from math import sqrt
from random import gauss, random, seed
from typing import List

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.
    """
    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(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)
    ]

    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]