8000 MAINT: Extract helper functions from histogram by eric-wieser · Pull Request #10261 · numpy/numpy · GitHub
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MAINT: Extract helper functions from histogram #10261

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merged 1 commit into from
Dec 22, 2017
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MAINT: Extract helper functions from histogram #10261

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71 changes: 44 additions & 27 deletions numpy/lib/histograms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,6 +199,45 @@ def _hist_bin_auto(x):
'sturges': _hist_bin_sturges}


def _ravel_and_check_weights(a, weights):
""" Check a and weights have matching shapes, and ravel both """
a = np.asarray(a)
if weights is not None:
weights = np.asarray(weights)
if weights.shape != a.shape:
raise ValueError(
'weights should have the same shape as a.')
weights = weights.ravel()
a = a.ravel()
return a, weights


def _get_outer_edges(a, range):
"""
Determine the outer bin edges to use, from either the data or the range
argument
"""
if range is None:
if a.size == 0:
# handle empty arrays. Can't determine range, so use 0-1.
first_edge, last_edge = 0.0, 1.0
else:
first_edge, last_edge = a.min() + 0.0, a.max() + 0.0
else:
first_edge, last_edge = [mi + 0.0 for mi in range]
if first_edge > last_edge:
raise ValueError(
'max must be larger than min in range parameter.')
if not np.all(np.isfinite([first_edge, last_edge])):
raise ValueError(
'range parameter must be finite.')
if first_edge == last_edge:
first_edge -= 0.5
last_edge += 0.5

return first_edge, last_edge


def histogram(a, bins=10, range=None, normed=False, weights=None,
density=None):
r"""
Expand Down Expand Up @@ -414,38 +453,16 @@ def histogram(a, bins=10, range=None, normed=False, weights=None,
>>> plt.show()

"""
a = np.asarray(a)
if weights is not None:
weights = np.asarray(weights)
if weights.shape != a.shape:
raise ValueError(
'weights should have the same shape as a.')
weights = weights.ravel()
a = a.ravel()

# Do not modify the original value of range so we can check for `None`
if range is None:
if a.size == 0:
# handle empty arrays. Can't determine range, so use 0-1.
first_edge, last_edge = 0.0, 1.0
else:
first_edge, last_edge = a.min() + 0.0, a.max() + 0.0
else:
first_edge, last_edge = [mi + 0.0 for mi in range]
if first_edge > last_edge:
raise ValueError(
'max must be larger than min in range parameter.')
if not np.all(np.isfinite([first_edge, last_edge])):
raise ValueError(
'range parameter must be finite.')
if first_edge == last_edge:
first_edge -= 0.5
last_edge += 0.5

# density overrides the normed keyword
if density is not None:
normed = False

a, weights = _ravel_and_check_weights(a, weights)

# Do not modify the original value of range so we can check for `None`
first_edge, last_edge = _get_outer_edges(a, range)

# parse the overloaded bins argument
n_equal_bins = None
bin_edges = None
Expand Down
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