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BUG: Fix performance regression when plotting values from Numpy array sub-classes #14289

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Merged
merged 1 commit into from
May 21, 2019
Merged

BUG: Fix performance regression when plotting values from Numpy array sub-classes #14289

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2 changes: 1 addition & 1 deletion lib/matplotlib/cbook/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1421,7 +1421,7 @@ def _reshape_2D(X, name):
X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))
if len(X) == 0:
return [[]]
if X.ndim == 1 and not isinstance(X[0], collections.abc.Iterable):
elif X.ndim == 1 and np.ndim(X[0]) == 0:
# 1D array of scalars: directly return it.
return [X]
elif X.ndim in [1, 2]:
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37 changes: 37 additions & 0 deletions lib/matplotlib/tests/test_cbook.py
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Expand Up @@ -492,8 +492,10 @@ def test_flatiter():


def test_reshape2d():

class dummy():
pass

xnew = cbook._reshape_2D([], 'x')
assert np.shape(xnew) == (1, 0)

Expand All @@ -515,6 +517,41 @@ class dummy():
xnew = cbook._reshape_2D(x, 'x')
assert np.shape(xnew) == (5, 3)

# Now test with a list of lists with different lengths, which means the
# array will internally be converted to a 1D object array of lists
x = [[1, 2, 3], [3, 4], [2]]
xnew = cbook._reshape_2D(x, 'x')
assert isinstance(xnew, list)
assert isinstance(xnew[0], np.ndarray) and xnew[0].shape == (3,)
assert isinstance(xnew[1], np.ndarray) and xnew[1].shape == (2,)
assert isinstance(xnew[2], np.ndarray) and xnew[2].shape == (1,)

# We now need to make sure that this works correctly for Numpy subclasses
# where iterating over items can return subclasses too, which may be
# iterable even if they are scalars. To emulate this, we make a Numpy
# array subclass that returns Numpy 'scalars' when iterating or accessing
# values, and these are technically iterable if checking for example
# isinstance(x, collections.abc.Iterable).

class ArraySubclass(np.ndarray):

def __iter__(self):
for value in super().__iter__():
yield np.array(value)

def __getitem__(self, item):
return np.array(super().__getitem__(item))

v = np.arange(10, dtype=float)
x = ArraySubclass((10,), dtype=float, buffer=v.data)

xnew = cbook._reshape_2D(x, 'x')

# We check here that the array wasn't split up into many individual
# ArraySubclass, which is what used to happen due to a bug in _reshape_2D
assert len(xnew) == 1
assert isinstance(xnew[0], ArraySubclass)


def test_contiguous_regions():
a, b, c = 3, 4, 5
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