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69 lines
3.2 KiB
Python
69 lines
3.2 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 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):
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# type: (int, float, float, float, float, float, float, str, bool) -> 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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"""
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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 ValueError("Unknown frequency: %s" % (frequency,))
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if length < 2:
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raise ValueError("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(26)
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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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