mirror of https://github.com/zulip/zulip.git
71 lines
3.4 KiB
Python
71 lines
3.4 KiB
Python
from __future__ import division, absolute_import
|
|
|
|
from zerver.models import Realm, UserProfile, Stream, Message
|
|
from analytics.models import InstallationCount, RealmCount, UserCount, StreamCount
|
|
from analytics.lib.counts import CountStat
|
|
from analytics.lib.time_utils import time_range
|
|
|
|
from datetime import datetime
|
|
from math import sqrt
|
|
from random import gauss, random, seed
|
|
from typing import List
|
|
|
|
from six.moves import range, zip
|
|
|
|
def generate_time_series_data(days=100, business_hours_base=10, non_business_hours_base=10,
|
|
growth=1, autocorrelation=0, spikiness=1, holiday_rate=0,
|
|
frequency=CountStat.DAY, partial_sum=False, random_seed=26):
|
|
# type: (int, float, float, float, float, float, float, str, bool, int) -> List[int]
|
|
"""
|
|
Generate semi-realistic looking time series data for testing analytics graphs.
|
|
|
|
days -- Number of days of data. Is the number of data points generated if
|
|
frequency is CountStat.DAY.
|
|
business_hours_base -- Average value during a business hour (or day) at beginning of
|
|
time series, if frequency is CountStat.HOUR (CountStat.DAY, respectively).
|
|
non_business_hours_base -- The above, for non-business hours/days.
|
|
growth -- Ratio between average values at end of time series and beginning of time series.
|
|
autocorrelation -- Makes neighboring data points look more like each other. At 0 each
|
|
point is unaffected by the previous point, and at 1 each point is a deterministic
|
|
function of the previous point.
|
|
spikiness -- 0 means no randomness (other than holiday_rate), higher values increase
|
|
the variance.
|
|
holiday_rate -- Fraction of days randomly set to 0, largely for testing how we handle 0s.
|
|
frequency -- Should be CountStat.HOUR or CountStat.DAY.
|
|
partial_sum -- If True, return partial sum of the series.
|
|
random_seed -- Seed for random number generator.
|
|
"""
|
|
if frequency == CountStat.HOUR:
|
|
length = days*24
|
|
seasonality = [non_business_hours_base] * 24 * 7
|
|
for day in range(5):
|
|
for hour in range(8):
|
|
seasonality[24*day + hour] = business_hours_base
|
|
holidays = []
|
|
for i in range(days):
|
|
holidays.extend([random() < holiday_rate] * 24)
|
|
elif frequency == CountStat.DAY:
|
|
length = days
|
|
seasonality = [8*business_hours_base + 16*non_business_hours_base] * 5 + \
|
|
[24*non_business_hours_base] * 2
|
|
holidays = [random() < holiday_rate for i in range(days)]
|
|
else:
|
|
raise AssertionError("Unknown frequency: %s" % (frequency,))
|
|
if length < 2:
|
|
raise AssertionError("Must be generating at least 2 data points. "
|
|
"Currently generating %s" % (length,))
|
|
growth_base = growth ** (1. / (length-1))
|
|
values_no_noise = [seasonality[i % len(seasonality)] * (growth_base**i) for i in range(length)]
|
|
|
|
seed(random_seed)
|
|
noise_scalars = [gauss(0, 1)]
|
|
for i in range(1, length):
|
|
noise_scalars.append(noise_scalars[-1]*autocorrelation + gauss(0, 1)*(1-autocorrelation))
|
|
|
|
values = [0 if holiday else int(v + sqrt(v)*noise_scalar*spikiness)
|
|
for v, noise_scalar, holiday in zip(values_no_noise, noise_scalars, holidays)]
|
|
if partial_sum:
|
|
for i in range(1, length):
|
|
values[i] = values[i-1] + values[i]
|
|
return [max(v, 0) for v in values]
|