438 lines
13 KiB
Python
438 lines
13 KiB
Python
|
from __future__ import division, print_function, absolute_import
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from numpy.testing import assert_allclose
|
||
|
|
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)
|
||
|
|
|
||
|
|
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:
|
||
|
|
res = binned_statistic(x, v, statistic, 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])
|