diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index bf6e70c15eb3..30b5ec0078b0 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -5871,7 +5871,7 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
Parameters
----------
x : (n,) array or sequence of (n,) arrays
- Input values, this takes either a single array or a sequency of
+ Input values, this takes either a single array or a sequence of
arrays which are not required to be of the same length
bins : integer or array_like or 'auto', optional
@@ -6042,8 +6042,8 @@ def hist(self, x, bins=None, range=None, normed=False, weights=None,
"""
def _normalize_input(inp, ename='input'):
- """Normalize 1 or 2d input into list of np.ndarray or
- a single 2D np.ndarray.
+ """Normalize 1 or 2d input into a list of one or more np.ndarrays
+ which have potentially different lengths.
Parameters
----------
@@ -6054,25 +6054,30 @@ def _normalize_input(inp, ename='input'):
"""
if (isinstance(x, np.ndarray) or
not iterable(cbook.safe_first_element(inp))):
- # TODO: support masked arrays;
- inp = np.asarray(inp)
+
+ inp = cbook.safe_masked_invalid(inp)
if inp.ndim == 2:
# 2-D input with columns as datasets; switch to rows
inp = inp.T
+
+ 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])
+
+ # Change to a list of arrays
+ inp = [inp[i, :] for i in range(inp.shape[0])]
+
elif inp.ndim == 1:
- # new view, single row
- inp = inp.reshape(1, inp.shape[0])
+ # Change to a list with a single array
+ 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]
+ # Change to a list of arrays
+ inp = [cbook.safe_masked_invalid(arr) for arr in inp]
return inp
@@ -6117,23 +6122,23 @@ 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 = len(np.ravel(x)) == 0
- # Massage 'x' for processing.
+ # Massage shape of 'x' to be a list of arrays
if input_empty:
- x = np.array([[]])
+ x = [np.array([[]])]
else:
x = _normalize_input(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')
- else:
+ # Massage shape of 'weights' to be a list, where each element
+ # weights[i] is either None or an array with the same shape as x[i]
+ if weights is None:
w = [None]*nx
+ else:
+ w = _normalize_input(weights, 'weights')
+ # Comparing shape of weights vs. x
if len(w) != nx:
raise ValueError('weights should have the same shape as x')
@@ -6142,6 +6147,16 @@ def _normalize_input(inp, ename='input'):
raise ValueError(
'weights should have the same shape as x')
+ # Combine the masks from x[i] and w[i] (if applicable) into a single
+ # mask and apply it to both.
+ if not input_empty:
+ for i in range(len(x)):
+ mask_i = x[i].mask
+ if w[i] is not None:
+ mask_i = mask_i | w[i].mask
+ w[i] = np.ma.masked_array(w[i], mask=mask_i).compressed()
+ x[i] = np.ma.masked_array(x[i], mask=mask_i).compressed()
+
if color is None:
color = [self._get_lines.get_next_color() for i in xrange(nx)]
else:
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