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FIX: handle fully masked data #9285

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Merged
merged 3 commits into from
Oct 12, 2017
Merged

FIX: handle fully masked data #9285

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07eaa4c
FIX: handle fully masked data
tacaswell Oct 5, 2017
8c3ad29
Handle min, max of possibly all-masked array
efiring Oct 12, 2017
63d3fb0
Remove trailing whitespace that the github editor so helpfully added
efiring Oct 12, 2017
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FIX: handle fully masked data 
As min/max do not make sense for an array with no non-masked values
numpy returns a singleton, `np.ma.masked`, which can not be cast to
a number.  In this case just treat numbers as in range (0, 1) (even
though it will just be ignored due to the masking).

closes #9280
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@tacaswell
tacaswell committed Oct 12, 2017
commit 07eaa4ca538b042b352ec98b4ef1348a7d610ad7
15 changes: 13 additions & 2 deletions 15 lib/matplotlib/image.py
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Expand Up @@ -370,8 +370,19 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
scaled_dtype = np.float32
# old versions of numpy do not work with `np.nammin`
# and `np.nanmax` as inputs
a_min = np.ma.min(A).astype(scaled_dtype)
a_max = np.ma.max(A).astype(scaled_dtype)
a_min = np.ma.min(A)
a_max = np.ma.max(A)

# we need these try/except blocks to handle
# fully-masked/invalid input
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And delete the 4 lines above.

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details....

try:
a_min = a_min.astype(scaled_dtype)
except AttributeError:
a_min = 0
try:
a_max = a_max.astype(scaled_dtype)
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Gee, can't leave it alone.

try:
    a_min  = a_min.astype(scaled_dtype)
except AttributeError:
    a_min, a_max = 0, 1
else:
    a_max = a_max.astype(scaled_dtype)

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Or shorten the whole block. From the top:

try:
    a_min = A.min().astype(scaled_dtype)
except AttributeError:
    a_min, a_max = 0, 1  # all masked, so values don't matter
else:
    a_max = A.max().astype(scaled_dtype)

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And taking @anntzer's suggestion:

a_min = A.min().astype(scaled_dtype)
if a_min is np.ma.masked:
    a_min, a_max = 0, 1  # all masked, so values don't matter
else:
    a_max = A.max().astype(scaled_dtype)

except AttributeError:
a_min = 1
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simpler?

if np.ma.count(A) == 0:
    a_min, a_max = 0, 1   # all masked, so values don't matter
else:
    a_min = np.ma.min(A).astype(scaled_dtype)
    a_max = np.ma.max(A).astype(scaled_dtype)

# scale the input data to [.1, .9]. The Agg
# interpolators clip to [0, 1] internally, use a
# smaller input scale to identify which of the
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10 changes: 10 additions & 0 deletions 10 lib/matplotlib/tests/test_image.py
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Expand Up @@ -835,3 +835,13 @@ def test_imshow_deprecated_interd_warn():
with warnings.catch_warnings(record=True) as warns:
getattr(im, k)
assert len(warns) == 1


def test_full_invalid():
x = np.ones((10, 10))
x[:] = np.nan

f, ax = plt.subplots()
ax.imshow(x)

f.canvas.draw()
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