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Use cbook._reshape_2D in hist. #8369

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Merged
merged 1 commit into from
Mar 25, 2017
Merged

Use cbook._reshape_2D in hist. #8369

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43 changes: 3 additions & 40 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6106,41 +6106,6 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
bin_range = range
del range

def _normalize_input(inp, ename='input'):
"""Normalize 1 or 2d input into list of np.ndarray or
a single 2D np.ndarray.

Parameters
----------
inp : iterable
ename : str, optional
Name to use in ValueError if `inp` can not be normalized

"""
if (isinstance(x, np.ndarray) or
not iterable(cbook.safe_first_element(inp))):
# TODO: support masked arrays;
inp = np.asarray(inp)
if inp.ndim == 2:
# 2-D input with columns as datasets; switch to rows
inp = inp.T
elif inp.ndim == 1:
# new view, single row
inp = inp.reshape(1, inp.shape[0])
else:
raise ValueError(
"{ename} must be 1D or 2D".format(ename=ename))
if inp.shape[1] < inp.shape[0]:
warnings.warn(
'2D hist input should be nsamples x nvariables;\n '
'this looks transposed '
'(shape is %d x %d)' % inp.shape[::-1])
else:
# multiple hist with data of different length
inp = [np.asarray(xi) for xi in inp]

return inp

if not self._hold:
self.cla()

Expand Down Expand Up @@ -6175,20 +6140,18 @@ def _normalize_input(inp, ename='input'):
binsgiven = (cbook.iterable(bins) or bin_range is not None)

# basic input validation
flat = np.ravel(x)

input_empty = len(flat) == 0
input_empty = np.size(x) == 0

# Massage 'x' for processing.
if input_empty:
x = np.array([[]])
else:
x = _normalize_input(x, 'x')
x = cbook._reshape_2D(x, 'x')
nx = len(x) # number of datasets

# We need to do to 'weights' what was done to 'x'
if weights is not None:
w = _normalize_input(weights, 'weights')
w = cbook._reshape_2D(weights, 'weights')
else:
w = [None]*nx

Expand Down
18 changes: 10 additions & 8 deletions lib/matplotlib/cbook/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1744,7 +1744,7 @@ def _compute_conf_interval(data, med, iqr, bootstrap):
bxpstats = []

# convert X to a list of lists
X = _reshape_2D(X)
X = _reshape_2D(X, "X")

ncols = len(X)
if labels is None:
Expand Down Expand Up @@ -1974,14 +1974,16 @@ def _check_1d(x):
return np.atleast_1d(x)


def _reshape_2D(X):
def _reshape_2D(X, name):
"""
Converts a non-empty list or an ndarray of two or fewer dimensions
into a list of iterable objects so that in
Use Fortran ordering to convert ndarrays and lists of iterables to lists of
1D arrays.

for v in _reshape_2D(X):
Lists of iterables are converted by applying `np.asarray` to each of their
elements. 1D ndarrays are returned in a singleton list containing them.
2D ndarrays are converted to the list of their *columns*.

v is iterable and can be used to instantiate a 1D array.
*name* is used to generate the error message for invalid inputs.
"""
# Iterate over columns for ndarrays, over rows otherwise.
X = X.T if isinstance(X, np.ndarray) else np.asarray(X)
Expand All @@ -1992,7 +1994,7 @@ def _reshape_2D(X):
# 2D array, or 1D array of iterables: flatten them first.
return [np.reshape(x, -1) for x in X]
else:
raise ValueError("input `X` must have 2 or fewer dimensions")
raise ValueError("{} must have 2 or fewer dimensions".format(name))


def violin_stats(X, method, points=100):
Expand Down Expand Up @@ -2039,7 +2041,7 @@ def violin_stats(X, method, points=100):
vpstats = []

# Want X to be a list of data sequences
X = _reshape_2D(X)
X = _reshape_2D(X, "X")

for x in X:
# Dictionary of results for this distribution
Expand Down