X-Git-Url: https://adrianiainlam.tk/git/?a=blobdiff_plain;f=src%2Fdatahandler%2Fbars-error.js;fp=src%2Fdatahandler%2Fbars-error.js;h=71dbe34ce01b8ccec83f04bd91ede6f9571ddcf9;hb=3123ca57f71d145bb5bcc4a2f754d3dff3225346;hp=0000000000000000000000000000000000000000;hpb=26ee953643ccd2d32e38e6b60b20e6a01c1dc9ba;p=dygraphs.git

diff --git a/src/datahandler/bars-error.js b/src/datahandler/bars-error.js
new file mode 100644
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--- /dev/null
+++ b/src/datahandler/bars-error.js
@@ -0,0 +1,102 @@
+/**
+ * @license
+ * Copyright 2013 David Eberlein (david.eberlein@ch.sauter-bc.com)
+ * MIT-licensed (http://opensource.org/licenses/MIT)
+ */
+
+/**
+ * @fileoverview DataHandler implementation for the error bars option.
+ * @author David Eberlein (david.eberlein@ch.sauter-bc.com)
+ */
+
+(function() {
+
+/*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 ] ]);
+      }
+    } else {
+      series.push([ x, null, [ null, null, null ] ]);
+    }
+  }
+  return series;
+};
+
+/** @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;
+
+  // 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;
+};
+
+})();