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MNT: fix __array__ to numpy #22975

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Merged
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May 18, 2022
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MNT: fix __array__ to numpy #22975

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7 changes: 6 additions & 1 deletion lib/matplotlib/cbook/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1333,7 +1333,12 @@ def _check_1d(x):
"""Convert scalars to 1D arrays; pass-through arrays as is."""
# Unpack in case of e.g. Pandas or xarray object
x = _unpack_to_numpy(x)
if not hasattr(x, 'shape') or len(x.shape) < 1:
# plot requires `shape` and `ndim`. If passed an
# object that doesn't provide them, then force to numpy array.
# Note this will strip unit information.
if (not hasattr(x, 'shape') or
not hasattr(x, 'ndim') or
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This seems to fix the problem. and see the test. However I am not sure if this is really on us, or the Kernel, which is missing ndim. Or maybe we should not be relying on ndim.

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Is ndim always len(shape)? (I guess not, but otherwise it make make sense to just use that instead?)

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There are 120 ndim in the Python code base (although some are in tests or non-Python-code), so even though we probably should fix it here, in the long run it may be better that Kernel gets ndim.

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The problem is that Kernel does not really properly behave like an ndarray, so it does need to be converted to an array here. We're (ab)using the fact that it does not define ndim as a proxy for "I'm not a proper duck type, just happen to have shape". Really one would like the np.asduckarray that has been discussed in numpy...

len(x.shape) < 1):
return np.atleast_1d(x)
else:
return x
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19 changes: 19 additions & 0 deletions lib/matplotlib/tests/test_units.py
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Expand Up @@ -264,3 +264,22 @@ def test_empty_default_limits(quantity_converter):
fig.draw_without_rendering()
assert ax.get_ylim() == (0, 100)
assert ax.get_xlim() == (28.5, 31.5)


# test array-like objects...
class Kernel:
def __init__(self, array):
self._array = np.asanyarray(array)

def __array__(self):
return self._array

@property
def shape(self):
return self._array.shape


def test_plot_kernel():
# just a smoketest that fail
kernel = Kernel([1, 2, 3, 4, 5])
plt.plot(kernel)