Ensure image scale factors are scalars #10289
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jklymak
requested changes
Jan 22, 2018
| @@ -394,8 +394,8 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0, | |||
| A_scaled = np.empty(A.shape, dtype=scaled_dtype) | |||
| A_scaled[:] = A | |||
| A_scaled -= a_min | |||
| a_min = a_min.astype(scaled_dtype) | |||
| a_max = a_max.astype(scaled_dtype) | |||
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It took me a bit to understand why this was necessary (because ytarray inherits nparray). Can you add a comment that makes it clear why this extra call is needed? Otherwise looks fine to me!
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This avoids errors that get triggered by passing an ndarray subclass to i 8000 mshow. For example: ```python import numpy as np from yt.units import km from matplotlib import pyplot as plt data = np.random.random((100, 100))*km plt.imshow(data) plt.show() ``` Or similarly with `astropy`: ```python import numpy as np from astropy.units import km from matplotlib import pyplot as plt data = np.random.random((100, 100))*km plt.imshow(data) plt.show() ``` Before this patch, both of these commands raise an error because `a_min` and `a_max` have units attached. By explicitly converting these to scalars we avoid this failure mode.
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dopplershift
approved these changes
Jan 22, 2018
|
The problem with duck typing is you have to agree on what a duck is. |
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The real problem we have with numpy though is that it's too easy to take any duck and force it to be a mallard. (Have I taken the metaphor too far yet?) |
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This avoids errors that get triggered by passing an ndarray subclass to imshow.
For example:
Or similarly with
astropy:Before this patch, both of these commands raise an error because
a_minanda_maxhave units attached. By explicitly converting these to scalars weavoid this failure mode.
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