snapshot of https://github.com/zulip/zulip.git @ 48c69911c7dd4e03b20552eb3bd77ad8151c1357 (2026-09-15)

analytics/lib/fixtures.py

@@ -0,0 +1,77 @@
+from math import sqrt
+from random import Random
+
+from analytics.lib.counts import CountStat
+
+
+def generate_time_series_data(
+    days: int = 100,
+    business_hours_base: float = 10,
+    non_business_hours_base: float = 10,
+    growth: float = 1,
+    autocorrelation: float = 0,
+    spikiness: float = 1,
+    holiday_rate: float = 0,
+    frequency: str = CountStat.DAY,
+    partial_sum: bool = False,
+    random_seed: int = 26,
+) -> 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.
+    """
+    rng = Random(random_seed)
+
+    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([rng.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 = [rng.random() < holiday_rate for i in range(days)]
+    else:
+        raise AssertionError(f"Unknown frequency: {frequency}")
+    if length < 2:
+        raise AssertionError(
+            f"Must be generating at least 2 data points. Currently generating {length}"
+        )
+    growth_base = growth ** (1.0 / (length - 1))
+    values_no_noise = [seasonality[i % len(seasonality)] * (growth_base**i) for i in range(length)]
+
+    noise_scalars = [rng.gauss(0, 1)]
+    for i in range(1, length):
+        noise_scalars.append(
+            noise_scalars[-1] * autocorrelation + rng.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, strict=False)
+    ]
+    if partial_sum:
+        for i in range(1, length):
+            values[i] = values[i - 1] + values[i]
+    return [max(v, 0) for v in values]

analytics/lib/time_utils.py

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+from datetime import datetime, timedelta
+
+from analytics.lib.counts import CountStat
+from zerver.lib.timestamp import floor_to_day, floor_to_hour, verify_UTC
+
+
+# If min_length is None, returns end_times from ceiling(start) to floor(end), inclusive.
+# If min_length is greater than 0, pads the list to the left.
+# So informally, time_range(Sep 20, Sep 22, day, None) returns [Sep 20, Sep 21, Sep 22],
+# and time_range(Sep 20, Sep 22, day, 5) returns [Sep 18, Sep 19, Sep 20, Sep 21, Sep 22]
+def time_range(
+    start: datetime, end: datetime, frequency: str, min_length: int | None
+) -> list[datetime]:
+    verify_UTC(start)
+    verify_UTC(end)
+    if frequency == CountStat.HOUR:
+        end = floor_to_hour(end)
+        step = timedelta(hours=1)
+    elif frequency == CountStat.DAY:
+        end = floor_to_day(end)
+        step = timedelta(days=1)
+    else:
+        raise AssertionError(f"Unknown frequency: {frequency}")
+
+    times = []
+    if min_length is not None:
+        start = min(start, end - (min_length - 1) * step)
+    current = end
+    while current >= start:
+        times.append(current)
+        current -= step
+    times.reverse()
+    return times