569 lines
18 KiB
Python
569 lines
18 KiB
Python
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import numpy as np
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from numpy.testing import assert_allclose
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import pytest
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from pytest import raises as assert_raises
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from scipy.stats import (binned_statistic, binned_statistic_2d,
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binned_statistic_dd)
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from scipy._lib._util import check_random_state
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from .common_tests import check_named_results
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class TestBinnedStatistic:
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@classmethod
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def setup_class(cls):
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rng = check_random_state(9865)
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cls.x = rng.uniform(size=100)
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cls.y = rng.uniform(size=100)
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cls.v = rng.uniform(size=100)
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cls.X = rng.uniform(size=(100, 3))
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cls.w = rng.uniform(size=100)
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cls.u = rng.uniform(size=100) + 1e6
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def test_1d_count(self):
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x = self.x
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v = self.v
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count1, edges1, bc = binned_statistic(x, v, 'count', bins=10)
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count2, edges2 = np.histogram(x, bins=10)
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assert_allclose(count1, count2)
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assert_allclose(edges1, edges2)
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def test_gh5927(self):
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# smoke test for gh5927 - binned_statistic was using `is` for string
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# comparison
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x = self.x
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v = self.v
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statistics = ['mean', 'median', 'count', 'sum']
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for statistic in statistics:
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binned_statistic(x, v, statistic, bins=10)
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def test_big_number_std(self):
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# tests for numerical stability of std calculation
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# see issue gh-10126 for more
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x = self.x
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u = self.u
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stat1, edges1, bc = binned_statistic(x, u, 'std', bins=10)
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stat2, edges2, bc = binned_statistic(x, u, np.std, bins=10)
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assert_allclose(stat1, stat2)
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def test_empty_bins_std(self):
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# tests that std returns gives nan for empty bins
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x = self.x
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u = self.u
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print(binned_statistic(x, u, 'count', bins=1000))
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stat1, edges1, bc = binned_statistic(x, u, 'std', bins=1000)
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stat2, edges2, bc = binned_statistic(x, u, np.std, bins=1000)
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assert_allclose(stat1, stat2)
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def test_non_finite_inputs_and_int_bins(self):
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# if either `values` or `sample` contain np.inf or np.nan throw
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# see issue gh-9010 for more
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x = self.x
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u = self.u
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orig = u[0]
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u[0] = np.inf
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assert_raises(ValueError, binned_statistic, u, x, 'std', bins=10)
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# need to test for non-python specific ints, e.g. np.int8, np.int64
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assert_raises(ValueError, binned_statistic, u, x, 'std',
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bins=np.int64(10))
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u[0] = np.nan
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assert_raises(ValueError, binned_statistic, u, x, 'count', bins=10)
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# replace original value, u belongs the class
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u[0] = orig
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def test_1d_result_attributes(self):
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x = self.x
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v = self.v
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res = binned_statistic(x, v, 'count', bins=10)
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attributes = ('statistic', 'bin_edges', 'binnumber')
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check_named_results(res, attributes)
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def test_1d_sum(self):
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x = self.x
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v = self.v
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sum1, edges1, bc = binned_statistic(x, v, 'sum', bins=10)
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sum2, edges2 = np.histogram(x, bins=10, weights=v)
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assert_allclose(sum1, sum2)
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assert_allclose(edges1, edges2)
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def test_1d_mean(self):
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x = self.x
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v = self.v
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stat1, edges1, bc = binned_statistic(x, v, 'mean', bins=10)
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stat2, edges2, bc = binned_statistic(x, v, np.mean, bins=10)
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assert_allclose(stat1, stat2)
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assert_allclose(edges1, edges2)
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def test_1d_std(self):
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x = self.x
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v = self.v
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stat1, edges1, bc = binned_statistic(x, v, 'std', bins=10)
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stat2, edges2, bc = binned_statistic(x, v, np.std, bins=10)
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assert_allclose(stat1, stat2)
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assert_allclose(edges1, edges2)
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def test_1d_min(self):
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x = self.x
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v = self.v
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stat1, edges1, bc = binned_statistic(x, v, 'min', bins=10)
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stat2, edges2, bc = binned_statistic(x, v, np.min, bins=10)
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assert_allclose(stat1, stat2)
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assert_allclose(edges1, edges2)
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def test_1d_max(self):
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x = self.x
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v = self.v
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stat1, edges1, bc = binned_statistic(x, v, 'max', bins=10)
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stat2, edges2, bc = binned_statistic(x, v, np.max, bins=10)
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assert_allclose(stat1, stat2)
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assert_allclose(edges1, edges2)
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def test_1d_median(self):
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x = self.x
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v = self.v
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stat1, edges1, bc = binned_statistic(x, v, 'median', bins=10)
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stat2, edges2, bc = binned_statistic(x, v, np.median, bins=10)
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assert_allclose(stat1, stat2)
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assert_allclose(edges1, edges2)
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def test_1d_bincode(self):
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x = self.x[:20]
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v = self.v[:20]
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count1, edges1, bc = binned_statistic(x, v, 'count', bins=3)
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bc2 = np.array([3, 2, 1, 3, 2, 3, 3, 3, 3, 1, 1, 3, 3, 1, 2, 3, 1,
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1, 2, 1])
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bcount = [(bc == i).sum() for i in np.unique(bc)]
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assert_allclose(bc, bc2)
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assert_allclose(bcount, count1)
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def test_1d_range_keyword(self):
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# Regression test for gh-3063, range can be (min, max) or [(min, max)]
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np.random.seed(9865)
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x = np.arange(30)
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data = np.random.random(30)
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mean, bins, _ = binned_statistic(x[:15], data[:15])
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mean_range, bins_range, _ = binned_statistic(x, data, range=[(0, 14)])
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mean_range2, bins_range2, _ = binned_statistic(x, data, range=(0, 14))
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assert_allclose(mean, mean_range)
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assert_allclose(bins, bins_range)
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assert_allclose(mean, mean_range2)
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assert_allclose(bins, bins_range2)
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def test_1d_multi_values(self):
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x = self.x
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v = self.v
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w = self.w
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stat1v, edges1v, bc1v = binned_statistic(x, v, 'mean', bins=10)
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stat1w, edges1w, bc1w = binned_statistic(x, w, 'mean', bins=10)
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stat2, edges2, bc2 = binned_statistic(x, [v, w], 'mean', bins=10)
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assert_allclose(stat2[0], stat1v)
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assert_allclose(stat2[1], stat1w)
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assert_allclose(edges1v, edges2)
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assert_allclose(bc1v, bc2)
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def test_2d_count(self):
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x = self.x
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y = self.y
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v = self.v
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count1, binx1, biny1, bc = binned_statistic_2d(
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x, y, v, 'count', bins=5)
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count2, binx2, biny2 = np.histogram2d(x, y, bins=5)
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assert_allclose(count1, count2)
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assert_allclose(binx1, binx2)
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assert_allclose(biny1, biny2)
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def test_2d_result_attributes(self):
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x = self.x
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y = self.y
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v = self.v
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res = binned_statistic_2d(x, y, v, 'count', bins=5)
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attributes = ('statistic', 'x_edge', 'y_edge', 'binnumber')
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check_named_results(res, attributes)
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def test_2d_sum(self):
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x = self.x
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y = self.y
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v = self.v
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sum1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'sum', bins=5)
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sum2, binx2, biny2 = np.histogram2d(x, y, bins=5, weights=v)
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assert_allclose(sum1, sum2)
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assert_allclose(binx1, binx2)
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assert_allclose(biny1, biny2)
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def test_2d_mean(self):
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x = self.x
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y = self.y
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v = self.v
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stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'mean', bins=5)
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stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.mean, bins=5)
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assert_allclose(stat1, stat2)
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assert_allclose(binx1, binx2)
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assert_allclose(biny1, biny2)
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def test_2d_mean_unicode(self):
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x = self.x
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y = self.y
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v = self.v
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stat1, binx1, biny1, bc = binned_statistic_2d(
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x, y, v, 'mean', bins=5)
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stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.mean, bins=5)
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assert_allclose(stat1, stat2)
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assert_allclose(binx1, binx2)
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assert_allclose(biny1, biny2)
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def test_2d_std(self):
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x = self.x
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y = self.y
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v = self.v
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stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'std', bins=5)
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stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.std, bins=5)
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assert_allclose(stat1, stat2)
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assert_allclose(binx1, binx2)
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assert_allclose(biny1, biny2)
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def test_2d_min(self):
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x = self.x
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y = self.y
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v = self.v
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stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'min', bins=5)
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stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.min, bins=5)
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assert_allclose(stat1, stat2)
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assert_allclose(binx1, binx2)
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assert_allclose(biny1, biny2)
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def test_2d_max(self):
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x = self.x
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y = self.y
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v = self.v
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stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'max', bins=5)
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stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.max, bins=5)
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assert_allclose(stat1, stat2)
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assert_allclose(binx1, binx2)
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assert_allclose(biny1, biny2)
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def test_2d_median(self):
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x = self.x
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y = self.y
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v = self.v
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stat1, binx1, biny1, bc = binned_statistic_2d(
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x, y, v, 'median', bins=5)
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stat2, binx2, biny2, bc = binned_statistic_2d(
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x, y, v, np.median, bins=5)
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assert_allclose(stat1, stat2)
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assert_allclose(binx1, binx2)
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assert_allclose(biny1, biny2)
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def test_2d_bincode(self):
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x = self.x[:20]
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y = self.y[:20]
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v = self.v[:20]
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count1, binx1, biny1, bc = binned_statistic_2d(
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x, y, v, 'count', bins=3)
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bc2 = np.array([17, 11, 6, 16, 11, 17, 18, 17, 17, 7, 6, 18, 16,
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6, 11, 16, 6, 6, 11, 8])
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bcount = [(bc == i).sum() for i in np.unique(bc)]
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assert_allclose(bc, bc2)
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count1adj = count1[count1.nonzero()]
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assert_allclose(bcount, count1adj)
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def test_2d_multi_values(self):
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x = self.x
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y = self.y
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v = self.v
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w = self.w
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stat1v, binx1v, biny1v, bc1v = binned_statistic_2d(
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x, y, v, 'mean', bins=8)
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stat1w, binx1w, biny1w, bc1w = binned_statistic_2d(
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x, y, w, 'mean', bins=8)
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stat2, binx2, biny2, bc2 = binned_statistic_2d(
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x, y, [v, w], 'mean', bins=8)
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assert_allclose(stat2[0], stat1v)
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assert_allclose(stat2[1], stat1w)
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assert_allclose(binx1v, binx2)
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assert_allclose(biny1w, biny2)
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assert_allclose(bc1v, bc2)
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def test_2d_binnumbers_unraveled(self):
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x = self.x
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y = self.y
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v = self.v
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stat, edgesx, bcx = binned_statistic(x, v, 'mean', bins=20)
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stat, edgesy, bcy = binned_statistic(y, v, 'mean', bins=10)
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stat2, edgesx2, edgesy2, bc2 = binned_statistic_2d(
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x, y, v, 'mean', bins=(20, 10), expand_binnumbers=True)
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bcx3 = np.searchsorted(edgesx, x, side='right')
|
||
|
|
bcy3 = np.searchsorted(edgesy, y, side='right')
|
||
|
|
|
||
|
|
# `numpy.searchsorted` is non-inclusive on right-edge, compensate
|
||
|
|
bcx3[x == x.max()] -= 1
|
||
|
|
bcy3[y == y.max()] -= 1
|
||
|
|
|
||
|
|
assert_allclose(bcx, bc2[0])
|
||
|
|
assert_allclose(bcy, bc2[1])
|
||
|
|
assert_allclose(bcx3, bc2[0])
|
||
|
|
assert_allclose(bcy3, bc2[1])
|
||
|
|
|
||
|
|
def test_dd_count(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
|
||
|
|
count1, edges1, bc = binned_statistic_dd(X, v, 'count', bins=3)
|
||
|
|
count2, edges2 = np.histogramdd(X, bins=3)
|
||
|
|
|
||
|
|
assert_allclose(count1, count2)
|
||
|
|
assert_allclose(edges1, edges2)
|
||
|
|
|
||
|
|
def test_dd_result_attributes(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
|
||
|
|
res = binned_statistic_dd(X, v, 'count', bins=3)
|
||
|
|
attributes = ('statistic', 'bin_edges', 'binnumber')
|
||
|
|
check_named_results(res, attributes)
|
||
|
|
|
||
|
|
def test_dd_sum(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
|
||
|
|
sum1, edges1, bc = binned_statistic_dd(X, v, 'sum', bins=3)
|
||
|
|
sum2, edges2 = np.histogramdd(X, bins=3, weights=v)
|
||
|
|
sum3, edges3, bc = binned_statistic_dd(X, v, np.sum, bins=3)
|
||
|
|
|
||
|
|
assert_allclose(sum1, sum2)
|
||
|
|
assert_allclose(edges1, edges2)
|
||
|
|
assert_allclose(sum1, sum3)
|
||
|
|
assert_allclose(edges1, edges3)
|
||
|
|
|
||
|
|
def test_dd_mean(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
|
||
|
|
stat1, edges1, bc = binned_statistic_dd(X, v, 'mean', bins=3)
|
||
|
|
stat2, edges2, bc = binned_statistic_dd(X, v, np.mean, bins=3)
|
||
|
|
|
||
|
|
assert_allclose(stat1, stat2)
|
||
|
|
assert_allclose(edges1, edges2)
|
||
|
|
|
||
|
|
def test_dd_std(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
|
||
|
|
stat1, edges1, bc = binned_statistic_dd(X, v, 'std', bins=3)
|
||
|
|
stat2, edges2, bc = binned_statistic_dd(X, v, np.std, bins=3)
|
||
|
|
|
||
|
|
assert_allclose(stat1, stat2)
|
||
|
|
assert_allclose(edges1, edges2)
|
||
|
|
|
||
|
|
def test_dd_min(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
|
||
|
|
stat1, edges1, bc = binned_statistic_dd(X, v, 'min', bins=3)
|
||
|
|
stat2, edges2, bc = binned_statistic_dd(X, v, np.min, bins=3)
|
||
|
|
|
||
|
|
assert_allclose(stat1, stat2)
|
||
|
|
assert_allclose(edges1, edges2)
|
||
|
|
|
||
|
|
def test_dd_max(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
|
||
|
|
stat1, edges1, bc = binned_statistic_dd(X, v, 'max', bins=3)
|
||
|
|
stat2, edges2, bc = binned_statistic_dd(X, v, np.max, bins=3)
|
||
|
|
|
||
|
|
assert_allclose(stat1, stat2)
|
||
|
|
assert_allclose(edges1, edges2)
|
||
|
|
|
||
|
|
def test_dd_median(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
|
||
|
|
stat1, edges1, bc = binned_statistic_dd(X, v, 'median', bins=3)
|
||
|
|
stat2, edges2, bc = binned_statistic_dd(X, v, np.median, bins=3)
|
||
|
|
|
||
|
|
assert_allclose(stat1, stat2)
|
||
|
|
assert_allclose(edges1, edges2)
|
||
|
|
|
||
|
|
def test_dd_bincode(self):
|
||
|
|
X = self.X[:20]
|
||
|
|
v = self.v[:20]
|
||
|
|
|
||
|
|
count1, edges1, bc = binned_statistic_dd(X, v, 'count', bins=3)
|
||
|
|
bc2 = np.array([63, 33, 86, 83, 88, 67, 57, 33, 42, 41, 82, 83, 92,
|
||
|
|
32, 36, 91, 43, 87, 81, 81])
|
||
|
|
|
||
|
|
bcount = [(bc == i).sum() for i in np.unique(bc)]
|
||
|
|
|
||
|
|
assert_allclose(bc, bc2)
|
||
|
|
count1adj = count1[count1.nonzero()]
|
||
|
|
assert_allclose(bcount, count1adj)
|
||
|
|
|
||
|
|
def test_dd_multi_values(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
w = self.w
|
||
|
|
|
||
|
|
for stat in ["count", "sum", "mean", "std", "min", "max", "median",
|
||
|
|
np.std]:
|
||
|
|
stat1v, edges1v, bc1v = binned_statistic_dd(X, v, stat, bins=8)
|
||
|
|
stat1w, edges1w, bc1w = binned_statistic_dd(X, w, stat, bins=8)
|
||
|
|
stat2, edges2, bc2 = binned_statistic_dd(X, [v, w], stat, bins=8)
|
||
|
|
assert_allclose(stat2[0], stat1v)
|
||
|
|
assert_allclose(stat2[1], stat1w)
|
||
|
|
assert_allclose(edges1v, edges2)
|
||
|
|
assert_allclose(edges1w, edges2)
|
||
|
|
assert_allclose(bc1v, bc2)
|
||
|
|
|
||
|
|
def test_dd_binnumbers_unraveled(self):
|
||
|
|
X = self.X
|
||
|
|
v = self.v
|
||
|
|
|
||
|
|
stat, edgesx, bcx = binned_statistic(X[:, 0], v, 'mean', bins=15)
|
||
|
|
stat, edgesy, bcy = binned_statistic(X[:, 1], v, 'mean', bins=20)
|
||
|
|
stat, edgesz, bcz = binned_statistic(X[:, 2], v, 'mean', bins=10)
|
||
|
|
|
||
|
|
stat2, edges2, bc2 = binned_statistic_dd(
|
||
|
|
X, v, 'mean', bins=(15, 20, 10), expand_binnumbers=True)
|
||
|
|
|
||
|
|
assert_allclose(bcx, bc2[0])
|
||
|
|
assert_allclose(bcy, bc2[1])
|
||
|
|
assert_allclose(bcz, bc2[2])
|
||
|
|
|
||
|
|
def test_dd_binned_statistic_result(self):
|
||
|
|
# NOTE: tests the reuse of bin_edges from previous call
|
||
|
|
x = np.random.random((10000, 3))
|
||
|
|
v = np.random.random(10000)
|
||
|
|
bins = np.linspace(0, 1, 10)
|
||
|
|
bins = (bins, bins, bins)
|
||
|
|
|
||
|
|
result = binned_statistic_dd(x, v, 'mean', bins=bins)
|
||
|
|
stat = result.statistic
|
||
|
|
|
||
|
|
result = binned_statistic_dd(x, v, 'mean',
|
||
|
|
binned_statistic_result=result)
|
||
|
|
stat2 = result.statistic
|
||
|
|
|
||
|
|
assert_allclose(stat, stat2)
|
||
|
|
|
||
|
|
def test_dd_zero_dedges(self):
|
||
|
|
x = np.random.random((10000, 3))
|
||
|
|
v = np.random.random(10000)
|
||
|
|
bins = np.linspace(0, 1, 10)
|
||
|
|
bins = np.append(bins, 1)
|
||
|
|
bins = (bins, bins, bins)
|
||
|
|
with assert_raises(ValueError, match='difference is numerically 0'):
|
||
|
|
binned_statistic_dd(x, v, 'mean', bins=bins)
|
||
|
|
|
||
|
|
def test_dd_range_errors(self):
|
||
|
|
# Test that descriptive exceptions are raised as appropriate for bad
|
||
|
|
# values of the `range` argument. (See gh-12996)
|
||
|
|
with assert_raises(ValueError,
|
||
|
|
match='In range, start must be <= stop'):
|
||
|
|
binned_statistic_dd([self.y], self.v,
|
||
|
|
range=[[1, 0]])
|
||
|
|
with assert_raises(
|
||
|
|
ValueError,
|
||
|
|
match='In dimension 1 of range, start must be <= stop'):
|
||
|
|
binned_statistic_dd([self.x, self.y], self.v,
|
||
|
|
range=[[1, 0], [0, 1]])
|
||
|
|
with assert_raises(
|
||
|
|
ValueError,
|
||
|
|
match='In dimension 2 of range, start must be <= stop'):
|
||
|
|
binned_statistic_dd([self.x, self.y], self.v,
|
||
|
|
range=[[0, 1], [1, 0]])
|
||
|
|
with assert_raises(
|
||
|
|
ValueError,
|
||
|
|
match='range given for 1 dimensions; 2 required'):
|
||
|
|
binned_statistic_dd([self.x, self.y], self.v,
|
||
|
|
range=[[0, 1]])
|
||
|
|
|
||
|
|
def test_binned_statistic_float32(self):
|
||
|
|
X = np.array([0, 0.42358226], dtype=np.float32)
|
||
|
|
stat, _, _ = binned_statistic(X, None, 'count', bins=5)
|
||
|
|
assert_allclose(stat, np.array([1, 0, 0, 0, 1], dtype=np.float64))
|
||
|
|
|
||
|
|
def test_gh14332(self):
|
||
|
|
# Test the wrong output when the `sample` is close to bin edge
|
||
|
|
x = []
|
||
|
|
size = 20
|
||
|
|
for i in range(size):
|
||
|
|
x += [1-0.1**i]
|
||
|
|
|
||
|
|
bins = np.linspace(0,1,11)
|
||
|
|
sum1, edges1, bc = binned_statistic_dd(x, np.ones(len(x)),
|
||
|
|
bins=[bins], statistic='sum')
|
||
|
|
sum2, edges2 = np.histogram(x, bins=bins)
|
||
|
|
|
||
|
|
assert_allclose(sum1, sum2)
|
||
|
|
assert_allclose(edges1[0], edges2)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", [np.float64, np.complex128])
|
||
|
|
@pytest.mark.parametrize("statistic", [np.mean, np.median, np.sum, np.std,
|
||
|
|
np.min, np.max, 'count',
|
||
|
|
lambda x: (x**2).sum(),
|
||
|
|
lambda x: (x**2).sum() * 1j])
|
||
|
|
def test_dd_all(self, dtype, statistic):
|
||
|
|
def ref_statistic(x):
|
||
|
|
return len(x) if statistic == 'count' else statistic(x)
|
||
|
|
|
||
|
|
rng = np.random.default_rng(3704743126639371)
|
||
|
|
n = 10
|
||
|
|
x = rng.random(size=n)
|
||
|
|
i = x >= 0.5
|
||
|
|
v = rng.random(size=n)
|
||
|
|
if dtype is np.complex128:
|
||
|
|
v = v + rng.random(size=n)*1j
|
||
|
|
|
||
|
|
stat, _, _ = binned_statistic_dd(x, v, statistic, bins=2)
|
||
|
|
ref = np.array([ref_statistic(v[~i]), ref_statistic(v[i])])
|
||
|
|
assert_allclose(stat, ref)
|
||
|
|
assert stat.dtype == np.result_type(ref.dtype, np.float64)
|