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Commit a1fbee5

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tacaswelltacaswell
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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 the result. 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. print out exact hash on travis
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‎.travis.yml

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@@ -76,6 +76,7 @@ before_install:
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install:
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- ccache -s
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- git describe
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# Upgrade pip and setuptools. Mock has issues with the default version of
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# setuptools
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- |

‎lib/matplotlib/image.py

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+43-7Lines changed: 43 additions & 7 deletions
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@@ -371,9 +371,20 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
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# this is to work around spurious warnings coming
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# out of masked arrays.
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with np.errstate(invalid='ignore'):
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rgba[..., 1] = A < 0 # under data
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rgba[..., 2] = A > 1 # over data
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rgba[..., 3] = ~A.mask # bad data
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rgba[..., 1] = np.where(A < 0, np.nan, 1) # under data
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rgba[..., 2] = np.where(A > 1, np.nan, 1) # over data
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# Have to invert mask, Agg knows what alpha means
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# so if you put this in as 0 for 'good' points, they
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# all get zeroed out
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rgba[..., 3] = 1
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if A.mask.shape == A.shape:
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# this is the case of a nontrivial mask
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mask = np.where(A.mask, np.nan, 1)
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else:
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# this is the case that the mask is a
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# numpy.bool_ of False
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mask = A.mask
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# ~A.mask # masked data
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A = rgba
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output = np.zeros((out_height, out_width, 4),
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dtype=A.dtype)
@@ -414,12 +425,37 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
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# Convert back to a masked greyscale array so
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# colormapping works correctly
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hid_output = output
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# any pixel where the a masked pixel is included
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# in the kernel (pulling this down from 1) needs to
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# be masked in the output
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if len(mask.shape) == 2:
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out_mask = np.empty((out_height, out_width),
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dtype=mask.dtype)
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_image.resample(mask, out_mask, t,
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_interpd_[self.get_interpolation()],
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True, 1,
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self.get_filternorm() or 0.0,
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self.get_filterrad() or 0.0)
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out_mask = np.isnan(out_mask)
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else:
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out_mask = mask
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# we need to mask both pixels which came in as masked
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# and the pixels that Agg is telling us to ignore (relavent
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# to non-affine transforms)
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# Use half alpha as the threshold for pixels to mask.
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out_mask = out_mask | (hid_output[..., 3] < .5)
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output = np.ma.masked_array(
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hid_output[..., 0], hid_output[..., 3] < 0.5)
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# relabel under data
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output[hid_output[..., 1] > .5] = -1
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hid_output[..., 0],
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out_mask)
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# 'unshare' the mask array to
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# needed to suppress numpy warning
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del out_mask
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invalid_mask = ~output.mask * ~np.isnan(output.data)
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# relabel under data. If any of the input data for
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# the pixel has input out of the norm bounds,
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output[np.isnan(hid_output[..., 1]) * invalid_mask] = -1
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# relabel over data
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output[hid_output[..., 2] > .5] = 2
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output[np.isnan(hid_output[..., 2]) * invalid_mask] = 2
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output = self.to_rgba(output, bytes=True, norm=False)
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‎lib/matplotlib/tests/baseline_images/test_image/imshow_masked_interpolation.pdf

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

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

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‎lib/matplotlib/tests/baseline_images/test_image/mask_image_over_under.png

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‎lib/matplotlib/tests/test_image.py

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@@ -60,6 +60,7 @@ def test_image_interps():
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ax3.imshow(X, interpolation='bicubic')
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ax3.set_ylabel('bicubic')
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@image_comparison(baseline_images=['interp_nearest_vs_none'],
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extensions=['pdf', 'svg'], remove_text=True)
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def test_interp_nearest_vs_none():
@@ -757,6 +758,23 @@ def test_imshow_endianess():
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ax2.imshow(Z.astype('>f8'), **kwargs)
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@image_comparison(baseline_images=['imshow_masked_interpolation'],
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remove_text=True, style='default')
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def test_imshow_masked_interpolation():
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np.random.seed(19680801)
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a = np.random.random((7, 7))*1000
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ndv = -9999.0
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a[2:4, 1:5] = ndv
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am = np.ma.masked_equal(a, ndv)
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
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ax1.imshow(am, interpolation='nearest')
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ax1.set_title("interpolation='nearest'")
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ax2.imshow(am, interpolation='bilinear')
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ax2.set_title("interpolation='bilinear'")
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plt.show()
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@cleanup
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def test_imshow_no_warn_invalid():
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with warnings.catch_warnings(record=True) as warns:

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