All files / ee/app/assets/javascripts/analytics/productivity_analytics utils.js

98.18% Statements 54/55
85% Branches 17/20
100% Functions 12/12
98% Lines 49/50

Press n or j to go to the next uncovered block, b, p or k for the previous block.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195                        5x 2x 1x     1x                 5x 2x 1x     1x                   5x 2x 2x   2x       2x                         5x 3x 10x                                                 5x 2x 2x   2x 7x 7x   7x 7x         7x 6x         2x                                                   5x 165x   9x 9x     20x           9x                                     5x 165x 165x 165x 165x   165x           165x 331x 331x 3x 3x 3x 3x 3x     331x     165x    
import { flatten } from 'lodash';
import { dateFormats } from '~/analytics/shared/constants';
import dateFormat from '~/lib/dateformat';
import { getDayDifference, getDateInPast } from '~/lib/utils/datetime_utility';
import { median } from '~/lib/utils/number_utils';
 
/**
 * Gets the labels endpoint for a given group or project
 * @param {String} namespacePath - The group's namespace.
 * @param {String} projectPathWithNamespace - The project's name including the group's namespace
 * @returns {String} - The labels endpoint path
 */
export const getLabelsEndpoint = (namespacePath, projectPathWithNamespace) => {
  if (projectPathWithNamespace) {
    return `/${projectPathWithNamespace}/-/labels`;
  }
 
  return `/groups/${namespacePath}/-/labels`;
};
 
/**
 * Gets the milestones endpoint for a given group or project
 * @param {String} namespacePath - The group's namespace.
 * @param {String} projectPathWithNamespace - The project's name including the group's namespace
 * @returns {String} - The milestones endpoint path
 */
export const getMilestonesEndpoint = (namespacePath, projectPathWithNamespace) => {
  if (projectPathWithNamespace) {
    return `/${projectPathWithNamespace}/-/milestones`;
  }
 
  return `/groups/${namespacePath}/-/milestones`;
};
 
/**
 * Determines the default min date for the productivity analytics date picker
 * by taking the minDate (provided by the BE) into account.
 * @param {Date} minDate - The min start date provided by the backend.
 * @param {Number} defaultDaysInPast - The number of days in the past (used for computing the start date).
 * @returns {Date} - The computed default start date.
 */
export const getDefaultStartDate = (minDate, defaultDaysInPast) => {
  const now = new Date(Date.now());
  const dateInPast = getDateInPast(now, defaultDaysInPast);
 
  Iif (!minDate) {
    return dateInPast;
  }
 
  return minDate > dateInPast ? minDate : dateInPast;
};
 
/**
 * Computes the day difference 'days' between a given start and end date
 * and creates an array of length 'days'.
 * For each day in the array it initializes an empty array.
 *
 * E.g. initDateArray(new Date('2019-01-01'), new Date('2019-01-03'))
 * the following data structure gets generated: [ [], [], [] ]
 * @param {Date} startDate - The start date
 * @param {Date} endDate - The end date
 */
export const initDateArray = (startDate, endDate) => {
  const days = getDayDifference(startDate, endDate);
  return Array.from({ length: days + 1 }, () => []);
};
 
/**
 * Transforms the raw scatter data into a data strucuture that allows easy access.
 * It creates a two dimensional array where each item in the first dimension corresponds to one day (date).
 *
 * I.e., the first item corresponds to the start date, the second item corresponds to the start date plus one day,
 * the last item corresponds to the end date.
 *
 * For each date, we store an array of individual MRs for the particular date (i.e, the second dimension) in the following form:
 * { merged_at: '2019-09-01T04:55:05.757Z', metric: 10 }
 *
 * Given that startDate=2019-09-01 and endDate=2019-09-03 we receive the following data structure:
 * [
 *   [{ merged_at: '2019-09-01T04:55:05.757Z', metric: 10 }, { merged_at: '2019-09-01T14:12:09.757Z', metric: 8 }, { ... }] // 2019-09-01
 *   [{ merged_at: '2019-09-02T08:29:33.748Z', metric: 7 }, ... ] // 2019-09-02
 *   [{ merged_at: '2019-09-03T21:29:49.351Z', metric: 24 }, ... ] // 2019-09-03
 * ]
 *
 * @param {Object} data - The raw data received from the API.
 * @param {Date} startDate - The start date selected by the user minus an additional offset in days (e.g., 30 days).
 * @param {Date} endDate - The end date selected by the user.
 * @returns {Array} The transformed data array (first item corresponds to start date, last item to end date)
 */
export const transformScatterData = (data, startDate, endDate) => {
  const result = initDateArray(startDate, endDate);
  const totalItems = result.length;
 
  Object.keys(data).forEach((id) => {
    const mergedAtDate = new Date(data[id].merged_at);
    const dayDiff = getDayDifference(mergedAtDate, endDate);
 
    Eif (dayDiff > -1) {
      const idx = totalItems - (dayDiff + 1);
      // When dealing with different time zones (merged_at is in UTC but endDate is in the browser's timezone)
      // the computed idx might be out of bounds because the result array (and its length) is initialized
      // via initDateArray (which takes only the browser's timezone into account)
      // This would lead to result[idx] being undefined, thus we need a sanity check here
      if (result[idx]) {
        result[idx].push(data[id]);
      }
    }
  });
 
  return result;
};
 
/**
 * Brings the data the we receive from transformScatterData into a format that can be passed to the chart.
 * Since transformScatterData contains more data than we actually want to display on the scatterplot
 * (it also contains historical data for median computation), we need to extract only the relevant portion of data.
 * First, this method computes the visibleData based on the number of days between startDate and the endDate.
 * Eventually it flattens the data an returns an array of the following structure:
 * [
 *  ['2019-09-02', 7, '2019-09-02T16:21:29.512Z'],
 *  ['2019-09-03', 10, '2019-09-03T04:55:05.757Z'],
 *  ['2019-09-03', 8, '2019-09-03T12:00:01.432Z'],
 *  ...
 * ]
 *
 * In the data above, each array i represents an MR in the scatterplot with the following data:
 * i[0] = date, displayed on x axis
 * i[1] = metric, displayed on y axis
 * i[2] = datetime, used in the tooltip
 *
 * @param {Array} data - The already transformed scatterplot data (which is computed by transformScatterData)
 * @param {Date} startDate - The start date selected by the user
 * @param {Date} endDate - The end date selected by the user
 * @returns {Array} An array with each item being another arry of two items (date, computed median)
 */
export const getScatterPlotData = (data, startDate, endDate) => {
  if (!data.length) return [];
 
  const startIndex = data.length - 1 - getDayDifference(startDate, endDate);
  const visibleData = data.slice(startIndex);
 
  // group by date
  const result = flatten(visibleData).map((item) => [
    dateFormat(item.merged_at, dateFormats.isoDate),
    item.metric,
    item.merged_at,
  ]);
 
  return result;
};
 
/**
 * Computes the moving median line data, i.e, it computes the 30 day rolling median for every item displayd on the scatterplot
 * For example the 30 day rolling median for startDate=2019-09-01 and endDate=2019-09-03 is computed as follows:
 * First we get the number of days between start and end date.
 * Then, we make sure to simplify our data by only storing an array of the metric values (without other meta info) every datebundleRenderer.renderToStream
 * Finally, for every day i between start and end date, we compute the median of all the metric values for that given day i
 *
 * Example: Rolling median for m days:
 * Calculate median for day i: median[i - m + 1 ... i]
 *
 * @param {Array} data - The already transformed scatterplot data (which is computed by transformScatterData)
 * @param {Date} startDate - The start date selected by the user
 * @param {Date} endDate - The end date selected by the user
 * @param {Number} daysOffset The number of days that to look up data in the past (e.g. 30 days in the past for 30 day rolling median)
 * @returns {Array} An array with each item being another arry of two items (date, computed median)
 */
export const getMedianLineData = (data, startDate, endDate, daysOffset) => {
  const result = [];
  const dayDiff = getDayDifference(startDate, endDate);
  const transformedData = data.map((arr) => arr.map((x) => x.metric));
  const len = data.length;
 
  let i = len - dayDiff;
  let medianData;
  let flattenedData;
  let startIndex;
  let d;
 
  while (i <= len) {
    startIndex = i - daysOffset - 1;
    if (transformedData[startIndex] && transformedData[i - 1]) {
      medianData = transformedData.slice(startIndex, i);
      flattenedData = flatten(medianData);
      Eif (flattenedData.length) {
        d = getDateInPast(endDate, len - i).toISOString();
        result.push([dateFormat(d, dateFormats.isoDate), median(flattenedData)]);
      }
    }
    i += 1;
  }
 
  return result;
};