| 1 | /** |
| 2 | * @license |
| 3 | * Copyright 2013 David Eberlein (david.eberlein@ch.sauter-bc.com) |
| 4 | * MIT-licensed (http://opensource.org/licenses/MIT) |
| 5 | */ |
| 6 | |
| 7 | /** |
| 8 | * @fileoverview DataHandler implementation for the error bars option. |
| 9 | * @author David Eberlein (david.eberlein@ch.sauter-bc.com) |
| 10 | */ |
| 11 | |
| 12 | /*global Dygraph:false */ |
| 13 | "use strict"; |
| 14 | |
| 15 | import BarsHandler from './bars'; |
| 16 | |
| 17 | /** |
| 18 | * @constructor |
| 19 | * @extends BarsHandler |
| 20 | */ |
| 21 | var ErrorBarsHandler = function() { |
| 22 | }; |
| 23 | |
| 24 | ErrorBarsHandler.prototype = new BarsHandler(); |
| 25 | |
| 26 | /** @inheritDoc */ |
| 27 | ErrorBarsHandler.prototype.extractSeries = function(rawData, i, options) { |
| 28 | // TODO(danvk): pre-allocate series here. |
| 29 | var series = []; |
| 30 | var x, y, variance, point; |
| 31 | var sigma = options.get("sigma"); |
| 32 | var logScale = options.get('logscale'); |
| 33 | for ( var j = 0; j < rawData.length; j++) { |
| 34 | x = rawData[j][0]; |
| 35 | point = rawData[j][i]; |
| 36 | if (logScale && point !== null) { |
| 37 | // On the log scale, points less than zero do not exist. |
| 38 | // This will create a gap in the chart. |
| 39 | if (point[0] <= 0 || point[0] - sigma * point[1] <= 0) { |
| 40 | point = null; |
| 41 | } |
| 42 | } |
| 43 | // Extract to the unified data format. |
| 44 | if (point !== null) { |
| 45 | y = point[0]; |
| 46 | if (y !== null && !isNaN(y)) { |
| 47 | variance = sigma * point[1]; |
| 48 | // preserve original error value in extras for further |
| 49 | // filtering |
| 50 | series.push([ x, y, [ y - variance, y + variance, point[1] ] ]); |
| 51 | } else { |
| 52 | series.push([ x, y, [ y, y, y ] ]); |
| 53 | } |
| 54 | } else { |
| 55 | series.push([ x, null, [ null, null, null ] ]); |
| 56 | } |
| 57 | } |
| 58 | return series; |
| 59 | }; |
| 60 | |
| 61 | /** @inheritDoc */ |
| 62 | ErrorBarsHandler.prototype.rollingAverage = |
| 63 | function(originalData, rollPeriod, options) { |
| 64 | rollPeriod = Math.min(rollPeriod, originalData.length); |
| 65 | var rollingData = []; |
| 66 | var sigma = options.get("sigma"); |
| 67 | |
| 68 | var i, j, y, v, sum, num_ok, stddev, variance, value; |
| 69 | |
| 70 | // Calculate the rolling average for the first rollPeriod - 1 points |
| 71 | // where there is not enough data to roll over the full number of points |
| 72 | for (i = 0; i < originalData.length; i++) { |
| 73 | sum = 0; |
| 74 | variance = 0; |
| 75 | num_ok = 0; |
| 76 | for (j = Math.max(0, i - rollPeriod + 1); j < i + 1; j++) { |
| 77 | y = originalData[j][1]; |
| 78 | if (y === null || isNaN(y)) |
| 79 | continue; |
| 80 | num_ok++; |
| 81 | sum += y; |
| 82 | variance += Math.pow(originalData[j][2][2], 2); |
| 83 | } |
| 84 | if (num_ok) { |
| 85 | stddev = Math.sqrt(variance) / num_ok; |
| 86 | value = sum / num_ok; |
| 87 | rollingData[i] = [ originalData[i][0], value, |
| 88 | [value - sigma * stddev, value + sigma * stddev] ]; |
| 89 | } else { |
| 90 | // This explicitly preserves NaNs to aid with "independent |
| 91 | // series". |
| 92 | // See testRollingAveragePreservesNaNs. |
| 93 | v = (rollPeriod == 1) ? originalData[i][1] : null; |
| 94 | rollingData[i] = [ originalData[i][0], v, [ v, v ] ]; |
| 95 | } |
| 96 | } |
| 97 | |
| 98 | return rollingData; |
| 99 | }; |
| 100 | |
| 101 | export default ErrorBarsHandler; |