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December 3, 2020 16:24
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csv_js
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| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Chart using XML Data</title> | |
| <script type="text/javascript" src="https://canvasjs.com/assets/script/jquery-1.11.1.min.js"></script> | |
| <script type="text/javascript" src="https://canvasjs.com/assets/script/canvasjs.min.js"></script> | |
| <script type="text/javascript"> | |
| window.onload = function() { | |
| var dataPoints = []; | |
| // var dataPoints_1 = []; | |
| function getDataPointsFromCSV(csv) { | |
| var dataPoints = csvLines = points = []; | |
| csvLines = csv.split(/[\r?\n|\r|\n]+/); | |
| for (var i = 0; i < csvLines.length; i++) | |
| if (csvLines[i].length > 0) { | |
| points = csvLines[i].split(","); | |
| dataPoints.push({ | |
| x: parseFloat(points[0]), | |
| y: parseFloat(points[1]) | |
| }); | |
| } | |
| return dataPoints; | |
| } | |
| function getDataPointsFromCSV_1(csv) { | |
| var dataPoints = csvLines = points = []; | |
| csvLines = csv.split(/[\r?\n|\r|\n]+/); | |
| for (var i = 0; i < csvLines.length; i++) | |
| if (csvLines[i].length > 0) { | |
| points = csvLines[i].split(","); | |
| dataPoints.push({ | |
| x: parseFloat(points[0]), | |
| // y: parseFloat(points[1]), | |
| y: parseFloat(points[2]) | |
| }); | |
| } | |
| return dataPoints; | |
| } | |
| $.get("https://raw.githubusercontent.com/atifkarim/Time-Series-Forecasting-Using-Machine-Learning-Algorithm/develop/csv_column_2.csv", function(data) { | |
| var chart = new CanvasJS.Chart("chartContainer", { | |
| title: { | |
| text: "Chart from CSV", | |
| }, | |
| data: [{ | |
| type: "line", | |
| dataPoints: getDataPointsFromCSV(data) | |
| }, | |
| { | |
| type: "line", | |
| dataPoints: getDataPointsFromCSV_1(data) | |
| } | |
| ] | |
| }); | |
| chart.render(); | |
| }); | |
| } | |
| </script> | |
| </head> | |
| <body> | |
| <div id="chartContainer" style="width:100%; height:300px;"></div> | |
| </body> | |
| </html> |
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