From 99675d0f1fb88a43b95c67b1788e88479f972fa1 Mon Sep 17 00:00:00 2001 From: Alinson S Xavier Date: Fri, 18 Sep 2020 11:31:15 -0500 Subject: [PATCH] Move compiled docs to its own branch --- docs/404.html | 220 -- docs/css/base.css | 281 -- docs/css/base.min.css | 1 - docs/css/bootstrap-custom.css | 5309 --------------------------- docs/css/bootstrap-custom.min.css | 1 - docs/css/cinder.css | 88 - docs/css/cinder.min.css | 1 - docs/css/custom.css | 28 - docs/css/highlight.css | 99 - docs/css/highlight.min.css | 1 - docs/fonts/fontawesome-webfont.eot | Bin 38205 -> 0 bytes docs/fonts/fontawesome-webfont.svg | 414 --- docs/fonts/fontawesome-webfont.ttf | Bin 80652 -> 0 bytes docs/fonts/fontawesome-webfont.woff | Bin 44432 -> 0 bytes docs/format/index.html | 515 --- docs/img/favicon.ico | Bin 1150 -> 0 bytes docs/img/grid1.png | Bin 251 -> 0 bytes docs/img/grid10.png | Bin 495 -> 0 bytes docs/img/grid11.png | Bin 253 -> 0 bytes docs/img/grid12.png | Bin 260 -> 0 bytes docs/img/grid13.png | Bin 266 -> 0 bytes docs/img/grid14.png | Bin 240 -> 0 bytes docs/img/grid15.png | Bin 442 -> 0 bytes docs/img/grid16.png | Bin 442 -> 0 bytes docs/img/grid17.png | Bin 442 -> 0 bytes docs/img/grid18.png | Bin 457 -> 0 bytes docs/img/grid19.png | Bin 427 -> 0 bytes docs/img/grid2.png | Bin 271 -> 0 bytes docs/img/grid20.png | Bin 493 -> 0 bytes docs/img/grid3.png | Bin 266 -> 0 bytes docs/img/grid4.png | Bin 244 -> 0 bytes docs/img/grid5.png | Bin 442 -> 0 bytes docs/img/grid6.png | Bin 460 -> 0 bytes docs/img/grid7.png | Bin 442 -> 0 bytes docs/img/grid8.png | Bin 457 -> 0 bytes docs/img/grid9.png | Bin 456 -> 0 bytes docs/index.html | 286 -- docs/js/base.js | 225 -- docs/js/bootstrap-3.0.3.min.js | 7 - docs/js/highlight.pack.js | 2 - docs/js/mathjax.js | 8 - docs/model/index.html | 422 --- docs/search/lunr.js | 3475 ------------------ docs/search/main.js | 98 - docs/search/search_index.json | 1 - docs/search/worker.js | 130 - docs/sitemap.xml | 19 - docs/sitemap.xml.gz | Bin 196 -> 0 bytes docs/usage/index.html | 298 -- 49 files changed, 11929 deletions(-) delete mode 100644 docs/404.html delete mode 100644 docs/css/base.css delete mode 100644 docs/css/base.min.css delete mode 100644 docs/css/bootstrap-custom.css delete mode 100644 docs/css/bootstrap-custom.min.css delete mode 100644 docs/css/cinder.css delete mode 100644 docs/css/cinder.min.css delete mode 100644 docs/css/custom.css delete mode 100644 docs/css/highlight.css delete mode 100644 docs/css/highlight.min.css delete mode 100644 docs/fonts/fontawesome-webfont.eot delete mode 100644 docs/fonts/fontawesome-webfont.svg delete mode 100644 docs/fonts/fontawesome-webfont.ttf delete mode 100644 docs/fonts/fontawesome-webfont.woff delete mode 100644 docs/format/index.html delete mode 100644 docs/img/favicon.ico delete mode 100644 docs/img/grid1.png delete mode 100644 docs/img/grid10.png delete mode 100644 docs/img/grid11.png delete mode 100644 docs/img/grid12.png delete mode 100644 docs/img/grid13.png delete mode 100644 docs/img/grid14.png delete mode 100644 docs/img/grid15.png delete mode 100644 docs/img/grid16.png delete mode 100644 docs/img/grid17.png delete mode 100644 docs/img/grid18.png delete mode 100644 docs/img/grid19.png delete mode 100644 docs/img/grid2.png delete mode 100644 docs/img/grid20.png delete mode 100644 docs/img/grid3.png delete mode 100644 docs/img/grid4.png delete mode 100644 docs/img/grid5.png delete mode 100644 docs/img/grid6.png delete mode 100644 docs/img/grid7.png delete mode 100644 docs/img/grid8.png delete mode 100644 docs/img/grid9.png delete mode 100644 docs/index.html delete mode 100644 docs/js/base.js delete mode 100644 docs/js/bootstrap-3.0.3.min.js delete mode 100644 docs/js/highlight.pack.js delete mode 100644 docs/js/mathjax.js delete mode 100644 docs/model/index.html delete mode 100644 docs/search/lunr.js delete mode 100644 docs/search/main.js delete mode 100644 docs/search/search_index.json delete mode 100644 docs/search/worker.js delete mode 100644 docs/sitemap.xml delete mode 100644 docs/sitemap.xml.gz delete mode 100644 docs/usage/index.html diff --git a/docs/404.html b/docs/404.html deleted file mode 100644 index e989583..0000000 --- a/docs/404.html +++ /dev/null @@ -1,220 +0,0 @@ - - - - - - - - - - - - - - RELOG - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - -
-
-

404

-

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-

Home

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- - -
- - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/css/base.css b/docs/css/base.css deleted file mode 100644 index 7b45cbb..0000000 --- a/docs/css/base.css +++ /dev/null @@ -1,281 +0,0 @@ -body { - padding-top: 70px; -} - -h1[id]:before, h2[id]:before, h3[id]:before, h4[id]:before, h5[id]:before, h6[id]:before { - content: ""; - display: block; - margin-top: -75px; - height: 75px; -} - -p > img { - max-width: 100%; - height: auto; -} - -ul.nav li.first-level { - font-weight: bold; -} - -ul.nav li.third-level { - padding-left: 12px; -} - -div.col-md-3 { - padding-left: 0; -} - -div.col-md-9 { - padding-bottom: 100px; -} - -div.source-links { - float: right; -} - -/* - * Side navigation - * - * Scrollspy and affixed enhanced navigation to highlight sections and secondary - * sections of docs content. - */ - -/* By default it's not affixed in mobile views, so undo that */ -.bs-sidebar.affix { - position: static; -} - -.bs-sidebar.well { - padding: 0; -} - -/* First level of nav */ -.bs-sidenav { - margin-top: 30px; - margin-bottom: 30px; - padding-top: 10px; - padding-bottom: 10px; - border-radius: 5px; -} - -/* All levels of nav */ -.bs-sidebar .nav > li > a { - display: block; - padding: 5px 20px; - z-index: 1; -} -.bs-sidebar .nav > li > a:hover, -.bs-sidebar .nav > li > a:focus { - text-decoration: none; - border-right: 1px solid; -} -.bs-sidebar .nav > .active > a, -.bs-sidebar .nav > .active:hover > a, -.bs-sidebar .nav > .active:focus > a { - font-weight: bold; - background-color: transparent; - border-right: 1px solid; -} - -/* Nav: second level (shown on .active) */ -.bs-sidebar .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - margin-bottom: 8px; -} -.bs-sidebar .nav .nav > li > a { - padding-top: 3px; - padding-bottom: 3px; - padding-left: 30px; - font-size: 90%; -} - -/* Show and affix the side nav when space allows it */ -@media (min-width: 992px) { - .bs-sidebar .nav > .active > ul { - display: block; - } - /* Widen the fixed sidebar */ - .bs-sidebar.affix, - .bs-sidebar.affix-bottom { - width: 213px; - } - .bs-sidebar.affix { - position: fixed; /* Undo the static from mobile first approach */ - top: 80px; - max-height: calc(100% - 90px); - } - .bs-sidebar.affix-bottom { - position: absolute; /* Undo the static from mobile first approach */ - } - .bs-sidebar.affix-bottom .bs-sidenav, - .bs-sidebar.affix .bs-sidenav { - margin-top: 0; - margin-bottom: 0; - } -} -@media (min-width: 1200px) { - /* Widen the fixed sidebar again */ - .bs-sidebar.affix-bottom, - .bs-sidebar.affix { - width: 263px; - } -} - - -/* Added to support >2 level nav in drop down */ - -.dropdown-submenu { - position: relative; -} - -.dropdown-submenu>.dropdown-menu { - top: 0; - left: 100%; - margin-top: 0px; - margin-left: 0px; -} - -.dropdown-submenu:hover>.dropdown-menu { - display: block; -} - -.dropdown-submenu>a:after { - display: block; - content: " "; - float: right; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; - border-width: 5px 0 5px 5px; - border-left-color: #ccc; - margin-top: 5px; - margin-right: -10px; -} - -.dropdown-submenu:hover>a:after { - border-left-color: #fff; -} - -.dropdown-submenu.pull-left { - float: none; -} - -.dropdown-submenu.pull-left>.dropdown-menu { - left: -100%; - margin-left: 00px; -} -/* Start Bootstrap Callouts CSS Source by Chris Pratt (https://codepen.io/chrisdpratt/pen/IAymB) MIT License*/ -.bs-callout { - padding: 20px; - margin: 20px 0; - border: 1px solid #eee; - border-left-width: 5px; - border-radius: 3px; - background-color: #FCFDFF; -} -.bs-callout h4 { - font-style: normal; - font-weight: 400; - margin-top: 0; - margin-bottom: 5px; -} -.bs-callout p:last-child { - margin-bottom: 0; -} -.bs-callout code { - border-radius: 3px; -} -.bs-callout+.bs-callout { - margin-top: -5px; -} -.bs-callout-default { - border-left-color: #FA023C; /*modified from upstream default by Christopher Simpkins*/ -} -.bs-callout-default h4 { - color: #FA023C; /*modified from upstream default by Christopher Simpkins*/ -} -.bs-callout-primary { - border-left-color: #428bca; -} -.bs-callout-primary h4 { - color: #428bca; -} -.bs-callout-success { - border-left-color: #5cb85c; -} -.bs-callout-success h4 { - color: #5cb85c; -} -.bs-callout-danger { - border-left-color: #d9534f; -} -.bs-callout-danger h4 { - color: #d9534f; -} -.bs-callout-warning { - border-left-color: #f0ad4e; -} -.bs-callout-warning h4 { - color: #f0ad4e; -} -.bs-callout-info { - border-left-color: #5bc0de; -} -.bs-callout-info h4 { - color: #5bc0de; -} -/* End Bootstrap Callouts CSS Source by Chris Pratt */ - -/* Admonitions */ -.admonition { - padding: 20px; - margin: 20px 0; - border: 1px solid #eee; - border-left-width: 5px; - border-radius: 3px; - background-color: #FCFDFF; -} - -.admonition p:last-child { - margin-bottom: 0; -} -.admonition code { - border-radius: 3px; -} -.admonition+.admonition { - margin-top: -5px; -} - -.admonition.note { /* csslint allow: adjoining-classes */ - border-left-color: #428bca; -} - -.admonition.warning { /* csslint allow: adjoining-classes */ - border-left-color: #f0ad4e; -} - -.admonition.danger { /* csslint allow: adjoining-classes */ - border-left-color: #d9534f; -} - -.admonition-title { - font-size: 19px; - font-style: normal; - font-weight: 400; - margin-top: 0; - margin-bottom: 5px; -} - -.admonition.note > .admonition-title { - color: #428bca; -} - -.admonition.warning > .admonition-title { - color: #f0ad4e; -} - -.admonition.danger > .admonition-title { - color: #d9534f; -} diff --git a/docs/css/base.min.css b/docs/css/base.min.css deleted file mode 100644 index c5454f8..0000000 --- a/docs/css/base.min.css +++ /dev/null @@ -1 +0,0 @@ -body{padding-top:70px}h1[id]:before,h2[id]:before,h3[id]:before,h4[id]:before,h5[id]:before,h6[id]:before{content:"";display:block;margin-top:-75px;height:75px}p>img{max-width:100%;height:auto}ul.nav li.first-level{font-weight:bold}ul.nav li.third-level{padding-left:12px}div.col-md-3{padding-left:0}div.col-md-9{padding-bottom:100px}div.source-links{float:right}.bs-sidebar.affix{position:static}.bs-sidebar.well{padding:0}.bs-sidenav{margin-top:30px;margin-bottom:30px;padding-top:10px;padding-bottom:10px;border-radius:5px}.bs-sidebar .nav>li>a{display:block;padding:5px 20px;z-index:1}.bs-sidebar .nav>li>a:hover,.bs-sidebar .nav>li>a:focus{text-decoration:none;border-right:1px solid}.bs-sidebar .nav>.active>a,.bs-sidebar .nav>.active:hover>a,.bs-sidebar .nav>.active:focus>a{font-weight:bold;background-color:transparent;border-right:1px solid}.bs-sidebar .nav .nav{display:none;margin-bottom:8px}.bs-sidebar .nav .nav>li>a{padding-top:3px;padding-bottom:3px;padding-left:30px;font-size:90%}@media(min-width:992px){.bs-sidebar .nav>.active>ul{display:block}.bs-sidebar.affix,.bs-sidebar.affix-bottom{width:213px}.bs-sidebar.affix{position:fixed;top:80px;max-height:calc(100% - 90px)}.bs-sidebar.affix-bottom{position:absolute}.bs-sidebar.affix-bottom .bs-sidenav,.bs-sidebar.affix .bs-sidenav{margin-top:0;margin-bottom:0}}@media(min-width:1200px){.bs-sidebar.affix-bottom,.bs-sidebar.affix{width:263px}}.dropdown-submenu{position:relative}.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:0;margin-left:0}.dropdown-submenu:hover>.dropdown-menu{display:block}.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#ccc;margin-top:5px;margin-right:-10px}.dropdown-submenu:hover>a:after{border-left-color:#fff}.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:00px}.bs-callout{padding:20px;margin:20px 0;border:1px solid #eee;border-left-width:5px;border-radius:3px;background-color:#fcfdff}.bs-callout h4{font-style:normal;font-weight:400;margin-top:0;margin-bottom:5px}.bs-callout p:last-child{margin-bottom:0}.bs-callout code{border-radius:3px}.bs-callout+.bs-callout{margin-top:-5px}.bs-callout-default{border-left-color:#fa023c}.bs-callout-default h4{color:#fa023c}.bs-callout-primary{border-left-color:#428bca}.bs-callout-primary h4{color:#428bca}.bs-callout-success{border-left-color:#5cb85c}.bs-callout-success h4{color:#5cb85c}.bs-callout-danger{border-left-color:#d9534f}.bs-callout-danger h4{color:#d9534f}.bs-callout-warning{border-left-color:#f0ad4e}.bs-callout-warning h4{color:#f0ad4e}.bs-callout-info{border-left-color:#5bc0de}.bs-callout-info h4{color:#5bc0de}.admonition{padding:20px;margin:20px 0;border:1px solid #eee;border-left-width:5px;border-radius:3px;background-color:#fcfdff}.admonition p:last-child{margin-bottom:0}.admonition code{border-radius:3px}.admonition+.admonition{margin-top:-5px}.admonition.note{border-left-color:#428bca}.admonition.warning{border-left-color:#f0ad4e}.admonition.danger{border-left-color:#d9534f}.admonition-title{font-size:19px;font-style:normal;font-weight:400;margin-top:0;margin-bottom:5px}.admonition.note>.admonition-title{color:#428bca}.admonition.warning>.admonition-title{color:#f0ad4e}.admonition.danger>.admonition-title{color:#d9534f} diff --git a/docs/css/bootstrap-custom.css b/docs/css/bootstrap-custom.css deleted file mode 100644 index 631252d..0000000 --- a/docs/css/bootstrap-custom.css +++ /dev/null @@ -1,5309 +0,0 @@ -/*! normalize.css v2.1.3 | MIT License | git.io/normalize */ -article, aside, details, figcaption, figure, footer, header, hgroup, main, nav, section, summary { - display: block; -} -audio, canvas, video { - display: inline-block; -} -audio:not([controls]) { - display: none; - height: 0; -} -[hidden], template { - display: none; -} -html { - font-family: sans-serif; - -webkit-text-size-adjust: 100%; - -ms-text-size-adjust: 100%} -body { - margin: 0; -} -a { - background: transparent; -} -a:focus { - outline: thin dotted; -} -a:active, a:hover { - outline: 0; -} -h1 { - margin: .67em 0; - font-size: 2em; -} -abbr[title] { - border-bottom: 1px dotted; -} -b, strong { - font-weight: bold; -} -dfn { - font-style: italic; -} -hr { - height: 0; - -moz-box-sizing: content-box; - box-sizing: content-box; -} -mark { - color: #000; - background: #ff0; -} -code, kbd, pre, samp { - font-family: Hack, monospace, serif; - font-size: 1em; -} -pre { - white-space: pre-wrap; -} -q { - quotes: "\201C" "\201D" "\2018" "\2019"} -small { - font-size: 80%} -sub, sup { - position: relative; - font-size: 75%; - line-height: 0; - vertical-align: baseline; -} -sup { - top: -0.5em; -} -sub { - bottom: -0.25em; -} -img { - border: 0; -} -svg:not(:root) { - overflow: hidden; -} -figure { - margin: 0; -} -fieldset { - padding: .35em .625em .75em; - margin: 0 2px; - border: 1px solid #c0c0c0; -} -legend { - padding: 0; - border: 0; -} -button, input, select, textarea { - margin: 0; - font-family: inherit; - font-size: 100%} -button, input { - line-height: normal; -} -button, select { - text-transform: none; -} -button, html input[type="button"], input[type="reset"], input[type="submit"] { - cursor: pointer; - -webkit-appearance: button; -} -button[disabled], html input[disabled] { - cursor: default; -} -input[type="checkbox"], input[type="radio"] { - padding: 0; - box-sizing: border-box; -} -input[type="search"] { - -webkit-box-sizing: content-box; - -moz-box-sizing: content-box; - box-sizing: content-box; - -webkit-appearance: textfield; -} -input[type="search"]::-webkit-search-cancel-button, input[type="search"]::-webkit-search-decoration { - -webkit-appearance: none; -} -button::-moz-focus-inner, input::-moz-focus-inner { - padding: 0; - border: 0; -} -textarea { - overflow: auto; - vertical-align: top; -} -table { - border-collapse: collapse; - border-spacing: 0; -} -@media print { - * { - color: #000!important; - text-shadow: none!important; - background: transparent!important; - box-shadow: none!important; -} -a, a:visited { - text-decoration: underline; -} -a[href]:after { - content: " (" attr(href) ")"} -abbr[title]:after { - content: " (" attr(title) ")"} -a[href^="javascript:"]:after, a[href^="#"]:after { - content: ""} -pre, blockquote { - border: 1px solid #999; - page-break-inside: avoid; -} -thead { - display: table-header-group; -} -tr, img { - page-break-inside: avoid; -} -img { - max-width: 100%!important; -} -@page { - margin: 2cm .5cm; -} -p, h2, h3 { - orphans: 3; - widows: 3; -} -h2, h3 { - page-break-after: avoid; -} -select { - background: #fff!important; -} -.navbar { - display: none; -} -.table td, .table th { - background-color: #fff!important; -} -.btn>.caret, .dropup>.btn>.caret { - border-top-color: #000!important; -} -.label { - border: 1px solid #000; -} -.table { - border-collapse: collapse!important; -} -.table-bordered th, .table-bordered td { - border: 1px solid #ddd!important; -} -}*, *:before, *:after { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; -} -html { - font-size: 62.5%; - -webkit-tap-highlight-color: rgba(0, 0, 0, 0); -} -body { - font-family: Merriweather, Georgia, serif; - font-size: 14px; - line-height: 1.428571429; - color: #222; - background-color: #fff; -} -input, button, select, textarea { - font-family: inherit; - font-size: inherit; - line-height: inherit; -} -a { - color: #008cba; - text-decoration: none; -} -a:hover, a:focus { - color: #00526e; - text-decoration: underline; -} -a:focus { - outline: thin dotted; - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -img { - vertical-align: middle; -} -.img-responsive { - display: block; - height: auto; - max-width: 100%} -.img-rounded { - border-radius: 0; -} -.img-thumbnail { - display: inline-block; - height: auto; - max-width: 100%; - padding: 4px; - line-height: 1.428571429; - background-color: #fff; - border: 1px solid #ddd; - border-radius: 0; - -webkit-transition: all .2s ease-in-out; - transition: all .2s ease-in-out; -} -.img-circle { - border-radius: 50%} -hr { - margin-top: 21px; - margin-bottom: 21px; - border: 0; - border-top: 1px solid #ddd; -} -.sr-only { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - border: 0; -} -h1, h2, h3, h4, h5, h6, .h1, .h2, .h3, .h4, .h5, .h6 { - font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; - font-weight: 300; - line-height: 1.1; - color: inherit; -} -h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small { - font-weight: normal; - line-height: 1; - color: #999; -} -h1, h2, h3 { - margin-top: 21px; - margin-bottom: 10.5px; -} -h1 small, h2 small, h3 small, h1 .small, h2 .small, h3 .small { - font-size: 65%} -h4, h5, h6 { - margin-top: 10.5px; - margin-bottom: 10.5px; -} -h4 small, h5 small, h6 small, h4 .small, h5 .small, h6 .small { - font-size: 75%} -h1, .h1 { - font-size: 39px; -} -h2, .h2 { - font-size: 32px; -} -h3, .h3 { - font-size: 26px; -} -h4, .h4 { - font-size: 19px; -} -h5, .h5 { - font-size: 15px; -} -h6, .h6 { - font-size: 13px; -} -p { - margin: 0 0 10.5px; -} -.lead { - margin-bottom: 21px; - font-size: 17px; - font-weight: 200; - line-height: 1.4; -} -@media(min-width:768px) { - .lead { - font-size: 22.5px; -} -}small, .small { - font-size: 85%} -cite { - font-style: normal; -} -.text-muted { - color: #999; -} -.text-primary { - color: #008cba; -} -.text-primary:hover { - color: #006687; -} -.text-warning { - color: #e99002; -} -.text-warning:hover { - color: #b67102; -} -.text-danger { - color: #f04124; -} 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-} -input[type="file"]:focus, input[type="radio"]:focus, input[type="checkbox"]:focus { - outline: thin dotted; - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -input[type="number"]::-webkit-outer-spin-button, input[type="number"]::-webkit-inner-spin-button { - height: auto; -} -output { - display: block; - padding-top: 7px; - font-size: 15px; - line-height: 1.428571429; - color: #6f6f6f; - vertical-align: middle; -} -.form-control { - display: block; - width: 100%; - height: 35px; - padding: 6px 12px; - font-size: 15px; - line-height: 1.428571429; - color: #6f6f6f; - vertical-align: middle; - background-color: #fff; - background-image: none; - border: 1px solid #ccc; - border-radius: 0; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; - transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -} 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input[type="radio"], .radio-inline input[type="radio"], .checkbox input[type="checkbox"], .checkbox-inline input[type="checkbox"] { - float: left; - margin-left: -20px; -} -.radio+.radio, .checkbox+.checkbox { - margin-top: -5px; -} -.radio-inline, .checkbox-inline { - display: inline-block; - padding-left: 20px; - margin-bottom: 0; - font-weight: normal; - vertical-align: middle; - cursor: pointer; -} -.radio-inline+.radio-inline, .checkbox-inline+.checkbox-inline { - margin-top: 0; - margin-left: 10px; -} -input[type="radio"][disabled], input[type="checkbox"][disabled], .radio[disabled], .radio-inline[disabled], .checkbox[disabled], .checkbox-inline[disabled], fieldset[disabled] input[type="radio"], fieldset[disabled] input[type="checkbox"], fieldset[disabled] .radio, fieldset[disabled] .radio-inline, fieldset[disabled] .checkbox, fieldset[disabled] .checkbox-inline { - cursor: not-allowed; -} -.input-sm { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 0; -} -select.input-sm { - height: 30px; - line-height: 30px; -} -textarea.input-sm { - height: auto; -} -.input-lg { - height: 48px; - padding: 10px 16px; - font-size: 19px; - line-height: 1.33; - border-radius: 0; -} -select.input-lg { - height: 48px; - line-height: 48px; -} -textarea.input-lg { - height: auto; -} -.has-warning .help-block, .has-warning .control-label, .has-warning .radio, .has-warning .checkbox, .has-warning .radio-inline, .has-warning .checkbox-inline { - color: #e99002; -} -.has-warning .form-control { - border-color: #e99002; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} -.has-warning .form-control:focus { - border-color: #b67102; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #febc53; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #febc53; -} -.has-warning .input-group-addon { - color: #e99002; - background-color: #fcf8e3; - border-color: #e99002; -} -.has-error .help-block, .has-error .control-label, .has-error .radio, .has-error .checkbox, .has-error .radio-inline, .has-error .checkbox-inline { - color: #f04124; -} -.has-error .form-control { - border-color: #f04124; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} -.has-error .form-control:focus { - border-color: #d32a0e; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #f79483; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #f79483; -} -.has-error .input-group-addon { - color: #f04124; - background-color: #f2dede; - border-color: #f04124; -} -.has-success .help-block, .has-success .control-label, .has-success .radio, .has-success .checkbox, .has-success .radio-inline, .has-success .checkbox-inline { - color: #43ac6a; -} -.has-success .form-control { - border-color: #43ac6a; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} -.has-success .form-control:focus { - border-color: #358753; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #85d0a1; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #85d0a1; -} -.has-success .input-group-addon { - color: #43ac6a; - background-color: #dff0d8; - border-color: #43ac6a; -} -.form-control-static { - margin-bottom: 0; -} -.help-block { - display: block; - margin-top: 5px; - margin-bottom: 10px; - color: #626262; -} -@media(min-width:768px) { - .form-inline .form-group { - display: inline-block; - margin-bottom: 0; - vertical-align: middle; -} -.form-inline .form-control { - display: inline-block; -} -.form-inline select.form-control { - width: auto; -} -.form-inline .radio, .form-inline .checkbox { - display: inline-block; - padding-left: 0; - margin-top: 0; - margin-bottom: 0; -} -.form-inline .radio input[type="radio"], .form-inline .checkbox input[type="checkbox"] { - float: none; - margin-left: 0; -} -}.form-horizontal .control-label, .form-horizontal .radio, .form-horizontal .checkbox, .form-horizontal .radio-inline, .form-horizontal .checkbox-inline { - padding-top: 7px; - margin-top: 0; - margin-bottom: 0; -} -.form-horizontal .radio, .form-horizontal .checkbox { - min-height: 28px; -} -.form-horizontal .form-group { - margin-right: -15px; - margin-left: -15px; -} -.form-horizontal .form-group:before, .form-horizontal .form-group:after { - display: table; - content: " "} -.form-horizontal .form-group:after { - clear: both; -} -.form-horizontal .form-group:before, .form-horizontal .form-group:after { - display: table; - content: " "} -.form-horizontal .form-group:after { - clear: both; -} -.form-horizontal .form-group:before, .form-horizontal .form-group:after { - display: table; - content: " "} -.form-horizontal .form-group:after { - clear: both; -} -.form-horizontal .form-group:before, .form-horizontal .form-group:after { - display: table; - content: " "} -.form-horizontal .form-group:after { - clear: both; -} -.form-horizontal .form-group:before, .form-horizontal .form-group:after { - display: table; - content: " "} -.form-horizontal .form-group:after { - clear: both; -} -.form-horizontal .form-control-static { - padding-top: 7px; -} -@media(min-width:768px) { - .form-horizontal .control-label { - text-align: right; -} -}.btn { - display: inline-block; - padding: 6px 12px; - margin-bottom: 0; - font-size: 15px; - font-weight: normal; - line-height: 1.428571429; - text-align: center; - white-space: nowrap; - vertical-align: middle; - cursor: pointer; - background-image: none; - border: 1px solid transparent; - border-radius: 0; - -webkit-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - -o-user-select: none; - user-select: none; -} -.btn:focus { - outline: thin dotted; - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -.btn:hover, .btn:focus { - color: #333; - text-decoration: none; -} -.btn:active, .btn.active { - background-image: none; - outline: 0; - -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); - box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); -} -.btn.disabled, .btn[disabled], fieldset[disabled] .btn { - pointer-events: none; - cursor: not-allowed; - opacity: .65; - filter: alpha(opacity=65); - -webkit-box-shadow: none; - box-shadow: none; -} -.btn-default { - color: #333; - background-color: #e7e7e7; - border-color: #dadada; -} -.btn-default:hover, .btn-default:focus, .btn-default:active, .btn-default.active, .open .dropdown-toggle.btn-default { - color: #333; - background-color: #d3d3d3; - border-color: #bbb; -} -.btn-default:active, .btn-default.active, .open .dropdown-toggle.btn-default { - background-image: none; -} -.btn-default.disabled, .btn-default[disabled], fieldset[disabled] .btn-default, .btn-default.disabled:hover, .btn-default[disabled]:hover, fieldset[disabled] .btn-default:hover, .btn-default.disabled:focus, .btn-default[disabled]:focus, fieldset[disabled] .btn-default:focus, .btn-default.disabled:active, .btn-default[disabled]:active, fieldset[disabled] .btn-default:active, .btn-default.disabled.active, .btn-default[disabled].active, fieldset[disabled] .btn-default.active { - background-color: #e7e7e7; - border-color: #dadada; -} -.btn-default .badge { - color: #e7e7e7; - background-color: #fff; -} -.btn-primary { - color: #fff; - background-color: #008cba; - border-color: #0079a1; -} -.btn-primary:hover, .btn-primary:focus, .btn-primary:active, .btn-primary.active, .open .dropdown-toggle.btn-primary { - color: #fff; - background-color: #006d91; - border-color: #004b63; -} -.btn-primary:active, .btn-primary.active, .open .dropdown-toggle.btn-primary { - background-image: none; -} -.btn-primary.disabled, .btn-primary[disabled], fieldset[disabled] .btn-primary, .btn-primary.disabled:hover, .btn-primary[disabled]:hover, fieldset[disabled] .btn-primary:hover, .btn-primary.disabled:focus, .btn-primary[disabled]:focus, fieldset[disabled] .btn-primary:focus, .btn-primary.disabled:active, .btn-primary[disabled]:active, fieldset[disabled] .btn-primary:active, .btn-primary.disabled.active, .btn-primary[disabled].active, fieldset[disabled] .btn-primary.active { - background-color: #008cba; - border-color: #0079a1; -} -.btn-primary .badge { - color: #008cba; - background-color: #fff; -} -.btn-warning { - color: #fff; - background-color: #e99002; - border-color: #d08002; -} -.btn-warning:hover, .btn-warning:focus, .btn-warning:active, .btn-warning.active, .open .dropdown-toggle.btn-warning { - color: #fff; - background-color: #c17702; - border-color: #935b01; -} -.btn-warning:active, .btn-warning.active, .open .dropdown-toggle.btn-warning { - background-image: none; -} -.btn-warning.disabled, .btn-warning[disabled], fieldset[disabled] .btn-warning, .btn-warning.disabled:hover, .btn-warning[disabled]:hover, fieldset[disabled] .btn-warning:hover, .btn-warning.disabled:focus, .btn-warning[disabled]:focus, fieldset[disabled] .btn-warning:focus, .btn-warning.disabled:active, .btn-warning[disabled]:active, fieldset[disabled] .btn-warning:active, .btn-warning.disabled.active, .btn-warning[disabled].active, fieldset[disabled] .btn-warning.active { - background-color: #e99002; - border-color: #d08002; -} -.btn-warning .badge { - color: #e99002; - background-color: #fff; -} -.btn-danger { - color: #fff; - background-color: #f04124; - border-color: #ea2f10; -} -.btn-danger:hover, .btn-danger:focus, .btn-danger:active, .btn-danger.active, .open .dropdown-toggle.btn-danger { - color: #fff; - background-color: #dc2c0f; - border-color: #b1240c; -} -.btn-danger:active, .btn-danger.active, .open .dropdown-toggle.btn-danger { - background-image: none; -} -.btn-danger.disabled, .btn-danger[disabled], fieldset[disabled] .btn-danger, .btn-danger.disabled:hover, .btn-danger[disabled]:hover, fieldset[disabled] .btn-danger:hover, .btn-danger.disabled:focus, .btn-danger[disabled]:focus, fieldset[disabled] .btn-danger:focus, .btn-danger.disabled:active, .btn-danger[disabled]:active, fieldset[disabled] .btn-danger:active, .btn-danger.disabled.active, .btn-danger[disabled].active, fieldset[disabled] .btn-danger.active { - background-color: #f04124; - border-color: #ea2f10; -} -.btn-danger .badge { - color: #f04124; - background-color: #fff; -} -.btn-success { - color: #fff; - background-color: #43ac6a; - border-color: #3c9a5f; -} -.btn-success:hover, .btn-success:focus, .btn-success:active, .btn-success.active, .open .dropdown-toggle.btn-success { - color: #fff; - background-color: #388f58; - border-color: #2b6e44; -} -.btn-success:active, .btn-success.active, .open .dropdown-toggle.btn-success { - background-image: none; -} -.btn-success.disabled, .btn-success[disabled], fieldset[disabled] .btn-success, .btn-success.disabled:hover, .btn-success[disabled]:hover, fieldset[disabled] .btn-success:hover, .btn-success.disabled:focus, .btn-success[disabled]:focus, fieldset[disabled] .btn-success:focus, .btn-success.disabled:active, .btn-success[disabled]:active, fieldset[disabled] .btn-success:active, .btn-success.disabled.active, .btn-success[disabled].active, fieldset[disabled] .btn-success.active { - background-color: #43ac6a; - border-color: #3c9a5f; -} -.btn-success .badge { - color: #43ac6a; - background-color: #fff; -} -.btn-info { - color: #fff; - background-color: #5bc0de; - border-color: #46b8da; -} -.btn-info:hover, .btn-info:focus, .btn-info:active, .btn-info.active, .open .dropdown-toggle.btn-info { - color: #fff; - background-color: #39b3d7; - border-color: #269abc; -} -.btn-info:active, .btn-info.active, .open .dropdown-toggle.btn-info { - background-image: none; -} -.btn-info.disabled, .btn-info[disabled], fieldset[disabled] .btn-info, .btn-info.disabled:hover, .btn-info[disabled]:hover, fieldset[disabled] .btn-info:hover, .btn-info.disabled:focus, .btn-info[disabled]:focus, fieldset[disabled] .btn-info:focus, .btn-info.disabled:active, .btn-info[disabled]:active, fieldset[disabled] .btn-info:active, .btn-info.disabled.active, .btn-info[disabled].active, fieldset[disabled] .btn-info.active { - background-color: #5bc0de; - border-color: #46b8da; -} -.btn-info .badge { - color: #5bc0de; - background-color: #fff; -} -.btn-link { - font-weight: normal; - color: #008cba; - cursor: pointer; - border-radius: 0; -} -.btn-link, .btn-link:active, .btn-link[disabled], fieldset[disabled] .btn-link { - background-color: transparent; - -webkit-box-shadow: none; - box-shadow: none; -} -.btn-link, .btn-link:hover, .btn-link:focus, .btn-link:active { - border-color: transparent; -} -.btn-link:hover, .btn-link:focus { - color: #00526e; - text-decoration: underline; - background-color: transparent; -} -.btn-link[disabled]:hover, fieldset[disabled] .btn-link:hover, .btn-link[disabled]:focus, fieldset[disabled] .btn-link:focus { - color: #999; - text-decoration: none; -} -.btn-lg { - padding: 10px 16px; - font-size: 19px; - line-height: 1.33; - border-radius: 0; -} -.btn-sm { - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 0; -} -.btn-xs { - padding: 1px 5px; - font-size: 12px; - line-height: 1.5; - border-radius: 0; -} -.btn-block { - display: block; - width: 100%; - padding-right: 0; - padding-left: 0; -} -.btn-block+.btn-block { - margin-top: 5px; -} -input[type="submit"].btn-block, input[type="reset"].btn-block, input[type="button"].btn-block { - width: 100%} -.fade { - opacity: 0; - -webkit-transition: opacity .15s linear; - transition: opacity .15s linear; -} -.fade.in { - opacity: 1; -} -.collapse { - display: none; -} -.collapse.in { - display: block; -} -.collapsing { - position: relative; - height: 0; - overflow: hidden; - -webkit-transition: height .35s ease; - transition: height .35s ease; -} -@font-face { - font-family: 'Glyphicons Halflings'; - src: url('../fonts/glyphicons-halflings-regular.eot'); - src: url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../fonts/glyphicons-halflings-regular.woff') format('woff'), url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../fonts/glyphicons-halflings-regular.svg#glyphicons-halflingsregular') format('svg'); -} -.glyphicon { - position: relative; - top: 1px; - display: inline-block; - font-family: 'Glyphicons Halflings'; - -webkit-font-smoothing: antialiased; - font-style: normal; - font-weight: normal; - line-height: 1; - -moz-osx-font-smoothing: grayscale; -} -.glyphicon:empty { - width: 1em; -} -.glyphicon-asterisk:before { - content: "\2a"} -.glyphicon-plus:before { - content: "\2b"} -.glyphicon-euro:before { - content: "\20ac"} -.glyphicon-minus:before { - content: "\2212"} -.glyphicon-cloud:before { - content: "\2601"} -.glyphicon-envelope:before { - content: "\2709"} -.glyphicon-pencil:before { - content: "\270f"} -.glyphicon-glass:before { - content: "\e001"} -.glyphicon-music:before { - content: "\e002"} -.glyphicon-search:before { - content: "\e003"} -.glyphicon-heart:before { - content: "\e005"} -.glyphicon-star:before { - content: "\e006"} -.glyphicon-star-empty:before { - content: "\e007"} -.glyphicon-user:before { - content: "\e008"} -.glyphicon-film:before { - content: "\e009"} -.glyphicon-th-large:before { - content: "\e010"} -.glyphicon-th:before { - content: "\e011"} -.glyphicon-th-list:before { - content: "\e012"} -.glyphicon-ok:before { - content: "\e013"} -.glyphicon-remove:before { - content: "\e014"} -.glyphicon-zoom-in:before { - content: "\e015"} -.glyphicon-zoom-out:before { - content: "\e016"} -.glyphicon-off:before { - content: "\e017"} -.glyphicon-signal:before { - content: "\e018"} -.glyphicon-cog:before { - content: "\e019"} -.glyphicon-trash:before { - content: "\e020"} -.glyphicon-home:before { - content: "\e021"} -.glyphicon-file:before { - content: "\e022"} -.glyphicon-time:before { - content: "\e023"} -.glyphicon-road:before { - content: "\e024"} -.glyphicon-download-alt:before { - content: "\e025"} 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Input Data Format

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The first step when using RELOG is to describe the reverse logistics pipeline and the relevant data. RELOG accepts as input a JSON file with three sections: parameters, products and plants. Below, we describe each section in more detail.

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Parameters

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The parameters section describes details about the simulation itself.

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KeyDescription
time horizon (years)Number of years in the simulation.
building period (years)List of years in which we are allowed to open new plants. For example, if this parameter is set to [1,2,3], we can only open plants during the first three years. By default, this equals [1]; that is, plants can only be opened during the first year.
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Example

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{
-    "parameters": {
-        "time horizon (years)": 2,
-        "building period (years)": [1]
-    }
-}
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Products

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The products section describes all products and subproducts in the simulation. The field instance["Products"] is a dictionary mapping the name of the product to a dictionary which describes its characteristics. Each product description contains the following keys:

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KeyDescription
transportation cost ($/km/tonne)The cost to transport this product. Must be a timeseries.
transportation energy (J/km/tonne)The energy required to transport this product. Must be a timeseries. Optional.
transportation emissions (tonne/km/tonne)A dictionary mapping the name of each greenhouse gas, produced to transport one tonne of this product along one kilometer, to the amount of gas produced (in tonnes). Must be a timeseries. Optional.
initial amountsA dictionary mapping the name of each location to its description (see below). If this product is not initially available, this key may be omitted. Must be a timeseries.
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Each product may have some amount available at the beginning of each time period. In this case, the key initial amounts maps to a dictionary with the following keys:

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KeyDescription
latitude (deg)The latitude of the location.
longitude (deg)The longitude of the location.
amount (tonne)The amount of the product initially available at the location. Must be a timeseries.
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Example

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{
-    "products": {
-        "P1": {
-            "initial amounts": {
-                "C1": {
-                    "latitude (deg)": 7.0,
-                    "longitude (deg)": 7.0,
-                    "amount (tonne)": [934.56, 934.56]
-                },
-                "C2": {
-                    "latitude (deg)": 7.0,
-                    "longitude (deg)": 19.0,
-                    "amount (tonne)": [198.95, 198.95]
-                },
-                "C3": {
-                    "latitude (deg)": 84.0,
-                    "longitude (deg)": 76.0,
-                    "amount (tonne)": [212.97, 212.97]
-                }
-            },
-            "transportation cost ($/km/tonne)": [0.015, 0.015],
-            "transportation energy (J/km/tonne)": [0.12, 0.11],
-            "transportation emissions (tonne/km/tonne)": {
-                "CO2": [0.052, 0.050],
-                "CH4": [0.003, 0.002]
-            }
-        },
-        "P2": {
-            "transportation cost ($/km/tonne)": [0.022, 0.020]
-        },
-        "P3": {
-            "transportation cost ($/km/tonne)": [0.0125, 0.0125]
-        },
-        "P4": {
-            "transportation cost ($/km/tonne)": [0.0175, 0.0175]
-        }
-    }
-}
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Processing Plants

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The plants section describes the available types of reverse manufacturing plants, their potential locations and associated costs, as well as their inputs and outputs. The field instance["Plants"] is a dictionary mapping the name of the plant to a dictionary with the following keys:

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KeyDescription
inputThe name of the product that this plant takes as input. Only one input is accepted per plant.
outputs (tonne/tonne)A dictionary specifying how many tonnes of each product is produced for each tonnes of input. For example, if the plant outputs 0.5 tonnes of P2 and 0.25 tonnes of P3 for each tonnes of P1 provided, then this entry should be {"P2": 0.5, "P3": 0.25}. If the plant does not output anything, this key may be omitted.
energy (GJ/tonne)The energy required to process 1 tonne of the input. Must be a timeseries. Optional.
emissions (tonne/tonne)A dictionary mapping the name of each greenhouse gas, produced to process each tonne of input, to the amount of gas produced (in tonne). Must be a timeseries. Optional.
locationsA dictionary mapping the name of the location to a dictionary which describes the site characteristics (see below).
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Each type of plant is associated with a set of potential locations where it can be built. Each location is represented by a dictionary with the following keys:

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KeyDescription
latitude (deg)The latitude of the location, in degrees.
longitude (deg)The longitude of the location, in degrees.
disposalA dictionary describing what products can be disposed locally at the plant.
capacities (tonne)A dictionary describing what plant sizes are allowed, and their characteristics.
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The keys in the disposal dictionary should be the names of the products. The values are dictionaries with the following keys:

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KeyDescription
cost ($/tonne)The cost to dispose of the product. Must be a timeseries.
limit (tonne)The maximum amount that can be disposed of. If an unlimited amount can be disposed, this key may be omitted. Must be a timeseries.
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The keys in the capacities (tonne) dictionary should be the amounts (in tonnes). The values are dictionaries with the following keys:

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KeyDescription
opening cost ($)The cost to open a plant of this size.
fixed operating cost ($)The cost to keep the plant open, even if the plant doesn't process anything. Must be a timeseries.
variable operating cost ($/tonne)The cost that the plant incurs to process each tonne of input. Must be a timeseries.
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Example

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{
-    "plants": {
-        "F1": {
-            "input": "P1",
-            "outputs (tonne/tonne)": {
-                "P2": 0.2,
-                "P3": 0.5
-            },
-            "energy (GJ/tonne)": [0.12, 0.11],
-            "emissions (tonne/tonne)": {
-                "CO2": [0.052, 0.050],
-                "CH4": [0.003, 0.002]
-            },
-            "locations": {
-                "L1": {
-                    "latitude (deg)": 0.0,
-                    "longitude (deg)": 0.0,
-                    "disposal": {
-                        "P2": {
-                            "cost ($/tonne)": [-10.0, -12.0],
-                            "limit (tonne)": [1.0, 1.0]
-                        }
-                    },
-                    "capacities (tonne)": {
-                        "100": {
-                            "opening cost ($)": [500, 530],
-                            "fixed operating cost ($)": [300.0, 310.0],
-                            "variable operating cost ($/tonne)": [5.0, 5.2]
-                        },
-                        "500": {
-                            "opening cost ($)": [750, 760],
-                            "fixed operating cost ($)": [400.0, 450.0],
-                            "variable operating cost ($/tonne)": [5.0, 5.2]
-                        }
-                    }
-                }
-            }
-        }
-    }
-}
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Current limitations

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  • Each plant can only be opened exactly once. After open, the plant remains open until the end of the simulation.
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  • Plants can be expanded at any time, even long after they are open.
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  • All material available at the beginning of a time period must be entirely processed by the end of that time period. It is not possible to store unprocessed materials from one time period to the next.
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  • Up to two plant sizes are currently supported. Variable operating costs must be the same for all plant sizes.
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- Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
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RELOG: Reverse Logistics Optimization

-

RELOG is an open-source supply chain optimization package focusing on reverse logistics and reverse manufacturing. The package uses Mixed-Integer Linear Programming to determine where to build recycling plants, what size should these plants have and which customers should be served by which plants. The package supports custom reverse logistics pipelines, with multiple types of plants, multiple types of product and multiple time periods.

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Table of Contents

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Source Code

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Authors

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License

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RELOG: Reverse Logistics Optimization
-Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
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-Redistribution and use in source and binary forms, with or without modification, are permitted
-provided that the following conditions are met:
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-   conditions and the following disclaimer.
-2. Redistributions in binary form must reproduce the above copyright notice, this list of
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-   with the distribution.
-3. Neither the name of the copyright holder nor the names of its contributors may be used to
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-AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
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-OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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-MathJax.Hub.Config({ - "tex2jax": { inlineMath: [ [ '$', '$' ] ] } -}); -MathJax.Hub.Config({ - config: ["MMLorHTML.js"], - jax: ["input/TeX", "output/HTML-CSS", "output/NativeMML"], - extensions: ["MathMenu.js", "MathZoom.js"] -}); \ No newline at end of file diff --git a/docs/model/index.html b/docs/model/index.html deleted file mode 100644 index 20a058c..0000000 --- a/docs/model/index.html +++ /dev/null @@ -1,422 +0,0 @@ - - - - - - - - - - - - - - Optimization Model - RELOG - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - -
-
- -

Optimization Model

-

In this page, we describe the precise mathematical optimization model used by RELOG to find the optimal logistics plan. This model is a variation of the classical Facility Location Problem, which has been widely studied in the operations research literature. To simplify the exposition, we present the simplified case where there is only one type of plant.

-

Mathematical Description

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Sets

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    -
  • $L$ - Set of locations holding the original material to be recycled
    • -
  • $M$ - Set of materials recovered during the reverse manufacturing process
    • -
  • $P$ - Set of potential plants to open
    • -
  • $T = { 1, \ldots, t^{max} } $ - Set of time periods
    • -
-

Constants

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Plants:

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    -
  • $c^\text{disp}_{pmt}$ - Cost of disposing one tonne of material $m$ at plant $p$ during time $t$ ($/tonne/km)
    • -
  • $c^\text{exp}_{pt}$ - Cost of adding one tonne of capacity to plant $p$ at time $t$ ($/tonne)
    • -
  • $c^\text{open}_{pt}$ - Cost of opening plant $p$ at time $t$, at minimum capacity ($)
    • -
  • $c^\text{f-base}_{pt}$ - Fixed cost of keeping plant $p$ open during time period $t$ ($)
    • -
  • $c^\text{f-exp}_{pt}$ - Increase in fixed cost for each additional tonne of capacity ($/tonne)
    • -
  • $c^\text{var}_{pt}$ - Variable cost of processing one tonne of input at plant $p$ at time $t$ ($/tonne)
    • -
  • $m^\text{min}_p$ - Minimum capacity of plant $p$ (tonne)
    • -
  • $m^\text{max}_p$ - Maximum capacity of plant $p$ (tonne)
    • -
  • $m^\text{disp}_{pmt}$ - Maximum amount of material $m$ that plant $p$ can dispose of during time $t$ (tonne)
    • -
-

Products:

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    -
  • $\alpha_{pm}$ - Amount of material $m$ recovered by plant $t$ for each tonne of original material (tonne/tonne)
    • -
  • $m^\text{initial}_{lt}$ - Amount of original material to be recycled at location $l$ during time $t$ (tonne)
    • -
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Transportation:

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    -
  • $c^\text{tr}_{t}$ - Transportation cost during time $t$ ($/tonne/km)
    • -
  • $d_{lp}$ - Distance between plant $p$ and location $l$ (km)
    • -
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Decision variables

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    -
  • $q_{mpt}$ - Amount of material $m$ recovered by plant $p$ during time $t$ (tonne)
    • -
  • $u_{pt}$ - Binary variable that equals 1 if plant $p$ starts operating at time $t$ (bool)
    • -
  • $w_{pt}$ - Extra capacity (amount above the minimum) added to plant $p$ during time $t$ (tonne)
    • -
  • $x_{pt}$ - Binary variable that equals 1 if plant $p$ is operational at time $t$ (bool)
    • -
  • $y_{lpt}$ - Amount of product sent from location $l$ to plant $p$ during time $t$ (tonne)
    • -
  • $z_{mpt}$ - Amount of material $m$ disposed of by plant $p$ during time $t$ (tonne)
    • -
-

Objective function

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RELOG minimizes the overall capital, production and transportation costs:

-

- -

-

In the first line, we have (i) opening costs, if plant starts operating at time $t$, (ii) fixed operating costs, if plant is operational, (iii) additional fixed operating costs coming from expansion performed in all previous time periods up to the current one, and finally (iv) the expansion costs during the current time period. -In the second line, we have the transportation costs and the variable operating costs. -In the third line, we have the disposal costs.

-

Constraints

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    -
  • All original materials must be sent to a plant:
    • -
-

- -

-
    -
  • Plants have a limited capacity:
    • -
-

- -

-
    -
  • Plants can only be expanded up to their maximum capacity. Furthermore, if a plant is closed, it cannot be expanded:
    • -
-

- -

-
    -
  • Amount of recovered material is proportional to the plant input:
    • -
-

- -

-
    -
  • Because we only consider a single type of plant, all recovered material must be immediately disposed of. In RELOG's full model, recovered materials may be sent to another plant for further processing.
    • -
-

- -

-
    -
  • A plant is operation at time $t$ if it was operational at time $t-1$ or it was built at time $t$. This constraint also prevents a plant from being built multiple times.
    • -
-

- -

-
    -
  • Variable bounds:
    • -
-

- -

-

Complete optimization model

-

- -

- - -
- -
- -
-

- Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
- - Documentation built with MkDocs. -

- - - -
- - - - - - - - - - - - - - - - - - - - diff --git a/docs/search/lunr.js b/docs/search/lunr.js deleted file mode 100644 index c353765..0000000 --- a/docs/search/lunr.js +++ /dev/null @@ -1,3475 +0,0 @@ -/** - * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.8 - * Copyright (C) 2019 Oliver Nightingale - * @license MIT - */ - -;(function(){ - -/** - * A convenience function for configuring and constructing - * a new lunr Index. - * - * A lunr.Builder instance is created and the pipeline setup - * with a trimmer, stop word filter and stemmer. - * - * This builder object is yielded to the configuration function - * that is passed as a parameter, allowing the list of fields - * and other builder parameters to be customised. - * - * All documents _must_ be added within the passed config function. - * - * @example - * var idx = lunr(function () { - * this.field('title') - * this.field('body') - * this.ref('id') - * - * documents.forEach(function (doc) { - * this.add(doc) - * }, this) - * }) - * - * @see {@link lunr.Builder} - * @see {@link lunr.Pipeline} - * @see {@link lunr.trimmer} - * @see {@link lunr.stopWordFilter} - * @see {@link lunr.stemmer} - * @namespace {function} lunr - */ -var lunr = function (config) { - var builder = new lunr.Builder - - builder.pipeline.add( - lunr.trimmer, - lunr.stopWordFilter, - lunr.stemmer - ) - - builder.searchPipeline.add( - lunr.stemmer - ) - - config.call(builder, builder) - return builder.build() -} - -lunr.version = "2.3.8" -/*! - * lunr.utils - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * A namespace containing utils for the rest of the lunr library - * @namespace lunr.utils - */ -lunr.utils = {} - -/** - * Print a warning message to the console. - * - * @param {String} message The message to be printed. - * @memberOf lunr.utils - * @function - */ -lunr.utils.warn = (function (global) { - /* eslint-disable no-console */ - return function (message) { - if (global.console && console.warn) { - console.warn(message) - } - } - /* eslint-enable no-console */ -})(this) - -/** - * Convert an object to a string. - * - * In the case of `null` and `undefined` the function returns - * the empty string, in all other cases the result of calling - * `toString` on the passed object is returned. - * - * @param {Any} obj The object to convert to a string. - * @return {String} string representation of the passed object. - * @memberOf lunr.utils - */ -lunr.utils.asString = function (obj) { - if (obj === void 0 || obj === null) { - return "" - } else { - return obj.toString() - } -} - -/** - * Clones an object. - * - * Will create a copy of an existing object such that any mutations - * on the copy cannot affect the original. - * - * Only shallow objects are supported, passing a nested object to this - * function will cause a TypeError. - * - * Objects with primitives, and arrays of primitives are supported. - * - * @param {Object} obj The object to clone. - * @return {Object} a clone of the passed object. - * @throws {TypeError} when a nested object is passed. - * @memberOf Utils - */ -lunr.utils.clone = function (obj) { - if (obj === null || obj === undefined) { - return obj - } - - var clone = Object.create(null), - keys = Object.keys(obj) - - for (var i = 0; i < keys.length; i++) { - var key = keys[i], - val = obj[key] - - if (Array.isArray(val)) { - clone[key] = val.slice() - continue - } - - if (typeof val === 'string' || - typeof val === 'number' || - typeof val === 'boolean') { - clone[key] = val - continue - } - - throw new TypeError("clone is not deep and does not support nested objects") - } - - return clone -} -lunr.FieldRef = function (docRef, fieldName, stringValue) { - this.docRef = docRef - this.fieldName = fieldName - this._stringValue = stringValue -} - -lunr.FieldRef.joiner = "/" - -lunr.FieldRef.fromString = function (s) { - var n = s.indexOf(lunr.FieldRef.joiner) - - if (n === -1) { - throw "malformed field ref string" - } - - var fieldRef = s.slice(0, n), - docRef = s.slice(n + 1) - - return new lunr.FieldRef (docRef, fieldRef, s) -} - -lunr.FieldRef.prototype.toString = function () { - if (this._stringValue == undefined) { - this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef - } - - return this._stringValue -} -/*! - * lunr.Set - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * A lunr set. - * - * @constructor - */ -lunr.Set = function (elements) { - this.elements = Object.create(null) - - if (elements) { - this.length = elements.length - - for (var i = 0; i < this.length; i++) { - this.elements[elements[i]] = true - } - } else { - this.length = 0 - } -} - -/** - * A complete set that contains all elements. - * - * @static - * @readonly - * @type {lunr.Set} - */ -lunr.Set.complete = { - intersect: function (other) { - return other - }, - - union: function (other) { - return other - }, - - contains: function () { - return true - } -} - -/** - * An empty set that contains no elements. - * - * @static - * @readonly - * @type {lunr.Set} - */ -lunr.Set.empty = { - intersect: function () { - return this - }, - - union: function (other) { - return other - }, - - contains: function () { - return false - } -} - -/** - * Returns true if this set contains the specified object. - * - * @param {object} object - Object whose presence in this set is to be tested. - * @returns {boolean} - True if this set contains the specified object. - */ -lunr.Set.prototype.contains = function (object) { - return !!this.elements[object] -} - -/** - * Returns a new set containing only the elements that are present in both - * this set and the specified set. - * - * @param {lunr.Set} other - set to intersect with this set. - * @returns {lunr.Set} a new set that is the intersection of this and the specified set. - */ - -lunr.Set.prototype.intersect = function (other) { - var a, b, elements, intersection = [] - - if (other === lunr.Set.complete) { - return this - } - - if (other === lunr.Set.empty) { - return other - } - - if (this.length < other.length) { - a = this - b = other - } else { - a = other - b = this - } - - elements = Object.keys(a.elements) - - for (var i = 0; i < elements.length; i++) { - var element = elements[i] - if (element in b.elements) { - intersection.push(element) - } - } - - return new lunr.Set (intersection) -} - -/** - * Returns a new set combining the elements of this and the specified set. - * - * @param {lunr.Set} other - set to union with this set. - * @return {lunr.Set} a new set that is the union of this and the specified set. - */ - -lunr.Set.prototype.union = function (other) { - if (other === lunr.Set.complete) { - return lunr.Set.complete - } - - if (other === lunr.Set.empty) { - return this - } - - return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements))) -} -/** - * A function to calculate the inverse document frequency for - * a posting. This is shared between the builder and the index - * - * @private - * @param {object} posting - The posting for a given term - * @param {number} documentCount - The total number of documents. - */ -lunr.idf = function (posting, documentCount) { - var documentsWithTerm = 0 - - for (var fieldName in posting) { - if (fieldName == '_index') continue // Ignore the term index, its not a field - documentsWithTerm += Object.keys(posting[fieldName]).length - } - - var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5) - - return Math.log(1 + Math.abs(x)) -} - -/** - * A token wraps a string representation of a token - * as it is passed through the text processing pipeline. - * - * @constructor - * @param {string} [str=''] - The string token being wrapped. - * @param {object} [metadata={}] - Metadata associated with this token. - */ -lunr.Token = function (str, metadata) { - this.str = str || "" - this.metadata = metadata || {} -} - -/** - * Returns the token string that is being wrapped by this object. - * - * @returns {string} - */ -lunr.Token.prototype.toString = function () { - return this.str -} - -/** - * A token update function is used when updating or optionally - * when cloning a token. - * - * @callback lunr.Token~updateFunction - * @param {string} str - The string representation of the token. - * @param {Object} metadata - All metadata associated with this token. - */ - -/** - * Applies the given function to the wrapped string token. - * - * @example - * token.update(function (str, metadata) { - * return str.toUpperCase() - * }) - * - * @param {lunr.Token~updateFunction} fn - A function to apply to the token string. - * @returns {lunr.Token} - */ -lunr.Token.prototype.update = function (fn) { - this.str = fn(this.str, this.metadata) - return this -} - -/** - * Creates a clone of this token. Optionally a function can be - * applied to the cloned token. - * - * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token. - * @returns {lunr.Token} - */ -lunr.Token.prototype.clone = function (fn) { - fn = fn || function (s) { return s } - return new lunr.Token (fn(this.str, this.metadata), this.metadata) -} -/*! - * lunr.tokenizer - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * A function for splitting a string into tokens ready to be inserted into - * the search index. Uses `lunr.tokenizer.separator` to split strings, change - * the value of this property to change how strings are split into tokens. - * - * This tokenizer will convert its parameter to a string by calling `toString` and - * then will split this string on the character in `lunr.tokenizer.separator`. - * Arrays will have their elements converted to strings and wrapped in a lunr.Token. - * - * Optional metadata can be passed to the tokenizer, this metadata will be cloned and - * added as metadata to every token that is created from the object to be tokenized. - * - * @static - * @param {?(string|object|object[])} obj - The object to convert into tokens - * @param {?object} metadata - Optional metadata to associate with every token - * @returns {lunr.Token[]} - * @see {@link lunr.Pipeline} - */ -lunr.tokenizer = function (obj, metadata) { - if (obj == null || obj == undefined) { - return [] - } - - if (Array.isArray(obj)) { - return obj.map(function (t) { - return new lunr.Token( - lunr.utils.asString(t).toLowerCase(), - lunr.utils.clone(metadata) - ) - }) - } - - var str = obj.toString().toLowerCase(), - len = str.length, - tokens = [] - - for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) { - var char = str.charAt(sliceEnd), - sliceLength = sliceEnd - sliceStart - - if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) { - - if (sliceLength > 0) { - var tokenMetadata = lunr.utils.clone(metadata) || {} - tokenMetadata["position"] = [sliceStart, sliceLength] - tokenMetadata["index"] = tokens.length - - tokens.push( - new lunr.Token ( - str.slice(sliceStart, sliceEnd), - tokenMetadata - ) - ) - } - - sliceStart = sliceEnd + 1 - } - - } - - return tokens -} - -/** - * The separator used to split a string into tokens. Override this property to change the behaviour of - * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens. - * - * @static - * @see lunr.tokenizer - */ -lunr.tokenizer.separator = /[\s\-]+/ -/*! - * lunr.Pipeline - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * lunr.Pipelines maintain an ordered list of functions to be applied to all - * tokens in documents entering the search index and queries being ran against - * the index. - * - * An instance of lunr.Index created with the lunr shortcut will contain a - * pipeline with a stop word filter and an English language stemmer. Extra - * functions can be added before or after either of these functions or these - * default functions can be removed. - * - * When run the pipeline will call each function in turn, passing a token, the - * index of that token in the original list of all tokens and finally a list of - * all the original tokens. - * - * The output of functions in the pipeline will be passed to the next function - * in the pipeline. To exclude a token from entering the index the function - * should return undefined, the rest of the pipeline will not be called with - * this token. - * - * For serialisation of pipelines to work, all functions used in an instance of - * a pipeline should be registered with lunr.Pipeline. Registered functions can - * then be loaded. If trying to load a serialised pipeline that uses functions - * that are not registered an error will be thrown. - * - * If not planning on serialising the pipeline then registering pipeline functions - * is not necessary. - * - * @constructor - */ -lunr.Pipeline = function () { - this._stack = [] -} - -lunr.Pipeline.registeredFunctions = Object.create(null) - -/** - * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token - * string as well as all known metadata. A pipeline function can mutate the token string - * or mutate (or add) metadata for a given token. - * - * A pipeline function can indicate that the passed token should be discarded by returning - * null, undefined or an empty string. This token will not be passed to any downstream pipeline - * functions and will not be added to the index. - * - * Multiple tokens can be returned by returning an array of tokens. Each token will be passed - * to any downstream pipeline functions and all will returned tokens will be added to the index. - * - * Any number of pipeline functions may be chained together using a lunr.Pipeline. - * - * @interface lunr.PipelineFunction - * @param {lunr.Token} token - A token from the document being processed. - * @param {number} i - The index of this token in the complete list of tokens for this document/field. - * @param {lunr.Token[]} tokens - All tokens for this document/field. - * @returns {(?lunr.Token|lunr.Token[])} - */ - -/** - * Register a function with the pipeline. - * - * Functions that are used in the pipeline should be registered if the pipeline - * needs to be serialised, or a serialised pipeline needs to be loaded. - * - * Registering a function does not add it to a pipeline, functions must still be - * added to instances of the pipeline for them to be used when running a pipeline. - * - * @param {lunr.PipelineFunction} fn - The function to check for. - * @param {String} label - The label to register this function with - */ -lunr.Pipeline.registerFunction = function (fn, label) { - if (label in this.registeredFunctions) { - lunr.utils.warn('Overwriting existing registered function: ' + label) - } - - fn.label = label - lunr.Pipeline.registeredFunctions[fn.label] = fn -} - -/** - * Warns if the function is not registered as a Pipeline function. - * - * @param {lunr.PipelineFunction} fn - The function to check for. - * @private - */ -lunr.Pipeline.warnIfFunctionNotRegistered = function (fn) { - var isRegistered = fn.label && (fn.label in this.registeredFunctions) - - if (!isRegistered) { - lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\n', fn) - } -} - -/** - * Loads a previously serialised pipeline. - * - * All functions to be loaded must already be registered with lunr.Pipeline. - * If any function from the serialised data has not been registered then an - * error will be thrown. - * - * @param {Object} serialised - The serialised pipeline to load. - * @returns {lunr.Pipeline} - */ -lunr.Pipeline.load = function (serialised) { - var pipeline = new lunr.Pipeline - - serialised.forEach(function (fnName) { - var fn = lunr.Pipeline.registeredFunctions[fnName] - - if (fn) { - pipeline.add(fn) - } else { - throw new Error('Cannot load unregistered function: ' + fnName) - } - }) - - return pipeline -} - -/** - * Adds new functions to the end of the pipeline. - * - * Logs a warning if the function has not been registered. - * - * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline. - */ -lunr.Pipeline.prototype.add = function () { - var fns = Array.prototype.slice.call(arguments) - - fns.forEach(function (fn) { - lunr.Pipeline.warnIfFunctionNotRegistered(fn) - this._stack.push(fn) - }, this) -} - -/** - * Adds a single function after a function that already exists in the - * pipeline. - * - * Logs a warning if the function has not been registered. - * - * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. - * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. - */ -lunr.Pipeline.prototype.after = function (existingFn, newFn) { - lunr.Pipeline.warnIfFunctionNotRegistered(newFn) - - var pos = this._stack.indexOf(existingFn) - if (pos == -1) { - throw new Error('Cannot find existingFn') - } - - pos = pos + 1 - this._stack.splice(pos, 0, newFn) -} - -/** - * Adds a single function before a function that already exists in the - * pipeline. - * - * Logs a warning if the function has not been registered. - * - * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline. - * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline. - */ -lunr.Pipeline.prototype.before = function (existingFn, newFn) { - lunr.Pipeline.warnIfFunctionNotRegistered(newFn) - - var pos = this._stack.indexOf(existingFn) - if (pos == -1) { - throw new Error('Cannot find existingFn') - } - - this._stack.splice(pos, 0, newFn) -} - -/** - * Removes a function from the pipeline. - * - * @param {lunr.PipelineFunction} fn The function to remove from the pipeline. - */ -lunr.Pipeline.prototype.remove = function (fn) { - var pos = this._stack.indexOf(fn) - if (pos == -1) { - return - } - - this._stack.splice(pos, 1) -} - -/** - * Runs the current list of functions that make up the pipeline against the - * passed tokens. - * - * @param {Array} tokens The tokens to run through the pipeline. - * @returns {Array} - */ -lunr.Pipeline.prototype.run = function (tokens) { - var stackLength = this._stack.length - - for (var i = 0; i < stackLength; i++) { - var fn = this._stack[i] - var memo = [] - - for (var j = 0; j < tokens.length; j++) { - var result = fn(tokens[j], j, tokens) - - if (result === null || result === void 0 || result === '') continue - - if (Array.isArray(result)) { - for (var k = 0; k < result.length; k++) { - memo.push(result[k]) - } - } else { - memo.push(result) - } - } - - tokens = memo - } - - return tokens -} - -/** - * Convenience method for passing a string through a pipeline and getting - * strings out. This method takes care of wrapping the passed string in a - * token and mapping the resulting tokens back to strings. - * - * @param {string} str - The string to pass through the pipeline. - * @param {?object} metadata - Optional metadata to associate with the token - * passed to the pipeline. - * @returns {string[]} - */ -lunr.Pipeline.prototype.runString = function (str, metadata) { - var token = new lunr.Token (str, metadata) - - return this.run([token]).map(function (t) { - return t.toString() - }) -} - -/** - * Resets the pipeline by removing any existing processors. - * - */ -lunr.Pipeline.prototype.reset = function () { - this._stack = [] -} - -/** - * Returns a representation of the pipeline ready for serialisation. - * - * Logs a warning if the function has not been registered. - * - * @returns {Array} - */ -lunr.Pipeline.prototype.toJSON = function () { - return this._stack.map(function (fn) { - lunr.Pipeline.warnIfFunctionNotRegistered(fn) - - return fn.label - }) -} -/*! - * lunr.Vector - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * A vector is used to construct the vector space of documents and queries. These - * vectors support operations to determine the similarity between two documents or - * a document and a query. - * - * Normally no parameters are required for initializing a vector, but in the case of - * loading a previously dumped vector the raw elements can be provided to the constructor. - * - * For performance reasons vectors are implemented with a flat array, where an elements - * index is immediately followed by its value. E.g. [index, value, index, value]. This - * allows the underlying array to be as sparse as possible and still offer decent - * performance when being used for vector calculations. - * - * @constructor - * @param {Number[]} [elements] - The flat list of element index and element value pairs. - */ -lunr.Vector = function (elements) { - this._magnitude = 0 - this.elements = elements || [] -} - - -/** - * Calculates the position within the vector to insert a given index. - * - * This is used internally by insert and upsert. If there are duplicate indexes then - * the position is returned as if the value for that index were to be updated, but it - * is the callers responsibility to check whether there is a duplicate at that index - * - * @param {Number} insertIdx - The index at which the element should be inserted. - * @returns {Number} - */ -lunr.Vector.prototype.positionForIndex = function (index) { - // For an empty vector the tuple can be inserted at the beginning - if (this.elements.length == 0) { - return 0 - } - - var start = 0, - end = this.elements.length / 2, - sliceLength = end - start, - pivotPoint = Math.floor(sliceLength / 2), - pivotIndex = this.elements[pivotPoint * 2] - - while (sliceLength > 1) { - if (pivotIndex < index) { - start = pivotPoint - } - - if (pivotIndex > index) { - end = pivotPoint - } - - if (pivotIndex == index) { - break - } - - sliceLength = end - start - pivotPoint = start + Math.floor(sliceLength / 2) - pivotIndex = this.elements[pivotPoint * 2] - } - - if (pivotIndex == index) { - return pivotPoint * 2 - } - - if (pivotIndex > index) { - return pivotPoint * 2 - } - - if (pivotIndex < index) { - return (pivotPoint + 1) * 2 - } -} - -/** - * Inserts an element at an index within the vector. - * - * Does not allow duplicates, will throw an error if there is already an entry - * for this index. - * - * @param {Number} insertIdx - The index at which the element should be inserted. - * @param {Number} val - The value to be inserted into the vector. - */ -lunr.Vector.prototype.insert = function (insertIdx, val) { - this.upsert(insertIdx, val, function () { - throw "duplicate index" - }) -} - -/** - * Inserts or updates an existing index within the vector. - * - * @param {Number} insertIdx - The index at which the element should be inserted. - * @param {Number} val - The value to be inserted into the vector. - * @param {function} fn - A function that is called for updates, the existing value and the - * requested value are passed as arguments - */ -lunr.Vector.prototype.upsert = function (insertIdx, val, fn) { - this._magnitude = 0 - var position = this.positionForIndex(insertIdx) - - if (this.elements[position] == insertIdx) { - this.elements[position + 1] = fn(this.elements[position + 1], val) - } else { - this.elements.splice(position, 0, insertIdx, val) - } -} - -/** - * Calculates the magnitude of this vector. - * - * @returns {Number} - */ -lunr.Vector.prototype.magnitude = function () { - if (this._magnitude) return this._magnitude - - var sumOfSquares = 0, - elementsLength = this.elements.length - - for (var i = 1; i < elementsLength; i += 2) { - var val = this.elements[i] - sumOfSquares += val * val - } - - return this._magnitude = Math.sqrt(sumOfSquares) -} - -/** - * Calculates the dot product of this vector and another vector. - * - * @param {lunr.Vector} otherVector - The vector to compute the dot product with. - * @returns {Number} - */ -lunr.Vector.prototype.dot = function (otherVector) { - var dotProduct = 0, - a = this.elements, b = otherVector.elements, - aLen = a.length, bLen = b.length, - aVal = 0, bVal = 0, - i = 0, j = 0 - - while (i < aLen && j < bLen) { - aVal = a[i], bVal = b[j] - if (aVal < bVal) { - i += 2 - } else if (aVal > bVal) { - j += 2 - } else if (aVal == bVal) { - dotProduct += a[i + 1] * b[j + 1] - i += 2 - j += 2 - } - } - - return dotProduct -} - -/** - * Calculates the similarity between this vector and another vector. - * - * @param {lunr.Vector} otherVector - The other vector to calculate the - * similarity with. - * @returns {Number} - */ -lunr.Vector.prototype.similarity = function (otherVector) { - return this.dot(otherVector) / this.magnitude() || 0 -} - -/** - * Converts the vector to an array of the elements within the vector. - * - * @returns {Number[]} - */ -lunr.Vector.prototype.toArray = function () { - var output = new Array (this.elements.length / 2) - - for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) { - output[j] = this.elements[i] - } - - return output -} - -/** - * A JSON serializable representation of the vector. - * - * @returns {Number[]} - */ -lunr.Vector.prototype.toJSON = function () { - return this.elements -} -/* eslint-disable */ -/*! - * lunr.stemmer - * Copyright (C) 2019 Oliver Nightingale - * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt - */ - -/** - * lunr.stemmer is an english language stemmer, this is a JavaScript - * implementation of the PorterStemmer taken from http://tartarus.org/~martin - * - * @static - * @implements {lunr.PipelineFunction} - * @param {lunr.Token} token - The string to stem - * @returns {lunr.Token} - * @see {@link lunr.Pipeline} - * @function - */ -lunr.stemmer = (function(){ - var step2list = { - "ational" : "ate", - "tional" : "tion", - "enci" : "ence", - "anci" : "ance", - "izer" : "ize", - "bli" : "ble", - "alli" : "al", - "entli" : "ent", - "eli" : "e", - "ousli" : "ous", - "ization" : "ize", - "ation" : "ate", - "ator" : "ate", - "alism" : "al", - "iveness" : "ive", - "fulness" : "ful", - "ousness" : "ous", - "aliti" : "al", - "iviti" : "ive", - "biliti" : "ble", - "logi" : "log" - }, - - step3list = { - "icate" : "ic", - "ative" : "", - "alize" : "al", - "iciti" : "ic", - "ical" : "ic", - "ful" : "", - "ness" : "" - }, - - c = "[^aeiou]", // consonant - v = "[aeiouy]", // vowel - C = c + "[^aeiouy]*", // consonant sequence - V = v + "[aeiou]*", // vowel sequence - - mgr0 = "^(" + C + ")?" + V + C, // [C]VC... is m>0 - meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$", // [C]VC[V] is m=1 - mgr1 = "^(" + C + ")?" + V + C + V + C, // [C]VCVC... is m>1 - s_v = "^(" + C + ")?" + v; // vowel in stem - - var re_mgr0 = new RegExp(mgr0); - var re_mgr1 = new RegExp(mgr1); - var re_meq1 = new RegExp(meq1); - var re_s_v = new RegExp(s_v); - - var re_1a = /^(.+?)(ss|i)es$/; - var re2_1a = /^(.+?)([^s])s$/; - var re_1b = /^(.+?)eed$/; - var re2_1b = /^(.+?)(ed|ing)$/; - var re_1b_2 = /.$/; - var re2_1b_2 = /(at|bl|iz)$/; - var re3_1b_2 = new RegExp("([^aeiouylsz])\\1$"); - var re4_1b_2 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - - var re_1c = /^(.+?[^aeiou])y$/; - var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; - - var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; - - var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; - var re2_4 = /^(.+?)(s|t)(ion)$/; - - var re_5 = /^(.+?)e$/; - var re_5_1 = /ll$/; - var re3_5 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - - var porterStemmer = function porterStemmer(w) { - var stem, - suffix, - firstch, - re, - re2, - re3, - re4; - - if (w.length < 3) { return w; } - - firstch = w.substr(0,1); - if (firstch == "y") { - w = firstch.toUpperCase() + w.substr(1); - } - - // Step 1a - re = re_1a - re2 = re2_1a; - - if (re.test(w)) { w = w.replace(re,"$1$2"); } - else if (re2.test(w)) { w = w.replace(re2,"$1$2"); } - - // Step 1b - re = re_1b; - re2 = re2_1b; - if (re.test(w)) { - var fp = re.exec(w); - re = re_mgr0; - if (re.test(fp[1])) { - re = re_1b_2; - w = w.replace(re,""); - } - } else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1]; - re2 = re_s_v; - if (re2.test(stem)) { - w = stem; - re2 = re2_1b_2; - re3 = re3_1b_2; - re4 = re4_1b_2; - if (re2.test(w)) { w = w + "e"; } - else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,""); } - else if (re4.test(w)) { w = w + "e"; } - } - } - - // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say) - re = re_1c; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - w = stem + "i"; - } - - // Step 2 - re = re_2; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = re_mgr0; - if (re.test(stem)) { - w = stem + step2list[suffix]; - } - } - - // Step 3 - re = re_3; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = re_mgr0; - if (re.test(stem)) { - w = stem + step3list[suffix]; - } - } - - // Step 4 - re = re_4; - re2 = re2_4; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = re_mgr1; - if (re.test(stem)) { - w = stem; - } - } else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1] + fp[2]; - re2 = re_mgr1; - if (re2.test(stem)) { - w = stem; - } - } - - // Step 5 - re = re_5; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = re_mgr1; - re2 = re_meq1; - re3 = re3_5; - if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) { - w = stem; - } - } - - re = re_5_1; - re2 = re_mgr1; - if (re.test(w) && re2.test(w)) { - re = re_1b_2; - w = w.replace(re,""); - } - - // and turn initial Y back to y - - if (firstch == "y") { - w = firstch.toLowerCase() + w.substr(1); - } - - return w; - }; - - return function (token) { - return token.update(porterStemmer); - } -})(); - -lunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer') -/*! - * lunr.stopWordFilter - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * lunr.generateStopWordFilter builds a stopWordFilter function from the provided - * list of stop words. - * - * The built in lunr.stopWordFilter is built using this generator and can be used - * to generate custom stopWordFilters for applications or non English languages. - * - * @function - * @param {Array} token The token to pass through the filter - * @returns {lunr.PipelineFunction} - * @see lunr.Pipeline - * @see lunr.stopWordFilter - */ -lunr.generateStopWordFilter = function (stopWords) { - var words = stopWords.reduce(function (memo, stopWord) { - memo[stopWord] = stopWord - return memo - }, {}) - - return function (token) { - if (token && words[token.toString()] !== token.toString()) return token - } -} - -/** - * lunr.stopWordFilter is an English language stop word list filter, any words - * contained in the list will not be passed through the filter. - * - * This is intended to be used in the Pipeline. If the token does not pass the - * filter then undefined will be returned. - * - * @function - * @implements {lunr.PipelineFunction} - * @params {lunr.Token} token - A token to check for being a stop word. - * @returns {lunr.Token} - * @see {@link lunr.Pipeline} - */ -lunr.stopWordFilter = lunr.generateStopWordFilter([ - 'a', - 'able', - 'about', - 'across', - 'after', - 'all', - 'almost', - 'also', - 'am', - 'among', - 'an', - 'and', - 'any', - 'are', - 'as', - 'at', - 'be', - 'because', - 'been', - 'but', - 'by', - 'can', - 'cannot', - 'could', - 'dear', - 'did', - 'do', - 'does', - 'either', - 'else', - 'ever', - 'every', - 'for', - 'from', - 'get', - 'got', - 'had', - 'has', - 'have', - 'he', - 'her', - 'hers', - 'him', - 'his', - 'how', - 'however', - 'i', - 'if', - 'in', - 'into', - 'is', - 'it', - 'its', - 'just', - 'least', - 'let', - 'like', - 'likely', - 'may', - 'me', - 'might', - 'most', - 'must', - 'my', - 'neither', - 'no', - 'nor', - 'not', - 'of', - 'off', - 'often', - 'on', - 'only', - 'or', - 'other', - 'our', - 'own', - 'rather', - 'said', - 'say', - 'says', - 'she', - 'should', - 'since', - 'so', - 'some', - 'than', - 'that', - 'the', - 'their', - 'them', - 'then', - 'there', - 'these', - 'they', - 'this', - 'tis', - 'to', - 'too', - 'twas', - 'us', - 'wants', - 'was', - 'we', - 'were', - 'what', - 'when', - 'where', - 'which', - 'while', - 'who', - 'whom', - 'why', - 'will', - 'with', - 'would', - 'yet', - 'you', - 'your' -]) - -lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter') -/*! - * lunr.trimmer - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * lunr.trimmer is a pipeline function for trimming non word - * characters from the beginning and end of tokens before they - * enter the index. - * - * This implementation may not work correctly for non latin - * characters and should either be removed or adapted for use - * with languages with non-latin characters. - * - * @static - * @implements {lunr.PipelineFunction} - * @param {lunr.Token} token The token to pass through the filter - * @returns {lunr.Token} - * @see lunr.Pipeline - */ -lunr.trimmer = function (token) { - return token.update(function (s) { - return s.replace(/^\W+/, '').replace(/\W+$/, '') - }) -} - -lunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer') -/*! - * lunr.TokenSet - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * A token set is used to store the unique list of all tokens - * within an index. Token sets are also used to represent an - * incoming query to the index, this query token set and index - * token set are then intersected to find which tokens to look - * up in the inverted index. - * - * A token set can hold multiple tokens, as in the case of the - * index token set, or it can hold a single token as in the - * case of a simple query token set. - * - * Additionally token sets are used to perform wildcard matching. - * Leading, contained and trailing wildcards are supported, and - * from this edit distance matching can also be provided. - * - * Token sets are implemented as a minimal finite state automata, - * where both common prefixes and suffixes are shared between tokens. - * This helps to reduce the space used for storing the token set. - * - * @constructor - */ -lunr.TokenSet = function () { - this.final = false - this.edges = {} - this.id = lunr.TokenSet._nextId - lunr.TokenSet._nextId += 1 -} - -/** - * Keeps track of the next, auto increment, identifier to assign - * to a new tokenSet. - * - * TokenSets require a unique identifier to be correctly minimised. - * - * @private - */ -lunr.TokenSet._nextId = 1 - -/** - * Creates a TokenSet instance from the given sorted array of words. - * - * @param {String[]} arr - A sorted array of strings to create the set from. - * @returns {lunr.TokenSet} - * @throws Will throw an error if the input array is not sorted. - */ -lunr.TokenSet.fromArray = function (arr) { - var builder = new lunr.TokenSet.Builder - - for (var i = 0, len = arr.length; i < len; i++) { - builder.insert(arr[i]) - } - - builder.finish() - return builder.root -} - -/** - * Creates a token set from a query clause. - * - * @private - * @param {Object} clause - A single clause from lunr.Query. - * @param {string} clause.term - The query clause term. - * @param {number} [clause.editDistance] - The optional edit distance for the term. - * @returns {lunr.TokenSet} - */ -lunr.TokenSet.fromClause = function (clause) { - if ('editDistance' in clause) { - return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance) - } else { - return lunr.TokenSet.fromString(clause.term) - } -} - -/** - * Creates a token set representing a single string with a specified - * edit distance. - * - * Insertions, deletions, substitutions and transpositions are each - * treated as an edit distance of 1. - * - * Increasing the allowed edit distance will have a dramatic impact - * on the performance of both creating and intersecting these TokenSets. - * It is advised to keep the edit distance less than 3. - * - * @param {string} str - The string to create the token set from. - * @param {number} editDistance - The allowed edit distance to match. - * @returns {lunr.Vector} - */ -lunr.TokenSet.fromFuzzyString = function (str, editDistance) { - var root = new lunr.TokenSet - - var stack = [{ - node: root, - editsRemaining: editDistance, - str: str - }] - - while (stack.length) { - var frame = stack.pop() - - // no edit - if (frame.str.length > 0) { - var char = frame.str.charAt(0), - noEditNode - - if (char in frame.node.edges) { - noEditNode = frame.node.edges[char] - } else { - noEditNode = new lunr.TokenSet - frame.node.edges[char] = noEditNode - } - - if (frame.str.length == 1) { - noEditNode.final = true - } - - stack.push({ - node: noEditNode, - editsRemaining: frame.editsRemaining, - str: frame.str.slice(1) - }) - } - - if (frame.editsRemaining == 0) { - continue - } - - // insertion - if ("*" in frame.node.edges) { - var insertionNode = frame.node.edges["*"] - } else { - var insertionNode = new lunr.TokenSet - frame.node.edges["*"] = insertionNode - } - - if (frame.str.length == 0) { - insertionNode.final = true - } - - stack.push({ - node: insertionNode, - editsRemaining: frame.editsRemaining - 1, - str: frame.str - }) - - // deletion - // can only do a deletion if we have enough edits remaining - // and if there are characters left to delete in the string - if (frame.str.length > 1) { - stack.push({ - node: frame.node, - editsRemaining: frame.editsRemaining - 1, - str: frame.str.slice(1) - }) - } - - // deletion - // just removing the last character from the str - if (frame.str.length == 1) { - frame.node.final = true - } - - // substitution - // can only do a substitution if we have enough edits remaining - // and if there are characters left to substitute - if (frame.str.length >= 1) { - if ("*" in frame.node.edges) { - var substitutionNode = frame.node.edges["*"] - } else { - var substitutionNode = new lunr.TokenSet - frame.node.edges["*"] = substitutionNode - } - - if (frame.str.length == 1) { - substitutionNode.final = true - } - - stack.push({ - node: substitutionNode, - editsRemaining: frame.editsRemaining - 1, - str: frame.str.slice(1) - }) - } - - // transposition - // can only do a transposition if there are edits remaining - // and there are enough characters to transpose - if (frame.str.length > 1) { - var charA = frame.str.charAt(0), - charB = frame.str.charAt(1), - transposeNode - - if (charB in frame.node.edges) { - transposeNode = frame.node.edges[charB] - } else { - transposeNode = new lunr.TokenSet - frame.node.edges[charB] = transposeNode - } - - if (frame.str.length == 1) { - transposeNode.final = true - } - - stack.push({ - node: transposeNode, - editsRemaining: frame.editsRemaining - 1, - str: charA + frame.str.slice(2) - }) - } - } - - return root -} - -/** - * Creates a TokenSet from a string. - * - * The string may contain one or more wildcard characters (*) - * that will allow wildcard matching when intersecting with - * another TokenSet. - * - * @param {string} str - The string to create a TokenSet from. - * @returns {lunr.TokenSet} - */ -lunr.TokenSet.fromString = function (str) { - var node = new lunr.TokenSet, - root = node - - /* - * Iterates through all characters within the passed string - * appending a node for each character. - * - * When a wildcard character is found then a self - * referencing edge is introduced to continually match - * any number of any characters. - */ - for (var i = 0, len = str.length; i < len; i++) { - var char = str[i], - final = (i == len - 1) - - if (char == "*") { - node.edges[char] = node - node.final = final - - } else { - var next = new lunr.TokenSet - next.final = final - - node.edges[char] = next - node = next - } - } - - return root -} - -/** - * Converts this TokenSet into an array of strings - * contained within the TokenSet. - * - * This is not intended to be used on a TokenSet that - * contains wildcards, in these cases the results are - * undefined and are likely to cause an infinite loop. - * - * @returns {string[]} - */ -lunr.TokenSet.prototype.toArray = function () { - var words = [] - - var stack = [{ - prefix: "", - node: this - }] - - while (stack.length) { - var frame = stack.pop(), - edges = Object.keys(frame.node.edges), - len = edges.length - - if (frame.node.final) { - /* In Safari, at this point the prefix is sometimes corrupted, see: - * https://github.com/olivernn/lunr.js/issues/279 Calling any - * String.prototype method forces Safari to "cast" this string to what - * it's supposed to be, fixing the bug. */ - frame.prefix.charAt(0) - words.push(frame.prefix) - } - - for (var i = 0; i < len; i++) { - var edge = edges[i] - - stack.push({ - prefix: frame.prefix.concat(edge), - node: frame.node.edges[edge] - }) - } - } - - return words -} - -/** - * Generates a string representation of a TokenSet. - * - * This is intended to allow TokenSets to be used as keys - * in objects, largely to aid the construction and minimisation - * of a TokenSet. As such it is not designed to be a human - * friendly representation of the TokenSet. - * - * @returns {string} - */ -lunr.TokenSet.prototype.toString = function () { - // NOTE: Using Object.keys here as this.edges is very likely - // to enter 'hash-mode' with many keys being added - // - // avoiding a for-in loop here as it leads to the function - // being de-optimised (at least in V8). From some simple - // benchmarks the performance is comparable, but allowing - // V8 to optimize may mean easy performance wins in the future. - - if (this._str) { - return this._str - } - - var str = this.final ? '1' : '0', - labels = Object.keys(this.edges).sort(), - len = labels.length - - for (var i = 0; i < len; i++) { - var label = labels[i], - node = this.edges[label] - - str = str + label + node.id - } - - return str -} - -/** - * Returns a new TokenSet that is the intersection of - * this TokenSet and the passed TokenSet. - * - * This intersection will take into account any wildcards - * contained within the TokenSet. - * - * @param {lunr.TokenSet} b - An other TokenSet to intersect with. - * @returns {lunr.TokenSet} - */ -lunr.TokenSet.prototype.intersect = function (b) { - var output = new lunr.TokenSet, - frame = undefined - - var stack = [{ - qNode: b, - output: output, - node: this - }] - - while (stack.length) { - frame = stack.pop() - - // NOTE: As with the #toString method, we are using - // Object.keys and a for loop instead of a for-in loop - // as both of these objects enter 'hash' mode, causing - // the function to be de-optimised in V8 - var qEdges = Object.keys(frame.qNode.edges), - qLen = qEdges.length, - nEdges = Object.keys(frame.node.edges), - nLen = nEdges.length - - for (var q = 0; q < qLen; q++) { - var qEdge = qEdges[q] - - for (var n = 0; n < nLen; n++) { - var nEdge = nEdges[n] - - if (nEdge == qEdge || qEdge == '*') { - var node = frame.node.edges[nEdge], - qNode = frame.qNode.edges[qEdge], - final = node.final && qNode.final, - next = undefined - - if (nEdge in frame.output.edges) { - // an edge already exists for this character - // no need to create a new node, just set the finality - // bit unless this node is already final - next = frame.output.edges[nEdge] - next.final = next.final || final - - } else { - // no edge exists yet, must create one - // set the finality bit and insert it - // into the output - next = new lunr.TokenSet - next.final = final - frame.output.edges[nEdge] = next - } - - stack.push({ - qNode: qNode, - output: next, - node: node - }) - } - } - } - } - - return output -} -lunr.TokenSet.Builder = function () { - this.previousWord = "" - this.root = new lunr.TokenSet - this.uncheckedNodes = [] - this.minimizedNodes = {} -} - -lunr.TokenSet.Builder.prototype.insert = function (word) { - var node, - commonPrefix = 0 - - if (word < this.previousWord) { - throw new Error ("Out of order word insertion") - } - - for (var i = 0; i < word.length && i < this.previousWord.length; i++) { - if (word[i] != this.previousWord[i]) break - commonPrefix++ - } - - this.minimize(commonPrefix) - - if (this.uncheckedNodes.length == 0) { - node = this.root - } else { - node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child - } - - for (var i = commonPrefix; i < word.length; i++) { - var nextNode = new lunr.TokenSet, - char = word[i] - - node.edges[char] = nextNode - - this.uncheckedNodes.push({ - parent: node, - char: char, - child: nextNode - }) - - node = nextNode - } - - node.final = true - this.previousWord = word -} - -lunr.TokenSet.Builder.prototype.finish = function () { - this.minimize(0) -} - -lunr.TokenSet.Builder.prototype.minimize = function (downTo) { - for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) { - var node = this.uncheckedNodes[i], - childKey = node.child.toString() - - if (childKey in this.minimizedNodes) { - node.parent.edges[node.char] = this.minimizedNodes[childKey] - } else { - // Cache the key for this node since - // we know it can't change anymore - node.child._str = childKey - - this.minimizedNodes[childKey] = node.child - } - - this.uncheckedNodes.pop() - } -} -/*! - * lunr.Index - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * An index contains the built index of all documents and provides a query interface - * to the index. - * - * Usually instances of lunr.Index will not be created using this constructor, instead - * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be - * used to load previously built and serialized indexes. - * - * @constructor - * @param {Object} attrs - The attributes of the built search index. - * @param {Object} attrs.invertedIndex - An index of term/field to document reference. - * @param {Object} attrs.fieldVectors - Field vectors - * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens. - * @param {string[]} attrs.fields - The names of indexed document fields. - * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms. - */ -lunr.Index = function (attrs) { - this.invertedIndex = attrs.invertedIndex - this.fieldVectors = attrs.fieldVectors - this.tokenSet = attrs.tokenSet - this.fields = attrs.fields - this.pipeline = attrs.pipeline -} - -/** - * A result contains details of a document matching a search query. - * @typedef {Object} lunr.Index~Result - * @property {string} ref - The reference of the document this result represents. - * @property {number} score - A number between 0 and 1 representing how similar this document is to the query. - * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match. - */ - -/** - * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple - * query language which itself is parsed into an instance of lunr.Query. - * - * For programmatically building queries it is advised to directly use lunr.Query, the query language - * is best used for human entered text rather than program generated text. - * - * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported - * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello' - * or 'world', though those that contain both will rank higher in the results. - * - * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can - * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding - * wildcards will increase the number of documents that will be found but can also have a negative - * impact on query performance, especially with wildcards at the beginning of a term. - * - * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term - * hello in the title field will match this query. Using a field not present in the index will lead - * to an error being thrown. - * - * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term - * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported - * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2. - * Avoid large values for edit distance to improve query performance. - * - * Each term also supports a presence modifier. By default a term's presence in document is optional, however - * this can be changed to either required or prohibited. For a term's presence to be required in a document the - * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and - * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not - * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'. - * - * To escape special characters the backslash character '\' can be used, this allows searches to include - * characters that would normally be considered modifiers, e.g. `foo\~2` will search for a term "foo~2" instead - * of attempting to apply a boost of 2 to the search term "foo". - * - * @typedef {string} lunr.Index~QueryString - * @example Simple single term query - * hello - * @example Multiple term query - * hello world - * @example term scoped to a field - * title:hello - * @example term with a boost of 10 - * hello^10 - * @example term with an edit distance of 2 - * hello~2 - * @example terms with presence modifiers - * -foo +bar baz - */ - -/** - * Performs a search against the index using lunr query syntax. - * - * Results will be returned sorted by their score, the most relevant results - * will be returned first. For details on how the score is calculated, please see - * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}. - * - * For more programmatic querying use lunr.Index#query. - * - * @param {lunr.Index~QueryString} queryString - A string containing a lunr query. - * @throws {lunr.QueryParseError} If the passed query string cannot be parsed. - * @returns {lunr.Index~Result[]} - */ -lunr.Index.prototype.search = function (queryString) { - return this.query(function (query) { - var parser = new lunr.QueryParser(queryString, query) - parser.parse() - }) -} - -/** - * A query builder callback provides a query object to be used to express - * the query to perform on the index. - * - * @callback lunr.Index~queryBuilder - * @param {lunr.Query} query - The query object to build up. - * @this lunr.Query - */ - -/** - * Performs a query against the index using the yielded lunr.Query object. - * - * If performing programmatic queries against the index, this method is preferred - * over lunr.Index#search so as to avoid the additional query parsing overhead. - * - * A query object is yielded to the supplied function which should be used to - * express the query to be run against the index. - * - * Note that although this function takes a callback parameter it is _not_ an - * asynchronous operation, the callback is just yielded a query object to be - * customized. - * - * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query. - * @returns {lunr.Index~Result[]} - */ -lunr.Index.prototype.query = function (fn) { - // for each query clause - // * process terms - // * expand terms from token set - // * find matching documents and metadata - // * get document vectors - // * score documents - - var query = new lunr.Query(this.fields), - matchingFields = Object.create(null), - queryVectors = Object.create(null), - termFieldCache = Object.create(null), - requiredMatches = Object.create(null), - prohibitedMatches = Object.create(null) - - /* - * To support field level boosts a query vector is created per - * field. An empty vector is eagerly created to support negated - * queries. - */ - for (var i = 0; i < this.fields.length; i++) { - queryVectors[this.fields[i]] = new lunr.Vector - } - - fn.call(query, query) - - for (var i = 0; i < query.clauses.length; i++) { - /* - * Unless the pipeline has been disabled for this term, which is - * the case for terms with wildcards, we need to pass the clause - * term through the search pipeline. A pipeline returns an array - * of processed terms. Pipeline functions may expand the passed - * term, which means we may end up performing multiple index lookups - * for a single query term. - */ - var clause = query.clauses[i], - terms = null, - clauseMatches = lunr.Set.complete - - if (clause.usePipeline) { - terms = this.pipeline.runString(clause.term, { - fields: clause.fields - }) - } else { - terms = [clause.term] - } - - for (var m = 0; m < terms.length; m++) { - var term = terms[m] - - /* - * Each term returned from the pipeline needs to use the same query - * clause object, e.g. the same boost and or edit distance. The - * simplest way to do this is to re-use the clause object but mutate - * its term property. - */ - clause.term = term - - /* - * From the term in the clause we create a token set which will then - * be used to intersect the indexes token set to get a list of terms - * to lookup in the inverted index - */ - var termTokenSet = lunr.TokenSet.fromClause(clause), - expandedTerms = this.tokenSet.intersect(termTokenSet).toArray() - - /* - * If a term marked as required does not exist in the tokenSet it is - * impossible for the search to return any matches. We set all the field - * scoped required matches set to empty and stop examining any further - * clauses. - */ - if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) { - for (var k = 0; k < clause.fields.length; k++) { - var field = clause.fields[k] - requiredMatches[field] = lunr.Set.empty - } - - break - } - - for (var j = 0; j < expandedTerms.length; j++) { - /* - * For each term get the posting and termIndex, this is required for - * building the query vector. - */ - var expandedTerm = expandedTerms[j], - posting = this.invertedIndex[expandedTerm], - termIndex = posting._index - - for (var k = 0; k < clause.fields.length; k++) { - /* - * For each field that this query term is scoped by (by default - * all fields are in scope) we need to get all the document refs - * that have this term in that field. - * - * The posting is the entry in the invertedIndex for the matching - * term from above. - */ - var field = clause.fields[k], - fieldPosting = posting[field], - matchingDocumentRefs = Object.keys(fieldPosting), - termField = expandedTerm + "/" + field, - matchingDocumentsSet = new lunr.Set(matchingDocumentRefs) - - /* - * if the presence of this term is required ensure that the matching - * documents are added to the set of required matches for this clause. - * - */ - if (clause.presence == lunr.Query.presence.REQUIRED) { - clauseMatches = clauseMatches.union(matchingDocumentsSet) - - if (requiredMatches[field] === undefined) { - requiredMatches[field] = lunr.Set.complete - } - } - - /* - * if the presence of this term is prohibited ensure that the matching - * documents are added to the set of prohibited matches for this field, - * creating that set if it does not yet exist. - */ - if (clause.presence == lunr.Query.presence.PROHIBITED) { - if (prohibitedMatches[field] === undefined) { - prohibitedMatches[field] = lunr.Set.empty - } - - prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet) - - /* - * Prohibited matches should not be part of the query vector used for - * similarity scoring and no metadata should be extracted so we continue - * to the next field - */ - continue - } - - /* - * The query field vector is populated using the termIndex found for - * the term and a unit value with the appropriate boost applied. - * Using upsert because there could already be an entry in the vector - * for the term we are working with. In that case we just add the scores - * together. - */ - queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b }) - - /** - * If we've already seen this term, field combo then we've already collected - * the matching documents and metadata, no need to go through all that again - */ - if (termFieldCache[termField]) { - continue - } - - for (var l = 0; l < matchingDocumentRefs.length; l++) { - /* - * All metadata for this term/field/document triple - * are then extracted and collected into an instance - * of lunr.MatchData ready to be returned in the query - * results - */ - var matchingDocumentRef = matchingDocumentRefs[l], - matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field), - metadata = fieldPosting[matchingDocumentRef], - fieldMatch - - if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) { - matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata) - } else { - fieldMatch.add(expandedTerm, field, metadata) - } - - } - - termFieldCache[termField] = true - } - } - } - - /** - * If the presence was required we need to update the requiredMatches field sets. - * We do this after all fields for the term have collected their matches because - * the clause terms presence is required in _any_ of the fields not _all_ of the - * fields. - */ - if (clause.presence === lunr.Query.presence.REQUIRED) { - for (var k = 0; k < clause.fields.length; k++) { - var field = clause.fields[k] - requiredMatches[field] = requiredMatches[field].intersect(clauseMatches) - } - } - } - - /** - * Need to combine the field scoped required and prohibited - * matching documents into a global set of required and prohibited - * matches - */ - var allRequiredMatches = lunr.Set.complete, - allProhibitedMatches = lunr.Set.empty - - for (var i = 0; i < this.fields.length; i++) { - var field = this.fields[i] - - if (requiredMatches[field]) { - allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field]) - } - - if (prohibitedMatches[field]) { - allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field]) - } - } - - var matchingFieldRefs = Object.keys(matchingFields), - results = [], - matches = Object.create(null) - - /* - * If the query is negated (contains only prohibited terms) - * we need to get _all_ fieldRefs currently existing in the - * index. This is only done when we know that the query is - * entirely prohibited terms to avoid any cost of getting all - * fieldRefs unnecessarily. - * - * Additionally, blank MatchData must be created to correctly - * populate the results. - */ - if (query.isNegated()) { - matchingFieldRefs = Object.keys(this.fieldVectors) - - for (var i = 0; i < matchingFieldRefs.length; i++) { - var matchingFieldRef = matchingFieldRefs[i] - var fieldRef = lunr.FieldRef.fromString(matchingFieldRef) - matchingFields[matchingFieldRef] = new lunr.MatchData - } - } - - for (var i = 0; i < matchingFieldRefs.length; i++) { - /* - * Currently we have document fields that match the query, but we - * need to return documents. The matchData and scores are combined - * from multiple fields belonging to the same document. - * - * Scores are calculated by field, using the query vectors created - * above, and combined into a final document score using addition. - */ - var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]), - docRef = fieldRef.docRef - - if (!allRequiredMatches.contains(docRef)) { - continue - } - - if (allProhibitedMatches.contains(docRef)) { - continue - } - - var fieldVector = this.fieldVectors[fieldRef], - score = queryVectors[fieldRef.fieldName].similarity(fieldVector), - docMatch - - if ((docMatch = matches[docRef]) !== undefined) { - docMatch.score += score - docMatch.matchData.combine(matchingFields[fieldRef]) - } else { - var match = { - ref: docRef, - score: score, - matchData: matchingFields[fieldRef] - } - matches[docRef] = match - results.push(match) - } - } - - /* - * Sort the results objects by score, highest first. - */ - return results.sort(function (a, b) { - return b.score - a.score - }) -} - -/** - * Prepares the index for JSON serialization. - * - * The schema for this JSON blob will be described in a - * separate JSON schema file. - * - * @returns {Object} - */ -lunr.Index.prototype.toJSON = function () { - var invertedIndex = Object.keys(this.invertedIndex) - .sort() - .map(function (term) { - return [term, this.invertedIndex[term]] - }, this) - - var fieldVectors = Object.keys(this.fieldVectors) - .map(function (ref) { - return [ref, this.fieldVectors[ref].toJSON()] - }, this) - - return { - version: lunr.version, - fields: this.fields, - fieldVectors: fieldVectors, - invertedIndex: invertedIndex, - pipeline: this.pipeline.toJSON() - } -} - -/** - * Loads a previously serialized lunr.Index - * - * @param {Object} serializedIndex - A previously serialized lunr.Index - * @returns {lunr.Index} - */ -lunr.Index.load = function (serializedIndex) { - var attrs = {}, - fieldVectors = {}, - serializedVectors = serializedIndex.fieldVectors, - invertedIndex = Object.create(null), - serializedInvertedIndex = serializedIndex.invertedIndex, - tokenSetBuilder = new lunr.TokenSet.Builder, - pipeline = lunr.Pipeline.load(serializedIndex.pipeline) - - if (serializedIndex.version != lunr.version) { - lunr.utils.warn("Version mismatch when loading serialised index. Current version of lunr '" + lunr.version + "' does not match serialized index '" + serializedIndex.version + "'") - } - - for (var i = 0; i < serializedVectors.length; i++) { - var tuple = serializedVectors[i], - ref = tuple[0], - elements = tuple[1] - - fieldVectors[ref] = new lunr.Vector(elements) - } - - for (var i = 0; i < serializedInvertedIndex.length; i++) { - var tuple = serializedInvertedIndex[i], - term = tuple[0], - posting = tuple[1] - - tokenSetBuilder.insert(term) - invertedIndex[term] = posting - } - - tokenSetBuilder.finish() - - attrs.fields = serializedIndex.fields - - attrs.fieldVectors = fieldVectors - attrs.invertedIndex = invertedIndex - attrs.tokenSet = tokenSetBuilder.root - attrs.pipeline = pipeline - - return new lunr.Index(attrs) -} -/*! - * lunr.Builder - * Copyright (C) 2019 Oliver Nightingale - */ - -/** - * lunr.Builder performs indexing on a set of documents and - * returns instances of lunr.Index ready for querying. - * - * All configuration of the index is done via the builder, the - * fields to index, the document reference, the text processing - * pipeline and document scoring parameters are all set on the - * builder before indexing. - * - * @constructor - * @property {string} _ref - Internal reference to the document reference field. - * @property {string[]} _fields - Internal reference to the document fields to index. - * @property {object} invertedIndex - The inverted index maps terms to document fields. - * @property {object} documentTermFrequencies - Keeps track of document term frequencies. - * @property {object} documentLengths - Keeps track of the length of documents added to the index. - * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing. - * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing. - * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index. - * @property {number} documentCount - Keeps track of the total number of documents indexed. - * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75. - * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2. - * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space. - * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index. - */ -lunr.Builder = function () { - this._ref = "id" - this._fields = Object.create(null) - this._documents = Object.create(null) - this.invertedIndex = Object.create(null) - this.fieldTermFrequencies = {} - this.fieldLengths = {} - this.tokenizer = lunr.tokenizer - this.pipeline = new lunr.Pipeline - this.searchPipeline = new lunr.Pipeline - this.documentCount = 0 - this._b = 0.75 - this._k1 = 1.2 - this.termIndex = 0 - this.metadataWhitelist = [] -} - -/** - * Sets the document field used as the document reference. Every document must have this field. - * The type of this field in the document should be a string, if it is not a string it will be - * coerced into a string by calling toString. - * - * The default ref is 'id'. - * - * The ref should _not_ be changed during indexing, it should be set before any documents are - * added to the index. Changing it during indexing can lead to inconsistent results. - * - * @param {string} ref - The name of the reference field in the document. - */ -lunr.Builder.prototype.ref = function (ref) { - this._ref = ref -} - -/** - * A function that is used to extract a field from a document. - * - * Lunr expects a field to be at the top level of a document, if however the field - * is deeply nested within a document an extractor function can be used to extract - * the right field for indexing. - * - * @callback fieldExtractor - * @param {object} doc - The document being added to the index. - * @returns {?(string|object|object[])} obj - The object that will be indexed for this field. - * @example Extracting a nested field - * function (doc) { return doc.nested.field } - */ - -/** - * Adds a field to the list of document fields that will be indexed. Every document being - * indexed should have this field. Null values for this field in indexed documents will - * not cause errors but will limit the chance of that document being retrieved by searches. - * - * All fields should be added before adding documents to the index. Adding fields after - * a document has been indexed will have no effect on already indexed documents. - * - * Fields can be boosted at build time. This allows terms within that field to have more - * importance when ranking search results. Use a field boost to specify that matches within - * one field are more important than other fields. - * - * @param {string} fieldName - The name of a field to index in all documents. - * @param {object} attributes - Optional attributes associated with this field. - * @param {number} [attributes.boost=1] - Boost applied to all terms within this field. - * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document. - * @throws {RangeError} fieldName cannot contain unsupported characters '/' - */ -lunr.Builder.prototype.field = function (fieldName, attributes) { - if (/\//.test(fieldName)) { - throw new RangeError ("Field '" + fieldName + "' contains illegal character '/'") - } - - this._fields[fieldName] = attributes || {} -} - -/** - * A parameter to tune the amount of field length normalisation that is applied when - * calculating relevance scores. A value of 0 will completely disable any normalisation - * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b - * will be clamped to the range 0 - 1. - * - * @param {number} number - The value to set for this tuning parameter. - */ -lunr.Builder.prototype.b = function (number) { - if (number < 0) { - this._b = 0 - } else if (number > 1) { - this._b = 1 - } else { - this._b = number - } -} - -/** - * A parameter that controls the speed at which a rise in term frequency results in term - * frequency saturation. The default value is 1.2. Setting this to a higher value will give - * slower saturation levels, a lower value will result in quicker saturation. - * - * @param {number} number - The value to set for this tuning parameter. - */ -lunr.Builder.prototype.k1 = function (number) { - this._k1 = number -} - -/** - * Adds a document to the index. - * - * Before adding fields to the index the index should have been fully setup, with the document - * ref and all fields to index already having been specified. - * - * The document must have a field name as specified by the ref (by default this is 'id') and - * it should have all fields defined for indexing, though null or undefined values will not - * cause errors. - * - * Entire documents can be boosted at build time. Applying a boost to a document indicates that - * this document should rank higher in search results than other documents. - * - * @param {object} doc - The document to add to the index. - * @param {object} attributes - Optional attributes associated with this document. - * @param {number} [attributes.boost=1] - Boost applied to all terms within this document. - */ -lunr.Builder.prototype.add = function (doc, attributes) { - var docRef = doc[this._ref], - fields = Object.keys(this._fields) - - this._documents[docRef] = attributes || {} - this.documentCount += 1 - - for (var i = 0; i < fields.length; i++) { - var fieldName = fields[i], - extractor = this._fields[fieldName].extractor, - field = extractor ? extractor(doc) : doc[fieldName], - tokens = this.tokenizer(field, { - fields: [fieldName] - }), - terms = this.pipeline.run(tokens), - fieldRef = new lunr.FieldRef (docRef, fieldName), - fieldTerms = Object.create(null) - - this.fieldTermFrequencies[fieldRef] = fieldTerms - this.fieldLengths[fieldRef] = 0 - - // store the length of this field for this document - this.fieldLengths[fieldRef] += terms.length - - // calculate term frequencies for this field - for (var j = 0; j < terms.length; j++) { - var term = terms[j] - - if (fieldTerms[term] == undefined) { - fieldTerms[term] = 0 - } - - fieldTerms[term] += 1 - - // add to inverted index - // create an initial posting if one doesn't exist - if (this.invertedIndex[term] == undefined) { - var posting = Object.create(null) - posting["_index"] = this.termIndex - this.termIndex += 1 - - for (var k = 0; k < fields.length; k++) { - posting[fields[k]] = Object.create(null) - } - - this.invertedIndex[term] = posting - } - - // add an entry for this term/fieldName/docRef to the invertedIndex - if (this.invertedIndex[term][fieldName][docRef] == undefined) { - this.invertedIndex[term][fieldName][docRef] = Object.create(null) - } - - // store all whitelisted metadata about this token in the - // inverted index - for (var l = 0; l < this.metadataWhitelist.length; l++) { - var metadataKey = this.metadataWhitelist[l], - metadata = term.metadata[metadataKey] - - if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) { - this.invertedIndex[term][fieldName][docRef][metadataKey] = [] - } - - this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata) - } - } - - } -} - -/** - * Calculates the average document length for this index - * - * @private - */ -lunr.Builder.prototype.calculateAverageFieldLengths = function () { - - var fieldRefs = Object.keys(this.fieldLengths), - numberOfFields = fieldRefs.length, - accumulator = {}, - documentsWithField = {} - - for (var i = 0; i < numberOfFields; i++) { - var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), - field = fieldRef.fieldName - - documentsWithField[field] || (documentsWithField[field] = 0) - documentsWithField[field] += 1 - - accumulator[field] || (accumulator[field] = 0) - accumulator[field] += this.fieldLengths[fieldRef] - } - - var fields = Object.keys(this._fields) - - for (var i = 0; i < fields.length; i++) { - var fieldName = fields[i] - accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName] - } - - this.averageFieldLength = accumulator -} - -/** - * Builds a vector space model of every document using lunr.Vector - * - * @private - */ -lunr.Builder.prototype.createFieldVectors = function () { - var fieldVectors = {}, - fieldRefs = Object.keys(this.fieldTermFrequencies), - fieldRefsLength = fieldRefs.length, - termIdfCache = Object.create(null) - - for (var i = 0; i < fieldRefsLength; i++) { - var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]), - fieldName = fieldRef.fieldName, - fieldLength = this.fieldLengths[fieldRef], - fieldVector = new lunr.Vector, - termFrequencies = this.fieldTermFrequencies[fieldRef], - terms = Object.keys(termFrequencies), - termsLength = terms.length - - - var fieldBoost = this._fields[fieldName].boost || 1, - docBoost = this._documents[fieldRef.docRef].boost || 1 - - for (var j = 0; j < termsLength; j++) { - var term = terms[j], - tf = termFrequencies[term], - termIndex = this.invertedIndex[term]._index, - idf, score, scoreWithPrecision - - if (termIdfCache[term] === undefined) { - idf = lunr.idf(this.invertedIndex[term], this.documentCount) - termIdfCache[term] = idf - } else { - idf = termIdfCache[term] - } - - score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf) - score *= fieldBoost - score *= docBoost - scoreWithPrecision = Math.round(score * 1000) / 1000 - // Converts 1.23456789 to 1.234. - // Reducing the precision so that the vectors take up less - // space when serialised. Doing it now so that they behave - // the same before and after serialisation. Also, this is - // the fastest approach to reducing a number's precision in - // JavaScript. - - fieldVector.insert(termIndex, scoreWithPrecision) - } - - fieldVectors[fieldRef] = fieldVector - } - - this.fieldVectors = fieldVectors -} - -/** - * Creates a token set of all tokens in the index using lunr.TokenSet - * - * @private - */ -lunr.Builder.prototype.createTokenSet = function () { - this.tokenSet = lunr.TokenSet.fromArray( - Object.keys(this.invertedIndex).sort() - ) -} - -/** - * Builds the index, creating an instance of lunr.Index. - * - * This completes the indexing process and should only be called - * once all documents have been added to the index. - * - * @returns {lunr.Index} - */ -lunr.Builder.prototype.build = function () { - this.calculateAverageFieldLengths() - this.createFieldVectors() - this.createTokenSet() - - return new lunr.Index({ - invertedIndex: this.invertedIndex, - fieldVectors: this.fieldVectors, - tokenSet: this.tokenSet, - fields: Object.keys(this._fields), - pipeline: this.searchPipeline - }) -} - -/** - * Applies a plugin to the index builder. - * - * A plugin is a function that is called with the index builder as its context. - * Plugins can be used to customise or extend the behaviour of the index - * in some way. A plugin is just a function, that encapsulated the custom - * behaviour that should be applied when building the index. - * - * The plugin function will be called with the index builder as its argument, additional - * arguments can also be passed when calling use. The function will be called - * with the index builder as its context. - * - * @param {Function} plugin The plugin to apply. - */ -lunr.Builder.prototype.use = function (fn) { - var args = Array.prototype.slice.call(arguments, 1) - args.unshift(this) - fn.apply(this, args) -} -/** - * Contains and collects metadata about a matching document. - * A single instance of lunr.MatchData is returned as part of every - * lunr.Index~Result. - * - * @constructor - * @param {string} term - The term this match data is associated with - * @param {string} field - The field in which the term was found - * @param {object} metadata - The metadata recorded about this term in this field - * @property {object} metadata - A cloned collection of metadata associated with this document. - * @see {@link lunr.Index~Result} - */ -lunr.MatchData = function (term, field, metadata) { - var clonedMetadata = Object.create(null), - metadataKeys = Object.keys(metadata || {}) - - // Cloning the metadata to prevent the original - // being mutated during match data combination. - // Metadata is kept in an array within the inverted - // index so cloning the data can be done with - // Array#slice - for (var i = 0; i < metadataKeys.length; i++) { - var key = metadataKeys[i] - clonedMetadata[key] = metadata[key].slice() - } - - this.metadata = Object.create(null) - - if (term !== undefined) { - this.metadata[term] = Object.create(null) - this.metadata[term][field] = clonedMetadata - } -} - -/** - * An instance of lunr.MatchData will be created for every term that matches a - * document. However only one instance is required in a lunr.Index~Result. This - * method combines metadata from another instance of lunr.MatchData with this - * objects metadata. - * - * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one. - * @see {@link lunr.Index~Result} - */ -lunr.MatchData.prototype.combine = function (otherMatchData) { - var terms = Object.keys(otherMatchData.metadata) - - for (var i = 0; i < terms.length; i++) { - var term = terms[i], - fields = Object.keys(otherMatchData.metadata[term]) - - if (this.metadata[term] == undefined) { - this.metadata[term] = Object.create(null) - } - - for (var j = 0; j < fields.length; j++) { - var field = fields[j], - keys = Object.keys(otherMatchData.metadata[term][field]) - - if (this.metadata[term][field] == undefined) { - this.metadata[term][field] = Object.create(null) - } - - for (var k = 0; k < keys.length; k++) { - var key = keys[k] - - if (this.metadata[term][field][key] == undefined) { - this.metadata[term][field][key] = otherMatchData.metadata[term][field][key] - } else { - this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key]) - } - - } - } - } -} - -/** - * Add metadata for a term/field pair to this instance of match data. - * - * @param {string} term - The term this match data is associated with - * @param {string} field - The field in which the term was found - * @param {object} metadata - The metadata recorded about this term in this field - */ -lunr.MatchData.prototype.add = function (term, field, metadata) { - if (!(term in this.metadata)) { - this.metadata[term] = Object.create(null) - this.metadata[term][field] = metadata - return - } - - if (!(field in this.metadata[term])) { - this.metadata[term][field] = metadata - return - } - - var metadataKeys = Object.keys(metadata) - - for (var i = 0; i < metadataKeys.length; i++) { - var key = metadataKeys[i] - - if (key in this.metadata[term][field]) { - this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key]) - } else { - this.metadata[term][field][key] = metadata[key] - } - } -} -/** - * A lunr.Query provides a programmatic way of defining queries to be performed - * against a {@link lunr.Index}. - * - * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method - * so the query object is pre-initialized with the right index fields. - * - * @constructor - * @property {lunr.Query~Clause[]} clauses - An array of query clauses. - * @property {string[]} allFields - An array of all available fields in a lunr.Index. - */ -lunr.Query = function (allFields) { - this.clauses = [] - this.allFields = allFields -} - -/** - * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause. - * - * This allows wildcards to be added to the beginning and end of a term without having to manually do any string - * concatenation. - * - * The wildcard constants can be bitwise combined to select both leading and trailing wildcards. - * - * @constant - * @default - * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour - * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists - * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists - * @see lunr.Query~Clause - * @see lunr.Query#clause - * @see lunr.Query#term - * @example query term with trailing wildcard - * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING }) - * @example query term with leading and trailing wildcard - * query.term('foo', { - * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING - * }) - */ - -lunr.Query.wildcard = new String ("*") -lunr.Query.wildcard.NONE = 0 -lunr.Query.wildcard.LEADING = 1 -lunr.Query.wildcard.TRAILING = 2 - -/** - * Constants for indicating what kind of presence a term must have in matching documents. - * - * @constant - * @enum {number} - * @see lunr.Query~Clause - * @see lunr.Query#clause - * @see lunr.Query#term - * @example query term with required presence - * query.term('foo', { presence: lunr.Query.presence.REQUIRED }) - */ -lunr.Query.presence = { - /** - * Term's presence in a document is optional, this is the default value. - */ - OPTIONAL: 1, - - /** - * Term's presence in a document is required, documents that do not contain - * this term will not be returned. - */ - REQUIRED: 2, - - /** - * Term's presence in a document is prohibited, documents that do contain - * this term will not be returned. - */ - PROHIBITED: 3 -} - -/** - * A single clause in a {@link lunr.Query} contains a term and details on how to - * match that term against a {@link lunr.Index}. - * - * @typedef {Object} lunr.Query~Clause - * @property {string[]} fields - The fields in an index this clause should be matched against. - * @property {number} [boost=1] - Any boost that should be applied when matching this clause. - * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be. - * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline. - * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended. - * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents. - */ - -/** - * Adds a {@link lunr.Query~Clause} to this query. - * - * Unless the clause contains the fields to be matched all fields will be matched. In addition - * a default boost of 1 is applied to the clause. - * - * @param {lunr.Query~Clause} clause - The clause to add to this query. - * @see lunr.Query~Clause - * @returns {lunr.Query} - */ -lunr.Query.prototype.clause = function (clause) { - if (!('fields' in clause)) { - clause.fields = this.allFields - } - - if (!('boost' in clause)) { - clause.boost = 1 - } - - if (!('usePipeline' in clause)) { - clause.usePipeline = true - } - - if (!('wildcard' in clause)) { - clause.wildcard = lunr.Query.wildcard.NONE - } - - if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) { - clause.term = "*" + clause.term - } - - if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) { - clause.term = "" + clause.term + "*" - } - - if (!('presence' in clause)) { - clause.presence = lunr.Query.presence.OPTIONAL - } - - this.clauses.push(clause) - - return this -} - -/** - * A negated query is one in which every clause has a presence of - * prohibited. These queries require some special processing to return - * the expected results. - * - * @returns boolean - */ -lunr.Query.prototype.isNegated = function () { - for (var i = 0; i < this.clauses.length; i++) { - if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) { - return false - } - } - - return true -} - -/** - * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause} - * to the list of clauses that make up this query. - * - * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion - * to a token or token-like string should be done before calling this method. - * - * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an - * array, each term in the array will share the same options. - * - * @param {object|object[]} term - The term(s) to add to the query. - * @param {object} [options] - Any additional properties to add to the query clause. - * @returns {lunr.Query} - * @see lunr.Query#clause - * @see lunr.Query~Clause - * @example adding a single term to a query - * query.term("foo") - * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard - * query.term("foo", { - * fields: ["title"], - * boost: 10, - * wildcard: lunr.Query.wildcard.TRAILING - * }) - * @example using lunr.tokenizer to convert a string to tokens before using them as terms - * query.term(lunr.tokenizer("foo bar")) - */ -lunr.Query.prototype.term = function (term, options) { - if (Array.isArray(term)) { - term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this) - return this - } - - var clause = options || {} - clause.term = term.toString() - - this.clause(clause) - - return this -} -lunr.QueryParseError = function (message, start, end) { - this.name = "QueryParseError" - this.message = message - this.start = start - this.end = end -} - -lunr.QueryParseError.prototype = new Error -lunr.QueryLexer = function (str) { - this.lexemes = [] - this.str = str - this.length = str.length - this.pos = 0 - this.start = 0 - this.escapeCharPositions = [] -} - -lunr.QueryLexer.prototype.run = function () { - var state = lunr.QueryLexer.lexText - - while (state) { - state = state(this) - } -} - -lunr.QueryLexer.prototype.sliceString = function () { - var subSlices = [], - sliceStart = this.start, - sliceEnd = this.pos - - for (var i = 0; i < this.escapeCharPositions.length; i++) { - sliceEnd = this.escapeCharPositions[i] - subSlices.push(this.str.slice(sliceStart, sliceEnd)) - sliceStart = sliceEnd + 1 - } - - subSlices.push(this.str.slice(sliceStart, this.pos)) - this.escapeCharPositions.length = 0 - - return subSlices.join('') -} - -lunr.QueryLexer.prototype.emit = function (type) { - this.lexemes.push({ - type: type, - str: this.sliceString(), - start: this.start, - end: this.pos - }) - - this.start = this.pos -} - -lunr.QueryLexer.prototype.escapeCharacter = function () { - this.escapeCharPositions.push(this.pos - 1) - this.pos += 1 -} - -lunr.QueryLexer.prototype.next = function () { - if (this.pos >= this.length) { - return lunr.QueryLexer.EOS - } - - var char = this.str.charAt(this.pos) - this.pos += 1 - return char -} - -lunr.QueryLexer.prototype.width = function () { - return this.pos - this.start -} - -lunr.QueryLexer.prototype.ignore = function () { - if (this.start == this.pos) { - this.pos += 1 - } - - this.start = this.pos -} - -lunr.QueryLexer.prototype.backup = function () { - this.pos -= 1 -} - -lunr.QueryLexer.prototype.acceptDigitRun = function () { - var char, charCode - - do { - char = this.next() - charCode = char.charCodeAt(0) - } while (charCode > 47 && charCode < 58) - - if (char != lunr.QueryLexer.EOS) { - this.backup() - } -} - -lunr.QueryLexer.prototype.more = function () { - return this.pos < this.length -} - -lunr.QueryLexer.EOS = 'EOS' -lunr.QueryLexer.FIELD = 'FIELD' -lunr.QueryLexer.TERM = 'TERM' -lunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE' -lunr.QueryLexer.BOOST = 'BOOST' -lunr.QueryLexer.PRESENCE = 'PRESENCE' - -lunr.QueryLexer.lexField = function (lexer) { - lexer.backup() - lexer.emit(lunr.QueryLexer.FIELD) - lexer.ignore() - return lunr.QueryLexer.lexText -} - -lunr.QueryLexer.lexTerm = function (lexer) { - if (lexer.width() > 1) { - lexer.backup() - lexer.emit(lunr.QueryLexer.TERM) - } - - lexer.ignore() - - if (lexer.more()) { - return lunr.QueryLexer.lexText - } -} - -lunr.QueryLexer.lexEditDistance = function (lexer) { - lexer.ignore() - lexer.acceptDigitRun() - lexer.emit(lunr.QueryLexer.EDIT_DISTANCE) - return lunr.QueryLexer.lexText -} - -lunr.QueryLexer.lexBoost = function (lexer) { - lexer.ignore() - lexer.acceptDigitRun() - lexer.emit(lunr.QueryLexer.BOOST) - return lunr.QueryLexer.lexText -} - -lunr.QueryLexer.lexEOS = function (lexer) { - if (lexer.width() > 0) { - lexer.emit(lunr.QueryLexer.TERM) - } -} - -// This matches the separator used when tokenising fields -// within a document. These should match otherwise it is -// not possible to search for some tokens within a document. -// -// It is possible for the user to change the separator on the -// tokenizer so it _might_ clash with any other of the special -// characters already used within the search string, e.g. :. -// -// This means that it is possible to change the separator in -// such a way that makes some words unsearchable using a search -// string. -lunr.QueryLexer.termSeparator = lunr.tokenizer.separator - -lunr.QueryLexer.lexText = function (lexer) { - while (true) { - var char = lexer.next() - - if (char == lunr.QueryLexer.EOS) { - return lunr.QueryLexer.lexEOS - } - - // Escape character is '\' - if (char.charCodeAt(0) == 92) { - lexer.escapeCharacter() - continue - } - - if (char == ":") { - return lunr.QueryLexer.lexField - } - - if (char == "~") { - lexer.backup() - if (lexer.width() > 0) { - lexer.emit(lunr.QueryLexer.TERM) - } - return lunr.QueryLexer.lexEditDistance - } - - if (char == "^") { - lexer.backup() - if (lexer.width() > 0) { - lexer.emit(lunr.QueryLexer.TERM) - } - return lunr.QueryLexer.lexBoost - } - - // "+" indicates term presence is required - // checking for length to ensure that only - // leading "+" are considered - if (char == "+" && lexer.width() === 1) { - lexer.emit(lunr.QueryLexer.PRESENCE) - return lunr.QueryLexer.lexText - } - - // "-" indicates term presence is prohibited - // checking for length to ensure that only - // leading "-" are considered - if (char == "-" && lexer.width() === 1) { - lexer.emit(lunr.QueryLexer.PRESENCE) - return lunr.QueryLexer.lexText - } - - if (char.match(lunr.QueryLexer.termSeparator)) { - return lunr.QueryLexer.lexTerm - } - } -} - -lunr.QueryParser = function (str, query) { - this.lexer = new lunr.QueryLexer (str) - this.query = query - this.currentClause = {} - this.lexemeIdx = 0 -} - -lunr.QueryParser.prototype.parse = function () { - this.lexer.run() - this.lexemes = this.lexer.lexemes - - var state = lunr.QueryParser.parseClause - - while (state) { - state = state(this) - } - - return this.query -} - -lunr.QueryParser.prototype.peekLexeme = function () { - return this.lexemes[this.lexemeIdx] -} - -lunr.QueryParser.prototype.consumeLexeme = function () { - var lexeme = this.peekLexeme() - this.lexemeIdx += 1 - return lexeme -} - -lunr.QueryParser.prototype.nextClause = function () { - var completedClause = this.currentClause - this.query.clause(completedClause) - this.currentClause = {} -} - -lunr.QueryParser.parseClause = function (parser) { - var lexeme = parser.peekLexeme() - - if (lexeme == undefined) { - return - } - - switch (lexeme.type) { - case lunr.QueryLexer.PRESENCE: - return lunr.QueryParser.parsePresence - case lunr.QueryLexer.FIELD: - return lunr.QueryParser.parseField - case lunr.QueryLexer.TERM: - return lunr.QueryParser.parseTerm - default: - var errorMessage = "expected either a field or a term, found " + lexeme.type - - if (lexeme.str.length >= 1) { - errorMessage += " with value '" + lexeme.str + "'" - } - - throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) - } -} - -lunr.QueryParser.parsePresence = function (parser) { - var lexeme = parser.consumeLexeme() - - if (lexeme == undefined) { - return - } - - switch (lexeme.str) { - case "-": - parser.currentClause.presence = lunr.Query.presence.PROHIBITED - break - case "+": - parser.currentClause.presence = lunr.Query.presence.REQUIRED - break - default: - var errorMessage = "unrecognised presence operator'" + lexeme.str + "'" - throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) - } - - var nextLexeme = parser.peekLexeme() - - if (nextLexeme == undefined) { - var errorMessage = "expecting term or field, found nothing" - throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) - } - - switch (nextLexeme.type) { - case lunr.QueryLexer.FIELD: - return lunr.QueryParser.parseField - case lunr.QueryLexer.TERM: - return lunr.QueryParser.parseTerm - default: - var errorMessage = "expecting term or field, found '" + nextLexeme.type + "'" - throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) - } -} - -lunr.QueryParser.parseField = function (parser) { - var lexeme = parser.consumeLexeme() - - if (lexeme == undefined) { - return - } - - if (parser.query.allFields.indexOf(lexeme.str) == -1) { - var possibleFields = parser.query.allFields.map(function (f) { return "'" + f + "'" }).join(', '), - errorMessage = "unrecognised field '" + lexeme.str + "', possible fields: " + possibleFields - - throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) - } - - parser.currentClause.fields = [lexeme.str] - - var nextLexeme = parser.peekLexeme() - - if (nextLexeme == undefined) { - var errorMessage = "expecting term, found nothing" - throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) - } - - switch (nextLexeme.type) { - case lunr.QueryLexer.TERM: - return lunr.QueryParser.parseTerm - default: - var errorMessage = "expecting term, found '" + nextLexeme.type + "'" - throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) - } -} - -lunr.QueryParser.parseTerm = function (parser) { - var lexeme = parser.consumeLexeme() - - if (lexeme == undefined) { - return - } - - parser.currentClause.term = lexeme.str.toLowerCase() - - if (lexeme.str.indexOf("*") != -1) { - parser.currentClause.usePipeline = false - } - - var nextLexeme = parser.peekLexeme() - - if (nextLexeme == undefined) { - parser.nextClause() - return - } - - switch (nextLexeme.type) { - case lunr.QueryLexer.TERM: - parser.nextClause() - return lunr.QueryParser.parseTerm - case lunr.QueryLexer.FIELD: - parser.nextClause() - return lunr.QueryParser.parseField - case lunr.QueryLexer.EDIT_DISTANCE: - return lunr.QueryParser.parseEditDistance - case lunr.QueryLexer.BOOST: - return lunr.QueryParser.parseBoost - case lunr.QueryLexer.PRESENCE: - parser.nextClause() - return lunr.QueryParser.parsePresence - default: - var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" - throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) - } -} - -lunr.QueryParser.parseEditDistance = function (parser) { - var lexeme = parser.consumeLexeme() - - if (lexeme == undefined) { - return - } - - var editDistance = parseInt(lexeme.str, 10) - - if (isNaN(editDistance)) { - var errorMessage = "edit distance must be numeric" - throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) - } - - parser.currentClause.editDistance = editDistance - - var nextLexeme = parser.peekLexeme() - - if (nextLexeme == undefined) { - parser.nextClause() - return - } - - switch (nextLexeme.type) { - case lunr.QueryLexer.TERM: - parser.nextClause() - return lunr.QueryParser.parseTerm - case lunr.QueryLexer.FIELD: - parser.nextClause() - return lunr.QueryParser.parseField - case lunr.QueryLexer.EDIT_DISTANCE: - return lunr.QueryParser.parseEditDistance - case lunr.QueryLexer.BOOST: - return lunr.QueryParser.parseBoost - case lunr.QueryLexer.PRESENCE: - parser.nextClause() - return lunr.QueryParser.parsePresence - default: - var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" - throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) - } -} - -lunr.QueryParser.parseBoost = function (parser) { - var lexeme = parser.consumeLexeme() - - if (lexeme == undefined) { - return - } - - var boost = parseInt(lexeme.str, 10) - - if (isNaN(boost)) { - var errorMessage = "boost must be numeric" - throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end) - } - - parser.currentClause.boost = boost - - var nextLexeme = parser.peekLexeme() - - if (nextLexeme == undefined) { - parser.nextClause() - return - } - - switch (nextLexeme.type) { - case lunr.QueryLexer.TERM: - parser.nextClause() - return lunr.QueryParser.parseTerm - case lunr.QueryLexer.FIELD: - parser.nextClause() - return lunr.QueryParser.parseField - case lunr.QueryLexer.EDIT_DISTANCE: - return lunr.QueryParser.parseEditDistance - case lunr.QueryLexer.BOOST: - return lunr.QueryParser.parseBoost - case lunr.QueryLexer.PRESENCE: - parser.nextClause() - return lunr.QueryParser.parsePresence - default: - var errorMessage = "Unexpected lexeme type '" + nextLexeme.type + "'" - throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end) - } -} - - /** - * export the module via AMD, CommonJS or as a browser global - * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js - */ - ;(function (root, factory) { - if (typeof define === 'function' && define.amd) { - // AMD. Register as an anonymous module. - define(factory) - } else if (typeof exports === 'object') { - /** - * Node. Does not work with strict CommonJS, but - * only CommonJS-like enviroments that support module.exports, - * like Node. - */ - module.exports = factory() - } else { - // Browser globals (root is window) - root.lunr = factory() - } - }(this, function () { - /** - * Just return a value to define the module export. - * This example returns an object, but the module - * can return a function as the exported value. - */ - return lunr - })) -})(); diff --git a/docs/search/main.js b/docs/search/main.js deleted file mode 100644 index 0e1fc81..0000000 --- a/docs/search/main.js +++ /dev/null @@ -1,98 +0,0 @@ -function getSearchTermFromLocation() { - var sPageURL = window.location.search.substring(1); - var sURLVariables = sPageURL.split('&'); - for (var i = 0; i < sURLVariables.length; i++) { - var sParameterName = sURLVariables[i].split('='); - if (sParameterName[0] == 'q') { - return decodeURIComponent(sParameterName[1].replace(/\+/g, '%20')); - } - } -} - -function joinUrl (base, path) { - if (path.substring(0, 1) === "/") { - // path starts with `/`. Thus it is absolute. - return path; - } - if (base.substring(base.length-1) === "/") { - // base ends with `/` - return base + path; - } - return base + "/" + path; -} - -function formatResult (location, title, summary) { - return ''; -} - -function displayResults (results) { - var search_results = document.getElementById("mkdocs-search-results"); - while (search_results.firstChild) { - search_results.removeChild(search_results.firstChild); - } - if (results.length > 0){ - for (var i=0; i < results.length; i++){ - var result = results[i]; - var html = formatResult(result.location, result.title, result.summary); - search_results.insertAdjacentHTML('beforeend', html); - } - } else { - search_results.insertAdjacentHTML('beforeend', "

No results found

"); - } -} - -function doSearch () { - var query = document.getElementById('mkdocs-search-query').value; - if (query.length > min_search_length) { - if (!window.Worker) { - displayResults(search(query)); - } else { - searchWorker.postMessage({query: query}); - } - } else { - // Clear results for short queries - displayResults([]); - } -} - -function initSearch () { - var search_input = document.getElementById('mkdocs-search-query'); - if (search_input) { - search_input.addEventListener("keyup", doSearch); - } - var term = getSearchTermFromLocation(); - if (term) { - search_input.value = term; - doSearch(); - } -} - -function onWorkerMessage (e) { - if (e.data.allowSearch) { - initSearch(); - } else if (e.data.results) { - var results = e.data.results; - displayResults(results); - } else if (e.data.config) { - min_search_length = e.data.config.min_search_length-1; - } -} - -if (!window.Worker) { - console.log('Web Worker API not supported'); - // load index in main thread - $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { - console.log('Loaded worker'); - init(); - window.postMessage = function (msg) { - onWorkerMessage({data: msg}); - }; - }).fail(function (jqxhr, settings, exception) { - console.error('Could not load worker.js'); - }); -} else { - // Wrap search in a web worker - var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); - searchWorker.postMessage({init: true}); - searchWorker.onmessage = onWorkerMessage; -} diff --git a/docs/search/search_index.json b/docs/search/search_index.json deleted file mode 100644 index f5ad5b9..0000000 --- a/docs/search/search_index.json +++ /dev/null @@ -1 +0,0 @@ -{"config":{"lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"RELOG: Reverse Logistics Optimization RELOG is an open-source supply chain optimization package focusing on reverse logistics and reverse manufacturing. The package uses Mixed-Integer Linear Programming to determine where to build recycling plants, what size should these plants have and which customers should be served by which plants. The package supports custom reverse logistics pipelines, with multiple types of plants, multiple types of product and multiple time periods. Table of Contents Usage Data Format Optimization model Source Code https://anl-ceeesa.github.io/RELOG/ Authors Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Nwike Iloeje, Argonne National Laboratory < ciloeje@anl.gov > License RELOG: Reverse Logistics Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. 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. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. 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.","title":"Home"},{"location":"#relog-reverse-logistics-optimization","text":"RELOG is an open-source supply chain optimization package focusing on reverse logistics and reverse manufacturing. The package uses Mixed-Integer Linear Programming to determine where to build recycling plants, what size should these plants have and which customers should be served by which plants. The package supports custom reverse logistics pipelines, with multiple types of plants, multiple types of product and multiple time periods.","title":"RELOG: Reverse Logistics Optimization"},{"location":"#table-of-contents","text":"Usage Data Format Optimization model","title":"Table of Contents"},{"location":"#source-code","text":"https://anl-ceeesa.github.io/RELOG/","title":"Source Code"},{"location":"#authors","text":"Alinson S. Xavier, Argonne National Laboratory < axavier@anl.gov > Nwike Iloeje, Argonne National Laboratory < ciloeje@anl.gov >","title":"Authors"},{"location":"#license","text":"RELOG: Reverse Logistics Optimization Copyright \u00a9 2020, UChicago Argonne, LLC. All Rights Reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. 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. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. 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.","title":"License"},{"location":"format/","text":"Input Data Format The first step when using RELOG is to describe the reverse logistics pipeline and the relevant data. RELOG accepts as input a JSON file with three sections: parameters , products and plants . Below, we describe each section in more detail. Parameters The parameters section describes details about the simulation itself. Key Description time horizon (years) Number of years in the simulation. building period (years) List of years in which we are allowed to open new plants. For example, if this parameter is set to [1,2,3] , we can only open plants during the first three years. By default, this equals [1] ; that is, plants can only be opened during the first year. Example { \"parameters\": { \"time horizon (years)\": 2, \"building period (years)\": [1] } } Products The products section describes all products and subproducts in the simulation. The field instance[\"Products\"] is a dictionary mapping the name of the product to a dictionary which describes its characteristics. Each product description contains the following keys: Key Description transportation cost ($/km/tonne) The cost to transport this product. Must be a timeseries. transportation energy (J/km/tonne) The energy required to transport this product. Must be a timeseries. Optional. transportation emissions (tonne/km/tonne) A dictionary mapping the name of each greenhouse gas, produced to transport one tonne of this product along one kilometer, to the amount of gas produced (in tonnes). Must be a timeseries. Optional. initial amounts A dictionary mapping the name of each location to its description (see below). If this product is not initially available, this key may be omitted. Must be a timeseries. Each product may have some amount available at the beginning of each time period. In this case, the key initial amounts maps to a dictionary with the following keys: Key Description latitude (deg) The latitude of the location. longitude (deg) The longitude of the location. amount (tonne) The amount of the product initially available at the location. Must be a timeseries. Example { \"products\": { \"P1\": { \"initial amounts\": { \"C1\": { \"latitude (deg)\": 7.0, \"longitude (deg)\": 7.0, \"amount (tonne)\": [934.56, 934.56] }, \"C2\": { \"latitude (deg)\": 7.0, \"longitude (deg)\": 19.0, \"amount (tonne)\": [198.95, 198.95] }, \"C3\": { \"latitude (deg)\": 84.0, \"longitude (deg)\": 76.0, \"amount (tonne)\": [212.97, 212.97] } }, \"transportation cost ($/km/tonne)\": [0.015, 0.015], \"transportation energy (J/km/tonne)\": [0.12, 0.11], \"transportation emissions (tonne/km/tonne)\": { \"CO2\": [0.052, 0.050], \"CH4\": [0.003, 0.002] } }, \"P2\": { \"transportation cost ($/km/tonne)\": [0.022, 0.020] }, \"P3\": { \"transportation cost ($/km/tonne)\": [0.0125, 0.0125] }, \"P4\": { \"transportation cost ($/km/tonne)\": [0.0175, 0.0175] } } } Processing Plants The plants section describes the available types of reverse manufacturing plants, their potential locations and associated costs, as well as their inputs and outputs. The field instance[\"Plants\"] is a dictionary mapping the name of the plant to a dictionary with the following keys: Key Description input The name of the product that this plant takes as input. Only one input is accepted per plant. outputs (tonne/tonne) A dictionary specifying how many tonnes of each product is produced for each tonnes of input. For example, if the plant outputs 0.5 tonnes of P2 and 0.25 tonnes of P3 for each tonnes of P1 provided, then this entry should be {\"P2\": 0.5, \"P3\": 0.25} . If the plant does not output anything, this key may be omitted. energy (GJ/tonne) The energy required to process 1 tonne of the input. Must be a timeseries. Optional. emissions (tonne/tonne) A dictionary mapping the name of each greenhouse gas, produced to process each tonne of input, to the amount of gas produced (in tonne). Must be a timeseries. Optional. locations A dictionary mapping the name of the location to a dictionary which describes the site characteristics (see below). Each type of plant is associated with a set of potential locations where it can be built. Each location is represented by a dictionary with the following keys: Key Description latitude (deg) The latitude of the location, in degrees. longitude (deg) The longitude of the location, in degrees. disposal A dictionary describing what products can be disposed locally at the plant. capacities (tonne) A dictionary describing what plant sizes are allowed, and their characteristics. The keys in the disposal dictionary should be the names of the products. The values are dictionaries with the following keys: Key Description cost ($/tonne) The cost to dispose of the product. Must be a timeseries. limit (tonne) The maximum amount that can be disposed of. If an unlimited amount can be disposed, this key may be omitted. Must be a timeseries. The keys in the capacities (tonne) dictionary should be the amounts (in tonnes). The values are dictionaries with the following keys: Key Description opening cost ($) The cost to open a plant of this size. fixed operating cost ($) The cost to keep the plant open, even if the plant doesn't process anything. Must be a timeseries. variable operating cost ($/tonne) The cost that the plant incurs to process each tonne of input. Must be a timeseries. Example { \"plants\": { \"F1\": { \"input\": \"P1\", \"outputs (tonne/tonne)\": { \"P2\": 0.2, \"P3\": 0.5 }, \"energy (GJ/tonne)\": [0.12, 0.11], \"emissions (tonne/tonne)\": { \"CO2\": [0.052, 0.050], \"CH4\": [0.003, 0.002] }, \"locations\": { \"L1\": { \"latitude (deg)\": 0.0, \"longitude (deg)\": 0.0, \"disposal\": { \"P2\": { \"cost ($/tonne)\": [-10.0, -12.0], \"limit (tonne)\": [1.0, 1.0] } }, \"capacities (tonne)\": { \"100\": { \"opening cost ($)\": [500, 530], \"fixed operating cost ($)\": [300.0, 310.0], \"variable operating cost ($/tonne)\": [5.0, 5.2] }, \"500\": { \"opening cost ($)\": [750, 760], \"fixed operating cost ($)\": [400.0, 450.0], \"variable operating cost ($/tonne)\": [5.0, 5.2] } } } } } } } Current limitations Each plant can only be opened exactly once. After open, the plant remains open until the end of the simulation. Plants can be expanded at any time, even long after they are open. All material available at the beginning of a time period must be entirely processed by the end of that time period. It is not possible to store unprocessed materials from one time period to the next. Up to two plant sizes are currently supported. Variable operating costs must be the same for all plant sizes.","title":"Data Format"},{"location":"format/#input-data-format","text":"The first step when using RELOG is to describe the reverse logistics pipeline and the relevant data. RELOG accepts as input a JSON file with three sections: parameters , products and plants . Below, we describe each section in more detail.","title":"Input Data Format"},{"location":"format/#parameters","text":"The parameters section describes details about the simulation itself. Key Description time horizon (years) Number of years in the simulation. building period (years) List of years in which we are allowed to open new plants. For example, if this parameter is set to [1,2,3] , we can only open plants during the first three years. By default, this equals [1] ; that is, plants can only be opened during the first year.","title":"Parameters"},{"location":"format/#example","text":"{ \"parameters\": { \"time horizon (years)\": 2, \"building period (years)\": [1] } }","title":"Example"},{"location":"format/#products","text":"The products section describes all products and subproducts in the simulation. The field instance[\"Products\"] is a dictionary mapping the name of the product to a dictionary which describes its characteristics. Each product description contains the following keys: Key Description transportation cost ($/km/tonne) The cost to transport this product. Must be a timeseries. transportation energy (J/km/tonne) The energy required to transport this product. Must be a timeseries. Optional. transportation emissions (tonne/km/tonne) A dictionary mapping the name of each greenhouse gas, produced to transport one tonne of this product along one kilometer, to the amount of gas produced (in tonnes). Must be a timeseries. Optional. initial amounts A dictionary mapping the name of each location to its description (see below). If this product is not initially available, this key may be omitted. Must be a timeseries. Each product may have some amount available at the beginning of each time period. In this case, the key initial amounts maps to a dictionary with the following keys: Key Description latitude (deg) The latitude of the location. longitude (deg) The longitude of the location. amount (tonne) The amount of the product initially available at the location. Must be a timeseries.","title":"Products"},{"location":"format/#example_1","text":"{ \"products\": { \"P1\": { \"initial amounts\": { \"C1\": { \"latitude (deg)\": 7.0, \"longitude (deg)\": 7.0, \"amount (tonne)\": [934.56, 934.56] }, \"C2\": { \"latitude (deg)\": 7.0, \"longitude (deg)\": 19.0, \"amount (tonne)\": [198.95, 198.95] }, \"C3\": { \"latitude (deg)\": 84.0, \"longitude (deg)\": 76.0, \"amount (tonne)\": [212.97, 212.97] } }, \"transportation cost ($/km/tonne)\": [0.015, 0.015], \"transportation energy (J/km/tonne)\": [0.12, 0.11], \"transportation emissions (tonne/km/tonne)\": { \"CO2\": [0.052, 0.050], \"CH4\": [0.003, 0.002] } }, \"P2\": { \"transportation cost ($/km/tonne)\": [0.022, 0.020] }, \"P3\": { \"transportation cost ($/km/tonne)\": [0.0125, 0.0125] }, \"P4\": { \"transportation cost ($/km/tonne)\": [0.0175, 0.0175] } } }","title":"Example"},{"location":"format/#processing-plants","text":"The plants section describes the available types of reverse manufacturing plants, their potential locations and associated costs, as well as their inputs and outputs. The field instance[\"Plants\"] is a dictionary mapping the name of the plant to a dictionary with the following keys: Key Description input The name of the product that this plant takes as input. Only one input is accepted per plant. outputs (tonne/tonne) A dictionary specifying how many tonnes of each product is produced for each tonnes of input. For example, if the plant outputs 0.5 tonnes of P2 and 0.25 tonnes of P3 for each tonnes of P1 provided, then this entry should be {\"P2\": 0.5, \"P3\": 0.25} . If the plant does not output anything, this key may be omitted. energy (GJ/tonne) The energy required to process 1 tonne of the input. Must be a timeseries. Optional. emissions (tonne/tonne) A dictionary mapping the name of each greenhouse gas, produced to process each tonne of input, to the amount of gas produced (in tonne). Must be a timeseries. Optional. locations A dictionary mapping the name of the location to a dictionary which describes the site characteristics (see below). Each type of plant is associated with a set of potential locations where it can be built. Each location is represented by a dictionary with the following keys: Key Description latitude (deg) The latitude of the location, in degrees. longitude (deg) The longitude of the location, in degrees. disposal A dictionary describing what products can be disposed locally at the plant. capacities (tonne) A dictionary describing what plant sizes are allowed, and their characteristics. The keys in the disposal dictionary should be the names of the products. The values are dictionaries with the following keys: Key Description cost ($/tonne) The cost to dispose of the product. Must be a timeseries. limit (tonne) The maximum amount that can be disposed of. If an unlimited amount can be disposed, this key may be omitted. Must be a timeseries. The keys in the capacities (tonne) dictionary should be the amounts (in tonnes). The values are dictionaries with the following keys: Key Description opening cost ($) The cost to open a plant of this size. fixed operating cost ($) The cost to keep the plant open, even if the plant doesn't process anything. Must be a timeseries. variable operating cost ($/tonne) The cost that the plant incurs to process each tonne of input. Must be a timeseries.","title":"Processing Plants"},{"location":"format/#example_2","text":"{ \"plants\": { \"F1\": { \"input\": \"P1\", \"outputs (tonne/tonne)\": { \"P2\": 0.2, \"P3\": 0.5 }, \"energy (GJ/tonne)\": [0.12, 0.11], \"emissions (tonne/tonne)\": { \"CO2\": [0.052, 0.050], \"CH4\": [0.003, 0.002] }, \"locations\": { \"L1\": { \"latitude (deg)\": 0.0, \"longitude (deg)\": 0.0, \"disposal\": { \"P2\": { \"cost ($/tonne)\": [-10.0, -12.0], \"limit (tonne)\": [1.0, 1.0] } }, \"capacities (tonne)\": { \"100\": { \"opening cost ($)\": [500, 530], \"fixed operating cost ($)\": [300.0, 310.0], \"variable operating cost ($/tonne)\": [5.0, 5.2] }, \"500\": { \"opening cost ($)\": [750, 760], \"fixed operating cost ($)\": [400.0, 450.0], \"variable operating cost ($/tonne)\": [5.0, 5.2] } } } } } } }","title":"Example"},{"location":"format/#current-limitations","text":"Each plant can only be opened exactly once. After open, the plant remains open until the end of the simulation. Plants can be expanded at any time, even long after they are open. All material available at the beginning of a time period must be entirely processed by the end of that time period. It is not possible to store unprocessed materials from one time period to the next. Up to two plant sizes are currently supported. Variable operating costs must be the same for all plant sizes.","title":"Current limitations"},{"location":"model/","text":"Optimization Model In this page, we describe the precise mathematical optimization model used by RELOG to find the optimal logistics plan. This model is a variation of the classical Facility Location Problem, which has been widely studied in the operations research literature. To simplify the exposition, we present the simplified case where there is only one type of plant. Mathematical Description Sets $L$ - Set of locations holding the original material to be recycled $M$ - Set of materials recovered during the reverse manufacturing process $P$ - Set of potential plants to open $T = { 1, \\ldots, t^{max} } $ - Set of time periods Constants Plants: $c^\\text{disp}_{pmt}$ - Cost of disposing one tonne of material $m$ at plant $p$ during time $t$ ( $/tonne/km ) $c^\\text{exp}_{pt}$ - Cost of adding one tonne of capacity to plant $p$ at time $t$ ( $/tonne ) $c^\\text{open}_{pt}$ - Cost of opening plant $p$ at time $t$, at minimum capacity ( $ ) $c^\\text{f-base}_{pt}$ - Fixed cost of keeping plant $p$ open during time period $t$ ( $ ) $c^\\text{f-exp}_{pt}$ - Increase in fixed cost for each additional tonne of capacity ( $/tonne ) $c^\\text{var}_{pt}$ - Variable cost of processing one tonne of input at plant $p$ at time $t$ ( $/tonne ) $m^\\text{min}_p$ - Minimum capacity of plant $p$ ( tonne ) $m^\\text{max}_p$ - Maximum capacity of plant $p$ ( tonne ) $m^\\text{disp}_{pmt}$ - Maximum amount of material $m$ that plant $p$ can dispose of during time $t$ ( tonne ) Products: $\\alpha_{pm}$ - Amount of material $m$ recovered by plant $t$ for each tonne of original material ( tonne/tonne ) $m^\\text{initial}_{lt}$ - Amount of original material to be recycled at location $l$ during time $t$ ( tonne ) Transportation: $c^\\text{tr}_{t}$ - Transportation cost during time $t$ ( $/tonne/km ) $d_{lp}$ - Distance between plant $p$ and location $l$ ( km ) Decision variables $q_{mpt}$ - Amount of material $m$ recovered by plant $p$ during time $t$ ( tonne ) $u_{pt}$ - Binary variable that equals 1 if plant $p$ starts operating at time $t$ ( bool ) $w_{pt}$ - Extra capacity (amount above the minimum) added to plant $p$ during time $t$ ( tonne ) $x_{pt}$ - Binary variable that equals 1 if plant $p$ is operational at time $t$ ( bool ) $y_{lpt}$ - Amount of product sent from location $l$ to plant $p$ during time $t$ ( tonne ) $z_{mpt}$ - Amount of material $m$ disposed of by plant $p$ during time $t$ ( tonne ) Objective function RELOG minimizes the overall capital, production and transportation costs: \\begin{align*} \\text{minimize} \\;\\; & \\sum_{t \\in T} \\sum_{p \\in P} \\left[ c^\\text{open}_{pt} u_{pt} + c^\\text{f-base}_{pt} x_{pt} + \\sum_{i=1}^t c^\\text{f-exp}_{pt} w_{pi} + c^{\\text{exp}}_{pt} w_{pt} \\right] + \\\\ & \\sum_{t \\in T} \\sum_{l \\in L} \\sum_{p \\in P} \\left[ c^{\\text{tr}}_t d_{lp} + c^{\\text{var}}_{pt} \\right] y_{lpt} + \\\\ & \\sum_{t \\in T} \\sum_{p \\in P} \\sum_{m \\in M} c^{\\text{disp}}_{pmt} z_{pmt} \\end{align*} In the first line, we have (i) opening costs, if plant starts operating at time $t$, (ii) fixed operating costs, if plant is operational, (iii) additional fixed operating costs coming from expansion performed in all previous time periods up to the current one, and finally (iv) the expansion costs during the current time period. In the second line, we have the transportation costs and the variable operating costs. In the third line, we have the disposal costs. Constraints All original materials must be sent to a plant: \\begin{align} & \\sum_{p \\in P} y_{lpt} = m^\\text{initial}_{lt} & \\forall l \\in L, t \\in T \\end{align} Plants have a limited capacity: \\begin{align} & \\sum_{l \\in L} y_{lpt} \\leq m^\\text{min}_p x_p + \\sum_{i=1}^t w_p & \\forall p \\in P, t \\in T \\end{align} Plants can only be expanded up to their maximum capacity. Furthermore, if a plant is closed, it cannot be expanded: \\begin{align} & \\sum_{i=1}^t w_p \\leq m^\\text{max}_p x_p & \\forall p \\in P, t \\in T \\end{align} Amount of recovered material is proportional to the plant input: \\begin{align} & q_{mpt} = \\alpha_{pm} \\sum_{l \\in L} y_{lpt} & \\forall m \\in M, p \\in P, t \\in T \\end{align} Because we only consider a single type of plant, all recovered material must be immediately disposed of. In RELOG's full model, recovered materials may be sent to another plant for further processing. \\begin{align} & q_{mpt} = z_{mpt} & \\forall m \\in M, p \\in P, t \\in T \\end{align} A plant is operation at time $t$ if it was operational at time $t-1$ or it was built at time $t$. This constraint also prevents a plant from being built multiple times. \\begin{align} & x_{pt} = x_{p,t-1} + u_{pt} & \\forall p \\in P, t \\in T \\setminus \\{1\\} \\\\ & x_{p,1} = u_{p,1} & \\forall p \\in P \\end{align} Variable bounds: \\begin{align} & q_{mpt} \\geq 0 & \\forall m \\in M, p \\in P, t \\in T \\\\ & u_{pt} \\in \\{0,1\\} & \\forall p \\in P, t \\in T \\\\ & w_{pt} \\geq 0 & \\forall p \\in P, t \\in T \\\\ & x_{pt} \\in \\{0,1\\} & \\forall p \\in P, t \\in T \\\\ & y_{lpt} \\geq 0 & \\forall l \\in L, p \\in P, t \\in T \\\\ & m^\\text{disp}_{mpt} \\geq z_{mpt} \\geq 0 & \\forall m \\in M, p \\in P, t \\in T \\end{align} Complete optimization model \\begin{align*} \\text{minimize} \\;\\; & \\sum_{t \\in T} \\sum_{p \\in P} \\left[ c^\\text{open}_{pt} u_{pt} + c^\\text{f-base}_{pt} x_{pt} + \\sum_{i=1}^t c^\\text{f-exp}_{pt} w_{pi} + c^{\\text{exp}}_{pt} w_{pt} \\right] + \\\\ & \\sum_{t \\in T} \\sum_{l \\in L} \\sum_{p \\in P} \\left[ c^{\\text{tr}}_t d_{lp} + c^{\\text{var}}_{pt} \\right] y_{lpt} + \\\\ & \\sum_{t \\in T} \\sum_{p \\in P} \\sum_{m \\in M} c^{\\text{disp}}_{pmt} z_{pmt} \\\\ \\text{subject to } & \\sum_{p \\in P} y_{lpt} = m^\\text{initial}_{lt} & \\forall l \\in L, t \\in T \\\\ & \\sum_{l \\in L} y_{lpt} \\leq m^\\text{min}_p x_p + \\sum_{i=1}^t w_p & \\forall p \\in P, t \\in T \\\\ & \\sum_{i=1}^t w_p \\leq m^\\text{max}_p x_p & \\forall p \\in P, t \\in T \\\\ & q_{mpt} = \\alpha_{pm} \\sum_{l \\in L} y_{lpt} & \\forall m \\in M, p \\in P, t \\in T \\\\ & q_{mpt} = z_{mpt} & \\forall m \\in M, p \\in P, t \\in T \\\\ & x_{pt} = x_{p,t-1} + u_{pt} & \\forall p \\in P, t \\in T \\setminus \\{1\\} \\\\ & x_{p,1} = u_{p,1} & \\forall p \\in P \\\\ & q_{mpt} \\geq 0 & \\forall m \\in M, p \\in P, t \\in T \\\\ & u_{pt} \\in \\{0,1\\} & \\forall p \\in P, t \\in T \\\\ & w_{pt} \\geq 0 & \\forall p \\in P, t \\in T \\\\ & x_{pt} \\in \\{0,1\\} & \\forall p \\in P, t \\in T \\\\ & y_{lpt} \\geq 0 & \\forall l \\in L, p \\in P, t \\in T \\\\ & m^\\text{disp}_{mpt} \\geq z_{mpt} \\geq 0 & \\forall m \\in M, p \\in P, t \\in T \\end{align*}","title":"Optimization Model"},{"location":"model/#optimization-model","text":"In this page, we describe the precise mathematical optimization model used by RELOG to find the optimal logistics plan. This model is a variation of the classical Facility Location Problem, which has been widely studied in the operations research literature. To simplify the exposition, we present the simplified case where there is only one type of plant.","title":"Optimization Model"},{"location":"model/#mathematical-description","text":"","title":"Mathematical Description"},{"location":"model/#sets","text":"$L$ - Set of locations holding the original material to be recycled $M$ - Set of materials recovered during the reverse manufacturing process $P$ - Set of potential plants to open $T = { 1, \\ldots, t^{max} } $ - Set of time periods","title":"Sets"},{"location":"model/#constants","text":"Plants: $c^\\text{disp}_{pmt}$ - Cost of disposing one tonne of material $m$ at plant $p$ during time $t$ ( $/tonne/km ) $c^\\text{exp}_{pt}$ - Cost of adding one tonne of capacity to plant $p$ at time $t$ ( $/tonne ) $c^\\text{open}_{pt}$ - Cost of opening plant $p$ at time $t$, at minimum capacity ( $ ) $c^\\text{f-base}_{pt}$ - Fixed cost of keeping plant $p$ open during time period $t$ ( $ ) $c^\\text{f-exp}_{pt}$ - Increase in fixed cost for each additional tonne of capacity ( $/tonne ) $c^\\text{var}_{pt}$ - Variable cost of processing one tonne of input at plant $p$ at time $t$ ( $/tonne ) $m^\\text{min}_p$ - Minimum capacity of plant $p$ ( tonne ) $m^\\text{max}_p$ - Maximum capacity of plant $p$ ( tonne ) $m^\\text{disp}_{pmt}$ - Maximum amount of material $m$ that plant $p$ can dispose of during time $t$ ( tonne ) Products: $\\alpha_{pm}$ - Amount of material $m$ recovered by plant $t$ for each tonne of original material ( tonne/tonne ) $m^\\text{initial}_{lt}$ - Amount of original material to be recycled at location $l$ during time $t$ ( tonne ) Transportation: $c^\\text{tr}_{t}$ - Transportation cost during time $t$ ( $/tonne/km ) $d_{lp}$ - Distance between plant $p$ and location $l$ ( km )","title":"Constants"},{"location":"model/#decision-variables","text":"$q_{mpt}$ - Amount of material $m$ recovered by plant $p$ during time $t$ ( tonne ) $u_{pt}$ - Binary variable that equals 1 if plant $p$ starts operating at time $t$ ( bool ) $w_{pt}$ - Extra capacity (amount above the minimum) added to plant $p$ during time $t$ ( tonne ) $x_{pt}$ - Binary variable that equals 1 if plant $p$ is operational at time $t$ ( bool ) $y_{lpt}$ - Amount of product sent from location $l$ to plant $p$ during time $t$ ( tonne ) $z_{mpt}$ - Amount of material $m$ disposed of by plant $p$ during time $t$ ( tonne )","title":"Decision variables"},{"location":"model/#objective-function","text":"RELOG minimizes the overall capital, production and transportation costs: \\begin{align*} \\text{minimize} \\;\\; & \\sum_{t \\in T} \\sum_{p \\in P} \\left[ c^\\text{open}_{pt} u_{pt} + c^\\text{f-base}_{pt} x_{pt} + \\sum_{i=1}^t c^\\text{f-exp}_{pt} w_{pi} + c^{\\text{exp}}_{pt} w_{pt} \\right] + \\\\ & \\sum_{t \\in T} \\sum_{l \\in L} \\sum_{p \\in P} \\left[ c^{\\text{tr}}_t d_{lp} + c^{\\text{var}}_{pt} \\right] y_{lpt} + \\\\ & \\sum_{t \\in T} \\sum_{p \\in P} \\sum_{m \\in M} c^{\\text{disp}}_{pmt} z_{pmt} \\end{align*} In the first line, we have (i) opening costs, if plant starts operating at time $t$, (ii) fixed operating costs, if plant is operational, (iii) additional fixed operating costs coming from expansion performed in all previous time periods up to the current one, and finally (iv) the expansion costs during the current time period. In the second line, we have the transportation costs and the variable operating costs. In the third line, we have the disposal costs.","title":"Objective function"},{"location":"model/#constraints","text":"All original materials must be sent to a plant: \\begin{align} & \\sum_{p \\in P} y_{lpt} = m^\\text{initial}_{lt} & \\forall l \\in L, t \\in T \\end{align} Plants have a limited capacity: \\begin{align} & \\sum_{l \\in L} y_{lpt} \\leq m^\\text{min}_p x_p + \\sum_{i=1}^t w_p & \\forall p \\in P, t \\in T \\end{align} Plants can only be expanded up to their maximum capacity. Furthermore, if a plant is closed, it cannot be expanded: \\begin{align} & \\sum_{i=1}^t w_p \\leq m^\\text{max}_p x_p & \\forall p \\in P, t \\in T \\end{align} Amount of recovered material is proportional to the plant input: \\begin{align} & q_{mpt} = \\alpha_{pm} \\sum_{l \\in L} y_{lpt} & \\forall m \\in M, p \\in P, t \\in T \\end{align} Because we only consider a single type of plant, all recovered material must be immediately disposed of. In RELOG's full model, recovered materials may be sent to another plant for further processing. \\begin{align} & q_{mpt} = z_{mpt} & \\forall m \\in M, p \\in P, t \\in T \\end{align} A plant is operation at time $t$ if it was operational at time $t-1$ or it was built at time $t$. This constraint also prevents a plant from being built multiple times. \\begin{align} & x_{pt} = x_{p,t-1} + u_{pt} & \\forall p \\in P, t \\in T \\setminus \\{1\\} \\\\ & x_{p,1} = u_{p,1} & \\forall p \\in P \\end{align} Variable bounds: \\begin{align} & q_{mpt} \\geq 0 & \\forall m \\in M, p \\in P, t \\in T \\\\ & u_{pt} \\in \\{0,1\\} & \\forall p \\in P, t \\in T \\\\ & w_{pt} \\geq 0 & \\forall p \\in P, t \\in T \\\\ & x_{pt} \\in \\{0,1\\} & \\forall p \\in P, t \\in T \\\\ & y_{lpt} \\geq 0 & \\forall l \\in L, p \\in P, t \\in T \\\\ & m^\\text{disp}_{mpt} \\geq z_{mpt} \\geq 0 & \\forall m \\in M, p \\in P, t \\in T \\end{align}","title":"Constraints"},{"location":"model/#complete-optimization-model","text":"\\begin{align*} \\text{minimize} \\;\\; & \\sum_{t \\in T} \\sum_{p \\in P} \\left[ c^\\text{open}_{pt} u_{pt} + c^\\text{f-base}_{pt} x_{pt} + \\sum_{i=1}^t c^\\text{f-exp}_{pt} w_{pi} + c^{\\text{exp}}_{pt} w_{pt} \\right] + \\\\ & \\sum_{t \\in T} \\sum_{l \\in L} \\sum_{p \\in P} \\left[ c^{\\text{tr}}_t d_{lp} + c^{\\text{var}}_{pt} \\right] y_{lpt} + \\\\ & \\sum_{t \\in T} \\sum_{p \\in P} \\sum_{m \\in M} c^{\\text{disp}}_{pmt} z_{pmt} \\\\ \\text{subject to } & \\sum_{p \\in P} y_{lpt} = m^\\text{initial}_{lt} & \\forall l \\in L, t \\in T \\\\ & \\sum_{l \\in L} y_{lpt} \\leq m^\\text{min}_p x_p + \\sum_{i=1}^t w_p & \\forall p \\in P, t \\in T \\\\ & \\sum_{i=1}^t w_p \\leq m^\\text{max}_p x_p & \\forall p \\in P, t \\in T \\\\ & q_{mpt} = \\alpha_{pm} \\sum_{l \\in L} y_{lpt} & \\forall m \\in M, p \\in P, t \\in T \\\\ & q_{mpt} = z_{mpt} & \\forall m \\in M, p \\in P, t \\in T \\\\ & x_{pt} = x_{p,t-1} + u_{pt} & \\forall p \\in P, t \\in T \\setminus \\{1\\} \\\\ & x_{p,1} = u_{p,1} & \\forall p \\in P \\\\ & q_{mpt} \\geq 0 & \\forall m \\in M, p \\in P, t \\in T \\\\ & u_{pt} \\in \\{0,1\\} & \\forall p \\in P, t \\in T \\\\ & w_{pt} \\geq 0 & \\forall p \\in P, t \\in T \\\\ & x_{pt} \\in \\{0,1\\} & \\forall p \\in P, t \\in T \\\\ & y_{lpt} \\geq 0 & \\forall l \\in L, p \\in P, t \\in T \\\\ & m^\\text{disp}_{mpt} \\geq z_{mpt} \\geq 0 & \\forall m \\in M, p \\in P, t \\in T \\end{align*}","title":"Complete optimization model"},{"location":"usage/","text":"Usage 1. Installation To use RELOG, the first step is to install the Julia programming language on your machine. Note that RELOG was developed and tested with Julia 1.5 and may not be compatible with newer versions. After Julia is installed, launch the Julia console, type ] to switch to package manger mode, then run: (@v1.5) pkg> add https://github.com/ANL-CEEESA/RELOG.git After the package and all its dependencies have been installed, please run the RELOG test suite, as shown below, to make sure that the package has been correctly installed: (@v1.5) pkg> test RELOG To update the package to a newer version, type ] to enter the package manager mode, then run: (@v1.5) pkg> update RELOG 2. Modeling the problem The two main model components in RELOG are products and plants . A product is any material that needs to be recycled, any intermediary product produced during the recycling process, or any product recovered at the end of the process. For example, in a NiMH battery recycling study case, products could include (i) the original batteries to be recycled; (ii) the cathode and anode parts of the battery; (iii) rare-earth elements and (iv) scrap metals. The model assumes that some products are initially available at user-specified locations (described by their latitude, longitude and the amount available), while other products only become available during the recycling process. Transporting products from one location to another incurs a transportation cost ( $/km/tonne ), spends some amount of energy ( J/km/tonne ) and may generate multiple types of emissions ( tonne/tonne ). All these parameters are user-specified and may be product- and time-specific. A plant is a facility that converts one type of product to another. RELOG assumes that each plant receives a single type of product as input and converts this input into multiple types of products. Multiple types of plants, with different inputs, outputs and performance characteristics, may be specified. In the NiMH battery recycling study case, for example, one type of plant could be a disassembly plant , which converts batteries into cathode and anode . Another type of plant could be anode recycling plant , which converts anode into rare-earth elements and scrap metals . To process each tonne of input material, plants incur a variable operating cost ( $/tonne ), spend some amount of energy ( GJ/tonne ), and produce multiple types of emissions ( tonne/tonne ). Plants also incur a fixed operating cost ( $ ) regardless of the amount of material they process. All these parameters are user-specified and may be region- and time-specific. Plants can be built at user-specified potential locations. Opening a plant incurs a one-time opening cost ( $ ) which may be region- and time-specific. Plants also have a limited capacity (in tonne ), which indicates the maximum amount of input material they are able to process per year. When specifying potential locations for each type of plant, it is also possible to specify the minimum and maximum capacity of the plants that can be built at that particular location. Different plants sizes may have different opening costs and fixed operating costs. After a plant is built, it can be further expanded in the following years, up to its maximum capacity. All products that are initially available must be sent to a plant for processing. All products that are generated by a plant can either be sent to another plant for further processing, or disposed of locally for either a profit or a loss ( $/tonne ). To model environmental regulations, it is also possible to specify the maximum amount of each product that can be disposed of at each location. All user parameters specified above must be provided to RELOG as a JSON file, which is fully described in the data format page . 3. Running the optimization After creating a JSON file describing the reverse manufacturing process and the input data, the following example illustrates how to use the package to find the optimal set of decisions: using RELOG RELOG.solve(\"/home/user/instance.json\", output=\"/home/user/solution.json\") The optimal logistics plan will be stored in the output file specified. See data format page for a description of this output file. 4. Advanced options 4.1 Changing the solver By default, RELOG internally uses Cbc , an open-source and freely-available Mixed-Integer Linear Programming solver developed by the COIN-OR Project . For larger-scale test cases, a commercial solver such as Gurobi, CPLEX or XPRESS is recommended. The following snippet shows how to switch from Cbc to Gurobi, for example: using RELOG, Gurobi, JuMP gurobi = optimizer_with_attributes(Gurobi.Optimizer, \"TimeLimit\" => 3600, \"MIPGap\" => 0.001) RELOG.solve(\"instance.json\", output=\"solution.json\", optimizer=gurobi)","title":"Usage"},{"location":"usage/#usage","text":"","title":"Usage"},{"location":"usage/#1-installation","text":"To use RELOG, the first step is to install the Julia programming language on your machine. Note that RELOG was developed and tested with Julia 1.5 and may not be compatible with newer versions. After Julia is installed, launch the Julia console, type ] to switch to package manger mode, then run: (@v1.5) pkg> add https://github.com/ANL-CEEESA/RELOG.git After the package and all its dependencies have been installed, please run the RELOG test suite, as shown below, to make sure that the package has been correctly installed: (@v1.5) pkg> test RELOG To update the package to a newer version, type ] to enter the package manager mode, then run: (@v1.5) pkg> update RELOG","title":"1. Installation"},{"location":"usage/#2-modeling-the-problem","text":"The two main model components in RELOG are products and plants . A product is any material that needs to be recycled, any intermediary product produced during the recycling process, or any product recovered at the end of the process. For example, in a NiMH battery recycling study case, products could include (i) the original batteries to be recycled; (ii) the cathode and anode parts of the battery; (iii) rare-earth elements and (iv) scrap metals. The model assumes that some products are initially available at user-specified locations (described by their latitude, longitude and the amount available), while other products only become available during the recycling process. Transporting products from one location to another incurs a transportation cost ( $/km/tonne ), spends some amount of energy ( J/km/tonne ) and may generate multiple types of emissions ( tonne/tonne ). All these parameters are user-specified and may be product- and time-specific. A plant is a facility that converts one type of product to another. RELOG assumes that each plant receives a single type of product as input and converts this input into multiple types of products. Multiple types of plants, with different inputs, outputs and performance characteristics, may be specified. In the NiMH battery recycling study case, for example, one type of plant could be a disassembly plant , which converts batteries into cathode and anode . Another type of plant could be anode recycling plant , which converts anode into rare-earth elements and scrap metals . To process each tonne of input material, plants incur a variable operating cost ( $/tonne ), spend some amount of energy ( GJ/tonne ), and produce multiple types of emissions ( tonne/tonne ). Plants also incur a fixed operating cost ( $ ) regardless of the amount of material they process. All these parameters are user-specified and may be region- and time-specific. Plants can be built at user-specified potential locations. Opening a plant incurs a one-time opening cost ( $ ) which may be region- and time-specific. Plants also have a limited capacity (in tonne ), which indicates the maximum amount of input material they are able to process per year. When specifying potential locations for each type of plant, it is also possible to specify the minimum and maximum capacity of the plants that can be built at that particular location. Different plants sizes may have different opening costs and fixed operating costs. After a plant is built, it can be further expanded in the following years, up to its maximum capacity. All products that are initially available must be sent to a plant for processing. All products that are generated by a plant can either be sent to another plant for further processing, or disposed of locally for either a profit or a loss ( $/tonne ). To model environmental regulations, it is also possible to specify the maximum amount of each product that can be disposed of at each location. All user parameters specified above must be provided to RELOG as a JSON file, which is fully described in the data format page .","title":"2. Modeling the problem"},{"location":"usage/#3-running-the-optimization","text":"After creating a JSON file describing the reverse manufacturing process and the input data, the following example illustrates how to use the package to find the optimal set of decisions: using RELOG RELOG.solve(\"/home/user/instance.json\", output=\"/home/user/solution.json\") The optimal logistics plan will be stored in the output file specified. See data format page for a description of this output file.","title":"3. Running the optimization"},{"location":"usage/#4-advanced-options","text":"","title":"4. Advanced options"},{"location":"usage/#41-changing-the-solver","text":"By default, RELOG internally uses Cbc , an open-source and freely-available Mixed-Integer Linear Programming solver developed by the COIN-OR Project . For larger-scale test cases, a commercial solver such as Gurobi, CPLEX or XPRESS is recommended. The following snippet shows how to switch from Cbc to Gurobi, for example: using RELOG, Gurobi, JuMP gurobi = optimizer_with_attributes(Gurobi.Optimizer, \"TimeLimit\" => 3600, \"MIPGap\" => 0.001) RELOG.solve(\"instance.json\", output=\"solution.json\", optimizer=gurobi)","title":"4.1 Changing the solver"}]} \ No newline at end of file diff --git a/docs/search/worker.js b/docs/search/worker.js deleted file mode 100644 index 9cce2f7..0000000 --- a/docs/search/worker.js +++ /dev/null @@ -1,130 +0,0 @@ -var base_path = 'function' === typeof importScripts ? 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- - -
-
- -

Usage

-

1. Installation

-

To use RELOG, the first step is to install the Julia programming language on your machine. Note that RELOG was developed and tested with Julia 1.5 and may not be compatible with newer versions. After Julia is installed, launch the Julia console, type ] to switch to package manger mode, then run:

-
(@v1.5) pkg> add https://github.com/ANL-CEEESA/RELOG.git
-
- -

After the package and all its dependencies have been installed, please run the RELOG test suite, as shown below, to make sure that the package has been correctly installed:

-
(@v1.5) pkg> test RELOG
-
- -

To update the package to a newer version, type ] to enter the package manager mode, then run:

-
(@v1.5) pkg> update RELOG
-
- -

2. Modeling the problem

-

The two main model components in RELOG are products and plants.

-

A product is any material that needs to be recycled, any intermediary product produced during the recycling process, or any product recovered at the end of the process. For example, in a NiMH battery recycling study case, products could include (i) the original batteries to be recycled; (ii) the cathode and anode parts of the battery; (iii) rare-earth elements and (iv) scrap metals.

-
    -
  • -

    The model assumes that some products are initially available at user-specified locations (described by their latitude, longitude and the amount available), while other products only become available during the recycling process.

    -
    • -
  • -

    Transporting products from one location to another incurs a transportation cost ($/km/tonne), spends some amount of energy (J/km/tonne) and may generate multiple types of emissions (tonne/tonne). All these parameters are user-specified and may be product- and time-specific.

    -
    • -
-

A plant is a facility that converts one type of product to another. RELOG assumes that each plant receives a single type of product as input and converts this input into multiple types of products. Multiple types of plants, with different inputs, outputs and performance characteristics, may be specified. In the NiMH battery recycling study case, for example, one type of plant could be a disassembly plant, which converts batteries into cathode and anode. Another type of plant could be anode recycling plant, which converts anode into rare-earth elements and scrap metals.

-
    -
  • -

    To process each tonne of input material, plants incur a variable operating cost ($/tonne), spend some amount of energy (GJ/tonne), and produce multiple types of emissions (tonne/tonne). Plants also incur a fixed operating cost ($) regardless of the amount of material they process. All these parameters are user-specified and may be region- and time-specific.

    -
    • -
  • -

    Plants can be built at user-specified potential locations. Opening a plant incurs a one-time opening cost ($) which may be region- and time-specific. Plants also have a limited capacity (in tonne), which indicates the maximum amount of input material they are able to process per year. When specifying potential locations for each type of plant, it is also possible to specify the minimum and maximum capacity of the plants that can be built at that particular location. Different plants sizes may have different opening costs and fixed operating costs. After a plant is built, it can be further expanded in the following years, up to its maximum capacity.

    -
    • -
  • -

    All products that are initially available must be sent to a plant for processing. All products that are generated by a plant can either be sent to another plant for further processing, or disposed of locally for either a profit or a loss ($/tonne). To model environmental regulations, it is also possible to specify the maximum amount of each product that can be disposed of at each location.

    -
    • -
-

All user parameters specified above must be provided to RELOG as a JSON file, which is fully described in the data format page.

-

3. Running the optimization

-

After creating a JSON file describing the reverse manufacturing process and the input data, the following example illustrates how to use the package to find the optimal set of decisions:

-
using RELOG
-RELOG.solve("/home/user/instance.json",
-            output="/home/user/solution.json")
-
- -

The optimal logistics plan will be stored in the output file specified. See data format page for a description of this output file.

-

4. Advanced options

-

4.1 Changing the solver

-

By default, RELOG internally uses Cbc, an open-source and freely-available Mixed-Integer Linear Programming solver developed by the COIN-OR Project. For larger-scale test cases, a commercial solver such as Gurobi, CPLEX or XPRESS is recommended. The following snippet shows how to switch from Cbc to Gurobi, for example:

-
using RELOG, Gurobi, JuMP
-
-gurobi = optimizer_with_attributes(Gurobi.Optimizer,
-                                   "TimeLimit" => 3600,
-                                   "MIPGap" => 0.001)
-
-RELOG.solve("instance.json",
-            output="solution.json",
-            optimizer=gurobi)
-
- - -
- -
- -
-

- Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
- - Documentation built with MkDocs. -

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