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.col-lg-pull-4{right:33.33333333333333%} .col-lg-pull-3{right:25%} .col-lg-pull-2{right:16.666666666666664%} .col-lg-pull-1{right:8.333333333333332%} .col-lg-pull-0{right:0} .col-lg-push-12{left:100%} .col-lg-push-11{left:91.66666666666666%} .col-lg-push-10{left:83.33333333333334%} .col-lg-push-9{left:75%} .col-lg-push-8{left:66.66666666666666%} .col-lg-push-7{left:58.333333333333336%} .col-lg-push-6{left:50%} .col-lg-push-5{left:41.66666666666667%} .col-lg-push-4{left:33.33333333333333%} .col-lg-push-3{left:25%} .col-lg-push-2{left:16.666666666666664%} .col-lg-push-1{left:8.333333333333332%} .col-lg-push-0{left:0} .col-lg-offset-12{margin-left:100%} .col-lg-offset-11{margin-left:91.66666666666666%} .col-lg-offset-10{margin-left:83.33333333333334%} .col-lg-offset-9{margin-left:75%} .col-lg-offset-8{margin-left:66.66666666666666%} .col-lg-offset-7{margin-left:58.333333333333336%} .col-lg-offset-6{margin-left:50%} .col-lg-offset-5{margin-left:41.66666666666667%} .col-lg-offset-4{margin-left:33.33333333333333%} .col-lg-offset-3{margin-left:25%} .col-lg-offset-2{margin-left:16.666666666666664%} .col-lg-offset-1{margin-left:8.333333333333332%} .col-lg-offset-0{margin-left:0}}table{max-width:100%;background-color:transparent}
th{text-align:left}
.table{width:100%;margin-bottom:18px}.table>thead>tr>th,.table>tbody>tr>th,.table>tfoot>tr>th,.table>thead>tr>td,.table>tbody>tr>td,.table>tfoot>tr>td{padding:8px;line-height:1.428571429;vertical-align:top;border-top:1px solid #ddd}
.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}
.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>th,.table>caption+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>td,.table>thead:first-child>tr:first-child>td{border-top:0}
.table>tbody+tbody{border-top:2px solid #ddd}
.table .table{background-color:#fff}
.table-condensed>thead>tr>th,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>tbody>tr>td,.table-condensed>tfoot>tr>td{padding:5px}
.table-bordered{border:1px solid #ddd}.table-bordered>thead>tr>th,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>tbody>tr>td,.table-bordered>tfoot>tr>td{border:1px solid #ddd}
.table-bordered>thead>tr>th,.table-bordered>thead>tr>td{border-bottom-width:2px}
.table-striped>tbody>tr:nth-child(odd)>td,.table-striped>tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}
.table-hover>tbody>tr:hover>td,.table-hover>tbody>tr:hover>th{background-color:#f5f5f5}
table col[class*="col-"]{position:static;float:none;display:table-column}
table td[class*="col-"],table th[class*="col-"]{position:static;float:none;display:table-cell}
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.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover,.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th{background-color:#e8e8e8}
.table>thead>tr>td.success,.table>tbody>tr>td.success,.table>tfoot>tr>td.success,.table>thead>tr>th.success,.table>tbody>tr>th.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>tbody>tr.success>td,.table>tfoot>tr.success>td,.table>thead>tr.success>th,.table>tbody>tr.success>th,.table>tfoot>tr.success>th{background-color:#dff0d8}
.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover,.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th{background-color:#d0e9c6}
.table>thead>tr>td.info,.table>tbody>tr>td.info,.table>tfoot>tr>td.info,.table>thead>tr>th.info,.table>tbody>tr>th.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>tbody>tr.info>td,.table>tfoot>tr.info>td,.table>thead>tr.info>th,.table>tbody>tr.info>th,.table>tfoot>tr.info>th{background-color:#d9edf7}
.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover,.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th{background-color:#c4e3f3}
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.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover,.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th{background-color:#faf2cc}
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.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover,.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th{background-color:#ebcccc}
@media (max-width:767px){.table-responsive{width:100%;margin-bottom:13.5px;overflow-y:hidden;overflow-x:scroll;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd;-webkit-overflow-scrolling:touch}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>thead>tr>th,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tfoot>tr>td{white-space:nowrap} .table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>thead>tr>th:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child{border-left:0} .table-responsive>.table-bordered>thead>tr>th:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child{border-right:0} .table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>th,.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>td{border-bottom:0}}fieldset{padding:0;margin:0;border:0;min-width:0}
legend{display:block;width:100%;padding:0;margin-bottom:18px;font-size:19.5px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}
label{display:inline-block;margin-bottom:5px;font-weight:bold}
input[type="search"]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
input[type="radio"],input[type="checkbox"]{margin:4px 0 0;margin-top:1px \9;line-height:normal}
input[type="file"]{display:block}
input[type="range"]{display:block;width:100%}
select[multiple],select[size]{height:auto}
input[type="file"]:focus,input[type="radio"]:focus,input[type="checkbox"]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
output{display:block;padding-top:7px;font-size:13px;line-height:1.428571429;color:#555}
.form-control{display:block;width:100%;height:32px;padding:6px 12px;font-size:13px;line-height:1.428571429;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-webkit-transition:border-color ease-in-out .15s, box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s, box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6)}
.form-control::-moz-placeholder{color:#999;opacity:1}
.form-control:-ms-input-placeholder{color:#999}
.form-control::-webkit-input-placeholder{color:#999}
.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{cursor:not-allowed;background-color:#eee;opacity:1}
textarea.form-control{height:auto}
input[type="search"]{-webkit-appearance:none}
input[type="date"]{line-height:32px}
.form-group{margin-bottom:15px}
.radio,.checkbox{display:block;min-height:18px;margin-top:10px;margin-bottom:10px;padding-left:20px}.radio label,.checkbox label{display:inline;font-weight:normal;cursor:pointer}
.radio input[type="radio"],.radio-inline input[type="radio"],.checkbox input[type="checkbox"],.checkbox-inline input[type="checkbox"]{float:left;margin-left:-20px}
.radio+.radio,.checkbox+.checkbox{margin-top:-5px}
.radio-inline,.checkbox-inline{display:inline-block;padding-left:20px;margin-bottom:0;vertical-align:middle;font-weight:normal;cursor:pointer}
.radio-inline+.radio-inline,.checkbox-inline+.checkbox-inline{margin-top:0;margin-left:10px}
input[type="radio"][disabled],input[type="checkbox"][disabled],.radio[disabled],.radio-inline[disabled],.checkbox[disabled],.checkbox-inline[disabled],fieldset[disabled] input[type="radio"],fieldset[disabled] input[type="checkbox"],fieldset[disabled] .radio,fieldset[disabled] .radio-inline,fieldset[disabled] .checkbox,fieldset[disabled] .checkbox-inline{cursor:not-allowed}
.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}
textarea.input-sm,select[multiple].input-sm{height:auto}
.input-lg{height:45px;padding:10px 16px;font-size:17px;line-height:1.33;border-radius:6px}select.input-lg{height:45px;line-height:45px}
textarea.input-lg,select[multiple].input-lg{height:auto}
.has-feedback{position:relative}.has-feedback .form-control{padding-right:40px}
.has-feedback .form-control-feedback{position:absolute;top:23px;right:0;display:block;width:32px;height:32px;line-height:32px;text-align:center}
.has-success .help-block,.has-success .control-label,.has-success .radio,.has-success .checkbox,.has-success .radio-inline,.has-success .checkbox-inline{color:#3c763d}
.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #67b168}
.has-success .input-group-addon{color:#3c763d;border-color:#3c763d;background-color:#dff0d8}
.has-success .form-control-feedback{color:#3c763d}
.has-warning .help-block,.has-warning .control-label,.has-warning .radio,.has-warning .checkbox,.has-warning .radio-inline,.has-warning .checkbox-inline{color:#8a6d3b}
.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #c0a16b}
.has-warning .input-group-addon{color:#8a6d3b;border-color:#8a6d3b;background-color:#fcf8e3}
.has-warning .form-control-feedback{color:#8a6d3b}
.has-error .help-block,.has-error .control-label,.has-error .radio,.has-error .checkbox,.has-error .radio-inline,.has-error .checkbox-inline{color:#a94442}
.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #ce8483}
.has-error .input-group-addon{color:#a94442;border-color:#a94442;background-color:#f2dede}
.has-error .form-control-feedback{color:#a94442}
.form-control-static{margin-bottom:0}
.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#404040}
@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle} .form-inline .form-control{display:inline-block;width:auto;vertical-align:middle} .form-inline .input-group>.form-control{width:100%} .form-inline .control-label{margin-bottom:0;vertical-align:middle} .form-inline .radio,.form-inline .checkbox{display:inline-block;margin-top:0;margin-bottom:0;padding-left:0;vertical-align:middle} .form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:none;margin-left:0} .form-inline .has-feedback .form-control-feedback{top:0}}
.form-horizontal .control-label,.form-horizontal .radio,.form-horizontal .checkbox,.form-horizontal .radio-inline,.form-horizontal .checkbox-inline{margin-top:0;margin-bottom:0;padding-top:7px}
.form-horizontal .radio,.form-horizontal .checkbox{min-height:25px}
.form-horizontal .form-group{margin-left:-15px;margin-right:-15px}
.form-horizontal .form-control-static{padding-top:7px}
@media (min-width:768px){.form-horizontal .control-label{text-align:right}}.form-horizontal .has-feedback .form-control-feedback{top:0;right:15px}
.btn{display:inline-block;margin-bottom:0;font-weight:normal;text-align:center;vertical-align:middle;cursor:pointer;background-image:none;border:1px solid transparent;white-space:nowrap;padding:6px 12px;font-size:13px;line-height:1.428571429;border-radius:4px;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.btn:focus,.btn:active:focus,.btn.active:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
.btn:hover,.btn:focus{color:#333;text-decoration:none}
.btn:active,.btn.active{outline:0;background-image:none;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,0.125);box-shadow:inset 0 3px 5px rgba(0,0,0,0.125)}
.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;pointer-events:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none}
.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default:hover,.btn-default:focus,.btn-default:active,.btn-default.active,.open .dropdown-toggle.btn-default{color:#333;background-color:#ebebeb;border-color:#adadad}
.btn-default:active,.btn-default.active,.open .dropdown-toggle.btn-default{background-image:none}
.btn-default.disabled,.btn-default[disabled],fieldset[disabled] .btn-default,.btn-default.disabled:hover,.btn-default[disabled]:hover,fieldset[disabled] .btn-default:hover,.btn-default.disabled:focus,.btn-default[disabled]:focus,fieldset[disabled] .btn-default:focus,.btn-default.disabled:active,.btn-default[disabled]:active,fieldset[disabled] .btn-default:active,.btn-default.disabled.active,.btn-default[disabled].active,fieldset[disabled] .btn-default.active{background-color:#fff;border-color:#ccc}
.btn-default .badge{color:#fff;background-color:#333}
.btn-primary{color:#fff;background-color:#428bca;border-color:#357ebd}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.open .dropdown-toggle.btn-primary{color:#fff;background-color:#3276b1;border-color:#285e8e}
.btn-primary:active,.btn-primary.active,.open .dropdown-toggle.btn-primary{background-image:none}
.btn-primary.disabled,.btn-primary[disabled],fieldset[disabled] .btn-primary,.btn-primary.disabled:hover,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary:hover,.btn-primary.disabled:focus,.btn-primary[disabled]:focus,fieldset[disabled] .btn-primary:focus,.btn-primary.disabled:active,.btn-primary[disabled]:active,fieldset[disabled] .btn-primary:active,.btn-primary.disabled.active,.btn-primary[disabled].active,fieldset[disabled] .btn-primary.active{background-color:#428bca;border-color:#357ebd}
.btn-primary .badge{color:#428bca;background-color:#fff}
.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.open .dropdown-toggle.btn-success{color:#fff;background-color:#47a447;border-color:#398439}
.btn-success:active,.btn-success.active,.open .dropdown-toggle.btn-success{background-image:none}
.btn-success.disabled,.btn-success[disabled],fieldset[disabled] .btn-success,.btn-success.disabled:hover,.btn-success[disabled]:hover,fieldset[disabled] .btn-success:hover,.btn-success.disabled:focus,.btn-success[disabled]:focus,fieldset[disabled] .btn-success:focus,.btn-success.disabled:active,.btn-success[disabled]:active,fieldset[disabled] .btn-success:active,.btn-success.disabled.active,.btn-success[disabled].active,fieldset[disabled] .btn-success.active{background-color:#5cb85c;border-color:#4cae4c}
.btn-success .badge{color:#5cb85c;background-color:#fff}
.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.open .dropdown-toggle.btn-info{color:#fff;background-color:#39b3d7;border-color:#269abc}
.btn-info:active,.btn-info.active,.open .dropdown-toggle.btn-info{background-image:none}
.btn-info.disabled,.btn-info[disabled],fieldset[disabled] .btn-info,.btn-info.disabled:hover,.btn-info[disabled]:hover,fieldset[disabled] .btn-info:hover,.btn-info.disabled:focus,.btn-info[disabled]:focus,fieldset[disabled] .btn-info:focus,.btn-info.disabled:active,.btn-info[disabled]:active,fieldset[disabled] .btn-info:active,.btn-info.disabled.active,.btn-info[disabled].active,fieldset[disabled] .btn-info.active{background-color:#5bc0de;border-color:#46b8da}
.btn-info .badge{color:#5bc0de;background-color:#fff}
.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.open .dropdown-toggle.btn-warning{color:#fff;background-color:#ed9c28;border-color:#d58512}
.btn-warning:active,.btn-warning.active,.open .dropdown-toggle.btn-warning{background-image:none}
.btn-warning.disabled,.btn-warning[disabled],fieldset[disabled] .btn-warning,.btn-warning.disabled:hover,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning:hover,.btn-warning.disabled:focus,.btn-warning[disabled]:focus,fieldset[disabled] .btn-warning:focus,.btn-warning.disabled:active,.btn-warning[disabled]:active,fieldset[disabled] .btn-warning:active,.btn-warning.disabled.active,.btn-warning[disabled].active,fieldset[disabled] .btn-warning.active{background-color:#f0ad4e;border-color:#eea236}
.btn-warning .badge{color:#f0ad4e;background-color:#fff}
.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.open .dropdown-toggle.btn-danger{color:#fff;background-color:#d2322d;border-color:#ac2925}
.btn-danger:active,.btn-danger.active,.open .dropdown-toggle.btn-danger{background-image:none}
.btn-danger.disabled,.btn-danger[disabled],fieldset[disabled] .btn-danger,.btn-danger.disabled:hover,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger:hover,.btn-danger.disabled:focus,.btn-danger[disabled]:focus,fieldset[disabled] .btn-danger:focus,.btn-danger.disabled:active,.btn-danger[disabled]:active,fieldset[disabled] .btn-danger:active,.btn-danger.disabled.active,.btn-danger[disabled].active,fieldset[disabled] .btn-danger.active{background-color:#d9534f;border-color:#d43f3a}
.btn-danger .badge{color:#d9534f;background-color:#fff}
.btn-link{color:#428bca;font-weight:normal;cursor:pointer;border-radius:0}.btn-link,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}
.btn-link,.btn-link:hover,.btn-link:focus,.btn-link:active{border-color:transparent}
.btn-link:hover,.btn-link:focus{color:#2a6496;text-decoration:underline;background-color:transparent}
.btn-link[disabled]:hover,fieldset[disabled] .btn-link:hover,.btn-link[disabled]:focus,fieldset[disabled] .btn-link:focus{color:#999;text-decoration:none}
.btn-lg,.btn-group-lg>.btn{padding:10px 16px;font-size:17px;line-height:1.33;border-radius:6px}
.btn-sm,.btn-group-sm>.btn{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}
.btn-xs,.btn-group-xs>.btn{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}
.btn-block{display:block;width:100%;padding-left:0;padding-right:0}
.btn-block+.btn-block{margin-top:5px}
input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}
.fade{opacity:0;-webkit-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}
.collapse{display:none}.collapse.in{display:block}
.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;transition:height .35s ease}
@font-face{font-family:'Glyphicons Halflings';src:url('../fonts/glyphicons-halflings-regular.eot');src:url('../fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'),url('../fonts/glyphicons-halflings-regular.woff') format('woff'),url('../fonts/glyphicons-halflings-regular.ttf') format('truetype'),url('../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:normal;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}
.glyphicon-asterisk:before{content:"\2a"}
.glyphicon-plus:before{content:"\2b"}
.glyphicon-euro:before{content:"\20ac"}
.glyphicon-minus:before{content:"\2212"}
.glyphicon-cloud:before{content:"\2601"}
.glyphicon-envelope:before{content:"\2709"}
.glyphicon-pencil:before{content:"\270f"}
.glyphicon-glass:before{content:"\e001"}
.glyphicon-music:before{content:"\e002"}
.glyphicon-search:before{content:"\e003"}
.glyphicon-heart:before{content:"\e005"}
.glyphicon-star:before{content:"\e006"}
.glyphicon-star-empty:before{content:"\e007"}
.glyphicon-user:before{content:"\e008"}
.glyphicon-film:before{content:"\e009"}
.glyphicon-th-large:before{content:"\e010"}
.glyphicon-th:before{content:"\e011"}
.glyphicon-th-list:before{content:"\e012"}
.glyphicon-ok:before{content:"\e013"}
.glyphicon-remove:before{content:"\e014"}
.glyphicon-zoom-in:before{content:"\e015"}
.glyphicon-zoom-out:before{content:"\e016"}
.glyphicon-off:before{content:"\e017"}
.glyphicon-signal:before{content:"\e018"}
.glyphicon-cog:before{content:"\e019"}
.glyphicon-trash:before{content:"\e020"}
.glyphicon-home:before{content:"\e021"}
.glyphicon-file:before{content:"\e022"}
.glyphicon-time:before{content:"\e023"}
.glyphicon-road:before{content:"\e024"}
.glyphicon-download-alt:before{content:"\e025"}
.glyphicon-download:before{content:"\e026"}
.glyphicon-upload:before{content:"\e027"}
.glyphicon-inbox:before{content:"\e028"}
.glyphicon-play-circle:before{content:"\e029"}
.glyphicon-repeat:before{content:"\e030"}
.glyphicon-refresh:before{content:"\e031"}
.glyphicon-list-alt:before{content:"\e032"}
.glyphicon-lock:before{content:"\e033"}
.glyphicon-flag:before{content:"\e034"}
.glyphicon-headphones:before{content:"\e035"}
.glyphicon-volume-off:before{content:"\e036"}
.glyphicon-volume-down:before{content:"\e037"}
.glyphicon-volume-up:before{content:"\e038"}
.glyphicon-qrcode:before{content:"\e039"}
.glyphicon-barcode:before{content:"\e040"}
.glyphicon-tag:before{content:"\e041"}
.glyphicon-tags:before{content:"\e042"}
.glyphicon-book:before{content:"\e043"}
.glyphicon-bookmark:before{content:"\e044"}
.glyphicon-print:before{content:"\e045"}
.glyphicon-camera:before{content:"\e046"}
.glyphicon-font:before{content:"\e047"}
.glyphicon-bold:before{content:"\e048"}
.glyphicon-italic:before{content:"\e049"}
.glyphicon-text-height:before{content:"\e050"}
.glyphicon-text-width:before{content:"\e051"}
.glyphicon-align-left:before{content:"\e052"}
.glyphicon-align-center:before{content:"\e053"}
.glyphicon-align-right:before{content:"\e054"}
.glyphicon-align-justify:before{content:"\e055"}
.glyphicon-list:before{content:"\e056"}
.glyphicon-indent-left:before{content:"\e057"}
.glyphicon-indent-right:before{content:"\e058"}
.glyphicon-facetime-video:before{content:"\e059"}
.glyphicon-picture:before{content:"\e060"}
.glyphicon-map-marker:before{content:"\e062"}
.glyphicon-adjust:before{content:"\e063"}
.glyphicon-tint:before{content:"\e064"}
.glyphicon-edit:before{content:"\e065"}
.glyphicon-share:before{content:"\e066"}
.glyphicon-check:before{content:"\e067"}
.glyphicon-move:before{content:"\e068"}
.glyphicon-step-backward:before{content:"\e069"}
.glyphicon-fast-backward:before{content:"\e070"}
.glyphicon-backward:before{content:"\e071"}
.glyphicon-play:before{content:"\e072"}
.glyphicon-pause:before{content:"\e073"}
.glyphicon-stop:before{content:"\e074"}
.glyphicon-forward:before{content:"\e075"}
.glyphicon-fast-forward:before{content:"\e076"}
.glyphicon-step-forward:before{content:"\e077"}
.glyphicon-eject:before{content:"\e078"}
.glyphicon-chevron-left:before{content:"\e079"}
.glyphicon-chevron-right:before{content:"\e080"}
.glyphicon-plus-sign:before{content:"\e081"}
.glyphicon-minus-sign:before{content:"\e082"}
.glyphicon-remove-sign:before{content:"\e083"}
.glyphicon-ok-sign:before{content:"\e084"}
.glyphicon-question-sign:before{content:"\e085"}
.glyphicon-info-sign:before{content:"\e086"}
.glyphicon-screenshot:before{content:"\e087"}
.glyphicon-remove-circle:before{content:"\e088"}
.glyphicon-ok-circle:before{content:"\e089"}
.glyphicon-ban-circle:before{content:"\e090"}
.glyphicon-arrow-left:before{content:"\e091"}
.glyphicon-arrow-right:before{content:"\e092"}
.glyphicon-arrow-up:before{content:"\e093"}
.glyphicon-arrow-down:before{content:"\e094"}
.glyphicon-share-alt:before{content:"\e095"}
.glyphicon-resize-full:before{content:"\e096"}
.glyphicon-resize-small:before{content:"\e097"}
.glyphicon-exclamation-sign:before{content:"\e101"}
.glyphicon-gift:before{content:"\e102"}
.glyphicon-leaf:before{content:"\e103"}
.glyphicon-fire:before{content:"\e104"}
.glyphicon-eye-open:before{content:"\e105"}
.glyphicon-eye-close:before{content:"\e106"}
.glyphicon-warning-sign:before{content:"\e107"}
.glyphicon-plane:before{content:"\e108"}
.glyphicon-calendar:before{content:"\e109"}
.glyphicon-random:before{content:"\e110"}
.glyphicon-comment:before{content:"\e111"}
.glyphicon-magnet:before{content:"\e112"}
.glyphicon-chevron-up:before{content:"\e113"}
.glyphicon-chevron-down:before{content:"\e114"}
.glyphicon-retweet:before{content:"\e115"}
.glyphicon-shopping-cart:before{content:"\e116"}
.glyphicon-folder-close:before{content:"\e117"}
.glyphicon-folder-open:before{content:"\e118"}
.glyphicon-resize-vertical:before{content:"\e119"}
.glyphicon-resize-horizontal:before{content:"\e120"}
.glyphicon-hdd:before{content:"\e121"}
.glyphicon-bullhorn:before{content:"\e122"}
.glyphicon-bell:before{content:"\e123"}
.glyphicon-certificate:before{content:"\e124"}
.glyphicon-thumbs-up:before{content:"\e125"}
.glyphicon-thumbs-down:before{content:"\e126"}
.glyphicon-hand-right:before{content:"\e127"}
.glyphicon-hand-left:before{content:"\e128"}
.glyphicon-hand-up:before{content:"\e129"}
.glyphicon-hand-down:before{content:"\e130"}
.glyphicon-circle-arrow-right:before{content:"\e131"}
.glyphicon-circle-arrow-left:before{content:"\e132"}
.glyphicon-circle-arrow-up:before{content:"\e133"}
.glyphicon-circle-arrow-down:before{content:"\e134"}
.glyphicon-globe:before{content:"\e135"}
.glyphicon-wrench:before{content:"\e136"}
.glyphicon-tasks:before{content:"\e137"}
.glyphicon-filter:before{content:"\e138"}
.glyphicon-briefcase:before{content:"\e139"}
.glyphicon-fullscreen:before{content:"\e140"}
.glyphicon-dashboard:before{content:"\e141"}
.glyphicon-paperclip:before{content:"\e142"}
.glyphicon-heart-empty:before{content:"\e143"}
.glyphicon-link:before{content:"\e144"}
.glyphicon-phone:before{content:"\e145"}
.glyphicon-pushpin:before{content:"\e146"}
.glyphicon-usd:before{content:"\e148"}
.glyphicon-gbp:before{content:"\e149"}
.glyphicon-sort:before{content:"\e150"}
.glyphicon-sort-by-alphabet:before{content:"\e151"}
.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}
.glyphicon-sort-by-order:before{content:"\e153"}
.glyphicon-sort-by-order-alt:before{content:"\e154"}
.glyphicon-sort-by-attributes:before{content:"\e155"}
.glyphicon-sort-by-attributes-alt:before{content:"\e156"}
.glyphicon-unchecked:before{content:"\e157"}
.glyphicon-expand:before{content:"\e158"}
.glyphicon-collapse-down:before{content:"\e159"}
.glyphicon-collapse-up:before{content:"\e160"}
.glyphicon-log-in:before{content:"\e161"}
.glyphicon-flash:before{content:"\e162"}
.glyphicon-log-out:before{content:"\e163"}
.glyphicon-new-window:before{content:"\e164"}
.glyphicon-record:before{content:"\e165"}
.glyphicon-save:before{content:"\e166"}
.glyphicon-open:before{content:"\e167"}
.glyphicon-saved:before{content:"\e168"}
.glyphicon-import:before{content:"\e169"}
.glyphicon-export:before{content:"\e170"}
.glyphicon-send:before{content:"\e171"}
.glyphicon-floppy-disk:before{content:"\e172"}
.glyphicon-floppy-saved:before{content:"\e173"}
.glyphicon-floppy-remove:before{content:"\e174"}
.glyphicon-floppy-save:before{content:"\e175"}
.glyphicon-floppy-open:before{content:"\e176"}
.glyphicon-credit-card:before{content:"\e177"}
.glyphicon-transfer:before{content:"\e178"}
.glyphicon-cutlery:before{content:"\e179"}
.glyphicon-header:before{content:"\e180"}
.glyphicon-compressed:before{content:"\e181"}
.glyphicon-earphone:before{content:"\e182"}
.glyphicon-phone-alt:before{content:"\e183"}
.glyphicon-tower:before{content:"\e184"}
.glyphicon-stats:before{content:"\e185"}
.glyphicon-sd-video:before{content:"\e186"}
.glyphicon-hd-video:before{content:"\e187"}
.glyphicon-subtitles:before{content:"\e188"}
.glyphicon-sound-stereo:before{content:"\e189"}
.glyphicon-sound-dolby:before{content:"\e190"}
.glyphicon-sound-5-1:before{content:"\e191"}
.glyphicon-sound-6-1:before{content:"\e192"}
.glyphicon-sound-7-1:before{content:"\e193"}
.glyphicon-copyright-mark:before{content:"\e194"}
.glyphicon-registration-mark:before{content:"\e195"}
.glyphicon-cloud-download:before{content:"\e197"}
.glyphicon-cloud-upload:before{content:"\e198"}
.glyphicon-tree-conifer:before{content:"\e199"}
.glyphicon-tree-deciduous:before{content:"\e200"}
.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px solid;border-right:4px solid transparent;border-left:4px solid transparent}
.dropdown{position:relative}
.dropdown-toggle:focus{outline:0}
.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;font-size:13px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,0.175);box-shadow:0 6px 12px rgba(0,0,0,0.175);background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}
.dropdown-menu .divider{height:1px;margin:8px 0;overflow:hidden;background-color:#e5e5e5}
.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:1.428571429;color:#333;white-space:nowrap}
.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus{text-decoration:none;color:#262626;background-color:#f5f5f5}
.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;outline:0;background-color:#428bca}
.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}
.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);cursor:not-allowed}
.open>.dropdown-menu{display:block}
.open>a{outline:0}
.dropdown-menu-right{left:auto;right:0}
.dropdown-menu-left{left:0;right:auto}
.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.428571429;color:#999}
.dropdown-backdrop{position:fixed;left:0;right:0;bottom:0;top:0;z-index:990}
.pull-right>.dropdown-menu{right:0;left:auto}
.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid;content:""}
.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}
@media (min-width:768px){.navbar-right .dropdown-menu{left:auto;right:0} .navbar-right .dropdown-menu-left{left:0;right:auto}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;float:left}.btn-group>.btn:hover,.btn-group-vertical>.btn:hover,.btn-group>.btn:focus,.btn-group-vertical>.btn:focus,.btn-group>.btn:active,.btn-group-vertical>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn.active{z-index:2}
.btn-group>.btn:focus,.btn-group-vertical>.btn:focus{outline:none}
.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}
.btn-toolbar{margin-left:-5px}.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}
.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}
.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}
.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-bottom-right-radius:0;border-top-right-radius:0}
.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}
.btn-group>.btn-group{float:left}
.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}
.btn-group>.btn-group:first-child>.btn:last-child,.btn-group>.btn-group:first-child>.dropdown-toggle{border-bottom-right-radius:0;border-top-right-radius:0}
.btn-group>.btn-group:last-child>.btn:first-child{border-bottom-left-radius:0;border-top-left-radius:0}
.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}
.btn-group>.btn+.dropdown-toggle{padding-left:8px;padding-right:8px}
.btn-group>.btn-lg+.dropdown-toggle{padding-left:12px;padding-right:12px}
.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,0.125);box-shadow:inset 0 3px 5px rgba(0,0,0,0.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}
.btn .caret{margin-left:0}
.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}
.dropup .btn-lg .caret{border-width:0 5px 5px}
.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}
.btn-group-vertical>.btn-group>.btn{float:none}
.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}
.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}
.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}
.btn-group-vertical>.btn:last-child:not(:first-child){border-bottom-left-radius:4px;border-top-right-radius:0;border-top-left-radius:0}
.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}
.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}
.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-right-radius:0;border-top-left-radius:0}
.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{float:none;display:table-cell;width:1%}
.btn-group-justified>.btn-group .btn{width:100%}
[data-toggle="buttons"]>.btn>input[type="radio"],[data-toggle="buttons"]>.btn>input[type="checkbox"]{display:none}
.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*="col-"]{float:none;padding-left:0;padding-right:0}
.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}
.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:45px;padding:10px 16px;font-size:17px;line-height:1.33;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:45px;line-height:45px}
textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn,select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn{height:auto}
.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}
textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn,select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn{height:auto}
.input-group-addon,.input-group-btn,.input-group .form-control{display:table-cell}.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child),.input-group .form-control:not(:first-child):not(:last-child){border-radius:0}
.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}
.input-group-addon{padding:6px 12px;font-size:13px;font-weight:normal;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}
.input-group-addon.input-lg{padding:10px 16px;font-size:17px;border-radius:6px}
.input-group-addon input[type="radio"],.input-group-addon input[type="checkbox"]{margin-top:0}
.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle),.input-group-btn:last-child>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-top-right-radius:0}
.input-group-addon:first-child{border-right:0}
.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:first-child>.btn-group:not(:first-child)>.btn{border-bottom-left-radius:0;border-top-left-radius:0}
.input-group-addon:last-child{border-left:0}
.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}
.input-group-btn>.btn:hover,.input-group-btn>.btn:focus,.input-group-btn>.btn:active{z-index:2}
.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}
.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{margin-left:-1px}
.nav{margin-bottom:0;padding-left:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:hover,.nav>li>a:focus{text-decoration:none;background-color:#eee}
.nav>li.disabled>a{color:#999}.nav>li.disabled>a:hover,.nav>li.disabled>a:focus{color:#999;text-decoration:none;background-color:transparent;cursor:not-allowed}
.nav .open>a,.nav .open>a:hover,.nav .open>a:focus{background-color:#eee;border-color:#428bca}
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.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.428571429;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}
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.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}
.nav-pills>li+li{margin-left:2px}
.nav-pills>li.active>a,.nav-pills>li.active>a:hover,.nav-pills>li.active>a:focus{color:#fff;background-color:#428bca}
.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}
.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{text-align:center;margin-bottom:5px}
.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}
@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}
.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}
.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:hover,.nav-tabs-justified>.active>a:focus{border:1px solid #ddd}
@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0} .nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:hover,.nav-tabs-justified>.active>a:focus{border-bottom-color:#fff}}
.tab-content>.tab-pane{display:none}
.tab-content>.active{display:block}
.nav-tabs .dropdown-menu{margin-top:-1px;border-top-right-radius:0;border-top-left-radius:0}
.navbar{position:relative;min-height:36px;margin-bottom:18px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}
@media (min-width:768px){.navbar-header{float:left}}
.navbar-collapse{max-height:340px;overflow-x:visible;padding-right:15px;padding-left:15px;border-top:1px solid transparent;box-shadow:inset 0 1px 0 rgba(255,255,255,0.1);-webkit-overflow-scrolling:touch}.navbar-collapse.in{overflow-y:auto}
@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;box-shadow:none}.navbar-collapse.collapse{display:block !important;height:auto !important;padding-bottom:0;overflow:visible !important} .navbar-collapse.in{overflow-y:visible} .navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse,.navbar-fixed-bottom .navbar-collapse{padding-left:0;padding-right:0}}
.container>.navbar-header,.container-fluid>.navbar-header,.container>.navbar-collapse,.container-fluid>.navbar-collapse{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container>.navbar-header,.container-fluid>.navbar-header,.container>.navbar-collapse,.container-fluid>.navbar-collapse{margin-right:0;margin-left:0}}
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.navbar-fixed-top,.navbar-fixed-bottom{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-top,.navbar-fixed-bottom{border-radius:0}}
.navbar-fixed-top{top:0;border-width:0 0 1px}
.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}
.navbar-brand{float:left;padding:9px 15px;font-size:17px;line-height:18px;height:36px}.navbar-brand:hover,.navbar-brand:focus{text-decoration:none}
@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}
.navbar-toggle{position:relative;float:right;margin-right:15px;padding:9px 10px;margin-top:1px;margin-bottom:1px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:none}
.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}
.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}
@media (min-width:768px){.navbar-toggle{display:none}}
.navbar-nav{margin:4.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:18px}
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@media (min-width:768px){.navbar-form{width:auto;border:0;margin-left:0;margin-right:0;padding-top:0;padding-bottom:0;-webkit-box-shadow:none;box-shadow:none}.navbar-form.navbar-right:last-child{margin-right:-15px}}
.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-right-radius:0;border-top-left-radius:0}
.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{border-bottom-right-radius:0;border-bottom-left-radius:0}
.navbar-btn{margin-top:2px;margin-bottom:2px}.navbar-btn.btn-sm{margin-top:3px;margin-bottom:3px}
.navbar-btn.btn-xs{margin-top:7px;margin-bottom:7px}
.navbar-text{margin-top:9px;margin-bottom:9px}@media (min-width:768px){.navbar-text{float:left;margin-left:15px;margin-right:15px}.navbar-text.navbar-right:last-child{margin-right:0}}
.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:hover,.navbar-default .navbar-brand:focus{color:#5e5e5e;background-color:transparent}
.navbar-default .navbar-text{color:#777}
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.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:hover,.navbar-default .navbar-toggle:focus{background-color:#ddd}
.navbar-default .navbar-toggle .icon-bar{background-color:#888}
.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}
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.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}
.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#999}.navbar-inverse .navbar-brand:hover,.navbar-inverse .navbar-brand:focus{color:#fff;background-color:transparent}
.navbar-inverse .navbar-text{color:#999}
.navbar-inverse .navbar-nav>li>a{color:#999}.navbar-inverse .navbar-nav>li>a:hover,.navbar-inverse .navbar-nav>li>a:focus{color:#fff;background-color:transparent}
.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:hover,.navbar-inverse .navbar-nav>.active>a:focus{color:#fff;background-color:#080808}
.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:hover,.navbar-inverse .navbar-nav>.disabled>a:focus{color:#444;background-color:transparent}
.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:hover,.navbar-inverse .navbar-toggle:focus{background-color:#333}
.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}
.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}
.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:hover,.navbar-inverse .navbar-nav>.open>a:focus{background-color:#080808;color:#fff}
@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808} .navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808} .navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#999}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus{color:#fff;background-color:transparent} .navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus{color:#fff;background-color:#080808} .navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus{color:#444;background-color:transparent}}
.navbar-inverse .navbar-link{color:#999}.navbar-inverse .navbar-link:hover{color:#fff}
.breadcrumb{padding:8px 15px;margin-bottom:18px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{content:"/\00a0";padding:0 5px;color:#5e5e5e}
.breadcrumb>.active{color:#999}
.pagination{display:inline-block;padding-left:0;margin:18px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;line-height:1.428571429;text-decoration:none;color:#428bca;background-color:#fff;border:1px solid #ddd;margin-left:-1px}
.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-bottom-left-radius:4px;border-top-left-radius:4px}
.pagination>li:last-child>a,.pagination>li:last-child>span{border-bottom-right-radius:4px;border-top-right-radius:4px}
.pagination>li>a:hover,.pagination>li>span:hover,.pagination>li>a:focus,.pagination>li>span:focus{color:#2a6496;background-color:#eee;border-color:#ddd}
.pagination>.active>a,.pagination>.active>span,.pagination>.active>a:hover,.pagination>.active>span:hover,.pagination>.active>a:focus,.pagination>.active>span:focus{z-index:2;color:#fff;background-color:#428bca;border-color:#428bca;cursor:default}
.pagination>.disabled>span,.pagination>.disabled>span:hover,.pagination>.disabled>span:focus,.pagination>.disabled>a,.pagination>.disabled>a:hover,.pagination>.disabled>a:focus{color:#999;background-color:#fff;border-color:#ddd;cursor:not-allowed}
.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:17px}
.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-bottom-left-radius:6px;border-top-left-radius:6px}
.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-bottom-right-radius:6px;border-top-right-radius:6px}
.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px}
.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-bottom-left-radius:3px;border-top-left-radius:3px}
.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-bottom-right-radius:3px;border-top-right-radius:3px}
.pager{padding-left:0;margin:18px 0;list-style:none;text-align:center}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}
.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#eee}
.pager .next>a,.pager .next>span{float:right}
.pager .previous>a,.pager .previous>span{float:left}
.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;background-color:#fff;cursor:not-allowed}
.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:bold;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}.label[href]:hover,.label[href]:focus{color:#fff;text-decoration:none;cursor:pointer}
.label:empty{display:none}
.btn .label{position:relative;top:-1px}
.label-default{background-color:#999}.label-default[href]:hover,.label-default[href]:focus{background-color:#808080}
.label-primary{background-color:#428bca}.label-primary[href]:hover,.label-primary[href]:focus{background-color:#3071a9}
.label-success{background-color:#5cb85c}.label-success[href]:hover,.label-success[href]:focus{background-color:#449d44}
.label-info{background-color:#5bc0de}.label-info[href]:hover,.label-info[href]:focus{background-color:#31b0d5}
.label-warning{background-color:#f0ad4e}.label-warning[href]:hover,.label-warning[href]:focus{background-color:#ec971f}
.label-danger{background-color:#d9534f}.label-danger[href]:hover,.label-danger[href]:focus{background-color:#c9302c}
.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:bold;color:#fff;line-height:1;vertical-align:baseline;white-space:nowrap;text-align:center;background-color:#999;border-radius:10px}.badge:empty{display:none}
.btn .badge{position:relative;top:-1px}
.btn-xs .badge{top:0;padding:1px 5px}
a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}
a.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#428bca;background-color:#fff}
.nav-pills>li>a>.badge{margin-left:3px}
.jumbotron{padding:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron h1,.jumbotron .h1{color:inherit}
.jumbotron p{margin-bottom:15px;font-size:20px;font-weight:200}
.container .jumbotron{border-radius:6px}
.jumbotron .container{max-width:100%}
@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron{padding-left:60px;padding-right:60px} .jumbotron h1,.jumbotron .h1{font-size:58.5px}}
.thumbnail{display:block;padding:4px;margin-bottom:18px;line-height:1.428571429;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.thumbnail>img,.thumbnail a>img{margin-left:auto;margin-right:auto}
a.thumbnail:hover,a.thumbnail:focus,a.thumbnail.active{border-color:#428bca}
.thumbnail .caption{padding:9px;color:#000}
.alert{padding:15px;margin-bottom:18px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}
.alert .alert-link{font-weight:bold}
.alert>p,.alert>ul{margin-bottom:0}
.alert>p+p{margin-top:5px}
.alert-dismissable{padding-right:35px}.alert-dismissable .close{position:relative;top:-2px;right:-21px;color:inherit}
.alert-success{background-color:#dff0d8;border-color:#d6e9c6;color:#3c763d}.alert-success hr{border-top-color:#c9e2b3}
.alert-success .alert-link{color:#2b542c}
.alert-info{background-color:#d9edf7;border-color:#bce8f1;color:#31708f}.alert-info hr{border-top-color:#a6e1ec}
.alert-info .alert-link{color:#245269}
.alert-warning{background-color:#fcf8e3;border-color:#faebcc;color:#8a6d3b}.alert-warning hr{border-top-color:#f7e1b5}
.alert-warning .alert-link{color:#66512c}
.alert-danger{background-color:#f2dede;border-color:#ebccd1;color:#a94442}.alert-danger hr{border-top-color:#e4b9c0}
.alert-danger .alert-link{color:#843534}
@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}.progress{overflow:hidden;height:18px;margin-bottom:18px;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1)}
.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:18px;color:#fff;text-align:center;background-color:#428bca;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-transition:width .6s ease;transition:width .6s ease}
.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:40px 40px}
.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}
.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
.media,.media-body{overflow:hidden;zoom:1}
.media,.media .media{margin-top:15px}
.media:first-child{margin-top:0}
.media-object{display:block}
.media-heading{margin:0 0 5px}
.media>.pull-left{margin-right:10px}
.media>.pull-right{margin-left:10px}
.media-list{padding-left:0;list-style:none}
.list-group{margin-bottom:20px;padding-left:0}
.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-right-radius:4px;border-top-left-radius:4px}
.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}
.list-group-item>.badge{float:right}
.list-group-item>.badge+.badge{margin-right:5px}
a.list-group-item{color:#555}a.list-group-item .list-group-item-heading{color:#333}
a.list-group-item:hover,a.list-group-item:focus{text-decoration:none;background-color:#f5f5f5}
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@media (max-width:767px){.visible-xs{display:block !important}table.visible-xs{display:table} tr.visible-xs{display:table-row !important} th.visible-xs,td.visible-xs{display:table-cell !important}}
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@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none !important}}
@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none !important}}
@media (min-width:1200px){.hidden-lg{display:none !important}}
.visible-print{display:none !important}@media print{.visible-print{display:block !important}table.visible-print{display:table} tr.visible-print{display:table-row !important} th.visible-print,td.visible-print{display:table-cell !important}}
@media print{.hidden-print{display:none !important}}
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.icon-stack .icon-stack-base{font-size:2em;*line-height:1em}
.icon-spin{display:inline-block;-moz-animation:spin 2s infinite linear;-o-animation:spin 2s infinite linear;-webkit-animation:spin 2s infinite linear;animation:spin 2s infinite linear}
a .icon-stack,a .icon-spin{display:inline-block;text-decoration:none}
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.icon-flip-horizontal:before{-webkit-transform:scale(-1, 1);-moz-transform:scale(-1, 1);-ms-transform:scale(-1, 1);-o-transform:scale(-1, 1);transform:scale(-1, 1)}
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a .icon-rotate-90:before,a .icon-rotate-180:before,a .icon-rotate-270:before,a .icon-flip-horizontal:before,a .icon-flip-vertical:before{display:inline-block}
.icon-glass:before{content:"\f000"}
.icon-music:before{content:"\f001"}
.icon-search:before{content:"\f002"}
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.icon-refresh:before{content:"\f021"}
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.icon-move:before{content:"\f047"}
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.icon-play:before{content:"\f04b"}
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.icon-mail-forward:before,.icon-share-alt:before{content:"\f064"}
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.icon-gears:before,.icon-cogs:before{content:"\f085"}
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.icon-linkedin-sign:before{content:"\f08c"}
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.icon-github-sign:before{content:"\f092"}
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.icon-phone-sign:before{content:"\f098"}
.icon-twitter:before{content:"\f099"}
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.icon-unlock:before{content:"\f09c"}
.icon-credit-card:before{content:"\f09d"}
.icon-rss:before{content:"\f09e"}
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<h1 id="manipulation-et-visualisation-de-donn-es-en-python">Manipulation et visualisation de données en Python</h1>
<h1 id="introduction-numpy-et-matplotlib">Introduction à NumPy et Matplotlib</h1>
<h3 id="alexandre-gramfort">Alexandre Gramfort</h3>
<h3 id="institut-mines-t-l-com-t-l-com-paristech">Institut Mines-Télécom - Télécom ParisTech</h3>
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<div class=" highlight hl-ipython"><pre><span class="c"># et JUSTE POUR MOI (pour avoir les figures dans le notebook)</span>
<span class="o">%</span><span class="k">matplotlib</span> <span class="n">inline</span>
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<h2 id="qu-est-ce-que-numpy-">Qu&#39;est-ce-que NumPy?</h2>
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<li><code>NumPy</code> est une librairie utilisée dans presque tous les projets de calcul numérique sous Python</li>
<li><code>NumPy</code> fournit des structures de données performantes pour la manipulation de vecteurs, de matrices, de tenseurs. On parlera de tableau ou <code>array</code> en Anglais</li>
<li><code>NumPy</code> est écrit en C et en Fortran d&#39;où ses performances élevées lorsque les calculs sont vectorisés (formulés comme des opérations sur des tableaux)</li>
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<h2 id="qu-est-ce-que-matplotlib-">Qu&#39;est-ce-que matplotlib?</h2>
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<li><code>matplotlib</code> est une librairie pour la génération de graphiques en 2D et 3D<ul>
<li>syntaxe très proche de celle de Matlab</li>
<li>supporte texte et étiquettes en LaTeX</li>
<li>sortie de qualité dans divers formats (PNG, PDF, SVG, EPS...)</li>
<li>interface graphique intéractive pour explorer les figures</li>
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<p>Pour utiliser <code>NumPy</code> et <code>matplotlib</code> il faut commencer par les importer :</p>
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<div class=" highlight hl-ipython"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
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<p><strong>np</strong> et <strong>plt</strong> sont les abréviations d&#39;import classiques.</p>
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<h2 id="cr-ation-de-tableaux-arrays-numpy-">Création de tableaux (arrays) <code>numpy</code></h2>
<p>Plusieurs possibilités:</p>
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<li>à partir de listes ou n-uplets Python</li>
<li>en utilisant des fonctions dédiées, telles que <code>arange</code>, <code>linspace</code>, etc.</li>
<li>par chargement à partir de fichiers</li>
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<h3 id="a-partir-de-listes">A partir de listes</h3>
<p>Au moyen de la fonction <code>np.array</code> :</p>
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<div class=" highlight hl-ipython"><pre><span class="c"># un vecteur à partir d&#39;une liste Python</span>
<span class="n">v</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="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">v</span><span class="p">))</span>
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<pre>[1 3 2 4]
&lt;type &apos;numpy.ndarray&apos;&gt;
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<p>On peut alors visualiser ces données avec matplotlib:</p>
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<div class=" highlight hl-ipython"><pre><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">v</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>On peut omettre <code>plt.show()</code>, lorsque la fonction <code>plt.ion()</code> a été appelée ; c&#39;est le cas dans Spyder.</p>
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<div class="text_cell_render border-box-sizing rendered_html">
<p>Un peu plus compliqué:</p>
</div>
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<div class="input">
<div class="prompt input_prompt">
In&nbsp;[5]:
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    <div class="input_area">
<div class=" highlight hl-ipython"><pre><span class="n">x</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="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</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">x</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="s">&#39;r--&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;vecteur&#39;</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="s">&#39;x&#39;</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="s">&#39;v&#39;</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="s">&#39;Exemple titre&#39;</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="s">&#39;lower right&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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WYpFOi4f0+IpsxxHunXBJLoqTNfXO3bDSbuFwFygwXYgHnGAN4BtwD9ajiWd/gY4glm2ZwfwFKda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"
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<p>Tableau de dimension 2 (matrice):</p>
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<div class=" highlight hl-ipython"><pre><span class="c"># une matrice: l&#39;argument est une liste emboitée</span>
<span class="n">M</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="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
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<pre>[[1 3]
 [2 4]]
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<span class="k">print</span><span class="p">(</span><span class="n">M</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="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
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<pre>1
4
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<p>La variable <code>M</code> est aussi du type <code>ndarray</code></p>
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<pre>&lt;type &apos;numpy.ndarray&apos;&gt;
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<h3 id="l-attribut-shape-">L&#39;attribut <code>shape</code></h3>
<p><code>v</code> et <code>M</code> diffèrent par leur taille:</p>
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<pre>(4,)
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<pre>(2, 2)
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<pre>&lt;type &apos;tuple&apos;&gt;
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<h3 id="l-attribut-ndim-">L&#39;attribut <code>ndim</code></h3>
<p>il permet de savoir quel est la dimension d&#39;un ndarray</p>
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<pre>1
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<pre>2
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<h3 id="l-attribut-size-">L&#39;attribut <code>size</code></h3>
<p>il permet de savoir quel est le nombre d&#39;éléments dans un <code>ndarray</code></p>
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<pre>4
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<pre>4
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<h3 id="exemple-d-un-tableau-3-dimensions">Exemple d&#39;un tableau à 3 dimensions</h3>
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<div class=" highlight hl-ipython"><pre><span class="n">T</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="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">]]])</span>
<span class="k">print</span><span class="p">(</span><span class="n">T</span><span class="p">)</span>
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<pre>[[[1 3 2 4]]]
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<pre>(1, 1, 4)
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<pre>3
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<pre>4
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<pre>4</pre>
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<p>Suite au prochain épisode...</p>
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<h2 id="tableaux-numpy-la-suite-">Tableaux NumPy (la suite)</h2>
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<span class="k">print</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
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<pre>[1 3 2 4]
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<span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
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<pre>[[1 3]
 [2 4]]
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<h3 id="l-attribut-dtype-">L&#39;attribut <code>dtype</code></h3>
<ul>
<li><p>Un <em>ndarray</em> peut contenir des nombres entiers, flottants, complexes etc.</p>
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<li><p>On obtient le type des données via l&#39;attribut <code>dtype</code></p>
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<li><p><code>dtype</code> est l&#39;abréviation de <em>data type</em></p>
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<span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
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<pre>[[1 3]
 [2 4]]
int64
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<h3 id="plus-d-attributs">Plus d&#39;attributs</h3>
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<div class=" highlight hl-ipython"><pre><span class="n">M</span><span class="o">.</span><span class="n">itemsize</span> <span class="c"># nombre d&#39;octets par élément</span>
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<pre>8</pre>
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<pre>32</pre>
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<pre>8</pre>
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<h3 id="assignation-l-ment-par-l-ment">Assignation élément par élément</h3>
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
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<pre>[[1 3]
 [2 4]]
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<div class=" highlight hl-ipython"><pre><span class="n">M</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="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
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<pre>[[-1  3]
 [ 2  4]]
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<p>Les types doivent être respectés lors d&#39;assignations</p>
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<div class=" highlight hl-ipython"><pre><span class="n">M</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="o">=</span> <span class="s">&quot;hello&quot;</span>
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<span class="ansired">---------------------------------------------------------------------------</span>
<span class="ansired">ValueError</span>                                Traceback (most recent call last)
<span class="ansigreen">&lt;ipython-input-29-a09d72434238&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span>M<span class="ansiblue">[</span><span class="ansicyan">0</span><span class="ansiblue">,</span><span class="ansicyan">0</span><span class="ansiblue">]</span> <span class="ansiblue">=</span> <span class="ansiblue">&quot;hello&quot;</span><span class="ansiblue"></span>

<span class="ansired">ValueError</span>: invalid literal for long() with base 10: &apos;hello&apos;</pre>
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<h3 id="attention-">Attention !</h3>
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<div class=" highlight hl-ipython"><pre><span class="n">a</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="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="k">print</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">3.2</span>  <span class="c"># perte de précision</span>
<span class="k">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
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<pre>int64
[3 0 0]
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<p>On peut définir le type de manière explicite en utilisant le mot clé <code>dtype</code> en argument:</p>
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<div class=" highlight hl-ipython"><pre><span class="n">a</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="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="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">3.2</span>
<span class="k">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
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<pre>[ 3.2  0.   0. ]
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<li><p>Autres options possibles de <code>dtype</code> : <code>int</code>, <code>float</code>, <code>complex</code>, <code>bool</code>, <code>object</code>, etc.</p>
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<li><p>On peut aussi spécifier la précision en bits: <code>int32</code>, <code>int16</code>, <code>float128</code>, <code>complex128</code>, etc.</p>
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<h2 id="changement-de-type">Changement de type</h2>
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<div class=" highlight hl-ipython"><pre><span class="n">M</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="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
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<pre>int64
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<div class=" highlight hl-ipython"><pre><span class="n">M2</span> <span class="o">=</span> <span class="n">M</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>  <span class="c"># conversion en float</span>
<span class="k">print</span><span class="p">(</span><span class="n">M2</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">M2</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
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<pre>[[-1.  2.]
 [ 0.  4.]]
float64
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<span class="k">print</span><span class="p">(</span><span class="n">M3</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">M3</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
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<pre>[[ True  True]
 [False  True]]
bool
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<h3 id="utilisation-de-fonction-de-g-n-ration-d-arrays-">Utilisation de fonction de génération d&#39;<em>arrays</em></h3>
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<h4 id="arange-array-range-">arange &quot;array range&quot;</h4>
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<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<pre>[0 2 4 6 8]
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<div class=" highlight hl-ipython"><pre><span class="n">x</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="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="c"># OK : plus efficace</span>
<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<pre>[0 2 4 6 8]
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<span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<pre>[-1.  -0.5  0.   0.5]
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<h4 id="linspace">linspace</h4>
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<div class=" highlight hl-ipython"><pre><span class="c"># avec linspace, le début et la fin SONT inclus</span>
<span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
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<pre>array([  0.,   2.,   4.,   6.,   8.,  10.])</pre>
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<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</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">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;$y = \sin(x)$&#39;</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>
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<div class=" highlight hl-ipython"><pre><span class="n">a</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="mi">5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
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<pre>[ 0.  1.  2.  3.  4.]
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<pre>[ 1.  2.  3.  4.  5.]
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<pre>[  0.   1.   4.   9.  16.]
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<div class=" highlight hl-ipython"><pre><span class="n">a</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="mi">5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
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<div class=" highlight hl-ipython"><pre><span class="n">a</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="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="n">b</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="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">a</span> <span class="o">/</span> <span class="n">b</span><span class="p">)</span>
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<div class=" highlight hl-ipython"><pre><span class="n">a</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="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="n">b</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="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">a</span> <span class="o">/</span> <span class="n">b</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">a</span> <span class="o">//</span> <span class="n">b</span><span class="p">)</span>
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<pre>[ 0.5  1. ]
[ 0.  1.]
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<div class=" highlight hl-ipython"><pre><span class="c"># tirage uniforme dans [0,1]</span>
<span class="n">random</span><span class="o">.</span><span class="n">random_sample</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>  <span class="c"># ou random.rand</span>
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<pre>array([[ 0.39901777,  0.6412892 ,  0.97677018],
       [ 0.88608808,  0.2866462 ,  0.19678252],
       [ 0.84092386,  0.39505946,  0.78899804]])</pre>
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<div class=" highlight hl-ipython"><pre><span class="c"># tirage suivant une loi normale standard</span>
<span class="n">random</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>  <span class="c"># ou random.randn</span>
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<pre>array([[ 0.08910219, -0.76917745, -1.59923397],
       [-0.31889444, -0.04475295, -1.17248134],
       [ 0.88410981, -1.10103646,  1.45595075]])</pre>
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<p>Affichage de l&#39;histogramme des tirages</p>
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<span class="n">hh</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">a</span><span class="p">,</span> <span class="mi">40</span><span class="p">)</span>
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    <section>
    
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<p>Affichage des tirages comme une image</p>
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In&nbsp;[53]:
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<div class=" highlight hl-ipython"><pre><span class="n">C</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="n">C</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(32, 32)
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In&nbsp;[54]:
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<div class=" highlight hl-ipython"><pre><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">C</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s">&#39;nearest&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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MlnmOZ+QRV2ZKLVz5k/X2hytLbrqEKnxpZZ8AjWwvaZONnO7TzaXjewvtb/ynNRW0VBqC05rKrUc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<h4 id="zeros-et-ones">zeros et ones</h4>
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<div class=" highlight hl-ipython"><pre><span class="c"># Création d&#39;un tableau 2D de zeros</span>
<span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="c"># Attention: zeros(2, 3) est FAUX</span>
<span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
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<pre>[[ 0.  0.  0.]
 [ 0.  0.  0.]]
float64
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">))</span>  <span class="c"># ligne</span>
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</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="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">))</span>  <span class="c"># colonne</span>
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<pre>[[0]
 [0]
 [0]]
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>  <span class="c"># tableau 3x3 de 1</span>
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<pre>[[ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]]
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<h4 id="diag">diag</h4>
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<div class=" highlight hl-ipython"><pre><span class="c"># création d&#39;une matrice diagonale</span>
<span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">D</span><span class="p">)</span>
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<pre>[[1 0 0]
 [0 2 0]
 [0 0 3]]
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<div class=" highlight hl-ipython"><pre><span class="c"># extraction de la diagonale</span>
<span class="k">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">D</span><span class="p">))</span>
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<pre>[1 2 3]
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<p>Suite au prochain épisode...</p>
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<h2 id="fichiers-entr-e-sortie">Fichiers - Entrée/Sortie</h2>
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<h3 id="fichiers-s-par-s-par-des-virgules-csv-">Fichiers séparés par des virgules (CSV)</h3>
<p>Un format fichier classique est le format CSV (comma-separated values), ou bien TSV (tab-separated values). Pour lire de tels fichiers utilisez <code>numpy.genfromtxt</code>.</p>
<p>Par exemple:</p>
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<pre>1,2,3,4,5
6,7,8,9,10
1,3,3,4,6
1,2,3,4,20</pre>
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<span class="k">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
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<pre>[[  1.   2.   3.   4.   5.]
 [  6.   7.   8.   9.  10.]
 [  1.   3.   3.   4.   6.]
 [  1.   2.   3.   4.  20.]]
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<pre>(4, 5)
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<p>A l&#39;aide de <code>numpy.savetxt</code> on peut enregistrer un <em>array</em> <code>numpy</code> dans un fichier txt:</p>
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<div class=" highlight hl-ipython"><pre><span class="n">M</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
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<pre>[[-2.3700268  -0.24567366 -2.33387944]
 [ 0.07599637  2.04665479  0.99520716]
 [ 1.89936908 -0.48671053  0.55652171]]
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<div class=" highlight hl-ipython"><pre><span class="n">np</span><span class="o">.</span><span class="n">savetxt</span><span class="p">(</span><span class="s">&quot;random-data.csv&quot;</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span>
           <span class="n">fmt</span><span class="o">=</span><span class="s">&#39; </span><span class="si">% 2.3f</span><span class="s">&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s">&#39;,&#39;</span><span class="p">)</span>
<span class="o">!</span>cat random-data.csv
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<pre> -2.370, -0.246, -2.334
  0.076,  2.047,  0.995
  1.899, -0.487,  0.557
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<h3 id="format-de-fichier-numpy-natif">Format de fichier Numpy natif</h3>
<p>Pour sauvegarder et recharger des tableaux <code>numpy</code> : <code>np.save</code> et <code>np.load</code> :</p>
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<pre>array([[-2.3700268 , -0.24567366, -2.33387944],
       [ 0.07599637,  2.04665479,  0.99520716],
       [ 1.89936908, -0.48671053,  0.55652171]])</pre>
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<h3 id="indexation-avanc-e">Indexation avancée</h3>
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<div class=" highlight hl-ipython"><pre><span class="c"># v ndarray à 1 dimension -&gt; un indice</span>
<span class="k">print</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<pre>1
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<div class=" highlight hl-ipython"><pre><span class="c"># M ndarray à 2 dimensions -&gt; deux indices </span>
<span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
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<pre>3
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In&nbsp;[72]:
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>  <span class="c"># La première ligne</span>
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<pre>[1 3]
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<p>On peut aussi utiliser <code>:</code> </p>
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In&nbsp;[73]:
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:])</span>  <span class="c"># 2 ème ligne (indice 1)</span>
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<pre>[2 4]
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>  <span class="c"># 2 ème colonne (indice 1)</span>
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<pre>[3 4]
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<h4 id="assignement-de-plus-d-un-l-ment">Assignement de plus d&#39;un élément</h4>
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<pre>[[1 3]
 [2 4]]
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<div class=" highlight hl-ipython"><pre><span class="n">M</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>  <span class="c"># assigne d&#39;un élément à toute une ligne</span>
<span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
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<pre>[[ 1  3]
 [-1 -1]]
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<div class=" highlight hl-ipython"><pre><span class="n">M</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>  <span class="c"># assignement de plusieurs éléments</span>
<span class="k">print</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
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<pre>[[ 1  5]
 [-1  6]]
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<h2 id="-slicing-"><em>Slicing</em></h2>
<p><em>Slicing</em> fait référence à la syntaxe <code>M[start:stop:step]</code> pour extraire une partie d&#39;un <em>array</em> :</p>
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<div class=" highlight hl-ipython"><pre><span class="n">A</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="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
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<pre>[1 2 3 4 5]
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">3</span><span class="p">])</span>
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<pre>[2 3]
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<p>On peut omettre n&#39;importe lequel des arguments dans <code>M[start:stop:step]</code>:</p>
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">[::])</span>
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<pre>[1 2 3 4 5]
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">[::</span><span class="mi">2</span><span class="p">])</span> <span class="c"># step = 2</span>
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<pre>[1 3 5]
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<pre>[1 2 3]
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<pre>[4 5]
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<p>On peut utiliser des indices négatifs :</p>
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<div class=" highlight hl-ipython"><pre><span class="n">A</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="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
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<pre>5
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:])</span> <span class="c"># les 3 derniers éléments</span>
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<pre>[3 4 5]
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<h3 id="-slicing-sur-les-tableaux-multi-dimensionnels"><em>slicing</em> sur les tableaux multi-dimensionnels</h3>
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<div class=" highlight hl-ipython"><pre><span class="n">A</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">n</span> <span class="o">+</span> <span class="n">m</span> <span class="o">*</span> <span class="mi">10</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)]</span>
              <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)])</span>
<span class="n">A</span>
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<pre>array([[ 0,  1,  2,  3],
       [10, 11, 12, 13],
       [20, 21, 22, 23],
       [30, 31, 32, 33]])</pre>
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<div class=" highlight hl-ipython"><pre><span class="c"># un bloc du tableau</span>
<span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="mi">1</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="mi">3</span><span class="p">])</span>
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<pre>[[11 12]
 [21 22]]
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">[::</span><span class="mi">2</span><span class="p">,</span> <span class="p">::</span><span class="mi">2</span><span class="p">])</span>  <span class="c"># 1 ligne sur 2 et 1 colonne sur 2</span>
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<pre>[[ 0  2]
 [20 22]]
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<h3 id="indexation-avanc-e-fancy-indexing-">Indexation avancée (<em>fancy indexing</em>)</h3>
<p>Utilisation de listes ou de tableaux pour accéder à plusieurs éléments.</p>
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<pre>[[ 0  1  2  3]
 [10 11 12 13]
 [20 21 22 23]
 [30 31 32 33]]
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<div class=" highlight hl-ipython"><pre><span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
<span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="n">indices</span><span class="p">])</span>  <span class="c"># lignes d&#39;indices 1 et 3</span>
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<pre>[[10 11 12 13]
 [30 31 32 33]]
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<span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">[:,</span> <span class="n">indices</span><span class="p">])</span>  <span class="c"># colonnes d&#39;indices 1 et 3</span>
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<pre>[[ 1  3]
 [11 13]
 [21 23]
 [31 33]]
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<div class=" highlight hl-ipython"><pre><span class="c"># éléments d&#39;indices [0,1] et [2,3]</span>
<span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
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<pre>[ 1 23]
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<div class=" highlight hl-ipython"><pre><span class="c"># sous-tableau lignes [0,1] et colonnes [2,3]</span>
<span class="k">print</span> <span class="n">A</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ix_</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">])]</span>
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<pre>[[ 1  3]
 [21 23]]
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<span class="k">print</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
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<pre>[0 1 2 3 4]
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<span class="k">print</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="n">mask</span><span class="p">])</span>
<span class="k">print</span><span class="p">(</span><span class="n">v</span><span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]])</span>
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[0 2]
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">v</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="n">v</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">])</span>
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<pre>[False False False  True  True]
[3 4]
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<p>Suite au prochain épisode...</p>
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<pre>[[ 0  1  2  3]
 [10 11 12 13]
 [20 21 22 23]
 [30 31 32 33]]
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<pre>[[ 0 10 20 30]
 [ 1 11 21 31]
 [ 2 12 22 32]
 [ 3 13 23 33]]
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<div class=" highlight hl-ipython"><pre><span class="p">(</span><span class="n">A</span> <span class="o">+</span> <span class="n">A</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>  <span class="c"># addition terme-à-terme</span>
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<pre>array([[ 0,  5, 11, 16],
       [ 5, 11, 16, 22],
       [11, 16, 22, 27],
       [16, 22, 27, 33]])</pre>
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">A</span><span class="p">))</span>  <span class="c"># trace de A</span>
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<pre>66
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<h3 id="multiplications-de-matrices">Multiplications de matrices</h3>
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<pre>(4, 4)
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<pre>[[   0    1    4    9]
 [ 100  121  144  169]
 [ 400  441  484  529]
 [ 900  961 1024 1089]]
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">A</span><span class="p">))</span>  <span class="c"># multiplication de matrices</span>
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<pre>[[ 140  146  152  158]
 [ 740  786  832  878]
 [1340 1426 1512 1598]
 [1940 2066 2192 2318]]
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<h3 id="multiplication-matrice-vecteur">Multiplication matrice vecteur</h3>
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">v</span><span class="p">))</span>
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<pre>[[ 0  1  2  3]
 [10 11 12 13]
 [20 21 22 23]
 [30 31 32 33]]
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<span class="ansired">---------------------------------------------------------------------------</span>
<span class="ansired">ValueError</span>                                Traceback (most recent call last)
<span class="ansigreen">&lt;ipython-input-105-3bef7781e90d&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
<span class="ansigreen">      1</span> <span class="ansigreen">print</span><span class="ansiblue">(</span>A<span class="ansiblue">)</span><span class="ansiblue"></span>
<span class="ansigreen">----&gt; 2</span><span class="ansired"> </span><span class="ansigreen">print</span><span class="ansiblue">(</span>np<span class="ansiblue">.</span>dot<span class="ansiblue">(</span>A<span class="ansiblue">,</span> v<span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>

<span class="ansired">ValueError</span>: objects are not aligned</pre>
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<pre>array([ 14,  74, 134, 194])</pre>
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<div class=" highlight hl-ipython"><pre><span class="n">B</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">Binv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">Binv</span><span class="p">)</span>
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<pre>[[-0.17547656  1.11964367  1.06455073]
 [-0.57835448 -0.53209909  0.71979545]
 [-0.61317879 -0.23518517 -1.82875743]]
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<pre>[[  1.00000000e+00   8.32667268e-17   0.00000000e+00]
 [  0.00000000e+00   1.00000000e+00   0.00000000e+00]
 [  0.00000000e+00   1.38777878e-17   1.00000000e+00]]
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<li>Calcul de vecteurs/valeurs propres <code>np.linalg.eig</code></li>
<li>Résolution de système linéaire <code>np.linalg.solve</code></li>
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<h3 id="op-rations-de-r-duction-ou-d-aggr-gation-">Opérations de réduction (ou d&#39;aggrégation)</h3>
<p>La performance des programmes écrits en Python/Numpy dépend de la capacité à vectoriser les calculs (les écrire comme des opérations sur des vecteurs/matrices) en évitant au maximum les boucles <code>for/while</code></p>
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<pre>[[ 0  1  2  3]
 [10 11 12 13]
 [20 21 22 23]
 [30 31 32 33]]
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<pre>264</pre>
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<div class=" highlight hl-ipython"><pre><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">A</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="p">:])</span>  <span class="c"># somme de la 2ième ligne</span>
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<pre>46</pre>
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<pre>array([  6,  46,  86, 126])</pre>
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<pre>array([60, 64, 68, 72])</pre>
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<pre>[[ 0  1  2  3]
 [10 11 12 13]
 [20 21 22 23]
 [30 31 32 33]]
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<pre>16.5</pre>
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<pre>11.5</pre>
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<pre>array([  1.5,  11.5,  21.5,  31.5])</pre>
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<pre>array([ 15.,  16.,  17.,  18.])</pre>
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<p>D&#39;autres fonctions de réduction existent: <code>np.var</code> pour la variance, <code>np.std</code> pour l&#39;écart type, <code>np.min</code> pour le minimum, <code>np.max</code> pour le maximum, <code>np.prod</code> pour le produit, <code>np.cumsum</code> pour la somme cumulée ou encore <code>np.cumprod</code> pour le produit cumulé.</p>
<p>Exemple:</p>
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<pre>[[ 0  1  2  3]
 [10 11 12 13]
 [20 21 22 23]
 [30 31 32 33]]
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<div class=" highlight hl-ipython"><pre><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>  <span class="c"># maximum de toutes les colonnes</span>
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<pre>array([30, 31, 32, 33])</pre>
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<p>Suite au prochain épisode...</p>
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<h2 id="copy">Copy</h2>
<p>NumPy ne copie pas automatiquement les tableaux.</p>
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<div class=" highlight hl-ipython"><pre><span class="n">A</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="mi">0</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
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<pre>[[0 2]
 [3 4]]
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<div class=" highlight hl-ipython"><pre><span class="n">B</span> <span class="o">=</span> <span class="n">A</span>  <span class="c"># assignation sans copie</span>
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<div class=" highlight hl-ipython"><pre><span class="n">B</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="o">=</span> <span class="mi">10</span>  <span class="c"># changer B affecte A</span>
<span class="k">print</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
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<pre>[[10  2]
 [ 3  4]]
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<pre>[[10  2]
 [ 3  4]]
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<p>Pour éviter ce comportement, on peut demander que <code>B</code> soit une <em>copie</em> de <code>A</code></p>
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<div class=" highlight hl-ipython"><pre><span class="n">A</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="mi">0</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
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<div class=" highlight hl-ipython"><pre><span class="n">B</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="o">=</span> <span class="o">-</span><span class="mi">5</span>  <span class="c"># B est modifié</span>
<span class="k">print</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
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<pre>[[-5  2]
 [ 3  4]]
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<pre>[[0 2]
 [3 4]]
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<h2 id="changement-de-taille">Changement de taille</h2>
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<span class="k">print</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
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<pre>(2, 2)
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<span class="k">print</span><span class="p">(</span><span class="n">B</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
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<pre>(4,)
[1 2 3 4]
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<span class="k">print</span><span class="p">(</span><span class="n">B</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">B</span><span class="p">)</span>
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<pre>(4, 1)
[[1]
 [2]
 [3]
 [4]]
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<h3 id="concat-ner-r-p-ter-des-arrays-">Concaténer, répéter des <em>arrays</em></h3>
<p>En utilisant les fonctions <code>tile</code>, <code>vstack</code>, <code>hstack</code>, et <code>concatenate</code>, on peut créer des vecteurs/matrices plus grandes à partir de vecteurs/matrices plus petites.</p>
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<span class="k">print</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
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<pre>[[1 2]
 [3 4]]
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<div class=" highlight hl-ipython"><pre><span class="k">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">A</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span> <span class="c"># répéter la matrice 2 fois</span>
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<pre>[[1 2 1 2]
 [3 4 3 4]]
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<h4 id="concatenate">concatenate</h4>
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<pre>[[1 2]
 [3 4]]
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<div class=" highlight hl-ipython"><pre><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<pre>array([[1, 2],
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<div class=" highlight hl-ipython"><pre><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="o">.</span><span class="n">T</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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<pre>array([[1, 2, 5],
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<h4 id="hstack-et-vstack">hstack et vstack</h4>
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<div class=" highlight hl-ipython"><pre><span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">))</span>
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<pre>array([[1, 2],
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       [5, 6]])</pre>
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<div class=" highlight hl-ipython"><pre><span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="o">.</span><span class="n">T</span><span class="p">))</span>
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<p>Rendez-vous sur <a href="http://docs.scipy.org/doc/scipy/reference/">http://docs.scipy.org/doc/scipy/reference/</a> pour découvrir cette librairie d&#39;algorithmes (optimisation, stats, signal, intégration numérique etc.)</p>
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