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Commit236b48f

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FIX: masked images with interpolation
When determining which pixels to mask in the resampled image, if _any_contribution to final value comes from a masked pixel, mask theresult.Due to Agg special-casing the meaning of the alpha channel,the interpolation for the mask channel needs to be done separately.This is probably a template for doing the over/under separately.
1 parentfbe562c commit236b48f

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6 files changed

+324
-7
lines changed

6 files changed

+324
-7
lines changed

‎lib/matplotlib/image.py

Lines changed: 38 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -371,9 +371,20 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
371371
# this is to work around spurious warnings coming
372372
# out of masked arrays.
373373
withnp.errstate(invalid='ignore'):
374-
rgba[...,1]=A<0# under data
375-
rgba[...,2]=A>1# over data
376-
rgba[...,3]=~A.mask# bad data
374+
rgba[...,1]=np.where(A<0,np.nan,1)# under data
375+
rgba[...,2]=np.where(A>1,np.nan,1)# over data
376+
# Have to invert mask, Agg knows what alpha means
377+
# so if you put this in as 0 for 'good' points, they
378+
# all get zeroed out
379+
rgba[...,3]=1
380+
ifA.mask.shape==A.shape:
381+
# this is the case of a nontrivial mask
382+
mask=np.where(A.mask,np.nan,1)
383+
else:
384+
# this is the case that the mask is a
385+
# numpy.bool_ of False
386+
mask=A.mask
387+
# ~A.mask # masked data
377388
A=rgba
378389
output=np.zeros((out_height,out_width,4),
379390
dtype=A.dtype)
@@ -414,12 +425,32 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
414425
# Convert back to a masked greyscale array so
415426
# colormapping works correctly
416427
hid_output=output
428+
# any pixel where the a masked pixel is included
429+
# in the kernel (pulling this down from 1) needs to
430+
# be masked in the output
431+
iflen(mask.shape)==2:
432+
out_mask=np.empty((out_height,out_width),
433+
dtype=mask.dtype)
434+
_image.resample(mask,out_mask,t,
435+
_interpd_[self.get_interpolation()],
436+
True,1,
437+
self.get_filternorm()or0.0,
438+
self.get_filterrad()or0.0)
439+
out_mask=np.isnan(out_mask)
440+
else:
441+
out_mask=mask
442+
417443
output=np.ma.masked_array(
418-
hid_output[...,0],hid_output[...,3]<0.5)
419-
# relabel under data
420-
output[hid_output[...,1]>.5]=-1
444+
hid_output[...,0],out_mask)
445+
# 'unshare' the mask array to
446+
# needed to suppress numpy warning
447+
delout_mask
448+
invalid_mask=~output.mask*~np.isnan(output.data)
449+
# relabel under data. If any of the input data for
450+
# the pixel has input out of the norm bounds,
451+
output[np.isnan(hid_output[...,1])*invalid_mask]=-1
421452
# relabel over data
422-
output[hid_output[...,2]>.5]=2
453+
output[np.isnan(hid_output[...,2])*invalid_mask]=2
423454

424455
output=self.to_rgba(output,bytes=True,norm=False)
425456

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‎lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.svg

Lines changed: 269 additions & 0 deletions
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‎lib/matplotlib/tests/test_image.py

Lines changed: 17 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -60,6 +60,7 @@ def test_image_interps():
6060
ax3.imshow(X,interpolation='bicubic')
6161
ax3.set_ylabel('bicubic')
6262

63+
6364
@image_comparison(baseline_images=['interp_nearest_vs_none'],
6465
extensions=['pdf','svg'],remove_text=True)
6566
deftest_interp_nearest_vs_none():
@@ -757,6 +758,22 @@ def test_imshow_endianess():
757758
ax2.imshow(Z.astype('>f8'),**kwargs)
758759

759760

761+
@image_comparison(baseline_images=['imshow_masked_interpolation'],
762+
remove_text=True,style='default')
763+
deftest_imshow_masked_interpolation():
764+
a=np.random.random((7,7))*1000
765+
ndv=-9999.0
766+
a[2:4,1:5]=ndv
767+
am=np.ma.masked_equal(a,ndv)
768+
769+
fig, (ax1,ax2)=plt.subplots(1,2,figsize=(6,3))
770+
ax1.imshow(am,interpolation='nearest')
771+
ax1.set_title("interpolation='nearest'")
772+
ax2.imshow(am,interpolation='bilinear')
773+
ax2.set_title("interpolation='bilinear'")
774+
plt.show()
775+
776+
760777
@cleanup
761778
deftest_imshow_no_warn_invalid():
762779
withwarnings.catch_warnings(record=True)aswarns:

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