Cleanup from DataHandler change.

[dygraphs.git] / datahandler / bars-error.js

diff --git a/datahandler/bars-error.js b/datahandler/bars-error.js
index ff4ba92..6da2fc5 100644 (file)
--- a/datahandler/bars-error.js
+++ b/datahandler/bars-error.js
@@ -10,83 +10,86 @@
  */
 
 (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";
+
+Dygraph.DataHandlers.ErrorBarsHandler = Dygraph.DataHandler();
+var ErrorBarsHandler = Dygraph.DataHandlers.ErrorBarsHandler;
+ErrorBarsHandler.prototype = new Dygraph.DataHandlers.BarsHandler();
+
+// 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;
       }
-      // 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");
+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;
-  };
 })();