8000 ENH: #7845. histogram2d and histogramdd can return int array when relevant by tom-bird · Pull Request #7886 · numpy/numpy · GitHub
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ENH: #7845. histogram2d and histogramdd can return int array when relevant #7886

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ENH: #7845. histogram2d and histogramdd can return int array when relevant #7886

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7 changes: 6 additions & 1 deletion numpy/lib/function_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -878,11 +878,16 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
N, D = sample.shape

nbin = empty(D, int)
ntype = np.dtype(np.intp)
edges = D*[None]
dedges = D*[None]
if weights is not None:
ntype = weights.dtype
weights = asarray(weights)

if normed:
ntype = np.dtype(np.float64)

try:
M = len(bins)
if M != D:
Expand Down Expand Up @@ -970,7 +975,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
# Flattened histogram matrix (1D)
# Reshape is used so that overlarge arrays
# will raise an error.
hist = zeros(nbin, float).reshape(-1)
hist = zeros(nbin, ntype).reshape(-1)

# Compute the sample indices in the flattened histogram matrix.
ni = nbin.argsort()
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10 changes: 10 additions & 0 deletions numpy/lib/tests/test_function_base.py
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Expand Up @@ -1761,6 +1761,16 @@ def test_finite_range(self):
assert_raises(ValueError, histogramdd, vals,
range=[[0.0, 1.0], [np.nan, 0.75], [0.25, 0.5]])

def test_type(self):
vals = np.random.random((10, 3))
w = np.ones(10, float)
assert_(np.issubdtype(np.histogramdd(vals)[0].dtype, int))
assert_(np.issubdtype(np.histogramdd(vals, weights=w)[0].dtype, float))
w = np.ones(10, int)
assert_(np.issubdtype(np.histogramdd(vals, weights=w)[0].dtype, int))
assert_(np.issubdtype(np.histogramdd(vals, normed=True)[0].dtype, float))
assert_(np.issubdtype(np.histogramdd(vals, weights=w, normed=True)[0].dtype, float))


class TestUnique(TestCase):

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0