mirror of https://github.com/zulip/zulip.git
80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
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 analytics.lib.counts import CountStat
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def generate_time_series_data(
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days: int = 100,
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business_hours_base: float = 10,
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non_business_hours_base: float = 10,
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growth: float = 1,
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autocorrelation: float = 0,
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spikiness: float = 1,
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holiday_rate: float = 0,
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frequency: str = CountStat.DAY,
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partial_sum: bool = False,
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random_seed: int = 26,
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) -> 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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partial_sum -- 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
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] * 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(f"Unknown frequency: {frequency}")
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if length < 2:
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raise AssertionError(
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f"Must be generating at least 2 data points. Currently generating {length}"
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)
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growth_base = growth ** (1.0 / (length - 1))
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values_no_noise = [
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seasonality[i % len(seasonality)] * (growth_base ** i) for i in range(length)
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]
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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(
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noise_scalars[-1] * autocorrelation + gauss(0, 1) * (1 - autocorrelation)
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)
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values = [
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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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]
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if partial_sum:
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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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