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ebebd04587
Pathways that only catch internal code errors should use AssertionError so that they are not included when computing test coverage.
71 lines
3.4 KiB
Python
71 lines
3.4 KiB
Python
from __future__ import division, absolute_import
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from zerver.models import Realm, UserProfile, Stream, Message
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from analytics.models import InstallationCount, RealmCount, UserCount, StreamCount
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from analytics.lib.counts import CountStat
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from analytics.lib.time_utils import time_range
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from datetime import datetime
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from math import sqrt
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from random import gauss, random, seed
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from typing import List
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from six.moves import range, zip
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def generate_time_series_data(days=100, business_hours_base=10, non_business_hours_base=10,
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growth=1, autocorrelation=0, spikiness=1, holiday_rate=0,
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frequency=CountStat.DAY, is_gauge=False, random_seed=26):
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# type: (int, float, float, float, float, float, float, str, bool, int) -> List[int]
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"""
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Generate semi-realistic looking time series data for testing analytics graphs.
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days -- Number of days of data. Is the number of data points generated if
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frequency is CountStat.DAY.
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business_hours_base -- Average value during a business hour (or day) at beginning of
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time series, if frequency is CountStat.HOUR (CountStat.DAY, respectively).
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non_business_hours_base -- The above, for non-business hours/days.
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growth -- Ratio between average values at end of time series and beginning of time series.
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autocorrelation -- Makes neighboring data points look more like each other. At 0 each
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point is unaffected by the previous point, and at 1 each point is a deterministic
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function of the previous point.
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spikiness -- 0 means no randomness (other than holiday_rate), higher values increase
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the variance.
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holiday_rate -- Fraction of days randomly set to 0, largely for testing how we handle 0s.
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frequency -- Should be CountStat.HOUR or CountStat.DAY.
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is_gauge -- If True, return partial sum of the series.
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random_seed -- Seed for random number generator.
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"""
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if frequency == CountStat.HOUR:
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length = days*24
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seasonality = [non_business_hours_base] * 24 * 7
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for day in range(5):
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for hour in range(8):
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seasonality[24*day + hour] = business_hours_base
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holidays = []
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for i in range(days):
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holidays.extend([random() < holiday_rate] * 24)
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elif frequency == CountStat.DAY:
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length = days
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seasonality = [8*business_hours_base + 16*non_business_hours_base] * 5 + \
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[24*non_business_hours_base] * 2
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holidays = [random() < holiday_rate for i in range(days)]
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else:
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raise AssertionError("Unknown frequency: %s" % (frequency,))
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if length < 2:
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raise AssertionError("Must be generating at least 2 data points. "
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"Currently generating %s" % (length,))
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growth_base = growth ** (1. / (length-1))
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values_no_noise = [seasonality[i % len(seasonality)] * (growth_base**i) for i in range(length)]
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seed(random_seed)
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noise_scalars = [gauss(0, 1)]
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for i in range(1, length):
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noise_scalars.append(noise_scalars[-1]*autocorrelation + gauss(0, 1)*(1-autocorrelation))
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values = [0 if holiday else int(v + sqrt(v)*noise_scalar*spikiness)
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for v, noise_scalar, holiday in zip(values_no_noise, noise_scalars, holidays)]
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if is_gauge:
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for i in range(1, length):
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values[i] = values[i-1] + values[i]
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return [max(v, 0) for v in values]
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