File size: 2,349 Bytes
7ffff7d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from numpy.testing import assert_allclose
try:
import matplotlib.pyplot as plt
HAS_PLT = True
except ImportError:
HAS_PLT = False
try:
import scipy # noqa
HAS_SCIPY = True
except ImportError:
HAS_SCIPY = False
import pytest
import numpy as np
from astropy.visualization import hist
from astropy.stats import histogram
@pytest.mark.skipif('not HAS_PLT')
def test_hist_basic(rseed=0):
rng = np.random.RandomState(rseed)
x = rng.randn(100)
for range in [None, (-2, 2)]:
n1, bins1, patches1 = plt.hist(x, 10, range=range)
n2, bins2, patches2 = hist(x, 10, range=range)
assert_allclose(n1, n2)
assert_allclose(bins1, bins2)
@pytest.mark.skipif('not HAS_PLT')
def test_hist_specify_ax(rseed=0):
rng = np.random.RandomState(rseed)
x = rng.randn(100)
fig, ax = plt.subplots(2)
n1, bins1, patches1 = hist(x, 10, ax=ax[0])
assert patches1[0].axes is ax[0]
n2, bins2, patches2 = hist(x, 10, ax=ax[1])
assert patches2[0].axes is ax[1]
@pytest.mark.skipif('not HAS_PLT')
def test_hist_autobin(rseed=0):
rng = np.random.RandomState(rseed)
x = rng.randn(100)
# 'knuth' bintype depends on scipy that is optional dependency
if HAS_SCIPY:
bintypes = [10, np.arange(-3, 3, 10), 'knuth', 'scott',
'freedman', 'blocks']
else:
bintypes = [10, np.arange(-3, 3, 10), 'scott',
'freedman', 'blocks']
for bintype in bintypes:
for range in [None, (-3, 3)]:
n1, bins1 = histogram(x, bintype, range=range)
n2, bins2, patches = hist(x, bintype, range=range)
assert_allclose(n1, n2)
assert_allclose(bins1, bins2)
def test_histogram_pathological_input():
# Regression test for https://github.com/astropy/astropy/issues/7758
# The key feature of the data below is that one of the points is very,
# very different than the rest. That leads to a large number of bins.
data = [9.99999914e+05, -8.31312483e-03, 6.52755852e-02, 1.43104653e-03,
-2.26311017e-02, 2.82660007e-03, 1.80307521e-02, 9.26294279e-03,
5.06606026e-02, 2.05418011e-03]
with pytest.raises(ValueError):
hist(data, bins='freedman', max_bins=10000)
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