8000 BUG: histogram small range robust by tylerjereddy · Pull Request #24161 · numpy/numpy · GitHub
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BUG: h 8000 istogram small range robust #24161

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Merged
merged 1 commit into from
Jul 11, 2023
Merged

BUG: histogram small range robust #24161

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6 changes: 4 additions & 2 deletions numpy/lib/histograms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -811,7 +811,8 @@ def histogram(a, bins=10, range=None, density=None, weights=None):
n = np.zeros(n_equal_bins, ntype)

# Pre-compute histogram scaling factor
norm = n_equal_bins / _unsigned_subtract(last_edge, first_edge)
norm_numerator = n_equal_bins
norm_denom = _unsigned_subtract(last_edge, first_edge)

# We iterate over blocks here for two reasons: the first is that for
# large arrays, it is actually faster (for example for a 10^8 array it
Expand Down Expand Up @@ -839,7 +840,8 @@ def histogram(a, bins=10, range=None, density=None, weights=None):

# Compute the bin indices, and for values that lie exactly on
# last_edge we need to subtract one
f_indices = _unsigned_subtract(tmp_a, first_edge) * norm
f_indices = ((_unsigned_subtract(tmp_a, first_edge) / norm_denom)
* norm_numerator)
indices = f_indices.astype(np.intp)
indices[indices == n_equal_bins] -= 1

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7 changes: 7 additions & 0 deletions numpy/lib/tests/test_histograms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -408,6 +408,13 @@ def test_big_arrays(self):
hist = np.histogramdd(sample=sample, bins=(xbins, ybins, zbins))
assert_equal(type(hist), type((1, 2)))

def test_gh_23110(self):
hist, e = np.histogram(np.array([-0.9e-308], dtype='>f8'),
bins=2,
range=(-1e-308, -2e-313))
expected_hist = np.array([1, 0])
assert_array_equal(hist, expected_hist)


class TestHistogramOptimBinNums:
"""
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0