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from __future__ import annotations
import copy
import numpy as np
import pytest
from pytest import approx
import boost_histogram as bh
np113 = tuple(int(x) for x in np.__version__.split(".")[:2]) >= (1, 13)
inputs_1d = (
[1, 2, 3, 4, 3, 4, 5, 10, 9, 11, 21, -2],
[
0.27237556,
0.72020987,
0.75204098,
-0.29265003,
-2.67332888,
0.68420365,
-0.60629843,
-0.6375687,
-1.00017927,
-0.07707552,
],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
)
opts = (
{},
{"bins": 10},
{"bins": "auto" if np113 else 20},
{"range": (0, 5), "bins": 30},
{"range": np.array((0, 5), dtype=float), "bins": np.int32(30)},
{"range": np.array((0, 3), dtype=np.double), "bins": np.uint32(10)},
{"range": np.array((0, 10), dtype=int), "bins": np.int8(30)},
{"bins": [0, 1, 1.2, 1.3, 4, 21]},
)
@pytest.mark.parametrize("a", inputs_1d)
@pytest.mark.parametrize("opt", opts)
def test_histogram1d(a, opt):
v = np.array(a)
h1, e1 = np.histogram(v, **opt)
h2, e2 = bh.numpy.histogram(v, **opt)
assert e1 == approx(e2)
assert h1 == approx(h2)
opt = copy.deepcopy(opt)
opt["density"] = True
h1, e1 = np.histogram(v, **opt)
h2, e2 = bh.numpy.histogram(v, **opt)
assert e1 == approx(e2)
assert h1 == approx(h2)
@pytest.mark.parametrize("a", inputs_1d)
@pytest.mark.parametrize("opt", opts)
def test_histogram1d_object(a, opt):
bh_opt = copy.deepcopy(opt)
bh_opt["histogram"] = bh.Histogram
v = np.array(a)
h1, e1 = np.histogram(v, **opt)
bh_h2 = bh.numpy.histogram(v, **bh_opt)
h2, e2 = bh_h2.to_numpy()
assert e1 == approx(e2)
assert h1 == approx(h2)
# Ensure reducible
assert bh_h2[:5].values() == approx(h1[:5])
opt = copy.deepcopy(opt)
opt["density"] = True
bh_opt = copy.deepcopy(bh_opt)
bh_opt["density"] = True
with pytest.raises(KeyError):
bh_h2 = bh.numpy.histogram(v, **bh_opt)
def test_histogram2d():
x = np.array([0.3, 0.3, 0.1, 0.8, 0.34, 0.03, 0.32, 0.65])
y = np.array([0.4, 0.5, 0.22, 0.65, 0.32, 0.01, 0.23, 1.98])
h1, e1x, e1y = np.histogram2d(x, y)
h2, e2x, e2y = bh.numpy.histogram2d(x, y)
assert e1x == approx(e2x)
assert e1y == approx(e2y)
assert h1 == approx(h2)
h1, e1x, e1y = np.histogram2d(x, y, density=True)
h2, e2x, e2y = bh.numpy.histogram2d(x, y, density=True)
assert e1x == approx(e2x)
assert e1y == approx(e2y)
assert h1 == approx(h2)
def test_histogram2d_object():
x = np.array([0.3, 0.3, 0.1, 0.8, 0.34, 0.03, 0.32, 0.65])
y = np.array([0.4, 0.5, 0.22, 0.65, 0.32, 0.01, 0.23, 1.98])
h1, e1x, e1y = np.histogram2d(x, y)
bh_h2 = bh.numpy.histogram2d(x, y, histogram=bh.Histogram)
h2, e2x, e2y = bh_h2.to_numpy()
assert e1x == approx(e2x)
assert e1y == approx(e2y)
assert h1 == approx(h2)
with pytest.raises(KeyError):
bh.numpy.histogram2d(x, y, density=True, histogram=bh.Histogram)
def test_histogramdd():
x = np.array([0.3, 0.3, 0.1, 0.8, 0.34, 0.03, 0.32, 0.65])
y = np.array([0.4, 0.5, 0.22, 0.65, 0.32, 0.01, 0.23, 1.98])
z = np.array([0.5, 0.7, 0.0, 0.65, 0.72, 0.01, 0.3, 1.4])
h1, (e1x, e1y, e1z) = np.histogramdd([x, y, z])
h2, (e2x, e2y, e2z) = bh.numpy.histogramdd([x, y, z])
assert e1x == approx(e2x)
assert e1y == approx(e2y)
assert e1z == approx(e2z)
assert h1 == approx(h2)
h1, (e1x, e1y, e1z) = np.histogramdd([x, y, z], density=True)
h2, (e2x, e2y, e2z) = bh.numpy.histogramdd([x, y, z], density=True)
assert e1x == approx(e2x)
assert e1y == approx(e2y)
assert e1z == approx(e2z)
assert h1 == approx(h2)
def test_histogramdd_object():
x = np.array([0.3, 0.3, 0.1, 0.8, 0.34, 0.03, 0.32, 0.65])
y = np.array([0.4, 0.5, 0.22, 0.65, 0.32, 0.01, 0.23, 1.98])
z = np.array([0.5, 0.7, 0.0, 0.65, 0.72, 0.01, 0.3, 1.4])
h1, (e1x, e1y, e1z) = np.histogramdd([x, y, z])
bh_h2 = bh.numpy.histogramdd([x, y, z], histogram=bh.Histogram)
h2, (e2x, e2y, e2z) = bh_h2.to_numpy(dd=True)
assert e1x == approx(e2x)
assert e1y == approx(e2y)
assert e1z == approx(e2z)
assert h1 == approx(h2)
with pytest.raises(KeyError):
bh.numpy.histogramdd([x, y, z], density=True, histogram=bh.Histogram)
def test_histogram_weights():
x = np.array([0.3, 0.3, 0.1, 0.8, 0.34, 0.03, 0.32, 0.65])
weights = np.array([0.4, 0.5, 0.22, 0.65, 0.32, 0.01, 0.23, 1.98])
h1, edges = np.histogram(x, weights=weights)
bh_h1, bh_edges = bh.numpy.histogram(x, weights=weights)
assert bh_h1 == approx(h1)
assert bh_edges == approx(edges)
def test_histogram_nans():
x = np.array([0, 1, 2, 3, np.nan])
with pytest.raises(ValueError):
np.histogram(x)
with pytest.raises(ValueError):
bh.numpy.histogram(x)
def test_histogram_all_zeros():
x = np.array([0, 0, 0, 0, 0, 0])
h1, edges = np.histogram(x)
bh_h1, bh_edges = bh.numpy.histogram(x)
assert bh_h1 == approx(h1)
assert bh_edges == approx(edges)
def test_histogram_all_ones():
x = np.array([0, 0, 0, 0, 0, 0])
h1, edges = np.histogram(x)
bh_h1, bh_edges = bh.numpy.histogram(x)
assert bh_h1 == approx(h1)
assert bh_edges == approx(edges)
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