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| """
| Numba 1D var kernels that can be shared by
| * Dataframe / Series
| * groupby
| * rolling / expanding
|
| Mirrors pandas/_libs/window/aggregation.pyx
| """
| from __future__ import annotations
|
| import numba
| import numpy as np
|
| from pandas.core._numba.kernels.shared import is_monotonic_increasing
|
|
| @numba.jit(nopython=True, nogil=True, parallel=False)
| def add_var(
| val: float,
| nobs: int,
| mean_x: float,
| ssqdm_x: float,
| compensation: float,
| num_consecutive_same_value: int,
| prev_value: float,
| ) -> tuple[int, float, float, float, int, float]:
| if not np.isnan(val):
| if val == prev_value:
| num_consecutive_same_value += 1
| else:
| num_consecutive_same_value = 1
| prev_value = val
|
| nobs += 1
| prev_mean = mean_x - compensation
| y = val - compensation
| t = y - mean_x
| compensation = t + mean_x - y
| delta = t
| if nobs:
| mean_x += delta / nobs
| else:
| mean_x = 0
| ssqdm_x += (val - prev_mean) * (val - mean_x)
| return nobs, mean_x, ssqdm_x, compensation, num_consecutive_same_value, prev_value
|
|
| @numba.jit(nopython=True, nogil=True, parallel=False)
| def remove_var(
| val: float, nobs: int, mean_x: float, ssqdm_x: float, compensation: float
| ) -> tuple[int, float, float, float]:
| if not np.isnan(val):
| nobs -= 1
| if nobs:
| prev_mean = mean_x - compensation
| y = val - compensation
| t = y - mean_x
| compensation = t + mean_x - y
| delta = t
| mean_x -= delta / nobs
| ssqdm_x -= (val - prev_mean) * (val - mean_x)
| else:
| mean_x = 0
| ssqdm_x = 0
| return nobs, mean_x, ssqdm_x, compensation
|
|
| @numba.jit(nopython=True, nogil=True, parallel=False)
| def sliding_var(
| values: np.ndarray,
| start: np.ndarray,
| end: np.ndarray,
| min_periods: int,
| ddof: int = 1,
| ) -> np.ndarray:
| N = len(start)
| nobs = 0
| mean_x = 0.0
| ssqdm_x = 0.0
| compensation_add = 0.0
| compensation_remove = 0.0
|
| min_periods = max(min_periods, 1)
| is_monotonic_increasing_bounds = is_monotonic_increasing(
| start
| ) and is_monotonic_increasing(end)
|
| output = np.empty(N, dtype=np.float64)
|
| for i in range(N):
| s = start[i]
| e = end[i]
| if i == 0 or not is_monotonic_increasing_bounds:
| prev_value = values[s]
| num_consecutive_same_value = 0
|
| for j in range(s, e):
| val = values[j]
| (
| nobs,
| mean_x,
| ssqdm_x,
| compensation_add,
| num_consecutive_same_value,
| prev_value,
| ) = add_var(
| val,
| nobs,
| mean_x,
| ssqdm_x,
| compensation_add,
| num_consecutive_same_value,
| prev_value,
| )
| else:
| for j in range(start[i - 1], s):
| val = values[j]
| nobs, mean_x, ssqdm_x, compensation_remove = remove_var(
| val, nobs, mean_x, ssqdm_x, compensation_remove
| )
|
| for j in range(end[i - 1], e):
| val = values[j]
| (
| nobs,
| mean_x,
| ssqdm_x,
| compensation_add,
| num_consecutive_same_value,
| prev_value,
| ) = add_var(
| val,
| nobs,
| mean_x,
| ssqdm_x,
| compensation_add,
| num_consecutive_same_value,
| prev_value,
| )
|
| if nobs >= min_periods and nobs > ddof:
| if nobs == 1 or num_consecutive_same_value >= nobs:
| result = 0.0
| else:
| result = ssqdm_x / (nobs - ddof)
| else:
| result = np.nan
|
| output[i] = result
|
| if not is_monotonic_increasing_bounds:
| nobs = 0
| mean_x = 0.0
| ssqdm_x = 0.0
| compensation_remove = 0.0
|
| return output
|
|
