From 67577d31bc076a5616a68efce1102316d1d9e3c1 Mon Sep 17 00:00:00 2001 From: Silvana Ayala Date: Fri, 13 Aug 2021 08:13:32 -0600 Subject: [PATCH] cleanup --- ...te-management scenarios using PV ICE.html} | 4256 ++-- ...e-management scenarios using PV ICE.ipynb} | 8 +- ...aste-management scenarios using PV ICE.py} | 6 +- ...rawatt Workshop 2021 Lifetime Effects.html | 21106 ---------------- ...awatt Workshop 2021 Lifetime Effects.ipynb | 7019 ----- ...Terawatt Workshop 2021 Lifetime Effects.py | 3475 --- docs/tutorials/Microsoft_Slides.html | 15434 ----------- docs/tutorials/Microsoft_Slides.ipynb | 2803 -- docs/tutorials/Microsoft_Slides.py | 520 - docs/tutorials/Microsoft_Slides.slides.html | 15521 ------------ docs/tutorials/PVSC 2021 Fig. 3.ipynb | 1841 -- docs/tutorials/PVSC 2021 Fig. 3.py | 147 - ...enarios - PCA simulation and plotting.html | 20000 --------------- ...narios - PCA simulation and plotting.ipynb | 5895 ----- ...Scenarios - PCA simulation and plotting.py | 991 - ...arios - STATE simulation and plotting.html | 19462 -------------- ...rios - STATE simulation and plotting.ipynb | 4955 ---- ...enarios - STATE simulation and plotting.py | 2890 --- ...ios - USA + Circular Economy Pathways.html | 14328 ----------- ...os - USA + Circular Economy Pathways.ipynb | 1204 - ...arios - USA + Circular Economy Pathways.py | 270 - .../(development) ReEDS Scenarios - USA.html | 18916 -------------- .../(development) ReEDS Scenarios - USA.ipynb | 6092 ----- .../(development) ReEDS Scenarios - USA.py | 1927 -- ...(development) ReEDS Scenarios - WORLD.html | 16068 ------------ ...development) ReEDS Scenarios - WORLD.ipynb | 2678 -- .../(development) ReEDS Scenarios - WORLD.py | 1111 - ...nt) ReEDS Scenarios Baseline Creation.html | 14788 ----------- ...t) ReEDS Scenarios Baseline Creation.ipynb | 771 - ...ment) ReEDS Scenarios Baseline Creation.py | 269 - 30 files changed, 1994 insertions(+), 202757 deletions(-) rename docs/tutorials/{in_development_ABM_Simulations.html => 12 - Evaluating Agent-Based-Modeling waste-management scenarios using PV ICE.html} (95%) rename docs/tutorials/{in_development_ABM_Simulations.ipynb => 12 - Evaluating Agent-Based-Modeling waste-management scenarios using PV ICE.ipynb} (99%) rename docs/tutorials/{in_development_ABM_Simulations.py => 12 - Evaluating Agent-Based-Modeling waste-management scenarios using PV ICE.py} (99%) delete mode 100644 docs/tutorials/12 - Terawatt Workshop 2021 Lifetime Effects.html delete mode 100644 docs/tutorials/12 - Terawatt Workshop 2021 Lifetime Effects.ipynb delete mode 100644 docs/tutorials/12 - Terawatt Workshop 2021 Lifetime Effects.py delete mode 100644 docs/tutorials/Microsoft_Slides.html delete mode 100644 docs/tutorials/Microsoft_Slides.ipynb delete mode 100644 docs/tutorials/Microsoft_Slides.py delete mode 100644 docs/tutorials/Microsoft_Slides.slides.html delete mode 100644 docs/tutorials/PVSC 2021 Fig. 3.ipynb delete mode 100644 docs/tutorials/PVSC 2021 Fig. 3.py delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - PCA simulation and plotting.html delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - PCA simulation and plotting.ipynb delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - PCA simulation and plotting.py delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - STATE simulation and plotting.html delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - STATE simulation and plotting.ipynb delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - STATE simulation and plotting.py delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.html delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.ipynb delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.py delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.html delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.ipynb delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.py delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.html delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.ipynb delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.py delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.html delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.ipynb delete mode 100644 docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.py diff --git a/docs/tutorials/in_development_ABM_Simulations.html b/docs/tutorials/12 - Evaluating Agent-Based-Modeling waste-management scenarios using PV ICE.html similarity index 95% rename from docs/tutorials/in_development_ABM_Simulations.html rename to docs/tutorials/12 - Evaluating Agent-Based-Modeling waste-management scenarios using PV ICE.html index aa789a9e..6d4c26be 100644 --- a/docs/tutorials/in_development_ABM_Simulations.html +++ b/docs/tutorials/12 - Evaluating Agent-Based-Modeling waste-management scenarios using PV ICE.html @@ -3,7 +3,7 @@ -in_development_ABM_Simulations +12 - Evaluating Agent-Based-Modeling waste-management scenarios using PV ICE @@ -83,6 +83,13 @@ scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent; } +/* tiny scrollbar */ + +.jp-scrollbar-tiny { + scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent; + scrollbar-width: thin; +} + /* * Webkit scrollbar styling */ @@ -148,6 +155,29 @@ border-bottom: var(--jp-scrollbar-endpad) solid transparent; } +/* tiny scrollbar */ + +.jp-scrollbar-tiny::-webkit-scrollbar, +.jp-scrollbar-tiny::-webkit-scrollbar-corner { + background-color: transparent; + height: 4px; + width: 4px; +} + +.jp-scrollbar-tiny::-webkit-scrollbar-thumb { + background: rgba(var(--jp-scrollbar-thumb-color), 0.5); +} + +.jp-scrollbar-tiny::-webkit-scrollbar-track:horizontal { + border-left: 0px solid transparent; + border-right: 0px solid transparent; +} + +.jp-scrollbar-tiny::-webkit-scrollbar-track:vertical { + border-top: 0px solid transparent; + border-bottom: 0px solid transparent; +} + /* * Phosphor */ @@ -340,6 +370,33 @@ text-overflow: ellipsis; } +.lm-close-icon { + border:1px solid transparent; + background-color: transparent; + position: absolute; + z-index:1; + right:3%; + top: 0; + bottom: 0; + margin: auto; + padding: 7px 0; + display: none; + vertical-align: middle; + outline: 0; + cursor: pointer; +} +.lm-close-icon:after { + content: "X"; + display: block; + width: 15px; + height: 15px; + text-align: center; + color:#000; + font-weight: normal; + font-size: 12px; + cursor: pointer; +} + /*----------------------------------------------------------------------------- | Copyright (c) Jupyter Development Team. | Copyright (c) 2014-2017, PhosphorJS Contributors @@ -776,6 +833,13 @@ } +.lm-TabBar-tabInput { + user-select: all; + width: 100%; + box-sizing : border-box; +} + + /* */ .p-TabBar-tab.p-mod-hidden, /* */ .lm-TabBar-tab.lm-mod-hidden { display: none !important; @@ -807,7 +871,7 @@ /* */ -.p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging +.p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging, /* */ .lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging { transition: none; @@ -844,12 +908,6 @@ |----------------------------------------------------------------------------*/ @charset "UTF-8"; -/*! - -Copyright 2015-present Palantir Technologies, Inc. All rights reserved. -Licensed under the Apache License, Version 2.0. - -*/ html{ -webkit-box-sizing:border-box; box-sizing:border-box; } @@ -861,17 +919,17 @@ box-sizing:inherit; } body{ - text-transform:none; - line-height:1.28581; - letter-spacing:0; font-size:14px; font-weight:400; + letter-spacing:0; + line-height:1.28581; + text-transform:none; color:#182026; font-family:-apple-system, "BlinkMacSystemFont", "Segoe UI", "Roboto", "Oxygen", "Ubuntu", "Cantarell", "Open Sans", "Helvetica Neue", "Icons16", sans-serif; } p{ - margin-top:0; - margin-bottom:10px; } + margin-bottom:10px; + margin-top:0; } small{ font-size:12px; } @@ -893,38 +951,38 @@ color:#f5f8fa; } h1.bp3-heading, .bp3-running-text h1{ - line-height:40px; - font-size:36px; } + font-size:36px; + line-height:40px; } h2.bp3-heading, .bp3-running-text h2{ - line-height:32px; - font-size:28px; } + font-size:28px; + line-height:32px; } h3.bp3-heading, .bp3-running-text h3{ - line-height:25px; - font-size:22px; } + font-size:22px; + line-height:25px; } h4.bp3-heading, .bp3-running-text h4{ - line-height:21px; - font-size:18px; } + font-size:18px; + line-height:21px; } h5.bp3-heading, .bp3-running-text h5{ - line-height:19px; - font-size:16px; } + font-size:16px; + line-height:19px; } h6.bp3-heading, .bp3-running-text h6{ - line-height:16px; - font-size:14px; } + font-size:14px; + line-height:16px; } .bp3-ui-text{ - text-transform:none; - line-height:1.28581; - letter-spacing:0; font-size:14px; - font-weight:400; } + font-weight:400; + letter-spacing:0; + line-height:1.28581; + text-transform:none; } .bp3-monospace-text{ - text-transform:none; - font-family:monospace; } + font-family:monospace; + text-transform:none; } .bp3-text-muted{ color:#5c7080; } @@ -942,54 +1000,54 @@ white-space:nowrap; word-wrap:normal; } .bp3-running-text{ - line-height:1.5; - font-size:14px; } + font-size:14px; + line-height:1.5; } .bp3-running-text h1{ color:#182026; font-weight:600; - margin-top:40px; - margin-bottom:20px; } + margin-bottom:20px; + margin-top:40px; } .bp3-dark .bp3-running-text h1{ color:#f5f8fa; } .bp3-running-text h2{ color:#182026; font-weight:600; - margin-top:40px; - margin-bottom:20px; } + margin-bottom:20px; + margin-top:40px; } .bp3-dark .bp3-running-text h2{ color:#f5f8fa; } .bp3-running-text h3{ color:#182026; font-weight:600; - margin-top:40px; - margin-bottom:20px; } + margin-bottom:20px; + margin-top:40px; } .bp3-dark .bp3-running-text h3{ color:#f5f8fa; } .bp3-running-text h4{ color:#182026; font-weight:600; - margin-top:40px; - margin-bottom:20px; } + margin-bottom:20px; + margin-top:40px; } .bp3-dark .bp3-running-text h4{ color:#f5f8fa; } .bp3-running-text h5{ color:#182026; font-weight:600; - margin-top:40px; - margin-bottom:20px; } + margin-bottom:20px; + margin-top:40px; } .bp3-dark .bp3-running-text h5{ color:#f5f8fa; } .bp3-running-text h6{ color:#182026; font-weight:600; - margin-top:40px; - margin-bottom:20px; } + margin-bottom:20px; + margin-top:40px; } .bp3-dark .bp3-running-text h6{ color:#f5f8fa; } .bp3-running-text hr{ - margin:20px 0; border:none; - border-bottom:1px solid rgba(16, 22, 26, 0.15); } + border-bottom:1px solid rgba(16, 22, 26, 0.15); + margin:20px 0; } .bp3-dark .bp3-running-text hr{ border-color:rgba(255, 255, 255, 0.15); } .bp3-running-text p{ @@ -1002,12 +1060,12 @@ .bp3-text-small{ font-size:12px; } a{ - text-decoration:none; - color:#106ba3; } + color:#106ba3; + text-decoration:none; } a:hover{ + color:#106ba3; cursor:pointer; - text-decoration:underline; - color:#106ba3; } + text-decoration:underline; } a .bp3-icon, a .bp3-icon-standard, a .bp3-icon-large{ color:inherit; } a code, @@ -1022,19 +1080,19 @@ .bp3-dark a:hover .bp3-icon-large{ color:inherit; } .bp3-running-text code, .bp3-code{ - text-transform:none; font-family:monospace; + text-transform:none; + background:rgba(255, 255, 255, 0.7); border-radius:3px; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2); - background:rgba(255, 255, 255, 0.7); - padding:2px 5px; color:#5c7080; - font-size:smaller; } + font-size:smaller; + padding:2px 5px; } .bp3-dark .bp3-running-text code, .bp3-running-text .bp3-dark code, .bp3-dark .bp3-code{ + background:rgba(16, 22, 26, 0.3); -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); - background:rgba(16, 22, 26, 0.3); color:#a7b6c2; } .bp3-running-text a > code, a > .bp3-code{ color:#137cbd; } @@ -1042,65 +1100,65 @@ color:inherit; } .bp3-running-text pre, .bp3-code-block{ - text-transform:none; font-family:monospace; - display:block; - margin:10px 0; + text-transform:none; + background:rgba(255, 255, 255, 0.7); border-radius:3px; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); - background:rgba(255, 255, 255, 0.7); - padding:13px 15px 12px; - line-height:1.4; color:#182026; + display:block; font-size:13px; + line-height:1.4; + margin:10px 0; + padding:13px 15px 12px; word-break:break-all; word-wrap:break-word; } .bp3-dark .bp3-running-text pre, .bp3-running-text .bp3-dark pre, .bp3-dark .bp3-code-block{ + background:rgba(16, 22, 26, 0.3); -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); - background:rgba(16, 22, 26, 0.3); color:#f5f8fa; } .bp3-running-text pre > code, .bp3-code-block > code{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; - padding:0; color:inherit; - font-size:inherit; } + font-size:inherit; + padding:0; } .bp3-running-text kbd, .bp3-key{ - display:-webkit-inline-box; - display:-ms-inline-flexbox; - display:inline-flex; -webkit-box-align:center; -ms-flex-align:center; align-items:center; - -webkit-box-pack:center; - -ms-flex-pack:center; - justify-content:center; + background:#ffffff; border-radius:3px; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); - background:#ffffff; - min-width:24px; - height:24px; - padding:3px 6px; - vertical-align:middle; - line-height:24px; color:#5c7080; + display:-webkit-inline-box; + display:-ms-inline-flexbox; + display:inline-flex; font-family:inherit; - font-size:12px; } + font-size:12px; + height:24px; + -webkit-box-pack:center; + -ms-flex-pack:center; + justify-content:center; + line-height:24px; + min-width:24px; + padding:3px 6px; + vertical-align:middle; } .bp3-running-text kbd .bp3-icon, .bp3-key .bp3-icon, .bp3-running-text kbd .bp3-icon-standard, .bp3-key .bp3-icon-standard, .bp3-running-text kbd .bp3-icon-large, .bp3-key .bp3-icon-large{ margin-right:5px; } .bp3-dark .bp3-running-text kbd, .bp3-running-text .bp3-dark kbd, .bp3-dark .bp3-key{ + background:#394b59; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); - background:#394b59; color:#a7b6c2; } .bp3-running-text blockquote, .bp3-blockquote{ - margin:0 0 10px; border-left:solid 4px rgba(167, 182, 194, 0.5); + margin:0 0 10px; padding:0 20px; } .bp3-dark .bp3-running-text blockquote, .bp3-running-text .bp3-dark blockquote, .bp3-dark .bp3-blockquote{ border-color:rgba(115, 134, 148, 0.5); } @@ -1117,9 +1175,9 @@ margin-top:5px; } .bp3-list-unstyled{ + list-style:none; margin:0; - padding:0; - list-style:none; } + padding:0; } .bp3-list-unstyled li{ padding:0; } .bp3-rtl{ @@ -1147,9 +1205,12 @@ display:-ms-flexbox; display:flex; } .bp3-alert-body .bp3-icon{ - margin-top:0; + font-size:40px; margin-right:20px; - font-size:40px; } + margin-top:0; } + +.bp3-alert-contents{ + word-break:break-word; } .bp3-alert-footer{ display:-webkit-box; @@ -1163,45 +1224,45 @@ .bp3-alert-footer .bp3-button{ margin-left:10px; } .bp3-breadcrumbs{ + -webkit-box-align:center; + -ms-flex-align:center; + align-items:center; + cursor:default; display:-webkit-box; display:-ms-flexbox; display:flex; -ms-flex-wrap:wrap; flex-wrap:wrap; - -webkit-box-align:center; - -ms-flex-align:center; - align-items:center; - margin:0; - cursor:default; height:30px; - padding:0; - list-style:none; } + list-style:none; + margin:0; + padding:0; } .bp3-breadcrumbs > li{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; -webkit-box-align:center; -ms-flex-align:center; - align-items:center; } + align-items:center; + display:-webkit-box; + display:-ms-flexbox; + display:flex; } .bp3-breadcrumbs > li::after{ + background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.71 7.29l-4-4a1.003 1.003 0 00-1.42 1.42L8.59 8 5.3 11.29c-.19.18-.3.43-.3.71a1.003 1.003 0 001.71.71l4-4c.18-.18.29-.43.29-.71 0-.28-.11-.53-.29-.71z' fill='%235C7080'/%3e%3c/svg%3e"); + content:""; display:block; - margin:0 5px; - background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.71 7.29l-4-4a1.003 1.003 0 0 0-1.42 1.42L8.59 8 5.3 11.29c-.19.18-.3.43-.3.71a1.003 1.003 0 0 0 1.71.71l4-4c.18-.18.29-.43.29-.71 0-.28-.11-.53-.29-.71z' fill='%235C7080'/%3e%3c/svg%3e"); - width:16px; height:16px; - content:""; } + margin:0 5px; + width:16px; } .bp3-breadcrumbs > li:last-of-type::after{ display:none; } .bp3-breadcrumb, .bp3-breadcrumb-current, .bp3-breadcrumbs-collapsed{ - display:-webkit-inline-box; - display:-ms-inline-flexbox; - display:inline-flex; -webkit-box-align:center; -ms-flex-align:center; align-items:center; + display:-webkit-inline-box; + display:-ms-inline-flexbox; + display:inline-flex; font-size:16px; } .bp3-breadcrumb, @@ -1212,8 +1273,8 @@ text-decoration:none; } .bp3-breadcrumb.bp3-disabled{ - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-breadcrumb .bp3-icon{ margin-right:5px; } @@ -1222,28 +1283,28 @@ color:inherit; font-weight:600; } .bp3-breadcrumb-current .bp3-input{ - vertical-align:baseline; font-size:inherit; - font-weight:inherit; } + font-weight:inherit; + vertical-align:baseline; } .bp3-breadcrumbs-collapsed{ - margin-right:2px; + background:#ced9e0; border:none; border-radius:3px; - background:#ced9e0; cursor:pointer; + margin-right:2px; padding:1px 5px; vertical-align:text-bottom; } .bp3-breadcrumbs-collapsed::before{ - display:block; background:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cg fill='%235C7080'%3e%3ccircle cx='2' cy='8.03' r='2'/%3e%3ccircle cx='14' cy='8.03' r='2'/%3e%3ccircle cx='8' cy='8.03' r='2'/%3e%3c/g%3e%3c/svg%3e") center no-repeat; - width:16px; + content:""; + display:block; height:16px; - content:""; } + width:16px; } .bp3-breadcrumbs-collapsed:hover{ background:#bfccd6; - text-decoration:none; - color:#182026; } + color:#182026; + text-decoration:none; } .bp3-dark .bp3-breadcrumb, .bp3-dark .bp3-breadcrumbs-collapsed{ @@ -1274,18 +1335,18 @@ -webkit-box-align:center; -ms-flex-align:center; align-items:center; - -webkit-box-pack:center; - -ms-flex-pack:center; - justify-content:center; border:none; border-radius:3px; cursor:pointer; + font-size:14px; + -webkit-box-pack:center; + -ms-flex-pack:center; + justify-content:center; padding:5px 10px; - vertical-align:middle; text-align:left; - font-size:14px; - min-width:30px; - min-height:30px; } + vertical-align:middle; + min-height:30px; + min-width:30px; } .bp3-button > *{ -webkit-box-flex:0; -ms-flex-positive:0; @@ -1320,140 +1381,140 @@ .bp3-align-left .bp3-button{ text-align:left; } .bp3-button:not([class*="bp3-intent-"]){ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-color:#f5f8fa; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); color:#182026; } .bp3-button:not([class*="bp3-intent-"]):hover{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-clip:padding-box; - background-color:#ebf1f5; } + background-color:#ebf1f5; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); } .bp3-button:not([class*="bp3-intent-"]):active, .bp3-button:not([class*="bp3-intent-"]).bp3-active{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#d8e1e8; - background-image:none; } + background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{ - outline:none; - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(206, 217, 224, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; + color:rgba(92, 112, 128, 0.6); cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + outline:none; } .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active:hover, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active, .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active:hover{ background:rgba(206, 217, 224, 0.7); } .bp3-button.bp3-intent-primary{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); background-color:#137cbd; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); color:#ffffff; } .bp3-button.bp3-intent-primary:hover, .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{ color:#ffffff; } .bp3-button.bp3-intent-primary:hover{ + background-color:#106ba3; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - background-color:#106ba3; } + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); } .bp3-button.bp3-intent-primary:active, .bp3-button.bp3-intent-primary.bp3-active{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#0e5a8a; - background-image:none; } + background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-button.bp3-intent-primary:disabled, .bp3-button.bp3-intent-primary.bp3-disabled{ + background-color:rgba(19, 124, 189, 0.5); + background-image:none; border-color:transparent; -webkit-box-shadow:none; box-shadow:none; - background-color:rgba(19, 124, 189, 0.5); - background-image:none; color:rgba(255, 255, 255, 0.6); } .bp3-button.bp3-intent-success{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); background-color:#0f9960; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); color:#ffffff; } .bp3-button.bp3-intent-success:hover, .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{ color:#ffffff; } .bp3-button.bp3-intent-success:hover{ + background-color:#0d8050; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - background-color:#0d8050; } + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); } .bp3-button.bp3-intent-success:active, .bp3-button.bp3-intent-success.bp3-active{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#0a6640; - background-image:none; } + background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-button.bp3-intent-success:disabled, .bp3-button.bp3-intent-success.bp3-disabled{ + background-color:rgba(15, 153, 96, 0.5); + background-image:none; border-color:transparent; -webkit-box-shadow:none; box-shadow:none; - background-color:rgba(15, 153, 96, 0.5); - background-image:none; color:rgba(255, 255, 255, 0.6); } .bp3-button.bp3-intent-warning{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); background-color:#d9822b; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); color:#ffffff; } .bp3-button.bp3-intent-warning:hover, .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{ color:#ffffff; } .bp3-button.bp3-intent-warning:hover{ + background-color:#bf7326; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - background-color:#bf7326; } + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); } .bp3-button.bp3-intent-warning:active, .bp3-button.bp3-intent-warning.bp3-active{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#a66321; - background-image:none; } + background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-button.bp3-intent-warning:disabled, .bp3-button.bp3-intent-warning.bp3-disabled{ + background-color:rgba(217, 130, 43, 0.5); + background-image:none; border-color:transparent; -webkit-box-shadow:none; box-shadow:none; - background-color:rgba(217, 130, 43, 0.5); - background-image:none; color:rgba(255, 255, 255, 0.6); } .bp3-button.bp3-intent-danger{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); background-color:#db3737; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); color:#ffffff; } .bp3-button.bp3-intent-danger:hover, .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{ color:#ffffff; } .bp3-button.bp3-intent-danger:hover{ + background-color:#c23030; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - background-color:#c23030; } + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); } .bp3-button.bp3-intent-danger:active, .bp3-button.bp3-intent-danger.bp3-active{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#a82a2a; - background-image:none; } + background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-button.bp3-intent-danger:disabled, .bp3-button.bp3-intent-danger.bp3-disabled{ + background-color:rgba(219, 55, 55, 0.5); + background-image:none; border-color:transparent; -webkit-box-shadow:none; box-shadow:none; - background-color:rgba(219, 55, 55, 0.5); - background-image:none; color:rgba(255, 255, 255, 0.6); } .bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{ stroke:#ffffff; } .bp3-button.bp3-large, .bp3-large .bp3-button{ - min-width:40px; min-height:40px; - padding:5px 15px; - font-size:16px; } + min-width:40px; + font-size:16px; + padding:5px 15px; } .bp3-button.bp3-large::before, .bp3-button.bp3-large > *, .bp3-large .bp3-button::before, @@ -1466,24 +1527,24 @@ margin-right:0; } .bp3-button.bp3-small, .bp3-small .bp3-button{ - min-width:24px; min-height:24px; + min-width:24px; padding:0 7px; } .bp3-button.bp3-loading{ position:relative; } .bp3-button.bp3-loading[class*="bp3-icon-"]::before{ visibility:hidden; } .bp3-button.bp3-loading .bp3-button-spinner{ - position:absolute; - margin:0; } + margin:0; + position:absolute; } .bp3-button.bp3-loading > :not(.bp3-button-spinner){ visibility:hidden; } .bp3-button[class*="bp3-icon-"]::before{ - line-height:1; font-family:"Icons16", sans-serif; font-size:16px; - font-weight:400; font-style:normal; + font-weight:400; + line-height:1; -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; color:#5c7080; } @@ -1495,28 +1556,28 @@ .bp3-button .bp3-spinner + .bp3-icon:last-child{ margin:0 -7px; } .bp3-dark .bp3-button:not([class*="bp3-intent-"]){ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); background-color:#394b59; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); color:#f5f8fa; } .bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover, .bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{ color:#f5f8fa; } .bp3-dark .bp3-button:not([class*="bp3-intent-"]):hover{ + background-color:#30404d; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - background-color:#30404d; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-button:not([class*="bp3-intent-"]):active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-active{ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#202b33; - background-image:none; } + background-image:none; + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled{ - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(57, 75, 89, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-button:not([class*="bp3-intent-"]):disabled.bp3-active, .bp3-dark .bp3-button:not([class*="bp3-intent-"]).bp3-disabled.bp3-active{ background:rgba(57, 75, 89, 0.7); } @@ -1537,9 +1598,9 @@ -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-dark .bp3-button[class*="bp3-intent-"]:disabled, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-disabled{ + background-image:none; -webkit-box-shadow:none; box-shadow:none; - background-image:none; color:rgba(255, 255, 255, 0.3); } .bp3-dark .bp3-button[class*="bp3-intent-"] .bp3-button-spinner .bp3-spinner-head{ stroke:#8a9ba8; } @@ -1549,35 +1610,35 @@ .bp3-button[class*="bp3-intent-"] .bp3-icon, .bp3-button[class*="bp3-intent-"] .bp3-icon-standard, .bp3-button[class*="bp3-intent-"] .bp3-icon-large{ color:inherit !important; } .bp3-button.bp3-minimal{ + background:none; -webkit-box-shadow:none; - box-shadow:none; - background:none; } + box-shadow:none; } .bp3-button.bp3-minimal:hover{ + background:rgba(167, 182, 194, 0.3); -webkit-box-shadow:none; box-shadow:none; - background:rgba(167, 182, 194, 0.3); - text-decoration:none; - color:#182026; } + color:#182026; + text-decoration:none; } .bp3-button.bp3-minimal:active, .bp3-button.bp3-minimal.bp3-active{ + background:rgba(115, 134, 148, 0.3); -webkit-box-shadow:none; box-shadow:none; - background:rgba(115, 134, 148, 0.3); color:#182026; } .bp3-button.bp3-minimal:disabled, .bp3-button.bp3-minimal:disabled:hover, .bp3-button.bp3-minimal.bp3-disabled, .bp3-button.bp3-minimal.bp3-disabled:hover{ background:none; - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{ background:rgba(115, 134, 148, 0.3); } .bp3-dark .bp3-button.bp3-minimal{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:inherit; } .bp3-dark .bp3-button.bp3-minimal:hover, .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{ + background:none; -webkit-box-shadow:none; - box-shadow:none; - background:none; } + box-shadow:none; } .bp3-dark .bp3-button.bp3-minimal:hover{ background:rgba(138, 155, 168, 0.15); } .bp3-dark .bp3-button.bp3-minimal:active, .bp3-dark .bp3-button.bp3-minimal.bp3-active{ @@ -1585,16 +1646,16 @@ color:#f5f8fa; } .bp3-dark .bp3-button.bp3-minimal:disabled, .bp3-dark .bp3-button.bp3-minimal:disabled:hover, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover{ background:none; - cursor:not-allowed; - color:rgba(167, 182, 194, 0.6); } + color:rgba(167, 182, 194, 0.6); + cursor:not-allowed; } .bp3-dark .bp3-button.bp3-minimal:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-disabled:hover.bp3-active{ background:rgba(138, 155, 168, 0.3); } .bp3-button.bp3-minimal.bp3-intent-primary{ color:#106ba3; } .bp3-button.bp3-minimal.bp3-intent-primary:hover, .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#106ba3; } .bp3-button.bp3-minimal.bp3-intent-primary:hover{ background:rgba(19, 124, 189, 0.15); @@ -1625,9 +1686,9 @@ .bp3-button.bp3-minimal.bp3-intent-success{ color:#0d8050; } .bp3-button.bp3-minimal.bp3-intent-success:hover, .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#0d8050; } .bp3-button.bp3-minimal.bp3-intent-success:hover{ background:rgba(15, 153, 96, 0.15); @@ -1658,9 +1719,9 @@ .bp3-button.bp3-minimal.bp3-intent-warning{ color:#bf7326; } .bp3-button.bp3-minimal.bp3-intent-warning:hover, .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#bf7326; } .bp3-button.bp3-minimal.bp3-intent-warning:hover{ background:rgba(217, 130, 43, 0.15); @@ -1691,9 +1752,9 @@ .bp3-button.bp3-minimal.bp3-intent-danger{ color:#c23030; } .bp3-button.bp3-minimal.bp3-intent-danger:hover, .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#c23030; } .bp3-button.bp3-minimal.bp3-intent-danger:hover{ background:rgba(219, 55, 55, 0.15); @@ -1721,6 +1782,220 @@ color:rgba(255, 115, 115, 0.5); } .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{ background:rgba(219, 55, 55, 0.3); } + .bp3-button.bp3-outlined{ + background:none; + -webkit-box-shadow:none; + box-shadow:none; + border:1px solid rgba(24, 32, 38, 0.2); + -webkit-box-sizing:border-box; + box-sizing:border-box; } + .bp3-button.bp3-outlined:hover{ + background:rgba(167, 182, 194, 0.3); + -webkit-box-shadow:none; + box-shadow:none; + color:#182026; + text-decoration:none; } + .bp3-button.bp3-outlined:active, .bp3-button.bp3-outlined.bp3-active{ + background:rgba(115, 134, 148, 0.3); + -webkit-box-shadow:none; + box-shadow:none; + color:#182026; } + .bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined.bp3-disabled:hover{ + background:none; + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } + .bp3-button.bp3-outlined:disabled.bp3-active, .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{ + background:rgba(115, 134, 148, 0.3); } + .bp3-dark .bp3-button.bp3-outlined{ + background:none; + -webkit-box-shadow:none; + box-shadow:none; + color:inherit; } + .bp3-dark .bp3-button.bp3-outlined:hover, .bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{ + background:none; + -webkit-box-shadow:none; + box-shadow:none; } + .bp3-dark .bp3-button.bp3-outlined:hover{ + background:rgba(138, 155, 168, 0.15); } + .bp3-dark .bp3-button.bp3-outlined:active, .bp3-dark .bp3-button.bp3-outlined.bp3-active{ + background:rgba(138, 155, 168, 0.3); + color:#f5f8fa; } + .bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{ + background:none; + color:rgba(167, 182, 194, 0.6); + cursor:not-allowed; } + .bp3-dark .bp3-button.bp3-outlined:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined:disabled:hover.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover.bp3-active{ + background:rgba(138, 155, 168, 0.3); } + .bp3-button.bp3-outlined.bp3-intent-primary{ + color:#106ba3; } + .bp3-button.bp3-outlined.bp3-intent-primary:hover, .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{ + background:none; + -webkit-box-shadow:none; + box-shadow:none; + color:#106ba3; } + .bp3-button.bp3-outlined.bp3-intent-primary:hover{ + background:rgba(19, 124, 189, 0.15); + color:#106ba3; } + .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{ + background:rgba(19, 124, 189, 0.3); + color:#106ba3; } + .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{ + background:none; + color:rgba(16, 107, 163, 0.5); } + .bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{ + background:rgba(19, 124, 189, 0.3); } + .bp3-button.bp3-outlined.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{ + stroke:#106ba3; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{ + color:#48aff0; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:hover{ + background:rgba(19, 124, 189, 0.2); + color:#48aff0; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-active{ + background:rgba(19, 124, 189, 0.3); + color:#48aff0; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{ + background:none; + color:rgba(72, 175, 240, 0.5); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled.bp3-active{ + background:rgba(19, 124, 189, 0.3); } + .bp3-button.bp3-outlined.bp3-intent-success{ + color:#0d8050; } + .bp3-button.bp3-outlined.bp3-intent-success:hover, .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{ + background:none; + -webkit-box-shadow:none; + box-shadow:none; + color:#0d8050; } + .bp3-button.bp3-outlined.bp3-intent-success:hover{ + background:rgba(15, 153, 96, 0.15); + color:#0d8050; } + .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{ + background:rgba(15, 153, 96, 0.3); + color:#0d8050; } + .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{ + background:none; + color:rgba(13, 128, 80, 0.5); } + .bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{ + background:rgba(15, 153, 96, 0.3); } + .bp3-button.bp3-outlined.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{ + stroke:#0d8050; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{ + color:#3dcc91; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:hover{ + background:rgba(15, 153, 96, 0.2); + color:#3dcc91; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-active{ + background:rgba(15, 153, 96, 0.3); + color:#3dcc91; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{ + background:none; + color:rgba(61, 204, 145, 0.5); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled.bp3-active{ + background:rgba(15, 153, 96, 0.3); } + .bp3-button.bp3-outlined.bp3-intent-warning{ + color:#bf7326; } + .bp3-button.bp3-outlined.bp3-intent-warning:hover, .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{ + background:none; + -webkit-box-shadow:none; + box-shadow:none; + color:#bf7326; } + .bp3-button.bp3-outlined.bp3-intent-warning:hover{ + background:rgba(217, 130, 43, 0.15); + color:#bf7326; } + .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{ + background:rgba(217, 130, 43, 0.3); + color:#bf7326; } + .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{ + background:none; + color:rgba(191, 115, 38, 0.5); } + .bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{ + background:rgba(217, 130, 43, 0.3); } + .bp3-button.bp3-outlined.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{ + stroke:#bf7326; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{ + color:#ffb366; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:hover{ + background:rgba(217, 130, 43, 0.2); + color:#ffb366; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-active{ + background:rgba(217, 130, 43, 0.3); + color:#ffb366; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{ + background:none; + color:rgba(255, 179, 102, 0.5); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled.bp3-active{ + background:rgba(217, 130, 43, 0.3); } + .bp3-button.bp3-outlined.bp3-intent-danger{ + color:#c23030; } + .bp3-button.bp3-outlined.bp3-intent-danger:hover, .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{ + background:none; + -webkit-box-shadow:none; + box-shadow:none; + color:#c23030; } + .bp3-button.bp3-outlined.bp3-intent-danger:hover{ + background:rgba(219, 55, 55, 0.15); + color:#c23030; } + .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{ + background:rgba(219, 55, 55, 0.3); + color:#c23030; } + .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{ + background:none; + color:rgba(194, 48, 48, 0.5); } + .bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{ + background:rgba(219, 55, 55, 0.3); } + .bp3-button.bp3-outlined.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{ + stroke:#c23030; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{ + color:#ff7373; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:hover{ + background:rgba(219, 55, 55, 0.2); + color:#ff7373; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-active{ + background:rgba(219, 55, 55, 0.3); + color:#ff7373; } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{ + background:none; + color:rgba(255, 115, 115, 0.5); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled.bp3-active{ + background:rgba(219, 55, 55, 0.3); } + .bp3-button.bp3-outlined:disabled, .bp3-button.bp3-outlined.bp3-disabled, .bp3-button.bp3-outlined:disabled:hover, .bp3-button.bp3-outlined.bp3-disabled:hover{ + border-color:rgba(92, 112, 128, 0.1); } + .bp3-dark .bp3-button.bp3-outlined{ + border-color:rgba(255, 255, 255, 0.4); } + .bp3-dark .bp3-button.bp3-outlined:disabled, .bp3-dark .bp3-button.bp3-outlined:disabled:hover, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-disabled:hover{ + border-color:rgba(255, 255, 255, 0.2); } + .bp3-button.bp3-outlined.bp3-intent-primary{ + border-color:rgba(16, 107, 163, 0.6); } + .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{ + border-color:rgba(16, 107, 163, 0.2); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary{ + border-color:rgba(72, 175, 240, 0.6); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-primary.bp3-disabled{ + border-color:rgba(72, 175, 240, 0.2); } + .bp3-button.bp3-outlined.bp3-intent-success{ + border-color:rgba(13, 128, 80, 0.6); } + .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{ + border-color:rgba(13, 128, 80, 0.2); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success{ + border-color:rgba(61, 204, 145, 0.6); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-success.bp3-disabled{ + border-color:rgba(61, 204, 145, 0.2); } + .bp3-button.bp3-outlined.bp3-intent-warning{ + border-color:rgba(191, 115, 38, 0.6); } + .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{ + border-color:rgba(191, 115, 38, 0.2); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning{ + border-color:rgba(255, 179, 102, 0.6); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-warning.bp3-disabled{ + border-color:rgba(255, 179, 102, 0.2); } + .bp3-button.bp3-outlined.bp3-intent-danger{ + border-color:rgba(194, 48, 48, 0.6); } + .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{ + border-color:rgba(194, 48, 48, 0.2); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger{ + border-color:rgba(255, 115, 115, 0.6); } + .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-outlined.bp3-intent-danger.bp3-disabled{ + border-color:rgba(255, 115, 115, 0.2); } a.bp3-button{ text-align:center; @@ -1773,43 +2048,43 @@ z-index:8; } .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:first-child) .bp3-button, .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:first-child){ - border-top-left-radius:0; - border-bottom-left-radius:0; } + border-bottom-left-radius:0; + border-top-left-radius:0; } .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button, .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){ - margin-right:-1px; + border-bottom-right-radius:0; border-top-right-radius:0; - border-bottom-right-radius:0; } + margin-right:-1px; } .bp3-button-group.bp3-minimal .bp3-button{ + background:none; -webkit-box-shadow:none; - box-shadow:none; - background:none; } + box-shadow:none; } .bp3-button-group.bp3-minimal .bp3-button:hover{ + background:rgba(167, 182, 194, 0.3); -webkit-box-shadow:none; box-shadow:none; - background:rgba(167, 182, 194, 0.3); - text-decoration:none; - color:#182026; } + color:#182026; + text-decoration:none; } .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-active{ + background:rgba(115, 134, 148, 0.3); -webkit-box-shadow:none; box-shadow:none; - background:rgba(115, 134, 148, 0.3); color:#182026; } .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{ background:none; - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{ background:rgba(115, 134, 148, 0.3); } .bp3-dark .bp3-button-group.bp3-minimal .bp3-button{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:inherit; } .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{ + background:none; -webkit-box-shadow:none; - box-shadow:none; - background:none; } + box-shadow:none; } .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:hover{ background:rgba(138, 155, 168, 0.15); } .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-active{ @@ -1817,16 +2092,16 @@ color:#f5f8fa; } .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover{ background:none; - cursor:not-allowed; - color:rgba(167, 182, 194, 0.6); } + color:rgba(167, 182, 194, 0.6); + cursor:not-allowed; } .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button:disabled:hover.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-disabled:hover.bp3-active{ background:rgba(138, 155, 168, 0.3); } .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{ color:#106ba3; } .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#106ba3; } .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{ background:rgba(19, 124, 189, 0.15); @@ -1857,9 +2132,9 @@ .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{ color:#0d8050; } .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#0d8050; } .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{ background:rgba(15, 153, 96, 0.15); @@ -1890,9 +2165,9 @@ .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{ color:#bf7326; } .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#bf7326; } .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{ background:rgba(217, 130, 43, 0.15); @@ -1923,9 +2198,9 @@ .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{ color:#c23030; } .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#c23030; } .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{ background:rgba(219, 55, 55, 0.15); @@ -1972,17 +2247,17 @@ -ms-flex:1 1 auto; flex:1 1 auto; } .bp3-button-group.bp3-vertical{ + -webkit-box-align:stretch; + -ms-flex-align:stretch; + align-items:stretch; -webkit-box-orient:vertical; -webkit-box-direction:normal; -ms-flex-direction:column; flex-direction:column; - -webkit-box-align:stretch; - -ms-flex-align:stretch; - align-items:stretch; vertical-align:top; } .bp3-button-group.bp3-vertical.bp3-fill{ - width:unset; - height:100%; } + height:100%; + width:unset; } .bp3-button-group.bp3-vertical .bp3-button{ margin-right:0 !important; width:100%; } @@ -2004,38 +2279,38 @@ .bp3-dark .bp3-button-group.bp3-vertical > .bp3-button:not(:last-child){ margin-bottom:1px; } .bp3-callout{ - line-height:1.5; font-size:14px; - position:relative; - border-radius:3px; + line-height:1.5; background-color:rgba(138, 155, 168, 0.15); - width:100%; - padding:10px 12px 9px; } + border-radius:3px; + padding:10px 12px 9px; + position:relative; + width:100%; } .bp3-callout[class*="bp3-icon-"]{ padding-left:40px; } .bp3-callout[class*="bp3-icon-"]::before{ - line-height:1; font-family:"Icons20", sans-serif; font-size:20px; - font-weight:400; font-style:normal; + font-weight:400; + line-height:1; -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; - position:absolute; - top:10px; + color:#5c7080; left:10px; - color:#5c7080; } + position:absolute; + top:10px; } .bp3-callout.bp3-callout-icon{ padding-left:40px; } .bp3-callout.bp3-callout-icon > .bp3-icon:first-child{ - position:absolute; - top:10px; + color:#5c7080; left:10px; - color:#5c7080; } + position:absolute; + top:10px; } .bp3-callout .bp3-heading{ - margin-top:0; + line-height:20px; margin-bottom:5px; - line-height:20px; } + margin-top:0; } .bp3-callout .bp3-heading:last-child{ margin-bottom:0; } .bp3-dark .bp3-callout{ @@ -2093,10 +2368,10 @@ .bp3-running-text .bp3-callout{ margin:20px 0; } .bp3-card{ + background-color:#ffffff; border-radius:3px; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); - background-color:#ffffff; padding:20px; -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); @@ -2104,9 +2379,9 @@ transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-card.bp3-dark, .bp3-dark .bp3-card{ + background-color:#30404d; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); - background-color:#30404d; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); } .bp3-elevation-0{ -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.15), 0 0 0 rgba(16, 22, 26, 0), 0 0 0 rgba(16, 22, 26, 0); @@ -2158,9 +2433,9 @@ box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); } .bp3-card.bp3-interactive:active{ - opacity:0.9; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); + opacity:0.9; -webkit-transition-duration:0; transition-duration:0; } .bp3-card.bp3-interactive:active.bp3-dark, @@ -2188,31 +2463,31 @@ position:fixed; } .bp3-divider{ - margin:5px; + border-bottom:1px solid rgba(16, 22, 26, 0.15); border-right:1px solid rgba(16, 22, 26, 0.15); - border-bottom:1px solid rgba(16, 22, 26, 0.15); } + margin:5px; } .bp3-dark .bp3-divider{ border-color:rgba(16, 22, 26, 0.4); } .bp3-dialog-container{ opacity:1; -webkit-transform:scale(1); transform:scale(1); - display:-webkit-box; - display:-ms-flexbox; - display:flex; -webkit-box-align:center; -ms-flex-align:center; align-items:center; + display:-webkit-box; + display:-ms-flexbox; + display:flex; -webkit-box-pack:center; -ms-flex-pack:center; justify-content:center; - width:100%; min-height:100%; pointer-events:none; -webkit-user-select:none; -moz-user-select:none; -ms-user-select:none; - user-select:none; } + user-select:none; + width:100%; } .bp3-dialog-container.bp3-overlay-enter > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear > .bp3-dialog{ opacity:0; -webkit-transform:scale(0.5); @@ -2221,16 +2496,16 @@ opacity:1; -webkit-transform:scale(1); transform:scale(1); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:300ms; + transition-duration:300ms; -webkit-transition-property:opacity, -webkit-transform; transition-property:opacity, -webkit-transform; transition-property:opacity, transform; transition-property:opacity, transform, -webkit-transform; - -webkit-transition-duration:300ms; - transition-duration:300ms; -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); } .bp3-dialog-container.bp3-overlay-exit > .bp3-dialog{ opacity:1; -webkit-transform:scale(1); @@ -2239,18 +2514,22 @@ opacity:0; -webkit-transform:scale(0.5); transform:scale(0.5); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:300ms; + transition-duration:300ms; -webkit-transition-property:opacity, -webkit-transform; transition-property:opacity, -webkit-transform; transition-property:opacity, transform; transition-property:opacity, transform, -webkit-transform; - -webkit-transition-duration:300ms; - transition-duration:300ms; -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); } .bp3-dialog{ + background:#ebf1f5; + border-radius:6px; + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); display:-webkit-box; display:-ms-flexbox; display:flex; @@ -2259,50 +2538,46 @@ -ms-flex-direction:column; flex-direction:column; margin:30px 0; - border-radius:6px; - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); - background:#ebf1f5; - width:500px; padding-bottom:20px; pointer-events:all; -webkit-user-select:text; -moz-user-select:text; -ms-user-select:text; - user-select:text; } + user-select:text; + width:500px; } .bp3-dialog:focus{ outline:0; } .bp3-dialog.bp3-dark, .bp3-dark .bp3-dialog{ + background:#293742; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); - background:#293742; color:#f5f8fa; } .bp3-dialog-header{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; - -webkit-box-flex:0; - -ms-flex:0 0 auto; - flex:0 0 auto; -webkit-box-align:center; -ms-flex-align:center; align-items:center; + background:#ffffff; border-radius:6px 6px 0 0; -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15); box-shadow:0 1px 0 rgba(16, 22, 26, 0.15); - background:#ffffff; - min-height:40px; - padding-right:5px; - padding-left:20px; } - .bp3-dialog-header .bp3-icon-large, + display:-webkit-box; + display:-ms-flexbox; + display:flex; + -webkit-box-flex:0; + -ms-flex:0 0 auto; + flex:0 0 auto; + min-height:40px; + padding-left:20px; + padding-right:5px; } + .bp3-dialog-header .bp3-icon-large, .bp3-dialog-header .bp3-icon{ + color:#5c7080; -webkit-box-flex:0; -ms-flex:0 0 auto; flex:0 0 auto; - margin-right:10px; - color:#5c7080; } + margin-right:10px; } .bp3-dialog-header .bp3-heading{ overflow:hidden; text-overflow:ellipsis; @@ -2311,14 +2586,14 @@ -webkit-box-flex:1; -ms-flex:1 1 auto; flex:1 1 auto; - margin:0; - line-height:inherit; } + line-height:inherit; + margin:0; } .bp3-dialog-header .bp3-heading:last-child{ margin-right:20px; } .bp3-dark .bp3-dialog-header{ + background:#30404d; -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); - box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); - background:#30404d; } + box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-dialog-header .bp3-icon-large, .bp3-dark .bp3-dialog-header .bp3-icon{ color:#a7b6c2; } @@ -2327,8 +2602,8 @@ -webkit-box-flex:1; -ms-flex:1 1 auto; flex:1 1 auto; - margin:20px; - line-height:18px; } + line-height:18px; + margin:20px; } .bp3-dialog-footer{ -webkit-box-flex:0; @@ -2346,6 +2621,9 @@ .bp3-dialog-footer-actions .bp3-button{ margin-left:10px; } .bp3-drawer{ + background:#ffffff; + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); display:-webkit-box; display:-ms-flexbox; display:flex; @@ -2354,90 +2632,87 @@ -ms-flex-direction:column; flex-direction:column; margin:0; - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); - background:#ffffff; padding:0; } .bp3-drawer:focus{ outline:0; } .bp3-drawer.bp3-position-top{ - top:0; - right:0; + height:50%; left:0; - height:50%; } + right:0; + top:0; } .bp3-drawer.bp3-position-top.bp3-overlay-enter, .bp3-drawer.bp3-position-top.bp3-overlay-appear{ -webkit-transform:translateY(-100%); transform:translateY(-100%); } .bp3-drawer.bp3-position-top.bp3-overlay-enter-active, .bp3-drawer.bp3-position-top.bp3-overlay-appear-active{ -webkit-transform:translateY(0); transform:translateY(0); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:200ms; + transition-duration:200ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:200ms; - transition-duration:200ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer.bp3-position-top.bp3-overlay-exit{ -webkit-transform:translateY(0); transform:translateY(0); } .bp3-drawer.bp3-position-top.bp3-overlay-exit-active{ -webkit-transform:translateY(-100%); transform:translateY(-100%); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer.bp3-position-bottom{ - right:0; bottom:0; + height:50%; left:0; - height:50%; } + right:0; } .bp3-drawer.bp3-position-bottom.bp3-overlay-enter, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear{ -webkit-transform:translateY(100%); transform:translateY(100%); } .bp3-drawer.bp3-position-bottom.bp3-overlay-enter-active, .bp3-drawer.bp3-position-bottom.bp3-overlay-appear-active{ -webkit-transform:translateY(0); transform:translateY(0); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:200ms; + transition-duration:200ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:200ms; - transition-duration:200ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer.bp3-position-bottom.bp3-overlay-exit{ -webkit-transform:translateY(0); transform:translateY(0); } .bp3-drawer.bp3-position-bottom.bp3-overlay-exit-active{ -webkit-transform:translateY(100%); transform:translateY(100%); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer.bp3-position-left{ - top:0; bottom:0; left:0; + top:0; width:50%; } .bp3-drawer.bp3-position-left.bp3-overlay-enter, .bp3-drawer.bp3-position-left.bp3-overlay-appear{ -webkit-transform:translateX(-100%); @@ -2445,36 +2720,36 @@ .bp3-drawer.bp3-position-left.bp3-overlay-enter-active, .bp3-drawer.bp3-position-left.bp3-overlay-appear-active{ -webkit-transform:translateX(0); transform:translateX(0); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:200ms; + transition-duration:200ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:200ms; - transition-duration:200ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer.bp3-position-left.bp3-overlay-exit{ -webkit-transform:translateX(0); transform:translateX(0); } .bp3-drawer.bp3-position-left.bp3-overlay-exit-active{ -webkit-transform:translateX(-100%); transform:translateX(-100%); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer.bp3-position-right{ - top:0; - right:0; bottom:0; + right:0; + top:0; width:50%; } .bp3-drawer.bp3-position-right.bp3-overlay-enter, .bp3-drawer.bp3-position-right.bp3-overlay-appear{ -webkit-transform:translateX(100%); @@ -2482,37 +2757,37 @@ .bp3-drawer.bp3-position-right.bp3-overlay-enter-active, .bp3-drawer.bp3-position-right.bp3-overlay-appear-active{ -webkit-transform:translateX(0); transform:translateX(0); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:200ms; + transition-duration:200ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:200ms; - transition-duration:200ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer.bp3-position-right.bp3-overlay-exit{ -webkit-transform:translateX(0); transform:translateX(0); } .bp3-drawer.bp3-position-right.bp3-overlay-exit-active{ -webkit-transform:translateX(100%); transform:translateX(100%); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( .bp3-position-right):not(.bp3-vertical){ - top:0; - right:0; bottom:0; + right:0; + top:0; width:50%; } .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( .bp3-position-right):not(.bp3-vertical).bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( @@ -2524,16 +2799,16 @@ .bp3-position-right):not(.bp3-vertical).bp3-overlay-appear-active{ -webkit-transform:translateX(0); transform:translateX(0); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:200ms; + transition-duration:200ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:200ms; - transition-duration:200ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( .bp3-position-right):not(.bp3-vertical).bp3-overlay-exit{ -webkit-transform:translateX(0); @@ -2542,22 +2817,22 @@ .bp3-position-right):not(.bp3-vertical).bp3-overlay-exit-active{ -webkit-transform:translateX(100%); transform:translateX(100%); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( .bp3-position-right).bp3-vertical{ - right:0; bottom:0; + height:50%; left:0; - height:50%; } + right:0; } .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( .bp3-position-right).bp3-vertical.bp3-overlay-enter, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( .bp3-position-right).bp3-vertical.bp3-overlay-appear{ @@ -2568,16 +2843,16 @@ .bp3-position-right).bp3-vertical.bp3-overlay-appear-active{ -webkit-transform:translateY(0); transform:translateY(0); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:200ms; + transition-duration:200ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:200ms; - transition-duration:200ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not( .bp3-position-right).bp3-vertical.bp3-overlay-exit{ -webkit-transform:translateY(0); @@ -2586,47 +2861,47 @@ .bp3-position-right).bp3-vertical.bp3-overlay-exit-active{ -webkit-transform:translateY(100%); transform:translateY(100%); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-drawer.bp3-dark, .bp3-dark .bp3-drawer{ + background:#30404d; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); - background:#30404d; color:#f5f8fa; } .bp3-drawer-header{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; - -webkit-box-flex:0; - -ms-flex:0 0 auto; - flex:0 0 auto; -webkit-box-align:center; -ms-flex-align:center; align-items:center; - position:relative; border-radius:0; -webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.15); box-shadow:0 1px 0 rgba(16, 22, 26, 0.15); + display:-webkit-box; + display:-ms-flexbox; + display:flex; + -webkit-box-flex:0; + -ms-flex:0 0 auto; + flex:0 0 auto; min-height:40px; padding:5px; - padding-left:20px; } + padding-left:20px; + position:relative; } .bp3-drawer-header .bp3-icon-large, .bp3-drawer-header .bp3-icon{ + color:#5c7080; -webkit-box-flex:0; -ms-flex:0 0 auto; flex:0 0 auto; - margin-right:10px; - color:#5c7080; } + margin-right:10px; } .bp3-drawer-header .bp3-heading{ overflow:hidden; text-overflow:ellipsis; @@ -2635,8 +2910,8 @@ -webkit-box-flex:1; -ms-flex:1 1 auto; flex:1 1 auto; - margin:0; - line-height:inherit; } + line-height:inherit; + margin:0; } .bp3-drawer-header .bp3-heading:last-child{ margin-right:20px; } .bp3-dark .bp3-drawer-header{ @@ -2650,33 +2925,33 @@ -webkit-box-flex:1; -ms-flex:1 1 auto; flex:1 1 auto; - overflow:auto; - line-height:18px; } + line-height:18px; + overflow:auto; } .bp3-drawer-footer{ + -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15); + box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15); -webkit-box-flex:0; -ms-flex:0 0 auto; flex:0 0 auto; - position:relative; - -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15); - box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15); - padding:10px 20px; } + padding:10px 20px; + position:relative; } .bp3-dark .bp3-drawer-footer{ -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4); box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.4); } .bp3-editable-text{ - display:inline-block; - position:relative; cursor:text; + display:inline-block; max-width:100%; + position:relative; vertical-align:top; white-space:nowrap; } .bp3-editable-text::before{ - position:absolute; - top:-3px; - right:-3px; bottom:-3px; left:-3px; + position:absolute; + right:-3px; + top:-3px; border-radius:3px; content:""; -webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); @@ -2687,9 +2962,9 @@ -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15); box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15); } .bp3-editable-text.bp3-editable-text-editing::before{ + background-color:#ffffff; -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); - background-color:#ffffff; } + box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); } .bp3-editable-text.bp3-disabled::before{ -webkit-box-shadow:none; box-shadow:none; } @@ -2733,9 +3008,9 @@ -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15); box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(255, 255, 255, 0.15); } .bp3-dark .bp3-editable-text.bp3-editable-text-editing::before{ + background-color:rgba(16, 22, 26, 0.3); -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); - background-color:rgba(16, 22, 26, 0.3); } + box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-editable-text.bp3-disabled::before{ -webkit-box-shadow:none; box-shadow:none; } @@ -2774,40 +3049,40 @@ .bp3-editable-text-input, .bp3-editable-text-content{ + color:inherit; display:inherit; - position:relative; - min-width:inherit; + font:inherit; + letter-spacing:inherit; max-width:inherit; - vertical-align:top; + min-width:inherit; + position:relative; + resize:none; text-transform:inherit; - letter-spacing:inherit; - color:inherit; - font:inherit; - resize:none; } + vertical-align:top; } .bp3-editable-text-input{ + background:none; border:none; -webkit-box-shadow:none; box-shadow:none; - background:none; - width:100%; padding:0; - white-space:pre-wrap; } + white-space:pre-wrap; + width:100%; } .bp3-editable-text-input::-webkit-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-editable-text-input::-moz-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-editable-text-input:-ms-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-editable-text-input::-ms-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-editable-text-input::placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-editable-text-input:focus{ outline:none; } .bp3-editable-text-input::-ms-clear{ @@ -2819,8 +3094,8 @@ text-overflow:ellipsis; white-space:pre; } .bp3-editable-text-editing > .bp3-editable-text-content{ - position:absolute; left:0; + position:absolute; visibility:hidden; } .bp3-editable-text-placeholder > .bp3-editable-text-content{ color:rgba(92, 112, 128, 0.6); } @@ -2833,6 +3108,12 @@ overflow:auto; white-space:pre-wrap; word-wrap:break-word; } +.bp3-divider{ + border-bottom:1px solid rgba(16, 22, 26, 0.15); + border-right:1px solid rgba(16, 22, 26, 0.15); + margin:5px; } + .bp3-dark .bp3-divider{ + border-color:rgba(16, 22, 26, 0.4); } .bp3-control-group{ -webkit-transform:translateZ(0); transform:translateZ(0); @@ -2864,11 +3145,11 @@ .bp3-control-group .bp3-select{ position:relative; } .bp3-control-group .bp3-input{ - z-index:2; - border-radius:inherit; } + border-radius:inherit; + z-index:2; } .bp3-control-group .bp3-input:focus{ - z-index:14; - border-radius:3px; } + border-radius:3px; + z-index:14; } .bp3-control-group .bp3-input[class*="bp3-intent"]{ z-index:13; } .bp3-control-group .bp3-input[class*="bp3-intent"]:focus{ @@ -2884,8 +3165,8 @@ .bp3-control-group .bp3-select select{ -webkit-transform:translateZ(0); transform:translateZ(0); - z-index:4; - border-radius:inherit; } + border-radius:inherit; + z-index:4; } .bp3-control-group .bp3-button:focus, .bp3-control-group .bp3-html-select select:focus, .bp3-control-group .bp3-select select:focus{ @@ -2939,9 +3220,13 @@ .bp3-control-group .bp3-select > .bp3-icon, .bp3-control-group .bp3-html-select > .bp3-icon{ z-index:17; } - .bp3-control-group:not(.bp3-vertical) > *{ + .bp3-control-group .bp3-select:focus-within{ + z-index:5; } + .bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){ margin-right:-1px; } - .bp3-dark .bp3-control-group:not(.bp3-vertical) > *{ + .bp3-control-group:not(.bp3-vertical) > .bp3-divider:not(:first-child){ + margin-left:6px; } + .bp3-dark .bp3-control-group:not(.bp3-vertical) > *:not(.bp3-divider){ margin-right:0; } .bp3-dark .bp3-control-group:not(.bp3-vertical) > .bp3-button + .bp3-button{ margin-left:1px; } @@ -2951,13 +3236,18 @@ .bp3-control-group > :first-child{ border-radius:3px 0 0 3px; } .bp3-control-group > :last-child{ - margin-right:0; - border-radius:0 3px 3px 0; } + border-radius:0 3px 3px 0; + margin-right:0; } .bp3-control-group > :only-child{ - margin-right:0; - border-radius:3px; } + border-radius:3px; + margin-right:0; } .bp3-control-group .bp3-input-group .bp3-button{ border-radius:3px; } + .bp3-control-group .bp3-numeric-input:not(:first-child) .bp3-input-group{ + border-bottom-left-radius:0; + border-top-left-radius:0; } + .bp3-control-group.bp3-fill{ + width:100%; } .bp3-control-group > .bp3-fill{ -webkit-box-flex:1; -ms-flex:1 1 auto; @@ -2974,50 +3264,50 @@ .bp3-control-group.bp3-vertical > *{ margin-top:-1px; } .bp3-control-group.bp3-vertical > :first-child{ - margin-top:0; - border-radius:3px 3px 0 0; } + border-radius:3px 3px 0 0; + margin-top:0; } .bp3-control-group.bp3-vertical > :last-child{ border-radius:0 0 3px 3px; } .bp3-control{ + cursor:pointer; display:block; - position:relative; margin-bottom:10px; - cursor:pointer; + position:relative; text-transform:none; } .bp3-control input:checked ~ .bp3-control-indicator{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); background-color:#137cbd; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); color:#ffffff; } .bp3-control:hover input:checked ~ .bp3-control-indicator{ + background-color:#106ba3; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - background-color:#106ba3; } + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); } .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{ + background:#0e5a8a; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - background:#0e5a8a; } + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-control input:disabled:checked ~ .bp3-control-indicator{ + background:rgba(19, 124, 189, 0.5); -webkit-box-shadow:none; - box-shadow:none; - background:rgba(19, 124, 189, 0.5); } + box-shadow:none; } .bp3-dark .bp3-control input:checked ~ .bp3-control-indicator{ -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-control:hover input:checked ~ .bp3-control-indicator{ + background-color:#106ba3; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - background-color:#106ba3; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{ + background-color:#0e5a8a; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - background-color:#0e5a8a; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-dark .bp3-control input:disabled:checked ~ .bp3-control-indicator{ + background:rgba(14, 90, 138, 0.5); -webkit-box-shadow:none; - box-shadow:none; - background:rgba(14, 90, 138, 0.5); } + box-shadow:none; } .bp3-control:not(.bp3-align-right){ padding-left:26px; } .bp3-control:not(.bp3-align-right) .bp3-control-indicator{ @@ -3027,53 +3317,53 @@ .bp3-control.bp3-align-right .bp3-control-indicator{ margin-right:-26px; } .bp3-control.bp3-disabled{ - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-control.bp3-inline{ display:inline-block; margin-right:20px; } .bp3-control input{ - position:absolute; - top:0; left:0; opacity:0; + position:absolute; + top:0; z-index:-1; } .bp3-control .bp3-control-indicator{ - display:inline-block; - position:relative; - margin-top:-3px; - margin-right:10px; - border:none; - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-clip:padding-box; background-color:#f5f8fa; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); + border:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); cursor:pointer; - width:1em; - height:1em; - vertical-align:middle; + display:inline-block; font-size:16px; + height:1em; + margin-right:10px; + margin-top:-3px; + position:relative; -webkit-user-select:none; -moz-user-select:none; -ms-user-select:none; - user-select:none; } + user-select:none; + vertical-align:middle; + width:1em; } .bp3-control .bp3-control-indicator::before{ + content:""; display:block; - width:1em; height:1em; - content:""; } + width:1em; } .bp3-control:hover .bp3-control-indicator{ background-color:#ebf1f5; } .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{ + background:#d8e1e8; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); - background:#d8e1e8; } + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-control input:disabled ~ .bp3-control-indicator{ + background:rgba(206, 217, 224, 0.5); -webkit-box-shadow:none; box-shadow:none; - background:rgba(206, 217, 224, 0.5); cursor:not-allowed; } .bp3-control input:focus ~ .bp3-control-indicator{ outline:rgba(19, 124, 189, 0.6) auto 2px; @@ -3081,8 +3371,8 @@ -moz-outline-radius:6px; } .bp3-control.bp3-align-right .bp3-control-indicator{ float:right; - margin-top:1px; - margin-left:10px; } + margin-left:10px; + margin-top:1px; } .bp3-control.bp3-large{ font-size:16px; } .bp3-control.bp3-large:not(.bp3-align-right){ @@ -3098,43 +3388,43 @@ .bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{ margin-top:0; } .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); background-color:#137cbd; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.1)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0)); - color:#ffffff; } - .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{ -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); - background-color:#106ba3; } + color:#ffffff; } + .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{ + background-color:#106ba3; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 -1px 0 rgba(16, 22, 26, 0.2); } .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{ + background:#0e5a8a; -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - background:#0e5a8a; } + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{ + background:rgba(19, 124, 189, 0.5); -webkit-box-shadow:none; - box-shadow:none; - background:rgba(19, 124, 189, 0.5); } + box-shadow:none; } .bp3-dark .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator{ -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-control.bp3-checkbox:hover input:indeterminate ~ .bp3-control-indicator{ + background-color:#106ba3; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - background-color:#106ba3; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{ + background-color:#0e5a8a; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); - background-color:#0e5a8a; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{ + background:rgba(14, 90, 138, 0.5); -webkit-box-shadow:none; - box-shadow:none; - background:rgba(14, 90, 138, 0.5); } + box-shadow:none; } .bp3-control.bp3-checkbox .bp3-control-indicator{ border-radius:3px; } .bp3-control.bp3-checkbox input:checked ~ .bp3-control-indicator::before{ - background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M12 5c-.28 0-.53.11-.71.29L7 9.59l-2.29-2.3a1.003 1.003 0 0 0-1.42 1.42l3 3c.18.18.43.29.71.29s.53-.11.71-.29l5-5A1.003 1.003 0 0 0 12 5z' fill='white'/%3e%3c/svg%3e"); } + background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M12 5c-.28 0-.53.11-.71.29L7 9.59l-2.29-2.3a1.003 1.003 0 00-1.42 1.42l3 3c.18.18.43.29.71.29s.53-.11.71-.29l5-5A1.003 1.003 0 0012 5z' fill='white'/%3e%3c/svg%3e"); } .bp3-control.bp3-checkbox input:indeterminate ~ .bp3-control-indicator::before{ background-image:url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3e%3cpath fill-rule='evenodd' clip-rule='evenodd' d='M11 7H5c-.55 0-1 .45-1 1s.45 1 1 1h6c.55 0 1-.45 1-1s-.45-1-1-1z' fill='white'/%3e%3c/svg%3e"); } .bp3-control.bp3-radio .bp3-control-indicator{ @@ -3178,22 +3468,22 @@ border-radius:1.75em; -webkit-box-shadow:none !important; box-shadow:none !important; - width:auto; min-width:1.75em; -webkit-transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9); - transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9); } + transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9); + width:auto; } .bp3-control.bp3-switch .bp3-control-indicator::before{ - position:absolute; - left:0; - margin:2px; + background:#ffffff; border-radius:50%; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2); - background:#ffffff; - width:calc(1em - 4px); height:calc(1em - 4px); + left:0; + margin:2px; + position:absolute; -webkit-transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9); - transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9); } + transition:left 100ms cubic-bezier(0.4, 1, 0.75, 0.9); + width:calc(1em - 4px); } .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{ left:calc(100% - 1em); } .bp3-control.bp3-switch.bp3-large:not(.bp3-align-right){ @@ -3225,96 +3515,96 @@ .bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{ background:rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-control.bp3-switch .bp3-control-indicator::before{ + background:#394b59; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - background:#394b59; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{ -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-control.bp3-switch .bp3-switch-inner-text{ - text-align:center; - font-size:0.7em; } + font-size:0.7em; + text-align:center; } .bp3-control.bp3-switch .bp3-control-indicator-child:first-child{ - visibility:hidden; - margin-right:1.2em; + line-height:0; margin-left:0.5em; - line-height:0; } + margin-right:1.2em; + visibility:hidden; } .bp3-control.bp3-switch .bp3-control-indicator-child:last-child{ - visibility:visible; - margin-right:0.5em; + line-height:1em; margin-left:1.2em; - line-height:1em; } + margin-right:0.5em; + visibility:visible; } .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:first-child{ - visibility:visible; - line-height:1em; } + line-height:1em; + visibility:visible; } .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:last-child{ - visibility:hidden; - line-height:0; } + line-height:0; + visibility:hidden; } .bp3-dark .bp3-control{ color:#f5f8fa; } .bp3-dark .bp3-control.bp3-disabled{ color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-control .bp3-control-indicator{ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); background-color:#394b59; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); - background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); } + background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-control:hover .bp3-control-indicator{ background-color:#30404d; } .bp3-dark .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{ + background:#202b33; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); - background:#202b33; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-dark .bp3-control input:disabled ~ .bp3-control-indicator{ + background:rgba(57, 75, 89, 0.5); -webkit-box-shadow:none; box-shadow:none; - background:rgba(57, 75, 89, 0.5); cursor:not-allowed; } .bp3-dark .bp3-control.bp3-checkbox input:disabled:checked ~ .bp3-control-indicator, .bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{ color:rgba(167, 182, 194, 0.6); } .bp3-file-input{ - display:inline-block; - position:relative; cursor:pointer; - height:30px; } + display:inline-block; + height:30px; + position:relative; } .bp3-file-input input{ - opacity:0; margin:0; - min-width:200px; } + min-width:200px; + opacity:0; } .bp3-file-input input:disabled + .bp3-file-upload-input, .bp3-file-input input.bp3-disabled + .bp3-file-upload-input{ + background:rgba(206, 217, 224, 0.5); -webkit-box-shadow:none; box-shadow:none; - background:rgba(206, 217, 224, 0.5); - cursor:not-allowed; color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; resize:none; } .bp3-file-input input:disabled + .bp3-file-upload-input::after, .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{ - outline:none; - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(206, 217, 224, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; + color:rgba(92, 112, 128, 0.6); cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + outline:none; } .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active:hover, .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active, .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active:hover{ background:rgba(206, 217, 224, 0.7); } .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input, .bp3-dark .bp3-file-input input.bp3-disabled + .bp3-file-upload-input{ + background:rgba(57, 75, 89, 0.5); -webkit-box-shadow:none; box-shadow:none; - background:rgba(57, 75, 89, 0.5); color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after, .bp3-dark .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after{ - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(57, 75, 89, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-file-input input:disabled + .bp3-file-upload-input::after.bp3-active, .bp3-dark .bp3-file-input input.bp3-disabled + .bp3-file-upload-input::after.bp3-active{ @@ -3332,55 +3622,55 @@ content:attr(bp3-button-text); } .bp3-file-upload-input{ - outline:none; + -webkit-appearance:none; + -moz-appearance:none; + appearance:none; + background:#ffffff; border:none; border-radius:3px; -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); - background:#ffffff; - height:30px; - padding:0 10px; - vertical-align:middle; - line-height:30px; color:#182026; font-size:14px; font-weight:400; + height:30px; + line-height:30px; + outline:none; + padding:0 10px; -webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-appearance:none; - -moz-appearance:none; - appearance:none; + vertical-align:middle; overflow:hidden; text-overflow:ellipsis; white-space:nowrap; word-wrap:normal; - position:absolute; - top:0; - right:0; + color:rgba(92, 112, 128, 0.6); left:0; padding-right:80px; - color:rgba(92, 112, 128, 0.6); + position:absolute; + right:0; + top:0; -webkit-user-select:none; -moz-user-select:none; -ms-user-select:none; user-select:none; } .bp3-file-upload-input::-webkit-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-file-upload-input::-moz-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-file-upload-input:-ms-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-file-upload-input::-ms-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-file-upload-input::placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-file-upload-input:focus, .bp3-file-upload-input.bp3-active{ -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); } @@ -3393,81 +3683,81 @@ -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); } .bp3-file-upload-input:disabled, .bp3-file-upload-input.bp3-disabled{ + background:rgba(206, 217, 224, 0.5); -webkit-box-shadow:none; box-shadow:none; - background:rgba(206, 217, 224, 0.5); - cursor:not-allowed; color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; resize:none; } .bp3-file-upload-input::after{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-color:#f5f8fa; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); color:#182026; - min-width:24px; min-height:24px; + min-width:24px; overflow:hidden; text-overflow:ellipsis; white-space:nowrap; word-wrap:normal; + border-radius:3px; + content:"Browse"; + line-height:24px; + margin:3px; position:absolute; - top:0; right:0; - margin:3px; - border-radius:3px; - width:70px; text-align:center; - line-height:24px; - content:"Browse"; } + top:0; + width:70px; } .bp3-file-upload-input::after:hover{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-clip:padding-box; - background-color:#ebf1f5; } + background-color:#ebf1f5; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); } .bp3-file-upload-input::after:active, .bp3-file-upload-input::after.bp3-active{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#d8e1e8; - background-image:none; } + background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-file-upload-input::after:disabled, .bp3-file-upload-input::after.bp3-disabled{ - outline:none; - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(206, 217, 224, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; + color:rgba(92, 112, 128, 0.6); cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + outline:none; } .bp3-file-upload-input::after:disabled.bp3-active, .bp3-file-upload-input::after:disabled.bp3-active:hover, .bp3-file-upload-input::after.bp3-disabled.bp3-active, .bp3-file-upload-input::after.bp3-disabled.bp3-active:hover{ background:rgba(206, 217, 224, 0.7); } .bp3-file-upload-input:hover::after{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-clip:padding-box; - background-color:#ebf1f5; } + background-color:#ebf1f5; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); } .bp3-file-upload-input:active::after{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#d8e1e8; - background-image:none; } + background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-large .bp3-file-upload-input{ + font-size:16px; height:40px; line-height:40px; - font-size:16px; padding-right:95px; } .bp3-large .bp3-file-upload-input[type="search"], .bp3-large .bp3-file-upload-input.bp3-round{ padding:0 15px; } .bp3-large .bp3-file-upload-input::after{ - min-width:30px; min-height:30px; + min-width:30px; + line-height:30px; margin:5px; - width:85px; - line-height:30px; } + width:85px; } .bp3-dark .bp3-file-upload-input{ + background:rgba(16, 22, 26, 0.3); -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); - background:rgba(16, 22, 26, 0.3); color:#f5f8fa; color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-file-upload-input::-webkit-input-placeholder{ @@ -3487,33 +3777,33 @@ -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-file-upload-input:disabled, .bp3-dark .bp3-file-upload-input.bp3-disabled{ + background:rgba(57, 75, 89, 0.5); -webkit-box-shadow:none; box-shadow:none; - background:rgba(57, 75, 89, 0.5); color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-file-upload-input::after{ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); background-color:#394b59; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); color:#f5f8fa; } .bp3-dark .bp3-file-upload-input::after:hover, .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{ color:#f5f8fa; } .bp3-dark .bp3-file-upload-input::after:hover{ + background-color:#30404d; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - background-color:#30404d; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-file-upload-input::after:active, .bp3-dark .bp3-file-upload-input::after.bp3-active{ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#202b33; - background-image:none; } + background-image:none; + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-dark .bp3-file-upload-input::after:disabled, .bp3-dark .bp3-file-upload-input::after.bp3-disabled{ - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(57, 75, 89, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-file-upload-input::after:disabled.bp3-active, .bp3-dark .bp3-file-upload-input::after.bp3-disabled.bp3-active{ background:rgba(57, 75, 89, 0.7); } @@ -3521,15 +3811,14 @@ background:rgba(16, 22, 26, 0.5); stroke:#8a9ba8; } .bp3-dark .bp3-file-upload-input:hover::after{ + background-color:#30404d; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - background-color:#30404d; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-file-upload-input:active::after{ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#202b33; - background-image:none; } - + background-image:none; + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-file-upload-input::after{ -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); } @@ -3547,9 +3836,9 @@ .bp3-form-group .bp3-control{ margin-top:7px; } .bp3-form-group .bp3-form-helper-text{ - margin-top:5px; color:#5c7080; - font-size:12px; } + font-size:12px; + margin-top:5px; } .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{ color:#106ba3; } .bp3-form-group.bp3-intent-success .bp3-form-helper-text{ @@ -3559,19 +3848,19 @@ .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{ color:#c23030; } .bp3-form-group.bp3-inline{ + -webkit-box-align:start; + -ms-flex-align:start; + align-items:flex-start; -webkit-box-orient:horizontal; -webkit-box-direction:normal; -ms-flex-direction:row; - flex-direction:row; - -webkit-box-align:start; - -ms-flex-align:start; - align-items:flex-start; } + flex-direction:row; } .bp3-form-group.bp3-inline.bp3-large label.bp3-label{ - margin:0 10px 0 0; - line-height:40px; } + line-height:40px; + margin:0 10px 0 0; } .bp3-form-group.bp3-inline label.bp3-label{ - margin:0 10px 0 0; - line-height:30px; } + line-height:30px; + margin:0 10px 0 0; } .bp3-form-group.bp3-disabled .bp3-label, .bp3-form-group.bp3-disabled .bp3-text-muted, .bp3-form-group.bp3-disabled .bp3-form-helper-text{ @@ -3601,36 +3890,44 @@ .bp3-input-group .bp3-input:not(:last-child){ padding-right:30px; } .bp3-input-group .bp3-input-action, + .bp3-input-group > .bp3-input-left-container, .bp3-input-group > .bp3-button, .bp3-input-group > .bp3-icon{ position:absolute; top:0; } .bp3-input-group .bp3-input-action:first-child, + .bp3-input-group > .bp3-input-left-container:first-child, .bp3-input-group > .bp3-button:first-child, .bp3-input-group > .bp3-icon:first-child{ left:0; } .bp3-input-group .bp3-input-action:last-child, + .bp3-input-group > .bp3-input-left-container:last-child, .bp3-input-group > .bp3-button:last-child, .bp3-input-group > .bp3-icon:last-child{ right:0; } .bp3-input-group .bp3-button{ - min-width:24px; min-height:24px; + min-width:24px; margin:3px; padding:0 7px; } .bp3-input-group .bp3-button:empty{ padding:0; } + .bp3-input-group > .bp3-input-left-container, + .bp3-input-group > .bp3-icon{ + z-index:1; } + .bp3-input-group > .bp3-input-left-container > .bp3-icon, .bp3-input-group > .bp3-icon{ - z-index:1; color:#5c7080; } + .bp3-input-group > .bp3-input-left-container > .bp3-icon:empty, .bp3-input-group > .bp3-icon:empty{ - line-height:1; font-family:"Icons16", sans-serif; font-size:16px; - font-weight:400; font-style:normal; + font-weight:400; + line-height:1; -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; } + .bp3-input-group > .bp3-input-left-container > .bp3-icon, .bp3-input-group > .bp3-icon, .bp3-input-group .bp3-input-action > .bp3-spinner{ margin:7px; } @@ -3660,16 +3957,17 @@ .bp3-input-group.bp3-disabled .bp3-icon{ color:rgba(92, 112, 128, 0.6); } .bp3-input-group.bp3-large .bp3-button{ - min-width:30px; min-height:30px; + min-width:30px; margin:5px; } + .bp3-input-group.bp3-large > .bp3-input-left-container > .bp3-icon, .bp3-input-group.bp3-large > .bp3-icon, .bp3-input-group.bp3-large .bp3-input-action > .bp3-spinner{ margin:12px; } .bp3-input-group.bp3-large .bp3-input{ + font-size:16px; height:40px; - line-height:40px; - font-size:16px; } + line-height:40px; } .bp3-input-group.bp3-large .bp3-input[type="search"], .bp3-input-group.bp3-large .bp3-input.bp3-round{ padding:0 15px; } .bp3-input-group.bp3-large .bp3-input:not(:first-child){ @@ -3677,22 +3975,23 @@ .bp3-input-group.bp3-large .bp3-input:not(:last-child){ padding-right:40px; } .bp3-input-group.bp3-small .bp3-button{ - min-width:20px; min-height:20px; + min-width:20px; margin:2px; } .bp3-input-group.bp3-small .bp3-tag{ - min-width:20px; min-height:20px; + min-width:20px; margin:2px; } + .bp3-input-group.bp3-small > .bp3-input-left-container > .bp3-icon, .bp3-input-group.bp3-small > .bp3-icon, .bp3-input-group.bp3-small .bp3-input-action > .bp3-spinner{ margin:4px; } .bp3-input-group.bp3-small .bp3-input{ + font-size:12px; height:24px; - padding-right:8px; - padding-left:8px; line-height:24px; - font-size:12px; } + padding-left:8px; + padding-right:8px; } .bp3-input-group.bp3-small .bp3-input[type="search"], .bp3-input-group.bp3-small .bp3-input.bp3-round{ padding:0 12px; } .bp3-input-group.bp3-small .bp3-input:not(:first-child){ @@ -3777,41 +4076,41 @@ .bp3-dark .bp3-input-group.bp3-intent-danger > .bp3-icon{ color:#ff7373; } .bp3-input{ - outline:none; + -webkit-appearance:none; + -moz-appearance:none; + appearance:none; + background:#ffffff; border:none; border-radius:3px; -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); - background:#ffffff; - height:30px; - padding:0 10px; - vertical-align:middle; - line-height:30px; color:#182026; font-size:14px; font-weight:400; + height:30px; + line-height:30px; + outline:none; + padding:0 10px; -webkit-transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); transition:-webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); transition:box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-appearance:none; - -moz-appearance:none; - appearance:none; } + vertical-align:middle; } .bp3-input::-webkit-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input::-moz-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input:-ms-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input::-ms-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input::placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input:focus, .bp3-input.bp3-active{ -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); } @@ -3824,24 +4123,24 @@ -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.15); } .bp3-input:disabled, .bp3-input.bp3-disabled{ + background:rgba(206, 217, 224, 0.5); -webkit-box-shadow:none; box-shadow:none; - background:rgba(206, 217, 224, 0.5); - cursor:not-allowed; color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; resize:none; } .bp3-input.bp3-large{ + font-size:16px; height:40px; - line-height:40px; - font-size:16px; } + line-height:40px; } .bp3-input.bp3-large[type="search"], .bp3-input.bp3-large.bp3-round{ padding:0 15px; } .bp3-input.bp3-small{ + font-size:12px; height:24px; - padding-right:8px; - padding-left:8px; line-height:24px; - font-size:12px; } + padding-left:8px; + padding-right:8px; } .bp3-input.bp3-small[type="search"], .bp3-input.bp3-small.bp3-round{ padding:0 12px; } .bp3-input.bp3-fill{ @@ -3850,9 +4149,9 @@ flex:1 1 auto; width:100%; } .bp3-dark .bp3-input{ + background:rgba(16, 22, 26, 0.3); -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); - background:rgba(16, 22, 26, 0.3); color:#f5f8fa; } .bp3-dark .bp3-input::-webkit-input-placeholder{ color:rgba(167, 182, 194, 0.6); } @@ -3871,9 +4170,9 @@ -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-input:disabled, .bp3-dark .bp3-input.bp3-disabled{ + background:rgba(57, 75, 89, 0.5); -webkit-box-shadow:none; box-shadow:none; - background:rgba(57, 75, 89, 0.5); color:rgba(167, 182, 194, 0.6); } .bp3-input.bp3-intent-primary{ -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); @@ -3982,9 +4281,9 @@ textarea.bp3-input.bp3-small{ padding:8px; } .bp3-dark textarea.bp3-input{ + background:rgba(16, 22, 26, 0.3); -webkit-box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); - background:rgba(16, 22, 26, 0.3); color:#f5f8fa; } .bp3-dark textarea.bp3-input::-webkit-input-placeholder{ color:rgba(167, 182, 194, 0.6); } @@ -4003,14 +4302,14 @@ -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark textarea.bp3-input:disabled, .bp3-dark textarea.bp3-input.bp3-disabled{ + background:rgba(57, 75, 89, 0.5); -webkit-box-shadow:none; box-shadow:none; - background:rgba(57, 75, 89, 0.5); color:rgba(167, 182, 194, 0.6); } label.bp3-label{ display:block; - margin-top:0; - margin-bottom:15px; } + margin-bottom:15px; + margin-top:0; } label.bp3-label .bp3-html-select, label.bp3-label .bp3-input, label.bp3-label .bp3-select, @@ -4023,9 +4322,9 @@ margin-top:5px; } label.bp3-label .bp3-select select, label.bp3-label .bp3-html-select select{ - width:100%; + font-weight:400; vertical-align:top; - font-weight:400; } + width:100%; } label.bp3-label.bp3-disabled, label.bp3-label.bp3-disabled .bp3-text-muted{ color:rgba(92, 112, 128, 0.6); } @@ -4056,9 +4355,9 @@ -webkit-box-flex:1; -ms-flex:1 1 14px; flex:1 1 14px; - width:30px; min-height:0; - padding:0; } + padding:0; + width:30px; } .bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:first-child{ border-radius:0 3px 0 0; } .bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button:last-child{ @@ -4087,28 +4386,28 @@ -webkit-box-align:center; -ms-flex-align:center; align-items:center; - -webkit-box-pack:center; - -ms-flex-pack:center; - justify-content:center; border:none; border-radius:3px; cursor:pointer; + font-size:14px; + -webkit-box-pack:center; + -ms-flex-pack:center; + justify-content:center; padding:5px 10px; - vertical-align:middle; text-align:left; - font-size:14px; - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + vertical-align:middle; background-color:#f5f8fa; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); color:#182026; + -moz-appearance:none; + -webkit-appearance:none; border-radius:3px; - width:100%; height:30px; padding:0 25px 0 10px; - -moz-appearance:none; - -webkit-appearance:none; } + width:100%; } .bp3-html-select select > *, .bp3-select select > *{ -webkit-box-flex:0; -ms-flex-positive:0; @@ -4131,27 +4430,27 @@ margin-right:0; } .bp3-html-select select:hover, .bp3-select select:hover{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-clip:padding-box; - background-color:#ebf1f5; } + background-color:#ebf1f5; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); } .bp3-html-select select:active, .bp3-select select:active, .bp3-html-select select.bp3-active, .bp3-select select.bp3-active{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#d8e1e8; - background-image:none; } + background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-html-select select:disabled, .bp3-select select:disabled, .bp3-html-select select.bp3-disabled, .bp3-select select.bp3-disabled{ - outline:none; - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(206, 217, 224, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; + color:rgba(92, 112, 128, 0.6); cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + outline:none; } .bp3-html-select select:disabled.bp3-active, .bp3-select select:disabled.bp3-active, .bp3-html-select select:disabled.bp3-active:hover, .bp3-select select:disabled.bp3-active:hover, .bp3-html-select select.bp3-disabled.bp3-active, @@ -4161,22 +4460,22 @@ .bp3-html-select.bp3-minimal select, .bp3-select.bp3-minimal select{ + background:none; -webkit-box-shadow:none; - box-shadow:none; - background:none; } + box-shadow:none; } .bp3-html-select.bp3-minimal select:hover, .bp3-select.bp3-minimal select:hover{ + background:rgba(167, 182, 194, 0.3); -webkit-box-shadow:none; box-shadow:none; - background:rgba(167, 182, 194, 0.3); - text-decoration:none; - color:#182026; } + color:#182026; + text-decoration:none; } .bp3-html-select.bp3-minimal select:active, .bp3-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal select.bp3-active{ + background:rgba(115, 134, 148, 0.3); -webkit-box-shadow:none; box-shadow:none; - background:rgba(115, 134, 148, 0.3); color:#182026; } .bp3-html-select.bp3-minimal select:disabled, .bp3-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal select:disabled:hover, @@ -4184,8 +4483,8 @@ .bp3-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal select.bp3-disabled:hover, .bp3-select.bp3-minimal select.bp3-disabled:hover{ background:none; - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-html-select.bp3-minimal select:disabled.bp3-active, .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active, @@ -4194,17 +4493,17 @@ background:rgba(115, 134, 148, 0.3); } .bp3-dark .bp3-html-select.bp3-minimal select, .bp3-html-select.bp3-minimal .bp3-dark select, .bp3-dark .bp3-select.bp3-minimal select, .bp3-select.bp3-minimal .bp3-dark select{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:inherit; } .bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover, .bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover, .bp3-dark .bp3-html-select.bp3-minimal select:active, .bp3-html-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-select.bp3-minimal select:active, .bp3-select.bp3-minimal .bp3-dark select:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-active, .bp3-dark .bp3-select.bp3-minimal select.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-active{ + background:none; -webkit-box-shadow:none; - box-shadow:none; - background:none; } + box-shadow:none; } .bp3-dark .bp3-html-select.bp3-minimal select:hover, .bp3-html-select.bp3-minimal .bp3-dark select:hover, .bp3-dark .bp3-select.bp3-minimal select:hover, .bp3-select.bp3-minimal .bp3-dark select:hover{ background:rgba(138, 155, 168, 0.15); } @@ -4219,8 +4518,8 @@ .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover, .bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover{ background:none; - cursor:not-allowed; - color:rgba(167, 182, 194, 0.6); } + color:rgba(167, 182, 194, 0.6); + cursor:not-allowed; } .bp3-dark .bp3-html-select.bp3-minimal select:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled.bp3-active, .bp3-dark .bp3-select.bp3-minimal select:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active, .bp3-dark .bp3-select.bp3-minimal select:disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active, @@ -4234,9 +4533,9 @@ .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal select.bp3-intent-primary:active, .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#106ba3; } .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-select.bp3-minimal select.bp3-intent-primary:hover{ @@ -4286,9 +4585,9 @@ .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal select.bp3-intent-success:active, .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#0d8050; } .bp3-html-select.bp3-minimal select.bp3-intent-success:hover, .bp3-select.bp3-minimal select.bp3-intent-success:hover{ @@ -4338,9 +4637,9 @@ .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal select.bp3-intent-warning:active, .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#bf7326; } .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-select.bp3-minimal select.bp3-intent-warning:hover{ @@ -4390,9 +4689,9 @@ .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal select.bp3-intent-danger:active, .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active{ + background:none; -webkit-box-shadow:none; box-shadow:none; - background:none; color:#c23030; } .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-select.bp3-minimal select.bp3-intent-danger:hover{ @@ -4438,33 +4737,33 @@ .bp3-html-select.bp3-large select, .bp3-select.bp3-large select{ + font-size:16px; height:40px; - padding-right:35px; - font-size:16px; } + padding-right:35px; } .bp3-dark .bp3-html-select select, .bp3-dark .bp3-select select{ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); background-color:#394b59; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); color:#f5f8fa; } .bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover, .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{ color:#f5f8fa; } .bp3-dark .bp3-html-select select:hover, .bp3-dark .bp3-select select:hover{ + background-color:#30404d; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - background-color:#30404d; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-html-select select:active, .bp3-dark .bp3-select select:active, .bp3-dark .bp3-html-select select.bp3-active, .bp3-dark .bp3-select select.bp3-active{ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#202b33; - background-image:none; } + background-image:none; + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-dark .bp3-html-select select:disabled, .bp3-dark .bp3-select select:disabled, .bp3-dark .bp3-html-select select.bp3-disabled, .bp3-dark .bp3-select select.bp3-disabled{ - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(57, 75, 89, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-html-select select:disabled.bp3-active, .bp3-dark .bp3-select select:disabled.bp3-active, .bp3-dark .bp3-html-select select.bp3-disabled.bp3-active, .bp3-dark .bp3-select select.bp3-disabled.bp3-active{ background:rgba(57, 75, 89, 0.7); } @@ -4474,28 +4773,28 @@ .bp3-html-select select:disabled, .bp3-select select:disabled{ + background-color:rgba(206, 217, 224, 0.5); -webkit-box-shadow:none; box-shadow:none; - background-color:rgba(206, 217, 224, 0.5); - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-html-select .bp3-icon, .bp3-select .bp3-icon, .bp3-select::after{ + color:#5c7080; + pointer-events:none; position:absolute; - top:7px; right:7px; - color:#5c7080; - pointer-events:none; } + top:7px; } .bp3-html-select .bp3-disabled.bp3-icon, .bp3-select .bp3-disabled.bp3-icon, .bp3-disabled.bp3-select::after{ color:rgba(92, 112, 128, 0.6); } .bp3-html-select, .bp3-select{ display:inline-block; + letter-spacing:normal; position:relative; - vertical-align:middle; - letter-spacing:normal; } + vertical-align:middle; } .bp3-html-select select::-ms-expand, .bp3-select select::-ms-expand{ display:none; } @@ -4515,8 +4814,8 @@ .bp3-html-select.bp3-large .bp3-icon, .bp3-select.bp3-large::after, .bp3-select.bp3-large .bp3-icon{ - top:12px; - right:12px; } + right:12px; + top:12px; } .bp3-html-select.bp3-fill, .bp3-html-select.bp3-fill select, .bp3-select.bp3-fill, @@ -4526,16 +4825,19 @@ .bp3-select option{ background-color:#30404d; color:#f5f8fa; } + .bp3-dark .bp3-html-select option:disabled, .bp3-dark + .bp3-select option:disabled{ + color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-html-select::after, .bp3-dark .bp3-select::after{ color:#a7b6c2; } .bp3-select::after{ - line-height:1; font-family:"Icons16", sans-serif; font-size:16px; - font-weight:400; font-style:normal; + font-weight:400; + line-height:1; -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; content:"ī›†"; } @@ -4546,8 +4848,8 @@ .bp3-running-text table td, table.bp3-html-table td{ padding:11px; - vertical-align:top; - text-align:left; } + text-align:left; + vertical-align:top; } .bp3-running-text table th, table.bp3-html-table th{ color:#182026; font-weight:600; } @@ -4574,8 +4876,8 @@ table.bp3-html-table.bp3-html-table-condensed th, table.bp3-html-table.bp3-html-table-condensed td, table.bp3-html-table.bp3-small th, table.bp3-html-table.bp3-small td{ - padding-top:6px; - padding-bottom:6px; } + padding-bottom:6px; + padding-top:6px; } table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{ background:rgba(191, 204, 214, 0.15); } @@ -4605,33 +4907,29 @@ table.bp3-html-table.bp3-interactive tbody tr:active td{ background-color:rgba(191, 204, 214, 0.4); } -.bp3-dark table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{ - background:rgba(92, 112, 128, 0.15); } - -.bp3-dark table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){ - -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); - box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); } - -.bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td{ - -webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); - box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); } - .bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child){ - -webkit-box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15); - box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15); } - -.bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{ - -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); - box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); } - .bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:first-child{ - -webkit-box-shadow:none; - box-shadow:none; } - -.bp3-dark table.bp3-html-table.bp3-interactive tbody tr:hover td{ - background-color:rgba(92, 112, 128, 0.3); - cursor:pointer; } - -.bp3-dark table.bp3-html-table.bp3-interactive tbody tr:active td{ - background-color:rgba(92, 112, 128, 0.4); } +.bp3-dark table.bp3-html-table{ } + .bp3-dark table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{ + background:rgba(92, 112, 128, 0.15); } + .bp3-dark table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){ + -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); + box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); } + .bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td{ + -webkit-box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); + box-shadow:inset 0 1px 0 0 rgba(255, 255, 255, 0.15); } + .bp3-dark table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child){ + -webkit-box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15); + box-shadow:inset 1px 1px 0 0 rgba(255, 255, 255, 0.15); } + .bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{ + -webkit-box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); + box-shadow:inset 1px 0 0 0 rgba(255, 255, 255, 0.15); } + .bp3-dark table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:first-child{ + -webkit-box-shadow:none; + box-shadow:none; } + .bp3-dark table.bp3-html-table.bp3-interactive tbody tr:hover td{ + background-color:rgba(92, 112, 128, 0.3); + cursor:pointer; } + .bp3-dark table.bp3-html-table.bp3-interactive tbody tr:active td{ + background-color:rgba(92, 112, 128, 0.4); } .bp3-key-combo{ display:-webkit-box; @@ -4664,8 +4962,8 @@ margin-right:0; } .bp3-hotkey-dialog{ - top:40px; - padding-bottom:0; } + padding-bottom:0; + top:40px; } .bp3-hotkey-dialog .bp3-dialog-body{ margin:0; padding:0; } @@ -4685,17 +4983,17 @@ margin-top:40px; } .bp3-hotkey{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; -webkit-box-align:center; -ms-flex-align:center; align-items:center; + display:-webkit-box; + display:-ms-flexbox; + display:flex; -webkit-box-pack:justify; -ms-flex-pack:justify; justify-content:space-between; - margin-right:0; - margin-left:0; } + margin-left:0; + margin-right:0; } .bp3-hotkey:not(:last-child){ margin-bottom:10px; } .bp3-icon{ @@ -4733,31 +5031,31 @@ color:#ff7373; } span.bp3-icon-standard{ - line-height:1; font-family:"Icons16", sans-serif; font-size:16px; - font-weight:400; font-style:normal; + font-weight:400; + line-height:1; -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; display:inline-block; } span.bp3-icon-large{ - line-height:1; font-family:"Icons20", sans-serif; font-size:20px; - font-weight:400; font-style:normal; + font-weight:400; + line-height:1; -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; display:inline-block; } span.bp3-icon:empty{ - line-height:1; font-family:"Icons20"; font-size:inherit; + font-style:normal; font-weight:400; - font-style:normal; } + line-height:1; } span.bp3-icon:empty::before{ -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; } @@ -5071,6 +5369,9 @@ .bp3-icon-desktop::before{ content:"īšÆ"; } +.bp3-icon-diagnosis::before{ + content:"ī¤"; } + .bp3-icon-diagram-tree::before{ content:"īž³"; } @@ -5506,6 +5807,9 @@ .bp3-icon-known-vehicle::before{ content:"īœ¼"; } +.bp3-icon-lab-test::before{ + content:"ī¤Ž"; } + .bp3-icon-label::before{ content:"ī™„"; } @@ -6218,6 +6522,7 @@ .bp3-submenu .bp3-popover-target{ display:block; } + .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{ } .bp3-submenu.bp3-popover{ -webkit-box-shadow:none; @@ -6233,19 +6538,19 @@ -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); } .bp3-menu{ - margin:0; - border-radius:3px; background:#ffffff; + border-radius:3px; + color:#182026; + list-style:none; + margin:0; min-width:180px; padding:5px; - list-style:none; - text-align:left; - color:#182026; } + text-align:left; } .bp3-menu-divider{ + border-top:1px solid rgba(16, 22, 26, 0.15); display:block; - margin:5px; - border-top:1px solid rgba(16, 22, 26, 0.15); } + margin:5px; } .bp3-dark .bp3-menu-divider{ border-top-color:rgba(255, 255, 255, 0.15); } @@ -6261,10 +6566,10 @@ -ms-flex-align:start; align-items:flex-start; border-radius:2px; + color:inherit; + line-height:20px; padding:5px 7px; text-decoration:none; - line-height:20px; - color:inherit; -webkit-user-select:none; -moz-user-select:none; -ms-user-select:none; @@ -6295,8 +6600,8 @@ text-decoration:none; } .bp3-menu-item.bp3-disabled{ background-color:inherit; - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-dark .bp3-menu-item{ color:inherit; } .bp3-dark .bp3-menu-item:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-menu-item{ @@ -6374,18 +6679,18 @@ .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{ color:#ffffff; } .bp3-menu-item::before{ - line-height:1; font-family:"Icons16", sans-serif; font-size:16px; - font-weight:400; font-style:normal; + font-weight:400; + line-height:1; -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; margin-right:7px; } .bp3-menu-item::before, .bp3-menu-item > .bp3-icon{ - margin-top:2px; - color:#5c7080; } + color:#5c7080; + margin-top:2px; } .bp3-menu-item .bp3-menu-item-label{ color:#5c7080; } .bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{ @@ -6393,40 +6698,40 @@ .bp3-menu-item.bp3-active, .bp3-menu-item:active{ background-color:rgba(115, 134, 148, 0.3); } .bp3-menu-item.bp3-disabled{ - outline:none !important; background-color:inherit !important; + color:rgba(92, 112, 128, 0.6) !important; cursor:not-allowed !important; - color:rgba(92, 112, 128, 0.6) !important; } + outline:none !important; } .bp3-menu-item.bp3-disabled::before, .bp3-menu-item.bp3-disabled > .bp3-icon, .bp3-menu-item.bp3-disabled .bp3-menu-item-label{ color:rgba(92, 112, 128, 0.6) !important; } .bp3-large .bp3-menu-item{ - padding:9px 7px; + font-size:16px; line-height:22px; - font-size:16px; } + padding:9px 7px; } .bp3-large .bp3-menu-item .bp3-icon{ margin-top:3px; } .bp3-large .bp3-menu-item::before{ - line-height:1; font-family:"Icons20", sans-serif; font-size:20px; - font-weight:400; font-style:normal; + font-weight:400; + line-height:1; -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; - margin-top:1px; - margin-right:10px; } + margin-right:10px; + margin-top:1px; } button.bp3-menu-item{ - border:none; background:none; - width:100%; - text-align:left; } + border:none; + text-align:left; + width:100%; } .bp3-menu-header{ + border-top:1px solid rgba(16, 22, 26, 0.15); display:block; margin:5px; - border-top:1px solid rgba(16, 22, 26, 0.15); cursor:default; padding-left:2px; } .bp3-dark .bp3-menu-header{ @@ -6440,17 +6745,17 @@ text-overflow:ellipsis; white-space:nowrap; word-wrap:normal; + line-height:17px; margin:0; - padding:10px 7px 0 1px; - line-height:17px; } + padding:10px 7px 0 1px; } .bp3-dark .bp3-menu-header > h6{ color:#f5f8fa; } .bp3-menu-header:first-of-type > h6{ padding-top:0; } .bp3-large .bp3-menu-header > h6{ - padding-top:15px; + font-size:18px; padding-bottom:5px; - font-size:18px; } + padding-top:15px; } .bp3-large .bp3-menu-header:first-of-type > h6{ padding-top:0; } @@ -6458,98 +6763,92 @@ background:#30404d; color:#f5f8fa; } -.bp3-dark .bp3-menu-item.bp3-intent-primary{ - color:#48aff0; } - .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-icon{ - color:inherit; } - .bp3-dark .bp3-menu-item.bp3-intent-primary::before, .bp3-dark .bp3-menu-item.bp3-intent-primary::after, - .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{ +.bp3-dark .bp3-menu-item{ } + .bp3-dark .bp3-menu-item.bp3-intent-primary{ color:#48aff0; } - .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active{ - background-color:#137cbd; } - .bp3-dark .bp3-menu-item.bp3-intent-primary:active{ - background-color:#106ba3; } - .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, - .bp3-dark .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label, - .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, - .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary:active, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::after, - .bp3-dark .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::after, - .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{ - color:#ffffff; } - -.bp3-dark .bp3-menu-item.bp3-intent-success{ - color:#3dcc91; } - .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-icon{ - color:inherit; } - .bp3-dark .bp3-menu-item.bp3-intent-success::before, .bp3-dark .bp3-menu-item.bp3-intent-success::after, - .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{ + .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-icon{ + color:inherit; } + .bp3-dark .bp3-menu-item.bp3-intent-primary::before, .bp3-dark .bp3-menu-item.bp3-intent-primary::after, + .bp3-dark .bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{ + color:#48aff0; } + .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active{ + background-color:#137cbd; } + .bp3-dark .bp3-menu-item.bp3-intent-primary:active{ + background-color:#106ba3; } + .bp3-dark .bp3-menu-item.bp3-intent-primary:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after, + .bp3-dark .bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label, + .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, + .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary:active, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary:active::after, + .bp3-dark .bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active::after, + .bp3-dark .bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{ + color:#ffffff; } + .bp3-dark .bp3-menu-item.bp3-intent-success{ color:#3dcc91; } - .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active{ - background-color:#0f9960; } - .bp3-dark .bp3-menu-item.bp3-intent-success:active{ - background-color:#0d8050; } - .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, - .bp3-dark .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label, - .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, - .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success:active, .bp3-dark .bp3-menu-item.bp3-intent-success:active::before, .bp3-dark .bp3-menu-item.bp3-intent-success:active::after, - .bp3-dark .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::after, - .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{ - color:#ffffff; } - -.bp3-dark .bp3-menu-item.bp3-intent-warning{ - color:#ffb366; } - .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-icon{ - color:inherit; } - .bp3-dark .bp3-menu-item.bp3-intent-warning::before, .bp3-dark .bp3-menu-item.bp3-intent-warning::after, - .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{ + .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-icon{ + color:inherit; } + .bp3-dark .bp3-menu-item.bp3-intent-success::before, .bp3-dark .bp3-menu-item.bp3-intent-success::after, + .bp3-dark .bp3-menu-item.bp3-intent-success .bp3-menu-item-label{ + color:#3dcc91; } + .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active{ + background-color:#0f9960; } + .bp3-dark .bp3-menu-item.bp3-intent-success:active{ + background-color:#0d8050; } + .bp3-dark .bp3-menu-item.bp3-intent-success:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-success:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after, + .bp3-dark .bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label, + .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, + .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success:active, .bp3-dark .bp3-menu-item.bp3-intent-success:active::before, .bp3-dark .bp3-menu-item.bp3-intent-success:active::after, + .bp3-dark .bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active::after, + .bp3-dark .bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{ + color:#ffffff; } + .bp3-dark .bp3-menu-item.bp3-intent-warning{ color:#ffb366; } - .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active{ - background-color:#d9822b; } - .bp3-dark .bp3-menu-item.bp3-intent-warning:active{ - background-color:#bf7326; } - .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, - .bp3-dark .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label, - .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, - .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning:active, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::after, - .bp3-dark .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::after, - .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{ - color:#ffffff; } - -.bp3-dark .bp3-menu-item.bp3-intent-danger{ - color:#ff7373; } - .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-icon{ - color:inherit; } - .bp3-dark .bp3-menu-item.bp3-intent-danger::before, .bp3-dark .bp3-menu-item.bp3-intent-danger::after, - .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{ + .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-icon{ + color:inherit; } + .bp3-dark .bp3-menu-item.bp3-intent-warning::before, .bp3-dark .bp3-menu-item.bp3-intent-warning::after, + .bp3-dark .bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{ + color:#ffb366; } + .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active{ + background-color:#d9822b; } + .bp3-dark .bp3-menu-item.bp3-intent-warning:active{ + background-color:#bf7326; } + .bp3-dark .bp3-menu-item.bp3-intent-warning:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after, + .bp3-dark .bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label, + .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, + .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning:active, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning:active::after, + .bp3-dark .bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active::after, + .bp3-dark .bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{ + color:#ffffff; } + .bp3-dark .bp3-menu-item.bp3-intent-danger{ color:#ff7373; } - .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active{ - background-color:#db3737; } - .bp3-dark .bp3-menu-item.bp3-intent-danger:active{ - background-color:#c23030; } - .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, - .bp3-dark .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label, - .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, - .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger:active, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::after, - .bp3-dark .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::after, - .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{ - color:#ffffff; } - -.bp3-dark .bp3-menu-item::before, -.bp3-dark .bp3-menu-item > .bp3-icon{ - color:#a7b6c2; } - -.bp3-dark .bp3-menu-item .bp3-menu-item-label{ - color:#a7b6c2; } - -.bp3-dark .bp3-menu-item.bp3-active, .bp3-dark .bp3-menu-item:active{ - background-color:rgba(138, 155, 168, 0.3); } - -.bp3-dark .bp3-menu-item.bp3-disabled{ - color:rgba(167, 182, 194, 0.6) !important; } - .bp3-dark .bp3-menu-item.bp3-disabled::before, - .bp3-dark .bp3-menu-item.bp3-disabled > .bp3-icon, - .bp3-dark .bp3-menu-item.bp3-disabled .bp3-menu-item-label{ + .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-icon{ + color:inherit; } + .bp3-dark .bp3-menu-item.bp3-intent-danger::before, .bp3-dark .bp3-menu-item.bp3-intent-danger::after, + .bp3-dark .bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{ + color:#ff7373; } + .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active{ + background-color:#db3737; } + .bp3-dark .bp3-menu-item.bp3-intent-danger:active{ + background-color:#c23030; } + .bp3-dark .bp3-menu-item.bp3-intent-danger:hover, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after, + .bp3-dark .bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label, + .bp3-dark .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, + .bp3-submenu .bp3-dark .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger:active, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger:active::after, + .bp3-dark .bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active::after, + .bp3-dark .bp3-menu-item.bp3-intent-danger.bp3-active .bp3-menu-item-label{ + color:#ffffff; } + .bp3-dark .bp3-menu-item::before, + .bp3-dark .bp3-menu-item > .bp3-icon{ + color:#a7b6c2; } + .bp3-dark .bp3-menu-item .bp3-menu-item-label{ + color:#a7b6c2; } + .bp3-dark .bp3-menu-item.bp3-active, .bp3-dark .bp3-menu-item:active{ + background-color:rgba(138, 155, 168, 0.3); } + .bp3-dark .bp3-menu-item.bp3-disabled{ color:rgba(167, 182, 194, 0.6) !important; } + .bp3-dark .bp3-menu-item.bp3-disabled::before, + .bp3-dark .bp3-menu-item.bp3-disabled > .bp3-icon, + .bp3-dark .bp3-menu-item.bp3-disabled .bp3-menu-item-label{ + color:rgba(167, 182, 194, 0.6) !important; } .bp3-dark .bp3-menu-divider, .bp3-dark .bp3-menu-header{ @@ -6561,14 +6860,14 @@ .bp3-label .bp3-menu{ margin-top:5px; } .bp3-navbar{ - position:relative; - z-index:10; + background-color:#ffffff; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.2); - background-color:#ffffff; - width:100%; height:50px; - padding:0 15px; } + padding:0 15px; + position:relative; + width:100%; + z-index:10; } .bp3-navbar.bp3-dark, .bp3-dark .bp3-navbar{ background-color:#394b59; } @@ -6579,22 +6878,22 @@ -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 0 0 rgba(16, 22, 26, 0), 0 1px 1px rgba(16, 22, 26, 0.4); } .bp3-navbar.bp3-fixed-top{ + left:0; position:fixed; - top:0; right:0; - left:0; } + top:0; } .bp3-navbar-heading{ - margin-right:15px; - font-size:16px; } + font-size:16px; + margin-right:15px; } .bp3-navbar-group{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; -webkit-box-align:center; -ms-flex-align:center; align-items:center; + display:-webkit-box; + display:-ms-flexbox; + display:flex; height:50px; } .bp3-navbar-group.bp3-align-left{ float:left; } @@ -6602,9 +6901,9 @@ float:right; } .bp3-navbar-divider{ - margin:0 10px; border-left:1px solid rgba(16, 22, 26, 0.15); - height:20px; } + height:20px; + margin:0 10px; } .bp3-dark .bp3-navbar-divider{ border-left-color:rgba(255, 255, 255, 0.15); } .bp3-non-ideal-state{ @@ -6618,12 +6917,12 @@ -webkit-box-align:center; -ms-flex-align:center; align-items:center; + height:100%; -webkit-box-pack:center; -ms-flex-pack:center; justify-content:center; - width:100%; - height:100%; - text-align:center; } + text-align:center; + width:100%; } .bp3-non-ideal-state > *{ -webkit-box-flex:0; -ms-flex-positive:0; @@ -6668,22 +6967,22 @@ overflow:hidden; } .bp3-overlay{ - position:static; - top:0; - right:0; bottom:0; left:0; + position:static; + right:0; + top:0; z-index:20; } .bp3-overlay:not(.bp3-overlay-open){ pointer-events:none; } .bp3-overlay.bp3-overlay-container{ - position:fixed; - overflow:hidden; } + overflow:hidden; + position:fixed; } .bp3-overlay.bp3-overlay-container.bp3-overlay-inline{ position:absolute; } .bp3-overlay.bp3-overlay-scroll-container{ - position:fixed; - overflow:auto; } + overflow:auto; + position:fixed; } .bp3-overlay.bp3-overlay-scroll-container.bp3-overlay-inline{ position:absolute; } .bp3-overlay.bp3-overlay-inline{ @@ -6698,77 +6997,77 @@ position:absolute; } .bp3-overlay-backdrop{ - position:fixed; - top:0; - right:0; bottom:0; left:0; + position:fixed; + right:0; + top:0; opacity:1; - z-index:20; background-color:rgba(16, 22, 26, 0.7); overflow:auto; -webkit-user-select:none; -moz-user-select:none; -ms-user-select:none; - user-select:none; } + user-select:none; + z-index:20; } .bp3-overlay-backdrop.bp3-overlay-enter, .bp3-overlay-backdrop.bp3-overlay-appear{ opacity:0; } .bp3-overlay-backdrop.bp3-overlay-enter-active, .bp3-overlay-backdrop.bp3-overlay-appear-active{ opacity:1; - -webkit-transition-property:opacity; - transition-property:opacity; + -webkit-transition-delay:0; + transition-delay:0; -webkit-transition-duration:200ms; transition-duration:200ms; + -webkit-transition-property:opacity; + transition-property:opacity; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-overlay-backdrop.bp3-overlay-exit{ opacity:1; } .bp3-overlay-backdrop.bp3-overlay-exit-active{ opacity:0; - -webkit-transition-property:opacity; - transition-property:opacity; + -webkit-transition-delay:0; + transition-delay:0; -webkit-transition-duration:200ms; transition-duration:200ms; + -webkit-transition-property:opacity; + transition-property:opacity; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-overlay-backdrop:focus{ outline:none; } .bp3-overlay-inline .bp3-overlay-backdrop{ position:absolute; } .bp3-panel-stack{ - position:relative; - overflow:hidden; } + overflow:hidden; + position:relative; } .bp3-panel-stack-header{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; - -ms-flex-negative:0; - flex-shrink:0; -webkit-box-align:center; -ms-flex-align:center; align-items:center; - z-index:1; -webkit-box-shadow:0 1px rgba(16, 22, 26, 0.15); box-shadow:0 1px rgba(16, 22, 26, 0.15); - height:30px; } + display:-webkit-box; + display:-ms-flexbox; + display:flex; + -ms-flex-negative:0; + flex-shrink:0; + height:30px; + z-index:1; } .bp3-dark .bp3-panel-stack-header{ -webkit-box-shadow:0 1px rgba(255, 255, 255, 0.15); box-shadow:0 1px rgba(255, 255, 255, 0.15); } .bp3-panel-stack-header > span{ + -webkit-box-align:stretch; + -ms-flex-align:stretch; + align-items:stretch; display:-webkit-box; display:-ms-flexbox; display:flex; -webkit-box-flex:1; -ms-flex:1; - flex:1; - -webkit-box-align:stretch; - -ms-flex-align:stretch; - align-items:stretch; } + flex:1; } .bp3-panel-stack-header .bp3-heading{ margin:0 5px; } @@ -6780,11 +7079,13 @@ margin:0 2px; } .bp3-panel-stack-view{ - position:absolute; - top:0; - right:0; bottom:0; left:0; + position:absolute; + right:0; + top:0; + background-color:#ffffff; + border-right:1px solid rgba(16, 22, 26, 0.15); display:-webkit-box; display:-ms-flexbox; display:flex; @@ -6793,11 +7094,12 @@ -ms-flex-direction:column; flex-direction:column; margin-right:-1px; - border-right:1px solid rgba(16, 22, 26, 0.15); - background-color:#ffffff; - overflow-y:auto; } + overflow-y:auto; + z-index:1; } .bp3-dark .bp3-panel-stack-view{ background-color:#30404d; } + .bp3-panel-stack-view:nth-last-child(n + 4){ + display:none; } .bp3-panel-stack-push .bp3-panel-stack-enter, .bp3-panel-stack-push .bp3-panel-stack-appear{ -webkit-transform:translateX(100%); @@ -6808,16 +7110,16 @@ -webkit-transform:translate(0%); transform:translate(0%); opacity:1; + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:400ms; + transition-duration:400ms; -webkit-transition-property:opacity, -webkit-transform; transition-property:opacity, -webkit-transform; transition-property:transform, opacity; transition-property:transform, opacity, -webkit-transform; - -webkit-transition-duration:400ms; - transition-duration:400ms; -webkit-transition-timing-function:ease; - transition-timing-function:ease; - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:ease; } .bp3-panel-stack-push .bp3-panel-stack-exit{ -webkit-transform:translate(0%); @@ -6828,16 +7130,16 @@ -webkit-transform:translateX(-50%); transform:translateX(-50%); opacity:0; + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:400ms; + transition-duration:400ms; -webkit-transition-property:opacity, -webkit-transform; transition-property:opacity, -webkit-transform; transition-property:transform, opacity; transition-property:transform, opacity, -webkit-transform; - -webkit-transition-duration:400ms; - transition-duration:400ms; -webkit-transition-timing-function:ease; - transition-timing-function:ease; - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:ease; } .bp3-panel-stack-pop .bp3-panel-stack-enter, .bp3-panel-stack-pop .bp3-panel-stack-appear{ -webkit-transform:translateX(-50%); @@ -6848,16 +7150,16 @@ -webkit-transform:translate(0%); transform:translate(0%); opacity:1; + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:400ms; + transition-duration:400ms; -webkit-transition-property:opacity, -webkit-transform; transition-property:opacity, -webkit-transform; transition-property:transform, opacity; transition-property:transform, opacity, -webkit-transform; - -webkit-transition-duration:400ms; - transition-duration:400ms; -webkit-transition-timing-function:ease; - transition-timing-function:ease; - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:ease; } .bp3-panel-stack-pop .bp3-panel-stack-exit{ -webkit-transform:translate(0%); @@ -6868,35 +7170,35 @@ -webkit-transform:translateX(100%); transform:translateX(100%); opacity:0; + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:400ms; + transition-duration:400ms; -webkit-transition-property:opacity, -webkit-transform; transition-property:opacity, -webkit-transform; transition-property:transform, opacity; transition-property:transform, opacity, -webkit-transform; - -webkit-transition-duration:400ms; - transition-duration:400ms; -webkit-transition-timing-function:ease; - transition-timing-function:ease; - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:ease; } .bp3-popover{ -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); -webkit-transform:scale(1); transform:scale(1); + border-radius:3px; display:inline-block; - z-index:20; - border-radius:3px; } + z-index:20; } .bp3-popover .bp3-popover-arrow{ + height:30px; position:absolute; - width:30px; - height:30px; } + width:30px; } .bp3-popover .bp3-popover-arrow::before{ + height:20px; margin:5px; - width:20px; - height:20px; } + width:20px; } .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover{ - margin-top:-17px; - margin-bottom:17px; } + margin-bottom:17px; + margin-top:-17px; } .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{ bottom:-11px; } .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow svg{ @@ -6917,8 +7219,8 @@ -webkit-transform:rotate(90deg); transform:rotate(90deg); } .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover{ - margin-right:17px; - margin-left:-17px; } + margin-left:-17px; + margin-right:17px; } .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{ right:-11px; } .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow svg{ @@ -6984,35 +7286,35 @@ .bp3-popover-enter-active > .bp3-popover, .bp3-popover-appear-active > .bp3-popover{ -webkit-transform:scale(1); transform:scale(1); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:300ms; + transition-duration:300ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:300ms; - transition-duration:300ms; -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); } .bp3-popover-exit > .bp3-popover{ -webkit-transform:scale(1); transform:scale(1); } .bp3-popover-exit-active > .bp3-popover{ -webkit-transform:scale(0.3); transform:scale(0.3); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:300ms; + transition-duration:300ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:300ms; - transition-duration:300ms; -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); } .bp3-popover .bp3-popover-content{ - position:relative; - border-radius:3px; } + border-radius:3px; + position:relative; } .bp3-popover.bp3-popover-content-sizing .bp3-popover-content{ max-width:350px; padding:20px; } @@ -7031,32 +7333,32 @@ .bp3-popover-enter-active > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear-active > .bp3-popover.bp3-minimal.bp3-popover{ -webkit-transform:scale(1); transform:scale(1); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-popover-exit > .bp3-popover.bp3-minimal.bp3-popover{ -webkit-transform:scale(1); transform:scale(1); } .bp3-popover-exit-active > .bp3-popover.bp3-minimal.bp3-popover{ -webkit-transform:scale(1); transform:scale(1); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-popover.bp3-dark, .bp3-dark .bp3-popover{ -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); @@ -7078,12 +7380,12 @@ fill:#30404d; } .bp3-popover-arrow::before{ + border-radius:2px; + content:""; display:block; position:absolute; -webkit-transform:rotate(45deg); - transform:rotate(45deg); - border-radius:2px; - content:""; } + transform:rotate(45deg); } .bp3-tether-pinned .bp3-popover-arrow{ display:none; } @@ -7101,26 +7403,26 @@ opacity:0; } .bp3-transition-container.bp3-popover-enter-active, .bp3-transition-container.bp3-popover-appear-active{ opacity:1; - -webkit-transition-property:opacity; - transition-property:opacity; + -webkit-transition-delay:0; + transition-delay:0; -webkit-transition-duration:100ms; transition-duration:100ms; + -webkit-transition-property:opacity; + transition-property:opacity; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-transition-container.bp3-popover-exit{ opacity:1; } .bp3-transition-container.bp3-popover-exit-active{ opacity:0; - -webkit-transition-property:opacity; - transition-property:opacity; + -webkit-transition-delay:0; + transition-delay:0; -webkit-transition-duration:100ms; transition-duration:100ms; + -webkit-transition-property:opacity; + transition-property:opacity; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-transition-container:focus{ outline:none; } .bp3-transition-container.bp3-popover-leave .bp3-popover-content{ @@ -7135,10 +7437,10 @@ width:100%; } .bp3-portal{ + left:0; position:absolute; - top:0; right:0; - left:0; } + top:0; } @-webkit-keyframes linear-progress-bar-stripes{ from{ background-position:0 0; } @@ -7151,23 +7453,23 @@ background-position:30px 0; } } .bp3-progress-bar{ - display:block; - position:relative; - border-radius:40px; background:rgba(92, 112, 128, 0.2); - width:100%; + border-radius:40px; + display:block; height:8px; - overflow:hidden; } + overflow:hidden; + position:relative; + width:100%; } .bp3-progress-bar .bp3-progress-meter{ - position:absolute; - border-radius:40px; background:linear-gradient(-45deg, rgba(255, 255, 255, 0.2) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.2) 50%, rgba(255, 255, 255, 0.2) 75%, transparent 75%); background-color:rgba(92, 112, 128, 0.8); background-size:30px 30px; - width:100%; + border-radius:40px; height:100%; + position:absolute; -webkit-transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9); - transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9); } + transition:width 200ms cubic-bezier(0.4, 1, 0.75, 0.9); + width:100%; } .bp3-progress-bar:not(.bp3-no-animation):not(.bp3-no-stripes) .bp3-progress-meter{ animation:linear-progress-bar-stripes 300ms linear infinite reverse; } .bp3-progress-bar.bp3-no-stripes .bp3-progress-meter{ @@ -7191,29 +7493,29 @@ background-color:#db3737; } @-webkit-keyframes skeleton-glow{ from{ - border-color:rgba(206, 217, 224, 0.2); - background:rgba(206, 217, 224, 0.2); } + background:rgba(206, 217, 224, 0.2); + border-color:rgba(206, 217, 224, 0.2); } to{ - border-color:rgba(92, 112, 128, 0.2); - background:rgba(92, 112, 128, 0.2); } } + background:rgba(92, 112, 128, 0.2); + border-color:rgba(92, 112, 128, 0.2); } } @keyframes skeleton-glow{ from{ - border-color:rgba(206, 217, 224, 0.2); - background:rgba(206, 217, 224, 0.2); } + background:rgba(206, 217, 224, 0.2); + border-color:rgba(206, 217, 224, 0.2); } to{ - border-color:rgba(92, 112, 128, 0.2); - background:rgba(92, 112, 128, 0.2); } } + background:rgba(92, 112, 128, 0.2); + border-color:rgba(92, 112, 128, 0.2); } } .bp3-skeleton{ + -webkit-animation:1000ms linear infinite alternate skeleton-glow; + animation:1000ms linear infinite alternate skeleton-glow; + background:rgba(206, 217, 224, 0.2); + background-clip:padding-box !important; border-color:rgba(206, 217, 224, 0.2) !important; border-radius:2px; -webkit-box-shadow:none !important; box-shadow:none !important; - background:rgba(206, 217, 224, 0.2); - background-clip:padding-box !important; - cursor:default; color:transparent !important; - -webkit-animation:1000ms linear infinite alternate skeleton-glow; - animation:1000ms linear infinite alternate skeleton-glow; + cursor:default; pointer-events:none; -webkit-user-select:none; -moz-user-select:none; @@ -7223,12 +7525,12 @@ .bp3-skeleton *{ visibility:hidden !important; } .bp3-slider{ - width:100%; - min-width:150px; height:40px; - position:relative; - outline:none; + min-width:150px; + width:100%; cursor:default; + outline:none; + position:relative; -webkit-user-select:none; -moz-user-select:none; -ms-user-select:none; @@ -7239,17 +7541,17 @@ cursor:-webkit-grabbing; cursor:grabbing; } .bp3-slider.bp3-disabled{ - opacity:0.5; - cursor:not-allowed; } + cursor:not-allowed; + opacity:0.5; } .bp3-slider.bp3-slider-unlabeled{ height:16px; } .bp3-slider-track, .bp3-slider-progress{ - top:5px; - right:0; - left:0; height:6px; + left:0; + right:0; + top:5px; position:absolute; } .bp3-slider-track{ @@ -7270,90 +7572,90 @@ background-color:#db3737; } .bp3-slider-handle{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-color:#f5f8fa; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.8)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.8), rgba(255, 255, 255, 0)); + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); color:#182026; - position:absolute; - top:0; - left:0; border-radius:3px; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2); cursor:pointer; - width:16px; - height:16px; } + height:16px; + left:0; + position:absolute; + top:0; + width:16px; } .bp3-slider-handle:hover{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-clip:padding-box; - background-color:#ebf1f5; } + background-color:#ebf1f5; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); } .bp3-slider-handle:active, .bp3-slider-handle.bp3-active{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#d8e1e8; - background-image:none; } + background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-slider-handle:disabled, .bp3-slider-handle.bp3-disabled{ - outline:none; - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(206, 217, 224, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; + color:rgba(92, 112, 128, 0.6); cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + outline:none; } .bp3-slider-handle:disabled.bp3-active, .bp3-slider-handle:disabled.bp3-active:hover, .bp3-slider-handle.bp3-disabled.bp3-active, .bp3-slider-handle.bp3-disabled.bp3-active:hover{ background:rgba(206, 217, 224, 0.7); } .bp3-slider-handle:focus{ z-index:1; } .bp3-slider-handle:hover{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); background-clip:padding-box; background-color:#ebf1f5; - z-index:2; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 -1px 0 rgba(16, 22, 26, 0.1); -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.2); cursor:-webkit-grab; - cursor:grab; } + cursor:grab; + z-index:2; } .bp3-slider-handle.bp3-active{ - -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#d8e1e8; background-image:none; + -webkit-box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:inset 0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 2px rgba(16, 22, 26, 0.2); -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), inset 0 1px 1px rgba(16, 22, 26, 0.1); cursor:-webkit-grabbing; cursor:grabbing; } .bp3-disabled .bp3-slider-handle{ + background:#bfccd6; -webkit-box-shadow:none; box-shadow:none; - background:#bfccd6; pointer-events:none; } .bp3-dark .bp3-slider-handle{ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); background-color:#394b59; background-image:-webkit-gradient(linear, left top, left bottom, from(rgba(255, 255, 255, 0.05)), to(rgba(255, 255, 255, 0))); background-image:linear-gradient(to bottom, rgba(255, 255, 255, 0.05), rgba(255, 255, 255, 0)); + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); color:#f5f8fa; } .bp3-dark .bp3-slider-handle:hover, .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{ color:#f5f8fa; } .bp3-dark .bp3-slider-handle:hover{ + background-color:#30404d; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); - background-color:#30404d; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-slider-handle:active, .bp3-dark .bp3-slider-handle.bp3-active{ - -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); background-color:#202b33; - background-image:none; } + background-image:none; + -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.6), inset 0 1px 2px rgba(16, 22, 26, 0.2); } .bp3-dark .bp3-slider-handle:disabled, .bp3-dark .bp3-slider-handle.bp3-disabled{ - -webkit-box-shadow:none; - box-shadow:none; background-color:rgba(57, 75, 89, 0.5); background-image:none; + -webkit-box-shadow:none; + box-shadow:none; color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-slider-handle:disabled.bp3-active, .bp3-dark .bp3-slider-handle.bp3-disabled.bp3-active{ background:rgba(57, 75, 89, 0.7); } @@ -7365,21 +7667,21 @@ .bp3-dark .bp3-slider-handle.bp3-active{ background-color:#293742; } .bp3-dark .bp3-disabled .bp3-slider-handle{ + background:#5c7080; border-color:#5c7080; -webkit-box-shadow:none; - box-shadow:none; - background:#5c7080; } + box-shadow:none; } .bp3-slider-handle .bp3-slider-label{ - margin-left:8px; + background:#394b59; border-radius:3px; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); - background:#394b59; - color:#f5f8fa; } + color:#f5f8fa; + margin-left:8px; } .bp3-dark .bp3-slider-handle .bp3-slider-label{ + background:#e1e8ed; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); - background:#e1e8ed; color:#394b59; } .bp3-disabled .bp3-slider-handle .bp3-slider-label{ -webkit-box-shadow:none; @@ -7387,12 +7689,12 @@ .bp3-slider-handle.bp3-start, .bp3-slider-handle.bp3-end{ width:8px; } .bp3-slider-handle.bp3-start{ - border-top-right-radius:0; - border-bottom-right-radius:0; } + border-bottom-right-radius:0; + border-top-right-radius:0; } .bp3-slider-handle.bp3-end{ - margin-left:8px; + border-bottom-left-radius:0; border-top-left-radius:0; - border-bottom-left-radius:0; } + margin-left:8px; } .bp3-slider-handle.bp3-end .bp3-slider-label{ margin-left:0; } @@ -7400,23 +7702,23 @@ -webkit-transform:translate(-50%, 20px); transform:translate(-50%, 20px); display:inline-block; - position:absolute; - padding:2px 5px; - vertical-align:top; + font-size:12px; line-height:1; - font-size:12px; } + padding:2px 5px; + position:absolute; + vertical-align:top; } .bp3-slider.bp3-vertical{ - width:40px; + height:150px; min-width:40px; - height:150px; } + width:40px; } .bp3-slider.bp3-vertical .bp3-slider-track, .bp3-slider.bp3-vertical .bp3-slider-progress{ - top:0; bottom:0; + height:auto; left:5px; - width:6px; - height:auto; } + top:0; + width:6px; } .bp3-slider.bp3-vertical .bp3-slider-progress{ top:auto; } .bp3-slider.bp3-vertical .bp3-slider-label{ @@ -7425,23 +7727,23 @@ .bp3-slider.bp3-vertical .bp3-slider-handle{ top:auto; } .bp3-slider.bp3-vertical .bp3-slider-handle .bp3-slider-label{ - margin-top:-8px; - margin-left:0; } + margin-left:0; + margin-top:-8px; } .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end, .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{ + height:8px; margin-left:0; - width:16px; - height:8px; } + width:16px; } .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{ - border-top-left-radius:0; - border-bottom-right-radius:3px; } + border-bottom-right-radius:3px; + border-top-left-radius:0; } .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start .bp3-slider-label{ -webkit-transform:translate(20px); transform:translate(20px); } .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end{ - margin-bottom:8px; - border-top-left-radius:3px; border-bottom-left-radius:0; - border-bottom-right-radius:0; } + border-bottom-right-radius:0; + border-top-left-radius:3px; + margin-bottom:8px; } @-webkit-keyframes pt-spinner-animation{ from{ @@ -7460,12 +7762,12 @@ transform:rotate(360deg); } } .bp3-spinner{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; -webkit-box-align:center; -ms-flex-align:center; align-items:center; + display:-webkit-box; + display:-ms-flexbox; + display:flex; -webkit-box-pack:center; -ms-flex-pack:center; justify-content:center; @@ -7476,12 +7778,12 @@ .bp3-spinner path{ fill-opacity:0; } .bp3-spinner .bp3-spinner-head{ + stroke:rgba(92, 112, 128, 0.8); + stroke-linecap:round; -webkit-transform-origin:center; transform-origin:center; -webkit-transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9); - transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9); - stroke:rgba(92, 112, 128, 0.8); - stroke-linecap:round; } + transition:stroke-dashoffset 200ms cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-spinner .bp3-spinner-track{ stroke:rgba(92, 112, 128, 0.2); } @@ -7514,48 +7816,48 @@ display:-ms-flexbox; display:flex; } .bp3-tabs.bp3-vertical > .bp3-tab-list{ + -webkit-box-align:start; + -ms-flex-align:start; + align-items:flex-start; -webkit-box-orient:vertical; -webkit-box-direction:normal; -ms-flex-direction:column; - flex-direction:column; - -webkit-box-align:start; - -ms-flex-align:start; - align-items:flex-start; } + flex-direction:column; } .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab{ border-radius:3px; - width:100%; - padding:0 10px; } + padding:0 10px; + width:100%; } .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab[aria-selected="true"]{ + background-color:rgba(19, 124, 189, 0.2); -webkit-box-shadow:none; - box-shadow:none; - background-color:rgba(19, 124, 189, 0.2); } + box-shadow:none; } .bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab-indicator-wrapper .bp3-tab-indicator{ - top:0; - right:0; + background-color:rgba(19, 124, 189, 0.2); + border-radius:3px; bottom:0; + height:auto; left:0; - border-radius:3px; - background-color:rgba(19, 124, 189, 0.2); - height:auto; } + right:0; + top:0; } .bp3-tabs.bp3-vertical > .bp3-tab-panel{ margin-top:0; padding-left:20px; } .bp3-tab-list{ + -webkit-box-align:end; + -ms-flex-align:end; + align-items:flex-end; + border:none; display:-webkit-box; display:-ms-flexbox; display:flex; -webkit-box-flex:0; -ms-flex:0 0 auto; flex:0 0 auto; - -webkit-box-align:end; - -ms-flex-align:end; - align-items:flex-end; - position:relative; + list-style:none; margin:0; - border:none; padding:0; - list-style:none; } + position:relative; } .bp3-tab-list > *:not(:last-child){ margin-right:20px; } @@ -7564,27 +7866,27 @@ text-overflow:ellipsis; white-space:nowrap; word-wrap:normal; + color:#182026; + cursor:pointer; -webkit-box-flex:0; -ms-flex:0 0 auto; flex:0 0 auto; - position:relative; - cursor:pointer; - max-width:100%; - vertical-align:top; + font-size:14px; line-height:30px; - color:#182026; - font-size:14px; } + max-width:100%; + position:relative; + vertical-align:top; } .bp3-tab a{ + color:inherit; display:block; - text-decoration:none; - color:inherit; } + text-decoration:none; } .bp3-tab-indicator-wrapper ~ .bp3-tab{ + background-color:transparent !important; -webkit-box-shadow:none !important; - box-shadow:none !important; - background-color:transparent !important; } + box-shadow:none !important; } .bp3-tab[aria-disabled="true"]{ - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-tab[aria-selected="true"]{ border-radius:0; -webkit-box-shadow:inset 0 -3px 0 #106ba3; @@ -7594,8 +7896,8 @@ .bp3-tab:focus{ -moz-outline-radius:0; } .bp3-large > .bp3-tab{ - line-height:40px; - font-size:16px; } + font-size:16px; + line-height:40px; } .bp3-tab-panel{ margin-top:20px; } @@ -7603,9 +7905,10 @@ display:none; } .bp3-tab-indicator-wrapper{ + left:0; + pointer-events:none; position:absolute; top:0; - left:0; -webkit-transform:translateX(0), translateY(0); transform:translateX(0), translateY(0); -webkit-transition:height, width, -webkit-transform; @@ -7615,15 +7918,14 @@ -webkit-transition-duration:200ms; transition-duration:200ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - pointer-events:none; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-tab-indicator-wrapper .bp3-tab-indicator{ - position:absolute; - right:0; + background-color:#106ba3; bottom:0; + height:3px; left:0; - background-color:#106ba3; - height:3px; } + position:absolute; + right:0; } .bp3-tab-indicator-wrapper.bp3-no-animation{ -webkit-transition:none; transition:none; } @@ -7656,19 +7958,19 @@ -webkit-box-align:center; -ms-flex-align:center; align-items:center; - position:relative; + background-color:#5c7080; border:none; border-radius:3px; -webkit-box-shadow:none; box-shadow:none; - background-color:#5c7080; - min-width:20px; + color:#f5f8fa; + font-size:12px; + line-height:16px; max-width:100%; min-height:20px; + min-width:20px; padding:2px 6px; - line-height:16px; - color:#f5f8fa; - font-size:12px; } + position:relative; } .bp3-tag.bp3-interactive{ cursor:pointer; } .bp3-tag.bp3-interactive:hover{ @@ -7699,8 +8001,8 @@ -moz-outline-radius:6px; } .bp3-tag.bp3-round{ border-radius:30px; - padding-right:8px; - padding-left:8px; } + padding-left:8px; + padding-right:8px; } .bp3-dark .bp3-tag{ background-color:#bfccd6; color:#182026; } @@ -7716,11 +8018,11 @@ fill:#ffffff; } .bp3-tag.bp3-large, .bp3-large .bp3-tag{ - min-width:30px; - min-height:30px; - padding:0 10px; + font-size:14px; line-height:20px; - font-size:14px; } + min-height:30px; + min-width:30px; + padding:5px 10px; } .bp3-tag.bp3-large::before, .bp3-tag.bp3-large > *, .bp3-large .bp3-tag::before, @@ -7733,8 +8035,8 @@ margin-right:0; } .bp3-tag.bp3-large.bp3-round, .bp3-large .bp3-tag.bp3-round{ - padding-right:12px; - padding-left:12px; } + padding-left:12px; + padding-right:12px; } .bp3-tag.bp3-intent-primary{ background:#137cbd; color:#ffffff; } @@ -7879,44 +8181,43 @@ background-color:rgba(219, 55, 55, 0.45); } .bp3-tag-remove{ + background:none; + border:none; + color:inherit; + cursor:pointer; display:-webkit-box; display:-ms-flexbox; display:flex; - opacity:0.5; - margin-top:-2px; - margin-right:-6px !important; margin-bottom:-2px; - border:none; - background:none; - cursor:pointer; + margin-right:-6px !important; + margin-top:-2px; + opacity:0.5; padding:2px; - padding-left:0; - color:inherit; } + padding-left:0; } .bp3-tag-remove:hover{ - opacity:0.8; background:none; + opacity:0.8; text-decoration:none; } .bp3-tag-remove:active{ opacity:1; } .bp3-tag-remove:empty::before{ - line-height:1; font-family:"Icons16", sans-serif; font-size:16px; - font-weight:400; font-style:normal; + font-weight:400; + line-height:1; -moz-osx-font-smoothing:grayscale; -webkit-font-smoothing:antialiased; content:"ī›—"; } .bp3-large .bp3-tag-remove{ margin-right:-10px !important; - padding:5px; - padding-left:0; } + padding:0 5px 0 0; } .bp3-large .bp3-tag-remove:empty::before{ - line-height:1; font-family:"Icons20", sans-serif; font-size:20px; + font-style:normal; font-weight:400; - font-style:normal; } + line-height:1; } .bp3-tag-input{ display:-webkit-box; display:-ms-flexbox; @@ -7930,10 +8231,10 @@ align-items:flex-start; cursor:text; height:auto; + line-height:inherit; min-height:30px; - padding-right:0; padding-left:5px; - line-height:inherit; } + padding-right:0; } .bp3-tag-input > *{ -webkit-box-flex:0; -ms-flex-positive:0; @@ -7947,10 +8248,10 @@ -ms-flex-negative:1; flex-shrink:1; } .bp3-tag-input .bp3-tag-input-icon{ - margin-top:7px; - margin-right:7px; + color:#5c7080; margin-left:2px; - color:#5c7080; } + margin-right:7px; + margin-top:7px; } .bp3-tag-input .bp3-tag-input-values{ display:-webkit-box; display:-ms-flexbox; @@ -7959,15 +8260,15 @@ -webkit-box-direction:normal; -ms-flex-direction:row; flex-direction:row; - -ms-flex-wrap:wrap; - flex-wrap:wrap; -webkit-box-align:center; -ms-flex-align:center; align-items:center; -ms-flex-item-align:stretch; align-self:stretch; - margin-top:5px; + -ms-flex-wrap:wrap; + flex-wrap:wrap; margin-right:7px; + margin-top:5px; min-width:0; } .bp3-tag-input .bp3-tag-input-values > *{ -webkit-box-flex:0; @@ -8001,8 +8302,8 @@ -webkit-box-flex:1; -ms-flex:1 1 auto; flex:1 1 auto; - width:80px; - line-height:20px; } + line-height:20px; + width:80px; } .bp3-tag-input .bp3-input-ghost:disabled, .bp3-tag-input .bp3-input-ghost.bp3-disabled{ cursor:not-allowed; } .bp3-tag-input .bp3-button, @@ -8010,8 +8311,8 @@ margin:3px; margin-left:0; } .bp3-tag-input .bp3-button{ - min-width:24px; min-height:24px; + min-width:24px; padding:0 7px; } .bp3-tag-input.bp3-large{ height:auto; @@ -8023,13 +8324,13 @@ .bp3-tag-input.bp3-large > :last-child{ margin-right:0; } .bp3-tag-input.bp3-large .bp3-tag-input-icon{ - margin-top:10px; - margin-left:5px; } + margin-left:5px; + margin-top:10px; } .bp3-tag-input.bp3-large .bp3-input-ghost{ line-height:30px; } .bp3-tag-input.bp3-large .bp3-button{ - min-width:30px; min-height:30px; + min-width:30px; padding:5px 10px; margin:5px; margin-left:0; } @@ -8037,9 +8338,9 @@ margin:8px; margin-left:0; } .bp3-tag-input.bp3-active{ + background-color:#ffffff; -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); - box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); - background-color:#ffffff; } + box-shadow:0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); } .bp3-tag-input.bp3-active.bp3-intent-primary{ -webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); } @@ -8067,9 +8368,9 @@ .bp3-dark .bp3-tag-input .bp3-input-ghost::placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::placeholder{ color:rgba(167, 182, 194, 0.6); } .bp3-dark .bp3-tag-input.bp3-active, .bp3-tag-input.bp3-dark.bp3-active{ + background-color:rgba(16, 22, 26, 0.3); -webkit-box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); - background-color:rgba(16, 22, 26, 0.3); } + box-shadow:0 0 0 1px #137cbd, 0 0 0 1px #137cbd, 0 0 0 3px rgba(19, 124, 189, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); } .bp3-dark .bp3-tag-input.bp3-active.bp3-intent-primary, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-primary{ -webkit-box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); box-shadow:0 0 0 1px #106ba3, 0 0 0 3px rgba(16, 107, 163, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); } @@ -8084,76 +8385,76 @@ box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); } .bp3-input-ghost{ + background:none; border:none; -webkit-box-shadow:none; box-shadow:none; - background:none; padding:0; } .bp3-input-ghost::-webkit-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input-ghost::-moz-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input-ghost:-ms-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input-ghost::-ms-input-placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input-ghost::placeholder{ - opacity:1; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + opacity:1; } .bp3-input-ghost:focus{ outline:none !important; } .bp3-toast{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; -webkit-box-align:start; -ms-flex-align:start; align-items:flex-start; - position:relative !important; - margin:20px 0 0; + background-color:#ffffff; border-radius:3px; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 2px 4px rgba(16, 22, 26, 0.2), 0 8px 24px rgba(16, 22, 26, 0.2); - background-color:#ffffff; - min-width:300px; + display:-webkit-box; + display:-ms-flexbox; + display:flex; + margin:20px 0 0; max-width:500px; - pointer-events:all; } + min-width:300px; + pointer-events:all; + position:relative !important; } .bp3-toast.bp3-toast-enter, .bp3-toast.bp3-toast-appear{ -webkit-transform:translateY(-40px); transform:translateY(-40px); } .bp3-toast.bp3-toast-enter-active, .bp3-toast.bp3-toast-appear-active{ -webkit-transform:translateY(0); transform:translateY(0); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:300ms; + transition-duration:300ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:300ms; - transition-duration:300ms; -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); } .bp3-toast.bp3-toast-enter ~ .bp3-toast, .bp3-toast.bp3-toast-appear ~ .bp3-toast{ -webkit-transform:translateY(-40px); transform:translateY(-40px); } .bp3-toast.bp3-toast-enter-active ~ .bp3-toast, .bp3-toast.bp3-toast-appear-active ~ .bp3-toast{ -webkit-transform:translateY(0); transform:translateY(0); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:300ms; + transition-duration:300ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:300ms; - transition-duration:300ms; -webkit-transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.54, 1.12, 0.38, 1.11); } .bp3-toast.bp3-toast-exit{ opacity:1; -webkit-filter:blur(0); @@ -8162,32 +8463,32 @@ opacity:0; -webkit-filter:blur(10px); filter:blur(10px); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:300ms; + transition-duration:300ms; -webkit-transition-property:opacity, -webkit-filter; transition-property:opacity, -webkit-filter; transition-property:opacity, filter; transition-property:opacity, filter, -webkit-filter; - -webkit-transition-duration:300ms; - transition-duration:300ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-toast.bp3-toast-exit ~ .bp3-toast{ -webkit-transform:translateY(0); transform:translateY(0); } .bp3-toast.bp3-toast-exit-active ~ .bp3-toast{ -webkit-transform:translateY(-40px); transform:translateY(-40px); + -webkit-transition-delay:50ms; + transition-delay:50ms; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:50ms; - transition-delay:50ms; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-toast .bp3-button-group{ -webkit-box-flex:0; -ms-flex:0 0 auto; @@ -8195,14 +8496,14 @@ padding:5px; padding-left:0; } .bp3-toast > .bp3-icon{ + color:#5c7080; margin:12px; - margin-right:0; - color:#5c7080; } + margin-right:0; } .bp3-toast.bp3-dark, .bp3-dark .bp3-toast{ + background-color:#394b59; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); - background-color:#394b59; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 2px 4px rgba(16, 22, 26, 0.4), 0 8px 24px rgba(16, 22, 26, 0.4); } .bp3-toast.bp3-dark > .bp3-icon, .bp3-dark .bp3-toast > .bp3-icon{ color:#a7b6c2; } @@ -8246,6 +8547,9 @@ word-break:break-word; } .bp3-toast-container{ + -webkit-box-align:center; + -ms-flex-align:center; + align-items:center; display:-webkit-box !important; display:-ms-flexbox !important; display:flex !important; @@ -8253,26 +8557,22 @@ -webkit-box-direction:normal; -ms-flex-direction:column; flex-direction:column; - -webkit-box-align:center; - -ms-flex-align:center; - align-items:center; - position:fixed; - right:0; left:0; - z-index:40; overflow:hidden; padding:0 20px 20px; - pointer-events:none; } + pointer-events:none; + position:fixed; + right:0; + z-index:40; } .bp3-toast-container.bp3-toast-container-top{ - top:0; - bottom:auto; } + top:0; } .bp3-toast-container.bp3-toast-container-bottom{ + bottom:0; -webkit-box-orient:vertical; -webkit-box-direction:reverse; -ms-flex-direction:column-reverse; flex-direction:column-reverse; - top:auto; - bottom:0; } + top:auto; } .bp3-toast-container.bp3-toast-container-left{ -webkit-box-align:start; -ms-flex-align:start; @@ -8285,6 +8585,7 @@ .bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active), .bp3-toast-container-bottom .bp3-toast.bp3-toast-enter:not(.bp3-toast-enter-active) ~ .bp3-toast, .bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active), .bp3-toast-container-bottom .bp3-toast.bp3-toast-appear:not(.bp3-toast-appear-active) ~ .bp3-toast, +.bp3-toast-container-bottom .bp3-toast.bp3-toast-exit-active ~ .bp3-toast, .bp3-toast-container-bottom .bp3-toast.bp3-toast-leave-active ~ .bp3-toast{ -webkit-transform:translateY(60px); transform:translateY(60px); } @@ -8294,16 +8595,16 @@ -webkit-transform:scale(1); transform:scale(1); } .bp3-tooltip .bp3-popover-arrow{ + height:22px; position:absolute; - width:22px; - height:22px; } + width:22px; } .bp3-tooltip .bp3-popover-arrow::before{ + height:14px; margin:4px; - width:14px; - height:14px; } + width:14px; } .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip{ - margin-top:-11px; - margin-bottom:11px; } + margin-bottom:11px; + margin-top:-11px; } .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{ bottom:-8px; } .bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow svg{ @@ -8324,8 +8625,8 @@ -webkit-transform:rotate(90deg); transform:rotate(90deg); } .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip{ - margin-right:11px; - margin-left:-11px; } + margin-left:-11px; + margin-right:11px; } .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{ right:-8px; } .bp3-tether-element-attached-right.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow svg{ @@ -8391,32 +8692,32 @@ .bp3-popover-enter-active > .bp3-tooltip, .bp3-popover-appear-active > .bp3-tooltip{ -webkit-transform:scale(1); transform:scale(1); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-popover-exit > .bp3-tooltip{ -webkit-transform:scale(1); transform:scale(1); } .bp3-popover-exit-active > .bp3-tooltip{ -webkit-transform:scale(0.8); transform:scale(0.8); + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:100ms; + transition-duration:100ms; -webkit-transition-property:-webkit-transform; transition-property:-webkit-transform; transition-property:transform; transition-property:transform, -webkit-transform; - -webkit-transition-duration:100ms; - transition-duration:100ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-tooltip .bp3-popover-content{ padding:10px 12px; } .bp3-tooltip.bp3-dark, @@ -8474,15 +8775,15 @@ color:#db3737; } .bp3-tree-node-list{ + list-style:none; margin:0; - padding-left:0; - list-style:none; } + padding-left:0; } .bp3-tree-root{ - position:relative; background-color:transparent; cursor:default; - padding-left:0; } + padding-left:0; + position:relative; } .bp3-tree-node-content-0{ padding-left:0px; } @@ -8548,15 +8849,15 @@ padding-left:460px; } .bp3-tree-node-content{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; -webkit-box-align:center; -ms-flex-align:center; align-items:center; - width:100%; + display:-webkit-box; + display:-ms-flexbox; + display:flex; height:30px; - padding-right:5px; } + padding-right:5px; + width:100%; } .bp3-tree-node-content:hover{ background-color:rgba(191, 204, 214, 0.4); } @@ -8566,10 +8867,10 @@ .bp3-tree-node-caret{ color:#5c7080; - -webkit-transform:rotate(0deg); - transform:rotate(0deg); cursor:pointer; padding:7px; + -webkit-transform:rotate(0deg); + transform:rotate(0deg); -webkit-transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); transition:-webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); @@ -8587,8 +8888,8 @@ content:"īš•"; } .bp3-tree-node-icon{ - position:relative; - margin-right:7px; } + margin-right:7px; + position:relative; } .bp3-tree-node-label{ overflow:hidden; @@ -8614,22 +8915,22 @@ user-select:none; } .bp3-tree-node-secondary-label .bp3-popover-wrapper, .bp3-tree-node-secondary-label .bp3-popover-target{ - display:-webkit-box; - display:-ms-flexbox; - display:flex; -webkit-box-align:center; -ms-flex-align:center; - align-items:center; } + align-items:center; + display:-webkit-box; + display:-ms-flexbox; + display:flex; } .bp3-tree-node.bp3-disabled .bp3-tree-node-content{ background-color:inherit; - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-tree-node.bp3-disabled .bp3-tree-node-caret, .bp3-tree-node.bp3-disabled .bp3-tree-node-icon{ - cursor:not-allowed; - color:rgba(92, 112, 128, 0.6); } + color:rgba(92, 112, 128, 0.6); + cursor:not-allowed; } .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{ background-color:#137cbd; } @@ -8657,24 +8958,18 @@ .bp3-dark .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{ background-color:#137cbd; } -/*! - -Copyright 2017-present Palantir Technologies, Inc. All rights reserved. -Licensed under the Apache License, Version 2.0. - -*/ .bp3-omnibar{ -webkit-filter:blur(0); filter:blur(0); opacity:1; - top:20vh; - left:calc(50% - 250px); - z-index:21; + background-color:#ffffff; border-radius:3px; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 4px 8px rgba(16, 22, 26, 0.2), 0 18px 46px 6px rgba(16, 22, 26, 0.2); - background-color:#ffffff; - width:500px; } + left:calc(50% - 250px); + top:20vh; + width:500px; + z-index:21; } .bp3-omnibar.bp3-overlay-enter, .bp3-omnibar.bp3-overlay-appear{ -webkit-filter:blur(20px); filter:blur(20px); @@ -8683,16 +8978,16 @@ -webkit-filter:blur(0); filter:blur(0); opacity:1; + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:200ms; + transition-duration:200ms; -webkit-transition-property:opacity, -webkit-filter; transition-property:opacity, -webkit-filter; transition-property:filter, opacity; transition-property:filter, opacity, -webkit-filter; - -webkit-transition-duration:200ms; - transition-duration:200ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-omnibar.bp3-overlay-exit{ -webkit-filter:blur(0); filter:blur(0); @@ -8701,35 +8996,35 @@ -webkit-filter:blur(20px); filter:blur(20px); opacity:0.2; + -webkit-transition-delay:0; + transition-delay:0; + -webkit-transition-duration:200ms; + transition-duration:200ms; -webkit-transition-property:opacity, -webkit-filter; transition-property:opacity, -webkit-filter; transition-property:filter, opacity; transition-property:filter, opacity, -webkit-filter; - -webkit-transition-duration:200ms; - transition-duration:200ms; -webkit-transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); - -webkit-transition-delay:0; - transition-delay:0; } + transition-timing-function:cubic-bezier(0.4, 1, 0.75, 0.9); } .bp3-omnibar .bp3-input{ - border-radius:0; - background-color:transparent; } + background-color:transparent; + border-radius:0; } .bp3-omnibar .bp3-input, .bp3-omnibar .bp3-input:focus{ -webkit-box-shadow:none; box-shadow:none; } .bp3-omnibar .bp3-menu{ + background-color:transparent; border-radius:0; -webkit-box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15); box-shadow:inset 0 1px 0 rgba(16, 22, 26, 0.15); - background-color:transparent; max-height:calc(60vh - 40px); overflow:auto; } .bp3-omnibar .bp3-menu:empty{ display:none; } .bp3-dark .bp3-omnibar, .bp3-omnibar.bp3-dark{ + background-color:#30404d; -webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); - box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); - background-color:#30404d; } + box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 4px 8px rgba(16, 22, 26, 0.4), 0 18px 46px 6px rgba(16, 22, 26, 0.4); } .bp3-omnibar-overlay .bp3-overlay-backdrop{ background-color:rgba(16, 22, 26, 0.2); } @@ -8741,8 +9036,8 @@ margin-bottom:0; } .bp3-select-popover .bp3-menu{ - max-width:400px; max-height:300px; + max-width:400px; overflow:auto; padding:0; } .bp3-select-popover .bp3-menu:not(:first-child){ @@ -8752,8 +9047,8 @@ min-width:150px; } .bp3-multi-select-popover .bp3-menu{ - max-width:400px; max-height:300px; + max-width:400px; overflow:auto; } .bp3-select-popover .bp3-popover-content{ @@ -8763,8 +9058,8 @@ margin-bottom:0; } .bp3-select-popover .bp3-menu{ - max-width:400px; max-height:300px; + max-width:400px; overflow:auto; padding:0; } .bp3-select-popover .bp3-menu:not(:first-child){ @@ -8799,6 +9094,7 @@ --jp-icon-circle: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMTggMTgiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPGNpcmNsZSBjeD0iOSIgY3k9IjkiIHI9IjgiLz4KICA8L2c+Cjwvc3ZnPgo=); --jp-icon-clear: url(data:image/svg+xml;base64,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); --jp-icon-close: url(data:image/svg+xml;base64,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); + --jp-icon-code: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTExLjQgMTguNkw2LjggMTRMMTEuNCA5LjRMMTAgOEw0IDE0TDEwIDIwTDExLjQgMTguNlpNMTYuNiAxOC42TDIxLjIgMTRMMTYuNiA5LjRMMTggOEwyNCAxNEwxOCAyMEwxNi42IDE4LjZWMTguNloiLz4KCTwvZz4KPC9zdmc+Cg==); 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--jp-icon-cut: url(data:image/svg+xml;base64,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); @@ -8829,11 +9125,15 @@ --jp-icon-new-folder: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIwIDZoLThsLTItMkg0Yy0xLjExIDAtMS45OS44OS0xLjk5IDJMMiAxOGMwIDEuMTEuODkgMiAyIDJoMTZjMS4xMSAwIDItLjg5IDItMlY4YzAtMS4xMS0uODktMi0yLTJ6bS0xIDhoLTN2M2gtMnYtM2gtM3YtMmgzVjloMnYzaDN2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=); 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+ --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=); --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=); --jp-icon-regex: url(data:image/svg+xml;base64,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); --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K); @@ -8844,8 +9144,12 @@ --jp-icon-spreadsheet: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8cGF0aCBjbGFzcz0ianAtaWNvbi1jb250cmFzdDEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNENBRjUwIiBkPSJNMi4yIDIuMnYxNy42aDE3LjZWMi4ySDIuMnptMTUuNCA3LjdoLTUuNVY0LjRoNS41djUuNXpNOS45IDQuNHY1LjVINC40VjQuNGg1LjV6bS01LjUgNy43aDUuNXY1LjVINC40di01LjV6bTcuNyA1LjV2LTUuNWg1LjV2NS41aC01LjV6Ii8+Cjwvc3ZnPgo=); --jp-icon-stop: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik02IDZoMTJ2MTJINnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==); --jp-icon-tab: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTIxIDNIM2MtMS4xIDAtMiAuOS0yIDJ2MTRjMCAxLjEuOSAyIDIgMmgxOGMxLjEgMCAyLS45IDItMlY1YzAtMS4xLS45LTItMi0yem0wIDE2SDNWNWgxMHY0aDh2MTB6Ii8+CiAgPC9nPgo8L3N2Zz4K); + --jp-icon-table-rows: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMSw4SDNWNGgxOFY4eiBNMjEsMTBIM3Y0aDE4VjEweiBNMjEsMTZIM3Y0aDE4VjE2eiIvPgogICAgPC9nPgo8L3N2Zz4=); + --jp-icon-tag: url(data:image/svg+xml;base64,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); --jp-icon-terminal: url(data:image/svg+xml;base64,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); --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIiBkPSJNMTUgMTVIM3YyaDEydi0yem0wLThIM3YyaDEyVjd6TTMgMTNoMTh2LTJIM3Yyem0wIDhoMTh2LTJIM3Yyek0zIDN2MmgxOFYzSDN6Ii8+Cjwvc3ZnPgo=); + --jp-icon-toc: url(data:image/svg+xml;base64,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); + --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4=); --jp-icon-trusted: url(data:image/svg+xml;base64,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); --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==); --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==); @@ -8902,6 +9206,9 @@ .jp-CloseIcon { background-image: var(--jp-icon-close); } +.jp-CodeIcon { + background-image: var(--jp-icon-code); +} .jp-ConsoleIcon { background-image: var(--jp-icon-console); } @@ -8992,12 +9299,21 @@ .jp-NotebookIcon { background-image: var(--jp-icon-notebook); } +.jp-NumberingIcon { + background-image: var(--jp-icon-numbering); +} +.jp-OfflineBoltIcon { + background-image: var(--jp-icon-offline-bolt); +} .jp-PaletteIcon { background-image: var(--jp-icon-palette); } .jp-PasteIcon { background-image: var(--jp-icon-paste); } +.jp-PdfIcon { + background-image: var(--jp-icon-pdf); +} .jp-PythonIcon { background-image: var(--jp-icon-python); } @@ -9007,6 +9323,9 @@ .jp-ReactIcon { background-image: var(--jp-icon-react); } +.jp-RedoIcon { + background-image: var(--jp-icon-redo); +} .jp-RefreshIcon { background-image: var(--jp-icon-refresh); } @@ -9037,12 +9356,24 @@ .jp-TabIcon { background-image: var(--jp-icon-tab); } +.jp-TableRowsIcon { + background-image: var(--jp-icon-table-rows); +} +.jp-TagIcon { + background-image: var(--jp-icon-tag); +} .jp-TerminalIcon { background-image: var(--jp-icon-terminal); } .jp-TextEditorIcon { background-image: var(--jp-icon-text-editor); } +.jp-TocIcon { + background-image: var(--jp-icon-toc); +} +.jp-TreeViewIcon { + background-image: var(--jp-icon-tree-view); +} .jp-TrustedIcon { background-image: var(--jp-icon-trusted); } @@ -9625,6 +9956,64 @@ | Distributed under the terms of the Modified BSD License. |----------------------------------------------------------------------------*/ +.jp-switch { + display: flex; + align-items: center; + padding-left: 4px; + padding-right: 4px; + font-size: var(--jp-ui-font-size1); + background-color: transparent; + color: var(--jp-ui-font-color1); + border: none; + height: 20px; +} + +.jp-switch:hover { + background-color: var(--jp-layout-color2); +} + +.jp-switch-label { + margin-right: 5px; +} + +.jp-switch-track { + cursor: pointer; + background-color: var(--jp-border-color1); + -webkit-transition: 0.4s; + transition: 0.4s; + border-radius: 34px; + height: 16px; + width: 35px; + position: relative; +} + +.jp-switch-track::before { + content: ''; + position: absolute; + height: 10px; + width: 10px; + margin: 3px; + left: 0px; + background-color: var(--jp-ui-inverse-font-color1); + -webkit-transition: 0.4s; + transition: 0.4s; + border-radius: 50%; +} + +.jp-switch[aria-checked='true'] .jp-switch-track { + background-color: var(--jp-warn-color0); +} + +.jp-switch[aria-checked='true'] .jp-switch-track::before { + /* track width (35) - margins (3 + 3) - thumb width (10) */ + left: 19px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + /* Sibling imports */ /* Override Blueprint's _reset.scss styles */ @@ -9757,13 +10146,6 @@ | Distributed under the terms of the Modified BSD License. |----------------------------------------------------------------------------*/ -/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */ - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - .jp-Collapse { display: flex; flex-direction: column; @@ -9816,6 +10198,46 @@ font-size: var(--jp-ui-font-size1); } +/*----------------------------------------------------------------------------- +| Modal variant +|----------------------------------------------------------------------------*/ + +.jp-ModalCommandPalette { + position: absolute; + z-index: 10000; + top: 38px; + left: 30%; + margin: 0; + padding: 4px; + width: 40%; + box-shadow: var(--jp-elevation-z4); + border-radius: 4px; + background: var(--jp-layout-color0); +} + +.jp-ModalCommandPalette .lm-CommandPalette { + max-height: 40vh; +} + +.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after { + display: none; +} + +.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header { + display: none; +} + +.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item { + margin-left: 4px; + margin-right: 4px; +} + +.jp-ModalCommandPalette + .lm-CommandPalette + .lm-CommandPalette-item.lm-mod-disabled { + display: none; +} + /*----------------------------------------------------------------------------- | Search |----------------------------------------------------------------------------*/ @@ -10027,6 +10449,7 @@ * relative to this base size */ font-size: var(--jp-ui-font-size1); color: var(--jp-ui-font-color1); + resize: both; } .jp-Dialog-button { @@ -10043,7 +10466,16 @@ border: 0; } +button.jp-Dialog-close-button { + padding: 0; + height: 100%; + min-width: unset; + min-height: unset; +} + .jp-Dialog-header { + display: flex; + justify-content: space-between; flex: 0 0 auto; padding-bottom: 12px; font-size: var(--jp-ui-font-size3); @@ -10616,6 +11048,11 @@ min-height: var(--jp-toolbar-micro-height); padding: 2px; z-index: 1; + overflow-x: hidden; +} + +.jp-Toolbar:hover { + overflow-x: auto; } /* Toolbar items */ @@ -10694,19 +11131,26 @@ color: var(--jp-ui-font-color1); } +#jp-main-dock-panel[data-mode='single-document'] + .jp-MainAreaWidget + > .jp-Toolbar.jp-Toolbar-micro { + padding: 0; + min-height: 0; +} + +#jp-main-dock-panel[data-mode='single-document'] + .jp-MainAreaWidget + > .jp-Toolbar { + border: none; + box-shadow: none; +} + /*----------------------------------------------------------------------------- | Copyright (c) 2014-2017, Jupyter Development Team. | | Distributed under the terms of the Modified BSD License. |----------------------------------------------------------------------------*/ -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - -/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */ - /*----------------------------------------------------------------------------- | Copyright (c) Jupyter Development Team. | Copyright (c) 2014-2017, PhosphorJS Contributors @@ -10777,18 +11221,6 @@ box-shadow: var(--jp-input-box-shadow); } -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - -/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */ - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - /* BASICS */ .CodeMirror { @@ -10955,17 +11387,17 @@ .CodeMirror-scroll { overflow: scroll !important; /* Things will break if this is overridden */ - /* 30px is the magic margin used to hide the element's real scrollbars */ + /* 50px is the magic margin used to hide the element's real scrollbars */ /* See overflow: hidden in .CodeMirror */ - margin-bottom: -30px; margin-right: -30px; - padding-bottom: 30px; + margin-bottom: -50px; margin-right: -50px; + padding-bottom: 50px; height: 100%; outline: none; /* Prevent dragging from highlighting the element */ position: relative; } .CodeMirror-sizer { position: relative; - border-right: 30px solid transparent; + border-right: 50px solid transparent; } /* The fake, visible scrollbars. Used to force redraw during scrolling @@ -11003,7 +11435,7 @@ height: 100%; display: inline-block; vertical-align: top; - margin-bottom: -30px; + margin-bottom: -50px; } .CodeMirror-gutter-wrapper { position: absolute; @@ -11059,873 +11491,139 @@ position: absolute; left: 0; right: 0; top: 0; bottom: 0; z-index: 0; -} - -.CodeMirror-linewidget { - position: relative; - z-index: 2; - padding: 0.1px; /* Force widget margins to stay inside of the container */ -} - -.CodeMirror-widget {} - -.CodeMirror-rtl pre { direction: rtl; } - -.CodeMirror-code { - outline: none; -} - -/* Force content-box sizing for the elements where we expect it */ -.CodeMirror-scroll, -.CodeMirror-sizer, -.CodeMirror-gutter, -.CodeMirror-gutters, -.CodeMirror-linenumber { - -moz-box-sizing: content-box; - box-sizing: content-box; -} - -.CodeMirror-measure { - position: absolute; - width: 100%; - height: 0; - overflow: hidden; - visibility: hidden; -} - -.CodeMirror-cursor { - position: absolute; - pointer-events: none; -} -.CodeMirror-measure pre { position: static; } - -div.CodeMirror-cursors { - visibility: hidden; - position: relative; - z-index: 3; -} -div.CodeMirror-dragcursors { - visibility: visible; -} - -.CodeMirror-focused div.CodeMirror-cursors { - visibility: visible; -} - -.CodeMirror-selected { background: #d9d9d9; } -.CodeMirror-focused .CodeMirror-selected { background: #d7d4f0; } -.CodeMirror-crosshair { cursor: crosshair; } -.CodeMirror-line::selection, .CodeMirror-line > span::selection, .CodeMirror-line > span > span::selection { background: #d7d4f0; } -.CodeMirror-line::-moz-selection, .CodeMirror-line > span::-moz-selection, .CodeMirror-line > span > span::-moz-selection { background: #d7d4f0; } - -.cm-searching { - background-color: #ffa; - background-color: rgba(255, 255, 0, .4); -} - -/* Used to force a border model for a node */ -.cm-force-border { padding-right: .1px; } - -@media print { - /* Hide the cursor when printing */ - .CodeMirror div.CodeMirror-cursors { - visibility: hidden; - } -} - -/* See issue #2901 */ -.cm-tab-wrap-hack:after { content: ''; } - -/* Help users use markselection to safely style text background */ -span.CodeMirror-selectedtext { background: none; } - -.CodeMirror-dialog { - position: absolute; - left: 0; right: 0; - background: inherit; - z-index: 15; - padding: .1em .8em; - overflow: hidden; - color: inherit; -} - -.CodeMirror-dialog-top { - border-bottom: 1px solid #eee; - top: 0; -} - -.CodeMirror-dialog-bottom { - border-top: 1px solid #eee; - bottom: 0; -} - -.CodeMirror-dialog input { - border: none; - outline: none; - background: transparent; - width: 20em; - color: inherit; - font-family: monospace; -} - -.CodeMirror-dialog button { - font-size: 70%; -} - -.CodeMirror-foldmarker { - color: blue; - text-shadow: #b9f 1px 1px 2px, #b9f -1px -1px 2px, #b9f 1px -1px 2px, #b9f -1px 1px 2px; - font-family: arial; - line-height: .3; - cursor: pointer; -} -.CodeMirror-foldgutter { - width: .7em; -} -.CodeMirror-foldgutter-open, -.CodeMirror-foldgutter-folded { - cursor: pointer; -} -.CodeMirror-foldgutter-open:after { - content: "\25BE"; -} -.CodeMirror-foldgutter-folded:after { - content: "\25B8"; -} - -/* - Name: material - Author: Mattia Astorino (http://github.com/equinusocio) - Website: https://material-theme.site/ -*/ - -.cm-s-material.CodeMirror { - background-color: #263238; - color: #EEFFFF; -} - -.cm-s-material .CodeMirror-gutters { - background: #263238; - color: #546E7A; - border: none; -} - -.cm-s-material .CodeMirror-guttermarker, -.cm-s-material .CodeMirror-guttermarker-subtle, -.cm-s-material .CodeMirror-linenumber { - color: #546E7A; -} - -.cm-s-material .CodeMirror-cursor { - border-left: 1px solid #FFCC00; -} - -.cm-s-material div.CodeMirror-selected { - background: rgba(128, 203, 196, 0.2); -} - -.cm-s-material.CodeMirror-focused div.CodeMirror-selected { - background: rgba(128, 203, 196, 0.2); -} - -.cm-s-material .CodeMirror-line::selection, -.cm-s-material .CodeMirror-line>span::selection, -.cm-s-material .CodeMirror-line>span>span::selection { - background: rgba(128, 203, 196, 0.2); -} - -.cm-s-material .CodeMirror-line::-moz-selection, -.cm-s-material .CodeMirror-line>span::-moz-selection, -.cm-s-material .CodeMirror-line>span>span::-moz-selection { - background: rgba(128, 203, 196, 0.2); -} - -.cm-s-material .CodeMirror-activeline-background { - background: rgba(0, 0, 0, 0.5); -} - -.cm-s-material .cm-keyword { - color: #C792EA; -} - -.cm-s-material .cm-operator { - color: #89DDFF; -} - -.cm-s-material .cm-variable-2 { - color: #EEFFFF; -} - -.cm-s-material .cm-variable-3, -.cm-s-material .cm-type { - color: #f07178; -} - -.cm-s-material .cm-builtin { - color: #FFCB6B; -} - -.cm-s-material .cm-atom { - color: #F78C6C; -} - -.cm-s-material .cm-number { - color: #FF5370; -} - -.cm-s-material .cm-def { - color: #82AAFF; -} - -.cm-s-material .cm-string { - color: #C3E88D; -} - -.cm-s-material .cm-string-2 { - color: #f07178; -} - -.cm-s-material .cm-comment { - color: #546E7A; -} - -.cm-s-material .cm-variable { - color: #f07178; -} - -.cm-s-material .cm-tag { - color: #FF5370; -} - -.cm-s-material .cm-meta { - color: #FFCB6B; -} - -.cm-s-material .cm-attribute { - color: #C792EA; -} - -.cm-s-material .cm-property { - color: #C792EA; -} - -.cm-s-material .cm-qualifier { - color: #DECB6B; -} - -.cm-s-material .cm-variable-3, -.cm-s-material .cm-type { - color: #DECB6B; -} - - -.cm-s-material .cm-error { - color: rgba(255, 255, 255, 1.0); - background-color: #FF5370; -} - -.cm-s-material .CodeMirror-matchingbracket { - text-decoration: underline; - color: white !important; -} -/** - * " - * Using Zenburn color palette from the Emacs Zenburn Theme - * https://github.com/bbatsov/zenburn-emacs/blob/master/zenburn-theme.el - * - * Also using parts of https://github.com/xavi/coderay-lighttable-theme - * " - * From: https://github.com/wisenomad/zenburn-lighttable-theme/blob/master/zenburn.css - */ - -.cm-s-zenburn .CodeMirror-gutters { background: #3f3f3f !important; } -.cm-s-zenburn .CodeMirror-foldgutter-open, .CodeMirror-foldgutter-folded { color: #999; } -.cm-s-zenburn .CodeMirror-cursor { border-left: 1px solid white; } -.cm-s-zenburn { background-color: #3f3f3f; color: #dcdccc; } -.cm-s-zenburn span.cm-builtin { color: #dcdccc; font-weight: bold; } -.cm-s-zenburn span.cm-comment { color: #7f9f7f; } -.cm-s-zenburn span.cm-keyword { color: #f0dfaf; font-weight: bold; } -.cm-s-zenburn span.cm-atom { color: #bfebbf; } -.cm-s-zenburn span.cm-def { color: #dcdccc; } -.cm-s-zenburn span.cm-variable { color: #dfaf8f; } -.cm-s-zenburn span.cm-variable-2 { color: #dcdccc; } -.cm-s-zenburn span.cm-string { color: #cc9393; } -.cm-s-zenburn span.cm-string-2 { color: #cc9393; } -.cm-s-zenburn span.cm-number { color: #dcdccc; } -.cm-s-zenburn span.cm-tag { color: #93e0e3; } -.cm-s-zenburn span.cm-property { color: #dfaf8f; } -.cm-s-zenburn span.cm-attribute { color: #dfaf8f; } -.cm-s-zenburn span.cm-qualifier { color: #7cb8bb; } -.cm-s-zenburn span.cm-meta { color: #f0dfaf; } -.cm-s-zenburn span.cm-header { color: #f0efd0; } -.cm-s-zenburn span.cm-operator { color: #f0efd0; } -.cm-s-zenburn span.CodeMirror-matchingbracket { box-sizing: border-box; background: transparent; border-bottom: 1px solid; } -.cm-s-zenburn span.CodeMirror-nonmatchingbracket { border-bottom: 1px solid; background: none; } -.cm-s-zenburn .CodeMirror-activeline { background: #000000; } -.cm-s-zenburn .CodeMirror-activeline-background { background: #000000; } -.cm-s-zenburn div.CodeMirror-selected { background: #545454; } -.cm-s-zenburn .CodeMirror-focused div.CodeMirror-selected { background: #4f4f4f; } - -.cm-s-abcdef.CodeMirror { background: #0f0f0f; color: #defdef; } -.cm-s-abcdef div.CodeMirror-selected { background: #515151; } -.cm-s-abcdef .CodeMirror-line::selection, .cm-s-abcdef .CodeMirror-line > span::selection, .cm-s-abcdef .CodeMirror-line > span > span::selection { background: rgba(56, 56, 56, 0.99); } -.cm-s-abcdef .CodeMirror-line::-moz-selection, .cm-s-abcdef .CodeMirror-line > span::-moz-selection, .cm-s-abcdef .CodeMirror-line > span > span::-moz-selection { background: rgba(56, 56, 56, 0.99); } -.cm-s-abcdef .CodeMirror-gutters { background: #555; border-right: 2px solid #314151; } -.cm-s-abcdef .CodeMirror-guttermarker { color: #222; } -.cm-s-abcdef .CodeMirror-guttermarker-subtle { color: azure; } -.cm-s-abcdef .CodeMirror-linenumber { color: #FFFFFF; } -.cm-s-abcdef .CodeMirror-cursor { border-left: 1px solid #00FF00; } - -.cm-s-abcdef span.cm-keyword { color: darkgoldenrod; font-weight: bold; } -.cm-s-abcdef span.cm-atom { color: #77F; } -.cm-s-abcdef span.cm-number { color: violet; } -.cm-s-abcdef span.cm-def { color: #fffabc; } -.cm-s-abcdef span.cm-variable { color: #abcdef; } -.cm-s-abcdef span.cm-variable-2 { color: #cacbcc; } -.cm-s-abcdef span.cm-variable-3, .cm-s-abcdef span.cm-type { color: #def; } -.cm-s-abcdef span.cm-property { color: #fedcba; } -.cm-s-abcdef span.cm-operator { color: #ff0; } -.cm-s-abcdef span.cm-comment { color: #7a7b7c; font-style: italic;} -.cm-s-abcdef span.cm-string { color: #2b4; } -.cm-s-abcdef span.cm-meta { color: #C9F; } -.cm-s-abcdef span.cm-qualifier { color: #FFF700; } -.cm-s-abcdef span.cm-builtin { color: #30aabc; } -.cm-s-abcdef span.cm-bracket { color: #8a8a8a; } -.cm-s-abcdef span.cm-tag { color: #FFDD44; } -.cm-s-abcdef span.cm-attribute { color: #DDFF00; } -.cm-s-abcdef span.cm-error { color: #FF0000; } -.cm-s-abcdef span.cm-header { color: aquamarine; font-weight: bold; } -.cm-s-abcdef span.cm-link { color: blueviolet; } - -.cm-s-abcdef .CodeMirror-activeline-background { background: #314151; } - -/* - - Name: Base16 Default Light - Author: Chris Kempson (http://chriskempson.com) - - CodeMirror template by Jan T. Sott (https://github.com/idleberg/base16-codemirror) - Original Base16 color scheme by Chris Kempson (https://github.com/chriskempson/base16) - -*/ - -.cm-s-base16-light.CodeMirror { background: #f5f5f5; color: #202020; } -.cm-s-base16-light div.CodeMirror-selected { background: #e0e0e0; } -.cm-s-base16-light .CodeMirror-line::selection, .cm-s-base16-light .CodeMirror-line > span::selection, .cm-s-base16-light .CodeMirror-line > span > span::selection { background: #e0e0e0; } -.cm-s-base16-light .CodeMirror-line::-moz-selection, .cm-s-base16-light .CodeMirror-line > span::-moz-selection, .cm-s-base16-light .CodeMirror-line > span > span::-moz-selection { background: #e0e0e0; } -.cm-s-base16-light .CodeMirror-gutters { background: #f5f5f5; border-right: 0px; } -.cm-s-base16-light .CodeMirror-guttermarker { color: #ac4142; } -.cm-s-base16-light .CodeMirror-guttermarker-subtle { color: #b0b0b0; } -.cm-s-base16-light .CodeMirror-linenumber { color: #b0b0b0; } -.cm-s-base16-light .CodeMirror-cursor { border-left: 1px solid #505050; } - -.cm-s-base16-light span.cm-comment { color: #8f5536; } -.cm-s-base16-light span.cm-atom { color: #aa759f; } -.cm-s-base16-light span.cm-number { color: #aa759f; } - -.cm-s-base16-light span.cm-property, .cm-s-base16-light span.cm-attribute { color: #90a959; } -.cm-s-base16-light span.cm-keyword { color: #ac4142; } -.cm-s-base16-light span.cm-string { color: #f4bf75; } - -.cm-s-base16-light span.cm-variable { color: #90a959; } -.cm-s-base16-light span.cm-variable-2 { color: #6a9fb5; } -.cm-s-base16-light span.cm-def { color: #d28445; } -.cm-s-base16-light span.cm-bracket { color: #202020; } -.cm-s-base16-light span.cm-tag { color: #ac4142; } -.cm-s-base16-light span.cm-link { color: #aa759f; } -.cm-s-base16-light span.cm-error { background: #ac4142; color: #505050; } - -.cm-s-base16-light .CodeMirror-activeline-background { background: #DDDCDC; } -.cm-s-base16-light .CodeMirror-matchingbracket { color: #f5f5f5 !important; background-color: #6A9FB5 !important} - -/* - - Name: Base16 Default Dark - Author: Chris Kempson (http://chriskempson.com) - - CodeMirror template by Jan T. Sott (https://github.com/idleberg/base16-codemirror) - Original Base16 color scheme by Chris Kempson (https://github.com/chriskempson/base16) - -*/ - -.cm-s-base16-dark.CodeMirror { background: #151515; color: #e0e0e0; } -.cm-s-base16-dark div.CodeMirror-selected { background: #303030; } -.cm-s-base16-dark .CodeMirror-line::selection, .cm-s-base16-dark .CodeMirror-line > span::selection, .cm-s-base16-dark .CodeMirror-line > span > span::selection { background: rgba(48, 48, 48, .99); } -.cm-s-base16-dark .CodeMirror-line::-moz-selection, .cm-s-base16-dark .CodeMirror-line > span::-moz-selection, .cm-s-base16-dark .CodeMirror-line > span > span::-moz-selection { background: rgba(48, 48, 48, .99); } -.cm-s-base16-dark .CodeMirror-gutters { background: #151515; border-right: 0px; } -.cm-s-base16-dark .CodeMirror-guttermarker { color: #ac4142; } -.cm-s-base16-dark .CodeMirror-guttermarker-subtle { color: #505050; } -.cm-s-base16-dark .CodeMirror-linenumber { color: #505050; } -.cm-s-base16-dark .CodeMirror-cursor { border-left: 1px solid #b0b0b0; } - -.cm-s-base16-dark span.cm-comment { color: #8f5536; } -.cm-s-base16-dark span.cm-atom { color: #aa759f; } -.cm-s-base16-dark span.cm-number { color: #aa759f; } - -.cm-s-base16-dark span.cm-property, .cm-s-base16-dark span.cm-attribute { color: #90a959; } -.cm-s-base16-dark span.cm-keyword { color: #ac4142; } -.cm-s-base16-dark span.cm-string { color: #f4bf75; } - -.cm-s-base16-dark span.cm-variable { color: #90a959; } -.cm-s-base16-dark span.cm-variable-2 { color: #6a9fb5; } -.cm-s-base16-dark span.cm-def { color: #d28445; } -.cm-s-base16-dark span.cm-bracket { color: #e0e0e0; } -.cm-s-base16-dark span.cm-tag { color: #ac4142; } -.cm-s-base16-dark span.cm-link { color: #aa759f; } -.cm-s-base16-dark span.cm-error { background: #ac4142; color: #b0b0b0; } - -.cm-s-base16-dark .CodeMirror-activeline-background { background: #202020; } -.cm-s-base16-dark .CodeMirror-matchingbracket { text-decoration: underline; color: white !important; } - -/* - - Name: dracula - Author: Michael Kaminsky (http://github.com/mkaminsky11) - - Original dracula color scheme by Zeno Rocha (https://github.com/zenorocha/dracula-theme) - -*/ - - -.cm-s-dracula.CodeMirror, .cm-s-dracula .CodeMirror-gutters { - background-color: #282a36 !important; - color: #f8f8f2 !important; - border: none; -} -.cm-s-dracula .CodeMirror-gutters { color: #282a36; } -.cm-s-dracula .CodeMirror-cursor { border-left: solid thin #f8f8f0; } -.cm-s-dracula .CodeMirror-linenumber { color: #6D8A88; } -.cm-s-dracula .CodeMirror-selected { background: rgba(255, 255, 255, 0.10); } -.cm-s-dracula .CodeMirror-line::selection, .cm-s-dracula .CodeMirror-line > span::selection, .cm-s-dracula .CodeMirror-line > span > span::selection { background: rgba(255, 255, 255, 0.10); } -.cm-s-dracula .CodeMirror-line::-moz-selection, .cm-s-dracula .CodeMirror-line > span::-moz-selection, .cm-s-dracula .CodeMirror-line > span > span::-moz-selection { background: rgba(255, 255, 255, 0.10); } -.cm-s-dracula span.cm-comment { color: #6272a4; } -.cm-s-dracula span.cm-string, .cm-s-dracula span.cm-string-2 { color: #f1fa8c; } -.cm-s-dracula span.cm-number { color: #bd93f9; } -.cm-s-dracula span.cm-variable { color: #50fa7b; } -.cm-s-dracula span.cm-variable-2 { color: white; } -.cm-s-dracula span.cm-def { color: #50fa7b; } -.cm-s-dracula span.cm-operator { color: #ff79c6; } -.cm-s-dracula span.cm-keyword { color: #ff79c6; } -.cm-s-dracula span.cm-atom { color: #bd93f9; } -.cm-s-dracula span.cm-meta { color: #f8f8f2; } -.cm-s-dracula span.cm-tag { color: #ff79c6; } -.cm-s-dracula span.cm-attribute { color: #50fa7b; } -.cm-s-dracula span.cm-qualifier { color: #50fa7b; } -.cm-s-dracula span.cm-property { color: #66d9ef; } -.cm-s-dracula span.cm-builtin { color: #50fa7b; } -.cm-s-dracula span.cm-variable-3, .cm-s-dracula span.cm-type { color: #ffb86c; } - -.cm-s-dracula .CodeMirror-activeline-background { background: rgba(255,255,255,0.1); } -.cm-s-dracula .CodeMirror-matchingbracket { text-decoration: underline; color: white !important; } - -/* - - Name: Hopscotch - Author: Jan T. Sott - - CodeMirror template by Jan T. Sott (https://github.com/idleberg/base16-codemirror) - Original Base16 color scheme by Chris Kempson (https://github.com/chriskempson/base16) - -*/ - -.cm-s-hopscotch.CodeMirror {background: #322931; color: #d5d3d5;} -.cm-s-hopscotch div.CodeMirror-selected {background: #433b42 !important;} -.cm-s-hopscotch .CodeMirror-gutters {background: #322931; border-right: 0px;} -.cm-s-hopscotch .CodeMirror-linenumber {color: #797379;} -.cm-s-hopscotch .CodeMirror-cursor {border-left: 1px solid #989498 !important;} - -.cm-s-hopscotch span.cm-comment {color: #b33508;} -.cm-s-hopscotch span.cm-atom {color: #c85e7c;} -.cm-s-hopscotch span.cm-number {color: #c85e7c;} - -.cm-s-hopscotch span.cm-property, .cm-s-hopscotch span.cm-attribute {color: #8fc13e;} -.cm-s-hopscotch span.cm-keyword {color: #dd464c;} -.cm-s-hopscotch span.cm-string {color: #fdcc59;} - -.cm-s-hopscotch span.cm-variable {color: #8fc13e;} -.cm-s-hopscotch span.cm-variable-2 {color: #1290bf;} -.cm-s-hopscotch span.cm-def {color: #fd8b19;} -.cm-s-hopscotch span.cm-error {background: #dd464c; color: #989498;} -.cm-s-hopscotch span.cm-bracket {color: #d5d3d5;} -.cm-s-hopscotch span.cm-tag {color: #dd464c;} -.cm-s-hopscotch span.cm-link {color: #c85e7c;} - -.cm-s-hopscotch .CodeMirror-matchingbracket { text-decoration: underline; color: white !important;} -.cm-s-hopscotch .CodeMirror-activeline-background { background: #302020; } - -/****************************************************************/ -/* Based on mbonaci's Brackets mbo theme */ -/* https://github.com/mbonaci/global/blob/master/Mbo.tmTheme */ -/* Create your own: http://tmtheme-editor.herokuapp.com */ -/****************************************************************/ - -.cm-s-mbo.CodeMirror { background: #2c2c2c; color: #ffffec; } -.cm-s-mbo div.CodeMirror-selected { background: #716C62; } -.cm-s-mbo .CodeMirror-line::selection, .cm-s-mbo .CodeMirror-line > span::selection, .cm-s-mbo .CodeMirror-line > span > span::selection { background: rgba(113, 108, 98, .99); } -.cm-s-mbo .CodeMirror-line::-moz-selection, .cm-s-mbo .CodeMirror-line > span::-moz-selection, .cm-s-mbo .CodeMirror-line > span > span::-moz-selection { background: rgba(113, 108, 98, .99); } -.cm-s-mbo .CodeMirror-gutters { background: #4e4e4e; border-right: 0px; } -.cm-s-mbo .CodeMirror-guttermarker { color: white; } -.cm-s-mbo .CodeMirror-guttermarker-subtle { color: grey; } -.cm-s-mbo .CodeMirror-linenumber { color: #dadada; } -.cm-s-mbo .CodeMirror-cursor { border-left: 1px solid #ffffec; } - -.cm-s-mbo span.cm-comment { color: #95958a; } -.cm-s-mbo span.cm-atom { color: #00a8c6; } -.cm-s-mbo span.cm-number { color: #00a8c6; } - -.cm-s-mbo span.cm-property, .cm-s-mbo span.cm-attribute { color: #9ddfe9; } -.cm-s-mbo span.cm-keyword { color: #ffb928; } -.cm-s-mbo span.cm-string { color: #ffcf6c; } -.cm-s-mbo span.cm-string.cm-property { color: #ffffec; } - -.cm-s-mbo span.cm-variable { color: #ffffec; } -.cm-s-mbo span.cm-variable-2 { color: #00a8c6; } -.cm-s-mbo span.cm-def { color: #ffffec; } -.cm-s-mbo span.cm-bracket { color: #fffffc; font-weight: bold; } -.cm-s-mbo span.cm-tag { color: #9ddfe9; } -.cm-s-mbo span.cm-link { color: #f54b07; } -.cm-s-mbo span.cm-error { border-bottom: #636363; color: #ffffec; } -.cm-s-mbo span.cm-qualifier { color: #ffffec; } - -.cm-s-mbo .CodeMirror-activeline-background { background: #494b41; } -.cm-s-mbo .CodeMirror-matchingbracket { color: #ffb928 !important; } -.cm-s-mbo .CodeMirror-matchingtag { background: rgba(255, 255, 255, .37); } - -/* - MDN-LIKE Theme - Mozilla - Ported to CodeMirror by Peter Kroon - Report bugs/issues here: https://github.com/codemirror/CodeMirror/issues - GitHub: @peterkroon - - The mdn-like theme is inspired on the displayed code examples at: https://developer.mozilla.org/en-US/docs/Web/CSS/animation - -*/ -.cm-s-mdn-like.CodeMirror { color: #999; background-color: #fff; } -.cm-s-mdn-like div.CodeMirror-selected { background: #cfc; } -.cm-s-mdn-like .CodeMirror-line::selection, .cm-s-mdn-like .CodeMirror-line > span::selection, .cm-s-mdn-like .CodeMirror-line > span > span::selection { background: #cfc; } -.cm-s-mdn-like .CodeMirror-line::-moz-selection, .cm-s-mdn-like .CodeMirror-line > span::-moz-selection, .cm-s-mdn-like .CodeMirror-line > span > span::-moz-selection { background: #cfc; } - -.cm-s-mdn-like .CodeMirror-gutters { background: #f8f8f8; border-left: 6px solid rgba(0,83,159,0.65); color: #333; } -.cm-s-mdn-like .CodeMirror-linenumber { color: #aaa; padding-left: 8px; } -.cm-s-mdn-like .CodeMirror-cursor { border-left: 2px solid #222; } - -.cm-s-mdn-like .cm-keyword { color: #6262FF; } -.cm-s-mdn-like .cm-atom { color: #F90; } -.cm-s-mdn-like .cm-number { color: #ca7841; } -.cm-s-mdn-like .cm-def { color: #8DA6CE; } -.cm-s-mdn-like span.cm-variable-2, .cm-s-mdn-like span.cm-tag { color: #690; } -.cm-s-mdn-like span.cm-variable-3, .cm-s-mdn-like span.cm-def, .cm-s-mdn-like span.cm-type { color: #07a; } - -.cm-s-mdn-like .cm-variable { color: #07a; } -.cm-s-mdn-like .cm-property { color: #905; } -.cm-s-mdn-like .cm-qualifier { color: #690; } - -.cm-s-mdn-like .cm-operator { color: #cda869; } -.cm-s-mdn-like .cm-comment { color:#777; font-weight:normal; } -.cm-s-mdn-like .cm-string { color:#07a; font-style:italic; } -.cm-s-mdn-like .cm-string-2 { color:#bd6b18; } /*?*/ -.cm-s-mdn-like .cm-meta { color: #000; } /*?*/ -.cm-s-mdn-like .cm-builtin { color: #9B7536; } /*?*/ -.cm-s-mdn-like .cm-tag { color: #997643; } -.cm-s-mdn-like .cm-attribute { color: #d6bb6d; } /*?*/ -.cm-s-mdn-like .cm-header { color: #FF6400; } -.cm-s-mdn-like .cm-hr { color: #AEAEAE; } -.cm-s-mdn-like .cm-link { color:#ad9361; font-style:italic; text-decoration:none; } -.cm-s-mdn-like .cm-error { border-bottom: 1px solid red; } - -div.cm-s-mdn-like .CodeMirror-activeline-background { background: #efefff; } -div.cm-s-mdn-like span.CodeMirror-matchingbracket { outline:1px solid grey; color: inherit; } - -.cm-s-mdn-like.CodeMirror { background-image: url(data:image/png;base64,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); } - -/* - - Name: seti - Author: Michael Kaminsky (http://github.com/mkaminsky11) +} - Original seti color scheme by Jesse Weed (https://github.com/jesseweed/seti-syntax) +.CodeMirror-linewidget { + position: relative; + z-index: 2; + padding: 0.1px; /* Force widget margins to stay inside of the container */ +} -*/ +.CodeMirror-widget {} +.CodeMirror-rtl pre { direction: rtl; } -.cm-s-seti.CodeMirror { - background-color: #151718 !important; - color: #CFD2D1 !important; - border: none; -} -.cm-s-seti .CodeMirror-gutters { - color: #404b53; - background-color: #0E1112; - border: none; +.CodeMirror-code { + outline: none; } -.cm-s-seti .CodeMirror-cursor { border-left: solid thin #f8f8f0; } -.cm-s-seti .CodeMirror-linenumber { color: #6D8A88; } -.cm-s-seti.CodeMirror-focused div.CodeMirror-selected { background: rgba(255, 255, 255, 0.10); } -.cm-s-seti .CodeMirror-line::selection, .cm-s-seti .CodeMirror-line > span::selection, .cm-s-seti .CodeMirror-line > span > span::selection { background: rgba(255, 255, 255, 0.10); } -.cm-s-seti .CodeMirror-line::-moz-selection, .cm-s-seti .CodeMirror-line > span::-moz-selection, .cm-s-seti .CodeMirror-line > span > span::-moz-selection { background: rgba(255, 255, 255, 0.10); } -.cm-s-seti span.cm-comment { color: #41535b; } -.cm-s-seti span.cm-string, .cm-s-seti span.cm-string-2 { color: #55b5db; } -.cm-s-seti span.cm-number { color: #cd3f45; } -.cm-s-seti span.cm-variable { color: #55b5db; } -.cm-s-seti span.cm-variable-2 { color: #a074c4; } -.cm-s-seti span.cm-def { color: #55b5db; } -.cm-s-seti span.cm-keyword { color: #ff79c6; } -.cm-s-seti span.cm-operator { color: #9fca56; } -.cm-s-seti span.cm-keyword { color: #e6cd69; } -.cm-s-seti span.cm-atom { color: #cd3f45; } -.cm-s-seti span.cm-meta { color: #55b5db; } -.cm-s-seti span.cm-tag { color: #55b5db; } -.cm-s-seti span.cm-attribute { color: #9fca56; } -.cm-s-seti span.cm-qualifier { color: #9fca56; } -.cm-s-seti span.cm-property { color: #a074c4; } -.cm-s-seti span.cm-variable-3, .cm-s-seti span.cm-type { color: #9fca56; } -.cm-s-seti span.cm-builtin { color: #9fca56; } -.cm-s-seti .CodeMirror-activeline-background { background: #101213; } -.cm-s-seti .CodeMirror-matchingbracket { text-decoration: underline; color: white !important; } -/* -Solarized theme for code-mirror -http://ethanschoonover.com/solarized -*/ +/* Force content-box sizing for the elements where we expect it */ +.CodeMirror-scroll, +.CodeMirror-sizer, +.CodeMirror-gutter, +.CodeMirror-gutters, +.CodeMirror-linenumber { + -moz-box-sizing: content-box; + box-sizing: content-box; +} -/* -Solarized color palette -http://ethanschoonover.com/solarized/img/solarized-palette.png -*/ +.CodeMirror-measure { + position: absolute; + width: 100%; + height: 0; + overflow: hidden; + visibility: hidden; +} -.solarized.base03 { color: #002b36; } -.solarized.base02 { color: #073642; } -.solarized.base01 { color: #586e75; } -.solarized.base00 { color: #657b83; } -.solarized.base0 { color: #839496; } -.solarized.base1 { color: #93a1a1; } -.solarized.base2 { color: #eee8d5; } -.solarized.base3 { color: #fdf6e3; } -.solarized.solar-yellow { color: #b58900; } -.solarized.solar-orange { color: #cb4b16; } -.solarized.solar-red { color: #dc322f; } -.solarized.solar-magenta { color: #d33682; } -.solarized.solar-violet { color: #6c71c4; } -.solarized.solar-blue { color: #268bd2; } -.solarized.solar-cyan { color: #2aa198; } -.solarized.solar-green { color: #859900; } - -/* Color scheme for code-mirror */ - -.cm-s-solarized { - line-height: 1.45em; - color-profile: sRGB; - rendering-intent: auto; -} -.cm-s-solarized.cm-s-dark { - color: #839496; - background-color: #002b36; - text-shadow: #002b36 0 1px; -} -.cm-s-solarized.cm-s-light { - background-color: #fdf6e3; - color: #657b83; - text-shadow: #eee8d5 0 1px; -} - -.cm-s-solarized .CodeMirror-widget { - text-shadow: none; -} - -.cm-s-solarized .cm-header { color: #586e75; } -.cm-s-solarized .cm-quote { color: #93a1a1; } - -.cm-s-solarized .cm-keyword { color: #cb4b16; } -.cm-s-solarized .cm-atom { color: #d33682; } -.cm-s-solarized .cm-number { color: #d33682; } -.cm-s-solarized .cm-def { color: #2aa198; } - -.cm-s-solarized .cm-variable { color: #839496; } -.cm-s-solarized .cm-variable-2 { color: #b58900; } -.cm-s-solarized .cm-variable-3, .cm-s-solarized .cm-type { color: #6c71c4; } - -.cm-s-solarized .cm-property { color: #2aa198; } -.cm-s-solarized .cm-operator { color: #6c71c4; } - -.cm-s-solarized .cm-comment { color: #586e75; font-style:italic; } - -.cm-s-solarized .cm-string { color: #859900; } -.cm-s-solarized .cm-string-2 { color: #b58900; } - -.cm-s-solarized .cm-meta { color: #859900; } -.cm-s-solarized .cm-qualifier { color: #b58900; } -.cm-s-solarized .cm-builtin { color: #d33682; } -.cm-s-solarized .cm-bracket { color: #cb4b16; } -.cm-s-solarized .CodeMirror-matchingbracket { color: #859900; } -.cm-s-solarized .CodeMirror-nonmatchingbracket { color: #dc322f; } -.cm-s-solarized .cm-tag { color: #93a1a1; } -.cm-s-solarized .cm-attribute { color: #2aa198; } -.cm-s-solarized .cm-hr { - color: transparent; - border-top: 1px solid #586e75; - display: block; +.CodeMirror-cursor { + position: absolute; + pointer-events: none; } -.cm-s-solarized .cm-link { color: #93a1a1; cursor: pointer; } -.cm-s-solarized .cm-special { color: #6c71c4; } -.cm-s-solarized .cm-em { - color: #999; - text-decoration: underline; - text-decoration-style: dotted; +.CodeMirror-measure pre { position: static; } + +div.CodeMirror-cursors { + visibility: hidden; + position: relative; + z-index: 3; } -.cm-s-solarized .cm-error, -.cm-s-solarized .cm-invalidchar { - color: #586e75; - border-bottom: 1px dotted #dc322f; +div.CodeMirror-dragcursors { + visibility: visible; } -.cm-s-solarized.cm-s-dark div.CodeMirror-selected { background: #073642; } -.cm-s-solarized.cm-s-dark.CodeMirror ::selection { background: rgba(7, 54, 66, 0.99); } -.cm-s-solarized.cm-s-dark .CodeMirror-line::-moz-selection, .cm-s-dark .CodeMirror-line > span::-moz-selection, .cm-s-dark .CodeMirror-line > span > span::-moz-selection { background: rgba(7, 54, 66, 0.99); } - -.cm-s-solarized.cm-s-light div.CodeMirror-selected { background: #eee8d5; } -.cm-s-solarized.cm-s-light .CodeMirror-line::selection, .cm-s-light .CodeMirror-line > span::selection, .cm-s-light .CodeMirror-line > span > span::selection { background: #eee8d5; } -.cm-s-solarized.cm-s-light .CodeMirror-line::-moz-selection, .cm-s-ligh .CodeMirror-line > span::-moz-selection, .cm-s-ligh .CodeMirror-line > span > span::-moz-selection { background: #eee8d5; } +.CodeMirror-focused div.CodeMirror-cursors { + visibility: visible; +} -/* Editor styling */ +.CodeMirror-selected { background: #d9d9d9; } +.CodeMirror-focused .CodeMirror-selected { background: #d7d4f0; } +.CodeMirror-crosshair { cursor: crosshair; } +.CodeMirror-line::selection, .CodeMirror-line > span::selection, .CodeMirror-line > span > span::selection { background: #d7d4f0; } +.CodeMirror-line::-moz-selection, .CodeMirror-line > span::-moz-selection, .CodeMirror-line > span > span::-moz-selection { background: #d7d4f0; } +.cm-searching { + background-color: #ffa; + background-color: rgba(255, 255, 0, .4); +} +/* Used to force a border model for a node */ +.cm-force-border { padding-right: .1px; } -/* Little shadow on the view-port of the buffer view */ -.cm-s-solarized.CodeMirror { - -moz-box-shadow: inset 7px 0 12px -6px #000; - -webkit-box-shadow: inset 7px 0 12px -6px #000; - box-shadow: inset 7px 0 12px -6px #000; +@media print { + /* Hide the cursor when printing */ + .CodeMirror div.CodeMirror-cursors { + visibility: hidden; + } } -/* Remove gutter border */ -.cm-s-solarized .CodeMirror-gutters { - border-right: 0; -} +/* See issue #2901 */ +.cm-tab-wrap-hack:after { content: ''; } -/* Gutter colors and line number styling based of color scheme (dark / light) */ +/* Help users use markselection to safely style text background */ +span.CodeMirror-selectedtext { background: none; } -/* Dark */ -.cm-s-solarized.cm-s-dark .CodeMirror-gutters { - background-color: #073642; +.CodeMirror-dialog { + position: absolute; + left: 0; right: 0; + background: inherit; + z-index: 15; + padding: .1em .8em; + overflow: hidden; + color: inherit; } -.cm-s-solarized.cm-s-dark .CodeMirror-linenumber { - color: #586e75; - text-shadow: #021014 0 -1px; +.CodeMirror-dialog-top { + border-bottom: 1px solid #eee; + top: 0; } -/* Light */ -.cm-s-solarized.cm-s-light .CodeMirror-gutters { - background-color: #eee8d5; +.CodeMirror-dialog-bottom { + border-top: 1px solid #eee; + bottom: 0; } -.cm-s-solarized.cm-s-light .CodeMirror-linenumber { - color: #839496; +.CodeMirror-dialog input { + border: none; + outline: none; + background: transparent; + width: 20em; + color: inherit; + font-family: monospace; } -/* Common */ -.cm-s-solarized .CodeMirror-linenumber { - padding: 0 5px; +.CodeMirror-dialog button { + font-size: 70%; } -.cm-s-solarized .CodeMirror-guttermarker-subtle { color: #586e75; } -.cm-s-solarized.cm-s-dark .CodeMirror-guttermarker { color: #ddd; } -.cm-s-solarized.cm-s-light .CodeMirror-guttermarker { color: #cb4b16; } -.cm-s-solarized .CodeMirror-gutter .CodeMirror-gutter-text { - color: #586e75; 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} - -/* -Copyright (C) 2011 by MarkLogic Corporation -Author: Mike Brevoort - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in -all copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -THE SOFTWARE. -*/ -.cm-s-xq-light span.cm-keyword { line-height: 1em; font-weight: bold; color: #5A5CAD; } -.cm-s-xq-light span.cm-atom { color: #6C8CD5; } -.cm-s-xq-light span.cm-number { color: #164; } -.cm-s-xq-light span.cm-def { text-decoration:underline; } -.cm-s-xq-light span.cm-variable { color: black; } -.cm-s-xq-light span.cm-variable-2 { color:black; } -.cm-s-xq-light span.cm-variable-3, .cm-s-xq-light span.cm-type { color: black; } -.cm-s-xq-light span.cm-property {} -.cm-s-xq-light span.cm-operator {} -.cm-s-xq-light span.cm-comment { color: #0080FF; font-style: italic; } -.cm-s-xq-light span.cm-string { color: red; } -.cm-s-xq-light span.cm-meta { color: yellow; } -.cm-s-xq-light span.cm-qualifier { color: grey; } -.cm-s-xq-light span.cm-builtin { color: #7EA656; 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+} + .jp-RenderedText { text-align: left; padding-left: var(--jp-code-padding); @@ -12192,7 +11885,6 @@ border: none; margin: 0px; padding: 0px; - line-height: normal; } .jp-RenderedText pre a:link { @@ -12236,27 +11928,35 @@ .jp-RenderedText pre .ansi-black-bg { background-color: #3e424d; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-red-bg { background-color: #e75c58; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-green-bg { background-color: #00a250; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-yellow-bg { background-color: #ddb62b; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-blue-bg { background-color: #208ffb; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-magenta-bg { background-color: #d160c4; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-cyan-bg { background-color: #60c6c8; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-white-bg { background-color: #c5c1b4; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-black-intense-fg { @@ -12286,27 +11986,35 @@ .jp-RenderedText pre .ansi-black-intense-bg { background-color: #282c36; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-red-intense-bg { background-color: #b22b31; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-green-intense-bg { background-color: #007427; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-yellow-intense-bg { background-color: #b27d12; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-blue-intense-bg { background-color: #0065ca; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-magenta-intense-bg { background-color: #a03196; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-cyan-intense-bg { background-color: #258f8f; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-white-intense-bg { background-color: #a1a6b2; + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-default-inverse-fg { @@ -12314,6 +12022,7 @@ } .jp-RenderedText pre .ansi-default-inverse-bg { background-color: var(--jp-inverse-layout-color0); + padding: var(--jp-private-code-span-padding) 0; } .jp-RenderedText pre .ansi-bold { @@ -12735,18 +12444,6 @@ | Distributed under the terms of the Modified BSD License. |----------------------------------------------------------------------------*/ -/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */ - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - .jp-MimeDocument { outline: none; } @@ -12756,18 +12453,6 @@ | Distributed under the terms of the Modified BSD License. |----------------------------------------------------------------------------*/ -/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */ - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - /*----------------------------------------------------------------------------- | Variables |----------------------------------------------------------------------------*/ @@ -12801,7 +12486,7 @@ .jp-BreadCrumbs { flex: 0 0 auto; - margin: 4px 12px; + margin: 8px 12px 8px 12px; } .jp-BreadCrumbs-item { @@ -12830,18 +12515,57 @@ .jp-FileBrowser-toolbar.jp-Toolbar { padding: 0px; + margin: 8px 12px 0px 12px; } .jp-FileBrowser-toolbar.jp-Toolbar { - justify-content: space-evenly; + justify-content: flex-start; } .jp-FileBrowser-toolbar.jp-Toolbar .jp-Toolbar-item { - flex: 1; + flex: 0 0 auto; + padding-left: 0px; + padding-right: 2px; } .jp-FileBrowser-toolbar.jp-Toolbar .jp-ToolbarButtonComponent { - width: 100%; + width: 40px; +} + +.jp-FileBrowser-toolbar.jp-Toolbar + .jp-Toolbar-item:first-child + .jp-ToolbarButtonComponent { + width: 72px; + background: var(--jp-brand-color1); +} + +.jp-FileBrowser-toolbar.jp-Toolbar + .jp-Toolbar-item:first-child + .jp-ToolbarButtonComponent + .jp-icon3 { + fill: white; +} + +/*----------------------------------------------------------------------------- +| Other styles +|----------------------------------------------------------------------------*/ + +.jp-FileDialog.jp-mod-conflict input { + color: red; +} + +.jp-FileDialog .jp-new-name-title { + margin-top: 12px; +} + +.jp-LastModified-hidden { + display: none; +} + +.jp-FileBrowser-filterBox { + padding: 0px; + flex: 0 0 auto; + margin: 8px 12px 0px 12px; } /*----------------------------------------------------------------------------- @@ -12885,6 +12609,19 @@ text-align: right; } +.jp-id-narrow { + display: none; + flex: 0 0 5px; + padding: 4px 4px; + border-left: var(--jp-border-width) solid var(--jp-border-color2); + text-align: right; + color: var(--jp-border-color2); +} + +.jp-DirListing-narrow .jp-id-narrow { + display: block; +} + .jp-DirListing-narrow .jp-id-modified, .jp-DirListing-narrow .jp-DirListing-itemModified { display: none; @@ -12904,6 +12641,12 @@ background-color: var(--jp-layout-color1); } +.jp-DirListing-content mark { + color: var(--jp-ui-font-color0); + background-color: transparent; + font-weight: bold; +} + /* Style the directory listing content when a user drops a file to upload */ .jp-DirListing.jp-mod-native-drop .jp-DirListing-content { outline: 5px dashed rgba(128, 128, 128, 0.5); @@ -12921,6 +12664,10 @@ user-select: none; } +.jp-DirListing-item[data-is-dot] { + opacity: 75%; +} + .jp-DirListing-item.jp-mod-selected { color: white; background: var(--jp-brand-color1); @@ -12994,21 +12741,6 @@ outline: none; } -.jp-FileDialog.jp-mod-conflict input { - color: red; -} - -.jp-FileDialog .jp-new-name-title { - margin-top: 12px; -} - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - -/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */ - /*----------------------------------------------------------------------------- | Copyright (c) Jupyter Development Team. | Distributed under the terms of the Modified BSD License. @@ -13167,8 +12899,12 @@ flex: 1 1 auto; } -.jp-OutputArea-executeResult.jp-RenderedText { +/* Text output with the Out[] prompt needs a top padding to match the + * alignment of the Out[] prompt itself. + */ +.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output { padding-top: var(--jp-code-padding); + border-top: var(--jp-border-width) solid transparent; } /*----------------------------------------------------------------------------- @@ -13225,13 +12961,6 @@ | Distributed under the terms of the Modified BSD License. |----------------------------------------------------------------------------*/ -/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */ - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - .jp-Collapser { flex: 0 0 var(--jp-cell-collapser-width); padding: 0px; @@ -13287,10 +13016,12 @@ .jp-InputArea { display: flex; flex-direction: row; + overflow: hidden; } .jp-InputArea-editor { flex: 1 1 auto; + overflow: hidden; } .jp-InputArea-editor { @@ -13433,13 +13164,6 @@ margin-top: 5px; } -/* Text output with the Out[] prompt needs a top padding to match the - * alignment of the Out[] prompt itself. - */ -.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output { - padding-top: var(--jp-code-padding); -} - .jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea { overflow-y: auto; max-height: 200px; @@ -13484,13 +13208,6 @@ | Distributed under the terms of the Modified BSD License. |----------------------------------------------------------------------------*/ -/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */ - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - /*----------------------------------------------------------------------------- | Variables |----------------------------------------------------------------------------*/ @@ -13771,6 +13488,15 @@ line-height: 1.4; } +.jp-NotebookTools .jp-select-wrapper { + margin-top: 4px; + margin-bottom: 0px; +} + +.jp-NotebookTools .jp-Collapse { + margin-top: 16px; +} + /*----------------------------------------------------------------------------- | Presentation Mode (.jp-mod-presentationMode) |----------------------------------------------------------------------------*/ @@ -13785,18 +13511,6 @@ flex: 0 0 110px; } -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - -/* This file was auto-generated by ensurePackage() in @jupyterlab/buildutils */ - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - - - @@ -14259,12 +13971,14 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - STATE simulation and plotting.ipynb b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - STATE simulation and plotting.ipynb deleted file mode 100644 index 75003e91..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - STATE simulation and plotting.ipynb +++ /dev/null @@ -1,4955 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ReEDS Scenarios on PV ICE Tool STATES" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. \n", - "\n", - "Current sections include:\n", - "\n", - "
    \n", - "
  1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format
    2. \n", - "
  2. ### Reading scenarios of interest and running PV ICE tool
    3. \n", - "
  3. ###Plotting
    4. \n", - "
  4. ### GeoPlotting.
    5. \n", - "
\n", - " Notes:\n", - " \n", - "Scenarios of Interest:\n", - "\tthe Ref.Mod, \n", - "o\t95-by-35.Adv, and \n", - "o\t95-by-35+Elec.Adv+DR ones\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import PV_ICE\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os,sys\n", - "import matplotlib.pyplot as plt\n", - "from IPython.display import display\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.rcParams['figure.figsize'] = (12, 8)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your simulation will be stored in C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - } - ], - "source": [ - "import os\n", - "from pathlib import Path\n", - "\n", - "testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP')\n", - "\n", - "print (\"Your simulation will be stored in %s\" % testfolder)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reading REEDS original file to get list of SCENARIOs, PCAs, and STATEs " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\nreedsFile = str(Path().resolve().parent.parent.parent / \\'December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx\\')\\nprint (\"Input file is stored in %s\" % reedsFile)\\n\\nrawdf = pd.read_excel(reedsFile,\\n sheet_name=\"UPV Capacity (GW)\")\\n #index_col=[0,2,3]) #this casts scenario, PCA and State as levels\\n#now set year as an index in place\\n#rawdf.drop(columns=[\\'State\\'], inplace=True)\\nrawdf.drop(columns=[\\'Tech\\'], inplace=True)\\nrawdf.set_index([\\'Scenario\\',\\'Year\\',\\'PCA\\', \\'State\\'], inplace=True)\\n\\nscenarios = list(rawdf.index.get_level_values(\\'Scenario\\').unique())\\nPCAs = list(rawdf.index.get_level_values(\\'PCA\\').unique())\\nSTATEs = list(rawdf.index.get_level_values(\\'State\\').unique())\\n\\nsimulationname = scenarios\\nsimulationname = [w.replace(\\'+\\', \\'_\\') for w in simulationname]\\nsimulationname\\nSFscenarios = [simulationname[0], simulationname[4], simulationname[8]]\\n'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r\"\"\"\n", - "reedsFile = str(Path().resolve().parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx')\n", - "print (\"Input file is stored in %s\" % reedsFile)\n", - "\n", - "rawdf = pd.read_excel(reedsFile,\n", - " sheet_name=\"UPV Capacity (GW)\")\n", - " #index_col=[0,2,3]) #this casts scenario, PCA and State as levels\n", - "#now set year as an index in place\n", - "#rawdf.drop(columns=['State'], inplace=True)\n", - "rawdf.drop(columns=['Tech'], inplace=True)\n", - "rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True)\n", - "\n", - "scenarios = list(rawdf.index.get_level_values('Scenario').unique())\n", - "PCAs = list(rawdf.index.get_level_values('PCA').unique())\n", - "STATEs = list(rawdf.index.get_level_values('State').unique())\n", - "\n", - "simulationname = scenarios\n", - "simulationname = [w.replace('+', '_') for w in simulationname]\n", - "simulationname\n", - "SFscenarios = [simulationname[0], simulationname[4], simulationname[8]]\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reading GIS inputs" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"\\nGISfile = str(Path().resolve().parent.parent.parent.parent / 'gis_centroid_n.xlsx')\\nGIS = pd.read_excel(GISfile)\\nGIS = GIS.set_index('id')\\nGIS.head()\\nGIS.loc['p1'].long\\n\"" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r\"\"\"\n", - "GISfile = str(Path().resolve().parent.parent.parent.parent / 'gis_centroid_n.xlsx')\n", - "GIS = pd.read_excel(GISfile)\n", - "GIS = GIS.set_index('id')\n", - "GIS.head()\n", - "GIS.loc['p1'].long\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Scenarios in PV_ICE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Downselect to Solar Future scenarios of interest\n", - "\n", - "Scenarios of Interest:\n", - "
  • Ref.Mod\n", - "
  • 95-by-35.Adv \n", - "
  • 95-by-35+Elec.Adv+DR " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SFscenarios = ['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']\n", - "SFscenarios" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "STATEs = ['WA', 'CA', 'VA', 'FL', 'MI', 'IN', 'KY', 'OH', 'PA', 'WV', 'NV', 'MD',\n", - " 'DE', 'NJ', 'NY', 'VT', 'NH', 'MA', 'CT', 'RI', 'ME', 'ID', 'MT', 'WY', 'UT', 'AZ', 'NM',\n", - " 'SD', 'CO', 'ND', 'NE', 'MN', 'IA', 'WI', 'TX', 'OK', 'OR', 'KS', 'MO', 'AR', 'LA', 'IL', 'MS',\n", - " 'AL', 'TN', 'GA', 'SC', 'NC'] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create the 3 Scenarios and assign Baselines\n", - "\n", - "Keeping track of each scenario as its own PV ICE Object." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path = C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n", - "path = C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n", - "path = C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - } - ], - "source": [ - "#for ii in range (0, 1): #len(scenarios):\n", - "i = 0\n", - "r1 = PV_ICE.Simulation(name=SFscenarios[i], path=testfolder)\n", - "\n", - "for jj in range (0, len(STATEs)): \n", - " filetitle = SFscenarios[i]+'_'+STATEs[jj]+'.csv'\n", - " filetitle = os.path.join(testfolder, 'STATEs', filetitle) \n", - " r1.createScenario(name=STATEs[jj], file=filetitle)\n", - " r1.scenario[STATEs[jj]].addMaterial('glass', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_glass_Reeds.csv')\n", - " r1.scenario[STATEs[jj]].addMaterial('silicon', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_silicon_Reeds.csv')\n", - " r1.scenario[STATEs[jj]].addMaterial('silver', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_silver_Reeds.csv')\n", - " r1.scenario[STATEs[jj]].addMaterial('copper', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_copper_Reeds.csv')\n", - " r1.scenario[STATEs[jj]].addMaterial('aluminum', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_aluminium_Reeds.csv')\n", - "\n", - "\n", - "i = 1\n", - "r2 = PV_ICE.Simulation(name=SFscenarios[i], path=testfolder)\n", - "\n", - "for jj in range (0, len(STATEs)): \n", - " filetitle = SFscenarios[i]+'_'+STATEs[jj]+'.csv'\n", - " filetitle = os.path.join(testfolder, 'STATEs', filetitle) \n", - " r2.createScenario(name=STATEs[jj], file=filetitle)\n", - " r2.scenario[STATEs[jj]].addMaterial('glass', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_glass_Reeds.csv')\n", - " r2.scenario[STATEs[jj]].addMaterial('silicon', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_silicon_Reeds.csv')\n", - " r2.scenario[STATEs[jj]].addMaterial('silver', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_silver_Reeds.csv')\n", - " r2.scenario[STATEs[jj]].addMaterial('copper', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_copper_Reeds.csv')\n", - " r2.scenario[STATEs[jj]].addMaterial('aluminum', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_aluminium_Reeds.csv')\n", - "\n", - "\n", - "i = 2\n", - "r3 = PV_ICE.Simulation(name=SFscenarios[i], path=testfolder)\n", - "for jj in range (0, len(STATEs)): \n", - " filetitle = SFscenarios[i]+'_'+STATEs[jj]+'.csv'\n", - " filetitle = os.path.join(testfolder, 'STATEs', filetitle) \n", - " r3.createScenario(name=STATEs[jj], file=filetitle)\n", - " r3.scenario[STATEs[jj]].addMaterial('glass', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_glass_Reeds.csv')\n", - " r3.scenario[STATEs[jj]].addMaterial('silicon', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_silicon_Reeds.csv')\n", - " r3.scenario[STATEs[jj]].addMaterial('silver', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_silver_Reeds.csv')\n", - " r3.scenario[STATEs[jj]].addMaterial('copper', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_copper_Reeds.csv')\n", - " r3.scenario[STATEs[jj]].addMaterial('aluminum', file=r'..\\baselines\\SolarFutures_2021\\baseline_material_aluminium_Reeds.csv')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Calculate Mass Flow" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on Scenario: WA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: CA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: VA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: FL\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MI\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: IN\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: KY\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: OH\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: PA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: WV\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NV\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MD\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: DE\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NJ\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NY\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: VT\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NH\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: CT\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: RI\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: ME\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: ID\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MT\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: WY\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: UT\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: AZ\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NM\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: SD\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: CO\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: ND\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NE\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MN\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: IA\n", - "********************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: WI\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: TX\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: OK\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: OR\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: KS\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MO\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: AR\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: LA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: IL\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MS\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: AL\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: TN\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: GA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: SC\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NC\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: WA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: CA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: VA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: FL\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MI\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: IN\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: KY\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: OH\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: PA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: WV\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NV\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MD\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: DE\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NJ\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NY\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: VT\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NH\n", - "********************\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: CT\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - 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"==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: MS\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: AL\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: TN\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: GA\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: SC\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: NC\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n" - ] - } - ], - "source": [ - "IRENA= False\n", - "PERFECTMFG = True\n", - "\n", - "mats = ['glass', 'silicon','silver','copper','aluminum']\n", - "\n", - "ELorRL = 'EL'\n", - "if IRENA:\n", - " if ELorRL == 'RL':\n", - " weibullInputParams = {'alpha': 5.3759, 'beta':30} # Regular-loss scenario IRENA\n", - " if ELorRL == 'EL':\n", - " weibullInputParams = {'alpha': 2.49, 'beta':30} # Regular-loss scenario IRENA\n", - " \n", - " if PERFECTMFG:\n", - " for jj in range (0, len(r1.scenario.keys())):\n", - " r1.scenario[STATEs[jj]].data['mod_lifetime'] = 40\n", - " r1.scenario[STATEs[jj]].data['mod_MFG_eff'] = 100.0\n", - " r2.scenario[STATEs[jj]].data['mod_lifetime'] = 40\n", - " r2.scenario[STATEs[jj]].data['mod_MFG_eff'] = 100.0\n", - " r3.scenario[STATEs[jj]].data['mod_lifetime'] = 40\n", - " r3.scenario[STATEs[jj]].data['mod_MFG_eff'] = 100.0\n", - "\n", - " for kk in range(0, len(mats)):\n", - " mat = mats[kk]\n", - " r1.scenario[STATEs[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 \n", - " r2.scenario[STATEs[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 \n", - " r3.scenario[STATEs[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 \n", - " \n", - " r1.calculateMassFlow(weibullInputParams=weibullInputParams)\n", - " r2.calculateMassFlow(weibullInputParams=weibullInputParams)\n", - " r3.calculateMassFlow(weibullInputParams=weibullInputParams)\n", - " title_Method = 'Irena_'+ELorRL\n", - "else:\n", - " r1.calculateMassFlow()\n", - " r2.calculateMassFlow()\n", - " r3.calculateMassFlow()\n", - " title_Method = 'PVICE'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "STATEs: dict_keys(['WA', 'CA', 'VA', 'FL', 'MI', 'IN', 'KY', 'OH', 'PA', 'WV', 'NV', 'MD', 'DE', 'NJ', 'NY', 'VT', 'NH', 'MA', 'CT', 'RI', 'ME', 'ID', 'MT', 'WY', 'UT', 'AZ', 'NM', 'SD', 'CO', 'ND', 'NE', 'MN', 'IA', 'WI', 'TX', 'OK', 'OR', 'KS', 'MO', 'AR', 'LA', 'IL', 'MS', 'AL', 'TN', 'GA', 'SC', 'NC'])\n", - "Module Keys: Index(['year', 'new_Installed_Capacity_[MW]', 'mod_eff', 'mod_reliability_t50',\n", - " 'mod_reliability_t90', 'mod_degradation', 'mod_lifetime', 'mod_MFG_eff',\n", - " 'mod_EOL_collection_eff', 'mod_EOL_collected_recycled', 'mod_Repair',\n", - " 'mod_MerchantTail', 'mod_Reuse', 'irradiance_stc', 'Area',\n", - " 'Cumulative_Area_disposedby_Failure',\n", - " 'Cumulative_Area_disposedby_ProjectLifetime',\n", - " 'Cumulative_Area_disposed', 'Cumulative_Active_Area',\n", - " 'Installed_Capacity_[W]', 'WeibullParams', 'EOL_on_Year_0',\n", - " 'EOL_on_Year_1', 'EOL_on_Year_2', 'EOL_on_Year_3', 'EOL_on_Year_4',\n", - " 'EOL_on_Year_5', 'EOL_on_Year_6', 'EOL_on_Year_7', 'EOL_on_Year_8',\n", - " 'EOL_on_Year_9', 'EOL_on_Year_10', 'EOL_on_Year_11', 'EOL_on_Year_12',\n", - " 'EOL_on_Year_13', 'EOL_on_Year_14', 'EOL_on_Year_15', 'EOL_on_Year_16',\n", - " 'EOL_on_Year_17', 'EOL_on_Year_18', 'EOL_on_Year_19', 'EOL_on_Year_20',\n", - " 'EOL_on_Year_21', 'EOL_on_Year_22', 'EOL_on_Year_23', 'EOL_on_Year_24',\n", - " 'EOL_on_Year_25', 'EOL_on_Year_26', 'EOL_on_Year_27', 'EOL_on_Year_28',\n", - " 'EOL_on_Year_29', 'EOL_on_Year_30', 'EOL_on_Year_31', 'EOL_on_Year_32',\n", - " 'EOL_on_Year_33', 'EOL_on_Year_34', 'EOL_on_Year_35', 'EOL_on_Year_36',\n", - " 'EOL_on_Year_37', 'EOL_on_Year_38', 'EOL_on_Year_39', 'EOL_on_Year_40',\n", - " 'EoL_Collected', 'EoL_NotCollected', 'EoL_Recycled',\n", - " 'EoL_NotRecycled_Landfilled'],\n", - " dtype='object')\n", - "Material Keys: Index(['year', 'mat_virgin_eff', 'mat_massperm2', 'mat_MFG_eff',\n", - " 'mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff',\n", - " 'mat_MFG_scrap_Recycled_into_HQ',\n", - " 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG',\n", - " 'mat_EOL_collected_Recycled', 'mat_EOL_Recycling_eff',\n", - " 'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG',\n", - " 'mat_modules_Collected', 'mat_modules_NotCollected',\n", - " 'mat_modules_Recycled', 'mat_modules_NotRecycled',\n", - " 'mat_EOL_sento_Recycling', 'mat_EOL_NotRecycled_Landfilled',\n", - " 'mat_EOL_Recycled', 'mat_EOL_Recycled_Losses_Landfilled',\n", - " 'mat_EOL_Recycled_2_HQ', 'mat_EOL_Recycled_2_OQ',\n", - " 'mat_EoL_Recycled_HQ_into_MFG', 'mat_EOL_Recycled_HQ_into_OU',\n", - " 'mat_UsedSuccessfullyinModuleManufacturing',\n", - " 'mat_EnteringModuleManufacturing', 'mat_LostinModuleManufacturing',\n", - " 'mat_Manufacturing_Input', 'mat_MFG_Scrap',\n", - " 'mat_MFG_Scrap_Sentto_Recycling', 'mat_MFG_Scrap_Landfilled',\n", - " 'mat_MFG_Scrap_Recycled_Successfully',\n", - " 'mat_MFG_Scrap_Recycled_Losses_Landfilled', 'mat_MFG_Recycled_into_HQ',\n", - " 'mat_MFG_Recycled_into_OQ', 'mat_MFG_Recycled_HQ_into_MFG',\n", - " 'mat_MFG_Recycled_HQ_into_OU', 'mat_Virgin_Stock',\n", - " 'mat_Virgin_Stock_Raw', 'mat_Total_EOL_Landfilled',\n", - " 'mat_Total_MFG_Landfilled', 'mat_Total_Landfilled',\n", - " 'mat_Total_Recycled_OU'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "print(\"STATEs:\", r1.scenario.keys())\n", - "print(\"Module Keys:\", r1.scenario[STATEs[jj]].data.keys())\n", - "print(\"Material Keys: \", r1.scenario[STATEs[jj]].material['glass'].materialdata.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# OPEN EI" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Reference.Mod'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kk=0\n", - "SFScenarios = [r1, r2, r3]\n", - "SFScenarios[kk].name\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done\n" - ] - } - ], - "source": [ - "# WORK ON THIS FOIR OPENEI\n", - "\n", - "keyw=['mat_Virgin_Stock','mat_Total_EOL_Landfilled','mat_Total_MFG_Landfilled', 'mat_Total_Landfilled', \n", - " 'new_Installed_Capacity_[MW]','Installed_Capacity_[W]']\n", - "keywprint = ['VirginMaterialDemand','EOLMaterial', 'ManufacturingScrap','ManufacturingScrapAndEOLMaterial',\n", - " 'NewInstalledCapacity','InstalledCapacity'] \n", - "keywunits = ['MetricTonnes', 'MetricTonnes', 'MetricTonnes', 'MetricTonnes', \n", - " 'MW','MW']\n", - "keywdcumneed = [True,True,True,True,\n", - " True,False]\n", - "keywdlevel = ['material','material','material','material',\n", - " 'module','module']\n", - "keywscale = [1000000, 1000000, 1000000, 1000000,\n", - " 1,1e6]\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " \n", - " for zz in range (0, len(STATEs)):\n", - "\n", - " foo = pd.DataFrame()\n", - " for jj in range (0, len(keyw)):\n", - "\n", - " if keywdlevel[jj] == 'material':\n", - " for ii in range (0, len(materials)): \n", - " sentit = '@value|'+keywprint[jj]+'|'+materials[ii].capitalize() +'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyw[jj]]/keywscale[jj] \n", - " \n", - " if keywdcumneed[jj]:\n", - " for ii in range (0, len(materials)): \n", - " sentit = '@value|Cumulative'+keywprint[jj]+'|'+materials[ii].capitalize() +'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyw[jj]].cumsum()/keywscale[jj] \n", - "\n", - " else:\n", - " sentit = '@value|'+keywprint[jj]+'|'+'PV' +'#'+keywunits[jj]\n", - " #sentit = '@value|'+keywprint[jj]+'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]]/keywscale[jj] \n", - "\n", - " if keywdcumneed[jj]:\n", - " sentit = '@value|Cumulative'+keywprint[jj]+'|'+'PV' +'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]].cumsum()/keywscale[jj] \n", - " \n", - "\n", - " foo['@states'] = STATEs[zz]\n", - " foo['@scenario|Solar Futures'] = SFScenarios[kk].name\n", - " foo['@timeseries|Year'] = SFScenarios[kk].scenario[STATEs[zz]].data.year\n", - "\n", - " scenariolist = scenariolist.append(foo) \n", - "\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "#scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "#scenariolist = scenariolist.applymap(lambda x: int(x))\n", - "scenariolist.to_csv(title_Method+' OpenEI.csv', index=False)\n", - "\n", - "print(\"Done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done\n" - ] - } - ], - "source": [ - "# WORK ON THIS FOIR OPENEI\n", - "\n", - "keyw=['mat_Virgin_Stock','mat_Total_EOL_Landfilled','mat_Total_MFG_Landfilled', 'mat_Total_Landfilled', \n", - " 'new_Installed_Capacity_[MW]','Installed_Capacity_[W]']\n", - "keywprint = ['VirginMaterialDemand','EOLMaterial', 'ManufacturingScrap','ManufacturingScrapAndEOLMaterial',\n", - " 'NewInstalledCapacity','InstalledCapacity'] \n", - "keywunits = ['MetricTonnes', 'MetricTonnes', 'MetricTonnes', 'MetricTonnes', \n", - " 'MW','MW']\n", - "keywdcumneed = [True,True,True,True,\n", - " True,False]\n", - "keywdlevel = ['material','material','material','material',\n", - " 'module','module']\n", - "keywscale = [1000000, 1000000, 1000000, 1000000,\n", - " 1,1e6]\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " \n", - " for zz in range (0, len(STATEs)):\n", - "\n", - " foo = pd.DataFrame()\n", - " for jj in range (0, len(keyw)):\n", - "\n", - " if keywdlevel[jj] == 'material':\n", - " for ii in range (0, len(materials)): \n", - " sentit = '@value|'+keywprint[jj]+'|'+materials[ii].capitalize() +'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyw[jj]]/keywscale[jj] \n", - " \n", - " else:\n", - " sentit = '@value|'+keywprint[jj]+'|'+'PV' +'#'+keywunits[jj]\n", - " #sentit = '@value|'+keywprint[jj]+'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]]/keywscale[jj] \n", - "\n", - "\n", - "\n", - " foo['@states'] = STATEs[zz]\n", - " foo['@scenario|Solar Futures'] = SFScenarios[kk].name\n", - " foo['@timeseries|Year'] = SFScenarios[kk].scenario[STATEs[zz]].data.year\n", - "\n", - " scenariolist = scenariolist.append(foo) \n", - "\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "#scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "#scenariolist = scenariolist.applymap(lambda x: int(x))\n", - "scenariolist.to_csv(title_Method+' OpenEI Yearly Only.csv', index=False)\n", - "\n", - "print(\"Done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done\n" - ] - } - ], - "source": [ - "# WORK ON THIS FOIR OPENEI\n", - "\n", - "keyw=['mat_Virgin_Stock','mat_Total_EOL_Landfilled','mat_Total_MFG_Landfilled', 'mat_Total_Landfilled', \n", - " 'new_Installed_Capacity_[MW]','Installed_Capacity_[W]']\n", - "keywprint = ['VirginMaterialDemand','EOLMaterial', 'ManufacturingScrap','ManufacturingScrapAndEOLMaterial',\n", - " 'NewInstalledCapacity','InstalledCapacity'] \n", - "keywunits = ['MetricTonnes', 'MetricTonnes', 'MetricTonnes', 'MetricTonnes', \n", - " 'MW','MW']\n", - "keywdcumneed = [True,True,True,True,\n", - " True,False]\n", - "keywdlevel = ['material','material','material','material',\n", - " 'module','module']\n", - "keywscale = [1000000, 1000000, 1000000, 1000000,\n", - " 1,1e6]\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " \n", - " for zz in range (0, len(STATEs)):\n", - "\n", - " foo = pd.DataFrame()\n", - " for jj in range (0, len(keyw)):\n", - "\n", - " if keywdlevel[jj] == 'material':\n", - "\n", - " if keywdcumneed[jj]:\n", - " for ii in range (0, len(materials)): \n", - " sentit = '@value|Cumulative'+keywprint[jj]+'|'+materials[ii].capitalize() +'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyw[jj]].cumsum()/keywscale[jj] \n", - "\n", - " else:\n", - "\n", - " if keywdcumneed[jj]:\n", - " sentit = '@value|Cumulative'+keywprint[jj]+'|'+'PV' +'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]].cumsum()/keywscale[jj] \n", - " \n", - "\n", - " foo['@states'] = STATEs[zz]\n", - " foo['@scenario|Solar Futures'] = SFScenarios[kk].name\n", - " foo['@timeseries|Year'] = SFScenarios[kk].scenario[STATEs[zz]].data.year\n", - "\n", - " scenariolist = scenariolist.append(foo) \n", - "\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "#scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "#scenariolist = scenariolist.applymap(lambda x: int(x))\n", - "scenariolist.to_csv(title_Method+' OpenEI Cumulatives Only.csv', index=False)\n", - "\n", - "print(\"Done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done\n" - ] - } - ], - "source": [ - "# WORK ON THIS FOIR OPENEI\n", - "# SCENARIO DIFERENCeS\n", - "\n", - "keyw=['new_Installed_Capacity_[MW]','Installed_Capacity_[W]']\n", - "keywprint = ['NewInstalledCapacity','InstalledCapacity'] \n", - "keywprint = ['NewInstalledCapacity','InstalledCapacity'] \n", - "sfprint = ['Reference','Grid Decarbonization', 'High Electrification'] \n", - "\n", - "keywunits = ['MW','MW']\n", - "keywdcumneed = [True,False]\n", - "keywdlevel = ['module','module']\n", - "keywscale = [1,1e6]\n", - "materials = []\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - " \n", - "for zz in range (0, len(STATEs)):\n", - "\n", - " foo = pd.DataFrame()\n", - " \n", - " for jj in range (0, len(keyw)):\n", - " \n", - " # kk -- scenario\n", - " for kk in range(0, 3):\n", - " sentit = '@value|'+keywprint[jj]+'|'+sfprint[kk]+'#'+keywunits[jj]\n", - " #sentit = '@value|'+keywprint[jj]+'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]]/keywscale[jj] \n", - "\n", - " if keywdcumneed[jj]:\n", - " sentit = '@value|Cumulative'+keywprint[jj]+'|'+sfprint[kk]+'#'+keywunits[jj]\n", - " foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]].cumsum()/keywscale[jj] \n", - "\n", - " # foo['@value|scenario|Solar Futures'] = SFScenarios[kk].name\n", - " foo['@states'] = STATEs[zz]\n", - " foo['@timeseries|Year'] = SFScenarios[kk].scenario[STATEs[zz]].data.year\n", - " scenariolist = scenariolist.append(foo) \n", - "\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]]\n", - "scenariolist = scenariolist[cols]\n", - "\n", - "#scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "#scenariolist = scenariolist.applymap(lambda x: int(x))\n", - "scenariolist.to_csv(title_Method+' OpenEI ScenarioDifferences.csv', index=False)\n", - "\n", - "print(\"Done\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    @states@timeseries|Year@value|NewInstalledCapacity|Reference#MW@value|CumulativeNewInstalledCapacity|Reference#MW@value|NewInstalledCapacity|Grid Decarbonization#MW@value|CumulativeNewInstalledCapacity|Grid Decarbonization#MW@value|NewInstalledCapacity|High Electrification#MW@value|CumulativeNewInstalledCapacity|High Electrification#MW@value|InstalledCapacity|Reference#MW@value|InstalledCapacity|Grid Decarbonization#MW@value|InstalledCapacity|High Electrification#MW
    0WA20100.1810500.1810500.1810500.1810500.1810500.1810500.1822820.1822820.182282
    1WA20116.7519756.9330256.7519756.9330256.7519756.9330256.9777406.9777406.977740
    2WA20126.75197513.6850016.75197513.6850016.75197513.68500113.72830213.72830213.728302
    3WA20139.58247623.2674769.58247723.2674779.58247723.26747723.30602623.30602723.306027
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    \n", - "
    " - ], - "text/plain": [ - " @states @timeseries|Year @value|NewInstalledCapacity|Reference#MW \\\n", - "0 WA 2010 0.181050 \n", - "1 WA 2011 6.751975 \n", - "2 WA 2012 6.751975 \n", - "3 WA 2013 9.582476 \n", - "4 WA 2014 9.582476 \n", - "\n", - " @value|CumulativeNewInstalledCapacity|Reference#MW \\\n", - "0 0.181050 \n", - "1 6.933025 \n", - "2 13.685001 \n", - "3 23.267476 \n", - "4 32.849952 \n", - "\n", - " @value|NewInstalledCapacity|Grid Decarbonization#MW \\\n", - "0 0.181050 \n", - "1 6.751975 \n", - "2 6.751975 \n", - "3 9.582477 \n", - "4 9.582477 \n", - "\n", - " @value|CumulativeNewInstalledCapacity|Grid Decarbonization#MW \\\n", - "0 0.181050 \n", - "1 6.933025 \n", - "2 13.685001 \n", - "3 23.267477 \n", - "4 32.849954 \n", - "\n", - " @value|NewInstalledCapacity|High Electrification#MW \\\n", - "0 0.181050 \n", - "1 6.751975 \n", - "2 6.751975 \n", - "3 9.582477 \n", - "4 9.582477 \n", - "\n", - " @value|CumulativeNewInstalledCapacity|High Electrification#MW \\\n", - "0 0.181050 \n", - "1 6.933025 \n", - "2 13.685001 \n", - "3 23.267477 \n", - "4 32.849954 \n", - "\n", - " @value|InstalledCapacity|Reference#MW \\\n", - "0 0.182282 \n", - "1 6.977740 \n", - "2 13.728302 \n", - "3 23.306026 \n", - "4 32.846403 \n", - "\n", - " @value|InstalledCapacity|Grid Decarbonization#MW \\\n", - "0 0.182282 \n", - "1 6.977740 \n", - "2 13.728302 \n", - "3 23.306027 \n", - "4 32.846405 \n", - "\n", - " @value|InstalledCapacity|High Electrification#MW \n", - "0 0.182282 \n", - "1 6.977740 \n", - "2 13.728302 \n", - "3 23.306027 \n", - "4 32.846405 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "scenariolist.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# SAVE DATA FOR BILLY: STATES" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "#for 3 significant numbers rounding\n", - "N = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "SFScenarios[kk].scenario[PCAs[zz]].data.year\n", - "\n", - "Index 20 --> 2030\n", - "\n", - "Index 30 --> 2040\n", - "\n", - "Index 40 --> 2050" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index 20 is year 2030\n", - "index 30 is year 2040\n", - "index 40 is year 2050\n" - ] - } - ], - "source": [ - "idx2030 = 20\n", - "idx2040 = 30\n", - "idx2050 = 40\n", - "print(\"index \", idx2030, \" is year \", r1.scenario[STATEs[0]].data['year'].iloc[idx2030])\n", - "print(\"index \", idx2040, \" is year \", r1.scenario[STATEs[0]].data['year'].iloc[idx2040])\n", - "print(\"index \", idx2050, \" is year \", r1.scenario[STATEs[0]].data['year'].iloc[idx2050])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 6 - STATE Cumulative Virgin Needs by 2050\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Virgin_Stock'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " \n", - " materiallist = []\n", - " for ii in range (0, len(materials)): \n", - " \n", - " keywordsum = []\n", - " for zz in range (0, len(STATEs)):\n", - " keywordsum.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword].sum())\n", - " \n", - " materiallist.append(keywordsum)\n", - " df = pd.DataFrame (materiallist,columns=STATEs, index = materials)\n", - " df = df.T\n", - " df = df.add_prefix(SFScenarios[kk].name+'_')\n", - " scenariolist = pd.concat([scenariolist , df], axis=1)\n", - "\n", - "scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "scenariolist = scenariolist.applymap(lambda x: int(x))\n", - "scenariolist.to_csv(title_Method+' 6 - STATE Cumulative2050 VirginMaterialNeeds_tons.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1.453878e+08\n", - "1 1.107787e+09\n", - "2 1.084215e+09\n", - "3 3.383155e+09\n", - "4 3.266099e+09\n", - "5 8.643166e+09\n", - "6 7.931744e+09\n", - "7 7.173017e+09\n", - "8 6.809346e+09\n", - "9 4.285922e+09\n", - "10 3.976719e+09\n", - "11 4.714018e+08\n", - "12 4.534323e+08\n", - "13 6.387699e+08\n", - "14 6.078930e+08\n", - "15 9.829633e+08\n", - "16 9.488859e+08\n", - "17 1.950642e+10\n", - "18 1.901376e+10\n", - "19 3.409841e+09\n", - "20 3.326317e+09\n", - "21 3.470388e+10\n", - "22 3.450642e+10\n", - "23 2.131930e+10\n", - "24 2.121362e+10\n", - "25 3.140724e+10\n", - "26 3.126931e+10\n", - "27 1.985550e+10\n", - "28 1.977723e+10\n", - "29 1.580074e+09\n", - "30 1.574424e+09\n", - "31 1.917670e+09\n", - "32 1.911396e+09\n", - "33 1.843109e+09\n", - "34 1.837551e+09\n", - "35 9.851720e+09\n", - "36 9.824170e+09\n", - "37 4.815710e+09\n", - "38 4.803155e+09\n", - "39 1.260341e+09\n", - "40 1.257263e+09\n", - "Name: mat_Virgin_Stock, dtype: float64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 7 - STATE Cumulative EoL Only Waste by 2050" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Total_EOL_Landfilled'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " \n", - " materiallist = []\n", - " for ii in range (0, len(materials)): \n", - " \n", - " keywordsum = []\n", - " for zz in range (0, len(STATEs)):\n", - " keywordsum.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword].sum())\n", - " \n", - " materiallist.append(keywordsum)\n", - " df = pd.DataFrame (materiallist,columns=STATEs, index = materials)\n", - " df = df.T\n", - " df = df.add_prefix(SFScenarios[kk].name+'_')\n", - " scenariolist = pd.concat([scenariolist , df], axis=1)\n", - "\n", - "scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "scenariolist = scenariolist.applymap(lambda x: int(x))\n", - "scenariolist.to_csv(title_Method+' 7 - STATE Cumulative2050 Waste_EOL_tons.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### 8 - STATE Yearly Virgin Needs 2030 2040 2050" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Virgin_Stock'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " materiallist = pd.DataFrame()\n", - "\n", - " for ii in range (0, len(materials)): \n", - " \n", - " keywordsum2030 = []\n", - " keywordsum2040 = []\n", - " keywordsum2050 = []\n", - "\n", - " for zz in range (0, len(STATEs)):\n", - " keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030])\n", - " keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040])\n", - " keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050])\n", - " \n", - " yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050])\n", - " yearlylist = yearlylist.T\n", - " yearlylist = yearlylist.add_prefix(materials[ii]+'_')\n", - " materiallist = pd.concat([materiallist, yearlylist], axis=1)\n", - " materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_')\n", - " scenariolist = pd.concat([scenariolist , materiallist], axis=1)\n", - "\n", - "scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "#scenariolist = scenariolist.applymap(lambda x: int(x))\n", - "scenariolist.to_csv(title_Method+' 8 - STATE Yearly 2030 2040 2050 VirginMaterialNeeds_tons.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 9 - STATE Yearly EoL Waste 2030 2040 205" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Total_EOL_Landfilled'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " materiallist = pd.DataFrame()\n", - "\n", - " for ii in range (0, len(materials)): \n", - " \n", - " keywordsum2030 = []\n", - " keywordsum2040 = []\n", - " keywordsum2050 = []\n", - "\n", - " for zz in range (0, len(STATEs)):\n", - " keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030])\n", - " keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040])\n", - " keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050])\n", - " \n", - " yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050])\n", - " yearlylist = yearlylist.T\n", - " yearlylist = yearlylist.add_prefix(materials[ii]+'_')\n", - " materiallist = pd.concat([materiallist, yearlylist], axis=1)\n", - " materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_')\n", - " scenariolist = pd.concat([scenariolist , materiallist], axis=1)\n", - "\n", - "scenariolist = scenariolist/1000000 # Converting to Metric Tonnes\n", - "#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "#scenariolist = scenariolist.applymap(lambda x: int(x))\n", - "scenariolist.to_csv(title_Method+' 9 - STATE Yearly 2030 2040 2050 Waste_EOL_tons.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# APPENDIX TABLES\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Appendix - Cumulative Virgin Stock" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Virgin_Stock'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " \n", - " materiallist = pd.DataFrame()\n", - " for ii in range (0, len(materials)): \n", - " \n", - " keywordsum2030 = []\n", - " keywordsum2040 = []\n", - " keywordsum2050 = []\n", - " for zz in range (0, len(STATEs)):\n", - " keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:20].sum())\n", - " keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:30].sum())\n", - " keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:].sum())\n", - " \n", - " yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050])\n", - " yearlylist = yearlylist.T\n", - " yearlylist = yearlylist.add_prefix(materials[ii]+'_')\n", - " materiallist = pd.concat([materiallist, yearlylist], axis=1)\n", - " materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_')\n", - " scenariolist = pd.concat([scenariolist , materiallist], axis=1)\n", - "\n", - "scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " filter_col = [col for col in scenariolist if (col.startswith(SFScenarios[kk].name)) ]\n", - " scen = scenariolist[filter_col]\n", - " scen.columns = scen.columns.str.lstrip(SFScenarios[kk].name+'_') # strip suffix at the right end only.\n", - " scen = scen.rename_axis('State')\n", - " scen = scen.sort_values(by='glass_2050', ascending=False)\n", - " scen.sum(axis=0)\n", - " reduced = scen.iloc[0:23]\n", - " new_row = pd.Series(data=scen.iloc[23::].sum(axis=0), name='OTHER STATES')\n", - " new_row_2 = pd.Series(data=scen.sum(axis=0), name='US TOTAL')\n", - " reduced = reduced.append(new_row, ignore_index=False)\n", - " reduced = reduced.append(new_row_2, ignore_index=False)\n", - " reduced = reduced.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - " reduced = reduced.applymap(lambda x: int(x))\n", - " reduced.to_csv(title_Method+' Appendix - '+ SFScenarios[kk].name + ' Cumulative Virgin Stock by State.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Appendix - Yearly Virgin Stock" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Virgin_Stock'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " materiallist = pd.DataFrame()\n", - "\n", - " for ii in range (0, len(materials)): \n", - " \n", - " keywordsum2030 = []\n", - " keywordsum2040 = []\n", - " keywordsum2050 = []\n", - "\n", - " for zz in range (0, len(STATEs)):\n", - " keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030])\n", - " keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040])\n", - " keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050])\n", - " \n", - " yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050])\n", - " yearlylist = yearlylist.T\n", - " yearlylist = yearlylist.add_prefix(materials[ii]+'_')\n", - " materiallist = pd.concat([materiallist, yearlylist], axis=1)\n", - " materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_')\n", - " scenariolist = pd.concat([scenariolist , materiallist], axis=1)\n", - "\n", - "scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "\n", - "\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " filter_col = [col for col in scenariolist if (col.startswith(SFScenarios[kk].name)) ]\n", - " scen = scenariolist[filter_col]\n", - " scen.columns = scen.columns.str.lstrip(SFScenarios[kk].name+'_') # strip suffix at the right end only.\n", - " scen = scen.rename_axis('State')\n", - " scen = scen.sort_values(by='glass_2050', ascending=False)\n", - " reduced = scen.iloc[0:23]\n", - " new_row = pd.Series(data=scen.iloc[23::].sum(axis=0), name='OTHER STATES')\n", - " new_row_2 = pd.Series(data=scen.sum(axis=0), name='US TOTAL')\n", - " reduced = reduced.append(new_row, ignore_index=False)\n", - " reduced = reduced.append(new_row_2, ignore_index=False)\n", - " reduced = reduced.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - " reduced = reduced.applymap(lambda x: int(x))\n", - " reduced.to_csv(title_Method+' Appendix - '+ SFScenarios[kk].name + ' Yearly Virgin Stock by State.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Appendix - Cumulative EOL_ WASTE by State" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Total_EOL_Landfilled'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " \n", - " materiallist = pd.DataFrame()\n", - " for ii in range (0, len(materials)): \n", - " \n", - " keywordsum2030 = []\n", - " keywordsum2040 = []\n", - " keywordsum2050 = []\n", - " for zz in range (0, len(STATEs)):\n", - " keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:20].sum())\n", - " keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:30].sum())\n", - " keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:].sum())\n", - " \n", - " yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050])\n", - " yearlylist = yearlylist.T\n", - " yearlylist = yearlylist.add_prefix(materials[ii]+'_')\n", - " materiallist = pd.concat([materiallist, yearlylist], axis=1)\n", - " materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_')\n", - " scenariolist = pd.concat([scenariolist , materiallist], axis=1)\n", - "\n", - "scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " filter_col = [col for col in scenariolist if (col.startswith(SFScenarios[kk].name)) ]\n", - " scen = scenariolist[filter_col]\n", - " scen.columns = scen.columns.str.lstrip(SFScenarios[kk].name+'_') # strip suffix at the right end only.\n", - " scen = scen.rename_axis('State')\n", - " #scen = scen.sort_values(by='glass_2050', ascending=False)\n", - " reduced = scen\n", - " new_row = pd.Series(data=scen.sum(axis=0), name='US TOTAL')\n", - " reduced = reduced.append(new_row, ignore_index=False)\n", - " #reduced = reduced.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - " #reduced = reduced.applymap(lambda x: int(x))\n", - " reduced.to_csv(title_Method+' Appendix - '+ SFScenarios[kk].name + ' Cumulative EOL_ WASTE by State.csv')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Sparkplots + APPENDIX - Yearly EoL Waste " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "sparkplotfolder = os.path.join(testfolder, 'SPARKPLOTS')" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Total_EOL_Landfilled'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "\n", - "scenariolist = pd.DataFrame()\n", - "for kk in range(0, 3):\n", - " # Loop over Materials\n", - " materiallist = pd.DataFrame()\n", - "\n", - " for ii in range (0, len(materials)): \n", - " \n", - " keywordsum2030 = []\n", - " keywordsum2040 = []\n", - " keywordsum2050 = []\n", - "\n", - " for zz in range (0, len(STATEs)):\n", - " keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030])\n", - " keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040])\n", - " keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050])\n", - " \n", - " # SPARK PLOT\n", - " if materials[ii] == 'glass':\n", - " fig, axs = plt.subplots(figsize=(2, 1), facecolor='w', edgecolor='k')\n", - " #axs.ioff()\n", - " axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year, SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword]/1000000, 'k')\n", - " axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2030], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030]/1000000, 'r.',markersize=12)\n", - " axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2040], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040]/1000000, 'r.', markersize=12)\n", - " axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2050], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050]/1000000, 'r.', markersize=12)\n", - " #plt.ylabel('Tonnes')\n", - " axs.set_xlim([2020, 2052])\n", - " #axs.set_visible(False)\n", - " axs.axis('off')\n", - " figtitle = title_Method+ ' ' + SFScenarios[kk].name + ' Fig_2x1_GLASS_Waste_'+STATEs[zz]+'.png'\n", - " #figtitle = os.path.join('SPARKPLOTS', figtitle)\n", - " #fig.savefig(figtitle, dpi=600)\n", - " fig.savefig(os.path.join(sparkplotfolder, figtitle), dpi=600)\n", - " plt.close(fig) # This avoids the figure from displayig and getting all the warnings\n", - " \n", - " yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050])\n", - " yearlylist = yearlylist.T\n", - " yearlylist = yearlylist.add_prefix(materials[ii]+'_')\n", - " materiallist = pd.concat([materiallist, yearlylist], axis=1)\n", - " materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_')\n", - " scenariolist = pd.concat([scenariolist , materiallist], axis=1)\n", - "\n", - "scenariolist = scenariolist/1000000 # Converting to Metric Tons\n", - "\n", - "\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " filter_col = [col for col in scenariolist if (col.startswith(SFScenarios[kk].name)) ]\n", - " scen = scenariolist[filter_col]\n", - " scen.columns = scen.columns.str.lstrip(SFScenarios[kk].name+'_') # strip suffix at the right end only.\n", - " scen = scen.rename_axis('State')\n", - " scen = scen.sort_values(by='State')\n", - " reduced = scen\n", - " new_row = pd.Series(data=scen.sum(axis=0), name='US TOTAL')\n", - " reduced = reduced.append(new_row, ignore_index=False)\n", - "# reduced = reduced.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "# reduced = reduced.applymap(lambda x: int(x))\n", - " reduced.to_csv(title_Method+' Appendix - '+ SFScenarios[kk].name + ' Yearly EOL Waste by State.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# PLOT HERE\n", - "fig, axs = plt.subplots(figsize=(2, 1), facecolor='w', edgecolor='k')\n", - "axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year, SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword]/1000000, 'k')\n", - "axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2030], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030]/1000000, 'r.',markersize=12)\n", - "axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2040], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040]/1000000, 'r.', markersize=12)\n", - "axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2050], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050]/1000000, 'r.', markersize=12)\n", - "#plt.ylabel('Tonnes')\n", - "axs.set_xlim([2020, 2052])\n", - "#axs.set_visible(False)\n", - "axs.axis('off');\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# OBSOLETE BECAUSE FASTER TO DO ON NATION LEVEL" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'failtest' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfailtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;31m# so the simulation will stop when reaching here jic\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'failtest' is not defined" - ] - } - ], - "source": [ - "print(failtest)\n", - "# so the simulation will stop when reaching here jic" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#matplotlib.use('Agg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Yearly and Cumulative Tables 3 Sigs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "UScumsig3 = UScumsig3.drop(UScumsig3.index[0])\n", - "N = 2\n", - "\n", - "UScumsig3 = UScumsig3.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "UScumsig3 = UScumsig3.applymap(lambda x: int(x))\n", - "UScumsig3.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly3sig = USyearly.copy()\n", - "UScum3sig = UScum.copy()\n", - "\n", - "USyearly3sig = USyearly3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "USyearly3sig = USyearly3sig.applymap(lambda x: int(x))\n", - "\n", - "UScum3sig = UScum3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "UScum3sig = UScum3sig.applymap(lambda x: int(x))\n", - "\n", - "USyearly3sig.to_csv(title_Method+' US_Yearly.csv')\n", - "UScum3sig.to_csv(title_Method+' US_Cumulative.csv')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "r1.plotScenariosComparison(keyword='Cumulative_Area_disposedby_Failure')\n", - "r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_Total_Landfilled')\n", - "\"\"\"\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aggregating PCAs Material Landfilled to obtain US totals by Year" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "### Singe Material Example Aggregating PCAs to obtain US Total\n", - "\n", - "\"\"\"\n", - "keyword='mat_Total_Landfilled'\n", - "#keyword='new_Installed_Capacity_[MW]'\n", - "\n", - "plt.figure()\n", - "plt.plot(r1.scenario[PCAs[0]].data['year'], foo, label=PCAs[12])\n", - "plt.title(keyword)\n", - "plt.legend()\n", - "\n", - "for jj in range (1, len(PCAs)): \n", - " foo['silver'] += r1.scenario[PCAs[jj]].material['silver'].materialdata[keyword]\n", - "\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(2, 1, 1)\n", - "ax.plot(r1.scenario[PCAs[0]].data['year'], foo['silver'], label='US')\n", - "plt.title(\"Material Landfilled per Year US\")\n", - "#ax.set_yscale('log')\n", - "print(max(foo))\n", - "\"\"\"\n", - "pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "### Verbose Material Example Aggregating PCAs to obtain US Total\n", - "\n", - "\"\"\"\n", - "keyword='mat_Total_Landfilled'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "USyearlyWASTE=pd.DataFrame()\n", - "\n", - "# Loop over Materials\n", - "for ii in range (0, len(materials)): \n", - " material = materials[ii]\n", - " foo1 = r1.scenario[PCAs[0]].material[material].materialdata[keyword].copy()\n", - " foo1 = foo1.to_frame(name=material)\n", - " foo2 = r2.scenario[PCAs[0]].material[material].materialdata[keyword].copy()\n", - " foo2 = foo2.to_frame(name=material)\n", - " foo3 = r3.scenario[PCAs[0]].material[material].materialdata[keyword].copy()\n", - " foo3 = foo3.to_frame(name=material)\n", - "\n", - " USyearlyWASTE[r1.name + '_' + material] = foo1[material]\n", - " USyearlyWASTE[r2.name + '_' + material] = foo2[material]\n", - " USyearlyWASTE[r3.name + '_' + material] = foo3[material]\n", - "\n", - " # Loop over PCAs\n", - " for jj in range (1, len(PCAs)): \n", - " USyearlyWASTE[r1.name + '_' + material] += r1.scenario[PCAs[jj]].material[material].materialdata[keyword]\n", - " USyearlyWASTE[r2.name + '_' + material] += r2.scenario[PCAs[jj]].material[material].materialdata[keyword]\n", - " USyearlyWASTE[r3.name + '_' + material] += r3.scenario[PCAs[jj]].material[material].materialdata[keyword]\n", - "\n", - "# Converting to grams to Tons. \n", - "USyearlyWASTE.head(20)\n", - "\"\"\"\n", - "pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Total_Landfilled'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "USyearly=pd.DataFrame()\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " # Loop over Materials\n", - " for ii in range (0, len(materials)): \n", - " material = materials[ii]\n", - " foo = obj.scenario[STATEs[0]].material[material].materialdata[keyword].copy()\n", - " foo = foo.to_frame(name=material)\n", - " USyearly[\"Waste_\"+material+'_'+obj.name] = foo[material]\n", - "\n", - " # Loop over STATEs\n", - " for jj in range (1, len(STATEs)): \n", - " USyearly[\"Waste_\"+material+'_'+obj.name] += obj.scenario[STATEs[jj]].material[material].materialdata[keyword]\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj.name)) ]\n", - " USyearly['Waste_Module_'+obj.name] = USyearly[filter_col].sum(axis=1)\n", - " \n", - "# Converting to grams to Tons. \n", - "USyearly.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Virgin_Stock'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " # Loop over Materials\n", - " for ii in range (0, len(materials)): \n", - " material = materials[ii]\n", - " foo = obj.scenario[STATEs[0]].material[material].materialdata[keyword].copy()\n", - " foo = foo.to_frame(name=material)\n", - " USyearly[\"VirginStock_\"+material+'_'+obj.name] = foo[material]\n", - "\n", - " # Loop over STATEs\n", - " for jj in range (1, len(STATEs)): \n", - " USyearly[\"VirginStock_\"+material+'_'+obj.name] += obj.scenario[STATEs[jj]].material[material].materialdata[keyword]\n", - " \n", - " filter_col = [col for col in USyearly if (col.startswith('VirginStock_') and col.endswith(obj.name)) ]\n", - " USyearly['VirginStock_Module_'+obj.name] = USyearly[filter_col].sum(axis=1)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting to grams to METRIC Tons. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly = USyearly/1000000 # This is the ratio for Metric tonnes\n", - "#907185 -- this is for US tons\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding Installed Capacity to US" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " obj.scenario[STATEs[0]].data.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='new_Installed_Capacity_[MW]'\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " # Loop over Materials\n", - " foo = obj.scenario[STATEs[0]].data[keyword]\n", - " foo = foo.to_frame(name=keyword)\n", - " USyearly[keyword+obj.name] = foo[keyword]\n", - "\n", - " # Loop over STATEs\n", - " for jj in range (1, len(STATEs)): \n", - " USyearly[keyword+obj.name] += obj.scenario[STATEs[jj]].data[keyword]\n", - "\n", - "USyearly.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='Installed_Capacity_[W]'\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " # Loop over Materials\n", - " foo = obj.scenario[STATEs[0]].data[keyword]\n", - " foo = foo.to_frame(name=keyword)\n", - " USyearly[\"Capacity_\"+obj.name] = foo[keyword]\n", - "\n", - " # Loop over STATEs\n", - " for jj in range (1, len(STATEs)): \n", - " USyearly[\"Capacity_\"+obj.name] += obj.scenario[STATEs[jj]].data[keyword]\n", - "\n", - "USyearly.head(20)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creative Cumulative DataFrame and Saving" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly.index = obj.scenario[STATEs[0]].data['year']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "UScum = USyearly.copy()\n", - "UScum = UScum.cumsum()\n", - "UScum.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "USyearly.to_csv(title_Method+' US_Yearly.csv')\n", - "UScum.to_csv(title_Method+' US_Cumulative.csv')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PLOTTING GALORE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "keywords=['VirginStock_', 'Waste_', 'Capacity']\n", - "SFScenarios = [r1, r2, r3]\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "# Loop over Keywords\n", - "for ii in range(0, 2):\n", - " keyw = keywords[ii]\n", - " # Loop over SF Scenarios\n", - " for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " plt.figure()\n", - " plt.plot([],[],color='m', label='glass', linewidth=5)\n", - " plt.plot([],[],color='c', label='silicon', linewidth=5)\n", - " plt.plot([],[],color='r', label='silver', linewidth=5)\n", - " plt.plot([],[],color='k', label='copper', linewidth=5)\n", - " plt.plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " plt.stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], \n", - " USyearly[keyw+materials[1]+'_'+obj.name], \n", - " USyearly[keyw+materials[2]+'_'+obj.name], \n", - " USyearly[keyw+materials[3]+'_'+obj.name], \n", - " USyearly[keyw+materials[4]+'_'+obj.name], \n", - " colors=['m','c','r','k', 'g'])\n", - " plt.ylabel('Mass [Tons]')\n", - " plt.xlim([2010, 2050])\n", - " plt.title('Yearly '+keyw+ ' ' + obj.name)\n", - " plt.legend(materials)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 8})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_', 'Waste_', 'Capacity_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(2,3, figsize=(15, 6), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .5, wspace=.001)\n", - "axs = axs.ravel()\n", - "i = 0\n", - "\n", - "# Loop over Keywords\n", - "for ii in range(0, 2):\n", - " keyw = keywords[ii]\n", - " # Loop over SF Scenarios\n", - " for kk in range(0, 3):\n", - " \n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - " axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " axs[i].plot([],[],color='k', label='silicon', linewidth=5)\n", - " axs[i].plot([],[],color='m', label='silver', linewidth=5)\n", - " axs[i].plot([],[],color='r', label='copper', linewidth=5)\n", - " axs[i].plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], \n", - " USyearly[keyw+materials[1]+'_'+obj.name], \n", - " USyearly[keyw+materials[2]+'_'+obj.name], \n", - " USyearly[keyw+materials[3]+'_'+obj.name], \n", - " USyearly[keyw+materials[4]+'_'+obj.name], \n", - " colors=['c','k','m','r', 'g'])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2010, 2050])\n", - " axs[i].set_title(keyw+ ' ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " \n", - " i += 1 \n", - " \n", - "for i in range (0, 3):\n", - " axs[i].set_ylim([0, 4e7])\n", - " axs[i+3].set_ylim([0, 4e7])\n", - "\n", - "axs[0].set_ylabel('Mass [Tons]')\n", - "axs[3].set_ylabel('Mass [Tons]')\n", - "axs[5].legend(materials)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 8})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_', 'Waste_', 'Capacity_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(3,3, figsize=(15, 6), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .5, wspace=.001)\n", - "axs = axs.ravel()\n", - "i = 0\n", - "\n", - "# Loop over Keywords\n", - "for ii in range(0, 2):\n", - " keyw = keywords[ii]\n", - " # Loop over SF Scenarios\n", - " for kk in range(0, 3):\n", - " \n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - " axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " axs[i].plot([],[],color='k', label='silicon', linewidth=5)\n", - " axs[i].plot([],[],color='m', label='silver', linewidth=5)\n", - " axs[i].plot([],[],color='r', label='copper', linewidth=5)\n", - " axs[i].plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], \n", - " USyearly[keyw+materials[1]+'_'+obj.name], \n", - " USyearly[keyw+materials[2]+'_'+obj.name], \n", - " USyearly[keyw+materials[3]+'_'+obj.name], \n", - " USyearly[keyw+materials[4]+'_'+obj.name], \n", - " colors=['c','k','m','r', 'g'])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2010, 2050])\n", - " axs[i].set_title(keyw+ ' ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " \n", - " i += 1 \n", - "\n", - " \n", - "# CAPACITY IS BY MODULE NOT MATERIAL:\n", - "ii = 2\n", - "keyw = keywords[ii]\n", - "\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - " axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " #axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+obj.name])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2010, 2050])\n", - " axs[i].set_title(keyw+ ' ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " i += 1 \n", - "\n", - "\n", - "\n", - "\n", - "for i in range (0, 3):\n", - " axs[i].set_ylim([0, 4e7])\n", - " axs[i+3].set_ylim([0, 4e7])\n", - "\n", - "axs[0].set_ylabel('Mass [Tons]')\n", - "axs[3].set_ylabel('Mass [Tons]')\n", - "axs[5].legend(materials)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 8})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_', 'Waste_', 'Capacity_']\n", - "SFScenarios = [r1, r2, r3]\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - " \n", - "fig, axs = plt.subplots(3,3, figsize=(15, 6), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .5, wspace=.001)\n", - "axs = axs.ravel()\n", - "i = 0\n", - "\n", - "# Loop over Keywords\n", - "for ii in range(0, 2):\n", - " keyw = keywords[ii]\n", - " # Loop over SF Scenarios\n", - " for kk in range(0, 3):\n", - " \n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - " axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " axs[i].plot([],[],color='k', label='silicon', linewidth=5)\n", - " axs[i].plot([],[],color='m', label='silver', linewidth=5)\n", - " axs[i].plot([],[],color='r', label='copper', linewidth=5)\n", - " axs[i].plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], UScum[keyw+materials[0]+'_'+obj.name], \n", - " UScum[keyw+materials[1]+'_'+obj.name], \n", - " UScum[keyw+materials[2]+'_'+obj.name], \n", - " UScum[keyw+materials[3]+'_'+obj.name], \n", - " UScum[keyw+materials[4]+'_'+obj.name], \n", - " colors=['c','k','m','r', 'g'])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2010, 2050])\n", - " axs[i].set_title(keyw+ ' ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " \n", - " i += 1 \n", - "\n", - "# CAPACITY IS BY MODULE NOT MATERIAL:\n", - "ii = 2\n", - "keyw = keywords[ii]\n", - "\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - " axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " #axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+obj.name]/1e6)\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2010, 2050])\n", - " axs[i].set_title(keyw+ ' ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " i += 1 \n", - " \n", - "for i in range (0, 3):\n", - " axs[i].set_ylim([1e0, 10e8])\n", - " axs[i+3].set_ylim([1e0, 10e8])\n", - " axs[i+6].set_ylim([1e0, 10e7])\n", - "\n", - " # axs[i].set_yscale('log')\n", - " # axs[i+3].set_yscale('log')\n", - " # axs[i+6].set_yscale('log')\n", - " \n", - " \n", - "axs[0].set_ylabel('Mass [Tons]')\n", - "axs[3].set_ylabel('Mass [Tons]')\n", - "axs[6].set_ylabel('Installed Capacity [TW]')\n", - "axs[5].legend(materials)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 8})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_', 'Waste_', 'Capacity_']\n", - "SFScenarios = [r1, r2, r3]\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - " \n", - "fig, axs = plt.subplots(3,3, figsize=(15, 6), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .5, wspace=.001)\n", - "axs = axs.ravel()\n", - "i = 0\n", - "\n", - "# Loop over Keywords\n", - "for ii in range(0, 2):\n", - " keyw = keywords[ii]\n", - " # Loop over SF Scenarios\n", - " for kk in range(0, 3):\n", - " \n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - " axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " axs[i].plot([],[],color='k', label='silicon', linewidth=5)\n", - " axs[i].plot([],[],color='m', label='silver', linewidth=5)\n", - " axs[i].plot([],[],color='r', label='copper', linewidth=5)\n", - " axs[i].plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], UScum[keyw+materials[0]+'_'+obj.name], \n", - " UScum[keyw+materials[1]+'_'+obj.name], \n", - " UScum[keyw+materials[2]+'_'+obj.name], \n", - " UScum[keyw+materials[3]+'_'+obj.name], \n", - " UScum[keyw+materials[4]+'_'+obj.name], \n", - " colors=['c','k','m','r', 'g'])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2010, 2050])\n", - " axs[i].set_title(keyw+ ' ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " \n", - " i += 1 \n", - "\n", - "# CAPACITY IS BY MODULE NOT MATERIAL:\n", - "ii = 2\n", - "keyw = keywords[ii]\n", - "\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - " axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " #axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+obj.name]/1e6)\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2010, 2050])\n", - " axs[i].set_title(keyw+ ' ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " i += 1 \n", - " \n", - "for i in range (0, 3):\n", - " axs[i].set_ylim([1e0, 10e8])\n", - " axs[i+3].set_ylim([1e0, 10e8])\n", - " axs[i+6].set_ylim([1e0, 10e7])\n", - "\n", - " axs[i].set_yscale('log')\n", - " axs[i+3].set_yscale('log')\n", - " axs[i+6].set_yscale('log')\n", - " \n", - " \n", - "axs[0].set_ylabel('Mass [Tons]')\n", - "axs[3].set_ylabel('Mass [Tons]')\n", - "axs[6].set_ylabel('Installed Capacity [TW]')\n", - "axs[5].legend(materials)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 10})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(3,3, figsize=(15, 10), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .3, wspace=.2)\n", - "axs = axs.ravel()\n", - "i = 0\n", - "\n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - "# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " # axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " # axs[i].plot([],[],color='k', label='silicon', linewidth=5)\n", - " # axs[i].plot([],[],color='m', label='silver', linewidth=5)\n", - " # axs[i].plot([],[],color='r', label='copper', linewidth=5)\n", - " # axs[i].plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], \n", - " USyearly[keyw+materials[1]+'_'+obj.name], \n", - " USyearly[keyw+materials[2]+'_'+obj.name], \n", - " USyearly[keyw+materials[3]+'_'+obj.name], \n", - " USyearly[keyw+materials[4]+'_'+obj.name], \n", - " colors=['c','k','gray','orange', 'g'])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " axs[i].set_title(obj.name)\n", - " #axs[i].legend(materials)\n", - "\n", - " i += 1 \n", - "\n", - "# 2nd axis plot\n", - "i = 0\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFScenarios[kk]\n", - " ax2=axs[i].twinx()\n", - " ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], \n", - " color = 'r', linewidth=4.0, label='cumulative')\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " # axs[i].set_xlim([2010, 2050])\n", - " # axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " ax2.set_yscale('log')\n", - " ax2.set_ylim([1e3, 1e8])\n", - " i += 1 \n", - "\n", - " ax2.legend()\n", - "\n", - "\n", - "i = 3\n", - "# ROW 2, Aluminum and Silicon:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - "\n", - "\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - "\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[4]+'_'+obj.name], color='g', lw=3, label='Aluminum')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], \n", - " # color='g', lw=3, alpha=.6)\n", - " \n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[1]+'_'+obj.name], color='k', lw=3, label='Silicon')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], \n", - " # color='k', lw=3)# alpha=.3)\n", - "\n", - "\n", - " # silicon aluminum 'k ''g'\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " axs[i].legend()\n", - "\n", - " i += 1 \n", - "\n", - "\n", - "\n", - "# ROW 3:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[3]+'_'+obj.name], color='orange', lw=3, label='Copper')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], \n", - " # color='orange', lw=3)# alpha=.3)\n", - "\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[2]+'_'+obj.name], color='gray', lw=3, label='Silver')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], \n", - " # color='gray', lw=3)# , alpha=.6)\n", - " \n", - " \n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " axs[i].legend()\n", - " \n", - " i += 1 \n", - " \n", - "for i in range (0, 3):\n", - " axs[i].set_ylim([0, 5e7])\n", - " axs[i+3].set_ylim([0, 3e6])\n", - " axs[i+6].set_ylim([0, 2.5e4])\n", - "\n", - " #axs[i+3].set_ylim([1e0, 10e8])\n", - " #axs[i+6].set_ylim([1e0, 5e6])\n", - "\n", - "# axs[i+3].set_yscale('log')\n", - "# axs[i+6].set_yscale('log')\n", - "\n", - "axs[0].set_ylabel('Mass [Tons]')\n", - "axs[3].set_ylabel('Mass [Tons]')\n", - "#axs[5].legend(materials)\n", - " \n", - "axs[0].set_ylabel('Yearly Mass [Tonnes]')\n", - "axs[3].set_ylabel('Yearly Mass [Tonnes]')\n", - "axs[6].set_ylabel('Yearly Mass [Tonnes]')\n", - "\n", - "#axs[8].legend(materials)\n", - "\n", - "fig.savefig(title_Method+' Fig_3x3_MaterialNeeds.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib as mpl\n", - "\n", - "plt.rcParams.update({'font.size': 10})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(3,3, figsize=(15, 10), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .3, wspace=.2)\n", - "axs = axs.ravel()\n", - "i = 0\n", - "\n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "titlesscens = ['Reference Scenario', 'Grid Decarbonization Scenario', 'High Electrification Scenario']\n", - "\n", - "\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='c', alpha=0.5, label='Glass')\n", - "# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " # axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " # axs[i].plot([],[],color='k', label='silicon', linewidth=5)\n", - " # axs[i].plot([],[],color='m', label='silver', linewidth=5)\n", - " # axs[i].plot([],[],color='r', label='copper', linewidth=5)\n", - " # axs[i].plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name]/1e6, \n", - " USyearly[keyw+materials[1]+'_'+obj.name]/1e6, \n", - " USyearly[keyw+materials[2]+'_'+obj.name]/1e6, \n", - " USyearly[keyw+materials[3]+'_'+obj.name]/1e6, \n", - " USyearly[keyw+materials[4]+'_'+obj.name]/1e6, \n", - " colors=['c','k','gray','orange', 'g'])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " axs[i].set_title(titlesscens[kk])\n", - " axs[i].legend(loc='lower right')\n", - "\n", - " #axs[i].legend(materials)\n", - "\n", - " i += 1 \n", - "\n", - "# 2nd axis plot\n", - "i = 0\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFScenarios[kk]\n", - " ax2=axs[i].twinx()\n", - " ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name]/1e6, \n", - " color = 'r', linewidth=4.0, label='cumulative')\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " # axs[i].set_xlim([2010, 2050])\n", - " # axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " ax2.set_yscale('log')\n", - " ax2.set_ylim([1e3/1e6, 1e8/1e6])\n", - " i += 1 \n", - "\n", - " ax2.legend()\n", - "\n", - "\n", - "i = 3\n", - "# ROW 2, Aluminum and Silicon:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - "\n", - "\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - "\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[4]+'_'+obj.name]/1e6, color='g', lw=3, label='Aluminum')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], \n", - " # color='g', lw=3, alpha=.6)\n", - " \n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[1]+'_'+obj.name]/1e6, color='k', lw=3, label='Silicon')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], \n", - " # color='k', lw=3)# alpha=.3)\n", - "\n", - "\n", - " # silicon aluminum 'k ''g'\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " axs[i].legend()\n", - "\n", - " i += 1 \n", - "\n", - "\n", - "\n", - "# ROW 3:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[3]+'_'+obj.name], color='orange', lw=3, label='Copper')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], \n", - " # color='orange', lw=3)# alpha=.3)\n", - "\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[2]+'_'+obj.name], color='gray', lw=3, label='Silver')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], \n", - " # color='gray', lw=3)# , alpha=.6)\n", - " \n", - " \n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " axs[i].legend()\n", - " axs[i].yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n", - "\n", - " i += 1 \n", - " \n", - "for i in range (0, 3):\n", - " axs[i].set_ylim([0, 5e7/1e6])\n", - " axs[i+3].set_ylim([0, 3e6/1e6])\n", - " axs[i+6].set_ylim([0, 2.5e4])\n", - "\n", - " #axs[i+3].set_ylim([1e0, 10e8])\n", - " #axs[i+6].set_ylim([1e0, 5e6])\n", - "\n", - "# axs[i+3].set_yscale('log')\n", - "# axs[i+6].set_yscale('log')\n", - "\n", - "axs[0].set_ylabel('Mass [Tons]')\n", - "axs[3].set_ylabel('Mass [Tons]')\n", - "#axs[5].legend(materials)\n", - " \n", - "axs[0].set_ylabel('Yearly Mass [Million Tonnes]')\n", - "axs[3].set_ylabel('Yearly Mass [Million Tonnes]')\n", - "axs[6].set_ylabel('Yearly Mass [Tonnes]')\n", - "\n", - "#axs[8].legend(materials)\n", - "\n", - "fig.savefig(title_Method+' Fig_3x3_MaterialNeeds.png', dpi=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mining Capacity + Virgin Needs Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mining2020_aluminum = 65267000\n", - "mining2020_silver = 22260\n", - "mining2020_copper = 20000000\n", - "mining2020_silicon = 8000000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 10})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(3,3, figsize=(15, 10), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .3, wspace=.2)\n", - "axs = axs.ravel()\n", - "i = 0\n", - "\n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "titlesscens = ['Reference Scenario', 'Grid Decarbonization Scenario', 'High Electrification Scenario']\n", - "\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - "# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " # axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " # axs[i].plot([],[],color='k', label='silicon', linewidth=5)\n", - " # axs[i].plot([],[],color='m', label='silver', linewidth=5)\n", - " # axs[i].plot([],[],color='r', label='copper', linewidth=5)\n", - " # axs[i].plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], \n", - " USyearly[keyw+materials[1]+'_'+obj.name], \n", - " USyearly[keyw+materials[2]+'_'+obj.name], \n", - " USyearly[keyw+materials[3]+'_'+obj.name], \n", - " USyearly[keyw+materials[4]+'_'+obj.name], \n", - " colors=['c','k','gray','orange', 'g'])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " axs[i].set_title(titlesscens[kk])\n", - " #axs[i].legend(materials)\n", - "\n", - " i += 1 \n", - "\n", - "# 2nd axis plot\n", - "i = 0\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFScenarios[kk]\n", - " ax2=axs[i].twinx()\n", - " ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], \n", - " color = 'r', linewidth=4.0, label='cumulative')\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " # axs[i].set_xlim([2010, 2050])\n", - " # axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " ax2.set_yscale('log')\n", - " ax2.set_ylim([1e3, 1e8])\n", - " i += 1 \n", - "\n", - " ax2.legend()\n", - "\n", - "\n", - "i = 3\n", - "# ROW 2, Aluminum and Silicon:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - "\n", - "\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - "\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[4]+'_'+obj.name], color='g', lw=3, label='Aluminum')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], \n", - " # color='g', lw=3, alpha=.6)\n", - " \n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[1]+'_'+obj.name], color='k', lw=3, label='Silicon')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], \n", - " # color='k', lw=3)# alpha=.3)\n", - "\n", - "\n", - " # silicon aluminum 'k ''g'\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " axs[i].legend()\n", - "\n", - " ax2=axs[i].twinx()\n", - " ax2.axhline(mining2020_aluminum, color = 'g', linestyle='-', linewidth=1.0, label='Production Al')\n", - " ax2.axhline(mining2020_silicon, color = 'k', linestyle='-', linewidth=1.0, label='Production Si')\n", - "\n", - " i += 1 \n", - "\n", - "\n", - "\n", - "# ROW 3:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFScenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[3]+'_'+obj.name], color='orange', lw=3, label='Copper')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], \n", - " # color='orange', lw=3)# alpha=.3)\n", - "\n", - " axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[2]+'_'+obj.name], color='gray', lw=3, label='Silver')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], \n", - " # color='gray', lw=3)# , alpha=.6)\n", - " \n", - " \n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " axs[i].legend()\n", - "\n", - " ax2=axs[i].twinx()\n", - " ax2.axhline(mining2020_copper, color = 'orange', linestyle='-', linewidth=1.0, label='Production Copper')\n", - " ax2.axhline(mining2020_silver, color = 'gray', linestyle='-', linewidth=1.0, label='Production Silver')\n", - " \n", - " i += 1 \n", - " \n", - "for i in range (0, 3):\n", - " axs[i].set_ylim([0, 5e7])\n", - " axs[i+3].set_ylim([0, 3e6])\n", - " axs[i+6].set_ylim([0, 2.5e4])\n", - "\n", - " #axs[i+3].set_ylim([1e0, 10e8])\n", - " #axs[i+6].set_ylim([1e0, 5e6])\n", - "\n", - "# axs[i+3].set_yscale('log')\n", - "# axs[i+6].set_yscale('log')\n", - "\n", - "axs[0].set_ylabel('Mass [Tons]')\n", - "axs[3].set_ylabel('Mass [Tons]')\n", - "#axs[5].legend(materials)\n", - " \n", - "axs[0].set_ylabel('Yearly Mass [Tonnes]')\n", - "axs[3].set_ylabel('Yearly Mass [Tonnes]')\n", - "axs[6].set_ylabel('Yearly Mass [Tonnes]')\n", - "\n", - "#axs[8].legend(materials)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 10})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(1,1, figsize=(4, 6), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .3, wspace=.2)\n", - "i = 0\n", - "\n", - "obj = SFScenarios[2]\n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "# ROW 2, Aluminum and Silicon: g- 4 aluminum k - 1 silicon orange - 3 copper gray - 2 silver\n", - "axs.plot(USyearly[keyw+materials[2]+'_'+obj.name]*100/mining2020_silver, \n", - " color = 'gray', linewidth=2.0, label='Silver')\n", - "axs.plot(USyearly[keyw+materials[1]+'_'+obj.name]*100/mining2020_silicon, \n", - " color = 'k', linewidth=2.0, label='Silicon')\n", - "axs.plot(USyearly[keyw+materials[4]+'_'+obj.name]*100/mining2020_aluminum, \n", - " color = 'g', linewidth=2.0, label='Aluminum')\n", - "axs.plot(USyearly[keyw+materials[3]+'_'+obj.name]*100/mining2020_copper, \n", - " color = 'orange', linewidth=2.0, label='Copper')\n", - "\n", - "axs.set_xlim([2020,2050])\n", - "axs.legend()\n", - "#axs.set_yscale('log')\n", - "\n", - "axs.set_ylabel('Virgin Material Needs ratio to 2020 Production Capacity [%]')\n", - "\n", - "fig.savefig(title_Method+' Fig_1x1_MaterialNeeds Ratio to Production.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 10})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(1,1, figsize=(4, 6), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .3, wspace=.2)\n", - "i = 0\n", - "\n", - "obj = SFScenarios[2].name\n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "# ROW 2, Aluminum and Silicon: g- 4 aluminum k - 1 silicon orange - 3 copper gray - 2 silver\n", - "axs.plot(USyearly[keyw+materials[2]+'_'+SFScenarios[2].name]*100/mining2020_silver, \n", - " color = 'gray', linewidth=2.0, label='Silver')\n", - "axs.fill_between(USyearly.index, USyearly[keyw+materials[2]+'_'+SFScenarios[0].name]*100/mining2020_silver, USyearly[keyw+materials[2]+'_'+SFScenarios[2].name]*100/mining2020_silver,\n", - " color='gray', lw=3, alpha=.3)\n", - " \n", - "axs.plot(USyearly[keyw+materials[1]+'_'+SFScenarios[2].name]*100/mining2020_silicon, \n", - " color = 'k', linewidth=2.0, label='Silicon')\n", - "axs.fill_between(USyearly.index, USyearly[keyw+materials[1]+'_'+SFScenarios[0].name]*100/mining2020_silicon, \n", - " USyearly[keyw+materials[1]+'_'+SFScenarios[2].name]*100/mining2020_silicon,\n", - " color='k', lw=3, alpha=.5)\n", - "\n", - "axs.plot(USyearly[keyw+materials[4]+'_'+SFScenarios[2].name]*100/mining2020_aluminum, \n", - " color = 'g', linewidth=2.0, label='Aluminum')\n", - "\n", - "axs.fill_between(USyearly.index, USyearly[keyw+materials[4]+'_'+SFScenarios[0].name]*100/mining2020_aluminum, \n", - " USyearly[keyw+materials[4]+'_'+SFScenarios[2].name]*100/mining2020_aluminum,\n", - " color='g', lw=3, alpha=.3)\n", - "\n", - "\n", - "axs.plot(USyearly[keyw+materials[3]+'_'+SFScenarios[2].name]*100/mining2020_copper, \n", - " color = 'orange', linewidth=2.0, label='Copper')\n", - "\n", - "axs.fill_between(USyearly.index, USyearly[keyw+materials[3]+'_'+SFScenarios[0].name]*100/mining2020_copper, \n", - " USyearly[keyw+materials[3]+'_'+SFScenarios[2].name]*100/mining2020_copper,\n", - " color='orange', lw=3, alpha=.3)\n", - "\n", - "axs.set_xlim([2020,2050])\n", - "axs.legend()\n", - "#axs.set_yscale('log')\n", - "\n", - "axs.set_ylabel('Virgin material needs as a percentage of 2020 global mining production capacity [%]')\n", - "\n", - "fig.savefig(title_Method+' Fig_1x1_MaterialNeeds Ratio to Production.png', dpi=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TABLES " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "USyearlysig3 = USyearly.copy()\n", - "USyearlysig3 = USyearlysig3.drop(USyearlysig3.index[0])\n", - "N = 2\n", - "\n", - "USyearlysig3 = USyearlysig3.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "USyearlysig3 = USyearlysig3.applymap(lambda x: int(x))\n", - "USyearlysig3.head()\n", - "#for col in USyearlysig3:\n", - "# USyearlysig3[col].apply(lambda x: round(x, N - int(np.floor(np.log(abs(x))))))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "UScumsig3 = UScum.copy()\n", - "UScumsig3 = UScumsig3.drop(UScumsig3.index[0])\n", - "N = 2\n", - "\n", - "UScumsig3 = UScumsig3.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "UScumsig3 = UScumsig3.applymap(lambda x: int(x))\n", - "UScumsig3.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "materials = ['Module', 'glass', 'aluminum', 'copper', 'silicon', 'silver']\n", - "\n", - "print(\" Metric Tonnes Installed in field in 2030\")\n", - "print(\" ######################################### \\n\")\n", - "#Loop over scenarios\n", - "for kk in range (0, 3):\n", - " obj = SFScenarios[kk]\n", - " print(\"SCENARIO :\", obj.name)\n", - "\n", - " print(\"********************************\")\n", - " print(\"********************************\")\n", - "\n", - " modulemat = 0\n", - " for ii in range(0, len(materials)):\n", - " installedmat = (UScumsig3['VirginStock_'+materials[ii]+'_'+obj.name].loc[2030]-\n", - " UScumsig3['Waste_'+materials[ii]+'_'+obj.name].loc[2030])\n", - " print(materials[ii], ':', round(installedmat/1000)*1000, 'tons')\n", - "\n", - " print(\"Capacity in Year 2030 [GW]:\", round(USyearlysig3['Capacity_'+obj.name].loc[2030]/1e9))\n", - " print(\"Capacity in Year 2050 [GW]:\", round(USyearlysig3['Capacity_'+obj.name].loc[2050]/1e9))\n", - " print(\"****************************\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\" VIRGIN STOCK Yearly Needs \")\n", - "print(\" **************************\")\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " print(obj.name)\n", - " filter_col = [col for col in USyearlysig3 if (col.startswith('VirginStock_') and col.endswith(obj.name)) ]\n", - " display(USyearlysig3[filter_col].loc[[2030, 2040, 2050]])\n", - " print(\"\\n\\n\")\n", - " \n", - "print(\" VIRGIN STOCK Cumulative Needs \")\n", - "print(\" ***************************** \")\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " print(obj.name)\n", - " filter_col = [col for col in UScumsig3 if (col.startswith('VirginStock_') and col.endswith(obj.name)) ]\n", - " display(UScumsig3[filter_col].loc[[2030, 2040, 2050]])\n", - " print(\"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\" WASTE CUMULATIVE RESULTS [Tonnes] \")\n", - "print(\" ******************************************\")\n", - "filter_col = [col for col in UScumsig3 if (col.startswith('Waste_Module')) ]\n", - "display(UScumsig3[filter_col].loc[[2016,2020,2030, 2040, 2050]])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Same as above cell but in more lines\n", - "\"\"\"\n", - "materials = ['glass', 'aluminum', 'copper', 'silicon', 'silver']\n", - "\n", - "print(\" WASTE CUMULATIVE RESULTS \")\n", - "print(\" ***********************\")\n", - "#Loop over scenarios\n", - "for kk in range (0, 3):\n", - " obj = SFScenarios[kk]\n", - " print(\"SCENARIO :\", obj.name)\n", - " modulewaste2016 = 0\n", - " modulewaste2020 = 0\n", - " modulewaste2030 = 0\n", - " modulewaste2040 = 0\n", - " modulewaste2050 = 0\n", - "\n", - " for ii in range(0, len(materials)):\n", - " modulewaste2016 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[7]\n", - " modulewaste2020 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[11]\n", - " modulewaste2030 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[21]\n", - " modulewaste2040 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[31]\n", - " modulewaste2050 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[41]\n", - "\n", - " print('Module Waste 2016:', round(modulewaste2016/1000)*1000, 'tons') \n", - " print('Module Waste 2020:', round(modulewaste2020/1000)*1000, 'tons') \n", - " print('Module Waste 2030:', round(modulewaste2030/1000)*1000, 'tons') \n", - " print('Module Waste 2040:', round(modulewaste2040/1000)*1000, 'tons') \n", - " print('Module Waste 2050:', round(modulewaste2050/1000)*1000, 'tons') \n", - "\n", - " print(\"****************************\")\n", - "\"\"\"\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## DWARAKS PLOT" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, ax = plt.subplots()\n", - "\n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1\n", - "kk = 0\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "plt.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'k.', linewidth=5, label=obj.name+' module mass')\n", - "plt.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'k', linewidth=5, label=obj.name+' glass mass only')\n", - "ax.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 2\n", - "kk = 1\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "plt.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'g.', linewidth=5, label=obj.name+' module mass')\n", - "plt.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'g', linewidth=5, label=obj.name+' glass mass only')\n", - "ax.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='g', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 2\n", - "kk = 2\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "plt.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'c.', linewidth=5, label=obj.name+' module mass')\n", - "plt.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'c', linewidth=5, label=obj.name+' glass mass only')\n", - "\n", - "ax.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='c', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# color = 'C1'\n", - "plt.legend()\n", - "plt.title('Yearly Virgin Material Needs by Scenario')\n", - "plt.ylabel('Mass [tons]')\n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Organizing Cumulative 2050 material needs for Materials / Scenarios" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#This is in the plots\n", - "\"\"\"\n", - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 3):\n", - " obj = SFScenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj.name].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050)\n", - "dfcumulations2050\n", - "\"\"\"\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculating Bottoms for stacked bar plots... ugh." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#This is in the plots\n", - "\"\"\"\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\"\"\"\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Virgin Needs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "keywords" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'k.', linewidth=5, label='S1: Reference Scenario, module mass')\n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'k', linewidth=5, label='S1: Reference Scenario, glass mass only')\n", - "a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 2 ***************\n", - "kk = 1\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'g.', linewidth=5, label='S2: Grid Decarbonization Scenario, module mass')\n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'g', linewidth=5, label='S2: Grid Decarbonization Scenario, glass mass only')\n", - "a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='g', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 3 ***************\n", - "kk = 2\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'c.', linewidth=5, label='S3: High Electrification Scenario, module mass')\n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'c', linewidth=5, label='S3: High Electrification Scenario, glass mass only')\n", - "\n", - "a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='c', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "a0.legend()\n", - "a0.set_title('Yearly Virgin Material Needs by Scenario')\n", - "a0.set_ylabel('Mass [Million Tonnes]')\n", - "\n", - "a0.set_xlabel('Years')\n", - " \n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 3):\n", - " obj = SFScenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj.name].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(3)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]')\n", - "a1.set_xlabel('Scenario')\n", - "a1.set_xticks(ind)\n", - "a1.set_xticklabels(['S1', 'S2', 'S3'])\n", - "#plt.yticks(np.arange(0, 81, 10))\n", - "a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))\n", - "\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Waste" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "keywords=['Waste_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "print(keyw)\n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'k.', linewidth=5, label='S1: Reference Scenario, module mass')\n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'k', linewidth=5, label='S1: Reference Scenario, glass mass only')\n", - "a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 2 ***************\n", - "kk = 1\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'g.', linewidth=5, label='S2: Grid Decarbonization Scenario, module mass')\n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'g', linewidth=5, label='S2: Grid Decarbonization, glass mass only')\n", - "a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='g', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 3 ***************\n", - "kk = 2\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'c.', linewidth=5, label='S3: High Electrification Scenario, module mass')\n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'c', linewidth=5, label='S3:High Electrification Scenario, glass mass only')\n", - "\n", - "a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='c', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "a0.legend()\n", - "a0.set_title('Yearly Waste Material by Scenario')\n", - "a0.set_ylabel('Mass [Million Tonnes]')\n", - "\n", - "a0.set_xlabel('Years')\n", - " \n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "keywords=['Waste_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 3):\n", - " obj = SFScenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj.name].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050)\n", - "dfcumulations2050 = dfcumulations2050/1000000\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(3)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - " \n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Cumulative Waste by 2050 [Million Tonnes]')\n", - "a1.set_xlabel('Scenario')\n", - "a1.set_xticks(ind, ('S1', 'S2', 'S3'))\n", - "#plt.yticks(np.arange(0, 81, 10))\n", - "a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))\n", - "a1.set_xticks(ind)\n", - "a1.set_xticklabels(['S1', 'S2', 'S3'])\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly WASTE by Scenario and Cumulatives.png', dpi=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Another option" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 12})\n", - "plt.rcParams['figure.figsize'] = (14, 8)\n", - "keywords=['Waste_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, a0 = plt.subplots(1, 1)\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFScenarios[kk]\n", - "\n", - "othermat = (USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "a0.bar(obj.scenario[STATEs[0]].data['year'], glassmat, label='S1: '+obj.name+' glass mass only')\n", - "a0.bar(obj.scenario[STATEs[0]].data['year'], othermat,bottom=glassmat, label='S1: '+obj.name+'other materials mass')\n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat,'k', label='S1: '+obj.name+' Module mass | Cumulative Installed Capacity [GW] (right axis)')\n", - "\n", - "# SCENARIO 2 ***************\n", - "kk = 1\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'g', label='S2: '+obj.name+' Module mass | Cumulative Installed Capacity [GW] (right axis)')\n", - "\n", - "\n", - "\n", - "# SCENARIO 3***************\n", - "kk = 2\n", - "obj = SFScenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+\n", - " USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+\n", - " USyearly[keyw+materials[4]+'_'+obj.name])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj.name])\n", - "a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'c', label='S3: '+obj.name+' Module mass | Cumulative Installed Capacity [GW] (right axis)')\n", - "\n", - "\n", - "\n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "keywords=['Waste_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 3):\n", - " obj = SFScenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj.name].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050)\n", - "dfcumulations2050\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "### Install Capacity\n", - "\n", - "ax2=a0.twinx()\n", - "ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly['Capacity_'+SFScenarios[0].name]/1e9, 'k', label='S1: Cumulative Installed Capacity [GW] (right axis)')\n", - "ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly['Capacity_'+SFScenarios[1].name]/1e9, 'g', label='S2: Cumulative Installed Capacity [GW] (right axis)')\n", - "ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly['Capacity_'+SFScenarios[2].name]/1e9, 'c', label='S3: Cumulative Installed Capacity [GW] (right axis)')\n", - "ax2.set_yscale('log')\n", - "ax2.set_ylabel('Cumulative Installed Capacity [GW]')\n", - "\n", - "a0.set_ylabel('Mass [tons]')\n", - "a0.set_title('Yearly Waste Material by Scenario and Module Component')\n", - "a0.legend(bbox_to_anchor=(0.10, -0.3), loc='lower left')\n", - "#ax2.legend()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### SIZE WASTE COMPARISON\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='Cumulative_Area_disposed'\n", - "\n", - "USyearly_Areadisp=pd.DataFrame()\n", - "\n", - "SFScenarios = [r1, r2, r3]\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " obj = SFScenarios[kk]\n", - " # Loop over Materials\n", - " foo = obj.scenario[STATEs[0]].data[keyword].copy()\n", - " USyearly_Areadisp[\"Areadisp_\"+obj.name] = foo\n", - "\n", - " # Loop over STATEs\n", - " for jj in range (1, len(STATEs)): \n", - " USyearly_Areadisp[\"Areadisp_\"+obj.name] += obj.scenario[STATEs[jj]].data[keyword]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "UScum_Areadisp = USyearly_Areadisp.copy()\n", - "UScum_Areadisp = UScum_Areadisp.cumsum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "A = UScum['Waste_Module_Reference.Mod'].iloc[-1]\n", - "#47700000 # tonnes cumulative by 2050\n", - "A = A*1000 # convert to kg\n", - "A = A/10.05599 # convert to m2 if each m2 is ~avg 10 kg\n", - "#A = A*2 # convert to area if each module is ~2 m2\n", - "A = A/1e6 # Convert to km 2\n", - "print(A)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "B = UScum['Waste_Module_95-by-35_Elec.Adv_DR'].iloc[-1]\n", - "#47700000 # tonnes cumulative by 2050\n", - "B = B*1000 # convert to kg\n", - "B = B/10.05599 # convert to m2 if each m2 is ~avg 10 kg\n", - "#A = A*2 # convert to area if each module is ~2 m2\n", - "B = B/1e6 # Convert to km 2\n", - "print(B)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "C = UScum_Areadisp['Areadisp_Reference.Mod'].iloc[-1]/1e6\n", - "D = UScum_Areadisp['Areadisp_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# MANHATTAN SIZE:\n", - "manhattans = 59.103529" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Cumulative Area by 2050 of Waste PV Modules\", C, \" km^2\")\n", - "print(\"Cumulative Area by 2050 of Waste PV Modules\", D, \" km^2\")\n", - "print(\"Cumulative Area by 2050 of Waste PV Material\", A, \" km$^2$\")\n", - "print(\"Cumulative Area by 2050 of Waste PV Material\", B, \" km$^2$\")\n", - "print(\"\")\n", - "print(\"Reference Waste equals \", C/manhattans, \" Manhattans \")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "fig, axs = plt.subplots(figsize=(8, 8))\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'b', label='Cumulative New Yearly Installs')\n", - "axs.plot(USyearly['Capacity_Reference.Mod']/1e12, 'g', label='Active in Field Installs')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12, 'r', label='Decomissioned PV Panels')\n", - "axs.legend()\n", - "axs.set_xlim([2020,2050])\n", - "axs.set_ylabel('Power [TW]')\n", - "fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "E = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6).sum()\n", - "F = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12).sum()\n", - "print(\"Cumulative Installs\", E)\n", - "print(\"Cumulative Waste\", F)\n", - "print(\"Fraction of Decomisioned to Installed Cumulative by 2050\", F/E)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "fig, axs = plt.subplots(figsize=(8, 8))\n", - "axs.plot(USyearly['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'b', label='Yearly New Yearly Installs')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12, 'r', label='Decomissioned PV Panels')\n", - "axs.legend()\n", - "axs.set_xlim([2020,2050])\n", - "axs.set_ylabel('Power [TW]')\n", - "fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "\n", - "plt.plot(UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6/UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'b', label='Cumulative New Yearly Installs')\n", - "plt.plot(USyearly['Capacity_Reference.Mod']/1e12/UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'g', label='Active in Field Installs')\n", - "plt.plot((UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12)/UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'r', label='Decomissioned PV Panels')\n", - "plt.legend()\n", - "plt.xlim([2020,2050])\n", - "plt.ylabel('Power [TW]')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# VALIDATION COMPARISON VALUES" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "USyearly.iloc[21]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly['Capacity_Reference.Mod'].iloc[21]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "7.766749e+11" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "UScum.iloc[-1]\n", - "print(\" Cumulative Waste by 2050, Reference Scenario: \", UScum['Waste_Module_Reference.Mod'].iloc[-1]/1e6, ' Million Tonnes')\n", - "print(\" cumulative Waste by 2050, Grid Decarbonization Scenario: \", UScum['Waste_Module_95-by-35.Adv'].iloc[-1]/1e6, ' Million Tonnes')\n", - "print(\" cumulative Waste by 2050, High Electrification Scenario: \", UScum['Waste_Module_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6, ' Million Tonnes')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - STATE simulation and plotting.py b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - STATE simulation and plotting.py deleted file mode 100644 index 96cd40f6..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - STATE simulation and plotting.py +++ /dev/null @@ -1,2890 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # ReEDS Scenarios on PV ICE Tool STATES - -# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. -# -# Current sections include: -# -#
      -#
    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format
      2. -#
    2. ### Reading scenarios of interest and running PV ICE tool
      3. -#
    3. ###Plotting
      4. -#
    4. ### GeoPlotting.
      5. -#
    -# Notes: -# -# Scenarios of Interest: -# the Ref.Mod, -# o 95-by-35.Adv, and -# o 95-by-35+Elec.Adv+DR ones -# - -# In[1]: - - -import PV_ICE -import numpy as np -import pandas as pd -import os,sys -import matplotlib.pyplot as plt -from IPython.display import display -plt.rcParams.update({'font.size': 22}) -plt.rcParams['figure.figsize'] = (12, 8) - - -# In[2]: - - -import os -from pathlib import Path - -testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP') - -print ("Your simulation will be stored in %s" % testfolder) - - -# ### Reading REEDS original file to get list of SCENARIOs, PCAs, and STATEs - -# In[3]: - - -r""" -reedsFile = str(Path().resolve().parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx') -print ("Input file is stored in %s" % reedsFile) - -rawdf = pd.read_excel(reedsFile, - sheet_name="UPV Capacity (GW)") - #index_col=[0,2,3]) #this casts scenario, PCA and State as levels -#now set year as an index in place -#rawdf.drop(columns=['State'], inplace=True) -rawdf.drop(columns=['Tech'], inplace=True) -rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True) - -scenarios = list(rawdf.index.get_level_values('Scenario').unique()) -PCAs = list(rawdf.index.get_level_values('PCA').unique()) -STATEs = list(rawdf.index.get_level_values('State').unique()) - -simulationname = scenarios -simulationname = [w.replace('+', '_') for w in simulationname] -simulationname -SFscenarios = [simulationname[0], simulationname[4], simulationname[8]] -""" - - -# ### Reading GIS inputs - -# In[4]: - - -r""" -GISfile = str(Path().resolve().parent.parent.parent.parent / 'gis_centroid_n.xlsx') -GIS = pd.read_excel(GISfile) -GIS = GIS.set_index('id') -GIS.head() -GIS.loc['p1'].long -""" - - -# ### Create Scenarios in PV_ICE - -# #### Downselect to Solar Future scenarios of interest -# -# Scenarios of Interest: -#
  • Ref.Mod -#
  • 95-by-35.Adv -#
  • 95-by-35+Elec.Adv+DR - -# In[5]: - - -SFscenarios = ['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR'] -SFscenarios - - -# In[6]: - - -STATEs = ['WA', 'CA', 'VA', 'FL', 'MI', 'IN', 'KY', 'OH', 'PA', 'WV', 'NV', 'MD', - 'DE', 'NJ', 'NY', 'VT', 'NH', 'MA', 'CT', 'RI', 'ME', 'ID', 'MT', 'WY', 'UT', 'AZ', 'NM', - 'SD', 'CO', 'ND', 'NE', 'MN', 'IA', 'WI', 'TX', 'OK', 'OR', 'KS', 'MO', 'AR', 'LA', 'IL', 'MS', - 'AL', 'TN', 'GA', 'SC', 'NC'] - - -# ### Create the 3 Scenarios and assign Baselines -# -# Keeping track of each scenario as its own PV ICE Object. - -# In[7]: - - -#for ii in range (0, 1): #len(scenarios): -i = 0 -r1 = PV_ICE.Simulation(name=SFscenarios[i], path=testfolder) - -for jj in range (0, len(STATEs)): - filetitle = SFscenarios[i]+'_'+STATEs[jj]+'.csv' - filetitle = os.path.join(testfolder, 'STATEs', filetitle) - r1.createScenario(name=STATEs[jj], file=filetitle) - r1.scenario[STATEs[jj]].addMaterial('glass', file=r'..\baselines\SolarFutures_2021\baseline_material_glass_Reeds.csv') - r1.scenario[STATEs[jj]].addMaterial('silicon', file=r'..\baselines\SolarFutures_2021\baseline_material_silicon_Reeds.csv') - r1.scenario[STATEs[jj]].addMaterial('silver', file=r'..\baselines\SolarFutures_2021\baseline_material_silver_Reeds.csv') - r1.scenario[STATEs[jj]].addMaterial('copper', file=r'..\baselines\SolarFutures_2021\baseline_material_copper_Reeds.csv') - r1.scenario[STATEs[jj]].addMaterial('aluminum', file=r'..\baselines\SolarFutures_2021\baseline_material_aluminium_Reeds.csv') - - -i = 1 -r2 = PV_ICE.Simulation(name=SFscenarios[i], path=testfolder) - -for jj in range (0, len(STATEs)): - filetitle = SFscenarios[i]+'_'+STATEs[jj]+'.csv' - filetitle = os.path.join(testfolder, 'STATEs', filetitle) - r2.createScenario(name=STATEs[jj], file=filetitle) - r2.scenario[STATEs[jj]].addMaterial('glass', file=r'..\baselines\SolarFutures_2021\baseline_material_glass_Reeds.csv') - r2.scenario[STATEs[jj]].addMaterial('silicon', file=r'..\baselines\SolarFutures_2021\baseline_material_silicon_Reeds.csv') - r2.scenario[STATEs[jj]].addMaterial('silver', file=r'..\baselines\SolarFutures_2021\baseline_material_silver_Reeds.csv') - r2.scenario[STATEs[jj]].addMaterial('copper', file=r'..\baselines\SolarFutures_2021\baseline_material_copper_Reeds.csv') - r2.scenario[STATEs[jj]].addMaterial('aluminum', file=r'..\baselines\SolarFutures_2021\baseline_material_aluminium_Reeds.csv') - - -i = 2 -r3 = PV_ICE.Simulation(name=SFscenarios[i], path=testfolder) -for jj in range (0, len(STATEs)): - filetitle = SFscenarios[i]+'_'+STATEs[jj]+'.csv' - filetitle = os.path.join(testfolder, 'STATEs', filetitle) - r3.createScenario(name=STATEs[jj], file=filetitle) - r3.scenario[STATEs[jj]].addMaterial('glass', file=r'..\baselines\SolarFutures_2021\baseline_material_glass_Reeds.csv') - r3.scenario[STATEs[jj]].addMaterial('silicon', file=r'..\baselines\SolarFutures_2021\baseline_material_silicon_Reeds.csv') - r3.scenario[STATEs[jj]].addMaterial('silver', file=r'..\baselines\SolarFutures_2021\baseline_material_silver_Reeds.csv') - r3.scenario[STATEs[jj]].addMaterial('copper', file=r'..\baselines\SolarFutures_2021\baseline_material_copper_Reeds.csv') - r3.scenario[STATEs[jj]].addMaterial('aluminum', file=r'..\baselines\SolarFutures_2021\baseline_material_aluminium_Reeds.csv') - - -# # Calculate Mass Flow - -# In[8]: - - -IRENA= False -PERFECTMFG = True - -mats = ['glass', 'silicon','silver','copper','aluminum'] - -ELorRL = 'EL' -if IRENA: - if ELorRL == 'RL': - weibullInputParams = {'alpha': 5.3759, 'beta':30} # Regular-loss scenario IRENA - if ELorRL == 'EL': - weibullInputParams = {'alpha': 2.49, 'beta':30} # Regular-loss scenario IRENA - - if PERFECTMFG: - for jj in range (0, len(r1.scenario.keys())): - r1.scenario[STATEs[jj]].data['mod_lifetime'] = 40 - r1.scenario[STATEs[jj]].data['mod_MFG_eff'] = 100.0 - r2.scenario[STATEs[jj]].data['mod_lifetime'] = 40 - r2.scenario[STATEs[jj]].data['mod_MFG_eff'] = 100.0 - r3.scenario[STATEs[jj]].data['mod_lifetime'] = 40 - r3.scenario[STATEs[jj]].data['mod_MFG_eff'] = 100.0 - - for kk in range(0, len(mats)): - mat = mats[kk] - r1.scenario[STATEs[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 - r2.scenario[STATEs[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 - r3.scenario[STATEs[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 - - r1.calculateMassFlow(weibullInputParams=weibullInputParams) - r2.calculateMassFlow(weibullInputParams=weibullInputParams) - r3.calculateMassFlow(weibullInputParams=weibullInputParams) - title_Method = 'Irena_'+ELorRL -else: - r1.calculateMassFlow() - r2.calculateMassFlow() - r3.calculateMassFlow() - title_Method = 'PVICE' - - -# In[9]: - - -print("STATEs:", r1.scenario.keys()) -print("Module Keys:", r1.scenario[STATEs[jj]].data.keys()) -print("Material Keys: ", r1.scenario[STATEs[jj]].material['glass'].materialdata.keys()) - - -# # OPEN EI - -# In[10]: - - -kk=0 -SFScenarios = [r1, r2, r3] -SFScenarios[kk].name - - -# In[11]: - - -# WORK ON THIS FOIR OPENEI - -keyw=['mat_Virgin_Stock','mat_Total_EOL_Landfilled','mat_Total_MFG_Landfilled', 'mat_Total_Landfilled', - 'new_Installed_Capacity_[MW]','Installed_Capacity_[W]'] -keywprint = ['VirginMaterialDemand','EOLMaterial', 'ManufacturingScrap','ManufacturingScrapAndEOLMaterial', - 'NewInstalledCapacity','InstalledCapacity'] -keywunits = ['MetricTonnes', 'MetricTonnes', 'MetricTonnes', 'MetricTonnes', - 'MW','MW'] -keywdcumneed = [True,True,True,True, - True,False] -keywdlevel = ['material','material','material','material', - 'module','module'] -keywscale = [1000000, 1000000, 1000000, 1000000, - 1,1e6] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - - for zz in range (0, len(STATEs)): - - foo = pd.DataFrame() - for jj in range (0, len(keyw)): - - if keywdlevel[jj] == 'material': - for ii in range (0, len(materials)): - sentit = '@value|'+keywprint[jj]+'|'+materials[ii].capitalize() +'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyw[jj]]/keywscale[jj] - - if keywdcumneed[jj]: - for ii in range (0, len(materials)): - sentit = '@value|Cumulative'+keywprint[jj]+'|'+materials[ii].capitalize() +'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyw[jj]].cumsum()/keywscale[jj] - - else: - sentit = '@value|'+keywprint[jj]+'|'+'PV' +'#'+keywunits[jj] - #sentit = '@value|'+keywprint[jj]+'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]]/keywscale[jj] - - if keywdcumneed[jj]: - sentit = '@value|Cumulative'+keywprint[jj]+'|'+'PV' +'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]].cumsum()/keywscale[jj] - - - foo['@states'] = STATEs[zz] - foo['@scenario|Solar Futures'] = SFScenarios[kk].name - foo['@timeseries|Year'] = SFScenarios[kk].scenario[STATEs[zz]].data.year - - scenariolist = scenariolist.append(foo) - -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -#scenariolist = scenariolist/1000000 # Converting to Metric Tons -#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -#scenariolist = scenariolist.applymap(lambda x: int(x)) -scenariolist.to_csv(title_Method+' OpenEI.csv', index=False) - -print("Done") - - -# In[12]: - - -# WORK ON THIS FOIR OPENEI - -keyw=['mat_Virgin_Stock','mat_Total_EOL_Landfilled','mat_Total_MFG_Landfilled', 'mat_Total_Landfilled', - 'new_Installed_Capacity_[MW]','Installed_Capacity_[W]'] -keywprint = ['VirginMaterialDemand','EOLMaterial', 'ManufacturingScrap','ManufacturingScrapAndEOLMaterial', - 'NewInstalledCapacity','InstalledCapacity'] -keywunits = ['MetricTonnes', 'MetricTonnes', 'MetricTonnes', 'MetricTonnes', - 'MW','MW'] -keywdcumneed = [True,True,True,True, - True,False] -keywdlevel = ['material','material','material','material', - 'module','module'] -keywscale = [1000000, 1000000, 1000000, 1000000, - 1,1e6] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - - for zz in range (0, len(STATEs)): - - foo = pd.DataFrame() - for jj in range (0, len(keyw)): - - if keywdlevel[jj] == 'material': - for ii in range (0, len(materials)): - sentit = '@value|'+keywprint[jj]+'|'+materials[ii].capitalize() +'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyw[jj]]/keywscale[jj] - - else: - sentit = '@value|'+keywprint[jj]+'|'+'PV' +'#'+keywunits[jj] - #sentit = '@value|'+keywprint[jj]+'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]]/keywscale[jj] - - - - foo['@states'] = STATEs[zz] - foo['@scenario|Solar Futures'] = SFScenarios[kk].name - foo['@timeseries|Year'] = SFScenarios[kk].scenario[STATEs[zz]].data.year - - scenariolist = scenariolist.append(foo) - -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -#scenariolist = scenariolist/1000000 # Converting to Metric Tons -#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -#scenariolist = scenariolist.applymap(lambda x: int(x)) -scenariolist.to_csv(title_Method+' OpenEI Yearly Only.csv', index=False) - -print("Done") - - -# In[13]: - - -# WORK ON THIS FOIR OPENEI - -keyw=['mat_Virgin_Stock','mat_Total_EOL_Landfilled','mat_Total_MFG_Landfilled', 'mat_Total_Landfilled', - 'new_Installed_Capacity_[MW]','Installed_Capacity_[W]'] -keywprint = ['VirginMaterialDemand','EOLMaterial', 'ManufacturingScrap','ManufacturingScrapAndEOLMaterial', - 'NewInstalledCapacity','InstalledCapacity'] -keywunits = ['MetricTonnes', 'MetricTonnes', 'MetricTonnes', 'MetricTonnes', - 'MW','MW'] -keywdcumneed = [True,True,True,True, - True,False] -keywdlevel = ['material','material','material','material', - 'module','module'] -keywscale = [1000000, 1000000, 1000000, 1000000, - 1,1e6] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - - for zz in range (0, len(STATEs)): - - foo = pd.DataFrame() - for jj in range (0, len(keyw)): - - if keywdlevel[jj] == 'material': - - if keywdcumneed[jj]: - for ii in range (0, len(materials)): - sentit = '@value|Cumulative'+keywprint[jj]+'|'+materials[ii].capitalize() +'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyw[jj]].cumsum()/keywscale[jj] - - else: - - if keywdcumneed[jj]: - sentit = '@value|Cumulative'+keywprint[jj]+'|'+'PV' +'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]].cumsum()/keywscale[jj] - - - foo['@states'] = STATEs[zz] - foo['@scenario|Solar Futures'] = SFScenarios[kk].name - foo['@timeseries|Year'] = SFScenarios[kk].scenario[STATEs[zz]].data.year - - scenariolist = scenariolist.append(foo) - -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -#scenariolist = scenariolist/1000000 # Converting to Metric Tons -#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -#scenariolist = scenariolist.applymap(lambda x: int(x)) -scenariolist.to_csv(title_Method+' OpenEI Cumulatives Only.csv', index=False) - -print("Done") - - -# In[14]: - - -# WORK ON THIS FOIR OPENEI -# SCENARIO DIFERENCeS - -keyw=['new_Installed_Capacity_[MW]','Installed_Capacity_[W]'] -keywprint = ['NewInstalledCapacity','InstalledCapacity'] -keywprint = ['NewInstalledCapacity','InstalledCapacity'] -sfprint = ['Reference','Grid Decarbonization', 'High Electrification'] - -keywunits = ['MW','MW'] -keywdcumneed = [True,False] -keywdlevel = ['module','module'] -keywscale = [1,1e6] -materials = [] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() - -for zz in range (0, len(STATEs)): - - foo = pd.DataFrame() - - for jj in range (0, len(keyw)): - - # kk -- scenario - for kk in range(0, 3): - sentit = '@value|'+keywprint[jj]+'|'+sfprint[kk]+'#'+keywunits[jj] - #sentit = '@value|'+keywprint[jj]+'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]]/keywscale[jj] - - if keywdcumneed[jj]: - sentit = '@value|Cumulative'+keywprint[jj]+'|'+sfprint[kk]+'#'+keywunits[jj] - foo[sentit] = SFScenarios[kk].scenario[STATEs[zz]].data[keyw[jj]].cumsum()/keywscale[jj] - - # foo['@value|scenario|Solar Futures'] = SFScenarios[kk].name - foo['@states'] = STATEs[zz] - foo['@timeseries|Year'] = SFScenarios[kk].scenario[STATEs[zz]].data.year - scenariolist = scenariolist.append(foo) - -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] -cols = [scenariolist.columns[-1]] + [col for col in scenariolist if col != scenariolist.columns[-1]] -scenariolist = scenariolist[cols] - -#scenariolist = scenariolist/1000000 # Converting to Metric Tons -#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -#scenariolist = scenariolist.applymap(lambda x: int(x)) -scenariolist.to_csv(title_Method+' OpenEI ScenarioDifferences.csv', index=False) - -print("Done") - - -# In[15]: - - -scenariolist.head() - - -# # SAVE DATA FOR BILLY: STATES - -# In[16]: - - -#for 3 significant numbers rounding -N = 2 - - -# SFScenarios[kk].scenario[PCAs[zz]].data.year -# -# Index 20 --> 2030 -# -# Index 30 --> 2040 -# -# Index 40 --> 2050 - -# In[17]: - - -idx2030 = 20 -idx2040 = 30 -idx2050 = 40 -print("index ", idx2030, " is year ", r1.scenario[STATEs[0]].data['year'].iloc[idx2030]) -print("index ", idx2040, " is year ", r1.scenario[STATEs[0]].data['year'].iloc[idx2040]) -print("index ", idx2050, " is year ", r1.scenario[STATEs[0]].data['year'].iloc[idx2050]) - - -# #### 6 - STATE Cumulative Virgin Needs by 2050 -# - -# In[18]: - - -keyword='mat_Virgin_Stock' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - - materiallist = [] - for ii in range (0, len(materials)): - - keywordsum = [] - for zz in range (0, len(STATEs)): - keywordsum.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword].sum()) - - materiallist.append(keywordsum) - df = pd.DataFrame (materiallist,columns=STATEs, index = materials) - df = df.T - df = df.add_prefix(SFScenarios[kk].name+'_') - scenariolist = pd.concat([scenariolist , df], axis=1) - -scenariolist = scenariolist/1000000 # Converting to Metric Tons -scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -scenariolist = scenariolist.applymap(lambda x: int(x)) -scenariolist.to_csv(title_Method+' 6 - STATE Cumulative2050 VirginMaterialNeeds_tons.csv') - - -# In[19]: - - -SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword] - - -# #### 7 - STATE Cumulative EoL Only Waste by 2050 - -# In[20]: - - -keyword='mat_Total_EOL_Landfilled' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - - materiallist = [] - for ii in range (0, len(materials)): - - keywordsum = [] - for zz in range (0, len(STATEs)): - keywordsum.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword].sum()) - - materiallist.append(keywordsum) - df = pd.DataFrame (materiallist,columns=STATEs, index = materials) - df = df.T - df = df.add_prefix(SFScenarios[kk].name+'_') - scenariolist = pd.concat([scenariolist , df], axis=1) - -scenariolist = scenariolist/1000000 # Converting to Metric Tons -scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -scenariolist = scenariolist.applymap(lambda x: int(x)) -scenariolist.to_csv(title_Method+' 7 - STATE Cumulative2050 Waste_EOL_tons.csv') - - -# ##### 8 - STATE Yearly Virgin Needs 2030 2040 2050 - -# In[21]: - - -keyword='mat_Virgin_Stock' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - materiallist = pd.DataFrame() - - for ii in range (0, len(materials)): - - keywordsum2030 = [] - keywordsum2040 = [] - keywordsum2050 = [] - - for zz in range (0, len(STATEs)): - keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030]) - keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040]) - keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050]) - - yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050]) - yearlylist = yearlylist.T - yearlylist = yearlylist.add_prefix(materials[ii]+'_') - materiallist = pd.concat([materiallist, yearlylist], axis=1) - materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_') - scenariolist = pd.concat([scenariolist , materiallist], axis=1) - -scenariolist = scenariolist/1000000 # Converting to Metric Tons -#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -#scenariolist = scenariolist.applymap(lambda x: int(x)) -scenariolist.to_csv(title_Method+' 8 - STATE Yearly 2030 2040 2050 VirginMaterialNeeds_tons.csv') - - -# #### 9 - STATE Yearly EoL Waste 2030 2040 205 - -# In[22]: - - -keyword='mat_Total_EOL_Landfilled' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - materiallist = pd.DataFrame() - - for ii in range (0, len(materials)): - - keywordsum2030 = [] - keywordsum2040 = [] - keywordsum2050 = [] - - for zz in range (0, len(STATEs)): - keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030]) - keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040]) - keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050]) - - yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050]) - yearlylist = yearlylist.T - yearlylist = yearlylist.add_prefix(materials[ii]+'_') - materiallist = pd.concat([materiallist, yearlylist], axis=1) - materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_') - scenariolist = pd.concat([scenariolist , materiallist], axis=1) - -scenariolist = scenariolist/1000000 # Converting to Metric Tonnes -#scenariolist = scenariolist.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -#scenariolist = scenariolist.applymap(lambda x: int(x)) -scenariolist.to_csv(title_Method+' 9 - STATE Yearly 2030 2040 2050 Waste_EOL_tons.csv') - - -# # APPENDIX TABLES -# -# - -# #### Appendix - Cumulative Virgin Stock - -# In[23]: - - -keyword='mat_Virgin_Stock' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - - materiallist = pd.DataFrame() - for ii in range (0, len(materials)): - - keywordsum2030 = [] - keywordsum2040 = [] - keywordsum2050 = [] - for zz in range (0, len(STATEs)): - keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:20].sum()) - keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:30].sum()) - keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:].sum()) - - yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050]) - yearlylist = yearlylist.T - yearlylist = yearlylist.add_prefix(materials[ii]+'_') - materiallist = pd.concat([materiallist, yearlylist], axis=1) - materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_') - scenariolist = pd.concat([scenariolist , materiallist], axis=1) - -scenariolist = scenariolist/1000000 # Converting to Metric Tons - -# Loop over SF Scenarios -for kk in range(0, 3): - filter_col = [col for col in scenariolist if (col.startswith(SFScenarios[kk].name)) ] - scen = scenariolist[filter_col] - scen.columns = scen.columns.str.lstrip(SFScenarios[kk].name+'_') # strip suffix at the right end only. - scen = scen.rename_axis('State') - scen = scen.sort_values(by='glass_2050', ascending=False) - scen.sum(axis=0) - reduced = scen.iloc[0:23] - new_row = pd.Series(data=scen.iloc[23::].sum(axis=0), name='OTHER STATES') - new_row_2 = pd.Series(data=scen.sum(axis=0), name='US TOTAL') - reduced = reduced.append(new_row, ignore_index=False) - reduced = reduced.append(new_row_2, ignore_index=False) - reduced = reduced.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) - reduced = reduced.applymap(lambda x: int(x)) - reduced.to_csv(title_Method+' Appendix - '+ SFScenarios[kk].name + ' Cumulative Virgin Stock by State.csv') - - -# #### Appendix - Yearly Virgin Stock - -# In[24]: - - -keyword='mat_Virgin_Stock' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - materiallist = pd.DataFrame() - - for ii in range (0, len(materials)): - - keywordsum2030 = [] - keywordsum2040 = [] - keywordsum2050 = [] - - for zz in range (0, len(STATEs)): - keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030]) - keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040]) - keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050]) - - yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050]) - yearlylist = yearlylist.T - yearlylist = yearlylist.add_prefix(materials[ii]+'_') - materiallist = pd.concat([materiallist, yearlylist], axis=1) - materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_') - scenariolist = pd.concat([scenariolist , materiallist], axis=1) - -scenariolist = scenariolist/1000000 # Converting to Metric Tons - - -# Loop over SF Scenarios -for kk in range(0, 3): - filter_col = [col for col in scenariolist if (col.startswith(SFScenarios[kk].name)) ] - scen = scenariolist[filter_col] - scen.columns = scen.columns.str.lstrip(SFScenarios[kk].name+'_') # strip suffix at the right end only. - scen = scen.rename_axis('State') - scen = scen.sort_values(by='glass_2050', ascending=False) - reduced = scen.iloc[0:23] - new_row = pd.Series(data=scen.iloc[23::].sum(axis=0), name='OTHER STATES') - new_row_2 = pd.Series(data=scen.sum(axis=0), name='US TOTAL') - reduced = reduced.append(new_row, ignore_index=False) - reduced = reduced.append(new_row_2, ignore_index=False) - reduced = reduced.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) - reduced = reduced.applymap(lambda x: int(x)) - reduced.to_csv(title_Method+' Appendix - '+ SFScenarios[kk].name + ' Yearly Virgin Stock by State.csv') - - -# #### Appendix - Cumulative EOL_ WASTE by State - -# In[25]: - - -keyword='mat_Total_EOL_Landfilled' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - - materiallist = pd.DataFrame() - for ii in range (0, len(materials)): - - keywordsum2030 = [] - keywordsum2040 = [] - keywordsum2050 = [] - for zz in range (0, len(STATEs)): - keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:20].sum()) - keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:30].sum()) - keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][0:].sum()) - - yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050]) - yearlylist = yearlylist.T - yearlylist = yearlylist.add_prefix(materials[ii]+'_') - materiallist = pd.concat([materiallist, yearlylist], axis=1) - materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_') - scenariolist = pd.concat([scenariolist , materiallist], axis=1) - -scenariolist = scenariolist/1000000 # Converting to Metric Tons - -# Loop over SF Scenarios -for kk in range(0, 3): - filter_col = [col for col in scenariolist if (col.startswith(SFScenarios[kk].name)) ] - scen = scenariolist[filter_col] - scen.columns = scen.columns.str.lstrip(SFScenarios[kk].name+'_') # strip suffix at the right end only. - scen = scen.rename_axis('State') - #scen = scen.sort_values(by='glass_2050', ascending=False) - reduced = scen - new_row = pd.Series(data=scen.sum(axis=0), name='US TOTAL') - reduced = reduced.append(new_row, ignore_index=False) - #reduced = reduced.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) - #reduced = reduced.applymap(lambda x: int(x)) - reduced.to_csv(title_Method+' Appendix - '+ SFScenarios[kk].name + ' Cumulative EOL_ WASTE by State.csv') - - -# ##### Sparkplots + APPENDIX - Yearly EoL Waste - -# In[26]: - - -sparkplotfolder = os.path.join(testfolder, 'SPARKPLOTS') - - -# In[27]: - - -keyword='mat_Total_EOL_Landfilled' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios - -scenariolist = pd.DataFrame() -for kk in range(0, 3): - # Loop over Materials - materiallist = pd.DataFrame() - - for ii in range (0, len(materials)): - - keywordsum2030 = [] - keywordsum2040 = [] - keywordsum2050 = [] - - for zz in range (0, len(STATEs)): - keywordsum2030.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030]) - keywordsum2040.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040]) - keywordsum2050.append(SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050]) - - # SPARK PLOT - if materials[ii] == 'glass': - fig, axs = plt.subplots(figsize=(2, 1), facecolor='w', edgecolor='k') - #axs.ioff() - axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year, SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword]/1000000, 'k') - axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2030], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030]/1000000, 'r.',markersize=12) - axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2040], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040]/1000000, 'r.', markersize=12) - axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2050], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050]/1000000, 'r.', markersize=12) - #plt.ylabel('Tonnes') - axs.set_xlim([2020, 2052]) - #axs.set_visible(False) - axs.axis('off') - figtitle = title_Method+ ' ' + SFScenarios[kk].name + ' Fig_2x1_GLASS_Waste_'+STATEs[zz]+'.png' - #figtitle = os.path.join('SPARKPLOTS', figtitle) - #fig.savefig(figtitle, dpi=600) - fig.savefig(os.path.join(sparkplotfolder, figtitle), dpi=600) - plt.close(fig) # This avoids the figure from displayig and getting all the warnings - - yearlylist = pd.DataFrame([keywordsum2030, keywordsum2040, keywordsum2050], columns=STATEs, index = [2030, 2040, 2050]) - yearlylist = yearlylist.T - yearlylist = yearlylist.add_prefix(materials[ii]+'_') - materiallist = pd.concat([materiallist, yearlylist], axis=1) - materiallist = materiallist.add_prefix(SFScenarios[kk].name+'_') - scenariolist = pd.concat([scenariolist , materiallist], axis=1) - -scenariolist = scenariolist/1000000 # Converting to Metric Tons - - -# Loop over SF Scenarios -for kk in range(0, 3): - filter_col = [col for col in scenariolist if (col.startswith(SFScenarios[kk].name)) ] - scen = scenariolist[filter_col] - scen.columns = scen.columns.str.lstrip(SFScenarios[kk].name+'_') # strip suffix at the right end only. - scen = scen.rename_axis('State') - scen = scen.sort_values(by='State') - reduced = scen - new_row = pd.Series(data=scen.sum(axis=0), name='US TOTAL') - reduced = reduced.append(new_row, ignore_index=False) -# reduced = reduced.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -# reduced = reduced.applymap(lambda x: int(x)) - reduced.to_csv(title_Method+' Appendix - '+ SFScenarios[kk].name + ' Yearly EOL Waste by State.csv') - - -# In[28]: - - -# PLOT HERE -fig, axs = plt.subplots(figsize=(2, 1), facecolor='w', edgecolor='k') -axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year, SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword]/1000000, 'k') -axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2030], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2030]/1000000, 'r.',markersize=12) -axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2040], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2040]/1000000, 'r.', markersize=12) -axs.plot(SFScenarios[kk].scenario[STATEs[zz]].data.year.loc[idx2050], SFScenarios[kk].scenario[STATEs[zz]].material[materials[ii]].materialdata[keyword][idx2050]/1000000, 'r.', markersize=12) -#plt.ylabel('Tonnes') -axs.set_xlim([2020, 2052]) -#axs.set_visible(False) -axs.axis('off'); - - -# In[ ]: - - - - - -# In[ ]: - - - - - -# In[ ]: - - - - - -# In[ ]: - - - - - -# In[ ]: - - - - - -# In[ ]: - - - - - -# In[ ]: - - - - - -# In[ ]: - - - - - -# # OBSOLETE BECAUSE FASTER TO DO ON NATION LEVEL - -# In[29]: - - -print(failtest) -# so the simulation will stop when reaching here jic - - -# In[ ]: - - -#matplotlib.use('Agg') - - -# ### Yearly and Cumulative Tables 3 Sigs - -# In[ ]: - - -UScumsig3 = UScumsig3.drop(UScumsig3.index[0]) -N = 2 - -UScumsig3 = UScumsig3.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -UScumsig3 = UScumsig3.applymap(lambda x: int(x)) -UScumsig3.head() - - -# In[ ]: - - -USyearly3sig = USyearly.copy() -UScum3sig = UScum.copy() - -USyearly3sig = USyearly3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -USyearly3sig = USyearly3sig.applymap(lambda x: int(x)) - -UScum3sig = UScum3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -UScum3sig = UScum3sig.applymap(lambda x: int(x)) - -USyearly3sig.to_csv(title_Method+' US_Yearly.csv') -UScum3sig.to_csv(title_Method+' US_Cumulative.csv') - - -# In[ ]: - - -""" -r1.plotScenariosComparison(keyword='Cumulative_Area_disposedby_Failure') -r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_Total_Landfilled') -""" -pass - - -# ## Aggregating PCAs Material Landfilled to obtain US totals by Year - -# In[ ]: - - -### Singe Material Example Aggregating PCAs to obtain US Total - -""" -keyword='mat_Total_Landfilled' -#keyword='new_Installed_Capacity_[MW]' - -plt.figure() -plt.plot(r1.scenario[PCAs[0]].data['year'], foo, label=PCAs[12]) -plt.title(keyword) -plt.legend() - -for jj in range (1, len(PCAs)): - foo['silver'] += r1.scenario[PCAs[jj]].material['silver'].materialdata[keyword] - - -fig = plt.figure() -ax = fig.add_subplot(2, 1, 1) -ax.plot(r1.scenario[PCAs[0]].data['year'], foo['silver'], label='US') -plt.title("Material Landfilled per Year US") -#ax.set_yscale('log') -print(max(foo)) -""" -pass - - -# In[ ]: - - -### Verbose Material Example Aggregating PCAs to obtain US Total - -""" -keyword='mat_Total_Landfilled' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -USyearlyWASTE=pd.DataFrame() - -# Loop over Materials -for ii in range (0, len(materials)): - material = materials[ii] - foo1 = r1.scenario[PCAs[0]].material[material].materialdata[keyword].copy() - foo1 = foo1.to_frame(name=material) - foo2 = r2.scenario[PCAs[0]].material[material].materialdata[keyword].copy() - foo2 = foo2.to_frame(name=material) - foo3 = r3.scenario[PCAs[0]].material[material].materialdata[keyword].copy() - foo3 = foo3.to_frame(name=material) - - USyearlyWASTE[r1.name + '_' + material] = foo1[material] - USyearlyWASTE[r2.name + '_' + material] = foo2[material] - USyearlyWASTE[r3.name + '_' + material] = foo3[material] - - # Loop over PCAs - for jj in range (1, len(PCAs)): - USyearlyWASTE[r1.name + '_' + material] += r1.scenario[PCAs[jj]].material[material].materialdata[keyword] - USyearlyWASTE[r2.name + '_' + material] += r2.scenario[PCAs[jj]].material[material].materialdata[keyword] - USyearlyWASTE[r3.name + '_' + material] += r3.scenario[PCAs[jj]].material[material].materialdata[keyword] - -# Converting to grams to Tons. -USyearlyWASTE.head(20) -""" -pass - - -# In[ ]: - - -keyword='mat_Total_Landfilled' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -USyearly=pd.DataFrame() - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios -for kk in range(0, 3): - obj = SFScenarios[kk] - # Loop over Materials - for ii in range (0, len(materials)): - material = materials[ii] - foo = obj.scenario[STATEs[0]].material[material].materialdata[keyword].copy() - foo = foo.to_frame(name=material) - USyearly["Waste_"+material+'_'+obj.name] = foo[material] - - # Loop over STATEs - for jj in range (1, len(STATEs)): - USyearly["Waste_"+material+'_'+obj.name] += obj.scenario[STATEs[jj]].material[material].materialdata[keyword] - - filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj.name)) ] - USyearly['Waste_Module_'+obj.name] = USyearly[filter_col].sum(axis=1) - -# Converting to grams to Tons. -USyearly.head(20) - - -# In[ ]: - - -keyword='mat_Virgin_Stock' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios -for kk in range(0, 3): - obj = SFScenarios[kk] - # Loop over Materials - for ii in range (0, len(materials)): - material = materials[ii] - foo = obj.scenario[STATEs[0]].material[material].materialdata[keyword].copy() - foo = foo.to_frame(name=material) - USyearly["VirginStock_"+material+'_'+obj.name] = foo[material] - - # Loop over STATEs - for jj in range (1, len(STATEs)): - USyearly["VirginStock_"+material+'_'+obj.name] += obj.scenario[STATEs[jj]].material[material].materialdata[keyword] - - filter_col = [col for col in USyearly if (col.startswith('VirginStock_') and col.endswith(obj.name)) ] - USyearly['VirginStock_Module_'+obj.name] = USyearly[filter_col].sum(axis=1) - - -# ### Converting to grams to METRIC Tons. -# - -# In[ ]: - - -USyearly = USyearly/1000000 # This is the ratio for Metric tonnes -#907185 -- this is for US tons - - -# ### Adding Installed Capacity to US - -# In[ ]: - - -obj.scenario[STATEs[0]].data.keys() - - -# In[ ]: - - -keyword='new_Installed_Capacity_[MW]' - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios -for kk in range(0, 3): - obj = SFScenarios[kk] - # Loop over Materials - foo = obj.scenario[STATEs[0]].data[keyword] - foo = foo.to_frame(name=keyword) - USyearly[keyword+obj.name] = foo[keyword] - - # Loop over STATEs - for jj in range (1, len(STATEs)): - USyearly[keyword+obj.name] += obj.scenario[STATEs[jj]].data[keyword] - -USyearly.head(20) - - -# In[ ]: - - -keyword='Installed_Capacity_[W]' - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios -for kk in range(0, 3): - obj = SFScenarios[kk] - # Loop over Materials - foo = obj.scenario[STATEs[0]].data[keyword] - foo = foo.to_frame(name=keyword) - USyearly["Capacity_"+obj.name] = foo[keyword] - - # Loop over STATEs - for jj in range (1, len(STATEs)): - USyearly["Capacity_"+obj.name] += obj.scenario[STATEs[jj]].data[keyword] - -USyearly.head(20) - - -# ### Creative Cumulative DataFrame and Saving - -# In[ ]: - - -USyearly.index = obj.scenario[STATEs[0]].data['year'] - - -# In[ ]: - - -UScum = USyearly.copy() -UScum = UScum.cumsum() -UScum.head() - - -# In[ ]: - - - -USyearly.to_csv(title_Method+' US_Yearly.csv') -UScum.to_csv(title_Method+' US_Cumulative.csv') - - -# # PLOTTING GALORE - -# In[ ]: - - -keywords=['VirginStock_', 'Waste_', 'Capacity'] -SFScenarios = [r1, r2, r3] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -# Loop over Keywords -for ii in range(0, 2): - keyw = keywords[ii] - # Loop over SF Scenarios - for kk in range(0, 3): - obj = SFScenarios[kk] - plt.figure() - plt.plot([],[],color='m', label='glass', linewidth=5) - plt.plot([],[],color='c', label='silicon', linewidth=5) - plt.plot([],[],color='r', label='silver', linewidth=5) - plt.plot([],[],color='k', label='copper', linewidth=5) - plt.plot([],[],color='g', label='aluminum', linewidth=5) - - plt.stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], - USyearly[keyw+materials[1]+'_'+obj.name], - USyearly[keyw+materials[2]+'_'+obj.name], - USyearly[keyw+materials[3]+'_'+obj.name], - USyearly[keyw+materials[4]+'_'+obj.name], - colors=['m','c','r','k', 'g']) - plt.ylabel('Mass [Tons]') - plt.xlim([2010, 2050]) - plt.title('Yearly '+keyw+ ' ' + obj.name) - plt.legend(materials) - - -# In[ ]: - - -plt.rcParams.update({'font.size': 8}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_', 'Waste_', 'Capacity_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(2,3, figsize=(15, 6), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .5, wspace=.001) -axs = axs.ravel() -i = 0 - -# Loop over Keywords -for ii in range(0, 2): - keyw = keywords[ii] - # Loop over SF Scenarios - for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - axs[i].plot([],[],color='c', label='glass', linewidth=5) - axs[i].plot([],[],color='k', label='silicon', linewidth=5) - axs[i].plot([],[],color='m', label='silver', linewidth=5) - axs[i].plot([],[],color='r', label='copper', linewidth=5) - axs[i].plot([],[],color='g', label='aluminum', linewidth=5) - - axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], - USyearly[keyw+materials[1]+'_'+obj.name], - USyearly[keyw+materials[2]+'_'+obj.name], - USyearly[keyw+materials[3]+'_'+obj.name], - USyearly[keyw+materials[4]+'_'+obj.name], - colors=['c','k','m','r', 'g']) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2010, 2050]) - axs[i].set_title(keyw+ ' ' + obj.name) - #axs[i].legend(materials) - - i += 1 - -for i in range (0, 3): - axs[i].set_ylim([0, 4e7]) - axs[i+3].set_ylim([0, 4e7]) - -axs[0].set_ylabel('Mass [Tons]') -axs[3].set_ylabel('Mass [Tons]') -axs[5].legend(materials) - - - -# In[ ]: - - -plt.rcParams.update({'font.size': 8}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_', 'Waste_', 'Capacity_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(3,3, figsize=(15, 6), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .5, wspace=.001) -axs = axs.ravel() -i = 0 - -# Loop over Keywords -for ii in range(0, 2): - keyw = keywords[ii] - # Loop over SF Scenarios - for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - axs[i].plot([],[],color='c', label='glass', linewidth=5) - axs[i].plot([],[],color='k', label='silicon', linewidth=5) - axs[i].plot([],[],color='m', label='silver', linewidth=5) - axs[i].plot([],[],color='r', label='copper', linewidth=5) - axs[i].plot([],[],color='g', label='aluminum', linewidth=5) - - axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], - USyearly[keyw+materials[1]+'_'+obj.name], - USyearly[keyw+materials[2]+'_'+obj.name], - USyearly[keyw+materials[3]+'_'+obj.name], - USyearly[keyw+materials[4]+'_'+obj.name], - colors=['c','k','m','r', 'g']) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2010, 2050]) - axs[i].set_title(keyw+ ' ' + obj.name) - #axs[i].legend(materials) - - i += 1 - - -# CAPACITY IS BY MODULE NOT MATERIAL: -ii = 2 -keyw = keywords[ii] - -# Loop over SF Scenarios -for kk in range(0, 3): - obj = SFScenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - #axs[i].plot([],[],color='c', label='glass', linewidth=5) - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+obj.name]) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2010, 2050]) - axs[i].set_title(keyw+ ' ' + obj.name) - #axs[i].legend(materials) - i += 1 - - - - -for i in range (0, 3): - axs[i].set_ylim([0, 4e7]) - axs[i+3].set_ylim([0, 4e7]) - -axs[0].set_ylabel('Mass [Tons]') -axs[3].set_ylabel('Mass [Tons]') -axs[5].legend(materials) - - - -# In[ ]: - - - - - -# In[ ]: - - -plt.rcParams.update({'font.size': 8}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_', 'Waste_', 'Capacity_'] -SFScenarios = [r1, r2, r3] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - - -fig, axs = plt.subplots(3,3, figsize=(15, 6), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .5, wspace=.001) -axs = axs.ravel() -i = 0 - -# Loop over Keywords -for ii in range(0, 2): - keyw = keywords[ii] - # Loop over SF Scenarios - for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - axs[i].plot([],[],color='c', label='glass', linewidth=5) - axs[i].plot([],[],color='k', label='silicon', linewidth=5) - axs[i].plot([],[],color='m', label='silver', linewidth=5) - axs[i].plot([],[],color='r', label='copper', linewidth=5) - axs[i].plot([],[],color='g', label='aluminum', linewidth=5) - - axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], UScum[keyw+materials[0]+'_'+obj.name], - UScum[keyw+materials[1]+'_'+obj.name], - UScum[keyw+materials[2]+'_'+obj.name], - UScum[keyw+materials[3]+'_'+obj.name], - UScum[keyw+materials[4]+'_'+obj.name], - colors=['c','k','m','r', 'g']) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2010, 2050]) - axs[i].set_title(keyw+ ' ' + obj.name) - #axs[i].legend(materials) - - i += 1 - -# CAPACITY IS BY MODULE NOT MATERIAL: -ii = 2 -keyw = keywords[ii] - -# Loop over SF Scenarios -for kk in range(0, 3): - obj = SFScenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - #axs[i].plot([],[],color='c', label='glass', linewidth=5) - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+obj.name]/1e6) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2010, 2050]) - axs[i].set_title(keyw+ ' ' + obj.name) - #axs[i].legend(materials) - i += 1 - -for i in range (0, 3): - axs[i].set_ylim([1e0, 10e8]) - axs[i+3].set_ylim([1e0, 10e8]) - axs[i+6].set_ylim([1e0, 10e7]) - - # axs[i].set_yscale('log') - # axs[i+3].set_yscale('log') - # axs[i+6].set_yscale('log') - - -axs[0].set_ylabel('Mass [Tons]') -axs[3].set_ylabel('Mass [Tons]') -axs[6].set_ylabel('Installed Capacity [TW]') -axs[5].legend(materials) - - -# In[ ]: - - -plt.rcParams.update({'font.size': 8}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_', 'Waste_', 'Capacity_'] -SFScenarios = [r1, r2, r3] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - - -fig, axs = plt.subplots(3,3, figsize=(15, 6), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .5, wspace=.001) -axs = axs.ravel() -i = 0 - -# Loop over Keywords -for ii in range(0, 2): - keyw = keywords[ii] - # Loop over SF Scenarios - for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - axs[i].plot([],[],color='c', label='glass', linewidth=5) - axs[i].plot([],[],color='k', label='silicon', linewidth=5) - axs[i].plot([],[],color='m', label='silver', linewidth=5) - axs[i].plot([],[],color='r', label='copper', linewidth=5) - axs[i].plot([],[],color='g', label='aluminum', linewidth=5) - - axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], UScum[keyw+materials[0]+'_'+obj.name], - UScum[keyw+materials[1]+'_'+obj.name], - UScum[keyw+materials[2]+'_'+obj.name], - UScum[keyw+materials[3]+'_'+obj.name], - UScum[keyw+materials[4]+'_'+obj.name], - colors=['c','k','m','r', 'g']) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2010, 2050]) - axs[i].set_title(keyw+ ' ' + obj.name) - #axs[i].legend(materials) - - i += 1 - -# CAPACITY IS BY MODULE NOT MATERIAL: -ii = 2 -keyw = keywords[ii] - -# Loop over SF Scenarios -for kk in range(0, 3): - obj = SFScenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - #axs[i].plot([],[],color='c', label='glass', linewidth=5) - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+obj.name]/1e6) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2010, 2050]) - axs[i].set_title(keyw+ ' ' + obj.name) - #axs[i].legend(materials) - i += 1 - -for i in range (0, 3): - axs[i].set_ylim([1e0, 10e8]) - axs[i+3].set_ylim([1e0, 10e8]) - axs[i+6].set_ylim([1e0, 10e7]) - - axs[i].set_yscale('log') - axs[i+3].set_yscale('log') - axs[i+6].set_yscale('log') - - -axs[0].set_ylabel('Mass [Tons]') -axs[3].set_ylabel('Mass [Tons]') -axs[6].set_ylabel('Installed Capacity [TW]') -axs[5].legend(materials) - - -# In[ ]: - - - - - -# In[ ]: - - -plt.rcParams.update({'font.size': 10}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(3,3, figsize=(15, 10), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .3, wspace=.2) -axs = axs.ravel() -i = 0 - -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() -# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) -# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - # axs[i].plot([],[],color='c', label='glass', linewidth=5) - # axs[i].plot([],[],color='k', label='silicon', linewidth=5) - # axs[i].plot([],[],color='m', label='silver', linewidth=5) - # axs[i].plot([],[],color='r', label='copper', linewidth=5) - # axs[i].plot([],[],color='g', label='aluminum', linewidth=5) - - axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], - USyearly[keyw+materials[1]+'_'+obj.name], - USyearly[keyw+materials[2]+'_'+obj.name], - USyearly[keyw+materials[3]+'_'+obj.name], - USyearly[keyw+materials[4]+'_'+obj.name], - colors=['c','k','gray','orange', 'g']) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - axs[i].set_title(obj.name) - #axs[i].legend(materials) - - i += 1 - -# 2nd axis plot -i = 0 -for kk in range(0, 3): - - obj = SFScenarios[kk] - ax2=axs[i].twinx() - ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], - color = 'r', linewidth=4.0, label='cumulative') - #axs[i].ylabel('Mass [Tons]') - # axs[i].set_xlim([2010, 2050]) - # axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - ax2.set_yscale('log') - ax2.set_ylim([1e3, 1e8]) - i += 1 - - ax2.legend() - - -i = 3 -# ROW 2, Aluminum and Silicon: -# Loop over SF Scenarios -for kk in range(0, 3): - - - obj = SFScenarios[kk] - axs[i].yaxis.grid() -# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[4]+'_'+obj.name], color='g', lw=3, label='Aluminum') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], - # color='g', lw=3, alpha=.6) - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[1]+'_'+obj.name], color='k', lw=3, label='Silicon') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], - # color='k', lw=3)# alpha=.3) - - - # silicon aluminum 'k ''g' - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - axs[i].legend() - - i += 1 - - - -# ROW 3: -# Loop over SF Scenarios -for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[3]+'_'+obj.name], color='orange', lw=3, label='Copper') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], - # color='orange', lw=3)# alpha=.3) - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[2]+'_'+obj.name], color='gray', lw=3, label='Silver') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], - # color='gray', lw=3)# , alpha=.6) - - - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - axs[i].legend() - - i += 1 - -for i in range (0, 3): - axs[i].set_ylim([0, 5e7]) - axs[i+3].set_ylim([0, 3e6]) - axs[i+6].set_ylim([0, 2.5e4]) - - #axs[i+3].set_ylim([1e0, 10e8]) - #axs[i+6].set_ylim([1e0, 5e6]) - -# axs[i+3].set_yscale('log') -# axs[i+6].set_yscale('log') - -axs[0].set_ylabel('Mass [Tons]') -axs[3].set_ylabel('Mass [Tons]') -#axs[5].legend(materials) - -axs[0].set_ylabel('Yearly Mass [Tonnes]') -axs[3].set_ylabel('Yearly Mass [Tonnes]') -axs[6].set_ylabel('Yearly Mass [Tonnes]') - -#axs[8].legend(materials) - -fig.savefig(title_Method+' Fig_3x3_MaterialNeeds.png', dpi=600) - - -# In[ ]: - - -import matplotlib as mpl - -plt.rcParams.update({'font.size': 10}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(3,3, figsize=(15, 10), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .3, wspace=.2) -axs = axs.ravel() -i = 0 - -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -titlesscens = ['Reference Scenario', 'Grid Decarbonization Scenario', 'High Electrification Scenario'] - - -for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='c', alpha=0.5, label='Glass') -# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - # axs[i].plot([],[],color='c', label='glass', linewidth=5) - # axs[i].plot([],[],color='k', label='silicon', linewidth=5) - # axs[i].plot([],[],color='m', label='silver', linewidth=5) - # axs[i].plot([],[],color='r', label='copper', linewidth=5) - # axs[i].plot([],[],color='g', label='aluminum', linewidth=5) - - axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name]/1e6, - USyearly[keyw+materials[1]+'_'+obj.name]/1e6, - USyearly[keyw+materials[2]+'_'+obj.name]/1e6, - USyearly[keyw+materials[3]+'_'+obj.name]/1e6, - USyearly[keyw+materials[4]+'_'+obj.name]/1e6, - colors=['c','k','gray','orange', 'g']) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - axs[i].set_title(titlesscens[kk]) - axs[i].legend(loc='lower right') - - #axs[i].legend(materials) - - i += 1 - -# 2nd axis plot -i = 0 -for kk in range(0, 3): - - obj = SFScenarios[kk] - ax2=axs[i].twinx() - ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name]/1e6, - color = 'r', linewidth=4.0, label='cumulative') - #axs[i].ylabel('Mass [Tons]') - # axs[i].set_xlim([2010, 2050]) - # axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - ax2.set_yscale('log') - ax2.set_ylim([1e3/1e6, 1e8/1e6]) - i += 1 - - ax2.legend() - - -i = 3 -# ROW 2, Aluminum and Silicon: -# Loop over SF Scenarios -for kk in range(0, 3): - - - obj = SFScenarios[kk] - axs[i].yaxis.grid() -# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[4]+'_'+obj.name]/1e6, color='g', lw=3, label='Aluminum') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], - # color='g', lw=3, alpha=.6) - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[1]+'_'+obj.name]/1e6, color='k', lw=3, label='Silicon') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], - # color='k', lw=3)# alpha=.3) - - - # silicon aluminum 'k ''g' - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - axs[i].legend() - - i += 1 - - - -# ROW 3: -# Loop over SF Scenarios -for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[3]+'_'+obj.name], color='orange', lw=3, label='Copper') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], - # color='orange', lw=3)# alpha=.3) - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[2]+'_'+obj.name], color='gray', lw=3, label='Silver') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], - # color='gray', lw=3)# , alpha=.6) - - - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - axs[i].legend() - axs[i].yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}')) - - i += 1 - -for i in range (0, 3): - axs[i].set_ylim([0, 5e7/1e6]) - axs[i+3].set_ylim([0, 3e6/1e6]) - axs[i+6].set_ylim([0, 2.5e4]) - - #axs[i+3].set_ylim([1e0, 10e8]) - #axs[i+6].set_ylim([1e0, 5e6]) - -# axs[i+3].set_yscale('log') -# axs[i+6].set_yscale('log') - -axs[0].set_ylabel('Mass [Tons]') -axs[3].set_ylabel('Mass [Tons]') -#axs[5].legend(materials) - -axs[0].set_ylabel('Yearly Mass [Million Tonnes]') -axs[3].set_ylabel('Yearly Mass [Million Tonnes]') -axs[6].set_ylabel('Yearly Mass [Tonnes]') - -#axs[8].legend(materials) - -fig.savefig(title_Method+' Fig_3x3_MaterialNeeds.png', dpi=600) - - -# ## Mining Capacity + Virgin Needs Plot - -# In[ ]: - - -mining2020_aluminum = 65267000 -mining2020_silver = 22260 -mining2020_copper = 20000000 -mining2020_silicon = 8000000 - - -# In[ ]: - - -plt.rcParams.update({'font.size': 10}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(3,3, figsize=(15, 10), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .3, wspace=.2) -axs = axs.ravel() -i = 0 - -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -titlesscens = ['Reference Scenario', 'Grid Decarbonization Scenario', 'High Electrification Scenario'] - -for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() -# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) -# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - # axs[i].plot([],[],color='c', label='glass', linewidth=5) - # axs[i].plot([],[],color='k', label='silicon', linewidth=5) - # axs[i].plot([],[],color='m', label='silver', linewidth=5) - # axs[i].plot([],[],color='r', label='copper', linewidth=5) - # axs[i].plot([],[],color='g', label='aluminum', linewidth=5) - - axs[i].stackplot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], - USyearly[keyw+materials[1]+'_'+obj.name], - USyearly[keyw+materials[2]+'_'+obj.name], - USyearly[keyw+materials[3]+'_'+obj.name], - USyearly[keyw+materials[4]+'_'+obj.name], - colors=['c','k','gray','orange', 'g']) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - axs[i].set_title(titlesscens[kk]) - #axs[i].legend(materials) - - i += 1 - -# 2nd axis plot -i = 0 -for kk in range(0, 3): - - obj = SFScenarios[kk] - ax2=axs[i].twinx() - ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[0]+'_'+obj.name], - color = 'r', linewidth=4.0, label='cumulative') - #axs[i].ylabel('Mass [Tons]') - # axs[i].set_xlim([2010, 2050]) - # axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - ax2.set_yscale('log') - ax2.set_ylim([1e3, 1e8]) - i += 1 - - ax2.legend() - - -i = 3 -# ROW 2, Aluminum and Silicon: -# Loop over SF Scenarios -for kk in range(0, 3): - - - obj = SFScenarios[kk] - axs[i].yaxis.grid() -# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[4]+'_'+obj.name], color='g', lw=3, label='Aluminum') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], - # color='g', lw=3, alpha=.6) - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[1]+'_'+obj.name], color='k', lw=3, label='Silicon') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], - # color='k', lw=3)# alpha=.3) - - - # silicon aluminum 'k ''g' - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - axs[i].legend() - - ax2=axs[i].twinx() - ax2.axhline(mining2020_aluminum, color = 'g', linestyle='-', linewidth=1.0, label='Production Al') - ax2.axhline(mining2020_silicon, color = 'k', linestyle='-', linewidth=1.0, label='Production Si') - - i += 1 - - - -# ROW 3: -# Loop over SF Scenarios -for kk in range(0, 3): - - obj = SFScenarios[kk] - axs[i].yaxis.grid() - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[3]+'_'+obj.name], color='orange', lw=3, label='Copper') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], - # color='orange', lw=3)# alpha=.3) - - axs[i].plot(obj.scenario[STATEs[0]].data['year'], USyearly[keyw+materials[2]+'_'+obj.name], color='gray', lw=3, label='Silver') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], - # color='gray', lw=3)# , alpha=.6) - - - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - axs[i].legend() - - ax2=axs[i].twinx() - ax2.axhline(mining2020_copper, color = 'orange', linestyle='-', linewidth=1.0, label='Production Copper') - ax2.axhline(mining2020_silver, color = 'gray', linestyle='-', linewidth=1.0, label='Production Silver') - - i += 1 - -for i in range (0, 3): - axs[i].set_ylim([0, 5e7]) - axs[i+3].set_ylim([0, 3e6]) - axs[i+6].set_ylim([0, 2.5e4]) - - #axs[i+3].set_ylim([1e0, 10e8]) - #axs[i+6].set_ylim([1e0, 5e6]) - -# axs[i+3].set_yscale('log') -# axs[i+6].set_yscale('log') - -axs[0].set_ylabel('Mass [Tons]') -axs[3].set_ylabel('Mass [Tons]') -#axs[5].legend(materials) - -axs[0].set_ylabel('Yearly Mass [Tonnes]') -axs[3].set_ylabel('Yearly Mass [Tonnes]') -axs[6].set_ylabel('Yearly Mass [Tonnes]') - -#axs[8].legend(materials) - - -# In[ ]: - - -plt.rcParams.update({'font.size': 10}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(1,1, figsize=(4, 6), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .3, wspace=.2) -i = 0 - -obj = SFScenarios[2] -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -# ROW 2, Aluminum and Silicon: g- 4 aluminum k - 1 silicon orange - 3 copper gray - 2 silver -axs.plot(USyearly[keyw+materials[2]+'_'+obj.name]*100/mining2020_silver, - color = 'gray', linewidth=2.0, label='Silver') -axs.plot(USyearly[keyw+materials[1]+'_'+obj.name]*100/mining2020_silicon, - color = 'k', linewidth=2.0, label='Silicon') -axs.plot(USyearly[keyw+materials[4]+'_'+obj.name]*100/mining2020_aluminum, - color = 'g', linewidth=2.0, label='Aluminum') -axs.plot(USyearly[keyw+materials[3]+'_'+obj.name]*100/mining2020_copper, - color = 'orange', linewidth=2.0, label='Copper') - -axs.set_xlim([2020,2050]) -axs.legend() -#axs.set_yscale('log') - -axs.set_ylabel('Virgin Material Needs ratio to 2020 Production Capacity [%]') - -fig.savefig(title_Method+' Fig_1x1_MaterialNeeds Ratio to Production.png', dpi=600) - - -# In[ ]: - - -plt.rcParams.update({'font.size': 10}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(1,1, figsize=(4, 6), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .3, wspace=.2) -i = 0 - -obj = SFScenarios[2].name -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -# ROW 2, Aluminum and Silicon: g- 4 aluminum k - 1 silicon orange - 3 copper gray - 2 silver -axs.plot(USyearly[keyw+materials[2]+'_'+SFScenarios[2].name]*100/mining2020_silver, - color = 'gray', linewidth=2.0, label='Silver') -axs.fill_between(USyearly.index, USyearly[keyw+materials[2]+'_'+SFScenarios[0].name]*100/mining2020_silver, USyearly[keyw+materials[2]+'_'+SFScenarios[2].name]*100/mining2020_silver, - color='gray', lw=3, alpha=.3) - -axs.plot(USyearly[keyw+materials[1]+'_'+SFScenarios[2].name]*100/mining2020_silicon, - color = 'k', linewidth=2.0, label='Silicon') -axs.fill_between(USyearly.index, USyearly[keyw+materials[1]+'_'+SFScenarios[0].name]*100/mining2020_silicon, - USyearly[keyw+materials[1]+'_'+SFScenarios[2].name]*100/mining2020_silicon, - color='k', lw=3, alpha=.5) - -axs.plot(USyearly[keyw+materials[4]+'_'+SFScenarios[2].name]*100/mining2020_aluminum, - color = 'g', linewidth=2.0, label='Aluminum') - -axs.fill_between(USyearly.index, USyearly[keyw+materials[4]+'_'+SFScenarios[0].name]*100/mining2020_aluminum, - USyearly[keyw+materials[4]+'_'+SFScenarios[2].name]*100/mining2020_aluminum, - color='g', lw=3, alpha=.3) - - -axs.plot(USyearly[keyw+materials[3]+'_'+SFScenarios[2].name]*100/mining2020_copper, - color = 'orange', linewidth=2.0, label='Copper') - -axs.fill_between(USyearly.index, USyearly[keyw+materials[3]+'_'+SFScenarios[0].name]*100/mining2020_copper, - USyearly[keyw+materials[3]+'_'+SFScenarios[2].name]*100/mining2020_copper, - color='orange', lw=3, alpha=.3) - -axs.set_xlim([2020,2050]) -axs.legend() -#axs.set_yscale('log') - -axs.set_ylabel('Virgin material needs as a percentage of 2020 global mining production capacity [%]') - -fig.savefig(title_Method+' Fig_1x1_MaterialNeeds Ratio to Production.png', dpi=600) - - -# # TABLES - -# In[ ]: - - -USyearlysig3 = USyearly.copy() -USyearlysig3 = USyearlysig3.drop(USyearlysig3.index[0]) -N = 2 - -USyearlysig3 = USyearlysig3.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -USyearlysig3 = USyearlysig3.applymap(lambda x: int(x)) -USyearlysig3.head() -#for col in USyearlysig3: -# USyearlysig3[col].apply(lambda x: round(x, N - int(np.floor(np.log(abs(x)))))) - - -# In[ ]: - - -UScumsig3 = UScum.copy() -UScumsig3 = UScumsig3.drop(UScumsig3.index[0]) -N = 2 - -UScumsig3 = UScumsig3.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -UScumsig3 = UScumsig3.applymap(lambda x: int(x)) -UScumsig3.head() - - -# In[ ]: - - -materials = ['Module', 'glass', 'aluminum', 'copper', 'silicon', 'silver'] - -print(" Metric Tonnes Installed in field in 2030") -print(" ######################################### \n") -#Loop over scenarios -for kk in range (0, 3): - obj = SFScenarios[kk] - print("SCENARIO :", obj.name) - - print("********************************") - print("********************************") - - modulemat = 0 - for ii in range(0, len(materials)): - installedmat = (UScumsig3['VirginStock_'+materials[ii]+'_'+obj.name].loc[2030]- - UScumsig3['Waste_'+materials[ii]+'_'+obj.name].loc[2030]) - print(materials[ii], ':', round(installedmat/1000)*1000, 'tons') - - print("Capacity in Year 2030 [GW]:", round(USyearlysig3['Capacity_'+obj.name].loc[2030]/1e9)) - print("Capacity in Year 2050 [GW]:", round(USyearlysig3['Capacity_'+obj.name].loc[2050]/1e9)) - print("****************************\n") - - -# In[ ]: - - - - - -# In[ ]: - - - - - -# In[ ]: - - -print(" VIRGIN STOCK Yearly Needs ") -print(" **************************") -for kk in range(0, 3): - obj = SFScenarios[kk] - print(obj.name) - filter_col = [col for col in USyearlysig3 if (col.startswith('VirginStock_') and col.endswith(obj.name)) ] - display(USyearlysig3[filter_col].loc[[2030, 2040, 2050]]) - print("\n\n") - -print(" VIRGIN STOCK Cumulative Needs ") -print(" ***************************** ") -for kk in range(0, 3): - obj = SFScenarios[kk] - print(obj.name) - filter_col = [col for col in UScumsig3 if (col.startswith('VirginStock_') and col.endswith(obj.name)) ] - display(UScumsig3[filter_col].loc[[2030, 2040, 2050]]) - print("\n\n") - - -# In[ ]: - - -print(" WASTE CUMULATIVE RESULTS [Tonnes] ") -print(" ******************************************") -filter_col = [col for col in UScumsig3 if (col.startswith('Waste_Module')) ] -display(UScumsig3[filter_col].loc[[2016,2020,2030, 2040, 2050]]) - - -# In[ ]: - - -# Same as above cell but in more lines -""" -materials = ['glass', 'aluminum', 'copper', 'silicon', 'silver'] - -print(" WASTE CUMULATIVE RESULTS ") -print(" ***********************") -#Loop over scenarios -for kk in range (0, 3): - obj = SFScenarios[kk] - print("SCENARIO :", obj.name) - modulewaste2016 = 0 - modulewaste2020 = 0 - modulewaste2030 = 0 - modulewaste2040 = 0 - modulewaste2050 = 0 - - for ii in range(0, len(materials)): - modulewaste2016 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[7] - modulewaste2020 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[11] - modulewaste2030 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[21] - modulewaste2040 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[31] - modulewaste2050 +=UScum['Waste_'+materials[ii]+'_'+obj.name].iloc[41] - - print('Module Waste 2016:', round(modulewaste2016/1000)*1000, 'tons') - print('Module Waste 2020:', round(modulewaste2020/1000)*1000, 'tons') - print('Module Waste 2030:', round(modulewaste2030/1000)*1000, 'tons') - print('Module Waste 2040:', round(modulewaste2040/1000)*1000, 'tons') - print('Module Waste 2050:', round(modulewaste2050/1000)*1000, 'tons') - - print("****************************") -""" -pass - - -# ## DWARAKS PLOT - -# In[ ]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, ax = plt.subplots() - -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 -kk = 0 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -plt.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'k.', linewidth=5, label=obj.name+' module mass') -plt.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'k', linewidth=5, label=obj.name+' glass mass only') -ax.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - -# SCENARIO 2 -kk = 1 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -plt.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'g.', linewidth=5, label=obj.name+' module mass') -plt.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'g', linewidth=5, label=obj.name+' glass mass only') -ax.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='g', alpha=0.3, - interpolate=True) - -# SCENARIO 2 -kk = 2 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -plt.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'c.', linewidth=5, label=obj.name+' module mass') -plt.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'c', linewidth=5, label=obj.name+' glass mass only') - -ax.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='c', alpha=0.3, - interpolate=True) - -# color = 'C1' -plt.legend() -plt.title('Yearly Virgin Material Needs by Scenario') -plt.ylabel('Mass [tons]') - - - - -# #### Organizing Cumulative 2050 material needs for Materials / Scenarios - -# In[ ]: - - -#This is in the plots -""" -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 3): - obj = SFScenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj.name].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 -""" -pass - - -# #### Calculating Bottoms for stacked bar plots... ugh. - -# In[ ]: - - -#This is in the plots -""" -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] -""" -pass - - -# ##### Virgin Needs - -# In[ ]: - - -keywords - - -# In[ ]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - - - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'k.', linewidth=5, label='S1: Reference Scenario, module mass') -a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'k', linewidth=5, label='S1: Reference Scenario, glass mass only') -a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - -# SCENARIO 2 *************** -kk = 1 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'g.', linewidth=5, label='S2: Grid Decarbonization Scenario, module mass') -a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'g', linewidth=5, label='S2: Grid Decarbonization Scenario, glass mass only') -a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='g', alpha=0.3, - interpolate=True) - -# SCENARIO 3 *************** -kk = 2 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'c.', linewidth=5, label='S3: High Electrification Scenario, module mass') -a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'c', linewidth=5, label='S3: High Electrification Scenario, glass mass only') - -a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='c', alpha=0.3, - interpolate=True) - -a0.legend() -a0.set_title('Yearly Virgin Material Needs by Scenario') -a0.set_ylabel('Mass [Million Tonnes]') - -a0.set_xlabel('Years') - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 3): - obj = SFScenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj.name].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(3) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]') -a1.set_xlabel('Scenario') -a1.set_xticks(ind) -a1.set_xticklabels(['S1', 'S2', 'S3']) -#plt.yticks(np.arange(0, 81, 10)) -a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver')) - - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600) - - -# ##### Waste - -# In[ ]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) -keywords=['Waste_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -print(keyw) -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'k.', linewidth=5, label='S1: Reference Scenario, module mass') -a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'k', linewidth=5, label='S1: Reference Scenario, glass mass only') -a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - -# SCENARIO 2 *************** -kk = 1 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'g.', linewidth=5, label='S2: Grid Decarbonization Scenario, module mass') -a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'g', linewidth=5, label='S2: Grid Decarbonization, glass mass only') -a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='g', alpha=0.3, - interpolate=True) - -# SCENARIO 3 *************** -kk = 2 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'c.', linewidth=5, label='S3: High Electrification Scenario, module mass') -a0.plot(obj.scenario[STATEs[0]].data['year'], glassmat, 'c', linewidth=5, label='S3:High Electrification Scenario, glass mass only') - -a0.fill_between(obj.scenario[STATEs[0]].data['year'], glassmat, modulemat, color='c', alpha=0.3, - interpolate=True) - -a0.legend() -a0.set_title('Yearly Waste Material by Scenario') -a0.set_ylabel('Mass [Million Tonnes]') - -a0.set_xlabel('Years') - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -keywords=['Waste_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 3): - obj = SFScenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj.name].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(3) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Cumulative Waste by 2050 [Million Tonnes]') -a1.set_xlabel('Scenario') -a1.set_xticks(ind, ('S1', 'S2', 'S3')) -#plt.yticks(np.arange(0, 81, 10)) -a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver')) -a1.set_xticks(ind) -a1.set_xticklabels(['S1', 'S2', 'S3']) - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly WASTE by Scenario and Cumulatives.png', dpi=600) - - -# ##### Another option - -# In[ ]: - - -plt.rcParams.update({'font.size': 12}) -plt.rcParams['figure.figsize'] = (14, 8) -keywords=['Waste_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, a0 = plt.subplots(1, 1) - -######################## -# SUBPLOT 1 -######################## -####################### - -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFScenarios[kk] - -othermat = (USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -a0.bar(obj.scenario[STATEs[0]].data['year'], glassmat, label='S1: '+obj.name+' glass mass only') -a0.bar(obj.scenario[STATEs[0]].data['year'], othermat,bottom=glassmat, label='S1: '+obj.name+'other materials mass') -a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat,'k', label='S1: '+obj.name+' Module mass | Cumulative Installed Capacity [GW] (right axis)') - -# SCENARIO 2 *************** -kk = 1 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'g', label='S2: '+obj.name+' Module mass | Cumulative Installed Capacity [GW] (right axis)') - - - -# SCENARIO 3*************** -kk = 2 -obj = SFScenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj.name]+USyearly[keyw+materials[1]+'_'+obj.name]+ - USyearly[keyw+materials[2]+'_'+obj.name]+USyearly[keyw+materials[3]+'_'+obj.name]+ - USyearly[keyw+materials[4]+'_'+obj.name]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj.name]) -a0.plot(obj.scenario[STATEs[0]].data['year'], modulemat, 'c', label='S3: '+obj.name+' Module mass | Cumulative Installed Capacity [GW] (right axis)') - - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -keywords=['Waste_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 3): - obj = SFScenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj.name].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -### Install Capacity - -ax2=a0.twinx() -ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly['Capacity_'+SFScenarios[0].name]/1e9, 'k', label='S1: Cumulative Installed Capacity [GW] (right axis)') -ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly['Capacity_'+SFScenarios[1].name]/1e9, 'g', label='S2: Cumulative Installed Capacity [GW] (right axis)') -ax2.plot(obj.scenario[STATEs[0]].data['year'], USyearly['Capacity_'+SFScenarios[2].name]/1e9, 'c', label='S3: Cumulative Installed Capacity [GW] (right axis)') -ax2.set_yscale('log') -ax2.set_ylabel('Cumulative Installed Capacity [GW]') - -a0.set_ylabel('Mass [tons]') -a0.set_title('Yearly Waste Material by Scenario and Module Component') -a0.legend(bbox_to_anchor=(0.10, -0.3), loc='lower left') -#ax2.legend() - - -# ### SIZE WASTE COMPARISON -# - -# In[ ]: - - -keyword='Cumulative_Area_disposed' - -USyearly_Areadisp=pd.DataFrame() - -SFScenarios = [r1, r2, r3] -# Loop over SF Scenarios -for kk in range(0, 3): - obj = SFScenarios[kk] - # Loop over Materials - foo = obj.scenario[STATEs[0]].data[keyword].copy() - USyearly_Areadisp["Areadisp_"+obj.name] = foo - - # Loop over STATEs - for jj in range (1, len(STATEs)): - USyearly_Areadisp["Areadisp_"+obj.name] += obj.scenario[STATEs[jj]].data[keyword] - - -# In[ ]: - - -UScum_Areadisp = USyearly_Areadisp.copy() -UScum_Areadisp = UScum_Areadisp.cumsum() - - -# In[ ]: - - -A = UScum['Waste_Module_Reference.Mod'].iloc[-1] -#47700000 # tonnes cumulative by 2050 -A = A*1000 # convert to kg -A = A/10.05599 # convert to m2 if each m2 is ~avg 10 kg -#A = A*2 # convert to area if each module is ~2 m2 -A = A/1e6 # Convert to km 2 -print(A) - - -# In[ ]: - - -B = UScum['Waste_Module_95-by-35_Elec.Adv_DR'].iloc[-1] -#47700000 # tonnes cumulative by 2050 -B = B*1000 # convert to kg -B = B/10.05599 # convert to m2 if each m2 is ~avg 10 kg -#A = A*2 # convert to area if each module is ~2 m2 -B = B/1e6 # Convert to km 2 -print(B) - - -# In[ ]: - - -C = UScum_Areadisp['Areadisp_Reference.Mod'].iloc[-1]/1e6 -D = UScum_Areadisp['Areadisp_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6 - - -# In[ ]: - - -# MANHATTAN SIZE: -manhattans = 59.103529 - - -# In[ ]: - - -print("Cumulative Area by 2050 of Waste PV Modules", C, " km^2") -print("Cumulative Area by 2050 of Waste PV Modules", D, " km^2") -print("Cumulative Area by 2050 of Waste PV Material", A, " km$^2$") -print("Cumulative Area by 2050 of Waste PV Material", B, " km$^2$") -print("") -print("Reference Waste equals ", C/manhattans, " Manhattans ") - - -# In[ ]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'b', label='Cumulative New Yearly Installs') -axs.plot(USyearly['Capacity_Reference.Mod']/1e12, 'g', label='Active in Field Installs') -axs.plot(UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12, 'r', label='Decomissioned PV Panels') -axs.legend() -axs.set_xlim([2020,2050]) -axs.set_ylabel('Power [TW]') -fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600) - - -# In[ ]: - - -E = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6).sum() -F = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12).sum() -print("Cumulative Installs", E) -print("Cumulative Waste", F) -print("Fraction of Decomisioned to Installed Cumulative by 2050", F/E) - - -# In[ ]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(USyearly['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'b', label='Yearly New Yearly Installs') -axs.plot(UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12, 'r', label='Decomissioned PV Panels') -axs.legend() -axs.set_xlim([2020,2050]) -axs.set_ylabel('Power [TW]') -fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600) - - -# In[ ]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - - -plt.plot(UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6/UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'b', label='Cumulative New Yearly Installs') -plt.plot(USyearly['Capacity_Reference.Mod']/1e12/UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'g', label='Active in Field Installs') -plt.plot((UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12)/UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6, 'r', label='Decomissioned PV Panels') -plt.legend() -plt.xlim([2020,2050]) -plt.ylabel('Power [TW]') - - -# In[ ]: - - - - - -# In[ ]: - - -# VALIDATION COMPARISON VALUES - - -# In[ ]: - - -USyearly.iloc[21] - - -# In[ ]: - - -USyearly['Capacity_Reference.Mod'].iloc[21] - - -# In[ ]: - - -7.766749e+11 - - -# In[ ]: - - -UScum.iloc[-1] -print(" Cumulative Waste by 2050, Reference Scenario: ", UScum['Waste_Module_Reference.Mod'].iloc[-1]/1e6, ' Million Tonnes') -print(" cumulative Waste by 2050, Grid Decarbonization Scenario: ", UScum['Waste_Module_95-by-35.Adv'].iloc[-1]/1e6, ' Million Tonnes') -print(" cumulative Waste by 2050, High Electrification Scenario: ", UScum['Waste_Module_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6, ' Million Tonnes') - diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.html b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.html deleted file mode 100644 index f77065df..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.html +++ /dev/null @@ -1,14328 +0,0 @@ - - - - -(development) ReEDS Scenarios - USA + Circular Economy Pathways - - - - - - - - - - - - - - - - - - - - - - - -
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    ReEDS Scenarios on PV ICE Tool USA

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    To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool.

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    Current sections include:

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    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format

      2. -

    2. ### Reading scenarios of interest and running PV ICE tool

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    3. ###Plotting

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    4. ### GeoPlotting.
      5. -</ol> - Notes:

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      Scenarios of Interest: - the Ref.Mod, -o 95-by-35.Adv, and -o 95-by-35+Elec.Adv+DR ones

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    In [1]:
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    import PV_ICE
-import numpy as np
-import pandas as pd
-import os,sys
-import matplotlib.pyplot as plt
-from IPython.display import display
-plt.rcParams.update({'font.size': 22})
-plt.rcParams['figure.figsize'] = (12, 8)
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    In [2]:
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    import os
-from pathlib import Path
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-testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
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-print ("Your simulation will be stored in %s" % testfolder)
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    Your simulation will be stored in C:\Users\sayala\Documents\GitHub\CircularEconomy-MassFlowCalculator\PV_ICE\TEMP
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    Reading REEDS original file to get list of SCENARIOs, PCAs, and STATEs

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    reedsFile = str(Path().resolve().parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx')
-print ("Input file is stored in %s" % reedsFile)
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-rawdf = pd.read_excel(reedsFile,
-                        sheet_name="UPV Capacity (GW)")
-                        #index_col=[0,2,3]) #this casts scenario, PCA and State as levels
-#now set year as an index in place
-#rawdf.drop(columns=['State'], inplace=True)
-rawdf.drop(columns=['Tech'], inplace=True)
-rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True)
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    Input file is stored in C:\Users\sayala\Documents\GitHub\December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx
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    In [4]:
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    scenarios = list(rawdf.index.get_level_values('Scenario').unique())
-PCAs = list(rawdf.index.get_level_values('PCA').unique())
-STATEs = list(rawdf.index.get_level_values('State').unique())
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    Reading GIS inputs

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    GISfile = str(Path().resolve().parent.parent.parent / 'gis_centroid_n.xlsx')
-GIS = pd.read_excel(GISfile)
-GIS = GIS.set_index('id')
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    GIS.head()
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    longlatcountry
    id
    p1-121.45225247.820991USA
    p10-117.15903935.120104USA
    p100-78.25771438.791108USA
    p101-82.19247728.708695USA
    p102-80.56373126.677092USA
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    GIS.loc['p1'].long
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    -121.4522522
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    Create Scenarios in PV_ICE

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    Rename difficult characters from Scenarios Names

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    simulationname = scenarios
-simulationname = [w.replace('+', '_') for w in simulationname]
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    ['Reference.Mod',
- 'Reference.Adv',
- 'Reference.Adv_DR',
- '95-by-35.Mod',
- '95-by-35.Adv',
- '95-by-35.Adv_DR',
- '95-by-35_Elec.Mod',
- '95-by-35_Elec.Adv',
- '95-by-35_Elec.Adv_DR']
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    Downselect to Solar Future scenarios of interest

    Scenarios of Interest:

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  • Ref.Mod

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  • 95-by-35.Adv

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  • 95-by-35+Elec.Adv+DR

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    SFscenarios = [simulationname[0], simulationname[4], simulationname[8]]
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    ['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']
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    Create the REFERENCE Scenario and assign Baselines

    Keeping track of each scenario as its own PV ICE Object.

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    #for ii in range (0, 1): #len(scenarios):
-i = 0
-rr = PV_ICE.Simulation(name='USA', path=testfolder)
-for i in range(0, 1):
-    filetitle = SFscenarios[i]+'.csv'
-    filetitle = os.path.join(testfolder, 'USA', filetitle)    
-    rr.createScenario(name=SFscenarios[i], file=filetitle)
-    rr.scenario[SFscenarios[i]].addMaterial('glass', file=r'..\baselines\ReedsSubset\baseline_material_glass_Reeds.csv')
-    rr.scenario[SFscenarios[i]].addMaterial('silicon', file=r'..\baselines\ReedsSubset\baseline_material_silicon_Reeds.csv')
-    rr.scenario[SFscenarios[i]].addMaterial('silver', file=r'..\baselines\ReedsSubset\baseline_material_silver_Reeds.csv')
-    rr.scenario[SFscenarios[i]].addMaterial('copper', file=r'..\baselines\ReedsSubset\baseline_material_copper_Reeds.csv')
-    rr.scenario[SFscenarios[i]].addMaterial('aluminum', file=r'..\baselines\ReedsSubset\baseline_material_aluminium_Reeds.csv')
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    path = C:\Users\sayala\Documents\GitHub\CircularEconomy-MassFlowCalculator\PV_ICE\TEMP
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    2 FINISH: Set characteristics of Recycling to SF values.

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    Calculate Mass Flow

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    IRENA= False
-ELorRL = 'RL'
-if IRENA:
-    if ELorRL == 'RL':
-        weibullInputParams = {'alpha': 5.3759}  # Regular-loss scenario IRENA
-    if ELorRL == 'EL':
-        weibullInputParams = {'alpha': 2.49}  # Regular-loss scenario IRENA
-    rr.calculateMassFlow(weibullInputParams=weibullInputParams, weibullAlphaOnly=True)
-    title_Method = 'Irena_'+ELorRL
-else:
-    rr.calculateMassFlow()
-    title_Method = 'PVICE'
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    Working on Scenario:  Reference.Mod
-********************
-Finished Area+Power Generation Calculations
-==> Working on Material :  glass
-==> Working on Material :  silicon
-==> Working on Material :  silver
-==> Working on Material :  copper
-==> Working on Material :  aluminum
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    In [12]:
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    print("Scenarios:", rr.scenario.keys())
-print("Module Keys:", rr.scenario[SFscenarios[0]].data.keys())
-print("Material Keys: ", rr.scenario[SFscenarios[0]].material['glass'].materialdata.keys())
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    Scenarios: dict_keys(['Reference.Mod'])
-Module Keys: Index(['year', 'new_Installed_Capacity_[MW]', 'mod_eff', 'mod_reliability_t50',
-       'mod_reliability_t90', 'mod_degradation', 'mod_lifetime', 'mod_MFG_eff',
-       'mod_EOL_collection_eff', 'mod_EOL_collected_recycled',
-       'mod_Repowering', 'mod_Repairing', 'Area',
-       'Cumulative_Area_disposedby_Failure',
-       'Cumulative_Area_disposedby_ProjectLifetime',
-       'Cumulative_Area_disposed', 'Cumulative_Active_Area',
-       'Installed_Capacity_[W]', 'WeibullParams', 'EOL_on_Year_0',
-       'EOL_on_Year_1', 'EOL_on_Year_2', 'EOL_on_Year_3', 'EOL_on_Year_4',
-       'EOL_on_Year_5', 'EOL_on_Year_6', 'EOL_on_Year_7', 'EOL_on_Year_8',
-       'EOL_on_Year_9', 'EOL_on_Year_10', 'EOL_on_Year_11', 'EOL_on_Year_12',
-       'EOL_on_Year_13', 'EOL_on_Year_14', 'EOL_on_Year_15', 'EOL_on_Year_16',
-       'EOL_on_Year_17', 'EOL_on_Year_18', 'EOL_on_Year_19', 'EOL_on_Year_20',
-       'EOL_on_Year_21', 'EOL_on_Year_22', 'EOL_on_Year_23', 'EOL_on_Year_24',
-       'EOL_on_Year_25', 'EOL_on_Year_26', 'EOL_on_Year_27', 'EOL_on_Year_28',
-       'EOL_on_Year_29', 'EOL_on_Year_30', 'EOL_on_Year_31', 'EOL_on_Year_32',
-       'EOL_on_Year_33', 'EOL_on_Year_34', 'EOL_on_Year_35', 'EOL_on_Year_36',
-       'EOL_on_Year_37', 'EOL_on_Year_38', 'EOL_on_Year_39', 'EOL_on_Year_40',
-       'EOL_on_Year_41', 'EoL_Collected', 'EoL_NotCollected', 'EoL_Recycled',
-       'EoL_NotRecycled_Landfilled'],
-      dtype='object')
-Material Keys:  Index(['year', 'mat_virgin_eff', 'mat_massperm2', 'mat_MFG_eff',
-       'mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff',
-       'mat_MFG_scrap_Recycled_into_HQ',
-       'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG',
-       'mat_EOL_collected_Recycled', 'mat_EOL_Recycling_eff',
-       'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG',
-       'mat_modules_NotRecycled', 'mat_modules_NotCollected',
-       'mat_EOL_sento_Recycling', 'mat_EOL_NotRecycled_Landfilled',
-       'mat_EOL_Recycled', 'mat_EOL_Recycled_Losses_Landfilled',
-       'mat_EOL_Recycled_2_HQ', 'mat_EOL_Recycled_2_OQ',
-       'mat_EoL_Recycled_HQ_into_MFG', 'mat_EOL_Recycled_HQ_into_OU',
-       'mat_UsedinManufacturing', 'mat_Manufacturing_Input', 'mat_MFG_Scrap',
-       'mat_MFG_Scrap_Sentto_Recycling', 'mat_MFG_Scrap_Landfilled',
-       'mat_MFG_Scrap_Recycled_Successfully',
-       'mat_MFG_Scrap_Recycled_Losses_Landfilled', 'mat_MFG_Recycled_into_HQ',
-       'mat_MFG_Recycled_into_OQ', 'mat_MFG_Recycled_HQ_into_MFG',
-       'mat_MFG_Recycled_HQ_into_OU', 'mat_Virgin_Stock',
-       'mat_Total_EOL_Landfilled', 'mat_Total_MFG_Landfilled',
-       'mat_Total_Landfilled', 'mat_Total_Recycled_OU'],
-      dtype='object')
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    """
-r1.plotScenariosComparison(keyword='Cumulative_Area_disposedby_Failure')
-r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_Total_Landfilled')
-"""
-pass
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    Aggregating PCAs Material Landfilled to obtain US totals by Year

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    keyword='mat_Total_Landfilled'
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    rr.scenario[SFscenarios[0]].material['glass'].materialdata['mat_Total_Landfilled']
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    Out[15]:
    - - - - -
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    0     1.677604e+09
-1     1.542129e+09
-2     1.864615e+10
-3     1.794693e+10
-4     5.755228e+10
-5     5.626462e+10
-6     1.190584e+11
-7     1.099151e+11
-8     1.465299e+11
-9     1.493990e+11
-10    2.683464e+11
-11    2.427442e+11
-12    2.560304e+11
-13    2.518589e+11
-14    3.347866e+11
-15    3.314195e+11
-16    5.099833e+11
-17    5.069446e+11
-18    6.629206e+11
-19    6.673354e+11
-20    9.351717e+11
-21    9.371938e+11
-22    9.883536e+11
-23    9.976634e+11
-24    1.063537e+12
-25    1.083313e+12
-26    1.167408e+12
-27    1.200502e+12
-28    1.360140e+12
-29    1.403517e+12
-30    1.564731e+12
-31    1.604215e+12
-32    1.736414e+12
-33    1.760313e+12
-34    1.967942e+12
-35    1.991998e+12
-36    2.157762e+12
-37    2.236637e+12
-38    2.591186e+12
-39    2.765081e+12
-40    3.447321e+12
-41    3.559147e+12
-Name: mat_Total_Landfilled, dtype: float64
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    materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-keywd = 'mat_Total_Landfilled'
-
-USyearly=pd.DataFrame()
-
-#for jj in range(len(SFscenarios)):
-for jj in range(0, 1):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['Waste_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled']
-
-    filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj)) ]
-    USyearly['Waste_Module_'+obj] = USyearly[filter_col].sum(axis=1)
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    In [19]:
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    materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-keywd = 'VirginStock_Module_'
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-#for jj in range(len(SFscenarios)):
-for jj in range(0,1):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['VirginStock_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled']
-
-    filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj)) ]
-    USyearly['VirginStock_Module_'+obj] = USyearly[filter_col].sum(axis=1)
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    Converting to grams to METRIC Tons.

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    USyearly = USyearly/1000000  # This is the ratio for Metric tonnes
-#907185 -- this is for US tons
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    Adding Installed Capacity to US

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    keyword='Installed_Capacity_[W]'
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-#for jj in range(len(SFscenarios)):
-for jj in range(0, 1):
-    obj = SFscenarios[jj]
-    USyearly["Capacity_"+obj] = rr.scenario[obj].data[keyword]
- 
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    USyearly.head(20)
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Waste_glass_Reference.ModWaste_silicon_Reference.ModWaste_silver_Reference.ModWaste_copper_Reference.ModWaste_aluminum_Reference.ModWaste_Module_Reference.ModVirginStock_glass_Reference.ModVirginStock_silicon_Reference.ModVirginStock_silver_Reference.ModVirginStock_copper_Reference.ModVirginStock_aluminum_Reference.ModVirginStock_Module_Reference.ModCapacity_Reference.Mod
    01677.604029645.5694954.4930610.4193520.000000e+002328.0859371677.604029645.5694954.4930610.4193520.000000e+004.656172e+031.039020e+08
    11542.129396294.2329882.1062740.4082443.034419e-071838.8769021542.129396294.2329882.1062740.4082443.034419e-073.677754e+032.070834e+08
    218646.1540823511.46303121.2227684.9361462.942192e-0522183.77605618646.1540823511.46303121.2227684.9361462.942192e-054.436755e+041.486954e+09
    317946.9266973521.23979916.3415424.7510424.524681e-0421489.25953317946.9266973521.23979916.3415424.7510424.524681e-044.297852e+042.758626e+09
    457552.28310911158.42947638.15924215.8488323.548157e-0368764.72420757552.28310911158.42947638.15924215.8488323.548157e-031.375294e+057.097585e+09
    556264.62371710608.34122633.97487615.1963211.984699e-0266922.15598856264.62371710608.34122633.97487615.1963211.984699e-021.338443e+051.141307e+10
    6119058.39106023596.22347364.99513236.9905338.563095e-02142756.685829119058.39106023596.22347364.99513236.9905338.563095e-022.855134e+052.148921e+10
    7109915.05768720789.52241458.28894139.1779192.989630e-01130802.345924109915.05768720789.52241458.28894139.1779192.989630e-012.616047e+053.151968e+10
    8146529.92039323328.10240382.77250864.9627078.845051e-01170006.642516146529.92039323328.10240382.77250864.9627078.845051e-013.400133e+054.641201e+10
    9149399.00032818288.27683983.88783764.4317142.303586e+00167837.900304149399.00032818288.27683983.88783764.4317142.303586e+003.356758e+056.124645e+10
    10268346.38163729993.592149144.082104127.5204785.435968e+00298617.012336268346.38163729993.592149144.082104127.5204785.435968e+005.972340e+058.929771e+10
    11242744.16005025518.195785121.143841123.1001641.187290e+01268518.472738242744.16005025518.195785121.143841123.1001641.187290e+015.370369e+051.172382e+11
    12256030.38904026657.458969118.860036130.5601192.437564e+01282961.643808256030.38904026657.458969118.860036130.5601192.437564e+015.659233e+051.472412e+11
    13251858.92509325985.937441108.453226129.1585014.756387e+01278130.038129251858.92509325985.937441108.453226129.1585014.756387e+015.562601e+051.770916e+11
    14334786.61299731874.954165135.290536173.1960738.889422e+01367058.947994334786.61299731874.954165135.290536173.1960738.889422e+017.341179e+052.173628e+11
    15331419.53947228517.495370124.956107173.0073761.599653e+02360394.963586331419.53947228517.495370124.956107173.0073761.599653e+027.207899e+052.574262e+11
    16509983.26833041891.286540181.799905258.0421302.781347e+02552592.531583509983.26833041891.286540181.799905258.0421302.781347e+021.105185e+063.199980e+11
    17506944.64131739930.745596171.904117248.6377984.683517e+02547764.280542506944.64131739930.745596171.904117248.6377984.683517e+021.095529e+063.822394e+11
    18662920.56521950372.466961213.158637314.7660547.649938e+02714585.950669662920.56521950372.466961213.158637314.7660547.649938e+021.429172e+064.642583e+11
    19667335.38476948638.144454202.550788304.0141951.213381e+03717693.475035667335.38476948638.144454202.550788304.0141951.213381e+031.435387e+065.458083e+11
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    - - - - - - - - diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.ipynb b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.ipynb deleted file mode 100644 index 09c1e072..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.ipynb +++ /dev/null @@ -1,1204 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ReEDS Scenarios on PV ICE Tool USA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. \n", - "\n", - "Current sections include:\n", - "\n", - "
      \n", - "
    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format
      2. \n", - "
    2. ### Reading scenarios of interest and running PV ICE tool
      3. \n", - "
    3. ###Plotting
      4. \n", - "
    4. ### GeoPlotting.
      5. \n", - "
    \n", - " Notes:\n", - " \n", - "Scenarios of Interest:\n", - "\tthe Ref.Mod, \n", - "o\t95-by-35.Adv, and \n", - "o\t95-by-35+Elec.Adv+DR ones\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import PV_ICE\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os,sys\n", - "import matplotlib.pyplot as plt\n", - "from IPython.display import display\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.rcParams['figure.figsize'] = (12, 8)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your simulation will be stored in C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - } - ], - "source": [ - "import os\n", - "from pathlib import Path\n", - "\n", - "testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')\n", - "\n", - "print (\"Your simulation will be stored in %s\" % testfolder)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reading REEDS original file to get list of SCENARIOs, PCAs, and STATEs " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input file is stored in C:\\Users\\sayala\\Documents\\GitHub\\December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx\n" - ] - } - ], - "source": [ - "reedsFile = str(Path().resolve().parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx')\n", - "print (\"Input file is stored in %s\" % reedsFile)\n", - "\n", - "rawdf = pd.read_excel(reedsFile,\n", - " sheet_name=\"UPV Capacity (GW)\")\n", - " #index_col=[0,2,3]) #this casts scenario, PCA and State as levels\n", - "#now set year as an index in place\n", - "#rawdf.drop(columns=['State'], inplace=True)\n", - "rawdf.drop(columns=['Tech'], inplace=True)\n", - "rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "scenarios = list(rawdf.index.get_level_values('Scenario').unique())\n", - "PCAs = list(rawdf.index.get_level_values('PCA').unique())\n", - "STATEs = list(rawdf.index.get_level_values('State').unique())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reading GIS inputs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "GISfile = str(Path().resolve().parent.parent.parent / 'gis_centroid_n.xlsx')\n", - "GIS = pd.read_excel(GISfile)\n", - "GIS = GIS.set_index('id')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    longlatcountry
    id
    p1-121.45225247.820991USA
    p10-117.15903935.120104USA
    p100-78.25771438.791108USA
    p101-82.19247728.708695USA
    p102-80.56373126.677092USA
    \n", - "
    " - ], - "text/plain": [ - " long lat country\n", - "id \n", - "p1 -121.452252 47.820991 USA\n", - "p10 -117.159039 35.120104 USA\n", - "p100 -78.257714 38.791108 USA\n", - "p101 -82.192477 28.708695 USA\n", - "p102 -80.563731 26.677092 USA" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "GIS.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-121.4522522" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "GIS.loc['p1'].long" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Scenarios in PV_ICE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rename difficult characters from Scenarios Names" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Reference.Mod',\n", - " 'Reference.Adv',\n", - " 'Reference.Adv_DR',\n", - " '95-by-35.Mod',\n", - " '95-by-35.Adv',\n", - " '95-by-35.Adv_DR',\n", - " '95-by-35_Elec.Mod',\n", - " '95-by-35_Elec.Adv',\n", - " '95-by-35_Elec.Adv_DR']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simulationname = scenarios\n", - "simulationname = [w.replace('+', '_') for w in simulationname]\n", - "simulationname" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Downselect to Solar Future scenarios of interest\n", - "\n", - "Scenarios of Interest:\n", - "
  • Ref.Mod\n", - "
  • 95-by-35.Adv \n", - "
  • 95-by-35+Elec.Adv+DR " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SFscenarios = [simulationname[0], simulationname[4], simulationname[8]]\n", - "SFscenarios" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the REFERENCE Scenario and assign Baselines\n", - "\n", - "Keeping track of each scenario as its own PV ICE Object." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path = C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - } - ], - "source": [ - "#for ii in range (0, 1): #len(scenarios):\n", - "i = 0\n", - "rr = PV_ICE.Simulation(name='USA', path=testfolder)\n", - "for i in range(0, 1):\n", - " filetitle = SFscenarios[i]+'.csv'\n", - " filetitle = os.path.join(testfolder, 'USA', filetitle) \n", - " rr.createScenario(name=SFscenarios[i], file=filetitle)\n", - " rr.scenario[SFscenarios[i]].addMaterial('glass', file=r'..\\baselines\\ReedsSubset\\baseline_material_glass_Reeds.csv')\n", - " rr.scenario[SFscenarios[i]].addMaterial('silicon', file=r'..\\baselines\\ReedsSubset\\baseline_material_silicon_Reeds.csv')\n", - " rr.scenario[SFscenarios[i]].addMaterial('silver', file=r'..\\baselines\\ReedsSubset\\baseline_material_silver_Reeds.csv')\n", - " rr.scenario[SFscenarios[i]].addMaterial('copper', file=r'..\\baselines\\ReedsSubset\\baseline_material_copper_Reeds.csv')\n", - " rr.scenario[SFscenarios[i]].addMaterial('aluminum', file=r'..\\baselines\\ReedsSubset\\baseline_material_aluminium_Reeds.csv')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2 FINISH: Set characteristics of Recycling to SF values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate Mass Flow" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on Scenario: Reference.Mod\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n" - ] - } - ], - "source": [ - "IRENA= False\n", - "ELorRL = 'RL'\n", - "if IRENA:\n", - " if ELorRL == 'RL':\n", - " weibullInputParams = {'alpha': 5.3759} # Regular-loss scenario IRENA\n", - " if ELorRL == 'EL':\n", - " weibullInputParams = {'alpha': 2.49} # Regular-loss scenario IRENA\n", - " rr.calculateMassFlow(weibullInputParams=weibullInputParams, weibullAlphaOnly=True)\n", - " title_Method = 'Irena_'+ELorRL\n", - "else:\n", - " rr.calculateMassFlow()\n", - " title_Method = 'PVICE'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scenarios: dict_keys(['Reference.Mod'])\n", - "Module Keys: Index(['year', 'new_Installed_Capacity_[MW]', 'mod_eff', 'mod_reliability_t50',\n", - " 'mod_reliability_t90', 'mod_degradation', 'mod_lifetime', 'mod_MFG_eff',\n", - " 'mod_EOL_collection_eff', 'mod_EOL_collected_recycled',\n", - " 'mod_Repowering', 'mod_Repairing', 'Area',\n", - " 'Cumulative_Area_disposedby_Failure',\n", - " 'Cumulative_Area_disposedby_ProjectLifetime',\n", - " 'Cumulative_Area_disposed', 'Cumulative_Active_Area',\n", - " 'Installed_Capacity_[W]', 'WeibullParams', 'EOL_on_Year_0',\n", - " 'EOL_on_Year_1', 'EOL_on_Year_2', 'EOL_on_Year_3', 'EOL_on_Year_4',\n", - " 'EOL_on_Year_5', 'EOL_on_Year_6', 'EOL_on_Year_7', 'EOL_on_Year_8',\n", - " 'EOL_on_Year_9', 'EOL_on_Year_10', 'EOL_on_Year_11', 'EOL_on_Year_12',\n", - " 'EOL_on_Year_13', 'EOL_on_Year_14', 'EOL_on_Year_15', 'EOL_on_Year_16',\n", - " 'EOL_on_Year_17', 'EOL_on_Year_18', 'EOL_on_Year_19', 'EOL_on_Year_20',\n", - " 'EOL_on_Year_21', 'EOL_on_Year_22', 'EOL_on_Year_23', 'EOL_on_Year_24',\n", - " 'EOL_on_Year_25', 'EOL_on_Year_26', 'EOL_on_Year_27', 'EOL_on_Year_28',\n", - " 'EOL_on_Year_29', 'EOL_on_Year_30', 'EOL_on_Year_31', 'EOL_on_Year_32',\n", - " 'EOL_on_Year_33', 'EOL_on_Year_34', 'EOL_on_Year_35', 'EOL_on_Year_36',\n", - " 'EOL_on_Year_37', 'EOL_on_Year_38', 'EOL_on_Year_39', 'EOL_on_Year_40',\n", - " 'EOL_on_Year_41', 'EoL_Collected', 'EoL_NotCollected', 'EoL_Recycled',\n", - " 'EoL_NotRecycled_Landfilled'],\n", - " dtype='object')\n", - "Material Keys: Index(['year', 'mat_virgin_eff', 'mat_massperm2', 'mat_MFG_eff',\n", - " 'mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff',\n", - " 'mat_MFG_scrap_Recycled_into_HQ',\n", - " 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG',\n", - " 'mat_EOL_collected_Recycled', 'mat_EOL_Recycling_eff',\n", - " 'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG',\n", - " 'mat_modules_NotRecycled', 'mat_modules_NotCollected',\n", - " 'mat_EOL_sento_Recycling', 'mat_EOL_NotRecycled_Landfilled',\n", - " 'mat_EOL_Recycled', 'mat_EOL_Recycled_Losses_Landfilled',\n", - " 'mat_EOL_Recycled_2_HQ', 'mat_EOL_Recycled_2_OQ',\n", - " 'mat_EoL_Recycled_HQ_into_MFG', 'mat_EOL_Recycled_HQ_into_OU',\n", - " 'mat_UsedinManufacturing', 'mat_Manufacturing_Input', 'mat_MFG_Scrap',\n", - " 'mat_MFG_Scrap_Sentto_Recycling', 'mat_MFG_Scrap_Landfilled',\n", - " 'mat_MFG_Scrap_Recycled_Successfully',\n", - " 'mat_MFG_Scrap_Recycled_Losses_Landfilled', 'mat_MFG_Recycled_into_HQ',\n", - " 'mat_MFG_Recycled_into_OQ', 'mat_MFG_Recycled_HQ_into_MFG',\n", - " 'mat_MFG_Recycled_HQ_into_OU', 'mat_Virgin_Stock',\n", - " 'mat_Total_EOL_Landfilled', 'mat_Total_MFG_Landfilled',\n", - " 'mat_Total_Landfilled', 'mat_Total_Recycled_OU'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "print(\"Scenarios:\", rr.scenario.keys())\n", - "print(\"Module Keys:\", rr.scenario[SFscenarios[0]].data.keys())\n", - "print(\"Material Keys: \", rr.scenario[SFscenarios[0]].material['glass'].materialdata.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "r1.plotScenariosComparison(keyword='Cumulative_Area_disposedby_Failure')\n", - "r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_Total_Landfilled')\n", - "\"\"\"\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aggregating PCAs Material Landfilled to obtain US totals by Year" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='mat_Total_Landfilled'\n", - "keyword='mat_Virgin_Stock'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1.677604e+09\n", - "1 1.542129e+09\n", - "2 1.864615e+10\n", - "3 1.794693e+10\n", - "4 5.755228e+10\n", - "5 5.626462e+10\n", - "6 1.190584e+11\n", - "7 1.099151e+11\n", - "8 1.465299e+11\n", - "9 1.493990e+11\n", - "10 2.683464e+11\n", - "11 2.427442e+11\n", - "12 2.560304e+11\n", - "13 2.518589e+11\n", - "14 3.347866e+11\n", - "15 3.314195e+11\n", - "16 5.099833e+11\n", - "17 5.069446e+11\n", - "18 6.629206e+11\n", - "19 6.673354e+11\n", - "20 9.351717e+11\n", - "21 9.371938e+11\n", - "22 9.883536e+11\n", - "23 9.976634e+11\n", - "24 1.063537e+12\n", - "25 1.083313e+12\n", - "26 1.167408e+12\n", - "27 1.200502e+12\n", - "28 1.360140e+12\n", - "29 1.403517e+12\n", - "30 1.564731e+12\n", - "31 1.604215e+12\n", - "32 1.736414e+12\n", - "33 1.760313e+12\n", - "34 1.967942e+12\n", - "35 1.991998e+12\n", - "36 2.157762e+12\n", - "37 2.236637e+12\n", - "38 2.591186e+12\n", - "39 2.765081e+12\n", - "40 3.447321e+12\n", - "41 3.559147e+12\n", - "Name: mat_Total_Landfilled, dtype: float64" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rr.scenario[SFscenarios[0]].material['glass'].materialdata['mat_Total_Landfilled']" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "keywd = 'mat_Total_Landfilled'\n", - "\n", - "USyearly=pd.DataFrame()\n", - "\n", - "#for jj in range(len(SFscenarios)):\n", - "for jj in range(0, 1):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['Waste_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled']\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj)) ]\n", - " USyearly['Waste_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "keywd = 'VirginStock_Module_'\n", - "\n", - "#for jj in range(len(SFscenarios)):\n", - "for jj in range(0,1):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['VirginStock_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled']\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj)) ]\n", - " USyearly['VirginStock_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting to grams to METRIC Tons. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly = USyearly/1000000 # This is the ratio for Metric tonnes\n", - "#907185 -- this is for US tons\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding Installed Capacity to US" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='Installed_Capacity_[W]'\n", - "\n", - "#for jj in range(len(SFscenarios)):\n", - "for jj in range(0, 1):\n", - " obj = SFscenarios[jj]\n", - " USyearly[\"Capacity_\"+obj] = rr.scenario[obj].data[keyword]\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    Waste_glass_Reference.ModWaste_silicon_Reference.ModWaste_silver_Reference.ModWaste_copper_Reference.ModWaste_aluminum_Reference.ModWaste_Module_Reference.ModVirginStock_glass_Reference.ModVirginStock_silicon_Reference.ModVirginStock_silver_Reference.ModVirginStock_copper_Reference.ModVirginStock_aluminum_Reference.ModVirginStock_Module_Reference.ModCapacity_Reference.Mod
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    \n", - "
    " - ], - "text/plain": [ - " Waste_glass_Reference.Mod Waste_silicon_Reference.Mod \\\n", - "0 1677.604029 645.569495 \n", - "1 1542.129396 294.232988 \n", - "2 18646.154082 3511.463031 \n", - "3 17946.926697 3521.239799 \n", - "4 57552.283109 11158.429476 \n", - "5 56264.623717 10608.341226 \n", - "6 119058.391060 23596.223473 \n", - "7 109915.057687 20789.522414 \n", - "8 146529.920393 23328.102403 \n", - "9 149399.000328 18288.276839 \n", - "10 268346.381637 29993.592149 \n", - "11 242744.160050 25518.195785 \n", - "12 256030.389040 26657.458969 \n", - "13 251858.925093 25985.937441 \n", - "14 334786.612997 31874.954165 \n", - "15 331419.539472 28517.495370 \n", - "16 509983.268330 41891.286540 \n", - "17 506944.641317 39930.745596 \n", - "18 662920.565219 50372.466961 \n", - "19 667335.384769 48638.144454 \n", - "\n", - " Waste_silver_Reference.Mod Waste_copper_Reference.Mod \\\n", - "0 4.493061 0.419352 \n", - "1 2.106274 0.408244 \n", - "2 21.222768 4.936146 \n", - "3 16.341542 4.751042 \n", - "4 38.159242 15.848832 \n", - "5 33.974876 15.196321 \n", - "6 64.995132 36.990533 \n", - "7 58.288941 39.177919 \n", - "8 82.772508 64.962707 \n", - "9 83.887837 64.431714 \n", - "10 144.082104 127.520478 \n", - "11 121.143841 123.100164 \n", - "12 118.860036 130.560119 \n", - "13 108.453226 129.158501 \n", - "14 135.290536 173.196073 \n", - "15 124.956107 173.007376 \n", - "16 181.799905 258.042130 \n", - "17 171.904117 248.637798 \n", - "18 213.158637 314.766054 \n", - "19 202.550788 304.014195 \n", - "\n", - " Waste_aluminum_Reference.Mod Waste_Module_Reference.Mod \\\n", - "0 0.000000e+00 2328.085937 \n", - "1 3.034419e-07 1838.876902 \n", - "2 2.942192e-05 22183.776056 \n", - "3 4.524681e-04 21489.259533 \n", - "4 3.548157e-03 68764.724207 \n", - "5 1.984699e-02 66922.155988 \n", - "6 8.563095e-02 142756.685829 \n", - "7 2.989630e-01 130802.345924 \n", - "8 8.845051e-01 170006.642516 \n", - "9 2.303586e+00 167837.900304 \n", - "10 5.435968e+00 298617.012336 \n", - "11 1.187290e+01 268518.472738 \n", - "12 2.437564e+01 282961.643808 \n", - "13 4.756387e+01 278130.038129 \n", - "14 8.889422e+01 367058.947994 \n", - "15 1.599653e+02 360394.963586 \n", - "16 2.781347e+02 552592.531583 \n", - "17 4.683517e+02 547764.280542 \n", - "18 7.649938e+02 714585.950669 \n", - "19 1.213381e+03 717693.475035 \n", - "\n", - " VirginStock_glass_Reference.Mod VirginStock_silicon_Reference.Mod \\\n", - "0 1677.604029 645.569495 \n", - "1 1542.129396 294.232988 \n", - "2 18646.154082 3511.463031 \n", - "3 17946.926697 3521.239799 \n", - "4 57552.283109 11158.429476 \n", - "5 56264.623717 10608.341226 \n", - "6 119058.391060 23596.223473 \n", - "7 109915.057687 20789.522414 \n", - "8 146529.920393 23328.102403 \n", - "9 149399.000328 18288.276839 \n", - "10 268346.381637 29993.592149 \n", - "11 242744.160050 25518.195785 \n", - "12 256030.389040 26657.458969 \n", - "13 251858.925093 25985.937441 \n", - "14 334786.612997 31874.954165 \n", - "15 331419.539472 28517.495370 \n", - "16 509983.268330 41891.286540 \n", - "17 506944.641317 39930.745596 \n", - "18 662920.565219 50372.466961 \n", - "19 667335.384769 48638.144454 \n", - "\n", - " VirginStock_silver_Reference.Mod VirginStock_copper_Reference.Mod \\\n", - "0 4.493061 0.419352 \n", - "1 2.106274 0.408244 \n", - "2 21.222768 4.936146 \n", - "3 16.341542 4.751042 \n", - "4 38.159242 15.848832 \n", - "5 33.974876 15.196321 \n", - "6 64.995132 36.990533 \n", - "7 58.288941 39.177919 \n", - "8 82.772508 64.962707 \n", - "9 83.887837 64.431714 \n", - "10 144.082104 127.520478 \n", - "11 121.143841 123.100164 \n", - "12 118.860036 130.560119 \n", - "13 108.453226 129.158501 \n", - "14 135.290536 173.196073 \n", - "15 124.956107 173.007376 \n", - "16 181.799905 258.042130 \n", - "17 171.904117 248.637798 \n", - "18 213.158637 314.766054 \n", - "19 202.550788 304.014195 \n", - "\n", - " VirginStock_aluminum_Reference.Mod VirginStock_Module_Reference.Mod \\\n", - "0 0.000000e+00 4.656172e+03 \n", - "1 3.034419e-07 3.677754e+03 \n", - "2 2.942192e-05 4.436755e+04 \n", - "3 4.524681e-04 4.297852e+04 \n", - "4 3.548157e-03 1.375294e+05 \n", - "5 1.984699e-02 1.338443e+05 \n", - "6 8.563095e-02 2.855134e+05 \n", - "7 2.989630e-01 2.616047e+05 \n", - "8 8.845051e-01 3.400133e+05 \n", - "9 2.303586e+00 3.356758e+05 \n", - "10 5.435968e+00 5.972340e+05 \n", - "11 1.187290e+01 5.370369e+05 \n", - "12 2.437564e+01 5.659233e+05 \n", - "13 4.756387e+01 5.562601e+05 \n", - "14 8.889422e+01 7.341179e+05 \n", - "15 1.599653e+02 7.207899e+05 \n", - "16 2.781347e+02 1.105185e+06 \n", - "17 4.683517e+02 1.095529e+06 \n", - "18 7.649938e+02 1.429172e+06 \n", - "19 1.213381e+03 1.435387e+06 \n", - "\n", - " Capacity_Reference.Mod \n", - "0 1.039020e+08 \n", - "1 2.070834e+08 \n", - "2 1.486954e+09 \n", - "3 2.758626e+09 \n", - "4 7.097585e+09 \n", - "5 1.141307e+10 \n", - "6 2.148921e+10 \n", - "7 3.151968e+10 \n", - "8 4.641201e+10 \n", - "9 6.124645e+10 \n", - "10 8.929771e+10 \n", - "11 1.172382e+11 \n", - "12 1.472412e+11 \n", - "13 1.770916e+11 \n", - "14 2.173628e+11 \n", - "15 2.574262e+11 \n", - "16 3.199980e+11 \n", - "17 3.822394e+11 \n", - "18 4.642583e+11 \n", - "19 5.458083e+11 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "USyearly.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.py b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.py deleted file mode 100644 index 0d20a037..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA + Circular Economy Pathways.py +++ /dev/null @@ -1,270 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # ReEDS Scenarios on PV ICE Tool USA - -# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. -# -# Current sections include: -# -#
      -#
    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format
      2. -#
    2. ### Reading scenarios of interest and running PV ICE tool
      3. -#
    3. ###Plotting
      4. -#
    4. ### GeoPlotting.
      5. -#
    -# Notes: -# -# Scenarios of Interest: -# the Ref.Mod, -# o 95-by-35.Adv, and -# o 95-by-35+Elec.Adv+DR ones -# - -# In[1]: - - -import PV_ICE -import numpy as np -import pandas as pd -import os,sys -import matplotlib.pyplot as plt -from IPython.display import display -plt.rcParams.update({'font.size': 22}) -plt.rcParams['figure.figsize'] = (12, 8) - - -# In[2]: - - -import os -from pathlib import Path - -testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP') - -print ("Your simulation will be stored in %s" % testfolder) - - -# ### Reading REEDS original file to get list of SCENARIOs, PCAs, and STATEs - -# In[3]: - - -reedsFile = str(Path().resolve().parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx') -print ("Input file is stored in %s" % reedsFile) - -rawdf = pd.read_excel(reedsFile, - sheet_name="UPV Capacity (GW)") - #index_col=[0,2,3]) #this casts scenario, PCA and State as levels -#now set year as an index in place -#rawdf.drop(columns=['State'], inplace=True) -rawdf.drop(columns=['Tech'], inplace=True) -rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True) - - -# In[4]: - - -scenarios = list(rawdf.index.get_level_values('Scenario').unique()) -PCAs = list(rawdf.index.get_level_values('PCA').unique()) -STATEs = list(rawdf.index.get_level_values('State').unique()) - - -# ### Reading GIS inputs - -# In[5]: - - -GISfile = str(Path().resolve().parent.parent.parent / 'gis_centroid_n.xlsx') -GIS = pd.read_excel(GISfile) -GIS = GIS.set_index('id') - - -# In[6]: - - -GIS.head() - - -# In[7]: - - -GIS.loc['p1'].long - - -# ### Create Scenarios in PV_ICE - -# #### Rename difficult characters from Scenarios Names - -# In[8]: - - -simulationname = scenarios -simulationname = [w.replace('+', '_') for w in simulationname] -simulationname - - -# #### Downselect to Solar Future scenarios of interest -# -# Scenarios of Interest: -#
  • Ref.Mod -#
  • 95-by-35.Adv -#
  • 95-by-35+Elec.Adv+DR - -# In[9]: - - -SFscenarios = [simulationname[0], simulationname[4], simulationname[8]] -SFscenarios - - -# #### Create the REFERENCE Scenario and assign Baselines -# -# Keeping track of each scenario as its own PV ICE Object. - -# In[10]: - - -#for ii in range (0, 1): #len(scenarios): -i = 0 -rr = PV_ICE.Simulation(name='USA', path=testfolder) -for i in range(0, 1): - filetitle = SFscenarios[i]+'.csv' - filetitle = os.path.join(testfolder, 'USA', filetitle) - rr.createScenario(name=SFscenarios[i], file=filetitle) - rr.scenario[SFscenarios[i]].addMaterial('glass', file=r'..\baselines\ReedsSubset\baseline_material_glass_Reeds.csv') - rr.scenario[SFscenarios[i]].addMaterial('silicon', file=r'..\baselines\ReedsSubset\baseline_material_silicon_Reeds.csv') - rr.scenario[SFscenarios[i]].addMaterial('silver', file=r'..\baselines\ReedsSubset\baseline_material_silver_Reeds.csv') - rr.scenario[SFscenarios[i]].addMaterial('copper', file=r'..\baselines\ReedsSubset\baseline_material_copper_Reeds.csv') - rr.scenario[SFscenarios[i]].addMaterial('aluminum', file=r'..\baselines\ReedsSubset\baseline_material_aluminium_Reeds.csv') - - -# # 2 FINISH: Set characteristics of Recycling to SF values. - -# #### Calculate Mass Flow - -# In[11]: - - -IRENA= False -ELorRL = 'RL' -if IRENA: - if ELorRL == 'RL': - weibullInputParams = {'alpha': 5.3759} # Regular-loss scenario IRENA - if ELorRL == 'EL': - weibullInputParams = {'alpha': 2.49} # Regular-loss scenario IRENA - rr.calculateMassFlow(weibullInputParams=weibullInputParams, weibullAlphaOnly=True) - title_Method = 'Irena_'+ELorRL -else: - rr.calculateMassFlow() - title_Method = 'PVICE' - - -# In[12]: - - -print("Scenarios:", rr.scenario.keys()) -print("Module Keys:", rr.scenario[SFscenarios[0]].data.keys()) -print("Material Keys: ", rr.scenario[SFscenarios[0]].material['glass'].materialdata.keys()) - - -# In[13]: - - -""" -r1.plotScenariosComparison(keyword='Cumulative_Area_disposedby_Failure') -r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_Total_Landfilled') -""" -pass - - -# ## Aggregating PCAs Material Landfilled to obtain US totals by Year - -# In[14]: - - -keyword='mat_Total_Landfilled' -keyword='mat_Virgin_Stock' - - -# In[15]: - - -rr.scenario[SFscenarios[0]].material['glass'].materialdata['mat_Total_Landfilled'] - - -# In[18]: - - -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] -keywd = 'mat_Total_Landfilled' - -USyearly=pd.DataFrame() - -#for jj in range(len(SFscenarios)): -for jj in range(0, 1): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['Waste_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled'] - - filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj)) ] - USyearly['Waste_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# In[19]: - - -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] -keywd = 'VirginStock_Module_' - -#for jj in range(len(SFscenarios)): -for jj in range(0,1): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['VirginStock_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled'] - - filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj)) ] - USyearly['VirginStock_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# ### Converting to grams to METRIC Tons. -# - -# In[20]: - - -USyearly = USyearly/1000000 # This is the ratio for Metric tonnes -#907185 -- this is for US tons - - -# ### Adding Installed Capacity to US - -# In[ ]: - - - - - -# In[23]: - - -keyword='Installed_Capacity_[W]' - -#for jj in range(len(SFscenarios)): -for jj in range(0, 1): - obj = SFscenarios[jj] - USyearly["Capacity_"+obj] = rr.scenario[obj].data[keyword] - - - -# In[24]: - - -USyearly.head(20) - - -# In[ ]: - - - - diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.html b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.html deleted file mode 100644 index df8a5be6..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.html +++ /dev/null @@ -1,18916 +0,0 @@ - - - - -(development) ReEDS Scenarios - USA - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - -
    -
    -
    -

    ReEDS Scenarios on PV ICE Tool USA

    -
    -
    -
    -
    -
    -
    -

    To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool.

    -

    Current sections include:

    -

      -
    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format

      2. -

    2. ### Reading scenarios of interest and running PV ICE tool

      3. -

    3. ###Plotting

      4. -

    4. ### GeoPlotting.
      5. -</ol> - Notes:

      -

      Scenarios of Interest: - the Ref.Mod, -o 95-by-35.Adv, and -o 95-by-35+Elec.Adv+DR ones

      - -
    -
    -
    -
    -
    -
    In [1]:
    -
    -
    -
    import PV_ICE
-import numpy as np
-import pandas as pd
-import os,sys
-import matplotlib.pyplot as plt
-from IPython.display import display
-plt.rcParams.update({'font.size': 22})
-plt.rcParams['figure.figsize'] = (12, 8)
-
    - -
    -
    -
    - -
    -
    -
    -
    In [2]:
    -
    -
    -
    import os
-from pathlib import Path
-
-testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP')
-
-print ("Your simulation will be stored in %s" % testfolder)
-
    - -
    -
    -
    - -
    -
    - - -
    - -
    - - -
    -
    Your simulation will be stored in C:\Users\sayala\Documents\GitHub\CircularEconomy-MassFlowCalculator\PV_ICE\TEMP
-
    -
    -
    - -
    -
    - -
    -
    -
    -
    -

    Reading REEDS original file to get list of SCENARIOs, PCAs, and STATEs

    -
    -
    -
    -
    -
    -
    In [3]:
    -
    -
    -
    r"""
-reedsFile = str(Path().resolve().parent.parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx')
-print ("Input file is stored in %s" % reedsFile)
-
-rawdf = pd.read_excel(reedsFile,
-                        sheet_name="UPV Capacity (GW)")
-                        #index_col=[0,2,3]) #this casts scenario, PCA and State as levels
-#now set year as an index in place
-#rawdf.drop(columns=['State'], inplace=True)
-rawdf.drop(columns=['Tech'], inplace=True)
-rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True)
-""";
-
    - -
    -
    -
    - -
    -
    -
    -
    In [4]:
    -
    -
    -
    #scenarios = list(rawdf.index.get_level_values('Scenario').unique())
-#PCAs = list(rawdf.index.get_level_values('PCA').unique())
-#STATEs = list(rawdf.index.get_level_values('State').unique())
-
    - -
    -
    -
    - -
    -
    -
    -
    -

    Create Scenarios in PV_ICE

    -
    -
    -
    -
    -
    -
    -

    Rename difficult characters from Scenarios Names

    -
    -
    -
    -
    -
    -
    In [5]:
    -
    -
    -
    scenarios = ['Reference.Mod',
- 'Reference.Adv',
- 'Reference.Adv_DR',
- '95-by-35.Mod',
- '95-by-35.Adv',
- '95-by-35.Adv_DR',
- '95-by-35_Elec.Mod',
- '95-by-35_Elec.Adv',
- '95-by-35_Elec.Adv_DR']
-
    - -
    -
    -
    - -
    -
    -
    -
    In [6]:
    -
    -
    -
    #simulationname = scenarios
-#simulationname = [w.replace('+', '_') for w in simulationname]
-#simulationname
-
    - -
    -
    -
    - -
    -
    -
    -
    -

    Downselect to Solar Future scenarios of interest

    Scenarios of Interest:

    -

  • Ref.Mod

    -

  • 95-by-35.Adv

    -

  • 95-by-35+Elec.Adv+DR

    - - - - -
    -
    -
    In [7]:
    -
    -
    -
    #SFscenarios = [simulationname[0], simulationname[4], simulationname[8]]
-SFscenarios = ['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']
-SFscenarios
-
    - -
    -
    -
    - -
    -
    - - -
    - -
    Out[7]:
    - - - - -
    -
    ['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']
    -
    - -
    - -
    -
    - -
    -
    -
    -
    -

    Create the 3 Scenarios and assign Baselines

    Keeping track of each scenario as its own PV ICE Object.

    - -
    -
    -
    -
    -
    -
    In [8]:
    -
    -
    -
    #for ii in range (0, 1): #len(scenarios):
-i = 0
-rr = PV_ICE.Simulation(name='USA', path=testfolder)
-for i in range(0, 3):
-    filetitle = SFscenarios[i]+'.csv'
-    filetitle = os.path.join(testfolder, 'USA', filetitle)    
-    rr.createScenario(name=SFscenarios[i], file=filetitle)
-    rr.scenario[SFscenarios[i]].addMaterial('glass', file=r'..\baselines\ReedsSubset\baseline_material_glass_Reeds.csv')
-    rr.scenario[SFscenarios[i]].addMaterial('silicon', file=r'..\baselines\ReedsSubset\baseline_material_silicon_Reeds.csv')
-    rr.scenario[SFscenarios[i]].addMaterial('silver', file=r'..\baselines\ReedsSubset\baseline_material_silver_Reeds.csv')
-    rr.scenario[SFscenarios[i]].addMaterial('copper', file=r'..\baselines\ReedsSubset\baseline_material_copper_Reeds.csv')
-    rr.scenario[SFscenarios[i]].addMaterial('aluminum', file=r'..\baselines\ReedsSubset\baseline_material_aluminium_Reeds.csv')
-
    - -
    -
    -
    - -
    -
    - - -
    - -
    - - -
    -
    path = C:\Users\sayala\Documents\GitHub\CircularEconomy-MassFlowCalculator\PV_ICE\TEMP
-
    -
    -
    - -
    -
    - -
    -
    -
    -
    -

    2 FINISH: Set characteristics of Recycling to SF values.

    -
    -
    -
    -
    -
    -
    -

    Calculate Mass Flow

    -
    -
    -
    -
    -
    -
    In [9]:
    -
    -
    -
    IRENA= False
-PERFECTMFG = True
-
-mats = ['glass', 'silicon','silver','copper','aluminum']
-
-ELorRL = 'EL'
-if IRENA:
-    if ELorRL == 'RL':
-        weibullInputParams = {'alpha': 5.3759, 'beta':30}  # Regular-loss scenario IRENA
-    if ELorRL == 'EL':
-        weibullInputParams = {'alpha': 2.49, 'beta':30}  # Regular-loss scenario IRENA
-    
-    if PERFECTMFG:
-        for jj in range (0, len(rr.scenario.keys())):
-            rr.scenario[list(rr.scenario.keys())[jj]].data['mod_lifetime'] = 40
-            rr.scenario[list(rr.scenario.keys())[jj]].data['mod_MFG_eff'] = 100.0
-
-            for kk in range(0, len(mats)):
-                mat = mats[kk]
-                rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0   
-                rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_scrap_Recycled'] = 0.0   
-               
-    
-    rr.calculateMassFlow(weibullInputParams=weibullInputParams)
-    title_Method = 'Irena_'+ELorRL
-else:
-    rr.calculateMassFlow()
-    title_Method = 'PVICE'
-
    - -
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    - - -
    -
    Working on Scenario:  Reference.Mod
-********************
-Finished Area+Power Generation Calculations
-==> Working on Material :  glass
-==> Working on Material :  silicon
-==> Working on Material :  silver
-==> Working on Material :  copper
-==> Working on Material :  aluminum
-Working on Scenario:  95-by-35.Adv
-********************
-Finished Area+Power Generation Calculations
-==> Working on Material :  glass
-==> Working on Material :  silicon
-==> Working on Material :  silver
-==> Working on Material :  copper
-==> Working on Material :  aluminum
-Working on Scenario:  95-by-35_Elec.Adv_DR
-********************
-Finished Area+Power Generation Calculations
-==> Working on Material :  glass
-==> Working on Material :  silicon
-==> Working on Material :  silver
-==> Working on Material :  copper
-==> Working on Material :  aluminum
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    In [10]:
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    print("Scenarios:", rr.scenario.keys())
-print("Module Keys:", rr.scenario[SFscenarios[0]].data.keys())
-print("Material Keys: ", rr.scenario[SFscenarios[0]].material['glass'].materialdata.keys())
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    Scenarios: dict_keys(['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR'])
-Module Keys: Index(['year', 'new_Installed_Capacity_[MW]', 'mod_eff', 'mod_reliability_t50',
-       'mod_reliability_t90', 'mod_degradation', 'mod_lifetime', 'mod_MFG_eff',
-       'mod_EOL_collection_eff', 'mod_EOL_collected_recycled',
-       'mod_Repowering', 'mod_Repairing', 'Area',
-       'Cumulative_Area_disposedby_Failure',
-       'Cumulative_Area_disposedby_ProjectLifetime',
-       'Cumulative_Area_disposed', 'Cumulative_Active_Area',
-       'Installed_Capacity_[W]', 'WeibullParams', 'EOL_on_Year_0',
-       'EOL_on_Year_1', 'EOL_on_Year_2', 'EOL_on_Year_3', 'EOL_on_Year_4',
-       'EOL_on_Year_5', 'EOL_on_Year_6', 'EOL_on_Year_7', 'EOL_on_Year_8',
-       'EOL_on_Year_9', 'EOL_on_Year_10', 'EOL_on_Year_11', 'EOL_on_Year_12',
-       'EOL_on_Year_13', 'EOL_on_Year_14', 'EOL_on_Year_15', 'EOL_on_Year_16',
-       'EOL_on_Year_17', 'EOL_on_Year_18', 'EOL_on_Year_19', 'EOL_on_Year_20',
-       'EOL_on_Year_21', 'EOL_on_Year_22', 'EOL_on_Year_23', 'EOL_on_Year_24',
-       'EOL_on_Year_25', 'EOL_on_Year_26', 'EOL_on_Year_27', 'EOL_on_Year_28',
-       'EOL_on_Year_29', 'EOL_on_Year_30', 'EOL_on_Year_31', 'EOL_on_Year_32',
-       'EOL_on_Year_33', 'EOL_on_Year_34', 'EOL_on_Year_35', 'EOL_on_Year_36',
-       'EOL_on_Year_37', 'EOL_on_Year_38', 'EOL_on_Year_39', 'EOL_on_Year_40',
-       'EoL_Collected', 'EoL_NotCollected', 'EoL_Recycled',
-       'EoL_NotRecycled_Landfilled'],
-      dtype='object')
-Material Keys:  Index(['year', 'mat_virgin_eff', 'mat_massperm2', 'mat_MFG_eff',
-       'mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff',
-       'mat_MFG_scrap_Recycled_into_HQ',
-       'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG',
-       'mat_EOL_collected_Recycled', 'mat_EOL_Recycling_eff',
-       'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG',
-       'mat_modules_Collected', 'mat_modules_NotCollected',
-       'mat_modules_Recycled', 'mat_modules_NotRecycled',
-       'mat_EOL_sento_Recycling', 'mat_EOL_NotRecycled_Landfilled',
-       'mat_EOL_Recycled', 'mat_EOL_Recycled_Losses_Landfilled',
-       'mat_EOL_Recycled_2_HQ', 'mat_EOL_Recycled_2_OQ',
-       'mat_EoL_Recycled_HQ_into_MFG', 'mat_EOL_Recycled_HQ_into_OU',
-       'mat_UsedSuccessfullyinModuleManufacturing',
-       'mat_EnteringModuleManufacturing', 'mat_LostinModuleManufacturing',
-       'mat_Manufacturing_Input', 'mat_MFG_Scrap',
-       'mat_MFG_Scrap_Sentto_Recycling', 'mat_MFG_Scrap_Landfilled',
-       'mat_MFG_Scrap_Recycled_Successfully',
-       'mat_MFG_Scrap_Recycled_Losses_Landfilled', 'mat_MFG_Recycled_into_HQ',
-       'mat_MFG_Recycled_into_OQ', 'mat_MFG_Recycled_HQ_into_MFG',
-       'mat_MFG_Recycled_HQ_into_OU', 'mat_Virgin_Stock',
-       'mat_Virgin_Stock_Raw', 'mat_Total_EOL_Landfilled',
-       'mat_Total_MFG_Landfilled', 'mat_Total_Landfilled',
-       'mat_Total_Recycled_OU'],
-      dtype='object')
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    In [11]:
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    """
-r1.plotScenariosComparison(keyword='Cumulative_Area_disposedby_Failure')
-r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_Total_Landfilled')
-"""
-pass
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    Aggregating PCAs Material Landfilled to obtain US totals by Year

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    In [12]:
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    USyearly=pd.DataFrame()
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    In [13]:
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    materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
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    In [14]:
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    keywd = 'mat_Virgin_Stock'
-
-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['VirginStock_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]
-
-    filter_col = [col for col in USyearly if (col.startswith('VirginStock_') and col.endswith(obj))]
-    USyearly['VirginStock_Module_'+obj] = USyearly[filter_col].sum(axis=1)
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    In [15]:
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    keywd = 'mat_Total_Landfilled'
-
-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['Waste_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]
-
-    filter_col = [col for col in USyearly if (col.startswith('Waste_') and col.endswith(obj)) ]
-    USyearly['Waste_Module_'+obj] = USyearly[filter_col].sum(axis=1)
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    In [16]:
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    keywd = 'mat_Total_EOL_Landfilled'
-
-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['Waste_EOL_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]
-
-    filter_col = [col for col in USyearly if (col.startswith('Waste_EOL_') and col.endswith(obj)) ]
-    USyearly['Waste_EOL_Module_'+obj] = USyearly[filter_col].sum(axis=1)
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    In [17]:
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    keywd = 'mat_Total_MFG_Landfilled'
-
-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['Waste_MFG_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]
-
-    filter_col = [col for col in USyearly if (col.startswith('Waste_MFG_') and col.endswith(obj)) ]
-    USyearly['Waste_MFG_Module_'+obj] = USyearly[filter_col].sum(axis=1)
-
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    Converting to grams to METRIC Tons.

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    In [18]:
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    USyearly = USyearly/1000000  # This is the ratio for Metric tonnes
-#907185 -- this is for US tons
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    Adding NEW Installed Capacity to US

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    In [19]:
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    keyword='new_Installed_Capacity_[MW]'
-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    USyearly[keyword+obj] = rr.scenario[obj].data[keyword]
- 
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    Reindexing and creating c umulative results

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    In [20]:
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    UScum = USyearly.copy()
-UScum = UScum.cumsum()
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    Adding Installed Capacity to US (This is already 'Cumulative') so not including it in UScum

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    In [21]:
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    keyword='Installed_Capacity_[W]'
-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    USyearly["Capacity_"+obj] = rr.scenario[obj].data[keyword]
- 
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    Set YEAR Index

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    In [22]:
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    USyearly.index = rr.scenario[obj].data['year']
-UScum.index = rr.scenario[obj].data['year']
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    In [23]:
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    USyearly.head().iloc[1]
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    Out[23]:
    - - - - -
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    VirginStock_glass_Reference.Mod                    1.442186e+05
-VirginStock_silicon_Reference.Mod                  1.445687e+04
-VirginStock_silver_Reference.Mod                   2.199155e+02
-VirginStock_copper_Reference.Mod                   1.022991e+02
-VirginStock_aluminum_Reference.Mod                 3.402000e+04
-VirginStock_Module_Reference.Mod                   1.930177e+05
-VirginStock_glass_95-by-35.Adv                     1.442186e+05
-VirginStock_silicon_95-by-35.Adv                   1.445687e+04
-VirginStock_silver_95-by-35.Adv                    2.199155e+02
-VirginStock_copper_95-by-35.Adv                    1.022991e+02
-VirginStock_aluminum_95-by-35.Adv                  3.402000e+04
-VirginStock_Module_95-by-35.Adv                    1.930177e+05
-VirginStock_glass_95-by-35_Elec.Adv_DR             1.442186e+05
-VirginStock_silicon_95-by-35_Elec.Adv_DR           1.445687e+04
-VirginStock_silver_95-by-35_Elec.Adv_DR            2.199155e+02
-VirginStock_copper_95-by-35_Elec.Adv_DR            1.022991e+02
-VirginStock_aluminum_95-by-35_Elec.Adv_DR          3.402000e+04
-VirginStock_Module_95-by-35_Elec.Adv_DR            1.930177e+05
-Waste_glass_Reference.Mod                          9.951086e+03
-Waste_silicon_Reference.Mod                        7.420918e+03
-Waste_silver_Reference.Mod                         4.750175e+01
-Waste_copper_Reference.Mod                         1.207129e+01
-Waste_aluminum_Reference.Mod                       1.013796e+03
-Waste_Module_Reference.Mod                         1.844537e+04
-Waste_glass_95-by-35.Adv                           9.951086e+03
-Waste_silicon_95-by-35.Adv                         7.420918e+03
-Waste_silver_95-by-35.Adv                          4.750175e+01
-Waste_copper_95-by-35.Adv                          1.207129e+01
-Waste_aluminum_95-by-35.Adv                        1.013796e+03
-Waste_Module_95-by-35.Adv                          1.844537e+04
-                                                       ...     
-Waste_EOL_glass_95-by-35_Elec.Adv_DR               2.824248e-05
-Waste_EOL_silicon_95-by-35_Elec.Adv_DR             1.479976e-06
-Waste_EOL_silver_95-by-35_Elec.Adv_DR              4.351959e-08
-Waste_EOL_copper_95-by-35_Elec.Adv_DR              1.897894e-08
-Waste_EOL_aluminum_95-by-35_Elec.Adv_DR            7.015257e-06
-Waste_EOL_Module_95-by-35_Elec.Adv_DR              3.680021e-05
-Waste_MFG_glass_Reference.Mod                      9.951086e+03
-Waste_MFG_silicon_Reference.Mod                    7.420918e+03
-Waste_MFG_silver_Reference.Mod                     4.750175e+01
-Waste_MFG_copper_Reference.Mod                     1.207129e+01
-Waste_MFG_aluminum_Reference.Mod                   1.013796e+03
-Waste_MFG_Module_Reference.Mod                     1.844537e+04
-Waste_MFG_glass_95-by-35.Adv                       9.951086e+03
-Waste_MFG_silicon_95-by-35.Adv                     7.420918e+03
-Waste_MFG_silver_95-by-35.Adv                      4.750175e+01
-Waste_MFG_copper_95-by-35.Adv                      1.207129e+01
-Waste_MFG_aluminum_95-by-35.Adv                    1.013796e+03
-Waste_MFG_Module_95-by-35.Adv                      1.844537e+04
-Waste_MFG_glass_95-by-35_Elec.Adv_DR               9.951086e+03
-Waste_MFG_silicon_95-by-35_Elec.Adv_DR             7.420918e+03
-Waste_MFG_silver_95-by-35_Elec.Adv_DR              4.750175e+01
-Waste_MFG_copper_95-by-35_Elec.Adv_DR              1.207129e+01
-Waste_MFG_aluminum_95-by-35_Elec.Adv_DR            1.013796e+03
-Waste_MFG_Module_95-by-35_Elec.Adv_DR              1.844537e+04
-new_Installed_Capacity_[MW]Reference.Mod           2.534300e+03
-new_Installed_Capacity_[MW]95-by-35.Adv            2.534300e+03
-new_Installed_Capacity_[MW]95-by-35_Elec.Adv_DR    2.534300e+03
-Capacity_Reference.Mod                             3.751735e+09
-Capacity_95-by-35.Adv                              3.751735e+09
-Capacity_95-by-35_Elec.Adv_DR                      3.751735e+09
-Name: 2011, Length: 78, dtype: float64
    -
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    In [24]:
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    USyearly.head()
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    Out[24]:
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    VirginStock_glass_Reference.ModVirginStock_silicon_Reference.ModVirginStock_silver_Reference.ModVirginStock_copper_Reference.ModVirginStock_aluminum_Reference.ModVirginStock_Module_Reference.ModVirginStock_glass_95-by-35.AdvVirginStock_silicon_95-by-35.AdvVirginStock_silver_95-by-35.AdvVirginStock_copper_95-by-35.Adv...Waste_MFG_silver_95-by-35_Elec.Adv_DRWaste_MFG_copper_95-by-35_Elec.Adv_DRWaste_MFG_aluminum_95-by-35_Elec.Adv_DRWaste_MFG_Module_95-by-35_Elec.Adv_DRnew_Installed_Capacity_[MW]Reference.Modnew_Installed_Capacity_[MW]95-by-35.Advnew_Installed_Capacity_[MW]95-by-35_Elec.Adv_DRCapacity_Reference.ModCapacity_95-by-35.AdvCapacity_95-by-35_Elec.Adv_DR
    year
    201070184.2857877082.020411128.42677449.78405316728.96619594173.48322170184.2857877082.020411128.42677449.784053...27.7401835.874518498.5231939032.8123991200.6513501200.6513501200.6513501.208819e+091.208819e+091.208819e+09
    2011144218.64171314456.874009219.915502102.29909034020.002590193017.732904144218.64171314456.874009219.915502102.299090...47.50174812.0712931013.79607718445.3738322534.3001092534.3001092534.3001093.751735e+093.751735e+093.751735e+09
    2012142823.27953014473.896289172.505147100.30625033296.094628190866.081844142823.27953014473.896289172.505147100.306250...37.26111211.836138992.22362018471.1319422534.3001092534.3001092534.3001096.282106e+096.282106e+096.282106e+09
    2013280638.79733327437.479671234.947057195.16319053446.058911361952.446162280638.72004427437.472114234.946992195.163136...50.74855023.0292501592.69211735045.0286555123.0337325123.0323215123.0323211.140951e+101.140951e+101.140951e+10
    2014279365.35148926818.242077214.149815191.57122951596.845772358186.160382279365.27455026818.234691214.149756191.571176...46.25634722.6053991537.58558133977.3937695123.0337325123.0323215123.0323211.651318e+101.651318e+101.651318e+10
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    5 rows Ɨ 78 columns

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    In [25]:
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    UScum.head()
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    Out[25]:
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    VirginStock_glass_Reference.ModVirginStock_silicon_Reference.ModVirginStock_silver_Reference.ModVirginStock_copper_Reference.ModVirginStock_aluminum_Reference.ModVirginStock_Module_Reference.ModVirginStock_glass_95-by-35.AdvVirginStock_silicon_95-by-35.AdvVirginStock_silver_95-by-35.AdvVirginStock_copper_95-by-35.Adv...Waste_MFG_Module_95-by-35.AdvWaste_MFG_glass_95-by-35_Elec.Adv_DRWaste_MFG_silicon_95-by-35_Elec.Adv_DRWaste_MFG_silver_95-by-35_Elec.Adv_DRWaste_MFG_copper_95-by-35_Elec.Adv_DRWaste_MFG_aluminum_95-by-35_Elec.Adv_DRWaste_MFG_Module_95-by-35_Elec.Adv_DRnew_Installed_Capacity_[MW]Reference.Modnew_Installed_Capacity_[MW]95-by-35.Advnew_Installed_Capacity_[MW]95-by-35_Elec.Adv_DR
    year
    201070184.2857877082.020411128.42677449.78405316728.9661959.417348e+0470184.2857877082.020411128.42677449.784053...9032.8123994842.7157193657.95878527.7401835.874518498.5231939032.8123991200.6513501200.6513501200.651350
    2011214402.92750021538.894420348.342276152.08314350748.9687852.871912e+05214402.92750021538.894420348.342276152.083143...27478.18623114793.80199811078.87722175.24193217.9458111512.31927027478.1862313734.9514593734.9514593734.951459
    2012357226.20703036012.790709520.847423252.38939484045.0634134.780573e+05357226.20703036012.790709520.847423252.389394...45949.31817224648.60828518653.882006112.50304329.7819482504.54289045949.3181726269.2515686269.2515686269.251568
    2013637865.00436463450.270380755.794480447.552583137491.1223248.400097e+05637864.92707463450.262823755.794415447.552530...80994.34682744012.67996832668.369060163.25159452.8111994097.23500780994.34682711392.28529911392.28388811392.283888
    2014917230.35585390268.512456969.944295639.123813189087.9680951.198196e+06917230.20162490268.497514969.944172639.123706...114971.74059662741.32797446310.667497209.50794175.4165975634.820587114971.74059616515.31903116515.31620916515.316209
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    5 rows Ɨ 75 columns

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    3 sig figures save Yearly and cumulative overview Nation

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    In [26]:
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    # Data for Jarett
-USyearly.to_csv(title_Method+' US_Yearly NATION_tonnes.csv')
-UScum.to_csv(title_Method+' US_Cumulative NATION_tonnes.csv')
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    USyearly3sig = USyearly.copy()
-UScum3sig = UScum.copy()
-N = 2
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-UScum3sig = UScum3sig.drop(UScum3sig.index[0])
-USyearly3sig = USyearly3sig.drop(USyearly3sig.index[0])
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-if IRENA:
-    UScum3sig = UScum3sig.loc[:, ~UScum3sig.columns.str.startswith('Waste_MFG_')]
-    USyearly3sig = USyearly3sig.loc[:, ~USyearly3sig.columns.str.startswith('Waste_MFG_')]
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-USyearly3sig = USyearly3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))
-USyearly3sig = USyearly3sig.applymap(lambda x: int(x))
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-UScum3sig = UScum3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))
-UScum3sig = UScum3sig.applymap(lambda x: int(x))
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-USyearly3sig.to_csv(title_Method+' US_Yearly NATION.csv')
-UScum3sig.to_csv(title_Method+' US_Cumulative NATION.csv')
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    print("Sanity check: mat_Total_Landfilled = mat_Total_EOL_Landfilled + mat_Total_MFG_Landfilled")
-A = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled'].iloc[5]
-B = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_EOL_Landfilled'].iloc[5]
-C = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_MFG_Landfilled'].iloc[5]
-A - B - C
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    Sanity check: mat_Total_Landfilled = mat_Total_EOL_Landfilled + mat_Total_MFG_Landfilled
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    0.0
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    PLOT

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    Yearly Virgin Material Needs by Scenario

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    In [29]:
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (15, 8)
-keyw='VirginStock_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
-
-########################    
-# SUBPLOT 1
-########################
-#######################
-
-foo = pd.DataFrame() 
-    
-# Loop over Keywords
-ii = 0 
-# Loop over SF Scenarios
-
-# loop plotting over scenarios
-
-# SCENARIO 1 ***************
-kk = 0
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,
-                 interpolate=True)
-
-
-# SCENARIO 2 ***************
-kk = 1
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='S2 Grid Decarbonization: module mass')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='S2 Grid Decarbonization: glass mass only')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,
-                 interpolate=True)
-
-# SCENARIO 3 ***************
-kk = 2
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='S3 High Electrification: module mass')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='S3 High Electrification: glass mass only')
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-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,
-                 interpolate=True)
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-a0.legend()
-a0.set_title('Yearly Virgin Material Needs by Scenario')
-a0.set_ylabel('Mass [Million Tonnes]')
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-a0.set_xlabel('Years')
-
-
-
-    
-########################    
-# SUBPLOT 2
-########################
-#######################
-# Calculate    
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-cumulations2050 = {}
-for ii in range(0, len(materials)):
-    matcum = []
-    for kk in range (0, 3):
-        obj = SFscenarios[kk]
-        matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])
-    cumulations2050[materials[ii]] = matcum
-
-dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) 
-dfcumulations2050 = dfcumulations2050/1000000   # in Million Tonnes
-
-dfcumulations2050['bottom1'] = dfcumulations2050['glass']
-dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']
-dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']
-dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']
-
-
-## Plot BARS Stuff
-ind=np.arange(3)
-width=0.35 # width of the bars.
-p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')
-p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,
-             bottom=dfcumulations2050['bottom1'])
-p2 = a1.bar(ind, dfcumulations2050['silicon'], width,
-             bottom=dfcumulations2050['bottom2'])
-p3 = a1.bar(ind, dfcumulations2050['copper'], width,
-             bottom=dfcumulations2050['bottom3'])
-p4 = a1.bar(ind, dfcumulations2050['silver'], width,
-             bottom=dfcumulations2050['bottom4'])
-
-a1.yaxis.set_label_position("right")
-a1.yaxis.tick_right()
-a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]')
-a1.set_xlabel('Scenario')
-a1.set_xticks(ind, ('S1', 'S2', 'S3'))
-#plt.yticks(np.arange(0, 81, 10))
-a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))
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-f.tight_layout()
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-f.savefig(title_Method+' Fig_2x1_Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600)
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    C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:118: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.
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    ['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']
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    rr.scenario['Reference.Mod'].material['glass'].materialdata['mat_Virgin_Stock'].tail(5)
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    36    1.021035e+12
-37    1.605191e+12
-38    1.601007e+12
-39    1.115273e+12
-40    1.112549e+12
-Name: mat_Virgin_Stock, dtype: float64
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    # Save Data for Jarett Zuboy
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    In [29]:
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (15, 8)
-keyw='VirginStock_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
-
-########################    
-# SUBPLOT 1
-########################
-#######################
-
-    
-    
-# Loop over Keywords
-ii = 0 
-# Loop over SF Scenarios
-
-# loop plotting over scenarios
-
-# SCENARIO 1 ***************
-kk = 0
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module ')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass ')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,
-                 interpolate=True)
-
-# SCENARIO 2 ***************
-kk = 1
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module ')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,
-                 interpolate=True)
-
-# SCENARIO 3 ***************
-kk = 2
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec: glass')
-
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,
-                 interpolate=True)
-
-a0.legend(loc='upper left')
-a0.set_title('Yearly Virgin Material Needs by Scenario')
-a0.set_ylabel('Mass [Million Tonnes]')
-
-a0.set_xlabel('Years')
-#a0.tick_params(axis='y', which='minor', length=3)
-#a0.set_yticks(minorbool=True) 
-a0.minorticks_on()
-a0.tick_params(axis='y', which='minor', bottom=False)
-    
-    
-########################    
-# SUBPLOT 2
-########################
-#######################
-# Calculate    
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-cumulations2050 = {}
-for ii in range(0, len(materials)):
-    matcum = []
-    for kk in range (0, 3):
-        obj = SFscenarios[kk]
-        matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])
-    cumulations2050[materials[ii]] = matcum
-
-dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) 
-dfcumulations2050 = dfcumulations2050/1000000   # in Million Tonnes
-
-dfcumulations2050['bottom1'] = dfcumulations2050['glass']
-dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']
-dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']
-dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']
-
-
-## Plot BARS Stuff
-ind=np.arange(3)
-width=0.35 # width of the bars.
-p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')
-p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,
-             bottom=dfcumulations2050['bottom1'])
-p2 = a1.bar(ind, dfcumulations2050['silicon'], width,
-             bottom=dfcumulations2050['bottom2'])
-p3 = a1.bar(ind, dfcumulations2050['copper'], width,
-             bottom=dfcumulations2050['bottom3'])
-p4 = a1.bar(ind, dfcumulations2050['silver'], width,
-             bottom=dfcumulations2050['bottom4'])
-
-a1.yaxis.set_label_position("right")
-a1.yaxis.tick_right()
-a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]')
-#a1.set_xlabel('Scenario')
-a1.tick_params(axis='y', which='minor', bottom='off')
-#a1.minorticks_on()
-
-plt.sca(a1)
-plt.xticks(range(3), ['Ref.', 'Grid\nDecarb.', 'High\nElec.'], color='black', rotation=0)
-plt.tick_params(axis='y', which='minor', bottom=False)
-#plt.yticks(minor=True)
-a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass'))
-
-f.tight_layout()
-
-f.savefig(title_Method+' Fig_2x1_Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600)
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (15, 8)
-
-keyw='Waste_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
-
-########################    
-# SUBPLOT 1
-########################
-#######################
-
-    
-    
-# Loop over Keywords
-ii = 0 
-# Loop over SF Scenarios
-
-# loop plotting over scenarios
-
-# SCENARIO 1 ***************
-kk = 0
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,
-                 interpolate=True)
-
-# SCENARIO 2 ***************
-kk = 1
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,
-                 interpolate=True)
-
-# SCENARIO 3 ***************
-kk = 2
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec.: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec.: glass')
-
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,
-                 interpolate=True)
-
-a0.legend()
-a0.set_title('Yearly Manufacturing Scrap and EoL Material by Scenario')
-a0.set_ylabel('Mass [Million Tonnes]')
-
-a0.set_xlabel('Years')
-a0.minorticks_on()
-a0.tick_params(axis='y', which='minor', bottom=False)
-
-
-    
-########################    
-# SUBPLOT 2
-########################
-#######################
-# Calculate    
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-cumulations2050 = {}
-for ii in range(0, len(materials)):
-    matcum = []
-    for kk in range (0, 3):
-        obj = SFscenarios[kk]
-        matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])
-    cumulations2050[materials[ii]] = matcum
-
-dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) 
-dfcumulations2050 = dfcumulations2050/1000000   # in Million Tonnes
-
-dfcumulations2050['bottom1'] = dfcumulations2050['glass']
-dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']
-dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']
-dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']
-
-
-## Plot BARS Stuff
-ind=np.arange(3)
-width=0.35 # width of the bars.
-p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')
-p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,
-             bottom=dfcumulations2050['bottom1'])
-p2 = a1.bar(ind, dfcumulations2050['silicon'], width,
-             bottom=dfcumulations2050['bottom2'])
-p3 = a1.bar(ind, dfcumulations2050['copper'], width,
-             bottom=dfcumulations2050['bottom3'])
-p4 = a1.bar(ind, dfcumulations2050['silver'], width,
-             bottom=dfcumulations2050['bottom4'])
-
-a1.yaxis.set_label_position("right")
-a1.yaxis.tick_right()
-a1.set_ylabel('Cumulative Manufacturing Scrap and EoL Material \n by 2050 [Million Tonnes]')
-
-plt.sca(a1)
-plt.xticks(range(3), ['Ref.', 'Grid\nDecarb.', 'High\nElec.'], color='black', rotation=0)
-plt.tick_params(axis='y', which='minor', bottom=False)
-#plt.yticks(minor=True)
-a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass'))
-
-
-
-f.tight_layout()
-
-f.savefig(title_Method+' Fig_2x1_Yearly MFG and EOL Material by Scenario and Cumulatives_Nation.png', dpi=600)
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (15, 8)
-keyw='Waste_EOL_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
-
-########################    
-# SUBPLOT 1
-########################
-#######################
-
-    
-    
-# Loop over Keywords
-ii = 0 
-# Loop over SF Scenarios
-
-# loop plotting over scenarios
-
-# SCENARIO 1 ***************
-kk = 0
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,
-                 interpolate=True)
-
-# SCENARIO 2 ***************
-kk = 1
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,
-                 interpolate=True)
-
-# SCENARIO 3 ***************
-kk = 2
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec.: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec.: glass ')
-
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,
-                 interpolate=True)
-
-a0.legend()
-a0.set_title('Yearly End of Life Material by Scenario')
-a0.set_ylabel('Mass [Million Tonnes]')
-
-a0.set_xlabel('Years')
-a0.minorticks_on()
-a0.tick_params(axis='y', which='minor', bottom=False)
-
-
-
-
-    
-########################    
-# SUBPLOT 2
-########################
-#######################
-# Calculate    
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-cumulations2050 = {}
-for ii in range(0, len(materials)):
-    matcum = []
-    for kk in range (0, 3):
-        obj = SFscenarios[kk]
-        matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])
-    cumulations2050[materials[ii]] = matcum
-
-dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) 
-dfcumulations2050 = dfcumulations2050/1000000   # in Million Tonnes
-
-dfcumulations2050['bottom1'] = dfcumulations2050['glass']
-dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']
-dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']
-dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']
-
-
-## Plot BARS Stuff
-ind=np.arange(3)
-width=0.35 # width of the bars.
-p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')
-p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,
-             bottom=dfcumulations2050['bottom1'])
-p2 = a1.bar(ind, dfcumulations2050['silicon'], width,
-             bottom=dfcumulations2050['bottom2'])
-p3 = a1.bar(ind, dfcumulations2050['copper'], width,
-             bottom=dfcumulations2050['bottom3'])
-p4 = a1.bar(ind, dfcumulations2050['silver'], width,
-             bottom=dfcumulations2050['bottom4'])
-
-a1.yaxis.set_label_position("right")
-a1.yaxis.tick_right()
-a1.set_ylabel('Cumulative End of Life Material by 2050 [Million Tonnes]')
-
-plt.sca(a1)
-plt.xticks(range(3), ['Ref.', 'Grid\nDecarb.', 'High\nElec.'], color='black', rotation=0)
-plt.tick_params(axis='y', which='minor', bottom=False)
-#plt.yticks(minor=True)
-a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass'))
-
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-f.tight_layout()
-
-f.savefig(title_Method+' Fig_2x1_Yearly EoL Waste by Scenario and Cumulatives_Nation.png', dpi=600)
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (15, 8)
-keyw='Waste_MFG_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
-
-########################    
-# SUBPLOT 1
-########################
-#######################
-
-    
-    
-# Loop over Keywords
-ii = 0 
-# Loop over SF Scenarios
-
-# loop plotting over scenarios
-
-# SCENARIO 1 ***************
-kk = 0
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,
-                 interpolate=True)
-
-# SCENARIO 2 ***************
-kk = 1
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,
-                 interpolate=True)
-
-# SCENARIO 3 ***************
-kk = 2
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec.: module')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec.: glass')
-
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,
-                 interpolate=True)
-
-a0.legend(loc='upper left')
-a0.set_title('Yearly Manufacturing Scrap by Scenario')
-a0.set_ylabel('Mass [Million Tonnes]')
-
-a0.set_xlabel('Years')
-a0.minorticks_on()
-a0.tick_params(axis='y', which='minor', bottom=False)
-
-
-
-
-    
-########################    
-# SUBPLOT 2
-########################
-#######################
-# Calculate    
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-cumulations2050 = {}
-for ii in range(0, len(materials)):
-    matcum = []
-    for kk in range (0, 3):
-        obj = SFscenarios[kk]
-        matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])
-    cumulations2050[materials[ii]] = matcum
-
-dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) 
-dfcumulations2050 = dfcumulations2050/1000000   # in Million Tonnes
-
-dfcumulations2050['bottom1'] = dfcumulations2050['glass']
-dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']
-dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']
-dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']
-
-
-## Plot BARS Stuff
-ind=np.arange(3)
-width=0.35 # width of the bars.
-p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')
-p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,
-             bottom=dfcumulations2050['bottom1'])
-p2 = a1.bar(ind, dfcumulations2050['silicon'], width,
-             bottom=dfcumulations2050['bottom2'])
-p3 = a1.bar(ind, dfcumulations2050['copper'], width,
-             bottom=dfcumulations2050['bottom3'])
-p4 = a1.bar(ind, dfcumulations2050['silver'], width,
-             bottom=dfcumulations2050['bottom4'])
-
-a1.yaxis.set_label_position("right")
-a1.yaxis.tick_right()
-a1.set_ylabel('Cumulative Manufacturing Scrap by 2050 [Million Tonnes]')
-
-plt.sca(a1)
-plt.xticks(range(3), ['Ref.', 'Grid\nDecarb.', 'High\nElec.'], color='black', rotation=0)
-plt.tick_params(axis='y', which='minor', bottom=False)
-#plt.yticks(minor=True)
-a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass'))
-
-
-
-f.tight_layout()
-
-f.savefig(title_Method+' Fig_2x1_Yearly MFG Waste by Scenario and Cumulatives_Nation.png', dpi=600)
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (8, 8)
-
-fig, axs = plt.subplots(figsize=(8, 8))
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6, 'k', label='Cumulative New Yearly Installs S3-S2')
-
-#axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'c', label='Cumulative New Yearly Installs')
-
-axs.legend()
-axs.set_xlim([2020,2030])
-axs.set_ylabel('Power [TW]')
-fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600)
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    WASTE COMPARISON SIZE

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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (8, 8)
-
-fig, axs = plt.subplots(figsize=(8, 8))
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Cumulative New Yearly Installs')
-axs.plot(USyearly['Capacity_'+SFscenarios[0]]/1e12, 'g', label='Active in Field Installs')
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels')
-axs.legend()
-axs.set_xlim([2020,2050])
-axs.set_ylabel('Power [TW]')
-fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600)
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (8, 8)
-
-fig, axs = plt.subplots(figsize=(8, 8))
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Cumulative New Yearly Installs Scen. 1 ')
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6, 'b--', label='Cumulative New Yearly Installs  Scen. 2 ')
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'b.', label='Cumulative New Yearly Installs  Scen. 3 ')
-axs.plot(USyearly['Capacity_'+SFscenarios[0]]/1e12, 'g', label='Active in Field Installs Scen. 1')
-axs.plot(USyearly['Capacity_'+SFscenarios[1]]/1e12, 'g--', label='Active in Field Installs Scen. 2')
-axs.plot(USyearly['Capacity_'+SFscenarios[2]]/1e12, 'g.', label='Active in Field Installs Scen. 3')
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels Scen. 1')
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6-USyearly['Capacity_'+SFscenarios[1]]/1e12, 'r--', label='Decomissioned PV Panels Scen 2')
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12, 'r.', label='Decomissioned PV Panels Scen 3')
-
-axs.legend()
-axs.set_xlim([2020,2050])
-axs.set_ylabel('Power [TW]')
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    foo0 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12).sum()
-foo1 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6-USyearly['Capacity_'+SFscenarios[1]]/1e12).sum()
-foo2 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12).sum()
-print(foo0, foo1, foo2)
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    1.3692474079346881 1.8234325025965212 2.0681043184189196
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    In [37]:
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    E = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6).sum()
-F = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12).sum()
-print("Cumulative Installs", E)
-print("Cumulative Waste", F)
-print("Fraction of Decomisioned to Installed Cumulative by 2050", F/E)
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    Cumulative Installs 12.481579419861925
-Cumulative Waste 1.3692474079346881
-Fraction of Decomisioned to Installed Cumulative by 2050 0.10970145378843692
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    SFscenarios[1]
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    '95-by-35.Adv'
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (8, 8)
-
-fig, axs = plt.subplots(figsize=(8, 8))
-axs.plot(USyearly['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Yearly New Yearly Installs')
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels')
-axs.legend()
-axs.set_xlim([2020,2050])
-axs.set_ylabel('Power [TW]')
-fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600)
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (8, 8)
-
-fig, axs = plt.subplots(figsize=(8, 8))
-axs.plot(USyearly['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Reference: New Installs')
-#axs.plot(USyearly['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6, 'b', label='Grid Decarb.: Yearly New Yearly Installs')
-axs.plot(USyearly['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'b--', label='High Elec.: New Installs')
-axs.fill_between(rr.scenario[obj].data['year'], USyearly['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6,
-                 USyearly['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, color='b', alpha=0.3,
-                 interpolate=True)
-
-
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Reference: Decomissioned PV Panels')
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12, 'r--', label='High Elec.: Decomissioned PV Panels')
-axs.fill_between(rr.scenario[obj].data['year'], UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12,
-                UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12, color='r', alpha=0.3,
-                 interpolate=True)
-axs.minorticks_on()
-
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-axs.legend()
-axs.set_xlim([2020,2050])
-axs.set_ylabel('Power [TW]')
-fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600)
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    In [41]:
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    print("CUMULATIVE WASTE by 2050")
-print("*************************")
-print("")
-UScum.iloc[-1]
-print("MFG Scrap + EoL Material Only")
-print("\t Reference Scenario: ", UScum['Waste_Module_Reference.Mod'].iloc[-1]/1e6, ' Million Tonnes')
-print("\t Grid Decarbonization Scenario: ", UScum['Waste_Module_95-by-35.Adv'].iloc[-1]/1e6, ' Million Tonnes')
-print("\t High Electrification Scenario: ", UScum['Waste_Module_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6, ' Million Tonnes')
-
-print("EoL Material Only")
-print("\t Reference Scenario: ", UScum['Waste_EOL_Module_Reference.Mod'].iloc[-1]/1e6, ' Million Tonnes')
-print("\t Grid Decarbonization Scenario: ", UScum['Waste_EOL_Module_95-by-35.Adv'].iloc[-1]/1e6, ' Million Tonnes')
-print("\t High Electrification Scenario: ", UScum['Waste_EOL_Module_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6, ' Million Tonnes')
-
-print("MFG Scrap Only")
-print("\t Reference Scenario: ", UScum['Waste_MFG_Module_Reference.Mod'].iloc[-1]/1e6, ' Million Tonnes')
-print("\t Grid Decarbonization Scenario: ", UScum['Waste_MFG_Module_95-by-35.Adv'].iloc[-1]/1e6, ' Million Tonnes')
-print("\t High Electrification Scenario: ", UScum['Waste_MFG_Module_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6, ' Million Tonnes')
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    CUMULATIVE WASTE by 2050
-*************************
-
-MFG Scrap + EoL Material Only
-	 Reference Scenario:  7.59887380714978  Million Tonnes
-	 Grid Decarbonization Scenario:  9.720686125963702  Million Tonnes
-	 High Electrification Scenario:  10.839108143474517  Million Tonnes
-EoL Material Only
-	 Reference Scenario:  5.768846179490254  Million Tonnes
-	 Grid Decarbonization Scenario:  7.081040756363473  Million Tonnes
-	 High Electrification Scenario:  7.058234739419352  Million Tonnes
-MFG Scrap Only
-	 Reference Scenario:  1.8300276276595273  Million Tonnes
-	 Grid Decarbonization Scenario:  2.6396453696002293  Million Tonnes
-	 High Electrification Scenario:  3.780873404055166  Million Tonnes
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    In [42]:
    -
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    print(" VIRGIN STOCK Yearly Needs ")
-print(" **************************")
-for kk in range(0, 3):
-    obj = SFscenarios[kk]
-    print(obj)
-    filter_col = [col for col in USyearly3sig if (col.startswith('VirginStock_') and col.endswith(obj)) ]
-    display(USyearly3sig[filter_col].loc[[2030, 2040, 2050]])
-    print("\n\n")
-    
-print(" VIRGIN STOCK Cumulative Needs ")
-print(" ***************************** ")
-for kk in range(0, 3):
-    obj = SFscenarios[kk]
-    print(obj)
-    filter_col = [col for col in UScum3sig if (col.startswith('VirginStock_') and col.endswith(obj)) ]
-    display(UScum3sig[filter_col].loc[[2030, 2040, 2050]])
-    print("\n\n")
-
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    -
     VIRGIN STOCK Yearly Needs 
- **************************
-Reference.Mod
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    VirginStock_glass_Reference.ModVirginStock_silicon_Reference.ModVirginStock_silver_Reference.ModVirginStock_copper_Reference.ModVirginStock_aluminum_Reference.ModVirginStock_Module_Reference.Mod
    year
    203018100008570045314802060002100000
    20407990003790020065791100929000
    20501110000528002799151270001290000
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-95-by-35.Adv
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    VirginStock_glass_95-by-35.AdvVirginStock_silicon_95-by-35.AdvVirginStock_silver_95-by-35.AdvVirginStock_copper_95-by-35.AdvVirginStock_aluminum_95-by-35.AdvVirginStock_Module_95-by-35.Adv
    year
    2030232000011000058219102640002700000
    2040322000153008026536700375000
    205012500005930031310301420001450000
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    VirginStock_glass_95-by-35_Elec.Adv_DRVirginStock_silicon_95-by-35_Elec.Adv_DRVirginStock_silver_95-by-35_Elec.Adv_DRVirginStock_copper_95-by-35_Elec.Adv_DRVirginStock_aluminum_95-by-35_Elec.Adv_DRVirginStock_Module_95-by-35_Elec.Adv_DR
    year
    2030330000015700082827103760003840000
    20405280002500013243460100613000
    2050264000012500066121703000003060000
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- VIRGIN STOCK Cumulative Needs 
- ***************************** 
-Reference.Mod
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    VirginStock_glass_Reference.ModVirginStock_silicon_Reference.ModVirginStock_silver_Reference.ModVirginStock_copper_Reference.ModVirginStock_aluminum_Reference.ModVirginStock_Module_Reference.Mod
    year
    203014100000847000597012700192000016800000
    2040202000001140000752017800262000024000000
    20503180000016900001040027300394000037500000
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-95-by-35.Adv
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    VirginStock_glass_95-by-35.AdvVirginStock_silicon_95-by-35.AdvVirginStock_silver_95-by-35.AdvVirginStock_copper_95-by-35.AdvVirginStock_aluminum_95-by-35.AdvVirginStock_Module_95-by-35.Adv
    year
    2030215000001240000845019900285000025600000
    20403680000019600001230032500459000043400000
    20504690000024400001480040800574000055100000
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    VirginStock_glass_95-by-35_Elec.Adv_DRVirginStock_silicon_95-by-35_Elec.Adv_DRVirginStock_silver_95-by-35_Elec.Adv_DRVirginStock_copper_95-by-35_Elec.Adv_DRVirginStock_aluminum_95-by-35_Elec.Adv_DRVirginStock_Module_95-by-35_Elec.Adv_DR
    year
    2030255000001430000957023400332000030300000
    20405030000026100001580043800615000059100000
    20507060000035700002090060500846000082700000
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    In [ ]:
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    In [43]:
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    print(" WASTE EoL CUMULATIVE RESULTS [Tonnes] ")
-print(" ******************************************")
-filter_col = [col for col in UScum3sig if (col.startswith('Waste_EOL_Module')) ]
-display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]])
-
    - -
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    -
     WASTE EoL CUMULATIVE RESULTS [Tonnes] 
- ******************************************
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    Waste_EOL_Module_Reference.ModWaste_EOL_Module_95-by-35.AdvWaste_EOL_Module_95-by-35_Elec.Adv_DR
    year
    2016111111
    2020643643643
    2030165000165000165000
    2040237000023800002380000
    2050577000070800007060000
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    In [44]:
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    print(" WASTE EoL + MfgScrap CUMULATIVE RESULTS [Tonnes] ")
-print(" ******************************************")
-filter_col = [col for col in UScum3sig if (col.startswith('Waste_Module')) ]
-display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]])
-
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     WASTE EoL + MfgScrap CUMULATIVE RESULTS [Tonnes] 
- ******************************************
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    Waste_Module_Reference.ModWaste_Module_95-by-35.AdvWaste_Module_95-by-35_Elec.Adv_DR
    year
    2016225000225000225000
    2020442000440000442000
    2030115000016000001810000
    2040365000045400005200000
    20507600000972000010800000
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    In [45]:
    -
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    print(" WASTE MfgScrap CUMULATIVE RESULTS [Tonnes] ")
-print(" ******************************************")
-filter_col = [col for col in UScum3sig if (col.startswith('Waste_MFG_Module')) ]
-display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]])
-
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     WASTE MfgScrap CUMULATIVE RESULTS [Tonnes] 
- ******************************************
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    Waste_MFG_Module_Reference.ModWaste_MFG_Module_95-by-35.AdvWaste_MFG_Module_95-by-35_Elec.Adv_DR
    year
    2016225000225000225000
    2020441000440000442000
    203098700014300001640000
    2040128000021600002820000
    2050183000026400003780000
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    In [46]:
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    materials = ['Module', 'glass', 'aluminum', 'copper', 'silicon', 'silver']
-
-print(" Appendix Table I: Metric Tonnes Installed in field in 2030")
-print(" ########################################################### \n")
-#Loop over scenarios
-for kk in range (0, 3):
-    obj = SFscenarios[kk]
-    print("SCENARIO :", obj)
-
-    print("********************************")
-    print("********************************")
-
-    modulemat = 0
-    for ii in range(0, len(materials)):
-        installedmat = (UScum3sig['VirginStock_'+materials[ii]+'_'+obj].loc[2030]-
-              UScum3sig['Waste_'+materials[ii]+'_'+obj].loc[2030])
-        print(materials[ii], ':', round(installedmat/1000)*1000, 'tons')
-
-    print("Capacity in Year 2030 [GW]:", round(USyearly3sig['Capacity_'+obj].loc[2030]/1e9))
-    print("Capacity in Year 2050 [GW]:", round(USyearly3sig['Capacity_'+obj].loc[2050]/1e9))
-    print("****************************\n")
-
    - -
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    - -
    - - -
    -
     Appendix Table I: Metric Tonnes Installed in field in 2030
- ########################################################### 
-
-SCENARIO : Reference.Mod
-********************************
-********************************
-Module : 15650000 tons
-glass : 13355000 tons
-aluminum : 1834000 tons
-copper : 11000 tons
-silicon : 530000 tons
-silver : 5000 tons
-Capacity in Year 2030 [GW]: 314
-Capacity in Year 2050 [GW]: 642
-****************************
-
-SCENARIO : 95-by-35.Adv
-********************************
-********************************
-Module : 24000000 tons
-glass : 20460000 tons
-aluminum : 2736000 tons
-copper : 17000 tons
-silicon : 799000 tons
-silver : 6000 tons
-Capacity in Year 2030 [GW]: 489
-Capacity in Year 2050 [GW]: 952
-****************************
-
-SCENARIO : 95-by-35_Elec.Adv_DR
-********************************
-********************************
-Module : 28490000 tons
-glass : 24330000 tons
-aluminum : 3192000 tons
-copper : 21000 tons
-silicon : 929000 tons
-silver : 7000 tons
-Capacity in Year 2030 [GW]: 587
-Capacity in Year 2050 [GW]: 1530
-****************************
-
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    Mining Capacity

    -
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    In [47]:
    -
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    mining2020_aluminum = 65267000
-mining2020_silver = 22260
-mining2020_copper = 20000000
-mining2020_silicon = 8000000
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    In [48]:
    -
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    plt.rcParams.update({'font.size': 10})
-plt.rcParams['figure.figsize'] = (12, 8)
-    
-keywords=['VirginStock_']
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-fig, axs = plt.subplots(1,1, figsize=(4, 6), facecolor='w', edgecolor='k')
-fig.subplots_adjust(hspace = .3, wspace=.2)
-i = 0
-
-obj = SFscenarios[2]
-# Loop over Keywords
-ii = 0 
-keyw = keywords[ii]
-# Loop over SF Scenarios
-
-# ROW 2, Aluminum and Silicon:        g-  4 aluminum k - 1 silicon   orange - 3 copper  gray - 2 silver
-axs.plot(USyearly[keyw+materials[2]+'_'+SFscenarios[2]]*100/mining2020_silver, 
-         color = 'gray', linewidth=2.0, label='Silver')
-axs.fill_between(USyearly.index, USyearly[keyw+materials[2]+'_'+SFscenarios[0]]*100/mining2020_silver, 
-                                 USyearly[keyw+materials[2]+'_'+SFscenarios[2]]*100/mining2020_silver,
-                   color='gray', lw=3, alpha=.3)
-    
-axs.plot(USyearly[keyw+materials[1]+'_'+SFscenarios[2]]*100/mining2020_silicon, 
-         color = 'k', linewidth=2.0, label='Silicon')
-axs.fill_between(USyearly.index, USyearly[keyw+materials[1]+'_'+SFscenarios[0]]*100/mining2020_silicon, 
-                                USyearly[keyw+materials[1]+'_'+SFscenarios[2]]*100/mining2020_silicon,
-                   color='k', lw=3, alpha=.5)
-
-axs.plot(USyearly[keyw+materials[4]+'_'+SFscenarios[2]]*100/mining2020_aluminum, 
-         color = 'g', linewidth=2.0, label='Aluminum')
-
-axs.fill_between(USyearly.index, USyearly[keyw+materials[4]+'_'+SFscenarios[0]]*100/mining2020_aluminum, 
-                                USyearly[keyw+materials[4]+'_'+SFscenarios[2]]*100/mining2020_aluminum,
-                   color='g', lw=3, alpha=.3)
-
-
-axs.plot(USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper, 
-         color = 'orange', linewidth=2.0, label='Copper')
-
-axs.fill_between(USyearly.index, USyearly[keyw+materials[3]+'_'+SFscenarios[0]]*100/mining2020_copper, 
-                                USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper,
-                   color='orange', lw=3, alpha=.3)
-
-axs.set_xlim([2020,2050])
-axs.legend()
-#axs.set_yscale('log')
-axs.minorticks_on()
-
-axs.set_ylabel('Virgin material needs as a percentage \nof 2020 global mining production capacity [%]')
-
-fig.savefig(title_Method+' Fig_1x1_MaterialNeeds Ratio to Production.png',  bbox_inches = "tight", dpi=600)
-
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    In [49]:
    -
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    keyw='VirginStock_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-newdf = pd.DataFrame()
-
-
-newdf['Silver_Ref'] = USyearly[keyw+materials[2]+'_'+SFscenarios[0]]*100/mining2020_silver
-newdf['Silver_High'] = USyearly[keyw+materials[2]+'_'+SFscenarios[2]]*100/mining2020_silver
-                  
-    
-    
-newdf['Silicon_Ref'] = USyearly[keyw+materials[1]+'_'+SFscenarios[0]]*100/mining2020_silicon
-newdf['Silicon_High'] = USyearly[keyw+materials[1]+'_'+SFscenarios[2]]*100/mining2020_silicon 
-                  
-    
-newdf['Aluminium_Ref'] = USyearly[keyw+materials[4]+'_'+SFscenarios[0]]*100/mining2020_aluminum
-newdf['Aluminum_High'] = USyearly[keyw+materials[4]+'_'+SFscenarios[2]]*100/mining2020_aluminum
-                         
-newdf['Copper_Ref'] = USyearly[keyw+materials[3]+'_'+SFscenarios[0]]*100/mining2020_copper
-newdf['Copper_High'] = USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper
-
-                        
-newdf['Copper_Ref'] = USyearly[keyw+materials[3]+'_'+SFscenarios[0]]*100/mining2020_copper
-newdf['Copper_High'] = USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper
-
-newdf.to_csv(title_Method+' Demand as Percentage of Mining.csv')
-
    - -
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    In [50]:
    -
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    import matplotlib as mpl
-
-plt.rcParams.update({'font.size': 10})
-plt.rcParams['figure.figsize'] = (12, 8)
-    
-keywords=['VirginStock_']
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-fig, axs = plt.subplots(3,3, figsize=(15, 10), facecolor='w', edgecolor='k')
-fig.subplots_adjust(hspace = .3, wspace=.2)
-axs = axs.ravel()
-i = 0
-
-# Loop over Keywords
-ii = 0 
-keyw = keywords[ii]
-# Loop over SF Scenarios
-
-titlesscens = ['Reference Scenario', 'Grid Decarbonization Scenario', 'High Electrification Scenario']
-
-
-for kk in range(0, 3):
-
-    obj = SFscenarios[kk]
-    axs[i].yaxis.grid()
-    axs[i].axvspan(2000, 2018, facecolor='c', alpha=0.5, label='Glass')
-#    axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)
- #   axs[i].plot([],[],color='c', label='glass', linewidth=5)
- #   axs[i].plot([],[],color='k', label='silicon', linewidth=5)
- #   axs[i].plot([],[],color='m', label='silver', linewidth=5)
- #   axs[i].plot([],[],color='r', label='copper', linewidth=5)
- #   axs[i].plot([],[],color='g', label='aluminum', linewidth=5)
-
-    axs[i].stackplot(rr.scenario[obj].data['year'], USyearly[keyw+materials[0]+'_'+obj]/1e6, 
-                                                      USyearly[keyw+materials[1]+'_'+obj]/1e6, 
-                                                      USyearly[keyw+materials[2]+'_'+obj]/1e6, 
-                                                      USyearly[keyw+materials[3]+'_'+obj]/1e6, 
-                                                      USyearly[keyw+materials[4]+'_'+obj]/1e6, 
-                                                      colors=['c','k','gray','orange', 'g'])
-    #axs[i].ylabel('Mass [Tons]')
-    axs[i].set_xlim([2020, 2050])
-    axs[i].set_title(titlesscens[kk])
-    axs[i].legend(loc='center right')
-
-    #axs[i].legend(materials)
-
-    i += 1 
-
-# 2nd axis plot
-i = 0
-for kk in range(0, 3):
-
-    obj = SFscenarios[kk]
-    ax2=axs[i].twinx()
-    
-    module = (UScum[keyw+materials[0]+'_'+obj]/1e6 + 
-             UScum[keyw+materials[1]+'_'+obj]/1e6 + 
-             UScum[keyw+materials[2]+'_'+obj]/1e6 +
-             UScum[keyw+materials[3]+'_'+obj]/1e6 +
-             UScum[keyw+materials[4]+'_'+obj]/1e6)
-    ax2.plot(rr.scenario[obj].data['year'], module, 
-             color = 'r', linewidth=4.0, label='cumulative')
-    #axs[i].ylabel('Mass [Tons]')
- #   axs[i].set_xlim([2010, 2050])
-  #  axs[i].set_title(keyw+ ' Yearly ' + obj.name)
-    #axs[i].legend(materials)
-    ax2.set_yscale('log')
-#    ax2.set_ylim([1e3/1e6, 1e8/1e6])
-    ax2.set_ylim([1e0, 1e2])
-
-    i += 1 
-
-    ax2.legend()
-
-
-i = 3
-# ROW 2, Aluminum and Silicon:
-# Loop over SF Scenarios
-for kk in range(0, 3):
-
-
-    obj = SFscenarios[kk]
-    axs[i].yaxis.grid()
-#    axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)
-
-    axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[4]+'_'+obj]/1e6, color='g', lw=3, label='Aluminum')
- #   axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], 
- #                   color='g', lw=3, alpha=.6)
-    
-    axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[1]+'_'+obj]/1e6, color='k', lw=3, label='Silicon')
-   # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], 
-   #                 color='k', lw=3)# alpha=.3)
-
-
-    # silicon aluminum 'k ''g'
-    #axs[i].ylabel('Mass [Tons]')
-    axs[i].set_xlim([2020, 2050])
-    #axs[i].set_title(keyw+ ' Yearly ' + obj.name)
-    #axs[i].legend(materials)
-    axs[i].legend()
-
-    i += 1 
-
-
-
-# ROW 3:
-# Loop over SF Scenarios
-for kk in range(0, 3):
-
-    obj = SFscenarios[kk]
-    axs[i].yaxis.grid()
-
-    axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[3]+'_'+obj], color='orange', lw=3, label='Copper')
- #   axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], 
-  #                  color='orange', lw=3)# alpha=.3)
-
-    axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[2]+'_'+obj], color='gray', lw=3, label='Silver')
- #   axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], 
- #                   color='gray', lw=3)# , alpha=.6)
-    
-    
-    #axs[i].ylabel('Mass [Tons]')
-    axs[i].set_xlim([2020, 2050])
-    #axs[i].set_title(keyw+ ' Yearly ' + obj.name)
-    axs[i].legend()
-    axs[i].yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))
-
-    i += 1 
-    
-for i in range (0, 3):
-    axs[i].set_ylim([0, 0.8e7/1e6])
-    axs[i].minorticks_on()
-
-    #a0.tick_params(axis='y', which='minor', bottom=False)
-    #    axs[i].set_ylim([0, 1e7/1e6])
-    
-    axs[i+3].set_ylim([0, 0.5e6/1e6])
-    axs[i+3].minorticks_on()
-
-    axs[i+6].set_ylim([0, 3500])
-
-    #axs[i+3].set_ylim([1e0, 10e8])
-    #axs[i+6].set_ylim([1e0, 5e6])
-
-#    axs[i+3].set_yscale('log')
-#    axs[i+6].set_yscale('log')
-
-axs[0].set_ylabel('Mass [Tons]')
-axs[3].set_ylabel('Mass [Tons]')
-#axs[5].legend(materials)
-    
-axs[0].set_ylabel('Yearly Mass [Million Tonnes]')
-axs[3].set_ylabel('Yearly Mass [Million Tonnes]')
-axs[6].set_ylabel('Yearly Mass [Tonnes]')
-
-#axs[8].legend(materials)
-
-fig.savefig(title_Method+' Fig_3x3_MaterialNeeds_Nation.png', dpi=600)
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    In [51]:
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    keyword='Cumulative_Area_disposed'
-
-USyearly_Areadisp=pd.DataFrame()
-
-# Loop over SF Scenarios
-for kk in range(0, 3):
-    obj = SFscenarios[kk]
-    # Loop over Materials
-    foo = rr.scenario[obj].data[keyword].copy()
-    USyearly_Areadisp["Areadisp_"+obj] = foo
-
-    # Loop over STATEs
-    #for jj in range (1, len(STATEs)): 
-     #   USyearly_Areadisp["Areadisp_"+obj] += rr.scenario[obj].data[keyword]
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    In [52]:
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    UScum_Areadisp = USyearly_Areadisp.copy()
-UScum_Areadisp = UScum_Areadisp.cumsum()
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    In [53]:
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    A = UScum['Waste_Module_Reference.Mod'].iloc[-1]
-#47700000 # tonnes cumulative by 2050
-A = A*1000 # convert to kg
-A = A/10.05599 # convert to m2 if each m2 is ~avg 10 kg
-#A = A*2 # convert to area if each module is ~2 m2
-A = A/1e6 # Convert to km 2
-print(A)
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    755.6564601943498
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    In [54]:
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    B = UScum['Waste_Module_95-by-35_Elec.Adv_DR'].iloc[-1]
-#47700000 # tonnes cumulative by 2050
-B = B*1000 # convert to kg
-B= B/10.05599 # convert to m2 if each m2 is ~avg 10 kg
-#A = A*2 # convert to area if each module is ~2 m2
-B =B/1e6 # Convert to km 2
-print(B)
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    1077.8757878114952
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    In [55]:
    -
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    C = UScum_Areadisp['Areadisp_Reference.Mod'].iloc[-1]/1e6
-D = UScum_Areadisp['Areadisp_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6
-
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    In [56]:
    -
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    # MANHATTAN SIZE:
-manhattans = 59.103529
-
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    In [57]:
    -
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    print("Reference Cumulative Area by 2050 of Waste PV Modules EoL", round(C), " km^2")
-print("High Electrification Cumulative Area by 2050 of Waste PV Modules EoL", round(D), " km^2")
-
-
-print("")
-print("Reference Waste equals ", round(C/manhattans), " Manhattans ")
-print("High Electrification equals ", round(D/manhattans), " Manhattans ")
-
-print("")
-print ("MFG SCrap + Eol Waste")
-print("Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL", round(A), " km^2")
-print("High Electrification Cumulative Area by 2050 of Waste PV Mfg + Modules EoL", round(B), " km^$")
-
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    Reference Cumulative Area by 2050 of Waste PV Modules EoL 560  km^2
-High Electrification Cumulative Area by 2050 of Waste PV Modules EoL 683  km^2
-
-Reference Waste equals  9  Manhattans 
-High Electrification equals  12  Manhattans 
-
-MFG SCrap + Eol Waste
-Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL 756  km^2
-High Electrification Cumulative Area by 2050 of Waste PV Mfg + Modules EoL 1078  km^$
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    New Section

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    -

    VirginStock_aluminum_Reference.Mod -VirginStock_aluminum_95-by-35.Adv
    -VirginStock_aluminum_95-by-35_Elec.Adv_DR -Waste_EOL_aluminum_Reference.Mod
    -Waste_EOL_aluminum_95-by-35.Adv
    -Waste_EOL_aluminum_95-by-35_Elec.Adv_DR

    -

    VirginStock_silver_Reference.Mod -VirginStock_silver_95-by-35.Adv
    -VirginStock_silver_95-by-35_Elec.Adv_DR -Waste_EOL_silver_Reference.Mod
    -Waste_EOL_silver_95-by-35.Adv
    -Waste_EOL_silver_95-by-35_Elec.Adv_DR

    - -
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    In [58]:
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    USyearly['VirginStock_silver_Reference.Mod']
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    Out[58]:
    - - - - -
    -
    year
-2010    128.426774
-2011    219.915502
-2012    172.505147
-2013    234.947057
-2014    214.149815
-2015    321.420822
-2016    283.204082
-2017    269.165286
-2018    273.761119
-2019    465.757234
-2020    382.744131
-2021     91.373232
-2022     82.747506
-2023    231.970049
-2024    212.484190
-2025    407.290500
-2026    380.662842
-2027    338.591666
-2028    315.521303
-2029    489.351669
-2030    452.797028
-2031    131.150695
-2032    130.404439
-2033    116.233802
-2034    115.657615
-2035    122.605382
-2036    122.066957
-2037    206.839493
-2038    206.024174
-2039    201.195632
-2040    200.476230
-2041    181.903956
-2042    181.308816
-2043    331.327899
-2044    330.328800
-2045    256.781267
-2046    256.063206
-2047    402.562428
-2048    401.512942
-2049    279.696766
-2050    279.013840
-Name: VirginStock_silver_Reference.Mod, dtype: float64
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    In [59]:
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    USyearly
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    VirginStock_glass_Reference.ModVirginStock_silicon_Reference.ModVirginStock_silver_Reference.ModVirginStock_copper_Reference.ModVirginStock_aluminum_Reference.ModVirginStock_Module_Reference.ModVirginStock_glass_95-by-35.AdvVirginStock_silicon_95-by-35.AdvVirginStock_silver_95-by-35.AdvVirginStock_copper_95-by-35.Adv...Waste_MFG_silver_95-by-35_Elec.Adv_DRWaste_MFG_copper_95-by-35_Elec.Adv_DRWaste_MFG_aluminum_95-by-35_Elec.Adv_DRWaste_MFG_Module_95-by-35_Elec.Adv_DRnew_Installed_Capacity_[MW]Reference.Modnew_Installed_Capacity_[MW]95-by-35.Advnew_Installed_Capacity_[MW]95-by-35_Elec.Adv_DRCapacity_Reference.ModCapacity_95-by-35.AdvCapacity_95-by-35_Elec.Adv_DR
    year
    20107.018429e+047082.020411128.42677449.78405316728.9661959.417348e+047.018429e+047082.020411128.42677449.784053...27.7401835.874518498.5231939032.8123991200.6513501200.6513501200.6513501.208819e+091.208819e+091.208819e+09
    20111.442186e+0514456.874009219.915502102.29909034020.0025901.930177e+051.442186e+0514456.874009219.915502102.299090...47.50174812.0712931013.79607718445.3738322534.3001092534.3001092534.3001093.751735e+093.751735e+093.751735e+09
    20121.428233e+0514473.896289172.505147100.30625033296.0946281.908661e+051.428233e+0514473.896289172.505147100.306250...37.26111211.836138992.22362018471.1319422534.3001092534.3001092534.3001096.282106e+096.282106e+096.282106e+09
    20132.806388e+0527437.479671234.947057195.16319053446.0589113.619524e+052.806387e+0527437.472114234.946992195.163136...50.74855023.0292501592.69211735045.0286555123.0337325123.0323215123.0323211.140951e+101.140951e+101.140951e+10
    20142.793654e+0526818.242077214.149815191.57122951596.8457723.581862e+052.793653e+0526818.234691214.149756191.571176...46.25634722.6053991537.58558133977.3937695123.0337325123.0323215123.0323211.651318e+101.651318e+101.651318e+10
    20154.899909e+0547187.198999321.420822365.86302885920.5249416.237859e+054.899912e+0547187.224863321.420998365.863228...69.42693643.1718612560.43304757897.0712309477.5373109477.5425059477.5425052.598093e+102.598093e+102.598093e+10
    20164.711404e+0543370.444699283.204082380.71761778848.3721235.940232e+054.711407e+0543370.468471283.204237380.717825...61.17211544.9247032349.68277752399.0719589477.5373109477.5425059477.5425053.540741e+103.540742e+103.540742e+10
    20174.563168e+0537304.395438269.165286422.53738073575.1420345.678880e+054.563165e+0537304.378239269.165162422.537185...58.13967549.8593882192.53822245378.4469539156.7831259156.7789039156.7789034.448377e+104.448377e+104.448377e+10
    20184.407763e+0532803.994115273.761119420.61800269844.8905545.441196e+054.407761e+0532803.978991273.760993420.617808...59.13237449.6329012081.37677939777.9031049156.7831259156.7789039156.7789035.353175e+105.353175e+105.353175e+10
    20198.022008e+0555529.114850465.757234824.668750121936.3323409.809567e+057.954251e+0555060.092196461.823249817.703245...101.14528797.8349073653.26925168667.32861216904.81495016762.02965316995.8429987.033950e+107.019597e+107.043100e+10
    20207.845582e+0548511.986589382.744131778.416420113139.3793759.473708e+057.779315e+0548102.233601379.511311771.841582...83.11790492.3477433389.70844762502.40027116904.81495016762.02965316995.8429988.707455e+108.678827e+108.725705e+10
    20212.014970e+0512144.50601191.373232201.03119328586.8949042.425208e+052.038239e+0512284.75413192.428435203.352757...20.81053925.012439898.24298116147.9929094479.8336684531.5680284723.5930239.127172e+109.103856e+109.169843e+10
    20221.972441e+0511572.39196782.747506197.69215827497.1783082.365941e+051.995219e+0511706.03315583.703097199.975162...18.84600324.596994864.00245615336.7550894479.8336684531.5680284723.5930239.543730e+109.525701e+109.610702e+10
    20235.876418e+0533306.015492231.970049596.53431079490.4009047.012668e+051.740481e+0698645.946912687.0502171766.818726...125.582329176.4250725937.092552101903.10348413634.58969140383.00562034173.1443281.087628e+111.354546e+111.300625e+11
    20245.774328e+0531411.747254212.484190593.24572575647.9864186.852983e+051.710244e+0693035.492425629.3368871757.078575...115.033210175.4524725650.10481394583.41805413634.58969140383.00562034173.1443281.219987e+111.754379e+111.638325e+11
    20251.172042e+0662055.099995407.2905001166.768295149852.3103901.385523e+062.717327e+06143872.011133944.2850522705.100808...230.039851360.00760311676.824513191573.11397628006.32474464931.42812973232.0457581.495638e+112.398391e+112.366270e+11
    20261.158703e+0659869.090073380.6628421117.797678144657.2270641.364728e+062.686401e+06138803.843587882.5500022591.564594...215.000407344.89766711272.012094178232.36271828006.32474464931.42812973232.0457581.769361e+113.038687e+113.090150e+11
    20271.091469e+0655065.927170338.5916661018.300894132846.3856771.280738e+061.611470e+0681300.609591499.9045741503.443013...170.187738279.6123719212.219072142769.57149426628.14038439314.40287661963.9490072.026970e+113.417583e+113.696333e+11
    20281.087800e+0653748.963838315.521303973.886665129491.2112951.272330e+061.606053e+0679356.214441465.8429561437.868817...158.591785267.4167938979.554847135712.06915226628.14038439314.40287661963.9490072.282008e+113.793160e+114.298146e+11
    20291.809482e+0687653.625830489.3516691554.185968210842.5465592.110021e+062.324661e+06112609.578471628.6754791996.679830...193.320389335.41912811491.516681165699.04731344459.75121457117.93203981314.8495712.713132e+114.343939e+115.089571e+11
    20301.805498e+0685658.543712452.7970281484.231439205677.9738452.098772e+062.319544e+06110046.474502581.7133291906.808476...178.879328320.32178011210.032823156850.12426044459.75121457117.93203981314.8495713.139969e+114.889834e+115.874918e+11
    20315.229548e+0524810.625586131.150695429.90119759573.7328626.079002e+051.521068e+0672164.274809381.4653831250.412169...107.567663192.6229586741.06419194320.57628112957.18735437687.32173849200.3422053.245497e+115.235060e+116.331016e+11
    20325.199791e+0524669.451544130.404439427.45503259234.7545226.044412e+051.512413e+0671753.655483379.2948211243.297244...106.955596191.5269216702.70710693783.88618612957.18735437687.32173849200.3422053.342861e+115.571010e+116.777191e+11
    20334.634747e+0521988.700426116.233802381.00484952797.9014725.387585e+052.092666e+0699282.682045524.8151721720.300996...217.825212390.06273413650.698544190999.77642511610.68853352424.212235100734.8920953.427705e+116.054757e+117.740140e+11
    20344.611772e+0521879.699388115.657615379.11615552536.1749515.360878e+052.082292e+0698790.524019522.2135911711.773225...216.745422388.12913913583.030138190052.96404111610.68853352424.212235100734.8920953.501751e+116.525823e+118.688293e+11
    20354.888809e+0523194.053430122.605382401.89036455692.1202255.682916e+052.690067e+06127625.273162674.6360832211.401626...192.503581344.71892612063.839314168796.53480312365.75409068042.55876689886.8442653.579958e+117.148294e+119.519686e+11
    20364.867340e+0523092.196125122.066957400.12545255447.5467915.657959e+052.678254e+06127064.803365671.6734012201.690196...191.658197343.20508412010.860642168055.26031012365.75409068042.55876689886.8442653.658960e+117.768905e+111.034795e+12
    20378.247589e+0539129.165183206.839493678.00285493954.5206389.587274e+051.040769e+0649377.354606261.012136855.576325...127.393125228.1246987983.488844111704.50943221040.88463026551.63269559996.0657853.794087e+117.939081e+111.084122e+12
    20388.215079e+0538974.926057206.024174675.33030793584.1712319.549483e+051.036666e+0649182.719224259.983279852.203819...126.890968227.2254777952.019572111264.19321721040.88463026551.63269559996.0657853.947659e+118.126903e+111.135060e+12
    20398.022544e+0538061.479491201.195632659.50274291390.8600819.325674e+053.234938e+0515347.56486581.128297265.931888...28.68443351.3656261797.59977525151.91101420625.0940158316.67534913613.5036854.087451e+118.121507e+111.138251e+12
    20407.993858e+0537925.385582200.476230657.14460291064.0798429.292329e+053.223371e+0515292.68759580.838212264.981013...28.58186851.1819621791.17221625061.97699320625.0940158316.67534913613.5036854.207746e+118.096610e+111.139448e+12
    20417.253301e+0534411.948436181.903956596.26621682627.8328308.431480e+053.543131e+0516809.73039888.857406291.267272...77.181043138.2092014836.79154967676.10474118778.5343219173.03766736887.2634074.318456e+118.089075e+111.164872e+12
    20427.229570e+0534299.361950181.308816594.31539682357.4971558.403895e+053.531539e+0516754.73355688.566689290.314325...76.928527137.7570184820.96689067454.68703218778.5343219173.03766736887.2634074.424574e+118.076129e+111.189647e+12
    20431.321148e+0662679.442535331.3278991086.065618150501.9836151.535747e+064.928646e+0523383.051811123.604441405.165196...92.162616165.0369195775.65872480812.67939334424.36131412842.27478144331.1427774.680619e+118.091276e+111.220898e+12
    20441.317164e+0662490.436489330.3288001082.790653150048.1540391.531116e+064.913784e+0523312.541641123.231719403.943445...91.884705164.5392605758.24257680568.99367534424.36131412842.27478144331.1427774.941152e+118.108800e+111.252251e+12
    20451.023898e+0648576.973797256.781267841.707886116640.0117611.190214e+069.645058e+0545759.200650241.886322792.883480...148.783663266.4290409323.993797130460.77641426837.67265425280.91710971991.7960505.135442e+118.257780e+111.311901e+12
    20461.021035e+0648441.133560256.063206839.354141116313.8406231.186886e+069.618087e+0545631.240010241.209913790.666267...148.367605265.6840009297.920260130095.95700826837.67265425280.91710971991.7960505.326798e+118.397897e+111.370475e+12
    20471.605191e+0676155.339399402.5624281319.566550182859.4700111.865928e+061.991206e+0694469.072431499.3700971636.894129...87.656616156.9679615493.27614576861.59884542306.23615252479.98261042648.3368645.675835e+118.805212e+111.398798e+12
    20481.601007e+0675956.801268401.5129421316.126419182382.7525841.861064e+061.986014e+0694222.790119498.0682311632.626721...87.428094156.5587435478.95509076661.21948542306.23615252479.98261042648.3368646.018977e+119.195459e+111.425437e+12
    20491.115273e+0652912.046993279.696766916.823007127049.1202151.296430e+061.252184e+0659407.561374314.0325071029.372745...143.123135256.2926538969.259151125497.34997429545.32687533172.32878769993.4572376.225061e+119.369104e+111.477322e+12
    20501.112549e+0652782.853553279.013840914.584433126738.9089511.293265e+061.249127e+0659262.508070313.2657451026.859363...142.773677255.6668738947.359234125190.92761229545.32687533172.32878769993.4572376.421114e+119.516972e+111.526481e+12
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    41 rows Ɨ 78 columns

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    In [61]:
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    plt.rcParams.update({'font.size': 14})
-plt.rcParams['figure.figsize'] = (12, 8)
-    
-fig, axs = plt.subplots(1,3, figsize=(16, 4), facecolor='w', edgecolor='k')
-fig.subplots_adjust(hspace = .1, wspace=.6)
-axs = axs.ravel()
-
-# PLOT 1
-i = 0
-axs[i].yaxis.grid()
-lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silver_Reference.Mod']/1e6, color='gray', linewidth=4.0, label='Virgin Material Demands')
-lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/1e6, color='gray', linestyle='dashed', linewidth=3.0, label='EoL Material')
-axs[i].set_ylabel('Mass [Tons]')
-axs[i].set_xlim([2020, 2050])
-axs[i].set_title('Silver')
-
-# 2nd axis plot
-ax2=axs[i].twinx()
-lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/USyearly['VirginStock_silver_Reference.Mod'], 
-             color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')
-ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')
-ax2.tick_params(axis='y', labelcolor='r')
-
-# LEGENDS
-# added these three lines
-lns = lns1+lns2+lns3
-labs = [l.get_label() for l in lns]
-#axs[0].legend(lns, labs, loc=0)
-
-# PLOT 2
-i = 1
-axs[i].yaxis.grid()
-lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_aluminum_Reference.Mod']/1e6, color='g', linewidth=4.0, label='Virgin Material Demands')
-lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/1e6, color='g', linestyle='dashed', linewidth=3.0, label='EoL Material')
-axs[i].set_ylabel('Mass [Tons]')
-axs[i].set_xlim([2020, 2050])
-axs[i].set_title('Aluminum')
-
-# 2nd axis plot
-ax2=axs[i].twinx()
-lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/USyearly['VirginStock_aluminum_Reference.Mod'], 
-             color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')
-
-ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')
-ax2.tick_params(axis='y', labelcolor='r')
-
-# LEGENDS
-# added these three lines
-lns = lns1+lns2+lns3
-labs = [l.get_label() for l in lns]
-#axs[1].legend(lns, labs, loc=0)
-
-
-
-# PLOT 3
-i = 2
-axs[i].yaxis.grid()
-lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silicon_Reference.Mod']/1e6, color='k', linewidth=4.0, label='Virgin Material Demands')
-lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/1e6, color='k', linestyle='dashed', linewidth=3.0, label='EoL Material')
-axs[i].set_ylabel('Mass [Tons]')
-axs[i].set_xlim([2020, 2050])
-axs[i].set_title('Silicon')
-
-# 2nd axis plot
-ax2=axs[i].twinx()
-lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/USyearly['VirginStock_silicon_Reference.Mod'], 
-             color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')
-
-#ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')
-ax2.tick_params(axis='y', labelcolor='r')
-
-# LEGENDS
-# added these three lines
-lns = lns1+lns2+lns3
-labs = [l.get_label() for l in lns]
-axs[2].legend(lns, labs, loc='upper center', bbox_to_anchor=(-0.95, -0.25),
-          fancybox=True, shadow=True, ncol=5)
-
-fig.savefig(title_Method+' Fig_1x3_VirginvsWaste_Fraction_Nation.png', bbox_inches = "tight", dpi=600)
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    In [62]:
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    from matplotlib.legend_handler import HandlerBase
-
-plt.rcParams.update({'font.size': 14})
-plt.rcParams['figure.figsize'] = (12, 8)
-
-class AnyObjectHandler(HandlerBase):
-    def create_artists(self, legend, orig_handle,
-                       x0, y0, width, height, fontsize, trans):
-        
-        if orig_handle[0] is 'r':
-            l1 = plt.Line2D([x0,y0+width], [0.4*height,0.4*height], color=orig_handle[0])
-            return [l1]
-
-        else:
-
-            l1 = plt.Line2D([x0,y0+width], [0.7*height,0.7*height], color=orig_handle[0], linestyle = orig_handle[3],)
-            l2 = plt.Line2D([x0,y0+width], [0.4*height,0.4*height], color=orig_handle[1], linestyle = orig_handle[4])
-            l3 = plt.Line2D([x0,y0+width], [0.1*height,0.1*height], color=orig_handle[2], linestyle = orig_handle[5])
-        
-        return [l1, l2, l3]
-
-    
-    
-fig, axs = plt.subplots(1,3, figsize=(16, 4), facecolor='w', edgecolor='k')
-fig.subplots_adjust(hspace = .1, wspace=.6)
-axs = axs.ravel()
-
-# PLOT 1
-i = 0
-axs[i].yaxis.grid()
-lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silver_Reference.Mod']/1e6, color='gray', linewidth=4.0, label='Virgin Material Demands')
-lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/1e6, color='gray', linestyle='dashed', linewidth=3.0, label='EoL Material')
-axs[i].set_ylabel('Mass [Tons]')
-axs[i].set_xlim([2020, 2050])
-axs[i].set_title('Silver')
-
-# 2nd axis plot
-ax2=axs[i].twinx()
-lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/USyearly['VirginStock_silver_Reference.Mod'], 
-             color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')
-ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')
-ax2.tick_params(axis='y', labelcolor='r')
-
-# LEGENDS
-# added these three lines
-lns = lns1+lns2+lns3
-labs = [l.get_label() for l in lns]
-#axs[0].legend(lns, labs, loc=0)
-
-# PLOT 2
-i = 1
-axs[i].yaxis.grid()
-lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_aluminum_Reference.Mod']/1e6, color='g', linewidth=4.0, label='Virgin Material Demands')
-lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/1e6, color='g', linestyle='dashed', linewidth=3.0, label='EoL Material')
-axs[i].set_ylabel('Mass [Tons]')
-axs[i].set_xlim([2020, 2050])
-axs[i].set_title('Aluminum')
-
-# 2nd axis plot
-ax2=axs[i].twinx()
-lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/USyearly['VirginStock_aluminum_Reference.Mod'], 
-             color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')
-
-ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')
-ax2.tick_params(axis='y', labelcolor='r')
-
-# LEGENDS
-# added these three lines
-lns = lns1+lns2+lns3
-labs = [l.get_label() for l in lns]
-#axs[1].legend(lns, labs, loc=0)
-
-
-
-# PLOT 3
-i = 2
-axs[i].yaxis.grid()
-lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silicon_Reference.Mod']/1e6, color='k', linewidth=4.0, label='Virgin Material Demands')
-lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/1e6, color='k', linestyle='dashed', linewidth=3.0, label='EoL Material')
-axs[i].set_ylabel('Mass [Tons]')
-axs[i].set_xlim([2020, 2050])
-axs[i].set_title('Silicon')
-
-# 2nd axis plot
-ax2=axs[i].twinx()
-lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/USyearly['VirginStock_silicon_Reference.Mod'], 
-             color = 'r', linewidth=1.0, label='EOl Material as fraction of Demand')
-
-#ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')
-ax2.tick_params(axis='y', labelcolor='r')
-
-# LEGENDS
-# added these three lines
-lns = lns1+lns2+lns3
-labs = [l.get_label() for l in lns]
-#axs[2].legend(lns, labs, loc='upper center', bbox_to_anchor=(-0.95, -0.25),
-#          fancybox=True, shadow=True, ncol=5) 
-
-#axs[2].legend([("gray","g","k","-","-","-"), ("gray","g","k","--","--","--"),("r","r","r","-","-","-")], ['Material Demands', "EoL Material", 'Fraction'],
-#           handler_map={tuple: AnyObjectHandler()}, loc='upper center', bbox_to_anchor=(-0.95, -0.25),
-#          fancybox=True, shadow=True, ncol=5)
-
-axs[2].legend([("gray","g","k","-","-","-"), ("gray","g","k","--","--","--"),("r","r","r")], [' Virgin material demands', "EOL material", 'EOL material as fraction of demand'],
-           handler_map={tuple: AnyObjectHandler()}, loc='upper center', bbox_to_anchor=(-0.95, -0.25),
-          fancybox=True, shadow=True, ncol=5)
-
-fig.savefig(title_Method+' Fig_1x3_VirginvsWaste_Fraction_Nation.png', bbox_inches = "tight", dpi=600)
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    In [63]:
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    newdf = pd.DataFrame()
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-newdf['Virgin material demands, Silicon, Reference'] = USyearly['VirginStock_silicon_Reference.Mod']/1e6 
-newdf['EOL material, Silicon, Reference'] = USyearly['Waste_EOL_silicon_Reference.Mod']/1e6
-newdf['EOL material as fraction of demand, Silicon, Reference'] = USyearly['Waste_EOL_silicon_Reference.Mod']/USyearly['VirginStock_silicon_Reference.Mod']
-
-newdf['Virgin material demands, Silver, Reference'] = USyearly['VirginStock_silver_Reference.Mod']/1e6 
-newdf['EOL material, Silver, Reference'] = USyearly['Waste_EOL_silver_Reference.Mod']/1e6
-newdf['EOL material as fraction of demand, Silver, Reference'] = USyearly['Waste_EOL_silver_Reference.Mod']/USyearly['VirginStock_silver_Reference.Mod']
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-newdf['Virgin material demands, Aluminum, Reference'] = USyearly['VirginStock_aluminum_Reference.Mod']/1e6 
-newdf['EOL material, Aluminum, Reference'] = USyearly['Waste_EOL_aluminum_Reference.Mod']/1e6
-newdf['EOL material as fraction of demand, Aluminum, Reference'] = USyearly['Waste_EOL_aluminum_Reference.Mod']/USyearly['VirginStock_aluminum_Reference.Mod']
-
-
-newdf['Virgin material demands, Silicon, Grid Decarb.'] = USyearly['VirginStock_silicon_'+SFscenarios[1]]/1e6 
-newdf['EOL material, Silicon, Grid Decarb.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[1]]/1e6
-newdf['EOL material as fraction of demand, Silicon, Grid Decarb.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[1]]/USyearly['VirginStock_silicon_'+SFscenarios[1]]
-
-newdf['Virgin material demands, Silver, Grid Decarb.'] = USyearly['VirginStock_silver_'+SFscenarios[1]]/1e6 
-newdf['EOL material, Silver, Grid Decarb.'] = USyearly['Waste_EOL_silver_'+SFscenarios[1]]/1e6
-newdf['EOL material as fraction of demand, Silver, Grid Decarb.'] = USyearly['Waste_EOL_silver_'+SFscenarios[1]]/USyearly['VirginStock_silver_'+SFscenarios[1]]
-
-newdf['Virgin material demands, Aluminum, Grid Decarb.'] = USyearly['VirginStock_aluminum_'+SFscenarios[1]]/1e6 
-newdf['EOL material, Aluminum, Grid Decarb.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[1]]/1e6
-newdf['EOL material as fraction of demand, Aluminum, Grid Decarb.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[1]]/USyearly['VirginStock_aluminum_'+SFscenarios[1]]
-
-
-newdf['Virgin material demands, Silicon, High Elec.'] = USyearly['VirginStock_silicon_'+SFscenarios[2]]/1e6 
-newdf['EOL material, Silicon, High Elec.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[2]]/1e6
-newdf['EOL material as fraction of demand, Silicon, High Elec.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[2]]/USyearly['VirginStock_silicon_'+SFscenarios[2]]
-
-newdf['Virgin material demands, Silver, High Elec.'] = USyearly['VirginStock_silver_'+SFscenarios[2]]/1e6 
-newdf['EOL material, Silver, High Elec.'] = USyearly['Waste_EOL_silver_'+SFscenarios[2]]/1e6
-newdf['EOL material as fraction of demand, Silver, High Elec.'] = USyearly['Waste_EOL_silver_'+SFscenarios[2]]/USyearly['VirginStock_silver_'+SFscenarios[2]]
-
-newdf['Virgin material demands, Aluminum, High Elec.'] = USyearly['VirginStock_aluminum_'+SFscenarios[2]]/1e6 
-newdf['EOL material, Aluminum, High Elec.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[2]]/1e6
-newdf['EOL material as fraction of demand, Aluminum, High Elec.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[2]]/USyearly['VirginStock_aluminum_'+SFscenarios[2]]
-
-newdf.to_csv(title_Method+' Demand vs EOL Fraction NATION.csv')
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    ['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']
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    - - - - - - - - diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.ipynb b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.ipynb deleted file mode 100644 index f7c6a5ba..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.ipynb +++ /dev/null @@ -1,6092 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ReEDS Scenarios on PV ICE Tool USA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. \n", - "\n", - "Current sections include:\n", - "\n", - "
      \n", - "
    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format
      2. \n", - "
    2. ### Reading scenarios of interest and running PV ICE tool
      3. \n", - "
    3. ###Plotting
      4. \n", - "
    4. ### GeoPlotting.
      5. \n", - "
    \n", - " Notes:\n", - " \n", - "Scenarios of Interest:\n", - "\tthe Ref.Mod, \n", - "o\t95-by-35.Adv, and \n", - "o\t95-by-35+Elec.Adv+DR ones\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import PV_ICE\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os,sys\n", - "import matplotlib.pyplot as plt\n", - "from IPython.display import display\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.rcParams['figure.figsize'] = (12, 8)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your simulation will be stored in C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - } - ], - "source": [ - "import os\n", - "from pathlib import Path\n", - "\n", - "testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP')\n", - "\n", - "print (\"Your simulation will be stored in %s\" % testfolder)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reading REEDS original file to get list of SCENARIOs, PCAs, and STATEs " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "r\"\"\"\n", - "reedsFile = str(Path().resolve().parent.parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx')\n", - "print (\"Input file is stored in %s\" % reedsFile)\n", - "\n", - "rawdf = pd.read_excel(reedsFile,\n", - " sheet_name=\"UPV Capacity (GW)\")\n", - " #index_col=[0,2,3]) #this casts scenario, PCA and State as levels\n", - "#now set year as an index in place\n", - "#rawdf.drop(columns=['State'], inplace=True)\n", - "rawdf.drop(columns=['Tech'], inplace=True)\n", - "rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True)\n", - "\"\"\";" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#scenarios = list(rawdf.index.get_level_values('Scenario').unique())\n", - "#PCAs = list(rawdf.index.get_level_values('PCA').unique())\n", - "#STATEs = list(rawdf.index.get_level_values('State').unique())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create Scenarios in PV_ICE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Rename difficult characters from Scenarios Names" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "scenarios = ['Reference.Mod',\n", - " 'Reference.Adv',\n", - " 'Reference.Adv_DR',\n", - " '95-by-35.Mod',\n", - " '95-by-35.Adv',\n", - " '95-by-35.Adv_DR',\n", - " '95-by-35_Elec.Mod',\n", - " '95-by-35_Elec.Adv',\n", - " '95-by-35_Elec.Adv_DR']" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "#simulationname = scenarios\n", - "#simulationname = [w.replace('+', '_') for w in simulationname]\n", - "#simulationname" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Downselect to Solar Future scenarios of interest\n", - "\n", - "Scenarios of Interest:\n", - "
  • Ref.Mod\n", - "
  • 95-by-35.Adv \n", - "
  • 95-by-35+Elec.Adv+DR " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#SFscenarios = [simulationname[0], simulationname[4], simulationname[8]]\n", - "SFscenarios = ['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']\n", - "SFscenarios" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the 3 Scenarios and assign Baselines\n", - "\n", - "Keeping track of each scenario as its own PV ICE Object." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path = C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - } - ], - "source": [ - "#for ii in range (0, 1): #len(scenarios):\n", - "i = 0\n", - "rr = PV_ICE.Simulation(name='USA', path=testfolder)\n", - "for i in range(0, 3):\n", - " filetitle = SFscenarios[i]+'.csv'\n", - " filetitle = os.path.join(testfolder, 'USA', filetitle) \n", - " rr.createScenario(name=SFscenarios[i], file=filetitle)\n", - " rr.scenario[SFscenarios[i]].addMaterial('glass', file=r'..\\baselines\\ReedsSubset\\baseline_material_glass_Reeds.csv')\n", - " rr.scenario[SFscenarios[i]].addMaterial('silicon', file=r'..\\baselines\\ReedsSubset\\baseline_material_silicon_Reeds.csv')\n", - " rr.scenario[SFscenarios[i]].addMaterial('silver', file=r'..\\baselines\\ReedsSubset\\baseline_material_silver_Reeds.csv')\n", - " rr.scenario[SFscenarios[i]].addMaterial('copper', file=r'..\\baselines\\ReedsSubset\\baseline_material_copper_Reeds.csv')\n", - " rr.scenario[SFscenarios[i]].addMaterial('aluminum', file=r'..\\baselines\\ReedsSubset\\baseline_material_aluminium_Reeds.csv')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2 FINISH: Set characteristics of Recycling to SF values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate Mass Flow" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on Scenario: Reference.Mod\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: 95-by-35.Adv\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n", - "Working on Scenario: 95-by-35_Elec.Adv_DR\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n" - ] - } - ], - "source": [ - "IRENA= False\n", - "PERFECTMFG = True\n", - "\n", - "mats = ['glass', 'silicon','silver','copper','aluminum']\n", - "\n", - "ELorRL = 'EL'\n", - "if IRENA:\n", - " if ELorRL == 'RL':\n", - " weibullInputParams = {'alpha': 5.3759, 'beta':30} # Regular-loss scenario IRENA\n", - " if ELorRL == 'EL':\n", - " weibullInputParams = {'alpha': 2.49, 'beta':30} # Regular-loss scenario IRENA\n", - " \n", - " if PERFECTMFG:\n", - " for jj in range (0, len(rr.scenario.keys())):\n", - " rr.scenario[list(rr.scenario.keys())[jj]].data['mod_lifetime'] = 40\n", - " rr.scenario[list(rr.scenario.keys())[jj]].data['mod_MFG_eff'] = 100.0\n", - "\n", - " for kk in range(0, len(mats)):\n", - " mat = mats[kk]\n", - " rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 \n", - " rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_scrap_Recycled'] = 0.0 \n", - " \n", - " \n", - " rr.calculateMassFlow(weibullInputParams=weibullInputParams)\n", - " title_Method = 'Irena_'+ELorRL\n", - "else:\n", - " rr.calculateMassFlow()\n", - " title_Method = 'PVICE'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scenarios: dict_keys(['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR'])\n", - "Module Keys: Index(['year', 'new_Installed_Capacity_[MW]', 'mod_eff', 'mod_reliability_t50',\n", - " 'mod_reliability_t90', 'mod_degradation', 'mod_lifetime', 'mod_MFG_eff',\n", - " 'mod_EOL_collection_eff', 'mod_EOL_collected_recycled',\n", - " 'mod_Repowering', 'mod_Repairing', 'Area',\n", - " 'Cumulative_Area_disposedby_Failure',\n", - " 'Cumulative_Area_disposedby_ProjectLifetime',\n", - " 'Cumulative_Area_disposed', 'Cumulative_Active_Area',\n", - " 'Installed_Capacity_[W]', 'WeibullParams', 'EOL_on_Year_0',\n", - " 'EOL_on_Year_1', 'EOL_on_Year_2', 'EOL_on_Year_3', 'EOL_on_Year_4',\n", - " 'EOL_on_Year_5', 'EOL_on_Year_6', 'EOL_on_Year_7', 'EOL_on_Year_8',\n", - " 'EOL_on_Year_9', 'EOL_on_Year_10', 'EOL_on_Year_11', 'EOL_on_Year_12',\n", - " 'EOL_on_Year_13', 'EOL_on_Year_14', 'EOL_on_Year_15', 'EOL_on_Year_16',\n", - " 'EOL_on_Year_17', 'EOL_on_Year_18', 'EOL_on_Year_19', 'EOL_on_Year_20',\n", - " 'EOL_on_Year_21', 'EOL_on_Year_22', 'EOL_on_Year_23', 'EOL_on_Year_24',\n", - " 'EOL_on_Year_25', 'EOL_on_Year_26', 'EOL_on_Year_27', 'EOL_on_Year_28',\n", - " 'EOL_on_Year_29', 'EOL_on_Year_30', 'EOL_on_Year_31', 'EOL_on_Year_32',\n", - " 'EOL_on_Year_33', 'EOL_on_Year_34', 'EOL_on_Year_35', 'EOL_on_Year_36',\n", - " 'EOL_on_Year_37', 'EOL_on_Year_38', 'EOL_on_Year_39', 'EOL_on_Year_40',\n", - " 'EoL_Collected', 'EoL_NotCollected', 'EoL_Recycled',\n", - " 'EoL_NotRecycled_Landfilled'],\n", - " dtype='object')\n", - "Material Keys: Index(['year', 'mat_virgin_eff', 'mat_massperm2', 'mat_MFG_eff',\n", - " 'mat_MFG_scrap_Recycled', 'mat_MFG_scrap_Recycling_eff',\n", - " 'mat_MFG_scrap_Recycled_into_HQ',\n", - " 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG',\n", - " 'mat_EOL_collected_Recycled', 'mat_EOL_Recycling_eff',\n", - " 'mat_EOL_Recycled_into_HQ', 'mat_EOL_RecycledHQ_Reused4MFG',\n", - " 'mat_modules_Collected', 'mat_modules_NotCollected',\n", - " 'mat_modules_Recycled', 'mat_modules_NotRecycled',\n", - " 'mat_EOL_sento_Recycling', 'mat_EOL_NotRecycled_Landfilled',\n", - " 'mat_EOL_Recycled', 'mat_EOL_Recycled_Losses_Landfilled',\n", - " 'mat_EOL_Recycled_2_HQ', 'mat_EOL_Recycled_2_OQ',\n", - " 'mat_EoL_Recycled_HQ_into_MFG', 'mat_EOL_Recycled_HQ_into_OU',\n", - " 'mat_UsedSuccessfullyinModuleManufacturing',\n", - " 'mat_EnteringModuleManufacturing', 'mat_LostinModuleManufacturing',\n", - " 'mat_Manufacturing_Input', 'mat_MFG_Scrap',\n", - " 'mat_MFG_Scrap_Sentto_Recycling', 'mat_MFG_Scrap_Landfilled',\n", - " 'mat_MFG_Scrap_Recycled_Successfully',\n", - " 'mat_MFG_Scrap_Recycled_Losses_Landfilled', 'mat_MFG_Recycled_into_HQ',\n", - " 'mat_MFG_Recycled_into_OQ', 'mat_MFG_Recycled_HQ_into_MFG',\n", - " 'mat_MFG_Recycled_HQ_into_OU', 'mat_Virgin_Stock',\n", - " 'mat_Virgin_Stock_Raw', 'mat_Total_EOL_Landfilled',\n", - " 'mat_Total_MFG_Landfilled', 'mat_Total_Landfilled',\n", - " 'mat_Total_Recycled_OU'],\n", - " dtype='object')\n" - ] - } - ], - "source": [ - "print(\"Scenarios:\", rr.scenario.keys())\n", - "print(\"Module Keys:\", rr.scenario[SFscenarios[0]].data.keys())\n", - "print(\"Material Keys: \", rr.scenario[SFscenarios[0]].material['glass'].materialdata.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "r1.plotScenariosComparison(keyword='Cumulative_Area_disposedby_Failure')\n", - "r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_Total_Landfilled')\n", - "\"\"\"\n", - "pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aggregating PCAs Material Landfilled to obtain US totals by Year" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly=pd.DataFrame()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "keywd = 'mat_Virgin_Stock'\n", - "\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['VirginStock_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('VirginStock_') and col.endswith(obj))]\n", - " USyearly['VirginStock_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "keywd = 'mat_Total_Landfilled'\n", - "\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['Waste_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('Waste_') and col.endswith(obj)) ]\n", - " USyearly['Waste_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "keywd = 'mat_Total_EOL_Landfilled'\n", - "\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['Waste_EOL_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('Waste_EOL_') and col.endswith(obj)) ]\n", - " USyearly['Waste_EOL_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "keywd = 'mat_Total_MFG_Landfilled'\n", - "\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['Waste_MFG_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('Waste_MFG_') and col.endswith(obj)) ]\n", - " USyearly['Waste_MFG_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting to grams to METRIC Tons. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly = USyearly/1000000 # This is the ratio for Metric tonnes\n", - "#907185 -- this is for US tons\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding NEW Installed Capacity to US" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='new_Installed_Capacity_[MW]'\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " USyearly[keyword+obj] = rr.scenario[obj].data[keyword]\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reindexing and creating c umulative results" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "UScum = USyearly.copy()\n", - "UScum = UScum.cumsum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding Installed Capacity to US (This is already 'Cumulative') so not including it in UScum" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='Installed_Capacity_[W]'\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " USyearly[\"Capacity_\"+obj] = rr.scenario[obj].data[keyword]\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Set YEAR Index" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly.index = rr.scenario[obj].data['year']\n", - "UScum.index = rr.scenario[obj].data['year']" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "VirginStock_glass_Reference.Mod 1.442186e+05\n", - "VirginStock_silicon_Reference.Mod 1.445687e+04\n", - "VirginStock_silver_Reference.Mod 2.199155e+02\n", - "VirginStock_copper_Reference.Mod 1.022991e+02\n", - "VirginStock_aluminum_Reference.Mod 3.402000e+04\n", - "VirginStock_Module_Reference.Mod 1.930177e+05\n", - "VirginStock_glass_95-by-35.Adv 1.442186e+05\n", - "VirginStock_silicon_95-by-35.Adv 1.445687e+04\n", - "VirginStock_silver_95-by-35.Adv 2.199155e+02\n", - "VirginStock_copper_95-by-35.Adv 1.022991e+02\n", - "VirginStock_aluminum_95-by-35.Adv 3.402000e+04\n", - "VirginStock_Module_95-by-35.Adv 1.930177e+05\n", - "VirginStock_glass_95-by-35_Elec.Adv_DR 1.442186e+05\n", - "VirginStock_silicon_95-by-35_Elec.Adv_DR 1.445687e+04\n", - "VirginStock_silver_95-by-35_Elec.Adv_DR 2.199155e+02\n", - "VirginStock_copper_95-by-35_Elec.Adv_DR 1.022991e+02\n", - "VirginStock_aluminum_95-by-35_Elec.Adv_DR 3.402000e+04\n", - "VirginStock_Module_95-by-35_Elec.Adv_DR 1.930177e+05\n", - "Waste_glass_Reference.Mod 9.951086e+03\n", - "Waste_silicon_Reference.Mod 7.420918e+03\n", - "Waste_silver_Reference.Mod 4.750175e+01\n", - "Waste_copper_Reference.Mod 1.207129e+01\n", - "Waste_aluminum_Reference.Mod 1.013796e+03\n", - "Waste_Module_Reference.Mod 1.844537e+04\n", - "Waste_glass_95-by-35.Adv 9.951086e+03\n", - "Waste_silicon_95-by-35.Adv 7.420918e+03\n", - "Waste_silver_95-by-35.Adv 4.750175e+01\n", - "Waste_copper_95-by-35.Adv 1.207129e+01\n", - "Waste_aluminum_95-by-35.Adv 1.013796e+03\n", - "Waste_Module_95-by-35.Adv 1.844537e+04\n", - " ... \n", - "Waste_EOL_glass_95-by-35_Elec.Adv_DR 2.824248e-05\n", - "Waste_EOL_silicon_95-by-35_Elec.Adv_DR 1.479976e-06\n", - "Waste_EOL_silver_95-by-35_Elec.Adv_DR 4.351959e-08\n", - "Waste_EOL_copper_95-by-35_Elec.Adv_DR 1.897894e-08\n", - "Waste_EOL_aluminum_95-by-35_Elec.Adv_DR 7.015257e-06\n", - "Waste_EOL_Module_95-by-35_Elec.Adv_DR 3.680021e-05\n", - "Waste_MFG_glass_Reference.Mod 9.951086e+03\n", - "Waste_MFG_silicon_Reference.Mod 7.420918e+03\n", - "Waste_MFG_silver_Reference.Mod 4.750175e+01\n", - "Waste_MFG_copper_Reference.Mod 1.207129e+01\n", - "Waste_MFG_aluminum_Reference.Mod 1.013796e+03\n", - "Waste_MFG_Module_Reference.Mod 1.844537e+04\n", - "Waste_MFG_glass_95-by-35.Adv 9.951086e+03\n", - "Waste_MFG_silicon_95-by-35.Adv 7.420918e+03\n", - "Waste_MFG_silver_95-by-35.Adv 4.750175e+01\n", - "Waste_MFG_copper_95-by-35.Adv 1.207129e+01\n", - "Waste_MFG_aluminum_95-by-35.Adv 1.013796e+03\n", - "Waste_MFG_Module_95-by-35.Adv 1.844537e+04\n", - "Waste_MFG_glass_95-by-35_Elec.Adv_DR 9.951086e+03\n", - "Waste_MFG_silicon_95-by-35_Elec.Adv_DR 7.420918e+03\n", - "Waste_MFG_silver_95-by-35_Elec.Adv_DR 4.750175e+01\n", - "Waste_MFG_copper_95-by-35_Elec.Adv_DR 1.207129e+01\n", - "Waste_MFG_aluminum_95-by-35_Elec.Adv_DR 1.013796e+03\n", - "Waste_MFG_Module_95-by-35_Elec.Adv_DR 1.844537e+04\n", - "new_Installed_Capacity_[MW]Reference.Mod 2.534300e+03\n", - "new_Installed_Capacity_[MW]95-by-35.Adv 2.534300e+03\n", - "new_Installed_Capacity_[MW]95-by-35_Elec.Adv_DR 2.534300e+03\n", - "Capacity_Reference.Mod 3.751735e+09\n", - "Capacity_95-by-35.Adv 3.751735e+09\n", - "Capacity_95-by-35_Elec.Adv_DR 3.751735e+09\n", - "Name: 2011, Length: 78, dtype: float64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "USyearly.head().iloc[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    2012142823.27953014473.896289172.505147100.30625033296.094628190866.081844142823.27953014473.896289172.505147100.306250...37.26111211.836138992.22362018471.1319422534.3001092534.3001092534.3001096.282106e+096.282106e+096.282106e+09
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    2014279365.35148926818.242077214.149815191.57122951596.845772358186.160382279365.27455026818.234691214.149756191.571176...46.25634722.6053991537.58558133977.3937695123.0337325123.0323215123.0323211.651318e+101.651318e+101.651318e+10
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    " - ], - "text/plain": [ - " VirginStock_glass_Reference.Mod VirginStock_silicon_Reference.Mod \\\n", - "year \n", - "2010 70184.285787 7082.020411 \n", - "2011 214402.927500 21538.894420 \n", - "2012 357226.207030 36012.790709 \n", - "2013 637865.004364 63450.270380 \n", - "2014 917230.355853 90268.512456 \n", - "\n", - " VirginStock_silver_Reference.Mod VirginStock_copper_Reference.Mod \\\n", - "year \n", - "2010 128.426774 49.784053 \n", - "2011 348.342276 152.083143 \n", - "2012 520.847423 252.389394 \n", - "2013 755.794480 447.552583 \n", - "2014 969.944295 639.123813 \n", - "\n", - " VirginStock_aluminum_Reference.Mod VirginStock_Module_Reference.Mod \\\n", - "year \n", - "2010 16728.966195 9.417348e+04 \n", - "2011 50748.968785 2.871912e+05 \n", - "2012 84045.063413 4.780573e+05 \n", - "2013 137491.122324 8.400097e+05 \n", - "2014 189087.968095 1.198196e+06 \n", - "\n", - " VirginStock_glass_95-by-35.Adv VirginStock_silicon_95-by-35.Adv \\\n", - "year \n", - "2010 70184.285787 7082.020411 \n", - "2011 214402.927500 21538.894420 \n", - "2012 357226.207030 36012.790709 \n", - "2013 637864.927074 63450.262823 \n", - "2014 917230.201624 90268.497514 \n", - "\n", - " VirginStock_silver_95-by-35.Adv VirginStock_copper_95-by-35.Adv ... \\\n", - "year ... \n", - "2010 128.426774 49.784053 ... \n", - "2011 348.342276 152.083143 ... \n", - "2012 520.847423 252.389394 ... \n", - "2013 755.794415 447.552530 ... \n", - "2014 969.944172 639.123706 ... \n", - "\n", - " Waste_MFG_Module_95-by-35.Adv Waste_MFG_glass_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 9032.812399 4842.715719 \n", - "2011 27478.186231 14793.801998 \n", - "2012 45949.318172 24648.608285 \n", - "2013 80994.346827 44012.679968 \n", - "2014 114971.740596 62741.327974 \n", - "\n", - " Waste_MFG_silicon_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 3657.958785 \n", - "2011 11078.877221 \n", - "2012 18653.882006 \n", - "2013 32668.369060 \n", - "2014 46310.667497 \n", - "\n", - " Waste_MFG_silver_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 27.740183 \n", - "2011 75.241932 \n", - "2012 112.503043 \n", - "2013 163.251594 \n", - "2014 209.507941 \n", - "\n", - " Waste_MFG_copper_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 5.874518 \n", - "2011 17.945811 \n", - "2012 29.781948 \n", - "2013 52.811199 \n", - "2014 75.416597 \n", - "\n", - " Waste_MFG_aluminum_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 498.523193 \n", - "2011 1512.319270 \n", - "2012 2504.542890 \n", - "2013 4097.235007 \n", - "2014 5634.820587 \n", - "\n", - " Waste_MFG_Module_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 9032.812399 \n", - "2011 27478.186231 \n", - "2012 45949.318172 \n", - "2013 80994.346827 \n", - "2014 114971.740596 \n", - "\n", - " new_Installed_Capacity_[MW]Reference.Mod \\\n", - "year \n", - "2010 1200.651350 \n", - "2011 3734.951459 \n", - "2012 6269.251568 \n", - "2013 11392.285299 \n", - "2014 16515.319031 \n", - "\n", - " new_Installed_Capacity_[MW]95-by-35.Adv \\\n", - "year \n", - "2010 1200.651350 \n", - "2011 3734.951459 \n", - "2012 6269.251568 \n", - "2013 11392.283888 \n", - "2014 16515.316209 \n", - "\n", - " new_Installed_Capacity_[MW]95-by-35_Elec.Adv_DR \n", - "year \n", - "2010 1200.651350 \n", - "2011 3734.951459 \n", - "2012 6269.251568 \n", - "2013 11392.283888 \n", - "2014 16515.316209 \n", - "\n", - "[5 rows x 75 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "UScum.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3 sig figures save Yearly and cumulative overview Nation" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Data for Jarett\n", - "USyearly.to_csv(title_Method+' US_Yearly NATION_tonnes.csv')\n", - "UScum.to_csv(title_Method+' US_Cumulative NATION_tonnes.csv')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly3sig = USyearly.copy()\n", - "UScum3sig = UScum.copy()\n", - "N = 2\n", - "\n", - "UScum3sig = UScum3sig.drop(UScum3sig.index[0])\n", - "USyearly3sig = USyearly3sig.drop(USyearly3sig.index[0])\n", - "\n", - "if IRENA:\n", - " UScum3sig = UScum3sig.loc[:, ~UScum3sig.columns.str.startswith('Waste_MFG_')]\n", - " USyearly3sig = USyearly3sig.loc[:, ~USyearly3sig.columns.str.startswith('Waste_MFG_')]\n", - "\n", - "USyearly3sig = USyearly3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "USyearly3sig = USyearly3sig.applymap(lambda x: int(x))\n", - "\n", - "UScum3sig = UScum3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "UScum3sig = UScum3sig.applymap(lambda x: int(x))\n", - "\n", - "USyearly3sig.to_csv(title_Method+' US_Yearly NATION.csv')\n", - "UScum3sig.to_csv(title_Method+' US_Cumulative NATION.csv')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sanity check: mat_Total_Landfilled = mat_Total_EOL_Landfilled + mat_Total_MFG_Landfilled\n" - ] - }, - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(\"Sanity check: mat_Total_Landfilled = mat_Total_EOL_Landfilled + mat_Total_MFG_Landfilled\")\n", - "A = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled'].iloc[5]\n", - "B = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_EOL_Landfilled'].iloc[5]\n", - "C = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_MFG_Landfilled'].iloc[5]\n", - "A - B - C" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PLOT" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Yearly Virgin Material Needs by Scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:118: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "keyw='VirginStock_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - "\n", - "foo = pd.DataFrame() \n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "\n", - "# SCENARIO 2 ***************\n", - "kk = 1\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='S2 Grid Decarbonization: module mass')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='S2 Grid Decarbonization: glass mass only')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 3 ***************\n", - "kk = 2\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='S3 High Electrification: module mass')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='S3 High Electrification: glass mass only')\n", - "\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "a0.legend()\n", - "a0.set_title('Yearly Virgin Material Needs by Scenario')\n", - "a0.set_ylabel('Mass [Million Tonnes]')\n", - "\n", - "a0.set_xlabel('Years')\n", - "\n", - "\n", - "\n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 3):\n", - " obj = SFscenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(3)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]')\n", - "a1.set_xlabel('Scenario')\n", - "a1.set_xticks(ind, ('S1', 'S2', 'S3'))\n", - "#plt.yticks(np.arange(0, 81, 10))\n", - "a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "36 1.021035e+12\n", - "37 1.605191e+12\n", - "38 1.601007e+12\n", - "39 1.115273e+12\n", - "40 1.112549e+12\n", - "Name: mat_Virgin_Stock, dtype: float64" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rr.scenario['Reference.Mod'].material['glass'].materialdata['mat_Virgin_Stock'].tail(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Save Data for Jarett Zuboy\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "keyw='VirginStock_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - "\n", - " \n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module ')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass ')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 2 ***************\n", - "kk = 1\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module ')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 3 ***************\n", - "kk = 2\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec: glass')\n", - "\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "a0.legend(loc='upper left')\n", - "a0.set_title('Yearly Virgin Material Needs by Scenario')\n", - "a0.set_ylabel('Mass [Million Tonnes]')\n", - "\n", - "a0.set_xlabel('Years')\n", - "#a0.tick_params(axis='y', which='minor', length=3)\n", - "#a0.set_yticks(minorbool=True) \n", - "a0.minorticks_on()\n", - "a0.tick_params(axis='y', which='minor', bottom=False)\n", - " \n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 3):\n", - " obj = SFscenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(3)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]')\n", - "#a1.set_xlabel('Scenario')\n", - "a1.tick_params(axis='y', which='minor', bottom='off')\n", - "#a1.minorticks_on()\n", - "\n", - "plt.sca(a1)\n", - "plt.xticks(range(3), ['Ref.', 'Grid\\nDecarb.', 'High\\nElec.'], color='black', rotation=0)\n", - "plt.tick_params(axis='y', which='minor', bottom=False)\n", - "#plt.yticks(minor=True)\n", - "a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass'))\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "\n", - "keyw='Waste_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - "\n", - " \n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 2 ***************\n", - "kk = 1\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 3 ***************\n", - "kk = 2\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec.: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec.: glass')\n", - "\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "a0.legend()\n", - "a0.set_title('Yearly Manufacturing Scrap and EoL Material by Scenario')\n", - "a0.set_ylabel('Mass [Million Tonnes]')\n", - "\n", - "a0.set_xlabel('Years')\n", - "a0.minorticks_on()\n", - "a0.tick_params(axis='y', which='minor', bottom=False)\n", - "\n", - "\n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 3):\n", - " obj = SFscenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(3)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Cumulative Manufacturing Scrap and EoL Material \\n by 2050 [Million Tonnes]')\n", - "\n", - "plt.sca(a1)\n", - "plt.xticks(range(3), ['Ref.', 'Grid\\nDecarb.', 'High\\nElec.'], color='black', rotation=0)\n", - "plt.tick_params(axis='y', which='minor', bottom=False)\n", - "#plt.yticks(minor=True)\n", - "a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass'))\n", - "\n", - "\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly MFG and EOL Material by Scenario and Cumulatives_Nation.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "keyw='Waste_EOL_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - "\n", - " \n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 2 ***************\n", - "kk = 1\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 3 ***************\n", - "kk = 2\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec.: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec.: glass ')\n", - "\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "a0.legend()\n", - "a0.set_title('Yearly End of Life Material by Scenario')\n", - "a0.set_ylabel('Mass [Million Tonnes]')\n", - "\n", - "a0.set_xlabel('Years')\n", - "a0.minorticks_on()\n", - "a0.tick_params(axis='y', which='minor', bottom=False)\n", - "\n", - "\n", - "\n", - "\n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 3):\n", - " obj = SFscenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(3)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Cumulative End of Life Material by 2050 [Million Tonnes]')\n", - "\n", - "plt.sca(a1)\n", - "plt.xticks(range(3), ['Ref.', 'Grid\\nDecarb.', 'High\\nElec.'], color='black', rotation=0)\n", - "plt.tick_params(axis='y', which='minor', bottom=False)\n", - "#plt.yticks(minor=True)\n", - "a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass'))\n", - "\n", - "\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly EoL Waste by Scenario and Cumulatives_Nation.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "keyw='Waste_MFG_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - "\n", - " \n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 2 ***************\n", - "kk = 1\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "# SCENARIO 3 ***************\n", - "kk = 2\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec.: module')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec.: glass')\n", - "\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "a0.legend(loc='upper left')\n", - "a0.set_title('Yearly Manufacturing Scrap by Scenario')\n", - "a0.set_ylabel('Mass [Million Tonnes]')\n", - "\n", - "a0.set_xlabel('Years')\n", - "a0.minorticks_on()\n", - "a0.tick_params(axis='y', which='minor', bottom=False)\n", - "\n", - "\n", - "\n", - "\n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 3):\n", - " obj = SFscenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(3)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Cumulative Manufacturing Scrap by 2050 [Million Tonnes]')\n", - "\n", - "plt.sca(a1)\n", - "plt.xticks(range(3), ['Ref.', 'Grid\\nDecarb.', 'High\\nElec.'], color='black', rotation=0)\n", - "plt.tick_params(axis='y', which='minor', bottom=False)\n", - "#plt.yticks(minor=True)\n", - "a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass'))\n", - "\n", - "\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly MFG Waste by Scenario and Cumulatives_Nation.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "fig, axs = plt.subplots(figsize=(8, 8))\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6, 'k', label='Cumulative New Yearly Installs S3-S2')\n", - "\n", - "#axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'c', label='Cumulative New Yearly Installs')\n", - "\n", - "axs.legend()\n", - "axs.set_xlim([2020,2030])\n", - "axs.set_ylabel('Power [TW]')\n", - "fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# WASTE COMPARISON SIZE" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Bo0ePJnrM9u3bCQ8PJygoCFtbW8DQjZbcNTWsrKwYN24c48aN448//mDevHl8+umnlCtXLtXHKQmRGD+//yUQ69bBSxoLU40kEVnEp59+iru7O0uXLo03pS8mJoYdO3bQvHlz47fr3377zTi47MqVK5w/f56yZcumOI58+fJhYWHBb7/9Zix7+PAhISEhFC9ePNHjIiIisLS0jFO2YsWKOO+T0+phZmZG27ZtCQgIwMrKCnt7+zh/0GMHDN6/fx8XF5dXurbEuLm5Ubly5XgJXLly5ShcuDCXL19Ocp0OpRS1a9cmMDCQixcvGscf1KtXj8WLF3Pjxo2XjodISuPGjQkMDOTtt99O9EFzERERWFhYxEkQXrz/yVW0aFGaNm3KmDFjOHToUJIDUFOLtbU1bm5unDlzhkmTJhnLn2+5iBUREYGZmRnm5v/7MxjbYvK63nrrLaZOncrs2bM5d+6cJBEiXfj5Qe/e0LKlIYF44U9pmpIkIotwc3NjyJAh9OjRg8OHD/PBBx9gZ2fH77//zrx58yhRooQxiahevTqjR4/GxsaGmJgYY/dBajAzM+ODDz5g+vTpFC9enJw5czJt2rSXPiXVxcWF/v37M2HCBJydndm6dSu7d++Os0/27NkpWbIkgYGBvPPOO1hZWVGpUqVE6/T09GTWrFlMnz4dDw+POE385cqVo0+fPnh5eTFs2DCcnJx4/PgxZ8+e5cKFCyxcuDBZ1z1y5Eg8PT3j3Ytp06bRqVMnHjx4QIsWLciePTuXLl1i48aNrF27FhsbG8CQMAwdOpRy5coZW0Hq1q3Ld999h42NjTHhex1Dhgxh+fLlNGrUiIEDB1K4cGH++ecf9u/fT506dfD29sbFxYUZM2bw6aef4ubmxpEjR1i+fPlrnzNWjx49aNeuHUWKFElxspaY4OBgFi9ezIcffkixYsW4du0a8+fPjzN2p3z58gQHB9O8eXPs7OwoV66cMZHs1q0bPXr04OzZs0ydOjXZ6414eHhQrVo1qlatirW1tXHW0ouDUYVIC88nEOvXp28CAZJEZCnTpk3j/fffZ9asWXTo0IGIiAhKlCiBu7s7n3/+v+eUrVy5ko8//piOHTtSpEgRpkyZEmeUekrNmjWLXr160a9fP3LlysWoUaM4cuRIvLUpnte7d28uXbrEzJkzefz4MS4uLqxcuTLeaPp58+bx+eef06RJE548ecJff/2VaJ21a9emaNGiXLlyBS8vr3jbZ8+eTdmyZVmwYAFffvkl9vb2VKxYkR49eiT7mtu2bUv58uX5/fe4j4Dx9PTE3t6eiRMnsnjxYrJly0apUqVo1apVnKQmtqXh+Q+e2DJnZ2csLCySHVOsPHnycPToUUaNGsXgwYMJCwujYMGC1KlTx5iEtWzZksmTJ/P999+zYMECatWqxZYtW1LcOtWqVSvMzc3p0qVLgt1GqaFMmTIopRg5ciS3bt0ib968tGrViokTJxr3+eabb+jfvz+urq6Eh4ezd+9eGjRowJIlSxg7diwbNmygcuXKrFmzJl4y+DLvv/8+AQEBfPPNN8TExFCxYkXWrVuXJst3C/G8BQtM1wIRS70JI4mdnJz086PQX/Tbb7+laHqiECJhW7dupVWrVly4cME4k0fI3xyRcgsXQs+e0KKFoQXixTEQSqlftNZpnslKS4QQItVdv36dP/74gy+++IKWLVtKAiFEKnpZApGeZJ0IIUSq8/PzM64n8f3335s6HCGyjEWLDAlE8+amTyBAkgghRBrw9fUlKiqKY8eOGaeyCiFSZtEi+PhjQwKxYYPpEwiQJEIIIYTI8BYvNrRANGuWcRIIkCRCCCGEyNAWLza0QDRtChs3ZpwEAiSJEEIIITKsJUsybgIBkkQIIYQQGdKSJdCjB7i4ZMwEAiSJEEIIITIcf/+Mn0CAJBFCCCFEhrJ0KXTvDk2aGBKIlzw1wKQkichitNaULFkSpRQXL15M9vHHjh3D19c3Xrmvr2+qPOXzVfj7+6OU4uHDhymuq2vXriil4r0+/vhj9u3bh1IqyeW4E9KgQQPatm2b5D4PHz5EKYW/v3+S+ymlmDVrVrLO/zIXLlzA19eXsLCwZB97+fJllFJs2bLFWFaiRIk4y6YLIdLO0qXQrZshgQgKytgJBEgSkeWEhIRw+fJlAFavXp3s448dO8bYsWPjlX/88cf8+OOPKQ3vlbi6uhISEmJ8OFVKlS9fnpCQkDivkSNH8t577xESEkLp0qVT5TwZxYULFxg7duxrJRFCCNOJTSAaN84cCQTIstdZzqpVq7C1teWdd95h1apV+Pj4pEq9RYoUMT5GPK3lzZuXvHnzplp9tra28R7kFSuxciGESE8//PC/BGLTpsyRQIC0RGQp0dHRrFmzBnd3d7p37865c+c4depUvP0OHDhAw4YNsbOzw8HBgQYNGvDrr7/i7+/PwIEDAYzN/g0aNADidmc8evQIW1tb5syZE69uJycnOnXqZHwfGhqKl5cXjo6O2NjY0KxZM86fP5/kdbzYnRHbxB4YGEjv3r1xcHCgSJEijBkzhpiYmNe6V0CC3RkxMTF8/fXXlClTBktLS8qWLcvSpUtfWte6desoW7Ys1tbW1KtXL97TPF9VbFfJypUrKVOmDPb29rRo0YKrV6/G2W/SpEmUKVMGKysr8ufPT/Pmzbl58yb79u3Dzc0NwNitVaJECQBu3LhB9+7dKVWqFNbW1pQtWxYfHx+ePn2arBjPnj1L8+bNcXR0xNbWlgoVKjB79uzXul4hBCxbBl27QqNGmacFIpYkEVnInj17+Oeff/Dy8qJt27ZYWFiwatWqOPvs27ePxo0bY2FhwdKlSwkICKBu3bpcu3YNV1dXPvvsMwBjs39CiYKtrS2tWrUiICAgTvmlS5f45ZdfjI9Svnv3LnXq1OH8+fPMmzePwMBAHj16RJMmTYiIiEj29Q0bNgw7OzvWrl1Lx44dGTduHGvXrn2lY6OiouK8EjNw4EDGjx9Pr169CA4OxsPDg+7du8cZI/CiEydO4OnpSeXKlVm/fj3u7u60b98+2dcX66effmLWrFlMmzYNPz8/Tpw4Qa9evYzbf/jhByZOnMiQIUP48ccfmTt3LmXKlOHRo0e89957TJ06FYD169cTEhLChg0bALh9+zaOjo58++23bN++naFDh7JkyRJj4viq3N3dyZYtG8uXL2fTpk0MHDiQ//7777WvV4g32bJl0KWLIYHYtAlSqRc3/Wits/yrWrVqOinnzp1Lcntm0a1bN50zZ0795MkTrbXWLVu21CVKlNAxMTHGfWrWrKmrVasWp+x533//vTb8WsQ1ZswYnTt3buP79evXazMzM33t2jVj2cSJE3WuXLmM5/fx8dGOjo76zp07xn3u3r2r7e3t9axZsxK9jiVLlmhA//fff1prrf/66y8N6E6dOsXZr3LlytrT0zPRerTWukuXLhqI9/rjjz/03r17NaBPnz6ttdb6jz/+0Eop7e/vH6eOTp06aScnJ+P7+vXr6zZt2hjft2vXTleoUCHOPR0/frwG9JIlS5KMD9Dff/99nLrt7e313bt3jWXTp0/XgA4PD9daa92/f3/dunXrROvcvHmzBvRff/2V5LkjIyP1ihUrtKWlpfFnFnuvN2/ebNyvePHi+rPPPtNaa/3vv/9qQJ86dSrJukXSssrfHJEyy5ZprZTWjRpp/ehR6tYNHNfp8PkqYyIS8en2Tzl586RJzl2lQBVmNJ+RrGOePHnChg0b8PDwIHv27AB4e3vTqVMnjh49Sq1atXj06BE//fQTM2fORCmVohhbtGiBnZ0da9asYdCgQQAEBATEOf+uXbtwcXHB3t7e+O0/R44cVKtWjePHjyf7nE2bNo3zvmLFioSGhr70uAoVKvDDDz/EKStatGi8LoLdu3djZmaGh4dHnNaKxo0bs2rVKqKjo8mWLVu8+o8dO4aXl1ece9q6devXHo9SvXp1cuXKZXxfsWJFAK5du0aZMmWoUqUKixYtYsyYMbi6ulKtWrUE43qR1pqZM2fi5+fHX3/9xePHj43bQkNDX+lx3Y6OjhQtWpQ+ffrwySef0LBhQ/Lly/caVynEm235ckMLRMOGsHlzJmyBeEa6M7KIbdu2ERYWRsuWLQkLCyMsLIwGDRpgaWlp7NK4d+8eWmsKFiyY4vNZWVnxwQcfGLs0zp8/z//93//h5eVl3Of27dsEBARgYWER57V3716uXLmS7HPmzJkzzvvs2bPH+SBMjI2NDU5OTnFelpaW8fa7ffs20dHRODg4xIm3a9euREVFcePGjQTrv3nzZrwP0pR8sCZ0nYDxWrt3787EiRMJDAzE2dmZ/PnzM3r0aKKjo5Osd8aMGXz22Wd4eHgQFBTEsWPHjGMZXuU+ApiZmbFjxw4KFChA9+7dKVCgAHXr1uXXX39N7mUK8cZascKQQNSvn7kTCJDZGYlKbkuAqcUmCu3atYu3LTAwkOnTp5MrVy7MzMwS/TBMLk9PT9zc3AgNDSUgIIC8efPSqFEj43ZHR0fc3d0ZPXp0vGNz5MiRKjGkJkdHR8zNzTl8+DBmZvHz68QSgwIFCnDr1q04ZS++T01mZmYMHjyYwYMHc+XKFVasWMGoUaMoXLgwffr0SfS4NWvW0K5dOyZMmGAsO3fuXLLPX758edatW0dkZCQHDx5k+PDhuLq6cvXq1QTvmxDif1auhM6dDQnEli2ZO4EASSKyhIcPH7Jlyxa8vb3jDMAD+PXXXxkyZAh79+6lSZMmODs788MPPzBgwIAEuzSe/9Zr9ZJ1Vps2bUquXLkIDAwkICCAtm3bxmlWb9y4MYGBgbz99ttYZ4Lhxo0aNSI6Opr79+/j4uLyysdVr16dTZs2MWnSJOM9Xb9+fVqFGUfRokX54osvWLJkiTEheLHlIlZERES8FpgVK1a89rktLCxo1KgRQ4YMoUOHDoSFheHo6Pja9QmR1a1cCZ06Qb16mb8FIpYkEVlAUFAQ4eHhDBo0CGdn5zjbateuzYQJE1i1ahVNmjTh66+/pkmTJrRo0YJevXpha2tLSEgITk5OtGrVivLlywMwc+ZMGjVqhL29PeXKlUvwvBYWFnh4ePDtt99y48aNeDM5hgwZwvLly2nUqBEDBw6kcOHC/PPPP+zfv586derg7e2dNjfkNZUrV44+ffrg5eXFsGHDcHJy4vHjx5w9e5YLFy6wcOHCBI8bPnw4zs7OtG/fnh49enDmzBkWLVqUZnH27t0bR0dHatasiYODA3v37uWPP/5g8uTJxusAmD9/Pl5eXtjY2PDuu+/i4uLCd999h7OzM6VLl2bFihXJXtX01KlTfP7553h6elKqVCnu3bvH5MmTqVy5siQQQiTh+QRiyxawtTV1RKlD2h6zgFWrVvHWW2/FSyDA8EHfvn171q9fz5MnT6hXrx47d+4kPDycjh074unpyf79+40LSdWtW5ehQ4cyc+ZMnJ2d6d27d5Ln9vLy4saNGxQqVIi6devG2ZYnTx6OHj1K+fLlGTx4ME2bNmXYsGHcv3+fSpUqpd4NSEWzZ89m9OjR/PDDD7Rs2ZKuXbsSHBxMvXr1Ej3GycmJ1atX8+uvv/Lhhx+ycePGeNNfU1OtWrU4cOAA3bp1o2XLlmzYsIEFCxbw4YcfAlC8eHGmTp3K+vXrqV27tnHdiC+//BJvb298fHzw9vYme/bsfPfdd8k6d4ECBcifPz8TJkygRYsW9OvXjwoVKrBp06ZUv04hsopVq7JmAgGgDDNBsjYnJyed1GyA3377jQoVKqRjREKIN5n8zXlzrF4NH30EdetCcHD6JRBKqV+01k5pfR5piRBCCCHSwM8/Q8eOUKdO+iYQ6UmSCCGEECKVhYcbEoiCBQ1LWWfFBAJkYKUQQgiR6oYOhQsXYPdueGHplyxFWiKEEEKIVLRtG8yZA4MHG56JkZVJEiGEEEKkkjt3oHt3ePttmDjR1NGkPenOEEIIIVKB1tC7tyGR2LYNXrJeX5YgSYQQQgiRCpYtg3Xr4OuvoUoVU0eTPtK9O0MpVVEptVspFa6Uuq6UGqeUSvIRhEopX6WUTuQ1Ir1iF0IIIRJy+TIMGGBYD+Lzz00dTfpJ15YIpVQuYBdwDt8Nb/gAACAASURBVPgAKA1Mw5DMJPXc5IXA9hfKPgSGA9tSP1IhhBDi1URHG57KCfDDD5Atya/FWUt6d2f0AayB1lrrB8BOpZQ94KuUmvKsLB6t9VXg6vNlSqnRwO9a65NpHbQQQgiRmG+/hQMHYMkSKFHC1NGkr/TuzmgB/PhCsrAaQ2JR/1UrUUo5Ai7AqtQNL/Py9fVFKYVSCjMzM3LlykX16tUZNWoUN2/eNHV4r6VBgwa0bds2w9aXWvLkyYOvr2+S+5QoUcL4882ePTvly5fnq6++4unTp/zzzz+Ym5szbdq0BI+NjIzE0dGRfv36vVb9puDr60uePHlMcm4hkuP//g9GjYLWrf/XGvEmSe+WiPLAnucLtNahSqnwZ9s2v2I9bQELDAmIeMbBwYHt2w29Pvfv3+fEiRPMnTsXPz8/tm/fTrVq1UwcYfLMmTMHCwuLDFtfeuvQoQMDBw7kyZMn7N27l7Fjx3L//n2mTp1Kw4YNWb16NZ999lm843788Ufu3bv30qemJlW/ECK+x48Nq1I6OsL8+aCUqSNKf+mdROQCwhIov/ds26vyAk5orS+kSlRZhLm5OTVr1jS+b9asGX379qVevXp4enpy/vx5smWizrqKFStm6PrSW8GCBY0/3/r163P16lXmzZvHN998g7e3Nz169ODPP/+kdOnScY5bvXo1RYoUoU6dOq9dv3oT/zoK8RI+PnDmjOG5GG9qw5kpFptK6LGhKpHy+DsqVRBD10eSXRlKqV5KqeNKqeP//vtv8qPMInLmzMmUKVP4888/2blzp7H88ePHDBs2jKJFi2JpaUnlypXZunVrvOMXLFjAu+++i5WVFfnz56dt27bcv3/fuD0wMJB3330XS0tLihYtyqhRo4iKijJu9/f3RynFiRMnaNCgATY2NlSpUoUTJ07w6NEjunXrhoODA6VKlWLVqrg/0he7H65evUr79u3Jly8f1tbWlC5dmtGjRxu3nz17lubNm+Po6IitrS0VKlRg9uzZidYHsGfPHpydnY3X169fPx4+fGjcvm/fPpRS7Nu3j3bt2mFnZ0epUqWYM2dOvHt16NAh6tevj42NDblz56Znz578999/cfY5cOAAlStXxsrKimrVqnHkyJH4P7RXVK1aNR49esTt27dp3bo1lpaWrF4dt3Hu8ePHbNq0CS8vr2QnAs/XHxISgru7O4UKFcLW1pYqVaqwYsWKOPvH/qxPnz6Ni4sLtra2lC9fnvXr18erOygoCCcnJ6ysrChQoADDhg0jMjIy0VgiIyP5/PPPKVasGJaWlhQqVAgPDw+TdbcIsXevYSxEnz7QsqWpozGd9E4i7gEJrSLuQMItFAlpjyHpCEhqJ621n9baSWvtlDdv3uRFmcU0bNgQc3Nzjh49aixr27Yt/v7+jBw5ks2bN1O9enXc3d05efJ/41THjx9P7969qV+/Phs3bmTu3Lk4ODgYP2R37NiBp6cn7733HkFBQQwcOJCpU6cyYMCAeDF06dIFb29v1q1bh9aatm3b0qNHDwoVKsTatWtxdnamc+fOXL16Nd6xsTp37syVK1fw8/Nj27ZtjBo1iidPnhi3u7u7ky1bNpYvX86mTZsYOHBgvA/x5507d47mzZuTJ08e1q1bx9ixY1m5cmWC4yZ69uxJ5cqV2bBhAw0aNKB///4cO3bMuP3w4cM0btyYAgUKsHbtWmbMmMHWrVvp1q2bcZ/r16/TokULHB0dWbt2Lb179+ajjz4iPDw80RiTcvnyZbJnz46joyM5c+akefPm8ZKILVu28N9//720K+Nl9f/999/Url2bhQsXsnnzZtq0aUO3bt3iJX5g6BZxd3dnw4YNvPXWW3h5ecX5uQYGBtK6dWtq1KjBpk2bGDNmDH5+fowYkfhs7UmTJrFixQq++uordu7cyYwZM3BwcCA6OjrZ1yVESt2/bxj/UKYMvPG9fVrrdHsBB4BVL5QVxdAK4faKdYQAB5Jz3mrVqumknDt3Ln7hoEFa169vmtegQUnGm5AxY8bo3LlzJ7q9QIECuk+fPlprrXft2qUBvW/fvjj71K1bV7dt21ZrrfW9e/e0tbW1Hjx4cKJ1Ojs76wYNGsQpmzx5sjYzM9NXrlzRWmu9ZMkSDWh/f3/jPsHBwRrQ3bp1M5aFhYVpc3NzPWfOHGNZ/fr1dZs2bYzvbW1t9aZNmxKM5d9//9WAPnXqVKLxvlifp6enLlOmjI6KijKWBQQEaEAfOXJEa6313r17NaBHjx5t3Ofp06c6T548evjw4cayOnXqxLsXu3fv1oA+ffq01lrroUOHakdHR/3o0SPjPsuXL9eAHjNmTKJxa6118eLF9ZAhQ3RkZKR+9OiR3rx5s7a3t49zPatXr9aAPnv2rLGsTZs2umzZsknW/ar1x4qJidGRkZG6V69eumHDhsby2J/1okWLjGW3b9/W2bJl03PnzjUeW6xYMd21a9c4dS5atEhbWVnp27dva63j/z67urrqIUOGvPQ6MosE/+aITKNjR62zZdP66FFTR5I44LhOh8/19G6J2AY0U0rleK7ME4gA9r/sYKVUCaAmMisj2Qy/Uwa7du2iQIEC1K5dm6ioKOOrcePGHD9+HICQkBAiIiLifJN+XnR0NCdOnKBdu3Zxyj09PYmJiSEkJCROeePGjY3/XaZMGQAaPfdkGgcHB/Lmzcu1a9cSvYYqVaowYsQI/P39CQ0NjbPN0dGRokWL0qdPHwICArh161ZStwOAY8eO4eHhEWecSJs2bTA3N+fQoUNx9m3atKnxvy0sLHjrrbeM367Dw8MJCQmhffv2ce5nnTp1sLCw4JdffjGez8XFBRsbG2NdrVu3fmmcsb799lssLCywtbXFzc2NevXqxemucXNzw87Oztga8fDhQ7Zu3frKrRBJ1X/v3j0++eQTihcvjoWFBRYWFvj5+XHhQvxhSc/fq9y5c5MvXz7jvbpw4QKhoaHx7lWjRo14/PgxZ86cSTC2KlWq4O/vz5QpUzh16lSc32ch0lNgICxfbhgP4exs6mhML70HVs4DPgHWK6UmA6UAX+Bb/dy0T6XURWC/1rrHC8d7AVHA2jSPdMaMND9Fenn8+DF37twhf/78ANy+fZubN28mOFMh9gP1zp07gGGwXUJu375NZGSksc5Yse/v3r0bpzznc8/CzZ49e7yy2PLHjx8neh0BAQGMGjWKwYMHExYWRuXKlZk2bRqNGzfGzMyMHTt2MGrUKLp3705ERAS1a9fmu+++o2rVqgnWd+PGjXjxZ8uWjdy5cycZ/4ux3rt3j+joaPr165fgNMorV64AcPPmTSpVqhRnm7W1NXZ2dole8/M6duzIoEGDsLS0pESJEuTIkSPOdhsbG9zd3Vm9ejXjxo0jKCiIiIgIvLy8Ulx/165dOXr0KKNHj6ZixYrY29szd+5cgoKC4tWT1L26ffs2AC0T6USOvVcv8vHxwczMjDlz5jB8+HAKFy7M0KFDGTRo0CtdmxCp4do1wxiIGjUM0zpFOicRWut7SqnGwCwM0znDgOkYEokX40poGoEXsFtr/eaOlHwNe/fuJSoqilq1agGGb+2FCxdm48aNiR6TO3duwPBBm9B8/Tx58mBhYRHvG/8///xjPEdqK1y4MP7+/sTExHDs2DF8fX1xd3cnNDSU3LlzU758edatW0dkZCQHDx5k+PDhuLq6cvXqVczM4je6FSxYMF780dHR3LlzJ1nx58yZE6UUvr6+CX44FipUCIACBQrEO19EREScgZxJyZ8/P05OTknu4+3tzcqVKzlx4gSrV6+matWqlC9fPkX1P378mODgYGbNmkWfPn2M5TExMa9U7/Ni76ufn1+CyV3JkiUTPM7Kyopx48Yxbtw4/vjjD+bNm8enn35KuXLlaN68ebLjECK5YmIMT+d8/NjwjIxMPFs8VaX77Ayt9TmtdSOttbXWuqDWerTWOvqFfUporbsmcGwVrbX8xUiGsLAwhg8fTpkyZWjSpAlg6Fq4efMmdnZ2ODk5xXsB1KpVC2tra5YuXZpgvdmyZaNatWqsWbMmTnlgYCBmZmbGhCUtmJmZUbNmTcaMGUN4eDh///13nO0WFhY0atSIIUOGcOPGDcLCEh6z6+zszIYNG+IMzlu/fr2xK+JV2draUrNmTc6fP5/g/YxNIqpXr87OnTvjDKRMaOZCSjRr1gxHR0fmzZvHjh07XmtA5YuePHlCdHQ0lpaWxrL//vuPTZs2JbuucuXKUbhwYS5fvpzgvYpNXpPy1ltvMXXqVCwtLTl37lyyYxDidcyZAzt2wLRpULasqaPJOOQpnllIVFSUcQbGf//9xy+//MLcuXMJDw9n+/btxq4KFxcXmjVrhouLC8OHD+ftt9/mwYMHnDx5ksePHzNp0iRy5szJ6NGjGTVqFE+fPqVly5Y8efKE4OBgxowZQ+HChRk7dizNmjWjW7dueHl5cfr0aUaPHk3Pnj0pUqRIql7b/fv3adasGZ07d6Zs2bI8efKEadOmUaBAASpUqMCpU6f4/PPP8fT0pFSpUty7d4/JkydTuXLlRFsVfHx8qFq1Kh9++CF9+/bl6tWrDB8+nGbNmiU7CZoyZYqxW6Vt27bkyJGD0NBQgoODmTBhAmXLluXTTz9l9uzZtGrViiFDhnD9+nUmTZqEtbV1atwiwJBAtWnThoULFwKGMSop5eDgQPXq1Rk3bhz29vaYmZnx9ddf4+DgwIMHCa5UnygzMzOmTZtGp06dePDgAS1atCB79uxcunSJjRs3snbt2jhjRmJ5eHhQrVo1qlatirW1NWvXriUqKop69eql+PqEeJnff4ehQ6FFC0N3hvgfSSKykPv371OrVi2UUtjb21OmTBk6duzIwIEDKVCggHE/pRTr169n4sSJzJgxg9DQUBwdHalSpQoDBw407jdixAgcHR2ZOXMm8+fPJ1euXNSrV8/YV960aVNWr17N+PHjWbFiBfny5eOzzz5j7NixqX5tVlZWvPvuu8ycOZMrV65gY2NDzZo12bFjB9bW1hQoUID8+fMzYcIErl+/Ts6cOWnYsCGTJ09OtM63336bbdu2MXLkSFq3bo29vT3e3t5MmTIl2fHVqVOHAwcOMGbMGDp16kR0dDTFixenefPmxnEXhQsXZuvWrXzyySe0adOGChUqsHz5cj744IPXvi8J8fb2ZsGCBdSuXZtixYqlSp0rV66kV69edO7cmdy5czNgwADCw8OZNWtWsuvy9PTE3t6eiRMnsnjxYrJly0apUqVo1aqVcbzMi95//30CAgL45ptviImJoWLFiqxbt+6l3TtCpFRkpGFVSltbWLTozVyVMinqTRjl7OTkpGNnHSTkt99+o0KFCukYkRDiTSZ/czKP0aNh/HhYuxbatDF1NK9OKfWL1jrNs2xTrFgphBBCZHghITBxomFhqcyUQKQnSSKEEEKIFzx8CJ06QdGi8N13po4m45IxEUIIIcQLhgyBS5dg/36wtzd1NBmXtEQIIYQQz9m8GRYsgGHDoG5dU0eTsUkS8cybMMBUCGF68rcmY7t1Cz7+GCpXhjSYaJblSHcGhrn1ERERCc5PF0KI1BQREZHgkvPC9LSGnj0NT+ncvRueW19NJEJaIoB8+fJx7do1wsPD5VuCECJNaK0JDw/n2rVr5MuXz9ThiAQsXgybNsGkSfDOO6aOJnOQlgjA/tmomevXrxMZGWniaIQQWZWFhQX58+c3/s0RGceff8KgQdCwoeFf8WokiXjG3t5e/scWQog3UFSUYTqnuTksXQoJPK9PJEKSCCGEEG+0KVMMC0utWGFYF0K8Osm3hBBCvLF++QXGjAFPT0iFh96+cSSJEEII8UaKiDA8XCt/fsOjvuXhWskn3RlCCCHeSF98YXjM944d4Oho6mgyJ2mJEEII8cbZudPwTIxPPgEXF1NHk3lJEiGEEOKNcvcudO0KFSrA11+bOprMTbozhBBCvDG0hr59Dctbb94M1tamjihzkyRCCCHEG2PlSggMhAkT4L33TB1N5ifdGUIIId4IoaHQvz+8/z4MH27qaLIGaYkQQgiR5cXEGMZBREfDsmWQLZupI0obMTqG73/6Pt3OJ0mEEEKILG/aNNi7FxYuhFKlTB1N2vjjzh9039SdQ6GH0u2c0p0hhBAiS5s6FYYNg9atoXt3U0eT+qJjopkeMp1K8ypx5tYZln64NN3OLUmEEEKILElrQ/IwdCi0b28YVJnVVqW8cOcC9fzrMWTHEJqUasLZfmfpXLlzup1fujOEEEJkOZGR0LOn4amc/fvDzJlZaxxEdEw0M3+ayag9o7Ayt+KHD3+gY6WOqHTOkiSJEEIIkaWEhxtaHoKDYexYGD06a7VAnL99nu6bunPkyhHcyroxv9V8CuYoaJJYJIkQQgiRZdy9C25uhkd7z50LffqYOqLUEx0TzYyjM/DZ64O1uTXLPJbx0bsfpXvrw/MkiRBCCJElXL0KzZrBxYuwZg20aWPqiFLP77d/p1tQN45ePYp7OXfmuc4zWevD8ySJEEIIken9/js0bQphYbB9OzRsaOqIUkd0TDTTj07HZ48PNhY2LPdYTod3O5i09eF5kkQIIYTI1H76CVxdwdwc9u+HqlVNHVHqyKitD8+TJEIIIUSm9eOPhvUfChSAHTugdGlTR5RyMTqG7376ji92fYFtdltWtF6B9zveGab14XmSRAghhMiUVq6ELl3gnXdg2zZDIpHZXX1wla4bu7L7r924vuXKQveFFLDLuBcmSYQQQohMZ+ZM+PRTaNAANm4EBwdTR5RyAWcC6BPch6fRT5nnOo9e1XplyNaH50kSIYQQItPQGkaNgkmTDN0YK1aAlZWpo0qZsMdhDNg6gBWnV1CjcA2WeSyjbO6ypg7rlUgSIYQQIlOIijKs+7BoEfTqBXPmZP5VKPdd3kfnDZ25/t91fOv7MqreKMzNMs9Hc+aJVAghxBsrIgK8vSEoCL78Enx9M/cqlE+inuCzx4dpIdMo7Viaw90P41zE2dRhJVu6P4BLKVVRKbVbKRWulLqulBqnlHqlXFIp1Vop9bNSKkIpdUcptV0pZZvWMQshhDCdsDDDIlKbNsH33xuWss7MCcTpf05TY2ENpoZMpVe1XpzsfTJTJhCQzi0RSqlcwC7gHPABUBqYhiGZ8XnJsR8Ds4ApwFAgF9AIaU0RQogs6/p1aN7csJjU6tWGZ2JkVjE6hhlHZzBi9whyWuVks/dmWpVtZeqwUiS9P4D7ANZAa631A2CnUsoe8FVKTXlWFo9SKg8wHRiotV7w3KYNaR6xEEIIk7hwwdACcfs2bN0KTZqYOqLXd+X+FboGdWXPX3twL+fOArcF5LPNZ+qwUiy9uzNaAD++kCysxpBY1E/iuNjcc2laBSaEECLj+OUXqFMHHj2CvXszdwKx6vQqKs2rxE9Xf2KB2wI2em7MEgkEpH8SUR74/fkCrXUoEP5sW2KcgfNAD6XUVaVUpFLqJ6XU+2kXqhBCCFPYtcuw/oONDRw6BE5Opo7o9URGR9J5Q2c6rO9A+TzlOdnnJB+/93GGX/shOdI7icgFhCVQfu/ZtsQUAMphGDcxHHADHgHblVL5UztIIYQQphEYCC1bQsmScOQIlM0cyyXEo7Wm/9b+LDu1jDH1x3Cw20HKOJYxdVipLt1nZwA6gTKVSHksM8AO6KG1XqG13g58CEQDAxI6QCnVSyl1XCl1/N9//01pzEIIIdLY7Nng5QXOznDgABQqZOqIXt/UI1NZcGIBI+uMxLeBb6Za+yE50juJuAfkTKDcgYRbKGLdffbvvtiCZ+MqfgEqJnSA1tpPa+2ktXbKmzfv60UrhBAizWkNY8bAgAHg5mZ4kFbOhD4pMom159YybNcwPN/25KtGX5k6nDSV3qnR77ww9kEpVRSw5YWxEi/4DUNLxYsdSQqISc0AhRBCpJ/oaOjfH+bPh+7dDf+aZ+Iv7T9d/YlOGzrxftH38f/QHzNligb/9JPeV7cNaKaUyvFcmScQAexP4rgtGBKGhrEFSikHoBrwf2kQpxBCiDT2+LFh3Yf582HECFi4MHMnEH/d+wv31e4UylGIjZ4bsTLP5A/1eAXpnUTMA54A65VSTZRSvQBf4Nvnp30qpS4qpRbFvtdaHweCgEVKqS5KKVdgExAJzE7PCxBCCJFy9+9Dixawfj3MmAETJ2buVSjDHofhutKVp9FPCe4QTF7bN6MbPV1zPq31PaVUYwwrT27GMA5iOoZE4sW4XlwKuyPwDfAtYAMcBhppre+lZcxCCCFS182bhgTizBnDUzg7dDB1RCkTGR1J28C2XLx7kR2ddlA+T1IrFmQt6d5wpLU+h2G56qT2KZFA2UOg77OXEEKITOjSJXBxMSQSmzcblrTOzLTW9A3uy+6/duP/gT8NSjQwdUjpKhP3PgkhhMhMbt82LGMdFgZ79himcmZ2kw9PZtGvixhdbzRdqnQxdTjpTpIIIYQQae7JE/DwgCtXDMtYZ4UEIvBsICN2j6DDux0Y22CsqcMxCUkihBBCpCmtoUcPwxLWq1dDrVqmjijlQq6E0HlDZ+oUq8Mi90VZainr5MjaE1iFEEKY3NixhgGU48eDp6epo0m5S/cu4b7anaIORdngueGNmMqZGEkihBBCpJnlyw1JRJcuMHKkqaNJuXsR92i5oiUxOoatHbaSxyaPqUMyKenOEEIIkSYOHjR0YzRoAH5+mXsdCICn0U9pHdiav8L+YlenXbyV+y1Th2RykkQIIYRIdX/8AR9+CCVKwLp1kD27qSNKGa01vbf0Zt/lfSzzWEbd4nVNHVKGIN0ZQgghUtWdO+Dqamh52LoVHB1NHVHKTTw4Ef+T/vjW96VjpY6mDifDkJYIIYQQqebJE2jdGv7+27AWROnSpo4o5VadXoXPXh86VurIl/W/NHU4GYokEUIIIVKF1tCzJxw4ACtXQu3apo4o5Y5cOULXoK7UK16PhW4L39ipnImR7gwhhBCp4quvYNkyGDcOvL1NHU3KXQ67zIerP6SYQzHWt1+PpbmlqUPKcCSJEEIIkWIrV8KYMdC5M/j4mDqalHvw5AGtVrYiMiaSLd5byG2T29QhZUjSnSGEECJFDh2Cbt2gXr2sMZUzKiYKz7WenL9znu0fbadcnnKmDinDkiRCCCHEa7t40TCVs3hxWL8eLLNAi/+QH4ew/eJ2/Fr50bhUY1OHk6FJd4YQQojXcveuYSqn1hAcDLmzQIv/7GOz+f7Y9wypOYSe1XqaOpwMT1oihBBCJNvTp9CmDVy+DLt2wVtZYPHGHy/+yKDtg3Ar68YUlymmDidTkCRCCCFEsmgNvXrBvn2GZ2PUzQKLN5779xzt17bnnXzvsLLNSrKZZTN1SJmCdGcIIYRIlokTYelS8PWFjz4ydTQp9++jf2m1shU2FjZs9t6MXXY7U4eUaUhLhBBCiFe2apVhCudHH8GXWWDxxidRT/AI8ODGwxvs77qfog5FTR1SpiJJhBBCiFdy5IhhKmfdurBoUeafyqm15uPNH3P4ymEC2gZQo3ANU4eU6Uh3hhBCiJf680/44AMoWhQ2bMgaUzknHpzI8lPL+arhV7R/u72pw8mUJIkQQgiRpHv3DFM5Y2IMT+XMClM515xdY3yo1qi6o0wdTqYl3RlCCCESFTuV89KlrDOV8+drP9N5Y2dqF60tD9VKIUkihBBCJEhr6NMH9u41PFirXj1TR5RyV+5fwX21OwXtCrLBc4M8VCuFJIkQQgiRoEmTYMkSwyyMjh1NHU3KPXz6ELdVboRHhrOr0y7y2uY1dUiZniQRQggh4gkIgFGjoEMHw3oQmV10TDQd1nXg9K3TbO2wlbfzvW3qkLIEGVgphBAijpAQ6NIF6tTJGlM5IyIj6Bvcl80XNvNd8+9oVqaZqUPKMqQlQgghhNGlS4apnEWKGKZyWlmZOqKUOfj3QT7e/DEX7lxgeO3h9K/R39QhZSmSRAghhAAgLMwwlTMqyjCVM08eU0f0+h48ecCIXSOYc3wOJXOWZFenXfJY7zQgSYQQQgjjVM4//4SdO6FsWVNH9Pq2/bGN3lt6c/XBVQbXHMxXDb/CNrutqcPKkiSJEEKIN5zW0Lcv7NljeLBW/fqmjuj13Am/w+AfB7Ps1DIq5q3IkR5HqFmkpqnDytIkiRBCiDfc5MmweDGMHg2dO5s6muTTWrPm3BoGbB3Avcf3+LLel4ysO1LWgEgHkkQIIcQbbM0aGDECvL1h7FhTR5N81/+7Tr/gfgSdD8KpkBO73HdRKX8lU4f1xpAkQggh3lBHjxpaHmrXNrREZKapnFprFv+6mM92fMaT6CdMdZnKoJqDMDeTj7X0JHdbCCHeQMePG6ZyFiqU+aZyXrp3iZ6be7Lnrz3UL16fhe4LKeNYxtRhvZFksSkhhHiDPHoEQ4aAszOYm0NwMOTNJKs/R8dEMz1kOu/MeYefr/3M/Fbz2dNljyQQJiQtEUII8YbYvt3wQK2//zb8+/XX4OBg6qhezbl/z9E9qDs/XfuJVmVbMdd1LkXsi5g6rDeeJBFCCJHF/fsvfPoprFwJ5cvDwYOGJa0zg6iYKL45/A2++33JkT0HK1qvwPsdb3l8dwaR7t0ZSqmKSqndSqlwpdR1pdQ4pVS2lxxTQimlE3itTq+4hRAis9EafvgBKlQwzML48ks4eTLzJBCn/zlNzYU1GblnJO7l3Dnb7ywd3u0gCUQGkq4tEUqpXMAu4BzwAVAamIYhmfF5hSo+Bw4/9/52ascohBBZwaVLhi6LnTvh/ffBzw/eziQPrnwa/ZSvD33N+APjyWWdizXt1tC2YltThyUSkN7dGX0Aa6C11voBsFMpZQ/4KqWmPCtLynmt9dE0j1IIITKpqCiYPh3GjDEMnJw925BMmGWSYfQnbpygW1A3Tv1zig7vdmBm85nkscnED/HI4tL716oF8OMLycJqDIlFcaGOowAAIABJREFUJl1oVQghMoYTJ6BGDRg2DJo2hXPnoF+/zJFAPIl6gs8eH2osqMG/j/4lyCuIFa1XSAKRwaX3r1Z54PfnC7TWoUD4s20vs0QpFa2UuqGU+lYpZZ0WQQohRGYSHg5Dh0L16nDjBqxda1j7oUgmmbxw7Nox3vN7jwkHJ9CxUkfO9juLezl3U4clXkF6d2fkAsISKL/3bFtingCzgR3AA6ABMBzDmIoPUjdEIYTIPHbuhN694a+/oFcvw3MwcuY0dVSvJiIygjH7xjAtZBqFchRia4ettHirhanDEslgiimeOoEylUi54QCtbwADnivap5T6B5ijlKqitT4Zr0KlegG9AIoVK5ayiIUQIoO5fduwaNSyZVCuHOzfD/XqmTqqV3fkyhG6BXXjwp0L9HyvJ9+4fIODVSZZtEIYpXd3xj0goRzZgYRbKJKy9tm/7yW0UWvtp7V20lo75c0sy7EJIcRLaA3Llxumba5aBT4+hmmbmSWBCI8MZ/D2wdRZXIcnUU/Y2Wknfm5+kkBkUundEvE7L4x9UEoVBWx5YazEK9Av/CuEEFnaX39B377w449QsyYsWADvvGPqqF7dzYc3cV3pyokbJ+hfvT+TGk8ih2UOU4clUiC9k4htwFClVA6t9X/PyjyBCGB/MuuKnTT8S2oFJ4QQGVFUFHz3HYwebZhp8f33hmQiW5LL9GUs52+fp/mK5tx6dIvN3ptpVbaVqUMSqSC9k4h5wCfAeqXUZKAU4At8+/y0T6XURWC/1vr/2bvv8KiqPIzj35PQe6+CVAEVBQTpUkIRkCqIsIgoiCJ2FxtYcF0r2CsiogK6gA2kSVeKKAiKAtIEpPcaEkhy9o8zkRCTMIRk7szk/TzPPJPcmbnzy3Ewb849pb/v+6eA/LiFpo4C1wBDgC+stb8G8gcQEQmkVatgwABYsQI6dnTrPpQr53VV52fxtsV0+qwT2SKysbDfQuqWqet1SZJBAjomwlp7CIgCIoGpwHDgFeDJZE/N5ntOonW4dSQ+BKYDvYGXfPciImEnOhoefhjq1oXt22HiRPj669ALEJ+v+Zyoj6MomrsoS/svVYAIMwGfnWGtXQO0PMdzKiT7/jPcolQiImFvzhw3bXPzZtcL8eKLUDitSfBB6vVlr3PfzPtocFEDpvSaooWjwlAIrGMmIpI1HDgA/fpB69ZuvMP8+W7wZKgFiASbwJBvh3DvzHvpXL0zc/rOUYAIU9oKXETEY9a66Zr33QeHDsHQoW7qZq5cXld2/mLjYun3dT8+++0zBtcbzGvXvkZkRAiNAJXzohAhIuKhnTvhtttg+nSoX9/1PNSs6XVV6XM45jBdPuvCwq0LeaHVCwxpNETbdoc5hQgREY9MnuzGPsTEwGuvweDBoTVtM6ltR7bRfnx71h9Yz/hu4+ldU+PeswKFCBGRADtyBO65Bz7+2G2aNW4cXHKJ11Wl3y+7f6H9hPYcP3WcWX1m0aJiC69LkgDRwEoRkQD6/nu48koYPx6eeAIWLw7tADF381yaftgUg2HRLYsUILIYhQgRkQA4dQoefRSaNYNs2WDRIhg+HLJn97qy9Bv36ziuHX8tFxe6mB8G/EDNkiE6mEPSLdXLGcaYvek4nwVaWWtXp78kEZHwsmYN9OkDK1e6dR9eeQXy5fO6qvSz1vL8oud5bN5jtKjQgi96fkGhXCGy/7hkqLTGRBQDPgC2+3muSGAoEMK5WkQk4yQkwJtvupUn8+d3K0526uR1VRcmLiGOu6ffzbsr3qV3zd6M6TSGnNlyel2WeORcAyvft9b+6M+JjDGRwLALL0lEJPTt3OkWjpo9Gzp0gA8+gJIlva7qwhyLPUbPyT2ZsXEGDzd+mGejniXC6Kp4VpZWiGgBrPH3RNbaeGNMC+CPC65KRCSEJZ26+e67MHAghPpyCTuP7aTDhA6s3rOa9657j4FXDfS6JAkCqYYIa+35bs2drteIiISLcJu6mWj1ntW0n9CewzGHmdprKu2qtvO6JAkSqfZDGWPeNcb0McZUCmRBIiKhKHHq5rhx4TF1M9HsTbNpPKYxCTaB72/5XgFCzpLWxayOwMfABmPMLmPM58aYB4wxDYwxGjwpIoKbuvnII2embi5eHPpTNxONWTmG9hPaU7FwRZYNWEatUrW8LkmCTFqXM8oaY8oDjYGGQCNcsIgEYo0xy4ElwGJgibX2QADqFREJGmvWwL/+BatWhcfUzUTWWp6Y/wTPfP8MrSu1ZvINkymQs4DXZUkQSnN2hrV2G7AN+BTAGJMHqM+ZUDEAGIJbH0JLaItIlhCOUzcTxcbFMmDqAMb9Oo7+tfvzTod3yB4ZBt0qkinO6xe/tTbaGPM7UBAoDBTBhYq4TKhNRCTo7NgBt9wSXlM3Ex06eYhuE7uxYMsC/tvyvzza5FHtwilpSjNEGPfpqYnrdUi8VQR2AD8Ak4AHgJ8zt0wREe9NmuSmbsbGhs/UzURbDm+h/fj2bDq0Sbtwit/SWvb6W1wvQ3ZgJS40PAL8YK31dxVLEZGQd+QI3H03fPJJeE3dTPTTjp+47tPrOB1/mm/7fEuzCs28LklCRFo9Ea2AaGA8sABYaq3dHIiiRESCxXffQd++sH27m7o5bFh4zLxI9PW6r+n1eS9K5SvF9H7TqV6sutclSQhJa4rnpcA9vq8fA9YbY/YYY74yxjxkjGlqjMmV+SWKiARe4tTN5s3DZ9fNpKy1vLHsDbr+rys1S9Zkaf+lChBy3tKa4rkOWAeMATDGFMSNiWgItMZttpXLGPMrbornvZlfrohI5vv9d7fr5qpVcNtt8PLL4TF1E9zYhwmrJzDu13Gs3b+WLtW7ML7bePJkz+N1aRKC/J6dYa09AswAZhhjCgNNgYFAO6AOoBAhIiEtIQHeeMNN3SxQIHymbh46eYjJaybzya+f8P227wFoWr4p73d8n1tq3UJkRKTHFUqo8itEGGOqc/YMjWq+h44D83ALTomIhKyku25edx2MHh3aUzdj42KZvmE641aP45v133Aq/hTVi1Xnvy3/S++avalQqILXJUoYSGt2xmO4wNAAtyaEAbbiAsObuNUqf7XWJgSgThGRTDNlCtx6K0RHh/bUzQSbwJK/lvDJL58wcc1EDsccpmTektxZ905uuvImapeqrXUfJEOl1RPxJG5q58e44LDYWrs7IFWJiARAdDQ8+KALDrVrw4QJUD0Exxau27+Ocb+OY/zq8Ww5vIU82fPQrUY3+tTsQ1SlKLJFaEFhyRxpfbLuBSZba/cHqhgRkUBZuRJ694Z162DIEPjPfyBnTq+r8t+B6AN8+tunjF01lhW7VhBhImhdqTXPtHiGztU7ky9HmIwElaCWVoh4C7cSpUKEiISNhAS3Udajj0Lx4jBnDkRFeV2Vf+IS4pi5cSZjV41lyh9TOJ1wmtqlavNK21e48fIbKZWvlNclShaTVojQhTMRCSs7d8LNN7vg0LUrvP8+FC3qdVXn9tve3/ho1Ud88usn7Dmxh+J5inPX1Xdx85U3c2WpK70uT7IwXSgTkSzh66+hf384eRJGjXJbdwfzGMODJw/y6epPGfvLWJbvXE62iGx0vKQj/Wr1o12VdtpZU4LCuULEAGPMtX6cx1pr/5MRBYmIZKToaHjgAXjvPahTxw2erFbt3K/zQlxCHN9u+pYPV33IlD+mcCr+FLVK1eLVtq/Su2Zviuct7nWJImc5V4jogX/bfFtAIUJEgsrKldCrF6xfDw895AZP5sjhdVX/tGbfGsauGssnv37C7uO7KZanGIPqDqJfrX7UKlXL6/JEUnWuENHWWvtjQCoREckgCQluqerHHjszeLJlS6+rOltsXCyfr/2cd5a/w6Jti8gWkY0OVTvQr1Y/2ldtT47IIEw7IsloTISIhJVgHzy56eAmRq0YxZhVY9gfvZ/KhSvzYqsXubnWzZTIW8Lr8kTOi0KEiISNr75ygydjYoJr8GRcQhzfrP+Gd5e/y6xNs4g0kXSq1olBdQcRVSmKCJPWhsoiwSutELEViA1UISIi6XXihBs8OWpUcA2e3HF0B6N/Hs37P7/PjmM7KJu/LMObD6d/7f6ULVDW6/JELlhaW4FXDGQhIiLpEWyDJxNsAnM3z+Wd5e8w5Y8pxNt42lZuy1vt36LDJR20BLWElVT70IwxXxhjqvh7IuN8YYxJM3wYYy41xsw1xkQbY3YaY542xvi9D60xJsIYs8IYY40x1/n7OhEJLwkJMGIE1K8Px465MRAvvOBdgNgfvZ8RS0ZQ7c1qtBnXhu+3fc+DDR9k490bmdlnJp2rd1aAkLCT1ie6C/DceZwrAugMPAP8mdITjDGFgTnAGt9zKwMjfa8d5uf7DADUDyiShe3Y4QZPzp3r/eDJrYe3MmLJCEavHE1MXAxNyzdlePPhXF/jenJmC6HNOETS4VyxeJYxxp91Ivx1B5Ab6GatPQrMNsYUAJ4yxrzoO5YqXwj5L/AIMDoD6xKREJF08OT777uvvRg8uXbfWl5Y/ALjV48HoO8Vfbm/4f1cXuLywBcj4pG0QsTwdJ5zZxqPtQNmJQsLnwEvAM2Aqec4939w25LPTWdtIhKikg6evOoqGD/em8GTy3cu57lFz/Hl2i/JlS0Xg+sN5sGGD1KuYLnAFyPisbQGVqY3RKSlOjAv2ftsM8ZE+x5LNUQYY64AbgG024xIFvPzz27bbq8GT1prWbh1Ic9+/yyzN8+mUK5CDG06lHvq36OlqCVLC/Qon8LA4RSOH/I9lpY3gLestRuNMRUyuC4RCUIJCTByJAwdCiVKBH7lyQSbwLT103h20bP8sP0HSuYtyQutXuCOundQIGeBwBUiEqS8GCpsUzhmUjnuHjTmRqAa0NHfNzHGDAQGApQvX/48SxQRryUdPNmtm7uMEajBk3EJcUz8fSLPLXqO3/b+RoVCFXi7/dvcUvsWcmXLFZgiREJAoEPEIaBQCscLknIPBcaY7MBLuHETEcaYQkDinwB5jTH5rbXHkr/OWjsKGAVQt27dVAOKiASXEydg3Di370VMDIweDbfeGpjBkzFxMYxdNZaXlrzE5kObuaz4ZYzrOo6el/fU9EyRFAT6X8U63NiHvxljygF5fY+lJC9wEfCy75bUZ8AmwO/1LEQkOG3YAG+/DR9+CEeOQIMG8NFHcMklmf/ep+JPMWblGP7z3X/YeWwn9cvW5+U2L9OxWkctSS2ShnOGCGNMTuDfwDfW2l8u8P1mAEOS9R70BE4CC1N5zXGgRbJjpYBPgcdINlBTREJHfDxMnw5vvQWzZkG2bNCjBwweDI0aZX7vQ3xCPBNWT+DJBU/y5+E/aVyuMZ90/YQWFVpggmHTDZEgd84QYa2NNcYMBRZlwPu9C9wDfGGMeQGoBDwFvJx02qcxZiOw0Frb31obByxIepIkAytXW2uXZUBdIhJABw7ABx/AO+/Ali1Qpgw8/TTcdhuUKpX572+t5ct1X/L4/MdZs28NtUvVZnrv6Vxb5VqFB5Hz4O/ljGXAVaTeW+AXa+0hY0wU8CZuOudh4BVckEhel99LYYtIaFi+3PU6fPopxMZCs2bw0kvQuTNkz57572+tZdamWQybN4wVu1ZQvVh1JvWYRLca3XTZQiQd/A0RDwETjDGngOnAHpLNprDWRvtzImvtGiDNSVrW2grneHwLbkaHiAS5mBiYNAnefBN+/BHy5nUDJe+8Ey4P4OKO32/9nqHzhvL9tu+pUKgCYzuPpc8VfYiM0N8rIul1Pj0RAK8Dr6XyHP1LFJG/bdsG777rlqbev9+tLvn669C3LxQsGLg6VuxcwbD5w5i5cSal85XmrfZvMaDOAHJEerjVp0iY8DdE3Eoa6ziIiABY69Z1eOstmDLFHevUyQ2UjIoK7B4Xa/at4fH5j/PF2i8okrsIL7V+iTvr3Ume7HkCV4RImPMrRFhrx2ZyHSISwo4eddMx334b1q2DYsXg4Yfh9tvh4osDW8umg5sYvnA4434dR74c+Xiq2VPc3/B+rTApkgnOa50IY8yluAGW5YAx1trdxpgqwJ6UFnwSkfD2+++u1+Hjj90iUfXru6979IBcAV7Y8acdP/HSkpf4fO3n5IjMwb8b/ZuHGz9M0Twe7REukgX4FSKMMfmAMUB34LTvdTOB3cCzwDbcWhIiEuZOn4avv3bhYcECyJkTevVylyzq1g1sLdZaZmycwYuLX2Th1oUUzFmQhxo9xD3176F0/tKBLUYkC/K3J+JloBEQhduKOybJY9NxAUIhQiSM7d7tBkm+957b16JCBXjhBTfTolixwNZyKv4UE1ZPYMSSEfy+73cuKnARI9uM5LY6t5E/Z/7AFiOShfkbIroB91pr5xtjks/C2AoE+KqniASCtbB0qZueOXmy64Vo29YtEtW+PUQGeE7WkZgjjFoxiteWvcaOYzuoWaImn3T9hJ6X9SR7ZAAWmhCRs/gbInIDB1J5LD8QnzHliEgwOHjQbYL1/vvw229uSubgwTBoUGD2skhux9EdvLbsNd5b8R5HY4/SsmJLPuj0AW0qt9EKkyIe8jdE/AT0xY2DSK47sCTDKhIRT1gLCxe64PD5525FyXr13BbcvXu7RaIC7fe9vzNi6QjG/zqeeBtPj0t7MKTREK4qc1XgixGRf/A3RAwD5hhj5gCTcGtGtDfG3I8LEddkUn0iksn27HHTM0ePdjtpFirk9rAYMACuvDLw9Rw6eYh5f87jw1UfMm3DNHJny83tV93OAw0foGLhioEvSERS5e86EYt8e148j9v3wgDDgR+AVtbanzKvRBHJaPHxMHu263WYMgXi4qBpU3j8cejeHXLnDlwtsXGxLN2+lNmbZjPnzzks37mcBJtAsTzFGN58OHfWu5NieQI8clNE/OL3OhHW2sVAU2NMbqAwcNjf/TJEJDhs3w5jxrgdNLdtc7Mq7r3X9TpUrx6YGqy1rN67mjmb5zB782y+2/od0aejiTSR1L+oPsOaDqN15dbUL1tfgyVFgpy/60REAUuttdHW2pPAycwtS0QySlwcTJvmeh1mzICEBGjdGkaMcEtS58yZ+TXsOLqD2ZtnM2fzHOZsnsOeE3sAqFa0GrfWupXWlVvT7OJmFMwVwE01ROSC+dsT8S0Qb4xZCXzvuy2y1qY2Y0NEPLZ5sxvnMHYs7NoFpUvDo49C//5QMZOHFhyNPcrCLQv/Dg5r968FoETeErSq1IpWFVvRqlIryhUsl7mFiEim8jdElMANnmwCNAPuBSKMMevwhQpr7fjMKVFE/BUbC1995Xod5s6FiAi3nsNtt7n7bOe10L3/TsWf4oftPzB381zm/DmHZduXEW/jyZ0tN9dcfA39a/enVaVW1CxZkwgTkTlFiEjAGWvPf3NOY0xeoCXwIC5cWGtt0G4FXrduXbt8+XKvyxDJNGvXuuDw8cdw4IDb9GrAAOjXDy66KOPfL8Em8Nve3/6+PPHd1u84cfoEESaCemXqud6GSq1oeFFDcmYLwPUSETmLMWaFtTbTF6L3++8S3/4ZjYCmvtvVuOWvp+F6I0QkgKKjYdIkFx4WL4bs2aFzZ9fr0KqV64XISNuObPs7NMz9cy57T+wF3LiGfrX60apSK5pXaE6hXIUy9o1FJGj5O7DyJ+BKYC8uMEwC7gFW2/R0ZYhIuq1a5YLD+PFw5IhbQfKll6BvXyhRIuPeZ++JvSzcspB5f85j7p9z2XBwAwCl8pWiTeU2RFWMIqpilMY1iGRh/vZE1MLt3rkUtzrlYhQgRAImOtqFhlGjYPlyt8129+6u16FpU8iIlZ/3ndjHwq0LWbBlAfO3zGfNvjUA5MuRj+YVmjO43mBaVWrFpcUv1VLTIgL4HyIKcuZSRjfcolOnjDGLge+A76y1P2ROiSJZ1/btbsvt996DQ4egZk14/XXo0wcKF76wc++P3s93W79j/p/zWbB1Ab/t/Q2AvNnz0qR8E/pe0ZfmFZpTp3QdrdcgIinyd8XKaGCO74YxJjtuW/BHcIHCAkE7sFIk1Pz4I7z6qhvzkJAAXbvCffdB48bp73U4EH2A77Z+93dPw+q9qwHIkz0PTco3offlvWleoTl1y9RVaBARv5zPwMrinBlU2RQ3RiIC+B0NrBS5YHFxbnrmK6/AkiVQoADccw/cfTdUqHD+5zsdf5olfy1hxsYZzNw4k1/3/IrFkjtbbhqXb8yNl9/4d2jIEZkjw38eEQl//g6sXAdUxW35vRKYDzyNW3DqYOaVJxL+Dh92y1C/8QZs3QqVKsFrr8Ett0D+/Od3rl3HdjFj4wxmbJzBt5u+5WjsUbJFZKNJ+SY83eJpmldoztVlr1ZoEJEM4W9PxP9wYx+War8MkYyxcaMb3zBmDJw4Ac2aufBw3XUQ6efFwbiEOJZtX8b0DdOZsXEGK3evBKBM/jLccOkNtK/anqhKURTIWSATfxIRyar8HRPxZGYXIpIVWAsLFrjxDlOnuhUke/Vym2DVqePfOfae2MvMjTOZvmE63276lkMxh4g0kTQq14jnop6jfdX21CxRUzMoRCTTnc+YiErAENzS10WAg7ixECOstZszpzyR8BAbC59+6sLDL7+43TOHDYNBg9yeFmlJ3PXyi7VfMG3DNJbvdKuvlspXii7Vu9CuSjtaV26tRZ5EJOD8HRNxFW4cRAzwDbAHKAlcD/zLGNPCWvtzplUpEqL27oV33oG333ZfX3aZWyjqX/+C3LlTf521ll/2/MLkNZOZtGYS6w+sJ8JE0OCiBjzT4hnaVW1HrVK1tA+FiHjK356IEbgBle2SjokwxuQBpvseb5nx5YmEptWrXa/D+PGuF6J9ezdFs1Wr1KdoWmtZuXslk36fxOS1k9l4cCMRJoIWFVrwQIMH6FK9CyXzlQzsDyIikgZ/Q8TVwA3JB1Vaa6ONMSNwAy9FsrSEBJg+3YWHuXNdT8Ott7ppmtWrp/waay0rdq34OzhsPrSZSBNJy4oteajRQ3Sp3oXieYsH9gcREfGTvyHiJFA0lceK4C5ziGRJJ07ARx+5mRXr10PZsvD8825J6iJF/vl8ay0/7fzp7+Cw5fAWskVkI6piFI81eYwu1btQNE9q/9xERIKHvyFiGvC8MWaztXZR4kFjTBPgOWBqZhQnEsz++gvefNPtZ3H4MNSrBxMmuD0tsidb8NFay6rdqxi/ejyT1kxi25FtZI/ITuvKrXnimifoXL0zRXKnkDhERIKYvyHiAeBrYKExZh9uYGUJ320J8GDmlCcSfH75BZ57DiZPdlM2r7/ejXdo2PCf4x22HN7ChNUTGL96PGv2rSF7RHbaVG7D082fplO1ThTOfYEbYIiIeMjfdSIOAE2MMdcC9YDSwC5gmbX220ysTySobN0KTZq4xaDuvx/uugsuvvjs5xw8eZBJv09i/OrxfL/NrQjfpHwT3u3wLj0u66EeBxEJG2mGCGNMbqA9UAEXGuZaa2cGoC6RoGMtDBjgvl616uz9LGLiYvhm/TeMXz2eaeuncTrhNDWK1eC/Lf9L75q9qVCoQkqnFBEJaamGCN/iUnNwASLRUWPMDep9kKxo9GiYM8et+VChAiTYBBZuWcj41eOZvGYyR2KPUDpfae6++m76XNGHWqVqadVIEQlrafVEvAgk4HbsXAFUBN4G3vN9LZJlbNsGDz4ILVpAVI+NPDz7fSb8NoHtR7eTL0c+rq9xPX2u6EOLCi2IjPBz4wsRkRCXVohoCDxorV3s+36tMeZ2331pa+2uzC9PxHvWwsCBbh2I/7z+F1ePrsuJ0ye4tsq1jGg9go7VOpInex6vyxQRCbi0QkRpIPmeGJsAA5TCjZEQCXsffgizZsFrryfwxM/9iEuI4/c7f+eSopd4XZqIiKfOtfC+zeg3NMZcaoyZa4yJNsbsNMY8bYxJs//XGHOZMWam7/mxxphtxpjRxphzbF0kcmG2b3ezMK65Bmy9N5n35zxeafuKAoSICOee4jnLGBOXwvG5yY9ba0uc682MMYVxgzXXAJ2BysBIXJgZlsZLCwJ/Ah8DO3FjMp4ErjLG1LPWplSjyAWxFm6/HU6fhsdeWUuXGQ/ToWoHBtQZ4HVpIiJBIa0QMTwT3u8OIDfQzVp7FJhtjCkAPGWMedF37B+stUtwi1olWmCM2Q58C1wBaAdRyXAff+z2wnj51dMMXX4TebPnZXSn0ZpxISLik2qIsNZmRohoB8xKFhY+A14AmnF+y2cf8N3nyKDaRP62c6dbhbJJEzhU8xlWfL+CyT0mUypfKa9LExEJGucaE5HRqgPrkh6w1m4Don2PpckYE2GMyWGMqQY8D/wE/JgZhUrWlXgZIyYG7n3xR55d9F9uuuImrr/0eq9LExEJKoEOEYWBwykcP+R77FymA7G4IFIEuM5am5Bx5YnA+PHwzTfw1H+jGbriJsrkL8Pr7V73uiwRkaAT6BABKc/4MKkcT+5uoAFwE5APmGGMyZXSE40xA40xy40xy/ft25fuYiVr2bUL7rnHbab1V7WHWX9gPR92/pBCuQp5XZqISNAJdIg4BKT0f+OCpNxDcRZr7QZr7TJr7TigLVAb6J3Kc0dZa+taa+sWL178QmqWLMJaGDQIoqNhwHOzeWv5m9xb/16iKkV5XZqISFAKdIhYR7KxD8aYckBeko2VOBdr7VbgIFApw6qTLO2zz+Drr+Gx/xziiRW3UKNYDZ6Les7rskREglagQ8QMoK0xJn+SYz2Bk8DC8zmRb3BlUdz6ESIXZM8et613gwawruJd7Dmxh0+6fkLu7Lm9Lk1EJGida7GpjPYucA/whTHmBVwvwlPAy0mnfRpjNgILrbX9fd+PAOKAZbjLHjWAh3DLcH8WyB9Awo+1cOedcOIE9Bg+kQeXTmB48+FcVeYqr0sTEQlqAQ0R1tpDxpgo4E3cmhCHgVdwQSJ5XUmXwl6OG1Q5EMgFbAM+B56z1p7I5LIlzE2aBF/nAC10AAAgAElEQVR8AY89t5NnVt7B1WWv5rGmj3ldlohI0At0TwTW2jVAy3M8p0Ky7z9DPQ6SCfbtg8GDoW49y/KytxKzLYZPun5CtoiA/9MQEQk5XkzxFAkad90FR49Cu2Hv8e3mWbzU+iVtriUi4ieFCMmyJk+GiRNh8BMbGPnbg7Su1JpB9QZ5XZaISMhQiJAsaf9+N5iyTt04lpTsS47IHHzY+UMijP5JiIj4S//HlCzpnnvg8GFo9NALLNvxA2+3f5uyBcp6XZaISEhRiJAs58sv4dNPYcCwlby77il6XtaTXjV7eV2WiEjI0RB0yVIOHHBLW19RJ4YFRftQPKY4b3d42+uyRERCknoiJEu57z4XJK68fyhr969hTOcxFMldxOuyRERCkkKEZBlTpsC4cfCvoQsZt+kV7rjqDq6tcq3XZYmIhCxdzpAs4dAhuOMOuKzOMRYU6keliEqMaDPC67JEREKaeiIkS7j/fti7Fy65+0G2HdnKR10+Im+OvF6XJSIS0hQiJOxNmwYffQQ9HpvOl1vfZ0ijITQu39jrskREQp4uZ0hYO3wYBg6E6rUPsrDgAC7PczlPt3ja67JERMKCeiIkrD34IOzZA+UHDWZf9D4+7vIxObPl9LosEZGwoBAhYWvmTBgzBjo+OpFvd37Gk82epHbp2l6XJSISNnQ5Q8LSkSNw221Qtc4uvss3iKuLXs0jTR7xuiwRkbCinggJS0OGwI6dlhL9byM6LpqPunxEtghlZhGRjKQQIWHn22/h/feh7SNjWLxvGs9HPU/1YtW9LktEJOzoTzMJK0ePussYla7awqJ899GiTAvurn+312WJiIQl9URIWHnoIfhrewIF+/bDYPiw84dEGH3MRUQyg3oiJGzMnQvvvQctHn2d+YcW8kGnD7i40MVelyUiErYUIiQsHDsG/ftDhbrrWJr3Ua6rdB231LrF67JERMKa+nklLDzyCGz9K47cvfuSN3te3u/4PsYYr8sSEQlr6omQkLdgAbz9NjR85DmWHv2Jid0nUipfKa/LEhEJewoREtJOnIBbb4WLrv6Zn/I8Ta9Le9Hjsh5elyUikiXocoaEtEcfhT//iiF7j74Uz1OcN9u/6XVJIiJZhnoiJGQtXAhvvAF1Hn6Cn0/8zvTe0ymSu4jXZYmIZBnqiZCQtGABdOoEZRsuYmXuEQysM5B2Vdt5XZaISJaiECEhZ/JkaNsWylQ4TmT3m6lQqAIj2ozwuiwRkSxHIUJCyttvww03wFX1Y6gy5Gb+OvYnH3X5iPw583tdmohIlqMQISHBWnj8cRg8GFp33g99W/HNpi8Y2WYkTS9u6nV5IiJZkgZWStCLi4NBg2D0aOh++wZWXdqev3b/xcTuEzWdU0TEQwoREtROnoQbb4QpU+DmYYv5Jn9niIF5N8+jUblGXpcnIpKl6XKGBK2DB6F1a5g6FW4Z+T8+yxlFkdxF+GHADwoQIiJBQCFCgtL27dC0Kfz4k6XXWy/w4bEbqVe2Hkv7L6VKkSpelyciIihESBBaswYaNYK/dp6mzeu3M2HvI/S6vBezb5pN0TxFvS5PRER8FCIkqCxZAk2aQCxHqflsR6btfp+hTYcyrts4cmXL5XV5IiKShAZWStCYOhV69oQSVf8i160dWLZvDaM7jqZ/nf5elyYiIikIeE+EMeZSY8xcY0y0MWanMeZpY0zkOV5TzxjzoTFmo+91fxhjnjTG6E/TMDFmDHTtChUbriL2pgbsOrmVGf+aoQAhIhLEAtoTYYwpDMwB1gCdgcrASFyYGZbGS3v6nvsCsAG4AviP7/76TCxZMpm18NxzMHQo1L5hOhuu7EnhyMIsumkRNUvW9Lo8ERFJQ6AvZ9wB5Aa6WWuPArONMQWAp4wxL/qOpeQFa+2+JN8vMMbEAO8ZYy621m7N5LolE8THw/33u5046935DitK3kWtYrWY2msqZfKX8bo8ERE5h0BfzmgHzEoWFj7DBYtmqb0oWYBItNJ3XyLjypNAiY2FXr3gjTcTuOrRIfxU4k7aV23Pwn4LFSBEREJEoENEdWBd0gPW2m1AtO+x89EISAD+yJjSJFCOHoX27WHSlye54umerMg5grvq3cVXPb8iX458XpcnIiJ+CvTljMLA4RSOH/I95hdjTClgKPBJGpdAJAjt3g3t2sHqTfup+p/OrI5dysg2I7m/wf0YY7wuT0REzoMXUzxtCsdMKsf/+URjcgATgePA/Wk8byAwEKB8+fLnX6VkuA0boG1b2BW7iVJD27Ht9DYm9phI90u7e12aiIikQ6AvZxwCCqVwvCAp91Ccxbg/VT8GLgPaW2sPpfZca+0oa21da23d4sWLp7deySDLl0PjxnAwzw/kGtyAGHOQeTfPU4AQEQlhgQ4R60g29sEYUw7IS7KxEql4BTc1tLO11p/nSxCYPRuaNwdqfEnMjS0omq8gS/sv1SZaIiIhLtAhYgbQ1hiTP8mxnsBJYGFaLzTGPArcDfSx1i7KvBIlI02YAB06QME2r7O/5fXULl2Lpf2XUrVoVa9LExGRCxToEPEuEAt8YYxp5Ru38BTwctIBkr6VKT9I8n1v4FncpYwdxpgGSW66VhGkXnkF/tUngRJ9H2DnlffSpXoX5vWdR/G8+k8mIhIOAjqw0lp7yBgTBbwJTMWNg3gFFySS15V0Kew2vvt+vltStwBjM7ZSuRDWwiOPwIsvn6TMfX3YUfAL7q1/LyPbjCQyIs0VzkVEJIQEfHaGtXYN0PIcz6mQ7Pt+/DM8SBA6fRoGDICPJ++j5MOd2ZX9B15p+wr3NbjP69JERCSDaRdPyTAnTkCPHjBj2UaKPNSOI9m2M7nbZLrV6OZ1aSIikgkUIiRD7N/vBlD+tHsp+e7rSEQuw7wb59GwXEOvSxMRkUwS8K3AJfxs3QpNmsDK2M/J3r8lpQoVZmn/pQoQIiJhTiFCLsj69dC0KWwt8ypxXXtwVdnaLO2/lCpFqnhdmoiIZDKFCEm31auh6TWW/XWGENPsfrrW6MrcvnMplqeY16WJiEgAKERIuvz0E1zTPI7jLQdwsvYIBtcbzMTuE8mdPbfXpYmISIAoRMh5++47aNkmllOdexJdbQxPXPMEb7R7Q2tAiIhkMZqdIedl1izocsNxInp3IbrUXF5t+yr3NrjX67JERMQDChHity+/hBv6HSBHvw7EFl3OR50/ou+Vfb0uS0REPKIQIX4ZNw5uvmsnOW9rQ3yhjXze/XM6V+/sdVkiIuIhhQg5p/fegzse20iuQa2JzL+fab1m0KJiC6/LEhERjylESJpGjIAhI34l56A25C0Qx8w+86lbpq7XZYmISBBQiJAUWQvDh8PwMUvIPrADxQvl49ub5lOjeA2vSxMRkSChECH/YC38+9/w8pSZRN7SjQrFLmL2TbO5uNDFXpcmIiJBRCFCzhIfD3feCaMW/4+If91EzVKXMeumWZTIW8Lr0kREJMhosSn5W1wc3HwzjFoxCrr3ovHFDVjQb4EChIiIpEghQgCIjYXuPSzjtz4PHW+n/SXtmNlnJgVzFfS6NBERCVK6nCFER0OXrpbZ9mFo9RK9a/ZmbOexZI/M7nVpIiISxNQTkcUdPQptr41ndq6B0Pgl7qx7J590/UQBQkREzkkhIgs7cABatDrF4tK9oM5ohjYdypvt3yTC6GMhIiLnpssZWdTu3RB1bTRrr7geW3kmI1qP4MFGD3pdloiIhBCFiCxo61Zo0e4wWxtfh7loKe93HE3/Ov29LktEREKMQkQWs2EDNO+wl92t2xJR8nc+7f4/ul/a3euyRETkQiUkwJIlMHFiwN5SISIL+e03aNFlG4eua02OYn/x1Y1TaVulrddliYhIeiUkwLJl8L//waRJsHMn5MoVsLdXiMgili+HVj3Xc6xrK/IWOcqMPrNpXL6x12WJiMj5shZ+/NH1OEyaBH/9BTlzQrt2cMMNcN11UKBAQEpRiMgCvv8err1lJTE92lKoEMztt4BapWp5XZaIiPjLWlixwgWHiRPd4Lbs2eHaa+HZZ6FTp4AFh6QUIsLct99Cx8GLiOvZgVKFCzL/ljlcUvQSr8sSEZFzsRZWrToTHDZvhmzZoE0bt81y585QqJCnJSpEhLGvvoIej84k/sZuVC5Wnnn9ZlOuYDmvyxIRkdTExbnBkVOnuv+Jb9wIkZEQFQVDh0KXLlCkiNdV/k0hIkyNHw99n5+EveFf1Cx5ObNvnqmNtEREgtHRozBrlgsO06bBwYPuUkWLFvDQQ9C1KxQr5nWVKVKICEOjRsHt742G62+n4UWNmNHnG22kJSISTLZudaFh6lSYPx9On3Y9DB06uPENbdp4MsbhfClEhBFrYeRIGPLFSOj0b9pUaseXN04mT/Y8XpcmIpK1JSS4gZFTprjg8Msv7ni1anDvvS44NGzoxjyEkNCqVlJ0+rQbc/PCS6dZXfQpaPss3WvcwPjrPyFHZA6vyxMRyZqOHYMFC1xo+OYb2LULIiKgSRN46SXo2NGFiBCmEBHCTpyADz6Al944zPaSo8jW9nXIs4MBtW/j3eveITIi0usSRUSyjvh4tyjP7NluatzSpW6gZP78bipmp05uLYeiRb2uNMMoRISgffvgzTfhtY+2cKTGq0Te+AFkO841FVryYKNRtKvSDmOM12WKiIS/zZtdaJg9G+bOhcOH3fE6deDf/4bWrV3PQ47w7BVWiAghmzfDyy/D6BnLiL1qJPT7nMiICHrVvJEHGjxA7dK1vS5RRCS8HT7sBkJ++60LDps2uePlykG3bi40REVB8eLe1hkgChEhYOVKeP7FeCb9MgUajcT2XUz+7AUZVO/f3F3/bi4qcJHXJYqIhKfYWLfE9Jw5Ljj8+KMbJJkvHzRv7gZFtm7txjZkwR5ghYggZa3rGXtuxAnmHRyLafQqtudGyuWrwIONX+XW2reSP2d+r8sUEQkvp07BTz+53oYFC9zCTydPugGR9erBY4+56Zf164ftJYrzoRARZOLi4PPP4b+v7WJ17jcxV78LuQ5yVan6PNTkWbrW6Eq2CP1nExHJEKdPu9CwYIELDosXu9AAcMUVMHCg63Fo1gwKF/ay0qAU8N9GxphLgTeAhsBhYDQw3Fobn8ZrcgD/BRoAdYFc1tqw6jeKjoaxY+G5D35je7mRmFYTMJGn6XRJF4Y0fpBG5RppsKSIyIU6fdqt15DY07BokfsfMEDNmjBggFsp8pprwmoWRWYJaIgwxhQG5gBrgM5AZWAkEAEMS+OleYABwI/AEqBl5lYaOAcOwFtvWUZ+OZejl42ETjPJafLQ/6rbuL/hfVQpUsXrEkVEQld8vBtYNneuCw6LFrn58QCXXw633nqmpyFIl5YOZoHuibgDyA10s9YeBWYbYwoATxljXvQd+wdr7WFjTBFrrTXG3EUYhIitW+Gll0/z/pL/caruCOjyC0VylOT+xs8wqO4dFM2jBCwict6shT/+cKEhMTgkTru89FLo18+FhmuugRLaT+hCBTpEtANmJQsLnwEvAM2Aqam90FprM7m2gPj1V3hmxBEmb34fW/81uG47lfLXYGiLD+hdsze5suXyukQRkdDy119nQsO8ebBzpzt+8cVu2mVUFLRsCaVKeVtnGAp0iKgOzEt6wFq7zRgT7Xss1RARyqx1l96Gv7qNhTGvwVXvQ+VjNCrdkqEt3uPaKtcSYSK8LlNEJDQcOOB6GBKDw4YN7njx4i4sREW5W8WKWXLaZSAFOkQUxg2mTO6Q77GwEh8PX34JT777M2sKjoRa/yMiArpV68mjzR6kTuk6XpcoIhL8jhxxYxnmz3c9DatWub/O8uVzYxkGDXKh4fLL3VRMCRgv5gqmdFnCpHI83YwxA4GBAOXLl8/IU6cqPh62bIH16+GXXxN4ffpMdlUcAU3nk8vk5/a69/FA43soXzAw9YiIhKSjR11oSJx2+fPPboGnHDncTpfDh7vQUK8eZM/udbVZWqBDxCGgUArHC5JyD0W6WWtHAaMA6tatm6EB5eBBN27njz/gtz9OsmrLZtbv38jOmI3EF9oARTZCsbXQcidFspXl4WYvcXvd2yiYq2BGliEiEh6OHTsTGhYscFMw4+NdQGjQAIYNc4MhGzSA3Lk9LlaSCnSIWIcb+/A3Y0w5IK/vsaBx6pTbq+KPP2D1uhOs+HMT6/ZuZNvxjUTn2uiCQpENUHD7WT9RvoiilM9XhRolW9D18nbccNkNZI9UUhYR+dvx425Rp8S1GpYvPxMa6teHRx91azU0aAB58nhdraQh0CFiBjDEGJPfWnvMd6wncBJYGOBasBb27HFB4Ze1x1m+aRO/79nA1qMbOchGbGFfr0KBnVAadwPymuKUz1OVaiVacmW5KlQrVoWqRatSuXBlCucOu6EdIiIXxlo3jmHGDJg588wW2dmyudDwyCOup6FRI4WGEBPoEPEucA/whTHmBaAS8BTwctJpn8aYjcBCa23/JMfa4Xosavm+7+576Cdr7da03jQhwU2tXLXmGMs2bGL1jg1sPrKRvXEbOJ3f16uQfxfkw92AvLYUZXNXoWrRNtS5uCqXl6lClSJVqFy4si5LiIicy8GDbpfLGTNg1izYvdsdr13bbZHdsqULDXnzelunXBAT6OUXfMtev8nZy14/lXTZa2PMFmCBtbZfsmMXp3DKW6y1Y9N8z2L5Lf3yQf7dZx3PE1+K0jmrUrlwFa4sV5WrKlahWjHXo6DNrUREzkNCghsAOWOGuy1b5o4VLuw2rGrXDtq21VoNAWKMWWGtrZvp7xMmazilKUeZfLbxQz2pWaYqV1epQs0yValcpDL5cuTzujQRkdC1f7/bHjuxt2HfPrcuQ926cO21LjhcfTVERnpdaZYTqBCRJbaDvKJMdebf94HXZYiIhLbEsQ3ffONuP/3kjhUt6noZ2rVzvQ5aTjrLyBIhQkRE0unkSbfA09SpLjjs2OF6G+rVgyefdMHhqqvU25BFKUSIiMjZdu6EadNccJgzxwWJfPlcL8N110H79lCypNdVShBQiBARyeqsdYMiv/nGBYcVK9zxiy+G/v2hY0e3vHTOnN7WKUFHIUJEJCs6ccJdpkgc37Bzp7tM0aABPPusCw6XXaYNrCRNChEiIlmBtfDLL24WxaxZbpnp06chf343KDLxMkXx4l5XKiFEIUJEJFzt3esWfJo1y03F3LPHHa9ZE+6914WHa65xG1uJpINChIhIuDh1CpYsORMafv7ZHS9aFFq3dqGhTRsoU8bbOiVsKESIiIQqa2HTpjOXKObPd5tbZcvmtsx+5hkXHOrUgYgIr6uVMKQQISISSnbvhrlzz9y2bXPHK1aEPn1caGjZEgoU8LZOyRIUIkREgtmRI2677MTQsGaNO16okNsu+6GH3CWKKlU0k0ICTiFCRCSYxMTA4sVnQsPy5W4jq9y5oWlTuPlmiIqCWrW0SqR4TiFCRMRLCQkuKMyZ40LD4sUQG+sCQv36MHSoCw0NGmixJwk6ChEiIoF2+LAbCDl9utsBc98+d/yKK+DOO11ouOYat4aDSBBTiBARyWzWwu+/u/0opk1z0zDj46FIEbeBVfv20KqVdr+UkKMQISKSGaKj3bLS06a5HofEWRS1asEjj7jgUL++xjVISFOIEBHJKJs3u8AwbZpbsyE2FvLmdQs9Pf6463UoW9brKkUyjEKEiEh6JCTA2rXu0sSSJW5A5IYN7rGqVWHQIOjQwc2o0IBICVMKESIi/jh6FJYtg6VLXWj44Qe3hgNAsWJuhcjBg91liqpVva1VJEAUIkREkktcTnrJkjOhYfVqd9wYuPxyuPFGFxwaNdJCT5JlKUSIiOzd6zarWrnS9TYsWXJm2mWBAm6Nhm7dXGioXx8KFvS2XpEgoRAhIlmHtW6WRGJgSLzfufPMc6pUcZckGjVyoeHSSzWDQiQVChEiEp7i42H9ehcSkgaGQ4fc4xERUKOG26yqdm2302WtWm5PChHxi0KEiISHI0fgu+/c2gzLlsEvv7i1GgBy5HCrQXbv7sJC7dpQsybkyeNtzSIhTiFCRELT8eOwaJFbj2H+fFixwk27zJkT6tWDAQPOBIYaNSB7dq8rFgk7ChEiEhpOnnQzJebNc6Hhxx8hLs6Fg/r1YdgwtzV2gwaQK5fX1YpkCQoRIhKcTp1ylyUSexqWLnUrQEZEuJ6Gf//bhYbGjd2qkCIScAoRIuKtmBjYuBH++OPMbf16+PVXN6bBGDfgcfBgNwiyaVM37VJEPKcQISKZz1rYvt2Fg6Rh4Y8/YOtW93iiMmXgkkugf3/X09CsmdvtUkSCjkKEiGSc48fPBIV1687uWUicKQHu8sMll7jxCzff7L6uVs3d58/vXf0icl4UIkTk/CT2KiSGhKT327efeZ4xUKGCCwfNmrn7xFuZMlomWiQMKESISMqOHnVjFTZsODsorF8PJ06ceV6BAi4YtGhxJiRUr+5WftQsCZGwphAhkpUlDQqJ94lf79175nkp9SpUr+7uS5VSr4JIFqUQIRLOEhLcRlJ//eV2pUwaFpIHBYCyZd021p06ufsqVc7c587tzc8gIkFLIUIkVMXEwI4d/7xt337m61274PTps1+XWlCoXFnLQIvIeVGIEAk21rrLDNu3p3xLDAgHD/7ztXnzupBw0UXuskPZsme+r1xZQUFEMpRChEggWet++acWEBJvx4//87UlS7pAUKECNGlyJiAkvRUooPEJIhIwAQ8RxphLgTeAhsBhYDQw3Fobf47XFQReBboAEcA3wD3W2gOZW7FIGhIS3NbS+/alfNu//5/HTp06+xwREVC6tOstuOwyaNvWfX3RRVCunLsvXdrtRCkiEkQCGiKMMYWBOcAaoDNQGRiJCwXDzvHy/wHVgAFAAvAC8BXQNLPqlSwiJsZtI3348Nn3aR07cMAFggMHID6V/Js/PxQv7m5ly7qlm4sXd2skJIaEiy5ysxuyqVNQREJPoP/PdQeQG+hmrT0KzDbGFACeMsa86Dv2D8aYhkBboJm19jvfsR3AMmNMK2vtnADVL16x1g0QjI52uzlGR7vbiRPudvy4u6X2dUqPHT3qAkHynoHkIiLcZYJChaBgQXe75BK38VNiSChW7MzXid9rjQQRCXOBDhHtgFnJwsJnuF6FZsDUNF63JzFAAFhrfzTG/Ol7TCEiMyX+Aj91yt0n/TrpfUyM22Ux6b0/x06ePDsYJP066fep/cWfmpw5IV8+d8ub98zXRYq4+/z5zwSD1O4LFnTPjYjInLYVEQlhgQ4R1YF5SQ9Ya7cZY6J9j6UWIqoD61I4vtb32LlZ634JJSS4W+LXye9TeyzxdiHfp/Z1So/Fxbmvk9+ndCyl+6S3lI6ldjylgBAX5/9/YX9ky+Z+wefK5W45c7oZA4m3EiXcmgRJj6X1fWI4SBoW8ubVJQIRkUwW6P/LFsYNpkzukO+x9Lyu0jnfdcWK0P9LMiICIiPdL8a07iMjIXt2933SxxJvefL881jS5+bI4V6fnvvEQHCu+8hIr1tTREQygBd/qtkUjplUjqf7dcaYgcBAgBoFC8L995/5RRwRcfbXaR1Lep94u5Dv/f06eTjQtD0REQkygQ4Rh4BCKRwvSMo9DUlfVzyF44VSe521dhQwCqBu3bqWJ588v0pFREQkTYHu419HsjEMxphyQF5SHvOQ6ut8UhsrISIiIpks0CFiBtDWGJM/ybGewElg4TleV8oY0yTxgDGmLm48xIzMKFRERETSFugQ8S4QC3xhjGnlG7fwFPBy0mmfxpiNxpgPEr+31i4FZgEfG2O6GWO6AOOBRVojQkRExBsBDRHW2kNAFBCJm845HHgFSD5gIZvvOUndiOutGAN8DKwAumZmvSIiIpK6gM/OsNauAVqe4zkVUjh2GLjFdxMRERGPhfjiCSIiIuIVhQgRERFJF4UIERERSReFCBEREUkXhQgRERFJF4UIERERSReFCBEREUkXhQgRERFJF4UIERERSReFCBEREUkXhQgRERFJF4UIERERSReFCBEREUkXY631uoZMZ4w5BvzhdR0hoBiw3+siQoTayj9qJ/+prfyjdvJPNWtt/sx+k4BvBe6RP6y1db0uItgZY5arnfyjtvKP2sl/aiv/qJ38Y4xZHoj30eUMERERSReFCBEREUmXrBIiRnldQIhQO/lPbeUftZP/1Fb+UTv5JyDtlCUGVoqIiEjGyyo9ESIiIpLBgjpEGGN6GGOmGGN2GGOOG2NWGGN6pfC824wxG4wxMb7nRCV7vJUx5n/GmK3GmGhjzG/GmLuMMZEpnKuzMWa171xrjDE9M/NnzAiBbidjzFhjjE3hVj2zf9YLlYFt1cwYM98Ys9cYE2uM2WyMGWmMKXC+5wpGgW4nY8yCVD5TuTL7Z71QGdVWyZ6b1xiz3dcGl1/IuYJFoNtJnykwxjRPpQ2eP99zpcpaG7Q3YCkwAbgBaAmMACxwd5Ln3AjEA48DLYCPgZPA5UmeMxH4BrgJaA4MA2KBkcnerwkQB7zuO9dLQALQxuu2CLJ2GgusBRoku+Xyui0C2FZdgZeBHr62Goybu/5Nsvc757mC8eZBOy0A5qXwmTJet0Wg2irZOZ8FdvvOc3myx7L0Z+o82inLf6Z8/+Ys0DtZG5TLqM+U5411joYslsKxCcCfSb7/AxiT5PsIYDUw7hznedbXSDmTHJsFzEv2vOnAIq/bIsjaaSyw3Ouf28u2SuXct/n+wRa50HN5ffOgnRYAk73+uYOhrYAqwHHgDlL+5ajPlH/tlOU/U5wJEWmGgQv5TAX15QxrbUqrkq0ESgAYYyoBl+D+gk58TQIwCWjnx3lyAQV858qJS2ATkz3vM6ChMaZgun+QTBbIdgp1GdVWqTjgu8+RAefyVCDbKdRlQlu9CowG1iV/QJ+ps6TaTqEuk//9neVCzxXUISIVjYA1vq8Tr8En/xCtBesyhKIAAASiSURBVIoYY4qf4zz7rbX7fN9XBrKncq4IXCOHksxqp0SXGmOO+q5zLzLGNLvwkj2T7rYyxkQaY3IaY2rhLv98Ya3dnZ5zhYDMaqdEbYwbixNtjJlljLkio3+AAEpXWxlj2uO6m4encl59pvCrnRJl+c+UzzxjTLwxZosxZpg5e5zbBX2mQipE+AZ6dAbe8h0q7Ls/nOyph5I9nvw8l+K6wN5Kcjhd5wpGmdxO4BLxg0BH4F9AJDDbGHP1hVUeeBnQVr8DMbg22YsbT0Ky5+ozlXY7ASwE7gXaAgOB8sD3xpgKF1h6wKW3rYwxOYDXgCestYdIWZb/TPnZTqDPFMAR4HmgH64dvsIFr5eTvOaCPlMhs3eG7z/8BOBra+3YZA8nX+zCpHIcY0xh4HPgV9z1/uT8PlcwCkQ7WWtfS/bcabiE/BjQJX2VB14GtdX1QEGgJvAEMMkYc531XVg8z3MFpUC0k7X2ySTP/d4YMwf3l9F9vltIuMC2egAXtN7z462y8mfKr3bSZwqstStxwT3RHGNMLPCAMeY/yS6bpOszFRI9EcaYIsAMYBvQJ8lDiUmpULKXJH5/VrLyTe35GsgJdLLWnkrvuYJRgNrpH6y1J3EDUOukr/LAy6i2stb+bq1dYq19D+gFtMeNrTnvcwWjALXTP/gudSwmi3ymfF3GQ4GngPzGmEJAPt/j+Y0xef09V7p/gAAJUDv9Q1b7TKVx2sm4DoTESzsX9JkK+hBhjMmDm3aYA+hgrT2R5OHEazjJ1yeoDhxMeh3fdw1oAnAZ0M5auyfZazYBp1M5VwKw/kJ+jswWwHZKS6j8FZQhbZWCn333lTLgXJ4LYDulJat8psrifhlOxv1P/RAw1fe8JbhQ7++5glYA2yktWeUzdS6J7XBh5/Ji+oq/N1xamgbsAy5JY2rK6GRTU34l2dQUXNdXNNAojfebBcxJduwbgn+KZ0DbKYVz5wY24wbLed4egWqrFF7X1vcPM+pCz+X1LdDtlMJzSuL+AnrZ67YIRFvhfjE2T3a7z9dOtwBX6jN1fu2U1T9TaZz7edwfzEUz4jPleWOd44cd5ftw3MM/FwzJ6XtOL9wiGcNw3aNj+eeCG4/5zvNsCucpkOR5iYtNver7cL5IaCw2FbB2wl3X/h64HYgCegI/4Balqut1WwSwrT7BLczS0dcOQ3z/4JcAEUmed85zBeMtkO2E61adhhv81QK4GffX0UGgvNdtEai2SuG8zUl5/YMs/Znyp530mfr7PO8AT/v+/bXFDUiN558LCKb7M+V5Y52jIbf4GjKlW4Ukz7sN2Ij7RfYzyf7CwS06ktp5mid7bhfgN9+51gE3et0OwdROuDUjvgD+8p3nCDATaOB1OwS4re4GVvh+/uO4hVkeB/Kl8J5pnisYb4FsJ1wX9XRgF3AKt47E50B1r9shkG2Vwnmbk8pCQVn5M+VPO+kz9ffj9/D/9u7QBoAQCIAg+f7bo5Jv4AUvUatOzGgSzInNhYSzUXj/M3udzc1zuTPNlF88AYBk/MNKAGAmEQEAJCICAEhEBACQiAgAIBERAEAiIgCAREQAAImIAACSD31dh0mT9gC1AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "fig, axs = plt.subplots(figsize=(8, 8))\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Cumulative New Yearly Installs')\n", - "axs.plot(USyearly['Capacity_'+SFscenarios[0]]/1e12, 'g', label='Active in Field Installs')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels')\n", - "axs.legend()\n", - "axs.set_xlim([2020,2050])\n", - "axs.set_ylabel('Power [TW]')\n", - "fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Power [TW]')" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "fig, axs = plt.subplots(figsize=(8, 8))\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Cumulative New Yearly Installs Scen. 1 ')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6, 'b--', label='Cumulative New Yearly Installs Scen. 2 ')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'b.', label='Cumulative New Yearly Installs Scen. 3 ')\n", - "axs.plot(USyearly['Capacity_'+SFscenarios[0]]/1e12, 'g', label='Active in Field Installs Scen. 1')\n", - "axs.plot(USyearly['Capacity_'+SFscenarios[1]]/1e12, 'g--', label='Active in Field Installs Scen. 2')\n", - "axs.plot(USyearly['Capacity_'+SFscenarios[2]]/1e12, 'g.', label='Active in Field Installs Scen. 3')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels Scen. 1')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6-USyearly['Capacity_'+SFscenarios[1]]/1e12, 'r--', label='Decomissioned PV Panels Scen 2')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12, 'r.', label='Decomissioned PV Panels Scen 3')\n", - "\n", - "axs.legend()\n", - "axs.set_xlim([2020,2050])\n", - "axs.set_ylabel('Power [TW]')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.3692474079346881 1.8234325025965212 2.0681043184189196\n" - ] - } - ], - "source": [ - "foo0 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12).sum()\n", - "foo1 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6-USyearly['Capacity_'+SFscenarios[1]]/1e12).sum()\n", - "foo2 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12).sum()\n", - "print(foo0, foo1, foo2)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cumulative Installs 12.481579419861925\n", - "Cumulative Waste 1.3692474079346881\n", - "Fraction of Decomisioned to Installed Cumulative by 2050 0.10970145378843692\n" - ] - } - ], - "source": [ - "E = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6).sum()\n", - "F = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12).sum()\n", - "print(\"Cumulative Installs\", E)\n", - "print(\"Cumulative Waste\", F)\n", - "print(\"Fraction of Decomisioned to Installed Cumulative by 2050\", F/E)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'95-by-35.Adv'" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SFscenarios[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "fig, axs = plt.subplots(figsize=(8, 8))\n", - "axs.plot(USyearly['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Yearly New Yearly Installs')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels')\n", - "axs.legend()\n", - "axs.set_xlim([2020,2050])\n", - "axs.set_ylabel('Power [TW]')\n", - "fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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in 2030\n", - " ########################################################### \n", - "\n", - "SCENARIO : Reference.Mod\n", - "********************************\n", - "********************************\n", - "Module : 15650000 tons\n", - "glass : 13355000 tons\n", - "aluminum : 1834000 tons\n", - "copper : 11000 tons\n", - "silicon : 530000 tons\n", - "silver : 5000 tons\n", - "Capacity in Year 2030 [GW]: 314\n", - "Capacity in Year 2050 [GW]: 642\n", - "****************************\n", - "\n", - "SCENARIO : 95-by-35.Adv\n", - "********************************\n", - "********************************\n", - "Module : 24000000 tons\n", - "glass : 20460000 tons\n", - "aluminum : 2736000 tons\n", - "copper : 17000 tons\n", - "silicon : 799000 tons\n", - "silver : 6000 tons\n", - "Capacity in Year 2030 [GW]: 489\n", - "Capacity in Year 2050 [GW]: 952\n", - "****************************\n", - "\n", - "SCENARIO : 95-by-35_Elec.Adv_DR\n", - "********************************\n", - "********************************\n", - "Module : 28490000 tons\n", - "glass : 24330000 tons\n", - "aluminum : 3192000 tons\n", - "copper : 21000 tons\n", - "silicon : 929000 tons\n", - "silver : 7000 tons\n", - "Capacity in Year 2030 [GW]: 587\n", - "Capacity in Year 2050 [GW]: 1530\n", - "****************************\n", - "\n" - ] - } - ], - "source": [ - "materials = ['Module', 'glass', 'aluminum', 'copper', 'silicon', 'silver']\n", - "\n", - "print(\" Appendix Table I: Metric Tonnes Installed in field in 2030\")\n", - "print(\" ########################################################### \\n\")\n", - "#Loop over scenarios\n", - "for kk in range (0, 3):\n", - " obj = SFscenarios[kk]\n", - " print(\"SCENARIO :\", obj)\n", - "\n", - " print(\"********************************\")\n", - " print(\"********************************\")\n", - "\n", - " modulemat = 0\n", - " for ii in range(0, len(materials)):\n", - " installedmat = (UScum3sig['VirginStock_'+materials[ii]+'_'+obj].loc[2030]-\n", - " UScum3sig['Waste_'+materials[ii]+'_'+obj].loc[2030])\n", - " print(materials[ii], ':', round(installedmat/1000)*1000, 'tons')\n", - "\n", - " print(\"Capacity in Year 2030 [GW]:\", round(USyearly3sig['Capacity_'+obj].loc[2030]/1e9))\n", - " print(\"Capacity in Year 2050 [GW]:\", round(USyearly3sig['Capacity_'+obj].loc[2050]/1e9))\n", - " print(\"****************************\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Mining Capacity" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "mining2020_aluminum = 65267000\n", - "mining2020_silver = 22260\n", - "mining2020_copper = 20000000\n", - "mining2020_silicon = 8000000" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 10})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(1,1, figsize=(4, 6), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .3, wspace=.2)\n", - "i = 0\n", - "\n", - "obj = SFscenarios[2]\n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "# ROW 2, Aluminum and Silicon: g- 4 aluminum k - 1 silicon orange - 3 copper gray - 2 silver\n", - "axs.plot(USyearly[keyw+materials[2]+'_'+SFscenarios[2]]*100/mining2020_silver, \n", - " color = 'gray', linewidth=2.0, label='Silver')\n", - "axs.fill_between(USyearly.index, USyearly[keyw+materials[2]+'_'+SFscenarios[0]]*100/mining2020_silver, \n", - " USyearly[keyw+materials[2]+'_'+SFscenarios[2]]*100/mining2020_silver,\n", - " color='gray', lw=3, alpha=.3)\n", - " \n", - "axs.plot(USyearly[keyw+materials[1]+'_'+SFscenarios[2]]*100/mining2020_silicon, \n", - " color = 'k', linewidth=2.0, label='Silicon')\n", - "axs.fill_between(USyearly.index, USyearly[keyw+materials[1]+'_'+SFscenarios[0]]*100/mining2020_silicon, \n", - " USyearly[keyw+materials[1]+'_'+SFscenarios[2]]*100/mining2020_silicon,\n", - " color='k', lw=3, alpha=.5)\n", - "\n", - "axs.plot(USyearly[keyw+materials[4]+'_'+SFscenarios[2]]*100/mining2020_aluminum, \n", - " color = 'g', linewidth=2.0, label='Aluminum')\n", - "\n", - "axs.fill_between(USyearly.index, USyearly[keyw+materials[4]+'_'+SFscenarios[0]]*100/mining2020_aluminum, \n", - " USyearly[keyw+materials[4]+'_'+SFscenarios[2]]*100/mining2020_aluminum,\n", - " color='g', lw=3, alpha=.3)\n", - "\n", - "\n", - "axs.plot(USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper, \n", - " color = 'orange', linewidth=2.0, label='Copper')\n", - "\n", - "axs.fill_between(USyearly.index, USyearly[keyw+materials[3]+'_'+SFscenarios[0]]*100/mining2020_copper, \n", - " USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper,\n", - " color='orange', lw=3, alpha=.3)\n", - "\n", - "axs.set_xlim([2020,2050])\n", - "axs.legend()\n", - "#axs.set_yscale('log')\n", - "axs.minorticks_on()\n", - "\n", - "axs.set_ylabel('Virgin material needs as a percentage \\nof 2020 global mining production capacity [%]')\n", - "\n", - "fig.savefig(title_Method+' Fig_1x1_MaterialNeeds Ratio to Production.png', bbox_inches = \"tight\", dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "keyw='VirginStock_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "newdf = pd.DataFrame()\n", - "\n", - "\n", - "newdf['Silver_Ref'] = USyearly[keyw+materials[2]+'_'+SFscenarios[0]]*100/mining2020_silver\n", - "newdf['Silver_High'] = USyearly[keyw+materials[2]+'_'+SFscenarios[2]]*100/mining2020_silver\n", - " \n", - " \n", - " \n", - "newdf['Silicon_Ref'] = USyearly[keyw+materials[1]+'_'+SFscenarios[0]]*100/mining2020_silicon\n", - "newdf['Silicon_High'] = USyearly[keyw+materials[1]+'_'+SFscenarios[2]]*100/mining2020_silicon \n", - " \n", - " \n", - "newdf['Aluminium_Ref'] = USyearly[keyw+materials[4]+'_'+SFscenarios[0]]*100/mining2020_aluminum\n", - "newdf['Aluminum_High'] = USyearly[keyw+materials[4]+'_'+SFscenarios[2]]*100/mining2020_aluminum\n", - " \n", - "newdf['Copper_Ref'] = USyearly[keyw+materials[3]+'_'+SFscenarios[0]]*100/mining2020_copper\n", - "newdf['Copper_High'] = USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper\n", - "\n", - " \n", - "newdf['Copper_Ref'] = USyearly[keyw+materials[3]+'_'+SFscenarios[0]]*100/mining2020_copper\n", - "newdf['Copper_High'] = USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper\n", - "\n", - "newdf.to_csv(title_Method+' Demand as Percentage of Mining.csv')\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib as mpl\n", - "\n", - "plt.rcParams.update({'font.size': 10})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(3,3, figsize=(15, 10), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .3, wspace=.2)\n", - "axs = axs.ravel()\n", - "i = 0\n", - "\n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "titlesscens = ['Reference Scenario', 'Grid Decarbonization Scenario', 'High Electrification Scenario']\n", - "\n", - "\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFscenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='c', alpha=0.5, label='Glass')\n", - "# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " # axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " # axs[i].plot([],[],color='k', label='silicon', linewidth=5)\n", - " # axs[i].plot([],[],color='m', label='silver', linewidth=5)\n", - " # axs[i].plot([],[],color='r', label='copper', linewidth=5)\n", - " # axs[i].plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " axs[i].stackplot(rr.scenario[obj].data['year'], USyearly[keyw+materials[0]+'_'+obj]/1e6, \n", - " USyearly[keyw+materials[1]+'_'+obj]/1e6, \n", - " USyearly[keyw+materials[2]+'_'+obj]/1e6, \n", - " USyearly[keyw+materials[3]+'_'+obj]/1e6, \n", - " USyearly[keyw+materials[4]+'_'+obj]/1e6, \n", - " colors=['c','k','gray','orange', 'g'])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " axs[i].set_title(titlesscens[kk])\n", - " axs[i].legend(loc='center right')\n", - "\n", - " #axs[i].legend(materials)\n", - "\n", - " i += 1 \n", - "\n", - "# 2nd axis plot\n", - "i = 0\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFscenarios[kk]\n", - " ax2=axs[i].twinx()\n", - " \n", - " module = (UScum[keyw+materials[0]+'_'+obj]/1e6 + \n", - " UScum[keyw+materials[1]+'_'+obj]/1e6 + \n", - " UScum[keyw+materials[2]+'_'+obj]/1e6 +\n", - " UScum[keyw+materials[3]+'_'+obj]/1e6 +\n", - " UScum[keyw+materials[4]+'_'+obj]/1e6)\n", - " ax2.plot(rr.scenario[obj].data['year'], module, \n", - " color = 'r', linewidth=4.0, label='cumulative')\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " # axs[i].set_xlim([2010, 2050])\n", - " # axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " ax2.set_yscale('log')\n", - "# ax2.set_ylim([1e3/1e6, 1e8/1e6])\n", - " ax2.set_ylim([1e0, 1e2])\n", - "\n", - " i += 1 \n", - "\n", - " ax2.legend()\n", - "\n", - "\n", - "i = 3\n", - "# ROW 2, Aluminum and Silicon:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - "\n", - "\n", - " obj = SFscenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - "\n", - " axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[4]+'_'+obj]/1e6, color='g', lw=3, label='Aluminum')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], \n", - " # color='g', lw=3, alpha=.6)\n", - " \n", - " axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[1]+'_'+obj]/1e6, color='k', lw=3, label='Silicon')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], \n", - " # color='k', lw=3)# alpha=.3)\n", - "\n", - "\n", - " # silicon aluminum 'k ''g'\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " axs[i].legend()\n", - "\n", - " i += 1 \n", - "\n", - "\n", - "\n", - "# ROW 3:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - "\n", - " obj = SFscenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "\n", - " axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[3]+'_'+obj], color='orange', lw=3, label='Copper')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], \n", - " # color='orange', lw=3)# alpha=.3)\n", - "\n", - " axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[2]+'_'+obj], color='gray', lw=3, label='Silver')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], \n", - " # color='gray', lw=3)# , alpha=.6)\n", - " \n", - " \n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " axs[i].legend()\n", - " axs[i].yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n", - "\n", - " i += 1 \n", - " \n", - "for i in range (0, 3):\n", - " axs[i].set_ylim([0, 0.8e7/1e6])\n", - " axs[i].minorticks_on()\n", - "\n", - " #a0.tick_params(axis='y', which='minor', bottom=False)\n", - " # axs[i].set_ylim([0, 1e7/1e6])\n", - " \n", - " axs[i+3].set_ylim([0, 0.5e6/1e6])\n", - " axs[i+3].minorticks_on()\n", - "\n", - " axs[i+6].set_ylim([0, 3500])\n", - "\n", - " #axs[i+3].set_ylim([1e0, 10e8])\n", - " #axs[i+6].set_ylim([1e0, 5e6])\n", - "\n", - "# axs[i+3].set_yscale('log')\n", - "# axs[i+6].set_yscale('log')\n", - "\n", - "axs[0].set_ylabel('Mass [Tons]')\n", - "axs[3].set_ylabel('Mass [Tons]')\n", - "#axs[5].legend(materials)\n", - " \n", - "axs[0].set_ylabel('Yearly Mass [Million Tonnes]')\n", - "axs[3].set_ylabel('Yearly Mass [Million Tonnes]')\n", - "axs[6].set_ylabel('Yearly Mass [Tonnes]')\n", - "\n", - "#axs[8].legend(materials)\n", - "\n", - "fig.savefig(title_Method+' Fig_3x3_MaterialNeeds_Nation.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='Cumulative_Area_disposed'\n", - "\n", - "USyearly_Areadisp=pd.DataFrame()\n", - "\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 3):\n", - " obj = SFscenarios[kk]\n", - " # Loop over Materials\n", - " foo = rr.scenario[obj].data[keyword].copy()\n", - " USyearly_Areadisp[\"Areadisp_\"+obj] = foo\n", - "\n", - " # Loop over STATEs\n", - " #for jj in range (1, len(STATEs)): \n", - " # USyearly_Areadisp[\"Areadisp_\"+obj] += rr.scenario[obj].data[keyword]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "UScum_Areadisp = USyearly_Areadisp.copy()\n", - "UScum_Areadisp = UScum_Areadisp.cumsum()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "755.6564601943498\n" - ] - } - ], - "source": [ - "A = UScum['Waste_Module_Reference.Mod'].iloc[-1]\n", - "#47700000 # tonnes cumulative by 2050\n", - "A = A*1000 # convert to kg\n", - "A = A/10.05599 # convert to m2 if each m2 is ~avg 10 kg\n", - "#A = A*2 # convert to area if each module is ~2 m2\n", - "A = A/1e6 # Convert to km 2\n", - "print(A)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1077.8757878114952\n" - ] - } - ], - "source": [ - "B = UScum['Waste_Module_95-by-35_Elec.Adv_DR'].iloc[-1]\n", - "#47700000 # tonnes cumulative by 2050\n", - "B = B*1000 # convert to kg\n", - "B= B/10.05599 # convert to m2 if each m2 is ~avg 10 kg\n", - "#A = A*2 # convert to area if each module is ~2 m2\n", - "B =B/1e6 # Convert to km 2\n", - "print(B)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "C = UScum_Areadisp['Areadisp_Reference.Mod'].iloc[-1]/1e6\n", - "D = UScum_Areadisp['Areadisp_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "# MANHATTAN SIZE:\n", - "manhattans = 59.103529" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reference Cumulative Area by 2050 of Waste PV Modules EoL 560 km^2\n", - "High Electrification Cumulative Area by 2050 of Waste PV Modules EoL 683 km^2\n", - "\n", - "Reference Waste equals 9 Manhattans \n", - "High Electrification equals 12 Manhattans \n", - "\n", - "MFG SCrap + Eol Waste\n", - "Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL 756 km^2\n", - "High Electrification Cumulative Area by 2050 of Waste PV Mfg + Modules EoL 1078 km^$\n" - ] - } - ], - "source": [ - "print(\"Reference Cumulative Area by 2050 of Waste PV Modules EoL\", round(C), \" km^2\")\n", - "print(\"High Electrification Cumulative Area by 2050 of Waste PV Modules EoL\", round(D), \" km^2\")\n", - "\n", - "\n", - "print(\"\")\n", - "print(\"Reference Waste equals \", round(C/manhattans), \" Manhattans \")\n", - "print(\"High Electrification equals \", round(D/manhattans), \" Manhattans \")\n", - "\n", - "print(\"\")\n", - "print (\"MFG SCrap + Eol Waste\")\n", - "print(\"Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL\", round(A), \" km^2\")\n", - "print(\"High Electrification Cumulative Area by 2050 of Waste PV Mfg + Modules EoL\", round(B), \" km^$\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### New Section" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "VirginStock_aluminum_Reference.Mod\n", - "VirginStock_aluminum_95-by-35.Adv \n", - "VirginStock_aluminum_95-by-35_Elec.Adv_DR \n", - "Waste_EOL_aluminum_Reference.Mod \n", - "Waste_EOL_aluminum_95-by-35.Adv \n", - "Waste_EOL_aluminum_95-by-35_Elec.Adv_DR \n", - "\n", - "VirginStock_silver_Reference.Mod\n", - "VirginStock_silver_95-by-35.Adv \n", - "VirginStock_silver_95-by-35_Elec.Adv_DR \n", - "Waste_EOL_silver_Reference.Mod \n", - "Waste_EOL_silver_95-by-35.Adv \n", - "Waste_EOL_silver_95-by-35_Elec.Adv_DR \n" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "year\n", - "2010 128.426774\n", - "2011 219.915502\n", - "2012 172.505147\n", - "2013 234.947057\n", - "2014 214.149815\n", - "2015 321.420822\n", - "2016 283.204082\n", - "2017 269.165286\n", - "2018 273.761119\n", - "2019 465.757234\n", - "2020 382.744131\n", - "2021 91.373232\n", - "2022 82.747506\n", - "2023 231.970049\n", - "2024 212.484190\n", - "2025 407.290500\n", - "2026 380.662842\n", - "2027 338.591666\n", - "2028 315.521303\n", - "2029 489.351669\n", - "2030 452.797028\n", - "2031 131.150695\n", - "2032 130.404439\n", - "2033 116.233802\n", - "2034 115.657615\n", - "2035 122.605382\n", - "2036 122.066957\n", - "2037 206.839493\n", - "2038 206.024174\n", - "2039 201.195632\n", - "2040 200.476230\n", - "2041 181.903956\n", - "2042 181.308816\n", - "2043 331.327899\n", - "2044 330.328800\n", - "2045 256.781267\n", - "2046 256.063206\n", - "2047 402.562428\n", - "2048 401.512942\n", - "2049 279.696766\n", - "2050 279.013840\n", - "Name: VirginStock_silver_Reference.Mod, dtype: float64" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "USyearly['VirginStock_silver_Reference.Mod']" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    VirginStock_glass_Reference.ModVirginStock_silicon_Reference.ModVirginStock_silver_Reference.ModVirginStock_copper_Reference.ModVirginStock_aluminum_Reference.ModVirginStock_Module_Reference.ModVirginStock_glass_95-by-35.AdvVirginStock_silicon_95-by-35.AdvVirginStock_silver_95-by-35.AdvVirginStock_copper_95-by-35.Adv...Waste_MFG_silver_95-by-35_Elec.Adv_DRWaste_MFG_copper_95-by-35_Elec.Adv_DRWaste_MFG_aluminum_95-by-35_Elec.Adv_DRWaste_MFG_Module_95-by-35_Elec.Adv_DRnew_Installed_Capacity_[MW]Reference.Modnew_Installed_Capacity_[MW]95-by-35.Advnew_Installed_Capacity_[MW]95-by-35_Elec.Adv_DRCapacity_Reference.ModCapacity_95-by-35.AdvCapacity_95-by-35_Elec.Adv_DR
    year
    20107.018429e+047082.020411128.42677449.78405316728.9661959.417348e+047.018429e+047082.020411128.42677449.784053...27.7401835.874518498.5231939032.8123991200.6513501200.6513501200.6513501.208819e+091.208819e+091.208819e+09
    20111.442186e+0514456.874009219.915502102.29909034020.0025901.930177e+051.442186e+0514456.874009219.915502102.299090...47.50174812.0712931013.79607718445.3738322534.3001092534.3001092534.3001093.751735e+093.751735e+093.751735e+09
    20121.428233e+0514473.896289172.505147100.30625033296.0946281.908661e+051.428233e+0514473.896289172.505147100.306250...37.26111211.836138992.22362018471.1319422534.3001092534.3001092534.3001096.282106e+096.282106e+096.282106e+09
    20132.806388e+0527437.479671234.947057195.16319053446.0589113.619524e+052.806387e+0527437.472114234.946992195.163136...50.74855023.0292501592.69211735045.0286555123.0337325123.0323215123.0323211.140951e+101.140951e+101.140951e+10
    20142.793654e+0526818.242077214.149815191.57122951596.8457723.581862e+052.793653e+0526818.234691214.149756191.571176...46.25634722.6053991537.58558133977.3937695123.0337325123.0323215123.0323211.651318e+101.651318e+101.651318e+10
    20154.899909e+0547187.198999321.420822365.86302885920.5249416.237859e+054.899912e+0547187.224863321.420998365.863228...69.42693643.1718612560.43304757897.0712309477.5373109477.5425059477.5425052.598093e+102.598093e+102.598093e+10
    20164.711404e+0543370.444699283.204082380.71761778848.3721235.940232e+054.711407e+0543370.468471283.204237380.717825...61.17211544.9247032349.68277752399.0719589477.5373109477.5425059477.5425053.540741e+103.540742e+103.540742e+10
    20174.563168e+0537304.395438269.165286422.53738073575.1420345.678880e+054.563165e+0537304.378239269.165162422.537185...58.13967549.8593882192.53822245378.4469539156.7831259156.7789039156.7789034.448377e+104.448377e+104.448377e+10
    20184.407763e+0532803.994115273.761119420.61800269844.8905545.441196e+054.407761e+0532803.978991273.760993420.617808...59.13237449.6329012081.37677939777.9031049156.7831259156.7789039156.7789035.353175e+105.353175e+105.353175e+10
    20198.022008e+0555529.114850465.757234824.668750121936.3323409.809567e+057.954251e+0555060.092196461.823249817.703245...101.14528797.8349073653.26925168667.32861216904.81495016762.02965316995.8429987.033950e+107.019597e+107.043100e+10
    20207.845582e+0548511.986589382.744131778.416420113139.3793759.473708e+057.779315e+0548102.233601379.511311771.841582...83.11790492.3477433389.70844762502.40027116904.81495016762.02965316995.8429988.707455e+108.678827e+108.725705e+10
    20212.014970e+0512144.50601191.373232201.03119328586.8949042.425208e+052.038239e+0512284.75413192.428435203.352757...20.81053925.012439898.24298116147.9929094479.8336684531.5680284723.5930239.127172e+109.103856e+109.169843e+10
    20221.972441e+0511572.39196782.747506197.69215827497.1783082.365941e+051.995219e+0511706.03315583.703097199.975162...18.84600324.596994864.00245615336.7550894479.8336684531.5680284723.5930239.543730e+109.525701e+109.610702e+10
    20235.876418e+0533306.015492231.970049596.53431079490.4009047.012668e+051.740481e+0698645.946912687.0502171766.818726...125.582329176.4250725937.092552101903.10348413634.58969140383.00562034173.1443281.087628e+111.354546e+111.300625e+11
    20245.774328e+0531411.747254212.484190593.24572575647.9864186.852983e+051.710244e+0693035.492425629.3368871757.078575...115.033210175.4524725650.10481394583.41805413634.58969140383.00562034173.1443281.219987e+111.754379e+111.638325e+11
    20251.172042e+0662055.099995407.2905001166.768295149852.3103901.385523e+062.717327e+06143872.011133944.2850522705.100808...230.039851360.00760311676.824513191573.11397628006.32474464931.42812973232.0457581.495638e+112.398391e+112.366270e+11
    20261.158703e+0659869.090073380.6628421117.797678144657.2270641.364728e+062.686401e+06138803.843587882.5500022591.564594...215.000407344.89766711272.012094178232.36271828006.32474464931.42812973232.0457581.769361e+113.038687e+113.090150e+11
    20271.091469e+0655065.927170338.5916661018.300894132846.3856771.280738e+061.611470e+0681300.609591499.9045741503.443013...170.187738279.6123719212.219072142769.57149426628.14038439314.40287661963.9490072.026970e+113.417583e+113.696333e+11
    20281.087800e+0653748.963838315.521303973.886665129491.2112951.272330e+061.606053e+0679356.214441465.8429561437.868817...158.591785267.4167938979.554847135712.06915226628.14038439314.40287661963.9490072.282008e+113.793160e+114.298146e+11
    20291.809482e+0687653.625830489.3516691554.185968210842.5465592.110021e+062.324661e+06112609.578471628.6754791996.679830...193.320389335.41912811491.516681165699.04731344459.75121457117.93203981314.8495712.713132e+114.343939e+115.089571e+11
    20301.805498e+0685658.543712452.7970281484.231439205677.9738452.098772e+062.319544e+06110046.474502581.7133291906.808476...178.879328320.32178011210.032823156850.12426044459.75121457117.93203981314.8495713.139969e+114.889834e+115.874918e+11
    20315.229548e+0524810.625586131.150695429.90119759573.7328626.079002e+051.521068e+0672164.274809381.4653831250.412169...107.567663192.6229586741.06419194320.57628112957.18735437687.32173849200.3422053.245497e+115.235060e+116.331016e+11
    20325.199791e+0524669.451544130.404439427.45503259234.7545226.044412e+051.512413e+0671753.655483379.2948211243.297244...106.955596191.5269216702.70710693783.88618612957.18735437687.32173849200.3422053.342861e+115.571010e+116.777191e+11
    20334.634747e+0521988.700426116.233802381.00484952797.9014725.387585e+052.092666e+0699282.682045524.8151721720.300996...217.825212390.06273413650.698544190999.77642511610.68853352424.212235100734.8920953.427705e+116.054757e+117.740140e+11
    20344.611772e+0521879.699388115.657615379.11615552536.1749515.360878e+052.082292e+0698790.524019522.2135911711.773225...216.745422388.12913913583.030138190052.96404111610.68853352424.212235100734.8920953.501751e+116.525823e+118.688293e+11
    20354.888809e+0523194.053430122.605382401.89036455692.1202255.682916e+052.690067e+06127625.273162674.6360832211.401626...192.503581344.71892612063.839314168796.53480312365.75409068042.55876689886.8442653.579958e+117.148294e+119.519686e+11
    20364.867340e+0523092.196125122.066957400.12545255447.5467915.657959e+052.678254e+06127064.803365671.6734012201.690196...191.658197343.20508412010.860642168055.26031012365.75409068042.55876689886.8442653.658960e+117.768905e+111.034795e+12
    20378.247589e+0539129.165183206.839493678.00285493954.5206389.587274e+051.040769e+0649377.354606261.012136855.576325...127.393125228.1246987983.488844111704.50943221040.88463026551.63269559996.0657853.794087e+117.939081e+111.084122e+12
    20388.215079e+0538974.926057206.024174675.33030793584.1712319.549483e+051.036666e+0649182.719224259.983279852.203819...126.890968227.2254777952.019572111264.19321721040.88463026551.63269559996.0657853.947659e+118.126903e+111.135060e+12
    20398.022544e+0538061.479491201.195632659.50274291390.8600819.325674e+053.234938e+0515347.56486581.128297265.931888...28.68443351.3656261797.59977525151.91101420625.0940158316.67534913613.5036854.087451e+118.121507e+111.138251e+12
    20407.993858e+0537925.385582200.476230657.14460291064.0798429.292329e+053.223371e+0515292.68759580.838212264.981013...28.58186851.1819621791.17221625061.97699320625.0940158316.67534913613.5036854.207746e+118.096610e+111.139448e+12
    20417.253301e+0534411.948436181.903956596.26621682627.8328308.431480e+053.543131e+0516809.73039888.857406291.267272...77.181043138.2092014836.79154967676.10474118778.5343219173.03766736887.2634074.318456e+118.089075e+111.164872e+12
    20427.229570e+0534299.361950181.308816594.31539682357.4971558.403895e+053.531539e+0516754.73355688.566689290.314325...76.928527137.7570184820.96689067454.68703218778.5343219173.03766736887.2634074.424574e+118.076129e+111.189647e+12
    20431.321148e+0662679.442535331.3278991086.065618150501.9836151.535747e+064.928646e+0523383.051811123.604441405.165196...92.162616165.0369195775.65872480812.67939334424.36131412842.27478144331.1427774.680619e+118.091276e+111.220898e+12
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    41 rows Ɨ 78 columns

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    " - ], - "text/plain": [ - " VirginStock_glass_Reference.Mod VirginStock_silicon_Reference.Mod \\\n", - "year \n", - "2010 7.018429e+04 7082.020411 \n", - "2011 1.442186e+05 14456.874009 \n", - "2012 1.428233e+05 14473.896289 \n", - "2013 2.806388e+05 27437.479671 \n", - "2014 2.793654e+05 26818.242077 \n", - "2015 4.899909e+05 47187.198999 \n", - "2016 4.711404e+05 43370.444699 \n", - "2017 4.563168e+05 37304.395438 \n", - "2018 4.407763e+05 32803.994115 \n", - "2019 8.022008e+05 55529.114850 \n", - "2020 7.845582e+05 48511.986589 \n", - "2021 2.014970e+05 12144.506011 \n", - "2022 1.972441e+05 11572.391967 \n", - "2023 5.876418e+05 33306.015492 \n", - "2024 5.774328e+05 31411.747254 \n", - "2025 1.172042e+06 62055.099995 \n", - "2026 1.158703e+06 59869.090073 \n", - "2027 1.091469e+06 55065.927170 \n", - "2028 1.087800e+06 53748.963838 \n", - "2029 1.809482e+06 87653.625830 \n", - "2030 1.805498e+06 85658.543712 \n", - "2031 5.229548e+05 24810.625586 \n", - "2032 5.199791e+05 24669.451544 \n", - "2033 4.634747e+05 21988.700426 \n", - "2034 4.611772e+05 21879.699388 \n", - "2035 4.888809e+05 23194.053430 \n", - "2036 4.867340e+05 23092.196125 \n", - "2037 8.247589e+05 39129.165183 \n", - "2038 8.215079e+05 38974.926057 \n", - "2039 8.022544e+05 38061.479491 \n", - "2040 7.993858e+05 37925.385582 \n", - "2041 7.253301e+05 34411.948436 \n", - "2042 7.229570e+05 34299.361950 \n", - "2043 1.321148e+06 62679.442535 \n", - "2044 1.317164e+06 62490.436489 \n", - "2045 1.023898e+06 48576.973797 \n", - "2046 1.021035e+06 48441.133560 \n", - "2047 1.605191e+06 76155.339399 \n", - "2048 1.601007e+06 75956.801268 \n", - "2049 1.115273e+06 52912.046993 \n", - "2050 1.112549e+06 52782.853553 \n", - "\n", - " VirginStock_silver_Reference.Mod VirginStock_copper_Reference.Mod \\\n", - "year \n", - "2010 128.426774 49.784053 \n", - "2011 219.915502 102.299090 \n", - "2012 172.505147 100.306250 \n", - "2013 234.947057 195.163190 \n", - "2014 214.149815 191.571229 \n", - "2015 321.420822 365.863028 \n", - "2016 283.204082 380.717617 \n", - "2017 269.165286 422.537380 \n", - "2018 273.761119 420.618002 \n", - "2019 465.757234 824.668750 \n", - "2020 382.744131 778.416420 \n", - "2021 91.373232 201.031193 \n", - "2022 82.747506 197.692158 \n", - "2023 231.970049 596.534310 \n", - "2024 212.484190 593.245725 \n", - "2025 407.290500 1166.768295 \n", - "2026 380.662842 1117.797678 \n", - "2027 338.591666 1018.300894 \n", - "2028 315.521303 973.886665 \n", - "2029 489.351669 1554.185968 \n", - "2030 452.797028 1484.231439 \n", - "2031 131.150695 429.901197 \n", - "2032 130.404439 427.455032 \n", - "2033 116.233802 381.004849 \n", - "2034 115.657615 379.116155 \n", - "2035 122.605382 401.890364 \n", - "2036 122.066957 400.125452 \n", - "2037 206.839493 678.002854 \n", - "2038 206.024174 675.330307 \n", - "2039 201.195632 659.502742 \n", - "2040 200.476230 657.144602 \n", - "2041 181.903956 596.266216 \n", - "2042 181.308816 594.315396 \n", - "2043 331.327899 1086.065618 \n", - "2044 330.328800 1082.790653 \n", - "2045 256.781267 841.707886 \n", - "2046 256.063206 839.354141 \n", - "2047 402.562428 1319.566550 \n", - "2048 401.512942 1316.126419 \n", - "2049 279.696766 916.823007 \n", - "2050 279.013840 914.584433 \n", - "\n", - " VirginStock_aluminum_Reference.Mod VirginStock_Module_Reference.Mod \\\n", - "year \n", - "2010 16728.966195 9.417348e+04 \n", - "2011 34020.002590 1.930177e+05 \n", - "2012 33296.094628 1.908661e+05 \n", - "2013 53446.058911 3.619524e+05 \n", - "2014 51596.845772 3.581862e+05 \n", - "2015 85920.524941 6.237859e+05 \n", - "2016 78848.372123 5.940232e+05 \n", - "2017 73575.142034 5.678880e+05 \n", - "2018 69844.890554 5.441196e+05 \n", - "2019 121936.332340 9.809567e+05 \n", - "2020 113139.379375 9.473708e+05 \n", - "2021 28586.894904 2.425208e+05 \n", - "2022 27497.178308 2.365941e+05 \n", - "2023 79490.400904 7.012668e+05 \n", - "2024 75647.986418 6.852983e+05 \n", - "2025 149852.310390 1.385523e+06 \n", - "2026 144657.227064 1.364728e+06 \n", - "2027 132846.385677 1.280738e+06 \n", - "2028 129491.211295 1.272330e+06 \n", - "2029 210842.546559 2.110021e+06 \n", - "2030 205677.973845 2.098772e+06 \n", - "2031 59573.732862 6.079002e+05 \n", - "2032 59234.754522 6.044412e+05 \n", - "2033 52797.901472 5.387585e+05 \n", - "2034 52536.174951 5.360878e+05 \n", - "2035 55692.120225 5.682916e+05 \n", - "2036 55447.546791 5.657959e+05 \n", - "2037 93954.520638 9.587274e+05 \n", - "2038 93584.171231 9.549483e+05 \n", - "2039 91390.860081 9.325674e+05 \n", - "2040 91064.079842 9.292329e+05 \n", - "2041 82627.832830 8.431480e+05 \n", - "2042 82357.497155 8.403895e+05 \n", - "2043 150501.983615 1.535747e+06 \n", - "2044 150048.154039 1.531116e+06 \n", - "2045 116640.011761 1.190214e+06 \n", - "2046 116313.840623 1.186886e+06 \n", - "2047 182859.470011 1.865928e+06 \n", - "2048 182382.752584 1.861064e+06 \n", - "2049 127049.120215 1.296430e+06 \n", - "2050 126738.908951 1.293265e+06 \n", - "\n", - " VirginStock_glass_95-by-35.Adv VirginStock_silicon_95-by-35.Adv \\\n", - "year \n", - "2010 7.018429e+04 7082.020411 \n", - "2011 1.442186e+05 14456.874009 \n", - "2012 1.428233e+05 14473.896289 \n", - "2013 2.806387e+05 27437.472114 \n", - "2014 2.793653e+05 26818.234691 \n", - "2015 4.899912e+05 47187.224863 \n", - "2016 4.711407e+05 43370.468471 \n", - "2017 4.563165e+05 37304.378239 \n", - "2018 4.407761e+05 32803.978991 \n", - "2019 7.954251e+05 55060.092196 \n", - "2020 7.779315e+05 48102.233601 \n", - "2021 2.038239e+05 12284.754131 \n", - "2022 1.995219e+05 11706.033155 \n", - "2023 1.740481e+06 98645.946912 \n", - "2024 1.710244e+06 93035.492425 \n", - "2025 2.717327e+06 143872.011133 \n", - "2026 2.686401e+06 138803.843587 \n", - "2027 1.611470e+06 81300.609591 \n", - "2028 1.606053e+06 79356.214441 \n", - "2029 2.324661e+06 112609.578471 \n", - "2030 2.319544e+06 110046.474502 \n", - "2031 1.521068e+06 72164.274809 \n", - "2032 1.512413e+06 71753.655483 \n", - "2033 2.092666e+06 99282.682045 \n", - "2034 2.082292e+06 98790.524019 \n", - "2035 2.690067e+06 127625.273162 \n", - "2036 2.678254e+06 127064.803365 \n", - "2037 1.040769e+06 49377.354606 \n", - "2038 1.036666e+06 49182.719224 \n", - "2039 3.234938e+05 15347.564865 \n", - "2040 3.223371e+05 15292.687595 \n", - "2041 3.543131e+05 16809.730398 \n", - "2042 3.531539e+05 16754.733556 \n", - "2043 4.928646e+05 23383.051811 \n", - "2044 4.913784e+05 23312.541641 \n", - "2045 9.645058e+05 45759.200650 \n", - "2046 9.618087e+05 45631.240010 \n", - "2047 1.991206e+06 94469.072431 \n", - "2048 1.986014e+06 94222.790119 \n", - "2049 1.252184e+06 59407.561374 \n", - "2050 1.249127e+06 59262.508070 \n", - "\n", - " VirginStock_silver_95-by-35.Adv VirginStock_copper_95-by-35.Adv ... \\\n", - "year ... \n", - "2010 128.426774 49.784053 ... \n", - "2011 219.915502 102.299090 ... \n", - "2012 172.505147 100.306250 ... \n", - "2013 234.946992 195.163136 ... \n", - "2014 214.149756 191.571176 ... \n", - "2015 321.420998 365.863228 ... \n", - "2016 283.204237 380.717825 ... \n", - "2017 269.165162 422.537185 ... \n", - "2018 273.760993 420.617808 ... \n", - "2019 461.823249 817.703245 ... \n", - "2020 379.511311 771.841582 ... \n", - "2021 92.428435 203.352757 ... \n", - "2022 83.703097 199.975162 ... \n", - "2023 687.050217 1766.818726 ... \n", - "2024 629.336887 1757.078575 ... \n", - "2025 944.285052 2705.100808 ... \n", - "2026 882.550002 2591.564594 ... \n", - "2027 499.904574 1503.443013 ... \n", - "2028 465.842956 1437.868817 ... \n", - "2029 628.675479 1996.679830 ... \n", - "2030 581.713329 1906.808476 ... \n", - "2031 381.465383 1250.412169 ... \n", - "2032 379.294821 1243.297244 ... \n", - "2033 524.815172 1720.300996 ... \n", - "2034 522.213591 1711.773225 ... \n", - "2035 674.636083 2211.401626 ... \n", - "2036 671.673401 2201.690196 ... \n", - "2037 261.012136 855.576325 ... \n", - "2038 259.983279 852.203819 ... \n", - "2039 81.128297 265.931888 ... \n", - "2040 80.838212 264.981013 ... \n", - "2041 88.857406 291.267272 ... \n", - "2042 88.566689 290.314325 ... \n", - "2043 123.604441 405.165196 ... \n", - "2044 123.231719 403.943445 ... \n", - "2045 241.886322 792.883480 ... \n", - "2046 241.209913 790.666267 ... \n", - "2047 499.370097 1636.894129 ... \n", - "2048 498.068231 1632.626721 ... \n", - "2049 314.032507 1029.372745 ... \n", - "2050 313.265745 1026.859363 ... \n", - "\n", - " Waste_MFG_silver_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 27.740183 \n", - "2011 47.501748 \n", - "2012 37.261112 \n", - "2013 50.748550 \n", - "2014 46.256347 \n", - "2015 69.426936 \n", - "2016 61.172115 \n", - "2017 58.139675 \n", - "2018 59.132374 \n", - "2019 101.145287 \n", - "2020 83.117904 \n", - "2021 20.810539 \n", - "2022 18.846003 \n", - "2023 125.582329 \n", - "2024 115.033210 \n", - "2025 230.039851 \n", - "2026 215.000407 \n", - "2027 170.187738 \n", - "2028 158.591785 \n", - "2029 193.320389 \n", - "2030 178.879328 \n", - "2031 107.567663 \n", - "2032 106.955596 \n", - "2033 217.825212 \n", - "2034 216.745422 \n", - "2035 192.503581 \n", - "2036 191.658197 \n", - "2037 127.393125 \n", - "2038 126.890968 \n", - "2039 28.684433 \n", - "2040 28.581868 \n", - "2041 77.181043 \n", - "2042 76.928527 \n", - "2043 92.162616 \n", - "2044 91.884705 \n", - "2045 148.783663 \n", - "2046 148.367605 \n", - "2047 87.656616 \n", - "2048 87.428094 \n", - "2049 143.123135 \n", - "2050 142.773677 \n", - "\n", - " Waste_MFG_copper_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 5.874518 \n", - "2011 12.071293 \n", - "2012 11.836138 \n", - "2013 23.029250 \n", - "2014 22.605399 \n", - "2015 43.171861 \n", - "2016 44.924703 \n", - "2017 49.859388 \n", - "2018 49.632901 \n", - "2019 97.834907 \n", - "2020 92.347743 \n", - "2021 25.012439 \n", - "2022 24.596994 \n", - "2023 176.425072 \n", - "2024 175.452472 \n", - "2025 360.007603 \n", - "2026 344.897667 \n", - "2027 279.612371 \n", - "2028 267.416793 \n", - "2029 335.419128 \n", - "2030 320.321780 \n", - "2031 192.622958 \n", - "2032 191.526921 \n", - "2033 390.062734 \n", - "2034 388.129139 \n", - "2035 344.718926 \n", - "2036 343.205084 \n", - "2037 228.124698 \n", - "2038 227.225477 \n", - "2039 51.365626 \n", - "2040 51.181962 \n", - "2041 138.209201 \n", - "2042 137.757018 \n", - "2043 165.036919 \n", - "2044 164.539260 \n", - "2045 266.429040 \n", - "2046 265.684000 \n", - "2047 156.967961 \n", - "2048 156.558743 \n", - "2049 256.292653 \n", - "2050 255.666873 \n", - "\n", - " Waste_MFG_aluminum_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 498.523193 \n", - "2011 1013.796077 \n", - "2012 992.223620 \n", - "2013 1592.692117 \n", - "2014 1537.585581 \n", - "2015 2560.433047 \n", - "2016 2349.682777 \n", - "2017 2192.538222 \n", - "2018 2081.376779 \n", - "2019 3653.269251 \n", - "2020 3389.708447 \n", - "2021 898.242981 \n", - "2022 864.002456 \n", - "2023 5937.092552 \n", - "2024 5650.104813 \n", - "2025 11676.824513 \n", - "2026 11272.012094 \n", - "2027 9212.219072 \n", - "2028 8979.554847 \n", - "2029 11491.516681 \n", - "2030 11210.032823 \n", - "2031 6741.064191 \n", - "2032 6702.707106 \n", - "2033 13650.698544 \n", - "2034 13583.030138 \n", - "2035 12063.839314 \n", - "2036 12010.860642 \n", - "2037 7983.488844 \n", - "2038 7952.019572 \n", - "2039 1797.599775 \n", - "2040 1791.172216 \n", - "2041 4836.791549 \n", - "2042 4820.966890 \n", - "2043 5775.658724 \n", - "2044 5758.242576 \n", - "2045 9323.993797 \n", - "2046 9297.920260 \n", - "2047 5493.276145 \n", - "2048 5478.955090 \n", - "2049 8969.259151 \n", - "2050 8947.359234 \n", - "\n", - " Waste_MFG_Module_95-by-35_Elec.Adv_DR \\\n", - "year \n", - "2010 9032.812399 \n", - "2011 18445.373832 \n", - "2012 18471.131942 \n", - "2013 35045.028655 \n", - "2014 33977.393769 \n", - "2015 57897.071230 \n", - "2016 52399.071958 \n", - "2017 45378.446953 \n", - "2018 39777.903104 \n", - "2019 68667.328612 \n", - "2020 62502.400271 \n", - "2021 16147.992909 \n", - "2022 15336.755089 \n", - "2023 101903.103484 \n", - "2024 94583.418054 \n", - "2025 191573.113976 \n", - "2026 178232.362718 \n", - "2027 142769.571494 \n", - "2028 135712.069152 \n", - "2029 165699.047313 \n", - "2030 156850.124260 \n", - "2031 94320.576281 \n", - "2032 93783.886186 \n", - "2033 190999.776425 \n", - "2034 190052.964041 \n", - "2035 168796.534803 \n", - "2036 168055.260310 \n", - "2037 111704.509432 \n", - "2038 111264.193217 \n", - "2039 25151.911014 \n", - "2040 25061.976993 \n", - "2041 67676.104741 \n", - "2042 67454.687032 \n", - "2043 80812.679393 \n", - "2044 80568.993675 \n", - "2045 130460.776414 \n", - "2046 130095.957008 \n", - "2047 76861.598845 \n", - "2048 76661.219485 \n", - "2049 125497.349974 \n", - "2050 125190.927612 \n", - "\n", - " new_Installed_Capacity_[MW]Reference.Mod \\\n", - "year \n", - "2010 1200.651350 \n", - "2011 2534.300109 \n", - "2012 2534.300109 \n", - "2013 5123.033732 \n", - "2014 5123.033732 \n", - "2015 9477.537310 \n", - "2016 9477.537310 \n", - "2017 9156.783125 \n", - "2018 9156.783125 \n", - "2019 16904.814950 \n", - "2020 16904.814950 \n", - "2021 4479.833668 \n", - "2022 4479.833668 \n", - "2023 13634.589691 \n", - "2024 13634.589691 \n", - "2025 28006.324744 \n", - "2026 28006.324744 \n", - "2027 26628.140384 \n", - "2028 26628.140384 \n", - "2029 44459.751214 \n", - "2030 44459.751214 \n", - "2031 12957.187354 \n", - "2032 12957.187354 \n", - "2033 11610.688533 \n", - "2034 11610.688533 \n", - "2035 12365.754090 \n", - "2036 12365.754090 \n", - "2037 21040.884630 \n", - "2038 21040.884630 \n", - "2039 20625.094015 \n", - "2040 20625.094015 \n", - "2041 18778.534321 \n", - "2042 18778.534321 \n", - "2043 34424.361314 \n", - "2044 34424.361314 \n", - "2045 26837.672654 \n", - "2046 26837.672654 \n", - "2047 42306.236152 \n", - "2048 42306.236152 \n", - "2049 29545.326875 \n", - "2050 29545.326875 \n", - "\n", - " new_Installed_Capacity_[MW]95-by-35.Adv \\\n", - "year \n", - "2010 1200.651350 \n", - "2011 2534.300109 \n", - "2012 2534.300109 \n", - "2013 5123.032321 \n", - "2014 5123.032321 \n", - "2015 9477.542505 \n", - "2016 9477.542505 \n", - "2017 9156.778903 \n", - "2018 9156.778903 \n", - "2019 16762.029653 \n", - "2020 16762.029653 \n", - "2021 4531.568028 \n", - "2022 4531.568028 \n", - "2023 40383.005620 \n", - "2024 40383.005620 \n", - "2025 64931.428129 \n", - "2026 64931.428129 \n", - "2027 39314.402876 \n", - "2028 39314.402876 \n", - "2029 57117.932039 \n", - "2030 57117.932039 \n", - "2031 37687.321738 \n", - "2032 37687.321738 \n", - "2033 52424.212235 \n", - "2034 52424.212235 \n", - "2035 68042.558766 \n", - "2036 68042.558766 \n", - "2037 26551.632695 \n", - "2038 26551.632695 \n", - "2039 8316.675349 \n", - "2040 8316.675349 \n", - "2041 9173.037667 \n", - "2042 9173.037667 \n", - "2043 12842.274781 \n", - "2044 12842.274781 \n", - "2045 25280.917109 \n", - "2046 25280.917109 \n", - "2047 52479.982610 \n", - "2048 52479.982610 \n", - "2049 33172.328787 \n", - "2050 33172.328787 \n", - "\n", - " new_Installed_Capacity_[MW]95-by-35_Elec.Adv_DR Capacity_Reference.Mod \\\n", - "year \n", - "2010 1200.651350 1.208819e+09 \n", - "2011 2534.300109 3.751735e+09 \n", - "2012 2534.300109 6.282106e+09 \n", - "2013 5123.032321 1.140951e+10 \n", - "2014 5123.032321 1.651318e+10 \n", - "2015 9477.542505 2.598093e+10 \n", - "2016 9477.542505 3.540741e+10 \n", - "2017 9156.778903 4.448377e+10 \n", - "2018 9156.778903 5.353175e+10 \n", - "2019 16995.842998 7.033950e+10 \n", - "2020 16995.842998 8.707455e+10 \n", - "2021 4723.593023 9.127172e+10 \n", - "2022 4723.593023 9.543730e+10 \n", - "2023 34173.144328 1.087628e+11 \n", - "2024 34173.144328 1.219987e+11 \n", - "2025 73232.045758 1.495638e+11 \n", - "2026 73232.045758 1.769361e+11 \n", - "2027 61963.949007 2.026970e+11 \n", - "2028 61963.949007 2.282008e+11 \n", - "2029 81314.849571 2.713132e+11 \n", - "2030 81314.849571 3.139969e+11 \n", - "2031 49200.342205 3.245497e+11 \n", - "2032 49200.342205 3.342861e+11 \n", - "2033 100734.892095 3.427705e+11 \n", - "2034 100734.892095 3.501751e+11 \n", - "2035 89886.844265 3.579958e+11 \n", - "2036 89886.844265 3.658960e+11 \n", - "2037 59996.065785 3.794087e+11 \n", - "2038 59996.065785 3.947659e+11 \n", - "2039 13613.503685 4.087451e+11 \n", - "2040 13613.503685 4.207746e+11 \n", - "2041 36887.263407 4.318456e+11 \n", - "2042 36887.263407 4.424574e+11 \n", - "2043 44331.142777 4.680619e+11 \n", - "2044 44331.142777 4.941152e+11 \n", - "2045 71991.796050 5.135442e+11 \n", - "2046 71991.796050 5.326798e+11 \n", - "2047 42648.336864 5.675835e+11 \n", - "2048 42648.336864 6.018977e+11 \n", - "2049 69993.457237 6.225061e+11 \n", - "2050 69993.457237 6.421114e+11 \n", - "\n", - " Capacity_95-by-35.Adv Capacity_95-by-35_Elec.Adv_DR \n", - "year \n", - "2010 1.208819e+09 1.208819e+09 \n", - "2011 3.751735e+09 3.751735e+09 \n", - "2012 6.282106e+09 6.282106e+09 \n", - "2013 1.140951e+10 1.140951e+10 \n", - "2014 1.651318e+10 1.651318e+10 \n", - "2015 2.598093e+10 2.598093e+10 \n", - "2016 3.540742e+10 3.540742e+10 \n", - "2017 4.448377e+10 4.448377e+10 \n", - "2018 5.353175e+10 5.353175e+10 \n", - "2019 7.019597e+10 7.043100e+10 \n", - "2020 8.678827e+10 8.725705e+10 \n", - "2021 9.103856e+10 9.169843e+10 \n", - "2022 9.525701e+10 9.610702e+10 \n", - "2023 1.354546e+11 1.300625e+11 \n", - "2024 1.754379e+11 1.638325e+11 \n", - "2025 2.398391e+11 2.366270e+11 \n", - "2026 3.038687e+11 3.090150e+11 \n", - "2027 3.417583e+11 3.696333e+11 \n", - "2028 3.793160e+11 4.298146e+11 \n", - "2029 4.343939e+11 5.089571e+11 \n", - "2030 4.889834e+11 5.874918e+11 \n", - "2031 5.235060e+11 6.331016e+11 \n", - "2032 5.571010e+11 6.777191e+11 \n", - "2033 6.054757e+11 7.740140e+11 \n", - "2034 6.525823e+11 8.688293e+11 \n", - "2035 7.148294e+11 9.519686e+11 \n", - "2036 7.768905e+11 1.034795e+12 \n", - "2037 7.939081e+11 1.084122e+12 \n", - "2038 8.126903e+11 1.135060e+12 \n", - "2039 8.121507e+11 1.138251e+12 \n", - "2040 8.096610e+11 1.139448e+12 \n", - "2041 8.089075e+11 1.164872e+12 \n", - "2042 8.076129e+11 1.189647e+12 \n", - "2043 8.091276e+11 1.220898e+12 \n", - "2044 8.108800e+11 1.252251e+12 \n", - "2045 8.257780e+11 1.311901e+12 \n", - "2046 8.397897e+11 1.370475e+12 \n", - "2047 8.805212e+11 1.398798e+12 \n", - "2048 9.195459e+11 1.425437e+12 \n", - "2049 9.369104e+11 1.477322e+12 \n", - "2050 9.516972e+11 1.526481e+12 \n", - "\n", - "[41 rows x 78 columns]" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "USyearly" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 14})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "fig, axs = plt.subplots(1,3, figsize=(16, 4), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .1, wspace=.6)\n", - "axs = axs.ravel()\n", - "\n", - "# PLOT 1\n", - "i = 0\n", - "axs[i].yaxis.grid()\n", - "lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silver_Reference.Mod']/1e6, color='gray', linewidth=4.0, label='Virgin Material Demands')\n", - "lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/1e6, color='gray', linestyle='dashed', linewidth=3.0, label='EoL Material')\n", - "axs[i].set_ylabel('Mass [Tons]')\n", - "axs[i].set_xlim([2020, 2050])\n", - "axs[i].set_title('Silver')\n", - "\n", - "# 2nd axis plot\n", - "ax2=axs[i].twinx()\n", - "lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/USyearly['VirginStock_silver_Reference.Mod'], \n", - " color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')\n", - "ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')\n", - "ax2.tick_params(axis='y', labelcolor='r')\n", - "\n", - "# LEGENDS\n", - "# added these three lines\n", - "lns = lns1+lns2+lns3\n", - "labs = [l.get_label() for l in lns]\n", - "#axs[0].legend(lns, labs, loc=0)\n", - "\n", - "# PLOT 2\n", - "i = 1\n", - "axs[i].yaxis.grid()\n", - "lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_aluminum_Reference.Mod']/1e6, color='g', linewidth=4.0, label='Virgin Material Demands')\n", - "lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/1e6, color='g', linestyle='dashed', linewidth=3.0, label='EoL Material')\n", - "axs[i].set_ylabel('Mass [Tons]')\n", - "axs[i].set_xlim([2020, 2050])\n", - "axs[i].set_title('Aluminum')\n", - "\n", - "# 2nd axis plot\n", - "ax2=axs[i].twinx()\n", - "lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/USyearly['VirginStock_aluminum_Reference.Mod'], \n", - " color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')\n", - "\n", - "ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')\n", - "ax2.tick_params(axis='y', labelcolor='r')\n", - "\n", - "# LEGENDS\n", - "# added these three lines\n", - "lns = lns1+lns2+lns3\n", - "labs = [l.get_label() for l in lns]\n", - "#axs[1].legend(lns, labs, loc=0)\n", - "\n", - "\n", - "\n", - "# PLOT 3\n", - "i = 2\n", - "axs[i].yaxis.grid()\n", - "lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silicon_Reference.Mod']/1e6, color='k', linewidth=4.0, label='Virgin Material Demands')\n", - "lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/1e6, color='k', linestyle='dashed', linewidth=3.0, label='EoL Material')\n", - "axs[i].set_ylabel('Mass [Tons]')\n", - "axs[i].set_xlim([2020, 2050])\n", - "axs[i].set_title('Silicon')\n", - "\n", - "# 2nd axis plot\n", - "ax2=axs[i].twinx()\n", - "lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/USyearly['VirginStock_silicon_Reference.Mod'], \n", - " color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')\n", - "\n", - "#ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')\n", - "ax2.tick_params(axis='y', labelcolor='r')\n", - "\n", - "# LEGENDS\n", - "# added these three lines\n", - "lns = lns1+lns2+lns3\n", - "labs = [l.get_label() for l in lns]\n", - "axs[2].legend(lns, labs, loc='upper center', bbox_to_anchor=(-0.95, -0.25),\n", - " fancybox=True, shadow=True, ncol=5)\n", - "\n", - "fig.savefig(title_Method+' Fig_1x3_VirginvsWaste_Fraction_Nation.png', bbox_inches = \"tight\", dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib.legend_handler import HandlerBase\n", - "\n", - "plt.rcParams.update({'font.size': 14})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - "\n", - "class AnyObjectHandler(HandlerBase):\n", - " def create_artists(self, legend, orig_handle,\n", - " x0, y0, width, height, fontsize, trans):\n", - " \n", - " if orig_handle[0] is 'r':\n", - " l1 = plt.Line2D([x0,y0+width], [0.4*height,0.4*height], color=orig_handle[0])\n", - " return [l1]\n", - "\n", - " else:\n", - "\n", - " l1 = plt.Line2D([x0,y0+width], [0.7*height,0.7*height], color=orig_handle[0], linestyle = orig_handle[3],)\n", - " l2 = plt.Line2D([x0,y0+width], [0.4*height,0.4*height], color=orig_handle[1], linestyle = orig_handle[4])\n", - " l3 = plt.Line2D([x0,y0+width], [0.1*height,0.1*height], color=orig_handle[2], linestyle = orig_handle[5])\n", - " \n", - " return [l1, l2, l3]\n", - "\n", - " \n", - " \n", - "fig, axs = plt.subplots(1,3, figsize=(16, 4), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .1, wspace=.6)\n", - "axs = axs.ravel()\n", - "\n", - "# PLOT 1\n", - "i = 0\n", - "axs[i].yaxis.grid()\n", - "lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silver_Reference.Mod']/1e6, color='gray', linewidth=4.0, label='Virgin Material Demands')\n", - "lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/1e6, color='gray', linestyle='dashed', linewidth=3.0, label='EoL Material')\n", - "axs[i].set_ylabel('Mass [Tons]')\n", - "axs[i].set_xlim([2020, 2050])\n", - "axs[i].set_title('Silver')\n", - "\n", - "# 2nd axis plot\n", - "ax2=axs[i].twinx()\n", - "lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/USyearly['VirginStock_silver_Reference.Mod'], \n", - " color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')\n", - "ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')\n", - "ax2.tick_params(axis='y', labelcolor='r')\n", - "\n", - "# LEGENDS\n", - "# added these three lines\n", - "lns = lns1+lns2+lns3\n", - "labs = [l.get_label() for l in lns]\n", - "#axs[0].legend(lns, labs, loc=0)\n", - "\n", - "# PLOT 2\n", - "i = 1\n", - "axs[i].yaxis.grid()\n", - "lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_aluminum_Reference.Mod']/1e6, color='g', linewidth=4.0, label='Virgin Material Demands')\n", - "lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/1e6, color='g', linestyle='dashed', linewidth=3.0, label='EoL Material')\n", - "axs[i].set_ylabel('Mass [Tons]')\n", - "axs[i].set_xlim([2020, 2050])\n", - "axs[i].set_title('Aluminum')\n", - "\n", - "# 2nd axis plot\n", - "ax2=axs[i].twinx()\n", - "lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/USyearly['VirginStock_aluminum_Reference.Mod'], \n", - " color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')\n", - "\n", - "ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')\n", - "ax2.tick_params(axis='y', labelcolor='r')\n", - "\n", - "# LEGENDS\n", - "# added these three lines\n", - "lns = lns1+lns2+lns3\n", - "labs = [l.get_label() for l in lns]\n", - "#axs[1].legend(lns, labs, loc=0)\n", - "\n", - "\n", - "\n", - "# PLOT 3\n", - "i = 2\n", - "axs[i].yaxis.grid()\n", - "lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silicon_Reference.Mod']/1e6, color='k', linewidth=4.0, label='Virgin Material Demands')\n", - "lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/1e6, color='k', linestyle='dashed', linewidth=3.0, label='EoL Material')\n", - "axs[i].set_ylabel('Mass [Tons]')\n", - "axs[i].set_xlim([2020, 2050])\n", - "axs[i].set_title('Silicon')\n", - "\n", - "# 2nd axis plot\n", - "ax2=axs[i].twinx()\n", - "lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/USyearly['VirginStock_silicon_Reference.Mod'], \n", - " color = 'r', linewidth=1.0, label='EOl Material as fraction of Demand')\n", - "\n", - "#ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')\n", - "ax2.tick_params(axis='y', labelcolor='r')\n", - "\n", - "# LEGENDS\n", - "# added these three lines\n", - "lns = lns1+lns2+lns3\n", - "labs = [l.get_label() for l in lns]\n", - "#axs[2].legend(lns, labs, loc='upper center', bbox_to_anchor=(-0.95, -0.25),\n", - "# fancybox=True, shadow=True, ncol=5) \n", - "\n", - "#axs[2].legend([(\"gray\",\"g\",\"k\",\"-\",\"-\",\"-\"), (\"gray\",\"g\",\"k\",\"--\",\"--\",\"--\"),(\"r\",\"r\",\"r\",\"-\",\"-\",\"-\")], ['Material Demands', \"EoL Material\", 'Fraction'],\n", - "# handler_map={tuple: AnyObjectHandler()}, loc='upper center', bbox_to_anchor=(-0.95, -0.25),\n", - "# fancybox=True, shadow=True, ncol=5)\n", - "\n", - "axs[2].legend([(\"gray\",\"g\",\"k\",\"-\",\"-\",\"-\"), (\"gray\",\"g\",\"k\",\"--\",\"--\",\"--\"),(\"r\",\"r\",\"r\")], [' Virgin material demands', \"EOL material\", 'EOL material as fraction of demand'],\n", - " handler_map={tuple: AnyObjectHandler()}, loc='upper center', bbox_to_anchor=(-0.95, -0.25),\n", - " fancybox=True, shadow=True, ncol=5)\n", - "\n", - "fig.savefig(title_Method+' Fig_1x3_VirginvsWaste_Fraction_Nation.png', bbox_inches = \"tight\", dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "newdf = pd.DataFrame()\n", - "\n", - "newdf['Virgin material demands, Silicon, Reference'] = USyearly['VirginStock_silicon_Reference.Mod']/1e6 \n", - "newdf['EOL material, Silicon, Reference'] = USyearly['Waste_EOL_silicon_Reference.Mod']/1e6\n", - "newdf['EOL material as fraction of demand, Silicon, Reference'] = USyearly['Waste_EOL_silicon_Reference.Mod']/USyearly['VirginStock_silicon_Reference.Mod']\n", - "\n", - "newdf['Virgin material demands, Silver, Reference'] = USyearly['VirginStock_silver_Reference.Mod']/1e6 \n", - "newdf['EOL material, Silver, Reference'] = USyearly['Waste_EOL_silver_Reference.Mod']/1e6\n", - "newdf['EOL material as fraction of demand, Silver, Reference'] = USyearly['Waste_EOL_silver_Reference.Mod']/USyearly['VirginStock_silver_Reference.Mod']\n", - "\n", - "newdf['Virgin material demands, Aluminum, Reference'] = USyearly['VirginStock_aluminum_Reference.Mod']/1e6 \n", - "newdf['EOL material, Aluminum, Reference'] = USyearly['Waste_EOL_aluminum_Reference.Mod']/1e6\n", - "newdf['EOL material as fraction of demand, Aluminum, Reference'] = USyearly['Waste_EOL_aluminum_Reference.Mod']/USyearly['VirginStock_aluminum_Reference.Mod']\n", - "\n", - "\n", - "newdf['Virgin material demands, Silicon, Grid Decarb.'] = USyearly['VirginStock_silicon_'+SFscenarios[1]]/1e6 \n", - "newdf['EOL material, Silicon, Grid Decarb.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[1]]/1e6\n", - "newdf['EOL material as fraction of demand, Silicon, Grid Decarb.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[1]]/USyearly['VirginStock_silicon_'+SFscenarios[1]]\n", - "\n", - "newdf['Virgin material demands, Silver, Grid Decarb.'] = USyearly['VirginStock_silver_'+SFscenarios[1]]/1e6 \n", - "newdf['EOL material, Silver, Grid Decarb.'] = USyearly['Waste_EOL_silver_'+SFscenarios[1]]/1e6\n", - "newdf['EOL material as fraction of demand, Silver, Grid Decarb.'] = USyearly['Waste_EOL_silver_'+SFscenarios[1]]/USyearly['VirginStock_silver_'+SFscenarios[1]]\n", - "\n", - "newdf['Virgin material demands, Aluminum, Grid Decarb.'] = USyearly['VirginStock_aluminum_'+SFscenarios[1]]/1e6 \n", - "newdf['EOL material, Aluminum, Grid Decarb.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[1]]/1e6\n", - "newdf['EOL material as fraction of demand, Aluminum, Grid Decarb.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[1]]/USyearly['VirginStock_aluminum_'+SFscenarios[1]]\n", - "\n", - "\n", - "newdf['Virgin material demands, Silicon, High Elec.'] = USyearly['VirginStock_silicon_'+SFscenarios[2]]/1e6 \n", - "newdf['EOL material, Silicon, High Elec.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[2]]/1e6\n", - "newdf['EOL material as fraction of demand, Silicon, High Elec.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[2]]/USyearly['VirginStock_silicon_'+SFscenarios[2]]\n", - "\n", - "newdf['Virgin material demands, Silver, High Elec.'] = USyearly['VirginStock_silver_'+SFscenarios[2]]/1e6 \n", - "newdf['EOL material, Silver, High Elec.'] = USyearly['Waste_EOL_silver_'+SFscenarios[2]]/1e6\n", - "newdf['EOL material as fraction of demand, Silver, High Elec.'] = USyearly['Waste_EOL_silver_'+SFscenarios[2]]/USyearly['VirginStock_silver_'+SFscenarios[2]]\n", - "\n", - "newdf['Virgin material demands, Aluminum, High Elec.'] = USyearly['VirginStock_aluminum_'+SFscenarios[2]]/1e6 \n", - "newdf['EOL material, Aluminum, High Elec.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[2]]/1e6\n", - "newdf['EOL material as fraction of demand, Aluminum, High Elec.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[2]]/USyearly['VirginStock_aluminum_'+SFscenarios[2]]\n", - "\n", - "newdf.to_csv(title_Method+' Demand vs EOL Fraction NATION.csv')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR']" - ] - }, - "execution_count": 195, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.py b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.py deleted file mode 100644 index c325619b..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - USA.py +++ /dev/null @@ -1,1927 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # ReEDS Scenarios on PV ICE Tool USA - -# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. -# -# Current sections include: -# -#
      -#
    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format
      2. -#
    2. ### Reading scenarios of interest and running PV ICE tool
      3. -#
    3. ###Plotting
      4. -#
    4. ### GeoPlotting.
      5. -#
    -# Notes: -# -# Scenarios of Interest: -# the Ref.Mod, -# o 95-by-35.Adv, and -# o 95-by-35+Elec.Adv+DR ones -# - -# In[1]: - - -import PV_ICE -import numpy as np -import pandas as pd -import os,sys -import matplotlib.pyplot as plt -from IPython.display import display -plt.rcParams.update({'font.size': 22}) -plt.rcParams['figure.figsize'] = (12, 8) - - -# In[2]: - - -import os -from pathlib import Path - -testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP') - -print ("Your simulation will be stored in %s" % testfolder) - - -# ### Reading REEDS original file to get list of SCENARIOs, PCAs, and STATEs - -# In[3]: - - -r""" -reedsFile = str(Path().resolve().parent.parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v2a.xlsx') -print ("Input file is stored in %s" % reedsFile) - -rawdf = pd.read_excel(reedsFile, - sheet_name="UPV Capacity (GW)") - #index_col=[0,2,3]) #this casts scenario, PCA and State as levels -#now set year as an index in place -#rawdf.drop(columns=['State'], inplace=True) -rawdf.drop(columns=['Tech'], inplace=True) -rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True) -"""; - - -# In[4]: - - -#scenarios = list(rawdf.index.get_level_values('Scenario').unique()) -#PCAs = list(rawdf.index.get_level_values('PCA').unique()) -#STATEs = list(rawdf.index.get_level_values('State').unique()) - - -# ### Create Scenarios in PV_ICE - -# #### Rename difficult characters from Scenarios Names - -# In[5]: - - -scenarios = ['Reference.Mod', - 'Reference.Adv', - 'Reference.Adv_DR', - '95-by-35.Mod', - '95-by-35.Adv', - '95-by-35.Adv_DR', - '95-by-35_Elec.Mod', - '95-by-35_Elec.Adv', - '95-by-35_Elec.Adv_DR'] - - -# In[6]: - - -#simulationname = scenarios -#simulationname = [w.replace('+', '_') for w in simulationname] -#simulationname - - -# #### Downselect to Solar Future scenarios of interest -# -# Scenarios of Interest: -#
  • Ref.Mod -#
  • 95-by-35.Adv -#
  • 95-by-35+Elec.Adv+DR - -# In[7]: - - -#SFscenarios = [simulationname[0], simulationname[4], simulationname[8]] -SFscenarios = ['Reference.Mod', '95-by-35.Adv', '95-by-35_Elec.Adv_DR'] -SFscenarios - - -# #### Create the 3 Scenarios and assign Baselines -# -# Keeping track of each scenario as its own PV ICE Object. - -# In[8]: - - -#for ii in range (0, 1): #len(scenarios): -i = 0 -rr = PV_ICE.Simulation(name='USA', path=testfolder) -for i in range(0, 3): - filetitle = SFscenarios[i]+'.csv' - filetitle = os.path.join(testfolder, 'USA', filetitle) - rr.createScenario(name=SFscenarios[i], file=filetitle) - rr.scenario[SFscenarios[i]].addMaterial('glass', file=r'..\baselines\ReedsSubset\baseline_material_glass_Reeds.csv') - rr.scenario[SFscenarios[i]].addMaterial('silicon', file=r'..\baselines\ReedsSubset\baseline_material_silicon_Reeds.csv') - rr.scenario[SFscenarios[i]].addMaterial('silver', file=r'..\baselines\ReedsSubset\baseline_material_silver_Reeds.csv') - rr.scenario[SFscenarios[i]].addMaterial('copper', file=r'..\baselines\ReedsSubset\baseline_material_copper_Reeds.csv') - rr.scenario[SFscenarios[i]].addMaterial('aluminum', file=r'..\baselines\ReedsSubset\baseline_material_aluminium_Reeds.csv') - - -# # 2 FINISH: Set characteristics of Recycling to SF values. - -# #### Calculate Mass Flow - -# In[9]: - - -IRENA= False -PERFECTMFG = True - -mats = ['glass', 'silicon','silver','copper','aluminum'] - -ELorRL = 'EL' -if IRENA: - if ELorRL == 'RL': - weibullInputParams = {'alpha': 5.3759, 'beta':30} # Regular-loss scenario IRENA - if ELorRL == 'EL': - weibullInputParams = {'alpha': 2.49, 'beta':30} # Regular-loss scenario IRENA - - if PERFECTMFG: - for jj in range (0, len(rr.scenario.keys())): - rr.scenario[list(rr.scenario.keys())[jj]].data['mod_lifetime'] = 40 - rr.scenario[list(rr.scenario.keys())[jj]].data['mod_MFG_eff'] = 100.0 - - for kk in range(0, len(mats)): - mat = mats[kk] - rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 - rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_scrap_Recycled'] = 0.0 - - - rr.calculateMassFlow(weibullInputParams=weibullInputParams) - title_Method = 'Irena_'+ELorRL -else: - rr.calculateMassFlow() - title_Method = 'PVICE' - - -# In[10]: - - -print("Scenarios:", rr.scenario.keys()) -print("Module Keys:", rr.scenario[SFscenarios[0]].data.keys()) -print("Material Keys: ", rr.scenario[SFscenarios[0]].material['glass'].materialdata.keys()) - - -# In[11]: - - -""" -r1.plotScenariosComparison(keyword='Cumulative_Area_disposedby_Failure') -r1.plotMaterialComparisonAcrossScenarios(material='silicon', keyword='mat_Total_Landfilled') -""" -pass - - -# ## Aggregating PCAs Material Landfilled to obtain US totals by Year - -# In[12]: - - -USyearly=pd.DataFrame() - - -# In[13]: - - -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - - -# In[14]: - - -keywd = 'mat_Virgin_Stock' - -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['VirginStock_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd] - - filter_col = [col for col in USyearly if (col.startswith('VirginStock_') and col.endswith(obj))] - USyearly['VirginStock_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# In[15]: - - -keywd = 'mat_Total_Landfilled' - -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['Waste_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd] - - filter_col = [col for col in USyearly if (col.startswith('Waste_') and col.endswith(obj)) ] - USyearly['Waste_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# In[16]: - - -keywd = 'mat_Total_EOL_Landfilled' - -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['Waste_EOL_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd] - - filter_col = [col for col in USyearly if (col.startswith('Waste_EOL_') and col.endswith(obj)) ] - USyearly['Waste_EOL_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# In[17]: - - -keywd = 'mat_Total_MFG_Landfilled' - -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['Waste_MFG_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd] - - filter_col = [col for col in USyearly if (col.startswith('Waste_MFG_') and col.endswith(obj)) ] - USyearly['Waste_MFG_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# ### Converting to grams to METRIC Tons. -# - -# In[18]: - - -USyearly = USyearly/1000000 # This is the ratio for Metric tonnes -#907185 -- this is for US tons - - -# ### Adding NEW Installed Capacity to US - -# In[19]: - - -keyword='new_Installed_Capacity_[MW]' -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - USyearly[keyword+obj] = rr.scenario[obj].data[keyword] - - - -# #### Reindexing and creating c umulative results - -# In[20]: - - -UScum = USyearly.copy() -UScum = UScum.cumsum() - - -# ### Adding Installed Capacity to US (This is already 'Cumulative') so not including it in UScum - -# In[21]: - - -keyword='Installed_Capacity_[W]' -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - USyearly["Capacity_"+obj] = rr.scenario[obj].data[keyword] - - - -# #### Set YEAR Index - -# In[22]: - - -USyearly.index = rr.scenario[obj].data['year'] -UScum.index = rr.scenario[obj].data['year'] - - -# In[23]: - - -USyearly.head().iloc[1] - - -# In[24]: - - -USyearly.head() - - -# In[25]: - - -UScum.head() - - -# ### 3 sig figures save Yearly and cumulative overview Nation - -# In[26]: - - -# Data for Jarett -USyearly.to_csv(title_Method+' US_Yearly NATION_tonnes.csv') -UScum.to_csv(title_Method+' US_Cumulative NATION_tonnes.csv') - - -# In[27]: - - -USyearly3sig = USyearly.copy() -UScum3sig = UScum.copy() -N = 2 - -UScum3sig = UScum3sig.drop(UScum3sig.index[0]) -USyearly3sig = USyearly3sig.drop(USyearly3sig.index[0]) - -if IRENA: - UScum3sig = UScum3sig.loc[:, ~UScum3sig.columns.str.startswith('Waste_MFG_')] - USyearly3sig = USyearly3sig.loc[:, ~USyearly3sig.columns.str.startswith('Waste_MFG_')] - -USyearly3sig = USyearly3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -USyearly3sig = USyearly3sig.applymap(lambda x: int(x)) - -UScum3sig = UScum3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -UScum3sig = UScum3sig.applymap(lambda x: int(x)) - -USyearly3sig.to_csv(title_Method+' US_Yearly NATION.csv') -UScum3sig.to_csv(title_Method+' US_Cumulative NATION.csv') - - -# In[28]: - - -print("Sanity check: mat_Total_Landfilled = mat_Total_EOL_Landfilled + mat_Total_MFG_Landfilled") -A = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled'].iloc[5] -B = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_EOL_Landfilled'].iloc[5] -C = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_MFG_Landfilled'].iloc[5] -A - B - C - - -# # PLOT - -# ## Yearly Virgin Material Needs by Scenario - -# In[29]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) -keyw='VirginStock_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - -foo = pd.DataFrame() - -# Loop over Keywords -ii = 0 -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - - -# SCENARIO 2 *************** -kk = 1 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='S2 Grid Decarbonization: module mass') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='S2 Grid Decarbonization: glass mass only') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3, - interpolate=True) - -# SCENARIO 3 *************** -kk = 2 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='S3 High Electrification: module mass') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='S3 High Electrification: glass mass only') - -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3, - interpolate=True) - -a0.legend() -a0.set_title('Yearly Virgin Material Needs by Scenario') -a0.set_ylabel('Mass [Million Tonnes]') - -a0.set_xlabel('Years') - - - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 3): - obj = SFscenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(3) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]') -a1.set_xlabel('Scenario') -a1.set_xticks(ind, ('S1', 'S2', 'S3')) -#plt.yticks(np.arange(0, 81, 10)) -a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver')) - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600) - - -# In[32]: - - - - - -# In[ ]: - - - - - -# In[34]: - - -rr.scenario['Reference.Mod'].material['glass'].materialdata['mat_Virgin_Stock'].tail(5) - - -# In[ ]: - - -# Save Data for Jarett Zuboy - - -# In[29]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) -keyw='VirginStock_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - - - -# Loop over Keywords -ii = 0 -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module ') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass ') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - -# SCENARIO 2 *************** -kk = 1 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module ') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3, - interpolate=True) - -# SCENARIO 3 *************** -kk = 2 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec: glass') - -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3, - interpolate=True) - -a0.legend(loc='upper left') -a0.set_title('Yearly Virgin Material Needs by Scenario') -a0.set_ylabel('Mass [Million Tonnes]') - -a0.set_xlabel('Years') -#a0.tick_params(axis='y', which='minor', length=3) -#a0.set_yticks(minorbool=True) -a0.minorticks_on() -a0.tick_params(axis='y', which='minor', bottom=False) - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 3): - obj = SFscenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(3) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]') -#a1.set_xlabel('Scenario') -a1.tick_params(axis='y', which='minor', bottom='off') -#a1.minorticks_on() - -plt.sca(a1) -plt.xticks(range(3), ['Ref.', 'Grid\nDecarb.', 'High\nElec.'], color='black', rotation=0) -plt.tick_params(axis='y', which='minor', bottom=False) -#plt.yticks(minor=True) -a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass')) - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600) - - -# In[30]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) - -keyw='Waste_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - - - -# Loop over Keywords -ii = 0 -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - -# SCENARIO 2 *************** -kk = 1 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3, - interpolate=True) - -# SCENARIO 3 *************** -kk = 2 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec.: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec.: glass') - -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3, - interpolate=True) - -a0.legend() -a0.set_title('Yearly Manufacturing Scrap and EoL Material by Scenario') -a0.set_ylabel('Mass [Million Tonnes]') - -a0.set_xlabel('Years') -a0.minorticks_on() -a0.tick_params(axis='y', which='minor', bottom=False) - - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 3): - obj = SFscenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(3) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Cumulative Manufacturing Scrap and EoL Material \n by 2050 [Million Tonnes]') - -plt.sca(a1) -plt.xticks(range(3), ['Ref.', 'Grid\nDecarb.', 'High\nElec.'], color='black', rotation=0) -plt.tick_params(axis='y', which='minor', bottom=False) -#plt.yticks(minor=True) -a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass')) - - - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly MFG and EOL Material by Scenario and Cumulatives_Nation.png', dpi=600) - - -# In[31]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) -keyw='Waste_EOL_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - - - -# Loop over Keywords -ii = 0 -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - -# SCENARIO 2 *************** -kk = 1 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3, - interpolate=True) - -# SCENARIO 3 *************** -kk = 2 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec.: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec.: glass ') - -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3, - interpolate=True) - -a0.legend() -a0.set_title('Yearly End of Life Material by Scenario') -a0.set_ylabel('Mass [Million Tonnes]') - -a0.set_xlabel('Years') -a0.minorticks_on() -a0.tick_params(axis='y', which='minor', bottom=False) - - - - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 3): - obj = SFscenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(3) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Cumulative End of Life Material by 2050 [Million Tonnes]') - -plt.sca(a1) -plt.xticks(range(3), ['Ref.', 'Grid\nDecarb.', 'High\nElec.'], color='black', rotation=0) -plt.tick_params(axis='y', which='minor', bottom=False) -#plt.yticks(minor=True) -a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass')) - - - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly EoL Waste by Scenario and Cumulatives_Nation.png', dpi=600) - - -# In[32]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) -keyw='Waste_MFG_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - - - -# Loop over Keywords -ii = 0 -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='Reference: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='Reference: glass') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - -# SCENARIO 2 *************** -kk = 1 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'g.', linewidth=5, label='Grid Decarb.: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'g', linewidth=5, label='Grid Decarb.: glass') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='g', alpha=0.3, - interpolate=True) - -# SCENARIO 3 *************** -kk = 2 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'c.', linewidth=5, label='High Elec.: module') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'c', linewidth=5, label='High Elec.: glass') - -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='c', alpha=0.3, - interpolate=True) - -a0.legend(loc='upper left') -a0.set_title('Yearly Manufacturing Scrap by Scenario') -a0.set_ylabel('Mass [Million Tonnes]') - -a0.set_xlabel('Years') -a0.minorticks_on() -a0.tick_params(axis='y', which='minor', bottom=False) - - - - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 3): - obj = SFscenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(3) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Cumulative Manufacturing Scrap by 2050 [Million Tonnes]') - -plt.sca(a1) -plt.xticks(range(3), ['Ref.', 'Grid\nDecarb.', 'High\nElec.'], color='black', rotation=0) -plt.tick_params(axis='y', which='minor', bottom=False) -#plt.yticks(minor=True) -a1.legend((p4[0], p3[0], p2[0], p1[0], p0[0] ), ('Silver', 'Copper', 'Silicon','Aluminum','Glass')) - - - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly MFG Waste by Scenario and Cumulatives_Nation.png', dpi=600) - - -# In[33]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6, 'k', label='Cumulative New Yearly Installs S3-S2') - -#axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'c', label='Cumulative New Yearly Installs') - -axs.legend() -axs.set_xlim([2020,2030]) -axs.set_ylabel('Power [TW]') -fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600) - - -# # WASTE COMPARISON SIZE - -# In[34]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Cumulative New Yearly Installs') -axs.plot(USyearly['Capacity_'+SFscenarios[0]]/1e12, 'g', label='Active in Field Installs') -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels') -axs.legend() -axs.set_xlim([2020,2050]) -axs.set_ylabel('Power [TW]') -fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600) - - -# In[35]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Cumulative New Yearly Installs Scen. 1 ') -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6, 'b--', label='Cumulative New Yearly Installs Scen. 2 ') -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'b.', label='Cumulative New Yearly Installs Scen. 3 ') -axs.plot(USyearly['Capacity_'+SFscenarios[0]]/1e12, 'g', label='Active in Field Installs Scen. 1') -axs.plot(USyearly['Capacity_'+SFscenarios[1]]/1e12, 'g--', label='Active in Field Installs Scen. 2') -axs.plot(USyearly['Capacity_'+SFscenarios[2]]/1e12, 'g.', label='Active in Field Installs Scen. 3') -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels Scen. 1') -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6-USyearly['Capacity_'+SFscenarios[1]]/1e12, 'r--', label='Decomissioned PV Panels Scen 2') -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12, 'r.', label='Decomissioned PV Panels Scen 3') - -axs.legend() -axs.set_xlim([2020,2050]) -axs.set_ylabel('Power [TW]') - - -# In[36]: - - -foo0 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12).sum() -foo1 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6-USyearly['Capacity_'+SFscenarios[1]]/1e12).sum() -foo2 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12).sum() -print(foo0, foo1, foo2) - - -# In[37]: - - -E = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6).sum() -F = (UScum['new_Installed_Capacity_[MW]Reference.Mod']/1e6-USyearly['Capacity_Reference.Mod']/1e12).sum() -print("Cumulative Installs", E) -print("Cumulative Waste", F) -print("Fraction of Decomisioned to Installed Cumulative by 2050", F/E) - - -# In[38]: - - -SFscenarios[1] - - -# In[39]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(USyearly['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Yearly New Yearly Installs') -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels') -axs.legend() -axs.set_xlim([2020,2050]) -axs.set_ylabel('Power [TW]') -fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600) - - -# In[40]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(USyearly['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Reference: New Installs') -#axs.plot(USyearly['new_Installed_Capacity_[MW]'+SFscenarios[1]]/1e6, 'b', label='Grid Decarb.: Yearly New Yearly Installs') -axs.plot(USyearly['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'b--', label='High Elec.: New Installs') -axs.fill_between(rr.scenario[obj].data['year'], USyearly['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, - USyearly['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, color='b', alpha=0.3, - interpolate=True) - - -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Reference: Decomissioned PV Panels') -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12, 'r--', label='High Elec.: Decomissioned PV Panels') -axs.fill_between(rr.scenario[obj].data['year'], UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, - UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6-USyearly['Capacity_'+SFscenarios[2]]/1e12, color='r', alpha=0.3, - interpolate=True) -axs.minorticks_on() - - -axs.legend() -axs.set_xlim([2020,2050]) -axs.set_ylabel('Power [TW]') -fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600) - - -# In[41]: - - -print("CUMULATIVE WASTE by 2050") -print("*************************") -print("") -UScum.iloc[-1] -print("MFG Scrap + EoL Material Only") -print("\t Reference Scenario: ", UScum['Waste_Module_Reference.Mod'].iloc[-1]/1e6, ' Million Tonnes') -print("\t Grid Decarbonization Scenario: ", UScum['Waste_Module_95-by-35.Adv'].iloc[-1]/1e6, ' Million Tonnes') -print("\t High Electrification Scenario: ", UScum['Waste_Module_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6, ' Million Tonnes') - -print("EoL Material Only") -print("\t Reference Scenario: ", UScum['Waste_EOL_Module_Reference.Mod'].iloc[-1]/1e6, ' Million Tonnes') -print("\t Grid Decarbonization Scenario: ", UScum['Waste_EOL_Module_95-by-35.Adv'].iloc[-1]/1e6, ' Million Tonnes') -print("\t High Electrification Scenario: ", UScum['Waste_EOL_Module_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6, ' Million Tonnes') - -print("MFG Scrap Only") -print("\t Reference Scenario: ", UScum['Waste_MFG_Module_Reference.Mod'].iloc[-1]/1e6, ' Million Tonnes') -print("\t Grid Decarbonization Scenario: ", UScum['Waste_MFG_Module_95-by-35.Adv'].iloc[-1]/1e6, ' Million Tonnes') -print("\t High Electrification Scenario: ", UScum['Waste_MFG_Module_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6, ' Million Tonnes') - - -# In[42]: - - -print(" VIRGIN STOCK Yearly Needs ") -print(" **************************") -for kk in range(0, 3): - obj = SFscenarios[kk] - print(obj) - filter_col = [col for col in USyearly3sig if (col.startswith('VirginStock_') and col.endswith(obj)) ] - display(USyearly3sig[filter_col].loc[[2030, 2040, 2050]]) - print("\n\n") - -print(" VIRGIN STOCK Cumulative Needs ") -print(" ***************************** ") -for kk in range(0, 3): - obj = SFscenarios[kk] - print(obj) - filter_col = [col for col in UScum3sig if (col.startswith('VirginStock_') and col.endswith(obj)) ] - display(UScum3sig[filter_col].loc[[2030, 2040, 2050]]) - print("\n\n") - - -# In[ ]: - - - - - -# In[43]: - - -print(" WASTE EoL CUMULATIVE RESULTS [Tonnes] ") -print(" ******************************************") -filter_col = [col for col in UScum3sig if (col.startswith('Waste_EOL_Module')) ] -display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]]) - - -# In[44]: - - -print(" WASTE EoL + MfgScrap CUMULATIVE RESULTS [Tonnes] ") -print(" ******************************************") -filter_col = [col for col in UScum3sig if (col.startswith('Waste_Module')) ] -display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]]) - - -# In[45]: - - -print(" WASTE MfgScrap CUMULATIVE RESULTS [Tonnes] ") -print(" ******************************************") -filter_col = [col for col in UScum3sig if (col.startswith('Waste_MFG_Module')) ] -display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]]) - - -# In[46]: - - -materials = ['Module', 'glass', 'aluminum', 'copper', 'silicon', 'silver'] - -print(" Appendix Table I: Metric Tonnes Installed in field in 2030") -print(" ########################################################### \n") -#Loop over scenarios -for kk in range (0, 3): - obj = SFscenarios[kk] - print("SCENARIO :", obj) - - print("********************************") - print("********************************") - - modulemat = 0 - for ii in range(0, len(materials)): - installedmat = (UScum3sig['VirginStock_'+materials[ii]+'_'+obj].loc[2030]- - UScum3sig['Waste_'+materials[ii]+'_'+obj].loc[2030]) - print(materials[ii], ':', round(installedmat/1000)*1000, 'tons') - - print("Capacity in Year 2030 [GW]:", round(USyearly3sig['Capacity_'+obj].loc[2030]/1e9)) - print("Capacity in Year 2050 [GW]:", round(USyearly3sig['Capacity_'+obj].loc[2050]/1e9)) - print("****************************\n") - - -# # Mining Capacity - -# In[47]: - - -mining2020_aluminum = 65267000 -mining2020_silver = 22260 -mining2020_copper = 20000000 -mining2020_silicon = 8000000 - - -# In[48]: - - -plt.rcParams.update({'font.size': 10}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(1,1, figsize=(4, 6), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .3, wspace=.2) -i = 0 - -obj = SFscenarios[2] -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -# ROW 2, Aluminum and Silicon: g- 4 aluminum k - 1 silicon orange - 3 copper gray - 2 silver -axs.plot(USyearly[keyw+materials[2]+'_'+SFscenarios[2]]*100/mining2020_silver, - color = 'gray', linewidth=2.0, label='Silver') -axs.fill_between(USyearly.index, USyearly[keyw+materials[2]+'_'+SFscenarios[0]]*100/mining2020_silver, - USyearly[keyw+materials[2]+'_'+SFscenarios[2]]*100/mining2020_silver, - color='gray', lw=3, alpha=.3) - -axs.plot(USyearly[keyw+materials[1]+'_'+SFscenarios[2]]*100/mining2020_silicon, - color = 'k', linewidth=2.0, label='Silicon') -axs.fill_between(USyearly.index, USyearly[keyw+materials[1]+'_'+SFscenarios[0]]*100/mining2020_silicon, - USyearly[keyw+materials[1]+'_'+SFscenarios[2]]*100/mining2020_silicon, - color='k', lw=3, alpha=.5) - -axs.plot(USyearly[keyw+materials[4]+'_'+SFscenarios[2]]*100/mining2020_aluminum, - color = 'g', linewidth=2.0, label='Aluminum') - -axs.fill_between(USyearly.index, USyearly[keyw+materials[4]+'_'+SFscenarios[0]]*100/mining2020_aluminum, - USyearly[keyw+materials[4]+'_'+SFscenarios[2]]*100/mining2020_aluminum, - color='g', lw=3, alpha=.3) - - -axs.plot(USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper, - color = 'orange', linewidth=2.0, label='Copper') - -axs.fill_between(USyearly.index, USyearly[keyw+materials[3]+'_'+SFscenarios[0]]*100/mining2020_copper, - USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper, - color='orange', lw=3, alpha=.3) - -axs.set_xlim([2020,2050]) -axs.legend() -#axs.set_yscale('log') -axs.minorticks_on() - -axs.set_ylabel('Virgin material needs as a percentage \nof 2020 global mining production capacity [%]') - -fig.savefig(title_Method+' Fig_1x1_MaterialNeeds Ratio to Production.png', bbox_inches = "tight", dpi=600) - - -# In[49]: - - - -keyw='VirginStock_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -newdf = pd.DataFrame() - - -newdf['Silver_Ref'] = USyearly[keyw+materials[2]+'_'+SFscenarios[0]]*100/mining2020_silver -newdf['Silver_High'] = USyearly[keyw+materials[2]+'_'+SFscenarios[2]]*100/mining2020_silver - - - -newdf['Silicon_Ref'] = USyearly[keyw+materials[1]+'_'+SFscenarios[0]]*100/mining2020_silicon -newdf['Silicon_High'] = USyearly[keyw+materials[1]+'_'+SFscenarios[2]]*100/mining2020_silicon - - -newdf['Aluminium_Ref'] = USyearly[keyw+materials[4]+'_'+SFscenarios[0]]*100/mining2020_aluminum -newdf['Aluminum_High'] = USyearly[keyw+materials[4]+'_'+SFscenarios[2]]*100/mining2020_aluminum - -newdf['Copper_Ref'] = USyearly[keyw+materials[3]+'_'+SFscenarios[0]]*100/mining2020_copper -newdf['Copper_High'] = USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper - - -newdf['Copper_Ref'] = USyearly[keyw+materials[3]+'_'+SFscenarios[0]]*100/mining2020_copper -newdf['Copper_High'] = USyearly[keyw+materials[3]+'_'+SFscenarios[2]]*100/mining2020_copper - -newdf.to_csv(title_Method+' Demand as Percentage of Mining.csv') - - -# In[50]: - - -import matplotlib as mpl - -plt.rcParams.update({'font.size': 10}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(3,3, figsize=(15, 10), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .3, wspace=.2) -axs = axs.ravel() -i = 0 - -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - -titlesscens = ['Reference Scenario', 'Grid Decarbonization Scenario', 'High Electrification Scenario'] - - -for kk in range(0, 3): - - obj = SFscenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='c', alpha=0.5, label='Glass') -# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - # axs[i].plot([],[],color='c', label='glass', linewidth=5) - # axs[i].plot([],[],color='k', label='silicon', linewidth=5) - # axs[i].plot([],[],color='m', label='silver', linewidth=5) - # axs[i].plot([],[],color='r', label='copper', linewidth=5) - # axs[i].plot([],[],color='g', label='aluminum', linewidth=5) - - axs[i].stackplot(rr.scenario[obj].data['year'], USyearly[keyw+materials[0]+'_'+obj]/1e6, - USyearly[keyw+materials[1]+'_'+obj]/1e6, - USyearly[keyw+materials[2]+'_'+obj]/1e6, - USyearly[keyw+materials[3]+'_'+obj]/1e6, - USyearly[keyw+materials[4]+'_'+obj]/1e6, - colors=['c','k','gray','orange', 'g']) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - axs[i].set_title(titlesscens[kk]) - axs[i].legend(loc='center right') - - #axs[i].legend(materials) - - i += 1 - -# 2nd axis plot -i = 0 -for kk in range(0, 3): - - obj = SFscenarios[kk] - ax2=axs[i].twinx() - - module = (UScum[keyw+materials[0]+'_'+obj]/1e6 + - UScum[keyw+materials[1]+'_'+obj]/1e6 + - UScum[keyw+materials[2]+'_'+obj]/1e6 + - UScum[keyw+materials[3]+'_'+obj]/1e6 + - UScum[keyw+materials[4]+'_'+obj]/1e6) - ax2.plot(rr.scenario[obj].data['year'], module, - color = 'r', linewidth=4.0, label='cumulative') - #axs[i].ylabel('Mass [Tons]') - # axs[i].set_xlim([2010, 2050]) - # axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - ax2.set_yscale('log') -# ax2.set_ylim([1e3/1e6, 1e8/1e6]) - ax2.set_ylim([1e0, 1e2]) - - i += 1 - - ax2.legend() - - -i = 3 -# ROW 2, Aluminum and Silicon: -# Loop over SF Scenarios -for kk in range(0, 3): - - - obj = SFscenarios[kk] - axs[i].yaxis.grid() -# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - - axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[4]+'_'+obj]/1e6, color='g', lw=3, label='Aluminum') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], - # color='g', lw=3, alpha=.6) - - axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[1]+'_'+obj]/1e6, color='k', lw=3, label='Silicon') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], - # color='k', lw=3)# alpha=.3) - - - # silicon aluminum 'k ''g' - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - axs[i].legend() - - i += 1 - - - -# ROW 3: -# Loop over SF Scenarios -for kk in range(0, 3): - - obj = SFscenarios[kk] - axs[i].yaxis.grid() - - axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[3]+'_'+obj], color='orange', lw=3, label='Copper') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], - # color='orange', lw=3)# alpha=.3) - - axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[2]+'_'+obj], color='gray', lw=3, label='Silver') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], - # color='gray', lw=3)# , alpha=.6) - - - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - axs[i].legend() - axs[i].yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}')) - - i += 1 - -for i in range (0, 3): - axs[i].set_ylim([0, 0.8e7/1e6]) - axs[i].minorticks_on() - - #a0.tick_params(axis='y', which='minor', bottom=False) - # axs[i].set_ylim([0, 1e7/1e6]) - - axs[i+3].set_ylim([0, 0.5e6/1e6]) - axs[i+3].minorticks_on() - - axs[i+6].set_ylim([0, 3500]) - - #axs[i+3].set_ylim([1e0, 10e8]) - #axs[i+6].set_ylim([1e0, 5e6]) - -# axs[i+3].set_yscale('log') -# axs[i+6].set_yscale('log') - -axs[0].set_ylabel('Mass [Tons]') -axs[3].set_ylabel('Mass [Tons]') -#axs[5].legend(materials) - -axs[0].set_ylabel('Yearly Mass [Million Tonnes]') -axs[3].set_ylabel('Yearly Mass [Million Tonnes]') -axs[6].set_ylabel('Yearly Mass [Tonnes]') - -#axs[8].legend(materials) - -fig.savefig(title_Method+' Fig_3x3_MaterialNeeds_Nation.png', dpi=600) - - -# In[51]: - - -keyword='Cumulative_Area_disposed' - -USyearly_Areadisp=pd.DataFrame() - -# Loop over SF Scenarios -for kk in range(0, 3): - obj = SFscenarios[kk] - # Loop over Materials - foo = rr.scenario[obj].data[keyword].copy() - USyearly_Areadisp["Areadisp_"+obj] = foo - - # Loop over STATEs - #for jj in range (1, len(STATEs)): - # USyearly_Areadisp["Areadisp_"+obj] += rr.scenario[obj].data[keyword] - - -# In[52]: - - -UScum_Areadisp = USyearly_Areadisp.copy() -UScum_Areadisp = UScum_Areadisp.cumsum() - - -# In[53]: - - -A = UScum['Waste_Module_Reference.Mod'].iloc[-1] -#47700000 # tonnes cumulative by 2050 -A = A*1000 # convert to kg -A = A/10.05599 # convert to m2 if each m2 is ~avg 10 kg -#A = A*2 # convert to area if each module is ~2 m2 -A = A/1e6 # Convert to km 2 -print(A) - - -# In[54]: - - -B = UScum['Waste_Module_95-by-35_Elec.Adv_DR'].iloc[-1] -#47700000 # tonnes cumulative by 2050 -B = B*1000 # convert to kg -B= B/10.05599 # convert to m2 if each m2 is ~avg 10 kg -#A = A*2 # convert to area if each module is ~2 m2 -B =B/1e6 # Convert to km 2 -print(B) - - -# In[55]: - - -C = UScum_Areadisp['Areadisp_Reference.Mod'].iloc[-1]/1e6 -D = UScum_Areadisp['Areadisp_95-by-35_Elec.Adv_DR'].iloc[-1]/1e6 - - -# In[56]: - - -# MANHATTAN SIZE: -manhattans = 59.103529 - - -# In[57]: - - -print("Reference Cumulative Area by 2050 of Waste PV Modules EoL", round(C), " km^2") -print("High Electrification Cumulative Area by 2050 of Waste PV Modules EoL", round(D), " km^2") - - -print("") -print("Reference Waste equals ", round(C/manhattans), " Manhattans ") -print("High Electrification equals ", round(D/manhattans), " Manhattans ") - -print("") -print ("MFG SCrap + Eol Waste") -print("Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL", round(A), " km^2") -print("High Electrification Cumulative Area by 2050 of Waste PV Mfg + Modules EoL", round(B), " km^$") - - -# ### New Section - -# VirginStock_aluminum_Reference.Mod -# VirginStock_aluminum_95-by-35.Adv -# VirginStock_aluminum_95-by-35_Elec.Adv_DR -# Waste_EOL_aluminum_Reference.Mod -# Waste_EOL_aluminum_95-by-35.Adv -# Waste_EOL_aluminum_95-by-35_Elec.Adv_DR -# -# VirginStock_silver_Reference.Mod -# VirginStock_silver_95-by-35.Adv -# VirginStock_silver_95-by-35_Elec.Adv_DR -# Waste_EOL_silver_Reference.Mod -# Waste_EOL_silver_95-by-35.Adv -# Waste_EOL_silver_95-by-35_Elec.Adv_DR -# - -# In[58]: - - -USyearly['VirginStock_silver_Reference.Mod'] - - -# In[59]: - - -USyearly - - -# In[61]: - - -plt.rcParams.update({'font.size': 14}) -plt.rcParams['figure.figsize'] = (12, 8) - -fig, axs = plt.subplots(1,3, figsize=(16, 4), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .1, wspace=.6) -axs = axs.ravel() - -# PLOT 1 -i = 0 -axs[i].yaxis.grid() -lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silver_Reference.Mod']/1e6, color='gray', linewidth=4.0, label='Virgin Material Demands') -lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/1e6, color='gray', linestyle='dashed', linewidth=3.0, label='EoL Material') -axs[i].set_ylabel('Mass [Tons]') -axs[i].set_xlim([2020, 2050]) -axs[i].set_title('Silver') - -# 2nd axis plot -ax2=axs[i].twinx() -lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/USyearly['VirginStock_silver_Reference.Mod'], - color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand') -ax2.set_ylabel('EoL Material as Fraction of Demand', color='r') -ax2.tick_params(axis='y', labelcolor='r') - -# LEGENDS -# added these three lines -lns = lns1+lns2+lns3 -labs = [l.get_label() for l in lns] -#axs[0].legend(lns, labs, loc=0) - -# PLOT 2 -i = 1 -axs[i].yaxis.grid() -lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_aluminum_Reference.Mod']/1e6, color='g', linewidth=4.0, label='Virgin Material Demands') -lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/1e6, color='g', linestyle='dashed', linewidth=3.0, label='EoL Material') -axs[i].set_ylabel('Mass [Tons]') -axs[i].set_xlim([2020, 2050]) -axs[i].set_title('Aluminum') - -# 2nd axis plot -ax2=axs[i].twinx() -lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/USyearly['VirginStock_aluminum_Reference.Mod'], - color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand') - -ax2.set_ylabel('EoL Material as Fraction of Demand', color='r') -ax2.tick_params(axis='y', labelcolor='r') - -# LEGENDS -# added these three lines -lns = lns1+lns2+lns3 -labs = [l.get_label() for l in lns] -#axs[1].legend(lns, labs, loc=0) - - - -# PLOT 3 -i = 2 -axs[i].yaxis.grid() -lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silicon_Reference.Mod']/1e6, color='k', linewidth=4.0, label='Virgin Material Demands') -lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/1e6, color='k', linestyle='dashed', linewidth=3.0, label='EoL Material') -axs[i].set_ylabel('Mass [Tons]') -axs[i].set_xlim([2020, 2050]) -axs[i].set_title('Silicon') - -# 2nd axis plot -ax2=axs[i].twinx() -lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/USyearly['VirginStock_silicon_Reference.Mod'], - color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand') - -#ax2.set_ylabel('EoL Material as Fraction of Demand', color='r') -ax2.tick_params(axis='y', labelcolor='r') - -# LEGENDS -# added these three lines -lns = lns1+lns2+lns3 -labs = [l.get_label() for l in lns] -axs[2].legend(lns, labs, loc='upper center', bbox_to_anchor=(-0.95, -0.25), - fancybox=True, shadow=True, ncol=5) - -fig.savefig(title_Method+' Fig_1x3_VirginvsWaste_Fraction_Nation.png', bbox_inches = "tight", dpi=600) - - -# In[62]: - - -from matplotlib.legend_handler import HandlerBase - -plt.rcParams.update({'font.size': 14}) -plt.rcParams['figure.figsize'] = (12, 8) - -class AnyObjectHandler(HandlerBase): - def create_artists(self, legend, orig_handle, - x0, y0, width, height, fontsize, trans): - - if orig_handle[0] is 'r': - l1 = plt.Line2D([x0,y0+width], [0.4*height,0.4*height], color=orig_handle[0]) - return [l1] - - else: - - l1 = plt.Line2D([x0,y0+width], [0.7*height,0.7*height], color=orig_handle[0], linestyle = orig_handle[3],) - l2 = plt.Line2D([x0,y0+width], [0.4*height,0.4*height], color=orig_handle[1], linestyle = orig_handle[4]) - l3 = plt.Line2D([x0,y0+width], [0.1*height,0.1*height], color=orig_handle[2], linestyle = orig_handle[5]) - - return [l1, l2, l3] - - - -fig, axs = plt.subplots(1,3, figsize=(16, 4), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .1, wspace=.6) -axs = axs.ravel() - -# PLOT 1 -i = 0 -axs[i].yaxis.grid() -lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silver_Reference.Mod']/1e6, color='gray', linewidth=4.0, label='Virgin Material Demands') -lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/1e6, color='gray', linestyle='dashed', linewidth=3.0, label='EoL Material') -axs[i].set_ylabel('Mass [Tons]') -axs[i].set_xlim([2020, 2050]) -axs[i].set_title('Silver') - -# 2nd axis plot -ax2=axs[i].twinx() -lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silver_Reference.Mod']/USyearly['VirginStock_silver_Reference.Mod'], - color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand') -ax2.set_ylabel('EoL Material as Fraction of Demand', color='r') -ax2.tick_params(axis='y', labelcolor='r') - -# LEGENDS -# added these three lines -lns = lns1+lns2+lns3 -labs = [l.get_label() for l in lns] -#axs[0].legend(lns, labs, loc=0) - -# PLOT 2 -i = 1 -axs[i].yaxis.grid() -lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_aluminum_Reference.Mod']/1e6, color='g', linewidth=4.0, label='Virgin Material Demands') -lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/1e6, color='g', linestyle='dashed', linewidth=3.0, label='EoL Material') -axs[i].set_ylabel('Mass [Tons]') -axs[i].set_xlim([2020, 2050]) -axs[i].set_title('Aluminum') - -# 2nd axis plot -ax2=axs[i].twinx() -lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_aluminum_Reference.Mod']/USyearly['VirginStock_aluminum_Reference.Mod'], - color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand') - -ax2.set_ylabel('EoL Material as Fraction of Demand', color='r') -ax2.tick_params(axis='y', labelcolor='r') - -# LEGENDS -# added these three lines -lns = lns1+lns2+lns3 -labs = [l.get_label() for l in lns] -#axs[1].legend(lns, labs, loc=0) - - - -# PLOT 3 -i = 2 -axs[i].yaxis.grid() -lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silicon_Reference.Mod']/1e6, color='k', linewidth=4.0, label='Virgin Material Demands') -lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/1e6, color='k', linestyle='dashed', linewidth=3.0, label='EoL Material') -axs[i].set_ylabel('Mass [Tons]') -axs[i].set_xlim([2020, 2050]) -axs[i].set_title('Silicon') - -# 2nd axis plot -ax2=axs[i].twinx() -lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silicon_Reference.Mod']/USyearly['VirginStock_silicon_Reference.Mod'], - color = 'r', linewidth=1.0, label='EOl Material as fraction of Demand') - -#ax2.set_ylabel('EoL Material as Fraction of Demand', color='r') -ax2.tick_params(axis='y', labelcolor='r') - -# LEGENDS -# added these three lines -lns = lns1+lns2+lns3 -labs = [l.get_label() for l in lns] -#axs[2].legend(lns, labs, loc='upper center', bbox_to_anchor=(-0.95, -0.25), -# fancybox=True, shadow=True, ncol=5) - -#axs[2].legend([("gray","g","k","-","-","-"), ("gray","g","k","--","--","--"),("r","r","r","-","-","-")], ['Material Demands', "EoL Material", 'Fraction'], -# handler_map={tuple: AnyObjectHandler()}, loc='upper center', bbox_to_anchor=(-0.95, -0.25), -# fancybox=True, shadow=True, ncol=5) - -axs[2].legend([("gray","g","k","-","-","-"), ("gray","g","k","--","--","--"),("r","r","r")], [' Virgin material demands', "EOL material", 'EOL material as fraction of demand'], - handler_map={tuple: AnyObjectHandler()}, loc='upper center', bbox_to_anchor=(-0.95, -0.25), - fancybox=True, shadow=True, ncol=5) - -fig.savefig(title_Method+' Fig_1x3_VirginvsWaste_Fraction_Nation.png', bbox_inches = "tight", dpi=600) - - -# In[63]: - - -newdf = pd.DataFrame() - -newdf['Virgin material demands, Silicon, Reference'] = USyearly['VirginStock_silicon_Reference.Mod']/1e6 -newdf['EOL material, Silicon, Reference'] = USyearly['Waste_EOL_silicon_Reference.Mod']/1e6 -newdf['EOL material as fraction of demand, Silicon, Reference'] = USyearly['Waste_EOL_silicon_Reference.Mod']/USyearly['VirginStock_silicon_Reference.Mod'] - -newdf['Virgin material demands, Silver, Reference'] = USyearly['VirginStock_silver_Reference.Mod']/1e6 -newdf['EOL material, Silver, Reference'] = USyearly['Waste_EOL_silver_Reference.Mod']/1e6 -newdf['EOL material as fraction of demand, Silver, Reference'] = USyearly['Waste_EOL_silver_Reference.Mod']/USyearly['VirginStock_silver_Reference.Mod'] - -newdf['Virgin material demands, Aluminum, Reference'] = USyearly['VirginStock_aluminum_Reference.Mod']/1e6 -newdf['EOL material, Aluminum, Reference'] = USyearly['Waste_EOL_aluminum_Reference.Mod']/1e6 -newdf['EOL material as fraction of demand, Aluminum, Reference'] = USyearly['Waste_EOL_aluminum_Reference.Mod']/USyearly['VirginStock_aluminum_Reference.Mod'] - - -newdf['Virgin material demands, Silicon, Grid Decarb.'] = USyearly['VirginStock_silicon_'+SFscenarios[1]]/1e6 -newdf['EOL material, Silicon, Grid Decarb.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[1]]/1e6 -newdf['EOL material as fraction of demand, Silicon, Grid Decarb.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[1]]/USyearly['VirginStock_silicon_'+SFscenarios[1]] - -newdf['Virgin material demands, Silver, Grid Decarb.'] = USyearly['VirginStock_silver_'+SFscenarios[1]]/1e6 -newdf['EOL material, Silver, Grid Decarb.'] = USyearly['Waste_EOL_silver_'+SFscenarios[1]]/1e6 -newdf['EOL material as fraction of demand, Silver, Grid Decarb.'] = USyearly['Waste_EOL_silver_'+SFscenarios[1]]/USyearly['VirginStock_silver_'+SFscenarios[1]] - -newdf['Virgin material demands, Aluminum, Grid Decarb.'] = USyearly['VirginStock_aluminum_'+SFscenarios[1]]/1e6 -newdf['EOL material, Aluminum, Grid Decarb.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[1]]/1e6 -newdf['EOL material as fraction of demand, Aluminum, Grid Decarb.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[1]]/USyearly['VirginStock_aluminum_'+SFscenarios[1]] - - -newdf['Virgin material demands, Silicon, High Elec.'] = USyearly['VirginStock_silicon_'+SFscenarios[2]]/1e6 -newdf['EOL material, Silicon, High Elec.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[2]]/1e6 -newdf['EOL material as fraction of demand, Silicon, High Elec.'] = USyearly['Waste_EOL_silicon_'+SFscenarios[2]]/USyearly['VirginStock_silicon_'+SFscenarios[2]] - -newdf['Virgin material demands, Silver, High Elec.'] = USyearly['VirginStock_silver_'+SFscenarios[2]]/1e6 -newdf['EOL material, Silver, High Elec.'] = USyearly['Waste_EOL_silver_'+SFscenarios[2]]/1e6 -newdf['EOL material as fraction of demand, Silver, High Elec.'] = USyearly['Waste_EOL_silver_'+SFscenarios[2]]/USyearly['VirginStock_silver_'+SFscenarios[2]] - -newdf['Virgin material demands, Aluminum, High Elec.'] = USyearly['VirginStock_aluminum_'+SFscenarios[2]]/1e6 -newdf['EOL material, Aluminum, High Elec.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[2]]/1e6 -newdf['EOL material as fraction of demand, Aluminum, High Elec.'] = USyearly['Waste_EOL_aluminum_'+SFscenarios[2]]/USyearly['VirginStock_aluminum_'+SFscenarios[2]] - -newdf.to_csv(title_Method+' Demand vs EOL Fraction NATION.csv') - - -# In[195]: - - - - - -# In[ ]: - - - - diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.html b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.html deleted file mode 100644 index 7b62413c..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.html +++ /dev/null @@ -1,16068 +0,0 @@ - - - - -(development) ReEDS Scenarios - WORLD - - - - - - - - - - - - - - - - - - - - - - - -
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    To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool.

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    Current sections include:

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    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format

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    2. ### Reading scenarios of interest and running PV ICE tool

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    3. ###Plotting

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    4. ### GeoPlotting.
      5. -</ol> - Notes:

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      Scenarios of Interest: - the Ref.Mod, -o 95-by-35.Adv, and -o 95-by-35+Elec.Adv+DR ones

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    In [1]:
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    import PV_ICE
-import numpy as np
-import pandas as pd
-import os,sys
-import matplotlib.pyplot as plt
-from IPython.display import display
-plt.rcParams.update({'font.size': 22})
-plt.rcParams['figure.figsize'] = (12, 8)
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    In [2]:
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    import os
-from pathlib import Path
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-testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP')
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-print ("Your simulation will be stored in %s" % testfolder)
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    Your simulation will be stored in C:\Users\sayala\Documents\GitHub\CircularEconomy-MassFlowCalculator\PV_ICE\TEMP
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    In [3]:
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    SFscenarios = ['World']
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    Create the 3 Scenarios and assign Baselines

    Keeping track of each scenario as its own PV ICE Object.

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    In [4]:
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    filetitle = r'..\baselines\ReedsSubset\baseline_modules_World_Bogdanov.csv'
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    In [5]:
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    #for ii in range (0, 1): #len(scenarios):
-i = 0
-rr = PV_ICE.Simulation(name='World', path=testfolder)
-rr.createScenario(name=SFscenarios[i], file=filetitle)
-rr.scenario[SFscenarios[i]].addMaterial('glass', file=r'..\baselines\ReedsSubset\baseline_material_glass_Reeds.csv')
-rr.scenario[SFscenarios[i]].addMaterial('silicon', file=r'..\baselines\ReedsSubset\baseline_material_silicon_Reeds.csv')
-rr.scenario[SFscenarios[i]].addMaterial('silver', file=r'..\baselines\ReedsSubset\baseline_material_silver_Reeds.csv')
-rr.scenario[SFscenarios[i]].addMaterial('copper', file=r'..\baselines\ReedsSubset\baseline_material_copper_Reeds.csv')
-rr.scenario[SFscenarios[i]].addMaterial('aluminum', file=r'..\baselines\ReedsSubset\baseline_material_aluminium_Reeds.csv')
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    path = C:\Users\sayala\Documents\GitHub\CircularEconomy-MassFlowCalculator\PV_ICE\TEMP
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    2 FINISH: Set characteristics of Recycling to SF values.

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    Calculate Mass Flow

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    In [6]:
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    IRENA= False
-PERFECTMFG = True
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-mats = ['glass', 'silicon','silver','copper','aluminum']
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-ELorRL = 'RL'
-if IRENA:
-    if ELorRL == 'RL':
-        weibullInputParams = {'alpha': 5.3759, 'beta':30}  # Regular-loss scenario IRENA
-    if ELorRL == 'EL':
-        weibullInputParams = {'alpha': 2.49, 'beta':30}  # Regular-loss scenario IRENA
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-    if PERFECTMFG:
-        for jj in range (0, len(rr.scenario.keys())):
-            rr.scenario[list(rr.scenario.keys())[jj]].data['mod_lifetime'] = 40
-            rr.scenario[list(rr.scenario.keys())[jj]].data['mod_MFG_eff'] = 100.0
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-            for kk in range(0, len(mats)):
-                mat = mats[kk]
-                rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0   
-                rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_scrap_Recycled'] = 0.0   
-               
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-    rr.calculateMassFlow(weibullInputParams=weibullInputParams)
-    title_Method = 'Irena_'+ELorRL
-else:
-    rr.calculateMassFlow()
-    title_Method = 'PVICE'
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    Working on Scenario:  World
-********************
-Finished Area+Power Generation Calculations
-==> Working on Material :  glass
-==> Working on Material :  silicon
-==> Working on Material :  silver
-==> Working on Material :  copper
-==> Working on Material :  aluminum
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    Aggregating PCAs Material Landfilled to obtain US totals by Year

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    In [7]:
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    USyearly=pd.DataFrame()
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    In [8]:
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    materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
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    In [9]:
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    keywd = 'mat_Virgin_Stock'
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-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['VirginStock_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]
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-    filter_col = [col for col in USyearly if (col.startswith('VirginStock_') and col.endswith(obj))]
-    USyearly['VirginStock_Module_'+obj] = USyearly[filter_col].sum(axis=1)
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    In [10]:
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    keywd = 'mat_Total_Landfilled'
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-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['Waste_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]
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-    filter_col = [col for col in USyearly if (col.startswith('Waste_') and col.endswith(obj)) ]
-    USyearly['Waste_Module_'+obj] = USyearly[filter_col].sum(axis=1)
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    In [11]:
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    keywd = 'mat_Total_EOL_Landfilled'
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-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['Waste_EOL_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]
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-    filter_col = [col for col in USyearly if (col.startswith('Waste_EOL_') and col.endswith(obj)) ]
-    USyearly['Waste_EOL_Module_'+obj] = USyearly[filter_col].sum(axis=1)
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    In [12]:
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    keywd = 'mat_Total_MFG_Landfilled'
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-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    for ii in range(len(materials)):
-        USyearly['Waste_MFG_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]
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-    filter_col = [col for col in USyearly if (col.startswith('Waste_MFG_') and col.endswith(obj)) ]
-    USyearly['Waste_MFG_Module_'+obj] = USyearly[filter_col].sum(axis=1)
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    Converting to grams to METRIC Tons.

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    In [13]:
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    USyearly = USyearly/1000000  # This is the ratio for Metric tonnes
-#907185 -- this is for US tons
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    Adding NEW Installed Capacity to US

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    In [14]:
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    keyword='new_Installed_Capacity_[MW]'
-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    USyearly[keyword+obj] = rr.scenario[obj].data[keyword]
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    Reindexing and creating c umulative results

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    UScum = USyearly.copy()
-UScum = UScum.cumsum()
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    Adding Installed Capacity to US (This is already 'Cumulative') so not including it in UScum

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    In [16]:
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    keyword='Installed_Capacity_[W]'
-for jj in range(len(SFscenarios)):
-    obj = SFscenarios[jj]
-    USyearly["Capacity_"+obj] = rr.scenario[obj].data[keyword]
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    Set YEAR Index

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    In [17]:
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    USyearly.index = rr.scenario[obj].data['year']
-UScum.index = rr.scenario[obj].data['year']
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    In [18]:
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    USyearly.head().iloc[1]
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    Out[18]:
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    VirginStock_glass_World             5.845518e+07
-VirginStock_silicon_World           5.898482e+06
-VirginStock_silver_World            1.069643e+05
-VirginStock_copper_World            4.146420e+04
-VirginStock_aluminum_World          1.393324e+07
-VirginStock_Module_World            7.843533e+07
-Waste_glass_World                   4.033407e+06
-Waste_silicon_World                 3.046645e+06
-Waste_silver_World                  2.310428e+04
-Waste_copper_World                  4.892776e+03
-Waste_aluminum_World                4.152106e+05
-Waste_Module_World                  7.523260e+06
-Waste_EOL_glass_World               6.671246e-02
-Waste_EOL_silicon_World             3.495899e-03
-Waste_EOL_silver_World              2.134799e-04
-Waste_EOL_copper_World              4.483077e-05
-Waste_EOL_aluminum_World            1.668896e-02
-Waste_EOL_Module_World              8.715562e-02
-Waste_MFG_glass_World               4.033407e+06
-Waste_MFG_silicon_World             3.046645e+06
-Waste_MFG_silver_World              2.310428e+04
-Waste_MFG_copper_World              4.892776e+03
-Waste_MFG_aluminum_World            4.152106e+05
-Waste_MFG_Module_World              7.523260e+06
-new_Installed_Capacity_[MW]World    1.000000e+06
-Capacity_World                      2.006803e+12
-Name: 2010, dtype: float64
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    In [19]:
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    USyearly.head()
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    Out[19]:
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    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    VirginStock_glass_WorldVirginStock_silicon_WorldVirginStock_silver_WorldVirginStock_copper_WorldVirginStock_aluminum_WorldVirginStock_Module_WorldWaste_glass_WorldWaste_silicon_WorldWaste_silver_WorldWaste_copper_World...Waste_EOL_aluminum_WorldWaste_EOL_Module_WorldWaste_MFG_glass_WorldWaste_MFG_silicon_WorldWaste_MFG_silver_WorldWaste_MFG_copper_WorldWaste_MFG_aluminum_WorldWaste_MFG_Module_Worldnew_Installed_Capacity_[MW]WorldCapacity_World
    year
    20095.894527e+079.371135e+06223992.03580541811.8466841.415011e+078.273232e+074.067224e+066.495388e+0648382.2797344933.797909...0.0000000.0000004.067224e+066.495388e+0648382.2797344933.797909421673.1498941.103760e+071000000.01.006860e+12
    20105.845518e+075.898482e+06106964.25248241464.2047301.393324e+077.843533e+074.033407e+063.046645e+0623104.2787504892.776203...0.0166890.0871564.033407e+063.046645e+0623104.2785364892.776158415210.6209837.523260e+061000000.02.006803e+12
    20115.690669e+075.704484e+0686775.63530540365.8152001.342383e+077.616215e+073.926567e+062.928193e+0618743.5546044763.169851...1.3615137.1104393.926562e+062.928192e+0618743.5372264763.166194400030.0018297.278291e+061000000.03.000622e+12
    20125.635610e+075.711201e+0668068.16068939579.4681511.313818e+077.531313e+073.888644e+062.988997e+0614702.9533084670.426463...18.31945195.6841233.888571e+062.988993e+0614702.7227094670.377242391517.8066147.288455e+061000000.03.991528e+12
    20135.477981e+075.355709e+0645860.92326238095.2380951.043250e+077.065197e+073.780282e+062.735609e+069907.4211594495.557312...118.766176620.4649133.779807e+062.735584e+069905.9594254495.238095310888.5552886.840681e+061000000.04.979357e+12
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    plt.plot(USyearly['new_Installed_Capacity_[MW]World'])
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    [<matplotlib.lines.Line2D at 0x156cf09fa90>]
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    VirginStock_glass_WorldVirginStock_silicon_WorldVirginStock_silver_WorldVirginStock_copper_WorldVirginStock_aluminum_WorldVirginStock_Module_WorldWaste_glass_WorldWaste_silicon_WorldWaste_silver_WorldWaste_copper_World...Waste_EOL_copper_WorldWaste_EOL_aluminum_WorldWaste_EOL_Module_WorldWaste_MFG_glass_WorldWaste_MFG_silicon_WorldWaste_MFG_silver_WorldWaste_MFG_copper_WorldWaste_MFG_aluminum_WorldWaste_MFG_Module_Worldnew_Installed_Capacity_[MW]World
    year
    20095.894527e+079.371135e+06223992.03580541811.8466841.415011e+078.273232e+074.067224e+066.495388e+0648382.2797344933.797909...0.0000000.0000000.0000004.067224e+066.495388e+0648382.2797344933.7979094.216731e+051.103760e+071000000.0
    20101.174004e+081.526962e+07330956.28828683276.0514132.808335e+071.611676e+088.100631e+069.542034e+0671486.5584839826.574112...0.0000450.0166890.0871568.100631e+069.542034e+0671486.5582709826.5740678.368838e+051.856086e+072000000.0
    20111.743071e+082.097410e+07417731.923592123641.8666134.150717e+072.373298e+081.202720e+071.247023e+0790230.11308714589.743963...0.0037021.3782027.1975941.202719e+071.247023e+0790230.09549614589.7402601.236914e+062.583915e+073000000.0
    20122.306632e+082.668530e+07485800.084281163221.3347645.464536e+073.126429e+081.591584e+071.545922e+07104933.06639519260.170426...0.05292419.697653102.8817171.591576e+071.545922e+07104932.81820519260.1175021.628432e+063.312761e+074000000.0
    20132.854431e+083.204101e+07531661.007543201316.5728596.507786e+073.832949e+081.969612e+071.819483e+07114840.48755423755.727738...0.372141138.463829723.3466311.969557e+071.819480e+07114838.77762923755.3555971.939320e+063.996829e+075000000.0
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    3 sig figures save Yearly and cumulative overview Nation

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    USyearly3sig = USyearly.copy()
-UScum3sig = UScum.copy()
-N = 2
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-UScum3sig = UScum3sig.drop(UScum3sig.index[0])
-USyearly3sig = USyearly3sig.drop(USyearly3sig.index[0])
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-if IRENA:
-    UScum3sig = UScum3sig.loc[:, ~UScum3sig.columns.str.startswith('Waste_MFG_')]
-    USyearly3sig = USyearly3sig.loc[:, ~USyearly3sig.columns.str.startswith('Waste_MFG_')]
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-USyearly3sig = USyearly3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))
-USyearly3sig = USyearly3sig.applymap(lambda x: int(x))
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-UScum3sig = UScum3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))
-UScum3sig = UScum3sig.applymap(lambda x: int(x))
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-USyearly3sig.to_csv(title_Method+' US_Yearly NATION.csv')
-UScum3sig.to_csv(title_Method+' US_Cumulative NATION.csv')
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    In [23]:
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    print("Sanity check: mat_Total_Landfilled = mat_Total_EOL_Landfilled + mat_Total_MFG_Landfilled")
-A = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled'].iloc[5]
-B = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_EOL_Landfilled'].iloc[5]
-C = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_MFG_Landfilled'].iloc[5]
-A - B - C
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    0.0
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    PLOT

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    Yearly Virgin Material Needs by Scenario

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    In [24]:
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (15, 8)
-keyw='VirginStock_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
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-f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
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-########################    
-# SUBPLOT 1
-########################
-#######################
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-    
-    
-# Loop over Keywords
-ii = 0 
-# Loop over SF Scenarios
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-# loop plotting over scenarios
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-# SCENARIO 1 ***************
-kk = 0
-obj = SFscenarios[kk]
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-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,
-                 interpolate=True)
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-########################    
-# SUBPLOT 2
-########################
-#######################
-# Calculate    
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-cumulations2050 = {}
-for ii in range(0, len(materials)):
-    matcum = []
-    for kk in range (0, 1):
-        obj = SFscenarios[kk]
-        matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])
-    cumulations2050[materials[ii]] = matcum
-
-dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) 
-dfcumulations2050 = dfcumulations2050/1000000   # in Million Tonnes
-
-dfcumulations2050['bottom1'] = dfcumulations2050['glass']
-dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']
-dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']
-dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']
-
-
-## Plot BARS Stuff
-ind=np.arange(1)
-width=0.35 # width of the bars.
-p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')
-p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,
-             bottom=dfcumulations2050['bottom1'])
-p2 = a1.bar(ind, dfcumulations2050['silicon'], width,
-             bottom=dfcumulations2050['bottom2'])
-p3 = a1.bar(ind, dfcumulations2050['copper'], width,
-             bottom=dfcumulations2050['bottom3'])
-p4 = a1.bar(ind, dfcumulations2050['silver'], width,
-             bottom=dfcumulations2050['bottom4'])
-
-a1.yaxis.set_label_position("right")
-a1.yaxis.tick_right()
-a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]')
-a1.set_xlabel('Scenario')
-a1.set_xticks(ind, ('S1'))
-#plt.yticks(np.arange(0, 81, 10))
-a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))
-
-f.tight_layout()
-
-f.savefig(title_Method+' Fig_2x1_WORLD Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600)
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    C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:79: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.
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    In [27]:
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (15, 8)
-
-keyw='Waste_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
-
-########################    
-# SUBPLOT 1
-########################
-#######################
-
-    
-    
-# Loop over Keywords
-ii = 0 
-# Loop over SF Scenarios
-
-# loop plotting over scenarios
-
-# SCENARIO 1 ***************
-kk = 0
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,
-                 interpolate=True)
-
-
-    
-########################    
-# SUBPLOT 2
-########################
-#######################
-# Calculate    
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-cumulations2050 = {}
-for ii in range(0, len(materials)):
-    matcum = []
-    for kk in range (0, 1):
-        obj = SFscenarios[kk]
-        matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])
-    cumulations2050[materials[ii]] = matcum
-
-dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) 
-dfcumulations2050 = dfcumulations2050/1000000   # in Million Tonnes
-
-dfcumulations2050['bottom1'] = dfcumulations2050['glass']
-dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']
-dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']
-dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']
-
-
-## Plot BARS Stuff
-ind=np.arange(1)
-width=0.35 # width of the bars.
-p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')
-p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,
-             bottom=dfcumulations2050['bottom1'])
-p2 = a1.bar(ind, dfcumulations2050['silicon'], width,
-             bottom=dfcumulations2050['bottom2'])
-p3 = a1.bar(ind, dfcumulations2050['copper'], width,
-             bottom=dfcumulations2050['bottom3'])
-p4 = a1.bar(ind, dfcumulations2050['silver'], width,
-             bottom=dfcumulations2050['bottom4'])
-
-a1.yaxis.set_label_position("right")
-a1.yaxis.tick_right()
-a1.set_ylabel('Cumulative Manufacturing Scrap and EoL Material \n by 2050 [Million Tonnes]')
-a1.set_xlabel('Scenario')
-a1.set_xticks(ind, ('S1'))
-#plt.yticks(np.arange(0, 81, 10))
-a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))
-
-f.tight_layout()
-
-f.savefig(title_Method+' Fig_2x1_Yearly MFG and EOL Material by Scenario and Cumulatives_Nation.png', dpi=600)
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    In [28]:
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (15, 8)
-keyw='Waste_EOL_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
-
-########################    
-# SUBPLOT 1
-########################
-#######################
-
-    
-    
-# Loop over Keywords
-ii = 0 
-# Loop over SF Scenarios
-
-# loop plotting over scenarios
-
-# SCENARIO 1 ***************
-kk = 0
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,
-                 interpolate=True)
-a0.legend()
-a0.set_title('Yearly End of Life Material by Scenario')
-a0.set_ylabel('Mass [Million Tonnes]')
-
-a0.set_xlabel('Years')
-
-
-
-    
-########################    
-# SUBPLOT 2
-########################
-#######################
-# Calculate    
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-cumulations2050 = {}
-for ii in range(0, len(materials)):
-    matcum = []
-    for kk in range (0, 1):
-        obj = SFscenarios[kk]
-        matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])
-    cumulations2050[materials[ii]] = matcum
-
-dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) 
-dfcumulations2050 = dfcumulations2050/1000000   # in Million Tonnes
-
-dfcumulations2050['bottom1'] = dfcumulations2050['glass']
-dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']
-dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']
-dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']
-
-
-## Plot BARS Stuff
-ind=np.arange(1)
-width=0.35 # width of the bars.
-p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')
-p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,
-             bottom=dfcumulations2050['bottom1'])
-p2 = a1.bar(ind, dfcumulations2050['silicon'], width,
-             bottom=dfcumulations2050['bottom2'])
-p3 = a1.bar(ind, dfcumulations2050['copper'], width,
-             bottom=dfcumulations2050['bottom3'])
-p4 = a1.bar(ind, dfcumulations2050['silver'], width,
-             bottom=dfcumulations2050['bottom4'])
-
-a1.yaxis.set_label_position("right")
-a1.yaxis.tick_right()
-a1.set_ylabel('Cumulative End of Life Material by 2050 [Million Tonnes]')
-a1.set_xlabel('Scenario')
-a1.set_xticks(ind, ('S1', 'S2', 'S3'))
-#plt.yticks(np.arange(0, 81, 10))
-a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))
-
-f.tight_layout()
-
-f.savefig(title_Method+' Fig_2x1_Yearly EoL Waste by Scenario and Cumulatives_Nation.png', dpi=600)
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    In [31]:
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (15, 8)
-keyw='Waste_MFG_'
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})
-
-########################    
-# SUBPLOT 1
-########################
-#######################
-
-    
-    
-# Loop over Keywords
-ii = 0 
-# Loop over SF Scenarios
-
-# loop plotting over scenarios
-
-# SCENARIO 1 ***************
-kk = 0
-obj = SFscenarios[kk]
-
-modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+
-            USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+
-            USyearly[keyw+materials[4]+'_'+obj])
-glassmat = (USyearly[keyw+materials[0]+'_'+obj])
-modulemat = modulemat/1000000
-glassmat = glassmat/1000000 
-a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')
-a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')
-a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,
-                 interpolate=True)
-
-
-
-########################    
-# SUBPLOT 2
-########################
-#######################
-# Calculate    
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-cumulations2050 = {}
-for ii in range(0, len(materials)):
-    matcum = []
-    for kk in range (0, 1):
-        obj = SFscenarios[kk]
-        matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])
-    cumulations2050[materials[ii]] = matcum
-
-dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) 
-dfcumulations2050 = dfcumulations2050/1000000   # in Million Tonnes
-
-dfcumulations2050['bottom1'] = dfcumulations2050['glass']
-dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']
-dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']
-dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']
-
-
-## Plot BARS Stuff
-ind=np.arange(1)
-width=0.35 # width of the bars.
-p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')
-p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,
-             bottom=dfcumulations2050['bottom1'])
-p2 = a1.bar(ind, dfcumulations2050['silicon'], width,
-             bottom=dfcumulations2050['bottom2'])
-p3 = a1.bar(ind, dfcumulations2050['copper'], width,
-             bottom=dfcumulations2050['bottom3'])
-p4 = a1.bar(ind, dfcumulations2050['silver'], width,
-             bottom=dfcumulations2050['bottom4'])
-
-a1.yaxis.set_label_position("right")
-a1.yaxis.tick_right()
-a1.set_ylabel('Cumulative Manufacturing Scrap by 2050 [Million Tonnes]')
-a1.set_xlabel('Scenario')
-a1.set_xticks(ind, ('S1'))
-#plt.yticks(np.arange(0, 81, 10))
-a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))
-
-f.tight_layout()
-
-f.savefig(title_Method+' Fig_2x1_Yearly MFG Waste by Scenario and Cumulatives_Nation.png', dpi=600)
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    In [33]:
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (8, 8)
-
-fig, axs = plt.subplots(figsize=(8, 8))
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'k', label='Cumulative New Yearly Installs S3-S2')
-
-#axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'c', label='Cumulative New Yearly Installs')
-
-axs.legend()
-axs.set_xlim([2020,2030])
-axs.set_ylabel('Power [TW]')
-fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600)
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    WASTE COMPARISON SIZE

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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (8, 8)
-
-fig, axs = plt.subplots(figsize=(8, 8))
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Cumulative New Yearly Installs')
-axs.plot(USyearly['Capacity_'+SFscenarios[0]]/1e12, 'g', label='Active in Field Installs')
-axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels')
-axs.legend()
-axs.set_xlim([2020,2050])
-axs.set_ylabel('Power [TW]')
-fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600)
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    foo0 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12).sum()
-print(foo0)
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    198.97552440190063
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    E = (UScum['new_Installed_Capacity_[MW]World']/1e6).sum()
-F = (UScum['new_Installed_Capacity_[MW]World']/1e6-USyearly['Capacity_World']/1e12).sum()
-print("Cumulative Installs", E)
-print("Cumulative Waste", F)
-print("Fraction of Decomisioned to Installed Cumulative by 2050", F/E)
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    Cumulative Installs 903.0
-Cumulative Waste 198.97552440190063
-Fraction of Decomisioned to Installed Cumulative by 2050 0.22034941794230412
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    plt.rcParams.update({'font.size': 15})
-plt.rcParams['figure.figsize'] = (8, 8)
-
-fig, axs = plt.subplots(figsize=(8, 8))
-axs.plot(USyearly['new_Installed_Capacity_[MW]World']/1e6, 'b', label='Yearly New Yearly Installs')
-axs.plot(UScum['new_Installed_Capacity_[MW]World']/1e6-USyearly['Capacity_World']/1e12, 'r', label='Decomissioned PV Panels')
-axs.legend()
-axs.set_xlim([2020,2050])
-axs.set_ylabel('Power [TW]')
-fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600)
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    In [40]:
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    print("CUMULATIVE WASTE by 2050")
-print("*************************")
-print("")
-UScum.iloc[-1]
-print("MFG Scrap + EoL Material Only")
-print("\t Reference Scenario: ", UScum['Waste_Module_World'].iloc[-1]/1e6, ' Million Tonnes')
-
-print("EoL Material Only")
-print("\t Reference Scenario: ", UScum['Waste_EOL_Module_World'].iloc[-1]/1e6, ' Million Tonnes')
-
-print("MFG Scrap Only")
-print("\t Reference Scenario: ", UScum['Waste_MFG_Module_World'].iloc[-1]/1e6, ' Million Tonnes')
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    CUMULATIVE WASTE by 2050
-*************************
-
-MFG Scrap + EoL Material Only
-	 Reference Scenario:  957.5969166556922  Million Tonnes
-EoL Material Only
-	 Reference Scenario:  819.552589911594  Million Tonnes
-MFG Scrap Only
-	 Reference Scenario:  138.04432674409833  Million Tonnes
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    In [41]:
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    print(" VIRGIN STOCK Yearly Needs ")
-print(" **************************")
-for kk in range(0, 1):
-    obj = SFscenarios[kk]
-    print(obj)
-    filter_col = [col for col in USyearly3sig if (col.startswith('VirginStock_') and col.endswith(obj)) ]
-    display(USyearly3sig[filter_col].loc[[2030, 2040, 2050]])
-    print("\n\n")
-    
-print(" VIRGIN STOCK Cumulative Needs ")
-print(" ***************************** ")
-for kk in range(0, 1):
-    obj = SFscenarios[kk]
-    print(obj)
-    filter_col = [col for col in UScum3sig if (col.startswith('VirginStock_') and col.endswith(obj)) ]
-    display(UScum3sig[filter_col].loc[[2030, 2040, 2050]])
-    print("\n\n")
-
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     VIRGIN STOCK Yearly Needs 
- **************************
-World
-
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    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    VirginStock_glass_WorldVirginStock_silicon_WorldVirginStock_silver_WorldVirginStock_copper_WorldVirginStock_aluminum_WorldVirginStock_Module_World
    year
    20304060000019300001020033400463000047200000
    2040388000001840000972031900442000045100000
    2050377000001790000944031000429000043800000
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- VIRGIN STOCK Cumulative Needs 
- ***************************** 
-World
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    VirginStock_glass_WorldVirginStock_silicon_WorldVirginStock_silver_WorldVirginStock_copper_WorldVirginStock_aluminum_WorldVirginStock_Module_World
    year
    20301050000000830000008920009050001760000001310000000
    2040145000000010200000099100012300002210000001770000000
    20501830000000120000000109000015400002640000002220000000
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    In [42]:
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    print(" WASTE EoL CUMULATIVE RESULTS [Tonnes] ")
-print(" ******************************************")
-filter_col = [col for col in UScum3sig if (col.startswith('Waste_EOL_Module')) ]
-display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]])
-
    - -
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     WASTE EoL CUMULATIVE RESULTS [Tonnes] 
- ******************************************
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    Waste_EOL_Module_World
    year
    201638100
    20201080000
    2030141000000
    2040533000000
    2050820000000
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    In [43]:
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    print(" WASTE EoL + MfgScrap CUMULATIVE RESULTS [Tonnes] ")
-print(" ******************************************")
-filter_col = [col for col in UScum3sig if (col.startswith('Waste_Module')) ]
-display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]])
-
    - -
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     WASTE EoL + MfgScrap CUMULATIVE RESULTS [Tonnes] 
- ******************************************
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    Waste_Module_World
    year
    201658300000
    202076300000
    2030242000000
    2040653000000
    2050958000000
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    In [44]:
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    print(" WASTE MfgScrap CUMULATIVE RESULTS [Tonnes] ")
-print(" ******************************************")
-filter_col = [col for col in UScum3sig if (col.startswith('Waste_MFG_Module')) ]
-display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]])
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     WASTE MfgScrap CUMULATIVE RESULTS [Tonnes] 
- ******************************************
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    Waste_MFG_Module_World
    year
    201658200000
    202075300000
    2030101000000
    2040120000000
    2050138000000
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    In [50]:
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    materials = ['Module', 'glass', 'aluminum', 'copper', 'silicon', 'silver']
-
-print(" Appendix Table I: Metric Tonnes Installed in field in 2030")
-print(" ########################################################### \n")
-#Loop over scenarios
-for kk in range (0, 1):
-    obj = SFscenarios[kk]
-    print("SCENARIO :", obj)
-
-    print("********************************")
-    print("********************************")
-
-    modulemat = 0
-    for ii in range(0, len(materials)):
-        installedmat = (UScum3sig['VirginStock_'+materials[ii]+'_'+obj].loc[2030]-
-              UScum3sig['Waste_'+materials[ii]+'_'+obj].loc[2030])
-        print(materials[ii], ':', round(installedmat/1000)*1000, 'tons')
-
-    print("Capacity in Year 2030 [GW]:", round(USyearly3sig['Capacity_'+obj].loc[2030]/1e9))
-    print("Capacity in Year 2050 [GW]:", round(USyearly3sig['Capacity_'+obj].loc[2050]/1e9))
-    print("****************************\n")
-
    - -
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    - -
    - - -
    -
     Appendix Table I: Metric Tonnes Installed in field in 2030
- ########################################################### 
-
-SCENARIO : World
-********************************
-********************************
-Module : 1068000000 tons
-glass : 884000000 tons
-aluminum : 144200000 tons
-copper : 725000 tons
-silicon : 39300000 tons
-silver : 456000 tons
-Capacity in Year 2030 [GW]: 19400
-Capacity in Year 2050 [GW]: 26600
-****************************
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    In [73]:
    -
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    plt.rcParams.update({'font.size': 10})
-plt.rcParams['figure.figsize'] = (12, 8)
-    
-keywords=['VirginStock_']
-materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']
-
-fig, axs = plt.subplots(1,3, figsize=(15, 5), facecolor='w', edgecolor='k')
-fig.subplots_adjust(hspace = .3, wspace=.5)
-axs = axs.ravel()
-i = 0
-
-# Loop over Keywords
-ii = 0 
-keyw = keywords[ii]
-# Loop over SF Scenarios
-
-
-
-for kk in range(0, 1):
-
-    obj = SFscenarios[kk]
-    axs[i].yaxis.grid()
-    axs[i].axvspan(2000, 2018, facecolor='c', alpha=0.5, label='Glass')
-#    axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)
- #   axs[i].plot([],[],color='c', label='glass', linewidth=5)
- #   axs[i].plot([],[],color='k', label='silicon', linewidth=5)
- #   axs[i].plot([],[],color='m', label='silver', linewidth=5)
- #   axs[i].plot([],[],color='r', label='copper', linewidth=5)
- #   axs[i].plot([],[],color='g', label='aluminum', linewidth=5)
-
-    axs[i].stackplot(rr.scenario[obj].data['year'], USyearly[keyw+materials[0]+'_'+obj]/1e6, 
-                                                      USyearly[keyw+materials[1]+'_'+obj]/1e6, 
-                                                      USyearly[keyw+materials[2]+'_'+obj]/1e6, 
-                                                      USyearly[keyw+materials[3]+'_'+obj]/1e6, 
-                                                      USyearly[keyw+materials[4]+'_'+obj]/1e6, 
-                                                      colors=['c','k','gray','orange', 'g'])
-    #axs[i].ylabel('Mass [Tons]')
-    axs[i].set_xlim([2020, 2050])
-    axs[i].set_title('WORLD')
-    axs[i].legend(loc='lower right')
-
-    #axs[i].legend(materials)
-
-
-# 2nd axis plot
-
-for kk in range(0, 1):
-
-    obj = SFscenarios[kk]
-    ax2=axs[i].twinx()
-    ax2.plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[0]+'_'+obj]/1e6, 
-             color = 'r', linewidth=4.0, label='cumulative')
-    #axs[i].ylabel('Mass [Tons]')
- #   axs[i].set_xlim([2010, 2050])
-  #  axs[i].set_title(keyw+ ' Yearly ' + obj.name)
-    #axs[i].legend(materials)
-    ax2.set_yscale('log')
-    ax2.set_ylim([1e3/1e6, 1e8/1e6])
- 
-
-    ax2.legend()
-
-
-i = 1
-# ROW 2, Aluminum and Silicon:
-# Loop over SF Scenarios
-for kk in range(0, 1):
-
-
-    obj = SFscenarios[kk]
-    axs[i].yaxis.grid()
-#    axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)
-
-    axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[4]+'_'+obj]/1e6, color='g', lw=3, label='Aluminum')
- #   axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], 
- #                   color='g', lw=3, alpha=.6)
-    
-    axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[1]+'_'+obj]/1e6, color='k', lw=3, label='Silicon')
-   # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], 
-   #                 color='k', lw=3)# alpha=.3)
-
-
-    # silicon aluminum 'k ''g'
-    #axs[i].ylabel('Mass [Tons]')
-    axs[i].set_xlim([2020, 2050])
-    #axs[i].set_title(keyw+ ' Yearly ' + obj.name)
-    #axs[i].legend(materials)
-    axs[i].legend()
-
-
-
-i=2
-
-# ROW 3:
-# Loop over SF Scenarios
-for kk in range(0, 1):
-
-    obj = SFscenarios[kk]
-    axs[i].yaxis.grid()
-
-    axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[3]+'_'+obj], color='orange', lw=3, label='Copper')
- #   axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], 
-  #                  color='orange', lw=3)# alpha=.3)
-
-    axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[2]+'_'+obj], color='gray', lw=3, label='Silver')
- #   axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], 
- #                   color='gray', lw=3)# , alpha=.6)
-    
-    
-    #axs[i].ylabel('Mass [Tons]')
-    axs[i].set_xlim([2020, 2050])
-    #axs[i].set_title(keyw+ ' Yearly ' + obj.name)
-    axs[i].legend()
-    axs[i].yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))
-
-
-    
-#axs[i].set_ylim([0, 5e7/1e6])
-#axs[i+3].set_ylim([0, 3e6/1e6])
-#axs[i+6].set_ylim([0, 2.5e4])
-
-#    axs[i+3].set_yscale('log')
-#    axs[i+6].set_yscale('log')
-
-axs[0].set_ylabel('Yearly Mass [Million Tonnes]')
-axs[1].set_ylabel('Yearly Mass [Million Tonnes]')
-axs[2].set_ylabel('Yearly Mass [Tonnes]')
-
-#axs[8].legend(materials)
-
-fig.savefig(title_Method+' Fig_3x3_WORLD_MaterialNeeds_Nation.png', dpi=600)
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    In [58]:
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    keyword='Cumulative_Area_disposed'
-
-USyearly_Areadisp=pd.DataFrame()
-
-# Loop over SF Scenarios
-for kk in range(0, 1):
-    obj = SFscenarios[kk]
-    # Loop over Materials
-    foo = rr.scenario[obj].data[keyword].copy()
-    USyearly_Areadisp["Areadisp_"+obj] = foo
-
-    # Loop over STATEs
-    #for jj in range (1, len(STATEs)): 
-     #   USyearly_Areadisp["Areadisp_"+obj] += rr.scenario[obj].data[keyword]
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    In [59]:
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    UScum_Areadisp = USyearly_Areadisp.copy()
-UScum_Areadisp = UScum_Areadisp.cumsum()
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    In [60]:
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    A = UScum['Waste_Module_World'].iloc[-1]
-#47700000 # tonnes cumulative by 2050
-A = A*1000 # convert to kg
-A = A/10.05599 # convert to m2 if each m2 is ~avg 10 kg
-#A = A*2 # convert to area if each module is ~2 m2
-A = A/1e6 # Convert to km 2
-print(A)
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    95226.5183891086
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    In [61]:
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    C = UScum_Areadisp['Areadisp_World'].iloc[-1]/1e6
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    In [62]:
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    # MANHATTAN SIZE:
-manhattans = 59.103529
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    In [63]:
    -
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    print("Reference Cumulative Area by 2050 of Waste PV Modules EoL", round(C), " km^2")
-
-print("")
-print("Reference Waste equals ", round(C/manhattans), " Manhattans ")
-
-print("")
-print ("MFG SCrap + Eol Waste")
-print("Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL", round(A), " km^2")
-
    - -
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    Reference Cumulative Area by 2050 of Waste PV Modules EoL 79360  km^2
-
-Reference Waste equals  1343  Manhattans 
-
-MFG SCrap + Eol Waste
-Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL 95227  km^2
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    New Section

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    VirginStock_aluminum_Reference.Mod -VirginStock_aluminum_95-by-35.Adv
    -VirginStock_aluminum_95-by-35_Elec.Adv_DR -Waste_EOL_aluminum_Reference.Mod
    -Waste_EOL_aluminum_95-by-35.Adv
    -Waste_EOL_aluminum_95-by-35_Elec.Adv_DR

    -

    VirginStock_silver_Reference.Mod -VirginStock_silver_95-by-35.Adv
    -VirginStock_silver_95-by-35_Elec.Adv_DR -Waste_EOL_silver_Reference.Mod
    -Waste_EOL_silver_95-by-35.Adv
    -Waste_EOL_silver_95-by-35_Elec.Adv_DR

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    In [67]:
    -
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    plt.rcParams.update({'font.size': 10})
-plt.rcParams['figure.figsize'] = (12, 8)
-    
-fig, axs = plt.subplots(1,2, figsize=(15, 6), facecolor='w', edgecolor='k')
-fig.subplots_adjust(hspace = .1, wspace=.4)
-axs = axs.ravel()
-
-# PLOT 1
-i = 0
-axs[i].yaxis.grid()
-lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silver_World']/1e6, color='gray', linewidth=4.0, label='Virgin Material Demands')
-lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silver_World']/1e6, color='k', linewidth=4.0, label='EoL Material')
-axs[i].set_ylabel('Mass [Tons]')
-axs[i].set_xlim([2020, 2050])
-axs[i].set_title('Silver')
-
-# 2nd axis plot
-ax2=axs[i].twinx()
-lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silver_World']/USyearly['VirginStock_silver_World'], 
-             color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')
-ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')
-ax2.tick_params(axis='y', labelcolor='r')
-
-# LEGENDS
-# added these three lines
-lns = lns1+lns2+lns3
-labs = [l.get_label() for l in lns]
-axs[0].legend(lns, labs, loc=0)
-
-# PLOT 2
-i = 1
-axs[i].yaxis.grid()
-lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_aluminum_World']/1e6, color='g', linewidth=4.0, label='Virgin Material Demands')
-lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_aluminum_World']/1e6, color='k', linewidth=4.0, label='EoL Material')
-axs[i].set_ylabel('Mass [Tons]')
-axs[i].set_xlim([2020, 2050])
-axs[i].set_title('Aluminum')
-
-# 2nd axis plot
-ax2=axs[i].twinx()
-lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_aluminum_World']/USyearly['VirginStock_aluminum_World'], 
-             color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')
-
-ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')
-ax2.tick_params(axis='y', labelcolor='r')
-
-# LEGENDS
-# added these three lines
-lns = lns1+lns2+lns3
-labs = [l.get_label() for l in lns]
-axs[1].legend(lns, labs, loc=0)
-
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    Out[67]:
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    <matplotlib.legend.Legend at 0x156d146f3c8>
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    - - - - - - diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.ipynb b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.ipynb deleted file mode 100644 index 29d81914..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.ipynb +++ /dev/null @@ -1,2678 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ReEDS Scenarios on PV ICE Tool WORLD\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. \n", - "\n", - "Current sections include:\n", - "\n", - "
      \n", - "
    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format
      2. \n", - "
    2. ### Reading scenarios of interest and running PV ICE tool
      3. \n", - "
    3. ###Plotting
      4. \n", - "
    4. ### GeoPlotting.
      5. \n", - "
    \n", - " Notes:\n", - " \n", - "Scenarios of Interest:\n", - "\tthe Ref.Mod, \n", - "o\t95-by-35.Adv, and \n", - "o\t95-by-35+Elec.Adv+DR ones\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import PV_ICE\n", - "import numpy as np\n", - "import pandas as pd\n", - "import os,sys\n", - "import matplotlib.pyplot as plt\n", - "from IPython.display import display\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.rcParams['figure.figsize'] = (12, 8)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your simulation will be stored in C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - } - ], - "source": [ - "import os\n", - "from pathlib import Path\n", - "\n", - "testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP')\n", - "\n", - "print (\"Your simulation will be stored in %s\" % testfolder)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "SFscenarios = ['World']\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the 3 Scenarios and assign Baselines\n", - "\n", - "Keeping track of each scenario as its own PV ICE Object." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "filetitle = r'..\\baselines\\ReedsSubset\\baseline_modules_World_Bogdanov.csv'" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path = C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - } - ], - "source": [ - "#for ii in range (0, 1): #len(scenarios):\n", - "i = 0\n", - "rr = PV_ICE.Simulation(name='World', path=testfolder)\n", - "rr.createScenario(name=SFscenarios[i], file=filetitle)\n", - "rr.scenario[SFscenarios[i]].addMaterial('glass', file=r'..\\baselines\\ReedsSubset\\baseline_material_glass_Reeds.csv')\n", - "rr.scenario[SFscenarios[i]].addMaterial('silicon', file=r'..\\baselines\\ReedsSubset\\baseline_material_silicon_Reeds.csv')\n", - "rr.scenario[SFscenarios[i]].addMaterial('silver', file=r'..\\baselines\\ReedsSubset\\baseline_material_silver_Reeds.csv')\n", - "rr.scenario[SFscenarios[i]].addMaterial('copper', file=r'..\\baselines\\ReedsSubset\\baseline_material_copper_Reeds.csv')\n", - "rr.scenario[SFscenarios[i]].addMaterial('aluminum', file=r'..\\baselines\\ReedsSubset\\baseline_material_aluminium_Reeds.csv')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2 FINISH: Set characteristics of Recycling to SF values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate Mass Flow" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Working on Scenario: World\n", - "********************\n", - "Finished Area+Power Generation Calculations\n", - "==> Working on Material : glass\n", - "==> Working on Material : silicon\n", - "==> Working on Material : silver\n", - "==> Working on Material : copper\n", - "==> Working on Material : aluminum\n" - ] - } - ], - "source": [ - "IRENA= False\n", - "PERFECTMFG = True\n", - "\n", - "mats = ['glass', 'silicon','silver','copper','aluminum']\n", - "\n", - "ELorRL = 'RL'\n", - "if IRENA:\n", - " if ELorRL == 'RL':\n", - " weibullInputParams = {'alpha': 5.3759, 'beta':30} # Regular-loss scenario IRENA\n", - " if ELorRL == 'EL':\n", - " weibullInputParams = {'alpha': 2.49, 'beta':30} # Regular-loss scenario IRENA\n", - " \n", - " if PERFECTMFG:\n", - " for jj in range (0, len(rr.scenario.keys())):\n", - " rr.scenario[list(rr.scenario.keys())[jj]].data['mod_lifetime'] = 40\n", - " rr.scenario[list(rr.scenario.keys())[jj]].data['mod_MFG_eff'] = 100.0\n", - "\n", - " for kk in range(0, len(mats)):\n", - " mat = mats[kk]\n", - " rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 \n", - " rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_scrap_Recycled'] = 0.0 \n", - " \n", - " \n", - " rr.calculateMassFlow(weibullInputParams=weibullInputParams)\n", - " title_Method = 'Irena_'+ELorRL\n", - "else:\n", - " rr.calculateMassFlow()\n", - " title_Method = 'PVICE'\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aggregating PCAs Material Landfilled to obtain US totals by Year" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly=pd.DataFrame()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "keywd = 'mat_Virgin_Stock'\n", - "\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['VirginStock_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('VirginStock_') and col.endswith(obj))]\n", - " USyearly['VirginStock_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "keywd = 'mat_Total_Landfilled'\n", - "\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['Waste_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('Waste_') and col.endswith(obj)) ]\n", - " USyearly['Waste_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "keywd = 'mat_Total_EOL_Landfilled'\n", - "\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['Waste_EOL_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('Waste_EOL_') and col.endswith(obj)) ]\n", - " USyearly['Waste_EOL_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "keywd = 'mat_Total_MFG_Landfilled'\n", - "\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " for ii in range(len(materials)):\n", - " USyearly['Waste_MFG_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd]\n", - "\n", - " filter_col = [col for col in USyearly if (col.startswith('Waste_MFG_') and col.endswith(obj)) ]\n", - " USyearly['Waste_MFG_Module_'+obj] = USyearly[filter_col].sum(axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Converting to grams to METRIC Tons. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly = USyearly/1000000 # This is the ratio for Metric tonnes\n", - "#907185 -- this is for US tons\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding NEW Installed Capacity to US" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='new_Installed_Capacity_[MW]'\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " USyearly[keyword+obj] = rr.scenario[obj].data[keyword]\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reindexing and creating c umulative results" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "UScum = USyearly.copy()\n", - "UScum = UScum.cumsum()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding Installed Capacity to US (This is already 'Cumulative') so not including it in UScum" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='Installed_Capacity_[W]'\n", - "for jj in range(len(SFscenarios)):\n", - " obj = SFscenarios[jj]\n", - " USyearly[\"Capacity_\"+obj] = rr.scenario[obj].data[keyword]\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Set YEAR Index" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly.index = rr.scenario[obj].data['year']\n", - "UScum.index = rr.scenario[obj].data['year']" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "VirginStock_glass_World 5.845518e+07\n", - "VirginStock_silicon_World 5.898482e+06\n", - "VirginStock_silver_World 1.069643e+05\n", - "VirginStock_copper_World 4.146420e+04\n", - "VirginStock_aluminum_World 1.393324e+07\n", - "VirginStock_Module_World 7.843533e+07\n", - "Waste_glass_World 4.033407e+06\n", - "Waste_silicon_World 3.046645e+06\n", - "Waste_silver_World 2.310428e+04\n", - "Waste_copper_World 4.892776e+03\n", - "Waste_aluminum_World 4.152106e+05\n", - "Waste_Module_World 7.523260e+06\n", - "Waste_EOL_glass_World 6.671246e-02\n", - "Waste_EOL_silicon_World 3.495899e-03\n", - "Waste_EOL_silver_World 2.134799e-04\n", - "Waste_EOL_copper_World 4.483077e-05\n", - "Waste_EOL_aluminum_World 1.668896e-02\n", - "Waste_EOL_Module_World 8.715562e-02\n", - "Waste_MFG_glass_World 4.033407e+06\n", - "Waste_MFG_silicon_World 3.046645e+06\n", - "Waste_MFG_silver_World 2.310428e+04\n", - "Waste_MFG_copper_World 4.892776e+03\n", - "Waste_MFG_aluminum_World 4.152106e+05\n", - "Waste_MFG_Module_World 7.523260e+06\n", - "new_Installed_Capacity_[MW]World 1.000000e+06\n", - "Capacity_World 2.006803e+12\n", - 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    5 rows Ɨ 26 columns

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    " - ], - "text/plain": [ - " VirginStock_glass_World VirginStock_silicon_World \\\n", - "year \n", - "2009 5.894527e+07 9.371135e+06 \n", - "2010 5.845518e+07 5.898482e+06 \n", - "2011 5.690669e+07 5.704484e+06 \n", - "2012 5.635610e+07 5.711201e+06 \n", - "2013 5.477981e+07 5.355709e+06 \n", - "\n", - " VirginStock_silver_World VirginStock_copper_World \\\n", - "year \n", - "2009 223992.035805 41811.846684 \n", - "2010 106964.252482 41464.204730 \n", - "2011 86775.635305 40365.815200 \n", - "2012 68068.160689 39579.468151 \n", - "2013 45860.923262 38095.238095 \n", - "\n", - " VirginStock_aluminum_World VirginStock_Module_World Waste_glass_World \\\n", - "year \n", - "2009 1.415011e+07 8.273232e+07 4.067224e+06 \n", - "2010 1.393324e+07 7.843533e+07 4.033407e+06 \n", - "2011 1.342383e+07 7.616215e+07 3.926567e+06 \n", - "2012 1.313818e+07 7.531313e+07 3.888644e+06 \n", - "2013 1.043250e+07 7.065197e+07 3.780282e+06 \n", - "\n", - " Waste_silicon_World Waste_silver_World Waste_copper_World ... \\\n", - "year ... \n", - "2009 6.495388e+06 48382.279734 4933.797909 ... \n", - "2010 3.046645e+06 23104.278750 4892.776203 ... \n", - "2011 2.928193e+06 18743.554604 4763.169851 ... \n", - "2012 2.988997e+06 14702.953308 4670.426463 ... \n", - "2013 2.735609e+06 9907.421159 4495.557312 ... \n", - "\n", - " Waste_EOL_aluminum_World Waste_EOL_Module_World Waste_MFG_glass_World \\\n", - "year \n", - "2009 0.000000 0.000000 4.067224e+06 \n", - "2010 0.016689 0.087156 4.033407e+06 \n", - "2011 1.361513 7.110439 3.926562e+06 \n", - "2012 18.319451 95.684123 3.888571e+06 \n", - "2013 118.766176 620.464913 3.779807e+06 \n", - "\n", - " Waste_MFG_silicon_World Waste_MFG_silver_World Waste_MFG_copper_World \\\n", - "year \n", - "2009 6.495388e+06 48382.279734 4933.797909 \n", - "2010 3.046645e+06 23104.278536 4892.776158 \n", - "2011 2.928192e+06 18743.537226 4763.166194 \n", - "2012 2.988993e+06 14702.722709 4670.377242 \n", - "2013 2.735584e+06 9905.959425 4495.238095 \n", - "\n", - " Waste_MFG_aluminum_World Waste_MFG_Module_World \\\n", - "year \n", - "2009 421673.149894 1.103760e+07 \n", - "2010 415210.620983 7.523260e+06 \n", - "2011 400030.001829 7.278291e+06 \n", - "2012 391517.806614 7.288455e+06 \n", - "2013 310888.555288 6.840681e+06 \n", - "\n", - " new_Installed_Capacity_[MW]World Capacity_World \n", - "year \n", - "2009 1000000.0 1.006860e+12 \n", - "2010 1000000.0 2.006803e+12 \n", - "2011 1000000.0 3.000622e+12 \n", - "2012 1000000.0 3.991528e+12 \n", - "2013 1000000.0 4.979357e+12 \n", - "\n", - "[5 rows x 26 columns]" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "USyearly.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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    20132.854431e+083.204101e+07531661.007543201316.5728596.507786e+073.832949e+081.969612e+071.819483e+07114840.48755423755.727738...0.372141138.463829723.3466311.969557e+071.819480e+07114838.77762923755.3555971.939320e+063.996829e+075000000.0
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    5 rows Ɨ 25 columns

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    " - ], - "text/plain": [ - " VirginStock_glass_World VirginStock_silicon_World \\\n", - "year \n", - "2009 5.894527e+07 9.371135e+06 \n", - "2010 1.174004e+08 1.526962e+07 \n", - "2011 1.743071e+08 2.097410e+07 \n", - "2012 2.306632e+08 2.668530e+07 \n", - "2013 2.854431e+08 3.204101e+07 \n", - "\n", - " VirginStock_silver_World VirginStock_copper_World \\\n", - "year \n", - "2009 223992.035805 41811.846684 \n", - "2010 330956.288286 83276.051413 \n", - "2011 417731.923592 123641.866613 \n", - "2012 485800.084281 163221.334764 \n", - "2013 531661.007543 201316.572859 \n", - "\n", - " VirginStock_aluminum_World VirginStock_Module_World Waste_glass_World \\\n", - "year \n", - "2009 1.415011e+07 8.273232e+07 4.067224e+06 \n", - "2010 2.808335e+07 1.611676e+08 8.100631e+06 \n", - "2011 4.150717e+07 2.373298e+08 1.202720e+07 \n", - "2012 5.464536e+07 3.126429e+08 1.591584e+07 \n", - "2013 6.507786e+07 3.832949e+08 1.969612e+07 \n", - "\n", - " Waste_silicon_World Waste_silver_World Waste_copper_World ... \\\n", - "year ... \n", - "2009 6.495388e+06 48382.279734 4933.797909 ... \n", - "2010 9.542034e+06 71486.558483 9826.574112 ... \n", - "2011 1.247023e+07 90230.113087 14589.743963 ... \n", - "2012 1.545922e+07 104933.066395 19260.170426 ... \n", - "2013 1.819483e+07 114840.487554 23755.727738 ... \n", - "\n", - " Waste_EOL_copper_World Waste_EOL_aluminum_World \\\n", - "year \n", - "2009 0.000000 0.000000 \n", - "2010 0.000045 0.016689 \n", - "2011 0.003702 1.378202 \n", - "2012 0.052924 19.697653 \n", - "2013 0.372141 138.463829 \n", - "\n", - " Waste_EOL_Module_World Waste_MFG_glass_World Waste_MFG_silicon_World \\\n", - "year \n", - "2009 0.000000 4.067224e+06 6.495388e+06 \n", - "2010 0.087156 8.100631e+06 9.542034e+06 \n", - "2011 7.197594 1.202719e+07 1.247023e+07 \n", - "2012 102.881717 1.591576e+07 1.545922e+07 \n", - "2013 723.346631 1.969557e+07 1.819480e+07 \n", - "\n", - " Waste_MFG_silver_World Waste_MFG_copper_World \\\n", - "year \n", - "2009 48382.279734 4933.797909 \n", - "2010 71486.558270 9826.574067 \n", - "2011 90230.095496 14589.740260 \n", - "2012 104932.818205 19260.117502 \n", - "2013 114838.777629 23755.355597 \n", - "\n", - " Waste_MFG_aluminum_World Waste_MFG_Module_World \\\n", - "year \n", - "2009 4.216731e+05 1.103760e+07 \n", - "2010 8.368838e+05 1.856086e+07 \n", - "2011 1.236914e+06 2.583915e+07 \n", - "2012 1.628432e+06 3.312761e+07 \n", - "2013 1.939320e+06 3.996829e+07 \n", - "\n", - " new_Installed_Capacity_[MW]World \n", - "year \n", - "2009 1000000.0 \n", - "2010 2000000.0 \n", - "2011 3000000.0 \n", - "2012 4000000.0 \n", - "2013 5000000.0 \n", - "\n", - "[5 rows x 25 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "UScum.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3 sig figures save Yearly and cumulative overview Nation" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "USyearly3sig = USyearly.copy()\n", - "UScum3sig = UScum.copy()\n", - "N = 2\n", - "\n", - "UScum3sig = UScum3sig.drop(UScum3sig.index[0])\n", - "USyearly3sig = USyearly3sig.drop(USyearly3sig.index[0])\n", - "\n", - "if IRENA:\n", - " UScum3sig = UScum3sig.loc[:, ~UScum3sig.columns.str.startswith('Waste_MFG_')]\n", - " USyearly3sig = USyearly3sig.loc[:, ~USyearly3sig.columns.str.startswith('Waste_MFG_')]\n", - "\n", - "USyearly3sig = USyearly3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "USyearly3sig = USyearly3sig.applymap(lambda x: int(x))\n", - "\n", - "UScum3sig = UScum3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x))))))\n", - "UScum3sig = UScum3sig.applymap(lambda x: int(x))\n", - "\n", - "USyearly3sig.to_csv(title_Method+' US_Yearly NATION.csv')\n", - "UScum3sig.to_csv(title_Method+' US_Cumulative NATION.csv')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sanity check: mat_Total_Landfilled = mat_Total_EOL_Landfilled + mat_Total_MFG_Landfilled\n" - ] - }, - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "print(\"Sanity check: mat_Total_Landfilled = mat_Total_EOL_Landfilled + mat_Total_MFG_Landfilled\")\n", - "A = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled'].iloc[5]\n", - "B = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_EOL_Landfilled'].iloc[5]\n", - "C = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_MFG_Landfilled'].iloc[5]\n", - "A - B - C" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# PLOT" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Yearly Virgin Material Needs by Scenario" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:79: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n" - ] - }, - { - "data": { - "image/png": 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SLyJiMHABcDHwBPCOlNK8nQNTStsj4u3A1cANwDbgOuAzHQtmqdVp/Ol788PtiqHEPtTY2MiqVat4/PHHOf744/NuR5IkSZJUplJKh2Qc90Pgh7sZ8zTwlz1Ra19zT4l97IADDqCpqYnnn39+94MlSZIkSepHDCX2saqqKhoaGpgxYwatra15tyNJkiRJUtkwlNgP6uvr2bRpEw8++GDerUiSJEmSVDbcU2I/GTNmDHPnzmX8+PEcfPDBebcjSZIkSdJeiYha4CBgQOd7KaX5WWo4U2I/qaioYMSIEcycOZOtW7fm3Y4kSZIkSXskIg6MiN8AW4ElQHOH17zS10ycKbEf1dXVsWnTJu69917e8pa3EBF5tyRJkiRJUnf9CDge+DQwH9jjDRQNJfazUaNGsXjxYsaPH8+RRx6ZdzuSJEmSJHXXG4EPp5Ru2NtCLt/YzyKCMWPGMHv2bDZs2JB3O5IkSZIkddfzwMs9UchQIge1tbVUV1dz11130dbWlnc7kiRJkiR1xyXABRFRv7eFXL6RkxEjRrBq1Sp++tOfsnr1aqZMmcLkyZPzbkuSJEmSpN15F3AwsCIiHgZe6nQ/pZTek6WQoUSONm7cyKc+9Sna29upqalh5syZBhOSJEmSpHI3Alhael8NjNzTQoYSOZo/fz5tbW20t7ezbds27rjjDkMJSZIkSVJZSymd3lO13FMiRxMnTqSqqoqKigoqKyt5+eWXefjhh3n55R7ZL0SSJEmSpH0uIqr39FlnSuSoUCgwbdo0mpubmThxIocffjiPPvoojz/+OCeffDKvf/3rqa7e4/+2kiRJkiTtExFxKnAxcBpQFxFbgXuAL6eUmrLWMZTIWaFQoFAovPL5gAMOoLW1lfvuu4/f//73TJ48mcMPP5zKysocu5QkSZIkqSgi3gL8FlgEfB1YA4wGzgZmRcSfp5RmZKllKFGGampqGDt2LC0tLdx555088sgjnHrqqRx88MFUVLjiRpIkSZKUq8uBW4BzUkqpw/UvRcQvga8AmUIJf8MtYwMGDGDs2LFEBLfddhs333wzzz77bN5tSZIkSZL6t4nADzsFEjtdU7qfiTMleoHBgwczePBgNmzYwM0338whhxzCpEmTWLJkCbNmzWLKlCme2iFJkiRJ2l9eAg7bxb3DS/czMZToRYYOHUp9fT1r167lyiuv5Oqrr2bHjh3U1NQwc+ZMgwlJkiRJ0v5wI3BFRGwEbkoptUTEAIp7SlwO/HfWQi7f6GUigsbGRtasWUNrayttbW20trYya9asvFuTJEmSJPUPFwC/oRg+bImIDcCW0ufflO5nYijRSx177LFUV1dTUVFBVVUVU6ZMybslSZIkSVI/kFJ6OaX0t8DRwPspzo54P3B0SunvUkotWWu5fKOXKhQKTJs2jccee4xDDz2USZMm5d2SJEmSJKkfSSktBBbuTQ1DiV6sUChQKBR4+umnWbt2LaNHj867JUmSJElSHxQRRwFLU0rbSu9fU0ppfpa6hhJ9QFVVFcuWLTOUkCRJkiTtK/OAU4CHSu+7Og4UIEr3KrMUNZToA4YPH86CBQs4+eSTqahwmxBJkiRJUo87HZjf4X2PMJToA2pqamhpaeH5559nzJgxebcjSZIkSepjUkqzu3q/t/xn9T6ipqaGpUuX5t2GJEmSJEmZOVOijxg2bBiLFi3ilFNOobIy09IdSZIkSZIyiYi17HofiVdJKY3KMs5Qoo+orq5m27ZtrFmzhgMPPDDvdiRJklRmVrx9UN4tSOrd/p1uhBJZGUr0IbW1tSxdutRQQpIkSZLUo1JKl+6Luu4p0YfsXMKxY8eOvFuRJEmSJGm3nCnRh1RVVbF9+3bWrFnDQQcdlHc7kiRJkqQ+IiJu6M74lNK7s4wzlOhjBgwYwOLFiw0lJEmSJEk9aeS+KGoo0ccMGzaMJ598ktNOO43q6uq825EkSZIk9QEppdP3RV33lOhjKisr2b59O88991zerUiSJEmS9JqcKdEH1dXVsXjxYsaNG5d3K5IkSZKkPiAiPgbcmFJaW3r/mlJK38tS11CiD2poaODJJ5/kT//0T6mpqcm7HUmSJElS73c1MAdYW3r/WhJgKNFfVVZW0t7ezurVqxk/fnze7UiSJEmSermUUkVX7/eWe0r0UXV1dSxatCjvNiRJkiRJ2iVnSvRRQ4cOZdmyZWzbto3a2tq825EkSZIk9WIRcXB3xqeUVmYZZyjRR3VcwnHIIYfk3Y4kSZIkqXdblnFcUNxTojLLYEOJPmzQoEEsWLDAUEKSJEmStLcC2AxMB24BtvZEUUOJPmzo0KGsWLGClpYWBgwYkHc7kiRJkqTe603AucDZwF8CtwLXAbenlLbvaVE3uuzDKiqK/3mfeeaZnDuRJEmSJPVmKaV7U0ofBw4E3gW8DPwEWBMRP46It0ZEtzMGQ4k+bvDgwSxcuDDvNiRJkiRJfUBKqT2l9LuU0oeA0cD7gIHAb4GfdbeeoUQfN2TIEFauXMnWrT2y3EeSJEmSpJ2Opbis441AG7CouwUMJfo4l3BIkiRJknpKRBwVEV+KiCVAEzARuAQYlVK6tLv1DCX6gfr6ehYsWJB3G5IkSZKkXioiPh8Rc4G5FGdHXAUcmFJ6e0rpJymljXtS19M3+oEhQ4bwzDPPsGXLFgYNGpR3O5IkSZKk3udyYBNwPfAMMAH4bER0NTallC7IUtRQoh+ICCKCp59+miOPPDLvdiRJkiRJvc9KIAGnZhibAEMJ/UF9fT1PPPGEoYQkSZIkqdtSSofsi7ruKdFPDB48mDVr1rBp06a8W5EkSZIkCTCU6Dc6LuGQJEmSJKk7IuLYiBiwB8/UvtYYQ4l+ZOjQocyfPz/vNiRJkiRJvc+jwLFZB0dEZemZo19rnHtK9CODBw/m6aefZuPGjdTX1+fdjiRJkiSp9wjgryPixIzjM02CMJToZyKCVatWcfTRrxlWSZIkSZLU2Wd6uqDLN/qZhoYG5s2bl3cbkiRJkqReJKVUsYev379WXUOJfmbQoEGsW7eOl156Ke9WJEmSJEn9nKFEPxQRrFy5Mu82JEmSJEn9nKFEPzRs2DDmz59PSinvViRJkiRJ/ZihRD9UV1fH+vXrWb9+fd6tSJIkSZL6MUOJfqqyspIVK1bk3YYkSZIkqR8zlOinGhoaXMIhSZIkSdpjEVEVEZV7U8NQop8aOHAgjz76KJdccglNTU15tyNJkiRJKnMRMSoivhQRD0fEJmAb0BoRm0rXLouIkd2pWbVvWlW5W7hwId/5zndoa2vjqquuYubMmUyePDnvtiRJkiRJZSgi/gSYASTgVuB6YD0QQANQAD4CfDQi3pxSmpulrqFEP9Xc3ExbWxvt7e20trYyY8YMQwlJkiRJ0q58B3gIOCeltLWrARFRB9xYGjslS9HdLt+IiFkRkXbxmlwaExFxYUSsioiXI+LuiDgu60+m/W/ixIlUVVVRUVFBRUUFmzdv5qGHHmLz5s15tyZJkiRJKj8nAVftKpAAKN27qjQ2kywzJT4G1He69iXgDcDDpc+fAy4GPgMsBD4NzIiIY1JKz2VtRvtPoVBg2rRpNDc3M3HiRA477DAee+wxHnnkEY4++miOOeYYhg8fnnebkiRJkqTy8AJwJHDnbsYVgBezFt1tKJFSmt/xc0TUACcC16eUdkTEAIqhxBUppatLY5qA5cDHgYuyNqP9q1AoUCgUXvl8wAEH0NbWxpIlS5g3bx4TJkzgDW94A6NGjSIicuxUkiRJkpSz7wPfiIjhFJdoLEml4xyj+Avj4cA5wIXA5VmL7smeEm8HhgG/KH0+leJMiht2DkgpbYmIW4EzMJToVSorKxk1ahQpJZ577jluuukmDjzwQE488UQOOuggKio8sEWSJEmS+puU0lciIgEXUFw90RYRmylufDkEqAQ2AdNSSldmrbsnocS5wDPAPaXPBaANWNJp3ALgPXtQX2UgImhsbKSxsZENGzZw6623MmzYME4++WTGjx9PVVUVTU1NzJo1iylTprhJpiRJkiT1cSmlKyLi3yhOTihQnLAAxVM4FgL3p5S2dadmt0KJ0k6a7wSu2TlNo9TE5pRSW6fh64G6iKhJKbV2Uet84HyAgw8+uDttaD8bOnQoQ4cOZcuWLdxxxx0MGjSIqqoqPvzhD9Pa2kpNTY1HikqSJElSP1AKHe4qvfZad+fivxMYzB+WbrzSVxdj4zXukVK6JqV0YkrpxJEjR3azDeVh0KBBjBs3jrq6Om644QZaWlpoa2ujtbWVWbNm5d2eJEmSJCkHEXFIRIzfk2e7G0qcCzyZUprT4dp6YEhEVHYa2wBsTSlt35PGVL4GDBjAaaedRnV1NRUVFdTU1DBlypS825IkSZIk7SMRcX5EjOh07ZMRsRZYCjwVEWsi4mPdqZs5lIiIoRQ3ruw8S2IhxQ0tDu90vVC6pz5o55Gi73jHO7jppptcuiFJkiRJfdt/ABN2fihtyfBNYCbF/STfQ3FJx3cj4rysRbuzp8RfAbW8OpS4H9hI8eiPaaXmXtl7ohv11csUCgUaGhpoaGjIuxVJkiRJ0r4VnT5/Grg2pfT+DtduiojtwD8DP89StDvLN84FHk8pLeh4MaXUAlwJXBgR/xgRUymeWVoBfLcb9dULjRgxgvnz57N9u6t0JEmSJKkfOYxXT1oAuA44KmuRTDMlSutGpgIX72LIlRRDiM8DjcAc4C0ppTVZG1HvVFVVxfbt21m1ahUTJkzY/QOSJEmSpN5qQGllBMCLQOdTOCld6/LAi65kCiVSSi8A1a9xPwGXl17qZ+rr62lubjaUkCRJkqS+reMxoAGcDMzoNOZYYFXWgt3ZU0Lq0pAhQ3jmmWfYsGEDQ4cOzbsdSZIkSVLP+0AX11Z3ce1k4JdZixpKaK9FBJWVlSxdupTjjz8+73YkSZIkST0spfTfGced3Z263dnoUtql4cOH8/jjj9PW1tWSIkmSJElSXxMRVRFRuTc1DCXUI2pra2lpaWH16q5m70iSJEmSeruIGBURX4qIhyNiE7ANaI2ITaVrl0XEyO7UdPmGekxdXR1PPPEEY8eOzbsVSZIkSVIPiog/obipZQJuBa4H1lPc8LIBKAAfAT4aEW9OKc3NUtdQQj2moaGBp556ii1btjBo0KC825EkSZIk9ZzvAA8B56SUtnY1oHRc6I2lsVOyFHX5hnpMRUUFEcHy5cvzbkWSJEmS1LNOAq7aVSABULp3VWlsJoYS6lHDhg3jscceI6WUdyuSJEmSpJ7zAnBkhnEF4MWsRQ0l1KPq6urYsGEDzz//fN6tSJIkSZJ6zveBb0TEFyLidRERO29E0RERcSHwNeA/shZ1Twn1uAEDBrBo0SJGjx6ddyuSJEmSpB6QUvpKRCTgAuBLQFtEbKa48eUQoBLYBExLKV2Zta6hhHrc8OHDWbhwIZMmTaK2tjbvdiRJkiRJPSCldEVE/BtwKsVlGsNKt9YDC4H7U0rbulPTUEI9rrKykra2NlatWsXhhx+edzuSJEmSpB5SCh3uKr32mntKaJ8YOnQojz/+eN5tSJIkSZLKmKGE9okhQ4bw/PPPs27durxbkSRJkqSyFxGHR8QPIuLxiGiLiFldjFkeEanT67kuxh0VETMjYmtEPBsRX4qIyk5jIiIujIhVEfFyRNwdEcftpsd3lOo2R8T1EfGmLsZMioi2rD+3oYT2maqqKp588sm825AkSZKk3uBo4Exgcem1Kz8HJnd4ndnxZkQMA2ZQ3IDyLIqbUv4LcFmnOp8DLga+CrwT2AzMiIgxXX3TiHgLMB0YAMwGDgfuioirOp7E0V3uKaF9prGxkXnz5nH88cdTVeX/1SRJkiTpNdyaUpoOEBE3ASN2MW51SumB16jzEWAg8K6U0kbgdxFRD1waEV9LKW2MiAEUQ4krUkpXl75nE7Ac+DhwURd1vwhcm1L6wM4LEfFB4DvAhIj4m5RSSzd+XsCZEtqHqquraWlp4dlnn827FUmSJEkqayml9h4qdQZwRymQ2Ok6ikHFn5U+nwrUAzd0+P5bgFtLz3flGOB/OvX841LNU4A7I2J4d5s1lNA+NXjwYObNm5d3G5IkSZLUV3wwIlojYkNE3BQR478GXVYAACAASURBVDvdL1A8nvMVKaWVwNbSvZ1j2oAlnZ5d0GFMZy3AoM4XU0qPAG8ERgL3A4d242cxlNC+1dDQwMqVK9m0aVPerUiSJElSbzcd+EdgKvAZintK3BMRQzuMGQa81MWz60v3do7ZnFLqvCHleqAuImq6eH4uu5hFkVJ6imIwsRn4SaafpMRQQvtURBARPPXUU3m3IkmSJEl5GhERczq8zu9ugZTSJ1NKv0gp3ZNSugZ4G3Ag8IHOQ7t4PDpd39WYXd37JXDmrpZopJSep7iU4+4OdXbL3Qe1zw0fPpzHH3+ciRMnUlFhDiZJkiSpX3ohpXRiTxZMKc2LiEXA8R0urwcauhg+lD/MoFgPDImIyk6zJRqArSml7V18rx8AP9hNP1uAt3bjR3CmhPa9AQMGsHnzZtasWZN3K5IkSZLUF3Wc2bCQTvtCRMQ4ivtBLOwwppLisZ4dvWo/iiwi4uCI2KNJD4YS2i8GDhzIggUL8m5DkiRJkvqMiDgGOBJ4pMPl24G3RcSQDtfeA7wMzC59vh/YCJzToVYd8M7S893poRJYBhzb3f7B5RvaT4YNG8aSJUuYPHkyAwcOzLsdSZIkSSorpVDgzNLHg4D6iDi79Pk24HTg74DfAM9SnNVwEbCSP95c8vvAPwG/ioivAhOAS4F/23lMaEqpJSKuBC6OiPUUZ0d8muLEhe/uSft78AxgKKH9pLKykpQSK1asoFDY1QkzkiRJktRvjQJu7HRt5+dDgVWlMd+iuPfDi8D/AhfuDBsAUkrrI2IqcDVwK8V9JL5JMZjo6EqKIcTngUZgDvCWlNJ+XXdvKKH9pqGhgccff5wjjzySiD0O0iRJkiSpz0kpLWf3Mw6mZqw1H/h/uxmTgMtLr72RgBXAtj152D0ltN8MGjSIF198kRdffDHvViRJkiRJPSCl1J5SOjSl9MSePO9MCe1XNTU1LF68mBEjRuTdiiRJkiRpD0TE0RT3tBhGcabES8DCPQkmDCW0XzU2NjJ//nxOOukkqqur825HkiRJkpRRRHwQ+CIwllcvNUkRsQq4LKX0X1lrunxD+1VVVRXbt29n1apVebciSZIkScooIj4B/IDi6R9TKG66WV16jQL+rHTv+xHxj1nrOlNC+92QIUNobm5mwoQJebciSZIkScrmn4EvpJS+1sW9F4B7gXsjYiXwL8C/ZynqTAntd/X19Tz77LNs2LAh71YkSZIkSdmMAR7KMO6h0thMDCW030UEy5cv5wtf+AJNTU15tyNJkiRJ2r25wIcjYpc5QkQE8OHS2ExcvqH9buHChXzrW99i+/bt/PjHP2bmzJlMnjw577YkSZIkSbv2L8D/AvMj4lfAQoqnbiSggeJpHH9FcRPMt2ctaiih/a65uZkdO3aQUqK1tZVZs2YZSkiSJElSGUsp3RcRxwGfBf4WGNdpyCrgduDrKaWlWesaSmi/mzhxIlVVVezYsYPKykre9KY35d2SJEmSJGk3SmHDPwBERB3FGRIAL6WUtu5JTUMJ7XeFQoFp06bR3NzMqFGjOOCAA/JuSZIkSZLUDaUQYo+CiI4MJZSLQqFAoVBgy5YtNDU1MX78eCorK/NuS5IkSZL0GiJiHDAKWJRS2tzF/RHAmSmla7PU8/QN5WrQoEFs2LCB5cuX592KJEmSJGkXIqI2Im4EllM89nN9RPxnRAztNPQw4L+y1jWUUO4aGxt54IEHaGtry7sVSZIkSVLXLgDeAnwEmAT8K/AOYE5EHLGnRQ0llLu6ujpnS0iSJElSeTsPuCil9MOU0pyU0reBPwFWA/dHxB4dqWgoobLQ2NhIU1OTsyUkSZIkqTyNA+Z2vJBSeg6YCswEZkTEX3a3qKGEykJdXR0bN27kqaeeyrsVSZIkSdKrPQu8aplGSml7Sulc4IfAjcD53SlqKKGysXNviR07duTdiiRJkiTpj90D/P2ubqaUPgVcAnygO0UNJVQ26urq2LRpk7MlJEmSJKn8fA94OCIadzUgpXQF8HdApuNAwVBCZWbEiBE0NTU5W0KSJEmSykhpc8vPpJRe3M24n6eUMs+WMJRQWRk4cCBbtmxh6dKlebciSZIkSepCRAyJiBMi4s0RMbX0fsie1DKUUNkZMWIEDzzwANu3b8+7FUmSJElSSSmEuBtYBzwE/B/wO+BhYF1E3B0RU7tT01BCZWfgwIFs3brV2RKSJEmSVCYi4t3AHcAm4IPAJIqncbwOOLl0bSNwR0Sck7VuVc+3Ku29ESNG8OCDD3LYYYdRXV2ddzuSJEmS1N99EfhGSumCXdyfA/w0Ir4GXEbxeNDdcqaEytKAAQN4+eWXWbJkSd6tSJIkSZJgAnBbhnG/BQ7NWtRQQmWrsbGRhx56yL0lJEmSJCl/S4C/zDDuL0tjM3H5hsrWgAEDeOGFF1iyZAlHHXVU3u1IkiRJUn92MXBTREwEbgAWAi8BCWgACsA5wBTg7KxFDSVU1kaOHMkDDzzAEUcc4d4SkiRJkpSTlNL0iDidYjjxXaCaYiABEMB24C7g9JTSfVnrGkqorNXW1rJt2zYWL17M0UcfnXc7kiRJktRvpZTuBd4WETXAYcCw0q31wNKUUmt3axpKqOyNHDmSBx98kCOOOIKampq825EkSZKkfq0UPizoiVpudKmyt3O2xKJFi/JuRZIkSZL6tYiojojG17g/JCLelLWeoYR6hZEjR/LQQw/R2trt2UCSJEmSpL0UERUR8XWKm1s+HxFrIuLiiOi8AuMointLZGIooV6htraW1tZWFi5cmHcrkiRJktQffRT4BPAt4D3Az4HPA3dHxMg9LWoooV5j1KhRPPzww2zbti3vViRJkiSpv/kI8KWU0hdSSjellP4ZOJHiZpdNEXH4nhQ1lFCvUVNTw/bt21mwoEf2U5EkSZIkZTcBuLfjhZTSfGAy8CzFYGJSd4saSqhXGTlyJHPmzKGlpSXvViRJkiSpP1kLjOt8MaX0EvAW4B5gJvDO7hQ1lFCvUlNTQ1tbm3tLSJIkSdL+9QDFvSReJaW0DTgb+BlwYXeKGkqo1xk5ciTXX389X/7yl2lqasq7HUmSJEnqD34IvBwRw7u6mVJqTyn9A3ARcHfWop2P7pDK3tKlS7nqqqtoa2vjiiuuYObMmUyePDnvtiRJkiSpz0opzaS4PGN3474CfCVrXWdKqNdpbm6mra2N9vZ2tm3bxk033URra2vebUmSJEmSuslQQr3OxIkTqaqqoqKi4pWv1157LQ888ADr16/Puz1JkiRJUkYu31CvUygUmDZtGs3NzUycOJFCocCOHTuYN28ev//97xk7dizHHXccBx10EJWVlXm3K0mSJEnaBUMJ9UqFQoFCofDK56qqKkaPHk1KiY0bN/Lb3/6WgQMHctxxx3HEEUcwaNCgHLuVJEmSJHXFUEJ9SkQwdOhQhg4dyrZt23jwwQdpamrida97HUcffTSjR48mImhqamLWrFlMmTLFTTIlSZIkKSeGEuqzamtrOfDAA2lvb2fVqlUsWrSIhoYGKioq+OAHP0hrays1NTWe3iFJkiRJ3RARo4BBKaVlpc8BfBg4CpiZUro1ay03ulSfV1FRQWNjI+PGjaOyspJf/OIXtLS00NbWRmtrK7Nmzcq7RUmSJEnqTX4C/HOHz5cB3wPeDtwcEe/PWshQQv1KXV0dp512GtXV1VRUVFBTU8OUKVPybkuSJEmSepPjgTsBIqIC+ChwYUqpAFwOfCprIZdvqN/ZeXpHU1MTU6dOdemGJEmSJHXPUODF0vsTgOHAz0qf7wT+JWshZ0qoXyoUCrzvfe8DYPXq1Tl3I0mSJEm9ytMU948A+HNgYUrpmdLnoUBL1kKGEuq3KioqaGhoYPbs2ezYsSPvdiRJkiSpt/gx8LWIuBH4LHBNh3unAAuyFjKUUL9WX1/P+vXrmT9/ft6tSJIkSVKvkFK6AvgE8Fzp63c63B4O/ChrLfeUUL83evRompqaOPTQQxkyZEje7UiSJElS2UspXQtc28X1j3SnjqGE+r3q6mqqqqq4//77eetb30rxiF1JkiRJ0k4RcXB3xqeUVmYZZyghASNHjuTJJ5/kqKOOYty4cXm3I0mSJEnlZjmQujG+MssgQwkJiAgaGxuZNWsW5557LtXV1Xm3JEmSJEnl5J0d3tcDX6O4oeWvgOeBUcBfAwXgM1mLGkpIJYMGDeLpp59m7ty5nHDCCXm3I0mSJEllI6X0253vI+InwG9SSh/tNOz7EfF9iseEXpelrqdvSB2MGTOGhx9+mPXr1+fdiiRJkiSVq3dRnCHRlV8Cf5G1kKGE1EFVVRUDBgzg3nvvJaXuLJeSJEmSpH7jZeC0Xdz7U6AlayGXb0idNDY2snLlSpYtW8aECRPybkeSJEmSys1/ABdHRCNwC3/YU+Is4B+Ay7MWMpSQujBy5Ehmz57NgQceyIABA/JuR5IkSZLKRkrp0ohYD3wW+BjFUzkCeA7415TSt7LWyrR8IyKqIuJzEbEkIrZFxNMR8c1OYyIiLoyIVRHxckTcHRHHZf+xpPIxcOBAWltbefTRR/NuRZIkSZLKTkrp28A4YAJwKnAoMLY7gQRk31Piv4B/Ar4BvBX4HMU1JB19DrgY+CrFo0I2AzMiYkx3GpLKxejRo3n00UdZu3Zt3q1IkiRJUtlJKbUDK4BVwDOlz92y21AiIt4OnAu8OaX0g5TS7JTS/6SULuwwZgDFUOKKlNLVKaUZwDkUp3B8vLtNSeWgsrKSwYMHc/fdd9Pe3u0/W5IkSZLUZ0XEmRHxIMVNLVcCx5auXxMRf5e1TpaZEh8E7kwpzX+NMacC9cANOy+klLYAtwJnZG1GKjfDhg3jueeeY9GiRXm3IkmSJEllISLeS3GDy4XA+fxxtrAE+FDWWllCiUnA4oi4OiI2RsTWiPhVRBzYYUwBaCt9844WlO5Jvdbo0aO577772LJlS96tSJIkSVI5+ALw9ZTS+4D/6XTvCeCorIWyhBJjgPcDx1FcxvEB4ATg5oiI0phhwOaUUlunZ9cDdRFR07loRJwfEXMiYo5r9lXOamtrSSnx8MMP592KJEmSJJWD8cDvdnGvheJKikyyhBJRep2VUrotpXQ98PfAycD/6zAu7eLZLu+llK5JKZ2YUjpx5MiRWfuVcjFq1Cjmz5/P6tWr825FkiRJkvK2CnjDLu6dCDyZtVCWUGI90JxSerHDtXuBVv4wJWM9MCQiKjs92wBsTSltz9qQVI4qKioYOnQos2fPZseOHXm3I0mSJEl5+k/gi6UNLQeWrkVETAU+C/wwa6EsocSCXVwPYOeRBAuBSuDwTmMKpXtSr1dfX8/69euZP/+19nyVJEmSpD7vq8BPgf8G1pWu3Q/cAVyfUvpO1kJZQonfAMdGxIgO194EVAOPd/jmGykeAwpARNQB7wRuz9qMVO5Gjx7Nz372My699FKamprybkeSJEmS9rtU9I/AkcAngIuATwJHla5nVpVhzDXAPwG3RsRXgCEUU5EZKaV7Sw21RMSVwMURsZ7i7IhPUww9vtudhqRytnTpUr75zW/S1tbG1772NWbOnMnkyZPzbkuSJEmS9ruU0pN0Y/+Irux2pkRKaSPFDS3XA9cB/w7MBN7daeiVwOXA5ynOrqgH3pJSWrM3DUrlpLm5mba2Ntrb22ltbeWuu+7KuyVJkiRJ2u8iYlREfDUiZkbEoog4unT9kxGR+V9us8yU2Jl+nLmbMYliKHF51m+et9/97nesWbOGQYMG5d2KeomJEydSVVXFjh07qKioYMiQIWzfvp3q6uq8W5MkSZKk/SIiTqZ4JOhaYDYwBagt3T4A+Bfg7Cy1MoUSfdU3v/lNbr/9dioqKigUCrzxjW9k0qRJjBo1Ku/WVKYKhQLTpk2jubmZY445hqqqKm6//Xbe+ta3MmDAgLzbkyRJkqT94ZvAXcC7KK7A+ECHew8B52Ut1G9DiU2bNjFz5kwA2tvbmT9/PvPnz+eHP/whhx56KJMmTWLSpElMmDCBiMi5W5WTQqFAoVB45fOaNWv49a9/zZlnnkl9fX2OnUmSJEnSfnE8cFZKqT1e/Qvzi0Dmf+nvt6HEHXfcQWtra5f3li1bxrJly7juuusYMWIEJ598MpMmTeKYY45xmr5eZfTo0bz44ov88pe/5B3veAcjR47MuyVJkiRJ2pc2ALv6xWcCkHlvyX4bSkyfPj3TuBdeeIHbbruN2267jUGDBnHCCScwadIkTjjhBOrq6vZxl+otGhsb2bhxIzfffDNnnHEG48aNy7slSZIkSdpXpgOXRUQTsKJ0LUXECOBfgV9lLdRvQ4nzzz+fYcOGMX36dFauXJnpmS1btnD33Xdz9913U1VVxcSJE19Z5tHY2LiPO1a5q6+vp7q6mltuuYWpU6f+0RIPSZIkSepDPkfxVM75wCOla98HDgeWAZdkLRTFQzPydeKJJ6Y5c+bk8r1TSjz++ONMnz6d6dOn8+ijj3a7RkVFBWeddRbvfe97qays3AddqjdpbW1l9erVnHLKKZxwwgnuSSJJknYpIh5JKZ24p8935+/RMWvWnn4bSbuQpkzJPHZv/7yXm4ioAf4emAqMANZRDCquTSlty1qn386U2CkiOO644zjuuOP44he/yIoVK7jllluYPn06s2fPZseOHbut0d7ezs0338y6dev41Kc+ZTDRz9XU1DB27FgefPBBNm/ezGmnnUZVVb//oyZJkiSpD0kptQL/WXrtsYqeaafvGD9+PJ/4xCeYMWMGa9eu5Wc/+xnvfve7GTJkyG6fnT17Nt/4xjcyBRnq2yorKxk7diwLFizgjjvuYNu2zEGhJEmSJPUb/vPta2hoaOC8887jvPPOY9u2bcyaNeuVZR7PPvtsl8/cd999tLW18ZnPfMaTOvq5iooKxo4dy7PPPsstt9zCGWecweDBg/NuS5IkSZK6LSKWAVn3f0gppcOyDHSmREa1tbW87W1v43vf+x6rVq3ioYce4pOf/GSXYx944AGuuOKKXR45qv5lzJgxbNmyhV/96lesW7cu73YkSZIkaU/8MsNrGXBI6ZWJMyX2QEVFBSeddBInnXQSRx55JB/72MdeNWbOnDlcfvnlXHjhhdTW1ubQpcpJY2MjGzZs4KabbmLMmDE88cQTTJkyhcmTJ+fdmiRJkiTtVkrpX3d1LyJOAS4GTgeeAL6Sta4zJfbSRz/6UX70ox91ecLCo48+ype//GVaWlpy6EzlZujQoaxZs4azzz6biy66iKlTp9LU1JR3W5IkSZK0RyLi9IiYCdwHjALelVKamFL6RdYahhI94EMf+hA/+clPqKh49f+cc+fO5dJLL2Xr1q05dKZys2TJEtra2mhvb6e1tZVZHs0lSZIkqZeJiDMj4j6KR4DWAH+eUjoppfTr7tYylOgh733ve/npT3/a5XGg8+fP59JLL2XLli05dKZyMnHiRKqqqqioqHhlGZAkSZIk9QYR8dcR8QjwG2ALcHpK6U9TSv+7pzXdU6IHnXfeedTU1PA3f/M3rzoWdOHChVxyySVcdtllnsDQjxUKBaZNm0ZzczPjxo1jw4YNbN++3ZNaJEmSJJW1iJgHvB6YBUwFHixdr+tqfEop03IBZ0r0sLPPPpubbrqpy18ylyxZwkUXXcTGjRtz6EzlolAocM4553DKKaewdu1a7r//flLKerKOJEmSJOXiKCAobmY5A/j/7N15XNZV+v/x17mBmx1ZxATR9gZ1nBaxXMYlzSyr6WeT9m2ctNUszZrWScuyLG2byrTMxhazzbTcxX0rNcPKNDWXysx9AVGR9T6/P4B7gBv0BoEb4f18PD6P4JzzubicyRm4OOc6R07yeEU7JarA9ddfz9SpU7nhhhvIysoqNvfLL78wZMgQnn32WSIjI32UodQUcXFxrFu3jtjYWJo1a+brdERERERERMpyW1UEVVGiinTv3p3p06dz/fXXe9y+sX37dndhIjo62kcZSk3gcDiIj49nyZIlREdH07BhQ1+nJCIiIiIi4sFa+0FVxNXxjSp05ZVXMmvWLEJCPI/Y7NixgyFDhnDw4EEfZCY1SUBAAFFRUcyZM4ejR4/6Oh0REREREZFqo6JEFevcuTPJycmlNrfcuXMnjz/+OPv37/dBZlKTFP77MW/ePHJycnycjYiIiIiIVDdjzHnGmLeNMWuNMXnGmCWlrDHGmMHGmB3GmOPGmGXGmItKWdfMGLPQGJNhjNlljHnGGONXkVhVTUWJatC+fXvmzZtHRESEx9yePXt4/PHHWbRoERkZXjUnlVqqfv367N27l5UrV6rxpYiIiIhI3dMc6A5sLnhK82/gSeAF4DrgKLDAGOM+B26MiSK/EaUFrgeeAR4ChpU3VnVQUaKatGnThgULFpTa3HLfvn289tpr9OnTh5EjR7JixQqPBplSN8THx/Pjjz+yceNGX6ciIiIiIiLVa4a1trG1tifwU8lJY0wQ+YWEEdba0dbaBUBP8osPA4ss7Q8EAzdYa+dba8eSX5B40BgTUc5YVU5FiWrUqlUrFi1aRExMTKnz2dnZrFixgpEjR9KnTx9effVV1qxZQ25ubjVnKr5S2Phy8eLF7Nmzx9fpiIiIiIhINbHWuk6ypC0QAUwq8s4xYAZwdZF1VwNzrbXpRcY+Jb9Q0bGcsaqcihLV7OKLL2bx4sXExsaecN3x48dZvHgxw4YNo2/fvrz55pusX78el+tk/57K6S4gIIDo6Gg1vhQRERERkaISgTxgS4nxjQVzRddtKrrAWvs7kFFknbexTsgYc7Ux5kljzDhjTJOCsQ7GmHhvY6go4QMtWrRg2bJlXHLJJV6tP3LkCMnJyQwePJg77riD8ePHs2XLFvUdqMXCwsKw1jJ//nw1vhQREREREYAo4Ki1Nq/EeCoQYoxxFlmXVsr7qQVz5YlVKmPMGcaYb8jfWdEXuAOoXzB9G/m9KryiooSPJCYm8u2337J8+XLuvffek+6cKHTw4EGmTZvGQw89RP/+/fnoo484dOhQFWcrvhAbG8uePXtYtWqVClAiIiIiIqe/+saYlCJPvwrEKO0HA1PKXFnrvFlT1lxRbwBh5O+qSCzyHuQ32exykvfdVJTwIYfDwV//+lfGjBnDrl27SE5O5tZbby31lo7S7N69m88++4x7772X77//voqzFV+Ij49n7dq1bNq06eSLRURERESkJjtgrU0q8owr5/upQHjJqz2BSCDDWptTZJ3nDQtQj//toPA2VlmuAp6w1m7Fs4DxB9DoJO+7qShRQ/j7+9OtWzfee+899u7dyxdffEHPnj0JCgo66bsZGRkMHz5chYlaqGjjy7179/o6HRERERER8Z1NgB9wXonxkj0kNlGiL4QxpjEQWmSdt7FOpOTRj0L1geNexlBRoiYKCgqiR48eTJo0iX379jFx4kSuueYa/P39y3wnJyeH4cOH891331VjplIdAgICiIqKYvbs2Wp8KSIiIiJSd60A0sm/uhMAY0wIcB0wp8i6OUA3Y0x4kbGbyC8ULC1nrLIsB+4rsdOicMfE7cAiL2IAKkrUeOHh4fTu3ZuZM2eyZ88e3n77bTp16oQxxmNtTk4Ozz33HGvWrPFBplKVChtfjh49mueee46VK1f6OiUREREREalExpgQY8yNxpgbyT/+EFv4uTEmxFqbCYwEBhtjBhhjugCfk/9z/RtFQo0FsoAvjDFXFPSueBr4T+E1oeWIVZbHgFbAeuBZ8gsSdxljlgFtgCe8/nPXhAZ6SUlJNiUlxddpnFZ27tzJjTfeyKpVqzzm/P39GTx4MElJST7ITKrKpk2bGDJkCHl5eQQGBrJw4ULatGnj67RERETkFBhj1lhrK/xNW3m+jzZLllT0y4hIGWynTl6vPdnfd2PMWcCvZUyfba39zeT/dnowcA8QA6QAg6y1xc7yG2OaAaPJLxCkAf8Fni5624a3sU6Q73nAU+Q3tawPHAIWFnydkleNlkk7JU5TjRo1Yu7cubRu3dpjLjc3l+eff55vv/3WB5lJVVm3bh15eXm4XC6ys7NZom8sRERERERqDWvtb9ZaU8bzW8Eaa619zlqbYK0Ntta2L62IYK3dYK3tXLAmzlr7ZMnrP72NdYJ8t1prb7HWxltrndbahtba3uUpSICKEqe1iIgI5s6dW+pvy3NzcxkxYgSrV6/2QWZSFVq0aIG/vz8OhwOHw0Hz5s19nZKIiIiIiNRBxphhxpimlRFLRYnTXGFhol27dh5zubm5jBw5km+++cYHmUllS0xMZPjw4fTu3ZsnnniCffv2cfjwYV+nJSIiIiIidc/dwHpjzDpjzGBjzLkVDaSiRC0QHh7OnDlz+Otf/+oxl5ubywsvvFBq7wk5/SQmJtKzZ08uvvhiAgICmDNnDpmZmb5OS0RERERE6pZ4oCv5t3g8AGw2xqQYYx4yxjQpTyAVJWqJ8PBwZs+eTfv27T3mCgsTurGhdomOjubIkSMsWrSIvLyyrggWERERERGpXNZal7V2kbX2biAO6A78CAwBfjXGfOVtLBUlapHCwkSHDh085vLy8njxxRdZsWKFDzKTqtKwYUN+++039Q4RERERERGfsNbmWWvnkn+LxwBgD/m3fnhFRYlaJiwsjNmzZ9OxY0ePuby8PF566SUVJmqZ+Ph4UlJS2Lhxo69TERERERGROsQYE2CMuc4YMxHYB3wAbAD6eRtDRYlaKDQ0lFmzZtGplDtzC3dMfPWV17tppIbz8/MjPj6exYsXs3v3bl+nIyIiIiIitZwx5ipjzHvkFyKmAo2Bx4FG1tqu1trx3sZSUaKWKixMdO7c2WPO5XLx8ssvqzBRizidTqKjo5k1a5Zu5BARERERkao2G2gKDAMaW2s7WmvftNbuL28gFSVqsZCQEGbMmEGXLl085goLE8uXL/dBZlIVQkNDdSOHiIiIiIhUh3Osta2tta9Za3ed44LGTwAAIABJREFUSiAVJWq5kJAQpk+fzhVXXOEx53K5eOWVV3j++ef58MMPWbx4MVu2bCEjI8MHmUpl0I0cIiIiIiJS1ay1v1VWLP/KCiQ1V2Fh4vrrr2f+/PnF5lwuF6tWrWLVqlXFxmNiYkhISKBRo0YkJCS4n5iYGIwx1Zm+lFPRGznatPG66a2IiIiIiEiZjDGrgVuttRuMMd8C9kTrrbWXehNXRYk6Ijg4mGnTptGjRw/mzp170vUHDx7k4MGDrF271iNO0UJFUlIS55xzTlWlLRUUHx/PmjVriIqKIjEx0dfpiIiIiIjI6e8n4HiRj09YlPCWihJ1SHBwMFOnTqVHjx4kJydXKMbx48fZunUrW7duBWDixIn069ePa6+9tjJTlVPk5+dHXFwcixYtol69esTFxfk6JREREREROY1Za28r8vGtlRVXPSXqmKCgIL788kv69OmDw1E5//W/++67uoqyBiq8kWP27Nm6kUNERERERCqNMeZdY8zZZcydaYx519tYKkrUQUFBQXzwwQccOHCAr7/+mvHjx/PII49w7bXXct5555W7WJGbm8sHH3xQRdnKqQgNDcXf3183coiIiIiISGW6FYgtY64+0NfbQDq+UYdFRUXRtm1b2rZtW2w8KyuLrVu3smnTJo/n6NGjpcZasWIFGzZsoFmzZtWRupRDdHQ0e/bsYcmSJXTt2hU/Pz9fpyQiIiIiIqe/snpK/BnY720QFSXEQ2BgIM2bN6d58+bFxq217Ny5k40bN9K3b1+PIxvvvvsuL774YqUdC5HK07BhQ3755RfGjx/PwYMH6dSpk27mEBERERERrxlj7gfuL/jUAlONMVkllgUBZwDvextXRQnxmjHGfevG8OHDueOOO4rNb968meXLl9OxY0cfZSgnkp6eztChQ8nLyyMwMJCFCxeqMCEiIiIiIt7aAEwBDPAgsBgo2VwwG9gETPI2qIoSUiF9+/bl9ddf58cffyw2PmHCBFq3bk1gYKCPMpOybNiwgby8PFwuF9nZ2SxZskRFCRERERER8Yq1dj4wH8AYcwT4r7V256nG1T57qRA/Pz9eeeUVj/H9+/czY8YMH2QkJ9OiRQv8/f1xOBw4HA5iY2NxuVy+TktERERERE4z1tphlVGQAO2UkFNwxRVXcM011zBr1qxi459//jlXXHEFkZGRPspMSpOYmMjw4cNZt24dzZo1IysriwULFtCxY0ftbBERERERkXIxxrQB7gAuIL+XRDHW2ku9iaOdEnJKXnrpJY/bHI4fP87HH3/so4zkRBITE+nZsyfNmzencePG/Pbbb0ybNo309HRfpyYiIiIiIqcJY0xXYBmQAPyV/Ns2jgIXAjHAem9jqSghp6Rp06bcfffdHuPz5s3j999/90FG4i1jDHFxcWRkZDB58mT27Nnj65REREREROT08AzwOnBNwedPWms7k79rIgdY4m0gFSXklD399NNEREQUG3O5XLz33ns+ykjKIyYmhuDgYL788ks2b97s63RERERERKTmawbMAVzkXw8aCmCt3Q48DQzxNpCKEnLKYmNjGTLE89+5NWvW8P333/sgIymvsLAwGjRowLx58/jmm2/UAFNERERERE4kE3BYay3514KeW2QunfxjHV5RUUIqxaBBgzjzzDM9xt99913y8vJ8kJGUl9PpJCEhgZSUFBYsWEBWVpavUxIRERERkZppLfCngo8XAo8bY7oaYzqSf7RjnbeBVJSQShEUFMQLL7zgMb59+3YWLlzog4ykIvz8/NQAU0RERERETuY18o9tAAwGjgFzgcVAA2CAt4FUlJBK06tXL1q3bu0xPnHiRDIyMnyQkVSEGmCKiIiIiMiJWGtnW2vHFHy8E2hJ/s6Ji4DzrLVrvI2looRUGmMM//nPfzzG09LS+OKLL3yQkZwKNcAUERERERFv2HxbrLU/Wmuzy/Ouf1UlJXVTmzZt6NWrF5MmTSo2PnXqVLp160ZsbKyPMpOKCAsLw+l0Mm/ePFJTU8nNzWXZsmV06tSJNm3a+Do9ERERERGpJsaYe8ux3Fpr3/JmoYoSUulGjhzJ1KlTyc7+X4EsOzubiRMn8q9//cuHmUlFFDbAnDRpEmPGjCE3Nxen08nChQtVmBARERERqTtGl2OtBbwqSuj4hlS6s88+m/vvv99jfPHixWzZssUHGcmp8vPzY//+/WRnZ5OXl0d2djZLlizxdVoiIiIiIlJNrLWOcjx+3sZVUUKqxODBg6lfv77H+Lvvvkv+VbZyumnRogUBAQE4HA78/Py4+OKLfZ2SiIiIiIic5nR8Q6pEZGQkTz/9NAMHDiw2/tNPP7Fq1Spt+z8NJSYmMnz4cNatW8c555zD77//zo4dO2jcuLGvUxMRERERkWpkjOl+sjXW2tnexFJRQqpMv379GD16NJs2bSo2/v7775OUlERAQICPMpOKSkxMJDExEYCMjAymT59Oly5d3GMiIiIiIlInzCS/b4QpMV50W7xXRzh0fEOqTEBAAC+99JLH+O7du5kzZ44PMpLKFBISQsOGDVmwYAEpKSm4XC5fpyQiIiIiItXjbOCcgn8WPi2BwcBmoJ23gVSUkCp1zTXX0LlzZ4/xTz/9lCNHjvggI6lMhTdzrF69mqVLl5Kbm+vrlEREREREpIpZa7eX8nxvrX0BGE9+ccIrKkpIlTLG8Morr2BM8V09R48eZdKkST7KSiqTn58fCQkJbNq0iTlz5pCZmenrlERERERExHe+Bzx/M10GFSWkyl100UXceuutHuOzZs1i165d1Z+QVDpjDAkJCezdu5epU6eSnp7u65RERERERKSaGWOcwK3Abm/fUVFCqsXw4cMJCQkpNpabm8sHH3zgo4ykKpxxxhlkZmYyZcoU9u/f7+t0RERERESkChhjvjXGrC7x/ADsA/4BPOttLBUlpFrEx8fz6KOPeoyvXLmStWvX+iAjqSoxMTEEBgYyZcoUtm/f7ut0RERERESk8v1UyrMSeAn4i7V2greBVJSQavPwww8THx/vMf7yyy9z4MABH2QkVSU8PJyYmBhmzpzJ+vXrsdae/CURERERETktWGtvtdbeVuK5x1r7nLX2p/LEUlFCqk1oaCjPPfecx/jhw4cZOXIk2dnZPshKqkpQUBBxcXEsWbKEVatW6cpQERERERHxoKKEVKs+ffrQvn17j/HNmzczduxY/Ua9lgkICCAhIYHvvvuO0aNHM3z4cFauXOnrtERERERE5BQZY3oZYz4xxiwrpb/Eam/jqCgh1crhcPDZZ58RFxfnMbdgwQKSk5N9kJVUJT8/P44dO8bDDz/MU089RZcuXVSYEBERERE5jRljRgKfAucAOyi9x4RX/KsiQZETiYuLY8qUKXTs2JGcnJxic++88w5nnnkmzZo181F2UhXWr19PXl4eLpeLrKwspk+fTps2bXydloiIiIiIVMztwBBr7YhTDaSdEuITbdq0YcyYMR7jubm5vPDCCxw8eNAHWUlVadGiBf7+/jgcDvz9/cnOziYlJYW8vDxfpyYiIiIiIuWXA6ypjEAqSojP3HXXXfTr189jPDU1lZEjR3rsopDTV2JiIsOHD6d3794MHz6c9u3bs3r1ambOnMmRI0d8nZ6IiIiIiJTP68CdxhhzqoFMTWgsmJSUZFNSUnydhvhAVlYWnTp1YtWqVR5z3bp1Y8CAAT7ISqrL/v37cblcdO3alTPPPNPX6YiIiFQ7Y8waa21SRd8vz/fRZsmSin4ZESmD7dTJ67Wn+ve9pjHGvAxcBywF0kpMW2vtY97EUU8J8anAwECmTJlCy5Yt2bNnT7G5uXPncu6553LVVVf5KDuparGxsWRkZDBjxgxatmxJq1at8PfX/yyJiIiIiNRkxpjewAOACwgDsksssYBXRQkd3xCfi4+PZ/LkyaX+MDpu3Dg2bdrkg6ykuoSEhJCQkMAPP/zAtGnTOHz4sK9TEhERERGRExsJfAbEWGsbWWvPLvGc420gFSWkRmjXrh2jRo3yGM/NzWXkyJEcOnTIB1lJdfHz8yMhIYEjR47w2WefsXXrVl+nJCIiIiIiZYsA3rXWnnKDOBUlpMbo378/d9xxh8f4oUOHeOGFF9T4sg6IiYkhOjqa5ORkli9fTnZ2yV1gIiIiIiJSA0wBLq+MQCctShhjbjXG2FKe/kXWGGPMYGPMDmPMcWPMMmPMRZWRoNQdxhhGjx7NpZde6jG3ceNG/vvf//ogK6lugYGBNG7cmA0bNvDFF19ol4yIiIiISM0zF+htjPmvMeYfxpjuJR9vA5Wno1xn4HiRz38p8vG/gSeBR4BNwIPAAmPMn621xbsXipxAUFCQu/Hlvn37is3NmTOHc889lyuvvNJH2Ul1cTgcxMfHk5qayqRJk7j88ss5ePAgS5cupVOnTrRp08bXKYqIiIiI1GWfFPzz9oKnJAv4eROoPEWJb621R0sOGmOCyC9KjLDWji4YWwn8BgwEnijH1xAhISGBzz//nC5dupCbm1tsbuzYsZx11llccMEFPspOqlNUVBShoaGMGzeOMWPGkJubi9PpZOHChSpMiIiIiIj4ztmVFagyekq0Jb/JxaTCAWvtMWAGcHUlxJc6qEOHDrz66qse47m5uYwYMYLU1FQfZCW+4HQ62b9/P9nZ2eTl5ZGdnc3ixYt9nZaIiIiISJ1lrd1+ssfbWOUpSmwzxuQaY342xtxdZDwRyAO2lFi/sWBOpEIGDBjArbfe6jF+8OBBXnjhBY9dFN7KyckhIyPjFLOT6tSiRQsCAgJwOBw4HA5ycnL45ZdfcLlcvk5NRERERKTOMcY0O9njbSxvjm/sJr9fxGryz4TcDIw1xoRYa18FooCj1tq8Eu+lAiHGGKe1Vi30pdyMMbz11lusX7+elJSUYnMbNmxg/Pjx3H777aSnp3PkyBHS09PdT1mfHzlyhOPH81ujnHnmmTz44IOcfXal7TySKpKYmMjw4cNZt24dLVq0ICEhgeTkZCIjI2ndujVnnnkmfn5eHVkTEREREZFTt578vhEn4tU36Mbak8Up5SVjPgOuAGKBx4GHrbVRJdbcBYwDnNZaj7scjTH9gH4ATZo0abl9u9e7O6SO2bFjBy1btmT//v2VHjs8PJwRI0bQpEmTSo8tVe/YsWMcOnSIevXq0bp1a8466ywVJ0RE5LRijFljrU2q6PtJSUm25C9vyvxaS5ZU9MuISBlsp05erz3Vv+81iTGmYynD0cCVBc/91tqZ3sSqaE+JyQVf8Czyd0SEG2NK/iQQCWSUVpAAsNaOs9YmWWuTYmNjK5iG1AWNGzdm0qRJVfLD5pEjRxg6dCh79uiSmNNRaGgojRs3xs/Pj7lz5/Lpp5+ybds28vJKbtwSEREREZHKYq1dWsrzpbX2HvJv5ujlbaxTbXRpyb8C1A84r8RcYsGcyCnr1KkTr7zySpXEPnToEEOHDuXQoUNVEl+qnooTIiIiIiI1xmLgem8XV7Qo8XfgALAdWAGkAz0LJ40xIcB1wJwKxhfxMGjQIP75z39WSew9e/bw1FNPceTIkSqJL9WjsDjh7+9PcnIyn3zyibs4sXLlSkaMGMHKlSt9naaIiIiISG12DZDm7eKTNro0xkwhv8nlj+TviLip4BlkrXUBmcaYkcCTxphU8ndHPEh+weONcqcvUgZjDOPGjcPPz48PP/wQl8uFMYaYmBjq16/v/mfRp+RYTEwMqampdOjQgV27dhWLv337doYNG8azzz5LcHCwj/6UUhlCQkJo0qQJGRkZzJ07l7179/Liiy+Sk5OD0+lk4cKFtGnTxtdpioiIiIiclowxk0oZdpJ/YuJ8YLC3sby5feNn4HagMWCADUAfa+2HRdaMJL8I8TgQA6QAXa21e71NRMQbwcHBvP/++4wdO5Zjx44RGRlZ7l4T0dHRzJ8/nw4dOnDw4MFic5s3b+b555/nySefxOl0Vmbq4gMhISGEhISwfPlysrKysNaSnZ2tooSIiIiIyKkprTFkJrAceNBaO9vbQCc9vmGtHWyt/ZO1NsRaG2ytbVmiIIHN95y1NqFgTXtr7ffeJiFSXkFBQcTExFS4+WWzZs2YM2cOYWFhHnNr167l5ZdfVj+CWuSSSy4hICAAh8OBw+EgIyODtWvXkpmZ6evUREREREROO9bay0t5rrbW3lWeggSceqNLkdNWq1atmDFjBoGBgR5zq1atYtSoUbhcLh9kJpUtMTGR4cOH07t3b5577jkuueQSVq1axYQJE/j22285evSor1MUEREREanxjDEtjDEJJ5hvZIxpUZ6YKkpIndapUyc+//zzUndcLF68mPHjx2Ot9UFmUtkSExPp2bMniYmJBAYGEh8fT/369fn+++/58MMPWbZsGWlpXvfjERERERGpU4wxV5LfbzLyBMuigG+MMVV++4ZIrXHdddfxwQcfYIzxmJsxYwaffPKJD7KS6hAQEEBcXBxxcXFs3ryZjz/+mAULFrB//35fpyYiIiIiUtM8ALxnrV1f1oKCufFAf2+DetPoUqTW6927N4cPH2bAgAEec59++imhoaFcf73XxT45zfj5+XHGGWfgcrnYsWMHmzdvJiEhgaSkJOLi4li1ahVLliyhU6dOapApIiIiInVVa2CMF+uSgQneBlVRQqTAvffeS1paGkOGDPGYGz9+PKGhoVxxxRU+yEyqi8PhoH79+lhrSUtLY+rUqaSmpjJ8+HBdJyoiIiIidV0IkO7FuvSCtV7R8Q2RIh5//HEefvjhUudGjx7NihUrqjkj8QVjDFFRUTRu3Jgff/yRrKws8vLyyM7OZsGCBb5OT0RERETEF/4Amnqxrhmw09ugKkqIFGGM4cUXX+TOO+/0mHO5XLz88st8/71uu61LkpKSil0nevToUZYsWcLevXvVBFVERERE6pKZwEPGmNCyFhhjwoB/ATO8DWpqwjfVSUlJNiUlxddpiLjl5eVx88038/nnn3vMBQYG8uyzz5KYmOiDzMQXNm3axLp162jRogXnn38+hw4dIjMzk8jISC6++GLOOussgoODfZ2miIichowxa6y1SRV9vzzfR5slSyr6ZUSkDLZTJ6/Xnurfd18zxjQAvgcOAI8DC621WQVzTqAL8DwQC1xird3nTVztlBAphZ+fHxMnTuSqq67ymMvKymLYsGH8+uuvPshMfKHodaJ+fn7ExsbSuHFj/Pz8WLp0KR988AGLFy9mz5492j0hIiIiIrVSQZGhM5BD/q6JI8aYncaYP4AjwCwgF+jsbUEC1OhSpExOp5MpU6Zw5ZVX8vXXXxebO3bsGP/+97+54ooruOaaa4iPj/dRluJLISEhhISEkJeXx6+//sqmTZuIiIjgoosu4pxzztHuCRERERGpVay1PwNJxpgOQAegUcHUTmCJtfar8sbU8Q2Rk0hLS+Pyyy/nhx9+KHNNUlIS1157LRdddBEOhzYg1WUZGRmkpqZijOH888+nWbNmbNu2jWXLlulKURER8aDjGyKnt7p0fKOqaKeEyElERkYyd+5c2rdvz+bNm0tdk5KSQkpKCgkJCVx77bVcfvnl+i15HVV098T27duZPXs2b7zxBrm5uTidTpKTk+nYsaOv0xQRERERqRFUlBDxQoMGDZg/fz7t27fn999/L3PdH3/8wdixY5kwYQJdu3ale/fuxMXFVWOmUlP4+flRv3599u3bR25uLi6Xi+zsbF599VX++OMPGjVqRFxcHFFRUdSrV4/AwEBfpywiIrVUeno6+/btY1a9emooV4e4gIPW8uaxY6zKy/N1OiJlUlFCxEtNmjThu+++Y+jQobz//vtkZGSUuTYjI4Np06Yxffp0WrVqxbXXXsuFF16IMaYaM5aaoEWLFvj7+5Obm4u/vz/t2rXD6XTy22+/sWnTJve68PBw4uPjiY+PJzIyksjISHehYuXKlSxZskTHP0REpNzS09PZu3cvjRo1Ii03F/S9SN1hLQ2ysxm6cyfPpKerMCE1lnpKiFRAamoq7733Hm+88Qa//fabV+80btzYfbQjKCioahOUGqXolaJlXSWblZXF0aNHyczMxBiDy+UiIiKC1NRUHnnkEXJycnA6nSxcuFCFCRGRWqSqe0ps3bqV+Ph4QkJCSDlypKJfRk5nWVls/P13+qSn+zqTWkk9JU6ddkqIVEBUVBQPPvgg999/PzNnzmTUqFEsWrTohO/s2LGDt956iwkTJnDllVdyww03UK9evWrKWHwpMTGxzGJEocDAQI8jHFlZWSxdupSsrCystWRlZfHaa6+RmZlJo0aN3Ec/nE5nVaYvIiKnsZycHPW5quucTmK0Q0ZqMBUlRE6Bn58f119/Pddffz3r16/njTfe4MMPP+T48eNlvnPs2DG+/PJLFixYQL9+/ejQoYOOdUipAgMDufTSS5k+fbr7+Mdf/vIXtm/fzs8//+zeUREVFUV8fHyxHhUBAQGAjn6IiAj6PqOuM0a9RKRSGGO6l2e9tXa2V3F1fEOkch06dIjx48czevToEzbFLHTZZZdxzz33EB0dXQ3ZyenoRMc/CndQHDt2jMzMTPd4dHQ0qampPPDAAzr6ISJSg1X18Y2NGzfStGlTAB3fqMMObN3K1YcP+zqNWqkuHd8wxrgAC3hT6bTWWj9v4mqnhEgli46O5pFHHuFf//oX06dPZ9SoUSxdurTM9d988w3r16/nzjvvpHPnzvpthng40fEPYwxBQUHF+pRYazl+/Djz588vdvTj1VdfJS0tjQYNGhAVFUV4eDhhYWEn3NarnRYiIiIiUuDsqgiqooRIFfH39+eGG27ghhtuYO3atYwaNYqPPvqIrKwsj7XHjh3j9ddfZ/ny5QwYMIDY2FgfZCy1hTGGkJAQLrvsMmbMmOE++nHRRRdx8OBB/vjjD/Ly8tzHPwIDA6lfvz7169cnNjbWXaxYu3YtXbt2JTs7WzstRESkRlgycyaT33mHTT/8wLGjR4mqX5+L2rThpv79ubB1awBaRUTwyEsv0evuu32crUjtYq3dXhVxVZQQqQYXXngh48eP57nnnmPgwIFMmTKl1HXfffcdAwcO5LbbbqNbt27aNSGnJDExkeHDh3sc/SjZYDU3N5djx45x4MABfvzxR/f43LlzycrKwuVykZ2dzYwZM7jwwgsJCQmp1j+HiIhUvlZr1vjk637bsmWF3/3Pv//NZ2PH0v3mm/n7nXdSLzqaPb//zrwpU7jzyiv58ocfSDjnnErMVkROxhjjDzQBPK4XtNZu8CaGihIi1ahhw4ZMnjyZyZMnc++997J//36PNcePH+fNN9/kq6++YuDAgTRs2NAHmUpt4c3NH/7+/oSFhREWFlZsvHXr1syePZvc3FwcDgd5eXlMmDABp9PJGWecQVxcHDExMURERBAREYGfn+exQR3/EBGRyrB01iw+efNNhr71Ftf17v2/iXbt6H7zzSybM4dA3TIiUm2MMQHAKKAvEFjGMvWUEKmpbrzxRjp16sT999/Pxx9/XOqaH3/8kfvuu4++ffvSvXt3HA71TZbq1axZs1J3WuTk5JCWlsauXbvIy8sD8o+MREVF0bBhQxo2bEi9evXYuHEj3bt31/EPERE5ZZ+8+SbNLrmkeEGiiA5XX13mu18lJ/PJW2+xZd06srOyOPtPf+LuIUNo3aWLe83enTt5bfBg1ixfTsbRo9Rv2JBuPXtyz5NPArBt40ZeGzyYDWvWkJ2dTcOEBHr260evfv0q9w8qcvoYClwL3AF8BAwAjgH/BM4F7vM2kIoSIj5Sv359PvroI2666Sb69+/P7t27PdZkZWUxbtw4vvrqK+677z4aNWrkg0ylLittp0VAQAABAQFERES4x1wuF5mZmfzyyy9s2JC/Uy85OZnMzEystWRnZ/PFF19w/vnnEx4eTmBgWQV1ERGR4nJzc1m3ejX/vM/rn3GK2bV9O+2vuop/3ncfDoeDFfPnc//f/8645GR3H4qn776brOPHGTxqFOH16rHzt9/4bfNmd4yHbrqJsy64gGfeeYeAwEC2b9nCMd1mInVbL+BpYBL5RYnV1to1wARjzAfA9YBXV4KqKCHiY3/729/o0KEDDz74IO+9916pazZs2MD9999P7969+dvf/lbqNnkRX3I4HISEhBTrN9GuXTvmzJnjPv5hjGHKlClYawkJCaFBgwY0aNCA6OhowsPDCQ8Px+l0ut/X0Q8REQE4fOgQ2VlZnJGQUGzcWuvesQfg5+dXaj+uog0vXS4XLTt04JeNG5k2YYK7KPHTmjUMf/dd946Llu3bu99JO3iQnb/9xsuffMJ5zZsDcGk5roEUqaUaA5uttXnGmEwgqsjcR8DHgFfdZlWUEKkBIiMjeffdd7npppu466672LFjh8ea7Oxs3nvvPb7++msGDRpEkyZNfJCpiPfKarQJ+f8+HzhwgB07dpCbm4sxBmstYWFhNGjQgN27d3PPPfeQk5Ojox8iInWctTb/gxIFh4lvvMGoJ55wf17WjRt7d+7krWeeYfWSJRzYs8cdr7AgAXBBixaMefppDh86RKsOHWjYuLF7LiIqijMSEhjxwAPc1L8/SR06EK2b0kR2A5EFH/8KdAAWFHx+bnkC6ZC6SA3SrVs31q9fT//+/ctcs3nzZh544AEWL15cjZmJVExiYiI9e/b0OALidDqpV68eDRs2JCEhgUaNGpGQkEBISAj79+9n+vTpZGVlkZeXR1ZWFv/5z3+YNWsWq1evZtu2bezdu5djx4797xvVUqxcuZIRI0awcuXKqv5jiohIFYqMicEZGMi+nTuLjXf/v//jgyVL+GDJkjLfdblcPPR//8eP33zD3UOG8NasWXywZAltu3Ylu8g17c+//z5NL76YVx9/nOuaN+cf7dqxuiCuw+Fg9NSpxJxxBs8OGMBV553HXd268fPatVXxx5U6zBhzqzHGlvL0L7LGGGMGG2N2GGOOG2NvyJqLAAAgAElEQVSWGWMuKiVWM2PMQmNMhjFmlzHmGWNMZW63XgIUbil6BxhsjPnYGPMe8AowzdtA2ikhUsNERETw1ltv0atXL+68805++eUXjzW5ubm8+uqrpKWl0aNHDx9kKVI1nE4nTqeTNm3aMGvWLHJzc/H39+eSSy4hLS2NPXv2kJOT417vcDiIiYmhfv36xMbGEhERQVhYGOvWraNbt25qsikiUgv4+/vT4tJL+WbRIvoX2RkR06ABMQ0anPDdHdu28fPatbw+ZQptu3Z1j2dlZhZb1yA+nqfHjsXlcvFTSgrjRozgof/7P2b89BORMTGcdcEFvDhxIrk5OXy/YgVvDB3KAz17MmvTJjUjl6rQGThe5POiPxD8G3gSeATYBDwILDDG/NlauwfAGBNF/q6FDeT3djiX/EKBA3iCyjEEqA9grX3N5J+duhEIBt4AnvE2kIoSIjXU5Zdfzo8//siQIUMYNWpUqb8Rfu+99zh06BC33Xab/g9RapWyjn4Uba4JuHdS/Prrr2zatMn99yQ5OZmsrCxcLpe7yeY555xDaGgoISEh+vsiInKaufnee3n45puZ/ckndL/5Zq/fKyw+OIs0WN79+++sXbWK8//8Z4/1DoeDFpdeyl3//jd3dO3Knh07iIyJcc/7BwTQqmNHeg8cyBN33MGRtDTqRUefwp9MpFTfWmuPlhw0xgSRX5QYYa0dXTC2EvgNGMj/Cg79yS8O3GCtTQfmG2MigKeNMS8WjJ2SggLIniKfvwq8WpFYKkqI1GChoaG89tpr9OzZk9tvv53NRbpAF5o2bRppaWkMGjSIgIAAH2QpUjVKu/mjJD8/P48GmwBt27b1aLI5depUrLUYY6hXrx7R0dHExsYSGRlJaGiou2BR2CRNjTZFRGqOjtdcw8333suwe+4hZfly2l99NZExMRw+dIhvFi0CIDgszOO9sy64gAaNGvHakCH0HzKEjKNHefv552kQH+9ec/TwYe7r0YPuN99Mk/POIycri49GjybmjDM4609/Ysv69bw+ZAhd//53Gp11FulpaXzw2muc36KFChJS3doCEeTfeAGAtfaYMWYGcDX/K0pcDcwtUXz4FHgB6AjMqJ50vaOihMhpoF27dvzwww/07duXzz//3GN+6dKlpKen89hjj3n8cCZSFzVt2rTMJpuFuyf27NnD9u3b3YULay0Oh4PIyEj27t3LI4884m60OWvWLDp16qQdFiJSa3zbsqWvUyi3B0eO5OJ27Zj83/8yfMAAjh09SlT9+rS49FJemzyZdlde6fGOMzCQFydO5MWHHuLfffrQID6e2x55hDXLl/PLxo35a4KCOLd5cz596y327txJUHAwLVq1YvTUqQQFBxNzxhlEN2jAuy+/zIHduwmrV4+k9u257xmvd6eLlNc2Y0wMsA34j7X27YLxRCAP2FJi/UbgpiKfJwKLii6w1v5ujMkomKtQUcIYsxq41Vq7wRjzLVB2c6/8r3mpN3FVlBA5TQQHB/PJJ5/QoEEDxowZ4zH//fff88QTTzB06FAiIyNLiSBSt5S108LhcBAUFERQUJDHnMvlIjMzk2XLlpGdnY21luzsbEaNGsWWLVsIDw8nJiaGqKgooqOjCQkJce+wKLpTSbssRESqxuXXXcfl1113wjXfphffmd68ZUuPZpjX9e7t/tgZGMgTb7xRZrzo2Fieeeed8icrUn67ye8XsRrwA24GxhpjQgqOR0QBR621eSXeSwVCjDFOa212wbq0UuKnUvzqzvL6if/1uviJkxQlvKWihMhpxM/PjzfeeIP4+HiGDBniMb9161Yee+wxhg0bRsOGDX2QocjpzeFwEBISwqWXXsr06dPdjTbbtWtHw4YNycnJYf/+/fzxxx/k5OS4rzIFCAoKIjo6mp07d/Lggw+Sk5NDQEAAs2fPpmPHjtplISIiIvWNMSlFPh9nrR1X+Im1di4wt8j8HGNMIPCEMeb1wmWlxDWlzJW1rsKFBGvtbUU+vrWicUpSUULkNGOMYfDgwTRs2JB+/fqRl1e8ULp7924effRRnnrqKc49t1xXBItIgbIabQYGBhJYpFlaUTk5ORw7doyFCxeSlZWFtRZrLa+//jpbtmwhLCyMqKgo91O4wyI4OJigoCB3LwvQTgsREZFa6oC1Nqmc70wGegFnkb/TIdwY41dit0QkkGGtLbyiLLVgrKR6lL6DolwKGm4eBm6y1k491XgqSoicpm6//XZiY2Pp1asXmSWutUpLS2Pw4MEMHjyYCy+80EcZipzevGm0WVRAQAABAQFcdtllzJgxo9guizPOOIOcnBwOHTrE7t27yc7OBnDvtCjsZREZGcmOHTsYOHCgu59F4U6LokULERERqXMs+VeA+gHnAT8XmUssmCu0qWDMzRjTGAgtsa5iiVibaYzZB+SeaixQUULktHbdddexcOFCrr32WlJTU4vNHT9+nGHDhvHAAw/QoUMHH2UoUvdUZJeFy+UiKyuLvXv3Mnv2bPdOi6ysLF577TU2b95MREQE9erVo169eu6dFsHBwe7Hz8/PHU87LURERGqNvwMHgO3k95xIB3oCwwGMMSHAdcC4Iu/MAR4xxoRba48UjN1Efj+IpZWU19vAIGPM3CI7NCpERQmR01zbtm35+uuv6datGzt27Cg2l5uby8svv8zhw4e57iRNoUSk8pR3l4XD4XAXF1q3bs3MmTM9dlpkZ2eXudMC8q8QrlevHn/88Ye7p4XT6WTatGl07NgRp9Pp1W4LFTRERER8wxgzhfwmlz+SvyPipoJnkLXWBWQaY0YCTxpjUsnf9fAg4ACKdmsdCwwCvjDGvACcAzxN/k0exTvBVlwk8GfgN2PMQmAvJXpaWGsf8yaQihIitUDTpk1ZsWIFV111FT/99JPH/DvvvMOhQ4fo06ePtoCL1HBl7bQo68YQAGutu6fFokWLit0c8tZbb7Ft2zb8/Pzcuy0iIiKIjIwsttsiKCiINWvW0KVLF7Kzs3E6nSxcuFCFCRERkerzM3A70Jj8ppQbgD7W2g+LrBlJfhHicSAGSAG6Wmv3Fi6w1qYaY7oAo8m//jMNeJX8wkRl+TuQVfBx+1LmLaCihEhdkpCQwPLly/nb3/7GV1995TE/ZcoUUlNTGThwIP7++qsvUpOVd6eFMQan04nT6fToadG2bVsSEhJwuVzu3RZ79uxxFy4K37fWMn/+fLKystzHSSZNmkRcXBxBQUHuwsXJdlxop4WIiEjFWGsHA4NPssYCzxU8J1q3Aehcedl5xD+7smLpJxORWiQqKop58+Zx8803M23aNI/5RYsWkZ6ezqOPPlrmb1xF5PRW1k4Lh8Nxwt0WAK1atXIfHfHz8yMgIIDk5GR3EaKwKWdYWJh7x0VERATh4eEEBQWxbt06evTooZ0WIiIi4jUVJURqmeDgYCZPnsyAAQMYN26cx3xKSgqPP/44AwYM4LzzzvNBhiJS1cq706JQ8+bNSy1oFOVyucjJyeHw4cPs37+f7OxsXC4XxhiSk5PJzMx0N+kcM2YMx44dcxcvCndbBAUFuRt/ltx1oZ0WIiIipwdjzF+AIUASkAC0sdZ+Z4x5DvjKWjvHmzgqSojUQv7+/owdO5a4uDiGDRvmMb9t2zYeeughunXrxi233EJ4eLgPshSRmuhkBQ2Hw1HmTSJt27Zl9uzZ7qMjF1xwAbt27eL33393HxdxOBzA/3ZdhIaGEhYWRkREBL/++isDBgwgJyeHgIAApk+fTvv27UstXpRGBQ2R2s1ay//7y1/YtX07X3z/PY3PPdc9N+Ojj3jmnntYumsXIWFh1ZJPq4gIHnnpJXrdfXe1fD2RmsQYczUwHVgBTACeKjKdBdxH/i0gJ6WihEgtZYzh6aefpmHDhgwYMACXy1Vs3lpLcnIyX3/9NX379uWKK65w/7AgIlIRZR0dKYvL5SI3N5eMjAwOHz7MzJkz3dehWmt588032bZtG8YYQkJCCA8PdxcwwsPDCQ4OJjAwkKCgIH744QeuueYaHR0R8dKNzy3zydedPKTi15T/uHo1u7ZvB2DelCnc8eijlZVWhby7YAHxZ53l0xxEfGgE8L619i5jjD/FixI/AP29DaSihEgt179/fxo0aMA//vEPsrKyPOaPHDnC6NGjmTdvHnfffTfnn3++D7IUkdqiPEdHHA6Hu0EnQJs2bZg1a1axJp2NGjVyFy8yMzNJT08vdefFnDlzih0defvtt8nLyyMsLIywsDD37o6ij46OiJxe5n3+OcGhoZzbtClzJ0/2eVGixaWX+vTri/hYIvBwwce2xFw6EO1tIP1aVKQOuOGGG1i6dCnNmjUrc83mzZt5+OGHGTNmDOnplXV9sYiI9wp3WvTu3Zvhw4cXa9LpdDoJDQ0lKiqK2NhYGjVqREJCAvHx8cTHx9OuXTsCAgJwOBz4+/tz1llnsXXrVr799lsWLFjArFmz+PLLL/n00095//33efvtt5kwYQJffPEFc+fOZdy4cXTu3JknnniCzp07M3v2bNLT092FjpNZuXIlI0aMYOXKlVX9H5NInZSXl8eCqVPpcPXVXHfLLfy6aRNb1q8vc/2a5ctpFRHB1g0bio3f3b07j91yi/vzp/v3p0/HjnyVnEyvVq346xln8MCNN3L40CF2bNtG/2uuoX3DhvTp2NHj67WKiGDS2297xE6eNIkeF15Ip0aNGHTDDezdubNa8xKpJvuAc8qYaw787m0g7ZQQqSMuu+wyfvjhB9544w2efvppjhw54rHGWsvcuXNZsWIFt9xyC127dsXPz88H2YpIXVXRJp3lPTpirSU3N5ecnBz279/P3Llz3UdHsrOzeeedd9ixY4e7IBEcHOzedREaGuruhREYGMi6devo2bPnKR0d0S4NkRNLWbqUQ/v20fXGG7modWteevhh5k6ezPl//vMpx96zYwdvP/88/Z98ksyMDF5+9FGev/9+dv/+O/+vb19uuf9+xgwbxpDbbuOz1atP2ONmfUoK+3fv5oHnnyfr+HFeeewxnh80iNenTPFpXiJV4FPgGWPMBqCwIm+NMRcAjwHjvQ2kooRIHRIQEMCDDz7IzTffzCOPPMJHH31U6rojR47w5ptvMm/ePPr3788FF1xQzZmKiJRfeQoaxhgCAgIICAgAoHXr1u7rUAuPjsTHxwPFCxgHDhxg9+7d5OTkkJeXB8DcuXOLHR15+eWX6dmzZ7HiRWhoKMHBwTidTvfxkcKjK9988w1dunRRPwyRE5g7eTLhkZG0veIKApxOLrv8cuZPmcKAp5465R/G01NTeXfBAhLOyf+l79affuLD11/n6bFjueYf/8hfZC0P9OzJb5s3c/af/lRmrGNHjvDa558TERUFwIG9e3n18cfJPH6coOBgn+UlUgWeBJoBS4E9BWPTgIbAPOB5bwOpKCFSB8XFxTFx4kTuuusuBg4cyPoytv1t3bqVRx55hK5du9KnTx8iIiKqOVMRkepxop0WJQsYJZW8daRVq1b4+flx9OhRUlNT3QUNyC9wFP4AVdjQc+HChWRlZeFyucjKymLixIlERka6CxdFCxiBgYGlNiXWTgupzbKzslgycyadrr2WgIIeNFfeeCNP9evHutWr+ctll51S/LgmTdw/+APuj5M6dvQY27dr1wl/+G92ySXuggTAOQX/W7J/165it4VUd14ilc1amwVca4zpAnQB6gOHgIXW2vnliaWihEgd1rFjR7777jvGjBnD0KFDyzzSMW/ePFasWME///lPunXrpiMdIlIrVdfRkaKstSQlJTFjxgx3UaNevXosW7bMfXSkaBED8ne9hYaGEhISQmhoKL/88guDBg1y77T4/PPPadu2rbuY4e/vf8LfJKugITXdivnzOZKWRrsrr+RIWhoALdu3xxkYyLzJk0+5KBEeGVns88LCR3i9eh5j2ZmZJ45V5J2i75XWbLw68xKpbMaYJsBua+1CYGGJOX8g3lrrVV8JFSVE6riAgAAeeOABbrrpJh599FEmTpxY6rqjR48yduxY5s+fz913312hb9xFRGqrihY0jDE0b968XEWNvLw8cnJyOHbsGGlpaSQnJxfrhzF+/Hh27txZrKgRFBRESEiI+yk8VrJhwwZuueUWcnJycDqdzJw5k/bt25e5K6Q0KmpIVZs7eTIA/+7Tx2Nu/pdf8q+RIz3GnYGBAORmZxcbT09NJTImpgqy9E5NzUukAn4F2gCrS5m7sGDcq99kqighIkD+kY4PP/yQfv36MWDAANatW1fqum3btvHoo49y+eWX07dvX6Kjvb7tR0REylCeooafn1+xHWsn6ocBxXtipKWlceDAAXJzc8nNzWXOnDnugkZWVhajRo1i8+bNOBwOAgMDPQoZoaGhxY6TrF27lh49eqjJp1SZjKNH+So5mW433kiP224rNvfz2rW8OngwKcuWebzXoFEjAH79+WcSL7oIgD1//MH2LVtoct55VZ94GWpqXiIVcKJmLkGA19uDVJQQkWLat29f7EhHWdeDLl68mFWrVnHTTTdx3XXXleu3aiIiUnlOdnzkRD0x2rVrx5w5c9wFjXbt2tGoUSNcLpd7R0ZaWhoHDx50FzaKXpFassnnK6+8wo033khwcDDBwcHuBp8hISHuQobT6SQgIACn08l3331Ht27dKlzUUEGj9ls6axaZGRn83z338OdWrYrNXdi6Ne++/DLzJk/morZti82d0agRzS65hLHPPUdQSAjW5eK9V14p1u/BF2pqXiLeMMb8BbioyFB3Y0zJinoQ0AvY7G1cFSVExIO/vz/3338/N910E4899hgTJkwodd3x48d5//33mT9/PnfeeSctW7as5kxFRAQqvx+Gw+HA4XCctOBcsslnUlISfn5+ZGRkkJ6e7i5sFN5UAsV7ZCQnJxcrarz11lscO3bMXcgovLGksJBRWMwICAggJSWFrl27apdGLTdv8mSanHuuR0ECwD8ggCt69GDelCk0L+V7kOHjxzP8vvsYetddNIiPZ9Czz/LxmDHVkfYJ1dS8RLzQA3iq4GMLDC1j3a/A3d4GNUWr3b6SlJRkU1JSfJ2GiJThq6++YuDAgaxdu/aE61q1asUdd9xRbNuwiIjUbps2bapQk8/Cd5944gl3UWPw4MGcc845Jy1mAMyZM4cZM2ZgrcXhcNCnTx9uvfVWd0Gj8JhJyWJGQEAA/v7+rFq1qkZcxWqMWWOtTaro+yf7Pnrjxo00bdoUgJRSGlpL3XBg61auPnzY12nUSrZTJ6/Xnurfd18zxgQATvKPbqQDnYFvSyzLttbmlCeudkqIyEn99a9/JSUlhXfeeYcnnniCQ4cOlbru22+/5fvvv+f666+nV69eBJfzPm4RETn9VHSXRuG7Fb25pF27diQnJ7sLGk2aNOHXX38lLy+P3Nxcj9tLoPiVrPPnz3dfxZqdnc2SJUu0W0JE5AQKig2FBQfP+6krSEUJEfGKv78/99xzD7169WLo0KGMHTsWl8vlsS43N5cpU6awePFibrvtNjp06HDCq+hERKRuq2hRo2nTpqd0FWvLli3dV7E6nU46leO3nSIiks8YkwBcQH4viWKstbO9iqHjGyJSEWvXrmXQoEEsK6XjdVFNmzalX79+nHvuudWUmYiIiHc2bdrE119/zUMPPUT79u19koOOb0h10PGNqlOXjm8UZcz/b+++46Oq8j6Of35pQCpJ6CBiQRGxrYCAiMhiAQVU3MfFVR9Zu6Cr6KPYscGqiwWVVXfXxYZtlVWwsKggrhVQbDQjvQVSaAkJJDnPHzMZJ5NJMkkmmZTv+/W6r7lz7rnnnjscJjO/OcWSgNeB00qTvI++AINzLqQlQcPW5UJEmpdjjjmGBQsW8Oqrr9KlS5cK8y1fvpwJEyYwffr0ClfyEBERiYQePXpwxhln0K9fv0hXRUSksZkCdAVOwhOQOAcYDPwDz0SXIb+xKighIjVmZpx//vm+icpatGgRNF/pDOtXXnkls2fPZt++ffVcUxERERERCaPhwAPAV97nm51zC51zVwBvA/8XakEKSohIrSUkJHDfffexbNkyzj777Arz5eXl8be//Y3LLruMN998k/z8/HqspYiIiIiIhEl7YINzrhjIA9L8jr3Hr8M6qqSghIiEzcEHH8ysWbOYO3cuhx9+eIX5duzYwfPPP8+ll17KCy+8wI4dO+qxliIiIiIiUksbgDbe/Z+Bs/yOnQAUhFqQghIiEnannXYa33//PVOnTiUpKanCfHl5efzrX//isssu4+mnnyYzM7MeaykiIiIiIjU0Dxjq3X8UGGdmn5vZfOA+4IVQC1JQQkTqRFxcHBMmTGDVqlWMHTu20rz79u3jvffe48orr+SRRx5h/fr19VRLERERERGpgVuAuwGccy8Co/FMcJkLjAcmhlqQghIiUqc6dOjAc889x3fffceYMWOIiqr4baekpIQFCxYwfvx47r//flasWFGPNRURERERkVA45/Kdc1l+z2c55/7gnDvXOfdX51xJqGUpKCEi9eLoo49m5syZrFq1iiuvvJK4uLhK83/99dfcfPPN3H777XzzzTc45yrNLyIiIk3X7Jdf5qJBgzi5UyeGdO3KHwYO5NFbb/Ud37xuHX2Sk/n0/fd9aSN79eKx22/3PZ901VVcfPLJ9VpvEalaTKQrICLNyyGHHMLTTz/N3XffzWOPPcZf//pXdu/eXWH+H374gR9++IFDDjmE0aNH079/f6Kjo+uxxiIiIk1P76ldInLdxTdurPY5/5w6lWfuv5+Lrr+e8ZMmUVhYyIpvv+X9117jhilTAGjToQPPffgh3Q47rMJyLrv5ZgoKQp57T0QCmNnq6uR3zh0cSj4FJUQkIjp27MiDDz7IxIkTmT59Oo899hhZWVkV5v/ll1946KGHSEpKom/fvpxwwgkcd9xxtGjRoh5rLSIiIvXtjWef5ZyxYxl3992+tEHDhnG5X0+JuBYtOKpv30rL6XJwSN+PRKRi3YDdwBxgc7gK1fANEYmo1NRUbr/9dtatW8e0adPo2rVrpfl3797NRx99xOTJk7nwwguZMmUK8+fPZ8+ePfVUYxEREalPu3fuJL19+3LpZubbDzZ8I1Cw4Rtb1q/n9rFjGdqtGwPbt2dM//588PrrvuM7srOZdOWVDD3wQAa2b8+Vw4ez7JtvypRROkxk5pNPcmaPHgzp2pXbLrmE3VryXJqeu4H1wPlAbzxLgU5xzv1fsC3UQhWUEJEGIT4+nmuvvZaMjAyef/55jjjiiCrPKSws5IsvvuDRRx/loosu4q677uK9994jOzu7HmosIiIi9aHHMcfw+jPPMOfll9kRxr/xOdu388ehQ1n2zTf86YEHeOS11xh50UVkbtrky3PTmDF88dFHXPfAA0yeMQNXUsLVZ53Fhl9+KVPWh7NmseiTT7jt8ccZf889/HfuXJ66556w1VWkIXDO3eecOwo4FvgMuBnYYmbvmdlFZpZUk3I1fENEGpTY2FguvvhiLrzwQt555x2mTJnC119/XeV5xcXFLF26lKVLl/L0009z+OGH069fP/r160fnzp3roeYiIiJSF26eOpWbLriAe66+GjOj2+GHM2TkSC687joSk5NrXO7Mp55iz65dvLhwIW06dACg7+DBvuOfz5vHd19+ydPvvcfxAwcC0GfQIEb26sWL06Zx2+OP+/LGxMTw8CuvEBPj+Xq1ZsUK/vPmm0x89NEa10+koXLO/QjcAdxhZn3x9JyYDDxjZg8656oVkVNPCRFpkKKiojj77LP58ssv+fjjjzn33HNp1apVyOevXLmS559/nquvvppx48bx0ksvkZGRoVU8REREGpnuvXrxxqJFTH3tNc677DJwjn889BD/e/LJ5Ndi+ObiTz6h/9ChvoBEoJ+WLCG1TRtfQAKgVUICA884g6VffFEmb+9Bg3wBCYCDevQgd/t29u/bV+P6iTQS3wAfA58CcUDV3Z0DqKeEiDRoZsYpp5zCKaecQn5+PvPmzWPWrFnMnj2bnJyckMrYsGEDGzZs4PXXXyctLY0+ffrQt29fjj76aE2UKSIi0gjEtWjBoGHDGDRsGABvv/AC948fz9svvMCYa66pUZk7c3Lo+ZvfVHg8OzOTtHbtyqWntWvHrtzcMmmJKSllnsfGxeGcY/++fcRWsQy6SGNjngldTgHGAOcA0cDbwFnAvOqWp6CEiDQa8fHxjBo1ilGjRlFUVMTChQuZNWsW//73v9m4MbQlxnJycpg7dy5z584lLi6OY445hj59+tCnTx/S09Pr+A5EREQkHEZdfDFP3Hkn637+ucZlpKSlkZWZWeHx9Pbtyd2+vVx6zrZtJKem1vi6Io2VmfXHE4g4D0gC3gUuA95zztW4W5CCEiLSKMXExDBkyBCGDBnCtGnTWLJkCbNmzWLWrFksX748pDL27dvHokWLWLRoEQCHHnqorxfFwQcfXGZWbxEREYmMnO3bSWvbtkxablYWe3btKpdeHX0GD+a1p58me9s20oP0iOjVuzfPTp7MN599xm9OPBGAgvx8Pps7l8EjRtT4uiKN2Gd4lgSdjadnRJ43fWiwz83OufdCKVRBCRFp9MyM3r1707t3bx544AFWrlzpC1CEMklmqYyMDDIyMnjllVdIT0/39aDQMA8REZHIGdOvH4POPJN+Q4aQ2rYtW9ev56UnnqBlfDxnXnBBjcu9YNw43nvlFa44/XTG3nQT7bt0Yc3KlRTk53Px9dfTf+hQjunXj9suuYTxkyaRkpbGS088QWFBARddd10Y71CkUUkCLsDTY6KyX/AcnmEdVVJQQkSanMMPP5yJEycyceJENm3axNtvv83bb7/N/Pnz2b9/f0hlZGdn88EHH/DBBx8QFxfHsccey3HHHUfPnj058MADiYrSPMEiItJ4Lb4xtGGPDcGlt9zCwnff5S8338yu3FzS27fn6GLsbwkAACAASURBVL59mTxjBp27datxualt2vD3//yHaXfdxSO33sq+wkK6HnIIl0yY4Mvz8MyZPHbbbZ7jBQX0PP54ps+ezQGHHBKGOxNpdA6qi0KtIcxE37t3b7d48eJIV0NEmrjdu3fzn//8h9mzZ/Puu++SlZVVo3ISExPp2bMnRx55JEceeSQHH3xwmRm3RUSk8di0aROXXnopsbGxEbm+mS1xzvWu6flVfY5evnw5RxzhmQx/8e7dNb2MNHJZGRkM27kz0tVokpzfMrJVqe3/96ZKn6JFpNlISkpi9OjRjB49muLiYr766itmz57NnDlz+PHHH0MuZ8+ePXz99de+oSEtW7akR48eviDFYYcdRpxm2hYRERERqZKCEiLSLEVHRzNgwAAGDBjAlClTWLNmDXPmzGH27NksWLAg5GEeAAUFBSxdupSlS5cCnkk4DzvsMF+QokePHsTHx9fVrYiIiIiINFoKSoiIAAcddBDXXnst1157Lbt27SozzCM7O7taZRUVFbFs2TKWLVvGG2+8QVRUFF27diUtLY2UlBRat24d9DElJSVi3YdFRERERCJBQQkRkQDJycmcd955nHfeeRQXF/Pll18yf/58Fi5cyOeff05eXl7VhfgpKSlh7dq1rF27tsq8CQkJZYIU/oGLtLQ0UlNTfZsCGCIiIiLS2CkoISJSiejoaE488URO9K5Pvn//fpYuXcrChQtZuHAhn376Kbm5uWG7Xl5eHnl5eWzatKnKvImJiaSmppKWluYLWgQ+pqamkpCQQLC1o0VEREREIk1BCRGRaoiNjaVPnz706dOHG2+8kZKSEn766SdfkGLhwoVs3bq1XuqyZ88e9uzZw4YNGyrNFxMTQ0JCAgkJCcTHx1drS0hIIDk5mRYtWtTLPYmIiIhI42FmhwFdgJaBx5xz74VShoISIiK1EBUVxVFHHcVRRx3FuHHjcM6RkZFRpifFmjVrIlrHoqIidu7cyc5aLAXWpk0bunTpwgEHHEDnzp3p0qULXbp0ITU1Vb0wRERERJoZM+sJvAb0BIJ9GHRAdChlKSghIhJGZkb37t3p3r07l156KQCbN29m/fr1bNu2zbdlZmaWe56VlYVzLsJ3EFxWVhZZWVm+FUZKJSQk+IIU/gGLDh06EBOjPzEiIiIiTdQzQBxwLrAM2FfTgvSJUUSkjnXq1IlOnTpVma+4uJjs7OxyQYvMzEy2bt3Kli1bfI/btm1rEAGMvLw8Vq1axapVq8qkx8TE0LFjRzp37kznzp19+506daJ169bqXSEiIiLSuB0H/N45N6e2BVU7KGFmnYGVQAKQ5Jzb40034FbgaqANsAi4zjm3tKKyRETkV9HR0bRr14527drRq1evSvMWFRWxffv2csGK0kf//b1799bTHZSt34YNG4LOd9GqVSs6duzoC9b47ycnJytgISIiFfr4nXd449lnWfHddxTu3UvHAw5g6DnnMGbcOFqnp0e6eiLNyS8EmUeiJmrSU+JhYA+eoIS/icCdwP8BK4AJwIdm1ss5Vz+zvomINBOlPRE6duzIcccdV2E+5xz5+fns2rWLnTt3Vvtxx44dbN26Nay9Mvbu3cvq1atZvXp1uWMJCQnlAhYdO3akQ4cOpKSkKGAhIhImY98aEJHr/vPcz2t87qO33car06cz4sILGTNuHAlJSaxZsYK3nnuO1StW8PDMmWGsqYhU4UbgITP7xjlX/kNdNVQrKGFmJwFnAJPxBCdK01viCUpMcc496U37AlgLjAfuqE0lRUSkZszMt/JGx44da1TG3r17WbVqFStWrPBty5cvZ+XKlRQUFIS1vnl5eWRkZJCRkVHuWMuWLWnfvj0dO3akffv2dOjQwbe1a9eO2NjYsNZFREQajoXvv8/MJ5/kzqeeYuRFF/nSjx84kHPGjuXLjz6KYO2qr7CggBYtw/Ijs0ikTAE6AyvMbC2wIzCDc65vKAWFHJQws2jgCeDeIBccACQDr/tVIM/MZgPDUFBCRKTRatWqFccccwzHHHNMmfSSkhLWr1/vC1L4Byy2b98e9noUFBSwbt061q1bV+6YmZGenl4mYFH62K5dO81jISLSyL3y1FP0OPbYMgGJUtHR0Zx42mkA7MjO5rHbbuO/H3xAQUEBRx5/PH+6/356/uY3vvwje/ViyKhRJCYn88bf/sbevDwGDR/OxEceITElBYAln37KVWeeyROzZvHa00+z+NNPSUlLY+yNNzLaO5F1qaVffMH0e+9l2Tff0LJlSwaPHMkNkyeTkJQEwOyXX+beq69mxscfM+2uu/hp8WIuufFGLrvllrp6uUTqw4/erdaq01PiKjxjRp4C/hBwrAdQDPwckL4cOL/GtRMRkQYrKiqKbt260a1bN84444wyx7Kzs1m5ciUrVqzg559/9m0ZGRnk5+eHvS7OOd8KIT/88EO543Fxcb75Otq1a0f79u3LPFfQQkSk4Srav5/vv/qKP1x7bZV5bxozhg2rV3PdAw/QOj2dlx5/nKvPOouXPv2UAw45xJfvP//6F10OPpjbn3iCrK1beeKuu7j/2mv58wsvlCnvvvHjGf773/M/V17J/Nmz+fMNN9CuUydOGjYMgO++/JJrRozg5LPO4sEXXmBnTg5PTprE7h07ePDFF8uUdfsf/8joyy7j8okTSfIGP0QaK+fc2HCVFVJQwszSgfuAC51z+4N8cEsF9jjnigPSc4F4M4tzzpVZIsTMrgCuAOjatWtN6i4iIg1Ueno6AwYMYMCAsmOWnXNs3ry5TKBi1apV/Pzzz/zyyy8UFhbWSX327dvHxo0b2bhxY9DjcXFxtG3bNmjAom3btrRu3Zro6JCW2hYRkTDbkZPDvsJCOnTpUmm+z+fN47svv+Tp997j+IEDAegzaBAje/XixWnTuO3xx315C/fu5bE33iA+MRGAVvHx3H3FFaxZuZKDDj/cl2/Aqacy7u67Aeg/dCib1q7luYcf9gUlnrz7bo4+4QSmzJjhO6dtp05cM2IEGcuWcWjPnr7086+6ijHXXFO7F0OkCQq1p8QDwFfOufcqyRNsFjSr6Jhz7lngWYDevXtHfl07ERGpc2bmWyZ08ODBZY6VlJSwYcOGMgGLX375xTcpZl30sCi1b98+Nm3axKZNm4Iej46OJj09nTZt2tC2bdugW3x8fJ3VT0REgCp6tP20ZAmpbdr4AhIArRISGHjGGSz94osyefsOGeILSACcMnIkd11+OcuWLCkTlBh81lllzjtlxAj+cvPNFBcXs7+wkB++/pqbHn6YoqIiX55j+/cnJjaWFd9+WyYoMfD006t3vyINjJk9BExzzm307lfKOXdzKOVWGZQwsyOBPwKDzKy1N7n0k1eKmRXj6RGRZGbRAb0lWgP5zrn9oVRGRESar6ioKA488EAOPPBAhg4dWuaYc47MzExWr15dJlBRur9ly5Y6rVtxcTHbtm1j27ZtFeZJSEjwBS38H0uDGenp6bRo0aJO6yki0hS1TksjrkULMoMsM+0vOzOTtHbtyqWntWvHrtzcsmlt2pR53rJVK+ITE8nKzCybr23bcs+Li4rYkZ1NcVERxcXFPDhhAg9OmFDuupkBge5gdRNpZH4HvAxs9O5XxgHhCUoA3YFY4IsgxzYC/wBmAtHAocBKv+M98CwPKiIiUmNm5ltpI3BICEB+fj5r1qzxBStKAxZr165l7dq15OXl1Xkd8/LyyMvLCzoRZ6mkpKQyQQr//dJH9bgQESkrJjaWo/v144uPPuLqu+6qMF96+/bkBploOWfbNpJTU8umZWWVeV6wdy/5e/bQpn37svkCysvZvp3omBhap6ezr6AAM+PyW2/1TbTpr23Aqleau0gaO+fcQcH2ayuUoMR/gVMC0s4AbgGGA6uBdcAuPNGS+wHMLB4YgXeIhoiISF2Jj4/nyCOP5Mgjjyx3zDlHdna2L0Cxbt063/7atWtZs2ZNvQQtAHbv3s3u3btZu3ZthXni4+N9AYv09HTS0tLKPWqOCxFpbsZccw03nn8+c15+mbP+UHbO/ZKSEr786CN69e7Ns5Mn881nn/GbE08EoCA/n8/mzmXwiBFlzvn644/J37PHN4Rj/jvvYGYc4bdKB8CCOXPKBBwWzJ7NEcceS3R0NK0SEujVpw/rfv6ZyydOrIvbFmkWqgxKOOeygAX+aWbWzbv7qXNujzftz8CdZpaLp3fEBCAKzzKiIiIiEWFmtGnThjZt2tC7d+9yx51z5OTklAlUlG4bNmxg/fr15AZ0+61L+fn55Ofns6GSbspRUVG0bt2atLQ031YasPDfT0pK0i9zItIkDBo2jAvGj+f+8eP57quvOHn4cFolJrJu1SrefO45OnXtysMzZ3JMv37cdskljJ80iZS0NF564gkKCwq46LrrypTXolUrrv/d77jouuvIysxk2p13MnjECA7u0aNMvs/nzWP6vffymxNPZP7s2Xw1fz5/efVV3/Hr7ruPa0aM4K6oKH579tnEJyaydeNGPps7l6vvvJMDu3evl9dHpL6Z2cWVHC7B02nhO+dcxV1IvaqzJGhV/ownCHErkA4sBk51zmVWepaIiEgEmZmvV8Lxxx8fNM/u3bvZsGGDL0gRuG3YsIH9++tv+qSSkhJycnLIycmpNF9MTIwveJGamkpqamqZ/dLn6nkhIo3BDZMnc8wJJ/D6s89yx2WXUbh3L526duWk4cO50Bt0eHjmTB677TYeufVW9hUU0PP445k+e3aZ5UABTh09moTERO4fP578vDwGDRvGxEcfLXfNO554glemT2fmU0+RkprKzVOncvLw4b7jx/bvz7Pvv88zkydz9xVXUFxcTMcDDqD/0KGkaw4Jadpm8OuCFv6/gPinOTObA/yhtDNDMOZc5Be+6N27t1u8eHGkqyEiIlIjJSUlbNu2zRekWLdunW8J0tJt8+bNlJSURLqqQZkZycnJ5QIWrVu3LrPfunVrEhIS1PtCmpRNmzZx6aWXEhsbG5Hrm9kS51z5blwhqupz9PLlyzniiCMAWLx7d00v06SM7NWLIaNGcf0DD1SYZ8mnn3LVmWfyypdflllBo7HKyshg2M6dka5Gk+QCVhOrTG3/vzckZnYs8Brwd+AdYDvQFhgFXAZcBXQCpgGvOOfGVVRWOHtKiIiINEtRUVG+iTj79u0bNE9RURGZmZnlghX+26ZNm+q1x0Up5xw7d+5k586drFmzptK8MTExZQIWpcGKYGmtWrVSAENERKRpmgpMd8497peWAzxkZvuAu51zJ5tZe+BGQEEJERGRSIqJiaFz58507tyZE044IWiekpIStm/fzoYNG9i8eTObN29m06ZN5fazs7Prufa/KioqYvv27WwPMsN9oNjYWFJSUnxBimD7pY/JyckaQiIiItJ49AcerODYcmCyd38JnukdKqSghIiISAMRFRVF+/btaR+wJF2ggoICtmzZUmngYsuWLeyOcFft/fv3k5WVRVbA0nvBmBlJSUm+QEVKSgrJycm+/cAtMTGRqKioergLEWlq3vnxxyrzHH/SSSzataseaiPSaG0ELgH+E+TYWO9xgFSg0l9TFJQQERFpZFq2bMlBBx3EQQdVvkR4Xl4eW7ZsqXKLZM+LUs45du3axa4QvwRERUWRlJRUJlDhH8RITk72bSkpKSQlJRETo489IiIiYXI78IqZ9QJm8+ucEiOAnsDvvflOBT6trCD9dRYREWmiEhISOPTQQzn00EMrzbdv3z62bt3qC1Js3bqVzMxMtm7dWm5/79699VT7ypWUlPjmwQhVQkKCL0jhH7TwT0tKSvKlxcfHqzeGiIhIEM65N8xsNTARuADoAGwFFgFjnXNLvPmuqaosBSVERESaubi4OLp27UrXrl0rzeecY8+ePWUCFcGCF5mZmWRmZlJYWFhPdxCavLw8X++RUJT2xvAPVlT1mJCQoLkxRESkSTOzFsB5wNfOud/VtjwFJURERCQkpfM+JCUl0b1790rzOufYvXs327ZtIzMzs8xjsLTc3Nx6uovQ1aQ3hpmRkJDge52CbYmJib5ARum+VioREZHGwjlXaGZ/B84Afq5teQpKiIiISNiZmW8YRFXDR8AzhGT79u1kZmb6VvfYtm1bhfuRnsSzIqW9Sfbs2RNyjwyA6OhoEhMTfYGKyh6TkpJ8gY/4+Hj1zBARkUj4ATgM+KS2BSkoISIiIhEXFxfnWzI1FAUFBWRlZZULVmRlZbF9+/Yyj1lZWeTk5OCcq+O7qLni4uJq98qAX3tmJCYm+rbSoIV/WrBNvTNERKQWbgBmmNkW4APnXFFNC1JQQkRERBqdli1b0qVLF7p06RJS/qKiInJzc4MGLPzTsrOzycrKIjs7mz179tTxXdSef8+M6oqKiioTpCgNZAQ+BktTDw0RkWbv30A88DbgzCwXKBP9d861C6UgBSVERESkyYuJiaFt27a0bds25HMKCwvLBClCeQx1SdOGoKSkpFrLsAaKj48vF7AIddPKJlJds19+mdefeYb1GRlEx8TQsWtXep90EjdMmQLA5nXrGHXUUTzy2mucNGwYACN79WLIqFFc/8ADkay6SFP1FAFBiJpSUEJEREQkiBYtWtCpUyc6deoU8jn79+8nNzeXnJwcsrOzyc7OrnI/JyeH/Pz8OryTupGfn1/jepsZ8fHxZYIUwfYrOxYXFxfmO2peEvr0jch18xZ9Xe1z/jl1Ks/cfz8XXX894ydNorCwkBXffsv7r73mC0q06dCB5z78kG6HHRbuKotIEM65SeEqS0EJERERkTCJjY2lXbt2tGsXUo9Vn7179/qCGaFspXkbU88Mf8453xKtNRUTE1MuWBEfH19u3/954KMCG43DG88+yzljxzLu7rt9aYOGDePyW2/1PY9r0YKj+kYm0OKcY19hIS1atozI9UUaOwUlRERERCKsVatWtGrVqlq9MsDTM2PHjh1kZ2eTm5vr20oDF5U9LygoqKO7qR9FRUU1mhzUX3R0NC1btuThhx9m1KhRPPLII2GsoYTL7p07SW/fvly6/0StwYZv+HvnpZf48/XXMzcjg6TWrX3pvyxfzu9POIGn3nmHvoMHA/DJu+/yj4ce4pdly0hMSeHMMWO45q67iImNBeDZyZN5/dln+csrr/DIxIlk/PQTdzzxBMPHjAnznYs0LGb2OnCrc+4X736lnHP/E0q5CkqIiIiINFKxsbHVniujVEFBQZkgxY4dO9ixYwe5ubm+R/99/8fG2kMjUHFxMXl5eaxevZqsrKxIV0cq0OOYY3j9mWfo0KULA884g9bp6dUuY8iIEfz5+uuZP2cOIy+80Jc+7803SWvbluNPOsnz/K23uOOPf+ScsWO55q672LhmDU/dcw8lJSVl5qYo2LuXSVdeycXXX0/XQw+lTceOtb9RkYavLRDr3W+H5pQQERERkZpq2bIlHTt2pGMNvkwVFRWxa9cuX+Bi586dvqBGKFtDXNkkJSUl0lWQCtw8dSo3XXAB91x9NWZGt8MPZ8jIkVx43XUkJieHVEZiSgr9hw5l3ptvlg1KvPUWvz37bKKjo3HOMe3OOxk+ZgwTH33UlyeuRQseuvFGLpkwwRcQKdy7lxumTOHkM88M782KNGynOef2AzjnBoerUAUlRERERKRaYmJiSEtLIy0trUbnlw69KO19UToMozQtcD9YWlFRUVjvSUGJhqt7r168sWgRX378MV9++CGLFy7kHw89xLw33+TFTz8lPjExpHJOPfdcJl11FTuys2mdns7K779nfUYGdzz5JADrMjLYumEDQ885p0z76j1oEIUFBfyyfDnHDxwIeIaODDj11PDfrEjDttXM3gReARY459RTQkREREQan5iYGNLT00mvQTd88EwsmJ+f7wtS7Nq1q0xgw3+r7Nj+/ft9ZSoo0bDFtWjBoGHDGOSdL+LtF17g/vHjefuFFxhzzTUhlTFo+HBiYmP5+J13OHfsWOa99RbtOnXi2P79AdiZnQ3A9eedF/T8zI0bffvJrVsTq4lSpfl5BRgNXApkmtlrwKvOua9qU6iCEiIiIiLSqJiZb+WN6k4OWso5R0FBgS9okZqaGuZaSl0adfHFPHHnnaz7+eeQz4lPTGTg6acz7623OHfsWD586y2GnnOOb8LMZG8buG3aNA4/+uhy53c68MBfn/hNsinSXDjnxpvZdcAQ4PfARcB1ZrYOmIknQPFjdctVUEJEREREmh0z86160j7Iyg7ScORs305awGSuuVlZ7Nm1q1x6VU4bPZrbLrmEhe+/z6a1azlt9GjfsQO7d6ddp05sWb+ecy65JBxVF2lynHMlwIfAh2Z2FXAGcD5wLXCrmS0HXgZec86tDqVMBSVERERERKTBGtOvH4POPJN+Q4aQ2rYtW9ev56UnnqBlfDxnXnBBtco68fTTaRkfz5Q//YlO3bpxZO/evmNRUVH86YEHuPuKK8jbtYsBp55KTFwcm9au5ZM5c3jwxRdpGR8f7tsTabScc0XAHGCOmbUAzgQuAO7zbiHFGxSUEBERERFpZvIWfR3pKoTs0ltuYeG77/KXm29mV24u6e3bc3TfvkyeMYPO3bpVq6wWLVty0rBhfPD66/zvhAnljp82ejQJSUnMmDqVd156iejoaDp168ZJp59OjOaQEKnMccAgYAAQBawN9UQL04SZtdK7d2+3ePHiSFdDRERERKRemdkS51zvqnMGV9Xn6OXLl3PEEUcAsHj37ppeRhq5rIwMhu3cGelqNElu8OCQ89b2/3tDY2bH4Zlb4n+ArsA24A3gFefcF6GWo54SIiIiIiIiIlIlMzsCTyDifKA7sBOYhWdljo+9c05Ui4ISIiIiIiIiIlIpM/seOBLYi2cuiVuA951z+2pTroISIiIiIiIiIlKVdcCfgbedc3nhKlRBCRERERERERGplHNuRF2UG1UXhYqIiIiISMPQECa2lwhyjmoP8hepRwpKiIiIiIg0UbGxsezduzfS1ZBI2rePbAWmpAFTUEJEREREpIlq164dmzZtIj8/H/TFtHlxDgoL2b5pE9Pzwjb8XyTsNKeEiIiIiEgTlZycDMDmzZvZtnu3fpFsRkqAbOeYnpfHl8XFka6OSIUUlBARERERacKSk5NJTk6m+4IFka6KiEg5CpaKiIiIiIiINABm1tPMPjKzfDPbbGb3mll0pOtVl9RTQkRERERERCTCzCwV+BBYBowCDgGm4ulMcEcEq1anFJQQERERERERibyrgFbAuc65XcA8M0sGJpnZQ960JkfDN0REREREREQibxgwNyD48CqeQMXJkalS3VNQQkRERERERCTyegAr/BOcc+uBfO+xJklBCREREREREZHISwV2BEnP9R5rkhrEnBJLlizJMrN1ka5HLbQBsiJdCWk21N6kvqnNSX1Tm5P6FOn2dmBtTm4Cn6NDEel/o0jQPTcSVr3sh5vZYr/nzzrnng3I4yq4TLD0JqFBBCWcc20jXYfaMLPFzrneka6HNA9qb1Lf1OakvqnNSX1q7O2tsX+ODkVj/zeqCd1zs5ULtA6SnkLwHhRNgoZviIiIiIiIiETeCgLmjjCzA4AEAuaaaEoUlBARERERERGJvPeB080syS/tfGAv8ElkqlT3FJQIj8BxQCJ1Se1N6pvanNQ3tTmpT2pvDV9z/DfSPTdPTwOFwFtmNtTMrgAmAY8ELBPapJhzTXa+DBEREREREZFGw8x6Ak8C/fHMI/F3YJJzrjiiFatDCkqIiIiIiIiISEQ06+EbZvY7M3vHzDaZ2R4zW2JmY4Lku9zMfjazAm+e3wYcb2tm08zsazPbZ2ZrK7lmpWVJ0xXG9jbUzF4zs3Vmlm9mP5rZeDOLDlLWKDP7wVvWMjM7vy7vURqWMLa5k81svpltM7NCM1ttZlPNLLm6ZUnTFq42F5A3wcw2mpkzs161KUualjC+xw32tq/A7c/VLUvCoyavs5n1NrMZZrbSzErMbEY9VLXazKynmX3k/Qy32czuDfYZLsh5KWb2TzPLNbOdZvaymaXXR51rqyb3bGZxZvawmX1qZnvNTL+kN2HNOigBTAD2ADcAI4H5wEwzu7Y0g5n9Hs/YnheAYcBPwJyAD0ad8UxAshVYWtHFQixLmq5wtbcr8MzAewcwHHgVmAo85H8xMxsIvOm9zjDgXeAVMzutLm5OGqRwtbk04FtgHHA6nvb2v8BM/4vpPU4IX5vzdzsVLGGuNtfshbu9/QFPd+nS7Sn/g2pv9aMWr/OJwEBgEZ7P5A2OmaUCHwIOGAXcC9wI3BPC6a8Bg4HLgEuAPsC/66Ke4VSLe47Hc6/5wOd1WUdpAJxzzXYD2gRJmwms8Xu+EnjO73kU8APwkn+a3/5fgLUVXK/KsrQ13S2M7S1YOZPxzMrbwi9tLvBxQL73gP9G+rXQVj9buNpcBWVfjucDRlpty9LWdLZwtzngUDxfOq/ytrdeAcfV5prxFsa/q4ODta8gZau91c+/a03/Lvl/Hl8MzIj0vQSp461ALpDsl3Yzni/eyZWc19/bRgf5pfX1pg2N9H3VxT1785VONTAecJG+F211tzXrnhLOuawgyd8C7QDM7GDgMOB1v3NKgDfwRG790yoValnSdIWxvVVUTksg2VtWC+AU/7K8XgX6m1lKjW9EGo1wtbkKZHsf48JQljQRddDmHsMzwVe5tdnV5qSO3+PKUHurH7V5nUP5PN4ADAPmurKrKLwKtAJOruK8TOfcwtIE59zXwBoafvur6T3jnNOQjWaiWQclKjAAWObd7+F9DPwwtBxIM7O21Sg3nGVJ0xGu9jYAyHLObfc+PwSIraCsKDx/8KV5qnGbM7NoM2thZsfiGT70lnOutIus3uOkIjVqc2Y2HOhHxV181eYkmNr8Xf3YzIrNbK2Z3REw5l3trX409de5BwH35pxbj6fXQI+gZ1RwntfyKs5rCGp6z9KMKCjhxzuJJwGq8wAACY5JREFUzih+HUOY6n3cEZA1N+B4KMJZljQB4Wpv5lk26CrKjn1Ve5NywtDmfgIK8PwSuQ24yO+Y2pyUU9M2Z2ZxwOPAXc65XIJTm5MyavEetxP4M55x+qfjGad/D/CI3zlqb/Wjqb/OqZS/N/DcX2X3VtPzGoLGXHepJ0EnjmqOzKwbnnGIbzvnZgQcDuw6ZBWkhyKcZUkjFa725p086E3gezzzSgRSexMgbG1uNJACHAXcBbxhZmcFdK9UmxOg1m1uAp4A2DMhXEptTmrV3pxz3+IJtpb60MwKgQlmdl/AMBG1t2ryDhntWFU+55z/r+lN+XUOdg9WQXo4zmsIGnPdpR4oKAGYWRrwPrAeuNDvUGlUtjWeKDp+zyF41K8i4SxLGrFwtTczawm8DbQARjrn9lVQlj+1t2YoXG3OOfeTd/dzM1sOfIJn7pKPq1uWNG21aXPe7tm34/nVOsnMABK9x5PMLME5lxdKWbW/E2kM6uhz3L/wTMZ3NHqPq63fAX8LIZ/R9F/nXMp/NgNPwL+ye8sFgg1daV3FeQ1BTe9ZmpFmP3zDzOKBOXgmazvT+0GnVGnENnC8Uw8gx2/8fijCWZY0UuFqb95xrjOBI4FhzrnMgHN+AfZXUFYJsKo29yGNRx2+x33jfTw4DGVJExKGNtcZTxDiX3g+zOYCs735PscTjA21LGni6uFzXOkvuWpvNeSc+7tzzqravNmb+uu8goB7M7MD8Cz1HmzOiArP86poromGpKb3LM1Isw5KmFkMntl8u+P5YrfN/7hzbjWeL2+/8zsnyvv8/epcK5xlSeMU5vY2HTgDGOGcWxl4LedcIZ712n8XcOh84Avn3M7Ac6TpqeP3uBO9j2vCUJY0EWFqcxl4euD4bzd4j/0Rz/r2anNS1+9xo4EiPMMj1d7qSTN4nd8HTjezJL+08/Es6/5JFed1MLOBpQlm1hvPDwMN/XWp6T1LM9Lch29MB4YDf8Izo28/v2Pfer/YTQJeMrO1wGfA/+L543eBf0Fmdp539zAg3u/5J35R3ZDKkiYrLO3NzG4DrgCmACUB5SzzW3LpPmCBmT2GZ9Ku4d7tjPDfmjRQ4WpzL+L5kLgUz2zZv8HTrfkLPMGvUlWWJU1erducc24PsMC/UO8QDoBFzrkf/Q5VWpY0eeF6j/srsB1YBOzzljkeeMw5l+1XZpVlSVhMoup/s5OBj4DfOuc+8aa15dclJlOBA0s/jzvn/lVfla/C08B1wFtm9iCeoMIk4BH/JTPNLAPPd4hLAZxzX5jZXOAFM7sJT6/XB4H/Ouc+rOd7qK4a3bM3bRieHhXHep+Xfr9a5JxbVz/Vl3rhnGu2G7AWT7e8YFs3v3yX4/nlphBPl+XfBimronIGB+SrsixtTXMLV3vD82E91PZ2NvCjt6wVwO8j/Tpoa5Rt7lpgCZ7xvXuAH4A7gcQg19R7XDPewvl3NaDcwd4yegU5pjbXTLcwvsddh6dHxG5vnp+A64GoINdUe6uff9uq/s1K3xMGB0krt0X6fgLq3hPPPCV7gS14fkSKDsizFpgRkNYa+CeeeRh24RnG2ybS91PH91zR//FLIn1P2sK7mfcfXERERERERESkXjXrOSVEREREREREJHIUlBARERERERGRiFBQQkREREREREQiQkEJEREREREREYkIBSVEREREREREJCIUlBARERERERGRiFBQQkREREREJICZXWJmS8xst5nlmtm3ZvZIpOtVE2Y2ycyyIl0PkWDMORfpOoiIiIiIiDQYZnYrcB/wEDAfaAkcD1zonDs0knWrCTPrArR3zi2JdF1EAikoISIiIiIi4sfMNgH/ds6NC0g314i+QJlZLFDinCuOdF1EKqLhGyIiIiIiImW1BrYGJgYGJMyslZk9ZGbrzKzQzNaY2ZSAPJeZ2U/e4+vM7OaA4zPMbLGZnWpm35tZnpn918yODMh3o5ktMrOdZpZpZrPN7NCAPAvM7F9mdoWZ/QIUAJ2CDd8ws4PM7N9mtss7RKVceSL1ISbSFRAREREREWlgvgGuNbP1wBznXHZgBjMz4G2gP56hHkuAzsBJfnn+D5iMZxjIAjxDQO4zs3zn3JN+xXUFHgYeAPYCfwFeN7NefoGQLsCTwDogGbgK+MzMDnPO7fQr60TgEOAWIB/wP1ZarxbAR8B+4HKgCLgH+MTMjnLO5YT4OonUmoISIiIiIiIiZY0D/g3MAJyZLQfeBP7inNvlzXMacCowyjn3jt+5LwCYWTJwN3C/c+4e77F5ZhYP3GFmf/UbVpEGnOic+9l7bhQwCzgcWAHgnLuh9AJmFg3MA7YBo0qv6dUaOM45t9Uvf+D9jcUTCDnMObfam+crYDVwJTAl8ASRuqLhGyIiIiIiIn6cc98DRwAjgemAAXcCi80s0ZttCJATEJDw1x9IAN4ws5jSDfgYaI+n50OptaUBCa9l3kdfHjPrZ2bzzCwbT8+GfCAROCzgukv8AxIV6At8UxqQ8N7zRuAzYGAV54qElYISIiIiIiIiAZxzhc652c658c65nsBlQHfgUm+WdGBLJUW08T7+hGeYROk235t+gF/eHQHn7vM+tgQws67Af/AER67EM0SjD56eEi0Dzs2s8uagYwX5MvH02hCpNxq+ISIiIiIiUgXn3D/M7CGghzcpG8+X+4qUzstwFsEDACurcfkzgHg8Q0XyALy9LoIFEEJZHWQLcGSQ9Pb8Wm+ReqGeEiIiIiIiIn7MrF2QtLZACr8GGD4C0szsrAqK+QLPpJWdnHOLg2y7q1GlVkAJnmEbpf6Hmv/I/BVwvJkdVJpgZp2BAcB/a1imSI2op4SIiIiIiEhZP5jZ23iGTGwDDgRuwjOPw/PePPOAucBMM7sXz4odHYFBzrkrnXM7zGwS8LiZHQgsxPOj8GHAKc65c6pRn4+BaOCfZvYPPL0cbqL8sI9QzcCzOsf7ZnYXUAxMArKAZ2pYpkiNKCghIiIiIiJS1r14VrWYhmeIxFbgc+B859waAOecM7Nz8CwHej3QFtgMzCwtxDn3kJltBm4AbgQKgFXAa9WpjHPuBzMbi2c1j3OA74DfVbccv/IKzWwo8AjwDzxzVSwAztVyoFLf7Ndlb0VERERERERE6o/mlBARERERERGRiFBQQkREREREREQiQkEJEREREREREYkIBSVEREREREREJCIUlBARERERERGRiFBQQkREREREREQiQkEJEREREREREYkIBSVEREREREREJCIUlBARERERERGRiPh/4vJ/I2SRnfkAAAAASUVORK5CYII=\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "keyw='VirginStock_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - "\n", - " \n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "\n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 1):\n", - " obj = SFscenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(1)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]')\n", - "a1.set_xlabel('Scenario')\n", - "a1.set_xticks(ind, ('S1'))\n", - "#plt.yticks(np.arange(0, 81, 10))\n", - "a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_WORLD Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:80: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "\n", - "keyw='Waste_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - "\n", - " \n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "\n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 1):\n", - " obj = SFscenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(1)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Cumulative Manufacturing Scrap and EoL Material \\n by 2050 [Million Tonnes]')\n", - "a1.set_xlabel('Scenario')\n", - "a1.set_xticks(ind, ('S1'))\n", - "#plt.yticks(np.arange(0, 81, 10))\n", - "a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly MFG and EOL Material by Scenario and Cumulatives_Nation.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:85: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "keyw='Waste_EOL_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - "\n", - " \n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "a0.legend()\n", - "a0.set_title('Yearly End of Life Material by Scenario')\n", - "a0.set_ylabel('Mass [Million Tonnes]')\n", - "\n", - "a0.set_xlabel('Years')\n", - "\n", - "\n", - "\n", - " \n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 1):\n", - " obj = SFscenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(1)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Cumulative End of Life Material by 2050 [Million Tonnes]')\n", - "a1.set_xlabel('Scenario')\n", - "a1.set_xticks(ind, ('S1', 'S2', 'S3'))\n", - "#plt.yticks(np.arange(0, 81, 10))\n", - "a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly EoL Waste by Scenario and Cumulatives_Nation.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:79: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (15, 8)\n", - "keyw='Waste_MFG_'\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]})\n", - "\n", - "######################## \n", - "# SUBPLOT 1\n", - "########################\n", - "#######################\n", - "\n", - " \n", - " \n", - "# Loop over Keywords\n", - "ii = 0 \n", - "# Loop over SF Scenarios\n", - "\n", - "# loop plotting over scenarios\n", - "\n", - "# SCENARIO 1 ***************\n", - "kk = 0\n", - "obj = SFscenarios[kk]\n", - "\n", - "modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+\n", - " USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+\n", - " USyearly[keyw+materials[4]+'_'+obj])\n", - "glassmat = (USyearly[keyw+materials[0]+'_'+obj])\n", - "modulemat = modulemat/1000000\n", - "glassmat = glassmat/1000000 \n", - "a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass')\n", - "a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only')\n", - "a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3,\n", - " interpolate=True)\n", - "\n", - "\n", - "\n", - "######################## \n", - "# SUBPLOT 2\n", - "########################\n", - "#######################\n", - "# Calculate \n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "cumulations2050 = {}\n", - "for ii in range(0, len(materials)):\n", - " matcum = []\n", - " for kk in range (0, 1):\n", - " obj = SFscenarios[kk]\n", - " matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050])\n", - " cumulations2050[materials[ii]] = matcum\n", - "\n", - "dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) \n", - "dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes\n", - "\n", - "dfcumulations2050['bottom1'] = dfcumulations2050['glass']\n", - "dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum']\n", - "dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon']\n", - "dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper']\n", - "\n", - "\n", - "## Plot BARS Stuff\n", - "ind=np.arange(1)\n", - "width=0.35 # width of the bars.\n", - "p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c')\n", - "p1 = a1.bar(ind, dfcumulations2050['aluminum'], width,\n", - " bottom=dfcumulations2050['bottom1'])\n", - "p2 = a1.bar(ind, dfcumulations2050['silicon'], width,\n", - " bottom=dfcumulations2050['bottom2'])\n", - "p3 = a1.bar(ind, dfcumulations2050['copper'], width,\n", - " bottom=dfcumulations2050['bottom3'])\n", - "p4 = a1.bar(ind, dfcumulations2050['silver'], width,\n", - " bottom=dfcumulations2050['bottom4'])\n", - "\n", - "a1.yaxis.set_label_position(\"right\")\n", - "a1.yaxis.tick_right()\n", - "a1.set_ylabel('Cumulative Manufacturing Scrap by 2050 [Million Tonnes]')\n", - "a1.set_xlabel('Scenario')\n", - "a1.set_xticks(ind, ('S1'))\n", - "#plt.yticks(np.arange(0, 81, 10))\n", - "a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver'))\n", - "\n", - "f.tight_layout()\n", - "\n", - "f.savefig(title_Method+' Fig_2x1_Yearly MFG Waste by Scenario and Cumulatives_Nation.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "fig, axs = plt.subplots(figsize=(8, 8))\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'k', label='Cumulative New Yearly Installs S3-S2')\n", - "\n", - "#axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'c', label='Cumulative New Yearly Installs')\n", - "\n", - "axs.legend()\n", - "axs.set_xlim([2020,2030])\n", - "axs.set_ylabel('Power [TW]')\n", - "fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# WASTE COMPARISON SIZE" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "fig, axs = plt.subplots(figsize=(8, 8))\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Cumulative New Yearly Installs')\n", - "axs.plot(USyearly['Capacity_'+SFscenarios[0]]/1e12, 'g', label='Active in Field Installs')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels')\n", - "axs.legend()\n", - "axs.set_xlim([2020,2050])\n", - "axs.set_ylabel('Power [TW]')\n", - "fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "198.97552440190063\n" - ] - } - ], - "source": [ - "foo0 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12).sum()\n", - "print(foo0)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cumulative Installs 903.0\n", - "Cumulative Waste 198.97552440190063\n", - "Fraction of Decomisioned to Installed Cumulative by 2050 0.22034941794230412\n" - ] - } - ], - "source": [ - "E = (UScum['new_Installed_Capacity_[MW]World']/1e6).sum()\n", - "F = (UScum['new_Installed_Capacity_[MW]World']/1e6-USyearly['Capacity_World']/1e12).sum()\n", - "print(\"Cumulative Installs\", E)\n", - "print(\"Cumulative Waste\", F)\n", - "print(\"Fraction of Decomisioned to Installed Cumulative by 2050\", F/E)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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    " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "plt.rcParams['figure.figsize'] = (8, 8)\n", - "\n", - "fig, axs = plt.subplots(figsize=(8, 8))\n", - "axs.plot(USyearly['new_Installed_Capacity_[MW]World']/1e6, 'b', label='Yearly New Yearly Installs')\n", - "axs.plot(UScum['new_Installed_Capacity_[MW]World']/1e6-USyearly['Capacity_World']/1e12, 'r', label='Decomissioned PV Panels')\n", - "axs.legend()\n", - "axs.set_xlim([2020,2050])\n", - "axs.set_ylabel('Power [TW]')\n", - "fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CUMULATIVE WASTE by 2050\n", - "*************************\n", - "\n", - "MFG Scrap + EoL Material Only\n", - "\t Reference Scenario: 957.5969166556922 Million Tonnes\n", - "EoL Material Only\n", - "\t Reference Scenario: 819.552589911594 Million Tonnes\n", - "MFG Scrap Only\n", - "\t Reference Scenario: 138.04432674409833 Million Tonnes\n" - ] - } - ], - "source": [ - "print(\"CUMULATIVE WASTE by 2050\")\n", - "print(\"*************************\")\n", - "print(\"\")\n", - "UScum.iloc[-1]\n", - "print(\"MFG Scrap + EoL Material Only\")\n", - "print(\"\\t Reference Scenario: \", UScum['Waste_Module_World'].iloc[-1]/1e6, ' Million Tonnes')\n", - "\n", - "print(\"EoL Material Only\")\n", - "print(\"\\t Reference Scenario: \", UScum['Waste_EOL_Module_World'].iloc[-1]/1e6, ' Million Tonnes')\n", - "\n", - "print(\"MFG Scrap Only\")\n", - "print(\"\\t Reference Scenario: \", UScum['Waste_MFG_Module_World'].iloc[-1]/1e6, ' Million Tonnes')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " VIRGIN STOCK Yearly Needs \n", - " **************************\n", - "World\n" - ] - }, - { - "data": { - "text/html": [ - "
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884000000 tons\n", - "aluminum : 144200000 tons\n", - "copper : 725000 tons\n", - "silicon : 39300000 tons\n", - "silver : 456000 tons\n", - "Capacity in Year 2030 [GW]: 19400\n", - "Capacity in Year 2050 [GW]: 26600\n", - "****************************\n", - "\n" - ] - } - ], - "source": [ - "materials = ['Module', 'glass', 'aluminum', 'copper', 'silicon', 'silver']\n", - "\n", - "print(\" Appendix Table I: Metric Tonnes Installed in field in 2030\")\n", - "print(\" ########################################################### \\n\")\n", - "#Loop over scenarios\n", - "for kk in range (0, 1):\n", - " obj = SFscenarios[kk]\n", - " print(\"SCENARIO :\", obj)\n", - "\n", - " print(\"********************************\")\n", - " print(\"********************************\")\n", - "\n", - " modulemat = 0\n", - " for ii in range(0, len(materials)):\n", - " installedmat = (UScum3sig['VirginStock_'+materials[ii]+'_'+obj].loc[2030]-\n", - " UScum3sig['Waste_'+materials[ii]+'_'+obj].loc[2030])\n", - " print(materials[ii], ':', round(installedmat/1000)*1000, 'tons')\n", - "\n", - " print(\"Capacity in Year 2030 [GW]:\", round(USyearly3sig['Capacity_'+obj].loc[2030]/1e9))\n", - " print(\"Capacity in Year 2050 [GW]:\", round(USyearly3sig['Capacity_'+obj].loc[2050]/1e9))\n", - " print(\"****************************\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 10})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "keywords=['VirginStock_']\n", - "materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum']\n", - "\n", - "fig, axs = plt.subplots(1,3, figsize=(15, 5), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .3, wspace=.5)\n", - "axs = axs.ravel()\n", - "i = 0\n", - "\n", - "# Loop over Keywords\n", - "ii = 0 \n", - "keyw = keywords[ii]\n", - "# Loop over SF Scenarios\n", - "\n", - "\n", - "\n", - "for kk in range(0, 1):\n", - "\n", - " obj = SFscenarios[kk]\n", - " axs[i].yaxis.grid()\n", - " axs[i].axvspan(2000, 2018, facecolor='c', alpha=0.5, label='Glass')\n", - "# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1)\n", - " # axs[i].plot([],[],color='c', label='glass', linewidth=5)\n", - " # axs[i].plot([],[],color='k', label='silicon', linewidth=5)\n", - " # axs[i].plot([],[],color='m', label='silver', linewidth=5)\n", - " # axs[i].plot([],[],color='r', label='copper', linewidth=5)\n", - " # axs[i].plot([],[],color='g', label='aluminum', linewidth=5)\n", - "\n", - " axs[i].stackplot(rr.scenario[obj].data['year'], USyearly[keyw+materials[0]+'_'+obj]/1e6, \n", - " USyearly[keyw+materials[1]+'_'+obj]/1e6, \n", - " USyearly[keyw+materials[2]+'_'+obj]/1e6, \n", - " USyearly[keyw+materials[3]+'_'+obj]/1e6, \n", - " USyearly[keyw+materials[4]+'_'+obj]/1e6, \n", - " colors=['c','k','gray','orange', 'g'])\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " axs[i].set_title('WORLD')\n", - " axs[i].legend(loc='lower right')\n", - "\n", - " #axs[i].legend(materials)\n", - "\n", - "\n", - "# 2nd axis plot\n", - "\n", - "for kk in range(0, 1):\n", - "\n", - " obj = SFscenarios[kk]\n", - " ax2=axs[i].twinx()\n", - " ax2.plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[0]+'_'+obj]/1e6, \n", - " color = 'r', linewidth=4.0, label='cumulative')\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " # axs[i].set_xlim([2010, 2050])\n", - " # axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " ax2.set_yscale('log')\n", - " ax2.set_ylim([1e3/1e6, 1e8/1e6])\n", - " \n", - "\n", - " ax2.legend()\n", - "\n", - "\n", - "i = 1\n", - "# ROW 2, Aluminum and Silicon:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 1):\n", - "\n", - "\n", - " obj = SFscenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5)\n", - "\n", - " axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[4]+'_'+obj]/1e6, color='g', lw=3, label='Aluminum')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], \n", - " # color='g', lw=3, alpha=.6)\n", - " \n", - " axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[1]+'_'+obj]/1e6, color='k', lw=3, label='Silicon')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], \n", - " # color='k', lw=3)# alpha=.3)\n", - "\n", - "\n", - " # silicon aluminum 'k ''g'\n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " #axs[i].legend(materials)\n", - " axs[i].legend()\n", - "\n", - "\n", - "\n", - "i=2\n", - "\n", - "# ROW 3:\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 1):\n", - "\n", - " obj = SFscenarios[kk]\n", - " axs[i].yaxis.grid()\n", - "\n", - " axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[3]+'_'+obj], color='orange', lw=3, label='Copper')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], \n", - " # color='orange', lw=3)# alpha=.3)\n", - "\n", - " axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[2]+'_'+obj], color='gray', lw=3, label='Silver')\n", - " # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], \n", - " # color='gray', lw=3)# , alpha=.6)\n", - " \n", - " \n", - " #axs[i].ylabel('Mass [Tons]')\n", - " axs[i].set_xlim([2020, 2050])\n", - " #axs[i].set_title(keyw+ ' Yearly ' + obj.name)\n", - " axs[i].legend()\n", - " axs[i].yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n", - "\n", - "\n", - " \n", - "#axs[i].set_ylim([0, 5e7/1e6])\n", - "#axs[i+3].set_ylim([0, 3e6/1e6])\n", - "#axs[i+6].set_ylim([0, 2.5e4])\n", - "\n", - "# axs[i+3].set_yscale('log')\n", - "# axs[i+6].set_yscale('log')\n", - "\n", - "axs[0].set_ylabel('Yearly Mass [Million Tonnes]')\n", - "axs[1].set_ylabel('Yearly Mass [Million Tonnes]')\n", - "axs[2].set_ylabel('Yearly Mass [Tonnes]')\n", - "\n", - "#axs[8].legend(materials)\n", - "\n", - "fig.savefig(title_Method+' Fig_3x3_WORLD_MaterialNeeds_Nation.png', dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "keyword='Cumulative_Area_disposed'\n", - "\n", - "USyearly_Areadisp=pd.DataFrame()\n", - "\n", - "# Loop over SF Scenarios\n", - "for kk in range(0, 1):\n", - " obj = SFscenarios[kk]\n", - " # Loop over Materials\n", - " foo = rr.scenario[obj].data[keyword].copy()\n", - " USyearly_Areadisp[\"Areadisp_\"+obj] = foo\n", - "\n", - " # Loop over STATEs\n", - " #for jj in range (1, len(STATEs)): \n", - " # USyearly_Areadisp[\"Areadisp_\"+obj] += rr.scenario[obj].data[keyword]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "UScum_Areadisp = USyearly_Areadisp.copy()\n", - "UScum_Areadisp = UScum_Areadisp.cumsum()" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "95226.5183891086\n" - ] - } - ], - "source": [ - "A = UScum['Waste_Module_World'].iloc[-1]\n", - "#47700000 # tonnes cumulative by 2050\n", - "A = A*1000 # convert to kg\n", - "A = A/10.05599 # convert to m2 if each m2 is ~avg 10 kg\n", - "#A = A*2 # convert to area if each module is ~2 m2\n", - "A = A/1e6 # Convert to km 2\n", - "print(A)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "C = UScum_Areadisp['Areadisp_World'].iloc[-1]/1e6\n" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "# MANHATTAN SIZE:\n", - "manhattans = 59.103529" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reference Cumulative Area by 2050 of Waste PV Modules EoL 79360 km^2\n", - "\n", - "Reference Waste equals 1343 Manhattans \n", - "\n", - "MFG SCrap + Eol Waste\n", - "Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL 95227 km^2\n" - ] - } - ], - "source": [ - "print(\"Reference Cumulative Area by 2050 of Waste PV Modules EoL\", round(C), \" km^2\")\n", - "\n", - "print(\"\")\n", - "print(\"Reference Waste equals \", round(C/manhattans), \" Manhattans \")\n", - "\n", - "print(\"\")\n", - "print (\"MFG SCrap + Eol Waste\")\n", - "print(\"Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL\", round(A), \" km^2\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### New Section" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "VirginStock_aluminum_Reference.Mod\n", - "VirginStock_aluminum_95-by-35.Adv \n", - "VirginStock_aluminum_95-by-35_Elec.Adv_DR \n", - "Waste_EOL_aluminum_Reference.Mod \n", - "Waste_EOL_aluminum_95-by-35.Adv \n", - "Waste_EOL_aluminum_95-by-35_Elec.Adv_DR \n", - "\n", - "VirginStock_silver_Reference.Mod\n", - "VirginStock_silver_95-by-35.Adv \n", - "VirginStock_silver_95-by-35_Elec.Adv_DR \n", - "Waste_EOL_silver_Reference.Mod \n", - "Waste_EOL_silver_95-by-35.Adv \n", - "Waste_EOL_silver_95-by-35_Elec.Adv_DR \n" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 10})\n", - "plt.rcParams['figure.figsize'] = (12, 8)\n", - " \n", - "fig, axs = plt.subplots(1,2, figsize=(15, 6), facecolor='w', edgecolor='k')\n", - "fig.subplots_adjust(hspace = .1, wspace=.4)\n", - "axs = axs.ravel()\n", - "\n", - "# PLOT 1\n", - "i = 0\n", - "axs[i].yaxis.grid()\n", - "lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silver_World']/1e6, color='gray', linewidth=4.0, label='Virgin Material Demands')\n", - "lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silver_World']/1e6, color='k', linewidth=4.0, label='EoL Material')\n", - "axs[i].set_ylabel('Mass [Tons]')\n", - "axs[i].set_xlim([2020, 2050])\n", - "axs[i].set_title('Silver')\n", - "\n", - "# 2nd axis plot\n", - "ax2=axs[i].twinx()\n", - "lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silver_World']/USyearly['VirginStock_silver_World'], \n", - " color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')\n", - "ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')\n", - "ax2.tick_params(axis='y', labelcolor='r')\n", - "\n", - "# LEGENDS\n", - "# added these three lines\n", - "lns = lns1+lns2+lns3\n", - "labs = [l.get_label() for l in lns]\n", - "axs[0].legend(lns, labs, loc=0)\n", - "\n", - "# PLOT 2\n", - "i = 1\n", - "axs[i].yaxis.grid()\n", - "lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_aluminum_World']/1e6, color='g', linewidth=4.0, label='Virgin Material Demands')\n", - "lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_aluminum_World']/1e6, color='k', linewidth=4.0, label='EoL Material')\n", - "axs[i].set_ylabel('Mass [Tons]')\n", - "axs[i].set_xlim([2020, 2050])\n", - "axs[i].set_title('Aluminum')\n", - "\n", - "# 2nd axis plot\n", - "ax2=axs[i].twinx()\n", - "lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_aluminum_World']/USyearly['VirginStock_aluminum_World'], \n", - " color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand')\n", - "\n", - "ax2.set_ylabel('EoL Material as Fraction of Demand', color='r')\n", - "ax2.tick_params(axis='y', labelcolor='r')\n", - "\n", - "# LEGENDS\n", - "# added these three lines\n", - "lns = lns1+lns2+lns3\n", - "labs = [l.get_label() for l in lns]\n", - "axs[1].legend(lns, labs, loc=0)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.py b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.py deleted file mode 100644 index 708f4e47..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios - WORLD.py +++ /dev/null @@ -1,1111 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # ReEDS Scenarios on PV ICE Tool WORLD -# - -# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. -# -# Current sections include: -# -#
      -#
    1. ### Reading a standard ReEDS output file and saving it in a PV ICE input format
      2. -#
    2. ### Reading scenarios of interest and running PV ICE tool
      3. -#
    3. ###Plotting
      4. -#
    4. ### GeoPlotting.
      5. -#
    -# Notes: -# -# Scenarios of Interest: -# the Ref.Mod, -# o 95-by-35.Adv, and -# o 95-by-35+Elec.Adv+DR ones -# - -# In[1]: - - -import PV_ICE -import numpy as np -import pandas as pd -import os,sys -import matplotlib.pyplot as plt -from IPython.display import display -plt.rcParams.update({'font.size': 22}) -plt.rcParams['figure.figsize'] = (12, 8) - - -# In[2]: - - -import os -from pathlib import Path - -testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP') - -print ("Your simulation will be stored in %s" % testfolder) - - -# In[3]: - - -SFscenarios = ['World'] - - -# #### Create the 3 Scenarios and assign Baselines -# -# Keeping track of each scenario as its own PV ICE Object. - -# In[4]: - - -filetitle = r'..\baselines\ReedsSubset\baseline_modules_World_Bogdanov.csv' - - -# In[5]: - - -#for ii in range (0, 1): #len(scenarios): -i = 0 -rr = PV_ICE.Simulation(name='World', path=testfolder) -rr.createScenario(name=SFscenarios[i], file=filetitle) -rr.scenario[SFscenarios[i]].addMaterial('glass', file=r'..\baselines\ReedsSubset\baseline_material_glass_Reeds.csv') -rr.scenario[SFscenarios[i]].addMaterial('silicon', file=r'..\baselines\ReedsSubset\baseline_material_silicon_Reeds.csv') -rr.scenario[SFscenarios[i]].addMaterial('silver', file=r'..\baselines\ReedsSubset\baseline_material_silver_Reeds.csv') -rr.scenario[SFscenarios[i]].addMaterial('copper', file=r'..\baselines\ReedsSubset\baseline_material_copper_Reeds.csv') -rr.scenario[SFscenarios[i]].addMaterial('aluminum', file=r'..\baselines\ReedsSubset\baseline_material_aluminium_Reeds.csv') - - -# # 2 FINISH: Set characteristics of Recycling to SF values. - -# #### Calculate Mass Flow - -# In[6]: - - -IRENA= False -PERFECTMFG = True - -mats = ['glass', 'silicon','silver','copper','aluminum'] - -ELorRL = 'RL' -if IRENA: - if ELorRL == 'RL': - weibullInputParams = {'alpha': 5.3759, 'beta':30} # Regular-loss scenario IRENA - if ELorRL == 'EL': - weibullInputParams = {'alpha': 2.49, 'beta':30} # Regular-loss scenario IRENA - - if PERFECTMFG: - for jj in range (0, len(rr.scenario.keys())): - rr.scenario[list(rr.scenario.keys())[jj]].data['mod_lifetime'] = 40 - rr.scenario[list(rr.scenario.keys())[jj]].data['mod_MFG_eff'] = 100.0 - - for kk in range(0, len(mats)): - mat = mats[kk] - rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_eff'] = 100.0 - rr.scenario[list(rr.scenario.keys())[jj]].material[mat].materialdata['mat_MFG_scrap_Recycled'] = 0.0 - - - rr.calculateMassFlow(weibullInputParams=weibullInputParams) - title_Method = 'Irena_'+ELorRL -else: - rr.calculateMassFlow() - title_Method = 'PVICE' - - -# ## Aggregating PCAs Material Landfilled to obtain US totals by Year - -# In[7]: - - -USyearly=pd.DataFrame() - - -# In[8]: - - -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - - -# In[9]: - - -keywd = 'mat_Virgin_Stock' - -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['VirginStock_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd] - - filter_col = [col for col in USyearly if (col.startswith('VirginStock_') and col.endswith(obj))] - USyearly['VirginStock_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# In[10]: - - -keywd = 'mat_Total_Landfilled' - -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['Waste_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd] - - filter_col = [col for col in USyearly if (col.startswith('Waste_') and col.endswith(obj)) ] - USyearly['Waste_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# In[11]: - - -keywd = 'mat_Total_EOL_Landfilled' - -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['Waste_EOL_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd] - - filter_col = [col for col in USyearly if (col.startswith('Waste_EOL_') and col.endswith(obj)) ] - USyearly['Waste_EOL_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# In[12]: - - -keywd = 'mat_Total_MFG_Landfilled' - -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - for ii in range(len(materials)): - USyearly['Waste_MFG_'+materials[ii]+'_'+obj] = rr.scenario[obj].material[materials[ii]].materialdata[keywd] - - filter_col = [col for col in USyearly if (col.startswith('Waste_MFG_') and col.endswith(obj)) ] - USyearly['Waste_MFG_Module_'+obj] = USyearly[filter_col].sum(axis=1) - - -# ### Converting to grams to METRIC Tons. -# - -# In[13]: - - -USyearly = USyearly/1000000 # This is the ratio for Metric tonnes -#907185 -- this is for US tons - - -# ### Adding NEW Installed Capacity to US - -# In[14]: - - -keyword='new_Installed_Capacity_[MW]' -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - USyearly[keyword+obj] = rr.scenario[obj].data[keyword] - - - -# #### Reindexing and creating c umulative results - -# In[15]: - - -UScum = USyearly.copy() -UScum = UScum.cumsum() - - -# ### Adding Installed Capacity to US (This is already 'Cumulative') so not including it in UScum - -# In[16]: - - -keyword='Installed_Capacity_[W]' -for jj in range(len(SFscenarios)): - obj = SFscenarios[jj] - USyearly["Capacity_"+obj] = rr.scenario[obj].data[keyword] - - - -# #### Set YEAR Index - -# In[17]: - - -USyearly.index = rr.scenario[obj].data['year'] -UScum.index = rr.scenario[obj].data['year'] - - -# In[18]: - - -USyearly.head().iloc[1] - - -# In[19]: - - -USyearly.head() - - -# In[20]: - - -plt.plot(USyearly['new_Installed_Capacity_[MW]World']) - - -# In[21]: - - -UScum.head() - - -# ### 3 sig figures save Yearly and cumulative overview Nation - -# In[22]: - - -USyearly3sig = USyearly.copy() -UScum3sig = UScum.copy() -N = 2 - -UScum3sig = UScum3sig.drop(UScum3sig.index[0]) -USyearly3sig = USyearly3sig.drop(USyearly3sig.index[0]) - -if IRENA: - UScum3sig = UScum3sig.loc[:, ~UScum3sig.columns.str.startswith('Waste_MFG_')] - USyearly3sig = USyearly3sig.loc[:, ~USyearly3sig.columns.str.startswith('Waste_MFG_')] - -USyearly3sig = USyearly3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -USyearly3sig = USyearly3sig.applymap(lambda x: int(x)) - -UScum3sig = UScum3sig.applymap(lambda x: round(x, N - int(np.floor(np.log10(abs(x)))))) -UScum3sig = UScum3sig.applymap(lambda x: int(x)) - -USyearly3sig.to_csv(title_Method+' US_Yearly NATION.csv') -UScum3sig.to_csv(title_Method+' US_Cumulative NATION.csv') - - -# In[23]: - - -print("Sanity check: mat_Total_Landfilled = mat_Total_EOL_Landfilled + mat_Total_MFG_Landfilled") -A = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_Landfilled'].iloc[5] -B = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_EOL_Landfilled'].iloc[5] -C = rr.scenario[obj].material[materials[ii]].materialdata['mat_Total_MFG_Landfilled'].iloc[5] -A - B - C - - -# # PLOT - -# ## Yearly Virgin Material Needs by Scenario - -# In[24]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) -keyw='VirginStock_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - - - -# Loop over Keywords -ii = 0 -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 1): - obj = SFscenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(1) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]') -a1.set_xlabel('Scenario') -a1.set_xticks(ind, ('S1')) -#plt.yticks(np.arange(0, 81, 10)) -a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver')) - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_WORLD Yearly Virgin Material Needs by Scenario and Cumulatives.png', dpi=600) - - -# In[27]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) - -keyw='Waste_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - - - -# Loop over Keywords -ii = 0 -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 1): - obj = SFscenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(1) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Cumulative Manufacturing Scrap and EoL Material \n by 2050 [Million Tonnes]') -a1.set_xlabel('Scenario') -a1.set_xticks(ind, ('S1')) -#plt.yticks(np.arange(0, 81, 10)) -a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver')) - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly MFG and EOL Material by Scenario and Cumulatives_Nation.png', dpi=600) - - -# In[28]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) -keyw='Waste_EOL_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - - - -# Loop over Keywords -ii = 0 -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) -a0.legend() -a0.set_title('Yearly End of Life Material by Scenario') -a0.set_ylabel('Mass [Million Tonnes]') - -a0.set_xlabel('Years') - - - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 1): - obj = SFscenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(1) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Cumulative End of Life Material by 2050 [Million Tonnes]') -a1.set_xlabel('Scenario') -a1.set_xticks(ind, ('S1', 'S2', 'S3')) -#plt.yticks(np.arange(0, 81, 10)) -a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver')) - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly EoL Waste by Scenario and Cumulatives_Nation.png', dpi=600) - - -# In[31]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (15, 8) -keyw='Waste_MFG_' -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) - -######################## -# SUBPLOT 1 -######################## -####################### - - - -# Loop over Keywords -ii = 0 -# Loop over SF Scenarios - -# loop plotting over scenarios - -# SCENARIO 1 *************** -kk = 0 -obj = SFscenarios[kk] - -modulemat = (USyearly[keyw+materials[0]+'_'+obj]+USyearly[keyw+materials[1]+'_'+obj]+ - USyearly[keyw+materials[2]+'_'+obj]+USyearly[keyw+materials[3]+'_'+obj]+ - USyearly[keyw+materials[4]+'_'+obj]) -glassmat = (USyearly[keyw+materials[0]+'_'+obj]) -modulemat = modulemat/1000000 -glassmat = glassmat/1000000 -a0.plot(rr.scenario[obj].data['year'], modulemat, 'k.', linewidth=5, label='S1 Reference Scenario: module mass') -a0.plot(rr.scenario[obj].data['year'], glassmat, 'k', linewidth=5, label='S1 Reference Scenario: glass mass only') -a0.fill_between(rr.scenario[obj].data['year'], glassmat, modulemat, color='k', alpha=0.3, - interpolate=True) - - - -######################## -# SUBPLOT 2 -######################## -####################### -# Calculate -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -cumulations2050 = {} -for ii in range(0, len(materials)): - matcum = [] - for kk in range (0, 1): - obj = SFscenarios[kk] - matcum.append(UScum[keyw+materials[ii]+'_'+obj].loc[2050]) - cumulations2050[materials[ii]] = matcum - -dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) -dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes - -dfcumulations2050['bottom1'] = dfcumulations2050['glass'] -dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminum'] -dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] -dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] - - -## Plot BARS Stuff -ind=np.arange(1) -width=0.35 # width of the bars. -p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') -p1 = a1.bar(ind, dfcumulations2050['aluminum'], width, - bottom=dfcumulations2050['bottom1']) -p2 = a1.bar(ind, dfcumulations2050['silicon'], width, - bottom=dfcumulations2050['bottom2']) -p3 = a1.bar(ind, dfcumulations2050['copper'], width, - bottom=dfcumulations2050['bottom3']) -p4 = a1.bar(ind, dfcumulations2050['silver'], width, - bottom=dfcumulations2050['bottom4']) - -a1.yaxis.set_label_position("right") -a1.yaxis.tick_right() -a1.set_ylabel('Cumulative Manufacturing Scrap by 2050 [Million Tonnes]') -a1.set_xlabel('Scenario') -a1.set_xticks(ind, ('S1')) -#plt.yticks(np.arange(0, 81, 10)) -a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'Aluminum', 'Silicon','Copper','Silver')) - -f.tight_layout() - -f.savefig(title_Method+' Fig_2x1_Yearly MFG Waste by Scenario and Cumulatives_Nation.png', dpi=600) - - -# In[33]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'k', label='Cumulative New Yearly Installs S3-S2') - -#axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[2]]/1e6, 'c', label='Cumulative New Yearly Installs') - -axs.legend() -axs.set_xlim([2020,2030]) -axs.set_ylabel('Power [TW]') -fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600) - - -# # WASTE COMPARISON SIZE - -# In[34]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6, 'b', label='Cumulative New Yearly Installs') -axs.plot(USyearly['Capacity_'+SFscenarios[0]]/1e12, 'g', label='Active in Field Installs') -axs.plot(UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12, 'r', label='Decomissioned PV Panels') -axs.legend() -axs.set_xlim([2020,2050]) -axs.set_ylabel('Power [TW]') -fig.savefig(title_Method+' Fig_New_Installs_vs_InstalledCapacity_vs_Waste', dpi=600) - - -# In[ ]: - - - - - -# In[37]: - - -foo0 = (UScum['new_Installed_Capacity_[MW]'+SFscenarios[0]]/1e6-USyearly['Capacity_'+SFscenarios[0]]/1e12).sum() -print(foo0) - - -# In[38]: - - -E = (UScum['new_Installed_Capacity_[MW]World']/1e6).sum() -F = (UScum['new_Installed_Capacity_[MW]World']/1e6-USyearly['Capacity_World']/1e12).sum() -print("Cumulative Installs", E) -print("Cumulative Waste", F) -print("Fraction of Decomisioned to Installed Cumulative by 2050", F/E) - - -# In[39]: - - -plt.rcParams.update({'font.size': 15}) -plt.rcParams['figure.figsize'] = (8, 8) - -fig, axs = plt.subplots(figsize=(8, 8)) -axs.plot(USyearly['new_Installed_Capacity_[MW]World']/1e6, 'b', label='Yearly New Yearly Installs') -axs.plot(UScum['new_Installed_Capacity_[MW]World']/1e6-USyearly['Capacity_World']/1e12, 'r', label='Decomissioned PV Panels') -axs.legend() -axs.set_xlim([2020,2050]) -axs.set_ylabel('Power [TW]') -fig.savefig(title_Method+' Fig_New_Installs_vs_Decomisions', dpi=600) - - -# In[40]: - - -print("CUMULATIVE WASTE by 2050") -print("*************************") -print("") -UScum.iloc[-1] -print("MFG Scrap + EoL Material Only") -print("\t Reference Scenario: ", UScum['Waste_Module_World'].iloc[-1]/1e6, ' Million Tonnes') - -print("EoL Material Only") -print("\t Reference Scenario: ", UScum['Waste_EOL_Module_World'].iloc[-1]/1e6, ' Million Tonnes') - -print("MFG Scrap Only") -print("\t Reference Scenario: ", UScum['Waste_MFG_Module_World'].iloc[-1]/1e6, ' Million Tonnes') - - -# In[41]: - - -print(" VIRGIN STOCK Yearly Needs ") -print(" **************************") -for kk in range(0, 1): - obj = SFscenarios[kk] - print(obj) - filter_col = [col for col in USyearly3sig if (col.startswith('VirginStock_') and col.endswith(obj)) ] - display(USyearly3sig[filter_col].loc[[2030, 2040, 2050]]) - print("\n\n") - -print(" VIRGIN STOCK Cumulative Needs ") -print(" ***************************** ") -for kk in range(0, 1): - obj = SFscenarios[kk] - print(obj) - filter_col = [col for col in UScum3sig if (col.startswith('VirginStock_') and col.endswith(obj)) ] - display(UScum3sig[filter_col].loc[[2030, 2040, 2050]]) - print("\n\n") - - -# In[ ]: - - - - - -# In[42]: - - -print(" WASTE EoL CUMULATIVE RESULTS [Tonnes] ") -print(" ******************************************") -filter_col = [col for col in UScum3sig if (col.startswith('Waste_EOL_Module')) ] -display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]]) - - -# In[43]: - - -print(" WASTE EoL + MfgScrap CUMULATIVE RESULTS [Tonnes] ") -print(" ******************************************") -filter_col = [col for col in UScum3sig if (col.startswith('Waste_Module')) ] -display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]]) - - -# In[44]: - - -print(" WASTE MfgScrap CUMULATIVE RESULTS [Tonnes] ") -print(" ******************************************") -filter_col = [col for col in UScum3sig if (col.startswith('Waste_MFG_Module')) ] -display(UScum3sig[filter_col].loc[[2016,2020,2030, 2040, 2050]]) - - -# In[50]: - - -materials = ['Module', 'glass', 'aluminum', 'copper', 'silicon', 'silver'] - -print(" Appendix Table I: Metric Tonnes Installed in field in 2030") -print(" ########################################################### \n") -#Loop over scenarios -for kk in range (0, 1): - obj = SFscenarios[kk] - print("SCENARIO :", obj) - - print("********************************") - print("********************************") - - modulemat = 0 - for ii in range(0, len(materials)): - installedmat = (UScum3sig['VirginStock_'+materials[ii]+'_'+obj].loc[2030]- - UScum3sig['Waste_'+materials[ii]+'_'+obj].loc[2030]) - print(materials[ii], ':', round(installedmat/1000)*1000, 'tons') - - print("Capacity in Year 2030 [GW]:", round(USyearly3sig['Capacity_'+obj].loc[2030]/1e9)) - print("Capacity in Year 2050 [GW]:", round(USyearly3sig['Capacity_'+obj].loc[2050]/1e9)) - print("****************************\n") - - -# In[73]: - - -plt.rcParams.update({'font.size': 10}) -plt.rcParams['figure.figsize'] = (12, 8) - -keywords=['VirginStock_'] -materials = ['glass', 'silicon', 'silver', 'copper', 'aluminum'] - -fig, axs = plt.subplots(1,3, figsize=(15, 5), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .3, wspace=.5) -axs = axs.ravel() -i = 0 - -# Loop over Keywords -ii = 0 -keyw = keywords[ii] -# Loop over SF Scenarios - - - -for kk in range(0, 1): - - obj = SFscenarios[kk] - axs[i].yaxis.grid() - axs[i].axvspan(2000, 2018, facecolor='c', alpha=0.5, label='Glass') -# axs[i].axvspan(2018, 2050.5, facecolor='yellow', alpha=0.1) - # axs[i].plot([],[],color='c', label='glass', linewidth=5) - # axs[i].plot([],[],color='k', label='silicon', linewidth=5) - # axs[i].plot([],[],color='m', label='silver', linewidth=5) - # axs[i].plot([],[],color='r', label='copper', linewidth=5) - # axs[i].plot([],[],color='g', label='aluminum', linewidth=5) - - axs[i].stackplot(rr.scenario[obj].data['year'], USyearly[keyw+materials[0]+'_'+obj]/1e6, - USyearly[keyw+materials[1]+'_'+obj]/1e6, - USyearly[keyw+materials[2]+'_'+obj]/1e6, - USyearly[keyw+materials[3]+'_'+obj]/1e6, - USyearly[keyw+materials[4]+'_'+obj]/1e6, - colors=['c','k','gray','orange', 'g']) - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - axs[i].set_title('WORLD') - axs[i].legend(loc='lower right') - - #axs[i].legend(materials) - - -# 2nd axis plot - -for kk in range(0, 1): - - obj = SFscenarios[kk] - ax2=axs[i].twinx() - ax2.plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[0]+'_'+obj]/1e6, - color = 'r', linewidth=4.0, label='cumulative') - #axs[i].ylabel('Mass [Tons]') - # axs[i].set_xlim([2010, 2050]) - # axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - ax2.set_yscale('log') - ax2.set_ylim([1e3/1e6, 1e8/1e6]) - - - ax2.legend() - - -i = 1 -# ROW 2, Aluminum and Silicon: -# Loop over SF Scenarios -for kk in range(0, 1): - - - obj = SFscenarios[kk] - axs[i].yaxis.grid() -# axs[i].axvspan(2000, 2018, facecolor='0.9', alpha=0.5) - - axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[4]+'_'+obj]/1e6, color='g', lw=3, label='Aluminum') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[4]+'_'+obj.name], - # color='g', lw=3, alpha=.6) - - axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[1]+'_'+obj]/1e6, color='k', lw=3, label='Silicon') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[1]+'_'+obj.name], - # color='k', lw=3)# alpha=.3) - - - # silicon aluminum 'k ''g' - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - #axs[i].legend(materials) - axs[i].legend() - - - -i=2 - -# ROW 3: -# Loop over SF Scenarios -for kk in range(0, 1): - - obj = SFscenarios[kk] - axs[i].yaxis.grid() - - axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[3]+'_'+obj], color='orange', lw=3, label='Copper') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[3]+'_'+obj.name], - # color='orange', lw=3)# alpha=.3) - - axs[i].plot(rr.scenario[obj].data['year'], USyearly[keyw+materials[2]+'_'+obj], color='gray', lw=3, label='Silver') - # axs[i].fill_between(obj.scenario[STATEs[0]].data['year'], 0, USyearly[keyw+materials[2]+'_'+obj.name], - # color='gray', lw=3)# , alpha=.6) - - - #axs[i].ylabel('Mass [Tons]') - axs[i].set_xlim([2020, 2050]) - #axs[i].set_title(keyw+ ' Yearly ' + obj.name) - axs[i].legend() - axs[i].yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}')) - - - -#axs[i].set_ylim([0, 5e7/1e6]) -#axs[i+3].set_ylim([0, 3e6/1e6]) -#axs[i+6].set_ylim([0, 2.5e4]) - -# axs[i+3].set_yscale('log') -# axs[i+6].set_yscale('log') - -axs[0].set_ylabel('Yearly Mass [Million Tonnes]') -axs[1].set_ylabel('Yearly Mass [Million Tonnes]') -axs[2].set_ylabel('Yearly Mass [Tonnes]') - -#axs[8].legend(materials) - -fig.savefig(title_Method+' Fig_3x3_WORLD_MaterialNeeds_Nation.png', dpi=600) - - -# In[58]: - - -keyword='Cumulative_Area_disposed' - -USyearly_Areadisp=pd.DataFrame() - -# Loop over SF Scenarios -for kk in range(0, 1): - obj = SFscenarios[kk] - # Loop over Materials - foo = rr.scenario[obj].data[keyword].copy() - USyearly_Areadisp["Areadisp_"+obj] = foo - - # Loop over STATEs - #for jj in range (1, len(STATEs)): - # USyearly_Areadisp["Areadisp_"+obj] += rr.scenario[obj].data[keyword] - - -# In[59]: - - -UScum_Areadisp = USyearly_Areadisp.copy() -UScum_Areadisp = UScum_Areadisp.cumsum() - - -# In[60]: - - -A = UScum['Waste_Module_World'].iloc[-1] -#47700000 # tonnes cumulative by 2050 -A = A*1000 # convert to kg -A = A/10.05599 # convert to m2 if each m2 is ~avg 10 kg -#A = A*2 # convert to area if each module is ~2 m2 -A = A/1e6 # Convert to km 2 -print(A) - - -# In[61]: - - -C = UScum_Areadisp['Areadisp_World'].iloc[-1]/1e6 - - -# In[62]: - - -# MANHATTAN SIZE: -manhattans = 59.103529 - - -# In[63]: - - -print("Reference Cumulative Area by 2050 of Waste PV Modules EoL", round(C), " km^2") - -print("") -print("Reference Waste equals ", round(C/manhattans), " Manhattans ") - -print("") -print ("MFG SCrap + Eol Waste") -print("Reference Cumulative Area by 2050 of Waste PV Mfg + Modules EoL", round(A), " km^2") - - -# ### New Section - -# VirginStock_aluminum_Reference.Mod -# VirginStock_aluminum_95-by-35.Adv -# VirginStock_aluminum_95-by-35_Elec.Adv_DR -# Waste_EOL_aluminum_Reference.Mod -# Waste_EOL_aluminum_95-by-35.Adv -# Waste_EOL_aluminum_95-by-35_Elec.Adv_DR -# -# VirginStock_silver_Reference.Mod -# VirginStock_silver_95-by-35.Adv -# VirginStock_silver_95-by-35_Elec.Adv_DR -# Waste_EOL_silver_Reference.Mod -# Waste_EOL_silver_95-by-35.Adv -# Waste_EOL_silver_95-by-35_Elec.Adv_DR -# - -# In[67]: - - -plt.rcParams.update({'font.size': 10}) -plt.rcParams['figure.figsize'] = (12, 8) - -fig, axs = plt.subplots(1,2, figsize=(15, 6), facecolor='w', edgecolor='k') -fig.subplots_adjust(hspace = .1, wspace=.4) -axs = axs.ravel() - -# PLOT 1 -i = 0 -axs[i].yaxis.grid() -lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_silver_World']/1e6, color='gray', linewidth=4.0, label='Virgin Material Demands') -lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_silver_World']/1e6, color='k', linewidth=4.0, label='EoL Material') -axs[i].set_ylabel('Mass [Tons]') -axs[i].set_xlim([2020, 2050]) -axs[i].set_title('Silver') - -# 2nd axis plot -ax2=axs[i].twinx() -lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_silver_World']/USyearly['VirginStock_silver_World'], - color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand') -ax2.set_ylabel('EoL Material as Fraction of Demand', color='r') -ax2.tick_params(axis='y', labelcolor='r') - -# LEGENDS -# added these three lines -lns = lns1+lns2+lns3 -labs = [l.get_label() for l in lns] -axs[0].legend(lns, labs, loc=0) - -# PLOT 2 -i = 1 -axs[i].yaxis.grid() -lns1 = axs[i].plot(USyearly.index, USyearly['VirginStock_aluminum_World']/1e6, color='g', linewidth=4.0, label='Virgin Material Demands') -lns2 = axs[i].plot(USyearly.index, USyearly['Waste_EOL_aluminum_World']/1e6, color='k', linewidth=4.0, label='EoL Material') -axs[i].set_ylabel('Mass [Tons]') -axs[i].set_xlim([2020, 2050]) -axs[i].set_title('Aluminum') - -# 2nd axis plot -ax2=axs[i].twinx() -lns3 = ax2.plot(USyearly.index, USyearly['Waste_EOL_aluminum_World']/USyearly['VirginStock_aluminum_World'], - color = 'r', linewidth=1.0, label='Eol Material as fraction of Demand') - -ax2.set_ylabel('EoL Material as Fraction of Demand', color='r') -ax2.tick_params(axis='y', labelcolor='r') - -# LEGENDS -# added these three lines -lns = lns1+lns2+lns3 -labs = [l.get_label() for l in lns] -axs[1].legend(lns, labs, loc=0) - diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.html b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.html deleted file mode 100644 index e72e6dc5..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.html +++ /dev/null @@ -1,14788 +0,0 @@ - - - - - -(development) ReEDS Scenarios Baseline Creation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.ipynb b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.ipynb deleted file mode 100644 index 0cf848f0..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.ipynb +++ /dev/null @@ -1,771 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ReEDS Scenarios on PV ICE Tool" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. \n", - "\n", - "Current sections include:\n", - "\n", - "
      \n", - "
    1. ### Reading a standard ReEDS output file
      2. \n", - "
    2. ### Saving PCA data as PV ICE input format
      3. \n", - "
    3. ### Saving State data as PV ICE input format
      4. \n", - "
    \n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import os,sys\n", - "import matplotlib.pyplot as plt\n", - "plt.rcParams.update({'font.size': 22})\n", - "plt.rcParams['figure.figsize'] = (12, 8)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your simulation will be stored in C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - } - ], - "source": [ - "import os\n", - "from pathlib import Path\n", - "\n", - "testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP')\n", - "\n", - "print (\"Your simulation will be stored in %s\" % testfolder)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reading a standard ReEDS output file" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input file is stored in C:\\Users\\sayala\\Documents\\GitHub\\December Core Scenarios ReEDS Outputs Solar Futures v3a.xlsx\n" - ] - } - ], - "source": [ - "reedsFile = str(Path().resolve().parent.parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v3a.xlsx')\n", - "print (\"Input file is stored in %s\" % reedsFile)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "REEDSInput = pd.read_excel(reedsFile,\n", - "# sheet_name=\"new installs PV (2)\")\n", - " sheet_name=\"new installs PV\")\n", - "\n", - "#index_col=[0,2,3]) #this casts scenario, PCA and State as levels\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save Input Files by PCA" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create a copy of the REEDS Input and modify structure for PCA focus" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    Capacity (GW)
    ScenarioYearPCA
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    2011p10.005158
    2012p10.005158
    2013p10.007146
    2014p10.007146
    2015p10.018253
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    2017p10.019460
    2018p10.019460
    2019p10.036320
    2020p10.036320
    2021p10.002434
    2022p10.002434
    2023p10.010066
    2024p10.010066
    2025p10.049445
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    2029p10.105931
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    " - ], - "text/plain": [ - " Capacity (GW)\n", - "Scenario Year PCA \n", - "95-by-35.Adv 2010 p1 0.000017\n", - " 2011 p1 0.005158\n", - " 2012 p1 0.005158\n", - " 2013 p1 0.007146\n", - " 2014 p1 0.007146\n", - " 2015 p1 0.018253\n", - " 2016 p1 0.018253\n", - " 2017 p1 0.019460\n", - " 2018 p1 0.019460\n", - " 2019 p1 0.036320\n", - " 2020 p1 0.036320\n", - " 2021 p1 0.002434\n", - " 2022 p1 0.002434\n", - " 2023 p1 0.010066\n", - " 2024 p1 0.010066\n", - " 2025 p1 0.049445\n", - " 2026 p1 0.049445\n", - " 2027 p1 0.051342\n", - " 2028 p1 0.051342\n", - " 2029 p1 0.105931\n", - " 2030 p1 0.105931" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rawdf = REEDSInput.copy()\n", - "rawdf.drop(columns=['State'], inplace=True)\n", - "rawdf.drop(columns=['Tech'], inplace=True)\n", - "rawdf.set_index(['Scenario','Year','PCA'], inplace=True)\n", - "rawdf.head(21)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Loading Module Baseline. Will be used later to populate all the columsn otehr than 'new_Installed_Capacity_[MW]' which will be supplied by the REEDS model" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "path = C:\\Users\\sayala\\Documents\\GitHub\\CircularEconomy-MassFlowCalculator\\PV_ICE\\TEMP\n" - ] - }, - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    mod_effmod_reliability_t50mod_reliability_t90mod_degradationmod_lifetimemod_MFG_effmod_EOL_collection_effmod_EOL_collected_recycledmod_Repairmod_MerchantTailmod_Reuse
    year
    201014.725.030.00.322.798.00.00.00.00.00.0
    201115.125.030.00.323.098.00.00.00.00.00.0
    201215.425.030.00.323.598.00.00.00.00.00.0
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    \n", - "
    " - ], - "text/plain": [ - " mod_eff mod_reliability_t50 mod_reliability_t90 mod_degradation \\\n", - "year \n", - "2010 14.7 25.0 30.0 0.3 \n", - "2011 15.1 25.0 30.0 0.3 \n", - "2012 15.4 25.0 30.0 0.3 \n", - "2013 16.0 28.0 33.0 0.3 \n", - "2014 16.3 28.0 33.0 0.3 \n", - "\n", - " mod_lifetime mod_MFG_eff mod_EOL_collection_eff \\\n", - "year \n", - "2010 22.7 98.0 0.0 \n", - "2011 23.0 98.0 0.0 \n", - "2012 23.5 98.0 0.0 \n", - "2013 24.2 98.0 0.0 \n", - "2014 26.0 98.0 0.0 \n", - "\n", - " mod_EOL_collected_recycled mod_Repair mod_MerchantTail mod_Reuse \n", - "year \n", - "2010 0.0 0.0 0.0 0.0 \n", - "2011 0.0 0.0 0.0 0.0 \n", - "2012 0.0 0.0 0.0 0.0 \n", - "2013 0.0 0.0 0.0 0.0 \n", - "2014 0.0 0.0 0.0 0.0 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import PV_ICE\n", - "r1 = PV_ICE.Simulation(name='Simulation1', path=testfolder)\n", - "r1.createScenario(name='US', file=r'..\\baselines\\SolarFutures_2021\\baseline_modules_US_Reeds.csv')\n", - "baseline = r1.scenario['US'].data\n", - "baseline = baseline.drop(columns=['new_Installed_Capacity_[MW]'])\n", - "baseline.set_index('year', inplace=True)\n", - "baseline.index = pd.PeriodIndex(baseline.index, freq='A') # A -- Annual\n", - "baseline.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### For each Scenario and for each PCA, combine with baseline and save as input file" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "for ii in range (len(rawdf.unstack(level=1))):\n", - " PCA = rawdf.unstack(level=1).iloc[ii].name[1]\n", - " SCEN = rawdf.unstack(level=1).iloc[ii].name[0]\n", - " SCEN=SCEN.replace('+', '_')\n", - " filetitle = SCEN+'_'+PCA +'.csv'\n", - " filetitle = os.path.join(testfolder, 'PCAs', filetitle)\n", - " A = rawdf.unstack(level=1).iloc[ii]\n", - " A = A.droplevel(level=0)\n", - " A.name = 'new_Installed_Capacity_[MW]'\n", - " A = pd.DataFrame(A)\n", - " A.index=pd.PeriodIndex(A.index, freq='A')\n", - " A = pd.DataFrame(A)\n", - " A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 0.85\n", - " A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 1000 # ReEDS file is in GW.\n", - " # Add other columns\n", - " A = pd.concat([A, baseline.reindex(A.index)], axis=1)\n", - " \n", - " header = \"year,new_Installed_Capacity_[MW],mod_eff,mod_reliability_t50,mod_reliability_t90,\"\\\n", - " \"mod_degradation,mod_lifetime,mod_MFG_eff,mod_EOL_collection_eff,mod_EOL_collected_recycled,\"\\\n", - " \"mod_Repair,mod_MerchantTail,mod_Reuse\\n\"\\\n", - " \"year,MW,%,years,years,%,years,%,%,%,%,%,%\\n\"\n", - "\n", - " with open(filetitle, 'w', newline='') as ict:\n", - " # Write the header lines, including the index variable for\n", - " # the last one if you're letting Pandas produce that for you.\n", - " # (see above).\n", - " for line in header:\n", - " ict.write(line)\n", - "\n", - " # savedata.to_csv(ict, index=False)\n", - " A.to_csv(ict, header=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save Input Files By States" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reassign data from REEDS Input, as we need one of the columns we dropped." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rawdf = REEDSInput.copy()\n", - "#rawdf.drop(columns=['State'], inplace=True)\n", - "rawdf.drop(columns=['Tech'], inplace=True)\n", - "rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True)\n", - "rawdf.head(21)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Group data so we can work with the States instead" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#df = rawdf.groupby(['Scenario','State', 'Year'])['Capacity (GW)'].sum(axis=0)\n", - "df = rawdf.groupby(['Scenario','State', 'Year'])['Capacity (GW)'].sum()\n", - "df = pd.DataFrame(df)\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### For each Scenario and for each STATE, combine with baseline and save as input file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for ii in range (len(df.unstack(level=2))): \n", - " STATE = df.unstack(level=2).iloc[ii].name[1]\n", - " SCEN = df.unstack(level=2).iloc[ii].name[0]\n", - " SCEN=SCEN.replace('+', '_')\n", - " filetitle = SCEN+'_'+STATE +'.csv'\n", - " filetitle = os.path.join(testfolder, 'STATEs', filetitle)\n", - " A = df.unstack(level=2).iloc[ii]\n", - " A = A.droplevel(level=0)\n", - " A.name = 'new_Installed_Capacity_[MW]'\n", - " A = pd.DataFrame(A)\n", - " A.index=pd.PeriodIndex(A.index, freq='A')\n", - " A = pd.DataFrame(A)\n", - " A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 0.85 # marketshares['Si']\n", - " A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 1000 # ReEDS file is in GW.\n", - " # Add other columns\n", - " A = pd.concat([A, baseline.reindex(A.index)], axis=1)\n", - " \n", - " \n", - " header = \"year,new_Installed_Capacity_[MW],mod_eff,mod_reliability_t50,mod_reliability_t90,\"\\\n", - " \"mod_degradation,mod_lifetime,mod_MFG_eff,mod_EOL_collection_eff,mod_EOL_collected_recycled,\"\\\n", - " \"mod_Repair,mod_MerchantTail,mod_Reuse\\n\"\\\n", - " \"year,MW,%,years,years,%,years,%,%,%,%,%,%\\n\"\n", - "\n", - " with open(filetitle, 'w', newline='') as ict:\n", - " # Write the header lines, including the index variable for\n", - " # the last one if you're letting Pandas produce that for you.\n", - " # (see above).\n", - " for line in header:\n", - " ict.write(line)\n", - "\n", - " # savedata.to_csv(ict, index=False)\n", - " A.to_csv(ict, header=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Saving US Baseline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a copy of the REEDS Input and modify structure for PCA focus" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "rawdf = REEDSInput.copy()\n", - "#rawdf.drop(columns=['State'], inplace=True)\n", - "rawdf.drop(columns=['Tech'], inplace=True)\n", - "rawdf.set_index(['Scenario','Year'], inplace=True)\n", - "rawdf.head(21)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#df = rawdf.groupby(['Scenario','Year'])['Capacity (GW)'].sum(axis=0)\n", - "df = rawdf.groupby(['Scenario','Year'])['Capacity (GW)'].sum()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading Module Baseline. Will be used later to populate all the columsn other than 'new_Installed_Capacity_[MW]' which will be supplied by the REEDS model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import PV_ICE\n", - "r1 = PV_ICE.Simulation(name='Simulation1', path=testfolder)\n", - "r1.createScenario(name='US', file=r'..\\baselines\\SolarFutures_2021\\baseline_modules_US_Reeds.csv')\n", - "baseline = r1.scenario['US'].data\n", - "baseline = baseline.drop(columns=['new_Installed_Capacity_[MW]'])\n", - "baseline.set_index('year', inplace=True)\n", - "baseline.index = pd.PeriodIndex(baseline.index, freq='A') # A -- Annual\n", - "baseline.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### For each Scenario, combine with baseline and save as input fileĀ¶" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for ii in range (len(df.unstack(level=1))):\n", - " SCEN = df.unstack(level=1).index[ii]\n", - " SCEN=SCEN.replace('+', '_')\n", - " filetitle = SCEN+'.csv'\n", - " filetitle = os.path.join(testfolder, 'USA', filetitle)\n", - " A = df.unstack(level=1).iloc[ii]\n", - "\n", - " A.name = 'new_Installed_Capacity_[MW]'\n", - " A = pd.DataFrame(A)\n", - " A.index=pd.PeriodIndex(A.index, freq='A')\n", - " A = pd.DataFrame(A)\n", - " A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 0.85 # marketshares['Si']\n", - " A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 1000 # ReEDS file is in GW.\n", - " # Add other columns\n", - " A = pd.concat([A, baseline.reindex(A.index)], axis=1)\n", - " \n", - " header = \"year,new_Installed_Capacity_[MW],mod_eff,mod_reliability_t50,mod_reliability_t90,\"\\\n", - " \"mod_degradation,mod_lifetime,mod_MFG_eff,mod_EOL_collection_eff,mod_EOL_collected_recycled,\"\\\n", - " \"mod_Repair,mod_MerchantTail,mod_Reuse\\n\"\\\n", - " \"year,MW,%,years,years,%,years,%,%,%,%,%,%\\n\"\n", - "\n", - " with open(filetitle, 'w', newline='') as ict:\n", - " # Write the header lines, including the index variable for\n", - " # the last one if you're letting Pandas produce that for you.\n", - " # (see above).\n", - " for line in header:\n", - " ict.write(line)\n", - "\n", - " # savedata.to_csv(ict, index=False)\n", - " A.to_csv(ict, header=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.py b/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.py deleted file mode 100644 index 3bc345db..00000000 --- a/docs/tutorials/ReEDS Simulations/(development) ReEDS Scenarios Baseline Creation.py +++ /dev/null @@ -1,269 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# # ReEDS Scenarios on PV ICE Tool - -# To explore different scenarios for furture installation projections of PV (or any technology), ReEDS output data can be useful in providing standard scenarios. ReEDS installation projections are used in this journal as input data to the PV ICE tool. -# -# Current sections include: -# -#
      -#
    1. ### Reading a standard ReEDS output file
      2. -#
    2. ### Saving PCA data as PV ICE input format
      3. -#
    3. ### Saving State data as PV ICE input format
      4. -#
    -# - -# In[1]: - - -import numpy as np -import pandas as pd -import os,sys -import matplotlib.pyplot as plt -plt.rcParams.update({'font.size': 22}) -plt.rcParams['figure.figsize'] = (12, 8) - - -# In[2]: - - -import os -from pathlib import Path - -testfolder = str(Path().resolve().parent.parent.parent / 'PV_ICE' / 'TEMP') - -print ("Your simulation will be stored in %s" % testfolder) - - -# ## Reading a standard ReEDS output file - -# In[3]: - - -reedsFile = str(Path().resolve().parent.parent.parent.parent / 'December Core Scenarios ReEDS Outputs Solar Futures v3a.xlsx') -print ("Input file is stored in %s" % reedsFile) - - -# In[6]: - - -REEDSInput = pd.read_excel(reedsFile, -# sheet_name="new installs PV (2)") - sheet_name="new installs PV") - -#index_col=[0,2,3]) #this casts scenario, PCA and State as levels - - -# ## Save Input Files by PCA - -# #### Create a copy of the REEDS Input and modify structure for PCA focus - -# In[7]: - - -rawdf = REEDSInput.copy() -rawdf.drop(columns=['State'], inplace=True) -rawdf.drop(columns=['Tech'], inplace=True) -rawdf.set_index(['Scenario','Year','PCA'], inplace=True) -rawdf.head(21) - - -# #### Loading Module Baseline. Will be used later to populate all the columsn otehr than 'new_Installed_Capacity_[MW]' which will be supplied by the REEDS model - -# In[8]: - - -import PV_ICE -r1 = PV_ICE.Simulation(name='Simulation1', path=testfolder) -r1.createScenario(name='US', file=r'..\baselines\SolarFutures_2021\baseline_modules_US_Reeds.csv') -baseline = r1.scenario['US'].data -baseline = baseline.drop(columns=['new_Installed_Capacity_[MW]']) -baseline.set_index('year', inplace=True) -baseline.index = pd.PeriodIndex(baseline.index, freq='A') # A -- Annual -baseline.head() - - -# #### For each Scenario and for each PCA, combine with baseline and save as input file - -# In[11]: - - -for ii in range (len(rawdf.unstack(level=1))): - PCA = rawdf.unstack(level=1).iloc[ii].name[1] - SCEN = rawdf.unstack(level=1).iloc[ii].name[0] - SCEN=SCEN.replace('+', '_') - filetitle = SCEN+'_'+PCA +'.csv' - filetitle = os.path.join(testfolder, 'PCAs', filetitle) - A = rawdf.unstack(level=1).iloc[ii] - A = A.droplevel(level=0) - A.name = 'new_Installed_Capacity_[MW]' - A = pd.DataFrame(A) - A.index=pd.PeriodIndex(A.index, freq='A') - A = pd.DataFrame(A) - A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 0.85 - A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 1000 # ReEDS file is in GW. - # Add other columns - A = pd.concat([A, baseline.reindex(A.index)], axis=1) - - header = "year,new_Installed_Capacity_[MW],mod_eff,mod_reliability_t50,mod_reliability_t90," "mod_degradation,mod_lifetime,mod_MFG_eff,mod_EOL_collection_eff,mod_EOL_collected_recycled," "mod_Repair,mod_MerchantTail,mod_Reuse\n" "year,MW,%,years,years,%,years,%,%,%,%,%,%\n" - - with open(filetitle, 'w', newline='') as ict: - # Write the header lines, including the index variable for - # the last one if you're letting Pandas produce that for you. - # (see above). - for line in header: - ict.write(line) - - # savedata.to_csv(ict, index=False) - A.to_csv(ict, header=False) - - -# In[ ]: - - - - - -# ## Save Input Files By States - -# #### Reassign data from REEDS Input, as we need one of the columns we dropped. - -# In[ ]: - - -rawdf = REEDSInput.copy() -#rawdf.drop(columns=['State'], inplace=True) -rawdf.drop(columns=['Tech'], inplace=True) -rawdf.set_index(['Scenario','Year','PCA', 'State'], inplace=True) -rawdf.head(21) - - -# #### Group data so we can work with the States instead - -# In[ ]: - - -#df = rawdf.groupby(['Scenario','State', 'Year'])['Capacity (GW)'].sum(axis=0) -df = rawdf.groupby(['Scenario','State', 'Year'])['Capacity (GW)'].sum() -df = pd.DataFrame(df) -df.head() - - -# #### For each Scenario and for each STATE, combine with baseline and save as input file - -# In[ ]: - - -for ii in range (len(df.unstack(level=2))): - STATE = df.unstack(level=2).iloc[ii].name[1] - SCEN = df.unstack(level=2).iloc[ii].name[0] - SCEN=SCEN.replace('+', '_') - filetitle = SCEN+'_'+STATE +'.csv' - filetitle = os.path.join(testfolder, 'STATEs', filetitle) - A = df.unstack(level=2).iloc[ii] - A = A.droplevel(level=0) - A.name = 'new_Installed_Capacity_[MW]' - A = pd.DataFrame(A) - A.index=pd.PeriodIndex(A.index, freq='A') - A = pd.DataFrame(A) - A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 0.85 # marketshares['Si'] - A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 1000 # ReEDS file is in GW. - # Add other columns - A = pd.concat([A, baseline.reindex(A.index)], axis=1) - - - header = "year,new_Installed_Capacity_[MW],mod_eff,mod_reliability_t50,mod_reliability_t90," "mod_degradation,mod_lifetime,mod_MFG_eff,mod_EOL_collection_eff,mod_EOL_collected_recycled," "mod_Repair,mod_MerchantTail,mod_Reuse\n" "year,MW,%,years,years,%,years,%,%,%,%,%,%\n" - - with open(filetitle, 'w', newline='') as ict: - # Write the header lines, including the index variable for - # the last one if you're letting Pandas produce that for you. - # (see above). - for line in header: - ict.write(line) - - # savedata.to_csv(ict, index=False) - A.to_csv(ict, header=False) - - -# # Saving US Baseline - -# ### Create a copy of the REEDS Input and modify structure for PCA focus - -# In[ ]: - - -rawdf = REEDSInput.copy() -#rawdf.drop(columns=['State'], inplace=True) -rawdf.drop(columns=['Tech'], inplace=True) -rawdf.set_index(['Scenario','Year'], inplace=True) -rawdf.head(21) - - -# In[ ]: - - -#df = rawdf.groupby(['Scenario','Year'])['Capacity (GW)'].sum(axis=0) -df = rawdf.groupby(['Scenario','Year'])['Capacity (GW)'].sum() - - -# ### Loading Module Baseline. Will be used later to populate all the columsn other than 'new_Installed_Capacity_[MW]' which will be supplied by the REEDS model - -# In[ ]: - - -import PV_ICE -r1 = PV_ICE.Simulation(name='Simulation1', path=testfolder) -r1.createScenario(name='US', file=r'..\baselines\SolarFutures_2021\baseline_modules_US_Reeds.csv') -baseline = r1.scenario['US'].data -baseline = baseline.drop(columns=['new_Installed_Capacity_[MW]']) -baseline.set_index('year', inplace=True) -baseline.index = pd.PeriodIndex(baseline.index, freq='A') # A -- Annual -baseline.head() - - -# ### For each Scenario, combine with baseline and save as input fileĀ¶ - -# In[ ]: - - -for ii in range (len(df.unstack(level=1))): - SCEN = df.unstack(level=1).index[ii] - SCEN=SCEN.replace('+', '_') - filetitle = SCEN+'.csv' - filetitle = os.path.join(testfolder, 'USA', filetitle) - A = df.unstack(level=1).iloc[ii] - - A.name = 'new_Installed_Capacity_[MW]' - A = pd.DataFrame(A) - A.index=pd.PeriodIndex(A.index, freq='A') - A = pd.DataFrame(A) - A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 0.85 # marketshares['Si'] - A['new_Installed_Capacity_[MW]'] = A['new_Installed_Capacity_[MW]'] * 1000 # ReEDS file is in GW. - # Add other columns - A = pd.concat([A, baseline.reindex(A.index)], axis=1) - - header = "year,new_Installed_Capacity_[MW],mod_eff,mod_reliability_t50,mod_reliability_t90," "mod_degradation,mod_lifetime,mod_MFG_eff,mod_EOL_collection_eff,mod_EOL_collected_recycled," "mod_Repair,mod_MerchantTail,mod_Reuse\n" "year,MW,%,years,years,%,years,%,%,%,%,%,%\n" - - with open(filetitle, 'w', newline='') as ict: - # Write the header lines, including the index variable for - # the last one if you're letting Pandas produce that for you. - # (see above). - for line in header: - ict.write(line) - - # savedata.to_csv(ict, index=False) - A.to_csv(ict, header=False) - - -# In[ ]: - - - - - -# In[ ]: - - - -