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111 lines
3.9 KiB
111 lines
3.9 KiB
from functools import partial
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import sys
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import numpy as np
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import pytest
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import pandas._libs.window.aggregations as window_aggregations
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from pandas import Series
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import pandas._testing as tm
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def _get_rolling_aggregations():
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# list pairs of name and function
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# each function has this signature:
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# (const float64_t[:] values, ndarray[int64_t] start,
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# ndarray[int64_t] end, int64_t minp) -> np.ndarray
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named_roll_aggs = (
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[
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("roll_sum", window_aggregations.roll_sum),
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("roll_mean", window_aggregations.roll_mean),
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]
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+ [
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(f"roll_var({ddof})", partial(window_aggregations.roll_var, ddof=ddof))
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for ddof in [0, 1]
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]
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+ [
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("roll_skew", window_aggregations.roll_skew),
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("roll_kurt", window_aggregations.roll_kurt),
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("roll_median_c", window_aggregations.roll_median_c),
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("roll_max", window_aggregations.roll_max),
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("roll_min", window_aggregations.roll_min),
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]
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+ [
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(
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f"roll_quantile({quantile},{interpolation})",
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partial(
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window_aggregations.roll_quantile,
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quantile=quantile,
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interpolation=interpolation,
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),
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)
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for quantile in [0.0001, 0.5, 0.9999]
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for interpolation in window_aggregations.interpolation_types
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]
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+ [
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(
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f"roll_rank({percentile},{method},{ascending})",
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partial(
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window_aggregations.roll_rank,
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percentile=percentile,
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method=method,
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ascending=ascending,
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),
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)
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for percentile in [True, False]
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for method in window_aggregations.rolling_rank_tiebreakers.keys()
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for ascending in [True, False]
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]
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)
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# unzip to a list of 2 tuples, names and functions
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unzipped = list(zip(*named_roll_aggs))
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return {"ids": unzipped[0], "params": unzipped[1]}
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_rolling_aggregations = _get_rolling_aggregations()
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@pytest.fixture(
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params=_rolling_aggregations["params"], ids=_rolling_aggregations["ids"]
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)
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def rolling_aggregation(request):
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"""Make a rolling aggregation function as fixture."""
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return request.param
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def test_rolling_aggregation_boundary_consistency(rolling_aggregation):
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# GH-45647
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minp, step, width, size, selection = 0, 1, 3, 11, [2, 7]
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values = np.arange(1, 1 + size, dtype=np.float64)
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end = np.arange(width, size, step, dtype=np.int64)
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start = end - width
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selarr = np.array(selection, dtype=np.int32)
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result = Series(rolling_aggregation(values, start[selarr], end[selarr], minp))
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expected = Series(rolling_aggregation(values, start, end, minp)[selarr])
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tm.assert_equal(expected, result)
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def test_rolling_aggregation_with_unused_elements(rolling_aggregation):
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# GH-45647
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minp, width = 0, 5 # width at least 4 for kurt
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size = 2 * width + 5
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values = np.arange(1, size + 1, dtype=np.float64)
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values[width : width + 2] = sys.float_info.min
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values[width + 2] = np.nan
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values[width + 3 : width + 5] = sys.float_info.max
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start = np.array([0, size - width], dtype=np.int64)
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end = np.array([width, size], dtype=np.int64)
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loc = np.array(
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[j for i in range(len(start)) for j in range(start[i], end[i])],
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dtype=np.int32,
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)
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result = Series(rolling_aggregation(values, start, end, minp))
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compact_values = np.array(values[loc], dtype=np.float64)
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compact_start = np.arange(0, len(start) * width, width, dtype=np.int64)
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compact_end = compact_start + width
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expected = Series(
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rolling_aggregation(compact_values, compact_start, compact_end, minp)
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)
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assert np.isfinite(expected.values).all(), "Not all expected values are finite"
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tm.assert_equal(expected, result)
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