EVOLUTION-MANAGER

Edit File: test_binned_statistic.py

from __future__ import division, print_function, absolute_import

import numpy as np
from numpy.testing import assert_allclose
from pytest import raises as assert_raises
from scipy.stats import (binned_statistic, binned_statistic_2d,
                         binned_statistic_dd)

from scipy._lib.six import u
from .common_tests import check_named_results

class TestBinnedStatistic(object):

@classmethod
    def setup_class(cls):
        np.random.seed(9865)
        cls.x = np.random.random(100)
        cls.y = np.random.random(100)
        cls.v = np.random.random(100)
        cls.X = np.random.random((100, 3))
        cls.w = np.random.random(100)
        cls.u = np.random.random(100) + 1e6

def test_1d_count(self):
        x = self.x
        v = self.v

count1, edges1, bc = binned_statistic(x, v, 'count', bins=10)
        count2, edges2 = np.histogram(x, bins=10)

assert_allclose(count1, count2)
        assert_allclose(edges1, edges2)

def test_gh5927(self):
        # smoke test for gh5927 - binned_statistic was using `is` for string
        # comparison
        x = self.x
        v = self.v
        statistics = [u'mean', u'median', u'count', u'sum']
        for statistic in statistics:
            binned_statistic(x, v, statistic, bins=10)

def test_big_number_std(self):
        # tests for numerical stability of std calculation
        # see issue gh-10126 for more
        x = self.x
        u = self.u

stat1, edges1, bc = binned_statistic(x, u, 'std', bins=10)
        stat2, edges2, bc = binned_statistic(x, u, np.std, bins=10)

assert_allclose(stat1, stat2)

def test_non_finite_inputs(self):
        # if either `values` or `sample` contain np.inf or np.nan throw
        # see issue gh-9010 for more
        x = self.x
        u = self.u
        u[0] = np.inf
        assert_raises(ValueError, binned_statistic, x, u, 'std', bins=10)
        u[0] = np.nan
        assert_raises(ValueError, binned_statistic, x, u, 'count', bins=10)

def test_1d_result_attributes(self):
        x = self.x
        v = self.v

res = binned_statistic(x, v, 'count', bins=10)
        attributes = ('statistic', 'bin_edges', 'binnumber')
        check_named_results(res, attributes)

def test_1d_sum(self):
        x = self.x
        v = self.v

sum1, edges1, bc = binned_statistic(x, v, 'sum', bins=10)
        sum2, edges2 = np.histogram(x, bins=10, weights=v)

assert_allclose(sum1, sum2)
        assert_allclose(edges1, edges2)

def test_1d_mean(self):
        x = self.x
        v = self.v

stat1, edges1, bc = binned_statistic(x, v, 'mean', bins=10)
        stat2, edges2, bc = binned_statistic(x, v, np.mean, bins=10)

assert_allclose(stat1, stat2)
        assert_allclose(edges1, edges2)

def test_1d_std(self):
        x = self.x
        v = self.v

stat1, edges1, bc = binned_statistic(x, v, 'std', bins=10)
        stat2, edges2, bc = binned_statistic(x, v, np.std, bins=10)

assert_allclose(stat1, stat2)
        assert_allclose(edges1, edges2)

def test_1d_min(self):
        x = self.x
        v = self.v

stat1, edges1, bc = binned_statistic(x, v, 'min', bins=10)
        stat2, edges2, bc = binned_statistic(x, v, np.min, bins=10)

assert_allclose(stat1, stat2)
        assert_allclose(edges1, edges2)

def test_1d_max(self):
        x = self.x
        v = self.v

stat1, edges1, bc = binned_statistic(x, v, 'max', bins=10)
        stat2, edges2, bc = binned_statistic(x, v, np.max, bins=10)

assert_allclose(stat1, stat2)
        assert_allclose(edges1, edges2)

def test_1d_median(self):
        x = self.x
        v = self.v

stat1, edges1, bc = binned_statistic(x, v, 'median', bins=10)
        stat2, edges2, bc = binned_statistic(x, v, np.median, bins=10)

assert_allclose(stat1, stat2)
        assert_allclose(edges1, edges2)

def test_1d_bincode(self):
        x = self.x[:20]
        v = self.v[:20]

count1, edges1, bc = binned_statistic(x, v, 'count', bins=3)
        bc2 = np.array([3, 2, 1, 3, 2, 3, 3, 3, 3, 1, 1, 3, 3, 1, 2, 3, 1,
                        1, 2, 1])

bcount = [(bc == i).sum() for i in np.unique(bc)]

assert_allclose(bc, bc2)
        assert_allclose(bcount, count1)

def test_1d_range_keyword(self):
        # Regression test for gh-3063, range can be (min, max) or [(min, max)]
        np.random.seed(9865)
        x = np.arange(30)
        data = np.random.random(30)

mean, bins, _ = binned_statistic(x[:15], data[:15])
        mean_range, bins_range, _ = binned_statistic(x, data, range=[(0, 14)])
        mean_range2, bins_range2, _ = binned_statistic(x, data, range=(0, 14))

assert_allclose(mean, mean_range)
        assert_allclose(bins, bins_range)
        assert_allclose(mean, mean_range2)
        assert_allclose(bins, bins_range2)

def test_1d_multi_values(self):
        x = self.x
        v = self.v
        w = self.w

stat1v, edges1v, bc1v = binned_statistic(x, v, 'mean', bins=10)
        stat1w, edges1w, bc1w = binned_statistic(x, w, 'mean', bins=10)
        stat2, edges2, bc2 = binned_statistic(x, [v, w], 'mean', bins=10)

assert_allclose(stat2[0], stat1v)
        assert_allclose(stat2[1], stat1w)
        assert_allclose(edges1v, edges2)
        assert_allclose(bc1v, bc2)

def test_2d_count(self):
        x = self.x
        y = self.y
        v = self.v

count1, binx1, biny1, bc = binned_statistic_2d(
            x, y, v, 'count', bins=5)
        count2, binx2, biny2 = np.histogram2d(x, y, bins=5)

assert_allclose(count1, count2)
        assert_allclose(binx1, binx2)
        assert_allclose(biny1, biny2)

def test_2d_result_attributes(self):
        x = self.x
        y = self.y
        v = self.v

res = binned_statistic_2d(x, y, v, 'count', bins=5)
        attributes = ('statistic', 'x_edge', 'y_edge', 'binnumber')
        check_named_results(res, attributes)

def test_2d_sum(self):
        x = self.x
        y = self.y
        v = self.v

sum1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'sum', bins=5)
        sum2, binx2, biny2 = np.histogram2d(x, y, bins=5, weights=v)

assert_allclose(sum1, sum2)
        assert_allclose(binx1, binx2)
        assert_allclose(biny1, biny2)

def test_2d_mean(self):
        x = self.x
        y = self.y
        v = self.v

stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'mean', bins=5)
        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.mean, bins=5)

assert_allclose(stat1, stat2)
        assert_allclose(binx1, binx2)
        assert_allclose(biny1, biny2)

def test_2d_mean_unicode(self):
        x = self.x
        y = self.y
        v = self.v
        stat1, binx1, biny1, bc = binned_statistic_2d(
            x, y, v, u('mean'), bins=5)
        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.mean, bins=5)
        assert_allclose(stat1, stat2)
        assert_allclose(binx1, binx2)
        assert_allclose(biny1, biny2)

def test_2d_std(self):
        x = self.x
        y = self.y
        v = self.v

stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'std', bins=5)
        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.std, bins=5)

assert_allclose(stat1, stat2)
        assert_allclose(binx1, binx2)
        assert_allclose(biny1, biny2)

def test_2d_min(self):
        x = self.x
        y = self.y
        v = self.v

stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'min', bins=5)
        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.min, bins=5)

assert_allclose(stat1, stat2)
        assert_allclose(binx1, binx2)
        assert_allclose(biny1, biny2)

def test_2d_max(self):
        x = self.x
        y = self.y
        v = self.v

stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'max', bins=5)
        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.max, bins=5)

assert_allclose(stat1, stat2)
        assert_allclose(binx1, binx2)
        assert_allclose(biny1, biny2)

def test_2d_median(self):
        x = self.x
        y = self.y
        v = self.v

stat1, binx1, biny1, bc = binned_statistic_2d(
            x, y, v, 'median', bins=5)
        stat2, binx2, biny2, bc = binned_statistic_2d(
            x, y, v, np.median, bins=5)

assert_allclose(stat1, stat2)
        assert_allclose(binx1, binx2)
        assert_allclose(biny1, biny2)

def test_2d_bincode(self):
        x = self.x[:20]
        y = self.y[:20]
        v = self.v[:20]

count1, binx1, biny1, bc = binned_statistic_2d(
            x, y, v, 'count', bins=3)
        bc2 = np.array([17, 11, 6, 16, 11, 17, 18, 17, 17, 7, 6, 18, 16,
                        6, 11, 16, 6, 6, 11, 8])

bcount = [(bc == i).sum() for i in np.unique(bc)]

assert_allclose(bc, bc2)
        count1adj = count1[count1.nonzero()]
        assert_allclose(bcount, count1adj)

def test_2d_multi_values(self):
        x = self.x
        y = self.y
        v = self.v
        w = self.w

stat1v, binx1v, biny1v, bc1v = binned_statistic_2d(
            x, y, v, 'mean', bins=8)
        stat1w, binx1w, biny1w, bc1w = binned_statistic_2d(
            x, y, w, 'mean', bins=8)
        stat2, binx2, biny2, bc2 = binned_statistic_2d(
            x, y, [v, w], 'mean', bins=8)

assert_allclose(stat2[0], stat1v)
        assert_allclose(stat2[1], stat1w)
        assert_allclose(binx1v, binx2)
        assert_allclose(biny1w, biny2)
        assert_allclose(bc1v, bc2)

def test_2d_binnumbers_unraveled(self):
        x = self.x
        y = self.y
        v = self.v

stat, edgesx, bcx = binned_statistic(x, v, 'mean', bins=20)
        stat, edgesy, bcy = binned_statistic(y, v, 'mean', bins=10)

stat2, edgesx2, edgesy2, bc2 = binned_statistic_2d(
            x, y, v, 'mean', bins=(20, 10), expand_binnumbers=True)

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)

assert_allclose(sum1, sum2)
        assert_allclose(edges1, edges2)

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

stat1v, edges1v, bc1v = binned_statistic_dd(X, v, np.std, bins=8)
        stat1w, edges1w, bc1w = binned_statistic_dd(X, w, np.std, bins=8)
        stat2, edges2, bc2 = binned_statistic_dd(X, [v, w], np.std, 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)