34507 lines (29566 with data), 4.0 MB
<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>ValveNet_JACC_Revisions_Final</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; margin: 0; }
td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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<style type="text/css">
@font-face {
font-family: "Fira Mono";
font-weight: normal;
font-style: normal;
src: local('"Fira Mono"'), url('fonts/fira.ttf') format('truetype');
}
@font-face {
font-family: "PT Sans";
font-weight: normal;
font-style: normal;
src: local('"PT Sans"'), url('fonts/pt-sans-regular.ttf') format('truetype');
}
div#notebook {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
line-height: 170%;
color: #f8f8f0;
-webkit-font-smoothing: antialiased !important;
padding-top: 25px !important;
}
body,
div.body {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
color: #f8f8f0;
background-color: #1e1e1e;
background: #1e1e1e;
-webkit-font-smoothing: antialiased !important;
}
body.notebook_app {
padding: 0;
background-color: #1e1e1e;
background: #1e1e1e;
padding-right: 0px !important;
overflow-y: hidden;
}
a {
font-family: "PT Sans", sans-serif;
color: #f8f8f0;
-webkit-font-smoothing: antialiased !important;
}
a:hover,
a:focus {
color: #f8f8f0;
-webkit-font-smoothing: antialiased !important;
}
div#maintoolbar {
position: absolute;
width: 90%;
margin-left: -10%;
padding-right: 8%;
float: left;
background: transparent !important;
}
#maintoolbar {
margin-bottom: -3px;
margin-top: 0px;
border: 0px;
min-height: 27px;
padding-top: 2px;
padding-bottom: 0px;
}
#maintoolbar .container {
width: 75%;
margin-right: auto;
margin-left: auto;
}
.list_header,
div#notebook_list_header.row.list_header {
font-size: 14pt;
color: #f8f8f0;
background-color: transparent;
height: 35px;
}
i.fa.fa-folder {
display: inline-block;
font: normal normal normal 14px "FontAwesome";
font-family: "FontAwesome" !important;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
font-size: 18px;
-moz-osx-font-smoothing: grayscale;
}
#running .panel-group .panel .panel-heading {
font-size: 14pt;
color: #f8f8f0;
padding: 8px 8px;
background: #2f2f2f;
background-color: #2f2f2f;
}
#running .panel-group .panel .panel-heading a {
font-size: 14pt;
color: #f8f8f0;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
font-size: 14pt;
color: #f8f8f0;
}
#running .panel-group .panel .panel-body .list_container .list_item {
background: #232323;
background-color: #232323;
padding: 2px;
border-bottom: 2px solid rgba(93,92,82,.25);
}
#running .panel-group .panel .panel-body .list_container .list_item:hover {
background: #232323;
background-color: #232323;
}
#running .panel-group .panel .panel-body {
padding: 2px;
}
button#refresh_running_list {
border: none !important;
}
button#refresh_cluster_list {
border: none !important;
}
div.running_list_info.toolbar_info {
font-size: 15px;
padding: 4px 0 4px 0;
margin-top: 5px;
margin-bottom: 8px;
height: 24px;
line-height: 24px;
text-shadow: none;
}
.list_placeholder {
font-weight: normal;
}
#tree-selector {
padding: 0px;
border-color: transparent;
}
#project_name > ul > li > a > i.fa.fa-home {
color: #a6e22e;
font-size: 17pt;
display: inline-block;
position: static;
padding: 0px 0px;
font-weight: normal;
text-align: center;
vertical-align: text-top;
}
.fa-folder:before {
color: #ae81ff;
}
.fa-arrow-up:before {
font-size: 14px;
}
.fa-arrow-down:before {
font-size: 14px;
}
span#last-modified.btn.btn-xs.btn-default.sort-action:hover .fa,
span#sort-name.btn.btn-xs.btn-default.sort-action:hover .fa {
color: #a6e22e;
}
.folder_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f07b";
color: #ae81ff;
}
.notebook_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f02d";
position: relative;
color: #a6e22e !important;
top: 0px;
}
.file_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f15b";
position: relative;
top: 0px;
color: #888888 !important;
}
#project_name a {
display: inline-flex;
padding-left: 7px;
margin-left: -2px;
text-align: -webkit-auto;
vertical-align: baseline;
font-size: 18px;
}
div#notebook_toolbar div.dynamic-instructions {
font-family: "PT Sans", sans-serif;
font-size: 17px;
color: #75715e;
}
span#login_widget > .button,
#logout {
font-family: "Proxima Nova", sans-serif;
color: #f8f8f0;
background: transparent;
background-color: transparent;
border: 2px solid #2f2f2f;
font-weight: normal;
box-shadow: none;
text-shadow: none;
border-radius: 3px;
margin-right: 10px;
padding: 2px 7px;
}
span#login_widget > .button:hover,
#logout:hover {
color: #a6e22e;
background-color: transparent;
background: transparent;
border: 2px solid #a6e22e;
background-image: none;
box-shadow: none !important;
border-radius: 3px;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus,
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
color: #f8f8f2;
background-color: #f8f8f0;
background: #f8f8f0;
border-color: #f8f8f0;
background-image: none;
box-shadow: none !important;
border-radius: 2px;
}
body > #header #header-container {
padding-bottom: 0px;
padding-top: 4px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
body > #header {
background: #1e1e1e;
background-color: #1e1e1e;
position: relative;
z-index: 100;
}
.list_container {
font-size: 11pt;
color: #f8f8f0;
border: none;
text-shadow: none !important;
}
.list_container > div {
border-bottom: 1px solid rgba(93,92,82,.25);
font-size: 11pt;
}
.list_header > div,
.list_item > div {
padding-top: 6px;
padding-bottom: 2px;
padding-left: 0px;
}
.list_header > div .item_link,
.list_item > div .item_link {
margin-left: -1px;
vertical-align: middle;
line-height: 22px;
font-size: 11pt;
}
.item_icon {
color: #ae81ff;
font-size: 13pt;
vertical-align: middle;
}
.list_item input:not([type="checkbox"]) {
padding-right: 0px;
height: 1.75em;
width: 25%;
margin: 0px 0 0;
margin-top: 0px;
}
.list_header > div .item_link,
.list_item > div .item_link {
margin-left: -1px;
vertical-align: middle;
line-height: 1.5em;
font-size: 12pt;
display: inline-table;
position: static;
}
#button-select-all {
height: 34px;
min-width: 55px;
z-index: 0;
border: none !important;
padding-top: 0px;
padding-bottom: 0px;
margin-bottom: 0px;
margin-top: 0px;
left: -3px;
border-radius: 0px !important;
}
#button-select-all:focus,
#button-select-all:active:focus,
#button-select-all.active:focus,
#button-select-all.focus,
#button-select-all:active.focus,
#button-select-all.active.focus {
background-color: #2f2f2f !important;
background: #2f2f2f !important;
}
button#tree-selector-btn {
height: 34px;
font-size: 10.0pt;
border: none;
left: 0px;
border-radius: 0px !important;
}
input#select-all.pull-left.tree-selector {
margin-left: 7px;
margin-right: 2px;
margin-top: 2px;
top: 4px;
}
input[type="radio"],
input[type="checkbox"] {
margin-top: 1px;
line-height: normal;
}
.delete-button {
border: none !important;
}
i.fa.fa-trash {
font-size: 13.5pt;
}
.list_container a {
font-size: 16px;
color: #f8f8f0;
border: none;
text-shadow: none !important;
font-weight: normal;
font-style: normal;
}
div.list_container a:hover {
color: #f8f8f0;
}
.list_header > div input,
.list_item > div input {
margin-right: 7px;
margin-left: 12px;
vertical-align: baseline;
line-height: 22px;
position: relative;
top: -1px;
}
div.list_item:hover {
background-color: rgba(93,92,82,.1);
}
.breadcrumb > li {
font-size: 10.0pt;
color: #f8f8f0;
border: none;
text-shadow: none !important;
}
.breadcrumb > li + li:before {
content: "/\00a0";
padding: 0px;
color: #f8f8f0;
font-size: 18px;
}
#project_name > .breadcrumb {
padding: 0px;
margin-bottom: 0px;
background-color: transparent;
font-weight: normal;
margin-top: -2px;
}
ul#tabs a {
font-family: "PT Sans", sans-serif;
font-size: 13.5pt;
font-weight: normal;
font-style: normal;
text-shadow: none !important;
}
.nav-tabs {
font-family: "PT Sans", sans-serif;
font-size: 13.5pt;
font-weight: normal;
font-style: normal;
background-color: transparent;
border-color: transparent;
text-shadow: none !important;
border: 2px solid transparent;
}
.nav-tabs > li > a:active,
.nav-tabs > li > a:focus,
.nav-tabs > li > a:hover,
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:focus,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
color: #a6e22e;
background-color: transparent;
border-color: transparent;
border-bottom: 2px solid transparent;
}
.nav > li.disabled > a,
.nav > li.disabled > a:hover {
color: #75715e;
}
.nav-tabs > li > a:before {
content: "";
position: absolute;
width: 100%;
height: 2px;
bottom: -2px;
left: 0;
background-color: #a6e22e;
visibility: hidden;
-webkit-transform: perspective(0)scaleX(0);
transform: perspective(0)scaleX(0);
-webkit-transition: ease 220ms;
transition: ease 220ms;
-webkit-font-smoothing: antialiased !important;
}
.nav-tabs > li > a:hover:before {
visibility: visible;
-webkit-transform: perspective(1)scaleX(1);
transform: perspective(1)scaleX(1);
}
.nav-tabs > li.active > a:before {
content: "";
position: absolute;
width: 100%;
height: 2px;
bottom: -2px;
left: 0;
background-color: #a6e22e;
visibility: visible;
-webkit-transform: perspective(1)scaleX(1);
transform: perspective(1)scaleX(1);
-webkit-font-smoothing: subpixel-antialiased !important;
}
div#notebook {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
padding-top: 4px;
}
.notebook_app {
background-color: #1e1e1e;
}
#notebook-container {
padding: 13px 2px;
background-color: #1e1e1e;
min-height: 0px;
box-shadow: none;
width: 85%;
margin-right: auto;
margin-left: auto;
}
div#ipython-main-app.container {
width: 85%;
margin-right: auto;
margin-left: auto;
margin-right: auto;
margin-left: auto;
}
.container {
width: 85%;
margin-right: auto;
margin-left: auto;
}
div#menubar-container {
width: 100%;
width: 85%;
}
div#header-container {
width: 85%;
}
.notebook_app #header,
.edit_app #header {
box-shadow: none !important;
background-color: #1e1e1e;
border-bottom: 2px solid rgba(93,92,82,.25);
}
#header,
.edit_app #header {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
box-shadow: none;
background-color: #1e1e1e;
}
#header .header-bar,
.edit_app #header .header-bar {
background: #1e1e1e;
background-color: #1e1e1e;
}
body > #header .header-bar {
width: 100%;
background: #1e1e1e;
}
span.checkpoint_status,
span.autosave_status {
font-size: small;
display: none;
}
#menubar,
div#menubar {
background-color: #1e1e1e;
padding-top: 0px !important;
}
#menubar .navbar,
.navbar-default {
background-color: #1e1e1e;
margin-bottom: 0px;
margin-top: 0px;
}
.navbar {
border: none;
}
div.navbar-text,
.navbar-text,
.navbar-text.indicator_area,
p.navbar-text.indicator_area {
margin-top: 8px !important;
margin-bottom: 0px;
color: #a6e22e;
}
.navbar-default {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
background-color: #1e1e1e;
border-color: rgba(93,92,82,.25);
line-height: 1.5em;
padding-bottom: 0px;
}
.navbar-default .navbar-nav > li > a {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
color: #f8f8f0;
display: block;
line-height: 1.5em;
padding-top: 14px;
padding-bottom: 11px;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
color: #f8f8f0 !important;
background-color: rgba(93,92,82,.25) !important;
border-color: rgba(93,92,82,.25) !important;
line-height: 1.5em;
transition: 80ms ease;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
color: #f8f8f2;
background-color: #383838;
border-color: #383838;
line-height: 1.5em;
}
.navbar-nav > li > .dropdown-menu {
margin-top: 0px;
}
.navbar-nav {
margin: 0;
}
div.notification_widget.info,
.notification_widget.info,
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus,
div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn,
div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:hover,
div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:focus {
color: #f8f8f0 !important;
background-color: transparent !important;
border-color: transparent !important;
padding-bottom: 0px !important;
margin-bottom: 0px !important;
font-size: 9pt !important;
z-index: 0;
}
div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn {
font-size: 9pt !important;
z-index: 0;
}
.notification_widget {
color: #ae81ff;
z-index: -500;
font-size: 9pt;
background: transparent;
background-color: transparent;
margin-right: 3px;
border: none;
}
.notification_widget,
div.notification_widget {
margin-right: 0px;
margin-left: 0px;
padding-right: 0px;
vertical-align: text-top !important;
margin-top: 6px !important;
background: transparent !important;
background-color: transparent !important;
font-size: 9pt !important;
border: none;
}
.navbar-btn.btn-xs:hover {
border: none !important;
background: transparent !important;
background-color: transparent !important;
color: #f8f8f0 !important;
}
div.notification_widget.info,
.notification_widget.info {
display: none !important;
}
.edit_mode .modal_indicator:before {
display: none;
}
.command_mode .modal_indicator:before {
display: none;
}
.item_icon {
color: #ae81ff;
}
.item_buttons .kernel-name {
font-size: 11pt;
color: #ae81ff;
}
.running_notebook_icon:before {
color: #a6e22e !important;
font: normal normal normal 15px/1 FontAwesome;
font-size: 15px;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f10c";
vertical-align: middle;
position: static;
display: inherit;
}
.item_buttons .running-indicator {
padding-top: 4px;
color: #a6e22e;
font-family: "PT Sans", sans-serif;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
}
#notification_trusted {
font-family: "PT Sans", sans-serif;
border: none;
background: transparent;
background-color: transparent;
margin-bottom: 0px !important;
vertical-align: bottom !important;
color: #75715e !important;
cursor: default !important;
}
#notification_area,
div.notification_area {
float: right !important;
position: static;
cursor: pointer;
padding-top: 6px;
padding-right: 4px;
}
div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn {
font-size: 9pt !important;
z-index: 0;
margin-top: -5px !important;
}
#modal_indicator {
float: right !important;
color: #4c8be2;
background: #1e1e1e;
background-color: #1e1e1e;
margin-top: 8px !important;
margin-left: 0px;
}
#kernel_indicator {
float: right !important;
color: #a6e22e;
background: #1e1e1e;
background-color: #1e1e1e;
border-left: 2px solid #a6e22e;
padding-top: 0px;
padding-bottom: 4px;
margin-top: 10px !important;
margin-left: -2px;
padding-left: 5px !important;
}
#kernel_indicator .kernel_indicator_name {
font-size: 17px;
color: #a6e22e;
background: #1e1e1e;
background-color: #1e1e1e;
padding-left: 5px;
padding-right: 5px;
margin-top: 4px;
vertical-align: text-top;
padding-bottom: 0px;
}
.kernel_idle_icon:before {
display: inline-block;
font: normal normal normal 22px/1 FontAwesome;
font-size: 22px;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
cursor: pointer;
margin-left: 0px !important;
opacity: 0.7;
vertical-align: bottom;
margin-top: 1px;
content: "\f1db";
}
.kernel_busy_icon:before {
display: inline-block;
font: normal normal normal 22px/1 FontAwesome;
font-size: 22px;
-webkit-animation: pulsate 2s infinite ease-out;
animation: pulsate 2s infinite ease-out;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
cursor: pointer;
margin-left: 0px !important;
vertical-align: bottom;
margin-top: 1px;
content: "\f111";
}
@-webkit-keyframes pulsate {
0% {
-webkit-transform: scale(1.0,1.0);
opacity: 0.8;
}
8% {
-webkit-transform: scale(1.0,1.0);
opacity: 0.8;
}
50% {
-webkit-transform: scale(0.75,0.75);
opacity: 0.3;
}
92% {
-webkit-transform: scale(1.0,1.0);
opacity: 0.8;
}
100% {
-webkit-transform: scale(1.0,1.0);
opacity: 0.8;
}
}
div.notification_widget.info,
.notification_widget.info,
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus,
div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn,
div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:hover,
div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:focus {
color: #f8f8f0;
background-color: #1e1e1e;
border-color: #1e1e1e;
}
#notification_area,
div.notification_area {
float: right !important;
position: static;
}
.notification_widget,
div.notification_widget {
margin-right: 0px;
margin-left: 0px;
padding-right: 0px;
vertical-align: text-top !important;
margin-top: 6px !important;
z-index: 1000;
}
#kernel_logo_widget,
#kernel_logo_widget .current_kernel_logo {
display: block;
}
div#ipython_notebook {
display: none;
}
i.fa.fa-icon {
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
text-rendering: auto;
}
.fa {
display: inline-block;
font: normal normal normal 10pt/1 "FontAwesome", "PT Sans", sans-serif;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.dropdown-menu {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
box-shadow: none;
padding: 0px;
text-align: left;
border: none;
background-color: #383838;
background: #383838;
line-height: 1;
}
.dropdown-menu:hover {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
box-shadow: none;
padding: 0px;
text-align: left;
border: none;
background-color: #383838;
box-shadow: none;
line-height: 1;
}
.dropdown-menu > li > a {
font-family: "PT Sans", sans-serif;
font-size: 10.0pt;
display: block;
padding: 10px 20px 9px 10px;
color: #f8f8f0;
background-color: #383838;
background: #383838;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
color: #f8f8f0;
background-color: rgba(93,92,82,.25);
background: rgba(93,92,82,.25);
border-color: rgba(93,92,82,.25);
transition: 200ms ease;
}
.dropdown-menu .divider {
height: 1px;
margin: 0px 0px;
overflow: hidden;
background-color: rgba(93,92,82,.5);
}
.dropdown-submenu > .dropdown-menu {
display: none;
top: 2px !important;
left: 100%;
margin-top: -2px;
margin-left: 0px;
padding-top: 0px;
transition: 200ms ease;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
font-family: "PT Sans", sans-serif;
font-size: 10.0pt;
font-weight: normal;
color: #75715e;
padding: none;
display: block;
clear: both;
white-space: nowrap;
}
.dropdown-submenu > a:after {
color: #f8f8f0;
margin-right: -16px;
margin-top: 0px;
display: inline-block;
}
.dropdown-submenu:hover > a:after,
.dropdown-submenu:active > a:after,
.dropdown-submenu:focus > a:after,
.dropdown-submenu:visited > a:after {
color: #a6e22e;
margin-right: -16px;
display: inline-block !important;
}
div.kse-dropdown > .dropdown-menu,
.kse-dropdown > .dropdown-menu {
min-width: 0;
top: 94%;
}
.btn,
.btn-default {
font-family: "PT Sans", sans-serif;
color: #f8f8f0;
background: #2f2f2f;
background-color: #2f2f2f;
border: 2px solid #2f2f2f;
font-weight: normal;
box-shadow: none;
text-shadow: none;
border-radius: 3px;
font-size: initial;
}
.btn:hover,
.btn:active:hover,
.btn.active:hover,
.btn-default:hover,
.open > .dropdown-toggle.btn-default:hover,
.open > .dropdown-toggle.btn:hover {
color: #a6e22e;
border: 2px solid #2a2a2a;
background-color: #2a2a2a;
background: #2a2a2a;
background-image: none;
box-shadow: none !important;
border-radius: 3px;
}
.btn:active,
.btn.active,
.btn:active:focus,
.btn.active:focus,
.btn:active.focus,
.btn.active.focus,
.btn-default:focus,
.btn-default.focus,
.btn-default:active,
.btn-default.active,
.btn-default:active:hover,
.btn-default.active:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn:focus,
.open > .dropdown-toggle.btn.focus,
.open > .dropdown-toggle.btn-default:hover,
.open > .dropdown-toggle.btn-default:focus,
.open > .dropdown-toggle.btn-default.hover,
.open > .dropdown-toggle.btn-default.focus {
color: #a6e22e;
border: 2px solid #2a2a2a;
background-color: #2a2a2a !important;
background: #2a2a2a !important;
background-image: none;
box-shadow: none !important;
border-radius: 3px;
}
.btn-default:active:hover,
.btn-default.active:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.btn-default:active.focus,
.btn-default.active.focus {
color: #a6e22e !important;
background-color: #2f2f2f;
border-color: #546745 !important;
transition: 2000ms ease;
}
.btn:focus,
.btn.focus,
.btn:active:focus,
.btn.active:focus,
.btn:active,
.btn.active,
.btn:active.focus,
.btn.active.focus {
color: #a6e22e !important;
outline: none !important;
outline-width: 0px !important;
background: #546745 !important;
background-color: #546745 !important;
border-color: #546745 !important;
transition: 200ms ease !important;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
font-size: 11pt;
background: transparent;
background-color: transparent;
border: 0px solid #2f2f2f;
border-bottom: 2px solid transparent;
margin-left: 5px;
padding-top: 4px !important;
}
.item_buttons > .btn:hover,
.item_buttons > .btn-group:hover,
.item_buttons > .input-group:hover,
.item_buttons > .btn.active,
.item_buttons > .btn-group.active,
.item_buttons > .input-group.active,
.item_buttons > .btn.focus {
margin-left: 5px;
background: #2a2a2a;
padding-top: 4px !important;
background-color: transparent;
border: 0px solid transparent;
border-bottom: 2px solid #a6e22e;
border-radius: 0px;
transition: none;
}
.item_buttons {
line-height: 1.5em !important;
}
.item_buttons .btn {
min-width: 11ex;
}
.btn-group > .btn:first-child {
margin-left: 3px;
}
.btn-group > .btn-mini,
.btn-sm,
.btn-group-sm > .btn,
.btn-xs,
.btn-group-xs > .btn,
.alternate_upload .btn-upload,
.btn-group,
.btn-group-vertical {
font-size: inherit;
font-weight: normal;
height: inherit;
line-height: inherit;
}
.btn-xs,
.btn-group-xs > .btn {
font-size: initial !important;
background-image: none;
font-weight: normal;
text-shadow: none;
display: inline-table;
padding: 2px 5px;
line-height: 1.45;
}
.btn-group > .btn:first-child {
margin-left: 3px;
}
div#new-buttons > button,
#new-buttons > button,
div#refresh_notebook_list,
#refresh_notebook_list {
background: transparent;
background-color: transparent;
border: none;
}
div#new-buttons > button:hover,
#new-buttons > button:hover,
div#refresh_notebook_list,
#refresh_notebook_list,
div.alternate_upload .btn-upload,
.alternate_upload .btn-upload,
div.dynamic-buttons > button,
.dynamic-buttons > button,
.dynamic-buttons > button:focus,
.dynamic-buttons > button:active:focus,
.dynamic-buttons > button.active:focus,
.dynamic-buttons > button.focus,
.dynamic-buttons > button:active.focus,
.dynamic-buttons > button.active.focus,
#new-buttons > button:focus,
#new-buttons > button:active:focus,
#new-buttons > button.active:focus,
#new-buttons > button.focus,
#new-buttons > button:active.focus,
#new-buttons > button.active.focus,
.alternate_upload .btn-upload:focus,
.alternate_upload .btn-upload:active:focus,
.alternate_upload .btn-upload.active:focus,
.alternate_upload .btn-upload.focus,
.alternate_upload .btn-upload:active.focus,
.alternate_upload .btn-upload.active.focus {
background: transparent !important;
background-color: transparent !important;
border: none !important;
}
.alternate_upload input.fileinput {
text-align: center;
vertical-align: bottom;
margin-left: -.5ex;
display: inline-table;
border: solid 0px #2f2f2f;
margin-bottom: -1ex;
}
.alternate_upload .btn-upload {
display: inline-table;
background: transparent;
border: none;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
margin-left: -2px;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
border-bottom-right-radius: 0;
border-top-right-radius: 0;
z-index: 2;
}
.dropdown-header {
font-family: "PT Sans", sans-serif !important;
font-size: 11pt !important;
color: #a6e22e !important;
border-bottom: none !important;
padding: 0px !important;
margin: 6px 6px 0px !important;
}
span#last-modified.btn.btn-xs.btn-default.sort-action,
span#sort-name.btn.btn-xs.btn-default.sort-action,
span#file-size.btn.btn-xs.btn-default.sort-action {
font-family: "PT Sans", sans-serif;
font-size: 16px;
background-color: transparent;
background: transparent;
border: none;
color: #f8f8f0;
padding-bottom: 0px;
margin-bottom: 0px;
vertical-align: sub;
}
span#last-modified.btn.btn-xs.btn-default.sort-action {
margin-left: 19px;
}
button.close {
border: 0px none;
font-family: sans-serif;
font-size: 20pt;
font-weight: normal;
}
.dynamic-buttons {
padding-top: 0px;
display: inline-block;
}
.close {
color: #f92672;
opacity: .5;
text-shadow: none;
font-weight: normal;
}
.close:hover {
color: #f92672;
opacity: 1;
font-weight: normal;
}
div.nbext-enable-btns .btn[disabled],
div.nbext-enable-btns .btn[disabled]:hover,
.btn-default.disabled,
.btn-default[disabled],
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
color: #888888;
background: #2c2c2c;
background-color: #2c2c2c;
border-color: #2c2c2c;
transition: 200ms ease;
}
.input-group-addon {
padding: 2px 5px;
font-size: 11pt;
font-weight: normal;
height: auto;
color: #f8f8f0;
text-align: center;
background-color: transparent;
border: 2px solid transparent !important;
text-transform: capitalize;
}
a.btn.btn-default.input-group-addon:hover {
background: transparent !important;
background-color: transparent !important;
}
.btn-group > .btn + .dropdown-toggle {
padding-left: 8px;
padding-right: 8px;
height: 100%;
}
.btn-group > .btn + .dropdown-toggle:hover {
background: #2a2a2a !important;
}
.input-group-btn {
position: relative;
font-size: inherit;
white-space: nowrap;
background: #2f2f2f;
background-color: #2f2f2f;
border: none;
}
.input-group-btn:hover {
background: #2a2a2a;
background-color: #2a2a2a;
border: none;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
background: #2f2f2f;
background-color: #2f2f2f;
border: none;
margin-left: 2px;
margin-right: -1px;
font-size: inherit;
}
.input-group-btn:first-child > .btn:hover,
.input-group-btn:first-child > .btn-group:hover {
background: #2a2a2a;
background-color: #2a2a2a;
border: none;
font-size: inherit;
transition: 200ms ease;
}
div.modal .btn-group > .btn:first-child {
background: #2f2f2f;
background-color: #2f2f2f;
border: 1px solid #2c2c2c;
margin-top: 0px !important;
margin-left: 0px;
margin-bottom: 2px;
}
div.modal .btn-group > .btn:first-child:hover {
background: #2a2a2a;
background-color: #2a2a2a;
border: 1px solid #2a2a2a;
transition: 200ms ease;
}
div.modal > button,
div.modal-footer > button {
background: #2f2f2f;
background-color: #2f2f2f;
border-color: #2f2f2f;
}
div.modal > button:hover,
div.modal-footer > button:hover {
background: #2a2a2a;
background-color: #2a2a2a;
border-color: #2a2a2a;
transition: 200ms ease;
}
.modal-content {
font-family: "PT Sans", sans-serif;
font-size: 10.0pt;
position: relative;
background: #2f2f2f;
background-color: #2f2f2f;
border: none;
border-radius: 1px;
background-clip: padding-box;
outline: none;
}
.modal-header {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
color: #f8f8f0;
background: #2f2f2f;
background-color: #2f2f2f;
border-color: rgba(93,92,82,.25);
padding: 12px;
min-height: 16.4286px;
}
.modal-content h4 {
font-family: "PT Sans", sans-serif;
font-size: 16pt;
color: #f8f8f0;
padding: 5px;
}
.modal-body {
background-color: #232323;
position: relative;
padding: 15px;
}
.modal-footer {
padding: 8px;
text-align: right;
background-color: #232323;
border-top: none;
}
.alert-info {
background-color: #2f2f2f;
border-color: rgba(93,92,82,.25);
color: #f8f8f0;
}
.modal-header .close {
margin-top: -5px;
font-size: 25pt;
}
.modal-backdrop,
.modal-backdrop.in {
opacity: 0.85;
background-color: notebook-bg;
}
div.panel,
div.panel-default,
.panel,
.panel-default {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
background-color: #232323;
color: #f8f8f0;
margin-bottom: 14px;
border: 0;
box-shadow: none;
}
div.panel > .panel-heading,
div.panel-default > .panel-heading {
font-size: 14pt;
color: #f8f8f0;
background: #2f2f2f;
background-color: #2f2f2f;
border: 0;
}
.modal .modal-dialog {
min-width: 950px;
margin: 50px auto;
}
div.container-fluid {
margin-right: auto;
margin-left: auto;
padding-left: 0px;
padding-right: 5px;
}
div.form-control,
.form-control {
font-family: "PT Sans", sans-serif;
font-size: initial;
color: #f8f8f0;
background-color: #282828;
border: 1px solid #282828 !important;
margin-left: 2px;
box-shadow: none;
transition: border-color 0.15s ease-in-out 0s, box-shadow 0.15s ease-in-out 0s;
}
.form-control-static {
min-height: inherit;
height: inherit;
}
.form-group.list-group-item {
color: #f8f8f0;
background-color: #232323;
border-color: rgba(93,92,82,.25);
margin-bottom: 0px;
}
.form-group .input-group {
float: left;
}
input,
button,
select,
textarea {
background-color: #282828;
font-weight: normal;
border: 1px solid rgba(93,92,82,.25);
}
select.form-control.select-xs {
height: 33px;
font-size: 11pt;
}
.toolbar select,
.toolbar label {
width: auto;
vertical-align: middle;
margin-right: 0px;
margin-bottom: 0px;
display: inline;
font-size: 92%;
margin-left: 10px;
padding: 0px;
background: #2f2f2f !important;
background-color: #2f2f2f !important;
border: 2px solid #2f2f2f !important;
}
.form-control:focus {
border-color: #a6e22e;
outline: 2px solid #49483e;
-webkit-box-shadow: none;
}
::-webkit-input-placeholder {
color: #75715e;
}
::-moz-placeholder {
color: #75715e;
}
:-ms-input-placeholder {
color: #75715e;
}
:-moz-placeholder {
color: #75715e;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
border: 2px solid rgba(93,92,82,.25) !important;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control:focus {
border-color: #a6e22e;
outline: 2px solid #49483e;
-webkit-box-shadow: none;
box-shadow: none;
}
div.output.output_scroll {
box-shadow: none;
}
::-webkit-scrollbar {
width: 11px;
max-height: 9px;
background-color: #2d2d2d;
border-radius: 3px;
border: none;
}
::-webkit-scrollbar-track {
background: #2d2d2d;
border: none;
width: 11px;
max-height: 9px;
}
::-webkit-scrollbar-thumb {
border-radius: 2px;
border: none;
background: #49483e;
background-clip: content-box;
width: 11px;
}
HTML,
body,
div,
dl,
dt,
dd,
ul,
ol,
li,
h1,
h2,
h3,
h4,
h5,
h6,
pre,
code,
form,
fieldset,
legend,
input,
button,
textarea,
p,
blockquote,
th,
td,
span,
a {
text-rendering: geometricPrecision;
-webkit-font-smoothing: subpixel-antialiased;
font-weight: 400;
}
div.input_area {
background-color: #282828;
background: #282828;
padding-right: 1.2em;
border: 0px;
border-radius: 0px;
border-top-right-radius: 4px;
border-bottom-right-radius: 4px;
}
div.cell {
padding: 0px;
background: #282828;
background-color: #282828;
border: medium solid #1e1e1e;
border-radius: 4px;
top: 0;
}
div.cell.selected {
background: #282828;
background-color: #282828;
border: medium solid #1e1e1e;
padding: 0px;
border-radius: 5px;
}
.edit_mode div.cell.selected {
padding: 0px;
background: #282828;
background-color: #282828;
border: medium solid #1e1e1e;
border-radius: 5px;
}
div.cell.edit_mode {
padding: 0px;
background: #282828;
background-color: #282828;
}
div.CodeMirror-sizer {
margin-left: 0px;
margin-bottom: -21px;
border-right-width: 16px;
min-height: 37px;
padding-right: 0px;
padding-bottom: 0px;
margin-top: 0px;
}
div.cell.selected:before,
.edit_mode div.cell.selected:before,
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
background: #282828 !important;
border: none;
border-radius: 3px;
position: absolute;
display: block;
top: 0px;
left: 0px;
width: 0px;
height: 100%;
}
div.cell.text_cell.selected::before,
.edit_mode div.cell.text_cell.selected:before,
div.cell.text_cell.selected:before,
div.cell.text_cell.selected.jupyter-soft-selected:before {
background: #282828 !important;
background-color: #282828 !important;
border-color: #57564b !important;
}
div.cell.code_cell .input {
border-left: 5px solid #282828 !important;
border-radius: 3px;
border-bottom-left-radius: 3px;
border-top-left-radius: 3px;
}
div.cell.code_cell.selected .input {
border-left: 5px solid #57564b !important;
border-radius: 3px;
}
.edit_mode div.cell.code_cell.selected .input {
border-left: 5px solid #33322b !important;
border-radius: 3px;
}
.edit_mode div.cell.selected:before {
height: 100%;
border-left: 5px solid #33322b !important;
border-radius: 3px;
}
div.cell.jupyter-soft-selected,
div.cell.selected.jupyter-soft-selected {
border-left-color: #33322b !important;
border-left-width: 0px !important;
padding-left: 7px !important;
border-right-color: #33322b !important;
border-right-width: 0px !important;
background: #33322b !important;
border-radius: 6px !important;
}
div.cell.selected.jupyter-soft-selected .input {
border-left: 5px solid #282828 !important;
}
div.cell.selected.jupyter-soft-selected {
border-left-color: #57564b;
border-color: #1e1e1e;
padding-left: 7px;
border-radius: 6px;
}
div.cell.code_cell.selected .input {
border-left: none;
border-radius: 3px;
}
div.cell.selected.jupyter-soft-selected .prompt,
div.cell.text_cell.selected.jupyter-soft-selected .prompt {
top: 0;
border-left: #282828 !important;
border-radius: 2px;
}
div.cell.text_cell.selected.jupyter-soft-selected .input_prompt {
border-left: none !important;
}
div.cell.text_cell.jupyter-soft-selected,
div.cell.text_cell.selected.jupyter-soft-selected {
border-left-color: #33322b !important;
border-left-width: 0px !important;
padding-left: 26px !important;
border-right-color: #33322b !important;
border-right-width: 0px !important;
background: #33322b !important;
border-radius: 5px !important;
}
div.cell.jupyter-soft-selected .input,
div.cell.selected.jupyter-soft-selected .input {
border-left-color: #33322b !important;
}
div.prompt,
.prompt {
font-family: "Fira Mono", monospace, monospace;
font-size: 9pt !important;
font-weight: normal;
color: #75715e;
line-height: 170%;
padding: 0px;
padding-top: 4px;
padding-left: 0px;
padding-right: 1px;
text-align: right !important;
min-width: 11.5ex !important;
width: 11.5ex !important;
}
div.prompt.input_prompt {
font-size: 9pt !important;
background-color: #282828;
border-top: 0px;
border-top-right-radius: 0px;
border-bottom-left-radius: 0px;
border-bottom-right-radius: 0px;
padding-right: 3px;
min-width: 11.5ex;
width: 11.5ex !important;
}
div.cell.code_cell .input_prompt {
border-right: 2px solid #49483e;
}
div.cell.selected .prompt {
top: 0;
}
.edit_mode div.cell.selected .prompt {
top: 0;
}
.edit_mode div.cell.selected .prompt {
top: 0;
}
.run_this_cell {
visibility: hidden;
color: transparent;
padding-top: 0px;
padding-bottom: 0px;
padding-left: 3px;
padding-right: 12px;
width: 1.5ex;
width: 0ex;
background: transparent;
background-color: transparent;
}
div.code_cell:hover div.input .run_this_cell {
visibility: visible;
}
div.cell.code_cell.rendered.selected .run_this_cell:hover {
background-color: #1e1e1e;
background: #1e1e1e;
color: #57564b !important;
}
div.cell.code_cell.rendered.unselected .run_this_cell:hover {
background-color: #1e1e1e;
background: #1e1e1e;
color: #57564b !important;
}
i.fa-step-forward.fa {
display: inline-block;
font: normal normal normal 9px "FontAwesome";
}
.fa-step-forward:before {
content: "\f04b";
}
div.cell.selected.jupyter-soft-selected .run_this_cell,
div.cell.selected.jupyter-soft-selected .run_this_cell:hover,
div.cell.unselected.jupyter-soft-selected .run_this_cell:hover,
div.cell.code_cell.rendered.selected.jupyter-soft-selected .run_this_cell:hover,
div.cell.code_cell.rendered.unselected.jupyter-soft-selected .run_this_cell:hover {
background-color: #33322b !important;
background: #33322b !important;
color: #33322b !important;
}
div.output_wrapper {
background-color: #232323;
border: 0px;
left: 0px;
margin-bottom: 0em;
margin-top: 0em;
border-top-right-radius: 0px;
border-top-left-radius: 0px;
}
div.output_subarea.output_text.output_stream.output_stdout,
div.output_subarea.output_text {
font-family: "Fira Mono", monospace, monospace;
font-size: 8.5pt !important;
line-height: 150% !important;
background-color: #232323;
color: #cccccc;
border-top-right-radius: 0px;
border-top-left-radius: 0px;
margin-left: 11.5px;
}
div.output_area pre {
font-family: "Fira Mono", monospace, monospace;
font-size: 8.5pt !important;
line-height: 151% !important;
color: #cccccc;
border-top-right-radius: 0px;
border-top-left-radius: 0px;
}
div.output_area {
display: -webkit-box;
}
div.output_html {
font-family: "Fira Mono", monospace, monospace;
font-size: 8.5pt;
color: #cccccc;
background-color: #232323;
background: #232323;
}
div.output_subarea {
overflow-x: auto;
padding: 1.2em !important;
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
flex: 1;
}
div.btn.btn-default.output_collapsed {
background: #222222;
background-color: #222222;
border-color: #222222;
}
div.btn.btn-default.output_collapsed:hover {
background: #1d1d1d;
background-color: #1d1d1d;
border-color: #1d1d1d;
}
div.prompt.output_prompt {
font-family: "Fira Mono", monospace, monospace;
font-weight: bold !important;
background-color: #232323;
color: transparent;
border-bottom-left-radius: 4px;
border-top-right-radius: 0px;
border-top-left-radius: 0px;
border-bottom-right-radius: 0px;
min-width: 11.5ex !important;
width: 11.5ex !important;
border-right: 2px solid transparent;
}
div.out_prompt_overlay.prompt {
font-family: "Fira Mono", monospace, monospace;
font-weight: bold !important;
background-color: #232323;
border-bottom-left-radius: 2px;
border-top-right-radius: 0px;
border-top-left-radius: 0px;
border-bottom-right-radius: 0px;
min-width: 11.5ex !important;
width: 11.5ex !important;
border-right: 2px solid transparent;
color: transparent;
}
div.out_prompt_overlay.prompt:hover {
background-color: #49483e;
box-shadow: none !important;
border: none;
border-bottom-left-radius: 2px;
-webkit-border-: 2px;
-moz-border-radius: 2px;
border-top-right-radius: 0px;
border-top-left-radius: 0px;
min-width: 11.5ex !important;
width: 11.5ex !important;
border-right: 2px solid #49483e !important;
}
div.cell.code_cell .output_prompt {
border-right: 2px solid transparent;
color: transparent;
}
div.cell.selected .output_prompt,
div.cell.selected .out_prompt_overlay.prompt {
border-left: 5px solid #33322b;
border-right: 2px solid #232323;
border-radius: 0px !important;
}
.edit_mode div.cell.selected .output_prompt,
.edit_mode div.cell.selected .out_prompt_overlay.prompt {
border-left: 5px solid #33322b;
border-right: 2px solid #232323;
border-radius: 0px !important;
}
div.text_cell,
div.text_cell_render pre,
div.text_cell_render {
font-family: sans-serif;
font-size: 13pt;
line-height: 130% !important;
color: #f8f8f0;
background: #282828;
background-color: #282828;
border-radius: 0px;
}
div .text_cell_render {
padding: 0.4em 0.4em 0.4em 0.4em;
}
div.cell.text_cell .CodeMirror-lines {
padding-top: .7em !important;
padding-bottom: .4em !important;
padding-left: .5em !important;
padding-right: .5em !important;
margin-top: .4em;
margin-bottom: .3em;
}
div.cell.text_cell.unrendered div.input_area,
div.cell.text_cell.rendered div.input_area {
background-color: #282828;
background: #282828;
border: 0px;
border-radius: 2px;
}
div.cell.text_cell .CodeMirror,
div.cell.text_cell .CodeMirror pre {
line-height: 170% !important;
}
div.cell.text_cell.rendered.selected {
font-family: sans-serif;
line-height: 170% !important;
background: #282828;
background-color: #282828;
border-radius: 0px;
}
div.cell.text_cell.unrendered.selected {
font-family: sans-serif;
line-height: 170% !important;
background: #282828;
background-color: #282828;
border-radius: 0px;
}
div.cell.text_cell.selected {
font-family: sans-serif;
line-height: 170% !important;
background: #282828;
background-color: #282828;
border-radius: 0px;
}
.edit_mode div.cell.text_cell.selected {
font-family: sans-serif;
line-height: 170% !important;
background: #282828;
background-color: #282828;
border-radius: 0px;
}
div.text_cell.unrendered,
div.text_cell.unrendered.selected,
div.edit_mode div.text_cell.unrendered {
font-family: sans-serif;
line-height: 170% !important;
background: #282828;
background-color: #282828;
border-radius: 0px;
}
div.cell.text_cell .prompt {
border-right: 0;
min-width: 11.5ex !important;
width: 11.5ex !important;
}
div.cell.text_cell.rendered .prompt {
font-family: "Fira Mono", monospace, monospace;
font-size: 9.5pt !important;
font-weight: normal;
color: #75715e !important;
text-align: right !important;
min-width: 14.5ex !important;
width: 14.5ex !important;
background-color: #282828;
border-right: 2px solid #49483e;
border-left: 4px solid #282828;
}
div.cell.text_cell.unrendered .prompt {
font-family: "Fira Mono", monospace, monospace;
font-size: 9.5pt !important;
font-weight: normal;
color: #75715e !important;
text-align: right !important;
min-width: 14.5ex !important;
width: 14.5ex !important;
border-right: 2px solid #49483e;
border-left: 4px solid #282828;
background-color: #282828;
}
div.cell.text_cell.rendered .prompt {
border-right: 2px solid #49483e;
}
div.cell.text_cell.rendered.selected .prompt {
top: 0;
border-left: 4px solid #57564b;
border-right: 2px solid #49483e;
}
div.text_cell.unrendered.selected .prompt,
div.text_cell.rendered.selected .prompt {
top: 0;
background: #282828;
border-left: 4px solid #33322b;
border-right: 2px solid #49483e;
}
div.rendered_html code {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt;
padding-top: 3px;
padding-left: 2px;
color: #f8f8f0;
background: #282828;
background-color: #282828;
}
pre,
code,
kbd,
samp {
white-space: pre-wrap;
}
.well code,
code {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt !important;
line-height: 170% !important;
color: #f8f8f0;
background: #282828;
background-color: #282828;
border-color: #282828;
}
kbd {
padding: 1px;
font-size: 12pt;
font-weight: 800;
color: #f8f8f0;
background-color: transparent !important;
border: 0;
box-shadow: none;
}
pre {
display: block;
padding: 8.5px;
margin: 0 0 9px;
font-size: 10.0pt;
line-height: 1.42857143;
color: #f8f8f0;
background-color: #282828;
border: 1px solid #282828;
border-radius: 2px;
}
div.rendered_html {
color: #f8f8f0;
}
.rendered_html * + ul {
margin-top: .4em;
margin-bottom: .3em;
}
.rendered_html * + p {
margin-top: .5em;
margin-bottom: .5em;
}
div.rendered_html pre {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt !important;
line-height: 170% !important;
color: #f8f8f0 !important;
background: #282828;
background-color: #282828;
max-width: 80%;
border-radius: 0px;
border-left: 3px solid #282828;
max-width: 80%;
border-radius: 0px;
padding-left: 5px;
margin-left: 6px;
}
div.text_cell_render pre,
div.text_cell_render code {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt !important;
line-height: 170% !important;
color: #f8f8f0;
background: #1e1e1e;
background-color: #1e1e1e;
max-width: 80%;
border-radius: 0px;
border-left: none;
}
div.text_cell_render pre {
border-left: 3px solid #49483e !important;
max-width: 80%;
border-radius: 0px;
padding-left: 5px;
margin-left: 6px;
}
div.text_cell_render h1,
div.rendered_html h1,
div.text_cell_render h2,
div.rendered_html h2,
div.text_cell_render h3,
div.rendered_html h3,
div.text_cell_render h4,
div.rendered_html h4,
div.text_cell_render h5,
div.rendered_html h5 {
font-family: "PT Sans", sans-serif;
margin: 0.4em .2em .3em .2em !important;
}
.rendered_html h1:first-child,
.rendered_html h2:first-child,
.rendered_html h3:first-child,
.rendered_html h4:first-child,
.rendered_html h5:first-child,
.rendered_html h6:first-child {
margin-top: 0.2em !important;
margin-bottom: 0.2em !important;
}
.rendered_html h1,
.text_cell_render h1 {
color: #a6e22e !important;
font-size: 200%;
text-align: left;
font-style: normal;
font-weight: normal;
}
.rendered_html h2,
.text_cell_render h2 {
color: #a6e22e !important;
font-size: 170%;
font-style: normal;
font-weight: normal;
}
.rendered_html h3,
.text_cell_render h3 {
color: #a6e22e !important;
font-size: 140%;
font-style: normal;
font-weight: normal;
}
.rendered_html h4,
.text_cell_render h4 {
color: #a6e22e !important;
font-size: 110%;
font-style: normal;
font-weight: normal;
}
.rendered_html h5,
.text_cell_render h5 {
color: #a6e22e !important;
font-size: 100%;
font-style: normal;
font-weight: normal;
}
hr {
margin-top: 8px;
margin-bottom: 10px;
border: 0;
border-top: 1px solid #a6e22e;
}
.rendered_html hr {
color: #a6e22e;
background-color: #a6e22e;
margin-right: 2em;
}
#complete > select > option:hover {
background: rgba(93,92,82,.25);
background-color: rgba(93,92,82,.25);
}
div#_vivaldi-spatnav-focus-indicator._vivaldi-spatnav-focus-indicator {
position: absolute;
z-index: 9999999999;
top: 0px;
left: 0px;
box-shadow: none;
pointer-events: none;
border-radius: 2px;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
text-align: left;
vertical-align: middle;
padding: 0.42em 0.47em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.rendered_html td {
font-family: "PT Sans", sans-serif !important;
font-size: 9.3pt;
}
.rendered_html table {
font-family: "PT Sans", sans-serif !important;
margin-left: 8px;
margin-right: auto;
border: none;
border-collapse: collapse;
border-spacing: 0;
color: #cccccc;
table-layout: fixed;
}
.rendered_html thead {
font-family: "PT Sans", sans-serif !important;
font-size: 10.3pt !important;
background: #1e1e1e;
color: #cccccc;
border-bottom: 1px solid #1e1e1e;
vertical-align: bottom;
}
.rendered_html tbody tr:nth-child(odd) {
background: #282828;
}
.rendered_html tbody tr {
background: #202020;
}
.rendered_html tbody tr:hover:nth-child(odd) {
background: #252525;
}
.rendered_html tbody tr:hover {
background: #1e1e1e;
}
.rendered_html * + table {
margin-top: .05em;
}
div.widget-area {
background-color: #232323;
background: #232323;
color: #cccccc;
}
div.widget-area a {
font-family: "PT Sans", sans-serif;
font-size: 10.0pt;
font-weight: normal;
font-style: normal;
color: #f8f8f0;
text-shadow: none !important;
}
div.widget-area a:hover,
div.widget-area a:focus {
font-family: "PT Sans", sans-serif;
font-size: 10.0pt;
font-weight: normal;
font-style: normal;
color: #f8f8f0;
background: rgba(93,92,82,.25);
background-color: rgba(93,92,82,.25);
border-color: transparent;
background-image: none;
text-shadow: none !important;
}
div.widget_item.btn-group > button.btn.btn-default.widget-combo-btn,
div.widget_item.btn-group > button.btn.btn-default.widget-combo-btn:hover {
background: #2c2c2c;
background-color: #2c2c2c;
border: 2px solid #2c2c2c !important;
font-size: inherit;
z-index: 0;
}
div.jupyter-widgets.widget-hprogress.widget-hbox {
display: inline-table !important;
width: 38% !important;
margin-left: 10px;
}
div.jupyter-widgets.widget-hprogress.widget-hbox .widget-label,
div.widget-hbox .widget-label,
.widget-hbox .widget-label,
.widget-inline-hbox .widget-label,
div.widget-label {
text-align: -webkit-auto !important;
margin-left: 15px !important;
max-width: 240px !important;
min-width: 100px !important;
vertical-align: text-top !important;
color: #cccccc !important;
font-size: 14px !important;
}
.widget-hprogress .progress {
flex-grow: 1;
height: 20px;
margin-top: auto;
margin-left: 12px;
margin-bottom: auto;
width: 300px;
}
.progress {
overflow: hidden;
height: 22px;
margin-bottom: 10px;
padding-left: 10px;
background-color: #49483e !important;
border-radius: 2px;
-webkit-box-shadow: none;
box-shadow: none;
z-index: 10;
}
.progress-bar-danger {
background-color: #e74c3c !important;
}
.progress-bar-info {
background-color: #3498db !important;
}
.progress-bar-warning {
background-color: #ff914d !important;
}
.progress-bar-success {
background-color: #83a83b !important;
}
.widget-select select {
margin-left: 12px;
}
.rendered_html :link {
font-family: "PT Sans", sans-serif;
font-size: 100%;
color: #a6e22e;
text-decoration: underline;
}
.rendered_html :visited,
.rendered_html :visited:active,
.rendered_html :visited:focus {
color: #acdf45;
}
.rendered_html :visited:hover,
.rendered_html :link:hover {
font-family: "PT Sans", sans-serif;
font-size: 100%;
color: #97dc0b;
}
div.cell.text_cell a.anchor-link:link {
font-size: inherit;
text-decoration: none;
padding: 0px 20px;
visibility: none;
color: rgba(0,0,0,.32);
}
div.cell.text_cell a.anchor-link:link:hover {
font-size: inherit;
color: #a6e22e;
}
.navbar-text {
margin-top: 4px;
margin-bottom: 0px;
}
#clusters > a {
color: #a6e22e;
text-decoration: underline;
cursor: auto;
}
#clusters > a:hover {
color: #ae81ff;
text-decoration: underline;
cursor: auto;
}
#nbextensions-configurator-container > div.row.container-fluid.nbext-selector > h3 {
font-size: 17px;
margin-top: 5px;
margin-bottom: 8px;
height: 24px;
padding: 4px 0 4px 0;
}
div#nbextensions-configurator-container.container,
#nbextensions-configurator-container.container {
width: 100%;
margin-right: auto;
margin-left: auto;
}
div.nbext-selector > nav > .nav > li > a {
font-family: "PT Sans", sans-serif;
font-size: 10.5pt;
padding: 2px 5px;
}
div.nbext-selector > nav > .nav > li > a:hover {
background: transparent;
}
div.nbext-selector > nav > .nav > li:hover {
background-color: rgba(93,92,82,.25) !important;
background: rgba(93,92,82,.25) !important;
}
div.nbext-selector > nav > .nav > li.active:hover {
background: transparent !important;
background-color: transparent !important;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:active,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
color: #f8f8f2;
background-color: rgba(93,92,82,.25) !important;
background: rgba(93,92,82,.25) !important;
-webkit-backface-visibility: hidden;
-webkit-font-smoothing: subpixel-antialiased !important;
}
div.nbext-readme > .nbext-readme-contents > .rendered_html {
font-family: "PT Sans", sans-serif;
font-size: 11.5pt;
line-height: 145%;
padding: 1em 1em;
color: #f8f8f0;
background-color: #282828;
-webkit-box-shadow: none;
-moz-box-shadow: none;
box-shadow: none;
}
.nbext-icon,
.nbext-desc,
.nbext-compat-div,
.nbext-enable-btns,
.nbext-params {
margin-bottom: 8px;
font-size: 11.5pt;
}
div.nbext-readme > .nbext-readme-contents {
padding: 0;
overflow-y: hidden;
}
div.nbext-readme > .nbext-readme-contents:not(:empty) {
margin-top: 0.5em;
margin-bottom: 2em;
border: none;
border-top-color: rgba(148,204,114,.2);
}
.nbext-showhide-incompat {
padding-bottom: 0.5em;
color: #888888;
font-size: 10.5pt;
}
.nbext-filter-menu.dropdown-menu > li > a:hover,
.nbext-filter-menu.dropdown-menu > li > a:focus,
.nbext-filter-menu.dropdown-menu > li > a.ui-state-focus {
color: #f8f8f0 !important;
background-color: rgba(93,92,82,.25) !important;
background: rgba(93,92,82,.25) !important;
border-color: rgba(93,92,82,.25) !important;
}
.nbext-filter-input-wrap > .nbext-filter-input-subwrap,
.nbext-filter-input-wrap > .nbext-filter-input-subwrap > input {
border: none;
outline: none;
background-color: transparent;
padding: 0;
vertical-align: middle;
margin-top: -2px;
}
span.rendered_html code {
background-color: transparent;
color: #f8f8f0;
}
#nbextensions-configurator-container > div.row.container-fluid.nbext-selector {
padding-left: 0px;
padding-right: 0px;
}
.nbext-filter-menu {
max-height: 55vh !important;
overflow-y: auto;
outline: none;
border: none;
}
.nbext-filter-menu:hover {
border: none;
}
.alert-warning {
background-color: #232323;
border-color: #232323;
color: #f8f8f0;
}
.notification_widget.danger {
color: #ffffff;
background-color: #e74c3c;
border-color: #e74c3c;
padding-right: 5px;
}
#nbextensions-configurator-container > div.nbext-buttons.tree-buttons.no-padding.pull-right > span > button {
border: none !important;
}
button#refresh_running_list {
border: none !important;
}
mark,
.mark {
background-color: #282828;
color: #f8f8f0;
padding: .15em;
}
a.text-warning,
a.text-warning:hover {
color: #75715e;
}
a.text-warning.bg-warning {
background-color: #1e1e1e;
}
span.bg-success.text-success {
background-color: transparent;
color: #a6e22e;
}
span.bg-danger.text-danger {
background-color: #1e1e1e;
color: #f92672;
}
.has-success .input-group-addon {
color: #a6e22e;
border-color: transparent;
background: inherit;
background-color: rgba(83,180,115,.10);
}
.has-success .form-control {
border-color: #a6e22e;
-webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);
box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);
}
.has-error .input-group-addon {
color: #f92672;
border-color: transparent;
background: inherit;
background-color: rgba(192,57,67,.10);
}
.has-error .form-control {
border-color: #f92672;
-webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);
box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);
}
.kse-input-group-pretty > kbd {
font-family: "Fira Mono", monospace, monospace;
color: #f8f8f0;
font-weight: normal;
background: transparent;
}
.kse-input-group-pretty > kbd {
font-family: "Fira Mono", monospace, monospace;
color: #f8f8f0;
font-weight: normal;
background: transparent;
}
div.nbext-enable-btns .btn[disabled],
div.nbext-enable-btns .btn[disabled]:hover,
.btn-default.disabled,
.btn-default[disabled] {
background: #2c2c2c;
background-color: #2c2c2c;
color: #f3f3e6;
}
label#Keyword-Filter {
display: none;
}
.input-group .nbext-list-btn-add,
.input-group-btn:last-child > .btn-group > .btn {
background: #2f2f2f;
background-color: #2f2f2f;
border-color: #2f2f2f;
border: 2px solid #2f2f2f;
}
.input-group .nbext-list-btn-add:hover,
.input-group-btn:last-child > .btn-group > .btn:hover {
background: #2a2a2a;
background-color: #2a2a2a;
border-color: #2a2a2a;
border: 2px solid #2a2a2a;
}
#notebook-container > div.cell.code_cell.rendered.selected > div.widget-area > div.widget-subarea > div > div.widget_item.btn-group > button.btn.btn-default.dropdown-toggle.widget-combo-carrot-btn {
background: #2f2f2f;
background-color: #2f2f2f;
border-color: #2f2f2f;
}
#notebook-container > div.cell.code_cell.rendered.selected > div.widget-area > div.widget-subarea > div > div.widget_item.btn-group > button.btn.btn-default.dropdown-toggle.widget-combo-carrot-btn:hover {
background: #2a2a2a;
background-color: #2a2a2a;
border-color: #2a2a2a;
}
.ui-widget-content {
background: #2f2f2f;
background-color: #2f2f2f;
border: 2px solid #2f2f2f;
color: #f8f8f0;
}
div.collapsible_headings_toggle {
color: rgba(93,92,82,.5) !important;
}
div.collapsible_headings_toggle:hover {
color: #a6e22e !important;
}
.collapsible_headings_toggle .h1,
.collapsible_headings_toggle .h2,
.collapsible_headings_toggle .h3,
.collapsible_headings_toggle .h4,
.collapsible_headings_toggle .h5,
.collapsible_headings_toggle .h6 {
margin: 0.3em .4em 0em 0em !important;
line-height: 1.2 !important;
}
div.collapsible_headings_toggle .fa-caret-down:before,
div.collapsible_headings_toggle .fa-caret-right:before {
font-size: xx-large;
transition: transform 1000ms;
transform: none !important;
}
.collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h1:after,
.collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h2:after,
.collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h3:after,
.collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h4:after,
.collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h5:after,
.collapsible_headings_collapsed.collapsible_headings_ellipsis .rendered_html h6:after {
position: absolute;
right: 0;
bottom: 20% !important;
content: "[\002026]";
color: rgba(93,92,82,.5) !important;
padding: 0.5em 0em 0em 0em !important;
}
.collapsible_headings_ellipsis .rendered_html h1,
.collapsible_headings_ellipsis .rendered_html h2,
.collapsible_headings_ellipsis .rendered_html h3,
.collapsible_headings_ellipsis .rendered_html h4,
.collapsible_headings_ellipsis .rendered_html h5,
.collapsible_headings_ellipsis .rendered_html h6,
.collapsible_headings_toggle .fa {
transition: transform 1000ms !important;
-webkit-transform: inherit !important;
-moz-transform: inherit !important;
-ms-transform: inherit !important;
-o-transform: inherit !important;
transform: inherit !important;
padding-right: 0px !important;
}
#toc-wrapper {
z-index: 90;
position: fixed !important;
display: flex;
flex-direction: column;
overflow: hidden;
padding: 10px;
border-style: solid;
border-width: thin;
border-right-width: medium !important;
background-color: #1e1e1e !important;
}
#toc-wrapper.ui-draggable.ui-resizable.sidebar-wrapper {
border-color: rgba(93,92,82,.25) !important;
}
#toc a,
#navigate_menu a,
.toc {
color: #f8f8f0 !important;
font-size: 12pt !important;
}
#toc li > span:hover {
background-color: rgba(93,92,82,.25) !important;
}
#toc a:hover,
#navigate_menu a:hover,
.toc {
color: #f8f8f2 !important;
font-size: 12pt !important;
}
#toc-wrapper .toc-item-num {
color: #a6e22e !important;
font-size: 12pt !important;
}
input.raw_input {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt !important;
color: #f8f8f0;
background-color: #282828;
border-color: #252525;
background: #252525;
width: auto;
vertical-align: baseline;
padding: 0em 0.25em;
margin: 0em 0.25em;
-webkit-box-shadow: none;
box-shadow: none;
}
audio,
video {
display: inline;
vertical-align: middle;
align-content: center;
margin-left: 20%;
}
.cmd-palette .modal-body {
padding: 0px;
margin: 0px;
}
.cmd-palette form {
background: #293547;
background-color: #293547;
}
.typeahead-field input:last-child,
.typeahead-hint {
background: #293547;
background-color: #293547;
z-index: 1;
}
.typeahead-field input {
font-family: "PT Sans", sans-serif;
color: #f8f8f0;
border: none;
font-size: 28pt;
display: inline-block;
line-height: inherit;
padding: 3px 10px;
height: 70px;
}
.typeahead-select {
background-color: #293547;
}
body > div.modal.cmd-palette.typeahead-field {
display: table;
border-collapse: separate;
background-color: #2b3850;
}
.typeahead-container button {
font-family: "PT Sans", sans-serif;
font-size: 28pt;
background-color: #2f2f2f;
border: none;
display: inline-block;
line-height: inherit;
padding: 3px 10px;
height: 70px;
}
.typeahead-search-icon {
min-width: 40px;
min-height: 55px;
display: block;
vertical-align: middle;
text-align: center;
}
.typeahead-container button:focus,
.typeahead-container button:hover {
color: #f8f8f0;
background-color: #2a2a2a;
border-color: #2a2a2a;
}
.typeahead-list > li.typeahead-group.active > a,
.typeahead-list > li.typeahead-group > a,
.typeahead-list > li.typeahead-group > a:focus,
.typeahead-list > li.typeahead-group > a:hover {
display: none;
}
.typeahead-dropdown > li > a,
.typeahead-list > li > a {
color: #f8f8f0;
text-decoration: none;
}
.typeahead-dropdown,
.typeahead-list {
font-family: "PT Sans", sans-serif;
font-size: 11pt;
color: #f8f8f0;
background-color: #202937;
border: none;
background-clip: padding-box;
margin-top: 0px;
padding: 3px 2px 3px 0px;
line-height: 1.7;
}
.typeahead-dropdown > li.active > a,
.typeahead-dropdown > li > a:focus,
.typeahead-dropdown > li > a:hover,
.typeahead-list > li.active > a,
.typeahead-list > li > a:focus,
.typeahead-list > li > a:hover {
color: #f8f8f0;
background-color: #2b3850;
border-color: #2b3850;
}
.command-shortcut:before {
content: "(command)";
padding-right: 3px;
color: #75715e;
}
.edit-shortcut:before {
content: "(edit)";
padding-right: 3px;
color: #75715e;
}
ul.typeahead-list i {
margin-left: 1px;
width: 18px;
margin-right: 10px;
}
ul.typeahead-list {
max-height: 50vh;
overflow: auto;
}
.typeahead-list > li {
position: relative;
border: none;
}
div.input.typeahead-hint,
input.typeahead-hint,
body > div.modal.cmd-palette.in > div > div > div > form > div > div.typeahead-field > span.typeahead-query > input.typeahead-hint {
color: #75715e !important;
background-color: transparent;
padding: 3px 10px;
}
.typeahead-dropdown > li > a,
.typeahead-list > li > a {
display: block;
padding: 5px;
clear: both;
font-weight: 400;
line-height: 1.7;
border: 1px solid #202937;
border-bottom-color: rgba(93,92,82,.5);
}
body > div.modal.cmd-palette.in > div {
min-width: 750px;
margin: 150px auto;
}
.typeahead-container strong {
font-weight: bolder;
color: #a6e22e;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
color: #ffffff;
background-color: #57564b;
border-color: #57564b;
border-style: solid;
border-width: 1px;
border-radius: 0px;
}
#find-and-replace #replace-preview .replace .match {
background-color: #f92672;
border-color: #f92672;
border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
background-color: #a6e22e;
border-color: #a6e22e;
border-radius: 0px;
}
.jupyter-dashboard-menu-item.selected::before {
font-family: 'FontAwesome' !important;
content: '\f00c' !important;
position: absolute !important;
color: #a6e22e !important;
left: 0px !important;
top: 13px !important;
font-size: 12px !important;
}
.shortcut_key,
span.shortcut_key {
display: inline-block;
width: 16ex;
text-align: right;
font-family: monospace;
}
.jupyter-keybindings {
padding: 1px;
line-height: 24px;
border-bottom: 1px solid rgba(93,92,82,.25);
}
.jupyter-keybindings i {
background: #282828;
font-size: small;
padding: 5px;
margin-left: 7px;
}
div#short-key-bindings-intro.well,
.well {
background-color: #2f2f2f;
border: 1px solid #2f2f2f;
color: #f8f8f0;
border-radius: 2px;
-webkit-box-shadow: none;
box-shadow: none;
}
#texteditor-backdrop {
background: #1e1e1e;
background-color: #1e1e1e;
}
#texteditor-backdrop #texteditor-container .CodeMirror-gutter,
#texteditor-backdrop #texteditor-container .CodeMirror-gutters {
background: #49483e;
background-color: #49483e;
color: #75715e;
}
.edit_app #menubar .navbar {
margin-bottom: 0px;
}
#texteditor-backdrop #texteditor-container {
padding: 0px;
background-color: #282828;
box-shadow: none;
}
.terminal-app {
background: #1e1e1e;
}
.terminal-app > #header {
background: #1e1e1e;
}
.terminal-app .terminal {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt;
line-height: 170%;
color: #f8f8f0;
background: #282828;
padding: 0.4em;
border-radius: 2px;
-webkit-box-shadow: none;
box-shadow: none;
}
.terminal .xterm-viewport {
background-color: #282828;
color: #f8f8f0;
overflow-y: auto;
}
.terminal .xterm-color-0 {
color: #a6e22e;
}
.terminal .xterm-color-1 {
color: #a6e22e;
}
.terminal .xterm-color-2 {
color: #f92672;
}
.terminal .xterm-color-3 {
color: #a6e22e;
}
.terminal .xterm-color-4 {
color: #ae81ff;
}
.terminal .xterm-color-5 {
color: #e6db74;
}
.terminal .xterm-color-6 {
color: #a6e22e;
}
.terminal .xterm-color-7 {
color: #a6e22e;
}
.terminal .xterm-color-8 {
color: #a6e22e;
}
.terminal .xterm-color-9 {
color: #e6db74;
}
.terminal .xterm-color-10 {
color: #a6e22e;
}
.terminal .xterm-color-14 {
color: #a6e22e;
}
.terminal .xterm-bg-color-15 {
background-color: #282828;
}
.terminal:not(.xterm-cursor-style-underline):not(.xterm-cursor-style-bar) .terminal-cursor {
background-color: #a6e22e;
color: #282828;
}
.terminal:not(.focus) .terminal-cursor {
outline: 1px solid #a6e22e;
outline-offset: -1px;
}
.celltoolbar {
font-size: 100%;
padding-top: 3px;
border-color: transparent;
border-bottom: thin solid rgba(148,204,114,.2);
background: transparent;
}
.cell-tag,
.tags-input input,
.tags-input button {
color: #f8f8f0;
background-color: #1e1e1e;
background-image: none;
border: 1px solid #f8f8f0;
border-radius: 1px;
box-shadow: none;
width: inherit;
font-size: inherit;
height: 22px;
line-height: 22px;
}
#notebook-container > div.cell.code_cell.rendered.selected > div.input > div.inner_cell > div.ctb_hideshow.ctb_show > div > div > button,
#notebook-container > div.input > div.inner_cell > div.ctb_hideshow.ctb_show > div > div > button {
font-size: 10pt;
color: #f8f8f0;
background-color: #1e1e1e;
background-image: none;
border: 1px solid #f8f8f0;
border-radius: 1px;
box-shadow: none;
width: inherit;
font-size: inherit;
height: 22px;
line-height: 22px;
}
div#pager #pager-contents {
background: #1e1e1e !important;
background-color: #1e1e1e !important;
}
div#pager pre {
color: #f8f8f0 !important;
background: #282828 !important;
background-color: #282828 !important;
padding: 0.4em;
}
div#pager .ui-resizable-handle {
top: 0px;
height: 8px;
background: #a6e22e !important;
border-top: 1px solid #a6e22e;
border-bottom: 1px solid #a6e22e;
}
div.CodeMirror,
div.CodeMirror pre {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt;
line-height: 170%;
color: #f8f8f0;
}
div.CodeMirror-lines {
padding-bottom: .9em;
padding-left: .5em;
padding-right: 1.5em;
padding-top: .7em;
}
span.ansiblack,
.ansi-black-fg {
color: #282828;
}
span.ansiblue,
.ansi-blue-fg,
.ansi-blue-intense-fg {
color: #66d9ef;
}
span.ansigray,
.ansi-gray-fg,
.ansi-gray-intense-fg {
color: #888888;
}
span.ansigreen,
.ansi-green-fg {
color: #a6e22e;
}
.ansi-green-intense-fg {
color: #888888;
}
span.ansipurple,
.ansi-purple-fg,
.ansi-purple-intense-fg {
color: #ae81ff;
}
span.ansicyan,
.ansi-cyan-fg,
.ansi-cyan-intense-fg {
color: #ae81ff;
}
span.ansiyellow,
.ansi-yellow-fg,
.ansi-yellow-intense-fg {
color: #e6db74;
}
span.ansired,
.ansi-red-fg,
.ansi-red-intense-fg {
color: #f92672;
}
div.output-stderr {
background-color: #f92672;
}
div.output-stderr pre {
color: #f8f8f2;
}
div.js-error {
color: #f92672;
}
.ipython_tooltip {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt;
line-height: 170%;
border: 2px solid #141414;
background: #282828;
background-color: #282828;
border-radius: 2px;
overflow-x: visible;
overflow-y: visible;
box-shadow: none;
position: absolute;
z-index: 1000;
}
.ipython_tooltip .tooltiptext pre {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt;
line-height: 170%;
background: #282828;
background-color: #282828;
color: #f8f8f0;
overflow-x: visible;
overflow-y: visible;
max-width: 900px;
}
div#tooltip.ipython_tooltip {
overflow-x: wrap;
overflow-y: visible;
max-width: 800px;
}
div.tooltiptext.bigtooltip {
overflow-x: visible;
overflow-y: scroll;
height: 400px;
max-width: 800px;
}
.cm-s-ipython.CodeMirror {
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt;
background: #282828;
color: #f8f8f0;
border-radius: 2px;
font-style: normal;
font-weight: normal;
}
.cm-s-ipython div.CodeMirror-selected {
background: #49483e;
}
.CodeMirror-gutters {
border: none;
border-right: 1px solid #49483e !important;
background-color: #49483e !important;
background: #49483e !important;
border-radius: 0px;
white-space: nowrap;
}
.cm-s-ipython .CodeMirror-gutters {
background: #49483e;
border: none;
border-radius: 0px;
width: 36px;
}
.cm-s-ipython .CodeMirror-linenumber {
color: #75715e;
}
.CodeMirror-sizer {
margin-left: 40px;
}
.CodeMirror-linenumber,
div.CodeMirror-linenumber,
.CodeMirror-gutter.CodeMirror-linenumberdiv.CodeMirror-gutter.CodeMirror-linenumber {
padding-right: 1px;
margin-left: 0px;
margin: 0px;
width: 26px !important;
padding: 0px;
text-align: right;
}
.CodeMirror-linenumber {
color: #75715e;
}
.cm-s-ipython .CodeMirror-cursor {
border-left: 5px solid #db797c !important;
}
.cm-s-ipython span.cm-comment {
color: #75715e;
font-style: italic;
}
.cm-s-ipython span.cm-atom {
color: #ae81ff;
}
.cm-s-ipython span.cm-number {
color: #ae81ff;
}
.cm-s-ipython span.cm-property {
color: #f8f8f0;
}
.cm-s-ipython span.cm-attribute {
color: #f8f8f0;
}
.cm-s-ipython span.cm-keyword {
color: #f92672;
font-weight: normal;
}
.cm-s-ipython span.cm-string {
color: #e6db74;
}
.cm-s-ipython span.cm-meta {
color: #fd971f;
}
.cm-s-ipython span.cm-operator {
color: #a6e22e;
}
.cm-s-ipython span.cm-builtin {
color: #a6e22e;
}
.cm-s-ipython span.cm-variable {
color: #f8f8f0;
}
.cm-s-ipython span.cm-variable-2 {
color: #a6e22e;
}
.cm-s-ipython span.cm-variable-3 {
color: #fd971f;
}
.cm-s-ipython span.cm-def {
color: #a6e22e;
font-weight: normal;
}
.cm-s-ipython span.cm-error {
background: rgba(249,38,114,.4);
}
.cm-s-ipython span.cm-tag {
color: #ae81ff;
}
.cm-s-ipython span.cm-link {
color: #a6e22e;
}
.cm-s-ipython span.cm-storage {
color: #ae81ff;
}
.cm-s-ipython span.cm-entity {
color: #a6e22e;
}
.cm-s-ipython span.cm-quote {
color: #e6db74;
}
div.CodeMirror span.CodeMirror-matchingbracket {
color: #ffffff;
font-weight: bold;
background-color: #49483e;
}
div.CodeMirror span.CodeMirror-nonmatchingbracket {
color: #ffffff;
font-weight: bold;
background: rgba(249,38,114,.4) !important;
}
.cm-header-1 {
font-size: 215%;
}
.cm-header-2 {
font-size: 180%;
}
.cm-header-3 {
font-size: 150%;
}
.cm-header-4 {
font-size: 120%;
}
.cm-header-5 {
font-size: 100%;
}
.cm-s-default .cm-hr {
color: #a6e22e;
}
div.cell.text_cell .cm-s-default .cm-header {
font-family: "PT Sans", sans-serif;
font-weight: normal;
color: #a6e22e !important;
margin-top: 0.3em !important;
margin-bottom: 0.3em !important;
}
div.cell.text_cell .cm-s-default span.cm-variable-2 {
color: #f8f8f0 !important;
}
div.cell.text_cell .cm-s-default span.cm-variable-3 {
color: #fd971f !important;
}
.cm-s-default span.cm-comment {
color: #75715e !important;
}
.cm-s-default .cm-tag {
color: #529b2f;
}
.cm-s-default .cm-builtin {
color: #a6e22e;
}
.cm-s-default .cm-string {
color: #e6db74;
}
.cm-s-default .cm-keyword {
color: #f92672;
}
.cm-s-default .cm-number {
color: #ae81ff;
}
.cm-s-default .cm-error {
color: #ae81ff;
}
.cm-s-default .cm-link {
color: #a6e22e;
}
.cm-s-default .cm-atom {
color: #ae81ff;
}
.cm-s-default .cm-def {
color: #a6e22e;
}
.CodeMirror-cursor {
border-left: 5px solid #db797c !important;
border-right: none;
width: 0;
}
.cm-s-default div.CodeMirror-selected {
background: #49483e;
}
.cm-s-default .cm-selected {
background: #49483e;
}
.MathJax_Display,
.MathJax {
border: 0 !important;
font-size: 100% !important;
text-align: center !important;
margin: 0em !important;
line-height: 2.25 !important;
}
.MathJax:focus,
body :focus .MathJax {
display: inline-block !important;
}
.MathJax:focus,
body :focus .MathJax {
display: inline-block !important;
}
.completions {
position: absolute;
z-index: 110;
overflow: hidden;
border: medium solid #49483e;
box-shadow: none;
line-height: 1;
}
.completions select {
background: #282828;
background-color: #282828;
outline: none;
border: none;
padding: 0px;
margin: 0px;
margin-left: 2px;
overflow: auto;
font-family: "Fira Mono", monospace, monospace;
font-size: 12pt;
color: #f8f8f0;
width: auto;
}
div#maintoolbar {
margin-left: 8px !important;
}
.toolbar.container {
width: 100% !important;
}
span.save_widget span.filename {
margin-left: 8px;
height: initial;
font-size: 100%;
color: #a6e22e;
background-color: #282828;
}
span.save_widget span.filename:hover {
color: #a6e22e;
background-color: #282828;
}
#menubar {
padding-top: 4px;
background-color: #1e1e1e;
}
<script>
MathJax.Hub.Config({
"HTML-CSS": {
/*preferredFont: "TeX",*/
/*availableFonts: ["TeX", "STIX"],*/
styles: {
scale: 100,
".MathJax_Display": {
"font-size": "100%",
}
}
}
});
</script>
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
* Mozilla scrollbar styling
*/
/* use standard opaque scrollbars for most nodes */
[data-jp-theme-scrollbars='true'] {
scrollbar-color: rgb(var(--jp-scrollbar-thumb-color))
var(--jp-scrollbar-background-color);
}
/* for code nodes, use a transparent style of scrollbar. These selectors
* will match lower in the tree, and so will override the above */
[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar,
[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar {
scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
}
/*
* Webkit scrollbar styling
*/
/* use standard opaque scrollbars for most nodes */
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar,
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-corner {
background: var(--jp-scrollbar-background-color);
}
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-thumb {
background: rgb(var(--jp-scrollbar-thumb-color));
border: var(--jp-scrollbar-thumb-margin) solid transparent;
background-clip: content-box;
border-radius: var(--jp-scrollbar-thumb-radius);
}
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-track:horizontal {
border-left: var(--jp-scrollbar-endpad) solid
var(--jp-scrollbar-background-color);
border-right: var(--jp-scrollbar-endpad) solid
var(--jp-scrollbar-background-color);
}
[data-jp-theme-scrollbars='true'] ::-webkit-scrollbar-track:vertical {
border-top: var(--jp-scrollbar-endpad) solid
var(--jp-scrollbar-background-color);
border-bottom: var(--jp-scrollbar-endpad) solid
var(--jp-scrollbar-background-color);
}
/* for code nodes, use a transparent style of scrollbar */
[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar::-webkit-scrollbar,
[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar::-webkit-scrollbar,
[data-jp-theme-scrollbars='true']
.CodeMirror-hscrollbar::-webkit-scrollbar-corner,
[data-jp-theme-scrollbars='true']
.CodeMirror-vscrollbar::-webkit-scrollbar-corner {
background-color: transparent;
}
[data-jp-theme-scrollbars='true']
.CodeMirror-hscrollbar::-webkit-scrollbar-thumb,
[data-jp-theme-scrollbars='true']
.CodeMirror-vscrollbar::-webkit-scrollbar-thumb {
background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
border: var(--jp-scrollbar-thumb-margin) solid transparent;
background-clip: content-box;
border-radius: var(--jp-scrollbar-thumb-radius);
}
[data-jp-theme-scrollbars='true']
.CodeMirror-hscrollbar::-webkit-scrollbar-track:horizontal {
border-left: var(--jp-scrollbar-endpad) solid transparent;
border-right: var(--jp-scrollbar-endpad) solid transparent;
}
[data-jp-theme-scrollbars='true']
.CodeMirror-vscrollbar::-webkit-scrollbar-track:vertical {
border-top: var(--jp-scrollbar-endpad) solid transparent;
border-bottom: var(--jp-scrollbar-endpad) solid transparent;
}
/*
* Phosphor
*/
.lm-ScrollBar[data-orientation='horizontal'] {
min-height: 16px;
max-height: 16px;
min-width: 45px;
border-top: 1px solid #a0a0a0;
}
.lm-ScrollBar[data-orientation='vertical'] {
min-width: 16px;
max-width: 16px;
min-height: 45px;
border-left: 1px solid #a0a0a0;
}
.lm-ScrollBar-button {
background-color: #f0f0f0;
background-position: center center;
min-height: 15px;
max-height: 15px;
min-width: 15px;
max-width: 15px;
}
.lm-ScrollBar-button:hover {
background-color: #dadada;
}
.lm-ScrollBar-button.lm-mod-active {
background-color: #cdcdcd;
}
.lm-ScrollBar-track {
background: #f0f0f0;
}
.lm-ScrollBar-thumb {
background: #cdcdcd;
}
.lm-ScrollBar-thumb:hover {
background: #bababa;
}
.lm-ScrollBar-thumb.lm-mod-active {
background: #a0a0a0;
}
.lm-ScrollBar[data-orientation='horizontal'] .lm-ScrollBar-thumb {
height: 100%;
min-width: 15px;
border-left: 1px solid #a0a0a0;
border-right: 1px solid #a0a0a0;
}
.lm-ScrollBar[data-orientation='vertical'] .lm-ScrollBar-thumb {
width: 100%;
min-height: 15px;
border-top: 1px solid #a0a0a0;
border-bottom: 1px solid #a0a0a0;
}
.lm-ScrollBar[data-orientation='horizontal']
.lm-ScrollBar-button[data-action='decrement'] {
background-image: var(--jp-icon-caret-left);
background-size: 17px;
}
.lm-ScrollBar[data-orientation='horizontal']
.lm-ScrollBar-button[data-action='increment'] {
background-image: var(--jp-icon-caret-right);
background-size: 17px;
}
.lm-ScrollBar[data-orientation='vertical']
.lm-ScrollBar-button[data-action='decrement'] {
background-image: var(--jp-icon-caret-up);
background-size: 17px;
}
.lm-ScrollBar[data-orientation='vertical']
.lm-ScrollBar-button[data-action='increment'] {
background-image: var(--jp-icon-caret-down);
background-size: 17px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-Widget, /* </DEPRECATED> */
.lm-Widget {
box-sizing: border-box;
position: relative;
overflow: hidden;
cursor: default;
}
/* <DEPRECATED> */ .p-Widget.p-mod-hidden, /* </DEPRECATED> */
.lm-Widget.lm-mod-hidden {
display: none !important;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-CommandPalette, /* </DEPRECATED> */
.lm-CommandPalette {
display: flex;
flex-direction: column;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */ .p-CommandPalette-search, /* </DEPRECATED> */
.lm-CommandPalette-search {
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-CommandPalette-content, /* </DEPRECATED> */
.lm-CommandPalette-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
min-height: 0;
overflow: auto;
list-style-type: none;
}
/* <DEPRECATED> */ .p-CommandPalette-header, /* </DEPRECATED> */
.lm-CommandPalette-header {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
/* <DEPRECATED> */ .p-CommandPalette-item, /* </DEPRECATED> */
.lm-CommandPalette-item {
display: flex;
flex-direction: row;
}
/* <DEPRECATED> */ .p-CommandPalette-itemIcon, /* </DEPRECATED> */
.lm-CommandPalette-itemIcon {
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-CommandPalette-itemContent, /* </DEPRECATED> */
.lm-CommandPalette-itemContent {
flex: 1 1 auto;
overflow: hidden;
}
/* <DEPRECATED> */ .p-CommandPalette-itemShortcut, /* </DEPRECATED> */
.lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-CommandPalette-itemLabel, /* </DEPRECATED> */
.lm-CommandPalette-itemLabel {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-DockPanel, /* </DEPRECATED> */
.lm-DockPanel {
z-index: 0;
}
/* <DEPRECATED> */ .p-DockPanel-widget, /* </DEPRECATED> */
.lm-DockPanel-widget {
z-index: 0;
}
/* <DEPRECATED> */ .p-DockPanel-tabBar, /* </DEPRECATED> */
.lm-DockPanel-tabBar {
z-index: 1;
}
/* <DEPRECATED> */ .p-DockPanel-handle, /* </DEPRECATED> */
.lm-DockPanel-handle {
z-index: 2;
}
/* <DEPRECATED> */ .p-DockPanel-handle.p-mod-hidden, /* </DEPRECATED> */
.lm-DockPanel-handle.lm-mod-hidden {
display: none !important;
}
/* <DEPRECATED> */ .p-DockPanel-handle:after, /* </DEPRECATED> */
.lm-DockPanel-handle:after {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
content: '';
}
/* <DEPRECATED> */
.p-DockPanel-handle[data-orientation='horizontal'],
/* </DEPRECATED> */
.lm-DockPanel-handle[data-orientation='horizontal'] {
cursor: ew-resize;
}
/* <DEPRECATED> */
.p-DockPanel-handle[data-orientation='vertical'],
/* </DEPRECATED> */
.lm-DockPanel-handle[data-orientation='vertical'] {
cursor: ns-resize;
}
/* <DEPRECATED> */
.p-DockPanel-handle[data-orientation='horizontal']:after,
/* </DEPRECATED> */
.lm-DockPanel-handle[data-orientation='horizontal']:after {
left: 50%;
min-width: 8px;
transform: translateX(-50%);
}
/* <DEPRECATED> */
.p-DockPanel-handle[data-orientation='vertical']:after,
/* </DEPRECATED> */
.lm-DockPanel-handle[data-orientation='vertical']:after {
top: 50%;
min-height: 8px;
transform: translateY(-50%);
}
/* <DEPRECATED> */ .p-DockPanel-overlay, /* </DEPRECATED> */
.lm-DockPanel-overlay {
z-index: 3;
box-sizing: border-box;
pointer-events: none;
}
/* <DEPRECATED> */ .p-DockPanel-overlay.p-mod-hidden, /* </DEPRECATED> */
.lm-DockPanel-overlay.lm-mod-hidden {
display: none !important;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-Menu, /* </DEPRECATED> */
.lm-Menu {
z-index: 10000;
position: absolute;
white-space: nowrap;
overflow-x: hidden;
overflow-y: auto;
outline: none;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */ .p-Menu-content, /* </DEPRECATED> */
.lm-Menu-content {
margin: 0;
padding: 0;
display: table;
list-style-type: none;
}
/* <DEPRECATED> */ .p-Menu-item, /* </DEPRECATED> */
.lm-Menu-item {
display: table-row;
}
/* <DEPRECATED> */
.p-Menu-item.p-mod-hidden,
.p-Menu-item.p-mod-collapsed,
/* </DEPRECATED> */
.lm-Menu-item.lm-mod-hidden,
.lm-Menu-item.lm-mod-collapsed {
display: none !important;
}
/* <DEPRECATED> */
.p-Menu-itemIcon,
.p-Menu-itemSubmenuIcon,
/* </DEPRECATED> */
.lm-Menu-itemIcon,
.lm-Menu-itemSubmenuIcon {
display: table-cell;
text-align: center;
}
/* <DEPRECATED> */ .p-Menu-itemLabel, /* </DEPRECATED> */
.lm-Menu-itemLabel {
display: table-cell;
text-align: left;
}
/* <DEPRECATED> */ .p-Menu-itemShortcut, /* </DEPRECATED> */
.lm-Menu-itemShortcut {
display: table-cell;
text-align: right;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-MenuBar, /* </DEPRECATED> */
.lm-MenuBar {
outline: none;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */ .p-MenuBar-content, /* </DEPRECATED> */
.lm-MenuBar-content {
margin: 0;
padding: 0;
display: flex;
flex-direction: row;
list-style-type: none;
}
/* <DEPRECATED> */ .p--MenuBar-item, /* </DEPRECATED> */
.lm-MenuBar-item {
box-sizing: border-box;
}
/* <DEPRECATED> */
.p-MenuBar-itemIcon,
.p-MenuBar-itemLabel,
/* </DEPRECATED> */
.lm-MenuBar-itemIcon,
.lm-MenuBar-itemLabel {
display: inline-block;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-ScrollBar, /* </DEPRECATED> */
.lm-ScrollBar {
display: flex;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */
.p-ScrollBar[data-orientation='horizontal'],
/* </DEPRECATED> */
.lm-ScrollBar[data-orientation='horizontal'] {
flex-direction: row;
}
/* <DEPRECATED> */
.p-ScrollBar[data-orientation='vertical'],
/* </DEPRECATED> */
.lm-ScrollBar[data-orientation='vertical'] {
flex-direction: column;
}
/* <DEPRECATED> */ .p-ScrollBar-button, /* </DEPRECATED> */
.lm-ScrollBar-button {
box-sizing: border-box;
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-ScrollBar-track, /* </DEPRECATED> */
.lm-ScrollBar-track {
box-sizing: border-box;
position: relative;
overflow: hidden;
flex: 1 1 auto;
}
/* <DEPRECATED> */ .p-ScrollBar-thumb, /* </DEPRECATED> */
.lm-ScrollBar-thumb {
box-sizing: border-box;
position: absolute;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-SplitPanel-child, /* </DEPRECATED> */
.lm-SplitPanel-child {
z-index: 0;
}
/* <DEPRECATED> */ .p-SplitPanel-handle, /* </DEPRECATED> */
.lm-SplitPanel-handle {
z-index: 1;
}
/* <DEPRECATED> */ .p-SplitPanel-handle.p-mod-hidden, /* </DEPRECATED> */
.lm-SplitPanel-handle.lm-mod-hidden {
display: none !important;
}
/* <DEPRECATED> */ .p-SplitPanel-handle:after, /* </DEPRECATED> */
.lm-SplitPanel-handle:after {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
content: '';
}
/* <DEPRECATED> */
.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle,
/* </DEPRECATED> */
.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
cursor: ew-resize;
}
/* <DEPRECATED> */
.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle,
/* </DEPRECATED> */
.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
cursor: ns-resize;
}
/* <DEPRECATED> */
.p-SplitPanel[data-orientation='horizontal'] > .p-SplitPanel-handle:after,
/* </DEPRECATED> */
.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
left: 50%;
min-width: 8px;
transform: translateX(-50%);
}
/* <DEPRECATED> */
.p-SplitPanel[data-orientation='vertical'] > .p-SplitPanel-handle:after,
/* </DEPRECATED> */
.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
top: 50%;
min-height: 8px;
transform: translateY(-50%);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-TabBar, /* </DEPRECATED> */
.lm-TabBar {
display: flex;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/* <DEPRECATED> */ .p-TabBar[data-orientation='horizontal'], /* </DEPRECATED> */
.lm-TabBar[data-orientation='horizontal'] {
flex-direction: row;
}
/* <DEPRECATED> */ .p-TabBar[data-orientation='vertical'], /* </DEPRECATED> */
.lm-TabBar[data-orientation='vertical'] {
flex-direction: column;
}
/* <DEPRECATED> */ .p-TabBar-content, /* </DEPRECATED> */
.lm-TabBar-content {
margin: 0;
padding: 0;
display: flex;
flex: 1 1 auto;
list-style-type: none;
}
/* <DEPRECATED> */
.p-TabBar[data-orientation='horizontal'] > .p-TabBar-content,
/* </DEPRECATED> */
.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
flex-direction: row;
}
/* <DEPRECATED> */
.p-TabBar[data-orientation='vertical'] > .p-TabBar-content,
/* </DEPRECATED> */
.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
flex-direction: column;
}
/* <DEPRECATED> */ .p-TabBar-tab, /* </DEPRECATED> */
.lm-TabBar-tab {
display: flex;
flex-direction: row;
box-sizing: border-box;
overflow: hidden;
}
/* <DEPRECATED> */
.p-TabBar-tabIcon,
.p-TabBar-tabCloseIcon,
/* </DEPRECATED> */
.lm-TabBar-tabIcon,
.lm-TabBar-tabCloseIcon {
flex: 0 0 auto;
}
/* <DEPRECATED> */ .p-TabBar-tabLabel, /* </DEPRECATED> */
.lm-TabBar-tabLabel {
flex: 1 1 auto;
overflow: hidden;
white-space: nowrap;
}
/* <DEPRECATED> */ .p-TabBar-tab.p-mod-hidden, /* </DEPRECATED> */
.lm-TabBar-tab.lm-mod-hidden {
display: none !important;
}
/* <DEPRECATED> */ .p-TabBar.p-mod-dragging .p-TabBar-tab, /* </DEPRECATED> */
.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
position: relative;
}
/* <DEPRECATED> */
.p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab,
/* </DEPRECATED> */
.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
left: 0;
transition: left 150ms ease;
}
/* <DEPRECATED> */
.p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab,
/* </DEPRECATED> */
.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
top: 0;
transition: top 150ms ease;
}
/* <DEPRECATED> */
.p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging
/* </DEPRECATED> */
.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
transition: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ .p-TabPanel-tabBar, /* </DEPRECATED> */
.lm-TabPanel-tabBar {
z-index: 1;
}
/* <DEPRECATED> */ .p-TabPanel-stackedPanel, /* </DEPRECATED> */
.lm-TabPanel-stackedPanel {
z-index: 0;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
@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; }
*,
*::before,
*::after{
-webkit-box-sizing:inherit;
box-sizing:inherit; }
body{
text-transform:none;
line-height:1.28581;
letter-spacing:0;
font-size:14px;
font-weight:400;
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; }
small{
font-size:12px; }
strong{
font-weight:600; }
::-moz-selection{
background:rgba(125, 188, 255, 0.6); }
::selection{
background:rgba(125, 188, 255, 0.6); }
.bp3-heading{
color:#182026;
font-weight:600;
margin:0 0 10px;
padding:0; }
.bp3-dark .bp3-heading{
color:#f5f8fa; }
h1.bp3-heading, .bp3-running-text h1{
line-height:40px;
font-size:36px; }
h2.bp3-heading, .bp3-running-text h2{
line-height:32px;
font-size:28px; }
h3.bp3-heading, .bp3-running-text h3{
line-height:25px;
font-size:22px; }
h4.bp3-heading, .bp3-running-text h4{
line-height:21px;
font-size:18px; }
h5.bp3-heading, .bp3-running-text h5{
line-height:19px;
font-size:16px; }
h6.bp3-heading, .bp3-running-text h6{
line-height:16px;
font-size:14px; }
.bp3-ui-text{
text-transform:none;
line-height:1.28581;
letter-spacing:0;
font-size:14px;
font-weight:400; }
.bp3-monospace-text{
text-transform:none;
font-family:monospace; }
.bp3-text-muted{
color:#5c7080; }
.bp3-dark .bp3-text-muted{
color:#a7b6c2; }
.bp3-text-disabled{
color:rgba(92, 112, 128, 0.6); }
.bp3-dark .bp3-text-disabled{
color:rgba(167, 182, 194, 0.6); }
.bp3-text-overflow-ellipsis{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal; }
.bp3-running-text{
line-height:1.5;
font-size:14px; }
.bp3-running-text h1{
color:#182026;
font-weight:600;
margin-top:40px;
margin-bottom:20px; }
.bp3-dark .bp3-running-text h1{
color:#f5f8fa; }
.bp3-running-text h2{
color:#182026;
font-weight:600;
margin-top:40px;
margin-bottom:20px; }
.bp3-dark .bp3-running-text h2{
color:#f5f8fa; }
.bp3-running-text h3{
color:#182026;
font-weight:600;
margin-top:40px;
margin-bottom:20px; }
.bp3-dark .bp3-running-text h3{
color:#f5f8fa; }
.bp3-running-text h4{
color:#182026;
font-weight:600;
margin-top:40px;
margin-bottom:20px; }
.bp3-dark .bp3-running-text h4{
color:#f5f8fa; }
.bp3-running-text h5{
color:#182026;
font-weight:600;
margin-top:40px;
margin-bottom:20px; }
.bp3-dark .bp3-running-text h5{
color:#f5f8fa; }
.bp3-running-text h6{
color:#182026;
font-weight:600;
margin-top:40px;
margin-bottom:20px; }
.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); }
.bp3-dark .bp3-running-text hr{
border-color:rgba(255, 255, 255, 0.15); }
.bp3-running-text p{
margin:0 0 10px;
padding:0; }
.bp3-text-large{
font-size:16px; }
.bp3-text-small{
font-size:12px; }
a{
text-decoration:none;
color:#106ba3; }
a:hover{
cursor:pointer;
text-decoration:underline;
color:#106ba3; }
a .bp3-icon, a .bp3-icon-standard, a .bp3-icon-large{
color:inherit; }
a code,
.bp3-dark a code{
color:inherit; }
.bp3-dark a,
.bp3-dark a:hover{
color:#48aff0; }
.bp3-dark a .bp3-icon, .bp3-dark a .bp3-icon-standard, .bp3-dark a .bp3-icon-large,
.bp3-dark a:hover .bp3-icon,
.bp3-dark a:hover .bp3-icon-standard,
.bp3-dark a:hover .bp3-icon-large{
color:inherit; }
.bp3-running-text code, .bp3-code{
text-transform:none;
font-family:monospace;
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; }
.bp3-dark .bp3-running-text code, .bp3-running-text .bp3-dark code, .bp3-dark .bp3-code{
-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; }
.bp3-dark .bp3-running-text a > code, .bp3-running-text .bp3-dark a > code, .bp3-dark a > .bp3-code{
color:inherit; }
.bp3-running-text pre, .bp3-code-block{
text-transform:none;
font-family:monospace;
display:block;
margin:10px 0;
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;
font-size:13px;
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{
-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{
-webkit-box-shadow:none;
box-shadow:none;
background:none;
padding:0;
color:inherit;
font-size:inherit; }
.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;
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;
font-family:inherit;
font-size:12px; }
.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{
-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);
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); }
.bp3-running-text ul,
.bp3-running-text ol, .bp3-list{
margin:10px 0;
padding-left:30px; }
.bp3-running-text ul li:not(:last-child), .bp3-running-text ol li:not(:last-child), .bp3-list li:not(:last-child){
margin-bottom:5px; }
.bp3-running-text ul ol, .bp3-running-text ol ol, .bp3-list ol,
.bp3-running-text ul ul,
.bp3-running-text ol ul,
.bp3-list ul{
margin-top:5px; }
.bp3-list-unstyled{
margin:0;
padding:0;
list-style:none; }
.bp3-list-unstyled li{
padding:0; }
.bp3-rtl{
text-align:right; }
.bp3-dark{
color:#f5f8fa; }
:focus{
outline:rgba(19, 124, 189, 0.6) auto 2px;
outline-offset:2px;
-moz-outline-radius:6px; }
.bp3-focus-disabled :focus{
outline:none !important; }
.bp3-focus-disabled :focus ~ .bp3-control-indicator{
outline:none !important; }
.bp3-alert{
max-width:400px;
padding:20px; }
.bp3-alert-body{
display:-webkit-box;
display:-ms-flexbox;
display:flex; }
.bp3-alert-body .bp3-icon{
margin-top:0;
margin-right:20px;
font-size:40px; }
.bp3-alert-footer{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:reverse;
-ms-flex-direction:row-reverse;
flex-direction:row-reverse;
margin-top:10px; }
.bp3-alert-footer .bp3-button{
margin-left:10px; }
.bp3-breadcrumbs{
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; }
.bp3-breadcrumbs > li{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center; }
.bp3-breadcrumbs > li::after{
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:""; }
.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;
font-size:16px; }
.bp3-breadcrumb,
.bp3-breadcrumbs-collapsed{
color:#5c7080; }
.bp3-breadcrumb:hover{
text-decoration:none; }
.bp3-breadcrumb.bp3-disabled{
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6); }
.bp3-breadcrumb .bp3-icon{
margin-right:5px; }
.bp3-breadcrumb-current{
color:inherit;
font-weight:600; }
.bp3-breadcrumb-current .bp3-input{
vertical-align:baseline;
font-size:inherit;
font-weight:inherit; }
.bp3-breadcrumbs-collapsed{
margin-right:2px;
border:none;
border-radius:3px;
background:#ced9e0;
cursor:pointer;
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;
height:16px;
content:""; }
.bp3-breadcrumbs-collapsed:hover{
background:#bfccd6;
text-decoration:none;
color:#182026; }
.bp3-dark .bp3-breadcrumb,
.bp3-dark .bp3-breadcrumbs-collapsed{
color:#a7b6c2; }
.bp3-dark .bp3-breadcrumbs > li::after{
color:#a7b6c2; }
.bp3-dark .bp3-breadcrumb.bp3-disabled{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-breadcrumb-current{
color:#f5f8fa; }
.bp3-dark .bp3-breadcrumbs-collapsed{
background:rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-breadcrumbs-collapsed:hover{
background:rgba(16, 22, 26, 0.6);
color:#f5f8fa; }
.bp3-button{
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-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;
padding:5px 10px;
vertical-align:middle;
text-align:left;
font-size:14px;
min-width:30px;
min-height:30px; }
.bp3-button > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-button > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-button::before,
.bp3-button > *{
margin-right:7px; }
.bp3-button:empty::before,
.bp3-button > :last-child{
margin-right:0; }
.bp3-button:empty{
padding:0 !important; }
.bp3-button:disabled, .bp3-button.bp3-disabled{
cursor:not-allowed; }
.bp3-button.bp3-fill{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
width:100%; }
.bp3-button.bp3-align-right,
.bp3-align-right .bp3-button{
text-align:right; }
.bp3-button.bp3-align-left,
.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));
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; }
.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; }
.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;
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6); }
.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));
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{
-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; }
.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; }
.bp3-button.bp3-intent-primary:disabled, .bp3-button.bp3-intent-primary.bp3-disabled{
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));
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{
-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; }
.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; }
.bp3-button.bp3-intent-success:disabled, .bp3-button.bp3-intent-success.bp3-disabled{
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));
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{
-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; }
.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; }
.bp3-button.bp3-intent-warning:disabled, .bp3-button.bp3-intent-warning.bp3-disabled{
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));
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{
-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; }
.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; }
.bp3-button.bp3-intent-danger:disabled, .bp3-button.bp3-intent-danger.bp3-disabled{
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; }
.bp3-button.bp3-large::before,
.bp3-button.bp3-large > *,
.bp3-large .bp3-button::before,
.bp3-large .bp3-button > *{
margin-right:10px; }
.bp3-button.bp3-large:empty::before,
.bp3-button.bp3-large > :last-child,
.bp3-large .bp3-button:empty::before,
.bp3-large .bp3-button > :last-child{
margin-right:0; }
.bp3-button.bp3-small,
.bp3-small .bp3-button{
min-width:24px;
min-height: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; }
.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;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
color:#5c7080; }
.bp3-button .bp3-icon, .bp3-button .bp3-icon-standard, .bp3-button .bp3-icon-large{
color:#5c7080; }
.bp3-button .bp3-icon.bp3-align-right, .bp3-button .bp3-icon-standard.bp3-align-right, .bp3-button .bp3-icon-large.bp3-align-right{
margin-left:7px; }
.bp3-button .bp3-icon:first-child:last-child,
.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));
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{
-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; }
.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; }
.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;
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); }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-button-spinner .bp3-spinner-head{
background:rgba(16, 22, 26, 0.5);
stroke:#8a9ba8; }
.bp3-dark .bp3-button:not([class*="bp3-intent-"])[class*="bp3-icon-"]::before{
color:#a7b6c2; }
.bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-button:not([class*="bp3-intent-"]) .bp3-icon-large{
color:#a7b6c2; }
.bp3-dark .bp3-button[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); }
.bp3-dark .bp3-button[class*="bp3-intent-"]:hover{
-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-button[class*="bp3-intent-"]:active, .bp3-dark .bp3-button[class*="bp3-intent-"].bp3-active{
-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{
-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; }
.bp3-button:disabled::before,
.bp3-button:disabled .bp3-icon, .bp3-button:disabled .bp3-icon-standard, .bp3-button:disabled .bp3-icon-large, .bp3-button.bp3-disabled::before,
.bp3-button.bp3-disabled .bp3-icon, .bp3-button.bp3-disabled .bp3-icon-standard, .bp3-button.bp3-disabled .bp3-icon-large, .bp3-button[class*="bp3-intent-"]::before,
.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{
-webkit-box-shadow:none;
box-shadow:none;
background:none; }
.bp3-button.bp3-minimal:hover{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(167, 182, 194, 0.3);
text-decoration:none;
color:#182026; }
.bp3-button.bp3-minimal:active, .bp3-button.bp3-minimal.bp3-active{
-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); }
.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{
-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{
-webkit-box-shadow:none;
box-shadow:none;
background: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{
background:rgba(138, 155, 168, 0.3);
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); }
.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{
-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);
color:#106ba3; }
.bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#106ba3; }
.bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(16, 107, 163, 0.5); }
.bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
stroke:#106ba3; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary{
color:#48aff0; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.2);
color:#48aff0; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#48aff0; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(72, 175, 240, 0.5); }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.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{
-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);
color:#0d8050; }
.bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#0d8050; }
.bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(13, 128, 80, 0.5); }
.bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
stroke:#0d8050; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success{
color:#3dcc91; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.2);
color:#3dcc91; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#3dcc91; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(61, 204, 145, 0.5); }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.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{
-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);
color:#bf7326; }
.bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#bf7326; }
.bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(191, 115, 38, 0.5); }
.bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
stroke:#bf7326; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning{
color:#ffb366; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.2);
color:#ffb366; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#ffb366; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(255, 179, 102, 0.5); }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.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{
-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);
color:#c23030; }
.bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#c23030; }
.bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(194, 48, 48, 0.5); }
.bp3-button.bp3-minimal.bp3-intent-danger:disabled.bp3-active, .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-button.bp3-minimal.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
stroke:#c23030; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger{
color:#ff7373; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.2);
color:#ff7373; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:active, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#ff7373; }
.bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger:disabled, .bp3-dark .bp3-button.bp3-minimal.bp3-intent-danger.bp3-disabled{
background:none;
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); }
a.bp3-button{
text-align:center;
text-decoration:none;
-webkit-transition:none;
transition:none; }
a.bp3-button, a.bp3-button:hover, a.bp3-button:active{
color:#182026; }
a.bp3-button.bp3-disabled{
color:rgba(92, 112, 128, 0.6); }
.bp3-button-text{
-webkit-box-flex:0;
-ms-flex:0 1 auto;
flex:0 1 auto; }
.bp3-button.bp3-align-left .bp3-button-text, .bp3-button.bp3-align-right .bp3-button-text,
.bp3-button-group.bp3-align-left .bp3-button-text,
.bp3-button-group.bp3-align-right .bp3-button-text{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-button-group{
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex; }
.bp3-button-group .bp3-button{
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
position:relative;
z-index:4; }
.bp3-button-group .bp3-button:focus{
z-index:5; }
.bp3-button-group .bp3-button:hover{
z-index:6; }
.bp3-button-group .bp3-button:active, .bp3-button-group .bp3-button.bp3-active{
z-index:7; }
.bp3-button-group .bp3-button:disabled, .bp3-button-group .bp3-button.bp3-disabled{
z-index:3; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]{
z-index:9; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]:focus{
z-index:10; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]:hover{
z-index:11; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]:active, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-active{
z-index:12; }
.bp3-button-group .bp3-button[class*="bp3-intent-"]:disabled, .bp3-button-group .bp3-button[class*="bp3-intent-"].bp3-disabled{
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; }
.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-top-right-radius:0;
border-bottom-right-radius:0; }
.bp3-button-group.bp3-minimal .bp3-button{
-webkit-box-shadow:none;
box-shadow:none;
background:none; }
.bp3-button-group.bp3-minimal .bp3-button:hover{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(167, 182, 194, 0.3);
text-decoration:none;
color:#182026; }
.bp3-button-group.bp3-minimal .bp3-button:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-active{
-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); }
.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{
-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{
-webkit-box-shadow:none;
box-shadow:none;
background: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{
background:rgba(138, 155, 168, 0.3);
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); }
.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{
-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);
color:#106ba3; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#106ba3; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(16, 107, 163, 0.5); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
stroke:#106ba3; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary{
color:#48aff0; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.2);
color:#48aff0; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#48aff0; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(72, 175, 240, 0.5); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.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{
-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);
color:#0d8050; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#0d8050; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(13, 128, 80, 0.5); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
stroke:#0d8050; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success{
color:#3dcc91; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.2);
color:#3dcc91; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#3dcc91; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(61, 204, 145, 0.5); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.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{
-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);
color:#bf7326; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#bf7326; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(191, 115, 38, 0.5); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
stroke:#bf7326; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning{
color:#ffb366; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.2);
color:#ffb366; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#ffb366; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(255, 179, 102, 0.5); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.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{
-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);
color:#c23030; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#c23030; }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(194, 48, 48, 0.5); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
stroke:#c23030; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger{
color:#ff7373; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.2);
color:#ff7373; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#ff7373; }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(255, 115, 115, 0.5); }
.bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-button-group.bp3-minimal .bp3-button.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-button-group .bp3-popover-wrapper,
.bp3-button-group .bp3-popover-target{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-button-group.bp3-fill{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
width:100%; }
.bp3-button-group .bp3-button.bp3-fill,
.bp3-button-group.bp3-fill .bp3-button:not(.bp3-fixed){
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-button-group.bp3-vertical{
-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%; }
.bp3-button-group.bp3-vertical .bp3-button{
margin-right:0 !important;
width:100%; }
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:first-child .bp3-button,
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:first-child{
border-radius:3px 3px 0 0; }
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:last-child .bp3-button,
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:last-child{
border-radius:0 0 3px 3px; }
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
.bp3-button-group.bp3-vertical:not(.bp3-minimal) > .bp3-button:not(:last-child){
margin-bottom:-1px; }
.bp3-button-group.bp3-align-left .bp3-button{
text-align:left; }
.bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-popover-wrapper:not(:last-child) .bp3-button,
.bp3-dark .bp3-button-group:not(.bp3-minimal) > .bp3-button:not(:last-child){
margin-right:1px; }
.bp3-dark .bp3-button-group.bp3-vertical > .bp3-popover-wrapper:not(:last-child) .bp3-button,
.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;
background-color:rgba(138, 155, 168, 0.15);
width:100%;
padding:10px 12px 9px; }
.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;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
position:absolute;
top:10px;
left:10px;
color:#5c7080; }
.bp3-callout.bp3-callout-icon{
padding-left:40px; }
.bp3-callout.bp3-callout-icon > .bp3-icon:first-child{
position:absolute;
top:10px;
left:10px;
color:#5c7080; }
.bp3-callout .bp3-heading{
margin-top:0;
margin-bottom:5px;
line-height:20px; }
.bp3-callout .bp3-heading:last-child{
margin-bottom:0; }
.bp3-dark .bp3-callout{
background-color:rgba(138, 155, 168, 0.2); }
.bp3-dark .bp3-callout[class*="bp3-icon-"]::before{
color:#a7b6c2; }
.bp3-callout.bp3-intent-primary{
background-color:rgba(19, 124, 189, 0.15); }
.bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
.bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
.bp3-callout.bp3-intent-primary .bp3-heading{
color:#106ba3; }
.bp3-dark .bp3-callout.bp3-intent-primary{
background-color:rgba(19, 124, 189, 0.25); }
.bp3-dark .bp3-callout.bp3-intent-primary[class*="bp3-icon-"]::before,
.bp3-dark .bp3-callout.bp3-intent-primary > .bp3-icon:first-child,
.bp3-dark .bp3-callout.bp3-intent-primary .bp3-heading{
color:#48aff0; }
.bp3-callout.bp3-intent-success{
background-color:rgba(15, 153, 96, 0.15); }
.bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
.bp3-callout.bp3-intent-success > .bp3-icon:first-child,
.bp3-callout.bp3-intent-success .bp3-heading{
color:#0d8050; }
.bp3-dark .bp3-callout.bp3-intent-success{
background-color:rgba(15, 153, 96, 0.25); }
.bp3-dark .bp3-callout.bp3-intent-success[class*="bp3-icon-"]::before,
.bp3-dark .bp3-callout.bp3-intent-success > .bp3-icon:first-child,
.bp3-dark .bp3-callout.bp3-intent-success .bp3-heading{
color:#3dcc91; }
.bp3-callout.bp3-intent-warning{
background-color:rgba(217, 130, 43, 0.15); }
.bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
.bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
.bp3-callout.bp3-intent-warning .bp3-heading{
color:#bf7326; }
.bp3-dark .bp3-callout.bp3-intent-warning{
background-color:rgba(217, 130, 43, 0.25); }
.bp3-dark .bp3-callout.bp3-intent-warning[class*="bp3-icon-"]::before,
.bp3-dark .bp3-callout.bp3-intent-warning > .bp3-icon:first-child,
.bp3-dark .bp3-callout.bp3-intent-warning .bp3-heading{
color:#ffb366; }
.bp3-callout.bp3-intent-danger{
background-color:rgba(219, 55, 55, 0.15); }
.bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
.bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
.bp3-callout.bp3-intent-danger .bp3-heading{
color:#c23030; }
.bp3-dark .bp3-callout.bp3-intent-danger{
background-color:rgba(219, 55, 55, 0.25); }
.bp3-dark .bp3-callout.bp3-intent-danger[class*="bp3-icon-"]::before,
.bp3-dark .bp3-callout.bp3-intent-danger > .bp3-icon:first-child,
.bp3-dark .bp3-callout.bp3-intent-danger .bp3-heading{
color:#ff7373; }
.bp3-running-text .bp3-callout{
margin:20px 0; }
.bp3-card{
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);
transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
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{
-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; }
.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);
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); }
.bp3-elevation-0.bp3-dark,
.bp3-dark .bp3-elevation-0{
-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); }
.bp3-elevation-1{
-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); }
.bp3-elevation-1.bp3-dark,
.bp3-dark .bp3-elevation-1{
-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-elevation-2{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.1), 0 1px 1px rgba(16, 22, 26, 0.2), 0 2px 6px rgba(16, 22, 26, 0.2); }
.bp3-elevation-2.bp3-dark,
.bp3-dark .bp3-elevation-2{
-webkit-box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4);
box-shadow:0 0 0 1px rgba(16, 22, 26, 0.2), 0 1px 1px rgba(16, 22, 26, 0.4), 0 2px 6px rgba(16, 22, 26, 0.4); }
.bp3-elevation-3{
-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); }
.bp3-elevation-3.bp3-dark,
.bp3-dark .bp3-elevation-3{
-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-elevation-4{
-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); }
.bp3-elevation-4.bp3-dark,
.bp3-dark .bp3-elevation-4{
-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); }
.bp3-card.bp3-interactive:hover{
-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);
cursor:pointer; }
.bp3-card.bp3-interactive:hover.bp3-dark,
.bp3-dark .bp3-card.bp3-interactive:hover{
-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-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);
-webkit-transition-duration:0;
transition-duration:0; }
.bp3-card.bp3-interactive:active.bp3-dark,
.bp3-dark .bp3-card.bp3-interactive:active{
-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-collapse{
height:0;
overflow-y:hidden;
-webkit-transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:height 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-collapse .bp3-collapse-body{
-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);
transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-collapse .bp3-collapse-body[aria-hidden="true"]{
display:none; }
.bp3-context-menu .bp3-popover-target{
display:block; }
.bp3-context-menu-popover-target{
position:fixed; }
.bp3-divider{
margin:5px;
border-right:1px solid rgba(16, 22, 26, 0.15);
border-bottom:1px solid rgba(16, 22, 26, 0.15); }
.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;
-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; }
.bp3-dialog-container.bp3-overlay-enter > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear > .bp3-dialog{
opacity:0;
-webkit-transform:scale(0.5);
transform:scale(0.5); }
.bp3-dialog-container.bp3-overlay-enter-active > .bp3-dialog, .bp3-dialog-container.bp3-overlay-appear-active > .bp3-dialog{
opacity:1;
-webkit-transform:scale(1);
transform:scale(1);
-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; }
.bp3-dialog-container.bp3-overlay-exit > .bp3-dialog{
opacity:1;
-webkit-transform:scale(1);
transform:scale(1); }
.bp3-dialog-container.bp3-overlay-exit-active > .bp3-dialog{
opacity:0;
-webkit-transform:scale(0.5);
transform:scale(0.5);
-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; }
.bp3-dialog{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-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; }
.bp3-dialog:focus{
outline:0; }
.bp3-dialog.bp3-dark,
.bp3-dark .bp3-dialog{
-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;
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,
.bp3-dialog-header .bp3-icon{
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
margin-right:10px;
color:#5c7080; }
.bp3-dialog-header .bp3-heading{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
margin:0;
line-height:inherit; }
.bp3-dialog-header .bp3-heading:last-child{
margin-right:20px; }
.bp3-dark .bp3-dialog-header{
-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; }
.bp3-dark .bp3-dialog-header .bp3-icon-large,
.bp3-dark .bp3-dialog-header .bp3-icon{
color:#a7b6c2; }
.bp3-dialog-body{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
margin:20px;
line-height:18px; }
.bp3-dialog-footer{
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
margin:0 20px; }
.bp3-dialog-footer-actions{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-pack:end;
-ms-flex-pack:end;
justify-content:flex-end; }
.bp3-dialog-footer-actions .bp3-button{
margin-left:10px; }
.bp3-drawer{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-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;
left:0;
height:50%; }
.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-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; }
.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-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; }
.bp3-drawer.bp3-position-bottom{
right:0;
bottom:0;
left:0;
height:50%; }
.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-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; }
.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-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; }
.bp3-drawer.bp3-position-left{
top:0;
bottom:0;
left:0;
width:50%; }
.bp3-drawer.bp3-position-left.bp3-overlay-enter, .bp3-drawer.bp3-position-left.bp3-overlay-appear{
-webkit-transform:translateX(-100%);
transform:translateX(-100%); }
.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-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; }
.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-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; }
.bp3-drawer.bp3-position-right{
top:0;
right:0;
bottom:0;
width:50%; }
.bp3-drawer.bp3-position-right.bp3-overlay-enter, .bp3-drawer.bp3-position-right.bp3-overlay-appear{
-webkit-transform:translateX(100%);
transform:translateX(100%); }
.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-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; }
.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-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; }
.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;
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(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-appear{
-webkit-transform:translateX(100%);
transform:translateX(100%); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-appear-active{
-webkit-transform:translateX(0);
transform:translateX(0);
-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; }
.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);
transform:translateX(0); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right):not(.bp3-vertical).bp3-overlay-exit-active{
-webkit-transform:translateX(100%);
transform:translateX(100%);
-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; }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical{
right:0;
bottom:0;
left:0;
height:50%; }
.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{
-webkit-transform:translateY(100%);
transform:translateY(100%); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical.bp3-overlay-enter-active, .bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical.bp3-overlay-appear-active{
-webkit-transform:translateY(0);
transform:translateY(0);
-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; }
.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);
transform:translateY(0); }
.bp3-drawer:not(.bp3-position-top):not(.bp3-position-bottom):not(.bp3-position-left):not(
.bp3-position-right).bp3-vertical.bp3-overlay-exit-active{
-webkit-transform:translateY(100%);
transform:translateY(100%);
-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; }
.bp3-drawer.bp3-dark,
.bp3-dark .bp3-drawer{
-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);
min-height:40px;
padding:5px;
padding-left:20px; }
.bp3-drawer-header .bp3-icon-large,
.bp3-drawer-header .bp3-icon{
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
margin-right:10px;
color:#5c7080; }
.bp3-drawer-header .bp3-heading{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
margin:0;
line-height:inherit; }
.bp3-drawer-header .bp3-heading:last-child{
margin-right:20px; }
.bp3-dark .bp3-drawer-header{
-webkit-box-shadow:0 1px 0 rgba(16, 22, 26, 0.4);
box-shadow:0 1px 0 rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-drawer-header .bp3-icon-large,
.bp3-dark .bp3-drawer-header .bp3-icon{
color:#a7b6c2; }
.bp3-drawer-body{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
overflow:auto;
line-height:18px; }
.bp3-drawer-footer{
-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; }
.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;
max-width:100%;
vertical-align:top;
white-space:nowrap; }
.bp3-editable-text::before{
position:absolute;
top:-3px;
right:-3px;
bottom:-3px;
left:-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);
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);
transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), box-shadow 100ms cubic-bezier(0.4, 1, 0.75, 0.9);
transition:background-color 100ms cubic-bezier(0.4, 1, 0.75, 0.9), 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); }
.bp3-editable-text:hover::before{
-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{
-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; }
.bp3-editable-text.bp3-disabled::before{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-editable-text.bp3-intent-primary .bp3-editable-text-input,
.bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
color:#137cbd; }
.bp3-editable-text.bp3-intent-primary:hover::before{
-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(19, 124, 189, 0.4);
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(19, 124, 189, 0.4); }
.bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
-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); }
.bp3-editable-text.bp3-intent-success .bp3-editable-text-input,
.bp3-editable-text.bp3-intent-success .bp3-editable-text-content{
color:#0f9960; }
.bp3-editable-text.bp3-intent-success:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4);
box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px rgba(15, 153, 96, 0.4); }
.bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-editable-text.bp3-intent-warning .bp3-editable-text-input,
.bp3-editable-text.bp3-intent-warning .bp3-editable-text-content{
color:#d9822b; }
.bp3-editable-text.bp3-intent-warning:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4);
box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px rgba(217, 130, 43, 0.4); }
.bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-editable-text.bp3-intent-danger .bp3-editable-text-input,
.bp3-editable-text.bp3-intent-danger .bp3-editable-text-content{
color:#db3737; }
.bp3-editable-text.bp3-intent-danger:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4);
box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px rgba(219, 55, 55, 0.4); }
.bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-editable-text:hover::before{
-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{
-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); }
.bp3-dark .bp3-editable-text.bp3-disabled::before{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-editable-text.bp3-intent-primary .bp3-editable-text-content{
color:#48aff0; }
.bp3-dark .bp3-editable-text.bp3-intent-primary:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4);
box-shadow:0 0 0 0 rgba(72, 175, 240, 0), 0 0 0 0 rgba(72, 175, 240, 0), inset 0 0 0 1px rgba(72, 175, 240, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-primary.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #48aff0, 0 0 0 3px rgba(72, 175, 240, 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 #48aff0, 0 0 0 3px rgba(72, 175, 240, 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-intent-success .bp3-editable-text-content{
color:#3dcc91; }
.bp3-dark .bp3-editable-text.bp3-intent-success:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4);
box-shadow:0 0 0 0 rgba(61, 204, 145, 0), 0 0 0 0 rgba(61, 204, 145, 0), inset 0 0 0 1px rgba(61, 204, 145, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-success.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #3dcc91, 0 0 0 3px rgba(61, 204, 145, 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 #3dcc91, 0 0 0 3px rgba(61, 204, 145, 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-intent-warning .bp3-editable-text-content{
color:#ffb366; }
.bp3-dark .bp3-editable-text.bp3-intent-warning:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4);
box-shadow:0 0 0 0 rgba(255, 179, 102, 0), 0 0 0 0 rgba(255, 179, 102, 0), inset 0 0 0 1px rgba(255, 179, 102, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-warning.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #ffb366, 0 0 0 3px rgba(255, 179, 102, 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 #ffb366, 0 0 0 3px rgba(255, 179, 102, 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-intent-danger .bp3-editable-text-content{
color:#ff7373; }
.bp3-dark .bp3-editable-text.bp3-intent-danger:hover::before{
-webkit-box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4);
box-shadow:0 0 0 0 rgba(255, 115, 115, 0), 0 0 0 0 rgba(255, 115, 115, 0), inset 0 0 0 1px rgba(255, 115, 115, 0.4); }
.bp3-dark .bp3-editable-text.bp3-intent-danger.bp3-editable-text-editing::before{
-webkit-box-shadow:0 0 0 1px #ff7373, 0 0 0 3px rgba(255, 115, 115, 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 #ff7373, 0 0 0 3px rgba(255, 115, 115, 0.3), inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-editable-text-input,
.bp3-editable-text-content{
display:inherit;
position:relative;
min-width:inherit;
max-width:inherit;
vertical-align:top;
text-transform:inherit;
letter-spacing:inherit;
color:inherit;
font:inherit;
resize:none; }
.bp3-editable-text-input{
border:none;
-webkit-box-shadow:none;
box-shadow:none;
background:none;
width:100%;
padding:0;
white-space:pre-wrap; }
.bp3-editable-text-input::-webkit-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-editable-text-input::-moz-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-editable-text-input:-ms-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-editable-text-input::-ms-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-editable-text-input::placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-editable-text-input:focus{
outline:none; }
.bp3-editable-text-input::-ms-clear{
display:none; }
.bp3-editable-text-content{
overflow:hidden;
padding-right:2px;
text-overflow:ellipsis;
white-space:pre; }
.bp3-editable-text-editing > .bp3-editable-text-content{
position:absolute;
left:0;
visibility:hidden; }
.bp3-editable-text-placeholder > .bp3-editable-text-content{
color:rgba(92, 112, 128, 0.6); }
.bp3-dark .bp3-editable-text-placeholder > .bp3-editable-text-content{
color:rgba(167, 182, 194, 0.6); }
.bp3-editable-text.bp3-multiline{
display:block; }
.bp3-editable-text.bp3-multiline .bp3-editable-text-content{
overflow:auto;
white-space:pre-wrap;
word-wrap:break-word; }
.bp3-control-group{
-webkit-transform:translateZ(0);
transform:translateZ(0);
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:stretch;
-ms-flex-align:stretch;
align-items:stretch; }
.bp3-control-group > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-control-group > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-control-group .bp3-button,
.bp3-control-group .bp3-html-select,
.bp3-control-group .bp3-input,
.bp3-control-group .bp3-select{
position:relative; }
.bp3-control-group .bp3-input{
z-index:2;
border-radius:inherit; }
.bp3-control-group .bp3-input:focus{
z-index:14;
border-radius:3px; }
.bp3-control-group .bp3-input[class*="bp3-intent"]{
z-index:13; }
.bp3-control-group .bp3-input[class*="bp3-intent"]:focus{
z-index:15; }
.bp3-control-group .bp3-input[readonly], .bp3-control-group .bp3-input:disabled, .bp3-control-group .bp3-input.bp3-disabled{
z-index:1; }
.bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input{
z-index:13; }
.bp3-control-group .bp3-input-group[class*="bp3-intent"] .bp3-input:focus{
z-index:15; }
.bp3-control-group .bp3-button,
.bp3-control-group .bp3-html-select select,
.bp3-control-group .bp3-select select{
-webkit-transform:translateZ(0);
transform:translateZ(0);
z-index:4;
border-radius:inherit; }
.bp3-control-group .bp3-button:focus,
.bp3-control-group .bp3-html-select select:focus,
.bp3-control-group .bp3-select select:focus{
z-index:5; }
.bp3-control-group .bp3-button:hover,
.bp3-control-group .bp3-html-select select:hover,
.bp3-control-group .bp3-select select:hover{
z-index:6; }
.bp3-control-group .bp3-button:active,
.bp3-control-group .bp3-html-select select:active,
.bp3-control-group .bp3-select select:active{
z-index:7; }
.bp3-control-group .bp3-button[readonly], .bp3-control-group .bp3-button:disabled, .bp3-control-group .bp3-button.bp3-disabled,
.bp3-control-group .bp3-html-select select[readonly],
.bp3-control-group .bp3-html-select select:disabled,
.bp3-control-group .bp3-html-select select.bp3-disabled,
.bp3-control-group .bp3-select select[readonly],
.bp3-control-group .bp3-select select:disabled,
.bp3-control-group .bp3-select select.bp3-disabled{
z-index:3; }
.bp3-control-group .bp3-button[class*="bp3-intent"],
.bp3-control-group .bp3-html-select select[class*="bp3-intent"],
.bp3-control-group .bp3-select select[class*="bp3-intent"]{
z-index:9; }
.bp3-control-group .bp3-button[class*="bp3-intent"]:focus,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"]:focus,
.bp3-control-group .bp3-select select[class*="bp3-intent"]:focus{
z-index:10; }
.bp3-control-group .bp3-button[class*="bp3-intent"]:hover,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"]:hover,
.bp3-control-group .bp3-select select[class*="bp3-intent"]:hover{
z-index:11; }
.bp3-control-group .bp3-button[class*="bp3-intent"]:active,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"]:active,
.bp3-control-group .bp3-select select[class*="bp3-intent"]:active{
z-index:12; }
.bp3-control-group .bp3-button[class*="bp3-intent"][readonly], .bp3-control-group .bp3-button[class*="bp3-intent"]:disabled, .bp3-control-group .bp3-button[class*="bp3-intent"].bp3-disabled,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"][readonly],
.bp3-control-group .bp3-html-select select[class*="bp3-intent"]:disabled,
.bp3-control-group .bp3-html-select select[class*="bp3-intent"].bp3-disabled,
.bp3-control-group .bp3-select select[class*="bp3-intent"][readonly],
.bp3-control-group .bp3-select select[class*="bp3-intent"]:disabled,
.bp3-control-group .bp3-select select[class*="bp3-intent"].bp3-disabled{
z-index:8; }
.bp3-control-group .bp3-input-group > .bp3-icon,
.bp3-control-group .bp3-input-group > .bp3-button,
.bp3-control-group .bp3-input-group > .bp3-input-action{
z-index:16; }
.bp3-control-group .bp3-select::after,
.bp3-control-group .bp3-html-select::after,
.bp3-control-group .bp3-select > .bp3-icon,
.bp3-control-group .bp3-html-select > .bp3-icon{
z-index:17; }
.bp3-control-group:not(.bp3-vertical) > *{
margin-right:-1px; }
.bp3-dark .bp3-control-group:not(.bp3-vertical) > *{
margin-right:0; }
.bp3-dark .bp3-control-group:not(.bp3-vertical) > .bp3-button + .bp3-button{
margin-left:1px; }
.bp3-control-group .bp3-popover-wrapper,
.bp3-control-group .bp3-popover-target{
border-radius:inherit; }
.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; }
.bp3-control-group > :only-child{
margin-right:0;
border-radius:3px; }
.bp3-control-group .bp3-input-group .bp3-button{
border-radius:3px; }
.bp3-control-group > .bp3-fill{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-control-group.bp3-fill > *:not(.bp3-fixed){
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto; }
.bp3-control-group.bp3-vertical{
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column; }
.bp3-control-group.bp3-vertical > *{
margin-top:-1px; }
.bp3-control-group.bp3-vertical > :first-child{
margin-top:0;
border-radius:3px 3px 0 0; }
.bp3-control-group.bp3-vertical > :last-child{
border-radius:0 0 3px 3px; }
.bp3-control{
display:block;
position:relative;
margin-bottom:10px;
cursor:pointer;
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));
color:#ffffff; }
.bp3-control:hover 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:#106ba3; }
.bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
-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; }
.bp3-control input:disabled:checked ~ .bp3-control-indicator{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(19, 124, 189, 0.5); }
.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{
-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; }
.bp3-dark .bp3-control input:not(:disabled):active:checked ~ .bp3-control-indicator{
-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; }
.bp3-dark .bp3-control input:disabled:checked ~ .bp3-control-indicator{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(14, 90, 138, 0.5); }
.bp3-control:not(.bp3-align-right){
padding-left:26px; }
.bp3-control:not(.bp3-align-right) .bp3-control-indicator{
margin-left:-26px; }
.bp3-control.bp3-align-right{
padding-right:26px; }
.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); }
.bp3-control.bp3-inline{
display:inline-block;
margin-right:20px; }
.bp3-control input{
position:absolute;
top:0;
left:0;
opacity: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));
cursor:pointer;
width:1em;
height:1em;
vertical-align:middle;
font-size:16px;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-control .bp3-control-indicator::before{
display:block;
width:1em;
height:1em;
content:""; }
.bp3-control:hover .bp3-control-indicator{
background-color:#ebf1f5; }
.bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
-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; }
.bp3-control input:disabled ~ .bp3-control-indicator{
-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;
outline-offset:2px;
-moz-outline-radius:6px; }
.bp3-control.bp3-align-right .bp3-control-indicator{
float:right;
margin-top:1px;
margin-left:10px; }
.bp3-control.bp3-large{
font-size:16px; }
.bp3-control.bp3-large:not(.bp3-align-right){
padding-left:30px; }
.bp3-control.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
margin-left:-30px; }
.bp3-control.bp3-large.bp3-align-right{
padding-right:30px; }
.bp3-control.bp3-large.bp3-align-right .bp3-control-indicator{
margin-right:-30px; }
.bp3-control.bp3-large .bp3-control-indicator{
font-size:20px; }
.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; }
.bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
-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; }
.bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(19, 124, 189, 0.5); }
.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{
-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; }
.bp3-dark .bp3-control.bp3-checkbox input:not(:disabled):active:indeterminate ~ .bp3-control-indicator{
-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; }
.bp3-dark .bp3-control.bp3-checkbox input:disabled:indeterminate ~ .bp3-control-indicator{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(14, 90, 138, 0.5); }
.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"); }
.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{
border-radius:50%; }
.bp3-control.bp3-radio input:checked ~ .bp3-control-indicator::before{
background-image:radial-gradient(#ffffff, #ffffff 28%, transparent 32%); }
.bp3-control.bp3-radio input:checked:disabled ~ .bp3-control-indicator::before{
opacity:0.5; }
.bp3-control.bp3-radio input:focus ~ .bp3-control-indicator{
-moz-outline-radius:16px; }
.bp3-control.bp3-switch input ~ .bp3-control-indicator{
background:rgba(167, 182, 194, 0.5); }
.bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
background:rgba(115, 134, 148, 0.5); }
.bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
background:rgba(92, 112, 128, 0.5); }
.bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
background:rgba(206, 217, 224, 0.5); }
.bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
background:rgba(255, 255, 255, 0.8); }
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
background:#137cbd; }
.bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
background:#106ba3; }
.bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
background:#0e5a8a; }
.bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
background:rgba(19, 124, 189, 0.5); }
.bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator::before{
background:rgba(255, 255, 255, 0.8); }
.bp3-control.bp3-switch:not(.bp3-align-right){
padding-left:38px; }
.bp3-control.bp3-switch:not(.bp3-align-right) .bp3-control-indicator{
margin-left:-38px; }
.bp3-control.bp3-switch.bp3-align-right{
padding-right:38px; }
.bp3-control.bp3-switch.bp3-align-right .bp3-control-indicator{
margin-right:-38px; }
.bp3-control.bp3-switch .bp3-control-indicator{
border:none;
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); }
.bp3-control.bp3-switch .bp3-control-indicator::before{
position:absolute;
left:0;
margin:2px;
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);
-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); }
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator::before{
left:calc(100% - 1em); }
.bp3-control.bp3-switch.bp3-large:not(.bp3-align-right){
padding-left:45px; }
.bp3-control.bp3-switch.bp3-large:not(.bp3-align-right) .bp3-control-indicator{
margin-left:-45px; }
.bp3-control.bp3-switch.bp3-large.bp3-align-right{
padding-right:45px; }
.bp3-control.bp3-switch.bp3-large.bp3-align-right .bp3-control-indicator{
margin-right:-45px; }
.bp3-dark .bp3-control.bp3-switch input ~ .bp3-control-indicator{
background:rgba(16, 22, 26, 0.5); }
.bp3-dark .bp3-control.bp3-switch:hover input ~ .bp3-control-indicator{
background:rgba(16, 22, 26, 0.7); }
.bp3-dark .bp3-control.bp3-switch input:not(:disabled):active ~ .bp3-control-indicator{
background:rgba(16, 22, 26, 0.9); }
.bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator{
background:rgba(57, 75, 89, 0.5); }
.bp3-dark .bp3-control.bp3-switch input:disabled ~ .bp3-control-indicator::before{
background:rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-control.bp3-switch input:checked ~ .bp3-control-indicator{
background:#137cbd; }
.bp3-dark .bp3-control.bp3-switch:hover input:checked ~ .bp3-control-indicator{
background:#106ba3; }
.bp3-dark .bp3-control.bp3-switch input:checked:not(:disabled):active ~ .bp3-control-indicator{
background:#0e5a8a; }
.bp3-dark .bp3-control.bp3-switch input:checked:disabled ~ .bp3-control-indicator{
background:rgba(14, 90, 138, 0.5); }
.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{
-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; }
.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; }
.bp3-control.bp3-switch .bp3-control-indicator-child:first-child{
visibility:hidden;
margin-right:1.2em;
margin-left:0.5em;
line-height:0; }
.bp3-control.bp3-switch .bp3-control-indicator-child:last-child{
visibility:visible;
margin-right:0.5em;
margin-left:1.2em;
line-height:1em; }
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:first-child{
visibility:visible;
line-height:1em; }
.bp3-control.bp3-switch input:checked ~ .bp3-control-indicator .bp3-control-indicator-child:last-child{
visibility:hidden;
line-height:0; }
.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)); }
.bp3-dark .bp3-control:hover .bp3-control-indicator{
background-color:#30404d; }
.bp3-dark .bp3-control input:not(:disabled):active ~ .bp3-control-indicator{
-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; }
.bp3-dark .bp3-control input:disabled ~ .bp3-control-indicator{
-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; }
.bp3-file-input input{
opacity:0;
margin:0;
min-width:200px; }
.bp3-file-input input:disabled + .bp3-file-upload-input,
.bp3-file-input input.bp3-disabled + .bp3-file-upload-input{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(206, 217, 224, 0.5);
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6);
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;
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6); }
.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{
-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;
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{
background:rgba(57, 75, 89, 0.7); }
.bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
color:#182026; }
.bp3-dark .bp3-file-input.bp3-file-input-has-selection .bp3-file-upload-input{
color:#f5f8fa; }
.bp3-file-input.bp3-fill{
width:100%; }
.bp3-file-input.bp3-large,
.bp3-large .bp3-file-input{
height:40px; }
.bp3-file-input .bp3-file-upload-input-custom-text::after{
content:attr(bp3-button-text); }
.bp3-file-upload-input{
outline:none;
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;
-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;
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
position:absolute;
top:0;
right:0;
left:0;
padding-right:80px;
color:rgba(92, 112, 128, 0.6);
-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); }
.bp3-file-upload-input::-moz-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-file-upload-input:-ms-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-file-upload-input::-ms-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-file-upload-input::placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.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); }
.bp3-file-upload-input[type="search"], .bp3-file-upload-input.bp3-round{
border-radius:30px;
-webkit-box-sizing:border-box;
box-sizing:border-box;
padding-left:10px; }
.bp3-file-upload-input[readonly]{
-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{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(206, 217, 224, 0.5);
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6);
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));
color:#182026;
min-width:24px;
min-height:24px;
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
position:absolute;
top:0;
right:0;
margin:3px;
border-radius:3px;
width:70px;
text-align:center;
line-height:24px;
content:"Browse"; }
.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; }
.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; }
.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;
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6); }
.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; }
.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; }
.bp3-large .bp3-file-upload-input{
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;
margin:5px;
width:85px;
line-height:30px; }
.bp3-dark .bp3-file-upload-input{
-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{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input::-moz-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input:-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input::-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input::placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-file-upload-input:focus{
-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); }
.bp3-dark .bp3-file-upload-input[readonly]{
-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{
-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));
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{
-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; }
.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; }
.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;
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); }
.bp3-dark .bp3-file-upload-input::after .bp3-button-spinner .bp3-spinner-head{
background:rgba(16, 22, 26, 0.5);
stroke:#8a9ba8; }
.bp3-dark .bp3-file-upload-input:hover::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:#30404d; }
.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; }
.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); }
.bp3-form-group{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
margin:0 0 15px; }
.bp3-form-group label.bp3-label{
margin-bottom:5px; }
.bp3-form-group .bp3-control{
margin-top:7px; }
.bp3-form-group .bp3-form-helper-text{
margin-top:5px;
color:#5c7080;
font-size:12px; }
.bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
color:#106ba3; }
.bp3-form-group.bp3-intent-success .bp3-form-helper-text{
color:#0d8050; }
.bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
color:#bf7326; }
.bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
color:#c23030; }
.bp3-form-group.bp3-inline{
-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; }
.bp3-form-group.bp3-inline.bp3-large label.bp3-label{
margin:0 10px 0 0;
line-height:40px; }
.bp3-form-group.bp3-inline label.bp3-label{
margin:0 10px 0 0;
line-height:30px; }
.bp3-form-group.bp3-disabled .bp3-label,
.bp3-form-group.bp3-disabled .bp3-text-muted,
.bp3-form-group.bp3-disabled .bp3-form-helper-text{
color:rgba(92, 112, 128, 0.6) !important; }
.bp3-dark .bp3-form-group.bp3-intent-primary .bp3-form-helper-text{
color:#48aff0; }
.bp3-dark .bp3-form-group.bp3-intent-success .bp3-form-helper-text{
color:#3dcc91; }
.bp3-dark .bp3-form-group.bp3-intent-warning .bp3-form-helper-text{
color:#ffb366; }
.bp3-dark .bp3-form-group.bp3-intent-danger .bp3-form-helper-text{
color:#ff7373; }
.bp3-dark .bp3-form-group .bp3-form-helper-text{
color:#a7b6c2; }
.bp3-dark .bp3-form-group.bp3-disabled .bp3-label,
.bp3-dark .bp3-form-group.bp3-disabled .bp3-text-muted,
.bp3-dark .bp3-form-group.bp3-disabled .bp3-form-helper-text{
color:rgba(167, 182, 194, 0.6) !important; }
.bp3-input-group{
display:block;
position:relative; }
.bp3-input-group .bp3-input{
position:relative;
width:100%; }
.bp3-input-group .bp3-input:not(:first-child){
padding-left:30px; }
.bp3-input-group .bp3-input:not(:last-child){
padding-right:30px; }
.bp3-input-group .bp3-input-action,
.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-button:first-child,
.bp3-input-group > .bp3-icon:first-child{
left:0; }
.bp3-input-group .bp3-input-action: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;
margin:3px;
padding:0 7px; }
.bp3-input-group .bp3-button:empty{
padding:0; }
.bp3-input-group > .bp3-icon{
z-index:1;
color:#5c7080; }
.bp3-input-group > .bp3-icon:empty{
line-height:1;
font-family:"Icons16", sans-serif;
font-size:16px;
font-weight:400;
font-style:normal;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased; }
.bp3-input-group > .bp3-icon,
.bp3-input-group .bp3-input-action > .bp3-spinner{
margin:7px; }
.bp3-input-group .bp3-tag{
margin:5px; }
.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus),
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
color:#5c7080; }
.bp3-dark .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus), .bp3-dark
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus){
color:#a7b6c2; }
.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-standard,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:not(:hover):not(:focus) .bp3-icon-large{
color:#5c7080; }
.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled{
color:rgba(92, 112, 128, 0.6) !important; }
.bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-standard, .bp3-input-group .bp3-input:not(:focus) + .bp3-button.bp3-minimal:disabled .bp3-icon-large,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-standard,
.bp3-input-group .bp3-input:not(:focus) + .bp3-input-action .bp3-button.bp3-minimal:disabled .bp3-icon-large{
color:rgba(92, 112, 128, 0.6) !important; }
.bp3-input-group.bp3-disabled{
cursor:not-allowed; }
.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;
margin:5px; }
.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{
height:40px;
line-height:40px;
font-size:16px; }
.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){
padding-left:40px; }
.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;
margin:2px; }
.bp3-input-group.bp3-small .bp3-tag{
min-width:20px;
min-height:20px;
margin:2px; }
.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{
height:24px;
padding-right:8px;
padding-left:8px;
line-height:24px;
font-size:12px; }
.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){
padding-left:24px; }
.bp3-input-group.bp3-small .bp3-input:not(:last-child){
padding-right:24px; }
.bp3-input-group.bp3-fill{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
width:100%; }
.bp3-input-group.bp3-round .bp3-button,
.bp3-input-group.bp3-round .bp3-input,
.bp3-input-group.bp3-round .bp3-tag{
border-radius:30px; }
.bp3-dark .bp3-input-group .bp3-icon{
color:#a7b6c2; }
.bp3-dark .bp3-input-group.bp3-disabled .bp3-icon{
color:rgba(167, 182, 194, 0.6); }
.bp3-input-group.bp3-intent-primary .bp3-input{
-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);
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); }
.bp3-input-group.bp3-intent-primary .bp3-input:focus{
-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); }
.bp3-input-group.bp3-intent-primary .bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #137cbd;
box-shadow:inset 0 0 0 1px #137cbd; }
.bp3-input-group.bp3-intent-primary .bp3-input:disabled, .bp3-input-group.bp3-intent-primary .bp3-input.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input-group.bp3-intent-primary > .bp3-icon{
color:#106ba3; }
.bp3-dark .bp3-input-group.bp3-intent-primary > .bp3-icon{
color:#48aff0; }
.bp3-input-group.bp3-intent-success .bp3-input{
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, 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(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-success .bp3-input:focus{
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-success .bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #0f9960;
box-shadow:inset 0 0 0 1px #0f9960; }
.bp3-input-group.bp3-intent-success .bp3-input:disabled, .bp3-input-group.bp3-intent-success .bp3-input.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input-group.bp3-intent-success > .bp3-icon{
color:#0d8050; }
.bp3-dark .bp3-input-group.bp3-intent-success > .bp3-icon{
color:#3dcc91; }
.bp3-input-group.bp3-intent-warning .bp3-input{
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, 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(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-warning .bp3-input:focus{
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-warning .bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #d9822b;
box-shadow:inset 0 0 0 1px #d9822b; }
.bp3-input-group.bp3-intent-warning .bp3-input:disabled, .bp3-input-group.bp3-intent-warning .bp3-input.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input-group.bp3-intent-warning > .bp3-icon{
color:#bf7326; }
.bp3-dark .bp3-input-group.bp3-intent-warning > .bp3-icon{
color:#ffb366; }
.bp3-input-group.bp3-intent-danger .bp3-input{
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, 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(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-danger .bp3-input:focus{
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input-group.bp3-intent-danger .bp3-input[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #db3737;
box-shadow:inset 0 0 0 1px #db3737; }
.bp3-input-group.bp3-intent-danger .bp3-input:disabled, .bp3-input-group.bp3-intent-danger .bp3-input.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input-group.bp3-intent-danger > .bp3-icon{
color:#c23030; }
.bp3-dark .bp3-input-group.bp3-intent-danger > .bp3-icon{
color:#ff7373; }
.bp3-input{
outline:none;
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;
-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; }
.bp3-input::-webkit-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-input::-moz-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-input:-ms-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-input::-ms-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-input::placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.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); }
.bp3-input[type="search"], .bp3-input.bp3-round{
border-radius:30px;
-webkit-box-sizing:border-box;
box-sizing:border-box;
padding-left:10px; }
.bp3-input[readonly]{
-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{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(206, 217, 224, 0.5);
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6);
resize:none; }
.bp3-input.bp3-large{
height:40px;
line-height:40px;
font-size:16px; }
.bp3-input.bp3-large[type="search"], .bp3-input.bp3-large.bp3-round{
padding:0 15px; }
.bp3-input.bp3-small{
height:24px;
padding-right:8px;
padding-left:8px;
line-height:24px;
font-size:12px; }
.bp3-input.bp3-small[type="search"], .bp3-input.bp3-small.bp3-round{
padding:0 12px; }
.bp3-input.bp3-fill{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
width:100%; }
.bp3-dark .bp3-input{
-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); }
.bp3-dark .bp3-input::-moz-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-input:-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-input::-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-input::placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-input:focus{
-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); }
.bp3-dark .bp3-input[readonly]{
-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{
-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);
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); }
.bp3-input.bp3-intent-primary:focus{
-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); }
.bp3-input.bp3-intent-primary[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #137cbd;
box-shadow:inset 0 0 0 1px #137cbd; }
.bp3-input.bp3-intent-primary:disabled, .bp3-input.bp3-intent-primary.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .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), 0 0 0 0 rgba(19, 124, 189, 0), inset 0 0 0 1px #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), 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 #137cbd, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-primary:focus{
-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); }
.bp3-dark .bp3-input.bp3-intent-primary[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #137cbd;
box-shadow:inset 0 0 0 1px #137cbd; }
.bp3-dark .bp3-input.bp3-intent-primary:disabled, .bp3-dark .bp3-input.bp3-intent-primary.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input.bp3-intent-success{
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, 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(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-success:focus{
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-success[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #0f9960;
box-shadow:inset 0 0 0 1px #0f9960; }
.bp3-input.bp3-intent-success:disabled, .bp3-input.bp3-intent-success.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-input.bp3-intent-success{
-webkit-box-shadow:0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, 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(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), 0 0 0 0 rgba(15, 153, 96, 0), inset 0 0 0 1px #0f9960, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-success:focus{
-webkit-box-shadow:0 0 0 1px #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 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 #0f9960, 0 0 0 1px #0f9960, 0 0 0 3px rgba(15, 153, 96, 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-input.bp3-intent-success[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #0f9960;
box-shadow:inset 0 0 0 1px #0f9960; }
.bp3-dark .bp3-input.bp3-intent-success:disabled, .bp3-dark .bp3-input.bp3-intent-success.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input.bp3-intent-warning{
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, 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(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-warning:focus{
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-warning[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #d9822b;
box-shadow:inset 0 0 0 1px #d9822b; }
.bp3-input.bp3-intent-warning:disabled, .bp3-input.bp3-intent-warning.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-input.bp3-intent-warning{
-webkit-box-shadow:0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, 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(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), 0 0 0 0 rgba(217, 130, 43, 0), inset 0 0 0 1px #d9822b, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-warning:focus{
-webkit-box-shadow:0 0 0 1px #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 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 #d9822b, 0 0 0 1px #d9822b, 0 0 0 3px rgba(217, 130, 43, 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-input.bp3-intent-warning[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #d9822b;
box-shadow:inset 0 0 0 1px #d9822b; }
.bp3-dark .bp3-input.bp3-intent-warning:disabled, .bp3-dark .bp3-input.bp3-intent-warning.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input.bp3-intent-danger{
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, 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(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.15), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-danger:focus{
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-input.bp3-intent-danger[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #db3737;
box-shadow:inset 0 0 0 1px #db3737; }
.bp3-input.bp3-intent-danger:disabled, .bp3-input.bp3-intent-danger.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-input.bp3-intent-danger{
-webkit-box-shadow:0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, 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(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), 0 0 0 0 rgba(219, 55, 55, 0), inset 0 0 0 1px #db3737, inset 0 0 0 1px rgba(16, 22, 26, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.4); }
.bp3-dark .bp3-input.bp3-intent-danger:focus{
-webkit-box-shadow:0 0 0 1px #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 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 #db3737, 0 0 0 1px #db3737, 0 0 0 3px rgba(219, 55, 55, 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-input.bp3-intent-danger[readonly]{
-webkit-box-shadow:inset 0 0 0 1px #db3737;
box-shadow:inset 0 0 0 1px #db3737; }
.bp3-dark .bp3-input.bp3-intent-danger:disabled, .bp3-dark .bp3-input.bp3-intent-danger.bp3-disabled{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-input::-ms-clear{
display:none; }
textarea.bp3-input{
max-width:100%;
padding:10px; }
textarea.bp3-input, textarea.bp3-input.bp3-large, textarea.bp3-input.bp3-small{
height:auto;
line-height:inherit; }
textarea.bp3-input.bp3-small{
padding:8px; }
.bp3-dark textarea.bp3-input{
-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); }
.bp3-dark textarea.bp3-input::-moz-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark textarea.bp3-input:-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark textarea.bp3-input::-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark textarea.bp3-input::placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark textarea.bp3-input:focus{
-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); }
.bp3-dark textarea.bp3-input[readonly]{
-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{
-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; }
label.bp3-label .bp3-html-select,
label.bp3-label .bp3-input,
label.bp3-label .bp3-select,
label.bp3-label .bp3-slider,
label.bp3-label .bp3-popover-wrapper{
display:block;
margin-top:5px;
text-transform:none; }
label.bp3-label .bp3-button-group{
margin-top:5px; }
label.bp3-label .bp3-select select,
label.bp3-label .bp3-html-select select{
width:100%;
vertical-align:top;
font-weight:400; }
label.bp3-label.bp3-disabled,
label.bp3-label.bp3-disabled .bp3-text-muted{
color:rgba(92, 112, 128, 0.6); }
label.bp3-label.bp3-inline{
line-height:30px; }
label.bp3-label.bp3-inline .bp3-html-select,
label.bp3-label.bp3-inline .bp3-input,
label.bp3-label.bp3-inline .bp3-input-group,
label.bp3-label.bp3-inline .bp3-select,
label.bp3-label.bp3-inline .bp3-popover-wrapper{
display:inline-block;
margin:0 0 0 5px;
vertical-align:top; }
label.bp3-label.bp3-inline .bp3-button-group{
margin:0 0 0 5px; }
label.bp3-label.bp3-inline .bp3-input-group .bp3-input{
margin-left:0; }
label.bp3-label.bp3-inline.bp3-large{
line-height:40px; }
label.bp3-label:not(.bp3-inline) .bp3-popover-target{
display:block; }
.bp3-dark label.bp3-label{
color:#f5f8fa; }
.bp3-dark label.bp3-label.bp3-disabled,
.bp3-dark label.bp3-label.bp3-disabled .bp3-text-muted{
color:rgba(167, 182, 194, 0.6); }
.bp3-numeric-input .bp3-button-group.bp3-vertical > .bp3-button{
-webkit-box-flex:1;
-ms-flex:1 1 14px;
flex:1 1 14px;
width:30px;
min-height:0;
padding:0; }
.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{
border-radius:0 0 3px 0; }
.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:first-child{
border-radius:3px 0 0 0; }
.bp3-numeric-input .bp3-button-group.bp3-vertical:first-child > .bp3-button:last-child{
border-radius:0 0 0 3px; }
.bp3-numeric-input.bp3-large .bp3-button-group.bp3-vertical > .bp3-button{
width:40px; }
form{
display:block; }
.bp3-html-select select,
.bp3-select select{
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-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;
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);
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));
color:#182026;
border-radius:3px;
width:100%;
height:30px;
padding:0 25px 0 10px;
-moz-appearance:none;
-webkit-appearance:none; }
.bp3-html-select select > *, .bp3-select select > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-html-select select > .bp3-fill, .bp3-select select > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-html-select select::before,
.bp3-select select::before, .bp3-html-select select > *, .bp3-select select > *{
margin-right:7px; }
.bp3-html-select select:empty::before,
.bp3-select select:empty::before,
.bp3-html-select select > :last-child,
.bp3-select select > :last-child{
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; }
.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; }
.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;
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6); }
.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,
.bp3-select select.bp3-disabled.bp3-active, .bp3-html-select select.bp3-disabled.bp3-active:hover,
.bp3-select select.bp3-disabled.bp3-active:hover{
background:rgba(206, 217, 224, 0.7); }
.bp3-html-select.bp3-minimal select,
.bp3-select.bp3-minimal select{
-webkit-box-shadow:none;
box-shadow:none;
background:none; }
.bp3-html-select.bp3-minimal select:hover,
.bp3-select.bp3-minimal select:hover{
-webkit-box-shadow:none;
box-shadow:none;
background:rgba(167, 182, 194, 0.3);
text-decoration:none;
color:#182026; }
.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{
-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,
.bp3-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal select.bp3-disabled,
.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); }
.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,
.bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active,
.bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active{
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{
-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{
-webkit-box-shadow:none;
box-shadow:none;
background: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); }
.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:rgba(138, 155, 168, 0.3);
color:#f5f8fa; }
.bp3-dark .bp3-html-select.bp3-minimal select:disabled, .bp3-html-select.bp3-minimal .bp3-dark select:disabled,
.bp3-dark .bp3-select.bp3-minimal select:disabled, .bp3-select.bp3-minimal .bp3-dark select:disabled, .bp3-dark .bp3-html-select.bp3-minimal select:disabled:hover, .bp3-html-select.bp3-minimal .bp3-dark select:disabled:hover,
.bp3-dark .bp3-select.bp3-minimal select:disabled:hover, .bp3-select.bp3-minimal .bp3-dark select:disabled:hover, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled,
.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); }
.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,
.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-disabled:hover.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-disabled:hover.bp3-active{
background:rgba(138, 155, 168, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-primary,
.bp3-select.bp3-minimal select.bp3-intent-primary{
color:#106ba3; }
.bp3-html-select.bp3-minimal select.bp3-intent-primary:hover,
.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{
-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{
background:rgba(19, 124, 189, 0.15);
color:#106ba3; }
.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:rgba(19, 124, 189, 0.3);
color:#106ba3; }
.bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled,
.bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled,
.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(16, 107, 163, 0.5); }
.bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-primary .bp3-button-spinner .bp3-spinner-head{
stroke:#106ba3; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary{
color:#48aff0; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:hover{
background:rgba(19, 124, 189, 0.2);
color:#48aff0; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-active{
background:rgba(19, 124, 189, 0.3);
color:#48aff0; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled{
background:none;
color:rgba(72, 175, 240, 0.5); }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-primary.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-primary.bp3-disabled.bp3-active{
background:rgba(19, 124, 189, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-success,
.bp3-select.bp3-minimal select.bp3-intent-success{
color:#0d8050; }
.bp3-html-select.bp3-minimal select.bp3-intent-success:hover,
.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{
-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{
background:rgba(15, 153, 96, 0.15);
color:#0d8050; }
.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:rgba(15, 153, 96, 0.3);
color:#0d8050; }
.bp3-html-select.bp3-minimal select.bp3-intent-success:disabled,
.bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled,
.bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(13, 128, 80, 0.5); }
.bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-success .bp3-button-spinner .bp3-spinner-head{
stroke:#0d8050; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success{
color:#3dcc91; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:hover{
background:rgba(15, 153, 96, 0.2);
color:#3dcc91; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-active{
background:rgba(15, 153, 96, 0.3);
color:#3dcc91; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled{
background:none;
color:rgba(61, 204, 145, 0.5); }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-success.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-success.bp3-disabled.bp3-active{
background:rgba(15, 153, 96, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-warning,
.bp3-select.bp3-minimal select.bp3-intent-warning{
color:#bf7326; }
.bp3-html-select.bp3-minimal select.bp3-intent-warning:hover,
.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{
-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{
background:rgba(217, 130, 43, 0.15);
color:#bf7326; }
.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:rgba(217, 130, 43, 0.3);
color:#bf7326; }
.bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled,
.bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled,
.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(191, 115, 38, 0.5); }
.bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-warning .bp3-button-spinner .bp3-spinner-head{
stroke:#bf7326; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning{
color:#ffb366; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:hover{
background:rgba(217, 130, 43, 0.2);
color:#ffb366; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-active{
background:rgba(217, 130, 43, 0.3);
color:#ffb366; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled{
background:none;
color:rgba(255, 179, 102, 0.5); }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-warning.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-warning.bp3-disabled.bp3-active{
background:rgba(217, 130, 43, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-danger,
.bp3-select.bp3-minimal select.bp3-intent-danger{
color:#c23030; }
.bp3-html-select.bp3-minimal select.bp3-intent-danger:hover,
.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{
-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{
background:rgba(219, 55, 55, 0.15);
color:#c23030; }
.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:rgba(219, 55, 55, 0.3);
color:#c23030; }
.bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled,
.bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled,
.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(194, 48, 48, 0.5); }
.bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active,
.bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-html-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head, .bp3-select.bp3-minimal select.bp3-intent-danger .bp3-button-spinner .bp3-spinner-head{
stroke:#c23030; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger{
color:#ff7373; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:hover, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:hover{
background:rgba(219, 55, 55, 0.2);
color:#ff7373; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-active{
background:rgba(219, 55, 55, 0.3);
color:#ff7373; }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled{
background:none;
color:rgba(255, 115, 115, 0.5); }
.bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger:disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger:disabled.bp3-active, .bp3-dark .bp3-html-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-html-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active,
.bp3-dark .bp3-select.bp3-minimal select.bp3-intent-danger.bp3-disabled.bp3-active, .bp3-select.bp3-minimal .bp3-dark select.bp3-intent-danger.bp3-disabled.bp3-active{
background:rgba(219, 55, 55, 0.3); }
.bp3-html-select.bp3-large select,
.bp3-select.bp3-large select{
height:40px;
padding-right:35px;
font-size:16px; }
.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));
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{
-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; }
.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; }
.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;
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); }
.bp3-dark .bp3-html-select select .bp3-button-spinner .bp3-spinner-head, .bp3-dark .bp3-select select .bp3-button-spinner .bp3-spinner-head{
background:rgba(16, 22, 26, 0.5);
stroke:#8a9ba8; }
.bp3-html-select select:disabled,
.bp3-select select:disabled{
-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); }
.bp3-html-select .bp3-icon,
.bp3-select .bp3-icon, .bp3-select::after{
position:absolute;
top:7px;
right:7px;
color:#5c7080;
pointer-events:none; }
.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;
position:relative;
vertical-align:middle;
letter-spacing:normal; }
.bp3-html-select select::-ms-expand,
.bp3-select select::-ms-expand{
display:none; }
.bp3-html-select .bp3-icon,
.bp3-select .bp3-icon{
color:#5c7080; }
.bp3-html-select .bp3-icon:hover,
.bp3-select .bp3-icon:hover{
color:#182026; }
.bp3-dark .bp3-html-select .bp3-icon, .bp3-dark
.bp3-select .bp3-icon{
color:#a7b6c2; }
.bp3-dark .bp3-html-select .bp3-icon:hover, .bp3-dark
.bp3-select .bp3-icon:hover{
color:#f5f8fa; }
.bp3-html-select.bp3-large::after,
.bp3-html-select.bp3-large .bp3-icon,
.bp3-select.bp3-large::after,
.bp3-select.bp3-large .bp3-icon{
top:12px;
right:12px; }
.bp3-html-select.bp3-fill,
.bp3-html-select.bp3-fill select,
.bp3-select.bp3-fill,
.bp3-select.bp3-fill select{
width:100%; }
.bp3-dark .bp3-html-select option, .bp3-dark
.bp3-select option{
background-color:#30404d;
color:#f5f8fa; }
.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;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
content:""; }
.bp3-running-text table, table.bp3-html-table{
border-spacing:0;
font-size:14px; }
.bp3-running-text table th, table.bp3-html-table th,
.bp3-running-text table td,
table.bp3-html-table td{
padding:11px;
vertical-align:top;
text-align:left; }
.bp3-running-text table th, table.bp3-html-table th{
color:#182026;
font-weight:600; }
.bp3-running-text table td,
table.bp3-html-table td{
color:#182026; }
.bp3-running-text table tbody tr:first-child th, table.bp3-html-table tbody tr:first-child th,
.bp3-running-text table tbody tr:first-child td,
table.bp3-html-table tbody tr:first-child td{
-webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
.bp3-dark .bp3-running-text table th, .bp3-running-text .bp3-dark table th, .bp3-dark table.bp3-html-table th{
color:#f5f8fa; }
.bp3-dark .bp3-running-text table td, .bp3-running-text .bp3-dark table td, .bp3-dark table.bp3-html-table td{
color:#f5f8fa; }
.bp3-dark .bp3-running-text table tbody tr:first-child th, .bp3-running-text .bp3-dark table tbody tr:first-child th, .bp3-dark table.bp3-html-table tbody tr:first-child th,
.bp3-dark .bp3-running-text table tbody tr:first-child td,
.bp3-running-text .bp3-dark table tbody tr:first-child td,
.bp3-dark table.bp3-html-table tbody tr:first-child 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); }
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; }
table.bp3-html-table.bp3-html-table-striped tbody tr:nth-child(odd) td{
background:rgba(191, 204, 214, 0.15); }
table.bp3-html-table.bp3-html-table-bordered th:not(:first-child){
-webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
table.bp3-html-table.bp3-html-table-bordered tbody tr td{
-webkit-box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 0 1px 0 0 rgba(16, 22, 26, 0.15); }
table.bp3-html-table.bp3-html-table-bordered tbody tr td:not(:first-child){
-webkit-box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 1px 1px 0 0 rgba(16, 22, 26, 0.15); }
table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td{
-webkit-box-shadow:none;
box-shadow:none; }
table.bp3-html-table.bp3-html-table-bordered.bp3-html-table-striped tbody tr:not(:first-child) td:not(:first-child){
-webkit-box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15);
box-shadow:inset 1px 0 0 0 rgba(16, 22, 26, 0.15); }
table.bp3-html-table.bp3-interactive tbody tr:hover td{
background-color:rgba(191, 204, 214, 0.3);
cursor:pointer; }
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-key-combo{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center; }
.bp3-key-combo > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-key-combo > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-key-combo::before,
.bp3-key-combo > *{
margin-right:5px; }
.bp3-key-combo:empty::before,
.bp3-key-combo > :last-child{
margin-right:0; }
.bp3-hotkey-dialog{
top:40px;
padding-bottom:0; }
.bp3-hotkey-dialog .bp3-dialog-body{
margin:0;
padding:0; }
.bp3-hotkey-dialog .bp3-hotkey-label{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1; }
.bp3-hotkey-column{
margin:auto;
max-height:80vh;
overflow-y:auto;
padding:30px; }
.bp3-hotkey-column .bp3-heading{
margin-bottom:20px; }
.bp3-hotkey-column .bp3-heading:not(:first-child){
margin-top:40px; }
.bp3-hotkey{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
-webkit-box-pack:justify;
-ms-flex-pack:justify;
justify-content:space-between;
margin-right:0;
margin-left:0; }
.bp3-hotkey:not(:last-child){
margin-bottom:10px; }
.bp3-icon{
display:inline-block;
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
vertical-align:text-bottom; }
.bp3-icon:not(:empty)::before{
content:"" !important;
content:unset !important; }
.bp3-icon > svg{
display:block; }
.bp3-icon > svg:not([fill]){
fill:currentColor; }
.bp3-icon.bp3-intent-primary, .bp3-icon-standard.bp3-intent-primary, .bp3-icon-large.bp3-intent-primary{
color:#106ba3; }
.bp3-dark .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-icon-large.bp3-intent-primary{
color:#48aff0; }
.bp3-icon.bp3-intent-success, .bp3-icon-standard.bp3-intent-success, .bp3-icon-large.bp3-intent-success{
color:#0d8050; }
.bp3-dark .bp3-icon.bp3-intent-success, .bp3-dark .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-icon-large.bp3-intent-success{
color:#3dcc91; }
.bp3-icon.bp3-intent-warning, .bp3-icon-standard.bp3-intent-warning, .bp3-icon-large.bp3-intent-warning{
color:#bf7326; }
.bp3-dark .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-icon-large.bp3-intent-warning{
color:#ffb366; }
.bp3-icon.bp3-intent-danger, .bp3-icon-standard.bp3-intent-danger, .bp3-icon-large.bp3-intent-danger{
color:#c23030; }
.bp3-dark .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-icon-large.bp3-intent-danger{
color:#ff7373; }
span.bp3-icon-standard{
line-height:1;
font-family:"Icons16", sans-serif;
font-size:16px;
font-weight:400;
font-style:normal;
-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;
-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-weight:400;
font-style:normal; }
span.bp3-icon:empty::before{
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased; }
.bp3-icon-add::before{
content:""; }
.bp3-icon-add-column-left::before{
content:""; }
.bp3-icon-add-column-right::before{
content:""; }
.bp3-icon-add-row-bottom::before{
content:""; }
.bp3-icon-add-row-top::before{
content:""; }
.bp3-icon-add-to-artifact::before{
content:""; }
.bp3-icon-add-to-folder::before{
content:""; }
.bp3-icon-airplane::before{
content:""; }
.bp3-icon-align-center::before{
content:""; }
.bp3-icon-align-justify::before{
content:""; }
.bp3-icon-align-left::before{
content:""; }
.bp3-icon-align-right::before{
content:""; }
.bp3-icon-alignment-bottom::before{
content:""; }
.bp3-icon-alignment-horizontal-center::before{
content:""; }
.bp3-icon-alignment-left::before{
content:""; }
.bp3-icon-alignment-right::before{
content:""; }
.bp3-icon-alignment-top::before{
content:""; }
.bp3-icon-alignment-vertical-center::before{
content:""; }
.bp3-icon-annotation::before{
content:""; }
.bp3-icon-application::before{
content:""; }
.bp3-icon-applications::before{
content:""; }
.bp3-icon-archive::before{
content:""; }
.bp3-icon-arrow-bottom-left::before{
content:"↙"; }
.bp3-icon-arrow-bottom-right::before{
content:"↘"; }
.bp3-icon-arrow-down::before{
content:"↓"; }
.bp3-icon-arrow-left::before{
content:"←"; }
.bp3-icon-arrow-right::before{
content:"→"; }
.bp3-icon-arrow-top-left::before{
content:"↖"; }
.bp3-icon-arrow-top-right::before{
content:"↗"; }
.bp3-icon-arrow-up::before{
content:"↑"; }
.bp3-icon-arrows-horizontal::before{
content:"↔"; }
.bp3-icon-arrows-vertical::before{
content:"↕"; }
.bp3-icon-asterisk::before{
content:"*"; }
.bp3-icon-automatic-updates::before{
content:""; }
.bp3-icon-badge::before{
content:""; }
.bp3-icon-ban-circle::before{
content:""; }
.bp3-icon-bank-account::before{
content:""; }
.bp3-icon-barcode::before{
content:""; }
.bp3-icon-blank::before{
content:""; }
.bp3-icon-blocked-person::before{
content:""; }
.bp3-icon-bold::before{
content:""; }
.bp3-icon-book::before{
content:""; }
.bp3-icon-bookmark::before{
content:""; }
.bp3-icon-box::before{
content:""; }
.bp3-icon-briefcase::before{
content:""; }
.bp3-icon-bring-data::before{
content:""; }
.bp3-icon-build::before{
content:""; }
.bp3-icon-calculator::before{
content:""; }
.bp3-icon-calendar::before{
content:""; }
.bp3-icon-camera::before{
content:""; }
.bp3-icon-caret-down::before{
content:"⌄"; }
.bp3-icon-caret-left::before{
content:"〈"; }
.bp3-icon-caret-right::before{
content:"〉"; }
.bp3-icon-caret-up::before{
content:"⌃"; }
.bp3-icon-cell-tower::before{
content:""; }
.bp3-icon-changes::before{
content:""; }
.bp3-icon-chart::before{
content:""; }
.bp3-icon-chat::before{
content:""; }
.bp3-icon-chevron-backward::before{
content:""; }
.bp3-icon-chevron-down::before{
content:""; }
.bp3-icon-chevron-forward::before{
content:""; }
.bp3-icon-chevron-left::before{
content:""; }
.bp3-icon-chevron-right::before{
content:""; }
.bp3-icon-chevron-up::before{
content:""; }
.bp3-icon-circle::before{
content:""; }
.bp3-icon-circle-arrow-down::before{
content:""; }
.bp3-icon-circle-arrow-left::before{
content:""; }
.bp3-icon-circle-arrow-right::before{
content:""; }
.bp3-icon-circle-arrow-up::before{
content:""; }
.bp3-icon-citation::before{
content:""; }
.bp3-icon-clean::before{
content:""; }
.bp3-icon-clipboard::before{
content:""; }
.bp3-icon-cloud::before{
content:"☁"; }
.bp3-icon-cloud-download::before{
content:""; }
.bp3-icon-cloud-upload::before{
content:""; }
.bp3-icon-code::before{
content:""; }
.bp3-icon-code-block::before{
content:""; }
.bp3-icon-cog::before{
content:""; }
.bp3-icon-collapse-all::before{
content:""; }
.bp3-icon-column-layout::before{
content:""; }
.bp3-icon-comment::before{
content:""; }
.bp3-icon-comparison::before{
content:""; }
.bp3-icon-compass::before{
content:""; }
.bp3-icon-compressed::before{
content:""; }
.bp3-icon-confirm::before{
content:""; }
.bp3-icon-console::before{
content:""; }
.bp3-icon-contrast::before{
content:""; }
.bp3-icon-control::before{
content:""; }
.bp3-icon-credit-card::before{
content:""; }
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-webkit-box-shadow:none;
box-shadow:none; }
.bp3-dark .bp3-submenu.bp3-popover > .bp3-popover-content, .bp3-submenu.bp3-popover.bp3-dark > .bp3-popover-content{
-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;
min-width:180px;
padding:5px;
list-style:none;
text-align:left;
color:#182026; }
.bp3-menu-divider{
display:block;
margin:5px;
border-top:1px solid rgba(16, 22, 26, 0.15); }
.bp3-dark .bp3-menu-divider{
border-top-color:rgba(255, 255, 255, 0.15); }
.bp3-menu-item{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-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;
border-radius:2px;
padding:5px 7px;
text-decoration:none;
line-height:20px;
color:inherit;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-menu-item > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-menu-item > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-menu-item::before,
.bp3-menu-item > *{
margin-right:7px; }
.bp3-menu-item:empty::before,
.bp3-menu-item > :last-child{
margin-right:0; }
.bp3-menu-item > .bp3-fill{
word-break:break-word; }
.bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
background-color:rgba(167, 182, 194, 0.3);
cursor:pointer;
text-decoration:none; }
.bp3-menu-item.bp3-disabled{
background-color:inherit;
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6); }
.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{
background-color:rgba(138, 155, 168, 0.15);
color:inherit; }
.bp3-dark .bp3-menu-item.bp3-disabled{
background-color:inherit;
color:rgba(167, 182, 194, 0.6); }
.bp3-menu-item.bp3-intent-primary{
color:#106ba3; }
.bp3-menu-item.bp3-intent-primary .bp3-icon{
color:inherit; }
.bp3-menu-item.bp3-intent-primary::before, .bp3-menu-item.bp3-intent-primary::after,
.bp3-menu-item.bp3-intent-primary .bp3-menu-item-label{
color:#106ba3; }
.bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary.bp3-active{
background-color:#137cbd; }
.bp3-menu-item.bp3-intent-primary:active{
background-color:#106ba3; }
.bp3-menu-item.bp3-intent-primary:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item, .bp3-menu-item.bp3-intent-primary:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::before, .bp3-menu-item.bp3-intent-primary:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item::after,
.bp3-menu-item.bp3-intent-primary:hover .bp3-menu-item-label,
.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-primary.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary:active, .bp3-menu-item.bp3-intent-primary:active::before, .bp3-menu-item.bp3-intent-primary:active::after,
.bp3-menu-item.bp3-intent-primary:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-primary.bp3-active, .bp3-menu-item.bp3-intent-primary.bp3-active::before, .bp3-menu-item.bp3-intent-primary.bp3-active::after,
.bp3-menu-item.bp3-intent-primary.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-menu-item.bp3-intent-success{
color:#0d8050; }
.bp3-menu-item.bp3-intent-success .bp3-icon{
color:inherit; }
.bp3-menu-item.bp3-intent-success::before, .bp3-menu-item.bp3-intent-success::after,
.bp3-menu-item.bp3-intent-success .bp3-menu-item-label{
color:#0d8050; }
.bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success.bp3-active{
background-color:#0f9960; }
.bp3-menu-item.bp3-intent-success:active{
background-color:#0d8050; }
.bp3-menu-item.bp3-intent-success:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item, .bp3-menu-item.bp3-intent-success:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::before, .bp3-menu-item.bp3-intent-success:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item::after,
.bp3-menu-item.bp3-intent-success:hover .bp3-menu-item-label,
.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-success.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success:active, .bp3-menu-item.bp3-intent-success:active::before, .bp3-menu-item.bp3-intent-success:active::after,
.bp3-menu-item.bp3-intent-success:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-success.bp3-active, .bp3-menu-item.bp3-intent-success.bp3-active::before, .bp3-menu-item.bp3-intent-success.bp3-active::after,
.bp3-menu-item.bp3-intent-success.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-menu-item.bp3-intent-warning{
color:#bf7326; }
.bp3-menu-item.bp3-intent-warning .bp3-icon{
color:inherit; }
.bp3-menu-item.bp3-intent-warning::before, .bp3-menu-item.bp3-intent-warning::after,
.bp3-menu-item.bp3-intent-warning .bp3-menu-item-label{
color:#bf7326; }
.bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning.bp3-active{
background-color:#d9822b; }
.bp3-menu-item.bp3-intent-warning:active{
background-color:#bf7326; }
.bp3-menu-item.bp3-intent-warning:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item, .bp3-menu-item.bp3-intent-warning:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::before, .bp3-menu-item.bp3-intent-warning:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item::after,
.bp3-menu-item.bp3-intent-warning:hover .bp3-menu-item-label,
.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-warning.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning:active, .bp3-menu-item.bp3-intent-warning:active::before, .bp3-menu-item.bp3-intent-warning:active::after,
.bp3-menu-item.bp3-intent-warning:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-warning.bp3-active, .bp3-menu-item.bp3-intent-warning.bp3-active::before, .bp3-menu-item.bp3-intent-warning.bp3-active::after,
.bp3-menu-item.bp3-intent-warning.bp3-active .bp3-menu-item-label{
color:#ffffff; }
.bp3-menu-item.bp3-intent-danger{
color:#c23030; }
.bp3-menu-item.bp3-intent-danger .bp3-icon{
color:inherit; }
.bp3-menu-item.bp3-intent-danger::before, .bp3-menu-item.bp3-intent-danger::after,
.bp3-menu-item.bp3-intent-danger .bp3-menu-item-label{
color:#c23030; }
.bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger.bp3-active{
background-color:#db3737; }
.bp3-menu-item.bp3-intent-danger:active{
background-color:#c23030; }
.bp3-menu-item.bp3-intent-danger:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item, .bp3-menu-item.bp3-intent-danger:hover::before, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::before, .bp3-menu-item.bp3-intent-danger:hover::after, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item::after,
.bp3-menu-item.bp3-intent-danger:hover .bp3-menu-item-label,
.bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-intent-danger.bp3-menu-item .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger:active, .bp3-menu-item.bp3-intent-danger:active::before, .bp3-menu-item.bp3-intent-danger:active::after,
.bp3-menu-item.bp3-intent-danger:active .bp3-menu-item-label, .bp3-menu-item.bp3-intent-danger.bp3-active, .bp3-menu-item.bp3-intent-danger.bp3-active::before, .bp3-menu-item.bp3-intent-danger.bp3-active::after,
.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;
-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; }
.bp3-menu-item .bp3-menu-item-label{
color:#5c7080; }
.bp3-menu-item:hover, .bp3-submenu .bp3-popover-target.bp3-popover-open > .bp3-menu-item{
color:inherit; }
.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;
cursor:not-allowed !important;
color:rgba(92, 112, 128, 0.6) !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;
line-height:22px;
font-size:16px; }
.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;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
margin-top:1px;
margin-right:10px; }
button.bp3-menu-item{
border:none;
background:none;
width:100%;
text-align:left; }
.bp3-menu-header{
display:block;
margin:5px;
border-top:1px solid rgba(16, 22, 26, 0.15);
cursor:default;
padding-left:2px; }
.bp3-dark .bp3-menu-header{
border-top-color:rgba(255, 255, 255, 0.15); }
.bp3-menu-header:first-of-type{
border-top:none; }
.bp3-menu-header > h6{
color:#182026;
font-weight:600;
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
margin:0;
padding:10px 7px 0 1px;
line-height:17px; }
.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;
padding-bottom:5px;
font-size:18px; }
.bp3-large .bp3-menu-header:first-of-type > h6{
padding-top:0; }
.bp3-dark .bp3-menu{
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{
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{
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{
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{
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{
border-color:rgba(255, 255, 255, 0.15); }
.bp3-dark .bp3-menu-header > h6{
color:#f5f8fa; }
.bp3-label .bp3-menu{
margin-top:5px; }
.bp3-navbar{
position:relative;
z-index:10;
-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; }
.bp3-navbar.bp3-dark,
.bp3-dark .bp3-navbar{
background-color:#394b59; }
.bp3-navbar.bp3-dark{
-webkit-box-shadow:inset 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:inset 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-dark .bp3-navbar{
-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{
position:fixed;
top:0;
right:0;
left:0; }
.bp3-navbar-heading{
margin-right:15px;
font-size:16px; }
.bp3-navbar-group{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
height:50px; }
.bp3-navbar-group.bp3-align-left{
float:left; }
.bp3-navbar-group.bp3-align-right{
float:right; }
.bp3-navbar-divider{
margin:0 10px;
border-left:1px solid rgba(16, 22, 26, 0.15);
height:20px; }
.bp3-dark .bp3-navbar-divider{
border-left-color:rgba(255, 255, 255, 0.15); }
.bp3-non-ideal-state{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-ms-flex-direction:column;
flex-direction:column;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
-webkit-box-pack:center;
-ms-flex-pack:center;
justify-content:center;
width:100%;
height:100%;
text-align:center; }
.bp3-non-ideal-state > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-non-ideal-state > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-non-ideal-state::before,
.bp3-non-ideal-state > *{
margin-bottom:20px; }
.bp3-non-ideal-state:empty::before,
.bp3-non-ideal-state > :last-child{
margin-bottom:0; }
.bp3-non-ideal-state > *{
max-width:400px; }
.bp3-non-ideal-state-visual{
color:rgba(92, 112, 128, 0.6);
font-size:60px; }
.bp3-dark .bp3-non-ideal-state-visual{
color:rgba(167, 182, 194, 0.6); }
.bp3-overflow-list{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-ms-flex-wrap:nowrap;
flex-wrap:nowrap;
min-width:0; }
.bp3-overflow-list-spacer{
-ms-flex-negative:1;
flex-shrink:1;
width:1px; }
body.bp3-overlay-open{
overflow:hidden; }
.bp3-overlay{
position:static;
top:0;
right:0;
bottom:0;
left:0;
z-index:20; }
.bp3-overlay:not(.bp3-overlay-open){
pointer-events:none; }
.bp3-overlay.bp3-overlay-container{
position:fixed;
overflow:hidden; }
.bp3-overlay.bp3-overlay-container.bp3-overlay-inline{
position:absolute; }
.bp3-overlay.bp3-overlay-scroll-container{
position:fixed;
overflow:auto; }
.bp3-overlay.bp3-overlay-scroll-container.bp3-overlay-inline{
position:absolute; }
.bp3-overlay.bp3-overlay-inline{
display:inline;
overflow:visible; }
.bp3-overlay-content{
position:fixed;
z-index:20; }
.bp3-overlay-inline .bp3-overlay-content,
.bp3-overlay-scroll-container .bp3-overlay-content{
position:absolute; }
.bp3-overlay-backdrop{
position:fixed;
top:0;
right:0;
bottom:0;
left: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; }
.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-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; }
.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-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; }
.bp3-overlay-backdrop:focus{
outline:none; }
.bp3-overlay-inline .bp3-overlay-backdrop{
position:absolute; }
.bp3-panel-stack{
position:relative;
overflow:hidden; }
.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; }
.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{
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; }
.bp3-panel-stack-header .bp3-heading{
margin:0 5px; }
.bp3-button.bp3-panel-stack-header-back{
margin-left:5px;
padding-left:0;
white-space:nowrap; }
.bp3-button.bp3-panel-stack-header-back .bp3-icon{
margin:0 2px; }
.bp3-panel-stack-view{
position:absolute;
top:0;
right:0;
bottom:0;
left:0;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:vertical;
-webkit-box-direction:normal;
-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; }
.bp3-dark .bp3-panel-stack-view{
background-color:#30404d; }
.bp3-panel-stack-push .bp3-panel-stack-enter, .bp3-panel-stack-push .bp3-panel-stack-appear{
-webkit-transform:translateX(100%);
transform:translateX(100%);
opacity:0; }
.bp3-panel-stack-push .bp3-panel-stack-enter-active, .bp3-panel-stack-push .bp3-panel-stack-appear-active{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1;
-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; }
.bp3-panel-stack-push .bp3-panel-stack-exit{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1; }
.bp3-panel-stack-push .bp3-panel-stack-exit-active{
-webkit-transform:translateX(-50%);
transform:translateX(-50%);
opacity:0;
-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; }
.bp3-panel-stack-pop .bp3-panel-stack-enter, .bp3-panel-stack-pop .bp3-panel-stack-appear{
-webkit-transform:translateX(-50%);
transform:translateX(-50%);
opacity:0; }
.bp3-panel-stack-pop .bp3-panel-stack-enter-active, .bp3-panel-stack-pop .bp3-panel-stack-appear-active{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1;
-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; }
.bp3-panel-stack-pop .bp3-panel-stack-exit{
-webkit-transform:translate(0%);
transform:translate(0%);
opacity:1; }
.bp3-panel-stack-pop .bp3-panel-stack-exit-active{
-webkit-transform:translateX(100%);
transform:translateX(100%);
opacity:0;
-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; }
.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);
display:inline-block;
z-index:20;
border-radius:3px; }
.bp3-popover .bp3-popover-arrow{
position:absolute;
width:30px;
height:30px; }
.bp3-popover .bp3-popover-arrow::before{
margin:5px;
width:20px;
height:20px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-popover{
margin-top:-17px;
margin-bottom: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{
-webkit-transform:rotate(-90deg);
transform:rotate(-90deg); }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover{
margin-left:17px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
left:-11px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow svg{
-webkit-transform:rotate(0);
transform:rotate(0); }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover{
margin-top:17px; }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
top:-11px; }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow svg{
-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; }
.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{
-webkit-transform:rotate(180deg);
transform:rotate(180deg); }
.bp3-tether-element-attached-middle > .bp3-popover > .bp3-popover-arrow{
top:50%;
-webkit-transform:translateY(-50%);
transform:translateY(-50%); }
.bp3-tether-element-attached-center > .bp3-popover > .bp3-popover-arrow{
right:50%;
-webkit-transform:translateX(50%);
transform:translateX(50%); }
.bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-popover > .bp3-popover-arrow{
top:-0.3934px; }
.bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-popover > .bp3-popover-arrow{
right:-0.3934px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-popover > .bp3-popover-arrow{
left:-0.3934px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-popover > .bp3-popover-arrow{
bottom:-0.3934px; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-popover{
-webkit-transform-origin:top left;
transform-origin:top left; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-popover{
-webkit-transform-origin:top center;
transform-origin:top center; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-popover{
-webkit-transform-origin:top right;
transform-origin:top right; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-popover{
-webkit-transform-origin:center left;
transform-origin:center left; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-popover{
-webkit-transform-origin:center center;
transform-origin:center center; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-popover{
-webkit-transform-origin:center right;
transform-origin:center right; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-popover{
-webkit-transform-origin:bottom left;
transform-origin:bottom left; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-popover{
-webkit-transform-origin:bottom center;
transform-origin:bottom center; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-popover{
-webkit-transform-origin:bottom right;
transform-origin:bottom right; }
.bp3-popover .bp3-popover-content{
background:#ffffff;
color:inherit; }
.bp3-popover .bp3-popover-arrow::before{
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
.bp3-popover .bp3-popover-arrow-border{
fill:#10161a;
fill-opacity:0.1; }
.bp3-popover .bp3-popover-arrow-fill{
fill:#ffffff; }
.bp3-popover-enter > .bp3-popover, .bp3-popover-appear > .bp3-popover{
-webkit-transform:scale(0.3);
transform:scale(0.3); }
.bp3-popover-enter-active > .bp3-popover, .bp3-popover-appear-active > .bp3-popover{
-webkit-transform:scale(1);
transform:scale(1);
-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; }
.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-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; }
.bp3-popover .bp3-popover-content{
position:relative;
border-radius:3px; }
.bp3-popover.bp3-popover-content-sizing .bp3-popover-content{
max-width:350px;
padding:20px; }
.bp3-popover-target + .bp3-overlay .bp3-popover.bp3-popover-content-sizing{
width:350px; }
.bp3-popover.bp3-minimal{
margin:0 !important; }
.bp3-popover.bp3-minimal .bp3-popover-arrow{
display:none; }
.bp3-popover.bp3-minimal.bp3-popover{
-webkit-transform:scale(1);
transform:scale(1); }
.bp3-popover-enter > .bp3-popover.bp3-minimal.bp3-popover, .bp3-popover-appear > .bp3-popover.bp3-minimal.bp3-popover{
-webkit-transform:scale(1);
transform:scale(1); }
.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-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; }
.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-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; }
.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);
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-popover.bp3-dark .bp3-popover-content,
.bp3-dark .bp3-popover .bp3-popover-content{
background:#30404d;
color:inherit; }
.bp3-popover.bp3-dark .bp3-popover-arrow::before,
.bp3-dark .bp3-popover .bp3-popover-arrow::before{
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
.bp3-popover.bp3-dark .bp3-popover-arrow-border,
.bp3-dark .bp3-popover .bp3-popover-arrow-border{
fill:#10161a;
fill-opacity:0.2; }
.bp3-popover.bp3-dark .bp3-popover-arrow-fill,
.bp3-dark .bp3-popover .bp3-popover-arrow-fill{
fill:#30404d; }
.bp3-popover-arrow::before{
display:block;
position:absolute;
-webkit-transform:rotate(45deg);
transform:rotate(45deg);
border-radius:2px;
content:""; }
.bp3-tether-pinned .bp3-popover-arrow{
display:none; }
.bp3-popover-backdrop{
background:rgba(255, 255, 255, 0); }
.bp3-transition-container{
opacity:1;
display:-webkit-box;
display:-ms-flexbox;
display:flex;
z-index:20; }
.bp3-transition-container.bp3-popover-enter, .bp3-transition-container.bp3-popover-appear{
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-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; }
.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-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; }
.bp3-transition-container:focus{
outline:none; }
.bp3-transition-container.bp3-popover-leave .bp3-popover-content{
pointer-events:none; }
.bp3-transition-container[data-x-out-of-boundaries]{
display:none; }
span.bp3-popover-target{
display:inline-block; }
.bp3-popover-wrapper.bp3-fill{
width:100%; }
.bp3-portal{
position:absolute;
top:0;
right:0;
left:0; }
@-webkit-keyframes linear-progress-bar-stripes{
from{
background-position:0 0; }
to{
background-position:30px 0; } }
@keyframes linear-progress-bar-stripes{
from{
background-position:0 0; }
to{
background-position:30px 0; } }
.bp3-progress-bar{
display:block;
position:relative;
border-radius:40px;
background:rgba(92, 112, 128, 0.2);
width:100%;
height:8px;
overflow:hidden; }
.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%;
height:100%;
-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); }
.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{
background-image:none; }
.bp3-dark .bp3-progress-bar{
background:rgba(16, 22, 26, 0.5); }
.bp3-dark .bp3-progress-bar .bp3-progress-meter{
background-color:#8a9ba8; }
.bp3-progress-bar.bp3-intent-primary .bp3-progress-meter{
background-color:#137cbd; }
.bp3-progress-bar.bp3-intent-success .bp3-progress-meter{
background-color:#0f9960; }
.bp3-progress-bar.bp3-intent-warning .bp3-progress-meter{
background-color:#d9822b; }
.bp3-progress-bar.bp3-intent-danger .bp3-progress-meter{
background-color:#db3737; }
@-webkit-keyframes skeleton-glow{
from{
border-color:rgba(206, 217, 224, 0.2);
background:rgba(206, 217, 224, 0.2); }
to{
border-color:rgba(92, 112, 128, 0.2);
background: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); }
to{
border-color:rgba(92, 112, 128, 0.2);
background:rgba(92, 112, 128, 0.2); } }
.bp3-skeleton{
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;
pointer-events:none;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-skeleton::before, .bp3-skeleton::after,
.bp3-skeleton *{
visibility:hidden !important; }
.bp3-slider{
width:100%;
min-width:150px;
height:40px;
position:relative;
outline:none;
cursor:default;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-slider:hover{
cursor:pointer; }
.bp3-slider:active{
cursor:-webkit-grabbing;
cursor:grabbing; }
.bp3-slider.bp3-disabled{
opacity:0.5;
cursor:not-allowed; }
.bp3-slider.bp3-slider-unlabeled{
height:16px; }
.bp3-slider-track,
.bp3-slider-progress{
top:5px;
right:0;
left:0;
height:6px;
position:absolute; }
.bp3-slider-track{
border-radius:3px;
overflow:hidden; }
.bp3-slider-progress{
background:rgba(92, 112, 128, 0.2); }
.bp3-dark .bp3-slider-progress{
background:rgba(16, 22, 26, 0.5); }
.bp3-slider-progress.bp3-intent-primary{
background-color:#137cbd; }
.bp3-slider-progress.bp3-intent-success{
background-color:#0f9960; }
.bp3-slider-progress.bp3-intent-warning{
background-color:#d9822b; }
.bp3-slider-progress.bp3-intent-danger{
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));
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; }
.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; }
.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; }
.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;
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6); }
.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: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; }
.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: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{
-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));
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{
-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; }
.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; }
.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;
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); }
.bp3-dark .bp3-slider-handle .bp3-button-spinner .bp3-spinner-head{
background:rgba(16, 22, 26, 0.5);
stroke:#8a9ba8; }
.bp3-dark .bp3-slider-handle, .bp3-dark .bp3-slider-handle:hover{
background-color:#394b59; }
.bp3-dark .bp3-slider-handle.bp3-active{
background-color:#293742; }
.bp3-dark .bp3-disabled .bp3-slider-handle{
border-color:#5c7080;
-webkit-box-shadow:none;
box-shadow:none;
background:#5c7080; }
.bp3-slider-handle .bp3-slider-label{
margin-left:8px;
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; }
.bp3-dark .bp3-slider-handle .bp3-slider-label{
-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;
box-shadow:none; }
.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; }
.bp3-slider-handle.bp3-end{
margin-left:8px;
border-top-left-radius:0;
border-bottom-left-radius:0; }
.bp3-slider-handle.bp3-end .bp3-slider-label{
margin-left:0; }
.bp3-slider-label{
-webkit-transform:translate(-50%, 20px);
transform:translate(-50%, 20px);
display:inline-block;
position:absolute;
padding:2px 5px;
vertical-align:top;
line-height:1;
font-size:12px; }
.bp3-slider.bp3-vertical{
width:40px;
min-width:40px;
height:150px; }
.bp3-slider.bp3-vertical .bp3-slider-track,
.bp3-slider.bp3-vertical .bp3-slider-progress{
top:0;
bottom:0;
left:5px;
width:6px;
height:auto; }
.bp3-slider.bp3-vertical .bp3-slider-progress{
top:auto; }
.bp3-slider.bp3-vertical .bp3-slider-label{
-webkit-transform:translate(20px, 50%);
transform:translate(20px, 50%); }
.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; }
.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-end, .bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
margin-left:0;
width:16px;
height:8px; }
.bp3-slider.bp3-vertical .bp3-slider-handle.bp3-start{
border-top-left-radius:0;
border-bottom-right-radius:3px; }
.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; }
@-webkit-keyframes pt-spinner-animation{
from{
-webkit-transform:rotate(0deg);
transform:rotate(0deg); }
to{
-webkit-transform:rotate(360deg);
transform:rotate(360deg); } }
@keyframes pt-spinner-animation{
from{
-webkit-transform:rotate(0deg);
transform:rotate(0deg); }
to{
-webkit-transform:rotate(360deg);
transform:rotate(360deg); } }
.bp3-spinner{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
-webkit-box-pack:center;
-ms-flex-pack:center;
justify-content:center;
overflow:visible;
vertical-align:middle; }
.bp3-spinner svg{
display:block; }
.bp3-spinner path{
fill-opacity:0; }
.bp3-spinner .bp3-spinner-head{
-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; }
.bp3-spinner .bp3-spinner-track{
stroke:rgba(92, 112, 128, 0.2); }
.bp3-spinner-animation{
-webkit-animation:pt-spinner-animation 500ms linear infinite;
animation:pt-spinner-animation 500ms linear infinite; }
.bp3-no-spin > .bp3-spinner-animation{
-webkit-animation:none;
animation:none; }
.bp3-dark .bp3-spinner .bp3-spinner-head{
stroke:#8a9ba8; }
.bp3-dark .bp3-spinner .bp3-spinner-track{
stroke:rgba(16, 22, 26, 0.5); }
.bp3-spinner.bp3-intent-primary .bp3-spinner-head{
stroke:#137cbd; }
.bp3-spinner.bp3-intent-success .bp3-spinner-head{
stroke:#0f9960; }
.bp3-spinner.bp3-intent-warning .bp3-spinner-head{
stroke:#d9822b; }
.bp3-spinner.bp3-intent-danger .bp3-spinner-head{
stroke:#db3737; }
.bp3-tabs.bp3-vertical{
display:-webkit-box;
display:-ms-flexbox;
display:flex; }
.bp3-tabs.bp3-vertical > .bp3-tab-list{
-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; }
.bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab{
border-radius:3px;
width:100%;
padding:0 10px; }
.bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab[aria-selected="true"]{
-webkit-box-shadow:none;
box-shadow:none;
background-color:rgba(19, 124, 189, 0.2); }
.bp3-tabs.bp3-vertical > .bp3-tab-list .bp3-tab-indicator-wrapper .bp3-tab-indicator{
top:0;
right:0;
bottom:0;
left:0;
border-radius:3px;
background-color:rgba(19, 124, 189, 0.2);
height:auto; }
.bp3-tabs.bp3-vertical > .bp3-tab-panel{
margin-top:0;
padding-left:20px; }
.bp3-tab-list{
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;
margin:0;
border:none;
padding:0;
list-style:none; }
.bp3-tab-list > *:not(:last-child){
margin-right:20px; }
.bp3-tab{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
position:relative;
cursor:pointer;
max-width:100%;
vertical-align:top;
line-height:30px;
color:#182026;
font-size:14px; }
.bp3-tab a{
display:block;
text-decoration:none;
color:inherit; }
.bp3-tab-indicator-wrapper ~ .bp3-tab{
-webkit-box-shadow:none !important;
box-shadow:none !important;
background-color:transparent !important; }
.bp3-tab[aria-disabled="true"]{
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6); }
.bp3-tab[aria-selected="true"]{
border-radius:0;
-webkit-box-shadow:inset 0 -3px 0 #106ba3;
box-shadow:inset 0 -3px 0 #106ba3; }
.bp3-tab[aria-selected="true"], .bp3-tab:not([aria-disabled="true"]):hover{
color:#106ba3; }
.bp3-tab:focus{
-moz-outline-radius:0; }
.bp3-large > .bp3-tab{
line-height:40px;
font-size:16px; }
.bp3-tab-panel{
margin-top:20px; }
.bp3-tab-panel[aria-hidden="true"]{
display:none; }
.bp3-tab-indicator-wrapper{
position:absolute;
top:0;
left:0;
-webkit-transform:translateX(0), translateY(0);
transform:translateX(0), translateY(0);
-webkit-transition:height, width, -webkit-transform;
transition:height, width, -webkit-transform;
transition:height, transform, width;
transition:height, transform, width, -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);
pointer-events:none; }
.bp3-tab-indicator-wrapper .bp3-tab-indicator{
position:absolute;
right:0;
bottom:0;
left:0;
background-color:#106ba3;
height:3px; }
.bp3-tab-indicator-wrapper.bp3-no-animation{
-webkit-transition:none;
transition:none; }
.bp3-dark .bp3-tab{
color:#f5f8fa; }
.bp3-dark .bp3-tab[aria-disabled="true"]{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tab[aria-selected="true"]{
-webkit-box-shadow:inset 0 -3px 0 #48aff0;
box-shadow:inset 0 -3px 0 #48aff0; }
.bp3-dark .bp3-tab[aria-selected="true"], .bp3-dark .bp3-tab:not([aria-disabled="true"]):hover{
color:#48aff0; }
.bp3-dark .bp3-tab-indicator{
background-color:#48aff0; }
.bp3-flex-expander{
-webkit-box-flex:1;
-ms-flex:1 1;
flex:1 1; }
.bp3-tag{
display:-webkit-inline-box;
display:-ms-inline-flexbox;
display:inline-flex;
-webkit-box-orient:horizontal;
-webkit-box-direction:normal;
-ms-flex-direction:row;
flex-direction:row;
-webkit-box-align:center;
-ms-flex-align:center;
align-items:center;
position:relative;
border:none;
border-radius:3px;
-webkit-box-shadow:none;
box-shadow:none;
background-color:#5c7080;
min-width:20px;
max-width:100%;
min-height:20px;
padding:2px 6px;
line-height:16px;
color:#f5f8fa;
font-size:12px; }
.bp3-tag.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-interactive:hover{
background-color:rgba(92, 112, 128, 0.85); }
.bp3-tag.bp3-interactive.bp3-active, .bp3-tag.bp3-interactive:active{
background-color:rgba(92, 112, 128, 0.7); }
.bp3-tag > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-tag > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-tag::before,
.bp3-tag > *{
margin-right:4px; }
.bp3-tag:empty::before,
.bp3-tag > :last-child{
margin-right:0; }
.bp3-tag:focus{
outline:rgba(19, 124, 189, 0.6) auto 2px;
outline-offset:0;
-moz-outline-radius:6px; }
.bp3-tag.bp3-round{
border-radius:30px;
padding-right:8px;
padding-left:8px; }
.bp3-dark .bp3-tag{
background-color:#bfccd6;
color:#182026; }
.bp3-dark .bp3-tag.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-interactive:hover{
background-color:rgba(191, 204, 214, 0.85); }
.bp3-dark .bp3-tag.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-interactive:active{
background-color:rgba(191, 204, 214, 0.7); }
.bp3-dark .bp3-tag > .bp3-icon, .bp3-dark .bp3-tag .bp3-icon-standard, .bp3-dark .bp3-tag .bp3-icon-large{
fill:currentColor; }
.bp3-tag > .bp3-icon, .bp3-tag .bp3-icon-standard, .bp3-tag .bp3-icon-large{
fill:#ffffff; }
.bp3-tag.bp3-large,
.bp3-large .bp3-tag{
min-width:30px;
min-height:30px;
padding:0 10px;
line-height:20px;
font-size:14px; }
.bp3-tag.bp3-large::before,
.bp3-tag.bp3-large > *,
.bp3-large .bp3-tag::before,
.bp3-large .bp3-tag > *{
margin-right:7px; }
.bp3-tag.bp3-large:empty::before,
.bp3-tag.bp3-large > :last-child,
.bp3-large .bp3-tag:empty::before,
.bp3-large .bp3-tag > :last-child{
margin-right:0; }
.bp3-tag.bp3-large.bp3-round,
.bp3-large .bp3-tag.bp3-round{
padding-right:12px;
padding-left:12px; }
.bp3-tag.bp3-intent-primary{
background:#137cbd;
color:#ffffff; }
.bp3-tag.bp3-intent-primary.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-intent-primary.bp3-interactive:hover{
background-color:rgba(19, 124, 189, 0.85); }
.bp3-tag.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-primary.bp3-interactive:active{
background-color:rgba(19, 124, 189, 0.7); }
.bp3-tag.bp3-intent-success{
background:#0f9960;
color:#ffffff; }
.bp3-tag.bp3-intent-success.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-intent-success.bp3-interactive:hover{
background-color:rgba(15, 153, 96, 0.85); }
.bp3-tag.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-success.bp3-interactive:active{
background-color:rgba(15, 153, 96, 0.7); }
.bp3-tag.bp3-intent-warning{
background:#d9822b;
color:#ffffff; }
.bp3-tag.bp3-intent-warning.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-intent-warning.bp3-interactive:hover{
background-color:rgba(217, 130, 43, 0.85); }
.bp3-tag.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-warning.bp3-interactive:active{
background-color:rgba(217, 130, 43, 0.7); }
.bp3-tag.bp3-intent-danger{
background:#db3737;
color:#ffffff; }
.bp3-tag.bp3-intent-danger.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-intent-danger.bp3-interactive:hover{
background-color:rgba(219, 55, 55, 0.85); }
.bp3-tag.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-intent-danger.bp3-interactive:active{
background-color:rgba(219, 55, 55, 0.7); }
.bp3-tag.bp3-fill{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
width:100%; }
.bp3-tag.bp3-minimal > .bp3-icon, .bp3-tag.bp3-minimal .bp3-icon-standard, .bp3-tag.bp3-minimal .bp3-icon-large{
fill:#5c7080; }
.bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
background-color:rgba(138, 155, 168, 0.2);
color:#182026; }
.bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
background-color:rgba(92, 112, 128, 0.3); }
.bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
background-color:rgba(92, 112, 128, 0.4); }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]){
color:#f5f8fa; }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:hover{
background-color:rgba(191, 204, 214, 0.3); }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]).bp3-interactive:active{
background-color:rgba(191, 204, 214, 0.4); }
.bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) > .bp3-icon, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-standard, .bp3-dark .bp3-tag.bp3-minimal:not([class*="bp3-intent-"]) .bp3-icon-large{
fill:#a7b6c2; }
.bp3-tag.bp3-minimal.bp3-intent-primary{
background-color:rgba(19, 124, 189, 0.15);
color:#106ba3; }
.bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
background-color:rgba(19, 124, 189, 0.25); }
.bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
background-color:rgba(19, 124, 189, 0.35); }
.bp3-tag.bp3-minimal.bp3-intent-primary > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-primary .bp3-icon-large{
fill:#137cbd; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary{
background-color:rgba(19, 124, 189, 0.25);
color:#48aff0; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:hover{
background-color:rgba(19, 124, 189, 0.35); }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-primary.bp3-interactive:active{
background-color:rgba(19, 124, 189, 0.45); }
.bp3-tag.bp3-minimal.bp3-intent-success{
background-color:rgba(15, 153, 96, 0.15);
color:#0d8050; }
.bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
background-color:rgba(15, 153, 96, 0.25); }
.bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
background-color:rgba(15, 153, 96, 0.35); }
.bp3-tag.bp3-minimal.bp3-intent-success > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-success .bp3-icon-large{
fill:#0f9960; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success{
background-color:rgba(15, 153, 96, 0.25);
color:#3dcc91; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:hover{
background-color:rgba(15, 153, 96, 0.35); }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-success.bp3-interactive:active{
background-color:rgba(15, 153, 96, 0.45); }
.bp3-tag.bp3-minimal.bp3-intent-warning{
background-color:rgba(217, 130, 43, 0.15);
color:#bf7326; }
.bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
background-color:rgba(217, 130, 43, 0.25); }
.bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
background-color:rgba(217, 130, 43, 0.35); }
.bp3-tag.bp3-minimal.bp3-intent-warning > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-warning .bp3-icon-large{
fill:#d9822b; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning{
background-color:rgba(217, 130, 43, 0.25);
color:#ffb366; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:hover{
background-color:rgba(217, 130, 43, 0.35); }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-warning.bp3-interactive:active{
background-color:rgba(217, 130, 43, 0.45); }
.bp3-tag.bp3-minimal.bp3-intent-danger{
background-color:rgba(219, 55, 55, 0.15);
color:#c23030; }
.bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
cursor:pointer; }
.bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
background-color:rgba(219, 55, 55, 0.25); }
.bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
background-color:rgba(219, 55, 55, 0.35); }
.bp3-tag.bp3-minimal.bp3-intent-danger > .bp3-icon, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-standard, .bp3-tag.bp3-minimal.bp3-intent-danger .bp3-icon-large{
fill:#db3737; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger{
background-color:rgba(219, 55, 55, 0.25);
color:#ff7373; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive{
cursor:pointer; }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:hover{
background-color:rgba(219, 55, 55, 0.35); }
.bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive.bp3-active, .bp3-dark .bp3-tag.bp3-minimal.bp3-intent-danger.bp3-interactive:active{
background-color:rgba(219, 55, 55, 0.45); }
.bp3-tag-remove{
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;
padding:2px;
padding-left:0;
color:inherit; }
.bp3-tag-remove:hover{
opacity:0.8;
background:none;
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;
-moz-osx-font-smoothing:grayscale;
-webkit-font-smoothing:antialiased;
content:""; }
.bp3-large .bp3-tag-remove{
margin-right:-10px !important;
padding:5px;
padding-left:0; }
.bp3-large .bp3-tag-remove:empty::before{
line-height:1;
font-family:"Icons20", sans-serif;
font-size:20px;
font-weight:400;
font-style:normal; }
.bp3-tag-input{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-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;
cursor:text;
height:auto;
min-height:30px;
padding-right:0;
padding-left:5px;
line-height:inherit; }
.bp3-tag-input > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-tag-input > .bp3-tag-input-values{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-tag-input .bp3-tag-input-icon{
margin-top:7px;
margin-right:7px;
margin-left:2px;
color:#5c7080; }
.bp3-tag-input .bp3-tag-input-values{
display:-webkit-box;
display:-ms-flexbox;
display:flex;
-webkit-box-orient:horizontal;
-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;
margin-right:7px;
min-width:0; }
.bp3-tag-input .bp3-tag-input-values > *{
-webkit-box-flex:0;
-ms-flex-positive:0;
flex-grow:0;
-ms-flex-negative:0;
flex-shrink:0; }
.bp3-tag-input .bp3-tag-input-values > .bp3-fill{
-webkit-box-flex:1;
-ms-flex-positive:1;
flex-grow:1;
-ms-flex-negative:1;
flex-shrink:1; }
.bp3-tag-input .bp3-tag-input-values::before,
.bp3-tag-input .bp3-tag-input-values > *{
margin-right:5px; }
.bp3-tag-input .bp3-tag-input-values:empty::before,
.bp3-tag-input .bp3-tag-input-values > :last-child{
margin-right:0; }
.bp3-tag-input .bp3-tag-input-values:first-child .bp3-input-ghost:first-child{
padding-left:5px; }
.bp3-tag-input .bp3-tag-input-values > *{
margin-bottom:5px; }
.bp3-tag-input .bp3-tag{
overflow-wrap:break-word; }
.bp3-tag-input .bp3-tag.bp3-active{
outline:rgba(19, 124, 189, 0.6) auto 2px;
outline-offset:0;
-moz-outline-radius:6px; }
.bp3-tag-input .bp3-input-ghost{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
width:80px;
line-height:20px; }
.bp3-tag-input .bp3-input-ghost:disabled, .bp3-tag-input .bp3-input-ghost.bp3-disabled{
cursor:not-allowed; }
.bp3-tag-input .bp3-button,
.bp3-tag-input .bp3-spinner{
margin:3px;
margin-left:0; }
.bp3-tag-input .bp3-button{
min-width:24px;
min-height:24px;
padding:0 7px; }
.bp3-tag-input.bp3-large{
height:auto;
min-height:40px; }
.bp3-tag-input.bp3-large::before,
.bp3-tag-input.bp3-large > *{
margin-right:10px; }
.bp3-tag-input.bp3-large:empty::before,
.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; }
.bp3-tag-input.bp3-large .bp3-input-ghost{
line-height:30px; }
.bp3-tag-input.bp3-large .bp3-button{
min-width:30px;
min-height:30px;
padding:5px 10px;
margin:5px;
margin-left:0; }
.bp3-tag-input.bp3-large .bp3-spinner{
margin:8px;
margin-left:0; }
.bp3-tag-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);
background-color:#ffffff; }
.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); }
.bp3-tag-input.bp3-active.bp3-intent-success{
-webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-tag-input.bp3-active.bp3-intent-warning{
-webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-tag-input.bp3-active.bp3-intent-danger{
-webkit-box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2);
box-shadow:0 0 0 1px #c23030, 0 0 0 3px rgba(194, 48, 48, 0.3), inset 0 1px 1px rgba(16, 22, 26, 0.2); }
.bp3-dark .bp3-tag-input .bp3-tag-input-icon, .bp3-tag-input.bp3-dark .bp3-tag-input-icon{
color:#a7b6c2; }
.bp3-dark .bp3-tag-input .bp3-input-ghost, .bp3-tag-input.bp3-dark .bp3-input-ghost{
color:#f5f8fa; }
.bp3-dark .bp3-tag-input .bp3-input-ghost::-webkit-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-webkit-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tag-input .bp3-input-ghost::-moz-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-moz-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tag-input .bp3-input-ghost:-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost:-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.bp3-dark .bp3-tag-input .bp3-input-ghost::-ms-input-placeholder, .bp3-tag-input.bp3-dark .bp3-input-ghost::-ms-input-placeholder{
color:rgba(167, 182, 194, 0.6); }
.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{
-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); }
.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); }
.bp3-dark .bp3-tag-input.bp3-active.bp3-intent-success, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-success{
-webkit-box-shadow:0 0 0 1px #0d8050, 0 0 0 3px rgba(13, 128, 80, 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 #0d8050, 0 0 0 3px rgba(13, 128, 80, 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-warning, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-warning{
-webkit-box-shadow:0 0 0 1px #bf7326, 0 0 0 3px rgba(191, 115, 38, 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 #bf7326, 0 0 0 3px rgba(191, 115, 38, 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-danger, .bp3-tag-input.bp3-dark.bp3-active.bp3-intent-danger{
-webkit-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);
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{
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); }
.bp3-input-ghost::-moz-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-input-ghost:-ms-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-input-ghost::-ms-input-placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.bp3-input-ghost::placeholder{
opacity:1;
color:rgba(92, 112, 128, 0.6); }
.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;
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;
max-width:500px;
pointer-events:all; }
.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-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; }
.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-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; }
.bp3-toast.bp3-toast-exit{
opacity:1;
-webkit-filter:blur(0);
filter:blur(0); }
.bp3-toast.bp3-toast-exit-active{
opacity:0;
-webkit-filter:blur(10px);
filter:blur(10px);
-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; }
.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-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; }
.bp3-toast .bp3-button-group{
-webkit-box-flex:0;
-ms-flex:0 0 auto;
flex:0 0 auto;
padding:5px;
padding-left:0; }
.bp3-toast > .bp3-icon{
margin:12px;
margin-right:0;
color:#5c7080; }
.bp3-toast.bp3-dark,
.bp3-dark .bp3-toast{
-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; }
.bp3-toast.bp3-dark > .bp3-icon,
.bp3-dark .bp3-toast > .bp3-icon{
color:#a7b6c2; }
.bp3-toast[class*="bp3-intent-"] a{
color:rgba(255, 255, 255, 0.7); }
.bp3-toast[class*="bp3-intent-"] a:hover{
color:#ffffff; }
.bp3-toast[class*="bp3-intent-"] > .bp3-icon{
color:#ffffff; }
.bp3-toast[class*="bp3-intent-"] .bp3-button, .bp3-toast[class*="bp3-intent-"] .bp3-button::before,
.bp3-toast[class*="bp3-intent-"] .bp3-button .bp3-icon, .bp3-toast[class*="bp3-intent-"] .bp3-button:active{
color:rgba(255, 255, 255, 0.7) !important; }
.bp3-toast[class*="bp3-intent-"] .bp3-button:focus{
outline-color:rgba(255, 255, 255, 0.5); }
.bp3-toast[class*="bp3-intent-"] .bp3-button:hover{
background-color:rgba(255, 255, 255, 0.15) !important;
color:#ffffff !important; }
.bp3-toast[class*="bp3-intent-"] .bp3-button:active{
background-color:rgba(255, 255, 255, 0.3) !important;
color:#ffffff !important; }
.bp3-toast[class*="bp3-intent-"] .bp3-button::after{
background:rgba(255, 255, 255, 0.3) !important; }
.bp3-toast.bp3-intent-primary{
background-color:#137cbd;
color:#ffffff; }
.bp3-toast.bp3-intent-success{
background-color:#0f9960;
color:#ffffff; }
.bp3-toast.bp3-intent-warning{
background-color:#d9822b;
color:#ffffff; }
.bp3-toast.bp3-intent-danger{
background-color:#db3737;
color:#ffffff; }
.bp3-toast-message{
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
padding:11px;
word-break:break-word; }
.bp3-toast-container{
display:-webkit-box !important;
display:-ms-flexbox !important;
display:flex !important;
-webkit-box-orient:vertical;
-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; }
.bp3-toast-container.bp3-toast-container-top{
top:0;
bottom:auto; }
.bp3-toast-container.bp3-toast-container-bottom{
-webkit-box-orient:vertical;
-webkit-box-direction:reverse;
-ms-flex-direction:column-reverse;
flex-direction:column-reverse;
top:auto;
bottom:0; }
.bp3-toast-container.bp3-toast-container-left{
-webkit-box-align:start;
-ms-flex-align:start;
align-items:flex-start; }
.bp3-toast-container.bp3-toast-container-right{
-webkit-box-align:end;
-ms-flex-align:end;
align-items:flex-end; }
.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-leave-active ~ .bp3-toast{
-webkit-transform:translateY(60px);
transform:translateY(60px); }
.bp3-tooltip{
-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); }
.bp3-tooltip .bp3-popover-arrow{
position:absolute;
width:22px;
height:22px; }
.bp3-tooltip .bp3-popover-arrow::before{
margin:4px;
width:14px;
height:14px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-top > .bp3-tooltip{
margin-top:-11px;
margin-bottom: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{
-webkit-transform:rotate(-90deg);
transform:rotate(-90deg); }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip{
margin-left:11px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
left:-8px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow svg{
-webkit-transform:rotate(0);
transform:rotate(0); }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip{
margin-top:11px; }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
top:-8px; }
.bp3-tether-element-attached-top.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow svg{
-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; }
.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{
-webkit-transform:rotate(180deg);
transform:rotate(180deg); }
.bp3-tether-element-attached-middle > .bp3-tooltip > .bp3-popover-arrow{
top:50%;
-webkit-transform:translateY(-50%);
transform:translateY(-50%); }
.bp3-tether-element-attached-center > .bp3-tooltip > .bp3-popover-arrow{
right:50%;
-webkit-transform:translateX(50%);
transform:translateX(50%); }
.bp3-tether-element-attached-top.bp3-tether-target-attached-top > .bp3-tooltip > .bp3-popover-arrow{
top:-0.22183px; }
.bp3-tether-element-attached-right.bp3-tether-target-attached-right > .bp3-tooltip > .bp3-popover-arrow{
right:-0.22183px; }
.bp3-tether-element-attached-left.bp3-tether-target-attached-left > .bp3-tooltip > .bp3-popover-arrow{
left:-0.22183px; }
.bp3-tether-element-attached-bottom.bp3-tether-target-attached-bottom > .bp3-tooltip > .bp3-popover-arrow{
bottom:-0.22183px; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-left > .bp3-tooltip{
-webkit-transform-origin:top left;
transform-origin:top left; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-center > .bp3-tooltip{
-webkit-transform-origin:top center;
transform-origin:top center; }
.bp3-tether-element-attached-top.bp3-tether-element-attached-right > .bp3-tooltip{
-webkit-transform-origin:top right;
transform-origin:top right; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-left > .bp3-tooltip{
-webkit-transform-origin:center left;
transform-origin:center left; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-center > .bp3-tooltip{
-webkit-transform-origin:center center;
transform-origin:center center; }
.bp3-tether-element-attached-middle.bp3-tether-element-attached-right > .bp3-tooltip{
-webkit-transform-origin:center right;
transform-origin:center right; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-left > .bp3-tooltip{
-webkit-transform-origin:bottom left;
transform-origin:bottom left; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-center > .bp3-tooltip{
-webkit-transform-origin:bottom center;
transform-origin:bottom center; }
.bp3-tether-element-attached-bottom.bp3-tether-element-attached-right > .bp3-tooltip{
-webkit-transform-origin:bottom right;
transform-origin:bottom right; }
.bp3-tooltip .bp3-popover-content{
background:#394b59;
color:#f5f8fa; }
.bp3-tooltip .bp3-popover-arrow::before{
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2);
box-shadow:1px 1px 6px rgba(16, 22, 26, 0.2); }
.bp3-tooltip .bp3-popover-arrow-border{
fill:#10161a;
fill-opacity:0.1; }
.bp3-tooltip .bp3-popover-arrow-fill{
fill:#394b59; }
.bp3-popover-enter > .bp3-tooltip, .bp3-popover-appear > .bp3-tooltip{
-webkit-transform:scale(0.8);
transform:scale(0.8); }
.bp3-popover-enter-active > .bp3-tooltip, .bp3-popover-appear-active > .bp3-tooltip{
-webkit-transform:scale(1);
transform:scale(1);
-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; }
.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-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; }
.bp3-tooltip .bp3-popover-content{
padding:10px 12px; }
.bp3-tooltip.bp3-dark,
.bp3-dark .bp3-tooltip{
-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-tooltip.bp3-dark .bp3-popover-content,
.bp3-dark .bp3-tooltip .bp3-popover-content{
background:#e1e8ed;
color:#394b59; }
.bp3-tooltip.bp3-dark .bp3-popover-arrow::before,
.bp3-dark .bp3-tooltip .bp3-popover-arrow::before{
-webkit-box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4);
box-shadow:1px 1px 6px rgba(16, 22, 26, 0.4); }
.bp3-tooltip.bp3-dark .bp3-popover-arrow-border,
.bp3-dark .bp3-tooltip .bp3-popover-arrow-border{
fill:#10161a;
fill-opacity:0.2; }
.bp3-tooltip.bp3-dark .bp3-popover-arrow-fill,
.bp3-dark .bp3-tooltip .bp3-popover-arrow-fill{
fill:#e1e8ed; }
.bp3-tooltip.bp3-intent-primary .bp3-popover-content{
background:#137cbd;
color:#ffffff; }
.bp3-tooltip.bp3-intent-primary .bp3-popover-arrow-fill{
fill:#137cbd; }
.bp3-tooltip.bp3-intent-success .bp3-popover-content{
background:#0f9960;
color:#ffffff; }
.bp3-tooltip.bp3-intent-success .bp3-popover-arrow-fill{
fill:#0f9960; }
.bp3-tooltip.bp3-intent-warning .bp3-popover-content{
background:#d9822b;
color:#ffffff; }
.bp3-tooltip.bp3-intent-warning .bp3-popover-arrow-fill{
fill:#d9822b; }
.bp3-tooltip.bp3-intent-danger .bp3-popover-content{
background:#db3737;
color:#ffffff; }
.bp3-tooltip.bp3-intent-danger .bp3-popover-arrow-fill{
fill:#db3737; }
.bp3-tooltip-indicator{
border-bottom:dotted 1px;
cursor:help; }
.bp3-tree .bp3-icon, .bp3-tree .bp3-icon-standard, .bp3-tree .bp3-icon-large{
color:#5c7080; }
.bp3-tree .bp3-icon.bp3-intent-primary, .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-tree .bp3-icon-large.bp3-intent-primary{
color:#137cbd; }
.bp3-tree .bp3-icon.bp3-intent-success, .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-tree .bp3-icon-large.bp3-intent-success{
color:#0f9960; }
.bp3-tree .bp3-icon.bp3-intent-warning, .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-tree .bp3-icon-large.bp3-intent-warning{
color:#d9822b; }
.bp3-tree .bp3-icon.bp3-intent-danger, .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-tree .bp3-icon-large.bp3-intent-danger{
color:#db3737; }
.bp3-tree-node-list{
margin:0;
padding-left:0;
list-style:none; }
.bp3-tree-root{
position:relative;
background-color:transparent;
cursor:default;
padding-left:0; }
.bp3-tree-node-content-0{
padding-left:0px; }
.bp3-tree-node-content-1{
padding-left:23px; }
.bp3-tree-node-content-2{
padding-left:46px; }
.bp3-tree-node-content-3{
padding-left:69px; }
.bp3-tree-node-content-4{
padding-left:92px; }
.bp3-tree-node-content-5{
padding-left:115px; }
.bp3-tree-node-content-6{
padding-left:138px; }
.bp3-tree-node-content-7{
padding-left:161px; }
.bp3-tree-node-content-8{
padding-left:184px; }
.bp3-tree-node-content-9{
padding-left:207px; }
.bp3-tree-node-content-10{
padding-left:230px; }
.bp3-tree-node-content-11{
padding-left:253px; }
.bp3-tree-node-content-12{
padding-left:276px; }
.bp3-tree-node-content-13{
padding-left:299px; }
.bp3-tree-node-content-14{
padding-left:322px; }
.bp3-tree-node-content-15{
padding-left:345px; }
.bp3-tree-node-content-16{
padding-left:368px; }
.bp3-tree-node-content-17{
padding-left:391px; }
.bp3-tree-node-content-18{
padding-left:414px; }
.bp3-tree-node-content-19{
padding-left:437px; }
.bp3-tree-node-content-20{
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%;
height:30px;
padding-right:5px; }
.bp3-tree-node-content:hover{
background-color:rgba(191, 204, 214, 0.4); }
.bp3-tree-node-caret,
.bp3-tree-node-caret-none{
min-width:30px; }
.bp3-tree-node-caret{
color:#5c7080;
-webkit-transform:rotate(0deg);
transform:rotate(0deg);
cursor:pointer;
padding:7px;
-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);
transition:transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9), -webkit-transform 200ms cubic-bezier(0.4, 1, 0.75, 0.9); }
.bp3-tree-node-caret:hover{
color:#182026; }
.bp3-dark .bp3-tree-node-caret{
color:#a7b6c2; }
.bp3-dark .bp3-tree-node-caret:hover{
color:#f5f8fa; }
.bp3-tree-node-caret.bp3-tree-node-caret-open{
-webkit-transform:rotate(90deg);
transform:rotate(90deg); }
.bp3-tree-node-caret.bp3-icon-standard::before{
content:""; }
.bp3-tree-node-icon{
position:relative;
margin-right:7px; }
.bp3-tree-node-label{
overflow:hidden;
text-overflow:ellipsis;
white-space:nowrap;
word-wrap:normal;
-webkit-box-flex:1;
-ms-flex:1 1 auto;
flex:1 1 auto;
position:relative;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
user-select:none; }
.bp3-tree-node-label span{
display:inline; }
.bp3-tree-node-secondary-label{
padding:0 5px;
-webkit-user-select:none;
-moz-user-select:none;
-ms-user-select:none;
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; }
.bp3-tree-node.bp3-disabled .bp3-tree-node-content{
background-color:inherit;
cursor:not-allowed;
color:rgba(92, 112, 128, 0.6); }
.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); }
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content{
background-color:#137cbd; }
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content,
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-standard, .bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-icon-large{
color:#ffffff; }
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret::before{
color:rgba(255, 255, 255, 0.7); }
.bp3-tree-node.bp3-tree-node-selected > .bp3-tree-node-content .bp3-tree-node-caret:hover::before{
color:#ffffff; }
.bp3-dark .bp3-tree-node-content:hover{
background-color:rgba(92, 112, 128, 0.3); }
.bp3-dark .bp3-tree .bp3-icon, .bp3-dark .bp3-tree .bp3-icon-standard, .bp3-dark .bp3-tree .bp3-icon-large{
color:#a7b6c2; }
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-primary, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-primary{
color:#137cbd; }
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-success, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-success{
color:#0f9960; }
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-warning, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-warning{
color:#d9822b; }
.bp3-dark .bp3-tree .bp3-icon.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-standard.bp3-intent-danger, .bp3-dark .bp3-tree .bp3-icon-large.bp3-intent-danger{
color:#db3737; }
.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;
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; }
.bp3-omnibar.bp3-overlay-enter, .bp3-omnibar.bp3-overlay-appear{
-webkit-filter:blur(20px);
filter:blur(20px);
opacity:0.2; }
.bp3-omnibar.bp3-overlay-enter-active, .bp3-omnibar.bp3-overlay-appear-active{
-webkit-filter:blur(0);
filter:blur(0);
opacity:1;
-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; }
.bp3-omnibar.bp3-overlay-exit{
-webkit-filter:blur(0);
filter:blur(0);
opacity:1; }
.bp3-omnibar.bp3-overlay-exit-active{
-webkit-filter:blur(20px);
filter:blur(20px);
opacity:0.2;
-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; }
.bp3-omnibar .bp3-input{
border-radius:0;
background-color:transparent; }
.bp3-omnibar .bp3-input, .bp3-omnibar .bp3-input:focus{
-webkit-box-shadow:none;
box-shadow:none; }
.bp3-omnibar .bp3-menu{
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{
-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; }
.bp3-omnibar-overlay .bp3-overlay-backdrop{
background-color:rgba(16, 22, 26, 0.2); }
.bp3-select-popover .bp3-popover-content{
padding:5px; }
.bp3-select-popover .bp3-input-group{
margin-bottom:0; }
.bp3-select-popover .bp3-menu{
max-width:400px;
max-height:300px;
overflow:auto;
padding:0; }
.bp3-select-popover .bp3-menu:not(:first-child){
padding-top:5px; }
.bp3-multi-select{
min-width:150px; }
.bp3-multi-select-popover .bp3-menu{
max-width:400px;
max-height:300px;
overflow:auto; }
.bp3-select-popover .bp3-popover-content{
padding:5px; }
.bp3-select-popover .bp3-input-group{
margin-bottom:0; }
.bp3-select-popover .bp3-menu{
max-width:400px;
max-height:300px;
overflow:auto;
padding:0; }
.bp3-select-popover .bp3-menu:not(:first-child){
padding-top:5px; }
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
/* Icons urls */
:root {
--jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-caret-right: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik03LjIsNS4yTDEwLjksOWwtMy44LDMuOFY1LjJINy4yeiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--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-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-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==);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-ListingsInfoIcon {
background-image: var(--jp-icon-listings-info);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
:root {
--jp-icon-search-white: url(data:image/svg+xml;base64,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);
}
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
/* CSS for icons in selected items in the settings editor */
#setting-editor .jp-PluginList .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#setting-editor
.jp-PluginList
.jp-mod-selected
.jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* CSS for icons in selected tabs in the sidebar tab manager */
#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable[fill] {
fill: #fff;
}
#tab-manager .lm-TabBar-tab.jp-mod-active .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
#tab-manager
.lm-TabBar-tab.jp-mod-active
.jp-icon-hover
:hover
.jp-icon-selectable[fill] {
fill: var(--jp-brand-color1);
}
#tab-manager
.lm-TabBar-tab.jp-mod-active
.jp-icon-hover
:hover
.jp-icon-selectable-inverse[fill] {
fill: #fff;
}
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the sidebar tab manager
*/
#tab-manager
.lm-TabBar-tab.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
#tab-manager
.lm-TabBar-tab.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
#tab-manager
.lm-TabBar-tab.jp-mod-dirty.jp-mod-active
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: #fff;
}
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) svg {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
/* Override Blueprint's _reset.scss styles */
html {
box-sizing: unset;
}
*,
*::before,
*::after {
box-sizing: unset;
}
body {
color: unset;
font-family: var(--jp-ui-font-family);
}
p {
margin-top: unset;
margin-bottom: unset;
}
small {
font-size: unset;
}
strong {
font-weight: unset;
}
/* Override Blueprint's _typography.scss styles */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Override Blueprint's _accessibility.scss styles */
:focus {
outline: unset;
outline-offset: unset;
-moz-outline-radius: unset;
}
/* Styles for ui-components */
.jp-Button {
border-radius: var(--jp-border-radius);
padding: 0px 12px;
font-size: var(--jp-ui-font-size1);
}
/* Use our own theme for hover styles */
button.jp-Button.bp3-button.bp3-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.minimal {
color: unset !important;
}
.jp-Button.jp-ToolbarButtonComponent {
text-transform: none;
}
.jp-InputGroup input {
box-sizing: border-box;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color3);
}
.jp-BPIcon {
display: inline-block;
vertical-align: middle;
margin: auto;
}
/* Stop blueprint futzing with our icon fills */
.bp3-icon.jp-BPIcon > svg:not([fill]) {
fill: var(--jp-inverse-layout-color3);
}
.jp-InputGroupAction {
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
/* Use our own theme for hover and option styles */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| 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.
|----------------------------------------------------------------------------*/
.jp-Collapse {
display: flex;
flex-direction: column;
align-items: stretch;
border-top: 1px solid var(--jp-border-color2);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-Collapse-header {
padding: 1px 12px;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size2);
}
.jp-Collapse-header:hover {
background-color: var(--jp-layout-color2);
}
.jp-Collapse-contents {
padding: 0px 12px 0px 12px;
background-color: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0px;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0px 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.lm-CommandPalette-wrapper::after {
content: ' ';
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
height: 30px;
width: 10px;
padding: 0px 10px;
background-image: var(--jp-icon-search-white);
background-size: 20px;
background-repeat: no-repeat;
background-position: center;
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color3);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0px;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color3);
}
.lm-CommandPalette-item.lm-mod-active {
background: var(--jp-layout-color3);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
background: var(--jp-layout-color4);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color3);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.4;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty:after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0px 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0px;
left: 0px;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px;
padding-bottom: 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0px;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
.jp-Dialog-header {
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0px 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
.jp-HoverBox.jp-mod-outofview {
display: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `p-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame:before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #aa00ff;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ffff00;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eeeeee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent:before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent:after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0px 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
height: 28px;
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
background-color: var(--jp-layout-color1);
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0px 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
height: 32px;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 1;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0px;
margin: 0px;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0px 6px;
margin: 0px;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent span {
padding: 0px;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
}
/*-----------------------------------------------------------------------------
| 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
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
/* <DEPRECATED> */ body.p-mod-override-cursor *, /* </DEPRECATED> */
body.lm-mod-override-cursor * {
cursor: inherit !important;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0px;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
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 {
/* Set height, width, borders, and global font properties here */
font-family: monospace;
height: 300px;
color: black;
direction: ltr;
}
/* PADDING */
.CodeMirror-lines {
padding: 4px 0; /* Vertical padding around content */
}
.CodeMirror pre.CodeMirror-line,
.CodeMirror pre.CodeMirror-line-like {
padding: 0 4px; /* Horizontal padding of content */
}
.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
background-color: white; /* The little square between H and V scrollbars */
}
/* GUTTER */
.CodeMirror-gutters {
border-right: 1px solid #ddd;
background-color: #f7f7f7;
white-space: nowrap;
}
.CodeMirror-linenumbers {}
.CodeMirror-linenumber {
padding: 0 3px 0 5px;
min-width: 20px;
text-align: right;
color: #999;
white-space: nowrap;
}
.CodeMirror-guttermarker { color: black; }
.CodeMirror-guttermarker-subtle { color: #999; }
/* CURSOR */
.CodeMirror-cursor {
border-left: 1px solid black;
border-right: none;
width: 0;
}
/* Shown when moving in bi-directional text */
.CodeMirror div.CodeMirror-secondarycursor {
border-left: 1px solid silver;
}
.cm-fat-cursor .CodeMirror-cursor {
width: auto;
border: 0 !important;
background: #7e7;
}
.cm-fat-cursor div.CodeMirror-cursors {
z-index: 1;
}
.cm-fat-cursor-mark {
background-color: rgba(20, 255, 20, 0.5);
-webkit-animation: blink 1.06s steps(1) infinite;
-moz-animation: blink 1.06s steps(1) infinite;
animation: blink 1.06s steps(1) infinite;
}
.cm-animate-fat-cursor {
width: auto;
border: 0;
-webkit-animation: blink 1.06s steps(1) infinite;
-moz-animation: blink 1.06s steps(1) infinite;
animation: blink 1.06s steps(1) infinite;
background-color: #7e7;
}
@-moz-keyframes blink {
0% {}
50% { background-color: transparent; }
100% {}
}
@-webkit-keyframes blink {
0% {}
50% { background-color: transparent; }
100% {}
}
@keyframes blink {
0% {}
50% { background-color: transparent; }
100% {}
}
/* Can style cursor different in overwrite (non-insert) mode */
.CodeMirror-overwrite .CodeMirror-cursor {}
.cm-tab { display: inline-block; text-decoration: inherit; }
.CodeMirror-rulers {
position: absolute;
left: 0; right: 0; top: -50px; bottom: 0;
overflow: hidden;
}
.CodeMirror-ruler {
border-left: 1px solid #ccc;
top: 0; bottom: 0;
position: absolute;
}
/* DEFAULT THEME */
.cm-s-default .cm-header {color: blue;}
.cm-s-default .cm-quote {color: #090;}
.cm-negative {color: #d44;}
.cm-positive {color: #292;}
.cm-header, .cm-strong {font-weight: bold;}
.cm-em {font-style: italic;}
.cm-link {text-decoration: underline;}
.cm-strikethrough {text-decoration: line-through;}
.cm-s-default .cm-keyword {color: #708;}
.cm-s-default .cm-atom {color: #219;}
.cm-s-default .cm-number {color: #164;}
.cm-s-default .cm-def {color: #00f;}
.cm-s-default .cm-variable,
.cm-s-default .cm-punctuation,
.cm-s-default .cm-property,
.cm-s-default .cm-operator {}
.cm-s-default .cm-variable-2 {color: #05a;}
.cm-s-default .cm-variable-3, .cm-s-default .cm-type {color: #085;}
.cm-s-default .cm-comment {color: #a50;}
.cm-s-default .cm-string {color: #a11;}
.cm-s-default .cm-string-2 {color: #f50;}
.cm-s-default .cm-meta {color: #555;}
.cm-s-default .cm-qualifier {color: #555;}
.cm-s-default .cm-builtin {color: #30a;}
.cm-s-default .cm-bracket {color: #997;}
.cm-s-default .cm-tag {color: #170;}
.cm-s-default .cm-attribute {color: #00c;}
.cm-s-default .cm-hr {color: #999;}
.cm-s-default .cm-link {color: #00c;}
.cm-s-default .cm-error {color: #f00;}
.cm-invalidchar {color: #f00;}
.CodeMirror-composing { border-bottom: 2px solid; }
/* Default styles for common addons */
div.CodeMirror span.CodeMirror-matchingbracket {color: #0b0;}
div.CodeMirror span.CodeMirror-nonmatchingbracket {color: #a22;}
.CodeMirror-matchingtag { background: rgba(255, 150, 0, .3); }
.CodeMirror-activeline-background {background: #e8f2ff;}
/* STOP */
/* The rest of this file contains styles related to the mechanics of
the editor. You probably shouldn't touch them. */
.CodeMirror {
position: relative;
overflow: hidden;
background: white;
}
.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 */
/* See overflow: hidden in .CodeMirror */
margin-bottom: -30px; margin-right: -30px;
padding-bottom: 30px;
height: 100%;
outline: none; /* Prevent dragging from highlighting the element */
position: relative;
}
.CodeMirror-sizer {
position: relative;
border-right: 30px solid transparent;
}
/* The fake, visible scrollbars. Used to force redraw during scrolling
before actual scrolling happens, thus preventing shaking and
flickering artifacts. */
.CodeMirror-vscrollbar, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {
position: absolute;
z-index: 6;
display: none;
}
.CodeMirror-vscrollbar {
right: 0; top: 0;
overflow-x: hidden;
overflow-y: scroll;
}
.CodeMirror-hscrollbar {
bottom: 0; left: 0;
overflow-y: hidden;
overflow-x: scroll;
}
.CodeMirror-scrollbar-filler {
right: 0; bottom: 0;
}
.CodeMirror-gutter-filler {
left: 0; bottom: 0;
}
.CodeMirror-gutters {
position: absolute; left: 0; top: 0;
min-height: 100%;
z-index: 3;
}
.CodeMirror-gutter {
white-space: normal;
height: 100%;
display: inline-block;
vertical-align: top;
margin-bottom: -30px;
}
.CodeMirror-gutter-wrapper {
position: absolute;
z-index: 4;
background: none !important;
border: none !important;
}
.CodeMirror-gutter-background {
position: absolute;
top: 0; bottom: 0;
z-index: 4;
}
.CodeMirror-gutter-elt {
position: absolute;
cursor: default;
z-index: 4;
}
.CodeMirror-gutter-wrapper ::selection { background-color: transparent }
.CodeMirror-gutter-wrapper ::-moz-selection { background-color: transparent }
.CodeMirror-lines {
cursor: text;
min-height: 1px; /* prevents collapsing before first draw */
}
.CodeMirror pre.CodeMirror-line,
.CodeMirror pre.CodeMirror-line-like {
/* Reset some styles that the rest of the page might have set */
-moz-border-radius: 0; -webkit-border-radius: 0; border-radius: 0;
border-width: 0;
background: transparent;
font-family: inherit;
font-size: inherit;
margin: 0;
white-space: pre;
word-wrap: normal;
line-height: inherit;
color: inherit;
z-index: 2;
position: relative;
overflow: visible;
-webkit-tap-highlight-color: transparent;
-webkit-font-variant-ligatures: contextual;
font-variant-ligatures: contextual;
}
.CodeMirror-wrap pre.CodeMirror-line,
.CodeMirror-wrap pre.CodeMirror-line-like {
word-wrap: break-word;
white-space: pre-wrap;
word-break: normal;
}
.CodeMirror-linebackground {
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 <plakroon@gmail.com>
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)
*/
.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;
}
.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
*/
/*
Solarized color palette
http://ethanschoonover.com/solarized/img/solarized-palette.png
*/
.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;
}
.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;
}
.cm-s-solarized .cm-error,
.cm-s-solarized .cm-invalidchar {
color: #586e75;
border-bottom: 1px dotted #dc322f;
}
.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; }
/* Editor styling */
/* 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;
}
/* Remove gutter border */
.cm-s-solarized .CodeMirror-gutters {
border-right: 0;
}
/* Gutter colors and line number styling based of color scheme (dark / light) */
/* Dark */
.cm-s-solarized.cm-s-dark .CodeMirror-gutters {
background-color: #073642;
}
.cm-s-solarized.cm-s-dark .CodeMirror-linenumber {
color: #586e75;
text-shadow: #021014 0 -1px;
}
/* Light */
.cm-s-solarized.cm-s-light .CodeMirror-gutters {
background-color: #eee8d5;
}
.cm-s-solarized.cm-s-light .CodeMirror-linenumber {
color: #839496;
}
/* Common */
.cm-s-solarized .CodeMirror-linenumber {
padding: 0 5px;
}
.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;
}
/* Cursor */
.cm-s-solarized .CodeMirror-cursor { border-left: 1px solid #819090; }
/* Fat cursor */
.cm-s-solarized.cm-s-light.cm-fat-cursor .CodeMirror-cursor { background: #77ee77; }
.cm-s-solarized.cm-s-light .cm-animate-fat-cursor { background-color: #77ee77; }
.cm-s-solarized.cm-s-dark.cm-fat-cursor .CodeMirror-cursor { background: #586e75; }
.cm-s-solarized.cm-s-dark .cm-animate-fat-cursor { background-color: #586e75; }
/* Active line */
.cm-s-solarized.cm-s-dark .CodeMirror-activeline-background {
background: rgba(255, 255, 255, 0.06);
}
.cm-s-solarized.cm-s-light .CodeMirror-activeline-background {
background: rgba(0, 0, 0, 0.06);
}
.cm-s-the-matrix.CodeMirror { background: #000000; color: #00FF00; }
.cm-s-the-matrix div.CodeMirror-selected { background: #2D2D2D; }
.cm-s-the-matrix .CodeMirror-line::selection, .cm-s-the-matrix .CodeMirror-line > span::selection, .cm-s-the-matrix .CodeMirror-line > span > span::selection { background: rgba(45, 45, 45, 0.99); }
.cm-s-the-matrix .CodeMirror-line::-moz-selection, .cm-s-the-matrix .CodeMirror-line > span::-moz-selection, .cm-s-the-matrix .CodeMirror-line > span > span::-moz-selection { background: rgba(45, 45, 45, 0.99); }
.cm-s-the-matrix .CodeMirror-gutters { background: #060; border-right: 2px solid #00FF00; }
.cm-s-the-matrix .CodeMirror-guttermarker { color: #0f0; }
.cm-s-the-matrix .CodeMirror-guttermarker-subtle { color: white; }
.cm-s-the-matrix .CodeMirror-linenumber { color: #FFFFFF; }
.cm-s-the-matrix .CodeMirror-cursor { border-left: 1px solid #00FF00; }
.cm-s-the-matrix span.cm-keyword { color: #008803; font-weight: bold; }
.cm-s-the-matrix span.cm-atom { color: #3FF; }
.cm-s-the-matrix span.cm-number { color: #FFB94F; }
.cm-s-the-matrix span.cm-def { color: #99C; }
.cm-s-the-matrix span.cm-variable { color: #F6C; }
.cm-s-the-matrix span.cm-variable-2 { color: #C6F; }
.cm-s-the-matrix span.cm-variable-3, .cm-s-the-matrix span.cm-type { color: #96F; }
.cm-s-the-matrix span.cm-property { color: #62FFA0; }
.cm-s-the-matrix span.cm-operator { color: #999; }
.cm-s-the-matrix span.cm-comment { color: #CCCCCC; }
.cm-s-the-matrix span.cm-string { color: #39C; }
.cm-s-the-matrix span.cm-meta { color: #C9F; }
.cm-s-the-matrix span.cm-qualifier { color: #FFF700; }
.cm-s-the-matrix span.cm-builtin { color: #30a; }
.cm-s-the-matrix span.cm-bracket { color: #cc7; }
.cm-s-the-matrix span.cm-tag { color: #FFBD40; }
.cm-s-the-matrix span.cm-attribute { color: #FFF700; }
.cm-s-the-matrix span.cm-error { color: #FF0000; }
.cm-s-the-matrix .CodeMirror-activeline-background { background: #040; }
/*
Copyright (C) 2011 by MarkLogic Corporation
Author: Mike Brevoort <mike@brevoort.com>
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; }
.cm-s-xq-light span.cm-bracket { color: #cc7; }
.cm-s-xq-light span.cm-tag { color: #3F7F7F; }
.cm-s-xq-light span.cm-attribute { color: #7F007F; }
.cm-s-xq-light span.cm-error { color: #f00; }
.cm-s-xq-light .CodeMirror-activeline-background { background: #e8f2ff; }
.cm-s-xq-light .CodeMirror-matchingbracket { outline:1px solid grey;color:black !important;background:yellow; }
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.CodeMirror {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.CodeMirror pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
/* This causes https://github.com/jupyter/jupyterlab/issues/522 */
/* May not cause it not because we changed it! */
.CodeMirror-lines {
padding: var(--jp-code-padding) 0;
}
.CodeMirror-linenumber {
padding: 0 8px;
}
.jp-CodeMirrorEditor-static {
margin: var(--jp-code-padding);
}
.jp-CodeMirrorEditor,
.jp-CodeMirrorEditor-static {
cursor: text;
}
.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .CodeMirror-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.CodeMirror.jp-mod-readOnly .CodeMirror-cursor {
display: none;
}
.CodeMirror-gutters {
border-right: 1px solid var(--jp-border-color2);
background-color: var(--jp-layout-color0);
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.CodeMirror-selectedtext.cm-searching {
background-color: var(--jp-search-selected-match-background-color) !important;
color: var(--jp-search-selected-match-color) !important;
}
.cm-searching {
background-color: var(
--jp-search-unselected-match-background-color
) !important;
color: var(--jp-search-unselected-match-color) !important;
}
.CodeMirror-focused .CodeMirror-selected {
background-color: var(--jp-editor-selected-focused-background);
}
.CodeMirror-selected {
background-color: var(--jp-editor-selected-background);
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/**
* Here is our jupyter theme for CodeMirror syntax highlighting
* This is used in our marked.js syntax highlighting and CodeMirror itself
* The string "jupyter" is set in ../codemirror/widget.DEFAULT_CODEMIRROR_THEME
* This came from the classic notebook, which came form highlight.js/GitHub
*/
/**
* CodeMirror themes are handling the background/color in this way. This works
* fine for CodeMirror editors outside the notebook, but the notebook styles
* these things differently.
*/
.CodeMirror.cm-s-jupyter {
background: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
/* In the notebook, we want this styling to be handled by its container */
.jp-CodeConsole .CodeMirror.cm-s-jupyter,
.jp-Notebook .CodeMirror.cm-s-jupyter {
background: transparent;
}
.cm-s-jupyter .CodeMirror-cursor {
border-left: var(--jp-code-cursor-width0) solid var(--jp-editor-cursor-color);
}
.cm-s-jupyter span.cm-keyword {
color: var(--jp-mirror-editor-keyword-color);
font-weight: bold;
}
.cm-s-jupyter span.cm-atom {
color: var(--jp-mirror-editor-atom-color);
}
.cm-s-jupyter span.cm-number {
color: var(--jp-mirror-editor-number-color);
}
.cm-s-jupyter span.cm-def {
color: var(--jp-mirror-editor-def-color);
}
.cm-s-jupyter span.cm-variable {
color: var(--jp-mirror-editor-variable-color);
}
.cm-s-jupyter span.cm-variable-2 {
color: var(--jp-mirror-editor-variable-2-color);
}
.cm-s-jupyter span.cm-variable-3 {
color: var(--jp-mirror-editor-variable-3-color);
}
.cm-s-jupyter span.cm-punctuation {
color: var(--jp-mirror-editor-punctuation-color);
}
.cm-s-jupyter span.cm-property {
color: var(--jp-mirror-editor-property-color);
}
.cm-s-jupyter span.cm-operator {
color: var(--jp-mirror-editor-operator-color);
font-weight: bold;
}
.cm-s-jupyter span.cm-comment {
color: var(--jp-mirror-editor-comment-color);
font-style: italic;
}
.cm-s-jupyter span.cm-string {
color: var(--jp-mirror-editor-string-color);
}
.cm-s-jupyter span.cm-string-2 {
color: var(--jp-mirror-editor-string-2-color);
}
.cm-s-jupyter span.cm-meta {
color: var(--jp-mirror-editor-meta-color);
}
.cm-s-jupyter span.cm-qualifier {
color: var(--jp-mirror-editor-qualifier-color);
}
.cm-s-jupyter span.cm-builtin {
color: var(--jp-mirror-editor-builtin-color);
}
.cm-s-jupyter span.cm-bracket {
color: var(--jp-mirror-editor-bracket-color);
}
.cm-s-jupyter span.cm-tag {
color: var(--jp-mirror-editor-tag-color);
}
.cm-s-jupyter span.cm-attribute {
color: var(--jp-mirror-editor-attribute-color);
}
.cm-s-jupyter span.cm-header {
color: var(--jp-mirror-editor-header-color);
}
.cm-s-jupyter span.cm-quote {
color: var(--jp-mirror-editor-quote-color);
}
.cm-s-jupyter span.cm-link {
color: var(--jp-mirror-editor-link-color);
}
.cm-s-jupyter span.cm-error {
color: var(--jp-mirror-editor-error-color);
}
.cm-s-jupyter span.cm-hr {
color: #999;
}
.cm-s-jupyter span.cm-tab {
background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
background-position: right;
background-repeat: no-repeat;
}
.cm-s-jupyter .CodeMirror-activeline-background,
.cm-s-jupyter .CodeMirror-gutter {
background-color: var(--jp-layout-color2);
}
/*-----------------------------------------------------------------------------
| 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.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0px;
padding: 0px;
line-height: normal;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0em;
}
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: 12px;
table-layout: fixed;
margin-left: auto;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon table {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0px;
}
.jp-RenderedHTMLCommon p {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
/* ...or leave it untouched if they don't */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-dark-background {
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-light-background {
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: 0.8em;
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*-----------------------------------------------------------------------------
| 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.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| 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.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser {
display: flex;
flex-direction: column;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
.jp-FileBrowser-toolbar.jp-Toolbar {
border-bottom: none;
height: auto;
margin: var(--jp-toolbar-header-margin);
box-shadow: none;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 4px 12px;
}
.jp-BreadCrumbs-item {
margin: 0px 2px;
padding: 0px 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0px;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar.jp-Toolbar {
padding: 0px;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
justify-content: space-evenly;
}
.jp-FileBrowser-toolbar.jp-Toolbar .jp-Toolbar-item {
flex: 1;
}
.jp-FileBrowser-toolbar.jp-Toolbar .jp-ToolbarButtonComponent {
width: 100%;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px 12px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
/* 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);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-item.jp-mod-selected {
color: white;
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon:before {
color: limegreen;
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0px;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-DirListing-deadSpace {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
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.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
}
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: flex;
flex-direction: row;
}
.jp-OutputPrompt {
flex: 0 0 var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-OutputArea-output {
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea-child .jp-OutputArea-output {
flex-grow: 1;
flex-shrink: 1;
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `p-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated:before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0px;
padding: 0px;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0px;
flex: 1 1 auto;
}
.jp-OutputArea-executeResult.jp-RenderedText {
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-OutputArea-stdin {
line-height: var(--jp-code-line-height);
padding-top: var(--jp-code-padding);
display: flex;
}
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0em 0.25em;
margin: 0em 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| 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.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0px;
margin: 0px;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0px;
bottom: 0px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0px;
width: 100%;
padding: 0px;
margin: 0px;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: flex;
flex-direction: row;
}
.jp-InputArea-editor {
flex: 1 1 auto;
}
.jp-InputArea-editor {
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0px;
background: var(--jp-cell-editor-background);
}
.jp-InputPrompt {
flex: 0 0 var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: flex;
flex-direction: row;
flex: 1 1 auto;
}
.jp-Placeholder-prompt {
box-sizing: border-box;
}
.jp-Placeholder-content {
flex: 1 1 auto;
border: none;
background: transparent;
height: 20px;
box-sizing: border-box;
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0px;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0px;
margin: 0px;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
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;
box-shadow: inset 0 0 6px 2px rgba(0, 0, 0, 0.3);
margin-left: var(--jp-private-cell-scrolling-output-offset);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
flex: 0 0
calc(
var(--jp-cell-prompt-width) -
var(--jp-private-cell-scrolling-output-offset)
);
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
flex: 1 1 auto;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
/*-----------------------------------------------------------------------------
| 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.
|----------------------------------------------------------------------------*/
/* 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
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: 2px;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: flex;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0px;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0px rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-NotebookTools-tool {
padding: 0px 12px 0 12px;
}
.jp-ActiveCellTool {
padding: 12px;
background-color: var(--jp-layout-color1);
border-top: none !important;
}
.jp-ActiveCellTool .jp-InputArea-prompt {
flex: 0 0 auto;
padding-left: 0px;
}
.jp-ActiveCellTool .jp-InputArea-editor {
flex: 1 1 auto;
background: var(--jp-cell-editor-background);
border-color: var(--jp-cell-editor-border-color);
}
.jp-ActiveCellTool .jp-InputArea-editor .CodeMirror {
background: transparent;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0px 12px 0px;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label {
line-height: 1.4;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
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.
|----------------------------------------------------------------------------*/
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
0px 1px 1px 0px var(--jp-shadow-penumbra-color),
0px 1px 3px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
0px 2px 2px 0px var(--jp-shadow-penumbra-color),
0px 1px 5px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
0px 4px 5px 0px var(--jp-shadow-penumbra-color),
0px 1px 10px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
0px 6px 10px 0px var(--jp-shadow-penumbra-color),
0px 1px 18px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
0px 8px 10px 1px var(--jp-shadow-penumbra-color),
0px 3px 14px 2px var(--jp-shadow-ambient-color);
--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
0px 12px 17px 2px var(--jp-shadow-penumbra-color),
0px 5px 22px 4px var(--jp-shadow-ambient-color);
--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
0px 16px 24px 2px var(--jp-shadow-penumbra-color),
0px 6px 30px 5px var(--jp-shadow-ambient-color);
--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
0px 20px 31px 3px var(--jp-shadow-penumbra-color),
0px 8px 38px 7px var(--jp-shadow-ambient-color);
--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
0px 24px 38px 3px var(--jp-shadow-penumbra-color),
0px 9px 46px 8px var(--jp-shadow-ambient-color);
/* Borders
*
* The following variables, specify the visual styling of borders in JupyterLab.
*/
--jp-border-width: 1px;
--jp-border-color0: var(--md-grey-400);
--jp-border-color1: var(--md-grey-400);
--jp-border-color2: var(--md-grey-300);
--jp-border-color3: var(--md-grey-200);
--jp-border-radius: 2px;
/* UI Fonts
*
* The UI font CSS variables are used for the typography all of the JupyterLab
* user interface elements that are not directly user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-ui-font-scale-factor: 1.2;
--jp-ui-font-size0: 0.83333em;
--jp-ui-font-size1: 13px; /* Base font size */
--jp-ui-font-size2: 1.2em;
--jp-ui-font-size3: 1.44em;
--jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica,
Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
/*
* Use these font colors against the corresponding main layout colors.
* In a light theme, these go from dark to light.
*/
/* Defaults use Material Design specification */
--jp-ui-font-color0: rgba(0, 0, 0, 1);
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
/*
* Use these against the brand/accent/warn/error colors.
* These will typically go from light to darker, in both a dark and light theme.
*/
--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
/* Content Fonts
*
* Content font variables are used for typography of user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-content-line-height: 1.6;
--jp-content-font-scale-factor: 1.2;
--jp-content-font-size0: 0.83333em;
--jp-content-font-size1: 14px; /* Base font size */
--jp-content-font-size2: 1.2em;
--jp-content-font-size3: 1.44em;
--jp-content-font-size4: 1.728em;
--jp-content-font-size5: 2.0736em;
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-content-presentation-font-size1: 17px;
--jp-content-heading-line-height: 1;
--jp-content-heading-margin-top: 1.2em;
--jp-content-heading-margin-bottom: 0.8em;
--jp-content-heading-font-weight: 500;
/* Defaults use Material Design specification */
--jp-content-font-color0: rgba(0, 0, 0, 1);
--jp-content-font-color1: rgba(0, 0, 0, 0.87);
--jp-content-font-color2: rgba(0, 0, 0, 0.54);
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
--jp-content-link-color: var(--md-blue-700);
--jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI',
Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
'Segoe UI Symbol';
/*
* Code Fonts
*
* Code font variables are used for typography of code and other monospaces content.
*/
--jp-code-font-size: 13px;
--jp-code-line-height: 1.3077; /* 17px for 13px base */
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
--jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace;
--jp-code-font-family: var(--jp-code-font-family-default);
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-code-presentation-font-size: 16px;
/* may need to tweak cursor width if you change font size */
--jp-code-cursor-width0: 1.4px;
--jp-code-cursor-width1: 2px;
--jp-code-cursor-width2: 4px;
/* Layout
*
* The following are the main layout colors use in JupyterLab. In a light
* theme these would go from light to dark.
*/
--jp-layout-color0: white;
--jp-layout-color1: white;
--jp-layout-color2: var(--md-grey-200);
--jp-layout-color3: var(--md-grey-400);
--jp-layout-color4: var(--md-grey-600);
/* Inverse Layout
*
* The following are the inverse layout colors use in JupyterLab. In a light
* theme these would go from dark to light.
*/
--jp-inverse-layout-color0: #111111;
--jp-inverse-layout-color1: var(--md-grey-900);
--jp-inverse-layout-color2: var(--md-grey-800);
--jp-inverse-layout-color3: var(--md-grey-700);
--jp-inverse-layout-color4: var(--md-grey-600);
/* Brand/accent */
--jp-brand-color0: var(--md-blue-700);
--jp-brand-color1: var(--md-blue-500);
--jp-brand-color2: var(--md-blue-300);
--jp-brand-color3: var(--md-blue-100);
--jp-brand-color4: var(--md-blue-50);
--jp-accent-color0: var(--md-green-700);
--jp-accent-color1: var(--md-green-500);
--jp-accent-color2: var(--md-green-300);
--jp-accent-color3: var(--md-green-100);
/* State colors (warn, error, success, info) */
--jp-warn-color0: var(--md-orange-700);
--jp-warn-color1: var(--md-orange-500);
--jp-warn-color2: var(--md-orange-300);
--jp-warn-color3: var(--md-orange-100);
--jp-error-color0: var(--md-red-700);
--jp-error-color1: var(--md-red-500);
--jp-error-color2: var(--md-red-300);
--jp-error-color3: var(--md-red-100);
--jp-success-color0: var(--md-green-700);
--jp-success-color1: var(--md-green-500);
--jp-success-color2: var(--md-green-300);
--jp-success-color3: var(--md-green-100);
--jp-info-color0: var(--md-cyan-700);
--jp-info-color1: var(--md-cyan-500);
--jp-info-color2: var(--md-cyan-300);
--jp-info-color3: var(--md-cyan-100);
/* Cell specific styles */
--jp-cell-padding: 5px;
--jp-cell-collapser-width: 8px;
--jp-cell-collapser-min-height: 20px;
--jp-cell-collapser-not-active-hover-opacity: 0.6;
--jp-cell-editor-background: var(--md-grey-100);
--jp-cell-editor-border-color: var(--md-grey-300);
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-cell-editor-active-background: var(--jp-layout-color0);
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
--jp-cell-prompt-width: 64px;
--jp-cell-prompt-font-family: 'Source Code Pro', monospace;
--jp-cell-prompt-letter-spacing: 0px;
--jp-cell-prompt-opacity: 1;
--jp-cell-prompt-not-active-opacity: 0.5;
--jp-cell-prompt-not-active-font-color: var(--md-grey-700);
/* A custom blend of MD grey and blue 600
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
--jp-cell-inprompt-font-color: #307fc1;
/* A custom blend of MD grey and orange 600
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
--jp-cell-outprompt-font-color: #bf5b3d;
/* Notebook specific styles */
--jp-notebook-padding: 10px;
--jp-notebook-select-background: var(--jp-layout-color1);
--jp-notebook-multiselected-color: var(--md-blue-50);
/* The scroll padding is calculated to fill enough space at the bottom of the
notebook to show one single-line cell (with appropriate padding) at the top
when the notebook is scrolled all the way to the bottom. We also subtract one
pixel so that no scrollbar appears if we have just one single-line cell in the
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
*/
--jp-notebook-scroll-padding: calc(
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
);
/* Rendermime styles */
--jp-rendermime-error-background: #fdd;
--jp-rendermime-table-row-background: var(--md-grey-100);
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
/* Dialog specific styles */
--jp-dialog-background: rgba(0, 0, 0, 0.25);
/* Console specific styles */
--jp-console-padding: 10px;
/* Toolbar specific styles */
--jp-toolbar-border-color: var(--jp-border-color1);
--jp-toolbar-micro-height: 8px;
--jp-toolbar-background: var(--jp-layout-color1);
--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
--jp-toolbar-header-margin: 4px 4px 0px 4px;
--jp-toolbar-active-background: var(--md-grey-300);
/* Input field styles */
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-input-active-background: var(--jp-layout-color1);
--jp-input-hover-background: var(--jp-layout-color1);
--jp-input-background: var(--md-grey-100);
--jp-input-border-color: var(--jp-border-color1);
--jp-input-active-border-color: var(--jp-brand-color1);
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
/* General editor styles */
--jp-editor-selected-background: #d9d9d9;
--jp-editor-selected-focused-background: #d7d4f0;
--jp-editor-cursor-color: var(--jp-ui-font-color0);
/* Code mirror specific styles */
--jp-mirror-editor-keyword-color: #008000;
--jp-mirror-editor-atom-color: #88f;
--jp-mirror-editor-number-color: #080;
--jp-mirror-editor-def-color: #00f;
--jp-mirror-editor-variable-color: var(--md-grey-900);
--jp-mirror-editor-variable-2-color: #05a;
--jp-mirror-editor-variable-3-color: #085;
--jp-mirror-editor-punctuation-color: #05a;
--jp-mirror-editor-property-color: #05a;
--jp-mirror-editor-operator-color: #aa22ff;
--jp-mirror-editor-comment-color: #408080;
--jp-mirror-editor-string-color: #ba2121;
--jp-mirror-editor-string-2-color: #708;
--jp-mirror-editor-meta-color: #aa22ff;
--jp-mirror-editor-qualifier-color: #555;
--jp-mirror-editor-builtin-color: #008000;
--jp-mirror-editor-bracket-color: #997;
--jp-mirror-editor-tag-color: #170;
--jp-mirror-editor-attribute-color: #00c;
--jp-mirror-editor-header-color: blue;
--jp-mirror-editor-quote-color: #090;
--jp-mirror-editor-link-color: #00c;
--jp-mirror-editor-error-color: #f00;
--jp-mirror-editor-hr-color: #999;
/* Vega extension styles */
--jp-vega-background: white;
/* Sidebar-related styles */
--jp-sidebar-min-width: 180px;
/* Search-related styles */
--jp-search-toggle-off-opacity: 0.5;
--jp-search-toggle-hover-opacity: 0.8;
--jp-search-toggle-on-opacity: 1;
--jp-search-selected-match-background-color: rgb(245, 200, 0);
--jp-search-selected-match-color: black;
--jp-search-unselected-match-background-color: var(
--jp-inverse-layout-color0
);
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
/* Icon colors that work well with light or dark backgrounds */
--jp-icon-contrast-color0: var(--md-purple-600);
--jp-icon-contrast-color1: var(--md-green-600);
--jp-icon-contrast-color2: var(--md-pink-600);
--jp-icon-contrast-color3: var(--md-blue-600);
}
</style>
<style type="text/css">
a.anchor-link {
display: none;
}
.highlight {
margin: 0.4em;
}
/* Input area styling */
.jp-InputArea {
overflow: hidden;
}
.jp-InputArea-editor {
overflow: hidden;
}
@media print {
body {
margin: 0;
}
}
</style>
<!-- Load mathjax -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-MML-AM_CHTML-full,Safe"> </script>
<!-- MathJax configuration -->
<script type="text/x-mathjax-config">
init_mathjax = function() {
if (window.MathJax) {
// MathJax loaded
MathJax.Hub.Config({
TeX: {
equationNumbers: {
autoNumber: "AMS",
useLabelIds: true
}
},
tex2jax: {
inlineMath: [ ['$','$'], ["\\(","\\)"] ],
displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
processEscapes: true,
processEnvironments: true
},
displayAlign: 'center',
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<h1 id="Table-of-Contents">Table of Contents<a class="anchor-link" href="#Table-of-Contents">¶</a></h1><ol>
<li><a href='#Notebook-order-to-get-to-this-point'>Notebook order to get to this point</a></li>
<li><a href='#Adding-Data'>Adding Data</a></li>
<li><a href='#Compute-Age/Sex-Stats-Using-Bootstrap'>Age & Sex</a></li>
<li><a href='#Compute-Race-Stats-Using-Bootstrap'>Race & Ethnicity</a></li>
<li><a href='#Compute-Time-Differential-ECG/Echo-Stats-Using-Bootstrap'>Time from ECG to Echo</a></li>
<li><a href='#Compute-Bundle-Branch-and-QRS-Stats-Using-Bootstrap'>Bundle Branch & QRS</a></li>
<li><a href='#Generate-Summary-Stats-for-Table-1-and-2'>Summary Stats for All Subgroups (Tables 1 & 2)</a></li>
<li><a href='#Now-we-run-the-vary_prevalance-scripts-from-ValveNet_v2-back-on-the-Dendrite-server'>Varying Prevalence (Table 3)</a></li>
<li><a href='#Test-Set-AUROC-and-AUPRC-Curves-(Fig-3A-and-3B)'>Overall AUROC & AUPRC (Figure 3A/3B)</a></li>
<li><a href='#Compute-a-Logistic-Regression-Model-using-Tabular-Data'>Logistic Regression on Tabular Data</a></li>
<li><a href='#NYP-Lawrence-Validation-with-and-without-Propensity-Score-Matching'>NYP Lawrence Validation</a></li>
<li><a href='#Model Calibration Curves'>Model Calibration Curves</a> </li>
<li><a href='#Determine PPV and NPV at Youden Index value per model'>Determining Performance per Model at Youden Index</a></li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pyodbc</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sqlalchemy</span> <span class="kn">import</span> <span class="n">create_engine</span>
<span class="kn">from</span> <span class="nn">timeit</span> <span class="kn">import</span> <span class="n">default_timer</span> <span class="k">as</span> <span class="n">timer</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">date</span><span class="p">,</span> <span class="n">timedelta</span>
<span class="kn">from</span> <span class="nn">openpyxl</span> <span class="kn">import</span> <span class="n">load_workbook</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">islice</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">matplotlib.pylab</span> <span class="k">as</span> <span class="nn">pl</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">auc</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">roc_curve</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">roc_auc_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">precision_recall_curve</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">average_precision_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">precision_recall_curve</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">auc</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">ConfusionMatrixDisplay</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">imblearn.datasets</span> <span class="kn">import</span> <span class="n">make_imbalance</span>
<span class="kn">from</span> <span class="nn">imblearn.under_sampling</span> <span class="kn">import</span> <span class="n">NearMiss</span>
<span class="kn">from</span> <span class="nn">imblearn.under_sampling</span> <span class="kn">import</span> <span class="n">RandomUnderSampler</span>
<span class="kn">from</span> <span class="nn">imblearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="kn">from</span> <span class="nn">imblearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report_imbalanced</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span><span class="p">,</span> <span class="n">tqdm_notebook</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="n">pd</span><span class="o">.</span><span class="n">options</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">max_rows</span> <span class="o">=</span> <span class="mi">500</span>
<span class="n">pd</span><span class="o">.</span><span class="n">options</span><span class="o">.</span><span class="n">display</span><span class="o">.</span><span class="n">max_columns</span> <span class="o">=</span> <span class="mi">500</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s2">"white"</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s2">"whitegrid"</span><span class="p">,</span> <span class="n">color_codes</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">cm</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span><span class="n">yhat_total</span><span class="p">,</span><span class="n">threshold</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">threshold</span><span class="p">)</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span><span class="n">yhat_total</span><span class="o">></span><span class="n">threshold</span><span class="p">)</span>
<span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span><span class="n">yhat_total</span><span class="o">></span><span class="n">threshold</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="n">specificity</span> <span class="o">=</span> <span class="p">(</span> <span class="n">tn</span> <span class="o">/</span> <span class="p">(</span><span class="n">tn</span><span class="o">+</span><span class="n">fp</span><span class="p">)</span> <span class="p">)</span>
<span class="n">sensitivity</span><span class="o">=</span> <span class="p">(</span> <span class="n">tp</span> <span class="o">/</span> <span class="p">(</span><span class="n">tp</span><span class="o">+</span><span class="n">fn</span><span class="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Positive Predictive Value'</span><span class="p">,</span><span class="nb">round</span><span class="p">(</span><span class="n">tp</span><span class="o">/</span><span class="p">(</span><span class="n">tp</span><span class="o">+</span><span class="n">fp</span><span class="p">),</span><span class="mi">2</span><span class="p">),</span><span class="s1">'Negative Predictive Value'</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">tn</span><span class="o">/</span><span class="p">(</span><span class="n">tn</span><span class="o">+</span><span class="n">fn</span><span class="p">),</span><span class="mi">2</span><span class="p">),</span> <span class="s1">' Specificty '</span><span class="p">,</span> <span class="n">specificity</span><span class="p">,</span> <span class="s1">'Sensitivity '</span><span class="p">,</span> <span class="n">sensitivity</span><span class="p">)</span>
<span class="n">disp</span> <span class="o">=</span> <span class="n">ConfusionMatrixDisplay</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="o">=</span><span class="n">cm</span><span class="p">,</span><span class="n">display_labels</span><span class="o">=</span><span class="p">[</span><span class="s1">'No Event'</span><span class="p">,</span><span class="s1">'Adverse Event'</span><span class="p">])</span>
<span class="n">disp</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Confusion Matrix"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">visible</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">point</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span><span class="n">yhat_total</span><span class="p">,</span><span class="n">t</span><span class="p">):</span>
<span class="n">specificity</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">sensitivity</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">t</span><span class="p">:</span>
<span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span> <span class="n">yhat_total</span><span class="o">></span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="n">specificity</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">tn</span> <span class="o">/</span> <span class="p">(</span><span class="n">tn</span><span class="o">+</span><span class="n">fp</span><span class="p">)</span> <span class="p">)</span>
<span class="n">sensitivity</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">tp</span> <span class="o">/</span> <span class="p">(</span><span class="n">tp</span><span class="o">+</span><span class="n">fn</span><span class="p">)</span> <span class="p">)</span>
<span class="k">return</span> <span class="n">t</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">specificity</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sensitivity</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">argmax</span><span class="p">()]</span>
<span class="k">def</span> <span class="nf">bootstrap</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">y_true_col</span><span class="p">,</span> <span class="n">y_score_col</span><span class="p">,</span> <span class="n">n_samples</span> <span class="o">=</span> <span class="mi">100</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">y_total</span><span class="p">,</span> <span class="n">yhat_total</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">y_true_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">y_score_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
<span class="n">fpr_boot</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">tpr_boot</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">aucs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span> <span class="n">yhat_total</span><span class="p">)</span>
<span class="n">thresh</span> <span class="o">=</span> <span class="n">point</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span> <span class="n">yhat_total</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
<span class="c1"># bootstrap for confidence interval</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">n_samples</span><span class="p">)):</span>
<span class="n">choices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">yhat_total</span><span class="p">)),</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">yhat_total</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span>
<span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_total</span><span class="p">[</span><span class="n">choices</span><span class="p">],</span><span class="n">yhat_total</span><span class="p">[</span><span class="n">choices</span><span class="p">])</span>
<span class="n">fpr_boot</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fpr</span><span class="p">)</span>
<span class="n">tpr_boot</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tpr</span><span class="p">)</span>
<span class="n">aucs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">low</span><span class="p">,</span><span class="n">high</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">aucs</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">aucs</span><span class="p">)</span><span class="o">*</span><span class="mf">1.96</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">aucs</span><span class="p">)</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">aucs</span><span class="p">)</span><span class="o">*</span><span class="mf">1.96</span>
<span class="n">lower_point</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">aucs</span><span class="p">,</span><span class="mf">2.5</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="n">higher_point</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">aucs</span><span class="p">,</span><span class="mf">97.5</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="n">mean_point</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">aucs</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">aucs</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">'mean: '</span><span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mean_point</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">aucs</span><span class="p">,</span><span class="mf">2.5</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">aucs</span><span class="p">,</span><span class="mf">2.5</span><span class="p">)],[</span><span class="mi">0</span><span class="p">,</span><span class="nb">max</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])],</span><span class="n">label</span> <span class="o">=</span> <span class="s1">'lower interval: '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">lower_point</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">aucs</span><span class="p">,</span><span class="mf">97.5</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">aucs</span><span class="p">,</span><span class="mf">97.5</span><span class="p">)],[</span><span class="mi">0</span><span class="p">,</span><span class="nb">max</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])],</span><span class="n">label</span> <span class="o">=</span> <span class="s1">'higher interval: '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">higher_point</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"AUC Histogram"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"AUC"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">):</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightblue'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span><span class="n">yhat_total</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'darkorange'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'ROC curve (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'navy'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'ROC Curve'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">plot_results_by_factor_by_target</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">factor</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="k">if</span> <span class="n">factor</span> <span class="o">==</span> <span class="s1">'All'</span><span class="p">:</span>
<span class="k">if</span> <span class="n">target</span> <span class="o">==</span> <span class="s1">'AS'</span><span class="p">:</span>
<span class="n">plot_results</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">factor_name</span> <span class="o">=</span> <span class="n">factor</span><span class="p">,</span> <span class="n">y_true_col</span> <span class="o">=</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="n">y_score_col</span> <span class="o">=</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">target</span> <span class="o">==</span> <span class="s1">'AI'</span><span class="p">:</span>
<span class="n">plot_results</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">factor_name</span> <span class="o">=</span> <span class="n">factor</span><span class="p">,</span> <span class="n">y_true_col</span> <span class="o">=</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="n">y_score_col</span> <span class="o">=</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">target</span> <span class="o">==</span> <span class="s1">'MR'</span><span class="p">:</span>
<span class="n">plot_results</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">factor_name</span> <span class="o">=</span> <span class="n">factor</span><span class="p">,</span> <span class="n">y_true_col</span> <span class="o">=</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="n">y_score_col</span> <span class="o">=</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">target</span> <span class="o">==</span> <span class="s1">'AS_AI_MR'</span><span class="p">:</span>
<span class="n">plot_results</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">factor_name</span> <span class="o">=</span> <span class="n">factor</span><span class="p">,</span> <span class="n">y_true_col</span> <span class="o">=</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="n">y_score_col</span> <span class="o">=</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">target</span> <span class="o">==</span> <span class="s1">'AS'</span><span class="p">:</span>
<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="n">factor</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">plot_results</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">factor_name</span> <span class="o">=</span> <span class="n">factor</span><span class="p">,</span> <span class="n">y_true_col</span> <span class="o">=</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="n">y_score_col</span> <span class="o">=</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">))</span>
<span class="k">if</span> <span class="n">target</span> <span class="o">==</span> <span class="s1">'AI'</span><span class="p">:</span>
<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="n">factor</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">plot_results</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">factor_name</span> <span class="o">=</span> <span class="n">factor</span><span class="p">,</span> <span class="n">y_true_col</span> <span class="o">=</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="n">y_score_col</span> <span class="o">=</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">))</span>
<span class="k">if</span> <span class="n">target</span> <span class="o">==</span> <span class="s1">'MR'</span><span class="p">:</span>
<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="n">factor</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">plot_results</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">factor_name</span> <span class="o">=</span> <span class="n">factor</span><span class="p">,</span> <span class="n">y_true_col</span> <span class="o">=</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="n">y_score_col</span> <span class="o">=</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">))</span>
<span class="k">if</span> <span class="n">target</span> <span class="o">==</span> <span class="s1">'AS_AI_MR'</span><span class="p">:</span>
<span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="n">factor</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">plot_results</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">factor_name</span> <span class="o">=</span> <span class="n">factor</span><span class="p">,</span> <span class="n">y_true_col</span> <span class="o">=</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="n">y_score_col</span> <span class="o">=</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">plot_results</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">factor_name</span><span class="p">,</span> <span class="n">y_true_col</span><span class="p">,</span> <span class="n">y_score_col</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="s1">'figures'</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="s1">'figures'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">level_name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">name</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">'/'</span><span class="p">,</span><span class="s1">'_'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">y_true_col</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="n">y_score_col</span><span class="p">]</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span> <span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">roc_auc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span> <span class="n">roc_auc</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># plot_roc_curve(fpr, tpr)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<span class="c1">#plt.savefig(f'figures/{df.name.replace('/','_')}_{y_true_col}_hist.png')</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_hist.png'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">factor_name</span><span class="p">,</span><span class="n">level_name</span><span class="p">,</span><span class="n">y_true_col</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">y_true_col</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">][</span><span class="n">y_score_col</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">"skyblue"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Pos"</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">y_true_col</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">][</span><span class="n">y_score_col</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s2">"red"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Neg"</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<span class="n">y_pred_optimal</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span> <span class="k">if</span> <span class="n">i</span> <span class="o">></span> <span class="n">optimal_threshold</span> <span class="k">else</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="n">y_score_col</span><span class="p">]]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_distplot.png'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">factor_name</span><span class="p">,</span><span class="n">level_name</span><span class="p">,</span><span class="n">y_true_col</span><span class="p">))</span>
<span class="c1">#plt.show()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="c1"># create heatmap</span>
<span class="n">cnf_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">y_scores</span><span class="p">))</span>
<span class="n">oddsratio</span><span class="p">,</span> <span class="n">pvalue</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">fisher_exact</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Odds Ratio is:'</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">oddsratio</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="s1">'P-Value is:'</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">pvalue</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="n">mtx</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">Table2x2</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">mtx</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
<span class="c1">#plt.savefig('figures/{}_{}_summary.png'.format(_name,y_true_col))</span>
<span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># name of classes</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">tick_marks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">tick_marks</span><span class="p">,</span> <span class="n">class_names</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">tick_marks</span><span class="p">,</span> <span class="n">class_names</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">),</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"YlGnBu"</span> <span class="p">,</span><span class="n">fmt</span><span class="o">=</span><span class="s1">'g'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">ymax</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_label_position</span><span class="p">(</span><span class="s2">"top"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Confusion matrix with threshold 0.5 [OR=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">oddsratio</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span><span class="si">}</span><span class="s1">,p=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">pvalue</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s1">]'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Actual label'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Predicted label'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_confusion_matrix_0.5.png'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">factor_name</span><span class="p">,</span><span class="n">level_name</span><span class="p">,</span><span class="n">y_true_col</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="n">cnf_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_optimal</span><span class="p">)</span>
<span class="n">oddsratio</span><span class="p">,</span> <span class="n">pvalue</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">fisher_exact</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Odds Ratio is:'</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">oddsratio</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="s1">'P-Value is:'</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">pvalue</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="n">mtx</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">Table2x2</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">mtx</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
<span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># name of classes</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">tick_marks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">tick_marks</span><span class="p">,</span> <span class="n">class_names</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">),</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"YlGnBu"</span> <span class="p">,</span><span class="n">fmt</span><span class="o">=</span><span class="s1">'g'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ymin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">ymax</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_label_position</span><span class="p">(</span><span class="s2">"top"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span> <span class="c1">#optimal threshold = (max(tpr-fpr))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Confusion matrix with optimal threshold </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">optimal_threshold</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s1"> [OR=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">oddsratio</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span><span class="si">}</span><span class="s1">,p=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">pvalue</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s1">]'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Actual label'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Predicted label'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_confusion_matrix_optimal.png'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">factor_name</span><span class="p">,</span><span class="n">level_name</span><span class="p">,</span><span class="n">y_true_col</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">bootstrap_stats</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">y_true_col</span><span class="p">,</span> <span class="n">y_score_col</span><span class="p">,</span> <span class="n">n_samples</span> <span class="o">=</span> <span class="mi">100</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">y_total</span><span class="p">,</span> <span class="n">yhat_total</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">y_true_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">y_score_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">fpr_boot</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">tpr_boot</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">aucs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">odds_ratios</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span> <span class="n">yhat_total</span><span class="p">)</span>
<span class="n">roc_auc_actual</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">)</span> <span class="c1"># from the complete data set</span>
<span class="n">roc_auc_actual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_actual</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
<span class="n">cnf_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_total</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">yhat_total</span><span class="p">))</span>
<span class="n">odds_ratio_actual</span><span class="p">,</span> <span class="n">pvalue</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">fisher_exact</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">)</span>
<span class="n">odds_ratio_actual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">odds_ratio_actual</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># set an arbitrary min size for bootstrapping</span>
<span class="k">if</span> <span class="n">n</span> <span class="o"><</span> <span class="mi">200</span><span class="p">:</span>
<span class="k">return</span> <span class="p">{</span><span class="s1">'n'</span><span class="p">:</span> <span class="n">n</span><span class="p">,</span> <span class="s1">'n_positives'</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y_total</span><span class="p">),</span> <span class="s1">'roc_auc_actual'</span><span class="p">:</span> <span class="n">roc_auc_actual</span><span class="p">,</span> <span class="s1">'AU-ROC (95% CI)'</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">'odds_ratio_actual'</span><span class="p">:</span> <span class="n">odds_ratio_actual</span><span class="p">,</span> <span class="s1">'OR (95% CI)'</span><span class="p">:</span> <span class="kc">None</span><span class="p">}</span>
<span class="c1"># bootstrap for confidence interval</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">n_samples</span><span class="p">)):</span>
<span class="n">choices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">yhat_total</span><span class="p">)),</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">yhat_total</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span>
<span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_total</span><span class="p">[</span><span class="n">choices</span><span class="p">],</span><span class="n">yhat_total</span><span class="p">[</span><span class="n">choices</span><span class="p">])</span>
<span class="n">cnf_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_total</span><span class="p">[</span><span class="n">choices</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">yhat_total</span><span class="p">[</span><span class="n">choices</span><span class="p">]))</span>
<span class="n">odds_ratio_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">fisher_exact</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">)</span>
<span class="n">fpr_boot</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fpr</span><span class="p">)</span>
<span class="n">tpr_boot</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tpr</span><span class="p">)</span>
<span class="n">aucs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">odds_ratios</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">odds_ratio_boot</span><span class="p">)</span>
<span class="n">auc_low</span><span class="p">,</span> <span class="n">auc_high</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">aucs</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">aucs</span><span class="p">)</span><span class="o">*</span><span class="mf">1.96</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">aucs</span><span class="p">)</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">aucs</span><span class="p">)</span><span class="o">*</span><span class="mf">1.96</span>
<span class="n">auc_lower_point</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">aucs</span><span class="p">,</span><span class="mf">2.5</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="n">auc_higher_point</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">aucs</span><span class="p">,</span><span class="mf">97.5</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="n">auc_mean_point</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">aucs</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">auc_lower_point</span><span class="p">,</span> <span class="n">auc_higher_point</span><span class="p">,</span> <span class="n">auc_low</span><span class="p">,</span> <span class="n">auc_high</span><span class="p">)</span>
<span class="n">odds_ratio_low</span><span class="p">,</span> <span class="n">odds_ratio_high</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">odds_ratios</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">odds_ratios</span><span class="p">)</span><span class="o">*</span><span class="mf">1.96</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">odds_ratios</span><span class="p">)</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">odds_ratios</span><span class="p">)</span><span class="o">*</span><span class="mf">1.96</span>
<span class="n">odds_ratio_lower_point</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">odds_ratios</span><span class="p">,</span><span class="mf">2.5</span><span class="p">),</span><span class="mi">1</span><span class="p">)</span>
<span class="n">odds_ratio_higher_point</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">odds_ratios</span><span class="p">,</span><span class="mf">97.5</span><span class="p">),</span><span class="mi">1</span><span class="p">)</span>
<span class="n">odds_ratio_mean_point</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">odds_ratios</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># plt.figure()</span>
<span class="c1"># lw = 2</span>
<span class="c1"># for i in range(0,n_samples):</span>
<span class="c1"># plt.plot(fpr_boot[i],tpr_boot[i], color='lightblue',lw=lw)</span>
<span class="c1"># fpr,tpr, _ = roc_curve(y_total,yhat_total)</span>
<span class="c1"># plt.plot(fpr, tpr, color='darkorange',lw=lw, label='ROC curve (AUROC = %0.3f)' % auc(fpr,tpr))</span>
<span class="c1"># plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="c1"># plt.xlim([0.0, 1.0])</span>
<span class="c1"># plt.ylim([0.0, 1.05])</span>
<span class="c1"># plt.xlabel('False Positive Rate')</span>
<span class="c1"># plt.ylabel('True Positive Rate')</span>
<span class="c1"># plt.title('ROC Curve')</span>
<span class="c1"># plt.legend(loc="lower right")</span>
<span class="c1"># plt.show()</span>
<span class="k">return</span> <span class="p">{</span><span class="s1">'n'</span><span class="p">:</span><span class="n">n</span><span class="p">,</span> <span class="s1">'n_positives'</span><span class="p">:</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y_total</span><span class="p">),</span> <span class="s1">'roc_auc_actual'</span><span class="p">:</span><span class="n">roc_auc_actual</span><span class="p">,</span> <span class="s1">'AU-ROC (95% CI)'</span><span class="p">:</span> <span class="p">[</span><span class="n">auc_lower_point</span><span class="p">,</span> <span class="n">auc_higher_point</span><span class="p">],</span> <span class="s1">'odds_ratio_actual'</span><span class="p">:</span><span class="n">odds_ratio_actual</span><span class="p">,</span> <span class="s1">'OR (95% CI)'</span><span class="p">:</span> <span class="p">[</span><span class="n">odds_ratio_lower_point</span><span class="p">,</span> <span class="n">odds_ratio_higher_point</span><span class="p">]}</span>
<span class="c1"># return (n, roc_auc_actual, odds_ratio_actual, lower_point, mean_point, higher_point)</span>
<span class="k">def</span> <span class="nf">get_odds_ratio</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
<span class="n">cnf_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="n">odds_ratio</span><span class="p">,</span> <span class="n">pvalue</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">fisher_exact</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">)</span>
<span class="k">return</span> <span class="n">odds_ratio</span>
<span class="c1"># Version of bootstrap which returns the fpr, tpr rather than plotting </span>
<span class="k">def</span> <span class="nf">boot</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr_boot</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">tpr_boot</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">aucs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">folder</span><span class="o">=</span><span class="kc">None</span>
<span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span> <span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span><span class="p">)</span>
<span class="n">thresh</span> <span class="o">=</span> <span class="n">point</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span><span class="p">,</span><span class="n">t</span><span class="p">)</span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="c1"># bootstrap for confidence interval</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">)):</span>
<span class="n">choices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">y_scores</span><span class="p">)),</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_scores</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span>
<span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">[</span><span class="n">choices</span><span class="p">],</span><span class="n">y_scores</span><span class="p">[</span><span class="n">choices</span><span class="p">])</span>
<span class="n">fpr_boot</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fpr</span><span class="p">)</span>
<span class="n">tpr_boot</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tpr</span><span class="p">)</span>
<span class="n">aucs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="k">return</span> <span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">aucs</span>
<span class="k">def</span> <span class="nf">query_sql</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">server</span> <span class="o">=</span> <span class="s1">'drtprd02jup01'</span><span class="p">,</span><span class="n">database</span> <span class="o">=</span> <span class="s1">'JupiterSCM'</span><span class="p">):</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
<span class="n">engine</span> <span class="o">=</span> <span class="n">create_engine</span><span class="p">(</span><span class="s2">"mssql+pyodbc://</span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2">?driver=SQL+Server"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">server</span><span class="p">,</span><span class="n">database</span><span class="p">),</span><span class="n">fast_executemany</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">conn</span> <span class="o">=</span> <span class="n">engine</span><span class="o">.</span><span class="n">connect</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Running query...'</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_sql</span><span class="p">(</span><span class="n">con</span><span class="o">=</span><span class="n">conn</span><span class="p">,</span> <span class="n">sql</span> <span class="o">=</span> <span class="n">query</span><span class="p">)</span>
<span class="n">conn</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">timer</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Query took </span><span class="si">%s</span><span class="s1"> minutes'</span> <span class="o">%</span> <span class="nb">round</span><span class="p">((</span><span class="n">end</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span><span class="o">/</span><span class="mi">60</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
<span class="k">return</span> <span class="n">result</span>
<span class="k">def</span> <span class="nf">generate_age_multisite</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="n">study_date</span><span class="p">,</span><span class="n">dob</span><span class="p">):</span>
<span class="n">df</span><span class="p">[</span><span class="n">study_date</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">study_date</span><span class="p">],</span><span class="n">errors</span><span class="o">=</span><span class="s1">'coerce'</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="n">dob</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">dob</span><span class="p">],</span><span class="n">errors</span><span class="o">=</span><span class="s1">'coerce'</span><span class="p">)</span>
<span class="n">DateofBirth_D</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">study_date</span><span class="p">]</span> <span class="o">-</span> <span class="n">df</span><span class="p">[</span><span class="n">dob</span><span class="p">])</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span><span class="o">//</span><span class="mi">365</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Number of rows where age not calculated:</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">PatientAge_Years</span><span class="o">.</span><span class="n">isnull</span><span class="p">())</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">df</span>
<span class="k">def</span> <span class="nf">balance_binary</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">desired_prevalence</span><span class="p">):</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">label</span><span class="p">]</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span>
<span class="n">binary_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bitwise_or</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">binary_y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">binary_mask</span><span class="p">]</span>
<span class="n">binary_X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">binary_mask</span><span class="p">]</span>
<span class="c1">#this is the ratio of minority:majority class, so if you want 30% prevalence of positive label sampling_strategy = 0.3/0.7</span>
<span class="n">rus</span> <span class="o">=</span> <span class="n">RandomUnderSampler</span><span class="p">(</span><span class="n">sampling_strategy</span><span class="o">=</span><span class="p">(</span><span class="n">desired_prevalence</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">desired_prevalence</span><span class="p">)))</span>
<span class="n">X_res</span><span class="p">,</span> <span class="n">y_res</span> <span class="o">=</span> <span class="n">rus</span><span class="o">.</span><span class="n">fit_resample</span><span class="p">(</span><span class="n">binary_X</span><span class="p">,</span> <span class="n">binary_y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">X_res</span>
<span class="k">def</span> <span class="nf">imbalance_binary</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">desired_prevalence</span><span class="p">,</span> <span class="n">minority_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">majority_class</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="n">majority_count</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">==</span><span class="n">majority_class</span><span class="p">]))</span>
<span class="n">X_resampled</span><span class="p">,</span> <span class="n">y_resampled</span> <span class="o">=</span> <span class="n">make_imbalance</span><span class="p">(</span>
<span class="n">df</span><span class="p">,</span>
<span class="n">df</span><span class="p">[</span><span class="n">label</span><span class="p">],</span>
<span class="n">sampling_strategy</span><span class="o">=</span><span class="p">{</span><span class="n">majority_class</span><span class="p">:</span><span class="nb">int</span><span class="p">(</span><span class="n">majority_count</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">desired_prevalence</span><span class="p">)),</span> <span class="n">minority_class</span><span class="p">:</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">*</span><span class="n">desired_prevalence</span><span class="p">)})</span>
<span class="k">return</span> <span class="n">X_resampled</span>
<span class="k">def</span> <span class="nf">adjust_prevalence</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">desired_prevalence</span><span class="p">,</span> <span class="n">minority_class</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="n">wanted_count</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">*</span><span class="n">desired_prevalence</span>
<span class="n">actual_count</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">label</span><span class="p">])[</span><span class="n">minority_class</span><span class="p">]</span>
<span class="n">ratio</span> <span class="o">=</span> <span class="n">wanted_count</span><span class="o">/</span><span class="n">actual_count</span>
<span class="k">if</span> <span class="n">ratio</span> <span class="o">>=</span> <span class="mi">1</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'using RandomUnderSampler'</span><span class="p">)</span>
<span class="n">new_df</span> <span class="o">=</span> <span class="n">balance_binary</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">desired_prevalence</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'using make_imbalance'</span><span class="p">)</span>
<span class="n">new_df</span> <span class="o">=</span> <span class="n">imbalance_binary</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">desired_prevalence</span><span class="p">,</span><span class="n">minority_class</span><span class="p">)</span>
<span class="k">return</span> <span class="n">new_df</span>
</pre></div>
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<h1 id="Notebook-order-to-get-to-this-point">Notebook order to get to this point<a class="anchor-link" href="#Notebook-order-to-get-to-this-point">¶</a></h1><ol>
<li>Generate an updated test dataset that removes bioprosthetic ECG-echo pairings and generates 1:1 pairing between ecg and echo, only including complete (all 3 labels present) echos and ECGs done 0 to 365 days prior to echo in question. One pair per patient, most recent period where pair is valid, with largest time difference between ECG and echo as tiebreaker (b/t 0 to 365 days)<br>
<code>/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Multivalvular_Label_Generation_No_Bioprosthesis_03_01_2022.ipynb</code></li>
</ol>
<hr>
<ol>
<li>Take that new list of ECG-echo pairs and run it on Dendrite to find the ECGs and preprocess the waveform arrays. Same for the tabular data. Then sanity check including looking at waveform/tabular output and going back into medical record to verify the label is correct, ecg is correct, and tabular data is correct.
<code>/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/ValveNet_JACC_Revision_Dataset_Generation_03_02_2022.ipynb</code></li>
</ol>
<hr>
<ol>
<li>Run the ValveNet_v2 engine on Dendrite (10.144.220.25). Running the CADnet branch of ValveNet_v2 files:</li>
</ol>
<ul>
<li>eval_AI_no_prosthetics.py</li>
<li>eval_AS_no_prosthetics.py</li>
<li>eval_MR_no_prosthetics.py</li>
<li><p>eval_AS_AI_MR_no_prosthetics.py
to generate the y,y_pred output for all 4 labels</p>
<p><code>as_path = os.path.join(box_path_prefix, 'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv')</code>
<code>ai_path = os.path.join(box_path_prefix, 'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv)</code>
<code>mr_path = os.path.join(box_path_prefix, 'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv')</code>
<code>as_ai_mr_path = os.path.join(box_path_prefix, 'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv')</code></p>
</li>
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<li>And now we're ready to run the post-hoc analyses in this notebook</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">user</span> <span class="o">=</span> <span class="s1">'pae2'</span>
<span class="n">box_path_prefix</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'/Users/</span><span class="si">{</span><span class="n">user</span><span class="si">}</span><span class="s1">'</span>
<span class="c1"># original_test_27k = pd.read_csv(os.path.join(box_path_prefix, 'Box/Heart Failure Analytics/Data/MuseLabelGeneration/27k_test_metadata_no_prev_adjustment.csv'))</span>
<span class="n">original_test_27k</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/JACC_REVISIONS_27k_test_metadata_no_prev_adjustment_new_ref.csv'</span> <span class="p">))</span>
<span class="n">original_test_27k</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">original_test_27k</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">original_test_27k</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">columns_needed</span> <span class="o">=</span> <span class="p">[</span>
<span class="s1">'filename'</span><span class="p">,</span>
<span class="s1">'AcquisitionDateTime_DT'</span><span class="p">,</span>
<span class="s1">'TestID'</span><span class="p">,</span>
<span class="s1">'PatientID'</span><span class="p">,</span>
<span class="s1">'DateofBirth_D'</span><span class="p">,</span>
<span class="s1">'Gender'</span><span class="p">,</span>
<span class="s1">'Race'</span><span class="p">,</span>
<span class="s1">'aortic_stenosis_label_four_grade'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_four_grade'</span><span class="p">,</span>
<span class="s1">'mitral_regurgitation_label_four_grade'</span><span class="p">,</span>
<span class="s1">'aortic_stenosis_label_binary'</span><span class="p">,</span>
<span class="s1">'mitral_regurgitation_label_binary'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_binary'</span><span class="p">,</span>
<span class="s1">'aortic_stenosis_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'mitral_regurgitation_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'AS_AI_MR_label_binary'</span><span class="p">,</span>
<span class="s1">'AS_AI_MR_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'QRSDuration'</span><span class="p">,</span>
<span class="s1">'RIGHT BUNDLE BRANCH BLOCK'</span><span class="p">,</span>
<span class="s1">'LEFT BUNDLE BRANCH BLOCK'</span><span class="p">,</span>
<span class="s1">'days_since_echo'</span>
<span class="p">]</span>
<span class="n">test_27k</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISION_test_df_newest_ecg_per_pt_tabular_metadata.csv'</span><span class="p">))</span>
<span class="c1">#print(test_27k.columns.tolist())</span>
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<pre>(26606, 161)
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (30,53,57,58,66) have mixed types.Specify dtype option on import or set low_memory=False.
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
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<h1 id="Adding-Data">Adding Data<a class="anchor-link" href="#Adding-Data">¶</a></h1>
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<h1 id="New-file-missing-LBBB-and-RBBB-so-we-need-to-add-those">New file missing LBBB and RBBB so we need to add those<a class="anchor-link" href="#New-file-missing-LBBB-and-RBBB-so-we-need-to-add-those">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tabular_labels</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/Muse_Tabular_Labels_Master_04_24_2021.csv'</span><span class="p">)</span>
<span class="n">selection</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'TestID'</span><span class="p">,</span><span class="s1">'LEFT BUNDLE BRANCH BLOCK'</span><span class="p">,</span> <span class="s1">'RIGHT BUNDLE BRANCH BLOCK'</span><span class="p">]</span>
<span class="n">test_27k</span> <span class="o">=</span> <span class="n">test_27k</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">tabular_labels</span><span class="p">[</span><span class="n">selection</span><span class="p">],</span> <span class="n">how</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span><span class="n">on</span><span class="o">=</span><span class="s1">'TestID'</span><span class="p">,</span> <span class="n">validate</span><span class="o">=</span><span class="s1">'1:1'</span><span class="p">)</span>
<span class="n">test_27k</span> <span class="o">=</span> <span class="n">test_27k</span><span class="p">[</span><span class="n">columns_needed</span><span class="p">]</span>
<span class="n">test_27k</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">test_27k</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">test_27k</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># test_27k.groupby(['Ventricular_Pacing_Present','Poor_Data_Quality_Present']).size()</span>
<span class="c1"># # We might need to reindex these rows based on the original DF </span>
<span class="c1"># test_27k = original_test_27k[['filename']].merge(test_27k, on = 'filename', suffixes = ('','_new'), how = 'inner', validate = '1:1')</span>
<span class="c1"># test_27k</span>
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<pre>(21048, 22)
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<h1 id="Backfill-four_grade-columns">Backfill four_grade columns<a class="anchor-link" href="#Backfill-four_grade-columns">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_27k</span><span class="p">[[</span><span class="s1">'aortic_stenosis_label_four_grade'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_four_grade'</span><span class="p">,</span>
<span class="s1">'mitral_regurgitation_label_four_grade'</span><span class="p">]]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">value_counts</span><span class="p">)</span>
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<th>aortic_stenosis_label_four_grade</th>
<th>aortic_insufficiency_label_four_grade</th>
<th>mitral_regurgitation_label_four_grade</th>
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<th>0.0</th>
<td>15424</td>
<td>17088</td>
<td>13770</td>
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<th>1.0</th>
<td>1166</td>
<td>2348</td>
<td>4902</td>
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<td>284</td>
<td>166</td>
<td>601</td>
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<th>3.0</th>
<td>623</td>
<td>30</td>
<td>141</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Backfill the four_grade columns</span>
<span class="n">fours</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'aortic_stenosis_label_four_grade'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_four_grade'</span><span class="p">,</span>
<span class="s1">'mitral_regurgitation_label_four_grade'</span><span class="p">]</span>
<span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">fours</span><span class="p">:</span>
<span class="n">test_27k</span><span class="p">[</span><span class="n">column</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_27k</span><span class="p">[</span><span class="n">column</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_27k</span><span class="p">[[</span><span class="s1">'aortic_stenosis_label_four_grade'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_four_grade'</span><span class="p">,</span>
<span class="s1">'mitral_regurgitation_label_four_grade'</span><span class="p">]]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">value_counts</span><span class="p">)</span>
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<th>aortic_stenosis_label_four_grade</th>
<th>aortic_insufficiency_label_four_grade</th>
<th>mitral_regurgitation_label_four_grade</th>
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<th>0.0</th>
<td>18975</td>
<td>18504</td>
<td>15404</td>
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<td>1166</td>
<td>2348</td>
<td>4902</td>
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<td>284</td>
<td>166</td>
<td>601</td>
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<td>623</td>
<td>30</td>
<td>141</td>
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<h1 id="Add-PatientAge_Years">Add PatientAge_Years<a class="anchor-link" href="#Add-PatientAge_Years">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_27k</span> <span class="o">=</span> <span class="n">generate_age_multisite</span><span class="p">(</span><span class="n">test_27k</span><span class="p">,</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">,</span><span class="s1">'DateofBirth_D'</span><span class="p">)</span>
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<pre>Number of rows where age not calculated:36
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<h1 id="Old-test-set-counts-vs-new-test-set">Old test set counts vs new test set<a class="anchor-link" href="#Old-test-set-counts-vs-new-test-set">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'four'</span> <span class="ow">in</span> <span class="n">x</span><span class="p">,</span> <span class="n">test_27k</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
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<pre>['aortic_stenosis_label_four_grade',
'aortic_insufficiency_label_four_grade',
'mitral_regurgitation_label_four_grade']</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">original_test_27k</span><span class="p">[[</span><span class="s1">'aortic_stenosis_label_binary_backfilled'</span><span class="p">,</span> <span class="s1">'mitral_regurgitation_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_binary_backfilled'</span><span class="p">]]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">value_counts</span><span class="p">)</span>
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<th>aortic_stenosis_label_binary_backfilled</th>
<th>mitral_regurgitation_label_binary_backfilled</th>
<th>aortic_insufficiency_label_binary_backfilled</th>
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<tbody>
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<th>0</th>
<td>25460</td>
<td>25449</td>
<td>26271</td>
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<th>1</th>
<td>1146</td>
<td>1157</td>
<td>335</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_27k</span><span class="p">[[</span><span class="s1">'aortic_stenosis_label_binary_backfilled'</span><span class="p">,</span> <span class="s1">'mitral_regurgitation_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_binary_backfilled'</span><span class="p">]]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">value_counts</span><span class="p">)</span>
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<th>aortic_insufficiency_label_binary_backfilled</th>
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<th>0.0</th>
<td>20141</td>
<td>20306</td>
<td>20852</td>
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<th>1.0</th>
<td>907</td>
<td>742</td>
<td>196</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_27k</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_tabular_metadata_additional_backfill.csv'</span><span class="p">),</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test_27k.to_csv(os.path.join(box_path_prefix, 'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/27k_test_metadata_no_prev_adjustment_newest_ecg_prosthetics_removed_metadata_reindexed.csv'))</span>
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<h1 id="Append-model-scores-to-the-data">Append model scores to the data<a class="anchor-link" href="#Append-model-scores-to-the-data">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Append model scores to the data</span>
<span class="c1"># Original</span>
<span class="n">as_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/Eval_Aortic_Stenosis_2022-01-18--04:38:05/y_y_pred_roc_0.8626.csv'</span><span class="p">)</span>
<span class="n">ai_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/Eval_Aortic_Insufficiency_2022-01-18--04:38:37/y_y_pred_roc_0.7661.csv'</span><span class="p">)</span>
<span class="n">mr_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/Eval_Mitral_Regurgitation_2022-01-18--04:39:09/y_y_pred_roc_0.8237.csv'</span><span class="p">)</span>
<span class="n">as_ai_mr_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/Eval_AS_AI_MR_Combination_2022-01-18--04:35:19/y_y_pred_roc_0.8281.csv'</span><span class="p">)</span>
<span class="n">as_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">as_path</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y_AS'</span><span class="p">,</span><span class="s1">'y_pred_AS_proba'</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'y_AS'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">})</span>
<span class="n">ai_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">ai_path</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y_AI'</span><span class="p">,</span><span class="s1">'y_pred_AI_proba'</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'y_AI'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">})</span>
<span class="n">mr_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">mr_path</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y_MR'</span><span class="p">,</span><span class="s1">'y_pred_MR_proba'</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'y_MR'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">})</span>
<span class="n">as_ai_mr_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">as_ai_mr_path</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y_AS_AI_MR'</span><span class="p">,</span><span class="s1">'y_pred_AS_AI_MR_proba'</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'y_AS_AI_M'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">})</span>
<span class="n">original_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_pred</span><span class="p">,</span> <span class="n">ai_pred</span><span class="p">,</span> <span class="n">mr_pred</span><span class="p">,</span> <span class="n">as_ai_mr_pred</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">original_pred</span><span class="p">[</span><span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">]</span> <span class="o">=</span> <span class="n">original_pred</span><span class="p">[</span><span class="s1">'y_pred_AS_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">original_pred</span><span class="p">[</span><span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">]</span> <span class="o">=</span> <span class="n">original_pred</span><span class="p">[</span><span class="s1">'y_pred_AI_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">original_pred</span><span class="p">[</span><span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">]</span> <span class="o">=</span> <span class="n">original_pred</span><span class="p">[</span><span class="s1">'y_pred_MR_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">original_pred</span><span class="p">[</span><span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">]</span> <span class="o">=</span> <span class="n">original_pred</span><span class="p">[</span><span class="s1">'y_pred_AS_AI_MR_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="c1"># Bioprosthesis removed</span>
<span class="n">as_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv'</span><span class="p">)</span>
<span class="n">ai_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv'</span><span class="p">)</span>
<span class="n">mr_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv'</span><span class="p">)</span>
<span class="n">as_ai_mr_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv'</span><span class="p">)</span>
<span class="n">as_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">as_path</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y_AS'</span><span class="p">,</span><span class="s1">'y_pred_AS_proba'</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'y_AS'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">})</span>
<span class="n">ai_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">ai_path</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y_AI'</span><span class="p">,</span><span class="s1">'y_pred_AI_proba'</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'y_AI'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">})</span>
<span class="n">mr_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">mr_path</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y_MR'</span><span class="p">,</span><span class="s1">'y_pred_MR_proba'</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'y_MR'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">})</span>
<span class="n">as_ai_mr_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">as_ai_mr_path</span><span class="p">,</span> <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y_AS_AI_MR'</span><span class="p">,</span><span class="s1">'y_pred_AS_AI_MR_proba'</span><span class="p">],</span> <span class="n">dtype</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'y_AS_AI_M'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">})</span>
<span class="n">pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_pred</span><span class="p">,</span> <span class="n">ai_pred</span><span class="p">,</span> <span class="n">mr_pred</span><span class="p">,</span> <span class="n">as_ai_mr_pred</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">pred</span><span class="p">[</span><span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="s1">'y_pred_AS_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">pred</span><span class="p">[</span><span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="s1">'y_pred_AI_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">pred</span><span class="p">[</span><span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="s1">'y_pred_MR_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">pred</span><span class="p">[</span><span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred</span><span class="p">[</span><span class="s1">'y_pred_AS_AI_MR_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">test_pred</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">test_27k</span><span class="p">,</span> <span class="n">pred</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">lstFiles</span> <span class="o">=</span> <span class="p">[</span><span class="n">as_path</span><span class="p">,</span><span class="n">ai_path</span><span class="p">,</span><span class="n">mr_path</span><span class="p">,</span><span class="n">as_ai_mr_path</span><span class="p">]</span>
<span class="n">study_name</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">lstFiles</span><span class="p">]</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">original_pred</span><span class="p">[[</span><span class="s1">'y_AS'</span><span class="p">,</span><span class="s1">'y_AI'</span><span class="p">,</span><span class="s1">'y_MR'</span><span class="p">,</span><span class="s1">'y_AS_AI_MR'</span><span class="p">]]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">value_counts</span><span class="p">)</span>
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<th>y_AS</th>
<th>y_AI</th>
<th>y_MR</th>
<th>y_AS_AI_MR</th>
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<th>0.0</th>
<td>25460</td>
<td>26271</td>
<td>25449</td>
<td>24307</td>
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<th>1.0</th>
<td>1146</td>
<td>335</td>
<td>1157</td>
<td>2299</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pred</span><span class="p">[[</span><span class="s1">'y_AS'</span><span class="p">,</span><span class="s1">'y_AI'</span><span class="p">,</span><span class="s1">'y_MR'</span><span class="p">,</span><span class="s1">'y_AS_AI_MR'</span><span class="p">]]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">value_counts</span><span class="p">)</span>
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<th>0</th>
<td>20141</td>
<td>20852</td>
<td>20306</td>
<td>19404</td>
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<th>1</th>
<td>907</td>
<td>196</td>
<td>742</td>
<td>1644</td>
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<h1 id="Generate-Model-Output-Summary-Table">Generate Model Output Summary Table<a class="anchor-link" href="#Generate-Model-Output-Summary-Table">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">as_summary</span> <span class="o">=</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'aortic_stenosis_label_four_grade'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)[</span><span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.975</span><span class="p">])</span>
<span class="n">mr_summary</span> <span class="o">=</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'mitral_regurgitation_label_four_grade'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)[</span><span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.975</span><span class="p">])</span>
<span class="n">ai_summary</span> <span class="o">=</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'aortic_insufficiency_label_four_grade'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)[</span><span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.975</span><span class="p">])</span>
<span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Insufficiency'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_summary</span><span class="p">,</span><span class="n">mr_summary</span><span class="p">,</span><span class="n">ai_summary</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">summary_stats</span> <span class="o">=</span> <span class="n">summary_stats</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">index</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">'None/trace*'</span><span class="p">,</span><span class="mi">1</span><span class="p">:</span><span class="s1">'Mild'</span><span class="p">,</span><span class="mi">2</span><span class="p">:</span><span class="s1">'Moderate'</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="s1">'Severe'</span><span class="p">})</span>
<span class="n">summary_stats</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'summary_stats.csv'</span><span class="p">)</span>
<span class="c1">#output_cols = ['y_pred_AS_proba', 'y_pred_AI_proba', 'y_pred_MR_proba', 'y_pred_AS_AI_MR_proba']</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">summary_stats</span>
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<th></th>
<th colspan="8" halign="left">Aortic Stenosis</th>
<th colspan="8" halign="left">Mitral Regurgitation</th>
<th colspan="8" halign="left">Aortic Insufficiency</th>
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<th></th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>2.5%</th>
<th>50%</th>
<th>97.5%</th>
<th>max</th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>2.5%</th>
<th>50%</th>
<th>97.5%</th>
<th>max</th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>2.5%</th>
<th>50%</th>
<th>97.5%</th>
<th>max</th>
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<th>None/trace*</th>
<td>18975.0</td>
<td>0.271788</td>
<td>0.219771</td>
<td>0.001363</td>
<td>0.012322</td>
<td>0.217372</td>
<td>0.775906</td>
<td>0.971297</td>
<td>15404.0</td>
<td>0.304829</td>
<td>0.162789</td>
<td>0.026033</td>
<td>0.085917</td>
<td>0.262900</td>
<td>0.672151</td>
<td>0.853389</td>
<td>18504.0</td>
<td>0.320664</td>
<td>0.134718</td>
<td>0.057341</td>
<td>0.159497</td>
<td>0.277066</td>
<td>0.660512</td>
<td>0.893703</td>
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<th>Mild</th>
<td>1166.0</td>
<td>0.536695</td>
<td>0.218282</td>
<td>0.017030</td>
<td>0.115173</td>
<td>0.564480</td>
<td>0.880108</td>
<td>0.976182</td>
<td>4902.0</td>
<td>0.436290</td>
<td>0.182823</td>
<td>0.047727</td>
<td>0.132331</td>
<td>0.433334</td>
<td>0.739360</td>
<td>0.850087</td>
<td>2348.0</td>
<td>0.410783</td>
<td>0.155863</td>
<td>0.131194</td>
<td>0.198045</td>
<td>0.374805</td>
<td>0.736602</td>
<td>0.940560</td>
</tr>
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<th>Moderate</th>
<td>284.0</td>
<td>0.590465</td>
<td>0.219712</td>
<td>0.044428</td>
<td>0.118110</td>
<td>0.627991</td>
<td>0.900383</td>
<td>0.928993</td>
<td>601.0</td>
<td>0.564647</td>
<td>0.157370</td>
<td>0.083580</td>
<td>0.192651</td>
<td>0.610530</td>
<td>0.768853</td>
<td>0.823377</td>
<td>166.0</td>
<td>0.476017</td>
<td>0.165181</td>
<td>0.129977</td>
<td>0.207978</td>
<td>0.489910</td>
<td>0.783583</td>
<td>0.869834</td>
</tr>
<tr>
<th>Severe</th>
<td>623.0</td>
<td>0.701876</td>
<td>0.179399</td>
<td>0.142367</td>
<td>0.271392</td>
<td>0.741752</td>
<td>0.934518</td>
<td>0.966101</td>
<td>141.0</td>
<td>0.588982</td>
<td>0.148862</td>
<td>0.098538</td>
<td>0.217909</td>
<td>0.637259</td>
<td>0.768774</td>
<td>0.800409</td>
<td>30.0</td>
<td>0.565792</td>
<td>0.157375</td>
<td>0.235375</td>
<td>0.254424</td>
<td>0.590199</td>
<td>0.808102</td>
<td>0.858896</td>
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<h1 id="Exploring-binary-label-vs.-y-discrepancies">Exploring binary label vs. y discrepancies<a class="anchor-link" href="#Exploring-binary-label-vs.-y-discrepancies">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_pred</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'y_AS'</span><span class="p">,</span><span class="s1">'y_AI'</span><span class="p">,</span><span class="s1">'y_MR'</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
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<pre>y_AS y_AI y_MR
0 0 0 19404
1 586
1 0 124
1 27
1 0 0 742
1 120
1 0 36
1 9
dtype: int64</pre>
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<h3 id="These-seem-to-be-consistent-(except-for-1-case)">These seem to be consistent (except for 1 case)<a class="anchor-link" href="#These-seem-to-be-consistent-(except-for-1-case)">¶</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_pred</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'aortic_stenosis_label_binary_backfilled'</span><span class="p">,</span><span class="s1">'aortic_stenosis_label_four_grade'</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
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<pre>aortic_stenosis_label_binary_backfilled aortic_stenosis_label_four_grade
0.0 0.0 18975
1.0 1166
1.0 2.0 284
3.0 623
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_pred</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'aortic_stenosis_label_binary_backfilled'</span><span class="p">,</span><span class="s1">'y_AS'</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
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<pre>aortic_stenosis_label_binary_backfilled y_AS
0.0 0 20141
1.0 1 907
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_pred</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'mitral_regurgitation_label_binary_backfilled'</span><span class="p">,</span><span class="s1">'y_MR'</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
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<pre>mitral_regurgitation_label_binary_backfilled y_MR
0.0 0 20306
1.0 1 742
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_pred</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'aortic_insufficiency_label_binary_backfilled'</span><span class="p">,</span><span class="s1">'y_AI'</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
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<pre>aortic_insufficiency_label_binary_backfilled y_AI
0.0 0 20852
1.0 1 196
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_pred</span><span class="p">[[</span><span class="s1">'y_AS'</span><span class="p">,</span> <span class="s1">'y_pred_AS_proba'</span><span class="p">,</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="s1">'y_pred_AI_proba'</span><span class="p">,</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="s1">'y_pred_MR_proba'</span><span class="p">,</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="s1">'y_pred_AS_AI_MR_proba'</span><span class="p">]]</span><span class="o">.</span><span class="n">apply</span><span class="p">([</span><span class="nb">min</span><span class="p">,</span> <span class="nb">max</span><span class="p">])</span>
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<style scoped>
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<th>y_AS</th>
<th>y_pred_AS_proba</th>
<th>y_AI</th>
<th>y_pred_AI_proba</th>
<th>y_MR</th>
<th>y_pred_MR_proba</th>
<th>y_AS_AI_MR</th>
<th>y_pred_AS_AI_MR_proba</th>
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<th>min</th>
<td>0</td>
<td>-6.597054</td>
<td>0</td>
<td>-2.799697</td>
<td>0</td>
<td>-3.622027</td>
<td>0</td>
<td>-4.252630</td>
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<th>max</th>
<td>1</td>
<td>3.713206</td>
<td>1</td>
<td>2.761508</td>
<td>1</td>
<td>1.761434</td>
<td>1</td>
<td>3.549835</td>
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<h1 id="Generate-Summary-Table-for-Race-/-Ethnicity">Generate Summary Table for Race / Ethnicity<a class="anchor-link" href="#Generate-Summary-Table-for-Race-/-Ethnicity">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">race_ethnicity</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/RACE_ETHNICITY/west_empi_race_ethnicity_simplified_version.csv'</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">race_ethnicity</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">race_ethnicity</span> <span class="o">=</span> <span class="n">race_ethnicity</span><span class="p">[</span><span class="n">race_ethnicity</span><span class="p">[</span><span class="s1">'EMPI'</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">race_ethnicity</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">race_ethnicity</span><span class="p">[</span><span class="s1">'EMPI'</span><span class="p">]</span> <span class="o">=</span> <span class="n">race_ethnicity</span><span class="p">[</span><span class="s1">'EMPI'</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="n">race_ethnicity</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">syngo</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/RACE_ETHNICITY/SYNGO study with race and ethnicity per patient aggregation and simplification.csv'</span><span class="p">))</span>
<span class="n">syngo</span><span class="p">[</span><span class="s1">'PATIENT_ID'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">syngo</span><span class="p">[</span><span class="s1">'PATIENT_ID'</span><span class="p">],</span> <span class="n">errors</span> <span class="o">=</span> <span class="s1">'coerce'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">syngo</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">syngo</span> <span class="o">=</span> <span class="n">syngo</span><span class="p">[</span><span class="n">syngo</span><span class="p">[</span><span class="s1">'PATIENT_ID'</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">syngo</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">syngo</span><span class="p">[</span><span class="s1">'PATIENT_ID'</span><span class="p">]</span> <span class="o">=</span> <span class="n">syngo</span><span class="p">[</span><span class="s1">'PATIENT_ID'</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="n">syngo</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<h3 id="Append-Race/Ethnicity-Data">Append Race/Ethnicity Data<a class="anchor-link" href="#Append-Race/Ethnicity-Data">¶</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test.set_index(['PatientID']).join(syngo.set_index('PATIENT_ID'), how = 'inner') # 23835</span>
<span class="c1"># test_race_ethnicity = test.drop('Race', axis = 1).set_index(['PatientID']).join(race_ethnicity.set_index('EMPI'), rsuffix = '_df', how = 'inner') # more rows with this join 25054</span>
<span class="n">test_race_ethnicity</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">left</span> <span class="o">=</span> <span class="n">test_pred</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">'Race'</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">),</span> <span class="n">left_on</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'PatientID'</span><span class="p">],</span> <span class="n">right</span> <span class="o">=</span> <span class="n">race_ethnicity</span><span class="p">,</span> <span class="n">right_on</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'EMPI'</span><span class="p">],</span> <span class="n">how</span> <span class="o">=</span> <span class="s1">'left'</span><span class="p">)</span>
<span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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<pre>21048</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Race'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
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<pre>Race
ASIAN 485
BLACK/AFRICAN AMERICAN 2559
DECLINED 1302
NATIVEAM 26
OTHER 7590
WHITE 7807
NaN 1279
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Ethnicity'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
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<pre>Ethnicity
NOT HISPANIC OR LATINO 5290
SPANISH/HISPANIC 4034
UNKNOWN 10370
NaN 1354
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">anna_race</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span><span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/NYP_Lawrence/propensity-score-matching-main/race_test_combined_anna upd.csv'</span><span class="p">))</span>
<span class="n">anna_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'filename'</span><span class="p">,</span><span class="s1">'RACE'</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_race_ethnicity</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">anna_race</span><span class="p">[</span><span class="n">anna_cols</span><span class="p">],</span><span class="n">how</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span><span class="n">on</span><span class="o">=</span><span class="s1">'filename'</span><span class="p">)</span>
<span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'Race'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'RACE'</span><span class="p">]</span>
<span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'RACE'</span><span class="p">],</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Race'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
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<pre>Race
ASIAN 485
BLACK/AFRICAN AMERICAN 3381
DECLINED 1203
NATIVEAM 26
OTHER 6347
WHITE 8589
NaN 1017
dtype: int64</pre>
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<h1 id="Compute-Age/Sex-Stats-Using-Bootstrap">Compute Age/Sex Stats Using Bootstrap<a class="anchor-link" href="#Compute-Age/Sex-Stats-Using-Bootstrap">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">female_index</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[</span><span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">Gender</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
<span class="n">male_index</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[</span><span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">Gender</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age18to60</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">0</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">60</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age60to70</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">61</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">70</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age70to80</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">71</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">80</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age80plus</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">81</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">130</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age18to65</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">0</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">65</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age65plus</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">65</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">130</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="k">def</span> <span class="nf">new_age_column</span><span class="p">(</span><span class="n">row</span><span class="p">):</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">row</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">60</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'18-60'</span>
<span class="k">elif</span> <span class="n">row</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">61</span> <span class="ow">and</span> <span class="n">row</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">70</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'60-70'</span>
<span class="k">elif</span> <span class="n">row</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">71</span> <span class="ow">and</span> <span class="n">row</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">80</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'70-80'</span>
<span class="k">elif</span> <span class="n">row</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">81</span> <span class="ow">and</span> <span class="n">row</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">120</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'80+'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [32]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'Patient_Age_Group'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">new_age_column</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [33]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'Patient_Age_Group'</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">()</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[33]:</div>
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<pre><AxesSubplot:></pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">factor_var</span> <span class="o">=</span> <span class="s1">'Patient_Age_Group'</span>
<span class="n">target_var</span> <span class="o">=</span> <span class="s1">'AS_AI_MR'</span>
<span class="n">plot_results_by_factor_by_target</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">factor_var</span><span class="p">,</span> <span class="n">target_var</span><span class="p">)</span>
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<pre>18-60
AUROC is: 0.81
Threshold value is: 0.27530762692370364
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 10.7 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>10.669</td> <td></td> <td>7.963</td> <td>14.294</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.367</td> <td>0.149</td> <td>2.075</td> <td>2.660</td> <td>0.000</td>
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<th>Risk ratio</th> <td>1.477</td> <td></td> <td>1.338</td> <td>1.630</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>0.390</td> <td>0.050</td> <td>0.291</td> <td>0.488</td> <td>0.000</td>
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<pre>Odds Ratio is: 9.1 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>9.053</td> <td></td> <td>6.581</td> <td>12.454</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.203</td> <td>0.163</td> <td>1.884</td> <td>2.522</td> <td>0.000</td>
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<th>Risk ratio</th> <td>3.189</td> <td></td> <td>2.499</td> <td>4.070</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.160</td> <td>0.124</td> <td>0.916</td> <td>1.404</td> <td>0.000</td>
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<pre>60-70
AUROC is: 0.79
Threshold value is: 0.41694409320792375
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 6.1 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>6.130</td> <td></td> <td>4.735</td> <td>7.935</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>1.813</td> <td>0.132</td> <td>1.555</td> <td>2.071</td> <td>0.000</td>
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<th>Risk ratio</th> <td>1.983</td> <td></td> <td>1.711</td> <td>2.297</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>0.685</td> <td>0.075</td> <td>0.537</td> <td>0.832</td> <td>0.000</td>
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<pre>Odds Ratio is: 7.5 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
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<th>Odds ratio</th> <td>7.472</td> <td></td> <td>5.534</td> <td>10.089</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.011</td> <td>0.153</td> <td>1.711</td> <td>2.311</td> <td>0.000</td>
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<th>Risk ratio</th> <td>3.089</td> <td></td> <td>2.454</td> <td>3.889</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.128</td> <td>0.117</td> <td>0.898</td> <td>1.358</td> <td>0.000</td>
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<pre>70-80
AUROC is: 0.72
Threshold value is: 0.5144382945813603
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 3.8 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>3.751</td> <td></td> <td>3.031</td> <td>4.642</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>1.322</td> <td>0.109</td> <td>1.109</td> <td>1.535</td> <td>0.000</td>
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<th>Risk ratio</th> <td>2.079</td> <td></td> <td>1.798</td> <td>2.405</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>0.732</td> <td>0.074</td> <td>0.587</td> <td>0.877</td> <td>0.000</td>
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<pre>Odds Ratio is: 3.9 P-Value is: 0.0
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<th>Odds ratio</th> <td>3.916</td> <td></td> <td>3.173</td> <td>4.832</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>1.365</td> <td>0.107</td> <td>1.155</td> <td>1.575</td> <td>0.000</td>
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<th>Risk ratio</th> <td>2.057</td> <td></td> <td>1.789</td> <td>2.365</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>0.721</td> <td>0.071</td> <td>0.582</td> <td>0.861</td> <td>0.000</td>
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<pre>80+
AUROC is: 0.73
Threshold value is: 0.6513456467300284
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 5.0 P-Value is: 0.0
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<th>Odds ratio</th> <td>4.956</td> <td></td> <td>3.792</td> <td>6.478</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>1.601</td> <td>0.137</td> <td>1.333</td> <td>1.868</td> <td>0.000</td>
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<th>Risk ratio</th> <td>3.617</td> <td></td> <td>2.854</td> <td>4.583</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.286</td> <td>0.121</td> <td>1.049</td> <td>1.522</td> <td>0.000</td>
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<pre>Odds Ratio is: 4.3 P-Value is: 0.0
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<th>Odds ratio</th> <td>4.265</td> <td></td> <td>3.551</td> <td>5.124</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>1.451</td> <td>0.094</td> <td>1.267</td> <td>1.634</td> <td>0.000</td>
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<th>Risk ratio</th> <td>2.183</td> <td></td> <td>1.938</td> <td>2.458</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>0.781</td> <td>0.061</td> <td>0.662</td> <td>0.900</td> <td>0.000</td>
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<pre>nan
AUROC is: 0.89
Threshold value is: 0.5335808680976182
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: inf P-Value is: 0.002
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<th>Odds ratio</th> <td>36.250</td> <td></td> <td>1.786</td> <td>735.877</td> <td>0.019</td>
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<th>Log odds ratio</th> <td>3.590</td> <td>1.536</td> <td>0.580</td> <td>6.601</td> <td>0.019</td>
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<th>Risk ratio</th> <td>8.622</td> <td></td> <td>0.610</td> <td>121.815</td> <td>0.111</td>
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<th>Log risk ratio</th> <td>2.154</td> <td>1.351</td> <td>-0.494</td> <td>4.803</td> <td>0.111</td>
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<pre>Odds Ratio is: 20.7 P-Value is: 0.008
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<th>Odds ratio</th> <td>20.667</td> <td></td> <td>1.953</td> <td>218.712</td> <td>0.012</td>
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<th>Log odds ratio</th> <td>3.029</td> <td>1.204</td> <td>0.669</td> <td>5.388</td> <td>0.012</td>
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<th>Risk ratio</th> <td>4.189</td> <td></td> <td>0.722</td> <td>24.319</td> <td>0.110</td>
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<th>Log risk ratio</th> <td>1.433</td> <td>0.897</td> <td>-0.326</td> <td>3.191</td> <td>0.110</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_samples</span> <span class="o">=</span> <span class="mi">10</span> <span class="c1"># bootstrap parameter</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">)</span>
<span class="n">as_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Patient_Age_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">mr_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Patient_Age_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Aortic Insufficiency'</span><span class="p">)</span>
<span class="n">ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Patient_Age_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">as_mr_ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Patient_Age_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Insufficiency'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">age_summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_summary</span><span class="p">,</span><span class="n">mr_summary</span><span class="p">,</span><span class="n">ai_summary</span><span class="p">,</span><span class="n">as_mr_ai_summary</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">age_summary_stats</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'age_summary_stats.csv'</span><span class="p">)</span>
</pre></div>
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<pre>Aortic Stenosis
18-60
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<pre>100%|██████████| 10/10 [00:01<00:00, 5.79it/s]
</pre>
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<pre>0.75 0.88 0.730097248666175 0.9176805793347693
60-70
</pre>
</div>
</div>
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<pre>100%|██████████| 10/10 [00:00<00:00, 11.47it/s]
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<pre>0.71 0.8 0.7019308909072053 0.8254304673223016
70-80
</pre>
</div>
</div>
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<pre>100%|██████████| 10/10 [00:00<00:00, 19.52it/s]
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<pre>0.72 0.79 0.718808566867564 0.7952340366099576
80+
</pre>
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<pre>100%|██████████| 10/10 [00:00<00:00, 31.05it/s]
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<pre>0.72 0.78 0.7164895487694941 0.7762557265479183
nan
Mitral Regurgitation
18-60
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<pre>100%|██████████| 10/10 [00:01<00:00, 6.24it/s]
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<pre>0.75 0.86 0.7508599733475103 0.8824598129228192
60-70
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</div>
</div>
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<pre>100%|██████████| 10/10 [00:00<00:00, 13.38it/s]
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<pre>0.82 0.87 0.8015219235996562 0.8839832015076687
70-80
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</div>
</div>
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<pre>100%|██████████| 10/10 [00:00<00:00, 16.72it/s]
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<pre>0.73 0.83 0.7180120179594174 0.8540374026348359
80+
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<pre>100%|██████████| 10/10 [00:00<00:00, 30.31it/s]
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<pre>0.67 0.76 0.6605119237164262 0.7638071307161832
nan
Aortic Insufficiency
18-60
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<pre>100%|██████████| 10/10 [00:01<00:00, 5.55it/s]
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<pre>0.73 0.87 0.7240681444570903 0.9009310520437414
60-70
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</div>
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<pre>100%|██████████| 10/10 [00:00<00:00, 11.40it/s]
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<pre>0.63 0.83 0.6190394863561854 0.8638125653025401
70-80
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<pre>100%|██████████| 10/10 [00:00<00:00, 14.89it/s]
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<pre>0.63 0.78 0.6044662864009422 0.8049715703823834
80+
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<pre>100%|██████████| 10/10 [00:00<00:00, 20.90it/s]
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_ranking.py:999: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless
warnings.warn(
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<pre>0.64 0.78 0.6076545211082022 0.8011775129506545
nan
Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation
18-60
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<pre>100%|██████████| 10/10 [00:01<00:00, 5.57it/s]
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<pre>0.8 0.83 0.7918608815291726 0.8389628150475155
60-70
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<pre>100%|██████████| 10/10 [00:00<00:00, 12.03it/s]
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<pre>0.76 0.82 0.7609023967654973 0.8255039023416939
70-80
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</div>
</div>
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<pre>100%|██████████| 10/10 [00:00<00:00, 18.52it/s]
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<pre>0.72 0.74 0.7122707064958184 0.7369380115398251
80+
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<pre>100%|██████████| 10/10 [00:00<00:00, 24.51it/s]
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<pre>0.71 0.76 0.7058830155136864 0.7610709448934017
nan
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<h1 id="Plot-Results-by-Age-Group">Plot Results by Age Group<a class="anchor-link" href="#Plot-Results-by-Age-Group">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [36]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#generate counts of each label number of studies, only non 65 plus</span>
<span class="n">n_AS</span><span class="p">,</span> <span class="n">n_AI</span><span class="p">,</span> <span class="n">n_MR</span><span class="p">,</span> <span class="n">n_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="k">for</span> <span class="n">study</span> <span class="ow">in</span> <span class="n">study_name</span><span class="p">:</span>
<span class="k">if</span> <span class="s1">'65plus'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">study</span><span class="p">:</span>
<span class="k">if</span> <span class="n">study</span><span class="o">==</span><span class="s1">'AS'</span><span class="p">:</span>
<span class="n">n_AS</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">study</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'AI'</span><span class="p">):</span>
<span class="n">n_AI</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">study</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'MR'</span><span class="p">):</span>
<span class="n">n_MR</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="s1">'AS_AI_MR'</span> <span class="ow">in</span> <span class="n">study</span><span class="p">:</span>
<span class="n">n_AS_AI_MR</span><span class="o">+=</span><span class="mi">1</span>
<span class="c1">#generate color counts (must make separate for age 65 if we want the two studies that only differ from age to have same color)</span>
<span class="n">c_AS</span><span class="p">,</span> <span class="n">c_AI</span><span class="p">,</span> <span class="n">c_MR</span><span class="p">,</span> <span class="n">c_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="n">c65_AS</span><span class="p">,</span> <span class="n">c65_AI</span><span class="p">,</span> <span class="n">c65_MR</span><span class="p">,</span> <span class="n">c65_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="nb">print</span><span class="p">(</span><span class="n">lstFiles</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">study_name</span><span class="p">)</span>
<span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'y_pred_proba'</span><span class="p">]</span>
<span class="c1">#a = plt.figure(figsize=(15, 15), subplots = [1,2]) </span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
<span class="k">for</span> <span class="n">file</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">lstFiles</span><span class="p">,</span><span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
<span class="c1">#plt.figure(figsize=(15, 15)) </span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">file</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">df</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">))</span>
<span class="n">neg_count</span><span class="p">,</span> <span class="n">pos_count</span> <span class="o">=</span> <span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="s1">',d'</span><span class="p">)),(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span><span class="s1">',d'</span><span class="p">))</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'All scores'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">auroc_total</span> <span class="o">=</span> <span class="n">auroc</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># test_pred[test_pred['QRSDuration'] <= 120]['QRSDuration'].value_counts()</span>
<span class="n">df_18_60</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Patient_Age_Group'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'18-60'</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_60_70</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Patient_Age_Group'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'60-70'</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_70_80</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Patient_Age_Group'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'70-80'</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_80_plus</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Patient_Age_Group'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'80+'</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="c1"># df_18_60</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_18_60</span><span class="p">)</span>
<span class="c1"># plotting bootstrap</span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightcoral'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_18_60</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_18_60</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># plottring ROC</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Age 18-60 (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="c1">#plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># df_60_70</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_60_70</span><span class="p">)</span>
<span class="c1"># plotting bootstrap</span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightblue'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_60_70</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_60_70</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># plotting ROC</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Age 61-70 (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="c1">#plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># df_70_80</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_70_80</span><span class="p">)</span>
<span class="c1"># plotting bootstrap</span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgreen'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_70_80</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_70_80</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># plotting ROC</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'green'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Age 71-80 (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="c1">#plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># df_80_plus</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_80_plus</span><span class="p">)</span>
<span class="c1"># plotting bootstrap</span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightyellow'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_80_plus</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_80_plus</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># plotting ROC</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'gold'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Age 81+ (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="c1">#plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># Plotting the Rest </span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' [</span><span class="si">{</span><span class="n">pos_count</span><span class="si">}</span><span class="s1"> / </span><span class="si">{</span><span class="n">neg_count</span><span class="si">}</span><span class="s1">]'</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' (AUROC=</span><span class="si">{</span><span class="n">auroc_total</span><span class="si">}</span><span class="s1">)'</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"AS_AI_MR"</span><span class="p">,</span> <span class="s2">"AS, AR, or MR"</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"_"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Aortic Insufficiency"</span><span class="p">,</span> <span class="s2">"Aortic Regurgitation"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
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<pre>['/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv', '/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv', '/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv', '/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv']
['Aortic_Stenosis', 'Aortic_Insufficiency', 'Mitral_Regurgitation', 'AS_AI_MR_Combination']
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv
All scores
AUROC is: 0.8804872725564813 0.8804872725564813
Threshold value is: 0.4290496913006215
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<pre>100%|██████████| 1000/1000 [00:03<00:00, 257.97it/s]
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<pre>AUROC is: 0.8331332744406912 0.8331332744406912
Threshold value is: 0.14236707472614496
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 379.22it/s]
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<pre>AUROC is: 0.7845235557450437 0.7845235557450437
Threshold value is: 0.37757042779559474
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 462.21it/s]
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<pre>AUROC is: 0.7588724569454022 0.7588724569454022
Threshold value is: 0.591104117144755
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 469.46it/s]
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<pre>AUROC is: 0.7490658459186391 0.7490658459186391
Threshold value is: 0.7445367314698965
Aortic Stenosis [907 / 20,141] (AUROC=0.88)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv
All scores
AUROC is: 0.7687817837666431 0.7687817837666431
Threshold value is: 0.3979350694912722
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<pre>100%|██████████| 1000/1000 [00:03<00:00, 268.13it/s]
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<pre>AUROC is: 0.7945912098298676 0.7945912098298676
Threshold value is: 0.450291893659322
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 422.46it/s]
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<pre>AUROC is: 0.7350061590401021 0.7350061590401021
Threshold value is: 0.4194478427674115
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 471.75it/s]
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<pre>AUROC is: 0.6974560916096925 0.6974560916096925
Threshold value is: 0.3979350694912722
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<pre>100%|██████████| 1000/1000 [00:01<00:00, 522.19it/s]
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<pre>AUROC is: 0.714252367613712 0.714252367613712
Threshold value is: 0.48813259378734075
Aortic Regurgitation [196 / 20,852] (AUROC=0.769)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv
All scores
AUROC is: 0.828017318849102 0.828017318849102
Threshold value is: 0.45940651160283796
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<pre>100%|██████████| 1000/1000 [00:03<00:00, 275.08it/s]
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<pre>AUROC is: 0.8211550864849018 0.8211550864849018
Threshold value is: 0.38141647091004066
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 427.12it/s]
</pre>
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<pre>AUROC is: 0.8425296239982376 0.8425296239982376
Threshold value is: 0.44406463805623175
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 466.26it/s]
</pre>
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<pre>AUROC is: 0.7860715229787394 0.7860715229787394
Threshold value is: 0.48397561958293783
</pre>
</div>
</div>
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<pre>100%|██████████| 1000/1000 [00:01<00:00, 526.02it/s]
</pre>
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<pre>AUROC is: 0.7132101489244347 0.7132101489244347
Threshold value is: 0.5709031391543532
Mitral Regurgitation [742 / 20,306] (AUROC=0.828)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv
All scores
AUROC is: 0.8354910330275293 0.8354910330275293
Threshold value is: 0.45716718045293103
</pre>
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<pre>100%|██████████| 1000/1000 [00:03<00:00, 285.58it/s]
</pre>
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<pre>AUROC is: 0.8087341223412461 0.8087341223412461
Threshold value is: 0.27530762692370364
</pre>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 414.08it/s]
</pre>
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<pre>AUROC is: 0.7878099487855584 0.7878099487855584
Threshold value is: 0.41694409320792375
</pre>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 449.85it/s]
</pre>
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<pre>AUROC is: 0.7159081854485794 0.7159081854485794
Threshold value is: 0.5144382945813603
</pre>
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<pre>100%|██████████| 1000/1000 [00:01<00:00, 542.46it/s]
</pre>
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<pre>AUROC is: 0.7270918262876714 0.7270918262876714
Threshold value is: 0.6513456467300284
AS, AR, or MR Combination [1,644 / 19,404] (AUROC=0.835)
</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [37]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/age_roc.png'</span><span class="p">)</span>
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<h1 id="Sex-Summary-Stats">Sex Summary Stats<a class="anchor-link" href="#Sex-Summary-Stats">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [40]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">new_gender_column</span><span class="p">(</span><span class="n">row</span><span class="p">):</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">]</span> <span class="o">==</span><span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'female'</span>
<span class="k">elif</span> <span class="n">row</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">]</span> <span class="o">==</span><span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'male'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'Gender_Group'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">new_gender_column</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [44]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_samples</span> <span class="o">=</span> <span class="mi">1000</span> <span class="c1"># bootstrap parameter</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">)</span>
<span class="n">as_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Gender_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">mr_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Gender_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Aortic Insufficiency'</span><span class="p">)</span>
<span class="n">ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Gender_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">as_mr_ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Gender_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Insufficiency'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">gender_summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_summary</span><span class="p">,</span><span class="n">mr_summary</span><span class="p">,</span><span class="n">ai_summary</span><span class="p">,</span><span class="n">as_mr_ai_summary</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">gender_summary_stats</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'gender_summary_stats.csv'</span><span class="p">)</span>
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<pre>Aortic Stenosis
female
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<pre>100%|██████████| 1000/1000 [02:56<00:00, 5.66it/s]
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<pre>0.87 0.91 0.8671946663860116 0.9067624353464864
male
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<pre>100%|██████████| 1000/1000 [02:55<00:00, 5.69it/s]
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<pre>0.85 0.89 0.853465851083383 0.8948331734730581
Mitral Regurgitation
female
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<pre>100%|██████████| 1000/1000 [02:59<00:00, 5.57it/s]
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<pre>0.79 0.85 0.7899195135781897 0.8483298850623756
male
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<pre>100%|██████████| 1000/1000 [02:41<00:00, 6.21it/s]
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<pre>0.81 0.87 0.8143669285891035 0.8685378008365738
Aortic Insufficiency
female
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<pre>100%|██████████| 1000/1000 [03:32<00:00, 4.71it/s]
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<pre>0.66 0.82 0.6673672645499634 0.8269134134028334
male
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<pre>100%|██████████| 1000/1000 [03:13<00:00, 5.17it/s]
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<pre>0.72 0.84 0.7188412947968956 0.8398562592208147
Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation
female
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<pre>100%|██████████| 1000/1000 [02:50<00:00, 5.85it/s]
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<pre>0.82 0.86 0.8210521960559966 0.8583817703873379
male
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<pre>100%|██████████| 1000/1000 [02:27<00:00, 6.78it/s]</pre>
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<pre>0.81 0.85 0.8126983460363397 0.851611709201546
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<pre>
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<h1 id="Compute-Race-Stats-Using-Bootstrap">Compute Race Stats Using Bootstrap<a class="anchor-link" href="#Compute-Race-Stats-Using-Bootstrap">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_samples</span> <span class="o">=</span> <span class="mi">1000</span> <span class="c1"># bootstrap parameter</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">)</span>
<span class="n">as_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Race'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">mr_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Race'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Aortic Insufficiency'</span><span class="p">)</span>
<span class="n">ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Race'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">as_mr_ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Race'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Insufficiency'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">race_summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_summary</span><span class="p">,</span><span class="n">mr_summary</span><span class="p">,</span><span class="n">ai_summary</span><span class="p">,</span><span class="n">as_mr_ai_summary</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">race_summary_stats</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'race_summary_stats.csv'</span><span class="p">)</span>
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<h1 id="Plot-Results-By-Race">Plot Results By Race<a class="anchor-link" href="#Plot-Results-By-Race">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">factor_var</span> <span class="o">=</span> <span class="s1">'Race'</span>
<span class="n">target_var</span> <span class="o">=</span> <span class="s1">'AS'</span>
<span class="n">plot_results_by_factor_by_target</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">factor_var</span><span class="p">,</span> <span class="n">target_var</span><span class="p">)</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [64]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'y_pred_proba'</span><span class="p">]</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
<span class="k">for</span> <span class="n">file</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">lstFiles</span><span class="p">,</span><span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
<span class="c1">#plt.figure(figsize=(15, 15)) </span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">file</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">df</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">))</span>
<span class="n">neg_count</span><span class="p">,</span> <span class="n">pos_count</span> <span class="o">=</span> <span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="s1">',d'</span><span class="p">)),(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span><span class="s1">',d'</span><span class="p">))</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'All scores'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="n">auroc_total</span> <span class="o">=</span> <span class="n">auroc</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="n">df_white</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Race'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'WHITE'</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_black</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Race'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'BLACK/AFRICAN AMERICAN'</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_white</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_black</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># Race white</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_white</span><span class="p">)</span>
<span class="c1"># plotting </span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightcoral'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'White'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_white</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_white</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="n">white_count</span> <span class="o">=</span> <span class="nb">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_white</span><span class="p">),</span><span class="s1">',d'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># Race black</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_black</span><span class="p">)</span>
<span class="c1"># plotting </span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightblue'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'White (N = </span><span class="si">{</span><span class="n">white_count</span><span class="si">}</span><span class="s1">, AUROC = %0.2f)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="c1">#plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Black'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_black</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_black</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="n">black_count</span> <span class="o">=</span> <span class="nb">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_black</span><span class="p">),</span><span class="s1">',d'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="n">total_pos_count</span> <span class="o">=</span> <span class="n">df_white</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">+</span> <span class="n">df_black</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">total_count</span><span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_white</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">df_black</span><span class="p">)</span>
<span class="n">total_count</span> <span class="o">=</span> <span class="nb">format</span><span class="p">(</span><span class="n">total_count</span><span class="p">,</span><span class="s1">',d'</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' [</span><span class="si">{</span><span class="n">pos_count</span><span class="si">}</span><span class="s1"> / </span><span class="si">{</span><span class="n">total_count</span><span class="si">}</span><span class="s1">]'</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' (AUROC=</span><span class="si">{</span><span class="n">auroc_total</span><span class="si">}</span><span class="s1">)'</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"AS_AI_MR"</span><span class="p">,</span> <span class="s2">"AS, AR, or MR"</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"_"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Aortic Insufficiency"</span><span class="p">,</span> <span class="s2">"Aortic Regurgitation"</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'navy'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Black or African American (N = </span><span class="si">{</span><span class="n">black_count</span><span class="si">}</span><span class="s1">, AUROC = %0.2f)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
</pre></div>
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</div>
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<pre>
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv
All scores
AUROC is: 0.8804872725564813 0.8804872725564813
Threshold value is: 0.4290496913006215
(8589, 41)
(3381, 41)
</pre>
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<pre>100%|██████████| 1000/1000 [00:04<00:00, 211.54it/s]
</pre>
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<pre>White
AUROC is: 0.8713834161233114 0.8713834161233114
Threshold value is: 0.48821429768368324
</pre>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 380.84it/s]
</pre>
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<pre>Black
AUROC is: 0.8679815838940801 0.8679815838940801
Threshold value is: 0.43497222371612443
Aortic Stenosis [907 / 11,970] (AUROC=0.88)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv
All scores
AUROC is: 0.7687817837666431 0.7687817837666431
Threshold value is: 0.3979350694912722
(8589, 41)
(3381, 41)
</pre>
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<pre>100%|██████████| 1000/1000 [00:03<00:00, 261.83it/s]
</pre>
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<pre>White
AUROC is: 0.7911882631674868 0.7911882631674868
Threshold value is: 0.4502594758980771
</pre>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 419.83it/s]
</pre>
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<pre>Black
AUROC is: 0.6937129556956887 0.6937129556956887
Threshold value is: 0.5060507886250929
Aortic Regurgitation [196 / 11,970] (AUROC=0.77)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv
All scores
AUROC is: 0.828017318849102 0.828017318849102
Threshold value is: 0.45940651160283796
(8589, 41)
(3381, 41)
</pre>
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<pre>100%|██████████| 1000/1000 [00:04<00:00, 207.37it/s]
</pre>
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<pre>White
AUROC is: 0.8286614061861972 0.8286614061861972
Threshold value is: 0.49102300861229714
</pre>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 399.57it/s]
</pre>
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<pre>Black
AUROC is: 0.7701759488148904 0.7701759488148904
Threshold value is: 0.433443442921303
Mitral Regurgitation [742 / 11,970] (AUROC=0.83)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv
All scores
AUROC is: 0.8354910330275293 0.8354910330275293
Threshold value is: 0.45716718045293103
(8589, 41)
(3381, 41)
</pre>
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<pre>100%|██████████| 1000/1000 [00:04<00:00, 223.86it/s]
</pre>
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<pre>White
AUROC is: 0.8373834239597975 0.8373834239597975
Threshold value is: 0.4820422523508854
</pre>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 402.33it/s]
</pre>
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<pre>Black
AUROC is: 0.7832236814606132 0.7832236814606132
Threshold value is: 0.45048856689025873
AS, AR, or MR Combination [1,644 / 11,970] (AUROC=0.84)
</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [65]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/race_roc.png'</span><span class="p">)</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [98]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_white</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.975</span><span class="p">])</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="s1">'</span><span class="si">{0:.3f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[98]:</div>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>2.5%</th>
<th>50%</th>
<th>97.5%</th>
<th>max</th>
</tr>
</thead>
<tbody>
<tr>
<th>index</th>
<td>8589.000</td>
<td>9909.026</td>
<td>5802.364</td>
<td>0.000</td>
<td>411.700</td>
<td>9934.000</td>
<td>19597.200</td>
<td>20459.000</td>
</tr>
<tr>
<th>TestID</th>
<td>8589.000</td>
<td>2447798.348</td>
<td>668207.679</td>
<td>1143854.000</td>
<td>1502944.200</td>
<td>2313477.000</td>
<td>3550858.100</td>
<td>3602346.000</td>
</tr>
<tr>
<th>PatientID</th>
<td>8589.000</td>
<td>1049367271.334</td>
<td>80630740.047</td>
<td>1000000178.000</td>
<td>1000466351.700</td>
<td>1008470110.000</td>
<td>1203584114.000</td>
<td>1400107077.000</td>
</tr>
<tr>
<th>Gender</th>
<td>8589.000</td>
<td>0.476</td>
<td>0.499</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>aortic_stenosis_label_four_grade</th>
<td>8589.000</td>
<td>0.243</td>
<td>0.711</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>3.000</td>
<td>3.000</td>
</tr>
<tr>
<th>aortic_insufficiency_label_four_grade</th>
<td>8589.000</td>
<td>0.150</td>
<td>0.394</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>3.000</td>
</tr>
<tr>
<th>mitral_regurgitation_label_four_grade</th>
<td>8589.000</td>
<td>0.325</td>
<td>0.568</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>2.000</td>
<td>3.000</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary</th>
<td>6991.000</td>
<td>0.081</td>
<td>0.273</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary</th>
<td>7821.000</td>
<td>0.041</td>
<td>0.198</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary</th>
<td>7913.000</td>
<td>0.012</td>
<td>0.108</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary_backfilled</th>
<td>8589.000</td>
<td>0.066</td>
<td>0.248</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary_backfilled</th>
<td>8589.000</td>
<td>0.011</td>
<td>0.103</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary_backfilled</th>
<td>8589.000</td>
<td>0.037</td>
<td>0.189</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary</th>
<td>8045.000</td>
<td>0.107</td>
<td>0.309</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary_backfilled</th>
<td>8589.000</td>
<td>0.100</td>
<td>0.300</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>QRSDuration</th>
<td>8589.000</td>
<td>94.944</td>
<td>21.407</td>
<td>0.000</td>
<td>68.000</td>
<td>90.000</td>
<td>154.000</td>
<td>208.000</td>
</tr>
<tr>
<th>RIGHT BUNDLE BRANCH BLOCK</th>
<td>8564.000</td>
<td>0.069</td>
<td>0.253</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>LEFT BUNDLE BRANCH BLOCK</th>
<td>8564.000</td>
<td>0.027</td>
<td>0.161</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>days_since_echo</th>
<td>8589.000</td>
<td>-83.908</td>
<td>117.018</td>
<td>-365.000</td>
<td>-357.000</td>
<td>-15.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>PatientAge_Years</th>
<td>8573.000</td>
<td>63.699</td>
<td>17.148</td>
<td>0.000</td>
<td>25.000</td>
<td>66.000</td>
<td>92.000</td>
<td>104.000</td>
</tr>
<tr>
<th>y_AS</th>
<td>8589.000</td>
<td>0.066</td>
<td>0.248</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_AS_proba</th>
<td>8589.000</td>
<td>-1.010</td>
<td>1.548</td>
<td>-6.597</td>
<td>-4.247</td>
<td>-0.940</td>
<td>1.747</td>
<td>3.350</td>
</tr>
<tr>
<th>y_AI</th>
<td>8589.000</td>
<td>0.011</td>
<td>0.103</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_AI_proba</th>
<td>8589.000</td>
<td>-0.722</td>
<td>0.635</td>
<td>-2.800</td>
<td>-1.610</td>
<td>-0.882</td>
<td>0.775</td>
<td>2.762</td>
</tr>
<tr>
<th>y_MR</th>
<td>8589.000</td>
<td>0.037</td>
<td>0.189</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_MR_proba</th>
<td>8589.000</td>
<td>-0.742</td>
<td>0.880</td>
<td>-3.415</td>
<td>-2.260</td>
<td>-0.837</td>
<td>0.959</td>
<td>1.735</td>
</tr>
<tr>
<th>y_AS_AI_MR</th>
<td>8589.000</td>
<td>0.100</td>
<td>0.300</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_AS_AI_MR_proba</th>
<td>8589.000</td>
<td>-0.661</td>
<td>1.066</td>
<td>-4.253</td>
<td>-2.675</td>
<td>-0.692</td>
<td>1.351</td>
<td>2.406</td>
</tr>
<tr>
<th>y_pred_AS_proba_sigmoid</th>
<td>8589.000</td>
<td>0.335</td>
<td>0.250</td>
<td>0.001</td>
<td>0.014</td>
<td>0.281</td>
<td>0.852</td>
<td>0.966</td>
</tr>
<tr>
<th>y_pred_AI_proba_sigmoid</th>
<td>8589.000</td>
<td>0.337</td>
<td>0.140</td>
<td>0.057</td>
<td>0.167</td>
<td>0.293</td>
<td>0.685</td>
<td>0.941</td>
</tr>
<tr>
<th>y_pred_MR_proba_sigmoid</th>
<td>8589.000</td>
<td>0.346</td>
<td>0.182</td>
<td>0.032</td>
<td>0.095</td>
<td>0.302</td>
<td>0.723</td>
<td>0.850</td>
</tr>
<tr>
<th>y_pred_AS_AI_MR_proba_sigmoid</th>
<td>8589.000</td>
<td>0.370</td>
<td>0.211</td>
<td>0.014</td>
<td>0.064</td>
<td>0.334</td>
<td>0.794</td>
<td>0.917</td>
</tr>
<tr>
<th>EMPI</th>
<td>8426.000</td>
<td>1049877479.927</td>
<td>81028006.870</td>
<td>1000000178.000</td>
<td>1000465134.875</td>
<td>1008427794.000</td>
<td>1203573022.250</td>
<td>1400107077.000</td>
</tr>
<tr>
<th>y</th>
<td>8589.000</td>
<td>0.100</td>
<td>0.300</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_proba</th>
<td>8589.000</td>
<td>-0.661</td>
<td>1.066</td>
<td>-4.253</td>
<td>-2.675</td>
<td>-0.692</td>
<td>1.351</td>
<td>2.406</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [99]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_black</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.975</span><span class="p">])</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="s1">'</span><span class="si">{0:.3f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
</pre></div>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[99]:</div>
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<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>2.5%</th>
<th>50%</th>
<th>97.5%</th>
<th>max</th>
</tr>
</thead>
<tbody>
<tr>
<th>index</th>
<td>3381.000</td>
<td>10168.915</td>
<td>6025.273</td>
<td>5.000</td>
<td>499.500</td>
<td>9964.000</td>
<td>19930.000</td>
<td>20455.000</td>
</tr>
<tr>
<th>TestID</th>
<td>3381.000</td>
<td>2581400.382</td>
<td>674721.852</td>
<td>1235844.000</td>
<td>1536035.500</td>
<td>2503321.000</td>
<td>3554799.500</td>
<td>3603193.000</td>
</tr>
<tr>
<th>PatientID</th>
<td>3381.000</td>
<td>1059993983.839</td>
<td>94728634.304</td>
<td>1000003252.000</td>
<td>1000590958.000</td>
<td>1008495056.000</td>
<td>1400036562.500</td>
<td>1400105959.000</td>
</tr>
<tr>
<th>Gender</th>
<td>3381.000</td>
<td>0.548</td>
<td>0.498</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>aortic_stenosis_label_four_grade</th>
<td>3381.000</td>
<td>0.064</td>
<td>0.332</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>3.000</td>
</tr>
<tr>
<th>aortic_insufficiency_label_four_grade</th>
<td>3381.000</td>
<td>0.093</td>
<td>0.314</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>3.000</td>
</tr>
<tr>
<th>mitral_regurgitation_label_four_grade</th>
<td>3381.000</td>
<td>0.282</td>
<td>0.521</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>2.000</td>
<td>3.000</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary</th>
<td>2809.000</td>
<td>0.016</td>
<td>0.124</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary</th>
<td>3154.000</td>
<td>0.029</td>
<td>0.168</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary</th>
<td>3189.000</td>
<td>0.007</td>
<td>0.083</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary_backfilled</th>
<td>3381.000</td>
<td>0.013</td>
<td>0.113</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary_backfilled</th>
<td>3381.000</td>
<td>0.007</td>
<td>0.080</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary_backfilled</th>
<td>3381.000</td>
<td>0.027</td>
<td>0.163</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary</th>
<td>3225.000</td>
<td>0.046</td>
<td>0.209</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary_backfilled</th>
<td>3381.000</td>
<td>0.044</td>
<td>0.205</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>QRSDuration</th>
<td>3381.000</td>
<td>90.171</td>
<td>18.728</td>
<td>4.000</td>
<td>66.000</td>
<td>86.000</td>
<td>144.000</td>
<td>202.000</td>
</tr>
<tr>
<th>RIGHT BUNDLE BRANCH BLOCK</th>
<td>3370.000</td>
<td>0.047</td>
<td>0.212</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>LEFT BUNDLE BRANCH BLOCK</th>
<td>3370.000</td>
<td>0.015</td>
<td>0.121</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>days_since_echo</th>
<td>3381.000</td>
<td>-89.997</td>
<td>122.262</td>
<td>-365.000</td>
<td>-360.000</td>
<td>-15.000</td>
<td>-1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>PatientAge_Years</th>
<td>3374.000</td>
<td>58.501</td>
<td>17.152</td>
<td>0.000</td>
<td>23.000</td>
<td>59.000</td>
<td>89.000</td>
<td>103.000</td>
</tr>
<tr>
<th>y_AS</th>
<td>3381.000</td>
<td>0.013</td>
<td>0.113</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_AS_proba</th>
<td>3381.000</td>
<td>-1.468</td>
<td>1.426</td>
<td>-5.327</td>
<td>-4.340</td>
<td>-1.403</td>
<td>1.168</td>
<td>2.733</td>
</tr>
<tr>
<th>y_AI</th>
<td>3381.000</td>
<td>0.007</td>
<td>0.080</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_AI_proba</th>
<td>3381.000</td>
<td>-0.774</td>
<td>0.654</td>
<td>-2.412</td>
<td>-1.668</td>
<td>-0.944</td>
<td>0.749</td>
<td>2.003</td>
</tr>
<tr>
<th>y_MR</th>
<td>3381.000</td>
<td>0.027</td>
<td>0.163</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_MR_proba</th>
<td>3381.000</td>
<td>-0.692</td>
<td>0.861</td>
<td>-3.087</td>
<td>-2.207</td>
<td>-0.773</td>
<td>0.857</td>
<td>1.530</td>
</tr>
<tr>
<th>y_AS_AI_MR</th>
<td>3381.000</td>
<td>0.044</td>
<td>0.205</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_AS_AI_MR_proba</th>
<td>3381.000</td>
<td>-0.811</td>
<td>0.997</td>
<td>-3.765</td>
<td>-2.760</td>
<td>-0.809</td>
<td>1.056</td>
<td>2.764</td>
</tr>
<tr>
<th>y_pred_AS_proba_sigmoid</th>
<td>3381.000</td>
<td>0.256</td>
<td>0.214</td>
<td>0.005</td>
<td>0.013</td>
<td>0.197</td>
<td>0.763</td>
<td>0.939</td>
</tr>
<tr>
<th>y_pred_AI_proba_sigmoid</th>
<td>3381.000</td>
<td>0.328</td>
<td>0.143</td>
<td>0.082</td>
<td>0.159</td>
<td>0.280</td>
<td>0.679</td>
<td>0.881</td>
</tr>
<tr>
<th>y_pred_MR_proba_sigmoid</th>
<td>3381.000</td>
<td>0.355</td>
<td>0.179</td>
<td>0.044</td>
<td>0.099</td>
<td>0.316</td>
<td>0.702</td>
<td>0.822</td>
</tr>
<tr>
<th>y_pred_AS_AI_MR_proba_sigmoid</th>
<td>3381.000</td>
<td>0.339</td>
<td>0.191</td>
<td>0.023</td>
<td>0.060</td>
<td>0.308</td>
<td>0.742</td>
<td>0.941</td>
</tr>
<tr>
<th>EMPI</th>
<td>3282.000</td>
<td>1061308748.772</td>
<td>95642515.005</td>
<td>1000003252.000</td>
<td>1000567756.975</td>
<td>1008529619.500</td>
<td>1400037685.275</td>
<td>1400105959.000</td>
</tr>
<tr>
<th>y</th>
<td>3381.000</td>
<td>0.044</td>
<td>0.205</td>
<td>0.000</td>
<td>0.000</td>
<td>0.000</td>
<td>1.000</td>
<td>1.000</td>
</tr>
<tr>
<th>y_pred_proba</th>
<td>3381.000</td>
<td>-0.811</td>
<td>0.997</td>
<td>-3.765</td>
<td>-2.760</td>
<td>-0.809</td>
<td>1.056</td>
<td>2.764</td>
</tr>
</tbody>
</table>
</div>
</div>
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<h1 id="Compute-Ethnicity-Stats-Using-Bootstrap">Compute Ethnicity Stats Using Bootstrap<a class="anchor-link" href="#Compute-Ethnicity-Stats-Using-Bootstrap">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_samples</span> <span class="o">=</span> <span class="mi">1000</span> <span class="c1"># bootstrap parameter</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">)</span>
<span class="n">as_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Ethnicity'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">mr_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Ethnicity'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Aortic Insufficiency'</span><span class="p">)</span>
<span class="n">ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Ethnicity'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">as_mr_ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'Ethnicity'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Insufficiency'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ethnicity_summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_summary</span><span class="p">,</span><span class="n">mr_summary</span><span class="p">,</span><span class="n">ai_summary</span><span class="p">,</span><span class="n">as_mr_ai_summary</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">ethnicity_summary_stats</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'ethnicity_summary_stats.csv'</span><span class="p">)</span>
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<h1 id="Plot-Results-By-Ethnicity">Plot Results By Ethnicity<a class="anchor-link" href="#Plot-Results-By-Ethnicity">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [69]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># This doesn't seem to work. Plotting comes out wierd.</span>
<span class="c1"># for y, y_true_col, y_score_col in [('Aortic Stenosis','y_AS','y_pred_AS_proba_sigmoid'), ('Aortic Insufficiency','y_AI','y_pred_AI_proba_sigmoid'), ('Mitral Regurgitation','y_MR','y_pred_MR_proba_sigmoid'), ('Combined','y_AS_AI_MR','y_pred_AS_AI_MR_proba_sigmoid')]:</span>
<span class="c1"># print(y)</span>
<span class="c1"># test_race_ethnicity.groupby(['Ethnicity'], dropna = False).apply(lambda x: plot_results(x, y_true_col, y_score_col))</span>
<span class="c1"># Still having trouble with groups overwriting one another </span>
<span class="n">factor_var</span> <span class="o">=</span> <span class="s1">'Ethnicity'</span>
<span class="n">target_var</span> <span class="o">=</span> <span class="s1">'AS'</span>
<span class="n">plot_results_by_factor_by_target</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">factor_var</span><span class="p">,</span> <span class="n">target_var</span><span class="p">)</span>
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<pre>NOT HISPANIC OR LATINO
AUROC is: 0.86
Threshold value is: 0.48230155998638263
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 14.5 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>14.457</td> <td></td> <td>10.778</td> <td>19.391</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.671</td> <td>0.150</td> <td>2.377</td> <td>2.965</td> <td>0.000</td>
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<th>Risk ratio</th> <td>4.052</td> <td></td> <td>3.213</td> <td>5.111</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.399</td> <td>0.118</td> <td>1.167</td> <td>1.631</td> <td>0.000</td>
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<pre>Odds Ratio is: 15.1 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>15.140</td> <td></td> <td>11.155</td> <td>20.549</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.717</td> <td>0.156</td> <td>2.412</td> <td>3.023</td> <td>0.000</td>
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<th>Risk ratio</th> <td>4.429</td> <td></td> <td>3.456</td> <td>5.675</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.488</td> <td>0.127</td> <td>1.240</td> <td>1.736</td> <td>0.000</td>
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<pre>SPANISH/HISPANIC
AUROC is: 0.88
Threshold value is: 0.44052041757831195
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 15.1 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>15.058</td> <td></td> <td>9.461</td> <td>23.967</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.712</td> <td>0.237</td> <td>2.247</td> <td>3.177</td> <td>0.000</td>
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<th>Risk ratio</th> <td>3.496</td> <td></td> <td>2.463</td> <td>4.961</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.251</td> <td>0.179</td> <td>0.901</td> <td>1.602</td> <td>0.000</td>
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<pre>Odds Ratio is: 15.8 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>15.839</td> <td></td> <td>9.567</td> <td>26.225</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.762</td> <td>0.257</td> <td>2.258</td> <td>3.267</td> <td>0.000</td>
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<th>Risk ratio</th> <td>4.208</td> <td></td> <td>2.804</td> <td>6.315</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.437</td> <td>0.207</td> <td>1.031</td> <td>1.843</td> <td>0.000</td>
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<pre>UNKNOWN
AUROC is: 0.89
Threshold value is: 0.4274447734495537
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 16.1 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>16.087</td> <td></td> <td>12.676</td> <td>20.417</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.778</td> <td>0.122</td> <td>2.540</td> <td>3.016</td> <td>0.000</td>
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<th>Risk ratio</th> <td>3.923</td> <td></td> <td>3.259</td> <td>4.722</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.367</td> <td>0.095</td> <td>1.181</td> <td>1.552</td> <td>0.000</td>
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<pre>Odds Ratio is: 21.4 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>21.371</td> <td></td> <td>15.940</td> <td>28.654</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>3.062</td> <td>0.150</td> <td>2.769</td> <td>3.355</td> <td>0.000</td>
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<th>Risk ratio</th> <td>6.201</td> <td></td> <td>4.804</td> <td>8.004</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.825</td> <td>0.130</td> <td>1.569</td> <td>2.080</td> <td>0.000</td>
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<pre>nan
AUROC is: 0.86
Threshold value is: 0.5918834778185068
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 10.5 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>10.490</td> <td></td> <td>5.965</td> <td>18.445</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.350</td> <td>0.288</td> <td>1.786</td> <td>2.915</td> <td>0.000</td>
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<th>Risk ratio</th> <td>3.110</td> <td></td> <td>2.059</td> <td>4.700</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.135</td> <td>0.211</td> <td>0.722</td> <td>1.547</td> <td>0.000</td>
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<pre>Odds Ratio is: 12.5 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
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<th>Odds ratio</th> <td>12.522</td> <td></td> <td>7.322</td> <td>21.413</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.527</td> <td>0.274</td> <td>1.991</td> <td>3.064</td> <td>0.000</td>
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<th>Risk ratio</th> <td>2.747</td> <td></td> <td>1.924</td> <td>3.923</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.011</td> <td>0.182</td> <td>0.654</td> <td>1.367</td> <td>0.000</td>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [70]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'y_pred_proba'</span><span class="p">]</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
<span class="k">for</span> <span class="n">file</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">lstFiles</span><span class="p">,</span><span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
<span class="c1">#plt.figure(figsize=(15, 15)) </span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">file</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">df</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">))</span>
<span class="n">neg_count</span><span class="p">,</span> <span class="n">pos_count</span> <span class="o">=</span> <span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="s1">',d'</span><span class="p">)),(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span><span class="s1">',d'</span><span class="p">))</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'All scores'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">auroc_total</span> <span class="o">=</span> <span class="n">auroc</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="n">df_not_hispanic</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Ethnicity'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'NOT HISPANIC OR LATINO'</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_hispanic</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Ethnicity'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'SPANISH/HISPANIC'</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_not_hispanic</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_hispanic</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># Ethnicity not hispanic</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_not_hispanic</span><span class="p">)</span>
<span class="c1"># plotting </span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightcoral'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Not Hispanic'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_not_hispanic</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_not_hispanic</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># Ethnicity hispanic</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_hispanic</span><span class="p">)</span>
<span class="c1"># plotting </span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightblue'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Hispanic ROC curve (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="c1">#plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Hispanic'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_hispanic</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_hispanic</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' [</span><span class="si">{</span><span class="n">pos_count</span><span class="si">}</span><span class="s1"> / </span><span class="si">{</span><span class="n">neg_count</span><span class="si">}</span><span class="s1">]'</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' (AUROC=</span><span class="si">{</span><span class="n">auroc_total</span><span class="si">}</span><span class="s1">)'</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"AS_AI_MR"</span><span class="p">,</span> <span class="s2">"AS, AR, or MR"</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"_"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Aortic Insufficiency"</span><span class="p">,</span> <span class="s2">"Aortic Regurgitation"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'navy'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Non-Hispanic ROC curve (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># marker=','</span>
<span class="c1"># linewidth=3</span>
<span class="c1"># linestyle='-'</span>
<span class="c1"># if labelname.startswith('Aortic Stenosis'):</span>
<span class="c1"># colorwheel=plt.cm.Reds(np.linspace(0.7,n_AS))</span>
<span class="c1"># color=colorwheel[c_AS]</span>
<span class="c1"># if labelname.startswith('Aortic Regurgitation'):</span>
<span class="c1"># colorwheel=plt.cm.Blues(np.linspace(0.6,n_AI))</span>
<span class="c1"># color=colorwheel[c_AI]</span>
<span class="c1"># if labelname.startswith('Mitral Regurgitation'):</span>
<span class="c1"># colorwheel=plt.cm.Greens(np.linspace(0.6,n_MR))</span>
<span class="c1"># color=colorwheel[c_MR]</span>
<span class="c1"># if 'AS/AR/MR' in labelname:</span>
<span class="c1"># colorwheel=plt.cm.Greys(np.linspace(0.8,n_AS_AI_MR))</span>
<span class="c1"># color=colorwheel[c_AS_AI_MR]</span>
</pre></div>
</div>
</div>
</div>
</div>
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<pre>
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv
All scores
AUROC is: 0.8804872725564813 0.8804872725564813
Threshold value is: 0.4290496913006215
(5290, 42)
(4034, 42)
</pre>
</div>
</div>
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<pre>100%|██████████| 1000/1000 [00:03<00:00, 325.89it/s]
</pre>
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<pre>Not Hispanic
AUROC is: 0.8639202926087781 0.8639202926087781
Threshold value is: 0.48230155998638263
</pre>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 348.32it/s]
</pre>
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<pre>Hispanic
AUROC is: 0.8819814293978019 0.8819814293978019
Threshold value is: 0.44052041757831195
Aortic Stenosis [907 / 20,141] (AUROC=0.88)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv
All scores
AUROC is: 0.7687817837666431 0.7687817837666431
Threshold value is: 0.3979350694912722
(5290, 42)
(4034, 42)
</pre>
</div>
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<pre>100%|██████████| 1000/1000 [00:03<00:00, 298.86it/s]
</pre>
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<pre>Not Hispanic
AUROC is: 0.721647272033575 0.721647272033575
Threshold value is: 0.47691565923631085
</pre>
</div>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 336.87it/s]
</pre>
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<pre>Hispanic
AUROC is: 0.6792457703732403 0.6792457703732403
Threshold value is: 0.3979350694912722
Aortic Regurgitation [196 / 20,852] (AUROC=0.769)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv
All scores
AUROC is: 0.828017318849102 0.828017318849102
Threshold value is: 0.45940651160283796
(5290, 42)
(4034, 42)
</pre>
</div>
</div>
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<pre>100%|██████████| 1000/1000 [00:03<00:00, 319.63it/s]
</pre>
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<pre>Not Hispanic
AUROC is: 0.8314383561643836 0.8314383561643836
Threshold value is: 0.4676501101949141
</pre>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 355.90it/s]
</pre>
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<pre>Hispanic
AUROC is: 0.8219206618456686 0.8219206618456686
Threshold value is: 0.5021834840032425
Mitral Regurgitation [742 / 20,306] (AUROC=0.828)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv
All scores
AUROC is: 0.8354910330275293 0.8354910330275293
Threshold value is: 0.45716718045293103
(5290, 42)
(4034, 42)
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
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<pre>100%|██████████| 1000/1000 [00:03<00:00, 329.72it/s]
</pre>
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<pre>Not Hispanic
AUROC is: 0.8333336276154322 0.8333336276154322
Threshold value is: 0.5103109684980559
</pre>
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 391.64it/s]
</pre>
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<pre>Hispanic
AUROC is: 0.8203316797819682 0.8203316797819682
Threshold value is: 0.4543221183650831
AS, AR, or MR Combination [1,644 / 19,404] (AUROC=0.835)
</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [71]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/ethnicity_roc.png'</span><span class="p">)</span>
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<h1 id="Compute-Time-Differential-ECG/Echo-Stats-Using-Bootstrap">Compute Time Differential ECG/Echo Stats Using Bootstrap<a class="anchor-link" href="#Compute-Time-Differential-ECG/Echo-Stats-Using-Bootstrap">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_samples</span> <span class="o">=</span> <span class="mi">1000</span> <span class="c1"># bootstrap parameter</span>
<span class="k">def</span> <span class="nf">new_time_column</span><span class="p">(</span><span class="n">row</span><span class="p">):</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'days_since_echo'</span><span class="p">]</span> <span class="o">>=</span> <span class="o">-</span><span class="mi">90</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'0-3 months'</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'days_since_echo'</span><span class="p">]</span> <span class="o"><</span> <span class="o">-</span><span class="mi">90</span> <span class="ow">and</span> <span class="n">row</span><span class="p">[</span><span class="s1">'days_since_echo'</span><span class="p">]</span> <span class="o">>=</span> <span class="o">-</span><span class="mi">180</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'3-6 months'</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'days_since_echo'</span><span class="p">]</span> <span class="o"><</span> <span class="o">-</span><span class="mi">180</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'6+ months'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'days_since_echo_group'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">new_time_column</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'days_since_echo_group'</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">)</span>
<span class="n">as_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'days_since_echo_group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">mr_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'days_since_echo_group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Aortic Insufficiency'</span><span class="p">)</span>
<span class="n">ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'days_since_echo_group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">as_mr_ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'days_since_echo_group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Insufficiency'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">days_since_echo_summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_summary</span><span class="p">,</span><span class="n">mr_summary</span><span class="p">,</span><span class="n">ai_summary</span><span class="p">,</span><span class="n">as_mr_ai_summary</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">days_since_echo_summary_stats</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'days_since_echo_summary_stats.csv'</span><span class="p">)</span>
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<h1 id="Compute-Bundle-Branch-and-QRS-Stats-Using-Bootstrap">Compute Bundle Branch and QRS Stats Using Bootstrap<a class="anchor-link" href="#Compute-Bundle-Branch-and-QRS-Stats-Using-Bootstrap">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">display</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'LEFT BUNDLE BRANCH BLOCK'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
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<pre>0.0 20491
1.0 493
Name: LEFT BUNDLE BRANCH BLOCK, dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_samples</span> <span class="o">=</span> <span class="mi">1000</span> <span class="c1"># bootstrap parameter</span>
<span class="k">def</span> <span class="nf">new_BBB_column</span><span class="p">(</span><span class="n">row</span><span class="p">):</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'RIGHT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span><span class="mi">0</span> <span class="ow">and</span> <span class="n">row</span><span class="p">[</span><span class="s1">'LEFT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span><span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'No Bundle Branch Block'</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'RIGHT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span><span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'Right Bundle Branch Block'</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'LEFT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span><span class="mi">1</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'Left Bundle Branch Block'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span>
<span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'BUNDLE_BRANCH_PRESENT_GROUP'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">new_BBB_column</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'BUNDLE_BRANCH_PRESENT_GROUP'</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">)</span>
<span class="n">as_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'BUNDLE_BRANCH_PRESENT_GROUP'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">mr_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'BUNDLE_BRANCH_PRESENT_GROUP'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Aortic Insufficiency'</span><span class="p">)</span>
<span class="n">ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'BUNDLE_BRANCH_PRESENT_GROUP'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">as_mr_ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'BUNDLE_BRANCH_PRESENT_GROUP'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Insufficiency'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">bbb_summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_summary</span><span class="p">,</span><span class="n">mr_summary</span><span class="p">,</span><span class="n">ai_summary</span><span class="p">,</span><span class="n">as_mr_ai_summary</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">bbb_summary_stats</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'bbb_summary_stats.csv'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_samples</span> <span class="o">=</span> <span class="mi">1000</span> <span class="c1"># bootstrap parameter</span>
<span class="k">def</span> <span class="nf">new_QRS_column</span><span class="p">(</span><span class="n">row</span><span class="p">):</span>
<span class="k">if</span> <span class="n">row</span><span class="p">[</span><span class="s1">'QRSDuration'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">120</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'QRS >= 120'</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="s1">'QRS < 120'</span>
<span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'QRSDuration_Group'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">new_QRS_column</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'QRSDuration_Group'</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">)</span>
<span class="n">as_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'QRSDuration_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">mr_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'QRSDuration_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Aortic Insufficiency'</span><span class="p">)</span>
<span class="n">ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'QRSDuration_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">as_mr_ai_summary</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'QRSDuration_Group'</span><span class="p">],</span> <span class="n">dropna</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n\n</span><span class="s1">'</span><span class="p">)</span>
<span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Insufficiency'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">qrs_summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_summary</span><span class="p">,</span><span class="n">mr_summary</span><span class="p">,</span><span class="n">ai_summary</span><span class="p">,</span><span class="n">as_mr_ai_summary</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">qrs_summary_stats</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'qrs_summary_stats.csv'</span><span class="p">)</span>
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<h1 id="Plot-results-by-QRS-Duration">Plot results by QRS Duration<a class="anchor-link" href="#Plot-results-by-QRS-Duration">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'QRSDurationFactor'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'QRSDuration'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'QRSDurationLessThan120'</span> <span class="k">if</span> <span class="n">x</span> <span class="o"><</span> <span class="mi">120</span> <span class="k">else</span> <span class="s1">'QRSDurationGreaterThan120'</span><span class="p">)</span>
<span class="n">factor_var</span> <span class="o">=</span> <span class="s1">'QRSDurationFactor'</span>
<span class="n">target_var</span> <span class="o">=</span> <span class="s1">'AS_AI_MR'</span>
<span class="n">plot_results_by_factor_by_target</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">factor_var</span><span class="p">,</span> <span class="n">target_var</span><span class="p">)</span>
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<pre>QRSDurationGreaterThan120
AUROC is: 0.78
Threshold value is: 0.6449750458368416
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 8.3 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
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<th>Odds ratio</th> <td>8.315</td> <td></td> <td>5.563</td> <td>12.428</td> <td>0.000</td>
</tr>
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<th>Log odds ratio</th> <td>2.118</td> <td>0.205</td> <td>1.716</td> <td>2.520</td> <td>0.000</td>
</tr>
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<th>Risk ratio</th> <td>5.562</td> <td></td> <td>3.846</td> <td>8.043</td> <td>0.000</td>
</tr>
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<th>Log risk ratio</th> <td>1.716</td> <td>0.188</td> <td>1.347</td> <td>2.085</td> <td>0.000</td>
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<pre>Odds Ratio is: 6.0 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
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<th>Odds ratio</th> <td>6.043</td> <td></td> <td>4.729</td> <td>7.722</td> <td>0.000</td>
</tr>
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<th>Log odds ratio</th> <td>1.799</td> <td>0.125</td> <td>1.554</td> <td>2.044</td> <td>0.000</td>
</tr>
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<th>Risk ratio</th> <td>2.640</td> <td></td> <td>2.226</td> <td>3.130</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>0.971</td> <td>0.087</td> <td>0.800</td> <td>1.141</td> <td>0.000</td>
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<pre>QRSDurationLessThan120
AUROC is: 0.83
Threshold value is: 0.4066919309601984
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 9.3 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
<tr>
<th>Odds ratio</th> <td>9.324</td> <td></td> <td>8.238</td> <td>10.554</td> <td>0.000</td>
</tr>
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<th>Log odds ratio</th> <td>2.233</td> <td>0.063</td> <td>2.109</td> <td>2.356</td> <td>0.000</td>
</tr>
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<th>Risk ratio</th> <td>2.453</td> <td></td> <td>2.268</td> <td>2.653</td> <td>0.000</td>
</tr>
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<th>Log risk ratio</th> <td>0.897</td> <td>0.040</td> <td>0.819</td> <td>0.976</td> <td>0.000</td>
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<pre>Odds Ratio is: 10.0 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
<tr>
<th>Odds ratio</th> <td>10.033</td> <td></td> <td>8.694</td> <td>11.578</td> <td>0.000</td>
</tr>
<tr>
<th>Log odds ratio</th> <td>2.306</td> <td>0.073</td> <td>2.163</td> <td>2.449</td> <td>0.000</td>
</tr>
<tr>
<th>Risk ratio</th> <td>3.603</td> <td></td> <td>3.220</td> <td>4.031</td> <td>0.000</td>
</tr>
<tr>
<th>Log risk ratio</th> <td>1.282</td> <td>0.057</td> <td>1.169</td> <td>1.394</td> <td>0.000</td>
</tr>
</table>
</div>
</div>
</div>
</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#generate counts of each label number of studies, only non 65 plus</span>
<span class="n">n_AS</span><span class="p">,</span> <span class="n">n_AI</span><span class="p">,</span> <span class="n">n_MR</span><span class="p">,</span> <span class="n">n_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="k">for</span> <span class="n">study</span> <span class="ow">in</span> <span class="n">study_name</span><span class="p">:</span>
<span class="k">if</span> <span class="s1">'65plus'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">study</span><span class="p">:</span>
<span class="k">if</span> <span class="n">study</span><span class="o">==</span><span class="s1">'AS'</span><span class="p">:</span>
<span class="n">n_AS</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">study</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'AI'</span><span class="p">):</span>
<span class="n">n_AI</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">study</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'MR'</span><span class="p">):</span>
<span class="n">n_MR</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="s1">'AS_AI_MR'</span> <span class="ow">in</span> <span class="n">study</span><span class="p">:</span>
<span class="n">n_AS_AI_MR</span><span class="o">+=</span><span class="mi">1</span>
<span class="c1">#generate color counts (must make separate for age 65 if we want the two studies that only differ from age to have same color)</span>
<span class="n">c_AS</span><span class="p">,</span> <span class="n">c_AI</span><span class="p">,</span> <span class="n">c_MR</span><span class="p">,</span> <span class="n">c_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="n">c65_AS</span><span class="p">,</span> <span class="n">c65_AI</span><span class="p">,</span> <span class="n">c65_MR</span><span class="p">,</span> <span class="n">c65_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="nb">print</span><span class="p">(</span><span class="n">lstFiles</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">study_name</span><span class="p">)</span>
<span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'y_pred_proba'</span><span class="p">]</span>
<span class="c1">#a = plt.figure(figsize=(15, 15), subplots = [1,2]) </span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
<span class="k">for</span> <span class="n">file</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">lstFiles</span><span class="p">,</span><span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
<span class="c1">#plt.figure(figsize=(15, 15)) </span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">file</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">df</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">))</span>
<span class="n">neg_count</span><span class="p">,</span> <span class="n">pos_count</span> <span class="o">=</span> <span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="s1">',d'</span><span class="p">)),(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span><span class="s1">',d'</span><span class="p">))</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'All scores'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">auroc_total</span> <span class="o">=</span> <span class="n">auroc</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># test_pred[test_pred['QRSDuration'] <= 120]['QRSDuration'].value_counts()</span>
<span class="n">df_qrs_less_than_120</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'QRSDuration'</span><span class="p">]</span> <span class="o"><</span> <span class="mi">120</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_qrs_greater_than_120</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'QRSDuration'</span><span class="p">]</span> <span class="o">>=</span> <span class="mi">120</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_qrs_less_than_120</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_qrs_greater_than_120</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># QRS <= 120</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_qrs_less_than_120</span><span class="p">)</span>
<span class="c1"># plotting </span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightcoral'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'QRS < 120'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_qrs_less_than_120</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_qrs_less_than_120</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># QRS >= 120 </span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_qrs_greater_than_120</span><span class="p">)</span>
<span class="c1"># plotting </span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightblue'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># QRS < 120 acual ROC curve</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'QRS < 120 ROC curve (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="c1">#plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'QRS >= 120'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_qrs_greater_than_120</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_qrs_greater_than_120</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' [</span><span class="si">{</span><span class="n">pos_count</span><span class="si">}</span><span class="s1"> / </span><span class="si">{</span><span class="n">neg_count</span><span class="si">}</span><span class="s1">]'</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' (AUROC=</span><span class="si">{</span><span class="n">auroc_total</span><span class="si">}</span><span class="s1">)'</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"AS_AI_MR"</span><span class="p">,</span> <span class="s2">"AS, AR, or MR"</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"_"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Aortic Insufficiency"</span><span class="p">,</span> <span class="s2">"Aortic Regurgitation"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'navy'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'QRS >= 120 ROC curve (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
</pre></div>
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<pre>['/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv', '/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv', '/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv', '/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv']
['Aortic_Stenosis', 'Aortic_Insufficiency', 'Mitral_Regurgitation', 'AS_AI_MR_Combination']
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv
All scores
AUROC is: 0.8804872725564813 0.8804872725564813
Threshold value is: 0.4290496913006215
(18813, 46)
(2235, 46)
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<pre>100%|██████████| 1000/1000 [00:06<00:00, 156.31it/s]
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<pre>QRS < 120
AUROC is: 0.8857389529349896 0.8857389529349896
Threshold value is: 0.4290496913006215
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<pre>100%|██████████| 1000/1000 [00:02<00:00, 496.16it/s]
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<pre>QRS >= 120
AUROC is: 0.7905320843851579 0.7905320843851579
Threshold value is: 0.591104117144755
Aortic Stenosis [907 / 20,141] (AUROC=0.88)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv
All scores
AUROC is: 0.7687817837666431 0.7687817837666431
Threshold value is: 0.3979350694912722
(18813, 46)
(2235, 46)
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<pre>100%|██████████| 1000/1000 [00:06<00:00, 145.69it/s]
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<pre>QRS < 120
AUROC is: 0.7764085872286653 0.7764085872286653
Threshold value is: 0.4481974703041241
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<pre>100%|██████████| 1000/1000 [00:01<00:00, 512.96it/s]
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<pre>QRS >= 120
AUROC is: 0.6583257506824386 0.6583257506824386
Threshold value is: 0.535040459125702
Aortic Regurgitation [196 / 20,852] (AUROC=0.769)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv
All scores
AUROC is: 0.828017318849102 0.828017318849102
Threshold value is: 0.45940651160283796
(18813, 46)
(2235, 46)
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<pre>100%|██████████| 1000/1000 [00:06<00:00, 160.46it/s]
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<pre>QRS < 120
AUROC is: 0.8221273438167755 0.8221273438167755
Threshold value is: 0.43892152481829055
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<pre>100%|██████████| 1000/1000 [00:01<00:00, 610.66it/s]
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<pre>QRS >= 120
AUROC is: 0.752938662644545 0.752938662644545
Threshold value is: 0.6198770145502475
Mitral Regurgitation [742 / 20,306] (AUROC=0.828)
/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv
All scores
AUROC is: 0.8354910330275293 0.8354910330275293
Threshold value is: 0.45716718045293103
(18813, 46)
(2235, 46)
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<pre>100%|██████████| 1000/1000 [00:05<00:00, 169.43it/s]
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<pre>QRS < 120
AUROC is: 0.8321187756490442 0.8321187756490442
Threshold value is: 0.4066919309601984
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<pre>100%|██████████| 1000/1000 [00:01<00:00, 607.17it/s]
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<pre>QRS >= 120
AUROC is: 0.7761847429030093 0.7761847429030093
Threshold value is: 0.6449750458368416
AS, AR, or MR Combination [1,644 / 19,404] (AUROC=0.835)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/qrs_duration_roc.png'</span><span class="p">)</span>
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<h1 id="Plot-results-by-LBBB/RBBB">Plot results by LBBB/RBBB<a class="anchor-link" href="#Plot-results-by-LBBB/RBBB">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [79]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_race_ethnicity</span><span class="p">[</span><span class="s1">'BundleBranchBlock'</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'RightBundleBranchBlock'</span> <span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="s1">'RIGHT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
<span class="k">else</span> <span class="s1">'LeftBundleBranchBlock'</span> <span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="s1">'LEFT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
<span class="k">else</span> <span class="s1">'No Bundle Branch Block'</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">factor_var</span> <span class="o">=</span> <span class="s1">'BundleBranchBlock'</span>
<span class="n">target_var</span> <span class="o">=</span> <span class="s1">'AS_AI_MR'</span>
<span class="n">plot_results_by_factor_by_target</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">factor_var</span><span class="p">,</span> <span class="n">target_var</span><span class="p">)</span>
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<pre>LeftBundleBranchBlock
AUROC is: 0.73
Threshold value is: 0.6692591309384145
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 7.9 P-Value is: 0.0
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<table class="simpletable">
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
<tr>
<th>Odds ratio</th> <td>7.948</td> <td></td> <td>2.847</td> <td>22.191</td> <td>0.000</td>
</tr>
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<th>Log odds ratio</th> <td>2.073</td> <td>0.524</td> <td>1.046</td> <td>3.100</td> <td>0.000</td>
</tr>
<tr>
<th>Risk ratio</th> <td>6.443</td> <td></td> <td>2.412</td> <td>17.210</td> <td>0.000</td>
</tr>
<tr>
<th>Log risk ratio</th> <td>1.863</td> <td>0.501</td> <td>0.881</td> <td>2.845</td> <td>0.000</td>
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<pre>Odds Ratio is: 4.2 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
<tr>
<th>Odds ratio</th> <td>4.227</td> <td></td> <td>2.709</td> <td>6.596</td> <td>0.000</td>
</tr>
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<th>Log odds ratio</th> <td>1.441</td> <td>0.227</td> <td>0.997</td> <td>1.886</td> <td>0.000</td>
</tr>
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<th>Risk ratio</th> <td>2.139</td> <td></td> <td>1.612</td> <td>2.838</td> <td>0.000</td>
</tr>
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<th>Log risk ratio</th> <td>0.760</td> <td>0.144</td> <td>0.477</td> <td>1.043</td> <td>0.000</td>
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<pre>No Bundle Branch Block
AUROC is: 0.84
Threshold value is: 0.45716718045293103
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 9.6 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
<tr>
<th>Odds ratio</th> <td>9.633</td> <td></td> <td>8.539</td> <td>10.867</td> <td>0.000</td>
</tr>
<tr>
<th>Log odds ratio</th> <td>2.265</td> <td>0.061</td> <td>2.145</td> <td>2.386</td> <td>0.000</td>
</tr>
<tr>
<th>Risk ratio</th> <td>2.606</td> <td></td> <td>2.408</td> <td>2.821</td> <td>0.000</td>
</tr>
<tr>
<th>Log risk ratio</th> <td>0.958</td> <td>0.040</td> <td>0.879</td> <td>1.037</td> <td>0.000</td>
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<pre>Odds Ratio is: 9.9 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
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<th>Odds ratio</th> <td>9.937</td> <td></td> <td>8.746</td> <td>11.289</td> <td>0.000</td>
</tr>
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<th>Log odds ratio</th> <td>2.296</td> <td>0.065</td> <td>2.169</td> <td>2.424</td> <td>0.000</td>
</tr>
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<th>Risk ratio</th> <td>3.085</td> <td></td> <td>2.812</td> <td>3.384</td> <td>0.000</td>
</tr>
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<th>Log risk ratio</th> <td>1.127</td> <td>0.047</td> <td>1.034</td> <td>1.219</td> <td>0.000</td>
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<pre>RightBundleBranchBlock
AUROC is: 0.79
Threshold value is: 0.6141482952187097
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 7.9 P-Value is: 0.0
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<table class="simpletable">
<tr>
<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
<tr>
<th>Odds ratio</th> <td>7.889</td> <td></td> <td>4.644</td> <td>13.401</td> <td>0.000</td>
</tr>
<tr>
<th>Log odds ratio</th> <td>2.065</td> <td>0.270</td> <td>1.535</td> <td>2.595</td> <td>0.000</td>
</tr>
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<th>Risk ratio</th> <td>4.683</td> <td></td> <td>2.929</td> <td>7.488</td> <td>0.000</td>
</tr>
<tr>
<th>Log risk ratio</th> <td>1.544</td> <td>0.239</td> <td>1.075</td> <td>2.013</td> <td>0.000</td>
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<pre>Odds Ratio is: 7.9 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
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<th>Odds ratio</th> <td>7.888</td> <td></td> <td>5.271</td> <td>11.804</td> <td>0.000</td>
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<th>Log odds ratio</th> <td>2.065</td> <td>0.206</td> <td>1.662</td> <td>2.468</td> <td>0.000</td>
</tr>
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<th>Risk ratio</th> <td>3.271</td> <td></td> <td>2.406</td> <td>4.446</td> <td>0.000</td>
</tr>
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<th>Log risk ratio</th> <td>1.185</td> <td>0.157</td> <td>0.878</td> <td>1.492</td> <td>0.000</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#generate counts of each label number of studies, only non 65 plus</span>
<span class="n">n_AS</span><span class="p">,</span> <span class="n">n_AI</span><span class="p">,</span> <span class="n">n_MR</span><span class="p">,</span> <span class="n">n_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="k">for</span> <span class="n">study</span> <span class="ow">in</span> <span class="n">study_name</span><span class="p">:</span>
<span class="k">if</span> <span class="s1">'65plus'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">study</span><span class="p">:</span>
<span class="k">if</span> <span class="n">study</span><span class="o">==</span><span class="s1">'AS'</span><span class="p">:</span>
<span class="n">n_AS</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">study</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'AI'</span><span class="p">):</span>
<span class="n">n_AI</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">study</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'MR'</span><span class="p">):</span>
<span class="n">n_MR</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="s1">'AS_AI_MR'</span> <span class="ow">in</span> <span class="n">study</span><span class="p">:</span>
<span class="n">n_AS_AI_MR</span><span class="o">+=</span><span class="mi">1</span>
<span class="c1">#generate color counts (must make separate for age 65 if we want the two studies that only differ from age to have same color)</span>
<span class="n">c_AS</span><span class="p">,</span> <span class="n">c_AI</span><span class="p">,</span> <span class="n">c_MR</span><span class="p">,</span> <span class="n">c_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="n">c65_AS</span><span class="p">,</span> <span class="n">c65_AI</span><span class="p">,</span> <span class="n">c65_MR</span><span class="p">,</span> <span class="n">c65_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="nb">print</span><span class="p">(</span><span class="n">lstFiles</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">study_name</span><span class="p">)</span>
<span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'y_pred_proba'</span><span class="p">]</span>
<span class="c1">#a = plt.figure(figsize=(15, 15), subplots = [1,2]) </span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
<span class="k">for</span> <span class="n">file</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">lstFiles</span><span class="p">,</span><span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
<span class="c1">#plt.figure(figsize=(15, 15)) </span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">file</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">df</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">))</span>
<span class="n">neg_count</span><span class="p">,</span> <span class="n">pos_count</span> <span class="o">=</span> <span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="s1">',d'</span><span class="p">)),(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span><span class="s1">',d'</span><span class="p">))</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'All scores'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">auroc_total</span> <span class="o">=</span> <span class="n">auroc</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="n">df_no_bundle_branch</span> <span class="o">=</span> <span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="p">[</span><span class="s1">'RIGHT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'LEFT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_left_bundle_branch</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'LEFT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">df_right_bundle_branch</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'RIGHT BUNDLE BRANCH BLOCK'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_no_bundle_branch</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_left_bundle_branch</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_right_bundle_branch</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1"># no_bundle_branch</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_no_bundle_branch</span><span class="p">)</span>
<span class="c1"># plotting </span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgreen'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'No Bundle'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_no_bundle_branch</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_no_bundle_branch</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'green'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'No Bundle Branch ROC curve (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="c1">#plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># left_bundle_branch</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_left_bundle_branch</span><span class="p">)</span>
<span class="c1"># plotting </span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightcoral'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Left Bundle'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_left_bundle_branch</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_left_bundle_branch</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Left Bundle Branch ROC curve (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="c1">#plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="c1"># Right_bundle_branch</span>
<span class="n">fpr_boot</span><span class="p">,</span> <span class="n">tpr_boot</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">boot</span><span class="p">(</span><span class="n">df_right_bundle_branch</span><span class="p">)</span>
<span class="c1"># plotting </span>
<span class="n">lw</span> <span class="o">=</span> <span class="mi">2</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">100</span><span class="p">):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">tpr_boot</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightblue'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="o">.</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Right Bundle'</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df_right_bundle_branch</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df_right_bundle_branch</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">3</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' [</span><span class="si">{</span><span class="n">pos_count</span><span class="si">}</span><span class="s1"> / </span><span class="si">{</span><span class="n">neg_count</span><span class="si">}</span><span class="s1">]'</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' (AUROC=</span><span class="si">{</span><span class="n">auroc_total</span><span class="si">}</span><span class="s1">)'</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"AS_AI_MR"</span><span class="p">,</span> <span class="s2">"AS, AR, or MR"</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"_"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">)</span>
<span class="n">titlename</span> <span class="o">=</span> <span class="n">titlename</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Aortic Insufficiency"</span><span class="p">,</span> <span class="s2">"Aortic Regurgitation"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'navy'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Right Bundle Branch ROC curve (AUROC = </span><span class="si">%0.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">titlename</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/bundle_branch_roc.png'</span><span class="p">)</span>
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<h1 id="Generate-Summary-Stats-for-Table-1-and-2">Generate Summary Stats for Table 1 and 2<a class="anchor-link" href="#Generate-Summary-Stats-for-Table-1-and-2">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Previously, I ran a for loop over each of the 4 target variables</span>
<span class="c1"># for y, y_true_col, y_score_col in [('Aortic Stenosis','y_AS','y_pred_AS_proba_sigmoid'), ('Aortic Insufficiency','y_AI','y_pred_AI_proba_sigmoid'), ('Mitral Regurgitation','y_MR','y_pred_MR_proba_sigmoid'), ('Combined','y_AS_AI_MR','y_pred_AS_AI_MR_proba_sigmoid')]:</span>
<span class="c1"># print(y)</span>
<span class="c1"># test_race_ethnicity.name = 'Overall'</span>
<span class="c1"># plot_results(test_race_ethnicity, y_true_col, y_score_col)</span>
<span class="c1"># Previous method used a loop. This doesn't seem to work. Plotting comes out wierd and plots are overwritten. Tried fixing by creating a function plot_results_by_factor_by_target and running in different cells.</span>
<span class="c1"># Pick target to be any of ['AS','AI','MR','AS_AI_MR']</span>
<span class="c1"># Plots get saved in figures/.. dir</span>
<span class="n">factor_var</span> <span class="o">=</span> <span class="s1">'All'</span> <span class="c1"># Since we are plotting all the data</span>
<span class="n">target_var</span> <span class="o">=</span> <span class="s1">'AS_AI_MR'</span>
<span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'All'</span>
<span class="n">plot_results_by_factor_by_target</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="n">factor_var</span><span class="p">,</span> <span class="n">target_var</span><span class="p">)</span>
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<pre>All
AUROC is: 0.84
Threshold value is: 0.45716718045293103
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
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<pre>Odds Ratio is: 9.7 P-Value is: 0.0
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<table class="simpletable">
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
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<th>Odds ratio</th> <td>9.691</td> <td></td> <td>8.648</td> <td>10.861</td> <td>0.000</td>
</tr>
<tr>
<th>Log odds ratio</th> <td>2.271</td> <td>0.058</td> <td>2.157</td> <td>2.385</td> <td>0.000</td>
</tr>
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<th>Risk ratio</th> <td>2.886</td> <td></td> <td>2.665</td> <td>3.125</td> <td>0.000</td>
</tr>
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<th>Log risk ratio</th> <td>1.060</td> <td>0.041</td> <td>0.980</td> <td>1.139</td> <td>0.000</td>
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<pre>Odds Ratio is: 10.1 P-Value is: 0.0
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<td></td> <th>Estimate</th> <th>SE</th> <th>LCB</th> <th>UCB</th> <th>p-value</th>
</tr>
<tr>
<th>Odds ratio</th> <td>10.113</td> <td></td> <td>8.951</td> <td>11.426</td> <td>0.000</td>
</tr>
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<th>Log odds ratio</th> <td>2.314</td> <td>0.062</td> <td>2.192</td> <td>2.436</td> <td>0.000</td>
</tr>
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<th>Risk ratio</th> <td>3.427</td> <td></td> <td>3.122</td> <td>3.761</td> <td>0.000</td>
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<th>Log risk ratio</th> <td>1.232</td> <td>0.047</td> <td>1.139</td> <td>1.325</td> <td>0.000</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/220k_multivalve_train_tabular_metadata_new_ref.csv'</span><span class="p">))</span>
<span class="n">eval_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/13k_multivalve_eval_tabular_metadata_new_ref.csv'</span><span class="p">))</span>
<span class="n">test_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_tabular_metadata_additional_backfill.csv'</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dfs</span> <span class="o">=</span> <span class="p">[</span><span class="n">train_df</span><span class="p">,</span><span class="n">eval_df</span><span class="p">,</span><span class="n">test_df</span><span class="p">]</span>
<span class="n">fours</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'aortic_stenosis_label_four_grade'</span><span class="p">,</span><span class="s1">'aortic_insufficiency_label_four_grade'</span><span class="p">,</span><span class="s1">'mitral_regurgitation_label_four_grade'</span><span class="p">]</span>
<span class="k">for</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">dfs</span><span class="p">:</span>
<span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">fours</span><span class="p">:</span>
<span class="n">df</span><span class="p">[</span><span class="n">column</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">column</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">column</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
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<pre>0.0 183755
3.0 18334
2.0 12091
1.0 7075
Name: aortic_stenosis_label_four_grade, dtype: int64
0.0 162862
1.0 49119
2.0 7898
3.0 1376
Name: aortic_insufficiency_label_four_grade, dtype: int64
0.0 88794
1.0 84270
2.0 38816
3.0 9375
Name: mitral_regurgitation_label_four_grade, dtype: int64
0.0 11351
3.0 852
2.0 475
1.0 272
Name: aortic_stenosis_label_four_grade, dtype: int64
0.0 10532
1.0 2117
2.0 249
3.0 52
Name: aortic_insufficiency_label_four_grade, dtype: int64
0.0 7648
1.0 4086
2.0 1005
3.0 211
Name: mitral_regurgitation_label_four_grade, dtype: int64
0.0 18975
1.0 1166
3.0 623
2.0 284
Name: aortic_stenosis_label_four_grade, dtype: int64
0.0 18504
1.0 2348
2.0 166
3.0 30
Name: aortic_insufficiency_label_four_grade, dtype: int64
0.0 15404
1.0 4902
2.0 601
3.0 141
Name: mitral_regurgitation_label_four_grade, dtype: int64
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">label_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'aortic_stenosis_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'mitral_regurgitation_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'AS_AI_MR_label_binary_backfilled'</span><span class="p">,</span>
<span class="s1">'aortic_stenosis_label_four_grade'</span><span class="p">,</span>
<span class="s1">'aortic_insufficiency_label_four_grade'</span><span class="p">,</span>
<span class="s1">'mitral_regurgitation_label_four_grade'</span><span class="p">]</span>
<span class="n">dfs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Train'</span><span class="p">:</span><span class="n">train_df</span><span class="p">,</span>
<span class="s1">'Eval'</span><span class="p">:</span><span class="n">eval_df</span><span class="p">,</span>
<span class="s1">'Test'</span><span class="p">:</span><span class="n">test_df</span><span class="p">}</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">value</span> <span class="ow">in</span> <span class="n">dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'--------------</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">key</span><span class="p">,</span><span class="s1">'</span><span class="se">\n</span><span class="s1">--------------'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">label_cols</span><span class="p">:</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">value</span><span class="p">[</span><span class="n">col</span><span class="p">]</span>
<span class="n">counts</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">percent</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">percent100</span> <span class="o">=</span> <span class="s1">'('</span> <span class="o">+</span> <span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">+</span> <span class="s1">')'</span>
<span class="nb">print</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'counts'</span><span class="p">:</span> <span class="n">counts</span><span class="p">,</span> <span class="s1">'percent'</span><span class="p">:</span> <span class="n">percent100</span><span class="p">})</span>
<span class="n">display</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">fours</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'aortic_stenosis_label_four_grade'</span><span class="p">,</span><span class="s1">'aortic_insufficiency_label_four_grade'</span><span class="p">,</span><span class="s1">'mitral_regurgitation_label_four_grade'</span><span class="p">]</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">value</span><span class="p">[</span><span class="n">fours</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">counts</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">percent</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">percent100</span> <span class="o">=</span> <span class="s1">'('</span> <span class="o">+</span> <span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">+</span> <span class="s1">')'</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AS_AI_MR_four_grade'</span><span class="p">)</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'counts'</span><span class="p">:</span> <span class="n">counts</span><span class="p">,</span> <span class="s1">'percent'</span><span class="p">:</span> <span class="n">percent100</span><span class="p">})</span>
<span class="n">display</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
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<pre>--------------
Train
--------------
aortic_stenosis_label_binary_backfilled
</pre>
</div>
</div>
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<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>190784</td>
<td>(86.2)</td>
</tr>
<tr>
<th>1.0</th>
<td>30471</td>
<td>(13.8)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>aortic_insufficiency_label_binary_backfilled
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>212312</td>
<td>(96.0)</td>
</tr>
<tr>
<th>1.0</th>
<td>8943</td>
<td>(4.0)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>mitral_regurgitation_label_binary_backfilled
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>174126</td>
<td>(78.7)</td>
</tr>
<tr>
<th>1.0</th>
<td>47129</td>
<td>(21.3)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>AS_AI_MR_label_binary_backfilled
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>147872</td>
<td>(66.8)</td>
</tr>
<tr>
<th>1.0</th>
<td>73383</td>
<td>(33.2)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>aortic_stenosis_label_four_grade
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>183755</td>
<td>(83.1)</td>
</tr>
<tr>
<th>1.0</th>
<td>7075</td>
<td>(3.2)</td>
</tr>
<tr>
<th>2.0</th>
<td>12091</td>
<td>(5.5)</td>
</tr>
<tr>
<th>3.0</th>
<td>18334</td>
<td>(8.3)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>aortic_insufficiency_label_four_grade
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>162862</td>
<td>(73.6)</td>
</tr>
<tr>
<th>1.0</th>
<td>49119</td>
<td>(22.2)</td>
</tr>
<tr>
<th>2.0</th>
<td>7898</td>
<td>(3.6)</td>
</tr>
<tr>
<th>3.0</th>
<td>1376</td>
<td>(0.6)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>mitral_regurgitation_label_four_grade
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>88794</td>
<td>(40.1)</td>
</tr>
<tr>
<th>1.0</th>
<td>84270</td>
<td>(38.1)</td>
</tr>
<tr>
<th>2.0</th>
<td>38816</td>
<td>(17.5)</td>
</tr>
<tr>
<th>3.0</th>
<td>9375</td>
<td>(4.2)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>AS_AI_MR_four_grade
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>74058</td>
<td>(33.5)</td>
</tr>
<tr>
<th>1.0</th>
<td>72906</td>
<td>(33.0)</td>
</tr>
<tr>
<th>2.0</th>
<td>46554</td>
<td>(21.0)</td>
</tr>
<tr>
<th>3.0</th>
<td>27737</td>
<td>(12.5)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
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<pre>
--------------
Eval
--------------
aortic_stenosis_label_binary_backfilled
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
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<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>11613</td>
<td>(89.7)</td>
</tr>
<tr>
<th>1.0</th>
<td>1337</td>
<td>(10.3)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>aortic_insufficiency_label_binary_backfilled
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>12653</td>
<td>(97.7)</td>
</tr>
<tr>
<th>1.0</th>
<td>297</td>
<td>(2.3)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>mitral_regurgitation_label_binary_backfilled
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>11751</td>
<td>(90.7)</td>
</tr>
<tr>
<th>1.0</th>
<td>1199</td>
<td>(9.3)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>AS_AI_MR_label_binary_backfilled
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>10539</td>
<td>(81.4)</td>
</tr>
<tr>
<th>1.0</th>
<td>2411</td>
<td>(18.6)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>aortic_stenosis_label_four_grade
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>11351</td>
<td>(87.7)</td>
</tr>
<tr>
<th>1.0</th>
<td>272</td>
<td>(2.1)</td>
</tr>
<tr>
<th>2.0</th>
<td>475</td>
<td>(3.7)</td>
</tr>
<tr>
<th>3.0</th>
<td>852</td>
<td>(6.6)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>aortic_insufficiency_label_four_grade
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>10532</td>
<td>(81.3)</td>
</tr>
<tr>
<th>1.0</th>
<td>2117</td>
<td>(16.3)</td>
</tr>
<tr>
<th>2.0</th>
<td>249</td>
<td>(1.9)</td>
</tr>
<tr>
<th>3.0</th>
<td>52</td>
<td>(0.4)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>mitral_regurgitation_label_four_grade
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>7648</td>
<td>(59.1)</td>
</tr>
<tr>
<th>1.0</th>
<td>4086</td>
<td>(31.6)</td>
</tr>
<tr>
<th>2.0</th>
<td>1005</td>
<td>(7.8)</td>
</tr>
<tr>
<th>3.0</th>
<td>211</td>
<td>(1.6)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>AS_AI_MR_four_grade
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>6632</td>
<td>(51.2)</td>
</tr>
<tr>
<th>1.0</th>
<td>3896</td>
<td>(30.1)</td>
</tr>
<tr>
<th>2.0</th>
<td>1343</td>
<td>(10.4)</td>
</tr>
<tr>
<th>3.0</th>
<td>1079</td>
<td>(8.3)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>
--------------
Test
--------------
aortic_stenosis_label_binary_backfilled
</pre>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>20141</td>
<td>(95.7)</td>
</tr>
<tr>
<th>1.0</th>
<td>907</td>
<td>(4.3)</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
<pre>aortic_insufficiency_label_binary_backfilled
</pre>
</div>
</div>
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<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>20852</td>
<td>(99.1)</td>
</tr>
<tr>
<th>1.0</th>
<td>196</td>
<td>(0.9)</td>
</tr>
</tbody>
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<pre>mitral_regurgitation_label_binary_backfilled
</pre>
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<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>20306</td>
<td>(96.5)</td>
</tr>
<tr>
<th>1.0</th>
<td>742</td>
<td>(3.5)</td>
</tr>
</tbody>
</table>
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<pre>AS_AI_MR_label_binary_backfilled
</pre>
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<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>19404</td>
<td>(92.2)</td>
</tr>
<tr>
<th>1.0</th>
<td>1644</td>
<td>(7.8)</td>
</tr>
</tbody>
</table>
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<pre>aortic_stenosis_label_four_grade
</pre>
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<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>18975</td>
<td>(90.2)</td>
</tr>
<tr>
<th>1.0</th>
<td>1166</td>
<td>(5.5)</td>
</tr>
<tr>
<th>3.0</th>
<td>623</td>
<td>(3.0)</td>
</tr>
<tr>
<th>2.0</th>
<td>284</td>
<td>(1.3)</td>
</tr>
</tbody>
</table>
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<pre>aortic_insufficiency_label_four_grade
</pre>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>18504</td>
<td>(87.9)</td>
</tr>
<tr>
<th>1.0</th>
<td>2348</td>
<td>(11.2)</td>
</tr>
<tr>
<th>2.0</th>
<td>166</td>
<td>(0.8)</td>
</tr>
<tr>
<th>3.0</th>
<td>30</td>
<td>(0.1)</td>
</tr>
</tbody>
</table>
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<pre>mitral_regurgitation_label_four_grade
</pre>
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<style scoped>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>15404</td>
<td>(73.2)</td>
</tr>
<tr>
<th>1.0</th>
<td>4902</td>
<td>(23.3)</td>
</tr>
<tr>
<th>2.0</th>
<td>601</td>
<td>(2.9)</td>
</tr>
<tr>
<th>3.0</th>
<td>141</td>
<td>(0.7)</td>
</tr>
</tbody>
</table>
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<pre>AS_AI_MR_four_grade
</pre>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>0.0</th>
<td>13593</td>
<td>(64.6)</td>
</tr>
<tr>
<th>1.0</th>
<td>5811</td>
<td>(27.6)</td>
</tr>
<tr>
<th>2.0</th>
<td>871</td>
<td>(4.1)</td>
</tr>
<tr>
<th>3.0</th>
<td>773</td>
<td>(3.7)</td>
</tr>
</tbody>
</table>
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<pre>
</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [86]:</div>
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<div class="CodeMirror cm-s-jupyter">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">value</span> <span class="ow">in</span> <span class="n">dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'----------------------------</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">key</span><span class="p">,</span><span class="s1">'</span><span class="se">\n</span><span class="s1">----------------------------'</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.975</span><span class="p">])</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="s1">'</span><span class="si">{0:.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">))</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
</pre></div>
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<pre>----------------------------
Train
----------------------------
</pre>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>2.5%</th>
<th>50%</th>
<th>97.5%</th>
<th>max</th>
</tr>
</thead>
<tbody>
<tr>
<th>TestID</th>
<td>221255.00</td>
<td>1916079.72</td>
<td>598942.33</td>
<td>653096.00</td>
<td>773689.25</td>
<td>1919853.00</td>
<td>3025918.60</td>
<td>3397880.00</td>
</tr>
<tr>
<th>PatientID</th>
<td>221255.00</td>
<td>1023185484.09</td>
<td>46698741.53</td>
<td>1000000037.00</td>
<td>1000448341.00</td>
<td>1004999452.00</td>
<td>1200497918.75</td>
<td>1400016028.00</td>
</tr>
<tr>
<th>Gender</th>
<td>221255.00</td>
<td>0.48</td>
<td>0.50</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>Race</th>
<td>210467.00</td>
<td>4.19</td>
<td>4.56</td>
<td>0.00</td>
<td>0.00</td>
<td>3.00</td>
<td>11.00</td>
<td>11.00</td>
</tr>
<tr>
<th>Ventricular_Pacing_Present</th>
<td>221255.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>Poor_Data_Quality</th>
<td>221255.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary</th>
<td>221255.00</td>
<td>0.14</td>
<td>0.34</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary</th>
<td>221255.00</td>
<td>0.21</td>
<td>0.41</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary</th>
<td>221255.00</td>
<td>0.04</td>
<td>0.20</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary_backfilled</th>
<td>221255.00</td>
<td>0.14</td>
<td>0.34</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary_backfilled</th>
<td>221255.00</td>
<td>0.04</td>
<td>0.20</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary_backfilled</th>
<td>221255.00</td>
<td>0.21</td>
<td>0.41</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary</th>
<td>221255.00</td>
<td>0.33</td>
<td>0.47</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary_backfilled</th>
<td>221255.00</td>
<td>0.33</td>
<td>0.47</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PatientAge_Years</th>
<td>221255.00</td>
<td>62.59</td>
<td>16.59</td>
<td>2.00</td>
<td>25.00</td>
<td>64.00</td>
<td>90.00</td>
<td>120.00</td>
</tr>
<tr>
<th>Poor_Data_Quality_Present</th>
<td>221255.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>AtrialRate</th>
<td>221255.00</td>
<td>89.85</td>
<td>49.20</td>
<td>0.00</td>
<td>50.00</td>
<td>80.00</td>
<td>258.00</td>
<td>750.00</td>
</tr>
<tr>
<th>VentricularRate</th>
<td>221255.00</td>
<td>81.62</td>
<td>19.76</td>
<td>0.00</td>
<td>51.00</td>
<td>79.00</td>
<td>126.00</td>
<td>304.00</td>
</tr>
<tr>
<th>P_RInterval</th>
<td>221255.00</td>
<td>143.69</td>
<td>63.27</td>
<td>0.00</td>
<td>0.00</td>
<td>154.00</td>
<td>244.00</td>
<td>1262.00</td>
</tr>
<tr>
<th>QRSDuration</th>
<td>221255.00</td>
<td>97.72</td>
<td>23.92</td>
<td>0.00</td>
<td>68.00</td>
<td>90.00</td>
<td>162.00</td>
<td>286.00</td>
</tr>
<tr>
<th>QTCCalculation</th>
<td>221255.00</td>
<td>451.59</td>
<td>53.19</td>
<td>0.00</td>
<td>390.00</td>
<td>447.00</td>
<td>546.00</td>
<td>12006.00</td>
</tr>
<tr>
<th>QTcFredericia</th>
<td>127353.00</td>
<td>436.71</td>
<td>40.15</td>
<td>0.00</td>
<td>370.00</td>
<td>433.00</td>
<td>526.00</td>
<td>796.00</td>
</tr>
<tr>
<th>AtrialRate_standard_scale</th>
<td>221255.00</td>
<td>-0.00</td>
<td>1.00</td>
<td>-1.83</td>
<td>-0.81</td>
<td>-0.20</td>
<td>3.42</td>
<td>13.42</td>
</tr>
<tr>
<th>VentricularRate_standard_scale</th>
<td>221255.00</td>
<td>-0.00</td>
<td>1.00</td>
<td>-4.13</td>
<td>-1.55</td>
<td>-0.13</td>
<td>2.25</td>
<td>11.25</td>
</tr>
<tr>
<th>P_RInterval_standard_scale</th>
<td>221255.00</td>
<td>-0.00</td>
<td>1.00</td>
<td>-2.27</td>
<td>-2.27</td>
<td>0.16</td>
<td>1.59</td>
<td>17.67</td>
</tr>
<tr>
<th>QRSDuration_standard_scale</th>
<td>221255.00</td>
<td>0.00</td>
<td>1.00</td>
<td>-4.09</td>
<td>-1.24</td>
<td>-0.32</td>
<td>2.69</td>
<td>7.87</td>
</tr>
<tr>
<th>QTCCalculation_standard_scale</th>
<td>221255.00</td>
<td>-0.00</td>
<td>1.00</td>
<td>-8.49</td>
<td>-1.16</td>
<td>-0.09</td>
<td>1.77</td>
<td>217.22</td>
</tr>
<tr>
<th>PatientAge_Years_standard_scale</th>
<td>221255.00</td>
<td>-0.00</td>
<td>1.00</td>
<td>-3.65</td>
<td>-2.27</td>
<td>0.08</td>
<td>1.65</td>
<td>3.46</td>
</tr>
<tr>
<th>SINUS TACHYCARDIA</th>
<td>221255.00</td>
<td>0.12</td>
<td>0.32</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OCCASIONAL PREMATURE VENTRICULAR COMPLEXES</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.15</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE LEFT ATRIAL ENLARGEMENT</th>
<td>221255.00</td>
<td>0.13</td>
<td>0.33</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LOW VOLTAGE QRS</th>
<td>221255.00</td>
<td>0.05</td>
<td>0.21</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CANNOT RULE OUT ANTERIOR INFARCT</th>
<td>221255.00</td>
<td>0.03</td>
<td>0.16</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ABNORMAL ECG</th>
<td>221255.00</td>
<td>0.73</td>
<td>0.45</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NO PREVIOUS ECGS AVAILABLE</th>
<td>221242.00</td>
<td>0.13</td>
<td>0.34</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ANTERIOR INFARCT</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>WHEN COMPARED</th>
<td>221242.00</td>
<td>0.86</td>
<td>0.35</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>DX STATEMENT IGNORED INCLUDES NO LONGER PRESENT</th>
<td>221242.00</td>
<td>0.11</td>
<td>0.31</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SINUS BRADYCARDIA</th>
<td>221255.00</td>
<td>0.10</td>
<td>0.29</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OTHERWISE NORMAL ECG</th>
<td>221255.00</td>
<td>0.06</td>
<td>0.24</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NO SIGNIFICANT CHANGE WAS FOUND</th>
<td>221242.00</td>
<td>0.39</td>
<td>0.49</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SINUS ARRHYTHMIA</th>
<td>221255.00</td>
<td>0.03</td>
<td>0.16</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RSR OR QR PATTERN IN V1 AND/OR V2 SUGGESTS RIGHT VENTRICULAR CONDUCTION DELAY</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.15</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BORDERLINE ECG</th>
<td>221255.00</td>
<td>0.07</td>
<td>0.25</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NORMAL SINUS RHYTHM</th>
<td>221255.00</td>
<td>0.65</td>
<td>0.48</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OCCASIONAL PREMATURE SUPRAVENTRICULAR COMPLEXES</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.03</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NORMAL ECG</th>
<td>221255.00</td>
<td>0.12</td>
<td>0.32</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CANNOT RULE OUT INFERIOR INFARCT</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POOR DATA QUALITY</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>LEFT VENTRICULAR HYPERTROPHY</th>
<td>221255.00</td>
<td>0.11</td>
<td>0.32</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>INFERIOR INFARCT</th>
<td>221255.00</td>
<td>0.06</td>
<td>0.24</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE VENTRICULAR COMPLEXES</th>
<td>221255.00</td>
<td>0.03</td>
<td>0.16</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>1ST DEGREE AV BLOCK</th>
<td>221255.00</td>
<td>0.07</td>
<td>0.25</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC ST ABNORMALITY</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.14</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC T WAVE ABNORMALITY</th>
<td>221255.00</td>
<td>0.17</td>
<td>0.37</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PROLONGED QT</th>
<td>221242.00</td>
<td>0.10</td>
<td>0.30</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ATRIAL FIBRILLATION</th>
<td>221255.00</td>
<td>0.08</td>
<td>0.28</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC INTRAVENTRICULAR CONDUCTION DELAY</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QT HAS SHORTENED</th>
<td>221242.00</td>
<td>0.03</td>
<td>0.16</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS WIDENING AND REPOLARIZATION ABNORMALITY</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RAPID VENTRICULAR RESPONSE</th>
<td>221242.00</td>
<td>0.03</td>
<td>0.16</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC ST AND T WAVE ABNORMALITY</th>
<td>221255.00</td>
<td>0.05</td>
<td>0.21</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ATRIAL FLUTTER</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>VARIABLE AV BLOCK</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST & T WAVE ABNORMALITY</th>
<td>221255.00</td>
<td>0.07</td>
<td>0.25</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST DEPRESSION</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>T WAVE INVERSION</th>
<td>221255.00</td>
<td>0.06</td>
<td>0.24</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT BUNDLE BRANCH BLOCK</th>
<td>221255.00</td>
<td>0.08</td>
<td>0.27</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT AXIS DEVIATION</th>
<td>221255.00</td>
<td>0.11</td>
<td>0.31</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS AXIS SHIFTED LEFT</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST NORMALIZED</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.12</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QT HAS LENGTHENED</th>
<td>221242.00</td>
<td>0.03</td>
<td>0.17</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST ABNORMALITY</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE VENTRICULAR OR ABERRANTLY CONDUCTED COMPLEXES</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>REPOLARIZATION ABNORMALITY</th>
<td>221255.00</td>
<td>0.03</td>
<td>0.17</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>UNUSUAL P AXIS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ANTEROSEPTAL INFARCT</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>MINIMAL VOLTAGE CRITERIA FOR LVH</th>
<td>221242.00</td>
<td>0.05</td>
<td>0.21</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PULMONARY DISEASE PATTERN</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>MARKED SINUS ARRHYTHMIA</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>T WAVE ABNORMALITY</th>
<td>221242.00</td>
<td>0.08</td>
<td>0.28</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC INTRAVENTRICULAR CONDUCTION BLOCK</th>
<td>221242.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE SUPRAVENTRICULAR COMPLEXES ARE NOW PRESENT</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BIATRIAL ENLARGEMENT</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SEPTAL INFARCT</th>
<td>221255.00</td>
<td>0.04</td>
<td>0.20</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE ANTERIOR INFARCT</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CANNOT RULE OUT SEPTAL INFARCT</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT ANTERIOR FASCICULAR BLOCK</th>
<td>221255.00</td>
<td>0.04</td>
<td>0.19</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT BUNDLE BRANCH BLOCK</th>
<td>221255.00</td>
<td>0.03</td>
<td>0.18</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT AXIS DEVIATION</th>
<td>221255.00</td>
<td>0.03</td>
<td>0.16</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE INFERIOR INFARCT</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST ELEVATION</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PR INTERVAL HAS DECREASED</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT ATRIAL ENLARGEMENT</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ELECTRONIC VENTRICULAR PACEMAKER</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SHORT PR</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ANTEROLATERAL INFARCT</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS WIDENING</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT VENTRICULAR HYPERTROPHY</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>EARLY REPOLARIZATION</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ECTOPIC ATRIAL RHYTHM</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT POSTERIOR FASCICULAR BLOCK</th>
<td>221255.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>INCOMPLETE RIGHT BUNDLE BRANCH BLOCK</th>
<td>221255.00</td>
<td>0.04</td>
<td>0.20</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QUESTIONABLE CHANGE IN QRS AXIS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SLOW VENTRICULAR RESPONSE</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PR INTERVAL HAS INCREASED</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC CHANGE IN ST SEGMENT IN ANTERIOR LEADS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT ATRIAL ENLARGEMENT</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.12</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>FREQUENT PREMATURE VENTRICULAR COMPLEXES</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CANNOT RULE OUT ANTEROSEPTAL INFARCT</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE LATERAL INFARCT</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>INCOMPLETE LEFT BUNDLE BRANCH BLOCK</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BIFASCICULAR BLOCK</th>
<td>221255.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSTERIOR EXTENSION</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>INVERTED T WAVES</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.12</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>T WAVE AMPLITUDE HAS DECREASED IN ANTERIOR LEADS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SIGNIFICANT CHANGES HAVE OCCURRED</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ABNORMAL QRST ANGLE</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SUSPECT ARM LEAD REVERSAL</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS AXIS SHIFTED RIGHT</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>AV SEQUENTIAL OR DUAL CHAMBER ELECTRONIC PACEMAKER</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>PEDIATRIC ECG ANALYSIS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS DURATION HAS DECREASED</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LATERAL INFARCT</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QUESTIONABLE CHANGE IN INITIAL FORCES OF ANTERIOR LEADS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE ATRIAL COMPLEXES ARE NOW PRESENT</th>
<td>221242.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QUESTIONABLE CHANGE IN INITIAL FORCES OF SEPTAL LEADS</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PROMINENT MIDPRECORDIAL VOLTAGE</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.02</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC CHANGE IN ST SEGMENT IN INFERIOR LEADS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>DEMAND PACEMAKER INTERPRETATION IS BASED ON INTRINSIC RHYTHM</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE BIVENTRICULAR HYPERTROPHY</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.01</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>EARLY TRANSITION</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE ATRIAL COMPLEXES</th>
<td>221255.00</td>
<td>0.03</td>
<td>0.18</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS DURATION HAS INCREASED</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE SUPRAVENTRICULAR COMPLEXES</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC CHANGE IN ST SEGMENT IN LATERAL LEADS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POOR PRECORDIAL R WAVE PROGRESSION</th>
<td>221242.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ELECTRONIC ATRIAL PACEMAKER</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ABERRANT CONDUCTION</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>FIRST DEGREE AV BLOCK</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OTHERWISE NORMAL</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>FUSION COMPLEXES</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OCCASIONAL</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT VENTRICULAR CONDUCTION DELAY</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.03</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BASELINE ARTIFACT</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.02</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT SUPERIOR AXIS DEVIATION</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ACUTE MI</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>UNCERTAIN ATRIAL RHYTHM</th>
<td>221255.00</td>
<td>0.00</td>
<td>0.03</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>FUSION COMPLEXES ARE NOW PRESENT</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CURRENT ARTIFACT PREVENTS MEANINGFUL COMPARISON</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.01</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>AGE AND GENDER SPECIFIC ECG ANALYSIS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>VENTRICULARPACED RHYTHM</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>AV DUALPACED RHYTHM</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>ATRIALSENSED VENTRICULARPACED RHYTHM</th>
<td>221255.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BIVENTRICULAR PACEMAKER DETECTED</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>NORMAL AXIS</th>
<td>221242.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_stenosis_label_four_grade</th>
<td>221255.00</td>
<td>0.39</td>
<td>0.92</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>3.00</td>
<td>3.00</td>
</tr>
<tr>
<th>aortic_insufficiency_label_four_grade</th>
<td>221255.00</td>
<td>0.31</td>
<td>0.57</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>2.00</td>
<td>3.00</td>
</tr>
<tr>
<th>mitral_regurgitation_label_four_grade</th>
<td>221255.00</td>
<td>0.86</td>
<td>0.85</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>3.00</td>
<td>3.00</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
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<pre>----------------------------
Eval
----------------------------
</pre>
</div>
</div>
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<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
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.dataframe tbody tr th {
vertical-align: top;
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.dataframe thead th {
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>2.5%</th>
<th>50%</th>
<th>97.5%</th>
<th>max</th>
</tr>
</thead>
<tbody>
<tr>
<th>TestID</th>
<td>12950.00</td>
<td>2098154.59</td>
<td>496993.03</td>
<td>659608.00</td>
<td>1076422.65</td>
<td>2065930.00</td>
<td>3044861.45</td>
<td>3423945.00</td>
</tr>
<tr>
<th>PatientID</th>
<td>12950.00</td>
<td>1032654872.34</td>
<td>55337536.15</td>
<td>1000003169.00</td>
<td>1000566122.53</td>
<td>1006747477.50</td>
<td>1201003125.25</td>
<td>1400012871.00</td>
</tr>
<tr>
<th>Gender</th>
<td>12950.00</td>
<td>0.49</td>
<td>0.50</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>Race</th>
<td>11832.00</td>
<td>4.21</td>
<td>4.65</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>11.00</td>
<td>11.00</td>
</tr>
<tr>
<th>Ventricular_Pacing_Present</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>Poor_Data_Quality</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary</th>
<td>12950.00</td>
<td>0.10</td>
<td>0.30</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary</th>
<td>12950.00</td>
<td>0.09</td>
<td>0.29</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.15</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary_backfilled</th>
<td>12950.00</td>
<td>0.10</td>
<td>0.30</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary_backfilled</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.15</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary_backfilled</th>
<td>12950.00</td>
<td>0.09</td>
<td>0.29</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary</th>
<td>12950.00</td>
<td>0.19</td>
<td>0.39</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary_backfilled</th>
<td>12950.00</td>
<td>0.19</td>
<td>0.39</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PatientAge_Years</th>
<td>12950.00</td>
<td>63.60</td>
<td>17.24</td>
<td>10.00</td>
<td>25.00</td>
<td>65.00</td>
<td>92.00</td>
<td>117.00</td>
</tr>
<tr>
<th>Poor_Data_Quality_Present</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>AtrialRate</th>
<td>12950.00</td>
<td>86.85</td>
<td>46.84</td>
<td>0.00</td>
<td>49.00</td>
<td>78.00</td>
<td>250.00</td>
<td>535.00</td>
</tr>
<tr>
<th>VentricularRate</th>
<td>12950.00</td>
<td>79.58</td>
<td>19.07</td>
<td>0.00</td>
<td>50.00</td>
<td>77.00</td>
<td>123.00</td>
<td>191.00</td>
</tr>
<tr>
<th>P_RInterval</th>
<td>12950.00</td>
<td>144.59</td>
<td>61.56</td>
<td>0.00</td>
<td>0.00</td>
<td>154.00</td>
<td>244.00</td>
<td>480.00</td>
</tr>
<tr>
<th>QRSDuration</th>
<td>12950.00</td>
<td>96.63</td>
<td>23.85</td>
<td>0.00</td>
<td>68.00</td>
<td>90.00</td>
<td>160.00</td>
<td>246.00</td>
</tr>
<tr>
<th>QTCCalculation</th>
<td>12950.00</td>
<td>448.50</td>
<td>39.53</td>
<td>0.00</td>
<td>386.00</td>
<td>444.00</td>
<td>540.00</td>
<td>709.00</td>
</tr>
<tr>
<th>QTcFredericia</th>
<td>9585.00</td>
<td>432.16</td>
<td>37.87</td>
<td>0.00</td>
<td>372.00</td>
<td>428.00</td>
<td>517.00</td>
<td>716.00</td>
</tr>
<tr>
<th>AtrialRate_standard_scale</th>
<td>12950.00</td>
<td>0.00</td>
<td>1.00</td>
<td>-1.85</td>
<td>-0.81</td>
<td>-0.19</td>
<td>3.48</td>
<td>9.57</td>
</tr>
<tr>
<th>VentricularRate_standard_scale</th>
<td>12950.00</td>
<td>0.00</td>
<td>1.00</td>
<td>-4.17</td>
<td>-1.55</td>
<td>-0.14</td>
<td>2.28</td>
<td>5.84</td>
</tr>
<tr>
<th>P_RInterval_standard_scale</th>
<td>12950.00</td>
<td>-0.00</td>
<td>1.00</td>
<td>-2.35</td>
<td>-2.35</td>
<td>0.15</td>
<td>1.61</td>
<td>5.45</td>
</tr>
<tr>
<th>QRSDuration_standard_scale</th>
<td>12950.00</td>
<td>0.00</td>
<td>1.00</td>
<td>-4.05</td>
<td>-1.20</td>
<td>-0.28</td>
<td>2.66</td>
<td>6.26</td>
</tr>
<tr>
<th>QTCCalculation_standard_scale</th>
<td>12950.00</td>
<td>-0.00</td>
<td>1.00</td>
<td>-11.35</td>
<td>-1.58</td>
<td>-0.11</td>
<td>2.31</td>
<td>6.59</td>
</tr>
<tr>
<th>PatientAge_Years_standard_scale</th>
<td>12950.00</td>
<td>-0.00</td>
<td>1.00</td>
<td>-3.11</td>
<td>-2.24</td>
<td>0.08</td>
<td>1.65</td>
<td>3.10</td>
</tr>
<tr>
<th>SINUS TACHYCARDIA</th>
<td>12950.00</td>
<td>0.10</td>
<td>0.30</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OCCASIONAL PREMATURE VENTRICULAR COMPLEXES</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.14</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE LEFT ATRIAL ENLARGEMENT</th>
<td>12950.00</td>
<td>0.12</td>
<td>0.32</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LOW VOLTAGE QRS</th>
<td>12950.00</td>
<td>0.05</td>
<td>0.21</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CANNOT RULE OUT ANTERIOR INFARCT</th>
<td>12950.00</td>
<td>0.03</td>
<td>0.17</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ABNORMAL ECG</th>
<td>12950.00</td>
<td>0.67</td>
<td>0.47</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NO PREVIOUS ECGS AVAILABLE</th>
<td>12950.00</td>
<td>0.21</td>
<td>0.41</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ANTERIOR INFARCT</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.12</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>WHEN COMPARED</th>
<td>12950.00</td>
<td>0.77</td>
<td>0.42</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>DX STATEMENT IGNORED INCLUDES NO LONGER PRESENT</th>
<td>12950.00</td>
<td>0.11</td>
<td>0.31</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SINUS BRADYCARDIA</th>
<td>12950.00</td>
<td>0.11</td>
<td>0.32</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OTHERWISE NORMAL ECG</th>
<td>12950.00</td>
<td>0.07</td>
<td>0.26</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NO SIGNIFICANT CHANGE WAS FOUND</th>
<td>12950.00</td>
<td>0.35</td>
<td>0.48</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SINUS ARRHYTHMIA</th>
<td>12950.00</td>
<td>0.03</td>
<td>0.18</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RSR OR QR PATTERN IN V1 AND/OR V2 SUGGESTS RIGHT VENTRICULAR CONDUCTION DELAY</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.14</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BORDERLINE ECG</th>
<td>12950.00</td>
<td>0.08</td>
<td>0.27</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NORMAL SINUS RHYTHM</th>
<td>12950.00</td>
<td>0.66</td>
<td>0.47</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OCCASIONAL PREMATURE SUPRAVENTRICULAR COMPLEXES</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.01</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NORMAL ECG</th>
<td>12950.00</td>
<td>0.15</td>
<td>0.36</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CANNOT RULE OUT INFERIOR INFARCT</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POOR DATA QUALITY</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>LEFT VENTRICULAR HYPERTROPHY</th>
<td>12950.00</td>
<td>0.10</td>
<td>0.30</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>INFERIOR INFARCT</th>
<td>12950.00</td>
<td>0.06</td>
<td>0.23</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE VENTRICULAR COMPLEXES</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.14</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>1ST DEGREE AV BLOCK</th>
<td>12950.00</td>
<td>0.06</td>
<td>0.25</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC ST ABNORMALITY</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.15</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC T WAVE ABNORMALITY</th>
<td>12950.00</td>
<td>0.15</td>
<td>0.36</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PROLONGED QT</th>
<td>12950.00</td>
<td>0.08</td>
<td>0.27</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ATRIAL FIBRILLATION</th>
<td>12950.00</td>
<td>0.08</td>
<td>0.28</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC INTRAVENTRICULAR CONDUCTION DELAY</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QT HAS SHORTENED</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.15</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS WIDENING AND REPOLARIZATION ABNORMALITY</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RAPID VENTRICULAR RESPONSE</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.16</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC ST AND T WAVE ABNORMALITY</th>
<td>12950.00</td>
<td>0.04</td>
<td>0.19</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ATRIAL FLUTTER</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>VARIABLE AV BLOCK</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST & T WAVE ABNORMALITY</th>
<td>12950.00</td>
<td>0.05</td>
<td>0.22</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST DEPRESSION</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>T WAVE INVERSION</th>
<td>12950.00</td>
<td>0.05</td>
<td>0.23</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT BUNDLE BRANCH BLOCK</th>
<td>12950.00</td>
<td>0.07</td>
<td>0.26</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT AXIS DEVIATION</th>
<td>12950.00</td>
<td>0.10</td>
<td>0.30</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS AXIS SHIFTED LEFT</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST NORMALIZED</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QT HAS LENGTHENED</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.15</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST ABNORMALITY</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.03</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE VENTRICULAR OR ABERRANTLY CONDUCTED COMPLEXES</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>REPOLARIZATION ABNORMALITY</th>
<td>12950.00</td>
<td>0.03</td>
<td>0.16</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>UNUSUAL P AXIS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ANTEROSEPTAL INFARCT</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>MINIMAL VOLTAGE CRITERIA FOR LVH</th>
<td>12950.00</td>
<td>0.05</td>
<td>0.21</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PULMONARY DISEASE PATTERN</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>MARKED SINUS ARRHYTHMIA</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>T WAVE ABNORMALITY</th>
<td>12950.00</td>
<td>0.07</td>
<td>0.26</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC INTRAVENTRICULAR CONDUCTION BLOCK</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.12</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE SUPRAVENTRICULAR COMPLEXES ARE NOW PRESENT</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BIATRIAL ENLARGEMENT</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SEPTAL INFARCT</th>
<td>12950.00</td>
<td>0.04</td>
<td>0.20</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE ANTERIOR INFARCT</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CANNOT RULE OUT SEPTAL INFARCT</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT ANTERIOR FASCICULAR BLOCK</th>
<td>12950.00</td>
<td>0.04</td>
<td>0.19</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT BUNDLE BRANCH BLOCK</th>
<td>12950.00</td>
<td>0.04</td>
<td>0.19</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT AXIS DEVIATION</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.14</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE INFERIOR INFARCT</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ST ELEVATION</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PR INTERVAL HAS DECREASED</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT ATRIAL ENLARGEMENT</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ELECTRONIC VENTRICULAR PACEMAKER</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>SHORT PR</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ANTEROLATERAL INFARCT</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS WIDENING</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT VENTRICULAR HYPERTROPHY</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>EARLY REPOLARIZATION</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ECTOPIC ATRIAL RHYTHM</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT POSTERIOR FASCICULAR BLOCK</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>INCOMPLETE RIGHT BUNDLE BRANCH BLOCK</th>
<td>12950.00</td>
<td>0.03</td>
<td>0.18</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QUESTIONABLE CHANGE IN QRS AXIS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SLOW VENTRICULAR RESPONSE</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PR INTERVAL HAS INCREASED</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.09</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC CHANGE IN ST SEGMENT IN ANTERIOR LEADS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT ATRIAL ENLARGEMENT</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>FREQUENT PREMATURE VENTRICULAR COMPLEXES</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.08</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CANNOT RULE OUT ANTEROSEPTAL INFARCT</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE LATERAL INFARCT</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>INCOMPLETE LEFT BUNDLE BRANCH BLOCK</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BIFASCICULAR BLOCK</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSTERIOR EXTENSION</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>INVERTED T WAVES</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>T WAVE AMPLITUDE HAS DECREASED IN ANTERIOR LEADS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SIGNIFICANT CHANGES HAVE OCCURRED</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ABNORMAL QRST ANGLE</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>SUSPECT ARM LEAD REVERSAL</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS AXIS SHIFTED RIGHT</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>AV SEQUENTIAL OR DUAL CHAMBER ELECTRONIC PACEMAKER</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>PEDIATRIC ECG ANALYSIS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.03</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS DURATION HAS DECREASED</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LATERAL INFARCT</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QUESTIONABLE CHANGE IN INITIAL FORCES OF ANTERIOR LEADS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE ATRIAL COMPLEXES ARE NOW PRESENT</th>
<td>12950.00</td>
<td>0.02</td>
<td>0.13</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QUESTIONABLE CHANGE IN INITIAL FORCES OF SEPTAL LEADS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PROMINENT MIDPRECORDIAL VOLTAGE</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.02</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC CHANGE IN ST SEGMENT IN INFERIOR LEADS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.07</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>DEMAND PACEMAKER INTERPRETATION IS BASED ON INTRINSIC RHYTHM</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POSSIBLE BIVENTRICULAR HYPERTROPHY</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>EARLY TRANSITION</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE ATRIAL COMPLEXES</th>
<td>12950.00</td>
<td>0.03</td>
<td>0.18</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRS DURATION HAS INCREASED</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PREMATURE SUPRAVENTRICULAR COMPLEXES</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.11</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>NONSPECIFIC CHANGE IN ST SEGMENT IN LATERAL LEADS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>POOR PRECORDIAL R WAVE PROGRESSION</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ELECTRONIC ATRIAL PACEMAKER</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ABERRANT CONDUCTION</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>FIRST DEGREE AV BLOCK</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.03</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OTHERWISE NORMAL</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>FUSION COMPLEXES</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>OCCASIONAL</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.03</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>RIGHT VENTRICULAR CONDUCTION DELAY</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.02</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BASELINE ARTIFACT</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>RIGHT SUPERIOR AXIS DEVIATION</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.06</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>ACUTE MI</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>UNCERTAIN ATRIAL RHYTHM</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.03</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>FUSION COMPLEXES ARE NOW PRESENT</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>CURRENT ARTIFACT PREVENTS MEANINGFUL COMPARISON</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>AGE AND GENDER SPECIFIC ECG ANALYSIS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>VENTRICULARPACED RHYTHM</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>AV DUALPACED RHYTHM</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>ATRIALSENSED VENTRICULARPACED RHYTHM</th>
<td>12950.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>BIVENTRICULAR PACEMAKER DETECTED</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
</tr>
<tr>
<th>NORMAL AXIS</th>
<td>12950.00</td>
<td>0.00</td>
<td>0.04</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_stenosis_label_four_grade</th>
<td>12950.00</td>
<td>0.29</td>
<td>0.82</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>3.00</td>
<td>3.00</td>
</tr>
<tr>
<th>aortic_insufficiency_label_four_grade</th>
<td>12950.00</td>
<td>0.21</td>
<td>0.48</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>3.00</td>
</tr>
<tr>
<th>mitral_regurgitation_label_four_grade</th>
<td>12950.00</td>
<td>0.52</td>
<td>0.71</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>2.00</td>
<td>3.00</td>
</tr>
</tbody>
</table>
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<pre>----------------------------
Test
----------------------------
</pre>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>count</th>
<th>mean</th>
<th>std</th>
<th>min</th>
<th>2.5%</th>
<th>50%</th>
<th>97.5%</th>
<th>max</th>
</tr>
</thead>
<tbody>
<tr>
<th>TestID</th>
<td>21048.00</td>
<td>2522514.51</td>
<td>695914.96</td>
<td>1143854.00</td>
<td>1510838.32</td>
<td>2408502.00</td>
<td>3560716.75</td>
<td>3603193.00</td>
</tr>
<tr>
<th>PatientID</th>
<td>21048.00</td>
<td>1067022344.92</td>
<td>107495409.63</td>
<td>1000000178.00</td>
<td>1000619042.52</td>
<td>1009046963.00</td>
<td>1400118709.95</td>
<td>1400299107.00</td>
</tr>
<tr>
<th>Gender</th>
<td>21048.00</td>
<td>0.50</td>
<td>0.50</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>Race</th>
<td>12163.00</td>
<td>4.33</td>
<td>4.64</td>
<td>0.00</td>
<td>0.00</td>
<td>3.00</td>
<td>11.00</td>
<td>11.00</td>
</tr>
<tr>
<th>aortic_stenosis_label_four_grade</th>
<td>21048.00</td>
<td>0.17</td>
<td>0.59</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>3.00</td>
<td>3.00</td>
</tr>
<tr>
<th>aortic_insufficiency_label_four_grade</th>
<td>21048.00</td>
<td>0.13</td>
<td>0.37</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>3.00</td>
</tr>
<tr>
<th>mitral_regurgitation_label_four_grade</th>
<td>21048.00</td>
<td>0.31</td>
<td>0.56</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>2.00</td>
<td>3.00</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary</th>
<td>17497.00</td>
<td>0.05</td>
<td>0.22</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary</th>
<td>19414.00</td>
<td>0.04</td>
<td>0.19</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary</th>
<td>19632.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_stenosis_label_binary_backfilled</th>
<td>21048.00</td>
<td>0.04</td>
<td>0.20</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>aortic_insufficiency_label_binary_backfilled</th>
<td>21048.00</td>
<td>0.01</td>
<td>0.10</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>mitral_regurgitation_label_binary_backfilled</th>
<td>21048.00</td>
<td>0.04</td>
<td>0.18</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary</th>
<td>19902.00</td>
<td>0.08</td>
<td>0.28</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>AS_AI_MR_label_binary_backfilled</th>
<td>21048.00</td>
<td>0.08</td>
<td>0.27</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>QRSDuration</th>
<td>21048.00</td>
<td>92.84</td>
<td>20.29</td>
<td>0.00</td>
<td>68.00</td>
<td>88.00</td>
<td>150.00</td>
<td>208.00</td>
</tr>
<tr>
<th>RIGHT BUNDLE BRANCH BLOCK</th>
<td>20984.00</td>
<td>0.06</td>
<td>0.24</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
<td>1.00</td>
</tr>
<tr>
<th>LEFT BUNDLE BRANCH BLOCK</th>
<td>20984.00</td>
<td>0.02</td>
<td>0.15</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>days_since_echo</th>
<td>21048.00</td>
<td>-77.68</td>
<td>113.89</td>
<td>-365.00</td>
<td>-357.00</td>
<td>-11.00</td>
<td>0.00</td>
<td>1.00</td>
</tr>
<tr>
<th>PatientAge_Years</th>
<td>21012.00</td>
<td>61.52</td>
<td>17.53</td>
<td>0.00</td>
<td>24.00</td>
<td>63.00</td>
<td>91.00</td>
<td>121.00</td>
</tr>
</tbody>
</table>
</div>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [87]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">value</span> <span class="ow">in</span> <span class="n">dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'----------------------------</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">key</span><span class="p">,</span><span class="s1">'(Male=0, Female=1)</span><span class="se">\n</span><span class="s1">----------------------------'</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">Gender</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">Gender</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
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<pre>----------------------------
Train (Male=0, Female=1)
----------------------------
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<pre>0.0 115330
1.0 105925
Name: Gender, dtype: int64</pre>
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<pre>0.0 0.521254
1.0 0.478746
Name: Gender, dtype: float64</pre>
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<pre>----------------------------
Eval (Male=0, Female=1)
----------------------------
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<pre>0.0 6543
1.0 6407
Name: Gender, dtype: int64</pre>
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<pre>0.0 0.505251
1.0 0.494749
Name: Gender, dtype: float64</pre>
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<pre>----------------------------
Test (Male=0, Female=1)
----------------------------
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<pre>0.0 10568
1.0 10480
Name: Gender, dtype: int64</pre>
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<pre>0.0 0.50209
1.0 0.49791
Name: Gender, dtype: float64</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [88]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">age_bins</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">60</span><span class="p">,</span><span class="mi">70</span><span class="p">,</span><span class="mi">80</span><span class="p">,</span><span class="mi">120</span><span class="p">]</span>
<span class="n">bincounts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">train_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="n">age_bins</span><span class="p">,</span><span class="n">include_lowest</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">binpct</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">train_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="n">age_bins</span><span class="p">,</span><span class="n">include_lowest</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<h1 id="Compute-Overall-Stats-Using-Bootstrap">Compute Overall Stats Using Bootstrap<a class="anchor-link" href="#Compute-Overall-Stats-Using-Bootstrap">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_samples</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="n">test_race_ethnicity</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'All'</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">)</span>
<span class="n">as_summary</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">json_normalize</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="s1">'y_AS'</span><span class="p">,</span> <span class="s1">'y_pred_AS_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">mr_summary</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">json_normalize</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="s1">'y_MR'</span><span class="p">,</span> <span class="s1">'y_pred_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Aortic Insufficiency'</span><span class="p">)</span>
<span class="n">ai_summary</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">json_normalize</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="s1">'y_AI'</span><span class="p">,</span> <span class="s1">'y_pred_AI_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">)</span>
<span class="n">as_mr_ai_summary</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">json_normalize</span><span class="p">(</span><span class="n">bootstrap_stats</span><span class="p">(</span><span class="n">test_race_ethnicity</span><span class="p">,</span> <span class="s1">'y_AS_AI_MR'</span><span class="p">,</span> <span class="s1">'y_pred_AS_AI_MR_proba_sigmoid'</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n\n\n</span><span class="s1">'</span><span class="p">)</span>
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<pre>Aortic Stenosis
All
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<pre>100%|██████████| 1000/1000 [06:12<00:00, 2.68it/s]
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<pre>0.87 0.9 0.8669916820715248 0.8949386072775464
Mitral Regurgitation
All
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<pre>100%|██████████| 1000/1000 [06:29<00:00, 2.57it/s]
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<pre>0.81 0.85 0.8068351190833125 0.8491307165193205
Aortic Insufficiency
All
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<pre>100%|██████████| 1000/1000 [05:54<00:00, 2.82it/s]
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<pre>0.72 0.81 0.7220325695511524 0.8148359849302759
Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation
All
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<pre>100%|██████████| 1000/1000 [04:19<00:00, 3.85it/s]</pre>
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<pre>0.82 0.85 0.8221128211597443 0.8480400196977026
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">mr_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Aortic Insufficiency'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_tuples</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="s1">'Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation'</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span> <span class="n">as_mr_ai_summary</span><span class="o">.</span><span class="n">columns</span><span class="p">))</span>
<span class="n">overall_summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">as_summary</span><span class="p">,</span><span class="n">mr_summary</span><span class="p">,</span><span class="n">ai_summary</span><span class="p">,</span><span class="n">as_mr_ai_summary</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">overall_summary_stats</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Overall'</span><span class="p">]</span>
<span class="n">overall_summary_stats</span>
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<th></th>
<th colspan="6" halign="left">Aortic Stenosis</th>
<th colspan="6" halign="left">Mitral Regurgitation</th>
<th colspan="6" halign="left">Aortic Insufficiency</th>
<th colspan="6" halign="left">Combined Aortic Stenosis, Aortic Regurgitation, and Mitral Regurgitation</th>
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<tr>
<th></th>
<th>n</th>
<th>n_positives</th>
<th>roc_auc_actual</th>
<th>AU-ROC (95% CI)</th>
<th>odds_ratio_actual</th>
<th>OR (95% CI)</th>
<th>n</th>
<th>n_positives</th>
<th>roc_auc_actual</th>
<th>AU-ROC (95% CI)</th>
<th>odds_ratio_actual</th>
<th>OR (95% CI)</th>
<th>n</th>
<th>n_positives</th>
<th>roc_auc_actual</th>
<th>AU-ROC (95% CI)</th>
<th>odds_ratio_actual</th>
<th>OR (95% CI)</th>
<th>n</th>
<th>n_positives</th>
<th>roc_auc_actual</th>
<th>AU-ROC (95% CI)</th>
<th>odds_ratio_actual</th>
<th>OR (95% CI)</th>
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<th>Overall</th>
<td>21048</td>
<td>907</td>
<td>0.88</td>
<td>[0.87, 0.9]</td>
<td>15.2</td>
<td>[12.4, 19.3]</td>
<td>21048</td>
<td>742</td>
<td>0.83</td>
<td>[0.81, 0.85]</td>
<td>10.4</td>
<td>[8.2, 13.2]</td>
<td>21048</td>
<td>196</td>
<td>0.77</td>
<td>[0.72, 0.81]</td>
<td>6.7</td>
<td>[4.4, 10.2]</td>
<td>21048</td>
<td>1644</td>
<td>0.84</td>
<td>[0.82, 0.85]</td>
<td>9.7</td>
<td>[8.3, 11.3]</td>
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<h1 id="Plot-Results-For-All-Data">Plot Results For All Data<a class="anchor-link" href="#Plot-Results-For-All-Data">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Now combine overall, race, ethnicity and save results</span>
<span class="n">race_ethnicity_summary_stats</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">overall_summary_stats</span><span class="p">,</span> <span class="n">gender_summary_stats</span><span class="p">,</span> <span class="n">age_summary_stats</span><span class="p">,</span><span class="n">race_summary_stats</span><span class="p">,</span> <span class="n">ethnicity_summary_stats</span><span class="p">,</span><span class="n">bbb_summary_stats</span><span class="p">,</span><span class="n">qrs_summary_stats</span><span class="p">])</span>
<span class="n">race_ethnicity_summary_stats</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'all_subset_summary_stats.csv'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">race_ethnicity_summary_stats</span>
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<h1 id="Look-at-age-breakdown-on-per-study-level">Look at age breakdown on per study level<a class="anchor-link" href="#Look-at-age-breakdown-on-per-study-level">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">value</span> <span class="ow">in</span> <span class="n">dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'--------------</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">key</span><span class="p">,</span><span class="s1">'</span><span class="se">\n</span><span class="s1">--------------'</span><span class="p">)</span>
<span class="n">df</span><span class="o">=</span><span class="n">value</span>
<span class="n">age_bins</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">60</span><span class="p">,</span><span class="mi">70</span><span class="p">,</span><span class="mi">80</span><span class="p">,</span><span class="mi">120</span><span class="p">]</span>
<span class="n">bincounts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="n">age_bins</span><span class="p">,</span><span class="n">include_lowest</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">binpct</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="n">age_bins</span><span class="p">,</span><span class="n">include_lowest</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">binpct</span> <span class="o">=</span> <span class="s1">'('</span> <span class="o">+</span> <span class="n">binpct</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">+</span> <span class="s1">')'</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'counts'</span><span class="p">:</span> <span class="n">bincounts</span><span class="p">,</span> <span class="s1">'percent'</span><span class="p">:</span> <span class="n">binpct</span><span class="p">})</span>
<span class="n">display</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
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<pre>--------------
Train
--------------
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<th>counts</th>
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<th>(-0.001, 60.0]</th>
<td>92088</td>
<td>(41.6)</td>
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<th>(60.0, 70.0]</th>
<td>53696</td>
<td>(24.3)</td>
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<th>(70.0, 80.0]</th>
<td>43669</td>
<td>(19.7)</td>
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<th>(80.0, 120.0]</th>
<td>31802</td>
<td>(14.4)</td>
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<pre>--------------
Eval
--------------
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<th>counts</th>
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<th>(-0.001, 60.0]</th>
<td>5087</td>
<td>(39.3)</td>
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<th>(60.0, 70.0]</th>
<td>2952</td>
<td>(22.8)</td>
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<th>(70.0, 80.0]</th>
<td>2669</td>
<td>(20.6)</td>
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<th>(80.0, 120.0]</th>
<td>2242</td>
<td>(17.3)</td>
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<pre>--------------
Test
--------------
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<th>counts</th>
<th>percent</th>
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<th>(-0.001, 60.0]</th>
<td>9246</td>
<td>(43.9)</td>
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<th>(60.0, 70.0]</th>
<td>4811</td>
<td>(22.9)</td>
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<th>(70.0, 80.0]</th>
<td>3958</td>
<td>(18.8)</td>
</tr>
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<th>(80.0, 120.0]</th>
<td>2991</td>
<td>(14.2)</td>
</tr>
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<th>NaN</th>
<td>42</td>
<td>(0.2)</td>
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<h1 id="Look-at-age-breakdown-on-per-patient-level">Look at age breakdown on per patient level<a class="anchor-link" href="#Look-at-age-breakdown-on-per-patient-level">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">value</span> <span class="ow">in</span> <span class="n">dfs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'--------------</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span><span class="n">key</span><span class="p">,</span><span class="s1">'</span><span class="se">\n</span><span class="s1">--------------'</span><span class="p">)</span>
<span class="n">df</span><span class="o">=</span><span class="n">value</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">></span> <span class="n">df</span><span class="o">.</span><span class="n">PatientID</span><span class="o">.</span><span class="n">nunique</span><span class="p">():</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">train_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'PatientID'</span><span class="p">,</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">age_bins</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">60</span><span class="p">,</span><span class="mi">70</span><span class="p">,</span><span class="mi">80</span><span class="p">,</span><span class="mi">120</span><span class="p">]</span>
<span class="n">bincounts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="n">age_bins</span><span class="p">,</span><span class="n">include_lowest</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">binpct</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="n">age_bins</span><span class="p">,</span><span class="n">include_lowest</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">binpct</span> <span class="o">=</span> <span class="s1">'('</span> <span class="o">+</span> <span class="n">binpct</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">+</span> <span class="s1">')'</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'counts'</span><span class="p">:</span> <span class="n">bincounts</span><span class="p">,</span> <span class="s1">'percent'</span><span class="p">:</span> <span class="n">binpct</span><span class="p">})</span>
<span class="n">display</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
</pre></div>
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<pre>--------------
Train
--------------
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<th></th>
<th>counts</th>
<th>percent</th>
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</thead>
<tbody>
<tr>
<th>(-0.001, 60.0]</th>
<td>16933</td>
<td>(39.2)</td>
</tr>
<tr>
<th>(60.0, 70.0]</th>
<td>10041</td>
<td>(23.3)</td>
</tr>
<tr>
<th>(70.0, 80.0]</th>
<td>8763</td>
<td>(20.3)</td>
</tr>
<tr>
<th>(80.0, 120.0]</th>
<td>7428</td>
<td>(17.2)</td>
</tr>
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<pre>--------------
Eval
--------------
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<th></th>
<th>counts</th>
<th>percent</th>
</tr>
</thead>
<tbody>
<tr>
<th>(-0.001, 60.0]</th>
<td>5087</td>
<td>(39.3)</td>
</tr>
<tr>
<th>(60.0, 70.0]</th>
<td>2952</td>
<td>(22.8)</td>
</tr>
<tr>
<th>(70.0, 80.0]</th>
<td>2669</td>
<td>(20.6)</td>
</tr>
<tr>
<th>(80.0, 120.0]</th>
<td>2242</td>
<td>(17.3)</td>
</tr>
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<pre>--------------
Test
--------------
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<th>counts</th>
<th>percent</th>
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</thead>
<tbody>
<tr>
<th>(-0.001, 60.0]</th>
<td>9246</td>
<td>(43.9)</td>
</tr>
<tr>
<th>(60.0, 70.0]</th>
<td>4811</td>
<td>(22.9)</td>
</tr>
<tr>
<th>(70.0, 80.0]</th>
<td>3958</td>
<td>(18.8)</td>
</tr>
<tr>
<th>(80.0, 120.0]</th>
<td>2991</td>
<td>(14.2)</td>
</tr>
<tr>
<th>NaN</th>
<td>42</td>
<td>(0.2)</td>
</tr>
</tbody>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [233]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">s</span> <span class="o">=</span> <span class="n">train_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'PatientID'</span><span class="p">,</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">)[</span><span class="s1">'Gender'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">]</span>
<span class="n">counts</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">percent</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">percent100</span> <span class="o">=</span> <span class="s1">'('</span> <span class="o">+</span> <span class="n">s</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">+</span> <span class="s1">')'</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'counts'</span><span class="p">:</span> <span class="n">counts</span><span class="p">,</span> <span class="s1">'percent'</span><span class="p">:</span> <span class="n">percent100</span><span class="p">})</span>
<span class="n">display</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
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<th>0.0</th>
<td>21663</td>
<td>(50.2)</td>
</tr>
<tr>
<th>1.0</th>
<td>21502</td>
<td>(49.8)</td>
</tr>
</tbody>
</table>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [234]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">female_index</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="n">test_df</span><span class="o">.</span><span class="n">Gender</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
<span class="n">male_index</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="n">test_df</span><span class="o">.</span><span class="n">Gender</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age18to60</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">0</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">60</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age60to70</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">61</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">70</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age70to80</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">71</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">80</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age80plus</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">81</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">130</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age18to65</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">0</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">65</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age65plus</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">65</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">130</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'JACC_REVISIONS_test_female_index.npy'</span><span class="p">,</span><span class="n">female_index</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'JACC_REVISIONS_test_male_index.npy'</span><span class="p">,</span><span class="n">male_index</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'JACC_REVISIONS_test_age18to60_index.npy'</span><span class="p">,</span><span class="n">age18to60</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'JACC_REVISIONS_test_age60to70_index.npy'</span><span class="p">,</span><span class="n">age60to70</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'JACC_REVISIONS_test_age70to80_index.npy'</span><span class="p">,</span><span class="n">age70to80</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'JACC_REVISIONS_test_age80plus_index.npy'</span><span class="p">,</span><span class="n">age80plus</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'JACC_REVISIONS_test_age18to65_index.npy'</span><span class="p">,</span><span class="n">age18to65</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'JACC_REVISIONS_test_age65plus_index.npy'</span><span class="p">,</span><span class="n">age65plus</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">base_filename</span> <span class="o">=</span> <span class="s1">'JACC_REVISIONS_test'</span>
<span class="n">base_df</span> <span class="o">=</span> <span class="n">test_df</span>
<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'AS_AI_MR_label_binary_backfilled'</span><span class="p">]</span>
<span class="n">sample_prevalences</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.005</span><span class="p">,</span><span class="mf">0.01</span><span class="p">,</span><span class="mf">0.02</span><span class="p">,</span><span class="mf">0.05</span><span class="p">,</span><span class="mf">0.1</span><span class="p">]</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [91]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">prev</span> <span class="ow">in</span> <span class="n">sample_prevalences</span><span class="p">:</span>
<span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">labels</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span><span class="n">label</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">adjust_prevalence</span><span class="p">(</span><span class="n">base_df</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">prev</span><span class="p">,</span> <span class="n">minority_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">base_df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">'filename'</span><span class="p">,</span><span class="n">how</span><span class="o">=</span><span class="s2">"inner"</span><span class="p">,</span><span class="n">validate</span><span class="o">=</span><span class="s1">'1:1'</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">'index'</span><span class="p">)</span><span class="o">.</span><span class="n">index</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">),</span> <span class="s1">'index length incorrect'</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="n">filename</span> <span class="o">=</span> <span class="n">base_filename</span> <span class="o">+</span> <span class="s1">'_'</span> <span class="o">+</span> <span class="n">label</span> <span class="o">+</span> <span class="s1">'_prev_'</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'_index.npy'</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">index</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\n</span><span class="si">{</span><span class="n">filename</span><span class="si">}</span><span class="s1"> saved. </span><span class="se">\n\n</span><span class="s1">'</span><span class="p">)</span>
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<pre>0.005 AS_AI_MR_label_binary_backfilled
using make_imbalance
0.0 0.994591
1.0 0.005409
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 19306
1.0 105
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.005_index.npy saved.
0.01 AS_AI_MR_label_binary_backfilled
using make_imbalance
0.0 0.989186
1.0 0.010814
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 19209
1.0 210
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.01_index.npy saved.
0.02 AS_AI_MR_label_binary_backfilled
using make_imbalance
0.0 0.97839
1.0 0.02161
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 19015
1.0 420
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.02_index.npy saved.
0.05 AS_AI_MR_label_binary_backfilled
using make_imbalance
0.0 0.94601
1.0 0.05399
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 18433
1.0 1052
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.05_index.npy saved.
0.1 AS_AI_MR_label_binary_backfilled
using RandomUnderSampler
0.0 0.899994
1.0 0.100006
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 14795
1.0 1644
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.1_index.npy saved.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">base_filename</span> <span class="o">=</span> <span class="s1">'JACC_REVISIONS_test'</span>
<span class="n">base_df</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span><span class="o">>=</span><span class="mi">65</span><span class="p">]</span>
<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'AS_AI_MR_label_binary_backfilled'</span><span class="p">]</span>
<span class="n">sample_prevalences</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.005</span><span class="p">,</span><span class="mf">0.01</span><span class="p">,</span><span class="mf">0.02</span><span class="p">,</span><span class="mf">0.05</span><span class="p">,</span><span class="mf">0.1</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">prev</span> <span class="ow">in</span> <span class="n">sample_prevalences</span><span class="p">:</span>
<span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">labels</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span><span class="n">label</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">adjust_prevalence</span><span class="p">(</span><span class="n">base_df</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">prev</span><span class="p">,</span> <span class="n">minority_class</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">base_df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">'filename'</span><span class="p">,</span><span class="n">how</span><span class="o">=</span><span class="s2">"inner"</span><span class="p">,</span><span class="n">validate</span><span class="o">=</span><span class="s1">'1:1'</span><span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">'index'</span><span class="p">)</span><span class="o">.</span><span class="n">index</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">),</span> <span class="s1">'index length incorrect'</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">label</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">dropna</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="n">filename</span> <span class="o">=</span> <span class="n">base_filename</span> <span class="o">+</span> <span class="s1">'_'</span> <span class="o">+</span> <span class="n">label</span> <span class="o">+</span> <span class="s1">'_prev_'</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'_age_65_plus_index.npy'</span>
<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">index</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">filename</span><span class="si">}</span><span class="s1"> saved. </span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
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<pre>0.005 AS_AI_MR_label_binary_backfilled
using make_imbalance
0.0 0.994249
1.0 0.005751
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 8471
1.0 49
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.005_age_65_plus_index.npy saved.
0.01 AS_AI_MR_label_binary_backfilled
using make_imbalance
0.0 0.988506
1.0 0.011494
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 8428
1.0 98
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.01_age_65_plus_index.npy saved.
0.02 AS_AI_MR_label_binary_backfilled
using make_imbalance
0.0 0.976932
1.0 0.023068
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 8343
1.0 197
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.02_age_65_plus_index.npy saved.
0.05 AS_AI_MR_label_binary_backfilled
using make_imbalance
0.0 0.942657
1.0 0.057343
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 8088
1.0 492
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.05_age_65_plus_index.npy saved.
0.1 AS_AI_MR_label_binary_backfilled
using make_imbalance
0.0 0.886088
1.0 0.113912
Name: AS_AI_MR_label_binary_backfilled, dtype: float64
0.0 7662
1.0 985
Name: AS_AI_MR_label_binary_backfilled, dtype: int64
JACC_REVISIONS_test_AS_AI_MR_label_binary_backfilled_prev_0.1_age_65_plus_index.npy saved.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">female_index</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="n">test_df</span><span class="o">.</span><span class="n">Gender</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
<span class="n">male_index</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="n">test_df</span><span class="o">.</span><span class="n">Gender</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age18to60</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">0</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">60</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age60to70</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">61</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">70</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age70to80</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">71</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">80</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age80plus</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">81</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">130</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age18to65</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">0</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">65</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
<span class="n">age65plus</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[(</span><span class="n">test_df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o">>=</span><span class="mi">65</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">]</span> <span class="o"><=</span><span class="mi">130</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span>
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<h1 id="Now-we-run-the-vary_prevalance-scripts-from-ValveNet_v2-back-on-the-Dendrite-server">Now we run the vary_prevalance scripts from ValveNet_v2 back on the Dendrite server<a class="anchor-link" href="#Now-we-run-the-vary_prevalance-scripts-from-ValveNet_v2-back-on-the-Dendrite-server">¶</a></h1><p><code>scp -r /Users/pae2/Box/Heart\ Failure\ Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/test_set_subsampling_indices pae2115@10.144.220.25:/home/pae2115/ValveNet_JACC_Revisions</code></p>
<p><code>!python /home/pae2115/ValveNet/ValveNet_Multisite_Validation/eval_AS_vary_prevalence.py</code></p>
<p><code>!python /home/pae2115/ValveNet/ValveNet_Multisite_Validation/eval_AI_vary_prevalence.py</code></p>
<p><code>!python /home/pae2115/ValveNet/ValveNet_Multisite_Validation/eval_MR_vary_prevalence.py</code></p>
<p><code>!python /home/pae2115/ValveNet/ValveNet_Multisite_Validation/eval_AS_AI_MR_vary_prevalence.py</code></p>
<h3 id="results-found-at-Box/Heart-Failure-Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/eval_vary_prevalence_script_outputs.ipynb">results found at <code>Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/eval_vary_prevalence_script_outputs.ipynb</code><a class="anchor-link" href="#results-found-at-Box/Heart-Failure-Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/eval_vary_prevalence_script_outputs.ipynb">¶</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">'''</span>
<span class="sd">-------------------Eval_AS_AI_MR_Combination Results-------------------</span>
<span class="sd">Loading from validation_data_config <DataConfig(batch_size=64, shuffle=False, sampler=None, data_type=2, pin_memory=True, features_path=/home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_wander_removed_pct_truncated_mean_normalized_waveform_features.npy, labels_path=/home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_27k_test_metadata_no_prev_adjustment_new_ref_pace_removed_poor_quality_removed_wander_removed_pct_truncated_mean_normalized_prosthethics_removed_AS_AI_MR_label.npy, tabular_path=/home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_tabular.npy, features_permute_axes_order=[], labels_permute_axes_order=[]) @a9fd0></span>
<span class="sd">Loading features data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_wander_removed_pct_truncated_mean_normalized_waveform_features.npy</span>
<span class="sd">Loading labels data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_27k_test_metadata_no_prev_adjustment_new_ref_pace_removed_poor_quality_removed_wander_removed_pct_truncated_mean_normalized_prosthethics_removed_AS_AI_MR_label.npy</span>
<span class="sd">Loading tabular data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_tabular.npy</span>
<span class="sd">result shape before permute_axes_order (21048, 1, 2500, 12)</span>
<span class="sd">result shape before permute_axes_order (21048,)</span>
<span class="sd">result shape before permute_axes_order (21048, 7)</span>
<span class="sd">Done loading features data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_wander_removed_pct_truncated_mean_normalized_waveform_features.npy (21048, 1, 2500, 12)</span>
<span class="sd">Done loading labels data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_27k_test_metadata_no_prev_adjustment_new_ref_pace_removed_poor_quality_removed_wander_removed_pct_truncated_mean_normalized_prosthethics_removed_AS_AI_MR_label.npy (21048,)</span>
<span class="sd">Done loading tabular data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_tabular.npy (21048, 7)</span>
<span class="sd">Number of tabular features loaded 7</span>
<span class="sd">/home/pae2115/ValveNet_v2/common/data_utils.py:9: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1634272204863/work/torch/csrc/utils/tensor_numpy.cpp:189.)</span>
<span class="sd"> return torch.from_numpy(np_array).float()</span>
<span class="sd">Eval label count: Counter({0.0: 19404, 1.0: 1644})</span>
<span class="sd">Eval on: cuda:0</span>
<span class="sd">Iteration: [329/329] 100%|████████████████████████████████████████ [00:06<00:00]</span>
<span class="sd">Validation Loss (no label weights passed): 0.4700</span>
<span class="sd">Validation ROC AUC: 0.8346</span>
<span class="sd">Validation AUPRC: 0.3202</span>
<span class="sd">Validation Precision at 2% Recall: 0.55</span>
<span class="sd">Validation Precision at 5% Recall: 0.5845070422535211</span>
<span class="sd">Validation Precision at 10% Recall: 0.5254777070063694</span>
<span class="sd">Validation Precision at 20% Recall: 0.45254470426409904</span>
<span class="sd">Validation Precision at 50% Recall: 0.30847076461769113</span>
<span class="sd">Validation Precision at 75% Recall: 0.21206392851005326</span>
<span class="sd">Validation Precision at 90% Recall: 0.152766308835673</span>
<span class="sd">Validation f1: 0.3390070921985812</span>
<span class="sd">-------------------JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plus Results-------------------</span>
<span class="sd">Loading from validation_data_config <DataConfig(batch_size=64, shuffle=False, sampler=None, data_type=2, pin_memory=True, features_path=./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_waveform_array.npy, labels_path=./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_label_array.npy, tabular_path=./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_tabular_array.npy, features_permute_axes_order=[], labels_permute_axes_order=[]) @1e0970></span>
<span class="sd">Loading features data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_waveform_array.npy</span>
<span class="sd">Loading labels data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_label_array.npy</span>
<span class="sd">Loading tabular data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_tabular_array.npy</span>
<span class="sd">result shape before permute_axes_order (9858, 1, 2500, 12)</span>
<span class="sd">result shape before permute_axes_order (9858,)</span>
<span class="sd">result shape before permute_axes_order (9858, 7)</span>
<span class="sd">Done loading features data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_waveform_array.npy (9858, 1, 2500, 12)</span>
<span class="sd">Done loading labels data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_label_array.npy (9858,)</span>
<span class="sd">Done loading tabular data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_tabular_array.npy (9858, 7)</span>
<span class="sd">Number of tabular features loaded 7</span>
<span class="sd">Eval label count: Counter({0.0: 8514, 1.0: 1344})</span>
<span class="sd">Eval on: cuda:0</span>
<span class="sd">Iteration: [155/155] 100%|████████████████████████████████████████ [00:03<00:00]</span>
<span class="sd">Validation Loss (no label weights passed): 0.6517</span>
<span class="sd">Validation ROC AUC: 0.7695</span>
<span class="sd">Validation AUPRC: 0.3502</span>
<span class="sd">Validation Precision at 2% Recall: 0.5094339622641509</span>
<span class="sd">Validation Precision at 5% Recall: 0.5714285714285714</span>
<span class="sd">Validation Precision at 10% Recall: 0.5625</span>
<span class="sd">Validation Precision at 20% Recall: 0.4786476868327402</span>
<span class="sd">Validation Precision at 50% Recall: 0.33465937344604674</span>
<span class="sd">Validation Precision at 75% Recall: 0.2518093336660844</span>
<span class="sd">Validation Precision at 90% Recall: 0.20026481297583582</span>
<span class="sd">Validation f1: 0.36382148311556045</span>
<span class="sd">'''</span>
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<pre>'\n-------------------Eval_AS_AI_MR_Combination Results-------------------\nLoading from validation_data_config <DataConfig(batch_size=64, shuffle=False, sampler=None, data_type=2, pin_memory=True, features_path=/home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_wander_removed_pct_truncated_mean_normalized_waveform_features.npy, labels_path=/home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_27k_test_metadata_no_prev_adjustment_new_ref_pace_removed_poor_quality_removed_wander_removed_pct_truncated_mean_normalized_prosthethics_removed_AS_AI_MR_label.npy, tabular_path=/home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_tabular.npy, features_permute_axes_order=[], labels_permute_axes_order=[]) @a9fd0>\n\n\nLoading features data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_wander_removed_pct_truncated_mean_normalized_waveform_features.npy\nLoading labels data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_27k_test_metadata_no_prev_adjustment_new_ref_pace_removed_poor_quality_removed_wander_removed_pct_truncated_mean_normalized_prosthethics_removed_AS_AI_MR_label.npy\nLoading tabular data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_tabular.npy\nresult shape before permute_axes_order (21048, 1, 2500, 12)\nresult shape before permute_axes_order (21048,)\nresult shape before permute_axes_order (21048, 7)\n\nDone loading features data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_wander_removed_pct_truncated_mean_normalized_waveform_features.npy (21048, 1, 2500, 12)\nDone loading labels data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_27k_test_metadata_no_prev_adjustment_new_ref_pace_removed_poor_quality_removed_wander_removed_pct_truncated_mean_normalized_prosthethics_removed_AS_AI_MR_label.npy (21048,)\nDone loading tabular data from /home/pae2115/ValveNet_JACC_Revisions/JACC_REVISION_test_df_newest_ecg_per_pt_tabular.npy (21048, 7)\nNumber of tabular features loaded 7\n/home/pae2115/ValveNet_v2/common/data_utils.py:9: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1634272204863/work/torch/csrc/utils/tensor_numpy.cpp:189.)\n return torch.from_numpy(np_array).float()\n\nEval label count: Counter({0.0: 19404, 1.0: 1644})\nEval on: cuda:0\nIteration: [329/329] 100%|████████████████████████████████████████ [00:06<00:00]\nValidation Loss (no label weights passed): 0.4700\nValidation ROC AUC: 0.8346\nValidation AUPRC: 0.3202\nValidation Precision at 2% Recall: 0.55\nValidation Precision at 5% Recall: 0.5845070422535211\nValidation Precision at 10% Recall: 0.5254777070063694\nValidation Precision at 20% Recall: 0.45254470426409904\nValidation Precision at 50% Recall: 0.30847076461769113\nValidation Precision at 75% Recall: 0.21206392851005326\nValidation Precision at 90% Recall: 0.152766308835673\nValidation f1: 0.3390070921985812\n\n\n-------------------JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plus Results-------------------\nLoading from validation_data_config <DataConfig(batch_size=64, shuffle=False, sampler=None, data_type=2, pin_memory=True, features_path=./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_waveform_array.npy, labels_path=./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_label_array.npy, tabular_path=./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_tabular_array.npy, features_permute_axes_order=[], labels_permute_axes_order=[]) @1e0970>\n\n\nLoading features data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_waveform_array.npy\nLoading labels data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_label_array.npy\nLoading tabular data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_tabular_array.npy\nresult shape before permute_axes_order (9858, 1, 2500, 12)\nresult shape before permute_axes_order (9858,)\nresult shape before permute_axes_order (9858, 7)\n\nDone loading features data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_waveform_array.npy (9858, 1, 2500, 12)\nDone loading labels data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_label_array.npy (9858,)\nDone loading tabular data from ./JACC_REVISION_Eval_AS_AI_MR_JACC_REVISIONS_test_age65plustmp_validation_tabular_array.npy (9858, 7)\nNumber of tabular features loaded 7\n\nEval label count: Counter({0.0: 8514, 1.0: 1344})\nEval on: cuda:0\nIteration: [155/155] 100%|████████████████████████████████████████ [00:03<00:00]\nValidation Loss (no label weights passed): 0.6517\nValidation ROC AUC: 0.7695\nValidation AUPRC: 0.3502\nValidation Precision at 2% Recall: 0.5094339622641509\nValidation Precision at 5% Recall: 0.5714285714285714\nValidation Precision at 10% Recall: 0.5625\nValidation Precision at 20% Recall: 0.4786476868327402\nValidation Precision at 50% Recall: 0.33465937344604674\nValidation Precision at 75% Recall: 0.2518093336660844\nValidation Precision at 90% Recall: 0.20026481297583582\nValidation f1: 0.36382148311556045\n'</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">as_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv'</span><span class="p">)</span>
<span class="n">ai_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv'</span><span class="p">)</span>
<span class="n">mr_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv'</span><span class="p">)</span>
<span class="n">as_ai_mr_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv'</span><span class="p">)</span>
<span class="n">lstFiles</span> <span class="o">=</span> <span class="p">[</span><span class="n">as_path</span><span class="p">,</span><span class="n">ai_path</span><span class="p">,</span><span class="n">mr_path</span><span class="p">,</span><span class="n">as_ai_mr_path</span><span class="p">]</span>
<span class="n">study_name</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">lstFiles</span><span class="p">]</span>
<span class="n">study_name</span>
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<pre>['Aortic_Stenosis',
'Aortic_Insufficiency',
'Mitral_Regurgitation',
'AS_AI_MR_Combination']</pre>
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<h1 id="Test-Set-AUROC-and-AUPRC-Curves-(Fig-3A-and-3B)">Test Set AUROC and AUPRC Curves (Fig 3A and 3B)<a class="anchor-link" href="#Test-Set-AUROC-and-AUPRC-Curves-(Fig-3A-and-3B)">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">lstFiles</span><span class="p">:</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">))</span>
<span class="n">testy</span><span class="p">,</span> <span class="n">lr_probs</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">neg_count</span><span class="p">,</span> <span class="n">pos_count</span> <span class="o">=</span> <span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="s1">',d'</span><span class="p">)),(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span><span class="s1">',d'</span><span class="p">))</span>
<span class="n">lr_precision</span><span class="p">,</span> <span class="n">lr_recall</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">precision_recall_curve</span><span class="p">(</span><span class="n">testy</span><span class="p">,</span> <span class="n">lr_probs</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="n">lr_f1</span><span class="p">,</span> <span class="n">lr_auc</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">testy</span><span class="p">,</span> <span class="n">yhat</span><span class="p">),</span> <span class="n">auc</span><span class="p">(</span><span class="n">lr_recall</span><span class="p">,</span> <span class="n">lr_precision</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">total_count</span> <span class="o">=</span> <span class="nb">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">),</span><span class="s1">',d'</span><span class="p">)</span>
<span class="c1"># print(tpr)</span>
<span class="c1"># print(fpr)</span>
<span class="c1"># print(thresholds)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUROC is:'</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">))</span>
<span class="n">auroc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="n">optimal_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">optimal_threshold</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">optimal_idx</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Threshold value is:"</span><span class="p">,</span> <span class="n">optimal_threshold</span><span class="p">)</span>
<span class="c1"># labelname = file.partition('Eval_')[2].partition('_2022')[0] + f' [{pos_count} / {total_count}]' + f' (AUROC={auroc})'</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' (AUROC = </span><span class="si">{</span><span class="n">auroc</span><span class="si">}</span><span class="s1">)'</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"AS_AI_MR"</span><span class="p">,</span> <span class="s2">"AS, AR, or MR"</span><span class="p">)</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"_"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">)</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Combination"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span>
<span class="c1"># labelname = labelname.replace("AI", "Aortic Regurgitation")</span>
<span class="c1"># labelname = labelname.replace("AS", "Aortic Stenosis")</span>
<span class="c1"># labelname = labelname.replace("MR", "Mitral Regurgitation")</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Aortic Insufficiency"</span><span class="p">,</span> <span class="s2">"Aortic Regurgitation"</span><span class="p">)</span>
<span class="n">marker</span><span class="o">=</span><span class="s1">','</span>
<span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span>
<span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span>
<span class="k">if</span> <span class="n">labelname</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">):</span>
<span class="n">colorwheel</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Reds</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span><span class="n">n_AS</span><span class="p">))</span>
<span class="n">color</span><span class="o">=</span><span class="n">colorwheel</span><span class="p">[</span><span class="n">c_AS</span><span class="p">]</span>
<span class="k">if</span> <span class="n">labelname</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'Aortic Regurgitation'</span><span class="p">):</span>
<span class="n">colorwheel</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Blues</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.6</span><span class="p">,</span><span class="n">n_AI</span><span class="p">))</span>
<span class="n">color</span><span class="o">=</span><span class="n">colorwheel</span><span class="p">[</span><span class="n">c_AI</span><span class="p">]</span>
<span class="k">if</span> <span class="n">labelname</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">):</span>
<span class="n">colorwheel</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Greens</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.6</span><span class="p">,</span><span class="n">n_MR</span><span class="p">))</span>
<span class="n">color</span><span class="o">=</span><span class="n">colorwheel</span><span class="p">[</span><span class="n">c_MR</span><span class="p">]</span>
<span class="k">if</span> <span class="s1">'AS, AR, or MR'</span> <span class="ow">in</span> <span class="n">labelname</span><span class="p">:</span>
<span class="n">colorwheel</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Greys</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.8</span><span class="p">,</span><span class="n">n_AS_AI_MR</span><span class="p">))</span>
<span class="n">color</span><span class="o">=</span><span class="n">colorwheel</span><span class="p">[</span><span class="n">c_AS_AI_MR</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">labelname</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span><span class="n">marker</span><span class="o">=</span><span class="n">marker</span><span class="p">,</span><span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span><span class="s1">'--k'</span><span class="p">)</span> <span class="c1">#reference line dashed black</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> <span class="c1">#remove gridlines</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'1-Specificity'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Sensitivity'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'AUROC per Valvular Disease and Combination Model in Test Cohort'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">,</span><span class="n">fontweight</span><span class="o">=</span><span class="s2">"bold"</span><span class="p">,</span><span class="n">pad</span> <span class="o">=</span> <span class="mi">34</span><span class="p">)</span>
<span class="c1"># show the legend</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">),</span><span class="s1">',d'</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s1">'ValveNet Model, N = </span><span class="si">{</span><span class="n">total_count</span><span class="si">}</span><span class="s1"> patients'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="o">.</span><span class="mi">50</span><span class="p">,</span><span class="o">.</span><span class="mi">20</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">get_legend</span><span class="p">()</span><span class="o">.</span><span class="n">get_title</span><span class="p">(),</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">'22'</span><span class="p">)</span> <span class="c1">#change legend title fontsize</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/Test_ROC.png'</span><span class="p">)</span>
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<pre>AUROC is: 0.8804872725564813
Threshold value is: 0.4290496913006215
AUROC is: 0.7687817837666431
Threshold value is: 0.3979350694912722
AUROC is: 0.828017318849102
Threshold value is: 0.45940651160283796
AUROC is: 0.8354910330275293
Threshold value is: 0.45716718045293103
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">cycle</span>
<span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'y_pred_proba'</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
<span class="c1">#generate counts of each label number of studies, only non 65 plus</span>
<span class="n">n_AS</span><span class="p">,</span> <span class="n">n_AI</span><span class="p">,</span> <span class="n">n_MR</span><span class="p">,</span> <span class="n">n_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="k">for</span> <span class="n">study</span> <span class="ow">in</span> <span class="n">study_name</span><span class="p">:</span>
<span class="k">if</span> <span class="s1">'65plus'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">study</span><span class="p">:</span>
<span class="k">if</span> <span class="n">study</span><span class="o">==</span><span class="s1">'AS'</span><span class="p">:</span>
<span class="n">n_AS</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">study</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'AI'</span><span class="p">):</span>
<span class="n">n_AI</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="n">study</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'MR'</span><span class="p">):</span>
<span class="n">n_MR</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="s1">'AS_AI_MR'</span> <span class="ow">in</span> <span class="n">study</span><span class="p">:</span>
<span class="n">n_AS_AI_MR</span><span class="o">+=</span><span class="mi">1</span>
<span class="c1">#generate color counts (must make separate for age 65 if we want the two studies that only differ from age to have same color)</span>
<span class="n">c_AS</span><span class="p">,</span> <span class="n">c_AI</span><span class="p">,</span> <span class="n">c_MR</span><span class="p">,</span> <span class="n">c_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="n">c65_AS</span><span class="p">,</span> <span class="n">c65_AI</span><span class="p">,</span> <span class="n">c65_MR</span><span class="p">,</span> <span class="n">c65_AS_AI_MR</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">lstFiles</span><span class="p">:</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">))</span>
<span class="n">testy</span><span class="p">,</span> <span class="n">lr_probs</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">neg_count</span><span class="p">,</span> <span class="n">pos_count</span> <span class="o">=</span> <span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="s1">',d'</span><span class="p">)),(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span><span class="s1">',d'</span><span class="p">))</span>
<span class="n">lr_precision</span><span class="p">,</span> <span class="n">lr_recall</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">precision_recall_curve</span><span class="p">(</span><span class="n">testy</span><span class="p">,</span> <span class="n">lr_probs</span><span class="p">)</span>
<span class="n">auprc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">average_precision_score</span><span class="p">(</span><span class="n">testy</span><span class="p">,</span> <span class="n">lr_probs</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
<span class="n">total_count</span> <span class="o">=</span> <span class="nb">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">),</span><span class="s1">',d'</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="n">lr_f1</span><span class="p">,</span> <span class="n">lr_auc</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">testy</span><span class="p">,</span> <span class="n">yhat</span><span class="p">),</span> <span class="n">auc</span><span class="p">(</span><span class="n">lr_recall</span><span class="p">,</span> <span class="n">lr_precision</span><span class="p">)</span>
<span class="c1"># summarize scores</span>
<span class="c1"># print('Logistic: f1=%.3f auc=%.3f' % (lr_f1, lr_auc))</span>
<span class="c1"># plot the precision-recall curves</span>
<span class="c1"># labelname = file.partition('Eval_')[2].partition('_2022')[0] + f' [{pos_count} / {total_count}]' + f' (AUPRC={auprc})'</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">' (AUPRC = </span><span class="si">{</span><span class="n">auprc</span><span class="si">}</span><span class="s1">)'</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"AS_AI_MR"</span><span class="p">,</span> <span class="s2">"AS, AR, or MR"</span><span class="p">)</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"_"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">)</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Combination"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span>
<span class="c1"># labelname = labelname.replace("AI", "Aortic Regurgitation")</span>
<span class="c1"># labelname = labelname.replace("AS", "Aortic Stenosis")</span>
<span class="c1"># labelname = labelname.replace("MR", "Mitral Regurgitation")</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Aortic Insufficiency"</span><span class="p">,</span> <span class="s2">"Aortic Regurgitation"</span><span class="p">)</span>
<span class="n">linestyle</span><span class="o">=</span><span class="s1">'solid'</span>
<span class="n">marker</span><span class="o">=</span><span class="s1">','</span>
<span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span>
<span class="k">if</span> <span class="n">labelname</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'Aortic Stenosis'</span><span class="p">):</span>
<span class="n">colorwheel</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Reds</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span><span class="n">n_AS</span><span class="p">))</span>
<span class="n">color</span><span class="o">=</span><span class="n">colorwheel</span><span class="p">[</span><span class="n">c_AS</span><span class="p">]</span>
<span class="k">if</span> <span class="n">labelname</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'Aortic Regurgitation'</span><span class="p">):</span>
<span class="n">colorwheel</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Blues</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.6</span><span class="p">,</span><span class="n">n_AI</span><span class="p">))</span>
<span class="n">color</span><span class="o">=</span><span class="n">colorwheel</span><span class="p">[</span><span class="n">c_AI</span><span class="p">]</span>
<span class="k">if</span> <span class="n">labelname</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'Mitral Regurgitation'</span><span class="p">):</span>
<span class="n">colorwheel</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Greens</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.6</span><span class="p">,</span><span class="n">n_MR</span><span class="p">))</span>
<span class="n">color</span><span class="o">=</span><span class="n">colorwheel</span><span class="p">[</span><span class="n">c_MR</span><span class="p">]</span>
<span class="k">if</span> <span class="s1">'AS, AR, or MR'</span> <span class="ow">in</span> <span class="n">labelname</span><span class="p">:</span>
<span class="n">colorwheel</span><span class="o">=</span><span class="n">pl</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Greys</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.8</span><span class="p">,</span><span class="n">n_AS_AI_MR</span><span class="p">))</span>
<span class="n">color</span><span class="o">=</span><span class="n">colorwheel</span><span class="p">[</span><span class="n">c_AS_AI_MR</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lr_recall</span><span class="p">,</span> <span class="n">lr_precision</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="n">marker</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">labelname</span><span class="p">,</span>
<span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span><span class="n">linestyle</span><span class="o">=</span><span class="n">linestyle</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
<span class="c1"># no_skill = len(testy[testy==1]) / len(testy) </span>
<span class="c1"># pyplot.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')</span>
<span class="c1"># axis labels</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Recall'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Precision'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Precision Recall Curves per Valvular Disease'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">,</span><span class="n">fontweight</span><span class="o">=</span><span class="s2">"bold"</span><span class="p">,</span><span class="n">pad</span> <span class="o">=</span> <span class="mi">34</span><span class="p">)</span>
<span class="c1"># show the legend</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s1">'ValveNet Model, N = </span><span class="si">{</span><span class="n">total_count</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="o">.</span><span class="mi">4</span><span class="p">,</span><span class="o">.</span><span class="mi">7</span><span class="p">),</span><span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">setp</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">get_legend</span><span class="p">()</span><span class="o">.</span><span class="n">get_title</span><span class="p">(),</span> <span class="n">fontsize</span><span class="o">=</span><span class="s1">'28'</span><span class="p">)</span> <span class="c1">#change legend title fontsize</span>
<span class="c1"># show the plot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/Test_AUPRC.png'</span><span class="p">)</span>
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<pre><Figure size 432x288 with 0 Axes></pre>
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<h1 id="Compute-a-Logistic-Regression-Model-using-Tabular-Data">Compute a Logistic Regression Model using Tabular Data<a class="anchor-link" href="#Compute-a-Logistic-Regression-Model-using-Tabular-Data">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [193]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">CONTINUOUS_COLUMNS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'PatientAge_Years'</span><span class="p">,</span><span class="s1">'QRSDuration'</span><span class="p">,</span><span class="s1">'P_RInterval'</span><span class="p">,</span><span class="s1">'QTCCalculation'</span><span class="p">,</span><span class="s1">'VentricularRate'</span><span class="p">,</span><span class="s1">'AtrialRate'</span><span class="p">]</span>
<span class="n">CONTINUOUS_COLUMNS_STANDARD_SCALE</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'PatientAge_Years_standard_scale'</span><span class="p">,</span><span class="s1">'QRSDuration_standard_scale'</span><span class="p">,</span><span class="s1">'P_RInterval_standard_scale'</span><span class="p">,</span><span class="s1">'QTCCalculation_standard_scale'</span><span class="p">,</span>
<span class="s1">'VentricularRate_standard_scale'</span><span class="p">,</span><span class="s1">'AtrialRate_standard_scale'</span><span class="p">]</span>
<span class="n">TABULAR_COLUMNS_TRUNCATED</span> <span class="o">=</span> <span class="n">CONTINUOUS_COLUMNS_STANDARD_SCALE</span> <span class="o">+</span> <span class="p">[</span><span class="s1">'Gender'</span><span class="p">]</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [198]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">logit_train_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/220k_multivalve_train_tabular_metadata_new_ref.csv'</span><span class="p">))</span>
<span class="n">logit_test_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISION_test_df_metadata_available.csv'</span><span class="p">))</span>
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (30,53,57,58,66) have mixed types.Specify dtype option on import or set low_memory=False.
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
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<div class="jp-InputPrompt jp-InputArea-prompt">In [199]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_tabular</span> <span class="o">=</span> <span class="n">logit_train_df</span><span class="p">[</span><span class="n">TABULAR_COLUMNS_TRUNCATED</span><span class="p">]</span>
<span class="n">test_tabular</span> <span class="o">=</span> <span class="n">logit_test_df</span><span class="p">[</span><span class="n">TABULAR_COLUMNS_TRUNCATED</span><span class="p">]</span>
<span class="n">train_label</span> <span class="o">=</span> <span class="n">logit_train_df</span><span class="p">[</span><span class="s1">'AS_AI_MR_label_binary_backfilled'</span><span class="p">]</span>
<span class="n">test_label</span> <span class="o">=</span> <span class="n">logit_test_df</span><span class="p">[</span><span class="s1">'AS_AI_MR_label_binary_backfilled'</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">train_tabular</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">test_tabular</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">train_label</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">test_label</span>
<span class="n">X_train_constant</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="c1"># logit_model_constant = sm.Logit(y_train, X_train_constant) #tried with and without adding a constant</span>
<span class="c1"># result_1 = logit_model_constant.fit()</span>
<span class="c1"># print (result_1.summary())</span>
<span class="c1"># params = result_1.params</span>
<span class="c1"># conf = result_1.conf_int()</span>
<span class="c1"># conf['OR'] = params</span>
<span class="c1"># conf.columns = ['2.5%', '97.5%', 'OR']</span>
<span class="c1"># print (np.exp(conf))</span>
<span class="c1"># X_train, X_test, y_train, y_test = train_test_split(sm.add_constant(X), y_Amyloid_Yes_No, test_size=0.5, random_state=0)</span>
<span class="n">logreg</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
<span class="n">logreg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_constant</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">X_test_constant</span> <span class="o">=</span> <span class="n">sm</span><span class="o">.</span><span class="n">add_constant</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">logreg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_constant</span><span class="p">)</span>
<span class="n">logit_roc_auc</span> <span class="o">=</span> <span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">logreg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_constant</span><span class="p">))</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">logreg</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test_constant</span><span class="p">)[:,</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Accuracy of logistic regression classifier on test set: </span><span class="si">{:.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">logreg</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_constant</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUC: </span><span class="si">%.3f</span><span class="s1">'</span> <span class="o">%</span> <span class="n">logit_roc_auc</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
<span class="n">report</span> <span class="o">=</span> <span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">output_dict</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df_classification_report</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">report</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
<span class="n">df_classification_report</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'logit_model_stats.csv'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'AS, AR, or MR (area = </span><span class="si">%0.2f</span><span class="s1">)'</span> <span class="o">%</span> <span class="n">logit_roc_auc</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span><span class="s1">'r--'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'AUROC of Logistic Regression using Tabular Data Only'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'figures/Logit_ROC.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'AUC: </span><span class="si">%.3f</span><span class="s1">'</span> <span class="o">%</span> <span class="n">logit_roc_auc</span><span class="p">)</span>
<span class="n">cnf_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
<span class="n">cnf_matrix</span>
<span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># name of classes</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">tick_marks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">tick_marks</span><span class="p">,</span> <span class="n">class_names</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">tick_marks</span><span class="p">,</span> <span class="n">class_names</span><span class="p">)</span>
<span class="c1"># create heatmap</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">cnf_matrix</span><span class="p">),</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"YlGnBu"</span> <span class="p">,</span><span class="n">fmt</span><span class="o">=</span><span class="s1">'g'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_label_position</span><span class="p">(</span><span class="s2">"top"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Confusion matrix'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Actual label'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Predicted label'</span><span class="p">)</span>
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<pre>Accuracy of logistic regression classifier on test set: 0.84
AUC: 0.663
precision recall f1-score support
0.0 0.95 0.87 0.91 19404
1.0 0.23 0.45 0.31 1644
accuracy 0.84 21048
macro avg 0.59 0.66 0.61 21048
weighted avg 0.89 0.84 0.86 21048
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<pre>AUC: 0.663
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[210]:</div>
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<pre>Text(0.5, 257.44, 'Predicted label')</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
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<pre> precision recall f1-score support
0.0 0.95 0.87 0.91 19404
1.0 0.23 0.45 0.31 1644
accuracy 0.84 21048
macro avg 0.59 0.66 0.61 21048
weighted avg 0.89 0.84 0.86 21048
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<h1 id="How-much-earlier-was-a-patient's-first-ECG-than-their-first-echo-among-severe-AS-patients?">How much earlier was a patient's first ECG than their first echo among severe AS patients?<a class="anchor-link" href="#How-much-earlier-was-a-patient's-first-ECG-than-their-first-echo-among-severe-AS-patients?">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [189]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dates_dx</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISION_test_df_newest_ecg_per_pt_tabular_metadata.csv'</span><span class="p">))</span>
<span class="n">master_muse</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/Master_File_MUSE_Pt_List.csv'</span><span class="p">)</span>
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (30,53,57,58,66) have mixed types.Specify dtype option on import or set low_memory=False.
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (1,2,6) have mixed types.Specify dtype option on import or set low_memory=False.
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">severe_as_only</span><span class="p">[</span><span class="s1">'first_AS_dx_DATE_TIME_CREATED'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">severe_as_only</span><span class="p">[</span><span class="s1">'first_AS_dx_DATE_TIME_CREATED'</span><span class="p">])</span>
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<pre><ipython-input-190-83008afd2160>:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
severe_as_only['first_AS_dx_DATE_TIME_CREATED'] = pd.to_datetime(severe_as_only['first_AS_dx_DATE_TIME_CREATED'])
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">master_muse</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">master_muse</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pt_as_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'PatientID'</span><span class="p">,</span><span class="s1">'first_AS_dx_DATE_TIME_CREATED'</span><span class="p">]</span>
<span class="n">pt_as</span> <span class="o">=</span> <span class="n">severe_as_only</span><span class="p">[</span><span class="n">pt_as_cols</span><span class="p">]</span>
<span class="n">pt_as</span> <span class="o">=</span> <span class="n">pt_as</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">()</span>
<span class="n">muse_pt_as</span> <span class="o">=</span> <span class="n">master_muse</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">pt_as</span><span class="p">,</span><span class="n">how</span><span class="o">=</span><span class="s1">'inner'</span><span class="p">,</span><span class="n">on</span><span class="o">=</span><span class="s1">'PatientID'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">muse_pt_as</span><span class="p">[</span><span class="s1">'days_since_dx'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">muse_pt_as</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">]</span> <span class="o">-</span> <span class="n">muse_pt_as</span><span class="p">[</span><span class="s1">'first_AS_dx_DATE_TIME_CREATED'</span><span class="p">])</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span> <span class="c1">#negative means ECG was prior to echo</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">muse_pt_as</span> <span class="o">=</span> <span class="n">muse_pt_as</span><span class="p">[</span><span class="n">muse_pt_as</span><span class="p">[</span><span class="s1">'days_since_dx'</span><span class="p">]</span><span class="o"><</span><span class="mi">3</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">muse_pt_as_grouped</span> <span class="o">=</span> <span class="n">muse_pt_as</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'PatientID'</span><span class="p">)[</span><span class="s1">'days_since_dx'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">muse_pt_as_grouped</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.975</span><span class="p">])</span>
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<pre>count 195.000000
mean -2035.435897
std 3081.856889
min -11505.000000
2.5% -9989.500000
50% -20.000000
97.5% 1.000000
max 1.000000
Name: days_since_dx, dtype: float64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">muse_pt_as_grouped</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">15</span><span class="p">),</span><span class="n">xlabelsize</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span> <span class="n">ylabelsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Days Prior to Diagnosis of Severe AS (All ECGs Associated with Patient)'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Number of Patients'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
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<pre>Text(0, 0.5, 'Number of Patients')</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [198]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dates_dx</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISION_test_df_newest_ecg_per_pt_tabular_metadata.csv'</span><span class="p">))</span>
<span class="n">master_muse</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/Master_File_MUSE_Pt_List.csv'</span><span class="p">)</span>
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (30,53,57,58,66) have mixed types.Specify dtype option on import or set low_memory=False.
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3146: DtypeWarning: Columns (1,2,6) have mixed types.Specify dtype option on import or set low_memory=False.
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">severe_as_only</span><span class="p">[</span><span class="s1">'first_AS_dx_DATE_TIME_CREATED'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">severe_as_only</span><span class="p">[</span><span class="s1">'first_AS_dx_DATE_TIME_CREATED'</span><span class="p">])</span>
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<pre><ipython-input-199-83008afd2160>:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
severe_as_only['first_AS_dx_DATE_TIME_CREATED'] = pd.to_datetime(severe_as_only['first_AS_dx_DATE_TIME_CREATED'])
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">master_muse</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">master_muse</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pt_as_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'TestID'</span><span class="p">,</span><span class="s1">'first_AS_dx_DATE_TIME_CREATED'</span><span class="p">]</span>
<span class="n">pt_as</span> <span class="o">=</span> <span class="n">severe_as_only</span><span class="p">[</span><span class="n">pt_as_cols</span><span class="p">]</span>
<span class="n">pt_as</span> <span class="o">=</span> <span class="n">pt_as</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">()</span>
<span class="n">muse_pt_as</span> <span class="o">=</span> <span class="n">master_muse</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">pt_as</span><span class="p">,</span><span class="n">how</span><span class="o">=</span><span class="s1">'inner'</span><span class="p">,</span><span class="n">on</span><span class="o">=</span><span class="s1">'TestID'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">muse_pt_as</span><span class="p">[</span><span class="s1">'days_since_dx'</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">muse_pt_as</span><span class="p">[</span><span class="s1">'AcquisitionDateTime_DT'</span><span class="p">]</span> <span class="o">-</span> <span class="n">muse_pt_as</span><span class="p">[</span><span class="s1">'first_AS_dx_DATE_TIME_CREATED'</span><span class="p">])</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span> <span class="c1">#negative means ECG was prior to echo</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">muse_pt_as</span> <span class="o">=</span> <span class="n">muse_pt_as</span><span class="p">[</span><span class="n">muse_pt_as</span><span class="p">[</span><span class="s1">'days_since_dx'</span><span class="p">]</span><span class="o"><</span><span class="mi">3</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">muse_pt_as_grouped</span> <span class="o">=</span> <span class="n">muse_pt_as</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'PatientID'</span><span class="p">)[</span><span class="s1">'days_since_dx'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">muse_pt_as_grouped</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.025</span><span class="p">,</span> <span class="mf">0.975</span><span class="p">])</span>
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<pre>count 388.000000
mean -22.329897
std 63.600290
min -359.000000
2.5% -269.925000
50% -1.000000
97.5% 0.000000
max 1.000000
Name: days_since_dx, dtype: float64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">muse_pt_as_grouped</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">15</span><span class="p">),</span><span class="n">xlabelsize</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span> <span class="n">ylabelsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Days Prior to Diagnosis of Severe AS (only ECGs in Test Set)'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Number of Patients'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
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<pre>Text(0, 0.5, 'Number of Patients')</pre>
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<h1 id="NYP-Lawrence-Validation-with-and-without-Propensity-Score-Matching">NYP Lawrence Validation with and without Propensity Score Matching<a class="anchor-link" href="#NYP-Lawrence-Validation-with-and-without-Propensity-Score-Matching">¶</a></h1><p>This can be found at <code>/Users/pae2/Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/NYP_Lawrence/propensity-score-matching-main/propensity_score_matching_v2_ValveNet.ipynb</code></p>
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<h1 id="Model-Calibration-Curves">Model Calibration Curves<a class="anchor-link" href="#Model-Calibration-Curves">¶</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="p">(</span><span class="n">brier_score_loss</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span><span class="p">,</span>
<span class="n">f1_score</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.calibration</span> <span class="kn">import</span> <span class="n">CalibratedClassifierCV</span><span class="p">,</span> <span class="n">calibration_curve</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">as_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Stenosis_2022-03-03--06:21:48/y_y_pred_roc_0.8805.csv'</span><span class="p">)</span>
<span class="n">ai_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Aortic_Insufficiency_2022-03-03--06:22:25/y_y_pred_roc_0.7688.csv'</span><span class="p">)</span>
<span class="n">mr_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_Mitral_Regurgitation_2022-03-03--06:23:05/y_y_pred_roc_0.8280.csv'</span><span class="p">)</span>
<span class="n">as_ai_mr_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">box_path_prefix</span><span class="p">,</span> <span class="s1">'Box/Heart Failure Analytics/Data/MuseLabelGeneration/ValveNet_JACC_Revisions/Dendrite_Transfer/JACC_REVISIONS_Eval_AS_AI_MR_Combination_2022-03-03--06:23:39/y_y_pred_roc_0.8351.csv'</span><span class="p">)</span>
<span class="n">lstFiles</span> <span class="o">=</span> <span class="p">[</span><span class="n">as_path</span><span class="p">,</span><span class="n">ai_path</span><span class="p">,</span><span class="n">mr_path</span><span class="p">,</span><span class="n">as_ai_mr_path</span><span class="p">]</span>
<span class="n">study_name</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">lstFiles</span><span class="p">]</span>
<span class="n">study_name</span>
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<pre>['Aortic_Stenosis',
'Aortic_Insufficiency',
'Mitral_Regurgitation',
'AS_AI_MR_Combination']</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'y'</span><span class="p">,</span><span class="s1">'y_pred_proba'</span><span class="p">]</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">lstFiles</span><span class="p">:</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">plot_calibration_curve</span><span class="p">(</span><span class="n">est</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">fig_index</span><span class="p">):</span>
<span class="sd">"""Plot calibration curve for est w/o and with calibration. """</span>
<span class="c1"># Calibrated with isotonic calibration</span>
<span class="c1"># isotonic = CalibratedClassifierCV(est, cv=2, method='isotonic')</span>
<span class="c1"># Calibrated with sigmoid calibration</span>
<span class="n">sigmoid</span> <span class="o">=</span> <span class="n">CalibratedClassifierCV</span><span class="p">(</span><span class="n">est</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'sigmoid'</span><span class="p">)</span>
<span class="c1"># Logistic regression with no calibration as baseline</span>
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mf">1.</span><span class="p">)</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">fig_index</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">rowspan</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'both'</span><span class="p">,</span> <span class="n">which</span><span class="o">=</span><span class="s1">'major'</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="s2">"k:"</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Perfectly calibrated"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">clf</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[(</span><span class="n">lr</span><span class="p">,</span> <span class="s1">'Logistic'</span><span class="p">),</span>
<span class="p">(</span><span class="n">est</span><span class="p">,</span> <span class="n">name</span><span class="p">),</span>
<span class="c1"># (isotonic, name + ' + Isotonic'),</span>
<span class="p">(</span><span class="n">sigmoid</span><span class="p">,</span> <span class="n">name</span> <span class="o">+</span> <span class="s1">' + Sigmoid'</span><span class="p">)]:</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s2">"predict_proba"</span><span class="p">):</span>
<span class="n">prob_pos</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)[:,</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span> <span class="c1"># use decision function</span>
<span class="n">prob_pos</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">prob_pos</span> <span class="o">=</span> \
<span class="p">(</span><span class="n">prob_pos</span> <span class="o">-</span> <span class="n">prob_pos</span><span class="o">.</span><span class="n">min</span><span class="p">())</span> <span class="o">/</span> <span class="p">(</span><span class="n">prob_pos</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">-</span> <span class="n">prob_pos</span><span class="o">.</span><span class="n">min</span><span class="p">())</span>
<span class="n">clf_score</span> <span class="o">=</span> <span class="n">brier_score_loss</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prob_pos</span><span class="p">,</span> <span class="n">pos_label</span><span class="o">=</span><span class="n">y</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">%s</span><span class="s2">:"</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">Brier: </span><span class="si">%1.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">clf_score</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">Precision: </span><span class="si">%1.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">Recall: </span><span class="si">%1.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">recall_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2">F1: </span><span class="si">%1.3f</span><span class="se">\n</span><span class="s2">"</span> <span class="o">%</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
<span class="n">fraction_of_positives</span><span class="p">,</span> <span class="n">mean_predicted_value</span> <span class="o">=</span> \
<span class="n">calibration_curve</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prob_pos</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">mean_predicted_value</span><span class="p">,</span> <span class="n">fraction_of_positives</span><span class="p">,</span> <span class="s2">"s-"</span><span class="p">,</span>
<span class="n">label</span><span class="o">=</span><span class="s2">"</span><span class="si">%s</span><span class="s2"> (</span><span class="si">%1.3f</span><span class="s2">)"</span> <span class="o">%</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">clf_score</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">prob_pos</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
<span class="n">histtype</span><span class="o">=</span><span class="s2">"step"</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Fraction of positives"</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">"lower right"</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">file</span> <span class="o">=</span> <span class="n">path</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'Eval_'</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">partition</span><span class="p">(</span><span class="s1">'_2022'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"AS_AI_MR"</span><span class="p">,</span> <span class="s2">"AS, AR, or MR"</span><span class="p">)</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"_"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">)</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Combination"</span><span class="p">,</span> <span class="s2">""</span><span class="p">)</span>
<span class="c1"># labelname = labelname.replace("AI", "Aortic Regurgitation")</span>
<span class="c1"># labelname = labelname.replace("AS", "Aortic Stenosis")</span>
<span class="c1"># labelname = labelname.replace("MR", "Mitral Regurgitation")</span>
<span class="n">labelname</span> <span class="o">=</span> <span class="n">labelname</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">"Aortic Insufficiency"</span><span class="p">,</span> <span class="s2">"Aortic Regurgitation"</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Calibration plots for </span><span class="si">{</span><span class="n">labelname</span><span class="si">}</span><span class="s1"> (reliability curve)'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">,</span><span class="n">fontweight</span><span class="o">=</span><span class="s2">"bold"</span><span class="p">,</span><span class="n">pad</span> <span class="o">=</span> <span class="mi">34</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Mean predicted value"</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Count"</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="c1"># ax2.legend(loc="upper center", ncol=2)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="c1"># Plot calibration curve for Gaussian Naive Bayes</span>
<span class="n">plot_calibration_curve</span><span class="p">(</span><span class="n">GaussianNB</span><span class="p">(),</span> <span class="s2">"Naive Bayes"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># Plot calibration curve for Linear SVC</span>
<span class="c1"># plot_calibration_curve(LinearSVC(max_iter=1000), "SVC", 2)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Logistic:
Brier: 0.035
Precision: 0.000
Recall: 0.000
F1: 0.000
Naive Bayes:
Brier: 0.035
Precision: 0.543
Recall: 0.041
F1: 0.077
Naive Bayes + Sigmoid:
Brier: 0.036
Precision: 0.471
Recall: 0.107
F1: 0.174
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
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<pre>Logistic:
Brier: 0.009
Precision: 0.000
Recall: 0.000
F1: 0.000
Naive Bayes:
Brier: 0.009
Precision: 0.000
Recall: 0.000
F1: 0.000
Naive Bayes + Sigmoid:
Brier: 0.009
Precision: 0.000
Recall: 0.000
F1: 0.000
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
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<pre>Logistic:
Brier: 0.031
Precision: 0.000
Recall: 0.000
F1: 0.000
Naive Bayes:
Brier: 0.031
Precision: 0.000
Recall: 0.000
F1: 0.000
Naive Bayes + Sigmoid:
Brier: 0.031
Precision: 0.250
Recall: 0.005
F1: 0.011
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<pre>/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/Users/pae2/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
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zF+/PhMGyP6+fmhb9++ePv2bZa34ePjg/nz56skEKklJiZi7ty5ePz4cZa3oQ5N7/f9+/crnQSBpBN7dhI5mUym1CgyZdIgCAJu3bolvpaid4/Tp0+rNAxs0KCBeE6bNm1amglEanfu3MG2bdtyIsR0JSQk4OvXr3j+/DmmTJkCPz8/8T09PT2VOq/Hjx/P9HiNjo7G+PHj4eXlpfLe7NmzVRKIlE6fPq30FDk3ZHasrl69WiWBSOnOnTvZOr9kRUJCAsaPH6+SQKTl9OnTSjcbmjR37lysX78+07rsnp6eGDp0KOLi4jKcb86cOSoJREpXr17F8ePHsxTjnTt3lM7Z6jzoSispSq4CFRsbi++//14lgUjtv//+w4QJEzTapioiIgITJ05USSDSsmXLlkzPy7Nnz053Xcm/W19f32zFmhPq1q2rVDpy//79NK9RKZNGIKmWi7qCgoIwePBglQQitZCQEPzwww8q5wpNS+981LBhQ6WS0Rs3bqR5v5n695LyAUdoaCgGDx6skkCk5uHhgQULFqT5XnLpOpB0X3Lp0qUM1wVkoSQi9U1U6dKl1V00TZ8+fVLqX9na2hrTp09HhQoVEBUVhX/++UfpopvyZiInJCYmok2bNujfvz9MTU1x5coVrFu3TsyAQ0JC8Oeff2LWrFkZrmfQoEHo3LkzAgIC8OHDB+jp6UEul+Ovv/4S5zEyMsLUqVPh5OSE2NhY3LlzB6tWrRJPdHfu3IFCoYCWlhaWLl2KmJgYlcZP27dvBwC1S2ciIyPx008/ITExUZxWtWpVjBo1CqVKlUJAQAA2bNggnkzlcjkmTJiAc+fOwdDQMM11Jh8Tffv2FX/YmzdvxsWLF8V57ty5I45dkB1BQUEwMDDADz/8gMaNG+PLly/466+/cOXKFXGe7du3o0+fPmofk+/evcPcuXOVptWrVw9Dhw6FnZ0dnj9/jnXr1omfLzw8HJMmTcLhw4chk8nU3ienTp1S+r579eqFTp06wcLCAsHBwdi5c6f4I01MTMSWLVvw+++/Zxq/nZ2d2KAtvRiS91lO7HcgqUh51qxZKFWqFP777z8xWc6OOnXq4J9//gEAsZqflZUVfHx8lE64derUwblz57K1jW/5Henp6WHmzJmoXr067t69K57sHzx4oJTgli5dGj///DNKliyJiIgIHDx4UOmkf+vWLYwaNSpb8asjraot6Rk9erRSSURkZKTShUVLSwv9+/dHmzZtACQl6/v37weQdKzOmTNH6Wb24cOHOHHihNI2OnbsiL59+0JfXx+enp7YvHmzWjfKmpbesfrp0yeVKroNGjTA8OHDYWlpievXr2Pt2rXZjnn79u149+6d0tPV6tWrY+nSpQD+VxJ0/vx5pUS0Ro0aGDt2LOzs7BASEoLt27crne9u3bqFjh07iueBCxcuYOXKleL7/fv3R//+/QEknSvUcf78eRw4cEB8LZPJ4Obmhk6dOsHQ0BDXr1/Hhg0bEBUVBSBpf69cuVKp2lBqb968gY2NDcaOHYuaNWvi7du3+O2335S+T09Pz0xrNKSUOnlVt5S7cuXKmDJlCvT19XHy5Ek0bdoUALB161alErrSpUtj7NixKFOmDHx9fbF69WqxqsrVq1dx9uxZtGvXTu14M7Jv3z6EhYWJrxs3bozvv/8eFhYW+PDhA9avX4+HDx+K79+6dSvDpCkxMRF16tTBiBEjYGtri7t37+L3338Xk67Y2FisWLECGzZs0Ej8GVHn2JfJZOjcubM4doQgCPjnn38wYMAAcRm5XK507CdXAVbXsmXLlG7GixUrhvHjx6NChQoIDAzE77//Lv72goODsWnTJkyfPj1bn1ldaZ2PZDIZevToIV7/ExMTcerUKQwePFhcLjY2Vun6V7x4caWqhcuXL1f6bVWvXh2jR49G0aJF8fDhQ6xZs0a8nu7fvx89e/ZUKXlJ/Xu6fft2ptXb1E4iktsqJPvWYj1dXV3MmzcPz58/x/PnzzF8+HClojYXFxdcvnwZr169AoBMn6J+q3bt2indwFWpUgUlSpTATz/9JE47fvw4fvnll3SLkr/77jv88ssvAJJ2YLKEhATMmjVL/Kxt27ZVqrri7OyM27dv47///gOQVIUnPDwcVlZWadZj1NXVzXKDphMnTih9h9WrV8fff/8t1oGvVKkSGjVqhAEDBohdYH769AkHDhxQOpBTGz16NCZNmiS+rlatGpo2bSoerIIgIDAw8Ju6G125cqXSDVKdOnWUqjglDx6Wcl9lZO/eveLFEEi6+Vq7dq04AFelSpXQuHFj9OjRQ7yAPHnyBJ6enmjZsqXa+yR1KcfYsWNRtGhRAEk/1rp16+KXX35BkSJFUK5cObW/o/T2f1rTc2q/T58+XUwcv7UIOGX9ckEQcPPmTbRv316pVEJbWxu1a9fOdhLxLb+jUaNGoW/fvgCSju9ktra2mDNnjvi7njt3LipVqiS+X7t2bVy4cEF8OpjT5zB1GBgYYMyYMSq9YJ08eVLpeB02bJhS9QlXV1cIgiDeaPr6+io1vDt8+LDS+tq3b6/U13j16tVRrFgx/Prrrxr/TJlJ71g9duyYUqlLrVq1sHnzZrF6TeXKlVGxYkV8//332dqug4ODyqB+aTVwrlixImbOnInnz5/j1atXWLNmjVK7k4oVKyqVGiUfR8nH74MHD5TWZ2lpmeXrQ1rtHYcNGya+rlq1KmrXro0BAwaI39mePXswcuRIWFtbp7lOXV1d7Ny5U4ylSpUq0NLSwvjx48V5MntqmtrTp0/F/+3t7dW6D9HS0sKaNWvE5D9lPe+9e/eK/5uammLXrl1icletWjXUqlULnTp1Ejuw2L9/v8aSiLp162LKlCl4/vw5Pn78iLVr14q1OypVqgQrKyul+4TMzh/Ozs7466+/xFK2SpUqoXLlyujXr59YgnLp0iWx3V9OUvfY79q1q9IAdOfOnVNKIq5evap0re7UqZPaVfnCw8OVHnSYmJjgwIEDSvu3atWqaN26NRQKBUxNTbN8PGZHeuej7t27Y82aNeIDv+PHjytdgz09PZW+i+7du4vf8devX5Ue4pQoUQI7d+6EkZERgKTPWqlSJfE6BiQdy6mv3aamprC3txfve9TpDl3tJCJ1Efe3dt9lZWWFXr16pfnep0+fcP36dURGRorTUvZCkxPSulB06NABy5Ytw8ePHwEkJVKvX79Ot358hw4d0pxuZGSUZv02IKkI6tatW/jw4YPSdE1/3pRdsQHApEmTVBrRGhgYYNKkSUoXjwsXLmR4M5n6PX19fVStWlVMiAAoHfhZVb58eZUnrFpaWhgxYoTSU+B79+6pvc7U38XUqVNVTniWlpYYPXo0Zs+eLU5LTiLUlXrgtC5duqBNmzZo2LAhXFxcYG1tneOjxubEftfW1hafUGtC+fLlYWVlJSae165dU0kiqlSpIlm3lOn1eFW8eHH069cvzfcCAgKUfgNAzp/DMuLs7IzOnTujbdu2adZxTV3Sm9ZnbtOmjdLT6jt37og3ZPfv31ead/jw4SrL9+rVC2vWrMnV0oiMjtXUMQ8dOlSlfn7Dhg1Ro0YNpSfCmubo6JjmTb8gCPDz81N6Egto/jgKDAxUSkSKFSuWZt10Z2dntG3bFidPngTwv6fE6ZUkNGjQQOVzpR4AU52qPCmlrBGhbilLjRo10uzwwc/PT+lYrF27tko7IQcHB1SuXFmsSuTt7Y2EhIRsVd1MK660HsAkJibi2bNnKtV4MtvvQ4YMUYmrVq1acHZ2Fq+PCoUCjx49+qaSY00qU6YMatasKR5/Xl5eCA4OFhvLJ5dQJ+vcubPa6753757SfWuzZs1U9m/JkiWxfft22NvbZ3m07+zI6HxkZ2eHRo0aibUTHj9+jNevX6NMmTIAkh56JJPJZEq/O29vb6V2I82aNRMTiGS1atWCra2teMzfuXMnzTiKFCkiJhEBAQEQBCHDxE3tX0LqBo0pb/C/lY+PD65evYp79+7hyZMn4k17Sjndv3vKJ4jJZDIZKleurBTP58+f000ikhtbZuTt27e4cuUK7t69i8ePH6eb+Wq6j+WUxeWp66Gn5OrqCplMJn7fGdWhNDExSfOGJPWxkrIqTValNYIxoPxEGIBKEpaeuLg4vH//XnxdpEiRdKtBpf6OsjpgYpcuXbBv3z6xqlB4eDj2798vVgspU6YM6tevj3bt2uXYYEk5sd+LFCmicoL6FslxJZcy3LhxA3K5XKkdllSjncpkMrU6OfD29saNGzfEc1hadWtz+hy2fft2WFpa4tOnTzh48KDSBfjp06fo2bNnuo3kUldXVaeHlpTHSMrflLa2dprnUy0tLVSrVk2pumN2ZOV7zOhYTRkzgHQbfed0EpEsMTERt2/fxs2bN+Ht7Y1nz56l2WZL08dR6jY9Li4uKg9Vkn333XdiEgFkfE5M68Y9Zc+DQNavDSmrpqhbGyK963LqY/7ixYuZVo+KjY1FYGDgN1fnTkkul+PatWu4ffs27t+/Dx8fnzSTq8z2e3ol2VWrVlV6yPb58+dvC1jDunbtKiYRCoUCFy5cQJ8+fZCYmKjUE1+VKlWyVMKW+t6qXLlyac6XsiQ8OzR1PgKSHrSkbIdw4sQJjB8/HqGhoUrj/NStW1cp6Ul9LO/atSvDgWiTl5HL5SqdfaT8XcXFxSEiIiLDDk3UTiKSq2EkU2cUwrCwMLx7907lhi+Zj48PZs2alWaRSZkyZRAWFpZhn/CaoqOjk25r/9RPPzN6GpBREeG7d+/w66+/ptnQzN7eXuyFI6ekrI5maGiYbi8xenp6MDQ0FE9iX79+TXed6Z3Es9JzQmbS62o29bbVfaIVERGh9KPP6MeR+r2Mvou06OvrY+fOnVi5ciWOHDmiEuPr16/x+vVr7N69G05OTnB3d1crEc2KnNjvOdFDUsr2Du/evcOxY8cQExOj9L4UTExMMnzqePv2bcyePVvlfKilpYXy5cvD399f6XPkpORxIipVqoQmTZpg9uzZYslBXFwcfvnlF8THxysVaSfLzkOhlMdIytJGfX39dHvcyUo12PQuzpl1VJBSRsdq6hJSY2PjNOfTdI88aTl37hwWLlyocoOno6ODKlWq5GgSkzpR0dQ5Ma3v7Vt7J0v5W1K3ZDK9z5PdB6GaHBl89+7d+OOPP1Sqi+vr66NcuXKZNvhOSd1rpZQlomnp0KEDFi1aJHbQcu7cOfTp00fsMj5ZVhpUA6q/76wOE5BaTp+PAKBp06ZKpQXJScSpU6eUtpO6x7jsHsuRkZEqD5bSuufVSBKRur/Yhw8fqgwMldrZs2cxd+5c2Nvbo1WrVujQoYNYfPfp0ycMGTJEbFhkZGSEPn36oF69eqhRowYsLCwwYMCAdItcNCkhIQGRkZFpnvRSH4gZXVDS+xFHR0dj6NChYraoq6uLHj16oFGjRqhZsyZsbW0xbdo0pe7ONM3GxkZ88hYTE5NmBgok3WykPFGnfnKUUm70e5+y4VlKqX806l7oraysoKWlJZb0pD55p5T6vYy+i/SYmJjg119/xZQpU3Dt2jVcvXoVd+7cgZ+fn9JJ6f79+xg+fDhOnTql0W5Ac2K/Z9TgOrtS9129Zs0a8X8dHR2VahC5Jb3fNJD0EGT48OFiLzXW1tZwc3NDnTp1UK1aNZiYmKBZs2a5lkSkNmvWLNy7d0+pxOC3335DtWrVlNpsAaoX2LRGv04t5RM1AwMD8VwZGxubbpWPrFzs0rs4Z+X7zOhYTb1vIyMj0+2KPCddvnwZEydOFM9JJUqUgJubG2rXro0qVapAX18/zZIdTUndpkFT58Scvj6kV1qSWnrHQOr936pVK6X2femxt7dXa7uZ2b9/P+bPny++rlixInr06AEXFxdUrFgRHz9+zFL12fDw8DSreGXlHkYK5ubmaN68ufgQ6fbt2wgLC1MqSdXS0kq3unh6Uj8UyOpDwNRy+nwEJF3runTpIrZR8vf3h4+Pj1LVNjMzM5VxS1IfywMGDEi3qm1Kaf1+U1ddyugaCGQhiahQoQJKlSol3ghHRkbi+PHj6bZrUCgUYqOld+/eYfv27VAoFGISsXv3bqUbRHd3d5UvJqdP3il5e3unWU/Qx8dH6XVGg2ml98TyxIkTSsVNP//8s0qd05z+rCVKlBBvJgVBwJ07d9Ic1vzOnTtKN7ffMsaDJty/f1/sqSqlZ8+eKb1W98Sura2NYsWKiXX+Pn/+jDdv3qRZPJ16cLLsfhdyuRwhISFo2bKleFH4+vUrbt68CXd3d7H7OX9/f3h5eaF+/frZ2k5acmK/a6I+cGqOjo5KT2BSNiJMviGXQkalaps3b1bq5nLLli0qVWJy8xyWmr6+PhYvXgw3NzfxBjU+Ph4zZ86Eh4eH0mcrWbKkUi81CoVCperAp0+fIJfLUaJECZULjb29vbi8QqHAs2fPVBKVxMTEDBvqpT6u0mpLFR0dnaWbgYyO1ZQxA0mdJ6R1fk/ddiIr1GkEumHDBnH/6OrqYv/+/UoDqGX2eb91zIjUdcHv3r2b5jkX0Nw5MbuMjY3FREbd0uf0joHU+zo4ODjN6jJPnz6Fg4ODxs9BKQd5s7W1xb59+5QS86y2JfT29k5zf6S+h8mNuv9A1o7LLl26iElEQkICLly4oNQlfp06ddRuA5Ms9f5N/T0kW7x4McLCwsS2SY0aNYK+vn6un4+S9erVS6mjg8OHDytV7e3UqZPKQ5/UnzU8PFzlWE4+Lzs6OmaYGKQsadPW1s704ana40TIZDKMGDFCaZq7u3u6fdv/8ccfSiP3aWlpKfU0kHqHpi4u8fX1zXId9G+xbt06lfEOzp8/rzQSob29fYYHcno/msw+a3BwcKYlLilP6OmNy5CR1AnaqlWrVPr5jouLU+liNLsjdWpKck9Bqe3cuVPptTojKyZL/Znc3d1V2qCEhYVh48aNGS6X0T5JSEjAjz/+iFatWqFmzZpo166d0o2xqakpWrVqJXY1mHK7mpQT+z0nBroCVNugJNNke4is/o4y+qyZ/a6vXr2q0aoP2VGjRg2lUVeBpIHNUo/XkPo73rx5s0rx/dy5c9GyZUs4OzujR48e4qBEgOq+S9mldbK9e/dmONZO6otVWuNvpG5smpmM9l/qmHfs2KFSR//ixYvfNH5L6hvxtI65lMeRjo6Oys1q6s4RUkv9GbNSvQJIuqlMmfx++PBB7Po4pfv37yv1jmZgYJDrDXRTXn/V/W2ldwyUL19eqQqyt7e3SpL09OlTdOvWDS4uLmjWrBmmTZuW9aDTEBoaqlR1zcDAQOUpdWb7PbWtW7eq3NA+fPhQqVvc5I5PcoM6x36yJk2aKFWrWb9+vdL1MqtVmYCktj0pb9qvXr2q0k7i06dP2LdvH44dO4aVK1di6tSp4vGS2+ejZKVLl1Y6N+3evVvpvNSjRw+VZWrVqqX0Wc+dO6dSxdbT0xPdu3eHs7MzWrdune74NykffFlaWmYas9pJBACVIe8jIiLQt29fLFq0CDdu3MDz58/x77//YsSIEUrddgFJH7x8+fLi69SZ1OzZs+Hp6YlHjx5h27ZtSl3JJUvZ+lzTvL29MWTIEFy+fBk+Pj7Ytm0bpk6dqvIZsiP1Z12+fDlOnjyJJ0+eYP/+/ejdu7dKMXHqH1zKE0xwcDBOnz6NW7dupTnoU1o6duyodJPz6NEj9O3bF+fPn4ePjw/++ecf9O3bV+lJob29fa6M1pqZ+fPnY+XKlXj06BG8vLwwceJEeHp6iu9ra2ur1RA0We/evZWewnp6emLYsGHivj927Bjc3NyUEkgnJyeV0T4z2ic6OjpQKBTw9/eHQqGAXC7H4MGDcezYMTx9+hT37t3Dpk2bVAa6ymrXjJnJT/s9vXYPmmwP8a2/o5RS/64nTpyI69ev48GDB1izZg3GjRun9H5Onr8yMmHCBJWSunXr1qlcpFM+Bb1x4waGDx+Oy5cv4+HDh1iwYIHYyDEmJgavXr1SeuqZuoee06dP46effsK9e/fw8OFDLF26FL/99luGcdrb2yv9Lt+8eYPff/8dsbGxiIuLg4eHh8r4Lt+iY8eOSlX77t27h5EjR+LmzZt48uQJNmzYgIkTJ37TNlLfGD5//hzXrl3Df//9JyYPKY+jmJgYjBs3Dl5eXvDy8sKiRYuUqrwAqteG1A01r1y5ggcPHuD06dNqtylMXfXB3d0dc+bMgZeXF54+fYrNmzdj2LBhStseOHCg2uMUaUpyTzVAUq9S30JHR0flmj5q1Chs27YNDx8+xPnz55V+w+/fv8/y0/D06OnpKd2cBQQEYPr06Xjw4AFu3ryJ6dOnqzzEyuyhh7+/P/r164dz587Bx8cHBw8exMiRI5UekHXs2DHT6imaos6xn0xHR0ccABBQ7vRAX18/W70BmpubK1WBiouLw6BBg3D06FH4+PjgwoULGD58uFIbkc6dO4vnhNw+H6WU8vqbMoGoVKlSmkmglZWVUtW3+Ph49OvXDwcOHMDjx49x9OhRcYwzhUKBt2/fplurJuV1QZ1qlFmql6CtrY2VK1fi+++/F/trlsvl2LFjR4YjkVatWhUzZ85Umta0aVOlLO7Nmzf48ccfM9x+eHh4pvV0s8PGxgZhYWHiiTstDg4OaXZ7p46mTZsqDZwXHByc6ZgGqU/+pUqVEr9zhUIh1t1MrjubGSsrKyxduhQ//PCD+ITxyZMnKjc6yQwNDbF69WqN1s/PDh0dHSQkJGDjxo0qJ9VkI0aMyFI9VUdHR8yYMUPp4nzjxo10R8a2srJSGswpWWb7ZMqUKbh165ZY7P769WuVxDSlRo0aabzuc37a72klC7q6ulkaXCgz3/o7Sqlp06ZKDR8fPnyIoUOHpjv/ly9fMu0uLycYGRlh3rx5St2uRkdHw93dXRzLwdraGpMmTVK60b969Wq6Iw5PmjRJ6QayevXq6N69Ozw8PMRpJ0+eVOrJB0h6upfeE2QjIyM0bNhQqfemDRs2iL/75JuhlNXevoWdnR1GjRql1P4mrc9sbm6eYTuBjFhYWMDCwkI8n0dHR4tdKU+ePBmVKlVC06ZNldrDXblyRaVb15RSl1am7j3s6dOnYqn/vn371BrVuVevXuJgakBS1cd9+/Zh3759ac7v4uKiNN5DbqlWrZpYGvLp06dM22Vm5ocffsC5c+fEhCQ6Ojrdbrft7e1VamNkl4mJCWrXrq1UA+Ho0aMZtovMKCE0NTVFQkICXrx4ke5+sbCwwIQJE7Ibcpapc+yn1LVrV5UaBkBSd6XZrUqW3BYxuQT0/fv36ZYmWVlZYcyYMeLr3D4fpdS2bVssXLhQpWQpo4d7U6ZMwc2bN8XvOyQkRKmL+pSqV6+e5kPxiIgIpQROnTGgslQSASR90bt370aPHj3UatjUvn17pUEvknXt2jXDolAdHR2VuuHfUqyckUqVKmHevHnp1n+2t7fHpk2b0u29IzN169ZNt+1IstTfRerP2qVLlzSXy0qPTs2aNcPGjRuVBjJKS/HixbFr1650e9XKTe3atVN6QpFa9+7ds3Ux69+/P5YsWZJpDx8VKlTA/v3700xSMtsnjo6O2Lx5c6bfN5BUHSutREUT8st+L1OmjMpDgurVq2u0O1lN/I6Sff/99+l2CwokXYRSJiZyuVypDn5uatSokUof6ydPnlR6aDJo0CBMmDAh03q7I0eOVKkiBSRVd8qoPc+YMWNUqhClvob88ssvKsepQqEQ6+iPHz8ezZs3zzC+rBgzZky6xwSQdH5J/b2p26A3WXp92ycfc5MnT05zMMRk1tbWSk8fAwIClBKxihUrptu9Z1aO62XLlmHYsGGZfr62bdtiy5YtkjxoSNkBg0KhyFLvRWkxNTXF9u3bMy0Btre3x+bNmzU6Vs2vv/6aYa83Dg4OSm31njx5km7X7zY2Nvjjjz/SPVdaWFhg48aNGitJUVdmx35KVatWRYUKFVSmZ6cqUzJbW1vs2LEj03YgRYoUwdatW1XOPbl9PkpmYGCgct+jp6eX4XdRokQJbNmyJdN9XKVKFWzYsCHN83zqtqZOTk6ZxprlJAJIujguWrQIJ06cwMiRI1GtWjVYWlpCR0cHpqamqFy5MgYMGIBDhw5h1apVaWaR2tra+PPPPzFjxgxUqVJF7H7S3t4enTp1woEDB7Bo0SKlp3YpRx/UtF69euHQoUPo2LEjbG1toa+vj7Jly+KHH37A8ePHlYpRs2PhwoVYvHgxnJ2dYWRkBF1dXdjZ2aFVq1bYsWMH1q1bp3QCSP1ZhwwZglmzZqF8+fLQ09MTv+esNsJt0qQJzp49i/nz56N+/fooVqwYdHV1YWlpiTp16mD27Nk4e/asSqNIqejo6GDFihVYtGgRqlWrBkNDQ5iamqJevXpYu3YtFi9enO1eQLp164Zz585hxowZcHFxga2tLXR1dWFra4smTZpgyZIlOHr0aLrjBKizT2rXro2zZ89i+vTp+O6772BlZQUdHR3o6+ujePHiaNmyJVatWoVdu3Zlq/cndeWX/Z66NELT40No6ncEJDXy3LNnD8aMGYNy5cpBT08P+vr6KF26NNzc3HDkyBGV3l5y8hyWmZkzZ6p057dgwQKl4vIff/wRR44cwYABA+Do6AhjY2Po6uqiWLFi6NixI/bu3ZtuKaq+vj62bt2KOXPmoHr16jA2NhZ/q5s3b04z2U/94KZkyZLw8PDAwIEDUaJECejp6cHOzg4dO3bE/v37lZ4UaoJMJoO7uztWrlwJV1dXmJmZwcjICM7Ozli2bBkWL16sUnKU1S6sp06dinHjxqFUqVLQ1dWFhYUFnJycxAu0nZ0dDh8+jMGDB8PBwQG6urowNDRE+fLlMWzYMBw9elRp4MfExEScOXNG6TNs3rwZPXr0gI2NDXR1dWFjY4PGjRtnqYRWT08P06ZNw8mTJzFixAhUrlwZFhYW0NfXh729PTp37oydO3dmeLOa06pVq6Z0U5d6tO7sKFmyJI4dO4Z58+ahQYMG4nXA2NgYNWrUwE8//YSTJ09qvKpphQoVcOTIEfTq1QvFihWDjo4OjI2NUaVKFYwbNw6HDx9WSnDDwsKUBuBMrUmTJjh+/Djc3NzE83uJEiUwaNAgnDp1Sq0bQk3L7NhPLfWgvObm5mjcuPE3xVCuXDmcOnUKv/zyC1xdXWFhYQEdHR2YmZnB2dkZP/30E06fPp3mA6HcPh+llPrBc4sWLTIdabx69eo4c+YMpk2bhtq1a8Pa2lr8rK6urpgzZw4OHDiQ7gPFlEm5kZGRWtdfmZDTIyARZcGtW7eUnnJ269Ytx0d1JqJvk17XwakNGzZMadCkhw8ffnP/7dkVHx8PHR2dTKuXpRxvA0hq76HpG0pS34oVK7Bp0yYASSOKb926VeKISFP+/PNPrFq1Snzt5uam0iaIcs6oUaPEwe46dOigVu2IbJVEEBERJdu6dStq1qyJNm3aYOjQofj1119V5omLi1NqUFmkSBHJEggAOHPmDGrUqIFWrVph4MCBmDp1aprdOKYc6E1bW1tj4wRQ9vTu3VtM/G7duiVpN8qkOVFRUdizZ4/StG+pykRZExMTg1u3bomv0xqUNC2a7/CdiIgKFQcHB8TGxuLNmzdi14LJdXj19PTw7t07/P333wgJCRGXSWu8ktzk4OAAuVwOf39/cayWhIQE9OvXDyYmJvj06RMOHz6slPi4uLjkWu82lLaSJUuiQYMGuHr1KuLj4/Hvv/+mW/ee8rZbt27B1NQUQUFB2LBhg1LPQGXKlMlyZxeUfRcvXhQHzqtWrVq6Xa6nxiSCiIi+SdOmTVV6Kdm1axd27dqV5vy6urrZ7u1OU2rWrIlKlSopJQmnTp3KsN1Kyh6uSDoTJkzAtWvXIAgCDh48yCQin1qxYkW67VpGjRqV6z3ZFWYpe2JTZ+T2ZKzORERE38TY2Bhr165Va9wAPT09LFiwQOPdGWeVTCbD6tWr1aqepKWlhUmTJqmMFUPSqFGjhtjo+Pbt2+mORkx5W9GiRdOc3rNnT5WxZyjnPH/+XKzK1KhRIzRs2FDtZVkSQURE38zJyQlnzpzBwYMHcfXqVbx69QpfvnyBQqGAiYkJSpYsie+++w59+/ZNd6Cj3FaqVCmcPHkSHh4euHTpEnx9fREWFob4+HgYGxujWLFiqF27Nnr37i150kPKJk+ejPPnzyM6Ohrbtm3D0qVLpQ6JsqhmzZp49OgRgoODYW5ujlKlSqFz586ZdolPmvXXX38BSOpads6cOVlalr0zERERERFRlrA6ExERERERZQmTCCIiIiIiyhImEURERERElCVMIoiIiIiIKEuYRBARERERUZYwiSAiIiIioixhEkFERERERFnCJIKIiIiIiLKESQQREREREWUJkwgiIiIiIsoSJhFERERERJQlTCKIiIiIiChLmEQQEREREVGWMIkgIiIiIqIsYRJBRERERERZwiSCiIiIiIiyhEkEERERERFlCZMIIiIiIiLKEiYRRERERESUJUwiiIiIiIgoS5hEEBERERFRljCJICIiIiKiLCl0SYRcLkfHjh1x/fr1dOfx8fGBm5sbatasie7du+Phw4e5GCERERERUd5WqJKIuLg4TJ48GS9fvkx3nujoaAwfPhw1a9aEh4cHXFxcMGrUKERGRuZipEREREREeVehSSJ8fX3Ru3dv+Pv7Zzjf6dOnoauri+nTp8PR0REzZ86Eqakpzpw5k0uREhERERHlbYUmifDy8kKDBg2wf//+DOd78OABatWqBS2tpK9GJpOhVq1a8Pb2zo0wiYiIiIjyPB2pA8gtffr0UWu+oKAglClTRmmatbU1fHx81FpeoVAgKioKurq6kMlkWY6TiIiIiDRLEATEx8fD2NhYfFBM36bQJBHqiomJgZ6entI0PT09yOVytZaPiorCixcvciI0IiIiIvoGFSpUgKmpqdRhFAhMIlLR19dXSRjkcjkMDAzUWl5XVxdA0kGaOhnJKY8fP0a1atVyZVukedx/+Rv3X/7G/Ze/cf/lLwkJCejevTvKli2L33//PVf3n1wux4sXL8T7NPp2TCJSsbOzQ1BQkNK04OBg2NraqrV8chUmPT096Ovrazy+9OTmtkjzuP/yN+6//I37L3/j/sv7Pn78CDs7O+jr62PevHkoW7asuN9ye/+xqrnmsFJYKjVr1oS3tzcEQQCQVIfO29sbTk5O0gZGRERElM88fvwYDRs2xJEjRwAAdevWRZEiRSSOijSBSQSSGlPHxsYCANq2bYvo6GgsWLAAvr6+WLx4MSIjI9G+fXuJoyQiIiLKHxQKBQCgcuXKGDJkCOrWrStxRKRpTCIANGzYEKdPnwYAmJiYYOPGjfD29ka3bt1w7949bNq0CSYmJhJHSURERJT3HT9+HK1bt0ZUVBS0tbUxa9YsFC9eXOqwSMMKZZuI58+fZ/i6Ro0aYrEbEREREamvaNGisLW1RVRUFIyNjaUOh3JIoUwiiIiIiEgzBEHAwYMHERkZiWHDhuG7777D3r17pQ6LchirMxERERHRN/nnn39w7tw5sS0EFXwsiSAiIiKiLBEEAcePH0fdunVhZ2eHVatWwdDQkKNBFyLc00RERESUJR8/fsTkyZOxdetWAEkd02hra0scFeUmlkQQERERkVqePHmCqlWrolixYjh8+DBHDC/EWBJBRERERJk6ePAgWrdujTt37gAAnJycoKPD59GFFfc8EREREaUrJiYGhoaG6NixI758+QInJyepQ6I8gCURRERERJSmefPmoWfPnkhISIChoSGGDx8OXV1dqcOiPIAlEURERESUJhcXFxgYGLDrVlLBkggiIiIiApBUdWnq1Kk4fPgwAKBjx46YNm0a9PT0JI6M8hqWRBARERERAEBPTw++vr4oXry41KFQHsckgoiIiKgQi46Oxvr16/HDDz/A2NgYBw4cYK9LlClWZyIiIiIqxJ4+fYrVq1fj4sWLAMAEgtTCJIKIiIiokImJicHVq1cBALVr18bVq1fRsWNHiaOi/IRJBBEREVEhs2TJEgwaNAifPn0CADg4OEgcEeU3LK8iIiIiKgTi4uIQHR0NS0tLjB8/Hi1atICdnZ3UYVE+xZIIIiIiogJOoVCge/fumDRpEgDA2toajRs3ljgqys9YEkFERERUQCkUCmhpaUFLSwsDBw6Era2t1CFRAcGSCCIiIqICKDAwEO3btxcbUPfp0wctWrSQOCoqKJhEEBERERVA1tbWMDQ0RHx8vNShUAHEJIKIiIiogHj58iWmTJmChIQEGBoawsPDA82aNZM6LCqAmEQQERERFRA+Pj44c+YM/Pz8AAAymUziiKigYhJBRERElI+9evUKly5dAgB06tQJ165dQ8WKFaUNigo89s5ERERElI/NnDkTb9++xX///QcdHR2Ym5tLHRIVAkwiiIiIiPIZf39/WFtbw9jYGO7u7tDV1YWODm/rKPewOhMRERFRPhIaGorWrVtj6dKlAAAHBwcUK1ZM4qiosGHKSkRERJQPxMTEwNDQEFZWVpgzZw5HnCZJsSSCiIiIKI+7evUq6tSpAx8fHwBA3759YW9vL3FUVJgxiSAiIiLK4ypXrow6derAyMhI6lCIADCJICIiIsqTjh07hgkTJkAQBFhbW2Pz5s1wcHCQOiwiAEwiiIiIiPKkT58+4fXr1/j69avUoRCpYBJBRERElEecOHECN27cAAAMHz4cR44cgZmZmcRREali70xEREREeUBcXByWLl2KypUro169etDS4rNeyruYRBARERFJ6L///kO9evWgr6+Pffv2oWjRolKHRJQpprhEREREErl37x769OmDPXv2AABKlCjBkacpX2ASQURERJTLgoODAQC1atXC+vXr0adPH4kjIsoaJhFEREREuWjjxo1o0qQJPn78CADo0qUL9PT0JI6KKGtYXkZERESUCwRBgEwmQ6tWrRAWFgZLS0upQyLKNiYRRERERDlIEATMnj0bCoUCixYtQtmyZTF9+nSpwyL6JkwiiIiIiHKQTCaDvr4+FAqFWBpBlN8xiSAiIiLSsJiYGLi7u8PNzQ2VKlXCrFmzmDxQgcKG1UREREQaFh0dDQ8PD1y+fBkAmEBQgcOSCCIiIiINiI2NxbFjx9C7d29YW1vj0qVLbDxNBRZLIoiIiIg04OjRo5g8eTK8vLwAgAkEFWgsiSAiIiLKJrlcjsDAQJQtWxa9e/dG2bJl4erqKnVYRDmOJRFERERE2TR27Fj07dsXsbGx0NLSwnfffSd1SES5giURRERERFmQkJAAANDR0cGoUaMQEhICAwMDiaMiyl0siSAiIiJSU2RkJDp37ox169YBAFxcXNC6dWuJoyLKfUwiiIiIiNRkYmICJycnlCtXTupQiCTFJIKIiIgoA2/evMHAgQPx8eNHAMCiRYvQoUMHiaMikhaTCCIiIqIMKBQKPH36FH5+flKHQpRnMIkgIiIiSuXt27fYtm0bAKBs2bK4ceMGGjRoIHFURHkHkwgiIiKiVHbv3g13d3cEBwcDAPT09CSOiChvYRJBREREBODdu3dilaVJkybhwoULsLGxkTgqoryJ40QQERFRoZeYmIjevXujWLFiOHToEAwNDWFvby91WER5FpMIIiIiKrRCQ0NhaWkJbW1tLFu2DCVKlJA6JKJ8gdWZiIiIqFB6+fIlGjZsiIMHDwIA6tevDwcHB4mjIsofmEQQERFRoSIIAgDA0dERPXv2RO3atSWOiCj/YRJBREREhYanpyfat2+PyMhIaGlpYf78+ShbtqzUYRHlO0wiiIiIqNAwMzODjo4Ovnz5InUoRPlankgioqKicO7cOcTGxipN37FjB5o3b45q1aqhbdu22L17t0QREhERUX519uxZceA4V1dXHD9+nD0vEX0jyZMIT09PtGjRAhMnTsSbN2/E6Vu3bsWSJUvw4cMHJCQk4M2bN1i4cCHmzZsnXbBERESU7xw7dgyHDx9GYmIiAEAmk0kcEVH+J2kSERAQgIkTJyI8PByCIOD9+/cAgNjYWKxfvx6CIMDMzAwDBw6Eq6srBEHAvn37cPv2bSnDJiIiojzO09MTHz9+BAC4u7vDw8MD2traEkdFVHBImkTs2rUL8fHxsLGxEasuAcDly5cRFRUFmUyGlStX4pdffsGuXbvQpk0bCIKAQ4cOZXlbcrkcs2fPhqurKxo0aIDNmzenO6+Xlxe6d+8OJycndOnSBVevXs32ZyQiIqLcFRwcjJEjR2LdunUAAFNTU+jp6UkcFVHBImkScfPmTchkMkyZMgV16tQRp1+6dAkAYGtriwYNGojThw4dCiDpJj+r3N3d4e3tjW3btmHevHnYsGEDTp06pTJfSEgIRo8ejbZt2+L48eNo164dxowZg3fv3mV5m0RERJR7fH19AQA2NjbYt28fZs2aJXFERAWXpEnEhw8fAADfffed0vTr169DJpMpJRAAxAFgQkJCsrSd6OhoHDhwADNnzkS1atXQsmVLDB8+HH///bfKvPfu3QMAjBw5Eg4ODhg9ejQMDAzw4MGDLG2TiIiIcs/p06fRtGlTXL9+HUBSA2p9fX2JoyIquCRNImJiYgBA6Ufu6+uLT58+AQDq1auX5vxZbRDl4+MDuVwOFxcXcZqLiwsePXqEhIQEpXktLCzw9etXnDlzBoIg4MKFC4iKikLFihWztE0iIiLKeXFxcQCAZs2aYdq0aXB2dpY4IqLCQdIkwsbGBgCUqgpduXIFQFKiUL9+faX5Hz16BAAoUqRIlrYTFBQEc3NzpWTFxsYG8fHxCA0NVZq3du3aGDBgACZNmoSqVatizJgxmDNnDhwdHbO0TSIiIspZ+/btQ7du3RAfHw9DQ0OMGzcOhoaGUodFVCjoSLnxWrVq4cyZM9i5cyeWLVuGqKgo7N+/HzKZDDVq1BCTDAAICwvD2rVrIZPJUKtWrSxtJyYmRqVBVfJruVyuND06OhqBgYH44Ycf0KpVK1y7dg2LFi1C+fLl4eTkpPY2Hz9+nKUYv9Xdu3dzdXukWdx/+Rv3X/7G/Zd/OTg44MuXL7hz5w6rLuVT/P3lX5ImET179sTp06dx8uRJPHz4ENHR0QgKCoJMJoObm5s434oVK3Ds2DF8/vwZWlpa6NevX5a2o6+vr5IsJL9O/cRi69atkMvlmDBhAgCgSpUq8PX1xYYNG7Bx40a1t1mtWrVcO6HdvXtXqaoW5S/cf/kb91/+xv2Xv8jlcri7u6NixYro1asXAGDcuHESR0XZlZu/v7i4uFx/wFvQSVqdqX79+vjxxx8hCALevn2LoKAgAECrVq3QrVs3cb4zZ87g8+fPAIBJkyahRo0aWdqOnZ0dIiIilBKJoKAg6OnpwdzcXGneR48eoXz58krTqlatioCAgCxtk4iIiDRLR0cH3t7e8PHxkToUokJP0pIIABg/fjwaN26M8+fPQy6Xw9XVFa1bt1aap3LlyihdujSGDx+OunXrZnkblStXhq6uLry9vcWuZO/evYuqVatCR0f5KyhSpAieP3+uNM3Pz0/sGYqIiIhyj1wux+bNmzFo0CCYmppiz549rLpElAdInkQAgJOTU4btDdasWfNN6zc0NETXrl0xb948LFmyBEFBQfjrr7+wYMECAEmlEqampjAwMICbmxv69OmDzZs3o23btrh58yY8PDywadOmb4qBiIiIsu7Zs2dYsmQJbG1t0bt3byYQRHmEpNWZctOMGTNQvXp1DB48GHPmzMGYMWPQvn17AEDDhg1x+vRpAECNGjWwYcMGnDlzBp07d8bOnTuxfPlyle5miYiIKGfEx8fj9u3bAICaNWvi4sWL6N27t8RREVFKeaIkAkgay+HAgQO4e/cuPnz4gKioKDx58gQAMH36dJQrVw4DBgyAgYFBttZvaGiIpUuXYunSpSrvpa6+1KRJEzRp0iRb2yEiIqJvs2LFCvz555+4evUqSpQogXLlykkdEhGlInkSkZiYiEWLFmHPnj0AAEEQACgPKHfjxg0cO3YMHh4e2Lx5M+zt7SWJlYiIiHJGYmIiIiMjYW5ujpEjR6JmzZooUaKE1GERUTokr840e/Zs7NmzB4IgwNraGs2aNVOZR19fH4Ig4NWrVxg5cqRKd61ERESUfwmCgL59+2Ls2LEQBAFWVlZo166d1GERUQYkTSKuX78ODw8PAEn9PF+6dAnLly9Xme/MmTOYOHEiAODVq1c4cOBAboZJREREOSBl7YNu3bqhR48eEkdEROqSNInYt28fgKRB58aMGQMdHR2lakzJtLW1MXr0aPTt2xeCIODs2bO5HSoRERFp0OfPn9GzZ09cuXIFANC3b1907do1zfsAIsp7JE0ivL29IZPJ0KdPH7XmTx7F+uXLlzkZFhEREeUwU1NTyOVyRERESB0KEWWDpElEeHg4AKBkyZJqzV+sWDEAQFRUVE6FRERERDkkMDAQs2bNQnx8PAwNDXH8+HF07NhR6rCIKBskTSJMTEwAAKGhoWrN//HjRwCAubl5jsVEREREOePhw4c4cOAAnj17BgCsukSUj0maRFSsWBEAcP78ebXmP3LkiNJyRERElLe9f/9ebPfQvn173LhxAzVq1JA4KiL6VpImER06dIAgCNiwYQMePHiQ4bynT5/Gzp07IZPJ0KZNm1yKkIiIiL7FzJkzMWnSJMTFxQEArK2tJY6IiDRB0sHmunfvjj179uDZs2cYMGAAOnbsiEqVKonvX79+HQEBAfjnn39w7do1CIIAR0dHdgFHRESUh33+/BmGhoYwNTXF/PnzkZiYCH19fanDIiINkjSJ0NbWxp9//olhw4bBz88PR48eBfC/OpLff/+9OK8gCChevDg2bNgAHR3JB9omIiKiNERERKBly5bo0KEDFi9eDAcHB6lDIqIcIPmI1XZ2djh8+DDGjBkDa2trCIKg8mdiYoIhQ4bgyJEjPBkRERHlQXK5HABgZmaGn3/+WelBIBEVPHnikb6BgQHGjRuHcePGwc/PD/7+/oiMjIShoSGKFSuGypUrQ0tL8nyHiIiI0nD37l2MHDkSO3fuRNWqVTFo0CCpQyKiHCZpEvHx40cULVpUaZqjoyMcHR0lioiIiIiyqkyZMqhSpQr09PSkDoWIcomkj/ebN2+OIUOG4OjRo4iOjpYyFCIiIsoCT09P/PTTTxAEAVZWVti1axfKly8vdVhElEskTSIUCgVu3bqFGTNmoEGDBpg6darYCxMRERHlXW/evMGDBw8QFhYmdShEJAFJk4jZs2fD2dkZABATE4MTJ05g+PDhaNKkCZYvX44XL15IGR4RERGlcPnyZVy/fh0AMHToUJw+fRpWVlYSR0VEUpA0iejfvz/27NmDf//9F5MnT0b58uUhCAI+f/6MrVu3okuXLujWrRt27NiBkJAQKUMlIiIq1BISEjBnzhysXbsWAKClpcU2EESFWJ7o8qhYsWIYOXIkjh8/jpMnT2LkyJEoXrw4BEHAs2fPsGTJEjRp0gSjRo3C6dOnxW7kiIiIKGd5eXkhPj4eOjo62LFjB7Zu3Sp1SESUB+SJJCKlcuXKYfLkyfD09MSePXvQv39/WFtbIyEhAVeuXMFPP/2Ehg0bSh0mERFRgff06VN06dIF27dvBwCUKlUKhoaG0gZFRHlCnksiUqpVqxZmz56Nv/76C02bNhUHn/v69avUoRERERVY4eHhAIAqVarg999/x4ABA6QNiIjynDwx2FxafHx8cOrUKZw7dw4BAQHidB0dHTRr1kzCyIiIiAquv//+G4sXL8b58+dhb2+PXr16SR0SEeVBeSqJ8Pf3x8mTJ3Hq1Cm8evUKAMTuXqtXr46uXbuiQ4cOsLCwkDBKIiKigkcQBMhkMjRs2BDdunWDubm51CERUR4meRLx+fNnnD59GqdOncLjx48B/C9xKFasGDp37owuXbqgbNmyUoZJRERUYC1btgwhISFYsmQJSpcujYULF0odEhHlcZImEYMGDcLdu3ehUCjExMHIyAitW7dG165dUbduXSnDIyIiKhTi4uKQkJCAxMREaGtrSx0OEeUDkiYRt2/fBpDU13S9evXQpUsXtG7dmj0/EBER5aD4+HisWbMGbdq0QdWqVfHLL79AJpNJHRYR5SOSJhHly5dH586d0blzZ9jZ2UkZChERUaHx9etX7Ny5E4IgoGrVqkwgiCjLJE0iTpw4IeXmiYiICo2EhAScOnUKnTt3hpWVFf755x/Y2tpKHRYR5VN5epwIIiIi0ozjx4/jxx9/xH///QcATCCI6JvkWklEnz59ACT1uLRq1Sqladmxb98+jcRFRERUUCUmJuL9+/coWbIkunbtCmtrazRu3FjqsIioAMi1JOL+/fuQyWQICwtTmZbcM5O6WHeTiIgoc1OmTMF///2HS5cuwdjYGE2aNJE6JCIqIHItiXB1dQUApQbUydOIiIhIMxQKBRQKBXR0dDBw4EDUr18fRkZGUodFRAVMriURu3btUmsaERERZU9MTAwGDRqE+vXrY9KkSXB2doazs7PUYRFRAZTvGlYrFAq8e/dO6jCIiIjyHENDQzg6OqJ48eJSh0JEBZykSUTz5s3RsmVLxMXFqTV/SEgInJycMHDgwByOjIiIKH/48OEDRo4ciffv3wMAlixZAjc3N4mjIqKCTtIk4v3793j37h0UCoVa88fHx0MulyM4ODiHIyMiIsof4uLicOvWLTx58kTqUIioEMmVNhEKhQInT55MN1k4ceIE9PT0MlxHfHw8zp8/DwAwNjbWeIxERET5xadPn3Du3DkMGjQIpUuXxs2bN2FoaCh1WERUiORKEqGlpQUvLy8cPHhQaXpyV61z5sxRe10ymQz16tXTaHxERET5yY4dO7Bx40a0bNkSxYsXZwJBRLku16ozTZo0CaamphAEQfxLlnJaRn8A4OLigpkzZ+ZW2ERERHlCcHAwXr16BQAYP348zp8/zwbURCSZXOvi1dLSEmfPnkVMTAyApMShZcuWkMlkOHXqFAwMDNJdViaTQUdHBxYWFplWeyIiIipoBEFAnz59YGBggBMnTsDAwACOjo5Sh0VEhViuJREAYGVlpfS6ePHikMlkKFGiBPT19XMzFCIiojwvIiICpqamkMlkmDdvHmxsbMSqwEREUpK0d6Z///0Xnp6eTCCIiIhSefv2LZo0aYL9+/cDABo0aICKFStKHBURUZJ8N9gcERFRQZbcBrBkyZJo27YtqlevLnFERESqcq06U58+fQAAxYoVw6pVq5SmZce+ffs0EhcREVFecePGDSxevBh///03zMzMsHjxYqlDIiJKU64lEffv34dMJkNYWJjKtJQ9NamD9UGJiKgg0tfXR3R0NIKCgmBmZiZ1OERE6cq1JMLV1RUAYGdnpzKNiIiosLp69Sp8fX0xZMgQ1KpVC+fPn4eWFmsbE1HelmtJxK5du9SaRkREVJjs378fT548Qb9+/aCnp8cEgojyhVzt4pWIiIiA27dvo0SJEihevDh+++036OrqchwkIspX8vzjjsDAQHh5eeHz589Sh0JERPTNvnz5goEDB2LFihUAADMzMxgaGkocFRFR1uSJkoh79+7h1KlTGDduHCwsLAAA0dHRmDJlCv79918ASY2pW7Vqhd9++w0mJiYSRktERJR1AQEBKFmyJMzNzbF9+3bUqFFD6pCIiLJN8pKI5cuXo3///tizZw/evn0rTl+4cCE8PT0hCAIEQYBCocD58+cxfPhwCaMlIiLKuosXL6JBgwa4cuUKAKBevXowNjaWOCoiouyTNInw8vLCli1bIAgCTExMEB8fDwAIDg7GsWPHIJPJUKVKFaxduxY//PADtLS08ODBAxw9elTKsImIiNSSkJAAIClpGDt2LJydnSWOiIhIMyRNIg4ePAgAqF69Ojw9PVG7dm0AwIULF5CYmAgAWLFiBVq2bIkJEyZgxIgREAQBp06dkixmIiIidWzevBmdO3eGXC6HgYEBpk6dClNTU6nDIiLSCEmTiLt370Imk2HSpElKg+okF/eWK1cOZcqUEad36NABAPDs2bPcDZSIiCiLSpUqhXLlykEul0sdChGRxknasDokJAQAUKlSJXFaYmIibt26BZlMhgYNGijNb2trCwAIDw/PtRiJiIjUkZiYiLVr16JYsWLo3bs3WrdujdatW0sdFhFRjpC0JEKhUACA2BYCALy9vREVFQUgqQ5pSqGhoQAAfX39XIqQiIhIPTKZDP/99x+8vLykDoWIKMdJmkTY29sDAHx9fcVpFy9eBADo6emhTp06SvNfvXoVAFCyZMlcipCIiCh9iYmJ2LZtGyIiIqClpYVdu3bB3d1d6rCIiHKcpElE3bp1IQgC/vjjD3z69AlPnjzBgQMHxKpMBgYG4rz37t3DunXrIJPJVEooiIiIpPD8+XP8+uuv8PDwAAAOGkdEhYakbSIGDhyIw4cP49GjR2jatCkAQBAEaGlpYdiwYeJ8ffv2xYMHD6BQKGBsbIzBgwdLFDERERV2CoUCDx48gLOzM6pUqYKzZ8+iSpUqUodFRJSrJC2JKFOmDFavXg0zMzNxUDkdHR1MmzZN7O4VACIjI6FQKGBubo61a9eiaNGiEkZNRESF2Zo1a9C1a1e8evUKAFC1alXIZDKJoyIiyl2SlkQAQJMmTXDp0iXcvn0bcrkcTk5OYi9Mydq1a4dOnTqhV69esLS0lChSIiIqrARBQFRUFExMTDBo0CAUL15cqQtyIqLCRvIkAkiqQ9qkSZN03//xxx9zMRoiIqL/EQQBI0aMQHR0NHbv3g1LS0v06tVL6rCIiCSVJ5KIZIIgwMfHB+/fv0d0dDQMDQ1hb2+PChUqQFtbW+rwiIioEJLJZGjRooVSd+RERIVdnkgioqOj8eeff2L//v2IiIhQed/IyAjdunXD5MmTYWRkJEGERERUmISGhmLixIkYOnQomjVrhr59+0odEhFRniJpw2oA+PjxI3r16oXNmzfjy5cvYgPrlH9RUVHYvXs3unfvjvfv32drO3K5HLNnz4arqysaNGiAzZs3pzuvn58fBg0ahJo1a6JNmzY4d+5cdj8eERHlQ0ZGRggODsbnz5+lDoWIKE+StCQiMTERP/74I/z8/AAADRo0QIcOHVCuXDkYGhoiKioKL1++xLlz53D16lW8ffsWkydPxu7du7Ncvcnd3R3e3t7Ytm0bPn78iKlTp6J48eLo0KGD0nxRUVEYOnQo6tati/nz5+PKlSv46aef4OjoiHLlymnssxMRUd4SFBSEDRs2YPr06TAwMMDJkyehpSX5szYiojxJ0iTiyJEjePr0KbS1tbFo0SJ06dJFZR4nJyf06tULp06dwrRp0/DgwQOcO3cO7du3V3s70dHROHDgAP78809Uq1YN1apVw/Dhw/H333+rJBFHjx6Fjo4OfvvtN+jq6qJ06dK4du0avL29mUQQERVg3t7e2L59O9q2bYvvvvuOCQQRUQYkPUOeOnUKMpkMgwYNSjOBSKlDhw4YPHgwBEHAkSNHsrQdHx8fyOVyuLi4iNNcXFzw6NEjJCQkKM1769YtNG/eHLq6uuK0jRs3sicOIqICKCIiAteuXQMAtG7dGtevX8d3330ncVRERHmfpEnE8+fPAQA9evRQa/7u3bsDAF6+fJml7QQFBcHc3Bz6+vriNBsbG8THxyM0NFRpXn9/f1hbW2Pu3Llo2LAhunXrhosXL2Zpe0RElD9s2LABo0ePRkxMDABwMFMiIjVJWp0puSemIkWKqDV/8iB0ISEhWdpOTEwM9PT0lKYlv5bL5UrTo6KisHXrVvTr1w+bNm3C1atXMWbMGBw4cADVqlVTe5uPHz/OUozf6u7du7m6PdIs7r/8jfsvf4mMjISWlhaMjIwwZMgQREdH4+nTp1KHRdnE31/+xv2Xf0maRFhYWCAkJAQBAQGoWrVqpvMHBAQAAMzNzbO0HX19fZVkIfm1oaGh0nRtbW1UqFABkydPBgBUqVIFd+/ezXISUa1aNaWSj5x09+5dpapalL9w/+Vv3H/5S0xMDJo0aYImTZpg2bJl3H/5HPdf/pab+y8uLi7XH/AWdJJWZ0q+Kd+3b59a8+/du1dpOXXZ2dkhIiJCKZEICgqCnp6eSkJSpEgRlC1bVmlamTJlst21LBERSS+5/ZuhoSF+/PFHDBw4UOKIiIjyN0mTiC5dukAQBBw6dAhbt27NcN6tW7fi0KFDkMlk6NSpU5a2U7lyZejq6sLb21ucdvfuXVStWhU6OsqFMc7OzirF2r6+vrC3t8/SNomIKG94/PgxmjRpIj6FHDJkCGrUqCFxVERE+Zuk1Znatm2LPXv24M6dO1i+fDmOHj2KNm3awNHREUZGRoiOjoafnx/OnTsHX19fAEm9KqXuljUzhoaG6Nq1K+bNm4clS5YgKCgIf/31FxYsWAAgqVTC1NQUBgYGcHNzw86dO7Fs2TK4ubnh33//xY0bN3Dw4EGNf34iIsp59vb2KFasGBQKhdShEBEVGJImETKZDGvXrsXIkSPx4MEDvHz5UkwWUhIEAQBQs2ZNrFmzJlvbmjFjBubOnYvBgwfD2NgYY8aMEceaaNiwIRYvXozu3bujePHi2LZtGxYuXIidO3eiZMmSWL16NapUqZL9D0pERLnq1q1bOHz4MJYuXQpLS0scOnRI6pCIiAoUSZMIIKmR9O7du3HgwAEcPHgQPj4+YtIAJCUaFSpUgJubG9zc3FSqH6nL0NAQS5cuxdKlS1XeS+5qNpmTkxMvOERE+dizZ89w9epVfP78GXZ2dlKHQ0RU4EieRACAjo4O+vXrh379+iEiIgIfP35EZGQkjIyMULx4cZiZmUkdIhER5XFeXl6Qy+WoX78+Bg0aBDc3N5Ue+IiISDPyRBKRkpmZGZMGIiLKEoVCgenTp8PExARHjhyBlpYWEwgiohyUZ5IIuVyOmzdv4saNG/j48SO+fv0KS0tL2Nvbo2nTpnBycpI6RCIiymOePHmC8uXLQ09PD5s2bYKtrS1kMpnUYRERFXh5Iok4evQoVqxYgeDg4DTf37hxIxwdHTFv3jwOKkNERAAAPz8/tGvXDlOnTsXYsWNVxvghIqKcI+k4EQCwfv16zJgxA0FBQRAEAXp6eihbtiyqVKmC0qVLQ0dHB4IgwNfXFwMHDsT58+elDpmIiCQUGRkJAHB0dMTixYs5cBwRkQQkLYnw9vbG6tWrAQDly5fHzz//jIYNG0JbW1ucJyEhAZcvX8bKlSvh5+eHqVOnokqVKihRooRUYRMRkUSOHDmCX3/9FWfPnoW9vT369+8vdUhERIWSpCUR27ZtAwBUqVIF+/fvR5MmTZQSCCCp56YWLVpg//79KF++POLi4rB9+3YJoiUiIqm5uLigZcuWbDRNRCQxSZOIx48fQyaTYcqUKTAyMspwXhMTE/z8888QBAFXr17NpQiJiEhqGzduxIwZMwAADg4OWLVqFaysrCSOioiocJM0iQgJCQEAtUeDrlmzJgDg48ePORYTERHlLaGhoQgODkZ8fLzUoRAR0f+TtE2EnZ0dAgICEBgYCHNz80znDw0NBQA+gSIiKsAUCgW2bt2KevXqoVq1apg6dSq0tLTYdSsRUR4iaUlEmzZtIAgCNmzYoNb8e/fuFZcjIqKC6evXr9iwYQMOHz4MANDW1mYCQUSUx0iaRPz444+oWLEiPD09MWXKFERERKQ5nyAI2LRpE3bt2oUSJUrgxx9/zOVIiYgoJykUCpw9exaCIMDc3BwnT57Er7/+KnVYRESUDkmrMx08eBBt2rSBv78/Tp48iQsXLqBBgwaoUKECzMzMEBsbi4CAAFy9ehWfP3+GTCaDnZ0d5s6dm+46V6xYkXsfgIiINOLs2bMYMWIEtm3bhtatW6N48eJSh0RERBmQNIlYtGiRWEQtCAJiYmLg6ekJT09PpfkEQQAAyGQy3Lt3L811CYIAmUzGJIKIKJ8QBAEfPnxA8eLF0bZtW2zduhWtWrWSOiwiIlKDpEkEnzQRERVec+bMwalTp3Dx4kWYmZmhbdu2UodERERqkjSJ+Pfff6XcPBER5TJBEKBQKKCtrY0ePXqgTJkyMDExkTosIiLKIkmTCCIiKjzkcjlGjx6NypUrY8qUKahZs6Y4/g8REeUvkvbOREREhYeenh5sbGxgYWEhdShERPSNmEQQEVGOCQ0NxcSJE/Hu3TsAgLu7O0aMGCFxVERE9K2YRBARUY6JjIzEP//8Ay8vL6lDISIiDWISQUREGhUWFoa9e/cCABwcHHDr1i106dJF4qiIiEiTmEQQEZFG7dixA9OnT8ebN28AgL0vEREVQEwiiIjom0VERIhJww8//IDTp0+jdOnSksZEREQ5J9e6eL127Rrs7OxQrly53NokERHlAkEQ0K9fP8THx+Ps2bPQ19dH1apVpQ6LiIhyUK4lEXPnzsW7d+/w77//omjRogCAo0ePAgA6d+4MLS0WihAR5SfR0dEwNDSETCbD9OnTYWRkBJlMJnVYRESUC3Ltzj04OBiCIMDIyEicNn36dMycORNxcXG5FQYREWnAhw8f0KJFC+zevRsA0LBhQ9SqVUviqIiIKLfkWhKRXNKQXGc2mSAIuRUCERFpiJ2dHRo2bIiKFStKHQoREUkg16ozlSpVCs+ePcPEiRPRtm1bpRKJP//8E7q6ulla39ixYzUdIhERZeD+/ftYuHAhtm7dCnNzcyxbtkzqkIiISCK5lkT06NEDCxYswIcPH7Bt2zZxuiAI2LRpU5bXxySCiCh3yWQyfPjwAe/fv4e5ubnU4RARkYRyLYno378/QkJCcPjwYQQHByMxMREymQyCILBKExFRHnX//n08ePAAgwcPRs2aNXH58mXo6OTapYOIiPKoXL0SjB8/HuPHjxdfV6pUCTKZDPfu3YOhoWFuhkJERGrYsWMHbty4gd69e8PQ0JAJBBERAcjlJIKIiPK+x48fw9LSEvb29pg3bx4A8EEPEREpkTSJ8PT0BMCLExFRXhEVFQU3Nzc0bdoU69atg5mZmdQhERFRHiRpEmFvb6/0+unTp7h9+zY+fPggDmJUvHhx1KpVCzVq1JAoSiKigu/jx48oWrQojI2N8eeff3LEaSIiylCeqM70/PlzzJkzBw8ePEh3ngoVKsDd3Z19khMRadiNGzfQr18/bNmyBS1atECjRo2kDomIiPK4XBtsLj137txBnz598ODBA7GnJj09PZibm0NXV1ec9vz5c/Tu3Ru3b9+WOmQiogJBoVAAAGrVqoVhw4bByclJ2oCIiCjfkDSJiIiIwPjx4xETEwNDQ0NMnjwZFy5cwIMHD3Dz5k08fPgQ58+fx+TJk2FiYoK4uDj8/PPPiIiIkDJsIqJ8b+/evejcuTPi4uKgr6+P2bNnw9raWuqwiIgon5A0idi5cyfCwsJgbm6OAwcOYOTIkShRooTSPA4ODhg5ciQOHjwIS0tLBAUFwcPDQ6KIiYgKBhsbG9ja2iImJkbqUIiIKB+SNIm4ePEiZDIZxo8fj3LlymU4b5kyZTB27FgIgoCzZ8/mUoRERAWDQqHA9u3bsX//fgBAq1atsG3bNlhYWEgbGBER5UuSJhH+/v4AgGbNmqk1f/PmzZWWIyIi9Z05cwb//vuv1GEQEVEBIGnvTHFxcQDUHyfCwMAAQFI/5kRElDFBEHDgwAG0bdsW5ubm2LJlC0xMTKQOi4iICgBJSyJsbW0BAD4+PmrN/+zZMwBJdXmJiChjfn5+mDJlCnbv3g0AMDU1hUwmkzgqIiIqCCRNIlxdXSEIAtavXy92NZiexMRErF+/HjKZDLVr186lCImI8hdBEPDkyRMAQLly5XD06FGMHj1a4qiIiKigkTSJ6NevHwDAy8sL48aNQ1BQUJrzBQUFYdy4cfDy8gIA9O3bN9diJCLKT7Zu3Yp27dqJJby1atWClpbkQwIREVEBI2mbiBo1amDAgAH4+++/8e+//+Ly5ctwdnZGuXLlYGRkhOjoaPj6+sLb2xuJiYkAkhIPDohERPQ/giAgNjYWhoaG6NmzJ3R0dFC+fHmpwyIiogJM0iQCAGbNmgVdXV3s2LEDCQkJ8PLyEksckgmCAAAYMmQIpkyZIkWYRER51uTJk/Hhwwfs3bsXFhYWGDJkiNQhERFRASd5EgEA06ZNQ48ePXDw4EHcunULHz58QFRUFIyMjGBvbw8XFxe4ubnxyRoRURq+++47hIaGQqFQQFtbW+pwiIioEMgTSQSQ1ABwxowZUodBRJTnff36FTNmzEDXrl3RsmVLthMjIqJcx9Z2RET5jJ6eHnx9fTnwJhERSSbPlEQQEVH6vnz5go0bN2LChAnQ19fHyZMnoaPDUzgREUmDJRFERPnA3bt3sXbtWty6dQsAmEAQEZGkmEQQEeVRkZGRuHnzJgCgefPmuHbtGho3bixxVEREREwiiIjyrF9++QVDhw5FREQEAKBkyZISR0RERJSESQQRUR4SHR2Nr1+/AgCmTJmCnTt3wszMTOKoiIiIlDGJICLKI+Li4tCuXTvMnj0bAFCiRAm4urpKHBUREZEqtswjIpKYQqGAlpYW9PX1MXjwYFSuXFnqkIiIiDLEkggiIgn5+vqiVatWePDgAQBg2LBhqFevnsRRERERZSxPlEQIgoD79+8jMDAQsbGxUCgUmS7j5uaWC5EREeUsGxsbGBsbIzY2VupQiIiI1CZ5EnHt2jXMnj0bHz58UHsZmUzGJIKI8q1Hjx5h3759WLhwISwsLHDs2DHIZDKpwyIiIlKbpNWZ/Pz8MHr0aHz48AGCIGTpj4gov7p//z7Onj2Ld+/eAQATCCIiynckLYnYunUr4uPjoa2tjYEDB6JFixawsbGBnp6elGEREWncs2fPEB4ejnr16mHAgAHo0qULu24lIqJ8S9Ik4saNG5DJZBg1ahTGjx8vZShERDlGEARMnjwZiYmJOHfuHGQyGRMIIiLK1yRNIoKDgwEA3bt3lzIMIqIc4efnh5IlS0JPTw9r1qyBpaUlqy4REVGBIGmbCHNzcwCAsbGxlGEQEWlcYGAgWrVqhXXr1gEAypUrB2tra4mjIiIi0gxJkwgnJycAST2V5DS5XI7Zs2fD1dUVDRo0wObNmzNdJjw8HPXr14eHh0eOx0dEBUNMTAyApNGmf/31VwwYMEDiiIiIiDRP0iRi4MCBAIC1a9ciPj4+R7fl7u4Ob29vbNu2DfPmzcOGDRtw6tSpDJdZtGgRQkJCcjQuIio4zp07h3r16iEgIAAAMGTIENja2kocFRERkeZJmkTUqVMHEyZMwMOHD9G3b1+cPn0agYGBiI6Ohlwuz/AvK6Kjo3HgwAHMnDkT1apVQ8uWLTF8+HD8/fff6S5z+fJlPHz4EFZWVt/6MYmokKhatSrq1KkDXV1dqUMhIiLKUZI2rO7Tpw8AwNDQEE+ePMFPP/2k1nIymQxPnz5Vezs+Pj6Qy+VwcXERp7m4uGD9+vVISEiAjo7y1xAZGYm5c+fC3d1d7ZiIqHA6f/48PDw88Ntvv6FEiRLYuHGj1CERERHlOEmTiPv37+fKdoKCgmBubg59fX1xmo2NDeLj4xEaGooiRYoozb9s2TI0atQIrq6uuRIfEeVfnz9/xocPHxAXF6d0jiEiIirIJE0ixo4dmyvbiYmJURnALvl16qpRt2/fxsWLFzNtL5GZx48ff9PyWXX37t1c3R5pFvdf/iEIAjw9PVGqVCmUL18effv2hZaWVq7/5klz+PvL37j/8jfuv/yrUCQR+vr6KslC8mtDQ0NxWmxsLGbNmoXZs2fD1NT0m7ZZrVq1XHsqeffuXaWqWpS/cP/lL5GRkRg1ahSaN2+OPn36cP/lc9x/+Rv3X/6Wm/svLi6OD3s0TNIkIrfY2dkhIiICcrlcLIEICgqCnp6eOFYFADx8+BBv377F1KlTxWkxMTGYM2cO7t+/j/nz5+d67EQkPUEQcPnyZTRp0gQmJiY4cuQISpQoIXVYREREkslTScTTp09x+/ZtfPjwAdHR0TA0NETx4sVRq1Yt1KhRI9vrrVy5MnR1deHt7Y06deoASMp+q1atqtSoukaNGjh//rzSsv3798fgwYM5qjZRIXbp0iUMGDAAGzZsQOfOneHg4CB1SERERJLKE0nE8+fPMWfOHDx48CDdeSpUqAB3d3dUrFgxy+s3NDRE165dMW/ePCxZsgRBQUH466+/sGDBAgBJpRKmpqYwMDBAqVKllJbV0tKCtbU1R5olKoSCg4NhY2ODpk2bYt26dejQoYPUIREREeUJko4TAQB37txBnz598ODBAwiCAEEQxGpGurq64rTnz5+jd+/euH37dra2M2PGDFSvXh2DBw/GnDlzMGbMGLRv3x4A0LBhQ5w+fVqTH4uI8jl3d3e0bt0aYWFhkMlk6Nq1K7S1taUOi4iIKE+QtCQiIiIC48ePR0xMDIyMjDB69Gi0b99eqa6xv78/zp49i02bNiEyMhI///wzTp48CTMzsyxty9DQEEuXLsXSpUtV3nv+/Hm6y125ciVL2yGi/E0QBMhkMrRr1w56enowMTGROiQiIqI8R9KSiJ07dyIsLAzm5uY4cOAARo4cqdJY0cHBASNHjsTBgwdhaWmJoKAgeHh4SBQxERVUiYmJmDBhgvigoXr16pg4cSJHnyYiIkqDpEnExYsXIZPJMH78eJQrVy7DecuUKYOxY8dCEAScPXs2lyIkosJCW1sb+vr6HDCOiIhIDZImEf7+/gCAZs2aqTV/8+bNlZYjIvoWX79+xS+//CKeU5YuXYpJkyZJHBUREVHeJ2kSERcXB0B5wLeMGBgYAACioqJyLCYiKjwiIiJw5MgRXL9+HQAgk8kkjoiIiCh/kDSJsLW1BQD4+PioNf+zZ88AADY2NjkWExEVbFFRUTh06BAAwN7eHtevX0efPn0kjoqIiCh/kTSJcHV1hSAIWL9+PRQKRYbzJiYmYv369ZDJZKhdu3YuRUhEBc2uXbswceJEvHjxAgBgYWEhbUBERET5kKRJRL9+/QAAXl5eGDduHIKCgtKcLygoCOPGjYOXlxcAoG/fvrkWIxHlfzExMWK7h2HDhuHYsWOoUKGCxFERERHlX5KOE1GjRg0MGDAAf//9N/79919cvnwZzs7OKFeuHIyMjBAdHQ1fX194e3sjMTERQFLi4eTkJGXYRJTPDBkyBMHBwTh//jz09PTg4uIidUhERET5mqRJBADMmjULurq62LFjBxISEuDl5SWWOCQTBAFA0o3AlClTpAiTiPKZuLg46OnpQSaTid1Dc8RpIiIizZA8iQCAadOmoUePHjh48CBu3bqFDx8+ICoqCkZGRrC3t4eLiwvc3NxQvnx5qUMlonwgODgYvXv3xuDBgzF48GA0atRI6pCIiIgKlDyRRABAuXLlMGPGDKnDIKICwNraGjVr1kSpUqWkDoWIiKhAkrRhNRGRpjx//hwDBw5EWFgYZDIZVq1ahaZNm0odFhERUYGUayURV69eBZA0sFxyo8bkadnRsGFDjcRFRAWDXC7Hs2fP8ObNG1haWkodDhERUYGWa0nE8OHDIZPJ4ODggHPnzilNyyqZTIanT59qOkQiymdevHiBO3fuoH///qhevTquX78OPT09qcMiIiIq8HK1OlNyL0upp2Xnj4hoy5YtWLZsGSIjIwGACQQREVEuybWSiJ07dwIADAwMVKYREanLz88P+vr6KFGiBGbNmoUpU6bAxMRE6rCIiIgKlVxLIr777ju1phERpSc2NhY9evSAi4sLtm7dCjMzM6lDIiIiKpQk7eL16NGjAIDOnTtDSyvzmlXR0dHYsWMHvnz5gunTp+dwdESUV4SEhMDa2hoGBgb4/fffUalSJalDIiIiKtQk7eJ1+vTpmDlzJuLi4tSaXy6X448//oCHh0cOR0ZEeYW3tzfq1q2L8+fPAwCaNm2KokWLShwVERFR4Sb5OBGCIKjdQ9O9e/cAQO2kg4jyr+QOFKpWrYrevXujatWqEkdEREREyXKlOpNCocDQoUMRGBiY5vvt27fPNJGIj49HcHCw2E0sERVcx48fx5YtW3DgwAEYGBjgt99+kzokIiIiSiFXkggtLS0MGjQIY8aMUXlPEAS8f/8+S+sbNmyYpkIjojzIxMQEenp6+Pr1q1KPbpR9cXFxCA0NxdevX5GYmCh1OHmGjo4Onj17JnUYlE3cf/mbJvafnp4ebGxsYG5urqGoSF251rC6RYsWGD16ND59+iROO3LkCGQyGTp27AgdnYxD0dXVhYWFBerXr4+6devmdLhElIsEQYCHhwcSEhLg5uaG5s2bo1mzZtkajJJUxcXFwd/fH5aWlihdujR0dXX53f6/qKgoGBsbSx0GZRP3X/72rftPEATExMQgMDAQ+vr6fOiUy3K1d6aJEycqvT5y5AgAYP78+TA0NMzNUIgojzl8+DAAoHfv3pDJZLzJ1aDQ0FBYWlrCxsZG6lCIiDRGJpPByMgINjY2CAoKQsmSJaUOqVCRtIvXxYsXAwD09fWlDIOIJHLq1CnUr18flpaW2LBhA0xMTJg85ICvX7+idOnSUodBRJQjTE1NERISInUYhY6kvTN169YN3bp1gyAIOHXqFI4dO6Yyz8WLFzF69GicOnVK7K2FiPI/f39//Pjjj9i6dSsAwNzcHNra2hJHVTAlJiZCV1dX6jCIiHKEjo4OEhISpA6j0JG8i9fXr1+jU6dO+Pnnn8XB51J6+fIlLl26hJ9//hl9+vRhpkmUz/n6+gIAHBwccODAAZVqjpQzWMJDRAUVz2/SkDSJ+Pr1K4YOHYrXr19DEARER0erzGNvb4/y5ctDEAQ8fPgQw4cPR3x8vATREtG32rt3L5o3b47Hjx8DAOrUqZNppwpERESU90iaRGzfvh0fP36EgYEB3N3dsW/fPpV5OnTogBMnTmDlypUwMDCAj48P9u7dK0G0RJRdyQNEdujQAdOnT0eFChUkjoiIiIi+haRJxIULFyCTyTB+/Hh07tw5w+Ko9u3bY/To0RAEASdPnszFKInoW8yZMwcDBgyAQqGAmZkZfvzxR+jp6UkdFhVQ06dPR8WKFZX+KleujFq1aqFXr15ir4CaIggCli1bhjp16sDJyQm7d+/W6PoBICQkRKmkfuDAgWjevLnGt/MtUseUvB/Se52T5HK5Unfy32rNmjWoWLFiugPmEhVWktYj8Pf3BwC0atVKrflbtWqFVatWiXWqiSjvq1y5MgwMDJCYmAgtLcmbYVEhMWPGDFhaWgJIutGPjIzE8ePHMX36dISFhWls0NJLly5hy5YtaNq0KVq2bAkXFxeNrDfZ5cuX8fPPP+PIkSMwMjLS6LpzkpubG+rVq5fr23337h2GDRuGUaNGoXv37rm+faLCRNIkIrnkQd0TY/IFgaOtEuVdMTExmD9/Ppo1a4bWrVujT58+UodEhVDLli1RokQJpWk9e/ZE+/btsW7dOgwYMEAjJWLPnz8HAEyePDlHnrQ/fPgQERERGl9vTnN2doazs3OubzcwMBBv3rzJ9e0SFUaSPha0t7cHAHh7e6s1/9OnTwEARYoUybGYiOjbaGtr4969e3j27JnUoRApMTAwQPPmzREZGYmXL19qZJ3JHX1w1GQiKmwkTSIaNGgAQRCwevVqxMTEZDivXC7H6tWrIZPJULdu3VyKkIjUERMTg9WrVyM2NhZ6eno4fvw4JkyYIHVYlAe9CQ+EX+hblb834blT3zy5BDxliba3tzeGDh0qPj0fNmwYHj58qLRc8+bNMWvWLMycORPVq1dH48aN4eTkhLVr1wIAWrRoodQmQJ11AsCDBw8wYsQIuLq6ok6dOhg5cqRYujF9+nSl9Q8cOFBl+X379qFixYq4fPmyynu9evVCz549M/w+/Pz8MGHCBNSpUwcuLi4YOHAgvLy8lOY5e/YsBgwYABcXF1SrVg3NmzeHu7s75HJ5uutNrw2Et7c3evTogerVq6N169bYvn27ynJt27bF7t274erqCldXV1y5cgUAcOPGDQwfPhx16tRB1apV0ahRIyxcuFAsqfHw8MCgQYMAJFVnS7n9L1++YMGCBWjUqBGqVauGdu3aYceOHSrjT/n7+2PcuHHi/li1ahXHqCJKh6RJhJubG3R1dfHixQv069cP169fh0KhUJnvzp07GDRoEB48eAAtLS3xJEFEeYOXlxfc3d3FGxmOQk/pSVSkXR01vemapFAocPv2bejp6cHR0REAcPPmTQwcOBBfv37FhAkT8MMPP+D9+/fo37+/ys30qVOn4OPjg19++QW9e/fG8uXLxTZ9M2bMwMyZMwEA165dU2udXl5e6N+/P/z8/PD999/jhx9+gK+vLwYNGoTAwEC4ubkprX/06NEqn6lt27bQ1dXFmTNnlKYHBATg4cOH6NixY7rfx5s3b9C7d2/cvHkTAwYMwOTJkxEeHo6hQ4eKCc/BgwcxYcIEmJqa4ueff8bUqVNhb2+PrVu3YtOmTVndBRg2bBjKli2L6dOnw97eHosXL8aaNWuU5vnw4QPWrVuHsWPHonfv3qhZsyauXr2KYcOGISYmBuPHj8cvv/yCGjVqwMPDA4sXLwYAuLq6it+Rm5sb3N3dAQDR0dEYMGAAjh07hm7dumHmzJkoX748Fi1ahPnz54vbDQ4ORp8+fXDz5k0MHjwYI0aMwIkTJ7Bz584sf06iQkGQ2O7du4WKFSsKlSpVEipVqiTUrFlT6Nixo9CzZ0+hY8eOgpOTk1CpUiWhYsWKQsWKFYW//vpL6pAzFBsbK3h5eQmxsbG5tk0vL69c2xZpXn7dfzExMcLt27fF1y9fvpQwGunkh/339OnTdN/r0aOHsG/fPkEQBEEulws9evQQDh06JAiCIERHRws9evQQjh49KgiCIHz58kXo0aOHcOrUKUEQBCEkJETo0aOHcO7cOUEQBOHTp09C9+7dhfMX/hFi4mOFl699hS7duggn/zktfI4MFu48uSu079xBOHT2iOAb8kb41+uy0L5zB+GI5wnBN+SN8OzZs2/+rNOmTRMqVKggPHnyRAgJCRFCQkKEz58/C97e3sKECROEChUqCIsWLRIEQRASExOF5s2bC3369BESEhLEdURFRQmtWrUSunTpIk5r1qyZUKlSJeHt27dK21u9erVQoUIFISAgQFxnixYt1Fpnz549hQYNGgihoaHitFevXgmVKlUSli5dmub6BUEQBgwYIDRr1kx8PWrUKKF27dpCXFycOO3PP/8UKlWqJHz8+DHd72rChAlCjRo1hDdv3ojTQkNDBRcXF2H8+PGCIAhC27ZtBTc3N0GhUIjzxMfHC40bNxY6duyYbkzJ+yH16+TPlfxdDRo0SKhWrZr4HSTPd/jwYaVYv//+e6FZs2ZKn1EQko5fZ2dn8fXNmzdVll+9erVQtWpVwcfHR2nZFStWCBUqVBCPuyVLlggVK1YUHj9+LM4THBws1K1bV2UfkGZERkZqbF0ZnecEQZr7s4JO8lGe+vXrBysrKyxcuBDBwcGIjY1Ns66qpaUlpk+fji5dukgQJRGlNnfuXBw+fBi3bt2ClZUVypUrJ3VIlIMSFQrIE+ORqFAgNj4W4bERCI0OhzwxHqEx4Qj48h6fv3xGbEIcgqJC8C7iIz5HhyA+MQGRcVGIkkcjQZGQa/F269ZNZZqenh4GDhyIn376CUBSO7vAwED069cPX758UZq3WbNm4lhGRYsWBZA0yrqDg0OG23369CkCAgLQt2/fDNepq6uLR48eYejQoWKnIQBQpkwZHD58GMWKFVP7s3bq1AkXL17EtWvX0KxZMwBJpSaurq6ws7NLcxmFQoHLly+jSZMmKFWqlDjd0tISe/bsEWM6fvw4YmJilLpgDwkJgZmZWZoDxGZm+PDh4v9aWloYMGAAbt68ievXr6NDhw7iew0bNlRabuPGjYiIiFBqDB8WFgZjY+NM4zh//jwqVKgAW1tbhIaGitNbtmyJjRs34uLFi6hUqRKuXLmC6tWro2rVquI81tbW6NChA3bt2pXlz0pU0EmeRABJxbEtWrQQTySfPn1CeHg4DA0NUaxYMdSqVQstWrRgFQkiicnlcsjlcpiYmGDcuHFo06YNrKyspA6LskkQBOw7sA+JCgWi5TFIEBKxeddWJCoS8TEyCImKRKzc8gcSFIl4Ex4AAFi+ZRUAICQ6DFpGOlizbT10tLSgLdOGQ/GS2LXvb+jItKGtpQ37CkVxzOMotLW0IZPJUNqyJFZvWydu36FMKaXXlSpV0thnW7ZsGWxsbAAk3ayamZnB0dFR6TqS3M24u7u7WPUltQ8fPohJhLW1dabbVXed2traEARB6QY+WZUqVTLdTkrNmzeHkZERzp49i2bNmsHPzw/Pnz/HwoUL010mPDwc0dHRaW4/5WCQurq6uHPnDk6ePIlXr17B398fISEhAP7XOYq6LCwsVM4XJUuWBJDUNWtKqb9rbW1tBAQE4I8//oCvry/8/f3VHgvC398fsbGx6XY5++HDBzGGFi1aqLxftmxZtbZDVNjkiSQCSDpRNWrUCI0aNZI6FCJKQ0JCArp06YJy5cphzZo1sLe3z/JNBGXPiGPT8CVWtZtPcwMzbO6yVGW6PEGO8NgIhMV+QVjMF+jFyxAaHY4EIRGJiv//ExKRkE47BC2ZFnS0tKEt04a+jh6MtLShI9OBtpYWtLW0xSRBS6aV4SChUqpVq5ZKF6+pJbfBmzBhApycnNKcJ+UNpLa2dqbbVXedr1+/BgCNjJ1iaGiIli1bwtPTE3K5HKdPn4auri5at26d7jLJDcsz2/6KFSuwadMmVKlSBU5OTujSpQucnZ2xYMEC8eZbXWkdK8L/N1pOHUfq73rfvn2YM2cOypQpg9q1a6N169aoWbMmtm3bptIeJLXExES4uLhg7Nixab6f3OOjTCZDXFxcujESkbI8k0QQUd4kCAJkMhl0dHTQo0ePTKtzkOallUAkT9/pfQhhsV8QHhuB8JikxCE6Xrm3u7GO/REW+0VMDLS1tKGnpScmA/9LDLTE5CCnaGtpp9mIWlsr8xt0TUtOgo2MjFC/fn2l9x4+fIgvX77AwMAgR9aZXF3p7du3KutYtmwZzM3NMXLkSLW327FjRxw/fhx37tyBp6cnGjVqBHNz83Tnt7S0hIGBQZrb37p1K4KDgzFgwABs2rQJXbp0USlVCQ4OVju2ZF++fEFkZCRMTEzEacljOmR0XomLi8OSJUtQp04d/PXXX9DR+d+tS1hYWKbbtbe3R1RUlMr++PLlC27cuCGWxpQoUSLNMSYCAgIy3QZRYZRnho+Vy+X4/PkzPnz4gPfv3yv9BQYG4vXr13j8+DFOnTqF8ePHSx0uUaHg7++PTp06iWO5DB8+PMOnm6Q5MfGxePr5BY77/JPhfP/4/Qff0LdIVCSipHlxNC5dB32qd8YPrgMxs/FYuLeeCWsjS5S1dEApixIoYV4MxUyLoIixNayMLGBuYAoTPSMY6OpDV1s3RxMIAChtUQKOVqXEPwsDMwBAURPbHN1uWqpVqwYbGxvs2rULUVFR4vTIyEhMnDgRM2bMUKv0IfU6bW1tM12nnZ0dKlWqhFOnTiEyMlKcLyAgADt37hRv0pOf0Gf2NLxBgwawsrLCwYMH4ePjk2GvTACgo6ODBg0a4PLly0olCl++fMHWrVvh7+8vtulI3d7p8uXLePPmDRISstbGRaFQ4NChQ+LrhIQE7NixA0ZGRhmObh0bG4uYmBiULl1aKYF49uwZ7t69K64L+F8JRsqeHps3bw4fHx9cunRJab0bNmzAhAkTxHaYrVu3xsuXL8UuZQHg69evOHbsWJY+J1FhIXlJhI+PD5YsWYI7d+6k2b0rEUnHwsICCQkJKg1ENeXt798jMSpcZbq2sQVKTdyaI9vMi+SJ8XgTFoBXYf7wDX0Dv9C3eB/xCQIyr0axs8fvmVYpevbxWZ6tdmRpYI6v8iiERIehuKldrsapq6uLadOmYdq0aejevTt69uwJfX19HDx4EO/fv8fy5cuVblrVXefs2bMxceLETNc5Y8YMDB8+HD169ECvXr2gpaWFv//+G2ZmZhgxYgQAiG0ItmzZgsaNG6dZZx9ISgratWuH3bt3w8jISGnMivT89NNP6NWrF3r16oX+/fvDxMQEBw4cQHR0NCZOnIhSpUqhePHi+PPPPxEXF4eiRYvi4cOHOHLkCPT19ZWSJHUYGhpi9erV+PDhAxwcHHD69Gl4e3tjzpw5MDU1TXc5c3Nz1KxZEx4eHjAxMUGZMmXw8uVLHDx4UEyyoqKiYG5urtQgXBAEdOvWDaNGjcL58+cxduxY9OnTB+XLl8fdu3dx7NgxNG7cGI0bNwYADB06FMePH8e4ceMwePBgWFlZYf/+/azORJQOSZOIT58+YfDgwYiIiMjSj5T1sIlyjq+vL3bt2oU5c+bAzMwMZ86cybEbu7QSiIymFwQJikQEfnkP3/8fZO1V6Fv4f3mHRCHpIYq5gRkcrUqhgUNtlLUsBUcrB4w4Ni3d9eXV5EBdWlpasDK0QFBUCCLl0TDVz92Rn1u0aIG//voLGzZswPr166GlpYXy5ctjw4YNYk9HWdWmTRu11lm3bl3s2LEDq1evxrp166Cvrw9XV1dMmTIFtrZJJTMdOnTA+fPn4eHhgdu3b6ebRABJvTTt3r0bzZs3h6GhYaZxOjo6Yv/+/Vi5ciW2bNkCLS0t1KhRA0uXLkX58uUBAJs2bcKSJUuwc+dOCIIABwcHzJw5EwkJCfjtt9/w+PFjVKtWTa3vxczMDEuXLsWiRYuwe/dulCpVCsuWLUPnzp0zXfaPP/7A4sWLcfjwYcjlctjb22PkyJGwt7fHlClTcPPmTbRp0waOjo4YOHAgPDw88OjRI9SpUwcODg7Yv38/Vq9ejbNnz2L//v0oXrw4fvzxR4wcOVJMRExMTLBnzx4sW7YM+/fvR2JiItq3b4/y5ctn2EidqLCSCRKm2MuXL8eWLVsgk8nQqFEjuLq64tWrVzhy5AgaN26MJk2aICwsDDdu3MDdu3chk8kwevToPD0SblxcnHhSza3epO7evQsXF5dc2RZpXl7bf3v27MFvv/2G48ePiwNy5ZRXv/VI972yvxzO0W1rSkb7TyEo8P7rJ/iFvIVf2P9GZo5PjAcAGOsZwdGyFMpaOaCcVWk4WpWClaGFSmLQe/8P6W7/gNuGTGN89uwZKleunIVPlbsEQcC7iI9IFJKqZOV0laqUoqKiYGycu4lLTnnw4AF69+6NTZs2oUmTJlKHkysK0v4rjDS5/zI7z0lxf1bQSVoScf36dchkMrRv3x4rVqwAALx+/RpHjhxBVFQU+vfvDwAYO3Ysdu/ejQULFmD79u3o2bMnSyOINOjNmzf4+PEj6tati759+6Jt27Y53nWrQh6bo+vPbYIg4HNUMPxC/eH3/1WSXocFICYh6XPq6+ijrGVJtHFsDEfrUnC0LAU7E1u1ShLMDczS7Z2pIJDJZLA2ssT7r58QHhsBK0MLqUPKl/bt24ciRYqojLFARJQTJE0ikvuFTk4WgKSBdoyNjfHo0SMkJCSIdUf79+8PLy8vnDlzBrt378bUqVMliZkoLxg49yzCv6p2RWhhqo9dc9tmeX0TJ05EaGgoLl26lFS9JAcTiMTYKER4ncGX2ydzbBu5ITQmHH6hb3E1xAtnL1/Hq9C3+CpPqiOuo6WD0hYl0Lh0HbEBsb1p0Wx355lWN64FjaGuAUz0jBAeEwFTPRPoakveZC/fmDVrFgICAnDz5k1Mnz49y43BiYiyQ9KzdHKjrNRdu5UrVw4PHz6En58fKlasKE53c3PDmTNncOPGjVyNkyivSSuByGh6Wt69ewdra2sYGBhg+fLlMDIy0kif9elJjPqCL7dP4IvXWQjyGBiVc0G0790c254mfY2LhN//t2FI/guLTWpsLoMMDubF4WpfE47/XyXJwbw4dHgTnGVWhpaIkscgNCYMdhL01pRfhYSE4OHDh3Bzc8OgQYOkDoeICglJr3LGxsaIiIhQ6ZWpZMmSaSYRyd3MBQYG5mqcRAXNp0+f0Lx5cwwbNgzTpk1T6cJRkxIiQhB+8yi+el+AkBAP48r1YNGgB/TtSmfYO5NUouNj8DosAH6hb+D7/w2fP0eFiO8XN7VDNbuKYglD2Otg1HWtI1m8BYmutg4sDMwQFvsFZvGxMNTN2hgNhdWGDZm3iyEi0jRJkwh7e3tERETg1atX4oiRQFLJhCAIYt/NyZL7gY6JUR5IiYjUI5fLoaenBzs7O0yZMgVt2rTJsW3Fh31E+PUj+PrwEgABJtUaw6J+N+hZ/689k9TduMoT5HgTHqhUwvD+6/+6VrU1toajZSm0cmwMR6tSKGvpACM95V5v7r4NlyDygsvC0Axf5ZEIiQ6DvVnRfN/7FBFRQSVpEuHq6oqnT59i8+bNcHFxga6uLoD/lThcvnxZqSem27dvAwB7YqBCLT4he+OpXLlyBZMmTYKHhwdKlSqF4cOHaziyJPIgf4Rf80Dk02uQaWnDzLklzOt2ga5FkcwXzkEJikQEfHmvVMIQ8OW92LWqRXLXqqVc4WjlAEfLUjAzSL/vesoZWjItWBla4nNUMCLlUTDVN8l8ISIiynWSJhFubm7YtWsXrl+/ju7du2PMmDFo27Yt6tWrBx0dHTx79gwLFy5E37594evri6VLl0Imk6ndJzVRQRMWEYslO+9ka1lHR0dUqVIlx57sxr33Rdi1w4h+cRsyXQOY1+kI8+86Q8fUUqPbGXFsWro9FSU3QFYokrpW9Q19g1f/31vSm/BAxCuSSjON9YxQzqoUnIu1hqNVKZSzKg1LQ3M+9c4jTPSMEBGnj5CYcBjr5mxbHSIiyh5Jk4iyZctizJgxWLNmDXx9fXH58mW0bdsWlpaW6N27N/bs2YPdu3dj9+7dAJK6UJTJZOjbt6+UYRNJ4oV/GBZtv43ImHgYGeggOjZBZR4LU+W+r48fP447d+5gwYIFsLe3x65duzQeV4z/U4RfO4yYV/ehZWAMi4a9YO7aAdpGOfMUP60EInn6zvuH/79rVX/EJiQ1Mk/qWtUBbco3TSphsCoNO2MbJgx5WHKXr+8iPiIs9gusjTSbiBIR0beTvPuQMWPGwNHREVu3bkXJkiXF6dOnT8enT5/g6ekpTpPJZBg1ahRatmwpRahEkrlw+y3WHXoIK3MDLBvXCGWKm4vvCYKAn1dfQeiXWPw5Q/m38fLlS3h7eyM6OhpGRkYai0cQBMS8uo/wa4cRG/AM2sbmsGo2AGYubaClr7ntZNW5l5dQ2qIEmpauJw7gVtzUjk+y8yEDHX2Y6hnjS+xXmOmbQFdbV+qQiIgoBcmTCABo27Yt2rZti5SDZ+vp6WHdunXw9vaGt7c3tLW10bBhwxwfQZcoL0lIVGDLscc4de01nMrbYsrA2jAz1lOaRyaTYUjHqpi5/hpOXX0Fg5iXKF68OJycnDB+/HhMmDBBHG/lWwmCAtHPbyPsmgfkH/2gbWoN69bfw9SpBbR0c24EUEEQ4Bf6FjcD72U4347uq9i1agFiZWiBSHk0QmLCUZRdvhIR5SmSXm3XrVsHa2trdOzYESYmJmlWL3B2doazs7ME0RFJK+xrLJbu9MKTVyHo1rQcBrevDG3ttJ+oV3e0Qe3Kdjhw4QVe/bsEtZ2rY/369WJnBd9KUCQi8slVhF/3QHxwIHQsi8Kmww8wrd4Eshx6QiwIAl6F+eNGwF3cCLiHoKgQaMsyLlFgAlGw6GjrwNLQHKEx4Yhhl69ERHmKpFfcw4cP48OHD9DT00P37t2lDIUoT3nhH4bF228jIjoeP/d3QZNaJTKc/9atWxjYthIm/n4ZfccswVi37zQSh5AQj68PLyL8xlEkhH+Crq0DinSdCOPK9SHT0vyouP9LHO7hZsBdfP7/xKG6XSX0rNIervY1MezozxrfLuVd5gamiIiLRHB0GEqwy1ciojxD0orCwcHBAIBGjRpJGQZRnuJ5xx/T112FlpYMy8Y1yjSBuHHjBrp37w6v6+fRzKUk/nsUivBI1UbXWaGIj8OX2yfhv/5HBJ/ZCG1DU9j1nIYSI1bApGojjSYQgiDgVehb/P3gCMadmo0Z/yzBqecXUNzUDqNdB2JzF3fMbDIOzcrWh4m+McwNzNJcT3rTKX/TkmnB2tAC8kQ5vsZFZjr/9OnTUbFixVwflHTNmjXZ3m5AQID4f2BgICpWrIg1a9ZoLDZPT0+0a9cOiYmJ4rSbN2+iT58+cHZ2RpMmTbBmzRpxLKbMqLOsQqHAnj170KFDB9SsWRPt2rXDrl27lKotpxYfH48OHTqofPbExES0bdtWqY0kEUlP0pIIGxsbfPjwASEhIbC1ZX1XKtwSEhXYevwxTl59jRrlbDB1YG2Ym6TfziA8PBwWFhaoW7cuVq5ciS5duuBLVCKueL/DnnM+mNAn69UAFbFR+HL3LL7cPglFdAQMHKrAtuMYGJapqdEnwIIg4LVY4nAPn6KCxRKH7lXa4zv7mjDRT3s8mORuXKnwMNYzgkGcPkJjwmGsZwztPNhQvlWrVnBwcICVlVWWlvv+++9ha2uLJUuWAACsrKzg7u6OihUraiSumJgY/Pbbb//H3n3H13T/Dxx/3Zu9pyD2DElIIkFRtEbtGZtEq6hRtNUqLS3VWqWqapTWbqm9G4pSW0UElSCoHRIRWTe5Gff3R765P1eGJJLcJN7Px8PjIed8zjnvcz8Z530+iwkTJmBgkJ78nzt3jqFDh1KnTh3Gjx/P9evXWbRoEZGRkUybNi3H8+X22MWLF7Nw4UI6dOjAoEGDOHv2LF9//TWPHj1i/Pjxmc6r0WiYMmUKYWFhmfYZGBgwduxYvvnmG5o2bYqZmVmmMkKIoqfXJOLdd99l+vTpTJs2jSVLlmBra6vPcITQm+jYJGav/YdL1x/TvWUN3u7kmu34B0j/A718+XIOHjyIvb09ffv2BcDJBDq/Xo2df1+ne8saVCmfu7fzqQkxPD2zm5izf5CWlIBZDS/smvliWqlugdwfZJ84uJetQw/XDjkmDuLVplAocDS34+7/pnx1LIZTvtapU4c6derk+bhjx47Ro0cP7dfm5uZ069atwOL65ZdfMDEx0Vmdfs6cOTg7O7N27VrtA7m1tTXLli3D398/xwlMcnNsdHQ0S5YsoUOHDnz//fcA9O/fn5SUFFatWsXQoUOxsfn/GeZiYmKYNGkSBw4cyPa67du3Z8GCBaxYsYLRo0e/zEcihCggek0imjZtyoABA/jtt9948803ady4MXXr1sXOzg4Tk5xnesl4aBKipLsfpebH748QE5fE+AENeMO7UrZlM9ZKadmyJVFRUVlO29q7dW32n77Fmr0hTHm3cY7XTomN4umpHcQE/YkmWY1FncbYNvXFpHz1l76vjHhvPrnDyTuB2sRB+b8Whx6u7WlYwUNWJBa5YmJogpWJJTH/m/LVWKZ8fSG1Ws369evp37+/tiXxwYMHBAUFMW7cOJ03+gMHDmTp0qUEBARk+5Ce22Pv379PvXr16NOnj87xjRo1IiAggP/++w8PDw8AQkNDeeedd4iNjWXIkCGsWLEiy2srlUo6duzI+vXrGTZsGMbGxlmWE0IUHb0mER07dgTS3zKpVCqOHDnCkSNHXnicQqGQJEKUCofO3uaX/Y+wtzFj9pjm1Kxom2W5tLQ0pk6dipmZGZMmTcLNzQ03N7csy1pbGNOrVS3W7A3h3xuPcavukKlMcvRDok9sJ/bCIUhLw9K9ObZNemBcJvsEJrcyEodTd89x8nbgM4mDiyQOpYzf1ACiY5Mybbe1MmHt1PYFfj17Mxvi1Qk8TnhCeSunlz7flStX+O677wgMDEStVlOnTh2GDx+eaS2i4OBg5s2bx8WLF7G0tKRPnz4oFAoWLlzIlStXgPQxET/++CMHDx6kYsX0cUzr16/nt99+4/bt25iamuLj48MHH3xArVq1uHv3Lq1btwZg27ZtbNu2jTVr1lChQgVat27N+++/z5gxY7Qx7NixgzVr1nD9+nWsra154403+OCDD3LsPrVv3z4iIyN17ufSpUsAmX5/ODk5UaZMGe3+rOT2WFdXVzZs2JDp+JCQEBQKBeXKldNuu3v3LtWqVWPy5MlYW1tnm0QAtGnThsWLF7Nv3z66dOmSbTkhRNHQaxLx/ACrnAZcCVGapKSmsXLXv+w8eoOqZU34elTLHMc/KJVKEhMTUSqV2taInHRpXp3dx26wave/zBnTXFteHXmX6BNbibt0FJRKrOq3wrZJN4zsyuV4vhfRaDT8F31XOx3rw7gIbeLQvW47Glb0xFoSh1InqwQip+0vy1BpiJ2pNY9V0SQkqzA3yn/f+AsXLuDv74+FhQXvvPMOFhYW7Nixg9GjR/PFF18wcOBAIP3B2d/fH0dHR0aPHo1KpWLNmjUvXMBw586dTJ06le7du+Pn50dUVBSrV6/Gz8+PP//8Uzv2YcKECfj4+NCnTx9q1KhBYmJipnMtX76cuXPn4u3tzUcffcTjx49ZvXo1ISEhrF+/Ptt1YA4fPky5cuV0ulk9fPgQgLJly2Yq7+TkxIMHD7K9p/wcm5yczJ07d9i1axebN2+mV69eOse3aNFCm+S8aFC6m5sbTk5OHDlyRJIIIYoBvSYRMtOCeBU9jUti9pqzXLweSdcW1fFwTsoygVCpVMyfP5+BAwdSpUoVZs+enevBzabGhgxoV4cfNwVz6lI4DcqoiD6+hfjQ0ygMjbBu2BHbxl0xtM7cSpFbOSYOdd6SxKEEOHT2Nn+euV0o5560+Fi+jmvbqDKtfCpnu9/6f1O+Pk54gpm1ab4H/H/99dcoFArWrl1L9erp3ff69+9P//79mTNnDh06dMDe3p5vv/0WY2NjNm3apH3r37p1a3x9fXM8/65du6hVqxazZ///RAB169Zlzpw5XL16FW9vb7p168aECROoVKmSdhzE8w/ST58+ZeHChTRv3pyffvpJOzi6YsWKTJ48mePHj9OyZcssYzh37lymAdrx8fEAWQ5ONjExISoqKtt7ys+xf/31l7ZFpVq1apm6SuW1W5KLiwuBgYF5OkYIUTj0mkRUqFChyK6lVquZPn06AQEBGBsb8/bbbzNs2LAsy+7du5fFixdz9+5dKleuzAcffECrVq2KLFZReoXdjWbGqjM8jU3iw/4NaOVTKds/iE+ePGHt2rWULVuWd999N88PS20aVubMX8eI3zWHe5rbKEzMsW3aA5tGnTGwsHnxCbLwbOJw6s45wv+XOLg7SeIgioZSocTB3I7wuAhikuKwMbXK8zkiIyMJDg6mf//+Om/FTUxMePfdd/noo484ceIEzZs358yZMwwcOFCn25CrqyvNmjXj6NGj2V6jXLlyHD9+nB9//JHu3btTsWJFWrZsme0Df3ZOnDhBUlISAwcO1CYQAF27dsXV1VWbAD0vJSWFBw8e8Oabb+psT0tLA8j290lOv2fyc2y1atVYtGgR9+/fZ9myZfTs2ZP169dTtWrVbK+Tk0qVKnHs2DFSU1N1Pg8hRNF7ZZZ3nTNnDkFBQaxcuZLw8HAmTJiAs7MznTp10il39uxZJkyYwBdffEHjxo05cuQIY8aMYdOmTbi6uuopelEaHA68w8KN57G2NGH2+82pWck2U5mkpCQCAgLo1q0bzs7OHD16FEdHxxzPe+v7d0mNj868Q2nAwLRU4tJMiKzVEZ/u/VCa5n32I41Gw63ou5zIInHoWuctGkniUGK18sn5rf+LdBm/I9t9M0e9nu/zvoi5kRlmhqY8UUVjaWyOQR7XLbl37x6Q/oD7vIyZie7fv8+dO3dIS0ujSpUqmcpVr149xyRi9OjRnD9/noULF7Jw4UJq1qxJq1at6N27N5Ur5/4zz4j1+RhMTEyyHRcF6S0YGo0GS0vdn00Li/TfAVl1m0pKStLuz0p+jq1Vqxa1atUCoEmTJnTv3p3FixczZ86cbK+TE0tLSzQaDdHR0Tg45L8lVQjx8oosifD390ehULB8+fIin1UhISGBjRs3snTpUtzd3XF3d2fo0KGsW7cuUxKxbds23nrrLe2sEv7+/hw+fJi9e/dKEiHyJTU1jZW7L7Pj7+u413DgU7+G2FplPf7h119/ZcqUKVSrVo369eu/MIEAsk4gANJSsW8zmOWnLLh/JYWflCaY5jLmjMTh5J1znLwTqE0c3JxqS+Ig9E6hUOBgbsfdmAc8UT3F0SJvazPkNP4u4227kZGRdgG1rP5mvWgGwXLlyrFjxw5Onz7NwYMHOXr0KMuWLWPlypWsWLGCRo1yt6p8RjwvGoPxvIxWgYzjMzg7OwMQERFB7dq1dfY9evSI+vXrZ3vOlzkW0hOKGjVqcPny5dzdRBby+3kIIQpekSURZ86cQaFQ6KyY+ay0tDT++usvAO2MFQUlNDQUtVqNt7e3dpu3tzeLFy8mJSVFZ1Can59fpkFqCoWCpKTCGSgoSrencUnMWXuWC2GRdGlenSFd3DB8bv2HlJQU7t27R4UKFfDz86N27dov/GOcW7aNuzKozGMmLjrGrqM36N26drZln00cTt05x4O4R88kDm1pVMET63x0HRGll62VSbazMxU2E0NjrE0seZr0vylfDXP/ciqjK+2NGzcy7bt58yaQngRUqpQ+W9l///2XqdytW7dyvEbGrE1NmjShSZMmAAQGBjJ48GDWrl2b6ySifPny2us92wVIrVbzySef0KVLl0yzSQHY2dlhaGhIdHS0zva6ddPXfvn3339p1qyZdvujR4+IiIigXr162caS22N37NjBnDlzWLFiRZZjMl5mTajo6GgMDQ1lXSkhioFi050pMTGR0aNHo1QqX+otRVYiIiKwsbHReXPk6OhIcnIyUVFRODn9/1SBzy8WdO3aNU6ePClTyoo8u3HvKd+sPM2T2CQ+6OdF64ZZd2GYPXs2jx8/5sCBAxgZGfH66wXbDcStugONXMux5dA12r1WFWuL/3/YSk8c7nHqbiAnb6cnDgqFAncnF7rUaSOJg8hRYUzjmhf2ZrbEqRN4rHpCOUunXI8bKlOmDO7u7uzcuZPBgwdrxxWo1WpWrlyJsbExzZo1w9raGi8vL3bv3s2IESO0C6TduXOHv//+O8drjBs3DhMTE7Zu3artu+/q6oqRkZHOW3SlUpmpteBZTZs2xcjIiI0bN9KiRQvtPQYEBBAQEJDtLEUZU6k+P2NShQoVcHd3Z8uWLfj7+2Nqmt4++euvv6JQKOjQoUO2seT22OrVqxMZGcmvv/7KV199pT3+zz//5O7duzqL6+VVeHg45cqVy/eAeiFEwSk2SUSGwpjmVaVSZWqOzvharVZne9zjx495//338fb2zvJNT05ymmu7MMhsFcXLxf8S2HH6CeYmSt5u7YitMoLAwAjt/tTUVBQKBUqlks6dOxMfH8+FCxfydpHkRMxD9pPdO9+vqzoQ9/vI9C8sgfowdPduzA3M6OvcntC4m1yJu0lU8lMUKKhsVp52ZV6ntmUVzA3MIBquRV/Nz+2/cor7z5+hoaF2Zp3SxsLAjNjkeJ7ERWOiNNZ2QZo7d26WizG2bduWRo0aMX78eEaMGIGfnx+9e/fG3NycvXv3EhISwoQJEzAwMCA+Pp6xY8cyfPhwevbsSa9evVCr1WzYsEH7tyrjc834W6JSqYiPj8fPz4+vvvoKPz8/2rRpg0ajYe/evSQlJdGzZ0/tcXZ2dpw+fZq1a9fSpEkT7XnVajXx8fGYmpoybNgwFi9ezODBg3njjTd49OgRGzZswMfHh0aNGmVbtz4+Phw6dIi4uDidh+7333+f0aNHM3DgQLp168a1a9fYtGkTffr0oUyZMtrzBQcHc/fuXVq1aqWdkSk3x1avXp3u3bvz+++/8/TpUxo0aMCNGzfYvHkztWvXpm/fvlnGrFKpdO79eRqNhgsXLtC6dWud/aX1e/tVUVD1p1ari/3v4tKm2CURhcHExCRTspDxdVZT1UH6244hQ4agVCr54Ycf8tz/0t3d/YV9ZgtKYGCgTlctoT+pqWms2nOZ7Sfu4lbdgU/9fbCz0h2JEB0dzaBBg+jZsydDhgwByHP9qf67yKNdy0iNzX46xjjDrAebJqSqWHln2/9aHGrjW7ETjSp6YGNqnacYRLqS8PMXEhKS44DZksxcY05iTBJxaQnYWdpqu6P+8ccfWZavXbs2b775Jk2bNmX9+vV89913rF27lrS0NOrUqcOiRYt0Xho1bdqUn3/+mfnz57N48WJsbW3x9/fn+vXr7Nu3T/u5ZryYMjMzw8LCgoEDB2JhYcGaNWtYtGgRaWlpuLu7s3z5cp2uQJ988gnz5s1jzpw5TJ8+HR8fH+35Ms49btw4KlSowJo1a5g/fz5lypShb9++jBkzJtPA6We1atWK7du3c//+fZ0xDG+++SZLlixhwYIFfPvtt5QpU4YxY8bw3nvv6XTn3blzJ9u2baNp06ba8Vm5Pfabb76hcuXKbN26lQMHDuDg4MDAgQMZO3ZstjFn/D1+9t6fdfXqVWJiYmjVqpV2f3x8fKn93n4VFGT9GRsba1dCz0pSUlKRv+At7RSaIlrhrU6dOigUCs6dO5flg3tCQgINGjRAoVAQEhJSoNc+d+4cAwcOJDg4WPuL/tSpUwwbNoygoKBMYyDu3LnD4MGDMTMzY/Xq1bka3Joh45tUkohXT0y8mjlr/yH4WiSdm1Xj3W7umcY/QPrbtLFjx9KuXTs6d+6cp/pLS04i6vBvxJzZjZF9ecp0HcvDTbOzHFw9sWb2K/oO8x4giUMBKQk/fyEhIdr+7KVRvDqB8LgIHMztsM3j9/SLHmIiIiIoU6ZMpu0jRowgNDSUw4cP5zXcIqNWq2nVqhW+vr58+OGH+g7npc2fP58tW7Zw6NAh7d9ySSJKtoKsvxf9ntPH81lp90q0RNStWxcjIyOCgoJo3LgxkP6H383NLVMCER0dzTvvvIOVlRUrV67UmRtciOzcvP+Ur1eeIeppIuP6etKmke50jP/99x/ffPMNs2fPxt7enoULF+b5GkkPbvBo5wKSI+9i7d0e+1Z+KI1NqfLBL9oy4XERBD+4TPDDELgXnO25aprXlwRClBrmRmaYG5nyRPUUK2OLPE/5mpM+ffpQvXp1fvnl/3/OIiMjOX36NM2bNy+w6xQGY2NjBg4cyKZNmxg3blyJntEoLS2NXbt2MWjQoCKf4VEIkbVXIokwMzOje/fuTJs2jVmzZhEREcGKFSuYPn06kP6mycrKClNTU+bPn8+TJ09YuHAhqampRESk92M3NTXFykoGmIrM/g66y4Lfz2NlbsTs91+ndmW7TGXi4+M5c+YMV65c0c7UkluatFSiT2zjydGNGJhbU67fZMxreAGQoFZx6dEVgsMvcyE8hIfxkQCUMc85+V2zN4Qvh76WpziEKK4UCgUOZnbciXlAlCqaMhYFt35A165dWbp0KePHj6dx48bExMSwceNG0tLSMq2+XBz5+fmxfv16du/eTdeuXfUdTr7t2rWLlJQUBg0apO9QhBD/80okEQCTJk1i6tSpDB48GAsLC0aPHk3Hjh0BeP3115k5cyY9e/YkICCAuLg4unfvrnN8ly5dmDt3rh4iF8VVapqGNXsus/VwGK7V7Jno3xA76/8f/3Dnzh2OHz9Ov379cHNz49SpU9mOwclOctQDHu38gaR7V7FwbYZ9u3e5mfCYC//uITg8hGuPb5KmScPU0AR3Jxc6ubTGo5wr5SzL0HfjqGzPezbkIRfDIqlXM/dd9YQozowNjbExsfrflK9WmORhytecjBs3DkdHRzZu3MjBgwcxMTGhQYMG/PDDD5mmLy2OLC0tmTJlCnPnzqVTp04lcpXn1NRUFi9ezBdffJHjGBAhRNF6ZZIIMzMzZs+ezezZszPty5jPG+D06dNFGZYooWIT1MxZe5bzVyPo0LQqw7rVw8hQt6vA0qVL2bZtG+3atcPOzi5PCYRGoyH23H4eH1xNtLER91t05ooyhYsB04hPVqFAQXX7ynSv+xb1y7pS26Eahga6P842ptY8TYzJdG4bEyvMbUxZtedf5o5tIVMlilLDzsyGOHU8jxOeUN4q91O+5kSpVOLn54efn18BRKgfbdu2pW3btvoOI98MDAzYt2+fvsMQQjynyJMIeWARJd3N+0+ZseoMkdGJjOnjyVuN/3/8w71799BoNFSsWJGJEycycuRI7Owyd2/KSdyTcE7uW8i/T+8SVtmeR4pUuH8GezNbGlX0wqNcXdzL1nnhitHLu2VOmDP8Wf4WP2w8z4mLD2hW3zlP8QlRXBkoDbAzsyUyIYqEZBUWxpmneBVCCFEwijyJ6NixY5aJxLOTRL1oxWqFQsGBAwcKPDYhXuTo+Xss+D0IC1MjZo5uRp0q/z/2IDk5mR49euDi4sLatWuxsrLK1TgajUbDjahbnA+/TNCN04TFhZOqUGBsZ4lrWRc6lHPFo5wrFawLboGlVj6V2HbkOmv3XqaxW7ksZ5ESoiSyNrEkJimWxwlPMDMyRamQ720hhCgMRZ5E3L9/P8f9Go2Ge/fu5VhGWjNEUUtN07B272W2/BVG3ar2TBzcEPv/jX94+vQpNjY2GBkZMWvWLO3qtzmJSogmODx9FqWguxdRXU8CoHxSMi0VFjR+rTdu1RtjbGBUKPdjYKBkcMe6fL3yDH+euU2HJlUL5TpCFDWFQoGDuR0PYh/xNDEWOzMbfYckhBClUpElEQ0bNiyqSwlRoGIT1MxdF8i5K4/o0KQqw7r///iHy5cv07t3b+bOnUuHDh1o1apVludISlETEnGN4PAQLoRf5k7MAyB93EJNQzvcI+5T/ckTKjfphW2znigMCv9Hs5FbOVyr2bN+XyhvNqiIqckrM0RKlHLpU76aEZ34FCsTCwyV8r0thBAFrch+s65du7aoLiVEgfnvQQzfrDxNZLSK93t70O61qkB6i5lCoaBWrVp07NiRWrVq6Ryn0Wi4FX2PCw8vExweQmhEGMlpKRgpDalbphYtqzWhnmNNLAMPEXt2L0YOzpTxm4Gpc80iuzeFQsHbndyY8ONRdhy9Tt82xX+mGSFyy8HcjrtPHxCleopTAU75KoQQIp28nhEiG8eD7/P9hnOYmxoyc9Tr1KmaPv7hwIEDLFmyhHXr1mFmZsa3334LQHRiDBfCQ7gQHkLwwxDtzEiVbJxpV7Ml9cu5UrdMTUwMjUm6H8ajzfOJfXyPxMreVO03HqVR0a+gWbeaPa+5l2PLoTDav1YVG0tZxVOUDsYGRlibWvE0MQYbE0tMDOV7WwghCpIkEUI8JzVNw68BIWw6eI06VeyYOLghDjb/Pz2rkZERKpWKh5GPiDVWpY9tCA/hVvRdAKxMLKlftg4e5VypX7Yu9ua22mM1aak8ObqJJ8c2YWBhQ7n+XxDyJEUvCUQG/46uvP/tITYevMqwbvX0FocQBc3O1Ia4pDgiE57gbFVWxtMJIUQBkiRCiGfEJaiZ+2sggaGPaPdaFd7rUQ8jQwP2799P1JMoXm/fkthyKTT7tAOTT89DnZqMgdKAOo416F+vGx7lXKlqVzHLGWHUj+8TsfMHku5fw8LtdRzbDcPAzBICA/Vwp/+vUlkr2jSqwt7jN+nyenXKOVjoNR4hCoqBUom9mS0RCVHEqxOwNJHvbSGEKCgy950Q/3MrPIaPvv+b4GsRjOrlwfu9PVGlqjh26x9WBG9kS8IhPgr4ijXnNxMR/5hW1ZsxsfkoVnafy5dvfkgP1/ZUt6+cKYHQaDQ8PRvAvZ/Hkxz1AKceH1G2+4fpCUQxMaCdC0qFgl8DQvUdihAvZeLEibi4uPDrr78C6S2DxgbGPFZFk6ZJ4+7du7i4uLBw4cI8n9vPzy/byRMKw8KFC3FxcdH55+bmRvPmzfn0008JDw8vslgK0+jRo1m6dKn265SUFBYvXkzr1q3x8PCgd+/enDhxIlfnyu2xkZGRTJo0iebNm+Pl5cW7777LxYsXczz32bNnqVOnDnfv3s20/Y033iAhISFXMQpRWkhLhHilDNvxaZarOJsbWBAf+AamJkqGDXQmWhHM+1uWE5HyBA0azKtb4e5UBy9nN+qXq0uZXA7UTImNImL3IlQ3zmNW3YMynUZjaF38Bnk62JjRtUUNNh+6RveWNahR0VbfIQnxUubPn0+7du1wdHTE0dyO+7EPeZoY+1LnHDFiBCqVqoAizNt1M6aOVqvV3L17lw0bNhAYGMj27duxtCw+LyTy6vDhwwQFBWnHlgHMmDGDX3/9lb59+1K3bl22bt3KsGHDWLNmDd7e3jmeLzfHqlQqBg8eTHh4OG+//Ta2trasX7+egQMHsnHjRurUqZPpvPfv3+ejjz7SWdMqg4+PDzVr1uTHH39kwoQJL/mJCFFySBIhXilZJRAACanxmNc5T5pFJKtCk1Ci4OnNx9SwqsK4fiOpYVcFpTJvDXdxl48T+ccyNClqHNoNw9q7XbHuk+3bqhYBJ/9jzd4Qpg1vou9whHgpsbGxzJw5k3nz5mFmZIqFsTlPVE8xSMv8EJhbzZo1K8AIc69p06Y0btxYZ1uDBg0YPnw427dvZ9CgQXqJ62WlpaUxY8YMBg8ejLl5+uriN2/e5LfffmPEiBF8+OGHAPTo0YOuXbvy7bffsmHDhmzPl9tjN2/eTFhYGMuWLaNly5ZA+kK4bdq0YenSpXz//fc65z137hzjxo3j0aNH2V57xIgRDB48mP79+1OpUqV8fyZClCTSnUmI/7F2SMLb0Y2Pm73Hih7z+KzZ+8x99ytqOVTLUwKRqorj4fb5PNr2HUb25akwdC42Pu2LdQIBYGlmRJ82tTl35RHBVyP0HY4QL6VVq1bs3r2bkydPAuBgZgvAk2xeJJQ0r732GgBhYWF6jiT/Dh06xK1bt+jSpYt22969e9FoNPTv31+7zdTUlF69ehEUFJTjgrW5PTYpKQkvLy9tAgHg4OBA9erVuXLlis45lyxZwoABAzAxMaFjx47ZXtvHx4eyZcuybt263H8AQpRwkkSIV4Lf1AC6jN+RY5nWKd585z8dowgN5sZmNG/eHFNT0zxdJ+FmMHeXf0j85RPYteiL8+BvMHao8DKhF6lOzarhaGvGqj3/kvYSb2zFq+HW9+9y4xvfTP9uff+uvkNj8uTJmJmZMXXqVNRqNUYGRtiYWpGQnLnfukajYf369fTq1QsvLy/q1atH+/btWbZsmU73lWfHRCxbtgwXFxf+/fffTOdr1aoV/v7+2q/DwsIYPXo0Pj4+eHh40K9fP44ePfpS9/fgQfqClc+/9T558iRDhw6lcePG2vETX3zxBTEx6cnT0aNHdcaMPOuDDz7g9ddfJzU1FYDw8HAmTJjAa6+9Rr169ejevTs7d+7UOUaj0fDjjz/Srl076tWrR9OmTfnkk0+08eXkt99+w9XVFWdnZ+22S5cu4eDgQLly5XTKurq6AmT5eef12KFDh2Zq0UhISOD27ds6sQBcvXoVf39/tm/fru1Slp1WrVqxZcsWEhMTcywnRGkhSYR4JUTHJoFRzr/YW7duzdixY7PsD/siaclJRO77hfDfvkJpbEaFt2di17wPCqVBfkPWC2MjAwa1r0PY3accv5D9Gz8hAFLjo/O0vShVqFCBUaNG8d9//7Fs2TIgfcpXpSL9Z/LZ5OD7779n6tSp1KxZk0mTJvHRRx9hYmLCvHnz2L59e5bn79KlCwqFgj/++ENne3BwMPfu3dO+Xb9y5Qp9+/YlLCyM9957jw8//JCUlBSGDx/O3r17c3UvsbGxREVFERUVRUREBOfOnWPixIk4Ozvj6+urLXfs2DGGDBmCSqVi7NixfP7559SvX5/ff/+dmTNnAuldoxwcHAgICNC5RkJCAocPH6Z9+/YYGBjw8OFD7aBkPz8/Pv30U+zs7Pjkk0/4+eeftcctXbqURYsWaZOV3r17c+DAAYYMGaJNRrKiUqk4c+aMTmsAwMOHDylbtmym8mXKlAHIsSUiP8fGxsZy9uxZ3nvvPRISEhg+fLjO/tmzZ/PZZ5/latzJm2++SWxsLOfOnXthWSFKAxkTIV4JSttHGFfPeeYNKysrPv744zyfO/F+GBE7F5D8+D7WDTti/+Ygva778LLe8K7EtsNhrN0bQpN65TE0kHcNpVXshcPEBh8qlHPfX/tFvo6z8miFVf03CiSGd955h507d7Js2TK6dOlClSpVsDO1BkCdmgxAcnIy69ato1OnTsyaNUt7bO/evWnSpAn79u2jR48emc5dvnx5fHx8CAgI0Pm9sXfvXoyNjWnXrh0AX3/9Nfb29mzbtk3b73/QoEEMHjyYb775hjZt2mBsbJzjfYwePTrTNqVSycKFC7G1tdVuW7VqFeXLl2flypXacw4YMIC+ffuyb98+Zs6ciYGBAR07duTXX38lIiJC+4B96NAhVCqVNvmZP38+arWaXbt24eTkpI17/PjxLFiwgB49euDg4MCuXbto0aIFkydP1vls1q9fz71796hcuXKW9xQcHExycjIuLi462+Pj47G3t89UPqNVOKeB7fk59pNPPuGvv/4C0luanh+4/aK6eVbGvZw9e5amTZvm+jghSip5OhClmjo1mRXnfsek9jk0SWZoko2yLGdjYpXnc2tSU3jy90bur5pEmjqJcgO+wPGtd0t0AgFgoFQwuJMrDx7Hs+/ULX2HI0S+GRkZabszffXVVwCYG6UvHKlKSSRNk4aRkREnTpzQ7s/w5MkTLC0tc5y2s0uXLty5c4dLly4B6a0bAQEBvPHGG1hbW/PkyRPt2/bExERta0JMTAxt27YlMjLyhdOKAnz66aesXLmSlStXsnz5cr7++ms8PDx4//332bZtm7bcTz/9xJYtW3QefLO6j86dO5OWlsa+ffu02/bs2UOlSpXw8PAgLS2NAwcO4OPjg6GhoTbuqKgo3nrrLdRqNcePHwegXLlynD59mtWrVxMZGQlAv3792LFjR7YJBMCdO3cAqFixos52jUaT4/ixnMan5efYPn368OOPP9K/f39+/fVXxo4dm+3xL+Lo6IiZmVmmKWCFKK2kJUKUWvdjwpl/8hduRd8lJbwKyXdcQJP5j8iued3yfG7143tE7PiBpAdhWLo1x6Hd0GK17sPL8qlbFvcaDmzYf4VWPpUwM5FfFaWRVf03Xuqt/41vfLPd5+z3Vbb7ipKPjw89evRg69at7NmzBw8PDwDSNGlEq2IwwQgjIyMOHz7MwYMHuXnzJrdu3eLp06cAWU7pmaF9+/ZMnz6dgIAA3N3dCQwMJDw8nM8//xz4/wfltWvXsnbt2izPkZuxA25ubplmZ+ratStdunRh1qxZdOjQAVNTUwwMDLhz5w4LFiwgLCyM27dv8/Dhw0zn8/T0pFKlSgQEBDBo0CBiY2O1XaEgPfGIjY3lwIEDHDhwIMe4J0yYwMiRI5kxYwYzZ87Ezc2NVq1a0adPH20rR1aio6MBMnUTsrCwyHJMQcY2C4vsFwzMz7EZY1zatm2LlZUVy5Yt4+TJkzRpkr8Z6iwtLXny5Em+jhWipJEnA1HqaDQajvx3il8CN5CaoiDpWgPSop0K7NwxgQFEHVyDwtAYpx4fYemqn2kfC5NCoeDtTq58/MNRth+5Tv+3XF58kBDF1CeffMKhQ4eYOXOmtj+/sYER0YkxOBjZ8sknn7B79268vb3x8vKib9++NGzYkMGDB+d4XhsbG5o3b67t0rR3716srKx44403ALRjAgYOHEibNm2yPEfNmjXzdU8mJia8+eabrFq1ihs3buDq6sqGDRv48ssvqVatGj4+Prz11lt4eHiwdu1adu3apXN8586d+emnn3j06BHHjh1DrVbTuXNnnbjbtWtHv379srx+xoDuOnXqsG/fPo4ePcpff/3F0aNH+eGHH1i1ahUbNmygRo0aWR6f0SqQlpams718+fJZts5kTK+a1ZiHgjgWoEOHDixbtozLly/nO4lIS0vDwKBkjYUTIr8kiRCliio5keWB6zl26wyGqjLEXXHDt5kbO/++ijo18y92W6vcdz1KiXlMxJ5FqG4EY1bdkzKdR2Nolbn/bWnhUsWepvXLs+3wNTo0qZqnz0q8GgwsbLMcRG1gYVvkseTE3t6ejz/+mMmTJ2vXADAzTO/WdPzsSXbv3s2oUaMYN26c9piUlBSio6NfOOd/ly5d+PDDDwkJCWH//v289dZb2u5EFSqkz8xmYGCQqY98WFgYd+/exczMLN/3lfEArlQqSUpKYtasWTRu3JgVK1ZgaPj/f94XLFiQZdxLlizh8OHDHDlyBBcXF2rVqgWkf15mZmakpKRkivv+/ftcvnwZMzMzUlNTCQ0NxdLSktatW9O6dWsgfVzIhx9+yKZNm5g4cWKWsTs4pC+6mdEikcHV1ZWDBw/qjNcACAkJAaBevXrZfh65PXb06NFER0dnmqEqPj4eIM+z8j3r6dOn2nsTorSTJEKUGtejbvH9yV94FBdJ6r1amMa68vW73qge3+CzX8ewaNEiunfvnq9zx/17jMiA5WhSk3FsPwyrBsV74biC4tehLqcuhfP7gSu816O+vsMRxUyVD37Rdwi51qtXL7Zu3aodRGugVGJrZs3jJ+n9+J9vEdi4cSMqlYqUlJQcz9uqVSssLCxYsGABEREROmseODk54e7uzrZt2xg6dKj2TXhycjKfffYZoaGhHDlyJF/3k5iYyMGDB7G3t6dmzZrEx8ejUqmoWrWqTgIREhLCmTNngPTEKGNfjRo1cHV15cCBA5w9e5YRI0ZojzE0NKRFixYcPHiQ0NBQnRnrZs2axb59+9i9ezfW1tb4+/vj4+PDTz/9pC2T0WUsp/ELGQlWeHi4zva33nqLhQsXsm7dOu2CcYmJiWzZsgVvb+8cWxNye2zZsmU5cOAAQUFBeHl5AemtzKtWrcLAwIDmzZtne42cREREkJKSQvny5fN1vBAljSQRosRL06Sx9+ohfg3ejjLVhMTQhnhVrEPbpsbUr1kGapZh//79uLm55fncqapYIgOWE3/5OCbOtXDqNhYje+cXH1hKVHSy4q3GVQg4+R9dm9egvGP2/ZGFKM4UCgVTp06lZ8+e2sTA1tQa9/r1sLCwYObMmdy/fx9ra2tOnz7N3r17MTEx0b6dzo6pqSlvvfUW27Ztw8nJKdPYhcmTJzN48GB8fX3p378/tra27Nmzh+DgYMaPH4+dnd0LYz9x4oTOw3ZUVBRbtmzh3r17fPXVVxgaGmJjY4OHhwdbt27F0tKSatWqce3aNTZt2qR9mI+Pj8fGxkZ7ns6dOzNnzhwUCgWdOnXSuebHH3/M6dOnGThwIAMHDsTZ2ZnDhw/z119/0bdvX22rhZ+fH0uWLGH06NE0b96cxMREfv/9d8zMzHSmn32eh4cH5ubmBAcH61y7du3a9OzZk59++omYmBjq1KmjvdeMaWoz7NixQ9sKkpdj33//ffbv3897772Hn58fdnZ27Nu3jzNnzjBu3LgcB4TnJDg4GCDfXaGEKGkkiRAlWkxiLIvOrCbowb8YxJYn4borQzp4EXRoHf6z1nDixAnKli2brwQi4cZ5InYtIjXhKXYt+2PbtEeJW/ehIPR/y4W/Au+w7o8QPvHz0Xc4QuSbi4sL/v7+rFixAgClQknlshWZOu9r1v20isWLF2NsbEy1atX47rvvuHDhAmvWrCEyMhJHR8dsz9ulSxe2bdtGp06dMr199/LyYv369SxcuJCVK1eSkpJCtWrVmDVrVpZTx2Zl6dKl2v8rlUqsrKyoU6cOCxYsoH379tp9CxYsYObMmWzZsgW1Wk2FChUYPnw4NWrUYMyYMZw6dUo79SykJxFz587Fw8ND2zKQoXLlymzcuJEffviBjRs3kpCQQKVKlZg0aRJ+fn7acmPHjsXW1pYtW7Ywe/ZsDAwMaNCgAd9++2224yEgferUxo0bc/bs2Uz7pk2bhqOjI9u2bWPbtm24uLjw888/06BBA51yEyZMwNvbW5tE5PZYe3t7fvvtN+bOncuaNWtITEykVq1azJ07V6clKa8CAwOxtrbG09Mz3+cQoiRRaHKaekLkWVJSEpcuXcLd3R0Tk6LpQx4YGJhpbutXwaWHofxwahUxiXEk3apNmdS6jO3ljnut8jx48ICzZ8/m6w9CWnISUQfXEBMYgJFDBZy6jcOkfPZ/DF9WSai/tX+EsPHAVeZ/0JKalWz1HU6xUhLqLyQkhLp16+o7jGIpPj6e6NRYktNSqGTtjEEOXXBKm0ePHtGyZUumTJnCgAEDivz6Bw4cYPTo0ezbt4+qVavm6xzx8fE5zthUVNLS0njzzTdp3749kyZN0nc4JUZB1t+Lfs/p4/mstJOWCFHipKalsunf3Wy7vA/DFCsSQhrTsq4rJ7Z/y+I79ixevJjy5cu/MIG49f27Wa+sq1CCJg3rhp2wf3NgiV/3oSD0fKMmf5z4j9V7LjN9hCyiJEoXB3M77sWEE534FAfzF3cvKi02btyIsbFxpq5MRaV169ZUrVqV7du388EHH+glhoJy+vRpIiMjXzijlxCliSQRokSJiH/MDydXcOXxDRRPKqG+48oHPbxp5VMJ64TW2Nvbv3DBoQxZJhAAmjTKD/gSs2oykDiDhZkRfdvW5ucdlwi68ggvl4KZMleI4sDU0AQrYwueJsZibWKJkUHWi1KWFvPmzePatWscOXKEgQMH6oyTKEoKhYLx48czZcoUhg4dmmnNiJLkp59+on///jg7vzpj5oR4ddptRYl3+m4Qn+z7hrDHd1CH1ccxpiH8txtr0hdTGjZsGL6+vgUya5IkEJl1bFoVJzszVu25TFqa9IIUpYu9uS0o4HFCtL5DKXQJCQmcOnWKNm3a8NFHH+k1lrfeeosGDRqwatUqvcbxMv755x9u3rxZ4ltThMgraYkQxZ46Rc2a81vYf/1vDNV2xIfUo2vjevRsUYk+vWdrFxIShcvI0IBBHery3W/nOHr+Hi0bVNR3SEIUGEOlIXamNkSpolElJ2JmlP+1Aoq7KVOmMGXKFH2HobVkyRJ9h/BSGjZsmO+peoUoySSJEMXa3acP+P7kz9x+eh/No+qkPapDbaMbDOniilKpZP/+/TnORZ4V9eP7RB/bVEgRl24tvSqy7XAYa/8IoWl9Z4wMpTFTlB42plbEJMURmRBFRevyr8RaMEIIkV/yBCCKJY1Gw8Hrx5j450zuR0eRdMWb2oZNae+iYsdvPxIaGgrkvJjR85Kj7vNo5w/c/Wkc8VdOF1bopZpSqWBwJ1ceRiUQcPI/fYcjRIFSKpQ4mNuiTk0mNilO3+EIIUSxJi0RothJUKtYdvZXTtwJxCChDAlX3Wjh4sgH/k1RKqB9mxaUK1cu1+dLfhLOk2ObiLv4NwoDQ2wadca2SXfuLv8oy8HVBha2BXczpVADFyfq13Rkw59XaN2wEuampXsQqni1WBiZY2poQpQqGgtjcwxewbVhhBAiNySJEMXKtcc3WXDyFyLio0i5WxsLlSvJd3ey6a9Axg06hsLQMNcJRHrysIW4i4fTk4eGHbFp0h1Dy/QpHKt88Esh3knppVCkt0aMX/A32w5fZ2D7OvoOSYgCo1AocDS3527MA54kPsXR3F7fIQkhRLEkSYQoFtI0aewKPcD6izswSDUjMbQRDau48MFIb8Lv10ChUGBomLtv1+ToR0Qf20zsxcMoFEqsfTpg26QHhlavzvzvha12ZTte93Bm+5EwOjatip116R2EKl49JobGWJlYEpMYi7WJFcalfMpXIYTID0kihN5FJ8aw6PQqgsNDUMaUR3XTnUdBu9EY3sXSvAk1a9bM1XmSnz4i+tgWYi/8lZ48NGiHbdMeGFrJm8TC4NehLicvPmDDn1cY6euh73CEKFD2ZrbEqxN4nPCE8layLooQQjxPkgihVxfCQ1h4aiWxSQmob7pSXunKxDEN+WPHI1q1apWrc6Q8jeDJ8a3EBh8CBVh7tcW2aU8MrR0KOfpXm3MZS9q9VoV9p27RrUUNnMuU3IWihHieodIAO1NrHquiSVCrMDc203dIQghRrEgSIfQiJS2V3y/uZEfofgyTrUkIfY2Em2F88GVVqpS3ZsSIES8+R8xjoo9vIeb8QQCsPFth18wXQ2vHwg5f/E+/ti4cOnuHNX+EMNG/ob7DEaJAWf9vytfHqieYGZnKlK9CCPEMmeJVFLlHcZF8eXAuO0L3w+PKpIY2Y1gbL2yTLqFUpL3w+JTYKCL3/cztxaOIOX8AK483qTRqIWU6vCcJRBGzszale8uaHA++z9XbT/QdjhBMnDgRFxcXfv311yz33717FxcXFxYuXPjCc6VP+WqHOjWZmKRY/Pz8ct1CWhAWLlyIi4uLzj83NzeaN2/Op59+Snh4eJHFUhzFxcWxcOFC2rdvT/369fHx8aFfv35s2LCB1NRUnbJFXXd5kfE9W1DlANLS0ujZsye7du3SbouPj2fGjBm0bNkST09P/P39+ffff3N1vtwee/v2bcaMGUOTJk1o2LAhY8aM4datWzmee+fOnVne144dO/D19SUt7cXPBUI/pCVCFKkTtwP56Z91qFPSSLrmidlTSxZMakNZe3O6vLE5x2NTYp8QfXIrsef+RKNJw6r+m9g288XIVvor61OPN2rwx8mbrNp9mW9GNpW3taJYmD9/Pu3atcPR8eVeLJgbmWFmZEqU6inDhw8nKSmpgCLMvREjRlC9enUA1Go1d+/eZcOGDQQGBrJ9+3YsLV+9roRxcXH07duX8PBwfH19qV69OiqVimPHjvHll19y4sQJFixYoP19NGLECFQqlZ6jzlrfvn1p0qRJgZ5z/fr1JCcn07lzZ+22jz76iOPHj+Pn50eFChVYt24dfn5+bNu2jSpVquR4vtwcGxkZyYABAwAYMmQISqWS1atX07dvX3bs2EHZsmUznffKlStMmzYty2t26dKF5cuXs379egYOHJjfj0IUIkkiRJFISlGzKmgTB28cwzDJnoRQdyoZq7kcuBIzwy45HpsS94Tok9uJPbcfTWoKVvXfwPb1XhjZZv6FJIqeuakRfdu4sGz7Rc5deYR3HakXoX+xsbHMnDmTefPmvdR5FAoFDmZ23I15QN0GbjhaFP1EDU2bNqVx48Y62xo0aMDw4cPZvn07gwYNKvKY9G3dunWEhYXx66+/4uPjo93+zjvvMG3aNH777Tf+/vtvWrZsCUCzZs30FeoLeXl54eXlVWDni4uLY/78+UydOlWbRB0/fpzDhw8zffp0+vTpA0CHDh3o0KEDP/zwQ44/J7k99ueffyYqKort27dTu3ZtAFq0aEHnzp1Zs2YNn3zyic55Dxw4wKeffkpcXNYLOyqVSoYNG8bXX39Nt27dXslkubiT7kyi0N2OvsekP2dx8MYxUh9UI+1KI756uw3fTx7IH3/swdraOsvjUuKieXxgFXcWjSLmn71YuDaj0siFlOk8WhKIYqZ9k6qUtTdn1e7LpKVp9B2OELRq1Yrdu3dz8uTJlz6XiaEx1iaWPE2KRZ2iLoDoXt5rr70GQFhYmJ4jeXkZXcy2bt2a62OCgoKwtbWlbt26mfYNHjwYgPPnzxdUiCXKli1bSElJoU2bNtpte/bswdTUlO7du2u3OTg40L59ew4ePJhjC1tuj9VoNLRq1UqbQADUqlULOzs7rly5onPOL774gtGjR1OpUiVef/31bK/91ltvoVar8/S9IYqOJBGi0Gg0GvaH/c2kP2cT/jSapFAfYoMNML6/B4/aZTA0NMTExCTTcanxT3l8cDV3Fo3k6Zk9WNRtSqURP+DU5X2M7HK/UrUoOkaGSvw61OW/BzEcCbqr73BEERm241P6/D4y079hOz7Vd2hMnjwZMzMzpk6dilqd84O/RqNh/fr19OrVCy8vL+rVq0f79u1ZtmwZGk16UmxvZsunoz6mTZu2aDQali1bhouLS5b9wlu1aoW/v7/267CwMEaPHo2Pjw8eHh7069ePo0ePvtT9PXjwAIBKlSrpbD958iRDhw6lcePG2vETX3zxBTExMQAcPXo02zEjH3zwAa+//rp2PEF4eDgTJkzgtddeo169enTv3p2dO3fqHKPRaPjxxx9p164d9erVo2nTpnzyySfa+AqLhYUF0dHRHDhwINO+qlWrcvHiRcaNG6fdltWYiODgYPz9/fHy8qJ58+YsXLiQH3/8Uad//sSJE+ncuTOBgYH07duX+vXr07p1a7Zt20ZycjLz5s2jWbNmNGrUiA8++IAnT3THhl25coVRo0bh4+ND/fr16dOnT6aYsxrrcOnSJYYMGaKNbc2aNbn+bH777Tdef/11TE3/f/2eS5cuUbt2bYyNjXXKurm5oVKpuH79erbny+2xkyZN4scff9QpEx4eTnR0NM7Ozjrbr1+/ztixY1m1ahVlypTJ9tpmZmY0adIk2zFOQr+kO5MoFHHqeH7651dO3w1CEeeI+np93mnnRd3yaVSuNCrLY1ITYog+tYOYs3+gSUnG0u11bF/vjbGDc5blRfHS3LMCWw+Hse6PEF73cMbI0EDfIYlC9jQxJk/bi1KFChUYNWoU8+bNY9myZbz//vvZlv3+++9ZunQpPXr0oE+fPsTHx7N9+3bmzZtHmTJl6NGjBwZKA4wMDNFoNCQkq+jSpQvfffcdf/zxB25ubtpzBQcHc+/ePUaOHAmkP0QOGDAAR0dH3nvvPYyMjNi9ezfDhw9n3rx5dOzY8YX3EhsbS1RUFACpqancuXOHOXPm4OzsjK+vr7bcsWPHGDZsGA0aNGDs2LEoFAqOHz/O77//TnJyMjNnzqRp06Y4ODgQEBCg0888ISGBw4cP06tXLwwMDHj48CG9e/dGo9Hg5+eHjY0NBw8e5JNPPuHRo0cMHToUgKVLl7Jo0SIGDhyIi4sLd+/eZc2aNVy6dIndu3djYFA4vwd8fX3Zu3cvEyZMYN26dbRu3Vqb7BgYGGR64H3epUuX8Pf3x9HRkdGjR6NSqVizZg1KZeZ3qxEREYwYMYLevXvTtWtX1qxZw2effcauXbuIjY1l1KhRXL9+nV9//RUzMzNmzpwJwIULF/D398fS0pJ33nkHCwsLduzYwejRo/niiy+y7ed/7do1/Pz8sLa2ZtSoUSQnJ7No0aJMg8Wz8t9///Hff/9p6yfDw4cPadgw8wx6GQ/w9+/fx9XVNctz5ufYqKgo/v33X7799lvMzMy0rUMZVq5cibGxMfHx8S+8p1atWjFlyhRu3br1wrEbomhJEiEK3JXI6yw4uYLHCdEk33Eh/qoFnX2S6d4y60XjUhNieXp6B0//+QNNctL/kodeGDtWLOLIxctQKhW83cmVL5adZO+J/+jWooa+QxIvcOTmKf66eaJQzj310Hf5Ou7Nak1pWe21AonhnXfeYefOnSxbtowuXbpk+QCSnJzMunXr6NSpE7NmzdJu7927N02aNGHfvn306NEDAAOFAQoFPE54QqVyzvj4+BAQEMDHH3+sPW7v3r0YGxvTrl07AL7++mvs7e3Ztm0b5ubmAAwaNIjBgwfzzTff0KZNmxc+8I4ePTrTNqVSycKFC7G1tdVuW7VqFeXLl9c+oAEMGDCAvn37sm/fPmbOnImBgQEdO3bk119/JSIiQvsgeOjQIVSq9OQI0gemq9Vqdu3ahZOTkzbu8ePHs2DBAnr06IGDgwO7du2iRYsWTJ48WRtH+fLlWb9+Pffu3aNy5cpZ3lN8fLy2G0xGK0lCQoI2WTIwMMDGxibbz6RZs2Z8/fXXzJgxgwsXLnDhwgUAbGxsaN++Pe+//7427qx8++23GBsbs2nTJuzt08e5tG7dWicpyxAdHc2UKVO0Y08qVqzI8OHD+e+//wgICNB+1iEhIRw7dkx73Ndff41CoWDz5s2UK5feit6/f3/69+/PnDlz6NChg/baz8qYOWzDhg2UL18egHbt2ul0J8pOYGAgQKaWjfj4eJ2WiQwZ23IadJ6fYwcPHszVq1cBmDBhAjVq6P49eNH3/LMy7uXs2bOSRBQz0p1JFJi0tDS2Xv6DLw99R3RsEqp/G9Gs/Ov0bWLC0EHdM5VPVcUS9dev3F40gugT2zGv5U3F977HqfsHkkCUUF4uTnjWKsPvf14lXpWs73DEK87IyEjbnemrr77KtsyJEycy7X/y5AmWlpYkJCRotykUCpQKJclpKTxNiqVLly7cuXOHS5cuAeldewICAnjjjTewtrbmyZMnnDlzhpYtW5KYmEhUVBRRUVHExMTQtm1bIiMjuXjx4gvv49NPP2XlypWsXLmS5cuX8/XXX+Ph4cH777/Ptm3btOV++ukntmzZovOAltV9dO7cmbS0NPbt26fdtmfPHipVqoSHhwdpaWkcOHAAHx8fDA0NtXFHRUVp+6gfP34cgHLlynH69GlWr15NZGQkAP369WPHjh3ZJhAA06dPp0mTJjRp0kSbpGW1LSe9evVi7969zJw5k3bt2mFra8vTp0/5/fff6dKlS7ZddJ4+fcqZM2fo1q2bzkO8q6trtgOw27Ztq/1/1apVAWjevLnOZ12xYkUiIiKA9JmKgoOD6datmzaBADAxMeHdd98lMTGREycyJ/BpaWkcPXqUli1bahMIgBo1auQ4diDDnTt3tLE8L6eZ87JqgXmZY0eOHMmCBQvo1KkTc+bM4Ztvvsnx/DnJ6LJ39650lS1upCVCFIgnqqf8eHolFx9eIS2qHKprtRnexYOub9RFofDRKZuqiuPp6V08/WcPGrUKi7pNsGveB+My2f/BESXH4M6ufDj/CFsPh+HXIfOgR1F8tKz22ku99e/z+8hs901t9VG+z1uQfHx86NGjB1u3bmXPnj14eHhkKmNkZMThw4c5ePAgN2/e5NatWzx9+hRAOyYig1KhxNzIlCeqp7R5qy3Tp08nICAAd3d3AgMDCQ8P5/PPPwf+/4Fu7dq1rF27Nsv4cjN2wM3NLdPsTF27dqVLly7MmjWLDh06YGpqioGBAXfu3GHBggWEhYVx+/ZtHj58mOl8np6eVKpUiYCAAAYNGkRsbCzHjh1jyJAhQHriERsby4EDB7Icc/Bs3BMmTGDkyJHMmDGDmTNn4ubmRqtWrejTp0+Ofd2HDh1K165dgfQH7k8++YR3331X+6Cc1Xi5rFhZWdGzZ0969uxJWloa586dY8mSJRw7doyZM2fy888/Zzrmzp07pKWlZflWu3r16lmOV3FwcND+P6OL1rPbMrZnfL/cu3cPgGrVqmU6V8Zb+fv372faFx0dTUJCQpYJWPXq1Tl06FCm7c8fD2Saycjc3DzLwdOJiYlA+hiT7OTn2Ixueu3btwfSfwYGDRqUr5aEjHt5fryJ0D9JIsRLC3pwiUWnVxOXlIj6hjtOmuqYG56ilU9nnbcXqYnxPD2zm6dndqNJSsCizmvpyYOTNE+WJjUr2tLCqwLbj1ynU7Nq2FtnbgYXoih98sknHDp0KMuHSo1GwyeffMLu3bvx9vbGy8uLvn370rBhw0z9uDM4mNtx5+kD0kw0NG/eXNulae/evVhZWfHGG28AaPuwDxw4UGemnGfVrJl1N88XMTEx4c0332TVqlXcuHEDV1dXNmzYwJdffkm1atXw8fHhrbfewsPDg7Vr1+osOgbprRE//fQTjx494tixY6jVau2aAhlxt2vXjn79+mV5/Yy3w3Xq1GHfvn0cPXqUv/76i6NHj/LDDz+watUqNmzYkKkby7P3nXHvGW+Ya9asSdOmTV947w8fPmTt2rW0aNFCZzyKUqnEx8eHZcuW0b17d86dO5fl8SkpKUDWXWqyS14MDTM/LuX0dv755PNZGYunGRkZZVsmq4f23Cy6ltEq8Pz1y5cvz6NHjzKVz9iW1RoOBXEspE8Hu2fPHkJCQvKVRGTcd2GNrxH5J0mEyLeU1BR+u7iD3VcOgMqCxGuv0blhfd7p7IqRYQdtubTEeJ7+s4enp3eRlpSAuUtj7Jr3waRsVf0FLwqVX4e6nLhwn/X7rzC6V+Y3v6J0sDG1znIQtY1p1tM264u9vT0ff/wxkydP5vvvv9fZd+7cOXbv3s2oUaN0ZvNJSUkhOjo60+xHAMYGxtiYWPE0KZYOnTpwaPwhQkJC2L9/P2+99Zb24bRChQpA+sPP8w/HYWFh3L17FzMzs3zfV8bDlVKpJCkpiVmzZtG4cWNWrFih89C7YMGCTMd26dKFJUuWcPjwYY4cOYKLiwu1atUC0j8vMzMzUlJSMsV9//59Ll++jJmZGampqYSGhmJpaUnr1q1p3bo1kD4u5MMPP2TTpk1MnDgx3/eX030vX76cx48f6yQRGQwMDKhWrRqPHz/O8viMOv3vv/8y7XvR6sq5lVH3N27cyLTv5s2bADrdnDLY2dlhaWmZZWy56c6T0ToSHR2t83Dv6upKQEAAKSkpOt8bly9fxsTERFv3WcnNsRqNhl69elG1atVMa05kDJ7OalxFbmS0rjzf8iP0T8ZEiHwJj33E5IPfsvvKATQRVVBfakg51X8M6+aunZUnLSmBJ8c2c3vRKJ78/TumVdyo8O5cyvWaIAlEKVfOwYL2Taqy//Qt7j6K1Xc4opAs7zabjX2XZPq3vNtsfYeWSa9evWjQoAF//fWXzvaMbkvPtwhs3LgRlUqlfWv9PDszGwwUSuo18sTCwoIFCxYQERGhHZgM4OTkhLu7O9u2bdPpVpScnMxnn33G2LFjsz3/iyQmJnLw4EHs7e2pWbMmiYmJqFQqqlatqvOgFxISwpkzZwB0rlWjRg1cXV05cOAAJ0+e1FnZ2NDQkBYtWnDkyBFCQ0N1rjtr1ixGjx7NkydPSE1Nxd/fnxkzZuiUyegy9qJ+9vlVvnx5fHx82LVrl/bennX37l2OHz+uTWqe5+DggJeXF7t379bWP6R3c/r7778LJMYyZcrg7u7Ozp07CQ8P125Xq9Xage9Zjb9QKBS0bduWo0ePagcmZ9zT4cOHX3jdjOTl+W5y7dq1IyEhQWcMTVRUFAEBAbRr1y7Llpa8HKtQKHB0dOTAgQPabnyQ/r2+du1aLC0tdRYFzIuMz+/5aWKF/klLhMizY7f+YdnZX0lMSiHphheu9q58+JknTvbp/RbTklQ8PbuXp6d3kqaKw7yWD3bN+2JSvrqeIxdFqW8bFw7+c5u1f4QwaXAjfYcjXnEKhYKpU6fSs2dPnYdpDw8PLC0tmTlzJvfv38fa2prTp0+zd+9eTExMsp2C0kBpgJ2ZLZGaKN5s3YrdO9NnMXp+7MLkyZMZPHgwvr6+9O/fH1tbW/bs2UNwcDDjx4/Hzs7uhbGfOHFC50E0KiqKLVu2cO/ePb766isMDQ2xsbHBw8ODrVu3YmlpSbVq1bh27RqbNm3SPszHx8frzHjUuXNn5syZg0KhoFOnTjrX/Pjjjzl9+jQDBw5k4MCBODs7c/jwYf766y/69u2rfXPt5+fHkiVLGD16NM2bNycxMZHff/8dMzOzLGc6ykrFihUzLUb2IjNmzGDAgAGMGjWKtm3b0rBhQ0xNTbl27Rrbtm3D3t6eDz/8MNvjP/30U/z8/OjVqxf9+vVDrVazdu3aHLsh5VVG3ffq1Yv+/ftjYWHBzp07+ffff5k8eXK2C62OGzeOw4cP4+fnx9tvv42BgQFr167FwsLihWueZCxCGBwcjKenp3b7G2+8QePGjZk2bRp37tyhbNmyrFu3Do1GozP7V0JCAn/++SeVK1fWrqKd22M//fRTfH19GTRoEAMHDsTY2Jjt27cTGhrKrFmz8r3idMaigU2aNMnX8aLwSBIhci0xJYkV537n8M2TKFX2qELdqGGezNcjmmGgVJCmVhFz9g+iT+0kTRWLeU3v9G5Lzvnr8ytKNlsrE3q8UYvf9oUSeiuKOlUyT2UoRFFycXHB39+fFStWaLc5ODiwbNky5s6dy+LFizE2NqZatWp89913XLhwgTVr1hAZGYmjo2Om81mbWBKTFMvrbZqze+cuOnXqlOntu5eXF+vXr2fhwoWsXLmSlJQUqlWrxqxZs3I1AxGkr8WQQalUYmVlRZ06dViwYIF24Cqkd1uaOXMmW7ZsQa1WU6FCBYYPH06NGjUYM2YMp06d0k49C+lJxNy5c/Hw8NC+wc5QuXJlNm7cyA8//MDGjRtJSEigUqVKTJo0CT8/P225sWPHYmtry5YtW5g9ezYGBgY0aNCAb7/9NtvxEAWhSpUq7N69myVLlnDy5EmOHTtGSkoKzs7O9OvXj+HDh2NlZZXt8V5eXvz888/Mnz+f77//HltbW/z8/Lh+/brOrFUvI6Puf/jhB1asWEFaWhp16tRh0aJF2Y6Rgf+fInfOnDn8/PPPGBsb07t3byB9Bq6clCtXjtq1axMYGJhpTM+iRYuYO3eudt2Q+vXr891332lnm4L0BHXChAn06NFDm0Tk9tjq1auzbt065s+fz5IlS0hLS8Pd3Z2ff/45VzNLZefcuXPUrl37hWMvRNFTaAoy7RYkJSVx6dIl3N3dcz27xMsKDAzE29u7UK/x35M7zD/xMw9iH5EaXgOrWHeGtKtGi0YupKkTiQkMIPrUDtISYjCr4YVd876YVsi+j6X4f0VRf/qiSkph+IwDVHCyZOaoZjkORCypSkL9hYSEULeuzJSVlfj4+BxnpnmRhGQVD2IfYW9mi51Z9usaFEePHj2iZcuWTJkyhQEDBug7nHzJb/09u0bGs0aMGEFoaGiuug4VV6tXr+a7777j+PHj+X77X1ReVH9xcXE0a9aM8ePH66wCn5UX/Z7Tx/NZaSdjIkSONBoNAdcO89mBOYQ/iSYptCHV0txZOL4Vr3tVJfrUTm4vGknUobWYlKuO89szKd9vsiQQAgAzE0P6veXCvzceczYk81STQpR05kZmWBiZEZ34lJS0/I1v0JeNGzdibGycqSvTq6BPnz68++67OtsiIyM5ffo09evX11NUBaNXr16YmJjwxx9/6DuUl/bHH39gYmJCr1699B2KyIJ0ZxLZikuKZ/GZNZy9fwFlXFmSb7jTvJo5H/i1JO78Pu6c3E5qfDRm1Tywa9EH04p19B2yKIbavVaFHX9fZ/WeyzSoUxYDZelrjRCvNntzO+4+fUCUKhoni8zdnoqbefPmce3aNY4cOcLAgQNzXBm6tOratStLly5l/PjxNG7cmJiYGDZu3EhaWlqWK4SXJBYWFowcOZJffvmFnj17ltipUVNTU/nll18YOXKkdrV3UbxIEiGyFBJxjQUnVxAVH436jgtl09z49D1PHMJPc2/J++nJQ9V62Pl+jGkl6SYhsmdooMSvQ13mrD3L4cA7tG4oiwqK0sXYwAgbUyuiE2OwMbHCxLB4d5VISEjg1KlTtGnTho8+Kh6LAha1cePG4ejoyMaNGzl48CAmJiY0aNCAH374ARcXF32H99L8/f3ZtWsXu3btonv37voOJ1927tyJubn5C7sxCf2RJELoSEtLY8vlP9j87x4MUixIDG1CLXNTJrVMJH7bJB7HPcG0ihtOPT/CrHLm+bmFyEqz+s7UrGTLuoBQmntWwNioZL4ZEyI7tqY2xCbFEZnwBGerssV6/M+UKVOYMmWKvsPQK6VSiZ+fn84g8dLEwMCArVu36juMl9KjR49cTz4g9EOSCKEVlRDN/OPLuRJ1g7TH5VE+cGdqgyQc7vxJzF9RmFZ2xan7B5hVcdd3qKKEUSoVvN3JlclLT7Dn+E16vCEzdonSxUCpxN7MloiEKOLVCVia5H+wthBClASSRAgAAu9fZNHp1SSok0i54cYbSXF0cfgDzeUojCrVxanrWEyruBfrt2uiePOoVYYGLk5sPHCVto2rYGlmpO+QhChQViaWPE2K47HqCebGZigVMneJEKL0kt9wr7jk1GQWHVvF7KOLUcUa4hJajq8NztHZ6B+M7cpQbsAXlPebjlnVepJAiJf2dmdX4hOT2XLomr5DeeXIbN6FT6FQ4GhuR0paKtGJMfoOR4hXhvx+0w9piXiFDNvxKU+z+cPm+MiKwU/vU8YwHhOn2ti1GItZNQ9JHESBquZsQ8sGFdn593U6v14NBxszfYf0SjAwMCA5ORljY2N9h1LqmRmZYmFsTrQqBmtjSwwN5M+sEIUtJSUFQ0P5WStq0hLxCskugQD4OOY65cqVo1y/yTgPnoF5dU9JIEShGNS+Lmka+G3fFX2H8sqwsrIiJkbejBcVBzM7AB6rovUbiBCviNjYWExNTfUdxitH0rZS7tb375IaH53+RU2nbMs59Z6ERS1vSRxEoStrb07HZlXZffQG3VvWoFJZK32HVOrZ29tz+/ZtAKytrTEyMpKf9UJkZGCIjak10YlPsUm2wtSoeE/5KkRJpdFoUKlUREZGUrmyTB9e1CSJKOW0CcQLWNb2KdxAhHhGn9a1+fP0bdbsvczn7zTWdzilnomJCZUrVyYqKor//vuP1NRUfYdUbKjV6kLp5qXRaIhSRfNQ8QBbM2sUSNJWGAqr/kTRKIj6MzExoWzZstISoQeSRAghipyNpQm+rWqy7o9QLt98jGs1B32HVOqZmJhQvnx5ypcvr+9QipXAwEA8PDwK5dyHb55k8ZnVvN/4bVpUlWS5MBRm/YnCJ/VXssmYCCGEXnRrXgM7KxNW7b4sM2uIUqlF1cbUsK/Crxe2kZicqO9whBCiQEkSUYpdv35d52vLlKy7MGjU0hQsip6piSH929Uh5L8ozvwbru9whChwSoWSt71680T1lO2h+/UdjhBCFKhXJolQq9VMmTKFhg0b0qxZM5YvX55t2dDQUPr27YuHhwc9e/bkwoULRRhpwalSpYrO15P/e8yssEfaf6oz7VGdaY/p9Q56ilC86to2qkyFMhas3htCamqavsMRosC5ONagWWUfdl05QET8Y32HI4QQBeaVSSLmzJlDUFAQK1euZNq0aSxZsoQ9e/ZkKpeQkMDQoUPx8PBg69ateHt789577xEXF6eHqPPu5MmT9OvXD5VKhaGhIQYWtlmWM7CwZde8buya1421U9sXbZBC/I+hgRK/jq7ceRjLobN39B2OEIVioEcPFMC64G36DkUIIQrMKzGwOiEhgY0bN7J06VLc3d1xd3dn6NChrFu3jk6dOumU3bt3L0ZGRkycOBGlUslnn33GkSNH+OOPP+jdu7ee7iD3UlJSCA8P5+HDh1StWpUqH/yi75CEyNHSLektfT9sPM8PG89rt9tamUiCK0oFR3N7FCg4eSeQk78H6uyzMbVmebfZeopMCCHy75VoiQgNDUWtVuPt7a3d5u3tzcWLF0lJSdEpGxwcTIMGDVAq0z8ahUJBgwYNCAoKKtKY8+LixYts3LgRgObNm3PgwAGqVq2q36CEyKXouKSst8dmvV2IkigpVZ3l9pwWARVCiOLslWiJiIiIwMbGBhOT/1/wx9HRkeTkZKKionByctIpW61aNZ3jHRwcCA0NzdW1MmaZUauz/oNRGA4fPkx8fDydO3fGwMAAQOaBL2GSkl7dB2ZbC4Ns95WUz6WkxCmyVhT1Z21oqdfrl2by+ZVsRVV/Gc9lMhtgwXklkgiVSpVpMZOMr59/2M+ubG6TguTkZACuXr2a33DzbMyYMQCEhIQU2TVFwbp06ZK+Q9CbD7plv25BSflcSkqcImtFUX8jq/bT6/VLM/n8Srairr/k5GRZmK6AvBJJhImJSaYkIONrMzOzXJXN7TechYUFtWvXxsjICIVCVigVQgghhNA3jUZDcnIyFhYW+g6l1HglkoiyZcsSExOjs7x6REQExsbG2NjYZCobERGhsy0yMpIyZcrk6lpKpRIrK6uCCVwIIYQQQhQIaYEoWK/EwOq6detiZGSkMzg6MDAQNzc3DA118ygPDw+CgoK0feY0Gg1BQUF4enoWZchCCCGEEEIUW69EEmFmZkb37t2ZNm0aFy5c4ODBg6xYsQJ/f38gvVUiMTERgPbt25OQkMD06dMJCwtj5syZxMXF0bFjR33eghBCCCGEEMWGQvOKDFNXqVRMnTqV/fv3Y2FhwZAhQxgyZAgALi4uzJw5k549ewJw4cIFvvzyS8LCwnBxcWHq1Km4u7vrM3whhBBCCCGKjVcmiRBCCCGEEEIUjFeiO5MQQgghhBCi4EgSIYQQQgghhMgTSSKEEEIIIYQQeSJJRAmgVquZMmUKDRs2pFmzZixfvjzbsqGhofTt2xcPDw969uzJhQsXijBSkZW81N/evXvp3Lkznp6edO3alUOHDhVhpCIream/DNHR0TRt2pStW7cWQYQiJ3mpv+vXr+Pv74+Hhwft2rVj3759RRipyEpe6u/s2bP07NkTT09PunXrxrFjx4owUpETtVpN586dOXHiRLZl5Pml5JEkogSYM2cOQUFBrFy5kmnTprFkyRL27NmTqVxCQgJDhw7Fw8ODrVu34u3tzXvvvUdcXJweohYZclt/Z8+eZcKECfj7+7Njxw569erFmDFjuHz5sh6iFhlyW3/PmjFjBo8fPy6iCEVOclt/8fHxvPPOO5QrV44dO3YwcOBAxo8fT1hYmB6iFhlyW3+PHz9mxIgRtG/fnp07d9KhQwdGjx7NvXv39BC1eFZSUhIfffQR165dy7aMPL+UUBpRrMXHx2vq1aunOX78uHbbokWLNP369ctUdtOmTZo33nhDk5qaqtFoNJq0tDRN27ZtNRs3biyyeIWuvNTfZ599pvnwww91tr3zzjuab7/9ttDjFFnLS/1lOHz4sKZdu3aa1157TbNly5aiCFNkIy/1t27dOs2bb76pUavV2m3Dhw+X3596lJf6279/v8bb21tnW6NGjTR79uwp9DhF9q5du6bp2rWrpkuXLpratWvr1OWz5PmlZJKWiGIuNDQUtVqNt7e3dpu3tzcXL14kJSVFp2xwcDANGjRAqUyvVoVCQYMGDXRW6hZFKy/15+fnx6hRo3S2KRQKkpKSiiRWkVle6g8gLi6OqVOnMn36dIyMjIoyVJGFvNTf6dOnadWqlU69/fTTT/Tu3bvI4hW68lJ/tra2xMbG8scff6DRaDhw4ADx8fG4uLgUddjiGWfPnqVZs2b8/vvvOZaT55eSSZKIYi4iIgIbGxtMTEy02xwdHUlOTiYqKipTWScnJ51tDg4OPHz4sEhiFZnlpf7q1KlDzZo1tV9fu3aNkydP0rBhwyKLV+jKS/0BfPvttzRv3lzqrJjIS/3dvn0bBwcHpk6dyuuvv06PHj3466+/ijpk8Yy81J+Pjw+DBg3iww8/xM3NjdGjR/Pll19So0aNog5bPKNfv35MmDABMzOzHMvJ80vJJElEMadSqTA2NtbZlvG1Wq3OVdnny4mik5f6e9bjx495//338fb2pk2bNoUao8heXurvzJkz/PXXX3zyySdFFp/IWV7qLz4+nl9++QVra2uWLVum7VN/6dKlIotX6MpL/SUkJHD37l1GjhzJ5s2b+fjjj5kxYwbnz58vqnDFS5Dnl5LJUN8BiJyZmJhk+iHK+Pr5zD67sqampoUbpMhWXuovQ3h4OEOGDEGpVPLDDz9om3dF0ctt/SUmJjJ58mSmTJmClZVVkcYospeXnz8DAwNq167NRx99BICrqyuBgYFs3LgRd3f3oglY6MhL/f3yyy+o1WrGjRsHpNdfWFgYS5Ys4aeffiqagEW+yfNLySRPJ8Vc2bJliYmJ0fnhioiIwNjYGBsbm0xlIyIidLZFRkZSpkyZIolVZJaX+gO4c+cOAwYMQKFQsHbtWuzs7IoyXPGc3NbfhQsXuHXrFhMmTMDLywsvLy8ePXrEl19+yRdffKGP0AV5+/lzcnKievXqOtuqVavG/fv3iyRWkVle6u/ixYvUqlVLZ5ubmxt37twpkljFy5Hnl5JJkohirm7duhgZGekMLgoMDMTNzQ1DQ92GJA8PD4KCgtBoNABoNBqCgoLw9PQsypDFM/JSf9HR0bzzzjtYWVmxdu1aHB0dizpc8Zzc1l/9+vXZv38/27dv1/5zdHRk7Nix2jejoujl5efPy8sr03TKYWFhVKhQoUhiFZnlpf6cnJy4cuWKzrbr169TuXLlIolVvBx5fimZJIko5szMzOjevTvTpk3jwoULHDx4kBUrVuDv7w+kv5VJTEwEoH379iQkJDB9+nTCwsKYOXMmcXFxdOzYUZ+38ErLS/3Nnz+fJ0+eMGvWLFJTU4mIiCAiIoLY2Fh93sIrLbf1Z2pqSpUqVXT+KZVKHBwccHBw0PNdvLry8vPXt29fbt68ybfffsvt27dZtWoVJ0+epG/fvvq8hVdaXuvvn3/+Yfny5dy5c4dNmzaxdetWBg8erM9bEDmQ55dSQK8TzIpcSUhI0EyYMEHj6empadasmeaXX37R7qtdu7bOXPTBwcGa7t27a9zd3TW+vr6aixcv6iNk8Yzc1l+jRo00tWvXzvRv/Pjx+gpdaPL28/es5s2byzoRxUBe6i8oKEjj6+urcXd313To0EFz4MABfYQsnpGX+jt8+LCmR48eGk9PT03nzp01AQEB+ghZZOP5dSLk+aXkU2g0/2s7EkIIIYQQQohckO5MQgghhBBCiDyRJEIIIYQQQgiRJ5JECCGEEEIIIfJEkgghhBBCCCFEnkgSIYQQQgghhMgTSSKEEEIIIYQQeSJJhBBCCCGEECJPJIkQQgghhBBC5IkkEUIIIYQQQog8kSRCCCGEEEIIkSeSRAghhBBCCCHyRJIIIYQQQgghRJ5IEiGEEEIIIYTIE0kihBBCCCGEEHkiSYQQQgghhBAiT0pEEqFWq+ncuTMnTpzQbrt37x5DhgzB09OTDh06cOTIEZ1jTp06RZcuXfDw8MDPz49bt27p7F+7di0tWrTAy8uLSZMmkZCQoHO9KVOm0LBhQ5o1a8by5csL9waFEEIIIYQoQQz1HcCLJCUlMX78eK5du6bdptFoGDVqFDVq1GDz5s0cOnSIsWPHsnv3bipVqsSDBw8YOXIko0aN4s0332TRokWMGjWKXbt2oVQq2b9/P99//z1z5szBycmJSZMmMWvWLL766isA5syZQ1BQECtXriQ8PJwJEybg7OxMp06dXhhvWloa8fHxGBkZoVAoCu1zEUIIIYQQuaPRaEhOTsbCwgKlskS8Qy/2FBqNRqPvILITFhbG+PHj0Wg0XLlyhZUrV9K0aVNOnjzJe++9x4kTJ7C0tATg7bffxsPDgw8//JAFCxZw6tQp1q9fD4BKpaJZs2b8+OOPNG3alIEDB+Lj48OHH34IwNmzZ3nnnXc4deoUCoWC1157jaVLl9K0aVMAFi9ezNGjR7Xny0lsbCxXr14tpE9ECCGEEELkV+3atbGystJ3GKVCsW6JOHv2LM2aNWPMmDF4enpqtwcHB+Pq6qpNIAC8vb05e/asdn/Dhg21+8zMzHBzcyMoKIjGjRtz8eJFRo4cqd3v6elJamoqISEhKJVK1Go13t7eOudevHgxKSkpGBrm/JEZGRkB6d+kxsbGL3X/uXXp0iXc3d2L5Fqi4En9lWxSfyWb1F/JJvVXshVl/anVaq5evap9ThMvr1gnEf369ctye0REBE5OTjrbHBwcCA8Pz3H/w4cPiYmJISkpSWe/oaEhtra2hIeHY2RkhI2NDSYmJtr9jo6OJCcnExUVlem8z8vowmRsbKxzjsJWlNcSBU/qr2ST+ivZpP5KNqm/kq2o60+6mhecYp1EZEelUmXKJI2NjUlOTtbuf74VwNjYGLVaTWJiovbrrPanpKRkuQ/Ss9jcunTpUq7LFoTAwMAivZ4oWFJ/JZvUX8km9VeySf2VbFJ/JVeJTCJMTEyIi4vT2aZWqzE1NdXuf/6BX61WY2trq814s9pvamqKQqHIch+kd4vKLXd39yLLrgMDA3W6X4mSReqvZJP6K9mk/ko2qb+SrSjrLykpqchf8JZ2JXJ4etmyZYmIiNDZFhkZSZkyZV64PyORiIyM1O5LSUkhOjoaJycnypYtS0xMjE4iERERgbGxMTY2NoV4V0IIIYQQQpQMJTKJ8PDwIDQ0VGdth8DAQO3gaw8PD86dO6fdp1KpuHz5Mp6eniiVSurVq6fTfHb+/HkMDAyoW7cudevWxcjIiKCgIJ1zu7m5vXBQtRBCCCGEEK+CEvlU3KhRI5ydnZk4cSJjxozhr7/+Ijg4mG+++QYAX19ffvnlF5YsWULbtm1ZvHgxzs7ONGnSBIABAwYwefJkXFxcKF++PNOmTcPX1xcLCwsAunfvzrRp05g1axYRERGsWLGC6dOn6+1+c9Ln9//NMhX2s34DKWQb+y7RdwhCCCGEEOJ/SmQSYWBgwOLFi/n888/p2bMnlStX5scff6RixYoAVKxYkYULFzJz5kyWLl2Kh4cHixcv1i4u0qlTJ+7du8fUqVNRq9W0bduWiRMnas8/adIkpk6dyuDBg7GwsGD06NF07NhRL/cqhBBCCCFEcVNikogrV67ofF2lShXWrVuXbfmWLVvSsmXLbPcPHz6c4cOHZ7nPzMyM2bNnM3v27PwFqwel9U29tqVFCCGEEEIUGyVyTIQQQgghhBBCfySJEEIIIYQQQuSJJBFCCCGEEEKIPCkxYyJEzrqM36HvEAqFWSN9RyCEEEIIIZ4nLRFCCCGEEEKIPJGWiFJi17xu+g6hUPT5PUDfIQghhBBCiOdIS4QQQgghhBAiTySJEEIIIYQQQuSJJBFCCCGEEEKIPJEkQgghhBBCCJEnMrC6lLjxja++QygcNZ30HYEQQgghhHiOtEQIIYQQQggh8kRaIkqJ6p9v0XcIheP3kfqOQAghhBBCPEdaIoQQQgghhBB5IkmEEEIIIYQQIk8kiRBCCCGEEELkiSQRQgghhBBCiDyRJEIIIYQQQgiRJ5JECCGEEEIIIfJEkgghhBBCCCFEnkgSIYQQQgghhMiTEp1EPH36lI8//phGjRrRvHlz5s6dS2pqKgD37t1jyJAheHp60qFDB44cOaJz7KlTp+jSpQseHh74+flx69Ytnf1r166lRYsWeHl5MWnSJBISEorsvoQQQgghhCjOSnQSMW3aNB4+fMi6dev49ttv2b59OytXrkSj0TBq1ChsbW3ZvHkzPXr0YOzYsdy5cweABw8eMHLkSLp27cqWLVtwdHRk1KhRpKWlAbB//36+//57vvzyS9asWcPFixeZNWuWPm9VCCGEEEKIYqNEJxFHjhxh8ODB1K5dm9dee43OnTtz6tQpTp06xc2bN/nqq6+oWbMmw4cPx8vLi82bNwOwceNG6tSpw7Bhw6hZsyYzZszgwYMHnDp1CoDVq1czaNAgWrduTb169Zg6dSrbtm0jPj5en7crhBBCCCFEsVCikwhbW1t27tyJSqXi4cOHHD16FDc3N4KDg3F1dcXS0lJb1tvbm/PnzwMQHBxMw4YNtfvMzMxwc3MjKCiI1NRULl68qLPf09OT1NRUQkJCiuzehBBCCCGEKK5KdBLx5ZdfcubMGRo0aECLFi1wdHRkzJgxRERE4OTkpFPWwcGB8PBwgGz3P3z4kJiYGJKSknT2GxoaYmtrqz1eCCGEEEKIV5mhvgN4Gbdv38bV1ZXRo0cTFxfH9OnTmT17NiqVCiMjI52yxsbGJCcnA6BSqTA2Ns60X61Wk5iYqP06q/25denSpfzcUr4FBgYW6fWKmtyfKM6k/ko2qb+STeqvZJP6K7lKbBJx+/ZtZsyYwaFDhyhXrhwAJiYmDBkyhN69exMXF6dTXq1WY2pqqi33fEKgVquxtbXFxMRE+3V2x+eGu7u79lyFKuxnIL27VqlU2u+P9F+gpfn+Sjupv5JN6q9kk/or2Yqy/pKSkor8BW9pV2K7M126dAkLCwttAgHpD+6pqamUKVOGiIgInfKRkZGUKVMGgLJly2a7PyORiIyM1O5LSUkhOjo6UxcoIYQQQgghXkUlNolwcnIiJiaGBw8eaLddv34dgOrVqxMaGqqztkNgYCCenp4AeHh4cO7cOe0+lUrF5cuX8fT0RKlUUq9ePZ3mtfPnz2NgYEDdunUL+a6EEEIIIYQo/kpsEuHp6UndunWZNGkSoaGhnD9/nilTptCtWzfatWuHs7MzEydO5Nq1ayxbtozg4GB69+4NgK+vL8HBwSxZsoSwsDA+//xznJ2dadKkCQADBgxgxYoV7N+/n4sXLzJt2jR8fX2xsLDQ5y0LIYQQQghRLJTYJMLQ0JCffvoJGxsbBg8ezPvvv0+jRo346quvMDAwYPHixURFRdGzZ0927NjBjz/+SMWKFQGoWLEiCxcuZMeOHfj6+hIZGcnixYtRKtM/jk6dOjFy5EimTp3KO++8g7u7OxMnTtTn7QohhBBCCFFslNiB1ZA+tmHBggVZ7qtSpQrr1q3L9tiWLVvSsmXLbPcPHz6c4cOHv3SMQgghhBBClDYltiVCCCGEEEIIoR+SRAghhBBCCCHyRJIIIYQQQgghRJ5IEiGEEEIIIYTIE0kihBBCCCGEEHkiSYQQQgghhBAiTySJEEIIIYQQQuRJvpKIf/75h3/++QeNRpOr8mq1mp07d7Jq1ar8XE4IIYQQQghRjORrsTk/Pz+USiWBgYGYmZm9sHx8fDwTJkzA3t6et99+Oz+XFEIIIYQQQhQT+e7OpNFoUCgUuSp748YNAOLi4vJ7OSGEEEIIIUQxkWNLRFpaGuPHj+fx48dZ7h86dChKZc55SHJyMlevXkWhUFC+fPn8RyqEEEIIIYQoFnJMIpRKJa+//jqff/45CoVCZwyERqPh7NmzebpYnz598helEEIIIYQQoth44ZgIX19fTp48ycOHD7Xb/vnnHxQKBQ0aNMixJUKhUGBkZIStrS1NmzbF19e3YKIWQgghhBBC6E2uBlbPnTtX5+s6deoA8PPPP+dqYLUQQgghhBCi9MjX7EyjR4/WtjIIIYQQQgghXi35SiLGjBlT0HEIIYQQQgghSoh8JRFZSUlJIS0t7YXljI2NC+qSQgghhBBCCD3IdxIRFxfH8uXLCQgI4N69e6Smpr7wGIVCweXLl/N7SSGEEEIIIUQxkK8kIjExkUGDBnHlyhUAnalfhRBCCCGEEKVbvpKINWvWEBoaCkDZsmVp2rQpZcqUka5KQgghhBBCvALylUQEBASgUCho1KgRy5Ytw8TEpKDjEkIIIYQQQhRT2a8Ul4ObN28C8OGHH+otgUhOTmbmzJk0btyYxo0b8+WXX6JWqwG4d+8eQ4YMwdPTkw4dOnDkyBGdY0+dOkWXLl3w8PDAz8+PW7du6exfu3YtLVq0wMvLi0mTJpGQkFBk9yWEEEIIIURxl68kwsDAAIBq1aoVaDB5MWfOHP78808WL17MkiVLOHr0KIsWLUKj0TBq1ChsbW3ZvHkzPXr0YOzYsdy5cweABw8eMHLkSLp27cqWLVtwdHRk1KhR2pml9u/fz/fff8+XX37JmjVruHjxIrNmzdLbfQohhBBCCFHc5CuJqFq1KgAPHz4syFhyLSYmhvXr1zN9+nS8vb1p0KAB77//Pv/++y+nTp3i5s2bfPXVV9SsWZPhw4fj5eXF5s2bAdi4cSN16tRh2LBh1KxZkxkzZvDgwQNOnToFwOrVqxk0aBCtW7emXr16TJ06lW3bthEfH6+XexVCCCGEEKK4yVcS0blzZzQaDRs3bizoeHIlMDAQU1NTmjZtqt3Ws2dPfv75Z4KDg3F1dcXS0lK7z9vbm/PnzwMQHBxMw4YNtfvMzMxwc3MjKCiI1NRULl68qLPf09OT1NRUQkJCCv/GhBBCCCGEKAHylUQMGjQINzc3fvvtN1atWkVycnJBx5Wj27dvU6FCBXbv3k2nTp148803mT17Nmq1moiICJycnHTKOzg4EB4eDpDt/ocPHxITE0NSUpLOfkNDQ2xtbbXHCyGEEEII8arL1+xMW7ZsoUOHDty4cYPZs2ezaNEi6tSpg52dHUZGRjkeO2/evHwF+qz4+Hju3r3LunXrmDZtGvHx8UybNo2UlBRUKlWmGIyNjbWJjkqlyjQVrbGxMWq1msTERO3XWe3Pi0uXLuX1tl5KYGBgkV6vqMn9ieJM6q9kk/or2aT+Sjapv5IrX0nEl19+iUKhANIXmouNjeXs2bM5HqPRaFAoFAWSRBgaGhIXF8e3335L5cqVAZgwYQITJkygR48exMXF6ZRXq9WYmpoCYGJikikhUKvV2Nraameaymp/xvG55e7uXjQzV4X9DKR32SqVSvv9kf4LtDTfX2kn9VeySf2VbFJ/JVtR1l9SUlKRv+At7fKVRDg7Oxd0HHni5OSEoaGhNoGA9JmikpKSKFOmDFevXtUpHxkZSZkyZYD0xfEiIiIy7a9Vq5Y2kYiMjKR27doApKSkEB0dnakLlBBCCCGEEK+qfCURhw4dKug48sTT05OUlBSuXLmCi4sLANevX8fCwgJPT09++eUXEhISMDc3B9IzXU9PTwA8PDx0Wk1UKhWXL19m5MiRKJVK6tWrR2BgoHbQ9vnz5zEwMKBu3bpFe5NCCCGEEEIUU/kaWK1vVatWpXXr1kyaNIlLly5x9uxZ5s6dS58+fWjSpAnOzs5MnDiRa9eusWzZMoKDg+nduzcAvr6+BAcHs2TJEsLCwvj8889xdnamSZMmAAwYMIAVK1awf/9+Ll68yLRp0/D19cXCwkKftyyEEEIIIUSxka+WiOJgzpw5fPPNNwwePBhDQ0O6d+/O+PHjMTAwYPHixXz++ef07NmTypUr8+OPP1KxYkUAKlasyMKFC5k5cyZLly7Fw8ODxYsXo1Sm51OdOnXi3r17TJ06FbVaTdu2bZk4caI+b1UIIYQQQohiJV9JxLFjx/J9wddffz3fxz7L0tKSmTNnMnPmzEz7qlSpwrp167I9tmXLlrRs2TLb/cOHD2f48OEFEqcQQgghhBClTb6SiKFDh2pnZ8oLhULB5cuX83NJIYQQQgghRDGR7+5MGo0m12UVCoW2u5AQQgghhBCiZMtXErFmzZoc9ycmJhIdHU1QUBDbt2/HyMiIRYsW0bBhw3wFKYQQQgghhCg+8pVENGrUKFflunbtyttvv03//v0ZPXo0O3bsoHz58vm5pBBCCCGEEKKYKPQ+RlWqVGHs2LHExMTw008/FfblhBBCCCGEEIWsSAYqZMyE9PfffxfF5YQQQgghhBCFqEiSiIxB1ZGRkUVxOSGEEEIIIUQhKpIkYv/+/QDY2NgUxeWEEEIIIYQQhShfA6vVavULy6SkpPDkyRP27dvH/PnzUSgUuR6QLYQQQgghhCi+8pVEeHh45Km8RqPBwMCAIUOG5OdyQgghhBBCiGIkX0lEXhaaA7C2tmbKlCm4ubnl53JCCCGEEEKIYiRfScT777//wjJKpRJzc3OqVKlC48aNMTc3z8+lhBBCCCGEEMVMoSURQgghhBBCiNKpSGZnEkIIIYQQQpQe+WqJeFZqaiqHDh3izJkzPHjwgISEBMzMzHB2dsbLy4vWrVtjYmJSELEKIYQQQgghioGXSiKOHz/OlClTePDgQZb7161bh4ODAzNnzqR58+YvcykhhBBCCCFEMZHv7kwBAQEMHz6cBw8eaKdwrVatGq6urlSpUgUDAwM0Gg2RkZG89957BAQEFGTcQgghhBBCCD3JV0vEo0ePmDRpEqmpqZQtW5YJEybQtm1bjI2NtWXUajV//vkn8+bN4/79+0yePJkGDRrg5ORUYMELIYQQQgghil6+WiJWrVqFSqWifPnybNq0iU6dOukkEADGxsZ06tSJTZs24ezsTHx8PJs2bSqQoIUQQgghhBD6k68k4ujRoygUCsaNG/fClgUHBwfGjRuHRqPh4MGD+QpSCCGEEEIIUXzkK4m4d+8eAE2bNs1V+YxyGccJIYQQQgghSq58JRFpaWkAGBrmbkiFgYEBkD5OQgghhBBCCFGy5SuJKFeuHABBQUG5Kn/u3DmAQhtU/fnnn+Pn56f9+t69ewwZMgRPT086dOjAkSNHdMqfOnWKLl264OHhgZ+fH7du3dLZv3btWlq0aIGXlxeTJk0iISGhUOIWQgghhBCiJMpXEvHaa6+h0Wj44YcfSExMzLGsSqXihx9+QKFQ8Nprr+UryJycPHmSzZs3a7/WaDSMGjUKW1tbNm/eTI8ePRg7dix37twB4MGDB4wcOZKuXbuyZcsWHB0dGTVqlLZ1Zf/+/Xz//fd8+eWXrFmzhosXLzJr1qwCj1sIIYQQQoiSKl9JhJ+fH4aGhly9ehV/f3/+/fffLMtdunQJf39/rl69ilKpxN/f/6WCfV5CQgJTpkyhQYMG2m2nTp3i5s2bfPXVV9SsWZPhw4fj5eWlTTQ2btxInTp1GDZsGDVr1mTGjBk8ePCAU6dOAbB69WoGDRpE69atqVevHlOnTmXbtm3Ex8cXaOxCCCGEEEKUVPlaJ6JGjRqMHTuW7777josXL9KrVy/Kly9PzZo1MTc3JyEhgbCwMJ2VrMeOHUuNGjUKLHCA+fPn06hRI8qUKaPtMhUcHIyrqyuWlpbact7e3pw9e1a7v2HDhtp9ZmZmuLm5ERQUROPGjbl48SIjR47U7vf09CQ1NZWQkBB8fHwKNH4hhBBCCCFKonwlEQDDhw/H3Nyc7777joSEBO7fv6+TNGg0GgBMTU0ZP368zpiFghAUFERAQAC7d+9mxYoV2u0RERGZxl44ODgQHh6e4/6HDx8SExNDUlKSzn5DQ0NsbW21xwshhBBCCPGqy3cSATBo0CA6duzI7t27OX36NA8ePCA+Ph5zc3MqVKiAOxfSfAAALHNJREFUj48P3bp1w87OrqDiBdJnefr888/57LPPsLGx0dmnUqkwMjLS2WZsbExycrJ2f1YL46nVau34juz258WlS5fyVP5lBQYGFun1iprcnyjOpP5KNqm/kk3qr2ST+iu5XiqJALC3t8ff319nvENqaqp2WtfCsGjRIqpUqUKHDh0y7TMxMSEuLk5nm1qtxtTUVLv/+YRArVZja2uLiYmJ9uvsjs8td3d37fkKVdjPQHqXrVKptN8f6b9AS/P9lXZSfyWb1F/JJvVXshVl/SUlJRX5C97SLs9JxJ49e3jy5AmDBg3KtszcuXP5559/GDhwIN27d0ehULxUkM/btWsXEREReHl5AZCcnExqaipeXl689957hIaG6pSPjIykTJkyAJQtW5aIiIhM+2vVqqVNJCIjI6lduzYAKSkpREdHF9r0tEIIIYQQQpQ0uZ6d6c6dO/Ts2ZOPP/6Y9evX51j21KlT/Pvvv3z22Wf06dOnwFeqXrt2Lbt372b79u1s376d3r174+7uzvbt2/Hw8CA0NFRnbYfAwEA8PT0B8PDw0A7ChvTuTZcvX8bT0xOlUkm9evV0mtbOnz+PgYEBdevWLdB7EEIIIYQQoqTKVRJx7949+vfvT0hICBqNhlu3bmU75WlaWhpOTk4YGRmh0Wi4dOkSfn5+PHz4sMCCrlChAlWqVNH+s7a2xtTUlCpVqtCoUSOcnZ2ZOHEi165dY9myZQQHB9O7d28AfH19CQ4OZsmSJYSFhfH555/j7OxMkyZNABgwYAArVqxg//79XLx4kWnTpuHr64uFhUWBxS+EEEIIIURJlqskYvz48URGRqLRaOjduzcHDhzI9qFaqVTy008/cejQIXr16oVGo+HBgwd89tlnBRp4dgwMDFi8eDFRUVH07NmTHTt28OOPP1KxYkUAKlasyMKFC9mxYwe+vr5ERkayePFilMr0j6JTp06MHDmSqVOn8s477+Du7s7EiROLJHYhhBBCCCFKgheOiThy5Ajnz59HoVAwffp0evXqlasTOzo68vXXX1O5cmW+++47Tpw4walTpwpl1eoPP/xQ5+sqVaqwbt26bMu3bNmSli1bZrt/+PDhDB8+vMDiE0IIIYQQojR5YUvErl27AGjWrFmuE4hnDR8+XDvyfseOHXk+XgghhBBCCFG8vDCJuHjxIgqF4qUWixs0aBAajYbz58/n+xxCCCGEEEKI4uGFScSjR48AqF69er4v4uHhAVCgg6uFEEIIIYQQ+vHCJCJjpWdbW9t8X8Tc3BzIvIibEEIIIYQQouR5YRKRkTw8v0BbXoSHhwPINKlCCCGEEEKUAi9MImrVqgWgs0BbXmUs3la5cuV8n0MIIYQQQghRPLwwiXj99dfRaDT89ttv+bqARqNh/fr1KBQKvLy88nUOIYQQQgghRPHxwiSiW7duGBsbExISwqxZs/J8gTlz5hAWFgZA586d8x6hEEIIIYQQolh5YRLh6OiIv78/Go2G1atXM2XKFKKiol544ujoaL744gtWrVqFQqGgTZs21K9fv0CCFkIIIYQQQujPC1esBhg7diz//PMPwcHBbN68mT179tC2bVsaNWpE9erVsbOzIzk5mSdPnnD79m1OnjzJ33//TVxcHBqNhurVq/P1118X9r0IIYQQQgghikCukghjY2N++eUXxo0bx/Hjx0lISGDnzp3s3Lkz22M0Gg0ATZo0Yd68edjY2BRMxEIIIYQQQgi9ylUSAWBpackvv/zCtm3b+Pnnn7l+/XqO5b28vHjnnXd46623XjpIIYQQQgghRPGR6yQiQ48ePejRowdXr17l3Llz3Lp1i7i4OJRKJfb29tSoUYOGDRtStmzZwohXCCGEEEIIoWd5TiIy1K5dm9q1axdkLEIIIYQQQogS4IWzMwkhhBBCCCHEsySJEEIIIYQQQuSJJBFCCCGEEEKIPJEkQgghhBBCCJEnkkQIIYQQQggh8kSSCCGEEEIIIUSeSBIhhBBCCCGEyJMSm0Tcvn2bESNG0LBhQ1q0aMGsWbNISkoC4N69ewwZMgRPT086dOjAkSNHdI49deoUXbp0wcPDAz8/P27duqWzf+3atbRo0QIvLy8mTZpEQkJCkd2XEEIIIYQQxV2JTCLUajUjRozA2NiYDRs2MHfuXA4cOMD8+fPRaDSMGjUKW1tbNm/eTI8ePRg7dix37twB4MGDB4wcOZKuXbuyZcsWHB0dGTVqFGlpaQDs37+f77//ni+//JI1a9Zw8eJFZs2apc/bFUIIIYQQolgpkUnEhQsXuH37NjNnzqRGjRo0atSIcePGsWvXLk6dOsXNmzf56quvqFmzJsOHD8fLy4vNmzcDsHHjRurUqcOwYcOoWbMmM2bM4MGDB5w6dQqA1atXM2jQIFq3bk29evWYOnUq27ZtIz4+Xp+3LIQQQgghRLFRIpOI6tWrs2zZMiwsLLTbFAoFarWa4OBgXF1dsbS01O7z9vbm/PnzAAQHB9OwYUPtPjMzM9zc3AgKCiI1NZWLFy/q7Pf09CQ1NZWQkJDCvzEhhBBCCCFKgBKZRNjb29O0aVPt12lpaaxbtw5vb28iIiJwcnLSKe/g4EB4eDhAtvsfPnxITEwMSUlJOvsNDQ2xtbXVHi+EEEIIIcSrzlDfARSEmTNnEhISwubNm1m5ciVGRkY6+42NjUlOTgZApVJhbGycab9arSYxMVH7dVb78+LSpUt5vY2XEhgYWKTXK2pyf6I4k/or2aT+Sjapv5JN6q/kKtFJhEaj4ZtvvmH9+vUsWLCAWrVqYWJiQlxcnE45tVqNqakpACYmJpkSArVaja2tLSYmJtqvszs+t9zd3bXnK1RhPwPpXbZKpdJ+f6T/Ai3N91faSf2VbFJ/JZvUX8lWlPWXlJRU5C94S7sS2Z0J0rswffbZZ2zYsIH58+fTpk0bAMqWLUtERIRO2cjISMqUKfPC/RmJRGRkpHZfSkoK0dHRmbpACSGEEEII8aoqsUnErFmz2LVrFwsXLuStt97Sbvfw8CA0NFRnbYfAwEA8PT21+8+dO6fdp1KpuHz5Mp6eniiVSurVq6fTtHb+/HkMDAyoW7du4d+UEEIIIYQQJUCJTCLOnz/P6tWrGTt2LO7u7kRERGj/NWrUCGdnZyZOnMi1a9dYtmwZwcHB9O7dGwBfX1+Cg4NZsmQJYWFhfP755zg7O9OkSRMABgwYwIoVK9i/fz8XL15k2rRp+Pr66swEJYQQQgghxKusRI6J2LdvHwDz5s1j3rx5Ovv+/fdfFi9ezOeff07Pnj2pXLkyP/74IxUrVgSgYsWKLFy4kJkzZ7J06VI8PDxYvHgxSmV6PtWpUyfu3bvH1KlTUavVtG3blokTJxbtDQohhBBCCFGMlcgk4tNPP+XTTz/Ndn+VKlVYt25dtvtbtmxJy5Yts90/fPhwhg8f/lIxCiGEEEIIUVqVyO5MQgghhBBCCP2RJEIIIYQQQgiRJ5JECCGEEEIIIfJEkgghhBBCCCFEnpTIgdXi1XPjG199h1Bo7AC8t+g7DCGEEEKIXJOWCCGEEEIIIUSeSEuEKBGqf14639SX5hYWIYQQQpRe0hIhhBBCCCGEyBNpiRAlQpfxO/QdQqFYYK/vCIQQQggh8k5aIoQQQgghhBB5Ii0RokTYNa+bvkMoFDe+WaPvEIQQQggh8kxaIoQQQgghhBB5IkmEEEIIIYQQIk8kiRBCCCGEEELkiSQRQgghhBBCiDyRJEIIIYQQQgiRJzI7kygR+vw+Ut8hFI6aTgBs1HMYQgghhBB5IS0RQgghhBBCiDyRlghRrG3su0TfIRSqUtvCIoQQQohSTVoihBBCCCGEEHkiLRFCFAM3vvHVdwiFqvrnW/QdghBCCCEKkLREZEOtVjNlyhQaNmxIs2bNWL58ub5DEkIIIYQQoliQlohszJkzh6CgIFauXEl4eDgTJkzA2dmZTp066Ts0UQpN/N8sTaWVzD4lhBBClC7SEpGFhIQENm7cyGeffYa7uztt2rRh6NChrFu3Tt+hCSGEEEIIoXfSEpGF0NBQ1Go13t7e2m3e3t4sXryYlJQUDA3lYxMFQ3Wmvb5DKFRmjQKA0j8L1cZnflcIIYQQrwJ5Gs5CREQENjY2mJiYaLc5OjqSnJxMVFQUTk7Zdz3RaDRA+piKomBtaAlAUlJSkVxPFKzNM9pz6dIl3N3d9R1KoRi9+5i+QygSQ7d8ou8QxMv4b4O+IxAvQ+qvxBpZtV+RPb9kPJdlPKeJlydJRBZUKhXGxsY62zK+flFykJycDMDVq1cLJ7jnjKzaD4BLly4VyfVE4Sit9Zfx/SmEEEJkpaj//iUnJ2Nqalqk1yytJInIgomJSaZkIeNrMzOzHI+1sLCgdu3aGBkZoVAoCi1GIYQQQgiROxqNhuTkZCwsLPQdSqkhSUQWypYtS0xMDGq1WtsCERERgbGxMTY2Njkeq1QqsbKyKoowhRBCCCFELkkLRMGS2ZmyULduXYyMjAgKCtJuCwwMxM3NTQZVCyGEEEKIV54kEVkwMzOje/fuTJs2jQsXLnDw4EFWrFiBv7+/vkMTQgghhBBC7xQaGaaeJZVKxdSpU9m/fz8WFhYMGTKEIUOG6DssIYQQQggh9E6SCCGEEEIIIUSeSHcmIYQQQgghRJ5IEiGEEEIIIYTIE0kihBBCCCGEEHkiSUQJoFarmTJlCg0bNqRZs2YsX74827KhoaH07dsXDw8PevbsyYULF4owUpGVvNTf3r176dy5M56ennTt2pVDhw4VYaQiK3mpvwzR0dE0bdqUrVu3FkGEIid5qb/r16/j7++Ph4cH7dq1Y9++fUUYqchKXurv7Nmz9OzZE09PT7p168axY8eKMFKRE7VaTefOnTlx4kS2ZeT5peSRJKIEmDNnDkFBQaxcuZJp06axZMkS9uzZk6lcQkICQ4cOxcPDg61bt+Lt7c17771HXFycHqIWGXJbf2fPnmXChAn4+/uzY8cOevXqxZgxY7h8+bIeohYZclt/z5oxYwaPHz8uoghFTnJbf/Hx8bzzzjuUK1eOHTt2MHDgQMaPH09YWJgeohYZclt/jx8/ZsSIEbRv356dO3fSoUMHRo8ezb179/QQtXhWUlISH330EdeuXcu2jDy/lFAaUazFx8dr6tWrpzl+/Lh226JFizT9+vXLVHbTpk2aN954Q5OamqrRaDSatLQ0Tdu2bTUbN24ssnjF/7V353E15f8fwF8tN9nSchMijeXGZCllm6wZVLasYcKgNJoMRpbvfFGJiW/m4TuESV++KjMjDKIklGQpRGSNVKpr4qbSckvb+f3R757vvd3FPYZivJ+PR4+Hzv2c0/ucz7ke530+mywu9ffDDz8wy5cvl9k2f/58JjAw8L3HSRTjUn8SCQkJzNixY5lBgwYxf/zxR2OESZTgUn8HDhxgRo4cyVRVVbHbFi1aRP9/NiEu9XfmzBnGxsZGZtuAAQOY6Ojo9x4nUe7x48fMxIkTmQkTJjACgUCmLqXR88vHiVoiPnAPHz5EVVUVbGxs2G02Nja4c+cOampqZMrevn0b/fr1g6ZmfbVqaGigX79+Mitvk8bFpf7mzJkDT09PmW0aGhp4/fp1o8RK5HGpPwAoKyuDr68v/P39wePxGjNUogCX+rt69Srs7e1l6i04OBjTp09vtHiJLC71p6+vj9LSUsTExIBhGJw7dw7l5eWwsLBo7LCJlJSUFNjZ2SEiIkJlOXp++ThREvGBE4lEaNOmDZo1a8Zu4/P5qK6uRmFhoVzZtm3bymwzMjLC8+fPGyVWIo9L/fXo0QPdunVjf3/8+DGSkpLQv3//RouXyOJSfwAQGBiIoUOHUp19ILjUX05ODoyMjODr64shQ4Zg8uTJOH/+fGOHTKRwqT9bW1u4urpi+fLlsLS0xLfffgsfHx907dq1scMmUmbOnIlVq1ahefPmKsvR88vHiZKID1xFRQV0dHRktkl+r6qqUqtsw3Kk8XCpP2kvX76El5cXbGxs8OWXX77XGIlyXOrv2rVrOH/+PFauXNlo8RHVuNRfeXk59u7dCz09PezZs4ftU3/37t1Gi5fI4lJ/YrEYeXl5WLx4MY4cOQJvb2/8+OOPuHXrVmOFS/4Cen75OGk3dQBEtWbNmsl9iSS/N8zslZXV1dV9v0ESpbjUn0R+fj4WLFgATU1NbN++nW3eJY1P3fqrrKzE2rVrsW7dOrRu3bpRYyTKcfn+aWlpQSAQ4PvvvwcAfP7557hx4wYOHTqEXr16NU7ARAaX+tu7dy+qqqqwdOlSAPX1l5GRgd27dyM4OLhxAiZvjZ5fPk70dPKBMzExQUlJicyXSyQSQUdHB23atJErKxKJZLYVFBTA2Ni4UWIl8rjUHwDk5uZi9uzZ0NDQQHh4OAwMDBozXNKAuvWXlpaGp0+fYtWqVbC2toa1tTVevHgBHx8frF+/vilCJ+D2/Wvbti26dOkis+2zzz7Ds2fPGiVWIo9L/d25cwfdu3eX2WZpaYnc3NxGiZX8NfT88nGiJOID17NnT/B4PJnBRTdu3IClpSW0tWUbkvr27YvU1FQwDAMAYBgGqampsLKyasyQiRQu9VdcXIz58+ejdevWCA8PB5/Pb+xwSQPq1l+fPn1w5swZHD9+nP3h8/n47rvv2DejpPFx+f5ZW1vLTaeckZEBU1PTRomVyONSf23btkV6errMtidPnsDMzKxRYiV/DT2/fJwoifjANW/eHM7OzvDz80NaWhri4uKwb98+zJ07F0D9W5nKykoAgIODA8RiMfz9/ZGRkYGAgACUlZXBycmpKU/hk8al/rZt24aioiJs3rwZtbW1EIlEEIlEKC0tbcpT+KSpW3+6urro3LmzzI+mpiaMjIxgZGTUxGfx6eLy/XNxcUFWVhYCAwORk5OD/fv3IykpCS4uLk15Cp80rvV3/fp1hISEIDc3F4cPH8bRo0cxb968pjwFogI9v/wNNOkEs0QtYrGYWbVqFWNlZcXY2dkxe/fuZT8TCAQyc9Hfvn2bcXZ2Znr16sVMnTqVuXPnTlOETKSoW38DBgxgBAKB3M+KFSuaKnTCcPv+SRs6dCitE/EB4FJ/qampzNSpU5levXoxjo6OzLlz55oiZCKFS/0lJCQwkydPZqysrJjx48czp0+fboqQiRIN14mg55ePnwbD/H/bESGEEEIIIYSogbozEUIIIYQQQjihJIIQQgghhBDCCSURhBBCCCGEEE4oiSCEEEIIIYRwQkkEIYQQQgghhBNKIgghhDSp2trapg6BEEIIR5REEEJk2Nvbw8LCgv1JSkpSe9/g4GCZfa9evfoeIyXv2tWrV5XWneS+WL58+Tv7e0KhEEuWLMGNGzfe2THVlZeXx57r0aNHG/3vc7Vjxw423tevXzd1OIQQQkkEIUS1mJgYtctGR0e/x0jI30l6ejqcnJxw5swZ0HJFhBDy8aEkghCi0pkzZ9TqbvLkyROkp6c3QkSkKZiamsLMzAx8Pv+dHK+4uBiVlZXv5FiEEEIan3ZTB0AI+TBZWFggPT0dRUVFSE5Ohp2dncryUVFRAABjY2OIRKLGCJE0ovDw8KYOgRBCyAeEWiIIIQp17doVAoEAgHpdmk6dOgUAcHBweK9xEUIIIaTpUUsEIUQpJycnPHr0CGfPnoWvry+0tRX/l3H37l1kZ2fDwMAAdnZ2b3xr/erVK4SHhyM+Ph5Pnz5FdXU1TExMMHjwYMyfPx+fffaZ0n3FYjGOHDmCCxcu4NGjRyguLoaWlhYMDQ1hZWWF6dOnY/DgwXL7rVmzBseOHYOTkxO2bduGs2fP4uDBg7h37x7Ky8vRrl07DB8+HG5ubmjXrh2n65SXl4dRo0YBAI4fP45WrVphx44duHLlCkpKStChQwfY2dlh/vz56Nixo9z+O3bsQFBQEPr27YuQkBD4+/sjPj4eDMOgc+fO8PX1hZWVFVs+MzMToaGhSE5ORn5+PrS0tGBmZoZRo0Zh7ty5aNOmjdJYCwsLERYWhri4OOTl5UFXVxf9+/eHp6enynO0t7eHUChkr19DYrEYkZGRiIqKQnZ2Nl69egUjIyP0798f8+bNQ+/evdmyFhYWMvvOnTsXADBgwAC5e+f58+fYv38/Ll68CKFQCIZh0KFDBwwfPhxff/01TExMlMZcVlaGiIgIREVF4enTp9DW1kafPn3g7u4OU1NTleerSExMDJYtWwYACAsLw8CBAxWWq6urw7BhwyASiTBt2jRs2rRJ5rMzZ84gNjYWaWlpKCwsRHV1NfT19dGjRw84ODjA2dlZ6XetoatXr7LXLyQkBMOGDVNYTnLN3d3d4e3tLfd5ZWUlfv/9d8TGxiIzMxMVFRXg8/no378/5syZI1N/hBACUBJBCFHByckJ//73v1FcXIykpCQMHTpUYTnJgGoHBwdoaWmpPGZKSgq8vLxQVFQksz0nJwc5OTn4448/sHbtWsyaNUtu3zt37mDx4sUKu0sJhUIIhUJER0djyZIl8PLyUhrD+vXrERERIff3w8PDcfz4cYSHh6Nnz54qz0OZ9PR0bNq0CSUlJey2rKwsZGVl4ejRo/j555+VPuhVV1dj0aJFuHXrFrstMzNTJqkKCwvDli1bUFNTI7PvgwcP8ODBA/z222/YuXMn+vXrJ3f8u3fvYtGiRXj58iW7TSwWIzY2FvHx8Zg9e/ZbnXNGRga8vLyQlZUlsz0/Px8nT55EdHQ01qxZg3nz5nE67unTp7FmzRpUVFTIbH/y5AmePHmCgwcPIjAwEF9++aXcvrm5uXBzc0N2drbM9osXL+LSpUuYP38+p1gAYNSoUdDT00NJSQmio6OVJhHJycnsPTpp0iR2e2FhITw9PZGamiq3j0gkgkgkwsWLFxEVFYW9e/e+8bv0rmRkZMDDwwN5eXky2589e4bIyEicOHECHh4e73RmLkLIx4+6MxFClOrcuTMsLS0BKO/SxDAM+9n48eNVHi8jIwNubm4oKiqCqakpNm/ejAsXLiApKQn79u2Dra0tampq4OvrK/f3ysrK2ATCyMgI/v7+iI2NRXJyMk6cOIGVK1dCT08PALBz507k5uYqjOH8+fOIiIiAnZ0dwsLCkJycjFOnTmHmzJkAgNLSUvj5+al/kRrw8/NDWVkZ3NzccObMGVy+fBmbN2+GoaEhxGIxPD095R5sJe7fv49bt27hm2++QWJiIqKiorBx40a2ZeHYsWPYtGkTampqYGtri5CQECQlJSExMRFbtmyBqakpCgsL4e7ujpycHJljFxUVYeHChXj58iX09PTg4+ODxMREJCYmwsfHBy1atEBoaCjn8y0rK8OCBQuQlZWF5s2bY9myZYiNjcWVK1ewZ88eCAQC1NXVISAgANeuXQMA3Lx5E3v27GGPsWfPHty8eRMhISHstqSkJCxfvhwVFRUQCATYvn07Ll26hMuXLyMoKAgCgQBisRhLly7FzZs3ZWKqqqpiEwgdHR18//33iIuLw+XLl7F161aYmJhg3759nM9VR0cHjo6OAIDY2FhUV1crLHfy5EkA9YPR+/fvz27/xz/+gdTUVGhqamLx4sU4ceIEkpKSEBsbi59++olNFpOSkthjvG8FBQWYN28e8vLyoK+vj3Xr1uHcuXNITk7Gr7/+ilGjRoFhGPzyyy/4z3/+0ygxEUI+DpREEEJUcnJyAgDExcUpfGi6ceMG/vzzT7Rv3x42NjYqj+Xn54eKigqYmpriyJEjmDx5Mtq1awdDQ0PY2dkhNDQUQ4YMAQBs3LgRVVVV7L6RkZHs293t27djxowZMDc3h4GBASwsLODm5sY+/NfV1eHKlSsKY6ioqMDIkSOxd+9eDBw4EAYGBujatSv8/PwwevRoAEBqaiqeP3/O8UrVE4vF8PHxwcqVK9G5c2fw+XxMnjwZ4eHh0NXVRXV1NQIDA5Xu7+DggOXLl8PExATdu3fHxIkTAdQ/rG/cuBEAMHLkSISFhWHYsGEwNDSEiYkJnJ2dcejQIRgbG6OsrAybN2+WOe7OnTtRXFwMbW1t7N27F7Nnz4aJiQlMTEwwe/Zs/Pe//wWPx+N8vkFBQXj+/Dk0NTXxyy+/YPHixTA3N4eRkRGGDx+O0NBQGBsbsw+iANCyZUvo6uqyx9DV1ZXZVltbi3Xr1qGurg69evXC4cOHMXbsWBgbG4PP52P06NE4ePAgBAIBampqsGHDBpmYfvvtNzZR++mnn+Dh4YGOHTuCz+djwoQJ+P3336Gvr8/5XIH/tSwUFxfj8uXLcp9XVVXh7NmzAICJEydCQ0MDQH0CnZCQAABYsmQJli1bBgsLCxgaGsLc3Bzjx4+XqYOLFy++VXxcbd26FQUFBdDT00NERARcXV3RqVMnGBgYwNbWFrt27cLUqVMBAD///DNNmkAIYVESQQhRydHRERoaGiguLlb4YC7pyuTk5MQ+MCmSkZHBvon29PSEoaGhXBltbW2sWrUKQP0b0nPnzrGftW/fHl999RVmzZoFW1tbhX9jwIAB7L8LCwuVxuLh4aEwVsm4BgBKWzLepG/fvmyrhrRu3bqx3YUuXLiA0tJShftL3nQ3FBkZibKyMgDADz/8oLCrC5/Ph4eHBwAgPj6efeBjGIYd+D5u3Dj06dNHbl9LS0v2YVFdDMOws3I5Ojpi0KBBcmUMDQ3x1VdfwdLSEiYmJmqtCXHp0iX2+nt7e8skHBItW7Zku9c8ePAAaWlp7GeSt/i2trYYM2aM3L4dOnTAwoUL1ThDeTY2NjAzMwPwvxnJpCUkJLB16+zszG6vra3FggULMHbsWKXdxtq3b49OnToBUH3/vislJSXsObi6usLc3FxhuVWrVkFbWxtVVVU4fvz4e4+LEPJxoCSCEKKSqakpO6j39OnTMp/V1tYiNjYWADBhwgSVx5FeAdnCwgLl5eUKfzp27Mh235HupmJvb4/169fD19dX4fGLiopw/fp1mdgU0dLSYrtoNSS9BsLbrmEgablRRJKkVFdXswlVQ8rGYkjKGxgYwMjISOn1kwyAZRiG7Xv/6NEjdhyEsvEYABSOLVDl8ePHbKIyYsQIpeUWL16Mo0ePIiAgQGWiKSF9rwgEAqXnamlpyR5Psup1aWkp7t27B+Ddnqs0SWtEXFyc3HgNyUO5lZWVzEO5hYUFVq9eje3btytsBamsrMS1a9fY1agbjnl5H1JTU9nWxR49eii9zjweD926dQOAJlldnBDyYaKB1YSQN3J0dERqairOnTsHPz8/6OjoAKjvu/3y5Ut06dLljQORpd/sT5s2Ta2/++zZM7ltNTU1SElJwb179/D06VPk5uYiKysLf/75p0w5ZW+8W7duzcbfkPT2uro6tWJsqOHMQ9KkB0jn5+crLGNgYKBwu+T6FRUVKRw0rYjk+kn/LclbdEW6dOmi1nElpI+r7C3225C+V7744gu19pGc6/Pnz9m6V3Wu5ubm0NLSUmshxYYmTZqEoKAgiMVinD9/nk0cy8rK2C5L0gOqG3r48CFu3ryJ7Oxs5OTk4OnTp3j69OlbxfJXSI+b+e6779Tap+H3jBDy6aIkghDyRo6Ojti8eTNKSkpw+fJljBw5EsD/ujKNGzfujceQdMXhouE+sbGx2LRpk8LxCmZmZhg8eLDcrEsNqTt15ttq3bq10s+ku+Uoux7NmjVTuP2vXD/pmaKaN2+utLyq2BUpLi5W67hcNca5ampqokWLFkq7lanSqVMn2NjYICUlBVFRUWwSERsbi9evX4PH4ylskUpPT8fatWtlul5JGBoaYuDAgbh9+7bC5Pl9eBffSULIp4uSCELIG7Vt2xa2tra4du0aYmJiMHLkSFRVVbFjFt40KxMg+wCdlpam9GFZmbNnz2Lp0qVgGAb6+voYM2YMLC0t0aVLF3Tv3h0GBgYoLy9/YxLxvqnqBiUWi9l/K2txUEZy/aysrDifo/S6EQ2730iTHsiujhYtWqh1XK4k52psbIxLly5x2lf6XKWvtyJcz1eas7MzUlJSkJiYiJKSEujp6bFdmUaMGCHXZSkvLw+urq4oKSkBj8eDvb09rKys0K1bN3Tt2pVdt2LKlCnvPIlQdk9KJ1kxMTGcW6IIIZ82SiIIIWpxdHTEtWvXEB8fj6qqKly8eBElJSXo1auXWl1ZOnTowP47Ly8PXbt2VVqWYRi5vvNbt24FwzDszE6KBmY3XHuiKeTl5Skd+C29jgLXxc46dOiA9PR0ubn81SH9tzIzMxUOrAa4DyZv3769zL6qjnv48GF07NgRY8aMeePMSJJ7pbCwEOXl5WjZsqXSsg3vlXbt2kFTUxN1dXXIzMxUut+LFy/Y8Qdvw9HREf7+/nj9+jXi4+MxZMgQdiyH9IBqieDgYJSUlEBLSwsHDhyQWTxQmnTrjjqkB9grG0eh7JjS9ZeXl6cyiVD0nSSEfNpoYDUhRC2SheRKS0tx5coVdrafNw2olpCeL1961qWGsrOzYWVlhdGjRyMsLAxA/cOkZMrOsWPHKkwggPoxGhJvO6bhr7pw4YLSz+Li4gDUv8GXvh7qkJQvKChQuFiZxOHDh2FtbY3x48ezg2Cl33SruvaJiYmcYrKwsGBbI1RNSXr27FkEBwdj/fr1bL2oeiCVnGttbS3Onz+vtFxSUhL69u0LBwcHdtB/y5Yt2SROcr0V4XquDbVq1YodKB8fH4/Y2FjU1tZCX18fw4cPlysvqbOePXsqTSBycnIgFAoBqH//SrcmKEuiG66jIWFjYwNNzfrHAFX3xatXrzBo0CDY29tj69atasVFCPn7oySCEKIWQ0NDdgrPkydPIj4+HpqamkqnJG2od+/e+PzzzwEAISEhePLkiVyZmpoabNq0CZWVlcjJyWFnGpIex6BoP6D+bfe2bdvY35UtBPa+xcbGIiUlRW57eno6Dh48CKA+8VI2uFsZZ2dntguYv78/ysvL5cqIRCLs3LkTYrEYIpEIPXr0YD+bMmUKgPqHRUWJTm5uLpu0qUtbW5t96x4VFaWwr39xcTG7iN3gwYPZBFC6ThvWlb29PYyNjQHUr/NQUFAgd9zy8nL861//wuvXr5GXlyfTCiKZqvb+/fvsNW8Y065du7icqkKSc7906RKbxIwbN07hehuSFgOhUKiwe1FFRQX++c9/sr+re/926tSJTQQkM6VJq6yslFnYTxqfz2cToaNHj8rMbiZt69atKC4uhlAofOuV3Akhfz+URBBC1CZJGKKjoyEWi2FrawsTExO19/fx8QGPx0NpaSlmzZqF0NBQ5ObmorCwEFevXoW7uzv7hnjChAmwtrYGAOjp6aFv374A6t/0b9y4ERkZGSgqKsKjR4+we/duTJkyhZ3GFIDCh+zGUFtbi0WLFiEsLAzPnz/HixcvcOjQIcydOxeVlZUwNDRk1zfgwsjIiN3v3r17mDFjBmJiYiASiZCfn49Tp07B1dWVnT3H29tbphuQu7s7zM3NwTAMlixZgj179kAoFKKgoACRkZGYNWvWW41r8PLygrGxMaqrq7FgwQKEhYVBKBTixYsXiIuLg6urK/Lz88Hj8bBixQp2P+kuTdHR0Xjx4gW7NoKOjg7Wr18PoH7WpWnTpuHIkSPIz8+HSCRCQkIC5syZg/v37wMAFi5cKNNdbtKkSeyaIX5+fggMDER2djYKCwsRFxeHmTNn4tmzZ3+5e86QIUNgbGyM8vJydgpeRV2ZAGDo0KEA6lsLFi9ejJs3b6KwsBA5OTk4cuQIpkyZIjPtr7r3r56eHpvcX7hwAevXr0dmZiYKCgoQHx+PmTNn4sGDB+xq7g2tXr0aenp6qK6uhpubG4KCgpCZmYnCwkLcunULy5Ytw6FDhwDUr7uh7ksDQsjfnwajzso/hJBPhr29PYRCIZycnGTe7AP13Rrs7OzYt6T+/v6YMWOGTJnExES4u7sDAMLCwjBw4ECZzxMSErBixQqVs7zY29tj27ZtMoOx7927hzlz5qh8uBo9ejSePXuGe/fuwc7ODvv27WM/W7NmDY4dOwY+n69wpWGgfn2CuXPnAqhvLVG1zoC0vLw89o3u2LFjkZCQoLC/vampKYKDg9G9e3eZ7Tt27EBQUBCANw86DwoKws6dO5V2d9HU1ISXlxe+/fZbuc+EQiHc3NwUjhXQ1NTE6tWrERAQAEC+7lTdFw8fPoSHh4fSaWt1dXWxZcsWODg4sNtqa2sxevRotvsOUP9WXbpbzeHDh+Hn56fyrbyLiwt8fHzkFt8rLi5mH9YVWb58OYKDgyEWixEQEMC21HAVEBCA/fv3A6ifwrfhWioSJSUlmD17Nh4/fqz0WAKBAN27d0d0dDSaN2+O69evs60aqu6Rx48fw9XVVeHYBw0NDaxcuRIXL15EUlIS3N3d4e3tLVMmLS0Nnp6eKlejtra2xq5du5R2JSSEfHqoJYIQorY2bdrAzs4OAMDj8RSuBvwmI0aMwNmzZ+Hp6QlLS0u0bt0a2tra4PP5GDlyJHbs2IHdu3fLrVJsaWmJyMhITJ8+HaampuDxeGjWrBlMTU0xZswYBAcHIygoiJ1+9vr16zItE41l8ODBOHr0KBwdHWFgYIAWLVqgZ8+eWLFiBSIjI+USCK68vLwQGRkJFxcXmJubo3nz5tDR0UHHjh0xZcoUHD58WGECAYAdlL5mzRpYWlqiZcuW0NPTw5AhQxAaGoqJEye+VUw9evRAdHQ0VqxYgd69e6NVq1bg8XgwNTWFi4sLTpw4IZNAAPXdeySJmmTtDoZhZJKv6dOn4/Tp0/j6668hEAjQsmVL8Hg8tGvXDo6Ojti/fz82bNigcPVufX19hIaGYuPGjbC2tkabNm3Y8RJBQUH45ptv3upcG5o8eTL7b2WtEEB9i0FERAQ8PT0hEAjQrFkz8Hg88Pl8DBo0CBs2bMCRI0fg4uICoL57k7rjNrp3746TJ09izpw5MDMzg46ODgwNDTFq1CgcOHDgjatz9+nTB6dPn4a3tzf69esHfX19aGtrQ19fH1988QV+/PFH/Prrr5RAEEJkUEsEIYT8RdItEb6+vpg1a1YTR0QIIYS8X9QSQQghhBBCCOGEkghCCCGEEEIIJ5REEEIIIYQQQjihJIIQQgghhBDCCSURhBBCCCGEEE5odiZCCCGEEEIIJ9QSQQghhBBCCOGEkghCCCGEEEIIJ5REEEIIIYQQQjihJIIQQgghhBDCCSURhBBCCCGEEE4oiSCEEEIIIYRw8n8kV59Sw33KowAAAABJRU5ErkJggg==
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<pre>Logistic:
Brier: 0.061
Precision: 0.690
Recall: 0.036
F1: 0.068
Naive Bayes:
Brier: 0.061
Precision: 0.558
Recall: 0.071
F1: 0.126
Naive Bayes + Sigmoid:
Brier: 0.062
Precision: 0.492
Recall: 0.117
F1: 0.189
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<h1 id="Determine-PPV-and-NPV-at-Youden-Index-value-per-model">Determine PPV and NPV at Youden Index value per model<a class="anchor-link" href="#Determine-PPV-and-NPV-at-Youden-Index-value-per-model">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [78]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">lstFiles</span><span class="p">:</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">))</span>
<span class="n">testy</span><span class="p">,</span> <span class="n">lr_probs</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">neg_count</span><span class="p">,</span> <span class="n">pos_count</span> <span class="o">=</span> <span class="p">(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="s1">',d'</span><span class="p">)),(</span><span class="nb">format</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span><span class="s1">',d'</span><span class="p">))</span>
<span class="n">lr_precision</span><span class="p">,</span> <span class="n">lr_recall</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">precision_recall_curve</span><span class="p">(</span><span class="n">testy</span><span class="p">,</span> <span class="n">lr_probs</span><span class="p">)</span>
<span class="c1"># calculate scores</span>
<span class="n">lr_f1</span><span class="p">,</span> <span class="n">lr_auc</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">testy</span><span class="p">,</span> <span class="n">yhat</span><span class="p">),</span> <span class="n">auc</span><span class="p">(</span><span class="n">lr_recall</span><span class="p">,</span> <span class="n">lr_precision</span><span class="p">)</span>
<span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'y'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'y_pred_proba'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">)</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_scores</span><span class="p">,</span><span class="n">pos_label</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">total_count</span> <span class="o">=</span> <span class="nb">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">),</span><span class="s1">',d'</span><span class="p">)</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span>
<span class="n">youden_index</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="n">cm</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span><span class="n">y_scores</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">youden_index</span><span class="p">)</span>
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<pre>1247
0.4290496913006215
0.4290496913006215
Positive Predictive Value 0.13 Negative Predictive Value 0.99 Specificty 0.742018767687801 Sensitivity 0.8621830209481808
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Cp8/s3f0w1Wn5OvyjGZt65iR3/9m5W+14MmNQohhLBARfooqisJFEIIYQFFMf+Gu4pOuKtqJFCISlEUiJrQhMfdbzBwrHHz2PuvP46DczHjZqUCcHifLf+e0RCdTkW9+lrGzEileesCo3PW/9uRpFUOLN1xCoD8HCtebdeGxs0LDXnenJFK+y559/jJaoaJPffz8tN/knOjpBkkJcuesHU9AXC2y+Or19Yz+POBXL3xCAAej6cysef3WFvpuXqjNlFbX+S3tMcAeK7JRca//AMaax15hbWISOhO6tXyjeR5GBRjRbGZQFBczccNVetAceHCBV566SW++OILunTpYkjv0aMHX3/9NY0aNSrXdVq1aoW7u7tRWrdu3Zg4ceJdK+vw4cOJiYm5a9d7kM79puGzqY04+XMdHne/YXQsdpETyQds8eqbDZT8wf/gjccJX3qWZ7vmce43DTNGNWPxd6eopSnpHjv2Y13WRjtRz15ruM6Jn+vyTMc8Zq/+8/49WA3SrnEaU9a9zJELLkbpfm1PMcb7J5zsrhvSbDWFRA3cyr/ievHjmUY83iCbea8m8erng6hf5wZRg7by1jf+nLzsyJAXjjClzx7GrfK734/0wJQ0PZl7Z7aK6rwuYLUOFAA2Nja8++67bNy4EVtb20pfJyEh4S6W6k4//vjjPb3+/bRxxWP4DsnCyc142v+v+235aWc9/EZkknu1ZDhg6hkNdevpebZrSU2gyZOF1Kmn58ShurR7MY/sDGsWTXPjH+9eZPWnToZrHT9Yl9yr1oz3f5LiQhW9h2URMDLr/j3kQ8xGraOVSyYjXzxMo/o5nLvyKB9vfRGt3orurc7y9ko/4setNuRv7HCNvMJa/Him5IvX2az65BfWom2jy7RyyWL/7004eblkpu+6Q0/z/R+NH8hzPShKOWZmWzrq6UGr3vUhSiaPvPjii3z00UelHl+yZAl9+vQhICCAyMhIdDpdua/93XffMWbMGMN+TEwMM2fORKfTMXv2bPr370/fvn358ssvAThw4ACvvfYab731Fj4+PrzzzjsUFRUxc+ZMAAYOHFj5B61Cxs1KpUfQVaO0rMvWLJ7uxuTPUrh1kqfbE4UUXLfi0M56AJw6/Agpp2pzJc0anQ4i327K6+EXaeBiHHTU1gqdel5j7rrfef/rM2z4tyP7tzx6rx+tRnCsl8/BMw2J3vECr34+kKMXnJn3ahKZeXWYtNaHc1fsjfKfy7LnERstnZ44D8DTDdN5wimbx2yv08ThKjeKrZkdtI1V/1hL5Cv/o1hX8ZVSqzOdoirXVp1V+0ABEBYWxt69e9m3b59R+q5du9i+fTvr1q1jw4YNpKSksHr16lKvERgYaLTt2bMHLy8vkpOTuXbtGgCbN2+mb9++htUZN2zYQFxcHN999x0//fQTAL/88gvTp09ny5YtXLx4kb179xIeHg7A2rVr79VH8EBpi2H2W48z5r1UGjhrjY7Vracn4oszrP7UmTEvt+J/ax1o55mLdS2FFbNceaZjHh287+x3GDoxjWH/L41aGoXHXIvpMyyLfUkSKO6Gi1fteOc/fvyR4QCo+Pr7djRyyKGhfW6p+fOLavHPWB9e8/yZ1aPX4t/2ND+daUixTo21Wo93q7NE73ye4H8P5MczbkQN2np/H+gBM7dy7M2tOqv2TU8Atra2fPDBB4YmqJt++OEH/Pz8eOSRkg65AQMGEB8fz9ChQ++4RllNTz179uS///0vXbp04erVq7Rt25Zly5Zx4sQJfvjhBwCuX7/OqVOnaNGiBU8++SQuLiXtvs2bNzcEmYfZ6V/rcCmlFp+/V7JoWXaGNXqdiuJCK8bPPU/tujrmrvvdkP81T3caPl5I9LRGPPqYln1b7Llx3YqsyzaMfbkVi/93ioTlj9HZ5xpOjUpqGooC1tYy5edueNIpi5bOWWw+2tKQpgK0utL/mKlQuF5kw+ivAw1pG97+D+ez7cjMrcuv5104/1ctJP4Xd/7luw+NtZZC7UPx58UspRzDY6t709ND85P09PS8owlKr9ffke/mSz3KKzAwkE8++YRr164ZlubV6XSEhobSq1cvoGQ1x7p163L48GGjyTQ3F+d62D3tcZ2Vh44b9mOiXLh2Rc24WakoCrw7/AneW3GGlu1usDPBnloahSeeLuA/h48Zzvl1vy2Lprmx+H8lo56Sf6zLhT81vP1hKjnZarb+pwFjP7hw35/tYaRXVIT67uWX8y5cvGrHQI9j/JbuQHpu6X18CvDpkEQmrvHlxCUnej39O0VaNb+lNUAFBHU4TkP7HC5eteMl9zP8nl6/xgQJAK2iQmumcUYrgaLqCAsLIyAgwLBIVqdOnVi8eDGvvvoq1tbWrFu3jk6dOlXomu3btyc9PZ2EhATDiz86depEbGws3bt3p6ioiODgYGbMmGHyOmq1Gq1Wa1j2t6ZQqSBsUQoLJjWmuFiFg5OWiC/OUMZClgZvz7rAwn815h/dWqHTquj7f5mlNlGJivsjw4E5SZ4sGLwFtUohLbcuU9e9bOIMFVM3vMy7/ruwUevJzKvDP9f4AipOpz1GZGJXPh60FWsrPTkFGibH9bpfj1Il6Msxj8Lc8aruofqrdbMJ6vXXXwege/funDhxggEDBqDVavH09GTYsGGlnhsYGGi037RpUxYuXAhA79692bt3L40bl4zmGDx4MCkpKfTv3x+tVktQUBAdO3bkwIEDZZbtpZdeIjAwkPXr11fLKfylmbTgXKnpwyddNtpv2zmf6G2nTV6r3Yt5hjkUAPYNdExfftbiMorSJR5tSeItTU+3e+79MUb7P6c0JPjfpQ/G2H7yCbaffOKulq86qQlNT7LWUxUkaz1VT7LWU/Vyt9Z6WlS4hmuYru0+ii1va16VtZ6EEKIm0qMyu5aT3lxbaxUngUIIISygV8oRKKp505MECiGEsIBOr0JrZp6ETmoUQghRc+nLsYSH1CiEEKIGk6YnIYQQJinlCBTVfXisBAohhLCA1CiEEEKYpFOs0OnNdGZbycxsIYSosWpCZ3b1DnNCCPGAKcrfzU9lbZVZ/yIvLw9/f38uXChZDHP//v0EBATQq1cv5s+fb8h34sQJgoKC8PHxYdq0aYaFTy9evMjQoUPx9fVl7Nix5OfnA5CTk8Po0aPp3bs3Q4cONayNZ4oECiGEsICiqMq1VcSvv/7KkCFDOHv2LAAFBQVMnTqV6OhoEhMTSU5OZteuXQCEhoYyffp0tm7diqIohvflzJgxg+DgYJKSkmjTpo1hUdMFCxbg4eHBli1bGDhwIB9++KHZ8kigEEIIC5irTdza2X358mUuXLhgtOXk5NxxzdjYWCIiInByKnk98JEjR2jatCmNGzfG2tqagIAAkpKSSE1NpaCggPbt2wMQFBREUlISxcXFHDx4EB8fH6N0gJ07dxpemeDv78/u3bspLi6+owy3kj4KIYSwQHlqDDePBwcHk5mZaXRs3LhxhISEGKXd/i0/PT0dR0dHw76TkxNpaWl3pDs6OpKWlkZ2dja2traG1xrcTL/9WtbW1tja2nLlyhWcnZ3LLL8ECiGEsIBer0KnL19n9qpVq1Crjd8pbmdnV4576FHdsgyIoiioVKoy02/+e6vb9289x8rMqCwJFEIIYYGKjHpycXGp1DLjLi4uRp3OGRkZODk53ZGemZmJk5MTDg4O5ObmotPpUKvVhvxQUhvJzMzExcUFrVZLfn4+9vb2Ju8vfRRCCGGBe9GZfbt27dpx5swZUlJS0Ol0bNq0CS8vL9zc3NBoNBw6dAiAhIQEvLy8sLGxwcPDg8TERADi4+Px8vICwNvbm/j4eAASExPx8PDAxsbG5P2lRiGEEBYo1/soLJxHodFoiIyMJCQkhMLCQry9vfH19QUgKiqK8PBw8vLyaN26NSNGjAAgIiKCsLAwFi9ejKurK/PmzQNg/PjxhIWF4efnR7169YiKijJ7fwkUQghhAUXB7DyJyr5HdPv27Yb/79y5Mxs3brwjj7u7O3FxcXeku7m5ERMTc0e6vb09S5YsqVA5JFAIIYQFSgKFuVFP96kw94gECiGEsIBeb36tJ72qencHS6AQQggL3Mump6pCAoUQQligIhPuqisJFEIIYYEaHSiuXr1q8kRzEzSEEKImUP7azOWpzsoMFJ06dTJMBb+dSqXixIkT97RgQghRHSiKCsXMEh6K1UNaozh58uT9LIcQQlRP5Zl5Xc2bnsyO2dLr9SxfvpywsDDy8vL4/PPP0el096NsQghR5d0c9WRuq87MdmbPmTOHK1eucPToURRFYc+ePWRkZBAeHn4/yieEEFVaTejMNluj+P7774mMjESj0VCvXj2++OIL9u3bdz/KJoQQVZ9CSdOSye1BF9IyZmsU1tbWRmuV16pVy/AyDCGEqOlkwh3QsmVLVq5ciU6n488//+TLL7/E3d39fpRNCCGqvHKNenrYm56mTZvGsWPHyMrKYsiQIeTn5zN16tT7UTYhhKj6lHJu1ZjZGoWtrS2zZs26H2URQohqRzqzgaysLP75z3/SsWNHPD09mTp1Kjk5OfejbEIIUfXVgBqF2UARHh5O48aNiYuL45tvvuHRRx9l+vTp96NsQghRDajKuVVfZpueUlNTWbx4sWF/8uTJBAQE3NNCCSFEtaH/azOXpxozW6NwcnLi/Pnzhv3Lly/j6Oh4TwslhBDVhtk5FKpqv4RHmTWKMWPGAHDlyhX69evHiy++iJWVFQcOHKBVq1b3rYBCCFGV1eh5FD4+PqWmd+vW7V6VRQghqqe7HAgSEhJYunQpAF5eXkyePJn9+/cze/ZsCgsL6d27NxMnTgTgxIkTTJs2jfz8fDw8PJgxYwbW1tZcvHiR0NBQsrKyaNasGVFRUdStW7dS5SkzUPTv37/UdEVRSElJqdTNhBDioVOepqUKND3duHGDDz/8kKSkJOzs7BgyZAjbt2/n/fffJyYmBldXV95880127dqFt7c3oaGhzJw5k/bt2zN16lRiY2MJDg5mxowZBAcH4+fnx6JFi4iOjiY0NLRSj2i2j2L16tU899xzPPXUUzz11FM8/fTTDB06tFI3E0KIh41KKd9WXjqdDr1ez40bN9BqtWi1WmxtbWnatCmNGzfG2tqagIAAkpKSSE1NpaCggPbt2wMQFBREUlISxcXFHDx40NAydDO9ssyOelq6dCkrVqxg8eLFTJgwgR07dnD58uVK31AIIR4qelXJZi4PJYOB1Gq10SE7Ozvs7OwM+7a2towfP57evXvzyCOP8Pzzz5Oenm40iMjJyYm0tLQ70h0dHUlLSyM7OxtbW1vDunw30yvLbKCwt7enXbt2PPXUU2RlZTF27Fj69OlT6RsKIcRDpQLvQg0ODiYzM9Po0Lhx4wgJCTHsnzx5knXr1rFjxw7q1avHpEmTOHv2LCrV38FIURRUKhV6vb7U9Jv/3ur2/Yoo1+qx165do2nTphw5coQuXbrIi4uEEOKmCgSKVatWlVqjuNXevXvp3LkzDRo0AEqajZYvX250XkZGBk5OTri4uJCRkWFIz8zMxMnJCQcHB3Jzc9HpdKjVakP+yjLbRzFo0CDefPNNunXrxpo1awgKCuKJJ56o9A2FEOKhUoElPFxcXGjUqJHRdnugcHd3Z//+/Vy/fh1FUdi+fTvt2rXjzJkzpKSkoNPp2LRpE15eXri5uaHRaDh06BBQMlrKy8sLGxsbPDw8SExMBCA+Ph4vL69KP6LZGsUrr7xCnz59qFOnDmvWrOHo0aN07dq10jcUQoiHS3km1JW/2cfT05Pjx48TFBSEjY0NzzzzDCEhIXTp0oWQkBAKCwvx9vbG19cXgKioKMLDw8nLy6N169aMGDECgIiICMLCwli8eDGurq7Mmzevsg+ISlFKnwqyYsUKkyeOGjWq0jcVphUWFpKcnMzTziForDPMnyCqhOfeH/ugiyAqoIGthjmvdqJNmzZoNJoKn3/z93Tszz+QUVhgMq+jpjaLn6v8vR60MmsUp0+fvp/lEEKI6qkCfRTVVZmBYvbs2fezHKIUIzs+Tfalaw+6GKKcXOyPP+giiAqwd64Hr3ay+DrlmSdRkXkUVZG8/FoIISyhUI6Z2felJPeMBAohhLBETW56EkIIUQ41IFCYnUeh1+tZtmwZkydPJi8vj88//1wm3AkhxF9UCqj0ZraHPVDMmTOH06dPc+TIEQD27NkjHd1CCHGTvDMbvv/+eyIjI9FoNNja2vLFF1+wb9+++1E2IYSo8u726rFVUbnWerKy+jue1KpVy7AioRBC1Hh3+X0UVZHZv/gtW7Zk5cqV6HQ6/vzzT7788kvc3d3vR9mEEKLqk85smDZtGseOHSMrK4shQ4aQn5/P1KlT70fZhBCiypOmJ0peojFr1qz7URYhhKh+/hrZZC5PdWY2UMycObPU9PDw8LteGCGEqJaqeY3BHLNNT/b29oatbt26/Pjjj/ejXEIIUT3UgOGxZmsU48aNM9r/xz/+wdixspyyEEJAzVgU0GyN4na2trakp6ffi7IIIYSogszWKD744APDS7kVReHYsWPyKlQhhLipBgyPNRso6tevb7Tft29f+vbte88KJIQQ1YmqHKOezI6KquLMBopz584xZ86c+1EWIYSofqRGASdPnkRRFEPzkxBCiL/VhM5ss4HC0dERPz8/2rVrR926dQ3pMo9CCCH+cpcDwfbt2/nss8+4ceMGXbp0ITw8nP379zN79mwKCwvp3bs3EydOBODEiRNMmzaN/Px8PDw8mDFjBtbW1ly8eJHQ0FCysrJo1qwZUVFRRn/DK6LMUU9FRUUAPPvss/Tp0wc3NzejORVCCCHu/hIe58+fJyIigujoaDZu3Mjx48fZtWsXU6dOJTo6msTERJKTk9m1axcAoaGhTJ8+na1bt6IoCrGxsQDMmDGD4OBgkpKSaNOmDdHR0ZV+xjJrFK+++iobNmy4Yx6FEEKIW+gxv0RHBTqzt23bRp8+fXBxcQFg/vz5pKSk0LRpUxo3bgxAQEAASUlJtGjRgoKCAtq3bw9AUFAQCxcuZODAgRw8eJBFixYZ0ocNG0ZoaGgFH65EmYFCUap5o5oQQtwHFemjuHz5Mmq12uiYnZ0ddnZ2hv2UlBRsbGwYM2YMly5dolu3bjz55JM4Ojoa8jg5OZGWlkZ6erpRuqOjI2lpaWRnZ2Nra2t4JcTN9MoqM1AUFhZy/PjxMgNG69atK31TIYR4aFRg1FNwcDCZmZlGh8aNG0dISIhhX6fT8dNPPxETE0OdOnUYO3YstWvXNhpQdHOAkV6vLzW9tAFIlgxIKjNQnD9/npCQkFIDhUql4rvvvqv0TYUQ4qFRgUCxatWqUmsUt3rsscfo3LkzDg4OALz88sskJSUZnZeRkYGTkxMuLi5kZGQY0jMzM3FycsLBwYHc3Fx0Oh1qtdqQv7LKDBQtWrQgPj6+0hcWQoiaoCJNTy4uLmg0GpN5u3fvzuTJk8nJyaFu3brs2bMHX19fli5dSkpKCo0aNWLTpk0MGDAANzc3NBoNhw4dokOHDiQkJODl5YWNjQ0eHh4kJiYSEBBAfHw8Xl5elX5GeaepEEJY6i526bZr14433niD4OBgiouL6dKlC0OGDOGJJ54gJCSEwsJCvL298fX1BSAqKorw8HDy8vJo3bo1I0aMACAiIoKwsDAWL16Mq6sr8+bNq3SZygwUHh4elb6oEELUFPdiCY9XXnmFV155xSitc+fObNy48Y687u7uxMXF3ZHu5uZGTExMxW5chjIDhUyoE0KIcpAlPIQQQpjzsC9wJIFCCCEsITUKIYQQpsiigEIIIUyTGoUQQghTVEo5Rj1JoBBCiBpMahRCCCFMkT4KIYQQpkmNQgghhClSoxBCCGHaXX5xUVUkgUIIISwgNQohhBDmVfNAYI4ECiGEsIBKUVCZeXW0ueNVnQQKIYSwhIx6EkIIYYr0UQghhDCtHC8uklFPQghRk0nTkxBCCFOk6UkIIYR51TwQmCOBQgghLFATahRWD7oAQghRnan0oNIrZrbKXfujjz4iLCwMgP379xMQEECvXr2YP3++Ic+JEycICgrCx8eHadOmodVqAbh48SJDhw7F19eXsWPHkp+fX+lnlBqFuAcUJi04z9mTtYlb4kSt2nrGzbpAq/Y3UKkUTv5Sh8+mNqKowIqOPa8RuuA86RdtDGf/v34tuJGvfoDlrzl69E2j//9dMOzXtdXymHMRI3p0ZOjbKTzz/DUADu52YPncZoCKJ9vk8mbYH2jq6FBbKaxd3pgd3zo/oCeoAu5RZ/b333/Phg0b6NatGwUFBUydOpWYmBhcXV1588032bVrF97e3oSGhjJz5kzat2/P1KlTiY2NJTg4mBkzZhAcHIyfnx+LFi0iOjqa0NDQyjzhva9RnD59mlatWrF169ZSjx84cIDhw4ff62KYNHz4cHr27ElgYKBhe/311+/qPRYuXMhPP/10V69ZFTVuUcBHsX/S1f+aIW3IO2mo1TDmpZaMeakVtWorDA5JB+Bpj+vELXHkrZ6tDJsEiftn+0ZnQoI6EBLUgQmDniU7sxaLP2yOR9crNHr8Om8FduDt/s/xjMdVPH0yAYVpnxznm0VNCQnqwLtvPsM//vUnDZveeNCP8sCo9OXbAC5fvsyFCxeMtpycnDuuefXqVebPn8+YMWMAOHLkCE2bNqVx48ZYW1sTEBBAUlISqampFBQU0L59ewCCgoJISkqiuLiYgwcP4uPjY5ReWfe8RrFu3Tp8fX1Zs2aNodBV0cyZM+nYseM9u/7Bgwfv6fWrir6jMkn6jwPpqX/XEI4esCXtfC0URYWiwB/Jj9C0ZQEAT3vko9Oq8O57let5alZEupB8wPZBFb9GG/j6ea5eqcWW2Ib0CrpE7Tp6bGrpUVmBtY1CUaEVNrUUVi1qwuHv6wOQlabhWrYNjzkXcjHlkQf8BA9IBWoUwcHBZGZmGh0aN24cISEhRmnTp09n4sSJXLp0CYD09HQcHR0Nx52cnEhLS7sj3dHRkbS0NLKzs7G1tcXa2toovbLuaaAoLi7m22+/ZeXKlQwePJhz587RpEkT9u7dy+zZs9FoNDRr1gyAkydPEhoayrfffgvA9u3bWbt2LYsXL2bp0qVs2bIFnU6Hp6cnoaGhpKam8sYbb1C/fn1q167N5MmTmT59OlqtFo1Gw+zZs3n88cfZvXs3CxcuRKvV0qhRIz744APq169frvJnZ2fj7+/Pzp07sbGx4fTp00yaNImNGzcSHx/PV199hV6vp3Xr1kRERKDRaPD09MTHx4dDhw6hVqtZsGABhw4dIjk5mfDwcD777DNatWp1zz7zB23RtEYAPOeVa0j7eVc9w/87uRXR/40MPvlXYwBysq3ZscGevZsfpfUL+bz3xVnG9mxJ5qVa97fgNZydfTH9/y+V8QOfBeB/8S54+mTy9Y4DqK0VftlXnx93NgDgv+tdDef5DrzEI3V1nPy1XqnXrQlUlKMz+69/V61ahVptXGO2s7Mz2l+7di2urq507tyZ9evXA6DX61GpVIY8iqKgUqnKTL/5r1EZbtuviHva9LRr1y4aNmxIs2bNePnll1mzZg1FRUWEhYWxcOFC1q9fT+3atQFwd3dHpVJx+vRpADZv3kzfvn3ZvXs3ycnJxMXFER8fT1paGhs3bgTgzJkzzJ07lxUrVvDVV18xatQo1q9fz6BBgzh8+DBXrlzh448/Zvny5cTHx+Pp6UlUVFSpZQ0PDzdqelq8eDH169enbdu27N2716hMv/32G7GxsaxevZqEhAQaNGjA8uXLAcjIyKBz587Ex8fz/PPPs3LlSvr160ebNm2YOXPmQx0kzGnxzHU+jv+djSse48D/Sn45PnjjcfZutgdUHPvRluOH6vKcV94DLWdN5DvoEj9sb8DlCyW1guC3UsjJtmGoVydGdO+I7aPFRn0ZAAPfOMewcSnMeKs1RYU1uLlQUcq3AS4uLjRq1Mhouz1QJCYmsm/fPgIDA1m4cKHhS3NGRoYhT0ZGBk5OTri4uBilZ2Zm4uTkhIODA7m5ueh0OqP8lXVPaxTr1q3D398fgD59+jBp0iR8fHxwcnKiefPmAPTv359PPvkEgL59+7J582aaNGnCwYMHmTVrFgsWLODIkSMEBQUBUFBQQMOGDenQoQMNGjSgUaOSb7De3t68//777Nmzhx49etC9e3d2797NpUuXGDFiBFASlR999NFSy1pW09PNMnXv3p0tW7YQExPDtm3bSElJYdCgQUBJzenpp582nNO1a1cAnnzyyRrRL1Ee3oHZhMxKZVG4Gzs2lNTo6trpCBiZyepPnbj5nUuFgra48t98ROV4+WawZFZzw/6LPTNZ8mELtMVWaIut+C7BGc9emWz4shHWNnr+OesUTZpf559D2pN+sfYDLPmDpyrHEh4VGfW0YsUKw/+vX7+eH3/8kRkzZtCrVy9SUlJo1KgRmzZtYsCAAbi5uaHRaDh06BAdOnQgISEBLy8vbGxs8PDwIDExkYCAAOLj4/Hy8qrkE97DQJGVlcWePXs4duwYX3/9NYqikJOTw759+1BuWXL31mpYQEAAI0eOxN3dHU9PTzQaDTqdjpEjRzJq1CgAcnJyUKvVZGdnG2ojAL6+vjz77LPs2LGDL7/8kp07d9KtWzeee+45lixZAkBhYWGFh4i99NJLREZGcvDgQVxdXXF2dkan09G7d2/Cw8MByM/PN0RuAI1GA2CoAtZ0HXte460PLjJlyBP8dqSOIf1GnhUB/5fJhT807E20p3mb67R69gZRE5s8wNLWPLZ2xTRscoMTh//+ZvvHcVu6+mZw5Ed71NZ6OnbPMjQvhc45iaa2nv83tD2FN2pwTeIv92MehUajITIykpCQEAoLC/H29sbX1xeAqKgowsPDycvLo3Xr1oYvxhEREYSFhbF48WJcXV2ZN29epe9/zwJFQkICnTp1YtmyZYa0Tz/9lF27dpGZmcnJkydxd3dn8+bNhuPOzs64urqydOlSJk+eDECnTp1YuHAhgwYNQqPR8Pbbb9O/f39eeOEFo/tNmDABf39/Bg8eTPPmzZk9ezbjx48nPDycM2fO0KxZM6Kjo0lLSyMyMrLcz1GrVi26du3KrFmzGDZsGAAdO3bkiy++YOzYsTg4OPDee+/RpEmTOzqkbqVWq42CSU3yj+mXQKUw8ePzhrRjB+uyaGoj3hvVjLdmpjJ8Uho6Hcwa05ScKzJq+35ybVLAlcxa6LR/t0QvjWzO2PDf+XzTQfR6FYd/sCfui8a4t8uhq08mF848QtTKw4b8Kz5uxs/7HB5A6auAW5qWTOaphKCgIENrSufOnQ3N7rdyd3cnLi7ujnQ3NzdiYmIqdd/b3bPfyA0bNjBx4kSjtKFDh7Js2TKWLVtGaGgo1tbWRk02AIGBgcyfP98QCHr06MHJkycZNGgQOp2Orl270r9/f1JTU43OGzNmDNOmTWPRokXY2Njw3nvv4ejoyKxZs5gwYQJ6vR5nZ2fmzp1bannDw8OpU6eOUVpMTAx2dnYEBgayceNGw6gtd3d3xo0bx8iRI9Hr9Tz11FOMHj3a5OfRtWtXIiIi+Oijj3juuefMf4DV3Me31Are6OpeZr7fjtRhYt8n70eRRBl+S67HG77GX7xyr9kwJ/SpO/Ke/NWOPk9XvgnjoVSOGkV1X+JDpUjbSJVTWFhIcnIyH/VdTPala+ZPEFWC2r70/i9RNdk71yP0m2G0adPG0FxcETd/T99fsJ/sawUm89Z/tDbTJ7xY6Xs9aFLHF0IIC1RkeGx1JYFCCCEsoVNKNnN5qjEJFEIIYYGasHqsBAohhLDEPRz1VFVIoBBCCAtIjUIIIYRp8s5sIYQQpqj0CiozndUqffWOFBIohBDCAipFQWWmD8Lc8apOAoUQQlhCmp6EEEKYJKOehBBCmCKjnoQQQpimUI4axX0pyT0jgUIIISwgo56EEEKYJp3ZQgghTCrH8FjpzBZCiJpMRj0JIYQwSf/XZi5PNSaBQgghLKCiHDOzq3knhZX5LEIIIcqk15dvq4DPPvsMPz8//Pz8mDNnDgD79+8nICCAXr16MX/+fEPeEydOEBQUhI+PD9OmTUOr1QJw8eJFhg4diq+vL2PHjiU/P7/SjyiBQgghLKEv51ZO+/fvZ+/evWzYsIH4+HiOHTvGpk2bmDp1KtHR0SQmJpKcnMyuXbsACA0NZfr06WzduhVFUYiNjQVgxowZBAcHk5SURJs2bYiOjq70I0qgEEIIC9xcFNDcVl6Ojo6EhYVRq1YtbGxsaN68OWfPnqVp06Y0btwYa2trAgICSEpKIjU1lYKCAtq3bw9AUFAQSUlJFBcXc/DgQXx8fIzSK0v6KIQQwhIVmJl9+fJl1Gq10SE7Ozvs7OwM+08++aTh/8+ePcuWLVsYNmwYjo6OhnQnJyfS0tJIT083Snd0dCQtLY3s7GxsbW2xtrY2Sq8sCRRCCGGJCgyPDQ4OJjMz0+jQuHHjCAkJueOU3377jTfffJN//etfqNVqzp49e8vlFFQqFXq9HpVKdUf6zX9vdft+RUigEEIIS+gVMLOEB38t4bFq1apSaxS3O3ToEO+88w5Tp07Fz8+PH3/8kYyMDMPxjIwMnJyccHFxMUrPzMzEyckJBwcHcnNz0el0qNVqQ/7Kkj4KIYSwQEX6KFxcXGjUqJHRdnuguHTpEm+//TZRUVH4+fkB0K5dO86cOUNKSgo6nY5Nmzbh5eWFm5sbGo2GQ4cOAZCQkICXlxc2NjZ4eHiQmJgIQHx8PF5eXpV+RqlRCCGEJe7yzOzly5dTWFhIZGSkIW3w4MFERkYSEhJCYWEh3t7e+Pr6AhAVFUV4eDh5eXm0bt2aESNGABAREUFYWBiLFy/G1dWVefPmVfzZ/iKBQgghLKFXDE1LJvOUU3h4OOHh4aUe27hx4x1p7u7uxMXF3ZHu5uZGTExMue9rigQKIYSwhKz1JIQQwrRyBIpqvoSHBAohhLCETgGdmanX5kZFVXESKIQQwhKKvmQzl6cak0AhhBCWkD4KIYQQJt3lUU9VkQQKIYSwiHRmCyGEMEWanoQQQpik04NOZz5PNSaBQgghLCE1CiGEECZJoBBCCGGSUo5RTxIohBCi5lIUPYqZCXXmjld1EiiEEMISsoSHEEIIk/T6ks1cnmpMAoUQQlhCOrOFEEKYoih6FDM1BumjEEKImkxqFEIIIUzSU45FAe9LSe4ZCRRCCGEBRadDMbOEh7njVZ0ECiGEsEg5XlxUzasUEiiqIOWv9kw7p3oPuCSiIqzs5OdVndg9Vhf4+/et8texNdv0ZPeYrUX3eNBUiqWfkrjrcnNzOX369IMuhhA1QsuWLalXr+JBXqvVkpycjK6czUpqtZo2bdpgbV39vp9LoKiC9Ho9+fn52NjYoFKpHnRxhHgoKYpCcXExdevWxcrKqlLX0Gq1FQoU1TFIgAQKIYQQZlQujAohhKgxJFAIIYQwSQKFEEIIkyRQCCGEMEkChRBCCJMkUAghhDBJAoUQQgiTJFCIUl24cIFWrVqxb98+o/QePXpw4cKFcl+nVatWBAYGGm3z58+/q2UdPnz4Xb1eVXP69GlatWrF1q1bSz1+4MCBB/4ZDB8+nJ49exr9nF9//fW7eo+FCxfy008/3dVrivKpntMExX1hY2PDu+++y8aNG7G1rfxaNQkJCXexVHf68ccf7+n1H7R169bh6+vLmjVr8PHxedDFKdPMmTPp2LHjPbv+wYMH7+n1RdmkRiHK5OTkxIsvvshHH31U6vElS5bQp08fAgICiIyMLPdSBgDfffcdY8aMMezHxMQwc+ZMdDods2fPpn///vTt25cvv/wSKPnW/Nprr/HWW2/h4+PDO++8Q1FRETNnzgRg4MCBlX/QKqy4uJhvv/2WCRMmcOzYMc6dOwfA3r178fPzIygoiNjYWABOnjxJQECA4dzt27czduxYAJYuXWr4TOfMmYOiKFy4cAFfX1+GDBnCqFGjOHnyJIMGDSIoKIghQ4Zw9uxZAHbv3s0rr7xCv379GDduHNnZ2eUuf3Z2Nl26dKG4uBgoqR317dsXgPj4ePr3709gYCBTp06lsLAQAE9PTz744AP69evHgAEDOH/+PPHx8SQnJxMeHs6pU6cs+1BFhUmgECaFhYWxd+/eO5qgdu3axfbt21m3bh0bNmwgJSWF1atXl3qN25ue9uzZg5eXF8nJyVy7dg2AzZs307dvX8MfvQ0bNhAXF8d3331naG745ZdfmD59Olu2bOHixYvs3buX8PBwANauXXuvPoIHateuXTRs2JBmzZrx8ssvs2bNGoqKiggLC2PhwoWsX7+e2rVrA+Du7o5KpTIsKHnzM929ezfJycnExcURHx9PWloaGzduBODMmTPMnTuXFStW8NVXXzFq1CjWr1/PoEGDOHz4MFeuXOHjjz9m+fLlxMfH4+npSVRUVKllDQ8PN/o5L168mPr169O2bVv27t1rVKbffvuN2NhYVq9eTUJCAg0aNGD58uUAZGRk0LlzZ+Lj43n++edZuXIl/fr1o02bNsycOZNWrVrd649d3EaanoRJtra2fPDBB4YmqJt++OEH/Pz8eOSRRwAYMGAA8fHxDB069I5rlNX01LNnT/773//SpUsXrl69Stu2bVm2bBknTpzghx9+AOD69eucOnWKFi1a8OSTT+Li4gJA8+bNDUHmYbZu3Tr8/f0B6NOnD5MmTcLHxwcnJyeaN28OQP/+/fnkk08A6Nu3L5s3b6ZJkyYcPHiQWbNmsWDBAo4cOUJQUBAABQUFNGzYkA4dOtCgQQMaNWoEgLe3N++//z579uyhR48edO/end27d3Pp0iVGjBgBlCxY+eijj5Za1rKanm6WqXv37mzZsoWYmBi2bdtGSkoKgwYNAkpqTk8//bThnK5duwLw5JNPSr9EFSCBQpjl6el5RxOUvpSXyWu12gpdNzAwkE8++YRr164Zmkx0Oh2hoaH06tULgCtXrlC3bl0OHz6MRqMxnKtSqSx+j0BVl5WVxZ49ezh27Bhff/01iqKQk5PDvn37jJ5drVYb/j8gIICRI0fi7u6Op6cnGo0GnU7HyJEjGTVqFAA5OTmo1Wqys7MNtREAX19fnn32WXbs2MGXX37Jzp076datG8899xxLliwBoLCwkPz8/Ao9x0svvURkZCQHDx7E1dUVZ2dndDodvXv3NtQI8/PzjZoub/6sa8LPuTqQpidRLjeboNLT0wHo1KkTmzdvpqCgAK1Wy7p16+jUqVOFrtm+fXvS09NJSEgwtFt36tSJ2NhYiouLyc/PJzg4mMOHD5u8jlqtrnCQqg4SEhLo1KkTu3fvZvv27ezYsYMxY8awa9cuMjMzOXnyJFDSnHOTs7Mzrq6uLF261OgzTUhIID8/H61Wy9tvv13qCKoJEyZw9OhRBg8ezPjx4zl+/Djt2rXj8OHDnDlzBoDo6GjmzJlToeeoVasWXbt2ZdasWYYydezYkW3btpGVlYWiKLz33nt89dVXJq+jVqsr1A8m7h6pUYhyudkEdXPIY/fu3Tlx4gQDBgxAq9Xi6enJsGHDSj03MDDQaL9p06YsXLgQgN69e7N3714aN24MwODBg0lJSaF///5otVqCgoLo2LEjBw4cKLNsL730EoGBgaxfv96o1lHdbdiwgYkTJxqlDR06lGXLlrFs2TJCQ0OxtrY2arIBDEOQX3jhBaBkSPPNjmqdTkfXrl3p378/qampRueNGTOGadOmsWjRImxsbHjvvfdwdHRk1qxZTJgwAb1ej7OzM3Pnzi21vOHh4dSpU8coLSYmBjs7OwIDA9m4caNh1Ja7uzvjxo1j5MiR6PV6nnrqKUaPHm3y8+jatSsRERF89NFHPPfcc+Y/QHHXyPsohBBCmCRNT0IIIUySQCGEEMIkCRRCCCFMkkAhhBDCJAkUQgghTJJAIaqNCxcu8NRTTxktE9G3b1/i4uIsvvabb77J+vXrgZLhpTk5OWXmzc3NNcxUroikpKRSV3k9cOCAYfa1Ka1ateLKlSsVumdYWJhhaQwhKkvmUYhqpXbt2kZLgqSlpeHv70+bNm1wd3e/K/cwt9rttWvXOHr06F25lxDVgQQKUa05OzvTtGlTzp49y/Hjx4mLi+PGjRvY2toSExPD2rVr+c9//oNer8fe3p53332X5s2bk5aWRlhYGOnp6TRs2JCsrCzDNVu1asX333+Pg4MDn3/+ORs2bMDa2pqmTZsSGRnJlClTKCgoMEzyO3v2LB9++CFXr15Fp9MxfPhwXnnlFQA++eQTvv32W+zt7WnatKnZ5zlz5gzvv/8++fn5ZGRk4O7uzoIFCwwTCRcsWMDRo0fR6/VMmDCB7t27A5T5nELcFYoQ1cT58+eV9u3bG6X9/PPPyvPPP69cvHhRWbdunfL8888rubm5iqIoyoEDB5Tg4GDl+vXriqIoyp49exRfX19FURTlrbfeUubPn68oiqKcPXtWad++vbJu3TpFURSlZcuWSlZWlvK///1P6dWrl3L16lVFURRl1qxZSnR0tFE5iouLlT59+ijJycmKoihKTk6O0rt3b+WXX35Rtm3bpvTp00fJzc1ViouLldGjRyvDhg2747l++OEHxc/PT1EURYmMjFTi4+MVRVGUoqIixd/fX0lKSjKU6/PPP1cURVFOnTqlvPDCC0pWVpbJ55w8ebKybNkyiz53IaRGIaqVm9/koWQBwfr16zN37lxcXV2BktrAzZcs7dy5k5SUFAYPHmw4Pycnh6tXr7J//34mT54MlCwpUtqqp99//z2+vr6G1VKnTJkCYPSGv7Nnz3Lu3DmmTp1qVMbjx4/zxx9/0LNnT0N5BgwYQExMjMnnCw0NZd++ffz73//m7NmzpKenc/36dcPxIUOGANCyZUuaN2/OL7/8wqFDh8p8TiHuBgkUolq5vY/idreuNaTX6wkMDCQ0NNSwn56ezqOPPnrHqqTW1nf+KqjValQqlWE/Jyfnjk5unU5HvXr1jMqUmZlJvXr1DC8IuvV65vzzn/80rKzarVs3Ll26ZHQNK6u/x5/o9Xqsra1NPqcQd4OMehIPLU9PTzZv3mxY8fY///kPI0eOBEoWmFuzZg0AFy9eLHXRwRdffJFt27aRl5cHwKeffsqXX36JtbU1Op0ORVFo1qyZUfC6dOkS/v7+JCcn4+XlRVJSEjk5Oej1+nK9Enbv3r28/fbb9OnTB4Bff/3VaMXUDRs2ABjedteuXTuTzynE3SA1CvHQ8vT05B//+AevvfYaKpUKW1tbPvvsM1QqFREREUyZMoXevXvj4uJS6ogpb29vfv/9d0NzT4sWLfjggw945JFHaNu2LX5+fqxcuZLo6Gg+/PBDli1bhlarZfz48XTo0AGAU6dOMWDAAOzs7HB3dzf7GtGJEyfy9ttvU6dOHWxtbXn++ecNrz8FOH/+PP369UOlUjFv3jzs7e1NPqcQd4OsHiuEEMIkaXoSQghhkgQKIYQQJkmgEEIIYZIECiGEECZJoBBCCGGSBAohhBAmSaAQQghhkgQKIYQQJv1/6M2JjtFrya8AAAAASUVORK5CYII=
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<pre>263
0.3979350694912722
0.3979350694912722
Positive Predictive Value 0.02 Negative Predictive Value 1.0 Specificty 0.7421830040283905 Sensitivity 0.6836734693877551
</pre>
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<pre>1023
0.45940651160283796
0.45940651160283796
Positive Predictive Value 0.1 Negative Predictive Value 0.99 Specificty 0.743868807249089 Sensitivity 0.7924528301886793
</pre>
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<pre>1916
0.45716718045293103
0.45716718045293103
Positive Predictive Value 0.2 Negative Predictive Value 0.98 Specificty 0.7337146980004123 Sensitivity 0.7858880778588808
</pre>
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Datasets
Models
