exploration|https://arxiv.org/abs/1706.10295]]
* [[Soft Optimality]]
* [[Hierarchical Reinforcement Learning]]
* [[Learn by Generation]]
* [[RL Return]]
! Implementations
Environments
* [[CommAI-env|https://github.com/facebookresearch/CommAI-env]]
* [[Universe|https://openai.com/blog/universe/]] with [[Gym|https://gym.openai.com/]]
* [[DeepMind Lab|https://github.com/deepmind/lab]]
* [[Facebook ELF|https://github.com/facebookresearch/ELF]]
* [[TextWorld|https://www.microsoft.com/en-us/research/project/textworld/]]
[[RL Applications]]
* [[A Survey|https://arxiv.org/abs/1712.05191]]
! Some derivations
[[http://www.tuananhle.co.uk/notes/rebar-relax.html]]
! Gradient estimation
$$
\min_h f(h) \le \min_\alpha E_{\alpha\sim p(h|\alpha)}[f(h)]
$$
!! DARTS
$$
\nabla_\alpha E_{p_\alpha(h)}[f(h)]\approx \nabla_\alpha f(\sigma(\alpha))
$$
!! Tempreture dependent sigmoid
$$
\nabla_\alpha E_{p_\alpha(h)}[f(h)]\approx \nabla_\alpha f(\sigma(\alpha/t))
$$
!! Gumbel softmax
$$
\nabla_\alpha E_{p_\alpha(h)}[f(h)]\approx E_{p(u)}[\nabla_\alpha f(\sigma(z/t))]
$$
$$
z := \log\frac{\alpha}{1-\alpha} + \log\frac{u}{1-u}
$$
$$
u\sim\text{Uniform}(0, 1)
$$
!! REINFORCE
$$
\nabla_\alpha E_{p_\alpha(h)}[f(h)]= E_{p_\alpha(h)}[f(h)\nabla_\alpha \log p_\alpha(h)]
$$
!! Rebar
! Using RELAX for RL
We want to estimate the gradient of an expectation of a black-box function: $$\frac{\partial}{\partial\theta}\mathbb E_{p(\tau|\theta)}[R(\tau)]$$. The //de facto// standard approach is the advantage actor-critic estimator (A2C).
Relay is an extensible deep learning IR, it generalizes NNVM. Expressivenss + Performance
* Relay is the high level IR of the TVM stack
* Generalize computation graphs to differentiable programs
* Enable whole-program optimization for deep learning
* Composed of new IR, auto-diff, optimizer, and backends
* Uses HalideIR to represent platform independent operators
!! IR
* A functional IR, an ML-like (ReasonML, OCaml, SML, ...) language tailored to machine learning
* Features, closures, reference, ADTs, and primitive operators, and tensors as teh primary value type
* Relay can represent full-models including a generative RNN and training loop
* The functional design enables analysis and transform of pure data-flow.
Low-cost abstraction enabled by:
* Tensor shape inference and specialization (Type system)
* High-level operator fusion
* Whole-program partial evaluation
Renewal processes are generalizations of the Poisson process on the real line whose intervals are drawn i.i.d. from some distribution.
! Formal definition
Let $S_1 , S_2 , S_3 , S_4 , S_5, \ldots$ be a sequence of positive independent identically distributed random variables such that
$$
0 < \mathbb{E}[S_i] < \infty.
$$
We refer to the random variable $S_i$ as the "$i$th" holding time.
Define for each $n > 0$:
$$
J_n = \sum_{i=1}^n S_i,
$$
each $J_n$ referred to as the "$n$th" //jump time// and the intervals $[J_n,J_{n+1}]$ being called renewal intervals.
Then the random variable $(X_t)_{t\geq0}$ given by
$$
X_t = \sum^{\infty}_{n=1} \mathbb{I}_{\{J_n \leq t\}}=\sup \left\{\, n: J_n \leq t\, \right\}
$$
(where $\mathbb{I}$ is the indicator function) represents the number of jumps that have occurred by time $t$, and is called a ''renewal process''.
! Special Cases
* [[Homogeneous Poisson Process]]
* Modulated Renewal Process
In variational inference, we often encouter gradients of the form:
$$
\frac{\partial}{\partial\theta_i}\mathbb E_{x\sim q_\theta}[f(x, q_\theta(x))],
$$
where the pdf of the variable appears in the integrand. If we can find a function $$h:(\mathcal E, \Theta)\mapsto \mathcal X$$ which is differentiable w.r.t. its second argument, and probability distribution $$p_\epsilon$$ over $$\mathcal E$$ which is easy to sample from, such that the following holds:
$$
x = h(\epsilon, \theta), \epsilon\sim p_\epsilon \Longleftrightarrow x\sim q_\theta
$$
We can use the following reformulation of integrals we often encouter in variational upper bounds
$$
\frac{\partial}{\partial\theta_i}\mathbb E_{x\sim q_\theta}[f(x, q_\theta(x))]=\mathbb E_{\epsilon\sim p_\epsilon}[\frac{\partial}{\partial\theta_i}f(h(\epsilon, \theta), q_\epsilon(h(\epsilon, \theta)))]
$$
A Monte Carlo estimators to this expectation typically have substaintially lower variance than [[REINFORCE]] estimator to the same quantity.
! Theory
!! Curse of Dimensionality
In a finite-dimensional, bounded space, all metrics are equivalent:
<<<
For each $x\in\Omega$, exists constants $c, C$ such that $\forall x'\in\Omega$, $cd(x, x')\le\tilde d(x, x')\le Cd(x, x')$.
<<<
But as the dimension increases, metrics start to diverge. In particular, the Euclidean distance in high-dimensional spaces is typically a poor measure of similarity for practical purposes. Local decisions around training do not extend to the whole space.
To avoid the curse of dimensionality, we need a guiding principle that plays well with our data (images, sounds, etc.) We need features that linearize intra-class variability and perserve inter-class variability.
!! Generalization Error
It is easy to construct discriminative features:
* Using a Gaussian RBF, it suffices to let $\sigma\rightarrow0$.
* The estimator converges to the nearest neighbor classifier:
$$
\hat f(x) = f(x_{i(x)}), i(x) = \arg\underset{i}{\min}\|x-x_i\|
$$
While it may be easy to correctly classify our training examples, we do not necessarily improve our generalization error:
$$
\mathbb E_{(x, y)}(\mathcal l(\hat f(x), y))
$$
!! Linearization
We want to obtain a representation $\Phi(x)$ such that
$$
\hat f(x) = \text{sign}(a^\top\Phi(x)+b)
$$
is a good approximation of $f(x)$. Thus $f(x)$ is approximately linearized by $\Phi(x)$:
$$
f(x) \approx \text{sign}(a^\top\Phi(x)+b)
$$
In particular, we should have
$$
a^\top(\Phi(x)-\Phi(x') = 0 \Rightarrow f(x)=f(x').
$$
!! Invariance and Symmetry
A global symmetry is an operator $\varphi\in Aut(\Omega)$ that leaves $f$ invariant:
$$
\forall x\in\Omega, f(\varphi(x)) = f(x)
$$
They can be absorbed by $\Phi$ to varying degrees:
* ''Invariants'': $\Phi(\varphi(x)) = \Phi(x)$ for each $x$.
* ''Covariants'': $\Phi(\varphi(x)) = A_\varphi\Phi(x)$ for each $x$, where $A_\varphi$ is "simpler" than $\varphi$.
[[Image Representation Learning]]
! Algorithms
* [[t-Distributed Stochastic Neighbor Embedding]]
* [[Random Projection]]
! Quick Facts
We define a feature map $\phi:\mathcal X\rightarrow\mathcal H$, for $\mathcal H$ an RKHS, $\varphi$ corresponds to a kernel $k: \mathcal X\times\mathcal X\rightarrow \mathbb R$ by $k(x, y) = \langle\varphi(x), \varphi(y)\rangle_{\mathcal H}$. For any p.s.d. $k$, a matching $\mathcal H$ and $\phi$ exist:
* e.g. we can use the Gaussian kernal: $k(x, y) = \exp(-\frac{1}{2\sigma^2}\|x-y\|^2)$
The **reproducing property** assures: $\forall f\in\mathcal H, f(x) = \langle f, \varphi(x)\rangle_{\mathcal H}$.
! Definition
; RKHS
: We say that $\mathcal H$ is a reproducing kernel Hilbert space of functions $f:\mathcal X\rightarrow\mathcal Y$, when for any $y\in\mathcal Y$ and $x\in\mathcal X$ the linear functional which maps $f\in\mathcal H$ to $(y,f(x))$ is continuous on $\mathcal H$.
We conclude from the [[Reisz Lemma]] that, for every $x\in\mathcal X$ and $y\in\mathcal Y$, there is a linear operator $K_x:\mathcal Y\rightarrow\mathcal H$ such that
$$
(y, f(x))=\langle K_xy,f\rangle.
$$
For every $x, t\in\mathcal X$ we also introduce the linear operator $K(x, t):\mathcal Y\rightarrow\mathcal Y$ defined, for every $y\in\mathcal Y$, by
$$
K(x,t)y:=(K_ty)(x).
$$
! Applications
* [[View of DL in RKHS]]
Residual connection were introduced by He et al. in [[Deep Residual Learning for Image Recognition|https://arxiv.org/abs/1512.03385]] in which they give convincing theoretical and practical evidence for the advantages of utilizing additive merging of signals both for image recognition, and especially for object detection.
* [[Wide Residual Network|http://arxiv.org/abs/1605.07146]]
* [[ResNeXt: Aggregated Residual Transformations for Deep Neural Networks|https://arxiv.org/abs/1611.05431]]
! Bibs
* [[Highway Connections]]
* [[Identity Matters in Deep Learning]]
! Repos
[[resnet.torch|https://github.com/facebook/fb.resnet.torch]]
RBMs can learn excellent generative models and RBM plays an important role in the training of Deep Belief Networks, as a good initialization for the FNN.
! Theory
!! Formalization
The RBM is a parameterized family of probability distributions over binary vectors. It defines a joint dsitribution over $v\in\{0, 1\}^{N_v}$ and $h\in\{0, 1\}^{N_h}$ via the following equation
$$
P(v, h) = \frac{\exp(h^\top Wv+v^\top b_v + h^\top b_h)}{Z(\theta)}
$$
The partition function $Z(\theta)$ is an sum of exponentially many terms and cannot be efficiently approximated to a constant multiplicative factor. The probability of the visible units is computed by marginalizing over the hidden units, also the probability of observing the data $X$, given the weights $W$:
$$p(X|W) = p(v) = \frac{1}{Z(v, h)}\sum_he^{-E(v, h)}$$
We can break the likelihood into 2 parts:
$$
\mathcal L = \ln p(v) = \ln\sum_he^{-E(v, h)}-\ln Z(v, h)
$$
//Clamped// ''Free Energy'' $F^c(v)$ and the standard ''Free Energy'' $F(v, h)$. First one is easy to evaluate in the RBM formalism, whereas $F(v, h)$ is computationally intractable. Knowing the $Z(v, h)$ is //like// knowing the equilibrium distribution function, and methods like RBMs appear to approximate it in some form or another.
!! Training
RBMs are usually trained via Contastive Divergence. The Energy funtion, being quadratic, lets us readily factor $Z$ using a mean field approximation, leading to simple expressions for the conditional probabilities:
$$
\begin{align}
p(h_i=1|v) &= \sigma(b_i+W_ih)\\
p(v_i=1|h) &= \sigma(a_i+W_iv)
\end{align}
$$
and weight update rule
$$
dw = \langle v, h\rangle_+-\langle v, h\rangle_\infty
$$
$\langle v, h\rangle_\infty$ is evaluated in the limit of infinite sampling, at the so-called equilibrium distribution. But we don't take the infinite limit.
CD approximates the (mean field) Free Energy by running only 1 (or more) steps of Gibbs Sampling.
! Applications
RBMs are basicly a solved problem. 10 years ago, we use them to pretrain deep supervised nets. They still outperform VAEs on simple datasets. RBM research continues in areas like semi-supervised deep learning with deep hybrid architectures, temperature dependence, infinitely deep RBMs, etc.
! Partition Functions related with Quantum Chemistry
refs
* [[Improving RBMs with physical chemistry|https://charlesmartin14.wordpress.com/2016/10/21/improving-rbms-with-physical-chemistry/]]
<<<
Knowing the Parittion function $Z(v, h)$ is not the same as knowing the distribution $p(v)$.
<<< [[Why Does Deep and Cheap Learning Work so Well?]]
This is a small but important technical oversight. Scaling Energy does not change $Z(v, h)$. A connection between RBMs and Kadanoff VRG approach is explained in [[Charlse's post|https://charlesmartin14.wordpress.com/2015/04/01/why-deep-learning-works-ii-the-renormalization-group/]]. A more detailed connection between deep learning and Statistical Mechanics is shown [[here|An exact mapping between the Variational Renormalization Group and Deep Learning]].
<<<
RG procedure must preserve more information about the distribution than the free energy.
<<< [[Comment on “Why does deep and cheap learning work so well?”|http://arxiv.org/pdf/1609.03541v1.pdf]]
Also in the training of RBMs, the idea of preserving Free Energy, via either a ''trace condition'' or say, by layer normalization, is the important point. In variational RG, on the renomalization operator $T(v, h)$:
$$
\text{Tr}_he^{T(v, h)}=1
$$
similar to ''Size-Consistency and/or Size-Extensivity'' in Quantum Chemistry.
! Bibs
* [[Recurrent Temporal RBM]]
! Bibs
* [[Understanding deep learning requires rethinking generalization|https://arxiv.org/abs/1611.03530]]
* [[Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz methods|https://arxiv.org/abs/1503.08235]]
* [[Train faster, generalize better: Stability of stochastic gradient descent|https://arxiv.org/abs/1509.01240]]
! Summary
By conventional wisdom, if the model happens to fit the training set exactly, then your model is probably not simple enough, meaning it will not fit the test set very well. According to Ben, this conventional wisdom is wrong. He demonstrates this by allowing the number of parameters to far exceed the size of the training set, and in doing so, he fit the training set exactly, and yet he still managed to fit the test set well. He suggested that generalization was successful here because stochastic gradient descent implicitly regularizes. For reference, in the linear case, stochastic gradient descent (aka the randomized Kaczmarz method) finds the solution of minimal 2-norm, and it converges faster when the optimal solution has smaller 2-norm. Along these lines, Ben has some work to demonstrate that even in the nonlinear case, fast convergence implies generalization.
! Bib
* [[Variational Autoencoder]]
!! Reversible Generative Models
They model a multivariate distribution $$p_X(x)$$ as an explicit invertible non-linear transformation $$x = f(z)$$ of a simple tractable distribution $$p_Z(z)$$.
$$
\log p_X(x) = \log p_Z(z) - \log|\frac{dx}{dz}|
$$
Aim at formulating the flow for which the Jacobian-determinant $$|\frac{dx}{dz}|$$ is relatively easy to compute.
!!! Normalizing flows
By restricting the functional form of $$f$$, various determinant identities can be exploited. These models cannot be trained directly on data and be able to sample because they do not have a tractable analytic inverse $$f^{-1}$$, but have been shown to be useful in representing the approximate posterior for variational inference.
* [[Variational Inference with Normalizing Flows|https://arxiv.org/abs/1505.05770]]
* [[Improving Variational Inference with Normalizing Flows]] [Rezende and Mohamed, 2016]
!!! Autoregressive transformations
By usign an autoregressive model and specifying an ordering in the dimensions, the Jacobian of $$f$$ is enforced to be lower triangular. These models excel at density estimation for tabular datasets, but requires $$D$$ sequential evaluations of $$f$$ to invert, which is prohibitive when $$D$$ is large.
* Improving Variational Inference with Inverse Autoregressive Flow [Kingma et al., NIPS 2016]: [[paper|https://arxiv.org/abs/1606.04934]], [[tf implementation|https://github.com/openai/iaf]]
** Inverse Autoregressive Flow: $$z_k = \frac{z_{k-1}-1-\mu_k(z_{<k, x})}{\sigma_k(z_{<k}, x)}$$
** Fast to sample, slow to calculate density
* [[Improving Variational Auto-Encoders using Householder Flow|http://arxiv.org/abs/1611.09630]] [Tomczak and Welling, 2017]
* [[Masked Autoregressive Flow|Parallel Multiscale Autoregresssive Density Estimation]]
!!! Partitioned transformations
Partitioning the dimensions and using an affine transform makes the detereminant cheap to compute, and the inverse $$f^{-1}$$ computable with the same cost as $$f$$. This method allows the use of convolutional architectures, excelling at density estimation for image data.
* NICE [Dinh et al., 2015]
* [[Real NVP|http://arxiv.org/abs/1605.08803]] [Dinh et al. 2016]:$$y_{1:d} = z_{k-1, 1:d}, y_{d+1:D}=t(z_{k-1, 1:d})+z_{d+1:D}\odot\exp(s(z_{k-1, 1:d}))$$
** Fast in both density calculation and sample, limited capacity
* [[Glow]]
!!! Not sure
* Hamiltonian variational inference [Salimans et al., 2015]
* Stochastic Backpropagation and Approximate Inference in Deep Generative Models
* Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
* Copula variational inference
* The Variational Gaussian Process
* Importance Weighted Autoencoders (IWAE) [Burda et al., 2016]
* Hierarchical Variational Models
* Auxiliary Deep Generative Models [Maaløe et al., 2016]
* Variational inference for Monte Carlo objectives
* [[Adversarial Variational Bayes]] (AVB) [Mescheder et al., 2017]
** Hierarchical Implicit Models [Tran et al., 2017]
** Variational Inference Using Implicit Distributions [Huszar, 2017]
** Adversarial Message Passing for Graphical Models [Karaletsos, 2016]
These models are easy to sample from, and can be trained by maximum likelihood using the change of variables formula. However, this requires placing awkward restrictions on their architectures, such as partitioning dimensions or using rank one weight matrices, in order to avoid an $$\mathcal O(D^3)$$ cost determinant computation.
!! ODE
Alternatively, the Jacobian trace can be used if the transformation is specified by an ordinary differential equation.
* Neural Ordinary Differentiable Equations
* [[FFJORD]]
! Theory
* [[Variational Inference for Generative Models]]
! Applications
* [[End-to-end Optimized Image Compression]]
* [[Video Streaming Optimization]]
* n-step return
* $$\lambda$$-return
* Meta-Gradient RL
Objective:
$$
\max_\theta\mathbb E_M\mathbb E_{\tau^{(k)}_M}[\sum_{k=1}^KR(\tau^{(k)}_M)|\text{RLagent}_\theta]
$$
! Computation requirement
For LSTM
* [[GEMM]] (input 2HxB, output 4HxB): for 3 gates (i, f, o) and cell
** #FLOPS = 16HHB
** Bytes through memory (fp32) = 4(8HH + 2HB + 4HB)
** flops:byte = 2HB:3B+4H, typically H>>B
** from roofline model, the efficient batchsize for H=2048 on P100 is 32
* Pointwise operations: tanh, sigmoid, add, pointwise mult
! Network Level Optimizations
# Reducing memory traffic
#* for small batch size, we reduce the amount of times we load matrix
#* by unrolling over time, freeing input, hidden dependencies
#* [[Persistent RNNs]]: keep recurrent matrix on-chip. impressive speedup, constraint by on-chip memory
# Reducing overheads
#* A naive implementation of pointwise operations is by separate [[GPU Kernel]]s: setup overhead and bandwidth are problem
#* Fuse into one kernel
# Increasing parallelism
! refs
* [[Optimizing Performance of Recurrent Neural Networks on GPUs|https://arxiv.org/abs/1604.01946]]
* [[Optimizing Recurrent Neural Networks in cuDNN 5|https://devblogs.nvidia.com/parallelforall/optimizing-recurrent-neural-networks-cudnn-5/]]
! Predict outputs
Meta-learner model $g$ with parameters $\phi$ takes input $x$ and predicts ouput $y$
$$
\hat y^* = g(\{(x, y)\}, x^*, \phi)
$$
$g$ is always a sequence model iterates over dataset.
!! Bibs
* ICML 2016, Santoro, Meta-learning with memory-augmented neural networks
* 2016 Duan, Rl2: Fast reinforcement learning via slow reinforcement learning
* 2016 Wang, Learning to Reinforcement Learn
* 2017 Mishra, Meta-learning with temporal convolutions
! Predict parameters
$g$ talks dataset $\mathcal D$ and current learner parameters $\theta$, outputs new parameter $\theta'$
!! Bibs
* 1992, Bengio, On the optimization of a synaptic learning rule
* 2001, Hochreiter, Learning to learn using gradient descent
* NIPS 2016, Andrychowicz, Learning to learn by gradient descent by gradient descent
* ICLR 2017, Li, Learning to optimize
* ICLR 2017, Ravi, Optimization as a model for few-shot learning
* ICLR 2017, Ha, Hypernetworks
! Feature map sizes
To adopt original strides:
* 1/8 from stage2_unit4: _plus6
* 1/16 from res4: _plus12
* 1/32 from res5: _plus15
* 1/64 from conv6:
@inproceedings{rosen2004author,
title={The author-topic model for authors and documents},
author={Rosen-Zvi, Michal and Griffiths, Thomas and Steyvers, Mark and Smyth, Padhraic},
booktitle={Proceedings of the 20th conference on Uncertainty in artificial intelligence},
pages={487--494},
year={2004},
organization={AUAI Press}
}
* Most of the focus has been on optimization methods
* Point estimate is probably not enough
Focus:
* Accurate uncertainty quantification
* Robustness of inferences
Classical posterior approximation:
$$
\pi_n(\theta|Y^{(n)}) = \frac{\pi(\theta)L(Y^{(n)}|\theta)}{\int(\pi(\theta)L(Y^{(n)}|\theta)d\theta)}
$$
* the integral of the marginal probability is intractable
* conjugate models have simple form
* Large sample Gaussian approximation can be more useful than variational Bayes sometimes (Bayesian central limit theorem (BVM))
* Or use laplace approximation to $$\int(\pi(\theta)L(Y^{(n)}|\theta)d\theta)$$
* Or [[variational Bayes|Variational Inference]]
! Big n problem
* Embarrassingly parallel (EP) MCMC: subsets of data & combine
** [[Wasserstein barycenter of subset posterior (WASP)|https://arxiv.org/abs/1508.05880]]
** [[Simple & fast posterior interval estimation (PIE)|https://arxiv.org/abs/1605.04029]]
*** WASP has explicit relationship with subset posteriors in 1-d.
*** Quantiles of WASP are simple averages of quantiles of subset posteriors.
*** So run MCMC, average quantiles. (reminiscent of bag of little boostraps)
* [[Approximate MCMC]]: Approximate expensive to evaluate transition kernels
* C-Bayes: Condition on observed data being in small neighborhood of data drawn from assumed model [ROBUST]
* Hybrid algorithms: run MCMC for a subset of the parameters & use a fast estimate for the others.
[[paper|https://arxiv.org/abs/1607.00360]]
The Bregman distortion is the residual of Taylor expansion of a function:
$$
R(x\|y) = \varphi(x) - \varphi(y)-(x-y)^\top\nabla\varphi(y)
$$
when $\varphi$ is not convex, this value can be positive, negative or zero. When $\varphi$ is convex, the Bregman divergence $D_\varphi(x\|y)$ has some good properties:
* triangle equality
* dual symmetry w.r.t. convex conjugate
* population minimizer = average
We can extend this with a differentiable function $g$. We define a new generator
$$
\breve{\varphi}\doteq g(x)\varphi\left(\frac{x}{g(x)}\right)
$$
in terms of the perspective transform of $\varphi$. It turns out the perspective transform of the divergence
$$
g(x)D_\varphi\left(\frac{x}{g(x)}\parallel\frac{y}{g(y)}\right) = D_{\breve\varphi}(x\|y)
$$
equals the divergence of the perspective transform iff $g$ is affine or $\varphi(x)$ is (restricted) positive homogenuos of degree 1. $\breve\varphi$ does not have to be convex.
! Variables
* `filt_opt`: filter bank options
** `Q`
** `J`: the maximal wavelet scale
! Functions
!! wavelet_factory_1d.m
<<<
Each filter bank is then used to create a layer operator, using the function WAVELET_LAYER_1D.
<<<
translation-invariant representations in one dimension
! Details
[[Understanding Deep ConvNets (Scattering Repr)]]
! Paper Summary
* [[Group Invariant Scattering]]
* [[Scattering Representation for Recognition]]
* [[Scattering Representation of Modulated Sounds]]
* [[Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants|http://openreview.net/pdf?id=SJiFvr9el]]
** $|c|^2$ as higher order nonlinearity. FFT computation is derived.
! Notes of the Talk
There was a very interesting [[talk on Techion|https://www.youtube.com/watch?v=wHhYvtnY2zI]]. I recently found my research is getting more and more related to it so I decide to sort it out.
Considering classification of high dimensional signals $x$. Dimension can be as large as $10^6$ for images. Interpolation methods that works well in low dimension meets problem in high dimensional counterparts. Because you cannot cover the whole very large space with samples anymore and the samples from the same class could be very far way from each other.
That leads to the need of dimension reduction. If the signal can be represented with low dimensional subsets $\Omega$, then there would be no curse of dimensionality. Find a good $\Omega$ turns to be problems like manifold learning or sparsity dictionary representations. However complex signals like image and audio have very correlated information that more or less influence the result, and thus such a subspace have to be deduced with the knowledge of $f$, the classification goal. That makes the problem supervised.
Let formulate the volume reduction problem. The signal domain $\Omega$ is reduced with a contractive operator $\Phi$ (non-linear) so that $\Phi(\Omega)$ could be a much smaller volume. A key element of this process is that $f$ should maintain regularity, i.e.,
$$
|f(x) - f(x')| \leq C\|\Phi(x) - \Phi(x')\|
$$
which is called Lipschitz regularity. This insures the reduced space preserves discriminability. The way of doing that is to build $\Phi(x)$ by contracting along directions where $f(x)$ is invariant: reduce intra-class variability.
On the other hand, we also want to increase the dimension so that original data can be easier to classify (e.g. by linear functions).
Deep neural networks have shown ability to do both things well. Typical deep neural nets combine linear and non-linear transformations and iterate between them and finally separate the output with linear classification. The impressive part of the work is the scale, a neural network can have billions of parameters.
The result on ImageNet containing $10^6$ images and $10^3$ classes is very impressive. The error rate is below $17\%$, very close to human ability. Speech recognition has not been moved for twenty years and now new speech recognition systems are implemented with neural nets. Now many companies are hiring people developing this kind of systems.
The question is why does it work. For the first two layers of image and audio classifications are learned with gradient descent, back propagation algorithms which looks like wavelets.
Let take an example in digit recognition, because the physical invariant is known. After a translation of rotations and small deformations, the sample remains in the same class. FYI, speech recognition requires subspace with time-frequecy translation and deformation invariance.
Ways to impose translate invariance is normalizing by zero mass or killing the face of Fourier transform. Diffeomorphisms is a little bit of different. We require Lipschitz stability,
$$
\forall\tau, \|\Phi(x_\tau) - \Phi(x)\|\leq C \underset{t}{\sup}|\nabla\tau(t)|\|x\|
$$
where $x_\tau(t) = x(t-\tau(t))$ is a small deformation of $x$ and the supremum is the diffeomorphism metric. This means the change of $\Phi(x)$ should be of the order of the deformation. If we move the distribution or perform Fourier transform, it will not be stable.
To deal with deformation, we have to use wavelets. Wavelets are functions that are localized, if we slightly deform a wavelet, we still have something that looks like a wavelet. To understand complex wavelet, think about a Gaussian modulated by sin and cos. We have a real part and an imaginary part,
$$
\psi(t) = \psi^a(t)+i\psi^b(t).
$$
And dilate it with
$$
\psi_\lambda(t)=2^{-j}\psi(2^{-j}t)
$$
with $\lambda = 2^{-j}$. The wavelet transformation averages the low frequencies and carries the high frequencies by their coefficients. The energy of wavelet transform will restore the original because it is unitary:
$$
\|Wx\|^2 = \|x\|^2.
$$
For scale and separation in 2D, the rotated and dilated wavelet is
$$
\psi_\lambda(t) = 2^{-j}\psi(2^{-j}r_\theta t)
$$
The wavelet can separate different different information within different scale.
To compute the invariance, we take a signal $x$ and make it as uniform as possible. The only linear function that is translation invariant is constant. To make a signal locally translation invariant, we locally average a signal by $x\star\phi(x)$. We take the modulus of the wavelet $|Wx|$
$$
|x\star\psi_{\lambda_t}(t)| = \sqrt{|x\star\psi^a_{\lambda_t}(t)|^2 + |x\star\psi^b_{\lambda_t}(t)|^2}
$$
which is unitary and contractive:
$$
\||W|x - |Wx'|\|\leq \|x-x'\|.
$$
The norm is preserved. The idea is to get the average of low frequencies and get the envelop of high frequencies, then we'll get a new set of invariant coefficients.
Put is all together, to get the first layer,
# we first compute the average of the image,
# then the high frequencies by wavelet transform,
# and take the modulus of the coefficients.
Now we compute the wavelet coefficients of these modulus, and there will be many images in the second layer of the network. The iteration goes on and on. The outcome is a convolution network. While compressing $x$ with the modulus, we keep increasing the dimensionality.
For other specific complex cases, we have to learn the invariant first. Basicly any contractive operator $|W_k|$ that preserves the norm can be used. The problem is how to learn it with small support.
Haar filtering is an orthogonal transformation computed by pairing the even and odd coefficients, adding and subtracting them. If we cascade it, we get the Haar wavelet. So we can learn the contraction by finding the right pairing that don't bring closer the points from different classes. Iin the unsupervised case, make sure points don't collapse (data aren't compressed too much), which is equivalent to minimize the mixed l-2/l-1 sparsity norm.
The structure resembles a [[convolutional network|Convolutional Neural Network]], instead of learning the [[filter banks|Filter Bank]], [[wavelets|Wavelet Transform]] are used to capture transform invariant features.
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! Tasks and data
* Large code corpus
** Karpathy et al. 2015 Linux Kernel Dataset
** [[Learning from "Big Code"|http://learnbigcode.github.io/]]
* human annotators, clean but homogeneous
** Project Euler dataset (pseudo-code as description)
** Parallel Django dataset (pseudo-code as description): This dataset includes all source code of Python web framework Django with line-by-line English annotation.
*** Diverse, spanning a wide variety of real-world use case like string manipulation, IO operations and exception handling.
** [[card2code|https://github.com/deepmind/card2code]]: DeepMind generates code for MTG and HS cards
** [[WikiSQL|https://github.com/salesforce/WikiSQL]]
* Assembled from user-generated descriptions
** StackOverflow: ACL 2016 Summarizing Source Code using a Neural Attention Model
** IFTTT: Language to code: Learning semantic parsers for if-this-then-that recipes
** Docstring: [[code-docstring-corpus|https://github.com/EdinburghNLP/code-docstring-corpus]]
** [[NL2Bash|https://github.com/TellinaTool/nl2bash]]: 10,000 bash one-liners
* Debugging
** Defects4J: a curated corpus of bugs (in total 395)
** 41 AspectJ bugs are from the iBugs dataset which were collected by Dallmeier and Zimmermann. (code before and after fix) plus some others collected by David Lo's lab. several hundreds in total
** Eclipse, etc 3k+ instances. Related file but not the exact line.
* Code review
** match best reviewer given descriptions
* Code Caption
** [[HabeasCorpus|https://github.com/kimiyoung/review_net]]: very small, 9 eclipse projects
* Social media
** find related tweets (keywords or social network mining stuff)
** text analysis of blogs, StackOverflow posts, feature descriptions
* Question-Code
** [[StaQC|https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset]]
** [[Conala Corpus|https://conala-corpus.github.io/]]
Make use of community: Open-source software systems such as Linux,MySQL, Django, Ant, and OpenEJB have become ubiquitous. These systems publicly expose not just source code, but also meta-data concerning authorship, bug-fixes, and review processes.
! Introduction
Reading comprehension and question answering in real world is enormously difficult to handle for computer. Teaching intelligent natural languages is one of the cornerstones to solve AI. If we could build models to comprehend natural languages, it wouldn't be thus far to build intelligent systems and understand human mind. And it has huge value for consumers.
Recurrent Neural Networks (RNN) trained by Back Propagation are up to now the closest imitation to what brain does. Wesbros et al.~\cite{werbos2016regular} carried out an empirical study to find their relationships. These are the models on our smartphones recognizing and answering our questions when we talk to Siri and Google Assistant. This subject is so challenging and promising that it has attracted some of the best research teams in AI fields such as DeepMind, Facebook Research and OpenAI. Mikolov et al.~\cite{mikolov2015roadmap} pose different intelligent tasks as dialogue, or Question Answering (QA), where the learner should develop memory, perform reasoning accordingly and understand indirect supervision.
Software society offers valuable datasets for NLP. Online code corpora like GitHub, question answering forums like StackOverflow and documentations of various softwares and programming tools are highly structured and rich in content. In return, training an agent who can understand Software Engineering (SE) materials can help develop SE theory and tools. We have seen applications of NLP in code refactoring, language models for code~\cite{dam2016deep} and information retrieval of answers, projects and documentation. But compared with other deep learning applications, this field is still growing and requires more research, tasks and well-established datasets.
! Research Questions and Aims
The ultimate goal of this project is to train an agent to be able to interact with humans, solving specific SE problems. However, this is too big a problem to solve, I decide to design incremental tasks and solve them in an iterative manner. Basic tasks are for example comment sentiment analysis and locating callings in code, basic fact Q\&A in documentation. More advanced tasks are like code completion, similar snippets retrieval and automatic problem solving or even let the program code itself. I will identify what tasks can be solved with current technologies and what are the preliminary tasks we have to solve before the ultimate problem. Although these tasks seem to be distantly related, answering the following questions is important to attack all of them.
# ''How to achieve indirect supervision through dialogue?'' Dialogue is human communication tool. This is how issues and knowledge are discussed on GitHub and StackOverflow and where we would like the agent to learn. But we have to design simpler tasks and rewarding mechanisms for the learner to understand normal feedback before the agent can learn from arbitrary forms of communication happening on these online communities.
# ''How to represent context and let the agent memorize it?'' Main stream NLP algorithms always use a RNN as an encoder of the context~\cite{cho2014learning} and use attention mechanism to associate certain parts of context to related queries~\cite{bahdanau2014neural}. In these 2 years, there are many advanced memory mechanisms and models being proposed. I will review them in the next part and show how to incorporate these structures to solve SE problems.
# ''How can we enhance current models?'' Sequence-to-sequence (seq2seq) models based on RNN~\cite{sutskever2014sequence} are widely used in Neural Machine Translation (NMT) and Automatic Speech Recognition (ASR). They have a chain-like structure. However, in computational linguistics and compiler theory, we use a different tree-like structure, the grammar tree, which shares similarities with another kind of deep model, the Recursive Neural Network. Combining Recurrent and Recursive Neural Networks we arrive at a structure-to-structure modeling. For example we can use Neural Tensor Network~\cite{socher2013reasoning} to do a syntactic to semantic tree reasoning.
! Related Work
!! Goal-Directed [[Dialog]]
* [[Learning End-to-End Goal-Oriented Dialog|https://arxiv.org/abs/1605.07683]] test on (6) Dialog bAbI task.
!! Program Induction
Popular program induction algorithm is [[Neural Abstract Machines]]
!! Memory Networks
''Seq2seq''~\cite{sutskever2014sequence,cho2014learning} learning is one of the most widely used models in NLP. In the original papers, an encoder RNN reads the input and generates a context vector $\mathbf c$ from the last hidden unit. Then $\mathbf c$ is connected to another RNN called the decoder, which generates the output sequence. Bahdanau et al.~\cite{bahdanau2014neural} use a sequence as the context and propose \textbf{attention mechanism} to adapt the weights of context associated with certain output stage.
More recent studies enable the intelligent learner to handle long and short term memories and apply them to reasoning for tasks such as language understanding and dialogue. The agent takes the following inputs:
* A ''query'' $q$ is the last utterance the speaker said in a general dialogue setting, or the question in a QA setting.
* A ''memory'' vector $\mathbf m$ is the knowledge of the agent plus the dialogue history. The knowledge could be as large as the whole code base or documentation as long as the model is powerful enough to scale to that.
The agent use these modules to handle the inputs:
* An ''encoder'' converts $q$ into a vector. This is a RNN in \cite{cho2014learning} or a simpler word embedding~\cite{mikolov2013efficient}.
* A ''memory module'' $M$ finds the best part of $\mathbf m$ related to $q$. This is called the \textbf{addressing} stage.
* A ''controller module'' $C$ sends $q$ to $M$ and reads back the relevant memory, adds that to the state. In practice we always cycle this process to enable complicate reasoning.
* A ''decoder'' generates output from the final states.
The classic Memory Network~\cite{weston2014memory} is trained in a fully supervised setting, labels of the best part of memory is given at every stage of the memory addressing. Its follow-up End-to-End Memory Network~\cite{sukhbaatar2015end} use soft attention for memory addressing to relax the supervision. This enables to train the attention in back propagation and the only supervision we need is on the output. Using one memory unit to match both the query and the state limits the expressiveness. By splitting the memory into a key-value pair, [[Millor et al.|Key-Value Memory Network]] encodes prior knowledge and gets better results. [[Recurrent Entity Network]] demonstrates how we can enhance the memory unit by letting the agent learn how to read and write the memory to track the facts. Weston~\cite{weston2016dialog} generalize this model to unsupervised learning, by adding a new stage to generate answers and predict replies.
This kind of model is tested on various tasks. For example, basic reasoning on 20 bAbI tasks~\cite{weston2015towards}, reading children's books~\cite{hill2015goldilocks}, understanding real dialogues from movies~\cite{dodge2015evaluating}, etc. All of the tasks plus some others can be found at the bAbI project~\footnote{https://research.fb.com/projects/babi/}. Machine comprehension is another important task, related corpora are CNN and Daily Mail datasets~\footnote{https://github.com/deepmind/rc-data}.
! Theoretical framework and methods
Considering the development of current NLP society, I sort these SE-related tasks by their difficulty.
''Code based reasoning.'' Like in the 20 bAbI task, we can teach our learner to understand the syntactic structure of code and query about the variables, operands or outputs. Of course we can achieve this with the compiler or a corresponding REPL, here we want to build a model to learn the programming language by itself. Applications of this system include code refactoring, auto-generation and translation between natural languages and programming languages.
Seq2seq~\cite{sutskever2014sequence} is state-of-the-art in solving this task. In theory, there exists finite RNNs that are Turing complete~\cite{siegelmann1992computational}, the question is can we train one with back propagation. To add structured information and build complicated models, I will evaluate the encoding performance of different sequence models, such as autoregressive models, RNN, recursive nets and generalize the model into a struct2struct fashion. We can utilize previous structure learning schemes such as \cite{huang2013learning} and \cite{socher2013reasoning}. Programming languages offer stricter grammars and more convenient parsing tools than natural language, which can be converted to supervision on the encoder training.
''Semi-close domain QA.'' In this stage, we no longer restrict the agent to answering fixed tasks but only specify a topic. we train a chatbot to simulate human responses to certain questions. There are already some datasets dedicated to this task. Google used IT troubleshooting chat logs to train their famous conversational agent~\cite{vinyals2015neural}. Lowe et al.~\cite{lowe2015ubuntu} released the Ubuntu Dialogue Corpus containing 1M dialogues on Ubuntu-related problems. Now the challenge is not only to generate answers which seem plausible, but really make sense. Thus we can lower the cost of labor in some software service areas.
Memory network is suitable for this task. To have more flexible memory, I will reach out to Neural Turing Machine~\cite{graves2014neural}, Dynamic Memory Network~\cite{kumar2015ask} and [[Differentiable Neural Computer]] for insights.
''Reading comprehension.'' Then we push the agent's reading and reasoning ability to the limit by letting it read longer and more complex documents. We pose a question to the agent and give it some documents as reference. Then the agent should locate related passages and summarize the answer. Microsoft released a general purpose reading comprehension dataset called MS MARCO~\cite{nguyen2016ms} and provided several benchmarks run by some of the current learning algorithms. We can migrate this case to SE scenario, where we search answers from software documents to users queries.
MS MARCO provides a [[leaderboard|http://www.msmarco.org/leaders.aspx]] to keep track of the best algorithms on this task. The current leaders are Prediction~\cite{wang2016machine} and ReasoNet~\cite{shen2016reasonet}. Prediction use match-LSTM~\cite{wang2015learning} to match the query to informative passages and a modified Pointer Network~\cite{kumar2015ask,trischler2016natural} for answer generation. ReasoNet is a memory network with a termination state.
''The great convergence.'' If we can solve previous individual problems, we are ready to combine these studies to create a universal Software Engineering bot, handling real world SE problems referring to some of the most popular communities. Such an agent could find answering to project, software or language specific questions and help us with development, documenting and quality assurance.
* Random Search
* Tree of Parzen estimators
* Sequential model-based algorithm configuration
2nd order methods are seldom used in deep learning mainly because we cannot approximate Hessian within batches. SDG has some advantages in stability.
! KFac
* [[paper|https://arxiv.org/abs/1503.05671]]
* useful for small autoencoder
! Newton Algorithm
Assuming a quadratic loss function $E$ we can compute the weight update along the lines of
$$
\Delta w = \eta\left(\frac{\partial^2E}{\partial W^2}\right)^{-1}\frac{\partial E}{\partial W} = \eta H^{-1}\frac{\partial E}{\partial w}
$$
The algorithm itself is unusable for neural nets because the Hessian is not positive definite and the algorithm will diverge.
! Conjugate Gradient
It works only for batch learning. The descent direction $\rho_k$ at iteration $k$ is given as
$$
\rho_k = -\nabla E(w_k) + \beta_k\rho_{k-1}
$$
where the choice of $\beta_k$ can be done either according to Fletcher and Reeves or Polak and Ribiere. Two directions $\rho_k$ and $\rho_{k-1}$ are defined as conjugate if
$$
\rho_k^\top H\rho_{k-1} = 0
$$
i.e. conjugate directions are orthogonal directions in the space of an identy Hessian matrix.
! Quasi-Newton
This kind of method iteratively computes an estimate of the inverse of Hssian and is $O(N^2)$. The estimated inverse Hessian $M$ is positive definite and the search direction is set to:
$$
\rho(t) = M(t)\nabla E(w(t))
$$
The most successful Quasi-Newton approach is BFGS, which updates the estimate as:
$$
M(t) = M(t-1)\left(1+\frac{\phi^\top M\phi}{\delta^\top\phi}\right)\frac{\delta\delta^\top}{\delta^\top\phi}-\left(\frac{\delta\phi^\top M+M\phi\delta^\top}{\delta^\top\phi}\right).
$$
where $\phi$ and $\delta$ are $N\times 1$ vectors:
$$
\begin{aligned}
\phi &= \nabla E(w(t)) - \nabla E(w(t-1))\\
\delta &= w(t)-w(t-1).
\end{aligned}
$$
! [[Gauss-Newton]] and Levenberg Marquardt
''They work only for [[mean square error loss functions|Neural Net Functions]]''. They use the square Jacobi approximation for Hessian:
$$
\Delta w = \left(\sum_p \frac{\partial f(w,x_p)^\top}{\partial w}\frac{\partial f(w,x_p)}{\partial w}\right)^{-1}\nabla E(w)
$$
The Leven Marquardt method has a regularization parameter $\mu$ that prevents it from blowing up, if some eigenvalues are small
$$
\Delta w = \left(\sum_p \frac{\partial f(w,x_p)^\top}{\partial w}\frac{\partial f(w,x_p)}{\partial w}+\mu I\right)^{-1}\nabla E(w)
$$
A similar procedure also works with Kullback-Leibler cost and is called [[Natural Gradient]].
[[Block-diagonal LM]] used the diagonal approximation of the Jacobi product in LM algorithm for RNN.
! [[Computing Hessian]]
! [[Apply to Multilayer Nets]]
Tactics
* Detect: intrusion, service denial, message integrity, delay
* Resist: actors, limit access, encrypt data, validate input, separate entities
* React: revoke access, lock computer, inform actors
* Recover: maintain audit trail, restore
Security patterns
* identity management
* authentication
* access control
* secure process management
* web services security
* cloud computing
Frameworks
* Spring Security
* Apache Shiro
* JGuard
* [[ZK|https://www.zkoss.org/]]: protects xss, ddos and cross-site request forgery attacks
! Perception
!! LiDAR
!!! Sensors
* Top LiDAR
* All LiDAR
!!! Representations
* Point Cloud: PointNet
* Voxel: SPConv
** 2D voxelization: pillar
* Range image
!! Camera
* [[Visual Odometry]]
!! Radar
* Semantic scene understanding
* Learned topological place recognition
* Introspective radar odometry
* System-level self-supervision for radar perception and navigation
** hard to work with: high return can be reflection & noise; low return can be occluded
! Prediction
* [[Pedestrain path prediction]]
* ICRA17 Self-Supervised segmentation of path proposals
* Raster-based motion prediction: [[video|https://slideslive.com/38930749/rasterbased-motion-prediction-for-safe-selfdriving]]
** Raster-based methods are unconstrained.
Papers
* CVPR20 STINet (detection+tracking+prediction)
** Need visual features of lanes and pedestrains to predict
* CVPR20 VectorNet
** In addition to prediction, can do map completion
! Planning
! Tools
!! Simulation platforms
* [[CARLA|https://leaderboard.carla.org/]]
!! Datasets
* Perception
** [[Waymo Open Dataset]]
* Prediction
** Argoverse
* blog: [[BN in BYOL|https://untitled-ai.github.io/understanding-self-supervised-contrastive-learning.html]]
* code: https://github.com/untitled-ai/self_supervised
* Train from manually ''annotated programs'' and avoiding program execution at training time.
* Train model with backprop: limited discrete operations
** low-level memory
** require memory to be differentiable
! refs
* introduced a floating parser to address the large amount of variation in utterances and table schema for semantic parsing on web tables.
* [[Seq2SQL|https://arxiv.org/abs/1709.00103]]
** attention on aggregation op, select column and where clause
** policy gradient training to deal with condition's order issue. @@color:#859900;Policy net is closely related to synthetic gradient@@
** WikiSQL dataset
* [[SQLNet|https://arxiv.org/abs/1711.04436]]
** better performance since direcly filling the clause.
** [[code|https://github.com/xxj96/SQLNet]]
! Semantic Role Labeling
* [[Syntactic Scaffolds for Semantic Structures]]
! Bib
* [[Multi-Scale Context Aggregation by Dilated Convolutions|https://arxiv.org/abs/1511.07122]]
** 2015 paper still state-of-the-art result
** [[Dilated Convolution]]s
* [[The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation|http://arxiv.org/abs/1611.09326v2]]
** similar architecture as PixelCNN++
* Fully Convolutional Instance-aware Semantic Segmentation
** 2016 COCO winner
** R-FCN for segmentation
** [[github|https://github.com/daijifeng001/TA-FCN]] code will come up
* [[EMA|Expectation-Maximization Attention Networks for Semantic Segmentation]]
** 88.2 on VOC
! Decoder Structure
* [[Segmentation Decoder Search Space]]
! Extentions
* [[Few-Shot Segmentation]]
! Bag of Words
! Position Encoding
* [[''MR'': sentence polarity dataset|http://www.cs.cornell.edu/people/pabo/movie-review-data/]], altogether 12662 sent.
* [[''Subj'': Subjectivity dataset|http://www.cs.cornell.edu/people/pabo/movie-review-data/]], 10000 sent., subject part of ''MR''.
* [[''SST'': Stanford Sentiment Treebank|http://nlp.stanford.edu/sentiment/treebank.html]], 9645 sent. Small but well organized with detailed structure.
* [[''TREC'': Question classification dataset|http://cogcomp.cs.illinois.edu/Data/QA/QC/]], 15500 sent. (?), if overlapped, use the last one.
* [[''CR'': Customer review dataset|https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html]], see Data Sets section.
* [[''MPQA'': Opinion polarity dataset|http://mpqa.cs.pitt.edu/corpora/mpqa_corpus/]], 535 articles.
* [[''Opi'': Opinosis Dataset|https://archive.ics.uci.edu/ml/datasets/Opinosis+Opinion+%26frasl%3B+Review]] 51 topics with each 100 sent., which comprises sentences extracted from user reviews on a given topic, e.g. "sound quality of ipod nano". Author's homepage won't work, pointing to UCI site.
* [[''Irony''|https://github.com/bwallace/ACL-2014-irony]] 16,006 sent. from reddit.
[[link|https://arxiv.org/abs/1609.05473]]
! Papers
# [[Robust Scene Text Recognition with Automatic Rectification|http://arxiv.org/abs/1603.03915]]
# [[An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition|http://arxiv.org/abs/1507.05717]]
! Github
# [[Convolutional Recurrent Neural Network|https://github.com/bgshih/crnn]]
# [[Attention OCR|https://github.com/da03/Attention-OCR]]
! Models
* [[Hopfield Net]]
* [[LSTM|Long short-term memory]]
* Tapes
* Arrays
* Stacks
* [[Stochastic Recurrent Network]]
* [[Neural HSMM]]
* Restrictions on recurrent weight matrix
** uRNN
** scoRNN
!! Advanced Structures
* [[Multi-Scale RNN]]
* [[Connectionist Temporal Classification]] loss
* Faster RNN
** [[Quasi-RNN]]
** [[SRU]]
* [[Attention Mechanism]]
* Memory networks
** External Memory
*** [[Memory Networks]]
*** [[Neural Machine Translation by Jointly Learning to Align and Translate|https://arxiv.org/abs/1409.0473]]
*** (Stack) RNNs with attention
*** Dynamic Mem. Nets
* [[Neural Abstract Machines]]
* Associative memory
** [[Using Fast Weights to Attend to the Recent Past]]
* Adaptive Computation Time
** [[Adaptive Computation Time for Recurrent Neural Networks|https://arxiv.org/abs/1603.08983]]
** [[tf implementation|https://github.com/DeNeutoy/act-tensorflow]], pretty slow
* Neural Programmer
! Bibs
* [[Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets]]
* [[Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision|https://arxiv.org/abs/1611.00020]]
** non-differentiable memory optimized with REINFORCE, improved weakly supervised semantic parsing on WebQuestionsSP.
* [[Differentiable neural computers|https://deepmind.com/blog/differentiable-neural-computers/]]
** DeepMind's nature paper
* [[Tracking the World State with Recurrent Entity Networks|https://openreview.net/forum?id=rJTKKKqeg]]
** [[tensorflow implementation|https://github.com/jimfleming/recurrent-entity-networks]]
** the results aren't easily comparable to DNC
Most networks with external memory use some form of ''content-based'' memory access, find the memory closest to some ''key vector'' emitted by the network, return either the memory content or an associated ''value vector''.
* [[MemN2N|http://arxiv.org/abs/1604.06045]]
* [[Can Active Memory Replace Attention?|https://arxiv.org/abs/1610.08613]]
** Extended Neural GPU with an output tape tensor $p$, which has access to @@color:#859900;all previous outputs@@.
** Use teacher forcing in training.
! Tools
* [[LSTMVis|https://github.com/HendrikStrobelt/LSTMVis]]
For simplicity, let's suppose $$\psi(x)=0$$. use $$\tilde\nabla f(x):=\nabla f_i(x)$$ SGD converges in rate $$\varepsilon\propto 1/\sqrt T$$
SGD converges slowly because the variance is large, $$\|\tilde\nabla f(x)-\nabla f(x)\|$$ can be "large"
[[paper|https://openreview.net/forum?id=HyWrIgW0W]]
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
* Data augmentation helps contrastive learning
* Learnable nonlinear transformation between the representation and the contrastive loss improves the quality of the learned representations
* Normalized embeddings and adjusted temperature
* larger batch sizes and training
# Details
* [[LARS|Layer-wise Adaptive Rate Scaling]] optimizer
*
SLAM does these two tasks simutanously
* Localization
** Estimate the robot's poses given landmarks
* Mapping
! Definition
Given
* the robot's control
** $$u_{1:T}=\{u_1, \dots, u_T\}$$
* Observations
** $$z_{1:T}=\{z_1, \dots, z_T\}$$
Wanted
* Map of the environment: $$m$$
* Path of the robot: $$x_{1:T}=\{x_1, \dots, x_T\}$$
!! Three Main Paradigms
* Kalman filter
* Particle filter
* [[Graphical Model]]: HMM with a map.
[[link|http://slidesnstories.tiddlyspot.com/]]
Should be pretty useful for scientific writing and presentation. But currently only using its footnote plugin.
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
! Algorithm
!! Advanced ES methods
* Hierarchical evolution
* Regularized evolution (tournament selection/remove oldest ones)
* [[CMA-ES|https://arxiv.org/abs/1604.00772]]
!! Aquisition function estimation
* TPE
* Neural network
!! Questions
* How many parameters in the actuall network, if <1k, could try TPE
* How long to evaluate one configuration, if long, can multi-fidelity speedup?
! Control Schemes
* Change R-stick to tilt
* Move major actions to shoulder buttons
* Sensitivity to high
* Stick jump off
! Sources
* lw
* ZeRo
! Practices
* Short hops
* Use HyperNet as auxiliary model to learn a transformation from a binary encoding of an architecture to the weight space.
* The weights for each node in our network are dynamically generated
Architecure sampling:
* memory-bank view
* random read/write
Weight sampling:
* HyperNet
[[link|https://blogs.princeton.edu/imabandit/2017/07/05/smooth-distributed-convex-optimization/]]
! Setting
The goal is to find in a distributed way the optimal "consensus" point
$$
x^*\in \arg\min_{x\in\mathbb R^d}\sum_{v\in V}f_v(x)
$$
where $v$ is a computing unit with access to a private dataset. $f$ is $\beta$-smooth and $\alpha$-strongly convex ($\kappa=\beta/\alpha$ is the condition number). We expect ''linear convergence'', i.e., the scaling of $T_\epsilon$ should be $\log(1/\epsilon)$.
* The discriminator family could bias the learning algorithm toward mode collapse.
* The training fails to equilibrate the game
** as said in [[this blog|http://www.inference.vc/my-notes-on-the-numerics-of-gans/]]
We make the discriminator $f$ $K$-Lipschitz continuous so that the derivative would be bounded.
[[Python Snippets]]
[[Config Files]]
[[System Snippets]]
[[paper|https://arxiv.org/abs/1706.04859]]
Optimising neural networks to not only approximate the function's outputs but also the function's derivatives. When the ground truth function is a network:
* model compression
* model distillation
* gradient synthesis
Foundations
* Hornik proved the universal approximation theorems for neural networks in Sobolev spaces - metric spaces where distances between functions are defined both in terms of their differences in values and differences in values of their derivatives.
* Sigmoid network's derivates w.r.t. its inputs can approximate the corresponding derivates of the ground truth function arbirarily well too.
Soft here means entropy regularized. With entropy regularization, we need to alter our notion of a greedy policy, as the optimal policy is stochastic.
$$
V(s') = \text{soft}\max_{a'}Q_\phi(s', a') = \log\int\exp(Q_\phi(s', a'))da'
$$
$\pi(a|s)=\exp(Q_\phi(s, a)-V(s))$ optimizes $\sum_tE_{\pi(s_t, a_t)}[r(s_t, a_t)]+E_{\pi(s_t)}[\mathcal H(\pi(a_t|s_t))]$
The intuition is that $\pi(a|s)\propto\exp(Q_\phi(s, a))$ when $\pi$ minimizes $D_{KL}(\pi(a|s)||\frac1Z\exp(Q(s,a)))$
$$
D_{KL}(\pi(a|s)||\frac1Z\exp(Q(s,a))) = E_{\pi(a|s)}[Q(s, a)]-\mathcal H(\pi)
$$
This method combats premature entropy collapse and is closely related to soft Q-learning.
!! Benefits
* Improve exploration and prevent entropy collapse
* Principled approach to break ties
* Can reduce to hard optimality as reward magnitude increases
* Good model for modeling human bebavior
* Compositionality: $Q_1+Q_2$ is the soft Q-function for $r_1+r_2$
!! Soft Q-learning
! Bibs
* Linearly solvable Markov decision problems: one framework for reasoning about soft optimality.
* General duality between optimal control and estimation: primer on the equivalence between inference and control.
* Optimal control as a graphical model inference problem: frames control as an inference problem in a graphical model.
* Modeling interaction via the principle of maximal causal entropy: connection between soft optimality and maximum entropy modeling.
* On stochastic optimal control and reinforcement learning by approximate inference: temporal difference style algorithm with soft optimality.
* Reinforcement learning with deep energy based models: soft Q-learning algorithm, deep RL with continuous actions and soft optimality
* Bridging the gap between value and policy based reinforcement learning.
* Equivalence between policy gradients and soft Q-learning.
** Soft Q-learning loss gradient can be interpreted as a policy gradient term plus a baseline-error-gradient term, corresponding to policy gradient instantiations such as A3C.
In ''multinomial logistic regression'', if we see the process as a set of independent binary regressions, we run $K-1$ independent binary logistic regression models for $K$ possible outcomes.
$$
\ln\frac{p(y_i=j)}{p(y_i=1)} = \mathbf w^T_j\mathbf x_i+b_j
$$
The probability sum to one so
$$
p(y_i=j) = \frac{\exp(w^T_j\mathbf x_i+b_j)}{1+\sum_{k=2}^K\exp(w^T_k\mathbf x_i+b_k)}
$$
Or we can see it as a log-linear model:
$$
p(y_i=j) = \text{softmax}(j, \mathbf w^T_1\mathbf x_i+b_1, \cdots, \mathbf w^T_K\mathbf x_i+b_K)
$$
The softmax function thus serves as the equivalent of the logistic function in binary logistic regression.
! Extensions
[[Sparsemax]]
Ahmed E. Hassan
* Analyzing a Decade of Linux System Calls
** System calls and related commits are extracted
** analyze the commit type and draw some empirical conclusions
** find patterns in API evolution
** some conclusions about software developments are drawn
* Towards a Better Understanding of the Impact of Experimental Components on Defect Prediction
** very nice thesis on defect prediction
David Lo
Bug localization
* Information Retrieval and Spectrum Based Bug Localizatin: Better Together
** use bug report to query defected code parts
* Compositional Vector Space Models for Improved Bug Localization
** more data but only searching for file
** solving LDA with GA seems so weird
similar to a learn to rank task
* Synergizing Specification Miners through Model Fissions and Fusions
** static program analysis for documentation
** construct FSA from execution traces
** fissing FSA to common blocks
** fuse FSA from a set of LTL constraints
* Who Should Review This Change? Putting Text and File Location Analyses Together for More Accurate Recommendations
** text recommendation system to suggest best reviewer for certain piece of code
** description field of review request in important, also the changed file directory
** another learn to rank task
* What Are the Characteristics of High-Rated Apps? A Case Study on Free Android Applications
** a hypothesis testing on 28 factors of 1,492 apps to the ratings
** didn't find the description but inferring from the dataset and results, I guess they are doing a 2-class classification.
* What’s Hot in Software Engineering Twitter Space?
** Social network mining on twitter
** Filter out related tweets, rule based methods
* Deep Learning for Just-In-Time Defect Prediction
** a simple classification task with RBM, no gimmicks.
[[OOPSLA]]
! Bibs
* [[A Survey of Machine Learning for Big Code and Naturalness|https://arxiv.org/abs/1709.06182]]
! Methods
* Theory first approach: formal, or logico-deductive, approach
** The elegance and rigor of definitions, abstractions, and formal proofs-ofproperties are of dominant concern
* Machine learning
! Applications
* [[Security]]
* program verification
** Behavioral consistency of C and Verilog programs using bounded model checking (2003)
* bug finding
** A few billion lines of code later: using static analysis to find bugs in the real world (2010)
* refactoring
** The ASTRÉE analyzer (2005)
* Knowledge Representation
** HDSKG (SANER 2017)
*** Extract relation triples from stackoverflow tagWiki
*** Parsing and POS using coreNLP
*** Relations discovered with rule based methods
*** Filtered with semi-supervised classification (garbage in garbage out?)
** Unsupervised Software-Specific Morphological Forms Inference from Informal Discussions (ICSE 2017)
*** Build SE thesaurus and detecting synonyms from unsupervised corpus i.e. Stack Overflow.
*** Train a new [[Word Embedding]] (skip-gram) to group similar words
*** Discriminate synonyms and abbreviations
** Mining Analogical Libaries in Q&A Discussions (SANER 2016)
*** Learn similar libraries (of different langs) from stackoverflow tags
*** association rule mining on tag co-occurence
*** POS and phrase chunking on tagWiki to classify tags
*** [[Word2Vec]] library grouping
** TechLand: Asisting Technology Landscape Inquiries with Insights from Stack Overflow (ICSME2016)
*** QA system based on knowledge graph
*** similar methods with previous paper
*** Queries are stemmed and parsed, greedily match tech terms
*** Visualization from tagWiki data and knowledge graph
*** Recommondation based on co-occurrence and trends
* Neural Language Interface
** Learning to Extract API Mentions from Informal Natural Language Discussions
*** matching natural language and APIs, similar to NER
*** @@color:red;relative small dataset@@
*** @@color:#859900;broaden the scope to doc2code@@
*** @@color:red;having trouble disambigurating keywords and common words@@
*** @@color:#859900;a pointer network should decide when to copy and when to generate@@
*** Brown clustering and word embeddings quantized features as input of
*** semi-supervised CRF classifier
* Predicting Semantically Linkable Knowledge in Developer Online Forums via CNN
** Use a siamese net for feature extraction
** @@color:red;sequential information is lost with CNNs@@
** @@color:#859900;use relation network to model covariance@@
** @@color:red;relatedness is not linear, should predict with multiheads@@
** @@color:#859900;triplet loss to learn to rank@@
! Directions
* Make use of community: Open-source software systems such as Linux,MySQL, Django, Ant, and OpenEJB have become ubiquitous. These systems publicly expose not just source code, but also meta-data concerning authorship, bug-fixes, and review processes.
* Literate programming: The naturalness hypothesis [[NLP for SE]]
* Automatic test cases generation, distributed system testing and bug finding. It would be great we can relate AI with the security community.
* [[AI for SE Mind Map]]
We can do GD for $$x$$ in the master problem or $$y$$ in its dual, but usuall we can get the same complexity. This is also true for doing mirror descent on the Primal-dual
<<<
''Theorem'' [Convergence rate]<br>
The convergence rate of GD is proportional to its conditional number $$\kappa = \frac{maxEV}{minEV}$$
<<<
!!! Stochastic Solvers
We can do accelerations,
| Slower | Median | Faster |
| !Primal |<|<|
| [[SGD|SGD for the Master Problem]]<br>Pegasos |@@color:red;variance reduction@@: <ul><li>SAG [leRoux-Schmidt-Bach, 2012</li><li>[[SVRG|Stochastic Variance Reduction Gradient]] [Johnson-Zhang, 2013]</li><li>SAGA [Defazio-Bach-LacosteJulien, 2014]</li></ul> |@@color:red;acceleration (momentum)@@: <ul><li>APPA [Frostig-Ge-Kakade-Sidford, 2015]</li><li>Catalyst [Lin-Mairal-Harchaoui, 2015]</li><li>Katyusha [AllenZhu, 2017]</li></ul> |
| !Primal-dual |<|<|
| | |@@color:red;acceleration (momentum)@@: <ul><li>[[SPDC|Stochastic Primal-Dual Coordinate]] [Zhang-Xiao, 2015]</li><li>RPDG [Lan-Zhou, 2015]</li></ul> |
| !Dual |<|<|
| |@@color:red;[[(randomized) coordinate descent|Difference between SGD and CD]]@@: <ul><li>[[SDCA|Stochastic Dual Coordinate Ascent]] [ShalevShwartz-Zhang, 2012]</li><li>RCDM [Nesterov, 2012]</li></ul> |@@color:red;acceleration (momentum)@@: <ul><li>AccSDCA [ShalevShwartz-Zhang, 2014</li><li>[[APCG|Accelerated Proximal Coordinate Gradient]] [Lin-Lu-Xiao, 2014]</li><li>ACDM [Lee-Sidford, 2013]</li><li>NUACDM [AllenZhu-Qu-Richarik-Yuan, 2016]</li></ul> |
$$\tilde O$$ notion hides log factors:
|!|!Case 1|!Case 2|!Case 3|!Case 4|
|SGD| $$O(\frac{L^2}{\sigma\varepsilon})$$ | $$O(\frac{G}{\sigma\varepsilon})$$ | $$O(\frac{L^2}{\varepsilon^2})$$ | $$O(\frac{G}{\varepsilon^2})$$<<ref "$$\|\nabla f_i(x)\|^2\le G$$">> |
|SVRG/[[SDCA|Stochastic Dual Coordinate Ascent]]| $$O((n+\frac L \sigma)\log\frac1\varepsilon)$$<<ref "$$\varepsilon\propto e^{-T}$$, linear convergence rate">> | $$\tilde O(n+\frac{G}{\sigma\varepsilon})$$ | $$O(n\log\frac1\varepsilon + \frac L \varepsilon)$$<<ref "SVRG++ [AllenZhu-Yuan 2015], [Mahdavi-Zhang-Jin 2013]">> | $$\tilde O(n+\frac{G}{\varepsilon^2})$$ |
|[[APCG|Accelerated Proximal Coordinate Gradient]]/[[SPDC|Stochastic Primal-Dual Coordinate]] | $$O\left(\left(n+\frac{\sqrt{nL}} {\sqrt\sigma}\right)\log\frac1\varepsilon\right)$$ | $$\tilde O\left(n+\frac{\sqrt{nG}} {\sqrt{\sigma\varepsilon}}\right)$$| $$\tilde O\left(n+\frac{\sqrt{nL}} {\sqrt{\varepsilon}}\right)$$ |$$\tilde O\left(n+\frac{\sqrt{nG}} {\varepsilon}\right)$$|
| =="Optimal" reductions [AllenZhu-Hazan 2016]==> |<|<|<|<|
!! [[Fenchel-Legendre Duality]]
{{Fenchel-Legendre Duality}}
!! [[Primal-Dual of the Master Problem]]
{{Primal-Dual of the Master Problem}}
!! [[Solvers of the Master Problem]]
{{Solvers of the Master Problem}}
* Language Models
** Code fixing
*** 2016 Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks.
** Code generation
*** 2016 Learning Python Code Suggestion with a Sparse Pointer Network
*** 2016 A deep language model for software code: meaningless paper with a concept software called DeepSoft that never exists
*** 2016 [[Neural Code Completion|https://openreview.net/forum?id=rJbPBt9lg]]: rejected ICLR17, read the review and traditional baseline methods
*** 2016 [[Program Synthesis for Character Level Language Modeling]]
*** 2017 Neural Attribute Machines for Program Generation
** Benchmark synthesis
*** 2017 Synthesizing benchmarks for predictive modeling
* Code Transducer Models: statistical machine translation or deep learning, evaluated by BLEU
** Code migration
** Pseudocode generation
** Code fixing
*** 2016 sk_p: a neural program corrector for MOOCs
* Multimodal Models
** Code systhesis: from natural language to code
*** 2014 Structured Generative Models of Natural Source Code
**** Read for a summary of traditional methods and program induction introduction
*** 2015 Bimodal modelling of source code and natural language: also traditional method
*** 2016 [[Latent Predictor Networks for Code Generation]]
*** 2017 Program synthesis from natural language using recurrent neural networks
*** 2017 Abstract Syntax Networks for Code Generation and Semantic Parsing
*** 2017 A Syntactic Neural Model for General-Purpose Code Generation
**** seq2action2tree permits syntax guarantee, right way to follow
**** Sequence model kind of simple. Can external memory improve parent supervision? Is conv encoder helpful?
**** Invent code evaluation metric: something like ''Semantic Textual Similarity''
*** Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
**** Policy-based RL helps with the where clause accuracy
** Documentation
*** 2016 Summarizing source code using a neural attention model
*** 2017 [[A parallel corpus of Python functions and documentation strings for automated code documentation and code generation|https://arxiv.org/abs/1707.02275]]: provides [[code-docstring-corpus|https://github.com/EdinburghNLP/code-docstring-corpus]] and use [[Nematus|https://github.com/EdinburghNLP/nematus]] as baseline model
* Program Induction
** [[Neural Abstract Machines]]
** Program by Examples
*** [[Glass-Box Program Synthesis: A Machine Learning Approach|https://scirate.com/arxiv/1709.08669]]: search based no ML
*** [[Neural Program Meta-Induction|https://arxiv.org/abs/1710.04157]]: DeepMind
**** No explicit program representation, task-specific learning, cross-task knowlege sharing
**** Conv encoder, lstm decoder
! Metrics
* Accuracy: exact match is too strong
* BLEU does not measure semanics
* Execution accuracy (for sql query results, and maybe #tests passed?)
* Static code analysis
国内melpa无法访问，添加下面的代码到`.spacemacs`的`dotspacemacs/user-init()`
```
(setq configuration-layer--elpa-archives
'(("melpa-cn" . "http://elpa.emacs-china.org/melpa/")
("org-cn" . "http://elpa.emacs-china.org/org/")
("gnu-cn" . "http://elpa.emacs-china.org/gnu/")))
```
! Helm
* projectile-ag search: `spc s p`, jump back with `Ctrl-o`
Gaussian likelihood model
$$
p(y|x)\approx\exp[-\frac{1}{2\lambda}\|y-\Phi x\|^2_2]
$$
and zero-mean Gaussian prior
$$
p(x; \gamma)\propto\exp[-\frac 1 2x^T\Gamma^{-1}x],\Gamma\triangleq\text{diag}[\gamma]
$$
The posterior $p(x|y;\gamma)$ is also Gaussian:
$$
\hat x=\Gamma\Phi^{-1}\Sigma_y^{-1}y,\Sigma_y\triangleq\Phi\Gamma\Phi^T+\lambda I
$$
The support pattern, meaning the locations of zero-valued elements will align for $x$ and $\gamma$. We marginalize over $x$ and maximize the resulting ''type-II likelihood function'' w.r.t. $\gamma$. The resulting convolution-of-Gaussian integral is available in closed-form such that we can equivalently minimize the negative log-likelihood
$$
\mathcal L(\gamma) = -\log\int p(y|x)p(x;\gamma)dx\equiv y^T\Sigma_y^{-1}y+\log|\Sigma_y|
$$
$$
sparsemax(z)=\arg\min_{p\in\Delta^{K-1}}\|p-z\|^2
$$
Typical speaker diarization tasks are carried with a two-stage procedure. The first is to segment the audio with some ([[changepoint algorithm|Changepoint algorithms]]).
Then the inferred segments are grouped into a set of speaker labels. A common approach is to use hierarchical agglomerative clustering with BIC stopping criterion [chen98].
This scheme suffers from the fact that errors made in the segmentation stage can degrade the performace of the subsequent clustering stage.
A number of algorithms iterate between multiple stages of resegmentation and clustering. But agglomerative clustering is extremely sensitive to the specified threshold for clustering merging, with different settings leading to either over- or under-clustering of the segments into speakers.
Clustering and segmentation are joint by employing HMMs to capture the repeated returns of speakers. Since the state space is unknown, [meignier00] uses an evolutive-HMM. The HMM is initialized to have one state and speakers are gradually added. [wooters07] starts with a number of states larger than the speakers and iteratively merge them according to metrics based on BIC. At each iteration, [[Viterbi decoding]] is performed to resegment the features of the audio.
A spectral envelope is a curve in the frequency-amplitude plane, derived from a Fourier magnitude spectrum. It describes one point in time (one window, to be precise).
! Spectral envelope correction
[[link|http://recherche.ircam.fr/anasyn/schwarz/da/specenv/3_3Spectral_Envelopes.html]]
In speech or in the singing voice, the spectral envelope is quite independent of the pitch (see section 2.4 for why this is so). However, if we transpose the vowel in figure 2.23 up by one octave by multiplying the frequencies of all partials by 2 and performing an additive resynthesis, the spectral envelope will necessarily be transposed also. Figure 2.26 shows this effect which sounds quite unnatural (it is sometimes termed the mickey mouse effect ). The unnaturalness comes from the fact that the formants are shifted up one octave, which corresponds to shrinking the vocal tract to half of its length. Obviously, this is not the natural behaviour of the vocal tract.
To avoid this, the spectral envelope has to be kept constant, while the partials ''slide'' along it to their new values. This means that the amplitude of a transposed partial is no longer determined by the amplitude of the original partial, but by the value of the spectral envelope at the frequency of the transposed partial, as in figure 2.27. This way, only the partials are shifted, but the spectral envelope and thus the formant locations stay the same, making the vowel sound natural.
! Reports
* [[Locally Linear Embedding]]
* [[LSTM Research Proposal]]
* [[Voice Conversion Research Proposal]]
! [[Speech Experiments]]
!! TODO
!!! Experiments
# Metric based classification
#* Theory: [[Accoustic Saliency Summary]]
#* Coding: [[Implementing Diarization]]
# LLE
#* Previous work: [[Emotion Code (Chen Li)]]
# ScatNet
#* [[Code reading|ScatNet Code]]
! Speaker Recognition Techniques
[[JFA & I-Vector]]
! Speech Recognition Techniques
!! [[HMM|Hidden Markov Model]]
[[Baum-Welch]]
[[HDP-HMM Diarization]]
!! [[Recurrent Temporal RBM]]
* [[TIMIT]]
* [[TIMIT HF]]
! Speaker Variations
!! GMM-HMM
vocal tract length normalization (VTLN): warps the frequency axis of the filter-bank analysis to account for the fact the locations of vocal-tract resonances vary roughly monotonically with the ''vocal tract length'' of the speaker. The optimal ''warping factor'' is found using EM by repeatedly selecting the best factor given the current model and then updating the model using the selected factor.
feature-space Maximum Likelihood Linear Regression (fMLLR): applies an affine transform to the feature vector so that the transformed feature better matches the model. It is typically applied to testing utterance by first generating recognition results using the raw feature and then re-recognizing the speech with the transformed feature, (which can be iterated several times).
!! DNN
For GMM-HMMs, fMLLR transforms are estimated to maximize the likelihood of the adaptation data given the model. For DNNs, they are optimized to maximize cross entropy (with backpropagation). This procedure is thus referred as feature-space discriminative linear regression (fDLR).
Experiments show that DNNs do not depend on these transforms as GMM-HMM and shallow MLPs.
! Problem
Given a bunch of training voices and target texts, generate the speech.
The output contains spectral features and excitation parameters (pitch and aperiodic energy) and their time derivatives of first and second order. Tools to extract these features include EMIME project and STRAIGHT acoustic analysis.
! HMM/GMM Based Systems
* MAP
* MLLR
* CMLLR derived from ML based optimization in HMM forced alignment
In the conventional HMM-based approach, a decision tree based contextual clustering is used to model the relationship between text and corresponding voice features.
!! Models
Multi-space probability distributions HMM (MSD HMM) to model observation sequences with continuous log F0 (voiced) and discrete symbol (unvoiced).
The use of HSMMs allow us to incorporate state duration models explicitly not only in the synthesis part but also in the training part of the system.
!! Examples
[[HTS]]
! DNN based models
[[Qian et al|DNN for parametric TTS]]
!! [[Google DNN TTS]]
! Data
CMU ARCTIC database of 7 speakers,
[[Voice Conversion]]
* [[paper|https://arxiv.org/abs/1709.02755]]
* [[code|https://github.com/taolei87/sru]]
A simplified GRU omitting $h_{t-1}$ in gemms in gates and input value computation. This makes GEMMs parallelizable. Use [[Highway Connections]] and [[Variational Dropout]]
!! CUDA optimization
Merging 3 GEMMs togather. This is a common LSTM speedup. But now we can do whole period at once.
! Implementation
Implemented with CuPy and pynvrtc
!! DrQA example
Different batch structure:
* SRU
** context_id, context_feature, context_tag, context_ent, context_mask, question_id, question_mask, (y_s, y_e,) text, span
**
* parlai
** x1, x1_f, x1_mask, x2, x2_mask, (y_s, y_e,) text, spans
** torch.cuda.ByteTensor of size 30x20
Have auxiliary input of tag and entity: POS and NER
`model.py` modifications:
* change the logger name
* save & load optimizer state dict
* change the dimension of inputs (for POS and NER features): 2 new features
`rnn_reader.py` modifications:
* different input order for question embedding
* add 'pos' and 'ner' features
* support embedding random initialization
`layers.py`
* don't pad during evaluation
* replace LSTM with SRUCell
* dropout implemented within layer
* the output is contiguous, input size could be different?
I migrated the code into ParlAI, still have to figure out the data changes
* where to get the embeddings?
* differences in opt?
! Remarks
* Compare embedding implementation
* Add POS and NER in parlai
* Prepare different initialization methods
[[Case Studies|http://mc-stan.org/users/documentation/case-studies.html]]
! From RNNs to CNNs
Recursive neural networks are a special type of trees. Recursive NN require a parse tree, which is in some way suboptimal because we have separate objective functions that need to find a good parse tree and then apply NN architecture. Ideally we want only raw input and perform a end 2 end learning.
Recurrent NN could do that but fails to capture specific phrases without prefix context (which always modifies our hidden representation) and often capture too much of last words in final vector (in standard RNN, which forgets).
RNN can only get compositional vectors for grammatical phrases. We can instead use CNN to compute every possible phrase. This method is not very linguistically or cognitively plausible but does not introduce much noise.
! Architecture in [[Kim (2014)|Convolutional Neural Networks for Sentence Classification]]
!! Conv
We can use the first layer to compute all bigrams as a convolution over the word vectors. For each pair:
$$
p = \tanh\left(W\left[\begin{array}{c} x \\ y \end{array}\right]+b\right)
$$
Here we are doing more matrix operations than in RNNs, but we can parallelize them. With a softmax classifier on top of this vector and propagate down to all the phrases.
A convolution filter $w\in\mathbb R^{hk}$ goes over $h$ words. Then the value of the neuron is:
$$
c_i = f(w^\top x_{i:i+h-1}+b)
$$
The resulting feature map: $c=[c_1, \cdots, c_{n-h+1}]$. We use zero-padding for short sentense, and for very long sentenses, we can do pooling.
!! [[Pooling Layer]]
Here we introduce the max-over-time pooling. Pooled single number: $\hat c = \max\{\mathbf c\}$. To have more features we can use different window sizes and multiple weights.
!! [[Dropout Layer]]
We randomly mask/dropout/set to 0 some of the feature weights $z$. First create masking vector $r$ of Bernoulli random variables with probability $p$ of being 1. And delete some of our feature $z$:
$$
y = softmax(W^{(S)}(r\circ z)+b)
$$
The idea behind this is we can prevent co-adaptation. If the model is too powerful, we can learn very specific feature constellations. At test time, there is no dropout
!! Regularization
Constrain $l_2$ norms of weight vectors of each class (row in softmax weight $W^{(S)}$ to fixed number $s$.
!! Details and Results
The hyperparameters chosen are:
* ReLU
* window filter size: 3-5
* each filter size has 100 feature maps
* dropout p=0.5
* L2 constraint s for rows of softmax s=3
* mini batch size for SGD training: 50
* word vectors: pre-training with word2vec, k=300
! Similarity between Recursive NN to CNN
* We can set stride size to make CNN word features never overlap, which gives a similar structure as RNN. And to use weighted average pooling instead of max pooling to make every child node contribute to the parent.
* We can tie weights of filters inside vs across different layers in CNN
* Other differences are CNN have multiple filters, and max-pooling.
* In RNN we use an input specific tree.
Computational trade-off.
At the time this note is edited, TensorFlow is still a quite new technology and this seems to be the first decent TensorFlow tutorial.
TensorFlow provides primitives for defining functions on tensors and automatically computing their derivatives.
! Deep-Learning Package Choices
There are a zoo of deep learning libraries. Each major company and many acedemic labs have their own deep learning libraries. We need to start ask the questions, what lib should we use and why should we use it.
The first property we concern is that, when we are writing deep learning models, are we writing configuration files or programs. Caffe, DistBelief and CNTK use files to specify the complicated deep models. This is kind of convenient and easy to copy-and-paste. However, for very deep models, things get exremely heavy. ResNet have 7k lines of configuration file. So we want the model description to be part of our programs and use standard programming structures like for-loops to deal with that. We can use 10 LOC to construct a RNN in tf, while caffe don't let us do that.
Numerical computation with high-level languages wrapped over cpp save us from concerning about memory management, etc, and let us focus on the model itself. Lua and Python are the two choices in deep learning area. Lua is a game engine allowing fast calling to C. Torch makes good use of Lua and ties directly down to the Lua numerical system. Python has a richer community and library infrastructure.
Theano is a symbolic tensor manipulation package in python. It has been around longer. TensorFlow has more support for building giant models with distributed systems and has support from Google.
The downside of TensorFlow is that it is a bit slower. We hope Google can fix that soon. Models from other library should have no problem migrating to TensorFlow.
! What is TensorFlow
TensorFlow is numerical packages dealing with math construct called tensors.
; Tensor
: are mulilinear maps from vector spaces to the real numbers ($V$ vector space, and $V^*$ dual space)
$$
f: V^*\times\cdots V^*\times V\times\cdots V\rightarrow \mathbb R
$$
Common to have fixed basis, so a tensor can be represented as a multidimensional array of numbers.
TensorFlow is similar to ''numpy'', but with GPU support and automatically compute derivatives.
numpy:
```python
a = np.zeros((2,2)); b = np.ones((2,2))
np.sum(b, axis=1) # array([ 2., 2.])
a.shape # (2,2)
np.reshape(a, (1,4)) # array([[ 0., 0., 0., 0.]])
```
TensorFlow:
```python
tf.InteractiveSession()
a = tf.zeros((2,2)); b = tf.ones((2,2))
tf.reduce_sum(b, reduction_indices=1).eval() # array([ 2., 2.], dtype=float32)
a.get_shape() # TensorShape([Dimension(2), Dimension(2)])
np.reshape(a, (1,4)).eval() # array([[ 0., 0., 0., 0.]], dtype=float32)
```
A lot of numpy operations can be carried to TensorFlow. But TensorFlow is different that it requires explicit evaluation. Its computations define a ''computation graph'' and `.eval()` runs the graph in the context. While numpy is doing those operations on memory, TensorFlow compiles them at first.
! [[TensorFlow Basics]]
! Examples
!! [[Linear Regression in TensorFlow]]
! Comments
TensorFlow can be seen as a numpy wrapper, but is less mature, not as many operations are supported. We can convert tensors to numpy arrays and print, e.g. matrices indexing not as convenient, so the natural softmax representation in numpy is not feasible in TensorFlow.
TensorBoard could be useful.
! Lecture Notes
* [[Lecture 2|Word2Vec]]
* [[Lecture 7|Stanford Introduction to TensorFlow]]
* [[Lecture 13|Stanford CNN for NLP]]
* [[paper|https://arxiv.org/abs/1711.09020]]
* [[code|https://github.com/yunjey/StarGAN]]
Training process:
* Training the discriminator
* Original-to-target domain
* Target-to-original domain: make sure features don't change much by checking the reconstruction ability
* Fooling the discriminator
[[link|https://www.youtube.com/watch?v=m8PLzDmW-TY]]
!! First Generation SLT
Empirical Risk: For one fixed (non data-dependent) $$h$$:
$$
\mathbb E[R_{in}(h)] = \mathbb E[\frac1m\sum_{i=1}^m l(h(X_i), Y_i)] = R_{out}(h)
$$
$$l(h(X_i)$$ are independent r.v.'s, (because of i.i.d. assumption)
if $$0\le l(h(X_i)\le 1$$, using Hoeffing's inequality:
$$
\mathbf P^m[\Delta(h)>\epsilon]\le \exp(-2m\epsilon^2) = \delta
$$
$$\delta$$ is the confidence. With probability $$\ge 1-\delta$$.
Theoretical Risk:
$$
R_{out}(h)\le R_{in}(h) +\sqrt{\frac{1}{2m}\log(\frac{1}{\delta})}
$$
!! Finite function class
* Structural Risk Minimization
* VC dimension, Rademacher complexity
[[IAS seminar|https://www.bilibili.com/video/BV14541187Ln]]
!! PAC-Bayes framework (Generalised Bayes)
* Before data, fix a distribution $$P\in M_1(\mathcal H)$$ "prior"
* Based on data, learn a distribution $$Q\in M_1(\mathcal H)$$ "posterior"
* Predictions:
** draw $$h\sim Q$$ and predict with the chosen $$h$$.
** each prediction wiht a fresh random draw.
The @@color:red;risk measures@@ $$R_{in}(h)$$ and $$R_{out}(h)$$ are @@color:red;extended by averaging@@:
$$
R(Q) \equiv \int_{\mathcal H}R(h)dQ(h)
$$
!!! PAC-Bayes vs Bayesian learning
* Prior
** PAC-Bayes: bounds hold for any distribution
** Bayes: prior choice impacts inference
* Posterior
** PAC-Bayes: bounds hold for any distribution
** Bayes: posterior uniquely defined by prior and statistical model
* Data distribution
** PAC-Bayes: bounds hold for any distribution
** Bayes: randomness lies in the noise model generating the output
!!! A General PAC-Bayesian Theorem
$$\Delta$$-function: "distance" between $$R_{in}(Q)$$ and $$R_{out}(Q)$$
Convex function $$\Delta: [0,1] \times [0,1]\rightarrow \mathbb R$$
For any distribution $$D$$ on $$\mathcal X\times \mathcal Y$$, for any set $$\mathcal H$$ of voters, for any distribution $$P$$ on $$\mathcal H$$, for any $$\delta\in[0, 1]$$, and for any $$\Delta$$-function, we have, with probability at least $$1-\delta$$ over the choice of $$S\sim D^m$$,
$$
\forall Q \text{ on } \mathcal H: \Delta(R_{in}(Q), R_{out}(Q))\le\frac1m[KL(Q\|P)+\ln\frac{\mathcal J_\Delta(m)}{\delta}]
$$
Proof: Change of measure inequality and Markov's inequality
!! Linear classifiers
* choose prior and posterior to be Gaussians
* $$P$$ centered at the origin
* $$Q\sim \mathcal N(\mathbf w, \mu)$$
Linear classifiers performance may be bounded by:
$$
KL(\hat Q_S(\mathbf w, \mu)\|Q_D(\mathbf w, \mu))\le\frac1m ( KL(P\|Q(\mathbf w, \mu)) +\ln\frac{m+1}{\delta})
$$
!!! Data- or distribution-dependent priors
* using part of the data to learn the prior for SVMs
* defining the prior in terms of the data generating distribution (aka localised PAC-Bayes)
$$\eta$$Prior SVM in case VC dimension does not work
* @@color:green;Bounds are tight@@
* @@color:green;Model selection from the bounds is as good as 10FCV@@
* @@color:red;The better bounds do not appear to give better model selection@@
!! Performance of deep NNs
* For SVMs we can think of the margin as capturing an accuracy with which we need to estimate the weights
* If we have a deep network solution with a wide basin of good performance we can take a similar approach using PAC-Bayes with a broad posterior around the solution
* (Dziugaite and Roy + Neyshabur) have derived some of the tightest deep learning bounds in this way
** by training to expand the basin of attraction
** hence not measuring good generalisation of normal training
A statistical manifold is a Riemannian manifold, each of whose points is a probability distribution. Statistical manifolds provide a setting for the field of [[Information geometry]]. The [[Fisher information metric]] provides a metric on these manifolds.
Let $X$ be an orientable manifold, and let $(X,\Sigma,\mu)$ be a measure on $X$. Equivalently, let $(\Omega,\mathcal F,P)$ be a probability space on $\Omega=X$, with [[sigma algebra]] $\mathcal F=\Sigma$ and probability $P=\mu$.
The statistical manifold $S(X)$ of $X$ is defined as the space of all measures $\mu$ on $X$ (with the sigma-algebra $\Sigma$ held fixed). Note that this space is infinite-dimensional; it is commonly taken to be a [[Frechet space]]. The points of $S(X)$ are measures.
Rather than dealing with an infinite-dimensional space $S(X)$, it is common to work with a finite-dimensional submanifold, defined by considering a set of probability distributions parameterized by some smooth, continuously-varying parameter $\theta$. That is, one considers only those measures that are selected by the parameter. If the parameter $\theta$ is n-dimensional, then, in general, the submanifold will be as well. All finite-dimensional statistical manifolds can be understood in this way
We want to learn a true distribution $Q$, which can be observed from i.i.d. samples. We have a parametric class of models $\mathcal P$ and we have parameters identify an individual model $P$. The task is to navigate through the class of models and find a good one.
We assume $P$ is capable of:
* sample from, (random field models, graphical models are examples cannot sample from)
* compute parameter gradient w.r.t. samples
* strongest assumption is we can compute the likelihood
[[GAN|Generative Adversarial Network]] is a likelihood-free model which can be sampled from. Because many possible input could be mapped to the same output. To compute a pointwise likelihood, we have to find the preimage, which is not tractable.
! Measure the distance
!! Integral Probability Metrics
* Muller, 1997
* Sriperumbunder et al., 2010
Taking the supremum over a difference of expectations.
$$
\gamma_{\mathcal F}(P, Q) = \underset{f\in\mathcal F}{\sup}\left|\int fdP-\int fdQ\right|
$$
Depending on the structure of $\mathcal F$, we get different measures.
* Energy statistics [Szekely, 1997]
* $\mathcal F$ is a unit-ball in a RKHS $\mathcal H$: Kernel [[MMD|Maximum Mean Discrepancy]]
* Wasserstein distance [Cuturi, 2013]
* [[DISCO Net]]s [Bouchacourt et al. 2016]
**Wasserstein Distance**:
$$
W_c(P\|Q):=\inf_{\rho:\rho_X=P, \rho_Y=Q}\int\int c(x, y)d\rho(x, y)
$$
Because we use integral here, we can compute this distance with samples.
!! Proper scoring rules
* [Gneiting and Raftery, 2007]
We take expectation of a score of how well the distribution $P$ fits the realization $x$.
$$
S(P, Q) = \int S(P, x)dQ(x)
$$
$$
S(P, P) \ge S(P, Q), \forall P, Q\in\mathcal P
$$
The scores can be used are:
* log-likelihood, $S(P, x)=\log P(x)$ then this is maximum likelihood
* Bayes score
* Quadratic score, $S(P, x) = 2P(x)-\|P\|^2_2$
* Pseudoshperical score, $S(P, x) = P(x)^{\alpha-1}/\|P\|_{\alpha}^{\alpha-1}$
!! $$f$$-divergence
* [Ali and Silvey, 1966]
* [[Divergence Classed GANs]]
$$f$$-divergence is a generalization of KL divergence. Now we require knowing the distribution of both $$P$$ and $$Q$$
$$
D_f(P\|Q) = \int f\left(\frac{dP(x)}{dQ(x)}\right)dQ(x)
$$
where $f$ is a convex function of likelihood ratio. $$f: \mathbb R\rightarrow\mathbb R\cup\{+\infty\}$$. Properties:
# $$D_f(P\|Q)\ge 0$$ and $$D_f(P\|Q) = 0$$ when $$P=Q$$;
# $$D_f(P\|Q)=D_g(P\|Q)$$ for any $$g(x) = f(x) + \lambda(x-1)$$
# Taking $$f_0(x) := f(x)-(x-1)f'(1)$$ is good. It's nonnegative, nonincreasing on $$(0, 1]$$ and nondecreasing on $$[1, \infty)$$;
# Taking the convec restriction $$f_+(x) = f(x)$$ if $$x\ge0$$ and $$\infty$$ otherwise.
Examples are
* [[Kullback-Leibler divergence]]: $$f = x\log x$$
* Reverse KL: $$f(x) = -\log x$$
* [[Jensen-Shannon divergence]]: $$f(x) = -(x+1)\log\frac{x+1}{2}+x\log x$$
* Total variation: $$f(x) = |x-1|$$
* Pearson $$\chi^2$$
Direct MLE
$$
\max_{\theta\in\mathbb R^d}\frac1m\sum_{i=1}^m\log P_\theta(x^i)
$$
is equivalent to minimizing the KL-divergence $$KL(P_r\|P_\theta)$$.
! Bibs
* [[Parallel Multiscale Autoregresssive Density Estimation]]
! Other distances
* [[Regular Bregman Divergences]]
* numpyro implementation: https://fehiepsi.github.io/rethinking-numpyro
* videos: https://www.bilibili.com/video/BV1ya411A7ih
! Foundamentals
* [[Principle of maximum entropy]]
* [[Statistical manifold]]
* [[Stochastic Processes]]
* [[Graphical Model]]
* [[Bayesian]]
* [[Influence Function]]
The three axioms of Probability theory
# $P(A)\ge0$ for all events $A$
# $P(\Omega) = 1$
# $P(A\cup B)=P(A)+P(B)$ for disjoint events $A$ and $B$
In probability theory, in contrast, it is the events and their probabilities that are viewed as being fundamental, with the sample space $\Omega$ being abstracted away as much as possible, and with the random variables and expectations being viewed as derived concepts.
[[Free Probability]]
! Distributions
* [[Gaussian]]
! [[Probabilistic Models]]
[[link|https://www.youtube.com/watch?v=lKVIXI8Djv4]] [[slides|https://drive.google.com/file/d/0BwMz1xbo9yErdEI0UHh3YzRLNXM/view]]
The lecturer discussed how to relate human brain with deep learning optimization.
The central problem is credit assignment in hidden layers (or and through time). [This work|?] explains some relation between hidden layers' credit with brain.
Spike-timing Dependent Plasticity (STDP): there is a relationship between the timing of pre-synaptic spike with post spike on the weight change. Learning rule is modeled like STDP. ''Hypothesis 1'':
$$
\delta W_{i,j}\propto \dot{s_i}\rho(s_j)
$$
''Coincidence'': This simulation works
''Hypothesis 2'':
This matches SGD if the rate of change of neurons corresponds to the gradient of an objective function.
$$
\dot{s_i} = -\frac{\partial J}{\partial s_i}
$$
and
$$
s_i=a+b\sum_iW_{ij}\rho(s_j)
$$
Leaky integrator neurons with state (integrated voltage) s:
$$
\dot{s_i} = \epsilon(R(s)-s)=-\epsilon\frac{\partial E}{\partial s}
$$
where $R(s)$ is where the neuron's activation would converge
$$
R(s)\propto b+W\rho(s)
$$
''coincidence'': Denoising auto-encoders with reconstruction function $R(s)$ converge towards $R(s)-s$ = gradient of energy.
''Hypothesis #3'': neural computation = inference:
Neural activations tend to noisily move towards configurations making neurons' activations more compatible with each other according to some energy function.
We would like to choose an energy function that:
$$
\frac{\partial E(s)}{\partial s} = s-R(s)
$$
Lyapunov function has this property, and are some other pretty ones.
''coincidence'': auto-encoders tends to be symmetric
''coincidence'': early inference in continuous-variable energy-based models approximates back propagation.
Near a fixed point, this kind of network updates like back propagation.
SGD update gives a contrastive Hebbian learning step:
$$
\delta W_{ij}\propto \frac{\partial}{\partial \beta_y}\rho(s_i)\rho(s_j)\approx\rho(s_i^+)\rho(s_j^+)-\rho(s_i^-)\rho(s_j^-)
$$
* Stein variational gradient descent
* Stein variational policy gradient
Obtain $$M$$ samples from target distribution. Have to compute a PSD of weights
! Stein's identity
For sufficient regular $$\phi$$,
$$
\mathbb E_{x\sim p}[\mathcal A_p\phi(x)] = 0, \qquad\text{where}\qquad \mathcal A_p\phi(x) = \phi(x)\nabla_x\log p(x)^T+\nabla_x\phi(x)
$$
The magnitude of $$\mathbb E_{x\sim q}\mathcal A_p\phi(x)$$ is known as Stein discrepancy, by considering the "maximum violation of Stein's identity" for $$\phi$$ in some proper function set $$\mathcal F$$:
$$
\mathbb S(q, p) = \underset{\phi\in\mathcal F}{\max}\{[\mathbb E_{x\sim q}\text{trace}(\mathcal A_p\phi(x))]^2\}.
$$
The choice of $$\mathcal F$$ is critical, and decides the discriminative power and computational tractability of Stein discrepancy. Traditionally, $$\mathcal F$$ is taken to be sets of functions with bounded Lipschitz norms, which casts a challenging functional optimization problem that is computationally intractable or requires special considerations.
! Kernelized Stein discrepancy
Bypasses this difficulty by maximizing $$\phi$$ in the unit ball of a reproducing kernel Hilbert space:
$$
\mathbb S(q, p) = \underset{\phi\in\mathcal H^d}{\max}\{[\mathbb E_{x\sim q}\text{trace}(\mathcal A_p\phi(x))]^2, s.t. \|\phi\|_{\mathcal H^d\le1}\}
$$
The optimal solution has been shown to be $$\phi(x) = \phi^*_{q,p}(x)/\|\phi^*_{q,p}\|_{\mathcal H^d}$$, where
$$
\phi^*_{q,p}(\cdot) = \mathbb E_{x\sim p}[\mathcal A_pk(x, \cdot)],
$$
for which we have
$$
\mathbb S(q, p) = \|\phi^*_{q,p}\|^2_{\mathcal H^d}
$$
! Variational Inference Using Smooth Transforms
variational inference
The formula as typically used in applications is
$$
\ln(n!) = n\ln(n) - n +O(\ln(n))
$$
The next term in the O(ln(n)) is (1/2)ln(2πn); a more precise variant of the formula is therefore
$$
n! \sim \sqrt{2 \pi n}\left(\frac{n}{e}\right)^n
$$
Being an asymptotic formula, Stirling's approximation has the property that
$$
\lim_{n \to \infty} \frac{n!}{\sqrt{2\pi n} \left(\frac{n}{e}\right)^n} = 1.
$$
[[paper|http://www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf]]
Master Problem:
{{primal_dual_of_master_problem}}
!! General theory for coordinate descent
[Coordinate Smoothness]: Assume each coordinate of $$g(y)$$ is $$L$$-smooth, i.e. $$\nabla_{ii}^2g(y)\in[0, L]$$, diagonal element of the Hessian is upperbounded by $$L$$.
[Algorithm]: $$y_{k+1} = y_k -\frac1L\nabla_ig(y_k)$$
[#Iters to converge]:
* $$T=O(\frac{dL}{\epsilon})$$, where $$d$$ is the number of parameters
* or $$T=O(\frac{dL}{\sigma}\log\frac1\epsilon)$$, if $$g(y)$$ is $$\sigma$$-SC, i.e. $$\nabla^2g(y)\succcurlyeq\sigma\cdot I$$ (Case 1)
Compute the smoothness and strong convexity:
if $$\psi(\cdot)$$ is $$\sigma$$-SC, $$\psi^*(\cdot)$$ is $$\frac1\sigma$$-smooth; if $$f_i(\cdot)$$ is $$L$$-smooth, $$f^*_i(\cdot)$$ is $$\frac1L$$-SC
$$
g(y) = \psi^*(-\frac1n A^Ty) + \frac1n\sum_{i=1}^nf^*_i(y_i)
$$
$$g(\cdot)$$ is $$\frac{1}{\sigma n^2}+\frac{1}{nL}$$ coordinate-smooth and $$\frac{1}{nL}$$-SC
Plugin to the convergence rate:
$$
T=O\left(\frac{n\times\left(\frac{1}{\sigma n^2}+\frac{1}{nL}\right)}{\frac{1}{nL}}\log\frac1\varepsilon\right) = O\left(\left(n+\frac L\sigma\right)\log\frac1\varepsilon\right)
$$
This matches ''Case 1 SVRG'': Computation of $$\nabla_ig(y)$$ is of the same complexity as computation of $$\nabla f_i(a_i^T x)$$ (Same constance factor).
! Videos
* [[Optimization for Machine Learning|https://simons.berkeley.edu/talks/elad-hazan-01-23-2017-1]]
! Theory
* [[Gradient Descent Basics]]
* [[Gradient Descent Properties]]
* [[Stochastic Gradient MCMC]]
* [[On SGD’s Failure in Practice: Characterizing and Overcoming Stalling]]
!! Deep Learning Insights
* [[Escaping Saddle Points]]
* [[Rethinking Generalization]]
!! [[A Variational Analysis of Stochastic Gradient Algorithms|https://arxiv.org/abs/1602.02666]]
[Robbins and Monro 1951] proves SGD reaches the optimum of the function.
!! Low Precision
* [[Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent]]
!! Constant Learning Rate
* [[Stochastic Gradient Descent as Approximate Bayesian Inference]]
Constant SGD can be interpreted as a stochastic process with a stationary distritbution. It can be interpreted as an Ornstein-Uhlenbeck process. Minimizing the KL, we can relate the optimal step size or preconditioning matrix to the Hessian and noise covariance near the optimum. The resulting criteria strongly resemble AdaGrad, RMSProp and classical Fisher scoring.
Using SGD with a constant learning rate allows simultaneous inference of the posterior and optimization of meta-level parameters.
$$
d\theta(t) = -g(\theta)dt+\sqrt{\frac\epsilon S}BdW(t)
$$
where $C = BB^\top$ is the constant noise covariance of the gradient $g$, $\Delta g(\theta)\sim\mathcal N(0, C(\theta))$. This results in a specific kind of stochastic process, the multivariate ''Ornstein-Uhlenbeck'' process:
$$
d\theta(t)=-A\theta(t)dt+B_{\epsilon/S}dW(t)
$$
which has an analytic stationary distribution $q(\theta)$ that is Gaussian $q(\theta)\propto\exp\{-\frac 1 2\theta^\top\Sigma^{-1}\theta\}$. The covariance satisfies
$$
\Sigma A^\top+A\Sigma=\frac\epsilon SBB^\top
$$
The resulting covariance is proportional to the learning rate $\epsilon$ and inversely proportional to the magnitude of $A$ and minibatch size $S$.
Under conditions of decaying learning rates, smoothness of gradients and the existence of a full rank stationary distribution, [[martingale|Martingale]] based analysis of stochastic gradient descent show that SGD has a Gaussian limiting distribution. In the limit as the time step goes to infinity,
$$
t^{1/2}(\theta_t - \theta^*)\rightarrow\mathcal N(0, \mathcal H(\theta)^{-1}\mathbb E(\nabla\log p(\theta)\nabla\log p(\theta)^T)\mathcal H(\theta)^{-1}),
$$
where $$\mathcal H(\theta)^{-1}$$ is the inverse Hessian matrix of the log-likelihood and $$\mathbb E(\nabla\log p(\theta)\nabla\log p(\theta)^T)$$ is the covariance of the gradients and $$\theta^*$$ is a stationary point or minima.
* Modify SGD in the simplest way s.t. it becomes an efficient approximate Bayesian sampling algorithm
* Construct other sampling algorithms such as preconditioning, momentum or Polyak averaging
Constant SGD is a stationary stochastic process centered on the optimum and has a certain covariance structure. It is a hybrid of MC and variational algorithms.
JMLR17 paper interpret Polyak-Ruppert averaging as a sampling procedure with convergence occurring to the true posterior under certain strong conditions.
Storing full-rank covariance is prohibitively expensive for deep learning.
!! Stochastic Weight Averaging
* [[paper|https://arxiv.org/abs/1902.02476]]
* [[code|https://github.com/wjmaddox/swa_gaussian]]
$$q(\theta) = \mathcal N(\theta_{SWA}, \frac12(\Sigma_{diag}+\Sigma_{low-rank}))$$
Similar to [[Izamailov UAI18|Averaging weights leads to wider optima and better generalization]], batch norm is updated after sampling.
! Bibs
* [[Bayesian Learning via stochastic Gradient Langevin Dynamics|http://people.ee.duke.edu/~lcarin/398_icmlpaper.pdf]]
* [[A Variational Analysis of Stochastic Gradient Algorithms|https://arxiv.org/abs/1602.02666]]
** Langevin dynamics is the discrete-time approximation of a continous-time stochastic differential equation.
* [[Approximation analysis of stochastic gradient Langevin dynamics by using Fokker-Planck equation and Ito process|http://www.jmlr.org/proceedings/papers/v32/satoa14.html]]
** detailed convergence analysis
** [[video|http://techtalks.tv/talks/approximation-analysis-of-stochastic-gradient-langevin-dynamics-by-using-fokker-planck-equation-and-ito-process/61010/]]
! Introduction
The Langevin method is a simpification of Hamiltonian dynamics where only a single leapfrog step is used.
This algorithm samples from a Bayesian posterior by adding artificial noise to the stochastic gradient which, at long times, dominates the SGD noise. Though elegant, one disadvantage of SGLD is that the learning rate must be decreased to achieve the correct sampling regime, and the algorithm can suffer from slow mixing times.
SG Fisher scoring speeds up mixing times in SGLD by preconditioning a gradient with the inverse sampling noise covariance.
! Computation
The SGLD technique generates a set of samples $$\{\hat\omega_t\}$$ from the model's posterior over the random variable $$\omega$$ by adding stochastic gradient steps to @@color:#859900;the previously generated samples@@:
$$
\delta\omega = \frac\epsilon2(\nabla\log p(\omega)+\frac NM\sum_{i\in S}\nabla\log p(y_i|x_i,\omega))+\eta
$$
where $$\eta\sim\mathcal N(0, \epsilon)$$ and $$S$$ is a randomly sampled set of $$M$$ indices from $$\{1, \dots, N\}$$. $$\epsilon$$ is decreased in magnitude following the Robbins-Monro equations. @@color:#859900;The approximate posterior structure need not be specified.@@
! Remarks
SGLD often collapses to a single mode.
! Bibs
* [[A Complete Recipe for Stochastic Gradient MCMC|https://arxiv.org/abs/1506.04696]]
! Models
* [[Stochastic Gradient Langevin Dynamics]]
* SG Hamilton
* SG thermostats
* SG Fisher scoring
SGD with a constant learning rate simulates a Markov chain with a stationary distribution. SG has been used in the service of scalable Bayesian MCMC methods, where the goal is to generate samples from a conditional distribution of latent variables given a data set.
SG MCMCs employ stochastic gradients of $\log p(\theta, \mathbf x)$ to improve convergence and computation of existing sampling algorithms.
! Quant's View
Summary of [[YLK Samo et al. 2016|https://arxiv.org/abs/1605.02654]]
!! Problem
The goal is to find a portfolio selection to outperform the market index with probability 1. Such a strategy is called a ''relative arbitrage''. We evaluate those strategies based on certain metrics. The chosen performance metric may depart from the excess return, for instance by adjusting the risk taken.
!! Assumptions
Relative arbitrage can be achieved under some certain assumtions of the market:
* Fernholz's ''master equation'': free from stochastic integration
* Stock capitalisation models
The dynamics of the $n$ positive stock capitalisation processes $X_i(\cdot)$, $i=1, \dots, n$ are described by the following system of SDEs:
$$
dX_i(t) = X_i(t)(b_i(t)dt+\sum_{\nu=1}^d\sigma_{i\nu}(t)dW_\nu(t)).
$$
for $t\ge0$ and $W_\nu$ are independent standard Brownian motions with $d\ge n$, and $X_i(0)>0$. The ''rates of return'' $b_i(\cdot)$, and ''volatilities'' $\sigma$ are some unspecified $\mathbb F$-progressively measurable processes (filtration for stochastic processes means cannot see into the future) and are assumed to satisfy the integrability condition
$$
\sum_{i=1}^n\int_0^T(|b_i(t)|+\sum_{\nu=1}^d\sigma_{i\nu}(t)^2)dt<\infty, \qquad \mathbb P-a.s.,
$$
for all $T\in(0, \infty)$, and the ''non-degeneracy'' condition
$$
\exists\epsilon>0:\xi^T\sigma(t)\sigma^T(t)\xi\ge\epsilon\|\xi\|^2,
$$
!! Difficulties
* Inverse problem: the search space is too large to prove a strategy is optimal.
* Several market imperfections are ignored, e.g. bankruptcy
* Driven only by @@color:#859900;market capitalisations@@
! DL Practitioner's View
!! Sequence Prediction
We can construct an end-to-end sequence model to predict the price, variance
[[SPDC|https://arxiv.org/abs/1409.3257]] works on the primal-dual formulation:
$$
\min_{x\in\mathbb R^d}\max_{y\in\mathbb R^n}\{\psi(x) - \frac1n\sum_{i=1}^nf_i^*(y_i)+\frac1ny^TAx\}
$$
per iteration, it keeps track of two vectors $$(x_k, y_k)$$, and performs:
* coordinate descent on $$y_{k+1}$$ ($$y_{k+1} = y_k - \eta_y\cdot (Ax_k)_i$$)
* full gradient descent on $$x_{k+1}$$ ($$x_{k+1} = x_k - \eta_x\cdot A^Ty_{k+1}$$)
SPDC introduces momentum:
* $$x_k + (x_k - x_{k-1})$$
! Continuous Time
! Discrete Time
! Both
[[HMM|Hidden Markov Model]]
* [[NIPS 2017 paper|https://arxiv.org/abs/1711.05411]]
A variational model with RNN as decoder. Similar works are
* STORN
* VRNN
* SRNN
In this work
* Backward recurrent network for approximate posterior (SRNN)
* Condition the recurrent state of the forward auto-regressive model with the stochastic variables and use a conditional prior (VRNN, STORN)
* auxiliary costs to force the latent variables to encode information about the future.
To deal with the "posterior collapse" issue, often caused by the latent being ignored, they force the latent variables to encode useful information by adding auxiliary training signals for the latent variables.
* [[paper|https://papers.nips.cc/paper/4937-accelerating-stochastic-gradient-descent-using-predictive-variance-reduction.pdf]]
* [[SVRG++|https://pdfs.semanticscholar.org/e8ea/05cc219f4baf10110a45dfcc75a3552bc635.pdf]]
* [[SCSG|https://arxiv.org/abs/1609.03261]]
Suppose each $$f_i(x)$$ is convex, $$L$$-smooth. First divide into epochs, where $$m \approx 2n$$, the beginning weight for each epoch $$\tilde x$$ is the "snapshot point". Compute the full gradient of the snapshot $$\tilde\nabla f(\tilde x)= \frac{\nabla f_1(\tilde x)+\cdots\nabla f_n(\tilde x)}{n}$$.
For the following updates, $$\tilde\nabla f(x_k) = \nabla f(\tilde x) - \nabla f_i(\tilde x) + \nabla f_i(x_k)$$.
Still unbiased, and $$\|\tilde\nabla f(x)-\nabla f(x)\|^2$$ approaches to zero. SVRG converges in rate $$\varepsilon\propto 1/T$$
<<<
''Remark'' [vs SGD]<br>
When the objective is non-smooth, the performance is slightly worse than SGD
<<<
Epoch length $$m$$ can vary:
* For non-SC objectives, one should double $$m$$ every epoch (SVRG++)
* If $$n$$ is huge, one can choose $$m<n$$ and compute $$\nabla f(\tilde x)$$ approximately (SCSG [Lei-Jordan, 2017])
This paper directly applied Mask R-CNN for stomata detection and pore measurement. Although experiments shows sufficient accuracy against manual measurement, the reviewer believe the following points require to be justified before the authors could safely make the statement that the method is "fast", "SOTA" and, especially "transferable".
* The authors should compare their Mask R-CNN results with some of the other reviewed methods, both speed-wise and accuracy-wise.
* A validation set of merely 99 images could be too small to justify a general complicated framework such as Mask R-CNN is necessary for this task.
* The reviewer is not sure what exactly transferable means in the abstract. Is it tranfering to other datasets or simple from training set to validation set? Either case, more experiments should be conducted to support this argument.
* Spoofing: violates the authentication security
** Android [[Intent spoofing|http://blog.palominolabs.com/2013/05/13/android-security/index.html], with a example of forging `SendMoneyActivity` to PayPal.
** App Cloning, Repacking or Piggybacking
* Tampering: affects the integrity property and involves a malicious modification of data.
** Content Pollution
* Repudiation
For RNNs, $\lambda$ can be adjusted to be much larger.
For large values of $\lambda$ any Tikhonov-damped 2nd-order optimization approach (e.g. HF) wil behave similarly to a 1st-order approach and crucial low-curvature directions whose associated curvature is significantly smaller than $\lambda$ will be effectively masked-out.
One hypothesis is that for certain small changes in $\theta$ there can be large and highly non-linear changes in the hidden state sequence $h$ (given that $W_{hh}$ is applied iteratively to potentially hundreds of temporal 'layers') and these will not be accurately approximated by $M_{\theta_n}$.
A damping function $R$ that can penalize directions in parameter space that despite not large in magnitude, can nontheless lead to large changes in the hidden-state sequence. The structural damping function:
$$
R_{\theta_n}(\theta) = \frac 1 2\|\delta_n\|^2+\mu S_{\theta_n}(\theta)
$$
where $\mu>0$ is a weighting constant and $S_{\theta_n}(\theta)$ is a function which quantifies change in the hidden units as a function of the change in parameters. With the inclusion of $\mu S_{\theta_n}(\theta)$, we hope that the quadratic model will tend to be more accurate at its optimum even for smaller values of $\lambda$.
Since $S$ is not generally quadratic in $\theta$, CG cannot be applied in $R$. We have to approximate $S$.
Pixel's Now Playing only works on Pixel because it works fully on device.
! Circulant matrix
O(d) storage/ O(dlogd) time
! Low displacement rank matrices
Original matrix $$M$$ is not low rank but its displaced version $$L$$ is
$$
L = \nabla_{A, B}[M] = AM-MB = GH^T
$$
* A & B: fixed $$d\times d$$ operator matrices
* G & H: $$m\times r$$ and $$n\times r$$ matrices of rank $$r$$
$$f$$-circular operator
! Kronecker product
$$
R = A_1 \otimes\cdots\otimes A_j \otimes\cdots\otimes A_M
$$
O(logd) storage/ O(dlogd) time
* Learned via sequential minimization of bilinear forms: O(dlogd) [ICCV15]
* Similar performance as that of unstructured weights
** Tensor train [Novikov et al., NIPS15]
! MIT 1999 paper
A general framework for learning to solve two-factor tasks using bilinear models, which provide sufficiently expressive representations of factor interactions but can nontheless be fit to data using efficient algorithms based on the singular value decomposition and expectation-maximization.
!! Bilinear models
!!! Symmetric model
$$
y_k^{sc} = \sum_{i=1}^I\sum_{j=1}^Jw_{ijk}a_i^sb_j^c.
$$
$a_i^s$ and $b_j^c$ are style and content parameters, $y_k^{sc}$ is a $K$-dimensional observation vector in style $s$ and content class $c$.
!!! Asymmetric model
$$
a_{jk}^s = \sum_iw_{ijk}^sa_i^s,
$$
giving
$$
y_k^{sc} = \sum_{j}a_{jk}^sb_j^c.
$$
Style and content can be viewed as 2 hidden layers in a neural net and they are interconnected by weight $W_k$.
!! Model fitting
Both model can be solved by matrix factorization techniques.
In the experiment, asymmetric model is fitted with 4-dimensional style parameter selected by cross validation. And before a test of recognition, a separable mixture model with $S\times C$ gaussian components is trained with EM.
The content is predicted while the style matrix of test case is learned in EM. However, the model is tricky to solve. "Overfitting" behavior is often observed, where as EM iterates, test case is assigned to incorrect class and the likelihood grows. With 15 speakers, best precision can get is 74.6% not significantly better than 1-NN, 63.9%.
* [[Style Transfer Taxonomy]]
! Basic
!! Objective
In its original formulation, the neural algorithm of artistic style proceeds as follows: starting from some innitialization of $p$ (e.g. $c$, or some random initialization), the algorithm adapts $p$ to minimize the loss function
$$
\cal L(s, c, p) = \lambda_s\cal L_s(p) + \lambda_c\cal L_c(p),
$$
where $\cal L_s(p)$ is the style loss, $\cal L_c(p)$ is the content loss. Given a set of "style layers" $\cal S$ and a set of "content layers" $\cal C$, the style and content losses are themselves defined as
$$
\cal L_s(p)=\sum_{i\in\cal S}\frac{1}{U_i}\|G(\phi_i(p))-G(\phi_i(s))\|^2_F
$$
$$
\cal L_c(p)=\sum_{j\in\cal C}\frac{1}{U_j}\|\phi_j(p)-\phi_j(s)\|^2_2
$$
where $\phi_l(x)$ are the classifier activations at layer $l$, $U_l$ is the total number of units at layer $l$ and $G(\phi_l(x))$ is the Gram matrix associated with the layer $l$ activations.
!! Fast neural style
A style transfer network $T$ takes a content image $c$ as input and output pastiche image $p$ directly. With this we can achieve real-time style transfer. The downside is one net is tied to one specific style.
!! Style representation
The straightforward solution is to condition the net on the style $s$: $p = T(c, s)$. A very surprising fact about the role of normalization in style transfer networks: to model a style, @@color:#859900;it is sufficient to specialize scaling and shifting parameters after normalization to each specific style@@. In other words, all convolutional weights of a style transfer network can be shared across many styles, and it is sufficient to tune parameters for an affine transformation after normalization for each style.
Conditioning on a style is achieved as follows:
$$
z = \gamma_s\left(\frac{x-\mu}{\sigma}\right)+\beta_s
$$
! Implementations
* Fast neural style: [[github|https://github.com/jcjohnson/fast-neural-style]].
** Use perceptual loss to guide image generation
** Downsample and then upsample
** Don't like VAE, no 1-d representation
** First realtime implementation
** written in lua
* Google's [[Supercharging Style Transfer|https://research.googleblog.com/2016/10/supercharging-style-transfer.html]]
** No code released
* Real otf one: [[AdaIN|https://github.com/xunhuang1995/AdaIN-style]]
* [[The Contextrual Loss]]
* [[Instance Normalization: The Missing Ingredient for Fast Stylization|https://arxiv.org/abs/1607.08022]]
** claims replacing batch normalization with instance normalization significantly improves the quality of feedforward style transfer models.
* [[Neural Style Representations and the Large-Scale Classification of Artistic Style|https://arxiv.org/abs/1611.05368]]
** for classification
* [[Scribbler: Controlling Deep Image Synthesis with Sketch and Color|https://arxiv.org/abs/1612.00835]]
** color sketches by training GAN on generated inputs
** brilliant idea and result
* [[Prisma's Texture Networks|https://github.com/DmitryUlyanov/texture_nets]]: an improved implementation
* [[Deep Photo Style Transfer|https://arxiv.org/abs/1703.07511]]
** preserve reality by making sure transformation is locally affine in color space
** Style loss term updated with segmentation mask
! Parametric
Start from random, iteratively update.
Style loss on Gram matrix $$G(i, j) = \sum_pF(i, p)F(j, p)$$.
* Gram matrix
** A neural algorithm of artistic style
** Perceptual losses for real-time style transfer and super-resolution
* Gram approximates
** Universal style transfer via feature transforms
** Decoder network over light weight reconstructed feature for fast semantic style transfer
* Mean/Variance
** A learned representation for artistic style
*** use a simple embedding lookup to produce instance normalization parameters
** Exploring the structure of a real-time, arbitrary neural artistic stylization network
** Arbitrary style transfer in real-time with adaptive instance normalization
** [[A Learned Representation For Artistic Style|http://arxiv.org/abs/1610.07629]]
*** conditional instance normalization, surprising result, why should this work?
* Histogram
** Stable and controllable neural texture synthesis and style transfer using histogram losses
! Non-parametric
Direcly find neural patches similar to the given example. Match loss (local style loss) is a sum over matching patches:
$$
\mathcal L_{match} = \sum_p\|\Psi_p(F_o)-\Psi_{NN(p)}(F_s)\|_F^2
$$
and $NN(p)$ is normalized cross-correlation over all local patches
* Combining Markov random fields and convolutional neural networks for image synthesis
* Precomputed real-time texture synthesis with markovian generative adversarial networks
* Fast patch-based style transfer of arbitrary style
** de-VGG
* Visual attribute transfer through deep image analogy
** considering bidirectional constraint and pyramids refinement
* [[Arbitrary Style Transfer with Deep Feature Reshuffle]]
@article{sudderth2008describing,
title={Describing visual scenes using transformed objects and parts},
author={Sudderth, Erik B and Torralba, Antonio and Freeman, William T and Willsky, Alan S},
journal={International Journal of Computer Vision},
volume={77},
number={1-3},
pages={291--330},
year={2008},
publisher={Springer}
}
* [[Optimization Methods for Large-Scale Machine Learning]]
* [[Recent Advances in Stochastic Convex and Non-Convex Optimization]]
* [[Geometric Insights into Support Vector Machine Behavior using the KKT Conditions|http://arxiv.org/abs/1704.00767]]
The naive Bayes classifier is equivalent to first standardizing the data then computing the mean difference. In the small $C$ regime soft margin SVM behaves like the MD classifier or a cropped version of the MD. In the large $C$ regime SVM is related to the maximal data piling direction.
The dimensionality of the space and the class sizes have strong impacts on SVM's behavior.
[[link|https://arxiv.org/abs/1604.06646]]
! Backgroud
We call the image patches containing single character proposals. The pipelines generating char proposal are suboptimal. They combines genral purpose features such as HoG, EdgeBoxes and Aggregate Channel Features. The proposals are then classified with e.g. CNN as specific letters/words.
!! [[Object Detection With CNNs]]
As in R-CNN, [[Jaderberg et al.'s text spotting method|Reading Text in the Wild with CNN]] also uses a similar pipeline for detection.
The performance of the detection pipeline becomes the new bottleneck of text spotting: recognition accuracy for correctly cropped words is 98% whereas the end-to-end text spotting F-score is only 69% mainly due to incorrect and missed word region proposals. Also, they are slow and inelegant.
! Synthetic datasets
Synthetic datasets with character-level region labels are very suitable for training detectors. Since we don't care natural image that much
! Fully-Convolutional Regression Network
!! Region proposals
Let $x$ denote an image, the most common approach for CNN-based detection is to propose a number of image regions $R$ that may contain the target object, and use a CNN $c = \phi(crop_R(x)) \in \{0, 1\}$ to classify. This approach, popularized by R-CNN, is considerably slow since it evaluates the CNN thousands of times per image.
An alternative faster way is to construct a fixed field of predictors $(c, \mathbf p) = \phi_{uv}(x)$, each of which specialises in predicting the presence $c\in\mathbb R$ and pose $\mathbf p = (x-u, y-v, w, h)$ of an object around a specific image location $(u, v)$. Here the pose parameters $(x,y)$ and $(w, h)$ denote respectively the location and size of a bounding box tightly enclosing the object. This can be implemented by Implicit Shape Models (ISM) and Hough voting. There a predictor $\phi_{uv}$ looks at a local image patch, centered at $(u,v)$, and tries to predict whether there is an object around $(u, v)$, and where the object is located relative to it.
To create a net which performs prediction densely, at every image location. Makes prediction about a class label (text/not text) and the parameters of a bounding box enclosing the word cnetered at that location (idea borrowed from You Only Look Once (YOLO) of Redmon et al.).
The differences:
# ISM and Hough voting aggregate individual predictions across the image by voting while YOLO uses individual predictions directly. This can accelerate detections.
# Hough voting predictors $\phi_{uv}(x)$ are local and translation invariant, whereas YOLO pools evidence from the whole image and uses different functions at different locations.
!! FCRN
''Single-scale features.'' Structure similar to VGG is used, only smaller. 9 CONVs each followed by a ReLU, and occasionally by a POOL. Max pooling is performed over 2x2 windows with a stride of 2 samples.
With 4 downsampling layers, the stride of these dense features is 16px, each containing 512 channels $\phi_{uv}^f(x)$.
''Bounding box prediction.'' The dense text predictors $\phi_{uv}(x) = \phi_{uv}^r\circ\phi^f(x)$. Where $\phi_{uv}^r$ is a (C-7-5x5) linear filter, regressing the object presence confidence c, and up to six object pose parameters $\mathbf p = (x-u, y-v, w, h, \cos\theta, \sin\theta)$ where $\theta$ is the bounding box rotation.
To constrain one word per cell, a denser predictor may be needed.
''Multi-scale detection.'' To detect larger text, input image is scaled down by factors $\{1, 1/2, 1/4, 1/8\}$. The largest scoring proposal over all overlapped detections is selected.
''Training loss.'' squared loss for each output. If a cell does not contain a ground-truth, loss ignores all parameters but $c$.
''Comparing to YOLO.'' YOLO imposes strong spatial constraints on bounding box predictions since each grid cell only predicts two boxes and can only have one class. This spatial constraint limits the number of nearby objects that our model can predict. Our model struggles with small objects that appear in groups, such as flocks of birds.
YOLO has its 90% parameters in the last two fully-connected layers. YOLO retains image size. This makes YOLO harder to train and less efficient.
! Experiment
Trained with 800,000 images from SynthText in the Wild dataset. Net is optimized with SGD with batch-normalization after each convolutional layer except the last one. Batch size is 16, momentum 0.9, weight-decay $5^{-4}$. Initial learning rate $10^{-4}$ and reduced to $10^{-5}$ when the training loss plateaus.
As only a small number of grid-cells contain text, non-text probability error term is weighed down by multiplying 0.01 at the begining, and gradually increased to 1. Or all result collapse to zero due to class imbalance.
The result is best on IC3 with high-recall profile. Not as good as [[Jaderberg 2015|Reading Text in the Wild with CNN]] on SVT but is 45 times faster.
! DNI
* [[Decoupled Neural Interfaces using Synthetic Gradients|https://arxiv.org/abs/1608.05343]]
** We can create a model of error gradients using local information
** The result is Layer 1 can now update before the execution of Layer 2.
** Analogous to return prediction bootstrapping in RL (TD learing/Actor-Critic): 'Learn a guess from a guess'
* [[Understanding Synthetic Gradients and Decoupled Neural Interfaces|https://arxiv.org/abs/1703.00522]]
** Synthetic gradients don't change the critical points
** Convergence proof: for very simple networks
** Difference of networks:
! Multi Network
Two RNNs tick at different clock speeds. Don't have to train synchronized.
! Windows add permanent static route
```
route add -p 10.0.0.0 mask 255.0.0.0 192.168.0.1
```
! Introduction
* PCA tries to perserve large pairwise distance, which is not a good idea for visualization. For instance, 2 distant points along a swiss roll manifold can be near in Euclidean space.
* Isomap tries to estimate the distance over the manifold.
* Locally linear embedding is similar with t-sne in the sense that they both try to preserve short distances within local structure.
T-sne is a very popular method to generate image feature 2-d visualization.
! Basic
The input of t-SNE consists of a collection of pairwise (Euclidean) distance $\delta_{ij}$ between the $n$ input objects, that are converted into conditional probabilities as $p_{j|i}=\frac{\exp(-\delta_{ij}^2/2\sigma_i)}{\sum_{k\ne i}\exp(-\delta_{ij}^2/2\sigma_i)}$, and which joint to obtain a joint probability matrix entries $p_{ij}=\frac{p_{j|i}+p_{i|j}}{2n}$ (for symmetry). @@color:#859900;$\sigma$ varies per object and are set such that the conditional has fixed perplexity.@@
The embedding similarity $Q$ is modeled by a normalized Student-t kernel with 1 DOF, which is very more heavy-tailed, making dissimilar points can be far enough in the embedding.
Locations are determined by minimizing $KL(P\|Q)$. Due to the asymmetry of the Kullback-Leibler divergence, the objective function focuses on modeling high values of $p_{ij}$ (similar objects) by high values of $q_{ij}$ (nearby points in the embedding space). This is typically solved by SGD:
$$
\frac{\partial C}{\partial\mathbf y_i} = 4\sum_{j\ne i}(p_{ij}-q_{ij})(1+\|\mathbf y_i - \mathbf y_j\|^2)^{-1}(\mathbf y_i - \mathbf y_j)
$$
! Tree-Based Algorithms
We can approximate the distribution with the $\lfloor 3u\rfloor$ neighbors.
!! Barnes-Hut approximation
Computing the attractive forces are easier. We can approximate repulsive forces by the intuition that many of the pairwise interactions are very similar. Barnes-Hut approximation is first used by astrophysicists to compute the interaction between stars. If a ceil in quadtree:
# is small enough
# sufficiently far away from point $\mathbf y_i$
<div class="tc-table-of-contents">
<<toc-selective-expandable 'TableOfContents'>>
</div>
!! Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
* [[paper|https://arxiv.org/pdf/1905.08233v1.pdf]]
* [[3rd party code|https://github.com/grey-eye/talking-heads]]
! By Conferences
* [[ICML 2015]]
* [[KDD 2016]]
! TODO List
# [[KDD 2016]]
# Shen: A sufficiently advanced Lisp
# [[SAHD]]
!! Others
* [[Towards bridging the gap between deep learning and biology|Deep learning in biology]]
! Players
Game changers past 10yrs
* Joe Maddon
* Andrew Friedman
* Alex Cobb: rising to be starting pitcher
! Operations
* Sum
* Cat
* Attention
** Spatial
** Channel
* predict conv
* Spatial transformation
* $$\text{FiML}(x) = \gamma(z)\odot x + \beta(z)$$
* Deformable convolution
* Box convolution
! Framework
* Mask Propagation
** ECCV18 [[Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation|https://arxiv.org/abs/1803.04242]]
[[Google Research Blog|https://research.googleblog.com/2017/09/build-your-own-machine-learning.html]]
Interesting demos
* [[Beholder|https://github.com/chrisranderson/beholder]]: A TensorBoard plugin for visualizing arbitrary tensors in a video as your network trains.
! Notes
* [[CS224D 2016|Stanford NLP Course]] has a decent tf tutorial: [[TensorFlow Basics]]
* [[TensorFlow Sessions]]
! [[Projects|TensorFlow Projects]]
! [[TensorFlow Workarounds]]
! Tools
* [[tfprof|https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tfprof]]: A Profiling Tool for TensorFlow Models
* Slim way of training
* [[TensorBoard]]
* TensorFlow Serving
* [[TensorFlow Fold|Deep Learning with Dynamic Computation Graphs]]: Parallel dynamic batching
!! Session object
<<<
Session object encapsulates the environment in which Tensor objects are evaluated.
<<< TensorFlow Docs
```python
c = a * b
with tf.Session() as sess:
print(sess.run(c))
print(c.eval()) # c.eval() is just syntatic sugar for sess.run(c) in the currently active session
```
* `c` is symbolic value until we actually evaluate it.
* `tf.InteractiveSession()` is useful for debugging
* `sess.run(c)` is an example of a TensorFlow ''Fetch''. First data are ''Fed'' into the computation graph and 'Fetch' take the result out.
!! TensorFlow computation graph
<<<
TensorFlow programs are usually structured into a construction phase, that assembles a graph, and an execution phase that uses a session to execute ops in the graph.
All computations add nodes to global default graph.
<<< TensorFlow Docs
Functions exist in global graph memory.
!! TensorFlow variables
<<<
When you train a model you use variables to hold and update parameters. Variables are in-memory buffers containing tensors.
<<< TensorFlow Docs
```python
W = tf.Variable(tf.zeros((2,2)), name="weights")
tf.initialize_all_variabes().eval() # explicit initialization.
W.eval()
```
Variable tensors always have to be intitialized.
```python
state = tf.Variable(0, name="counter")
new_value = tf.add(state, tf.constant(1)) # make a new value
update = tf.assign(state, new_value) # assign the value to var
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for _ in range(3):
sess.run(update)
print(sess.run(state))
```
The name of variables is like an index allowing other programs to communicate with the graph.
!! Inputting data
We can import tensors from numpy.
```python
a = np.zeros((3,3))
ta = tf.convert_to_tensor(a)
```
The above method does not scale. `tf.placeholder` vars are dummy nodes where we can put data in. And `feed_dict` is a python dictionary mapping from `tf.placeholder` vars to data.
```python
In [96]: input1 = tf.placeholder(tf.float32)
In [97]: input2 = tf.placeholder(tf.float32)
In [98]: output = tf.mul(input1, input2)
In [99]: with tf.Session() as sess:
....: print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))
....:
[array([ 14.], dtype=float32)]
```
!! Variable scope
* `tf.variable_scope()` provides name-spacing
* `tf.get_variable()` creates/accesses variables from within a variable scope.
```python
with tf.variable_scope("foo"):
with tf.variable_scope("bar"):
v = tf.get_variable("v", [1])
assert v.name == "foo/bar/v:0"
```
`reuse_variables()` is useful to implement weight sharing in RNNs:
```python
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
tf.get_variable_scope().reuse_variables() # setting reuse=True in the scope
v1 = tf.get_variable("v", [1])
assert v1 == v
```
!! Gradient Computation
When people define a graph, a gradient operation is attached to the node. Backprop computes gradients for all variables in graph.
! Tutorials
[[MNIST multiclass|https://www.tensorflow.org/versions/r0.8/tutorials/mnist/beginners/index.html]]
! Applications
* [[cnn-text-classification]]
Session handles
* initialization
* restoring
* hooks: tools that run in the process of training/evaluation of the model
! MonitoredSession
! Version 0.80r is not compatible with skimage
* workaround: import skimage before tf.
* clean solution: not supported yet.
! GPU1 not visible
* workaround: run as `CUDA_VISIBLE_DEVICES=gpuid python myscript.py`
* clean solution: maybe graph level specification works but not perceived.
! Imbalanced class
* workarounds:
** increase batch size
** oversampling
* should also try:
** advanced sampling methods (e.g. [[SMOTE|http://arxiv.org/abs/1106.1813]])
** modify loss fun
```python
z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
= x - x * z + log(1 + exp(-x))
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(_pred) + (1-y)*tf.log(1-_pred), reduction_indices=1))
```
Hidden states are represented by tensors and updated via a cross-layer convolution.
* [[cnn-text-classification]]
* [[Project|http://cgm.technion.ac.il/Computer-Graphics-Multimedia/Software/Contextual/]]
** A very nice implementation of content loss
** UNSQ: is this metric slow? how it works in SR?
[[HuggingFace Youtube talk|https://www.youtube.com/watch?v=G5lmya6eKtc]]
* model size and computational efficiency
** SustaiNLP 2020
** [[Language Model Compression]]
* out-of-domain generalization and model evaluation
* fine tuning and sample efficiency
* common sense and inductive biases
In early 2020, the language models are getting exponentially bigger (10B params)
[[blog|http://www.inference.vc/my-notes-on-the-numerics-of-gans/]]
Simultaneous gradient descent is a generalization, rather than a special case, of gradient descent.
Iff a vector field $v$ is conservative, is there a scalar potential $phi$ whose gradient equals $v$. A vector field defined by an AE is not necessarily conservative.
The solution proposed by Mescheder et al is to construct a conervative vector field from the original as follows: $-\nabla L(x):=-\frac{\partial}{\partial x}\|v(x)\|^2_2$. This new vector field $\-\nabla L$ has the same fix points as $v$.
<<<
''Theorem'' [name]<br>
Descriptions
<<<
<<<
''Theorem'' [name]<br>
Descriptions
<<<
* Fixed-parameter algorithms
** [[Open questions for scheduling|https://arxiv.org/abs/1709.01670]]
To predict stock values and other economic variables
Google Trends: interest over time.
[[ARMA]]
* Many other generative models exist
* Learning guarantees are asymptotic
* Model needs to be correctly specified
* Non-stationarity needs to be modeled explicitly.
[[Generalization Bounds for Non-stationary Mixing Processes]]
$$
Ax=b \qquad A\succ0\qquad\kappa=K(A)=\frac{\lambda_{\text{max}}(A)}{\lambda_{\text{min}}(A)}
$$
Taylor expansion
$$
A^{-1}=\frac{1}{\lambda_{\text{max}}}(\frac{A}{\lambda_{\text{max}}})^{-1} = \frac{1}{\lambda_{\text{max}}}(I-(I-\frac{A}{\lambda_{\text{max}}}))^{-1}=\sum_{k=0}^{\infty}(I-\frac{A}{\lambda_{\text{max}}})^k\frac{1}{\lambda_{\text{max}}}
$$
<<<
''Theorem'' [name]<br>
$$
\|p_n(A)b-A^{-1}b\|\le\epsilon\|b\|\qquad\ni\qquad n\ge\Omega(\kappa\log\frac1\epsilon)
$$
<<<
By acceleration, we mean to find a tighter bound such as $\Omega(\sqrt\kappa\log\frac1\epsilon)$
```
@phdthesis{sutskever2013training,
title={Training recurrent neural networks},
author={Sutskever, Ilya},
year={2013},
school={University of Toronto}
}
```
A modern theory of why RNNs fail to learn long-term dependencies because simple gradient descent fails to optimize them correctly. One attempt to mitigate this problem is Hessian Free (HF) optimization
Hessian Free
(Stocahstic) Gradient Descent
Momentum methods (Hinton, Nesterov)
Initialization is important in DNNs. Is pretraining important or not?
! Background
!! RNN Algorithms
!!! Echo-State Networks
The Echo-State Network (ESN; Jaeger and Haas, 2004) is a standard RNN that is trained with the ESN
training method, which learns neither the input-to-hidden nor the hidden-to-hidden connections, but sets them to ''draws from a well-chosen distribution'', and only uses the training data to learn the hidden-to-output connections.
Unlike random projections or convolutional neural networks with random weights, RNNs are highly sensitive to the scale of the random recurrent weight matrix, which is a consequence of the ''exponential relationship between the scale and the evolution of the hidden states''. When the recurrent connections are sparse and are scaled so that their spectral radius is slightly less than 1, the hidden state sequence remembers its inputs for a limited but nontrivial #timesteps while applying many random transformations to it.
Despite its impressive performance on the synthetic problems from Martens and Sutskever (2011),
the ESN has a number of limitations. Its capacity is limited because ''its recurrent connections are not learned'', so it cannot solve data-intensive problems where high-performing models must have millions of parameters. And the size of high-performing ESNs grows very quickly with the information that the hidden state needs to carry. This explanation is consistent with the performance characteristics of random convolutional networks, which excel only when the number of labelled cases is very small, so systems that adapt all the parameters of the neural network lose because of overfitting.
!!! Mapping Long Sequences to Short Sequences
Connectionist Sequence Classification
!!! Truncated Backpropagation Through Time
Truncated BPTT processes the sequence one timestep at a time, and every $k_1$ timesteps, it runs BPTT for $k_2$ timesteps, so a parameter update can be cheap if $k_2$ is small. Consequently, its hidden states have been exposed to many timesteps and so may contain useful information about the far past, which would be opportunistically exploited.
! Training RNNs with Hessian-Free Optimization
RNNs trained by HF can significantly outperform similarly sized LSTMs.
!! [[Hessian-Free Optimization]]
! Language Modelling with RNNs
!! Tensor RNN
$$
h_t = \tanh(W_{hv}v_t+W_{hh}^{(v_t)}h_{t-1}+b_h)
$$
each character specifies a different hidden-to-hidden weight matrix. $W_{hh}^{(v_t)}$ can be defined with a tensor
$$
W_{hh}^{(v_t)} = \sum_{m=1}^Mv_t^{(m)}W_{hh}^{(m)}
$$
!! Multiplicative RNN
The storage of $W_{hh}^{(m)}$ can be prohibitive. $W_{hh}^{(v)}$ can be factored with
$$
W_{hh}^{(v_t)} = W_{hf}\text{diag}(W_{fv}v_t)W_{fh}
$$
! Things to Do
Compare SGD and second order methods for RNN, CW-RNN and LSTM.
Second order algorithms to try:
* HF
* BP of diagonal Hessian
Plot the Eigenvalue order of the Hessian.
* On [[GoogLeNet]]
** Moving its POOLs outside of Inception module, do it in traditional way
** What about adding another layer of Inception with no POOL?
* Use handcrafted Gabor filters (or some wavelet filters) instead of first layer CONVs, which takes much space in optimization
! First Impression
Like it very much, customizable and beautiful!
! About TiddlyWiki
!! It is a Quine
And very interestingly, it is a practical Quine. Wikipedia [[defines a Quine|http://en.wikipedia.org/wiki/Quine_(computing)]] as //a computer program which takes no input and produces a copy of its own source code as its only output//.
TiddlyWiki is an unusual example of a practical quine: it is this ability to produce a copy of its own source code that lies at the heart of TiddlyWiki's ability to independently save changes to itself.
! Plugins
* [[MathJax Plugin]]
* [[Highlight]]
* [[CodeMirror]]
* [[SlidesNStories]]
! Problem
!! Time series forecasting
''Training data'': finite sample realization of some stochastic process
$$
(X_1, Y_1), \dots, (X_T, Y_T)\in \mathcal Z = \mathcal X\times\mathcal Y.
$$
''Loss function'': $L:H\times\mathcal Z\rightarrow[0, 1]$, where $H$ is a hypothesis set of functions mapping from $\mathcal X$ to $\mathcal Y$. The loss function is bounded by 1.
''Problem'': find $h\in H$ with small path-dependent expected loss
$$
\mathcal L(h, \mathbf Z_1^T) = \underset{Z_{T+1}}{\mathbb E}[L(h, Z_{T+1})|\mathbf Z_1^T].
$$
!! Standard Assumptions
* Stationarity
* Mixing: the dependency between 2 events decays with their gap.
** beta, alpha, etc. mixing
! Models
[[ARMA]]
! Papers
* [[Generalization Bounds for Non-stationary Mixing Processes]]
! Talks
NIPS 2016 Tutorial: [[Theory and Algorithms for Forecasting Non-Stationary Time Series]]
* [[link|https://arxiv.org/abs/1605.06336]]
* [[blog|http://www.inference.vc/temporal-contrastive-learning-for-latent-variable-models/]]
The activations in the last hidden layer will learn to represent something fundamental, the log-odds-ratios in a probabilistic generative model. The model relies on a not very practical assumption about the time series data.
It's not hard to see that TCL is analogous to a temporal version of the [[jigsaw puzzle method|http://www.inference.vc/notes-on-unsupervised-learning-of-visual-representations-by-solving-jigsaw-puzzles/]].
This is yet another example of using logistic regression as a proxy to estimating log-probability-ratios directly from data. This insight provides new ways in which unsupervised or semi-supervised tasks can be reduced to supervised learning problems.
As classification is now considered significantly easier than density estimation, direct probability ratio estimation may provide the easiest path forward for representation learning.
! Implementations
[[TIMIT HF]]
! Recent Results
Best TIMIT result in the literature: [[Transducer LSTM]]
TIMIT Results with End-To-End Training
|Training Method |Dev PER |Test PER |
|CTC |19.05+-0.11|21.57+-0.25|
|CTC (Noise) |16.34+-0.07|18.63+-0.16|
|Transducer |15.97+-0.28|18.07+-0.24|
Both networks consisted of five bidirectional hidden levels, each containing 2 LSTM layers of 250 cells, along with a size 62 softmax output layer (one unit for each phoneme, plus an extra blank unit).
* CTC: Connectionist Temporal Classification (6.8M weights). The CTC were first trained to convergence with no noise, then retrained with weight noise (std 0.075).
* Transducer: Sequence Transduction (7.4M weights) with an additional phoneme prediction network with a hidden layer of 250 LSTM cells, and an output network with a single hidden layer of 250 tanh units. Single best transducer run is 17.7.
All networks were trained using SGD, with learning rate $10^{-4}$, momentum 0.9 and random initial weights drawn uniformly from $[-0.1,0.1]$.
TIMIT Results with [[Hybrid Training|Hybrid LSTM]]i
|Network |Dev PER |Test PER |Dev FER |Test FER |Dev CE |Test CE |
|DBRNN |19.91+-0.22|21.92+-0.35|30.82+-0.31|31.91+-0.47|1.07+-0.010|1.12+-0.014|
|DBLSTM |17.44+-0.16|19.34+-0.15|28.43+-0.14|29.55+-0.31|0.93+-0.011|0.98+-0.019|
|DBLSTM (Noise) |16.11+-0.15|17.99+-0.13|26.64+-0.08|27.88+-0.16|0.88+-0.008|0.93+-0.004|
! Kaldi
!! Preprocessing
# Data preparation (`data_prep.sh`)
#* converting sphere files
#* and lexicon in text to fst format
#* generating utterance to speaker maps
#* transcriptions to symbol table ints
# Following steps
#* `prepare_graphs.sh`
#* symbol tables for words and phones
#* binary format FSTs: data/{G.fst, L.fst, L_disambig.fst}
#* files indicating silences
# Raw MFCC features
#* differ script (scp) and archive (ark) files (Table concept: specifiers)
# [[HMM Alignments]] Monophone training
#* `$feats` treated as an rspecifier
#* creates `$dir/topo` which specifies phone topologies
#* Initialization
#* creates decoding graphs
# Triphone training
!! Karel's setting
it relies on CUDA
!!! Original
Karel works on fMLLR and gmms.
# RBM pretraining [[geoff's tutorial|http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf]]
# frame cross-entropy
# sequence-training optimizing sMBR
!!! LSTM
Use fbank features.
# For dev & training set
## copy data
## make fbank
## compute cmvn
# split the training set with `subset_data_dir_tr_cv.sh`
```
# Train
$cuda_cmd $dir/log/train_nnet.log \
steps/nnet/train.sh --network-type lstm --learn-rate 0.0001 \
--cmvn-opts "--norm-means=true --norm-vars=true" --feat-type plain --splice 0 \
--train-opts "--momentum 0.9 --halving-factor 0.5" \
--train-tool "nnet-train-lstm-streams --num-stream=4 --targets-delay=5" \
--proto-opts "--num-cells 512 --num-recurrent 200 --num-layers 2 --clip-gradient 50.0" \
${train}_tr90 ${train}_cv10 data/lang $ali $ali $dir || exit 1;
```
!!! Multitask
```
$cuda_cmd $dir/log/train_nnet.log \
steps/nnet/train.sh \
--cmvn-opts "--norm-means=true --norm-vars=true" \
--delta-opts "--delta-order=2" --splice 5 \
--labels "scp:$dir/pasted_post.scp" --num-tgt $output_dim \
--proto-opts "--block-softmax-dims='$ali1_dim:$ali2_dim'" \
--learn-rate 0.008 \
${train_tr90_wsj} ${train}_cv10 lang-dummy ali-dummy ali-dummy $dir || exit 1;
```
| |lstm |multitask |
|network-type |lstm | |
|learn-rate |0.0001 |0.008 |
|cmvn-opts |norm-means/vars| |
|feat-type |plain | |
|delta-otps | |order =2 |
|splice |0 |5 |
|train-opts |momentum 0.9 halving-factor 0.5| |
|train-tool |nnet-train-lstm-streams num-stream=4 target-delay=5| |
|labels | |pasted_post.scp|
|num-tgt | |$output_dim|
|proto-opts |num-cells 512 num-recurrent 200 num-layers 2 clip-gradient 50.0|block-softmax-dims|
! Demo Comments
Initialization is important
RNNs can be used in phoneme classification but the final speech recognition system has to incorparate HMMs.
* [[cheatsheet|https://gist.github.com/MohamedAlaa/2961058]]
* [[configs|https://github.com/gpakosz/.tmux]]
|List sessions|`tmux ls`|
|Kill session|`tmux kill-session -t ID`|
! DR1Mask
!! Experiments
* split attention
* rank1-blendmask
** R_50
** remove gamma
** no tanh
** sum
** rank1 attention
! Bayesian
!! Experiments
* Rank-1 models
* Variational (VAdam?)
!! Reading
* [[NIPS11 Practical variational inference for neural networks]]
* [[ICML15 Weight uncertainty in neural networks]]
* [[Editor Shortcuts]]
* [[Linux Cheatsheet]]
* [[Org-mode]]
* [[WikiText]]
* [[Spacemacs]]
* [[Awesome WM]]
* [[bug.n]]
* [[tmux]]
* [[LaTeX]]
* [[Magit]]
* [[Dot Files]]
* [[Kitty]]
* [[Mirrors]]
* [[Websites]]
* [[arxiv|https://arxiv.org/abs/1706.02379]]
* [[ICML|http://vip.ece.cornell.edu/papers/17ICML_WS_qnets.pdf]]
Transducers promise to separate the element transformation from the actual traversal of the input data. This allows the very same transducer pipeline to be used in `reduce` (resp. the transducer specific variant `transduce`) and at the same time apply the exact same transformation logic to asynchronous channels as provided by `core.async`.
Just as well we could create a traditional lazy sequence from the transducer. The promise is to provide the laziness we know from sequences, but save the cons cells created in each step of the transformation pipeline. This is more efficient and reduces the load on the garbage collector.
Do transducer deliver on that promise?
Following is an example of a transducer:
```clojure
(defn neighbourhood
[n]
(list (dec n) n (inc n)))
(def example-t
(comp
(mapcat neighbourhood)
(map str))
```
```
@inproceedings{graves2013speech,
title={Speech recognition with deep recurrent neural networks},
author={Graves, Alan and Mohamed, Abdel-rahman and Hinton, Geoffrey},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on},
pages={6645--6649},
year={2013},
organization={IEEE}
}
```
! Experiments
|Training Method |Dev PER |Test PER |
|CTC |19.05+-0.11|21.57+-0.25|
|CTC (Noise) |16.34+-0.07|18.63+-0.16|
|Transducer |15.97+-0.28|18.07+-0.24|
Both networks consisted of five bidirectional hidden levels, each containing 2 LSTM layers of 250 cells, along with a size 62 softmax output layer (one unit for each phoneme, plus an extra blank unit).
* CTC: Connectionist Temporal Classification (6.8M weights). The CTC were first trained to convergence with no noise, then retrained with weight noise (std 0.075).
* Transducer: Sequence Transduction (7.4M weights) with an additional phoneme prediction network with a hidden layer of 250 LSTM cells, and an output network with a single hidden layer of 250 tanh units. Single best transducer run is 17.7.
All networks were trained using SGD, with learning rate $10^{-4}$, momentum 0.9 and random initial weights drawn uniformly from $[-0.1,0.1]$.
!! abstract
Allows for components to share perturbed copies of atoms.
! Details
convert 128-dimensional SIFT descriptor to a vector quantized discrete value.
The goal is to allow the same global cluster to describe objects at many different locations. To accomplish this, we augment topic models with transformation variables, thereby ''shifting'' global clusters from a "canonical" coordinate frame to the object positions underlying a particular image.
Let $\tau(\theta;\rho)$ denote a family of transformations of the parameter vector $\theta$, indexed by $\rho$
! Hierarchical Models
!! Fixed-Order Object Model
Fixed number of shared parts. LDA like model
* Unimodal Gaussian ''transformation priors'' $\rho_i$. Analogously to in-between-consonants (scale to uniform length).
* Mixture component $z_j$
* Multinomial appearance descriptors $W$
* Object transform displacement Gaussian $v$
<<<
Marginalizing the unobserved assignments $z_{ij}$ of features to parts, we find that the graph defines object appearance via a finite mixture model:
$$
p(w_{ji},v_{ji}|\rho_j, o_j=l)=\sum_{k=1}^K\pi_{lk}\eta_k(w_{ji})N(v_{ji}; \tau(\mu_k,\Lambda_k;\rho_j))
$$
Parts are thus latent variables which capture dependencies in feature location and appearance, while ''reference transformations'' allow a common set of parts to model unaligned images. Removing these transformations, we recover a variant of the author-topic model, where objects correspond to authors, features to words, and parts to the latent topics underlying a given text corpus.
<<<
How to marginalize reference transformation $\rho$?
!! Nonparametric Object Model
[[HDP|Dirichlet Process Mixtures]] to learn the number of shared parts (topics) underlying a set of object categories. The more samples, the more classes.
Let $H_w$ denote a Dirichlet prior on feature appearance distributions, $H_v$ a normal-inverse-Wishart prior on feature position distributions, and $H_w\times H_v$ the corresponding product measure. A global probability measure $G_0\sim DP(\gamma, H_w\times H_v)$ is first used to define an infinite set of shared parts:
$$
G_0(\theta)=\sum_{k=1}^\infty \beta_k\delta(\theta,\theta_k) \qquad \beta\sim GEM(\gamma) \qquad (\eta_k, \mu_k, \Lambda_k) = \theta_k\sim H_w\times H_v
$$
<<<
This generalizes the parametric model by defining an infinite set of potential global parts, and using the Dirichlet process's stick-breaking prior to automatically choose an ''appropriate model order''. It also extends the original HDP by associating a different reference transformation with each ''training image''.
<<<
!! Fixed-Order Scene Model
Learn the contextual relationship. (by transformations?)
! Sampling
Algorithm 1: Consider each training image $j$ in turn and sequential resampling of assignments $t_j$ of features to tables (category-specific copies of global parts) in the $j$th training image
$$
t_{ji}\sim\sum_{t=1}^{T_l}N_{lt}f_{k_{lt}}(w_{ji}, v_{ji})\delta(t_{ji},t)+\frac{\alpha}{\gamma+\sum_kM_k}\left[\sum_{k=1}^KM_kf_l(w_{ji},v_{ji}+\gamma f_{\bar k}(w_{ji}, v_{ji})\right]\delta(t_{ji},\bar t)
$$
and the image's coordinate frame $\rho_j$ with an auxiliary variable method.
<<<
Rao-Blackwellization
<<<
Algorithm 2: examine each object category $l$, and sequential resample all samples assigments $\mathbf k_l$ of local tables (category-specific parts) to global parts for the $l$th object category, as well as the HDP concentration parameters.
! Language modeling
* [[Transformer XL]]
* [[Compressed Transformer]]
! Vision
* ViT (pytorch repo: https://github.com/jeonsworld/ViT-pytorch)
! Reinforcement learning
* [[Iterated Amplification]]
This work is built upon Google's [[Character-Level Language Modeling with Deeper Self-Attention|https://arxiv.org/abs/1808.04444]]
* causal attention mask: limit history knowledge
* auxiliary loss: to train deep transformer
* multiple positions: not necessary by incorporating cached segment with XL
* learned positional embedding:
! Think of
Implement copynet with transformer
!
* Non-parametric KDEs for p($$\lambda$$ is good) and p($$\lambda$$ is bad) rather than $$p(y|\lambda)$$
! Intro
* ML based automatic optimization
* Python for easy customization and exploration; multiple runtime for product deployment
!! Tensor Expression and Optimization Search Space
tensor expression => program configuration. choices:
* CPU (scalar/AVX vector)
** loop transformations
** cache locality
** vectorization
* GPU
** Use of shared memory
** thread cooperation
* TPU (tensor)
** tensorization
* thread bindings
* latency hiding
Learning-based optimization:
* NIPS18 Learning to Optimize Tensor Programs
* Halide's compute/schedule separation
TVM backend lowers PyTorch IR to [[Relay|Relay IR]], and is able to transparently improve PyTorch performance with little user improvement.
[[Community Highlights 2019]]
VTA:
TSIM: Support for future hardware
! Roadmap
* Training
** AutoDiff with Relay
** Training on-device
** Tradeoff accuracy/throughput/Joules
* Automation
** Auto quantization
** Full-program optimization
** Automated HW design
* Hardware
** VTA Chisel design
** ASIC flow
** Training on VTA
! Researches
* PL: New high-level differentiable programing IR
* Architecture: New deep learning ASICs, RISC-V
* Security: trusted enclave for private aware ML
* ML: test bed for automatic optimization
[[link|https://joanbruna.github.io/stat212b/]]
! Lec1
We can take deep learing as
<<<
A class of parametrized non-linear representations encoding appropriate domain knowledge (invariance
and stationarity) that can be (massively) optimized efficiently using stochastic gradient descent
<<<
CNN is able to capture high level image properties more efficiently than previous models. How can we explain these advantage?
* model architecture? non-linearity/convolution/depth/invariance
* optimization? normalization/no local minima/stochastic optimization/equivalent local solutions
[[Deep Learning Theory]]
! Lec2
[[Representation Learning]]
! Lec3 Groups, Invariants and Filters
[[Understanding Deep ConvNets (Scattering Repr)]]
! Lec4 Scattering Convolutional Networks
Why are wavelets a good
! Paper reading lists
* [[Pure Exploration|https://homepages.cwi.nl/~wmkoolen/PureExploration18/]]
! Definition
* Epistemic (lack of knowledge), small dataset, OOD => can be reduced by larger datasets
* Aleatoric (inherent noise), overlapping labels
! Bayesian methods
* [[Bayesian Deep Learning]]
! Papers
* NIPS19
** [[Modeling Uncertainty by Learning A Hierarchy of Deep Neural Connections]]
[[link|https://sites.google.com/view/uncertainty2019]]
[[paper|http://web.stanford.edu/~cdesa/papers/isca_buckwild_submission.pdf]]
Proof of low precision doesn't harm training.
Things that matter:
* rounding strategy
* precision
* sparsity of input
basic facts:
* BUCKWILD! is based on HOGWILD!.
* The conversion in the AXPY operation decreases the number of bits used to represent the number and introduces round-off error.
! DMGC model
Numbers used by a parallel SGD can be separated into:
* data, quantized once and saved in DRAM. dataset precision, index precision for sparse data
* model, always stored in the last-level of cache. Quantize after AXPY, standard/unbiased rounding.
* gradient,
* communicate
[[link|https://arxiv.org/abs/1601.04920]]
! [[Image Representation Learning]]
! Averaging and Wavelets
[[Fourier transform|Fourier Transform vs Wavelet Transform]] are unstable in high-frequency. We apply a local translation invariance:
$$
\|\Phi(x)-\|\Phi(\varphi_vx)\|\le C2^{-J}\|v\|
$$
we can do a local averaging within the translation orbit with $\phi(v)$:
$$
\Phi(x)=2^{-dJ}\int_v\phi(2^{-J}v)\varphi_vxdv
$$
In coordinates, it becomes
$$
\Phi(x)(u)=\int\phi_J(v)v(u-v)dv=x\ast\phi_J(u)
$$
with
$$
\phi_J(v)=2^{-Jd}\phi(2^{-J}v)
$$
But the low-pass information is insufficient. We want to capture high-frequency with a measure involving a non-linearity. We want them to preserve stability to deformations and inter-class variability.
Have to understand why wavelets are a good idea. [[Think about 2|TODOs]]
! Definitions
; Separation
: If we can write $f(x)=f_0(\Phi(x))$ where $\Phi(x)$.
This is always used to reduce the dimension of the data. We further impose the separation is Lipschitz:
$$
\exists\epsilon>0 \forall(x, x')\in\Omega^2, \epsilon\|f(x) - f(x')\| \leq \|\Phi(x) - \Phi(x')\|.
$$ which implies $f_0$ is Lipschitz continuous.
; Linearization
: On the other hand, we can linearize the variations of $f$ by projecting the data into a much larger space of dim $d'$. $\Phi(x)=\{\phi_k(x)\}_{k\leq d'}$. We say that $\Phi$ separates $f$ linearly if $f(x)$ is well approximated by a one-dimensional projection:
$$
\tilde f(x) = \langle\Phi(x), w\rangle=\sum_{k=1}^{d'}w_k\phi_k(x).
$$
; Local symmetries
: We suppose there is a metric $|g|_G$ which measures the distance between $g\in G$ and the identity. A function $f$ is locally invariant to the action of G if
$$
\forall x\in \Omega, \exists C_x>0, \forall g\in G \ni |g|_G<C_x, f(g.x)=f(x)
$$
; Translations
: The translation group $G=\mathbb R^n$ is an example of Lie group. The action of $g\in G=\mathbb R^n$ over $x\in\Omega$ is $g.x(u)=x(u-g)$.
; Diffeomorphisms
: A small diffeomorphism acting on $x(u)$ can be written as a translation of $u$ by a $g(u)$: $g.x(u) = x(u-g(u))$ with $g\in\mathbf C^{\mathbf 1}(\mathbb R^n)$.
; Averaging
: A linear operator can compute local invariants to the action of the translation group $G$, by averaging $x$ along the orbit $\{g.x\}_{g\in G}$. This is done with a convolution by an averaging kernel $\phi_J(u)=2^{-nJ}\phi(2^{-J}u)$ of size $2^J$, with $\int\phi(u)du=1$:
$$
\Phi_Jx(u)=x\star\phi_J(u)
$$
It is Lipschitz continuous to diffeomorphisms but eliminates the variation above the frequency $c2^{-J}$.
; Wavelet transform
: We define $K$ wavelets $\psi_k(u)$ for $u\in\mathbb R^n$. They are regular functions with a fast decay and zero average. These $K$ wavelets are dilated by $2^j$: $\psi_{j,k}(u)=2^{-jn}\psi_k(2^{-j}u$.
! Remarks
The set of convolution filters, which are wavelets, is overcomplete in order to be able to fully recover the original input signals. On the on hand, similar to ReLU, each individual activation within the scattering network only preserves partial information of the input. On the other hand, different to ReLU, scattering network activatio perserves the energy information, i.e. keeping only the modulus of the convolution responses and erasing the phase information; ReLU retains the phase information but eliminates the modulus information when the phase of a response is negative.
See [[Concatenated Rectified Linear Units]] for an alternative ReLU.
The main motivation behind the algorithm is to prevent the co-adaptation of feature detectors, or overfitting, by forcing neurons to be robust and rely on population behavior, rather than on the activity of other specific units.
It is also noted that for a single logistic unit dropout performs a kind of "geometric averaging" (normalized weighted geometric mean) over the ensemble of possible subnetworks (proof in 3.1), and conjectured that something similar may occur also in multilayer networks.
! Definition
A one-parameter unitary group $\{\varphi_t\in Aut(\Omega)\}_{t\in\mathbb R}$ satisfies:
# $\forall t, s, \varphi_{s+t}=\varphi_t\varphi_s$,
# $\lim_{s\rightarrow t}\|\varphi_s-\varphi_t\| = 0$,
# $\forall t\in\mathbb R, x\in\Omega, \|\varphi_tx\|=\|x\|$.
In particular, these are Abelian groups
* Rotation and translations are $1$-parameter unitary groups
* Dilations can be made unitary: $\varphi_sx(u)=s^{1/2}x(su)$.
! Global Invariants
!! Stone's theorem
''Theorem:'' Suppose $\Omega$ is a Hilbert space. There is a one-to-one correspondence betweeen self-adjoint operators on $\Omega$ and one-parameter unitary groups of $Aut(\Omega)$. Given $\{\varphi_t\}_{t\in\mathbb R}$, there exists $A$ self-adjoint such that $\forall t$, $\varphi_t=e^{itA}$ ([[Matrix exponential]]). Conversely, if $A$ is self-adjoint, the family $\{e^{itA}\}_t$ is a one-parameter unitary group.
[[Proof|http://www2.maths.lth.se/media/thesis/2010/MATX01.pdf]]
!! Stone theorem, Fourier and Global Invariants
@@color:#859900;Translations are simultaneously diagonalized by Fourier atoms.@@
The Stone theorem formalizes the fact that a collection of “nice” commuting operators simultaneously diagonalizes (in a complex basis):
$$
A=V^*\text{diag}(\lambda_1, \dots, \lambda_n)V
$$
Unitary condition implies that eigenvalues are unitary complex numbers.
On larger Abelian (commuting) groups, we can factorize them into one-parameter groups (e.g. translations in R2)
$$
G=G_1\times G_2\times \dots G_l
$$
Thus $\Phi(x)=|Vx|$ is global invariant:
$$
\forall x, t, \Phi(\varphi_t(x))=\Phi(x).
$$
[[talk|https://www.youtube.com/watch?v=bBH8HVEr0-A]]
Encoding more structure into the model, combining neural networks with differential equations.
Julia is good because it is strongly typed. Can optimize and handle weird functions at the same time.
Julia keeps a generic algorithm (without specific type). @@color:#859900;When the types are specified, propagate to infer type?@@
Lecturer: Jiawang Bian
Time: 2019/8/11
Four categories:
# Supervised
#* (Eigen et al, NIPS 2014) Depth map prediction from a single image using a multi-scale deep network
# Unsupervised on Stereo Pair
#* (Graget al, ECCV 2016) Unsupervised CNN for Single View Depth Estimation: Geometry to Rescue–
# Unsupervised on Monocular Video
#* Convenient because can be trained and evaluated from the same sampled video
#* (Zhou et al, CVPR 2017) Unsupervised Learning of Depth and Ego-Motion from Video: predict relationship within consequetive frames
# Unsupervised on Stereo Video
#* (Zhan et al, CVPR 18) Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction: incoporated camera pose information
! Unsupervised on Stereo Pair
* depth $$D$$ is the inverse of disparity $$d$$: $$D(x) = fB/d(x)$$
** for stereo matching, the translation is clear, corresponding features are on the same line (horizontal displacement is zero).
* The problem with stereo matching is oclusion.
* Graget al, ECCV 2016
** Photometric loss by using warpped image from dense disparity
! Unsupervised on Monocular Video
* Zhou et al, CVPR 17
** Use camera pose prediction (from 5 frames) to guide depth
** Drawbacks
*** Occlusion
*** Moving object
*** Scale ambiguity: depth are relative
*** Scale inconsistency–Mask: each training sample is independent (depth scale independent from gt)
**Mitigate the issue by occlusion and moving object
! Unsupervised on Stereo Video
* Zhan et al, CVPR 18
** Based on previous two
** Visual loss (some other pixel-level feature distance)
! Using Optical flow for Occlusion and Moving Object
Optical flow masks oving regions for wrong depth estimation but introduces too much overhead
* (Yin et al, CVPR 2018) GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
* (Zou et al, ECCV 2018) DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
** alternative joint training
* (Ranjan et al, CVPR 2019) Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
! Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video
* Solve the scale-inconsistent issue for visual odometry
** consistent across frames within video
* Mitigate the issue by ''occlusion and moving objects''
* Simpler and more efficient than SOTA
!! Moving object from self discovered mask
* Predict masks from consecutive frames
** obvious depth estimation disagreements are from occluded regions and moving boundries
!! Geometry consistency loss
* enforce the same depth scale from consecutive frames
! Pseudo-RGBD SLAM
* Close loop to optmized camera pose
* Global bundle adjustment
* Motion model is replaced by CNN
Procedure
* Input
** RGB image and predicted Depth
** Predicted Camera Pose
* Output
** Refined Camera Pose and Depth
** Dense scene reconstruction
* Advantage
** Deep learning for front-end and Geometry for back-end
* [[Graph-powered Machine Learning at Google|https://research.googleblog.com/2016/10/graph-powered-machine-learning-at-google.html]]
** learn sentiment from word embedding for smart reply
* Pretext tasks
* Contrastive learning
** [[SimCLR]]
! Related works
''GANs''
* generator and discriminator nets
* unstable to train
this paper developed Deep Convolutional GAN
A stable Deep Convolutional GAN should:
* Replace any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator).
* Use batchnorm in both the generator and the discriminator.
* Remove fully connected hidden layers for deeper architectures.
* Use ReLU activation in generator for all layers except for the output, which uses Tanh.
* Use LeakyReLU activation in the discriminator for all layers.
! Implementations
* [[tensorflow|https://github.com/carpedm20/DCGAN-tensorflow]]
This paper propses a biologically plausible way of modeling memory in RNNs. The authors claim short-term memory is not modeled well by hidden layer in classic RNN, so they add more layers to the recurrent unit.
TL;DR: Autoassociative memory accumulates the outerproduct of hidden state to form the memory.
The recurrent update:
$$
\mathbf h_{t+1} = \tanh(W\mathbf x+A_t\mathbf h_t+\mathbf b)
$$
<<<
Plastic mechanisms are more accurately modeled by a memory with $O(H^2)$ capacity than the $O(H)$ of standard recurrent artificial recurrent neural nets and LSTMs.
<<<
I think this structure is similar to a RNN with deep hidden layer and skip connections under the hood. The Hebbian-like rule for updating the fast weights $A(t)$ is like embedding a Hopfield network.
$$
A(t) = \lambda A(t-1)+\eta h(t)h(t)^\top
$$
We don't have to compute the fast weight matrix:
$$
A(t)h_s(t+1) = \eta\sum_{\tau=1}{\tau=t}\lambda^{t-\tau}h(\tau)[h(\tau)^\top h_s(t+1)]
$$
The $h(\tau)^\top h_s(t+1)$ part can be interpreted as attention. This auto associate rule from [[Hopfield Net]] is not memory efficient. It can only store $\log(N)$ number of the patterns. The fact that is is learned through backprop makes it more efficient in some way.
The decay rate is different from the original idea.
! Remarks
Although driven by associative memory, the rationale of Fast Weight was explained by attention mechanism, where $A_t\mathbf h_t$ is the attentive sum of $\mathbf h_t$ in history. The reason why Fast Weights could better store inputs than RNNs is explained by correspondence to Hopfield net and its biological intuition.
[[Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization|https://scirate.com/arxiv/1709.06493]] change the update rule, specifically, the two hyperparameters $\lambda$ and $\eta$, into learnable weight matrices:
$$
A_t = W_A\odot A_{t-1} + W_h\odot \mathbf h_t \mathbf h_t^\top
$$
Using DNN to construct a global non-linear mapping relationship between the spectral envelopes of two speakers.
! Model
The DNN is trained by cascading 2 RBMs, which model the distributions of spectral envelopes of source and target speakers, using a Bernoulli BAM.
The model is proposed to convert the [[spectral envelopes|Spectral envelope]] directly instread of melcepstra.
! Learning
!! Training stage
Construct a generative model $\lambda^{(v)}$ to describe the distribution of the joint spectral space. The features of the joint space are ''concatenations'' of the time-aligned source and target spectral features, $v=[x, y]$.
!! Conversion stage
Maximum output probability parameter generation
$$
\tilde y^{(s)} = arg \underset{y^{(s)}}{\max}P(y|x,\lambda^{(v)})
$$
s.t. $y=My^{(s)}$. The constraint makes sure the observation sequences are fixed ''linear transformations'' of the static sequences (since the delta and delta-delta are computed by difference).
To eliminate variables in a factorized probability equation, repeat:
# choose a variable to eliminate
# push in its sum as far as possible
# compute the sum, resulting in a new factor
The time and space complexities of Variable Elimination are dominated by the size of the largest intermediate factor. Choosing the elimination ordering that minimizes this complexity is an NP-hard problem.
Graphical models are powerful tools for visualizing the independence properties of complex probability models.
Some references
* [[Neural Variational Inference and Learning in Belief Networks|https://arxiv.org/abs/1402.0030]]
* [[Tutorial on Variational Autoencoders|https://arxiv.org/abs/1606.05908]]
* [[Auto-Encoding Variational Bayes|https://arxiv.org/abs/1312.6114]]
* [[Stochastic Backpropagation and Approximate Inference in Deep Generative Models|https://arxiv.org/abs/1401.4082]]
* [[Semi-Supervised Learning with Deep Generative Models|https://arxiv.org/abs/1406.5298]]
* [[Rényi Divergence Variational Inference|https://arxiv.org/abs/1602.02311]]
** This paper is definitely worth a read, and has a lot more to it than just deriving the bound. It proposes a practical algorithm VR-max, which uses $$\alpha\rightarrow\infty$$ and a smaller number of Monte Carlo samples. This method perfroms similarly to IWAE but requires fewer backpropagation iterations.
Nice models
* [[PixelVAE|https://arxiv.org/abs/1611.05013]]: not much perceptual gain but a good point to start.
Inference networks
* Amortized inference [Stuhlmuller et al., NIPS 2013]
* Inference networks, recognition networks [Kingma and Welling, 2014]
* Informed sampler [Jampani et al., 2014]
* Memory-based approach [Kulkarni et al., 2015]
! Background
''Directed graphical models'' rely on an ''ancestral sampling procedure'', which is appealing both for its conceptual and computational simplicity. They lack, however, the conditional independence structure of undirected models, making exact and approximate posterior inference on latent variables cumbersome [[Saul et al. 1996|https://arxiv.org/abs/cs/9603102]]. Recent advances in ''stochastic variational inference'' and ''amortized inference'', allowed efficient approximate inference and learning of deep directed graphical models by maximizing a variational lower bound on the log-likelihood. In particular, VAE simultaneously learns a generative network, that maps gaussian latent variables $$z$$ to samples $$x$$, and semantically meaningful features by exploiting the reparametrization trick.
We optimize the ELBO:
$$
\max_{w, \theta}\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta)]-D_{KL}(q(z|x, w)\|p(z))
$$
! Introduction
[[Reddit discussion|https://www.reddit.com/r/MachineLearning/comments/4r3pjy/variational_autoencoders_vae_vs_generative/]]
VAEs are a probabilistic graphical model whose ''explicit goal'' is latent modeling, and accounting for or marginalizing out certain variables (as in the semi-supervised work above) as part of the modeling process. They ''can'' make good generations (cf [[inverse autoregressive flow|https://arxiv.org/abs/1606.04934]], [[discriminative regularization|https://arxiv.org/abs/1602.03220]]) though they are ideal in settings where the latent is important (see [[variational fair autoencoder|https://arxiv.org/abs/1511.00830]]). The VAE naturally collapses most dimensions in the latent representations, and you generally get very interpretable dimensions out, the the training dynamics are generally a bit weird.
The ability to set [[complex priors|https://arxiv.org/abs/1605.06197]] in the latent is also nice especially in cases where you know something should make sense or you have a desired latent distribution. You can also do distributed latents (and priors) over time, as in the [[VRNN paper|https://arxiv.org/abs/1506.02216]] or fixed latents over a sequence as in VRAE, STORN, and Generating Sentences from a Continuous Latent Space. These all learn interesting and powerful latent representations for sequences, and can be combined with many existing models for sequence modeling.
! Model
Let $$\bf x$$ be a vector of $$D$$ observable variables, $$\mathbf z\in \mathbb R^M$$ a vector of stochastic latent units and let $$p(\mathbf x, \mathbf z)$$ be a parametric model of the joint distribution. Estimating the marginal likelihood $$p(\mathbf x)$$ could be intractable when the model is parametrized by a neural network. We can instead optimize the variational lower bound:
$$
\ln p(\mathbf x)\ge\mathbf E_{q(\mathbf z|\mathbf x)}[\ln p(\mathbf x|\mathbf z)]-\operatorname{KL}(q(\mathbf z|\mathbf x)\|p(\mathbf z)),
$$
where $$p(\mathbf x|\mathbf z)$$ is called a decoder and $$p(\mathbf z)=\mathcal N(\mathbf z|\mathbf 0, \mathbf I)$$ is the prior.
In practice, $$q(\mathbf z|\mathbf x)$$ is reparametrized:
$$
q(\mathbf z|\mathbf x) = \mathcal N(z|\mu(x), \operatorname{diag}(\sigma^2(x)))
$$
! Theory
The basic idea is [[Variational Inference]]. We wish to optimize $\theta$ such that we can sample $$z$$ from $$P(z)$$ and with high probability, $$f(z;\theta)$$ will be like the $$X$$'s in the dataset.
VAE assumes the latent factor $$z$$ that decides the properties of the image can be drawn from a simple distribution, i.e., $$\mathcal N(0, I)$$. Take this as a normalized random variable. The straightforward MC estimation $$P(X)\approx\frac 1 n\sum_iP(X|z_i)$$ is not feasible, because very similar samples are hard to genrate.
VAE attempts to sample values of $$z$$ from a new distribution $$Q(z|X)$$ that are likely to produce $$X$$. And compute $$E_{z\sim Q}P(X|z)$$. The relationship between $$E_{z\sim Q}P(X|z)$$ from $$P(X)$$ is one of the cornerstones of variational Bayesian methods.
Within SGD scheme, we only have to compute the gradient of
$$
\log P(X|z)-\mathcal D[Q(z|X)\|P(z)]
$$
We assume $$Q(z|X)$$ is a Gaussian of $$X$$, $$\mathcal N(\mu(X), \Sigma(X))$$.
To derive the variational autoencoder (VAE) from here you only need two more ingredients. Following borrowed from [[here|http://www.inference.vc/variational-renyi-lower-bound/]].
!! Amortised Inference
The bound above assumes that we observe a single sample, but usually we want to apply it on an entire dataset of i.i.d. samples. In this case, we need to introduce a separate auxiliary $q_n$ for each datapoint $x_n$, each with paramters $\psi_n$ that we need to optimise. This is feasible if the number of datapoints is small. In VAE we instead make the $q_n$s share parameters and make them depend on the datapoint $x_n$, basically $q_n(z)=q(z|x_n,\psi)$. This conditional auxiliary distribution is called the recognition model or inference network and can be used directly to perform approximate inference of latent variables given previously unseen data.
!! Monte Carlo and reparametrisation
Often, the variational lower bound can just be computed in closed form and optimized via gradient descent or via iterative methods. However, $\mathbb E_q$ involves an integral that is not always trivial to compute, and this is the case when we use a deep generative model as in VAE. Instead, we can sample from $q$ and approximate the lower-bound via Monte Carlo. This estimate will be unbiased, but have high variance if the number of samples is small. VAE uses a small reparametrisation trick to backpropagate through the sampling process and calculate derivatives with respect to $\theta$. Since Backprop does not flow through stochastic units, we perform a ''reparameterization trick'', sampling $z$ from $z = \mu(X) + \Sigma^{1/2}(X)\epsilon$, where $\epsilon\sim\mathcal(0, I)$.
! Remarks
[[From Variational auto-encoders do not train complex generative models|http://dustintran.com/blog/variational-auto-encoders-do-not-train-complex-generative-models]]
The neural network used in the encoder (variational distribution) does not lead to any richer approximating distribution. It is a way to amortize inference such that the number of parameters does not grow with the size of the data (an incredible feat, but not one for expressivity!) ([[Stuhlmüller et al., 2013|https://papers.nips.cc/paper/4966-learning-stochastic-inverses]]). This is better explained from the perspective of variational inference than auto-encoders. For example, suppose we have a model with latent variables that are Gaussian a priori (as in VAEs). We may choose a fully factorized Gaussian as the variational distribution, where each latent variable is rendered independent. The inference network takes data as input and outputs the local variational parameters relevant to each data point. The optimal inference network outputs the set of Gaussian parameters which maximizes the variational objective. This means a variational auto-encoder with a perfect inference network can only do as well as a fully factorized Gaussian with no inference network.
[[Much recent work has tried to improve this simple approximation to improve our approximate inference.|Variational Generative Models]]
! Bib
* [[Auto-Encoding Variational Bayes|https://arxiv.org/abs/1312.6114/]]
!! Applications
* [[Variational Deep Embedding: A Generative Approach to Clustering|https://arxiv.org/abs/1611.05148]]
** Hidden units conditioned on GMM as clustering assignment
* [[Deep Variational Inference Without Pixel-Wise Reconstruction|https://arxiv.org/abs/1611.05209]]
** Improve real NVP with VAE.
* [[A Deep Generative Framework for Paraphrase Generation|https://arxiv.org/abs/1709.05074]]
** A VAE-LSTM architecture should be able to generate more general text: refer to [[RVAE|https://github.com/kefirski/pytorch_RVAE]]
** How does it capture higher level features
! Basics
* [[Variational Inference]]
! Tutorials
* [[Variational Bayes and Beyond: Bayesian Inference for Big Data]]
! Variations
* [[Variational Generative Models]]
! Computation
* Computing the expected log likelihood $$\mathbb E_{z\sim q(z)}[\log p(x|z, \theta)]$$
** MC estimator
** Adversarial Variational Bayes
* [[Reparametrization Trick]]
! Theory
* [[Variational Inference Diagnostics]]
! Discussions
* In general have no clue how accurate the approximation is. Often posterior uncertainty badly under-estimated
** Fix-ups
** [[On statistical optimality of variational Bayes|https://arxiv.org/abs/1712.08983]]
[[ICML 2018 Tamara Broderick|http://www.tamarabroderick.com/tutorial_2018_icml.html]]
! Uncertainty
Suppose we have a multivariate normal. Mean field approximation $$q(\theta)=\prod^J_{j=1}q_j(\theta_j)$$ underestimates the variance (sometimes severely). It makes us overly confident and makes wrong decisions. We don't get covariance estiamtes, which could leads to wrong mean estimate.
!! Posterior means revisited
Suppose we want to predict college GPA $$y_n$$, and collect standardized test scores (e.g., SAT, ACT) $$x_n$$. We also collect regional test scores $$r_n$$.
Model:
$$
y_n|\beta, z,\sigma^2\overset{indep}{\sim}\mathcal N(\beta^Tx_n+z_{k(n)}r_n,\sigma^2)
$$
Priors:
$$
z_k|\rho^2\overset{iid}{\sim}\mathcal N(0, \rho^2)
$$
$$
\beta\sim\mathcal(0,\Sigma)
$$
$$
(\sigma^2)^{-1}\sim\text{Gamma}(a_{\sigma^2}, b_{\sigma^2})
$$
$$
(\rho^2)^{-1}\sim\text{Gamma}(a_{\rho^2}, b_{\rho^2})
$$
!! What we can do
Reliable diagnostic:
* KL vs ELBO (Look into the talk: Yes, but did it work? Evaluating variational inference)
** When we minimize KL, we are maximizing the ELBO. The value of KL is more informative, the closer to zero, the better we are estimating.
* Richer "nice" set, alternative divergences
* Theoretical guarantees on finite-data quality
** data summarization
! Automated, Scalable Bayesian Inference via Data Summarization
For the classification task,
$$
p(\theta|y)\approx_\theta p(y|\theta)p(\theta)
$$
Propose a efficient approximation with error bounds for finite data.
!! Bayesian Coreset
Pre-process data to get a smaller, weighted dataset. Previous heuristics methods are data squashing, big data GPs.
Subsampling the dataset will loss some important samples. We add a constraint on the log likelihood:
$$
\|\mathcal L(w)-\mathcal L\|\le\epsilon
$$
Using optimization algorithms finding the coreset. Seems not very intuitive.
$$
\ln p(Y|X) = \ln \underset{\omega\sim q(\omega)}{\mathbb E}\frac{p(Y|X, \omega)p(\omega)}{q(\omega)}
$$
For variational dropout, $$q(\omega)$$ takes the form of a mixture of two gaussians with small variances (drop or not drop). To estimate teh integral in ELBO
$$
\int q(\omega)\ln p(y_i|x_i, \omega)d\omega
$$
a single sample $$\hat\omega\sim q(\omega)$$ is used. The KL term is implemented as weight decay.
* [[A Theoretically Grounded Application of Dropout in Recurrent Neural Networks|https://arxiv.org/abs/1512.05287]]
** If dropout masks are shared between time steps, the objective for their proposed variational model is the same as the commonly used dropout objective with an $$\mathcal l_2$$ penalty
Shared masks are not a crucial ingredient of variational dropout in theory or in practice if $$q$$ and $$p$$ are redefined for the non-shared setting.
Making $$q$$ more flexible to approximate the real posterior. In variational dropout the posterior over weights does not concentrate with more data.
We want to do MAP estimation for the model parameters (the means of the distribution of weights, $$\Theta$$):
<iframe style="border: 0; width: 100%; height: 120px;" src="https://bandcamp.com/EmbeddedPlayer/album=2182194154/size=large/bgcol=ffffff/linkcol=0687f5/tracklist=false/artwork=small/transparent=true/" seamless><a href="http://hallatar.bandcamp.com/album/no-stars-upon-the-bridge">No Stars Upon The Bridge by Hallatar</a></iframe>
! [[Variational Inference MDL reformulation]]
$$
\ln q_j^*(\mathbf Z_j) = \mathbb E_{i\ne j}[\ln p(\mathbf X, \mathbf Z)]+C
$$
Following borrowed from [[here|http://www.inference.vc/variational-renyi-lower-bound/]].
! Why do we need variational lower bounds?
One way to define a probabilistic generative model for some variable $x$ is via latent variables: We introduce hidden variables $z$ and define the joint distribution between $x$ and $z$. In such a model, typically:
* $$p(z)$$ is very easy 🐣,
* $$p(x|z)$$ is easy 🐹,
* $$p(x,z)$$ is easy 🐨,
* $$p(x)$$ is super-hard 🦂,
* $$p(z|x)$$ is mega-hard 🐉
to evaluate.
Unfortunately, in machine learning the things we need to calculate are exactly the bad guys, 🦂 and 🐉:
* inference is evaluating $$p(z|x)$$
* learning (via maximum likelihood) involves $$p(x)$$ or at least its gradients
Variational lower bounds give us ways to approximately perform both inference and maximum likelihood parameter learning.
! Standard variational (VI) lower bound
$$
p(x|\theta) = \int p(x|z, \theta)p(z)dz
$$
Maximizing this term is intractable, so we do @@color:#859900;per-instance@@ variational approximation.
$$
\begin{aligned}
\log p(x|\theta) &= \log\int p(x|z, \theta)p(z)dz\\
&= \log\int p(x|z, \theta)\frac{q(z)}{q(z)}p(z)dz\\
&= \log\int p(x|z, \theta)\frac{p(z)}{q(z)}q(z)dz\\
&= \log\mathbb E_{z\sim q(z)}[p(x|z, \theta)\frac{p(z)}{q(z)}]\\
&\ge \mathbb E_{z\sim q(z)}[\log p(x|z, \theta)\frac{p(z)}{q(z)}]\\
&= \mathbb E_{z\sim q(z)}[\log p(x|z, \theta)]-D_{KL}(q(z)\|p(z))
\end{aligned}
$$
To nice auxiliary distribution $$q(x,\psi)$$ 🦄, that is both easy to evaluate analytically and easy to sample from, and define the lower bound as follows:
$$
L(x,\theta,\psi)=\log p(x;\theta)-\operatorname{KL}[q(z;\psi)\|p(z|x;\theta)],
$$
where $$\theta$$ are the parameters of the generative model. Hey, but doesn't that formula have the two things we can't evaluate? The good thing is that the lower bound can be rearranged to a form where we don't need either of those:
$$
L(x,\theta,\psi)=\mathbb E_q\log p(x,y;\theta)q(x;\psi)
$$
So basically:
$$
\log(\text{🦂})-\operatorname{KL}[\text{🦄}\|\text{🐉}]=\mathbb E_\text{🦄}\log\frac{\text{🐨}}{\text{🦄}}
$$
so we only have to deal with koalas and unicorns. Sweet.
For the drawbacks of VI and ''Rényi Lower Bound'', refer to [[Rényi Divergence Variational Inference|https://arxiv.org/abs/1602.02311]] and [[this blog|http://www.inference.vc/variational-renyi-lower-bound/]].
! Relationship with EM
$$\mathbb E_🐉\log🐨$$ is the ''expected complete log-likelihood'', which is optimized by EM. Variational inference does not estimate fixed model parameters, it is often used in a Bayesian setting where classical parameters are treated as latent variables. Variational inference applies to models where we cannot compute the exact conditional of the latent variables.
! Coordinate ascent mean-field variational inference
CAVI fixes other variational factors and compute the optimal $$q_j(z_j)$$ by its ''complete conditional''. This can be seen as a ''message passing'' algorithm, and is closely related to Gibbs sampling.
* [[paper|https://arxiv.org/abs/1802.02538]]
* [[blog|https://apeiroto.pe/ml/yes-but-did-it-work-evaluating-variational-inference.html]]
! Pareto Smoothing Importance Sampling
# Run VI, get variational distribution $$q(z)$$ approximating $$p(z|x)$$
# Sample a bunch of $$z$$s from $$q(z)$$
# Calculate the importance ratios $$p(z, x)/q(z)$$
# Fit a generalised Pareto distribution to the largest $$M$$ importance ratios
# Report the (estimated) shape parameter $$k$$
! Variational Simulation-Based Calibration Diagnostic
PSIS diagnostics looks at the full approximate posterior. VSBC evaluates the quality of point estimates by checking how far the distribution of calibration probabilities deviates from normal, or in this paper, how asymmetric this probability is.
For neural networks, VSBC may be computationally impractical because it puts the whole VI procedure inside an inner loop.
Hopefully this would be the goto blog for variational inference if you are interested in Variational Autoencoder, GAN etc.
! References
* [[Variational Inference: Foundations and Modern Methods|https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Variational-Inference-Foundations-and-Modern-Methods]]
* Several posts from [[inFERENCe|http://www.inference.vc/]]
! Variational Inference
!! Introduction
We care about the posterior of hidden variable $$z$$ given observation $$x$$:
$$
p(z|x) = \frac{p(z, x)}{p(x)}
$$
This is always intractable because the denominator $$p(x)$$ is super hard to compute. Let use the notations from [[The Variational Rényi Lower Bound|http://www.inference.vc/variational-renyi-lower-bound/]] for the ease of reading, the evaluation of
* $$p(z)$$ is very easy 🐣,
* $$p(x|z)$$ is easy 🐹,
* $$p(x,z)$$ is easy 🐨,
* $$p(x)$$ is super-hard 🦂,
* $$p(z|x)$$ is mega-hard 🐉
The 2 major approximation methods are
* MCMC, which forms a Markov chain whose posterior is 🐉,
* Variational inference, which people use to call it what you do when you are waiting for your MCMC sampler to converge.
So it's fast, and it's not very accurate. Anyway, how does it work? VI posit a ''variational family'' of distributions over the latent variables, $$q(z;\nu)$$, 🦄, and minimize the KL-divergence between 🦄 and 🐉. KL itself is intractable, we turn to the ''evidence lower bound'':
$$
g(z, \nu) = \log(🦂)−\operatorname{KL}[🦄\|🐉]=\mathbb E_🦄\log 🐨 - \mathbb E_🦄\log 🦄
$$
The first term prefers 🦄 to place its mass on the MAP, @@color:#859900;and the second is the entropy of 🦄 and acts like a regulizer@@.
If we modify the divergence part, we arrive at other methods:
* Further factorizing 🦄 into $🦄_1(\theta)🦄_2(\mbox(tags))$, the alternating optimization scheme is similar to message passing
* This should remind you of belief propagation but this topic is beyond this post
* Rényi Divergence, then we get [[this paper|https://arxiv.org/abs/1602.02311]]
For the drawbacks of VI and ''Rényi Lower Bound'', refer to [[Rényi Divergence Variational Inference|https://arxiv.org/abs/1602.02311]] and [[this blog|http://www.inference.vc/variational-renyi-lower-bound/]].
VI was first introduced to AI world to solve neural networks and picked up by Jordan's lab and generalized to many probabilistic models. Modern VI research touches areas like Bayesian statistics, convex optimization, reinforcement learning and (probably you're interested) neural network.
! Stochastic Gradients of ELBO
When we have large dataset computing the gradient is expensive. We can update the parameters based on small batches of data.
To optimize ELBO, we have to first take the estimation and then take the gradient. The expectation is not always tractable, for specific models, we use conjugate priors to solve it. Unfortunately, most powerful models don't fall into this category.
In normal VI we are often limited to exponential family distributions. Here we introduce ways to enable VI for more generalized class of models.
!! REINFORCE gradients
The problem here is we try to compute the integration before gradient, when we switch these two (true for most cases), we get stochastic optimization scheme.
$$
\nabla_\nu\mathcal L = \nabla_\nu\int 🦄g(z, \nu)dz = \mathbb E_🦄[\nabla_\nu\log🦄(\log🐨-\log🦄)]
$$
This gradient is model independent. The problem with this gradient is that likelihood functions take large values on tail of the distribution, and the variance is too large.
!! Controling variance
We can subtract it with functions with expectation 0 to get likelihood functions with same expectation bu t smaller variance. This kind of function is called ''control variates''. For any 🦄, we can use the score function $\nabla_\nu\log 🦄$ as the controller.
Techniques from [[Monte Carlo Methods]] could help as well.
!! Pathwise estimator
Instead of sampling from 🦄, we sample $\epsilon$ from some standard distribution $s$ and then transform it to $z$ with some fixed function $t$. We also assume 🐨 and 🦄 are differentiable w.r.t. $z$.
$$
\nabla_\nu\mathcal L = \mathbb E_s[\nabla_\nu g(t(\epsilon, \nu), \nu)] = \mathbb E_s[\nabla_z[\log🐨-\log🦄]\nabla_\nu t(\epsilon, \nu)]
$$
This trick yield much better behaved variance. This is also known as the reparameterization gradient.
!! Amortized inference
We add a global variable $\beta$ to the model. The algorithm becomes slow because we have to run optimization in the stochastic inner loop for each data point.
We can learn a mapping $f_\theta$ from $x_i$ to $\phi_i$:
$$
\phi_i = f_\theta(x_i)
$$
This function is can be learned using neural network. VAE seems quite straight forward with pathwise estimator and amortized inference:
<<<
''Model'' [Variational Autoencoder]<br>
We reparametrize observation $x$ with hidden variables $z\sim\mathcal N(0, 1)$ from standard normal:
$$
p(x|z)$ = \mathcal N(\mu_\beta(z), \sigma_\beta^2(z)),
$$
and learn the parameters of 🦄 with amortized inference
$$
(q(z|x) = \mathcal N(f^\mu_\theta(x), f^\sigma_\theta(x)).
$$
Where all functions are deep networks.
<<<
Read more from [[Reparametrization Trick]]
! Variational Inference for Implicit Models
🦄 from ''Mean-field family'' is fully factorized, this cannot approximate complex 🐉. Fully factorized as VAE, at the end of the day, we usually output just a single Gaussian. The parametric assumptions we make in mean-field is too strong, and implicit models would be one way to relax these.
One step forward, we can use covariance matrix to control the dependencies between variables. Rank-1 approximation can give us better result than mean-field, but higher order is not acceptable because of the complexity. Another limitation of these methods is that the approximate posterior is always Gaussian.
[[DRAW: A Recurrent Neural Network For Image Generation|https://arxiv.org/abs/1502.04623]] imposes ordering and non-linear dependency on all preceding variables
$$
q_{AR}(z;\nu) = \prod_kq_k(z_k|z_{<k};\nu_k).
$$
This posterior is called autoregressive distribution. DRAW greatly improved the image generation quality.
A design principle for richer posteriors is as follow:
# introduce new variables
# adapt bound to compute entropy
# maintain computational efficiency
!! Change-of-variables
We transform the random variables with invertible functions, the final distribution is tractible with the rule of change of variables, multiplying the original distribution with the term of determinate of Jacobian:
$$
q(z') = q(z)|\det\frac{\partial f}{\partial z}|^{-1}
$$
By applying as many functions as we like, we can get models as complex as we need.
* Sampling of these models is easy
* And we can compute the entropy
This is called a normalising flow.
We want the Jacobian to be triangular so that the computation is fast. There are several choices of transformation functions:
* Planar flow: $z_k = z_{k-1} + uh(\omega^\top z_{k-1}+b)$ is able to learn an identity or an one-layer nonlinear transformation.
* Real NVP: $y_{1:d} = z_{k-1, 1:d}, y_{d+1:D}=t(z_{k-1, 1:d})+z_{d+1:D}\odot\exp(s(z_{k-1, 1:d}))$
* Inverse AR flow: $z_k = \frac{z_{k-1}-1-\mu_k(z_{<k, x})}{\sigma_k(z_{<k}, x)}$
We can use latent variables to enrich the posterior approximation. In previous setting, we only allow continuous hidden variable distributions. Now we can use stochastic latent variable $\omega$ and both continuous and discrete distributions.
We introduce ''auxiliary variable'' $\omega$ that depends on $z$ and $x$. We now use ''auxiliary variational bound'', a lower bound on minus entropy term to make the estimation of ELBO tractable:
$$
\log p(x)\ge\mathcal L - \mathbb E_{q(z|x)}\operatorname{KL}[q(\omega|z, x)\|r(\omega|z, x)]
$$
The way of choosing auxiliary prior $r(\omega|z, x)$ and auxiliary posterior $q(\omega|x, z)$ includes
* Hamilton flow: $r(\omega) = \mathcal N(\omega|0, M)$ as independent Gaussian.
* Input-dependent Gaussian: $r(\omega|x, z)$
* Auto-regressive: $r(\omega|x, z) = \prod_ir(\omega_t|f_\theta(\omega_{<t}, x))
* $q(\omega|x, z)$ can be a mixture model, normalising flow, Gaussian process
Another way of doing this is auxiliary variables, where we use entropy bounds and Monte Carlo sampling.
! Variational Inference for GANs
The idea of applying variational inference to adversarial training has long been establishded. [[Adversarial Autoencoder|http://www.inference.vc/adversarial-autoencoders/]] use amortized inference to
For estimating the density ratio $\frac{q(z|x}{p(z)}$, we use logistic regression, introducing a discriminator $D$. We amortise the discriminator, learn a logistic regression classifier $D(z, x)$.
The generator $G$ in GAN has loss approximating the ELBO:
$$
\mathcal L(G;D) = \frac 1 N\sum_{n=1}^N\mathbb E_q\log D(z, x_n)+\mathbb E_p\log(1-D(z, x_n) = \frac 1 N\sum_{n=1}^N\mathbb E_\epsilon\left[\log\frac{D(G(\epsilon, x_n), x_n)}{1-D(G(\epsilon, x_n), x_n)}-\log p(x_n|z)\right]
$$
We can minimise the same loss w.r.t. parameters of $p(x|z)$ to obtain a VAE-like learning algorithm.
*
* [[Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks|https://arxiv.org/abs/1701.04722]]
* [[ALI|https://arxiv.org/abs/1606.00704]]
* [[BiGAN|https://arxiv.org/abs/1605.09782]]
reference: Variational learning and bits-back coding: an information-theoretic view to bayesian learning
Variational inference can be reformulated as the optimization of a Minimum description length loss function. One advantange of the MDL interpretation is that it leads to a clear separation between prediction accuracy and model complexity.
''variational free energy'' $$\mathcal F$$ w.r.t. parameters $$\beta$$
$$
\mathcal F = -\langle\ln [\frac{Pr(\mathcal D|\mathbf w)P(\mathbf w|\mathbf \alpha)}{Q(\mathbf w|\mathbf \beta)}]\rangle_{\mathbf w\sim Q(\mathbf \beta)}
$$
Rearranging
$$
\mathcal F = \langle L^N(\mathbf w, \mathcal D)\rangle_{\mathbf w\sim Q(\mathbf \beta)}+D_{KL}(Q(\beta)\|P(\alpha))
$$
Shannon's source coding theorem tells us that the first term on the right hand side is a lower bound on the expected amount of information (measured in nats, due to the use of natural logarithms) required to transmit the targets in $$\mathcal D$$ to a receiver who knows the inputs. Error loss:
$$
L^E(\beta, \mathcal D) = \langle L^N(\mathbf w, \mathcal D)\rangle_{\mathbf w\sim Q(\mathbf \beta)}
$$
Complexity loss:
$$
L^C(\alpha, \beta) = D_{KL}(Q(\beta)\|P(\alpha))
$$
Two transmission costs were ignored in the above discussion. One is the cost of transmitting the model with $$\mathbf w$$ unspecified (e.g. training algorithm). The other is the cost of transmitting the prior
[[paper|https://arxiv.org/abs/1709.07116]]
Refer to this one to compute the discrete attention
! Variational distribution
For each observation $x_n$ we have a corresponding latent variable $z_n$ comprising a $1$-of-$K$ binary vector.
!! Priors
The prior of $\pi$ is Dirichlet distribution
$$
p(\pi)=\text{Dir}(\pi\mid\alpha_0)=C(\alpha_0)\prod_{k=1}^K\pi_k^{\alpha_0-1}
$$
The prior of the mean and precision of each Gaussian component is an independent Gaussian-Wishart prior
$$
p(\mu,\Lambda)=p(\mu\mid\Lambda)p(\Lambda)=\prod_{k=1}^K\mathcal N(\mu_k\mid m_0, (\beta_0\Lambda_k)^{-1})\mathcal W(\Lambda_k\mid W_0,\nu_0)
$$
!! Conditional Distributions
$$
p(X\mid Z, \mu,\Lambda) = \prod_{n=1}^N\prod_{k=1}^K\mathcal N\left(x_n\mid\mu_k,\Lambda_k^{-1}\right)^{z_{nk}}
$$
is the conditional distribution of the observed data vector.
$$
p(Z\mid\pi)=\prod_{n=1}^N\prod_{k=1}^K\pi_k^{z_{nk}}
$$
is the conditional distribution of $Z$ given the mixing coefficient $\pi$.
The joint distribution of all of the random variables,
$$
p(X,Z,\pi,\mu,\Lambda= p(X\mid Z, \mu,\Lambda)p(Z\mid\pi)p(\mu\mid\Lambda)p(\Lambda)
$$
We consider a variational distribution which factorizes between the latent variables and the parameters so that
$$
q(Z,\pi, \mu, \Lambda)=q(Z)q(\pi, \mu, \Lambda)
$$
[[general result|10.1 Variational Inference]]
! The Problem
In reinforcement learning, we choose the parameters $$\theta$$ of a policy distribution $$\pi(a|s,\theta)$$ to maximize the expected reward $$\mathbb E_{\tau\sim\pi}[R]$$ over state-action trajectories $$\tau$$. And in fitting latent-variable models, we wish to maximize the marginal probability $$p(x|\theta) = \mathbb E_{p(b|\theta)}[p(x|z)]$$. The general problem is
$$
\mathcal L(\theta) = \mathbb E_{p(b|\theta)}[f(b)]
$$
We want to build unbiased, stochastic gradient estimators $$\hat g$$ such that $$\mathbb E[\hat g] = \frac{\partial}{\partial\theta}\mathcal L(\theta)$$.
! Introduction
An ideal loss may be intractable to optimize because of some non-differentiable function. Variational optimization turns this into a differentiable one. This generally works by introducing a probability distribution $$p_\psi(\theta)$$ over parameters $$\theta$$. The average loss under $$p_\psi$$ may be differentiable w.r.t. $$\psi$$. To find the optimal $$\psi$$, one can generally use a [[REINFORCE]] gradient estimator, which results in [[Evolution Stragegies]].
But ES generally has very high variance, so we apply the reparametrization trick to $$p_\psi$$ to construct a lower-variance gradient estimator. This, however, only works for continuous variables. To deal with the discreteness, we turn to a concrete relaxation, which approximates the discrete random variable by a continuous approximation.
! Applications
* [[Learning Sparse Neural Networks through $$L_0$$ Regularization|https://arxiv.org/abs/1712.01312]]
! Gradient Estimators
* [[Gumbel Machinery]]
* [[REINFORCE]]
* [[Evolution Stragegies]]
* [[RELAX]]
* [[Improving Straight Through Estimator]]
! References
* Model-based VC [11-13]
* Maximum-likelihood parameter generation (MLPG) is robus against variance modifications [22]
The training of ENAS:
* Sample $$\phi$$ from $$q = RNN()$$ amortised inference?
* Compute adapted parameters with gradient descent on $$\log p(y|x, \phi)$$
The goal is handle the uncertainty and ambiguity that occurs when learning from sampled architectures. Trajectory distribution?
* best model architecture and weights $$p(\theta)$$, Gaussian
* sampled model architecture and weights $$p(\phi|\theta)$$, Gaussian with mean $$\theta$$
! Useful results
* Gradient descent for a fixed number of iterations using $$x$$, $$y$$ corresponds to MAP of $$p(\phi|x, y, \theta)$$ under a Gaussian prior $$p(\phi|\theta)$$. [[ref|https://www.sciencedirect.com/science/article/pii/0024379594001146]]
* [[SVGD|Stein Variational Gradient Descent]] reduces the distance between the approximate distribution defined by the particles and the target distribution.
* [[Combining VI and MCMC]],
! Related work
* [[Dropout as a Bayesian Approximation]]: The GP representation and infinite width limit seems also related to [[DL theory|http://www.offconvex.org/2019/07/10/trajectories-linear-nets/]]
* [[BayesNAS]]
* Single-Path NAS (as MCMC?). Search space restricted to sequential
! Approach
!! Structured Bayesian Pruning
Given a probabilistic model $$p(y|x, \theta)$$. Approximate the posterior $$p(\theta|\mathcal D)$$ by a parametric distribution $$q_\phi(\theta)$$ by minimizing the KL divergence. Which is equivalent to maximization of the variational lower bound $$\mathcal L(\phi)$$.
$$
\mathcal L(\phi) = L_D(\phi)-\text{KL}(q_\phi(\theta)\|p(\theta))
$$
where
$$
L_D(\phi) = \sum_{i=1}^N\mathbb E_{q_\phi(\theta)}\log p(y_i|x_i,\theta)
$$
To estimate expected log-likelihood, we use the Reparametrization trick to obtain a differentiable minibatch-based MC estimator. sample from $$f(\phi, \epsilon)$$
$$
L_D(\phi) \simeq L_D^{SGVB}(\phi) = \frac{N}{M}\sum_{k=1}^M\log p(y_{ik}|x_{ik}, w_{ik})
$$
! Experiments
* Variational Dropout [[https://github.com/HolyBayes/pytorch_ard]]
* Structured Bayesian Pruning [[https://github.com/gaosh/Structured-Bayesian-Pruning-pytorch]]
* Try more general bayesian networks [[https://github.com/JavierAntoran/Bayesian-Neural-Networks]]
Use HNAS structure for classification
! Search space
!! HR
Searching for based on autodeeplab
* check [[HRNet-FCOS|https://github.com/HRNet/HRNet-FCOS]] for detailed implementation
!! CS-HR
Look for hints from
* [[MixNet|https://github.com/rwightman/pytorch-image-models.git]]
* [[PC-DARTS|https://github.com/yuhuixu1993/PC-DARTS]]
Lets break down the VGGNet in more detail. The whole VGGNet is composed of CONV layers that perform 3x3 convolutions with stride 1 and pad 1, and of POOL layers that perform 2x2 max pooling with stride 2 (and no padding). We can write out the size of the representation at each step of the processing and keep track of both the representation size and the total number of weights:
```
INPUT: [224x224x3] memory: 224*224*3=150K weights: 0
CONV3-64: [224x224x64] memory: 224*224*64=3.2M weights: (3*3*3)*64 = 1,728
CONV3-64: [224x224x64] memory: 224*224*64=3.2M weights: (3*3*64)*64 = 36,864
POOL2: [112x112x64] memory: 112*112*64=800K weights: 0
CONV3-128: [112x112x128] memory: 112*112*128=1.6M weights: (3*3*64)*128 = 73,728
CONV3-128: [112x112x128] memory: 112*112*128=1.6M weights: (3*3*128)*128 = 147,456
POOL2: [56x56x128] memory: 56*56*128=400K weights: 0
CONV3-256: [56x56x256] memory: 56*56*256=800K weights: (3*3*128)*256 = 294,912
CONV3-256: [56x56x256] memory: 56*56*256=800K weights: (3*3*256)*256 = 589,824
CONV3-256: [56x56x256] memory: 56*56*256=800K weights: (3*3*256)*256 = 589,824
POOL2: [28x28x256] memory: 28*28*256=200K weights: 0
CONV3-512: [28x28x512] memory: 28*28*512=400K weights: (3*3*256)*512 = 1,179,648
CONV3-512: [28x28x512] memory: 28*28*512=400K weights: (3*3*512)*512 = 2,359,296
CONV3-512: [28x28x512] memory: 28*28*512=400K weights: (3*3*512)*512 = 2,359,296
POOL2: [14x14x512] memory: 14*14*512=100K weights: 0
CONV3-512: [14x14x512] memory: 14*14*512=100K weights: (3*3*512)*512 = 2,359,296
CONV3-512: [14x14x512] memory: 14*14*512=100K weights: (3*3*512)*512 = 2,359,296
CONV3-512: [14x14x512] memory: 14*14*512=100K weights: (3*3*512)*512 = 2,359,296
POOL2: [7x7x512] memory: 7*7*512=25K weights: 0
FC: [1x1x4096] memory: 4096 weights: 7*7*512*4096 = 102,760,448
FC: [1x1x4096] memory: 4096 weights: 4096*4096 = 16,777,216
FC: [1x1x1000] memory: 1000 weights: 4096*1000 = 4,096,000
TOTAL memory: 24M * 4 bytes ~= 93MB / image (only forward! ~*2 for bwd)
TOTAL params: 138M parameters
```
As is common with Convolutional Networks, notice that most of the memory is used in the early CONV layers, and that most of the parameters are in the last FC layers. In this particular case, the first FC layer contains 100M weights, out of a total of 140M.
# Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR, 2015.
# Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, arXiv:1412.4729.
# Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint Modeling Embedding and Translation to Bridge Video and Language, arXiv:1505.01861.
# Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko, Sequence to Sequence--Video to Text, arXiv:1505.00487.
# Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, Describing Videos by Exploiting Temporal Structure, arXiv:1502.08029
# Anna Rohrbach, Marcus Rohrbach, Bernt Schiele, The Long-Short Story of Movie Description, arXiv:1506.01698
# Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler, Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, arXiv:1506.06724
# Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053
! Dataset
* [[DAVIS Challenge 2017|http://davischallenge.org/code.html]]
** [[Video Object Segmentation with Re-identification|https://arxiv.org/abs/1708.00197]]: winner entry
! Networks
[[Temporal feature aggregations]]
! Bibs
to read:
* Cheng et al. SegFlow: Joint learning for video object segmentation and optical flow
* Tsai et al. Video segmentation via object flow
* Kundu et al. Feature Space Optimisation for semantic video segmentation
* Voigtlaender and Leibe. Online dapation of cnns for video object segmentation
* predicting kernels: Niklaus~\etal Video Frame interpolation via adaptive convolution
** Vondrick and Torralba Generating the future with adversarial transformers
** generating videos with scene dynamics
* Streeter: approximation algorithms for cascading prediction models
!! Semantic
* Dynamic Video Segmentation Network, Xu~\etal~CVPR,18
** Assign different images regions to two different networks: the first one is deeper and slower, while the second one is shallow and relies on the optical flow.
** CityScapes (70.4 - 19.8FPS, 63.2 - 30FPS)
* Low-Latency Video Semantic Segmentation Li~\etal~CVPR,18
** The authors propose to predict a set of local convolutional kernels based on the current frame that is applied on the decoder output from the previous frame.
** key frame prediction
** CityScapes (76.84, 720x960 - 171ms per frame), CamVid (94.6 mAcc, 82.9 gAcc)
* Semantic Video Segmentation by Gated Recurrent Flow Propagation Nilsson and Sminchisescu~CVPR,18
** The authors propose to use temporal propagation between the frames for semantic video segmentation: in particular, they rely on the optical flow and only propagate the labels from the previous frame at pixels where the optical flow estimate is confident.
** CityScapes (PSP-MSC, 80.6 512x512 training / 0.7s per frame for full size testing), CamVid (Dilation8, 66.1)
* Deep Spatio-Temporal Random Fields for Efficient Video Segmentation Chandra~\etal~CVPR,18
** CamVid (wout CityScapes - 67.0\%, with - 75.2\%s), DAVIS-val (86.5\%), DAVIS Person (86.98)
** adapt deep gaussian random field to handle temporal information by predicting besides unary and spatial pairwise terms, also temporal pairwise terms, efficiently propagating features between frames
%%%%%%%%
The best of both worlds: Combining CNNs and Geometric Constraints for Hierarchical Motion Segmentation
Pia Bideau~\etal CVPR,18
main benchmarks: Freiburg-Berkeley Motion, Complex Background, Camouflaged Animals; supp. DAVIS - does not achieve best results (\textcolor{red}{check out numbers})
the authors decompose motion segmentation into first predicting rigid motions (motions that can be described by translating rigid 3D regions) using optical flow and then combining them with object proposals (from SharpMask) to predict object instance segmentations. pixel displacement from one frame to the next is a function of depth and motion
%%%%%%%%
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
Chen~\etal~CVPR,18
DAVIS-2016 (77.5, 275ms per frame 480p resolution)
The authors assign per-pixel labels based on the similarity of their embeddings to the reference ones. It also allows to incorporate users' feedback on-line.
%%%%%%%%
Fast and Accurate Online Video Object Segmentation via tracking parts
Cheng~\etal~CVPR,18
DAVIS-2016 (82.4 - 1.8s, 77.9 - 0.60s), DAVIS-2017 (54.6)
Divide images into parts of interest, track and segment them along the video frames
%%%%%%%
SDC-Net: Video Prediction Using spatially-displaced convolution
Reda~\etal~ECCV,18
To predict next frame, predict pixel-kernels and offsets. Use previous flow estimates
%%%%%%%
Deep Kalman Filtering Network for Video Compression Artifact Reduction
Lu~\etal~ECCV,18
the authors propose to improve the quality of video frames by combining deep neural networks with Kalman filter: in particular, previous estimate is improved by adding a measurement from the residual of the prediction.
%%%%%%%%%%%%%%%
Video Object Segmentation by Learning Location-Sensitive Embeddings
Ci~\etal~ECCV,18
DAVIS (bounding box as input, 81.0, +CRF 82.9), SegTrack-v2 (69.7)
The authors suggest to learn location-sensitive embeddings and use those in combination with a foreground segmentation network, to perform per-frame segmentation.
%%%%%%%%%%%%%%%
Reinforcement Cutting-Agent Learning for Video Object Segmentation
Han~\etal~CVPR,18
DAVIS-2016 (84.1), YouTube-Objects (78.1)
The authors formulate video object segmentation as the MDP problem and train two RL-agents - one finding the right bounding box with the second one finding the right segmentation mask.
%%%%%%%%%%%%%%%%
Motion-guided cascaded refinement network for video object segmentation
Hu~\etal~CVPR,18
DAVIS-2016 (84.4, 0.73 sec/frame - online training), YouTube-Objects (76.6)
The authors first acquire a coarse segmentation mask using the active contour method on the optical flow, and then use it to refine the final prediction with the help of a larger network.
%%%%%%%%%%%%%%%%%%%
Retrospective Encoders for Video Summarisation
Zhang~\etal~ECCV,18
The problem of video summarisation is solved here by proposing to embed the video summary close to the embedding of the original video, which is done via Seq2seq.
%%%%%%%%%%%%%%%%%%%%
K for the price of 1: Parameter efficient multi-task and transfer learning
From Google
ICLR,19
Instead of fine-tuning only the last layer, the authors suggest to train a separate model patch - this path can contain BN and depthwise layers.
%%%%%%%%%%%%%%%%%%%%%
Mobile Video Object Detection with Temporally-Aware Feature Maps
Liu and Zhu, CVPR18
The authors insert LSTM blocks inside MobileNet-SSD: to reduce the number of parameters inside LSTM, they propose to reduce the number of input channels to LSTM.
%%%%%%%%%%%%%%%%%%%%
Learning to forecast and refine residual motion for Image-to-Video Generation
Zhao~\etal~ECCV,18
The authors propose to disentangle motion differences into a residual motion map and a context map, and combine this with the static image in order to generate future frames.
%%%%%%%%%%%%%%%%%%%%
Folded Recurrent Neural Networks for Future Video Prediction
Oliu~\etal~ECCV,18
The authors propose to stack GRUs in an encoder-decoder fashion to pass information spatially for the task of generating future frames.
%%%%%%%%%%%%%%%%%%%%%%
PhaseNet for Video Frame Interpolation
Meyer~\etal~CVPR,18
The authors learn phase of the intermediate frame based on its adjacent frames.
%%%%%%%%%%%%%%%%%%%%%%
Video Summarisation Using Fully Convolutional Sequence Networks,
Rochan~\etal~ECCV,18
The authors pinpoint the connection between video summarisation and semantic segmentation, as the result of which they are able to leverage existing segmentation models in an efficient and effective way.
! Intro
* [[Video Streaming Optimization Trends|https://sonnati.wordpress.com/2017/03/20/video-streaming-optimization-trends/]]
* [[Artificial Intelligence in video encoding optimization|https://sonnati.wordpress.com/2017/10/09/artificial-intelligence-in-video-encoding-optimization/]]
* [[Netflix Blog|https://medium.com/netflix-techblog/using-machine-learning-to-improve-streaming-quality-at-netflix-9651263ef09f]]
! Bibs
* [[A Survey on Quality of Experience of HTTP Adaptive Streaming|https://ieeexplore.ieee.org/document/6913491/]]
! Definition
* HAS: HTTP Adaptive Streaming
** Resolution ABR: Adaptive Bitrate Streaming
** Frame Rate
** Quantization
! Methods
* [[Neural Adaptive Video Streaming with Pensieve|http://web.mit.edu/pensieve/]]
* Descriptors
* [[Visual Attention]]
* [[Meta-Learning]]
* [[Efficient CNN]]
! Multiscale approaches
In LABGAN, generate a pyramid of images, and use the components of Laplacian filters to recover the whole image. This method is used for video prediction.
! Applications
* [[Pose Estimation]]
* [[Image Assessment with NIMA|https://github.com/kentsyx/Neural-IMage-Assessment]]
* [[Instance Segmentation]]
* [[Planar Reconstruction]]
! Repos
* [[GMS-Feature-Matcher|https://github.com/JiawangBian/GMS-Feature-Matcher]]
!! Bib
For supervised tasks
* [[Recurrent Models of Visual Attention|https://arxiv.org/abs/1406.6247]]
** Use RNN to generate glimpse location
** [[Torch blog|http://torch.ch/blog/2015/09/21/rmva.html]]
* Learning to combine foveal glimpses with a third-order boltzmann machine
* What and where: A Bayesian inference theory of attention
* [[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|https://arxiv.org/abs/1502.03044]]
** Hard/soft attention. focus on single element, hard to learn.
* Neural variational inference and learning in belief networks
* Learning wake-sleep recurrent attention models
For unsupervised tasks
* [[DRAW: A Recurrent Neural Network For Image Generation|https://arxiv.org/abs/1502.04623]]
** [[Magenta review|https://github.com/tensorflow/magenta/blob/master/magenta/reviews/draw.md]]: the foveation attention model is differentiable and so can be trained end to end with backpropagation. Instead of simply cropping a patch from the input image, DRAW extracts the pixels in the patch by Gaussian interpolation.
! Attention types
* Reading attentions are widely used in discriminative tasks, usually transforms an image into a representation in a canonical coordinate space, with the parameters controlling the attention learned by gradient descent.
* Writing (generative) attentions decide which part to generate
* Randomized
* Error-based
!! Spatially-transformed attention
refs:
* Spatial transformer networks
* Efficient inference in occlusion-aware generative models of images
** develop occlusion-aware generative models that make use of spatial transformers in this way.
Rather than selecting a patch of an image (taking glimpses) as other methods do, a more powerful approach is to use a mechanism that provides invariance to shape and size of objects (general affine transformations). ''Spatial transformers'' process an input image $x$ using parameters $\lambda$ to generate an output:
$$
\operatorname{ST}(x, \lambda) = [\kappa_h(\lambda)\otimes\kappa_w(\lambda)]\ast x,
$$
where $\kappa_h$ and $\kappa_w$ are $1$-dimensional kernels, $\otimes$ indicates the tensor outer-product of the two kernels and $\ast$ indicates a convolution. When used for reading attention, spatial transformers allow the model to observe the input image @@color:#859900;in a canonical form, providing the desired invariance@@. When used for writing attention, it allows the generative model to independently handle position, scale and rotation of parts of the generated image, as well as their content.
!! Other implementations
* [[Attention Pooling]]
* [[Feature-wise Transformation]]
* [[Context in Vision]]
aka ego-motion estimation
* ICRA18 Self-supervised distractor learning for VO
* CVPR20 D3VO: dual camera training, mono VO
* CVPR20 ViDAR
** Deep SfM from Waymo dataset
** Caliberate shutter time
** Large scale training
!! Radar
* CoRL19 Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information
! Bibs
* Learning to Compose Neural Networks for Question Answering
** Use modular neural network architectures.
* [[FiLM: Visual Reasoning with a General Conditioning Layer]]
* Learning visual reasoning without strong priors
** GRU encode NL queries into FiLM generator
* Modulating early visual processing by language
! Dataset
* [[COCO VQA Challenge|http://www.visualqa.org/]]
* [[GuessWhat?! Visual object discovery through multi-modal dialogue|https://github.com/GuessWhatGame/guesswhat]]
* [[MovieQA|http://movieqa.cs.toronto.edu/home/]]
* [[FigureQA|https://datasets.maluuba.com/FigureQA]]
* [[Textbook Question Answering|http://data.allenai.org/tqa/]]
! Tutorial
[[Visual Turing test tutorial|https://github.com/mateuszmalinowski/visual_turing_test-tutorial]]
Suppose that a ConvNet classifies an image as a dog. How can we be certain that it's actually picking up on the dog in the image as opposed to some contextual cues from the background or some other miscellaneous object? One way of investigating which part of the image some classification prediction is coming from is by plotting the probability of the class of interest (e.g. dog class) as a function of the position of an occluder object. That is, we iterate over regions of the image, set a patch of the image to be all zero, and look at the probability of the class. We can visualize the probability as a 2-dimensional heat map. This approach has been used in Matthew Zeiler's [[Visualizing and Understanding Convolutional Networks|http://arxiv.org/abs/1311.2901]]:
[img height=400 [http://cs231n.github.io/assets/cnnvis/occlude.jpeg]]
! Implementations
* [[Jason Yosinski|http://yosinski.com/]] prodived [[a great visualization tool|http://devblogs.nvidia.com/parallelforall/harnessing-caffe-framework-deep-visualization/]] based on caffe, implementing the method mentioned above (with slight difference).
* [[Network Dissection|http://netdissect.csail.mit.edu/]]
* [[Plug and Play Generative Networks|https://github.com/Evolving-AI-Lab/ppgn]]: generate features
* Interpreting Deep Visual Representations via Network Dissection
** [[paper|https://arxiv.org/abs/1711.05611]]: interesting training behaviour and layer property distribution can be inferred.
** [[code|https://github.com/CSAILVision/NetDissect]]
* SVCCA for Deep Learning Dynamics and Interpretability
** SVCCA could align activations by different networks.
** [[code|https://github.com/google/svcca]]
! Introduction
Many state-of-art researches focus on spectral conversion techniques. 2 categories
* rule based methods: e.g. move the position of formants. most of the details are retained. ''The information (e.g. formants) for building the converion function is usually difficult to be extracted accurately.''
* statistical methods: construct a more precise mapping function using complex statistical models.
! 3 major issues
Sinusoidal model can support modifications to both the prosody and the spectral characteristics.
Acoustic features which enable humans to identify speakers must be extracted and coded. These features should be independent of the message and the environment so that whatever and wherever the source speaker speaks, his/her voice characteristics can be successfully transformed to sound like the target speaker.
The type of conversion function and method of training and applying the conversion function must be decided.
! Basic methods: parallel training
* [[Codebook mapping method for VC]]
** Standard - vector quantization: generate the 1-1 correspondence between source and target spectral codebook. But the linear transformation from a limited set of vectors to form the target centroids may leads to discontinuous in the synthesised voice and degrade of speech quality.
** Weighted - Line spectral frequencies (LSFs) are used to weigh the target codebooks. This is proposed to leviate the discontinous problem.
* Formant mapping is prone to formant tracking errors.
* Linear multivariate regression for VC
$$
\hat y_t = A_mx_t+B_m,
$$
* ANN can handle nonlinear transformation functions.
! HMM/GMM
[[GMM for VC]]
!! HMM sequence-to-frame mapping
Let $p(x|s)$ denote a state-observation probability of frame vector $x$ given state $s$, and $p(s'|s)$ represent a state-transition probability from state $s$ to $s'$. Given state $s$ and source vector $x$, we assume that target vector $y$ has the following linear-Gaussian distribution,
$$
p(y|s, x) = N(y|B_sx+b_s,\Sigma_s),
$$
The mapping can be learned with least mean square or
!! GP based model
This is a variation of GMM. Solve the mapping function as a regression problem and apply Gaussian process to solve it. Assign a GP for each dimension of the target spectra.
! Segmentation Based
[[Maximum likelihood transformations for VC]]
! Model-based VC
!! Expression conversion model
The advantage of model based VC method is it does not require parallel corpora for training. It describe the relationship between expressions of source and target speaker with linear transforms.
$$
\log(f0_c) = \mu_t+\frac{\sigma_t}{\sigma_s}(\log(f0_s)-\mu_s)
$$
[[VC with factorised HMM-TTS models]]
! Speaker adaptation techniques
Generate "average voice" model from multiple speakers' recordings.
* Feature-space MLLR
! Deep Neural Network
[[USTC DNN VC]]
[[Proposal|VC Research Proposal]]
Build the speaker acoustic model with dynamic information.
* HMM
* Conditional RBM
* Gaussian process dynamic model
! Previous works
Related works of [[DNN ASR]]
[[DNN Adaptation]]
[[Multitask Learning for Phone label and State]]
! Model
An ASR-like model to extract speaker's acoustic feature from the text label (and prosody). Then learn the transformation between target and source features.
I kind of think a flexible voice conversion system can work as a semi-TTS. So the training data need to be as large unless personal acoustic features can be precisely modeled. The difference is personal prosody (and maybe [[glottal flow|Glottis]]) features does not need to be learnt.
We can see this problem as learning to sing.
# Extract target and source acoustic features (enough to reconstruct the language, or unless the target sentence).
# Learn the exact matching (the matching matrix is sparse, but linear is not a good idea)
# We learn the score and lyrics (prosody and text) from the target voice. Using the other part of the same model.
# Send the converted features and the script back to the TTS-like feature extraction model and get the voice back at the other end.
!! Extracting vocal tract
A time series indicating the speech text and prosody characteristics of the sentence, multiplying the speaker's acoustic features for these variables makes a series of spectra.
!!! Sparse coding
!!! Deep feature generation
!! Learning the transformation
maximum output probability parameter generation
$$
\tilde y^{(s)} = arg \underset{y^{(s)}}{\max}P(y|x,\lambda^{(v)})
$$
[[link|https://arxiv.org/abs/1711.00937]]
pretty nice result of combining VAE with pixelRNN. Further work should be done in speaker style conversion.
The quantization method used is so simple. Should consider other approximation methods. [[Continuous approximations to arithmetic functions]]
Fourier transforms suffer from [[resolution problem|Fourier Transform vs Wavelet Transform]]. Wavelet is here to rescue.
In mathematics, a ''wavelet series'' is a representation of a square-integrable function by a certain orthonormal series generated by a wavelet.
!! Formal definition
A function $\psi\in L^2(\mathbb{R})$ is called an ''orthonormal wavelet'' if it can be used to define a Hilbert basis, that is a complete orthonormal system, for the Hilbert space $L^2(\mathbb{R})$ of square integrable functions.
The Hilbert basis is constructed as the family of functions $\{\psi_{jk}: j, k \in \mathbb Z\}$ by means of dyadic translations and dilations of $\psi$,
$$
\psi_{jk}(x) = 2^\frac{j}{2} \psi(2^jx - k)
$$
for integers $j$, $k \in \mathbb{Z}$.
This family is an orthonormal system if it is orthonormal under the standard inner product $\langle f, g\rangle = \int_{-\infty}^\infty f(x)\overline{g(x)}dx$ on $L^2(\mathbb{R})$.
$$
\langle\psi_{jk},\psi_{lm}\rangle = \delta_{jl}\delta_{km}
$$
where $\delta_{jl}$, is the Kronecker delta.
Completeness is satisfied if every function $h \in L^2(\mathbb{R})$ may be expanded in the basis as
$$
h(x) = \sum_{j, k=-\infty}^\infty c_{jk} \psi_{jk}(x)
$$
with convergence of the series understood to be convergence in norm. Such a representation of a function $f$ is known as a ''wavelet series''. This implies that an orthonormal wavelet is self-dual.
!! Wavelet transform
A wavelet transform is defined by dilating a mother wavelet $\psi\in\mathbf L^2(\mathbb R^d)$ with scale factors $\{a^j\}_{j\in Z}$ for $a>1$. In image processing applications one usually sets $a=2$, whereas audio applications need smaller dilations factors, typically $a\leq2^{1/8}$. Wavelets are not only dilation
The integral wavelet transform is the integral transform defined as
$$
\left[W_\psi f\right](a, b) = \frac{1}{\sqrt{|a|}} \int_{-\infty}^\infty \overline{\psi\left(\frac{x-b}{a}\right)}f(x)dx,
$$
The ''wavelet coefficients'' $c_{jk}$ are then given by
$$
c_{jk} = \left[W_\psi f\right]\left(2^{-j}, k2^{-j}\right)
$$
We can construct wavelet functions which are //orthonormal//. Examples of prototype wavelet functions are [[Haar basis function|Haar Wavelet]], [[Daubechies basis function|Daubechies Wavelet]], [[Shannon basis funtion|Shannon Wavelet]], [[Mexican Hat funtion|Mexican Hat Wavelet]] etc. These basis functions can be implemented using filters. Longer filters lead to smoother functions.
!!! Basic idea
The fundamental idea of wavelet transforms is that the transformation should allow only changes in time extension, but not shape. This is effected by choosing suitable basis functions that allow for this. Changes in the time extension are expected to conform to the corresponding analysis frequency of the basis function. Based on the uncertainty principle of signal processing.
!!! Littlewood-Paley wavelet transform
A Littlewood-Paley wavelet transform is a redundant representation which computes the following filter banks, without subsampling:
$$
\forall u\in\mathbb R^d, \forall \lambda a^{\mathbb Z}\times G, W_{\lambda}x(u)=x\star\psi_\lambda(u)=\int x(v)\psi_\lambda(u-v)dv.
$$
If $x$ is real and the wavelet is chosen such that $\hat\psi$ is also real, then $W_{-\lambda}x=W_{\lambda}x^*$, which implies that in that case one can assimilate a rotation $r$ with its negative version $-r$ into an equivalence class of positive rotations $G^+=G/\{\pm1\}$.
!!! Filter Banks
Wavelet can be thought of as a [[band-pass filter|Band-pass Filter]] from signal processing point of view. In specific cases, wavelets can be considered as an octave band pass filter. So, we can use [[filter banks|Filter Bank]] to implement wavelet transform. Thus, wavelet transform can be interpreted as [[constant-Q filtering|Constant-Q Filtering]] with a set of octave band filters, followed by sampling at respective [[Nyquist frequencies|Nyquist Frequency]]. Also, by adding higher octave bands, we add resolution to the signal.
[[paper|https://arxiv.org/abs/1609.03499]]
* [[A clean tf implementation|https://github.com/ibab/tensorflow-wavenet]]
* [[Fast implementation by caching|https://github.com/tomlepaine/fast-wavenet]]
* [[ByteNet based on it|https://github.com/paarthneekhara/byteNet-tensorflow]]
** [[ByteNet paper|https://arxiv.org/abs/1610.10099]]
WaveNet like papers have been used in audio generation, NMT and ASR.
$$
h_i = \sigma(W_{g, i}\ast x_i+V^T_{g, i}c)\odot\tanh(W_{f, i}\ast x_i+V_{f, i}^Tc)
$$
where $c$ is the extra conditioning data, $i$ is the layer index. In cases when $c$ encodes spatial or sequential information, matrix products can be replaced by convolution.
To support higher sampling rate, improve convolution filter size from 2 to 3. 16bit audio, categorical distribution is too costly. Model sample with discretized mixture of logistic distribution.
We can use WaveNet as the transformation function for [[IAF|Improving Variational Inference with Normalizing Flows]]. Initially, a random sample is drawn from $z\sim\text{Logistic}(0, I)$. The following transformation is applied to $z$:
$$
x_t = z_t\cdot s(z_{<t}, \theta)+\mu(z_{<t}, \theta)
$$
$p(x_t|z_{<t})$ follows a logistic distribution:
$$
p(x_t|z_{<t}) = \mathbb L(x_t|\mu(z_{<t}, \theta), s(z_{<t}, \theta)),
$$
while $\mu(z_{<t}, \theta)$ and $s(z_{<t}, \theta)$ can be WaveNet model.
The inference procedure required to estimate the log-likelihood is sequential and slow.
Repeatedly running the model forward and backing it up again seems like a silly thing to do. Indeed online methods for training recurrent neural networks are an active area of research.
[[video|https://www.youtube.com/watch?v=9g9GsI33ol8]]
* 1950 20s-scenes at 10Hz
* 1 mid range lidar, 4 near range lidar
* 5 high resolution cameras
* 11M 2D image labels in 1000 segments
* 12M 3D LiDAR labels in 1200 segments
Horizon:
* Detection
** project box and segmentation results to point cloud
** point cloud rotation augmentation
** point cloud enhancement
* Tracking
** 3rd stage association
** ReID
[[500+ Colours|http://cloford.com/resources/colours/500col.htm]]
* eBooks: https://b-ok.global/
[[link|https://arxiv.org/abs/1602.07868]]
openai uses this on ResNet VAEs for IAF. We express the weight vectors in terms of the new parameters using
$$
\mathbf w = \frac{g}{\|\mathbf v\|}\mathbf v
$$
where $\mathbf v$ is a $k$-dimensional vector, $g$ is a scalar, and $\|\mathbf v\|$ denotes the Euclidean norm of $\mathbf v$. We can differentiate this to obtain the gradient of a loss function $L$ with
$$
\nabla_gL = \frac{\mathbf v^\top}{\|\mathbf v\|}\nabla_{\mathbf w}L
$$
and the gradient w.r.t. $\mathbf v$ is
$$
\begin{array}
&&\\
\nabla_\mathbf{v}L&=&\frac{\partial\mathbf w}{\partial\mathbf v}\nabla_\mathbf{w}L\\
&=&\frac{g}{\|\mathbf{v}\|}I\nabla_\mathbf{w}L-\frac{\partial\mathbf w}{\partial\|\mathbf v\|}\frac{\partial\|\mathbf v\|}{\partial\mathbf v}\nabla_\mathbf{w}L\\
&=&\frac{g}{\|\mathbf{v}\|}I\nabla_\mathbf{w}L-\left(\frac{g}{\|\mathbf v\|^2}\mathbf v\right)\frac{\partial\|\mathbf v\|}{\partial\mathbf v}\nabla_\mathbf{w}L\\
&=&\frac{g}{\|\mathbf{v}\|}\nabla_\mathbf{w}L-\frac{g\mathbf{v}\mathbf{v}^\top}{\|\mathbf{v}\|^3}\nabla_\mathbf{w}L\\
&=&\frac{g}{\|\mathbf{v}\|}\nabla_\mathbf{w}L-\frac{g\nabla_gL}{\|\mathbf{v}\|^2}\mathbf{v}\\
\end{array}
$$
Welcome to my personal wiki. I think it would be helpful if I share my notes. I kept my notes in a very non-linear way. So be sure to dig really deep until you find what you are looking for.
Here are some nice places to start:
* [[Deep Learning]]: This is the root of deep learning related materials I am following.
* [[Deep Learning Bib]]: I write some of my paper summaries here.
* [[Readings]]: Some good old resources.
* [[Speech]]: If you are interested in my works during graduate study.
* [[Programming]]: Here are some more hackery stuff, focusing majorly on [[clojure]].
''Notice'': please don't change anything.
* [[Wasserstein GAN|https://arxiv.org/abs/1701.07875]]
* [[Towards Principled Methods for Training Generative Adversarial Networks|https://arxiv.org/abs/1701.04862]]
* [[Improved Training of Wasserstein GANs|https://arxiv.org/abs/1704.00028]]
** A simple way to enforce the Lipschitz constraint on the class of functions, which can be modeled by the neural network, is weight clipping. It was proposed that training can be improved by instead augmenting the loss by a regularization term that penalizes the deviation of the gradient of the critic (as a function of the network's input) from one.
* [[On the regularization of Wasserstein GANs|https://arxiv.org/abs/1709.08894]]
** A theoretical proof of using a weaker regularization term enforcing the Lipschitz constraint is preferable
! Implementations
* [[tf|https://github.com/igul222/improved_wgan_training]]
! Idea
Directly learning $P_\theta$ is bad because it'll be very unlikely that all of $P_r$ lies within $P_\theta$'s support. We can add random noise to $P_\theta$.
! Details
WGAN uses a critic instead of a discriminator. The critic is trained to provide an approximation of the Wasserstein distance between the real data distribution $P_r$ and the generator distribution $P_g$. The approximation is based on the Kantorovich-Rubinstein duality (see [[this blog|https://vincentherrmann.github.io/blog/wasserstein/]] for more):
$$
W(P_r, P_g)\propto \underset{f}{\max}\mathbb E_{x\sim P_r}[f(x)]-\mathbb E_{x\sim P_g}[f(x)]
$$
[[Optimal Transport GAN and VAE Explaination|https://arxiv.org/abs/1706.01807]]
! Experiments
Strided convolution creates checkerboard patterns and introduced
Jose Abreu: top fisherman
A whitening tranformation is used for decorrelation. It transforms a set of variables into a new set with covariance equals $I$.
Typically whitening $X$ means multiplying by $M^{-1/2}$ where $M = E[XX^\top]$. Whitening transformation is not unique. Whitened data will stay whitened after rotations, which means $W = RM^{-1/2}$ with orthogonal matrix $R$ will also be a whitening transformation.
Whitening can be done with PCA, let $M$ have eigenvectors in columns of $E$ and eigenvalues on the diagonal of $D$, so that $C = EDE^\top$. A "normal" PCA transformation if given by $W_{PCA} = D^{-1/2}E^\top$. $M^{-1/2}$ can also be obtained with PCA by rotating with $E$, we call this ZCA whitening
$$
W_{ZCA} = ED^{-1/2}E^\top = M^{-1/2}.
$$
ZCA tries to transform the data as little as possible. Since correlations in natural data (e.g. images) are mostly very local, so decorrelation filters can also be local.
Just links between Tiddlers.
! Refs
* [[examples|https://meta.wikimedia.org/wiki/Help:Wikitext_examples]]
! Snippets
!! WikiLinks
* `[[Tiddler Name]]`
* `[[Link Name|Link Address]]`
!! Images
First import the image. Then include it with `[img[img_name.ext]]`
* BoW
* Distributed word embeddings
* Continuous ''vector'' representation of words
** co-occurrence stats to obtain vectors for long phrases
** LSA, fail to preserve linear regularities among words
** LDA, computationally expensive on large datasets
** [[Word2Vec]]
** [[GloVe|Global Vectors for Word Representation]]
** CoVe
** CoVeR: considers covariate
* [[Hashed Character $n$-gram|https://arxiv.org/abs/1511.06018]]
! Refs
* [[Supervised Learning of Universal Sentence Representations from Natural Language Inference Data|https://arxiv.org/abs/1705.02364]]
** embedding sentences on Stanford Natural Language Inference dataset
** BiLSTM with attention, max-pooling over hidden representation
* [[One-shot and few-shot learning of word embeddings|https://arxiv.org/abs/1710.10280]]
! Ideas to try
* character level word embedding learning
** learn word embedding model end2end to avoid storing large stuff
** probably better word root stuff?
* definition based word embedding learning
! refs
* [[Distributed Representations of Words and Phrases and their Compositionality|https://arxiv.org/abs/1310.4546]]
* [[Enriching Word Vectors with Subword Information|https://arxiv.org/abs/1607.04606]]
* [[Bag of Tricks for Efficient Text Classification|https://arxiv.org/abs/1607.01759]]
** [[fastText supervised tutorial|https://github.com/facebookresearch/fastText/blob/master/tutorials/supervised-learning.md]]
** Language detector in less than 1MB
!! Similarity to Count-Based Models
* [[Neural Word Embedding as Implicit Matrix Factorization|https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization]]: Word2Vec == PMI matrix factorization of count based models
* Count based and most neural models have equivalent performace when properly hyperoptimized.
Word similarity is hard to represent. High-dim vectors are not robust. Usually around 25-1000 dim.
Represent words as vectors in some space. Learn high-quality word vectors from hugh datasets with billions of words, and with millions of words in the vocabulary.
king:queen=man:woman
''The distributional hypothesis'': words with similar context have similar meaning.
We can represent words as cooccurence matrix. And reduce dim (by e.g. svd). This method suffer from frequency inbalance, computation inefficiency, etc.
!! Idea
By predicting words to the left and right, cooccurence will also be captured.
!! The model
Predicting surrounding words. The object of Skip-gram model is to maximize the average log probability:
$$
J(\Omega) = \frac 1 T\sum_{t=1}^T\sum_{-c\le j\le c, j\ne0}\log p(w_{t+j}|w_t)
$$
Where the input $w_t$ is the center word of a 1-of-V (or one-hot) encoding. The basic Skip-gram formulation defines $$p(w_{t+j}|w_t)$$ as softmax, whose computation is expensive when vocabulary is large (10^^6^^).
!!! Hierarchical softmax
$$
p(w|w_I) = \prod_{j=1}^{L(w)-1}\sigma([n(w, j+1)=ch(n(w,j))]\cdot v_{n(w,j)}'\top v_{w_I})
$$
where $$v$$ and $$v'$$ are the same as $$v_{in}$$, $$v_{out}$$, $$\sigma(x)=1/(1+\exp(-x))$$, $$[x]$$ is 1 if $$x$$ is true otherwise -1.
So the nagative sampling approximates it as:
$$
\log p(w_O|w_I)\approx\log\sigma(\langle v^{w_I}_{in}, v^{w_O}_{out}\rangle)+\sum_{k=1}^K\mathbb E_{wk\sim P_n(w)}[\log \sigma(-\langle v^{w_I}_{in}, v^{w_O}_{out}\rangle)],
$$
where $$\sigma(x)=\frac 1 {1+\exp(-x)}$$ is the sigmoid (logistic) function, and the expectations are computed by drawing random words.
* Extend attention and add reference of Grammar as a Foreign Language, [[Pointer Networks]]
* add proof of expressiveness for NN, RNN and SGD: [[Deep Approximiation Theory]] for the part of program induction
* statistics of previous publications
* go to li2017code for previous code completion papers and evaluation.
* go to evans2017learning for program induction related work.
[[link|http://www.stat.columbia.edu/~gelman/research/published/Evaluating_Variational_Inference.pdf]]
Without the need for a residual net, even in a linear regression I can find examples in which exact Bayesian posterior lead to worse predictive performance than ADVI (Reproducible code is available upon request). This is not a pathological edge example. The data is simulated from the correctly-specified regression model with n=100 and d=20 — and these are exactly the data one would simulate for linear regressions. The posterior is sampled using stan and is exact measured by all diagnostics. The predictive performance is evaluated using log predictive density on independent test data and the averaged over a large number of replications of both data and sampling to eliminate all other noises. But still, log predictive density from ADVI is higher than the exact posterior.
VI can be flawed:
* the slow convergence
* inability of the approximation family to capture the true posterior
* the asymmetry of the true distribution
* the direction of the KL divergence under penalizes approximation wiht too-light tails
To test VI:
* Has the objective converged?
** hypothesis testing: convergence tests based on the asymptotic property fo stochastic approximations. But that is too complex
* Is that optimum $$q^*(\theta)$$ a good approximation?
ELBO or predictive density is not enough
* unknown multiplicative constant
* uninterpretable scale
Diagnosis tools:
* [[PSIS|Pareto Smoothed Importance Sampling]] disgnosis
** A goodness of fit measurement for joint distributions;
** simultaneously improving the error in the estimate
* VSBC diagnosis
** the average performance of point estimate
** particulary, whether the estimates are first-order unbiased
!! From VI to IS to PSIS
$$(\theta_1, \dots, \theta_S)$$ drawn from the variational approximation $$q(\theta)$$.
$$
\mathbb E_p[h(\theta)] = \frac{\sum_{s-1}{S} h(\theta_s)w_s}{\sum_{s=1}^\theta w_s}
$$
* When $$w_s=1$$, this is plain VI
** small variance
** biased to an unknown extent
** inconsistent as $$S\rightarrow\infty$$
* When $$w_s=p(\theta_s, y)/q(\theta_s)$$, [[importance sampling|Importance sampling]]
** consistent and with small $$O(1/S)$$ bias
** may have a large or infintie variance
links: [[paper|https://arxiv.org/abs/1904.02689]], [[code|https://github.com/dbolya/yolact]]
! Training
800k iters with batchsize 8.
@inproceedings{ze2013statistical,
title={Statistical parametric speech synthesis using deep neural networks},
author={Ze, Heiga and Senior, Andrew and Schuster, Mike},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on},
pages={7962--7966},
year={2013},
organization={IEEE}
}
[[Recent Advances in Zero-shot Recognition|https://arxiv.org/abs/1710.04837]]
! Semantic Representations in Zero-shot Recognition
* Semantic Attributes: has wings, spotted...
** User-defined Attributes
** Relative Attributes
** Data-driven Attributes
** Video Attributes
* Semanatic Representation beyond Attributes
** Concept ontology: e.g. WordNet's semantic distance
** Semantic word vectors
! Models
* Embedding Models
** Bayesian Models
*** Direct Attribute Prediction
*** Indirect Attribute Prediction
** Semantic Embeddings
** Embeddings into Common Spaces
** Deep Embedding
*** [[Learning a Deep Embedding Model for Zero-shot Learning|https://arxiv.org/abs/1611.05088]]
* Recognition Models in the Embedding Space
* Problems in Zero-shot Recognition
** Projection Domain Shift problem
*** transductive learning
** Hubness problem
[[link|http://arxiv.org/pdf/1606.01305v1.pdf]]
In RNN, this has a similar structure as dropout, only different by passing the activations from the previous time-step. Idea should work with ConvNet, result not very pretty.
高一时写的小说。当时想模仿古龙的文笔写一个不严肃的故事。没有换行符，只能在编辑模式下阅读。
古龙是优秀勤奋热情的小说家
黄风中的刀声
瀚海结金城, 清潮授长生
九月月带刀, 风萧萧无情
(一)
九月九,柳叶城.
柳叶城是再平凡不过的小城, 平凡得没什么人知道.
除非你到了那里, 否则你不会知道它.
因为去了那里的人, 没有回来过.
/******/
萧无情却偏偏在那里, 走在城中唯一的一条街上, 紧握着他的刀.
张老实站在他的面摊边上老老实实地冲他打着招呼. 张老实之所以叫张老实, 是因为他是小城里出了名的老实人.
萧无情并不买账, 写在他脸上的永远只有轻蔑和仇恨. 虽然只到柳叶城第二天, 他已知道城里很多事情, 其中每一件都很有把握. 张老实不老实! 绝无例外!
萧无情看着漂着青菜和油花的粗汤面, 目光无法移开.
他忽然发现饥饿比仇恨更强烈.
日头正高, 街上的行人仿佛都消失了.
张老实在他的面摊前, 笑着.
"萧大爷吃面?"虽只一日, 大家都已知道这个萧无情.
萧无情把刀握得更紧了, 而他的脚迈进了门
/******/
一碗热气腾腾的面摆在萧无情面前.
萧无情右手用筷子挑起一根清面, 左手依然握着刀.
世上一共出现过一百四十六种下毒的方法, 萧无情至少知道其中的一百四十四种, 所以他还活着, 也会活下去.
萧无情盯着面上的热气, 忽然明白了.
一碗盖着油的汤面不该有热气, 除非它盖的不是油!(忍不住加句后话....还是忍住了)
张老实站在他面前, 等着他将这口面吃下. 张老实果然不老实!
但萧无情可还是萧无情, 萧无情不光知道如何下毒, 也知道如何解别人下的毒.
油状之毒, 来自七步鹰胃中的黄汤, 沙漠中曾有秀水宫中人用熬鹰的手法将其制成七步孟婆汤, 因为毒性全然不同于百草之毒, 根本察无所察.
其克星正是与其同生在腐肉上的生物. 万物生克, 天下之毒七步之内必有解药, 萧无情很明白这个道理.
那种生物就是苍蝇. 没错, 不然与七步鹰共食一尸, 怎能不死.
最平凡的事物都有意想不到的力量. 正如这柳叶城的暗藏杀机, 如这城中张老实的深藏不露, 如张老实店中密布的苍蝇.
张老实店中的苍蝇实在很多, 多得让人有些不舒服. 但不舒服总比死了要好.
萧无情放下筷子, 抓起一张桌布, 在空中一挥, 十几只黄豆大小的苍蝇竟被吸在了桌布上.
萧无情从容地伸出空闲的右手, 将一只只苍蝇捻碎, 撒在面上.
张老实面色忽然煞白, 仿佛胸口被人打了一拳, 想走, 又不敢走.
萧无情一手捻着苍蝇, 另一只手握着刀, 眼睛看也不看张老实. 他已猜到张老实的表情.
过了好久, 面汤上的热气终于消失了.
萧无情眯起了双眼, 似乎很期待将要品尝的美味. 因为这面中还有习惯性的胜利和残虐的快感. 他喜欢践踏自己对手自尊心的感觉.
萧无情用一只手吃着面, 吃得很仔细. 仿佛这是他一生的最后一顿饭.
他就像一只野兽, 珍惜眼前的每一丁点面.
他用一只手吃面, 吃得很慢.
(二)
夕阳西斜, 沙漠上瞬间凉了下来. 虽然阴云渐浓, 柳叶城却恢复了生机, 各样的人都出来消磨时光.
一个劲装男子走在路上, 走得很别扭. 一个跛子走路本来就别扭.
萧无情是个跛子, 这个跛子是萧无情.
他左脚先迈出, 右脚再慢慢跟上.
他的左手一直握着刀, 漆黑的刀!
用剑的人, 该把剑放在惯用手的另一侧. 正如当年迎风一刀斩描述的东洋刽子手拔刀术, 这样才能在剑出鞘的一刻使出夺命的一击.
刀却相反, 很少有人懂得这个道理.
有的人懂了, 却在同时死在了萧无情的刀下.
对一个刀客来说, 这样的道理还是不要懂的好.
萧无情一张紧绷的脸忽然涨红, 双目如要瞪出. 熟悉萧无情的人知道, 他从未有过这样的表情.
路人还未发现这名高贵刀客的异样, 萧无情已经不见了, 比风还快!
萧无情在飞奔, 人们如何都不肯相信跛子竟有如此敏捷的身法.
萧无情不能停下.
因为还没有活人被大便憋死!
因为解毒而加了苍蝇, 因为苍蝇白吃了面. 萧无情当然可以不吃那碗面. 但他不介意给张老实一个教训.
/******/
天皇皇, 地荒荒. 入了万草堂, 薰天香, 人断肠.
(三)
这就是万草堂, 柳叶城最气派的地方. 男人女人分别有一间半封顶的房间, 每间房只有两个位置.
气派的地方当然容不下太多人.
这里是城中最宝贵的位置. 之所以宝贵, 是因为这里可以使人舒畅.
最让人舒畅的事当然是薰香.
城中人每天都来这里, 所以这里经常特别热闹.
还好现下是柳叶城各家吃晚饭的时间.
好在晚饭时很少有人薰香.
/******/
万草堂男宾上房有一个空位置, 萧无情刚坐下, 屋外一声惊雷, 竟下起了暴雨!
哗--哗--
萧无情身边的黑衣人, 似乎已待了好久, 道:"好快!"
在做一些原始的事, 比如薰香时, 人总会坦诚起来.
萧无情开口说了自打来柳叶城的第一句话:
"我还未脱下裤子."
黑衣人竟疯狂了, 他也是江湖上有身份的人. 这种人最容易疯狂.
因为他看到了一个远比他幸福的人.
一个如暴风雨般薰香的人.
萧无情的刀还在手里.
黑衣人的刀却在心上!
薰香虽然使人舒畅, 薰得久了却会受不了. 而黑衣人已经薰了三个时辰.
漫长的三个时辰, 会有很多人走进万草堂.
所以黑衣人已经杀了很多人--薰得比他快的人.
而没有一个人比萧无情更快!
比他快的只有他自己的刀!
萧无情的刀, 是魔刀, 因仇恨而生, 带给人血与痛. 江湖上很少有人不知道.
黑衣人当然知道, 他还知道了萧无情胜人一筹的不只有他的刀, 他更羡慕的是萧无情风驰电掣的薰香速度. 黑衣人知道自己杀不了眼前这个人, 他本也没打算杀他. 这种优越与对他的羞辱, 是性命无法偿还的.
疯狂的人有疯狂的想法.
/******/
萧无情:"朋友,"
黑衣人:"没有朋友!朋友是用来出卖的!"
萧无情:"你有草纸吗?"
黑衣人冷笑道:"我用不着纸, 你也用不着纸."
"..." 萧无情抬起头, 看着身边这张可憎的脸. 眯起了双眼, 刀握得更紧了. 这是他胜利的表情.
黑衣人在这野兽的注视下显然无法保持镇静. 然而疯狂壮了他的胆.
黑衣人:"我知道你在想什么." 他当然不知道, 这是江湖人的坏习惯, 你越想知道什么, 就越要说你已经知道了.
萧无情面色没有丝毫变化. 冷峻得像一块石头, 会吃人的石头.
黑衣人的敌意明显被恐惧淹没了. 如果黑衣人知道张老实今天的事, 他可以预知自己的下场. 他已经像张老实一样, 后悔目睹这魔鬼刀客复仇的过程. 听说张老实后来吐了四天四宿, 他肯定不会好到哪里去.
萧无情已然发现这个外强中干的黑衣人根本不是张老实那样值得尊敬的对手, 他只是一个冲动的可悲的小丑. 忽然想起安慰自己刀下亡魂的话, 头疼的话, 砍掉就不疼了.
/******/
一阵寒光! 萧无情的刀出鞘了!
只有眼力最好的人才能看出他这一刀挥向哪里.
无情刀客终于出刀了, 而谁又能想到他这一刀竟挥向他自己!
头被砍下, 自然不会再疼, 其他地方, 其他毛病也一样.
萧无情身后一股黄色的疾风闪过.
随后是一声十分诡异的肃杀的啸声, 龙吟虎咆一般.
好快的刀!
萧无情从容地站起来, 用鞋底蹭了蹭刀上的血.
黑衣人竟流下了泪水.
因为在那出刀的一刻, 他薰出了香.