*/
(function() {
- /*global Dygraph:false */
- "use strict";
- var ErrorBarsHandler = Dygraph.DataHandler();
- ErrorBarsHandler.prototype = Dygraph.DataHandlers.createHandler("bars");
- Dygraph.DataHandlers.registerHandler("bars-error", ErrorBarsHandler);
- // errorBars
- ErrorBarsHandler.prototype.extractSeries = function(rawData, i, options) {
- // TODO(danvk): pre-allocate series here.
- var series = [];
- var x, y, variance, point;
- var sigma = options.get("sigma");
- var logScale = options.get('logscale');
- for ( var j = 0; j < rawData.length; j++) {
- x = rawData[j][0];
- point = rawData[j][i];
- if (logScale && point !== null) {
- // On the log scale, points less than zero do not exist.
- // This will create a gap in the chart.
- if (point[0] <= 0 || point[0] - sigma * point[1] <= 0) {
- point = null;
- }
+/*global Dygraph:false */
+"use strict";
+
+/**
+ * @constructor
+ * @extends Dygraph.DataHandlers.BarsHandler
+ */
+Dygraph.DataHandlers.ErrorBarsHandler = function() {
+};
+
+var ErrorBarsHandler = Dygraph.DataHandlers.ErrorBarsHandler;
+ErrorBarsHandler.prototype = new Dygraph.DataHandlers.BarsHandler();
+
+/** @inheritDoc */
+ErrorBarsHandler.prototype.extractSeries = function(rawData, i, options) {
+ // TODO(danvk): pre-allocate series here.
+ var series = [];
+ var x, y, variance, point;
+ var sigma = options.get("sigma");
+ var logScale = options.get('logscale');
+ for ( var j = 0; j < rawData.length; j++) {
+ x = rawData[j][0];
+ point = rawData[j][i];
+ if (logScale && point !== null) {
+ // On the log scale, points less than zero do not exist.
+ // This will create a gap in the chart.
+ if (point[0] <= 0 || point[0] - sigma * point[1] <= 0) {
+ point = null;
}
- // Extract to the unified data format.
- if (point !== null) {
- y = point[0];
- if (y !== null && !isNaN(y)) {
- variance = sigma * point[1];
- // preserve original error value in extras for further
- // filtering
- series.push([ x, y, [ y - variance, y + variance, point[1] ] ]);
- } else {
- series.push([ x, y, [ y, y, y ] ]);
- }
+ }
+ // Extract to the unified data format.
+ if (point !== null) {
+ y = point[0];
+ if (y !== null && !isNaN(y)) {
+ variance = sigma * point[1];
+ // preserve original error value in extras for further
+ // filtering
+ series.push([ x, y, [ y - variance, y + variance, point[1] ] ]);
} else {
- series.push([ x, null, [ null, null, null ] ]);
+ series.push([ x, y, [ y, y, y ] ]);
}
+ } else {
+ series.push([ x, null, [ null, null, null ] ]);
}
- return series;
- };
+ }
+ return series;
+};
- ErrorBarsHandler.prototype.rollingAverage = function(originalData, rollPeriod,
- options) {
- rollPeriod = Math.min(rollPeriod, originalData.length);
- var rollingData = [];
- var sigma = options.get("sigma");
+/** @inheritDoc */
+ErrorBarsHandler.prototype.rollingAverage =
+ function(originalData, rollPeriod, options) {
+ rollPeriod = Math.min(rollPeriod, originalData.length);
+ var rollingData = [];
+ var sigma = options.get("sigma");
- var i, j, y, v, sum, num_ok, stddev, variance, value;
+ var i, j, y, v, sum, num_ok, stddev, variance, value;
- // Calculate the rolling average for the first rollPeriod - 1 points
- // where there is not enough data to roll over the full number of points
- for (i = 0; i < originalData.length; i++) {
- sum = 0;
- variance = 0;
- num_ok = 0;
- for (j = Math.max(0, i - rollPeriod + 1); j < i + 1; j++) {
- y = originalData[j][1];
- if (y === null || isNaN(y))
- continue;
- num_ok++;
- sum += y;
- variance += Math.pow(originalData[j][2][2], 2);
- }
- if (num_ok) {
- stddev = Math.sqrt(variance) / num_ok;
- value = sum / num_ok;
- rollingData[i] = [ originalData[i][0], value,
- [value - sigma * stddev, value + sigma * stddev] ];
- } else {
- // This explicitly preserves NaNs to aid with "independent
- // series".
- // See testRollingAveragePreservesNaNs.
- v = (rollPeriod == 1) ? originalData[i][1] : null;
- rollingData[i] = [ originalData[i][0], v, [ v, v ] ];
- }
+ // Calculate the rolling average for the first rollPeriod - 1 points
+ // where there is not enough data to roll over the full number of points
+ for (i = 0; i < originalData.length; i++) {
+ sum = 0;
+ variance = 0;
+ num_ok = 0;
+ for (j = Math.max(0, i - rollPeriod + 1); j < i + 1; j++) {
+ y = originalData[j][1];
+ if (y === null || isNaN(y))
+ continue;
+ num_ok++;
+ sum += y;
+ variance += Math.pow(originalData[j][2][2], 2);
}
+ if (num_ok) {
+ stddev = Math.sqrt(variance) / num_ok;
+ value = sum / num_ok;
+ rollingData[i] = [ originalData[i][0], value,
+ [value - sigma * stddev, value + sigma * stddev] ];
+ } else {
+ // This explicitly preserves NaNs to aid with "independent
+ // series".
+ // See testRollingAveragePreservesNaNs.
+ v = (rollPeriod == 1) ? originalData[i][1] : null;
+ rollingData[i] = [ originalData[i][0], v, [ v, v ] ];
+ }
+ }
+
+ return rollingData;
+};
- return rollingData;
- };
})();