def test_resample_bins_nd():
from pyirf.binning import resample_histogram1d
n = 10
# shape 2x3x10
data = np.array([
[
np.ones(n),
np.ones(n) * 2,
np.ones(n) * 3,
],
[
np.ones(n) * 4,
np.ones(n) * 5,
np.ones(n) * 6,
],
])
edges = np.arange(n + 1)
new_data = resample_histogram1d(data, edges, edges, axis=2)
np.testing.assert_array_almost_equal(new_data, data)
edges = np.arange(3 + 1)
new_data = resample_histogram1d(data, edges, edges, axis=1)
np.testing.assert_array_almost_equal(new_data, data)
def test_resample_bins_1d():
from pyirf.binning import resample_histogram1d
n = 10
data = np.ones(n)
edges = np.arange(n + 1)
# no resampling
new_data = resample_histogram1d(data, edges, edges)
np.testing.assert_array_almost_equal(new_data, data)
# resampling, less bins
new_edges = np.arange(n + 1, step=2)
true_new_data = np.ones(n // 2) * 2
new_data = resample_histogram1d(data, edges, new_edges)
np.testing.assert_array_almost_equal(new_data, true_new_data)
# resampling, smaller range (lose normalization)
new_edges = np.arange(n // 2 + 1)
true_new_data = np.ones(n // 2)
new_data = resample_histogram1d(data, edges, new_edges)
np.testing.assert_array_almost_equal(new_data, true_new_data)
# resampling, larger range (fill with zeros)
new_edges = np.arange(-n, 2 * n + 1)
true_new_data = np.concatenate([np.zeros(n), np.ones(n), np.zeros(n)])
new_data = resample_histogram1d(data, edges, new_edges)
np.testing.assert_array_almost_equal(new_data, true_new_data)
def test_resample_bins_units():
from pyirf.binning import resample_histogram1d
import pytest
n = 10
data = np.ones(n) * u.m
old_edges = np.arange(n + 1) * u.TeV
new_edges = old_edges.copy().to(u.GeV)
new_data = resample_histogram1d(data, old_edges, new_edges)
np.testing.assert_array_almost_equal(
new_data.to_value(data.unit), data.to_value(data.unit),
)
with pytest.raises(ValueError):
new_data = resample_histogram1d(data, old_edges, new_edges.value)
with pytest.raises(u.core.UnitConversionError):
new_data = resample_histogram1d(data, old_edges, u.Quantity(new_edges.value, unit=u.m))