252 lines
6.9 KiB
JavaScript
252 lines
6.9 KiB
JavaScript
define([
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'angular',
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'lodash',
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'moment',
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'./utils'
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],
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function (angular, _, moment, utils) {
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'use strict';
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var module = angular.module('grafana.services');
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module.service('DataProcessingService', function() {
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var self = this;
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/**
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* Downsample datapoints series
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*/
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this.downsampleSeries = function(datapoints, time_to, ms_interval, func) {
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var downsampledSeries = [];
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var timeWindow = {
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from: time_to * 1000 - ms_interval,
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to: time_to * 1000
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};
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var points_sum = 0;
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var points_num = 0;
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var value_avg = 0;
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var frame = [];
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for (var i = datapoints.length - 1; i >= 0; i -= 1) {
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if (timeWindow.from < datapoints[i][1] && datapoints[i][1] <= timeWindow.to) {
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points_sum += datapoints[i][0];
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points_num++;
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frame.push(datapoints[i][0]);
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}
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else {
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value_avg = points_num ? points_sum / points_num : 0;
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if (func === "max") {
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downsampledSeries.push([_.max(frame), timeWindow.to]);
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}
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else if (func === "min") {
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downsampledSeries.push([_.min(frame), timeWindow.to]);
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}
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// avg by default
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else {
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downsampledSeries.push([value_avg, timeWindow.to]);
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}
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// Shift time window
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timeWindow.to = timeWindow.from;
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timeWindow.from -= ms_interval;
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points_sum = 0;
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points_num = 0;
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frame = [];
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// Process point again
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i++;
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}
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}
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return downsampledSeries.reverse();
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};
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/**
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* Group points by given time interval
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* datapoints: [[<value>, <unixtime>], ...]
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*/
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this.groupBy = function(interval, groupByCallback, datapoints) {
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var ms_interval = utils.parseInterval(interval);
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// Calculate frame timestamps
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var min_timestamp = datapoints[0][1];
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var frames = _.groupBy(datapoints, function(point) {
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var group_time = Math.floor(point[1] / ms_interval) * ms_interval;
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// Prevent points outside of time range
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if (group_time < min_timestamp) {
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group_time = min_timestamp;
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}
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return group_time;
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});
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// frame: { '<unixtime>': [[<value>, <unixtime>], ...] }
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// return [{ '<unixtime>': <value> }, { '<unixtime>': <value> }, ...]
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var grouped = _.mapValues(frames, function(frame) {
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var points = _.map(frame, function(point) {
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return point[0];
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});
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return groupByCallback(points);
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});
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// Convert points to Grafana format
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return _.map(grouped, function(value, timestamp) {
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return [Number(value), Number(timestamp)];
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});
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};
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this.sumSeries = function(timeseries) {
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// Calculate new points for interpolation
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var new_timestamps = _.uniq(_.map(_.flatten(timeseries, true), function(point) {
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return point[1];
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}));
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new_timestamps = _.sortBy(new_timestamps);
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var interpolated_timeseries = _.map(timeseries, function(series) {
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var timestamps = _.map(series, function(point) {
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return point[1];
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});
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var new_points = _.map(_.difference(new_timestamps, timestamps), function(timestamp) {
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return [null, timestamp];
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});
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var new_series = series.concat(new_points);
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return sortByTime(new_series);
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});
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_.each(interpolated_timeseries, interpolateSeries);
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var new_timeseries = [];
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var sum;
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for (var i = new_timestamps.length - 1; i >= 0; i--) {
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sum = 0;
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for (var j = interpolated_timeseries.length - 1; j >= 0; j--) {
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sum += interpolated_timeseries[j][i][0];
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}
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new_timeseries.push([sum, new_timestamps[i]]);
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}
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return sortByTime(new_timeseries);
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};
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function sortByTime(series) {
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return _.sortBy(series, function(point) {
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return point[1];
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});
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}
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/**
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* Interpolate series with gaps
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*/
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function interpolateSeries(series) {
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var left, right;
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// Interpolate series
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for (var i = series.length - 1; i >= 0; i--) {
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if (!series[i][0]) {
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left = findNearestLeft(series, series[i]);
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right = findNearestRight(series, series[i]);
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if (!left) {
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left = right;
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}
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if (!right) {
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right = left;
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}
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series[i][0] = linearInterpolation(series[i][1], left, right);
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}
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}
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return series;
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}
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function linearInterpolation(timestamp, left, right) {
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if (left[1] === right[1]) {
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return (left[0] + right[0]) / 2;
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} else {
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return (left[0] + (right[0] - left[0]) / (right[1] - left[1]) * (timestamp - left[1]));
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}
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}
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function findNearestRight(series, point) {
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var point_index = _.indexOf(series, point);
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var nearestRight;
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for (var i = point_index; i < series.length; i++) {
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if (series[i][0]) {
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return series[i];
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}
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}
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return nearestRight;
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}
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function findNearestLeft(series, point) {
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var point_index = _.indexOf(series, point);
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var nearestLeft;
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for (var i = point_index; i > 0; i--) {
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if (series[i][0]) {
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return series[i];
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}
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}
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return nearestLeft;
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}
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this.medianBy = function(interval, timeseries) {
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var flatten_series = _.flatten(timeseries, true);
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return self.groupBy(interval, self.MEDIAN, flatten_series);
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};
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this.AVERAGE = function(values) {
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var sum = 0;
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_.each(values, function(value) {
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sum += value;
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});
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return sum / values.length;
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};
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this.MIN = function(values) {
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return _.min(values);
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};
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this.MAX = function(values) {
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return _.max(values);
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};
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this.MEDIAN = function(values) {
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var sorted = _.sortBy(values);
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return sorted[Math.floor(sorted.length / 2)];
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};
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this.setAlias = function(alias, timeseries) {
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timeseries.target = alias;
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return timeseries;
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};
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this.aggregationFunctions = {
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avg: this.AVERAGE,
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min: this.MIN,
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max: this.MAX,
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median: this.MEDIAN
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};
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this.groupByWrapper = function(interval, groupFunc, datapoints) {
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var groupByCallback = self.aggregationFunctions[groupFunc];
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return self.groupBy(interval, groupByCallback, datapoints);
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};
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this.aggregateWrapper = function(groupByCallback, interval, datapoints) {
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var flattenedPoints = _.flatten(datapoints, true);
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return self.groupBy(interval, groupByCallback, flattenedPoints);
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};
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this.metricFunctions = {
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groupBy: this.groupByWrapper,
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average: _.partial(this.aggregateWrapper, this.AVERAGE),
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min: _.partial(this.aggregateWrapper, this.MIN),
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max: _.partial(this.aggregateWrapper, this.MAX),
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sumSeries: this.sumSeries,
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medianBy: this.medianBy,
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setAlias: this.setAlias,
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};
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});
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}); |