Add cartesian_to_spherical(). #1365
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MLopez-Ibanez
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@@ Coverage Diff @@
## main #1365 +/- ##
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+ Coverage 92.08% 92.38% +0.30%
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Files 82 82
Lines 4346 4283 -63
Branches 363 357 -6
==========================================
- Hits 4002 3957 -45
+ Misses 255 246 -9
+ Partials 89 80 -9
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MLopez-Ibanez
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@astrojuanlu Something seems wrong with the checks. Not sure why they fail. |
| sph_car(np.array([0.5, np.pi / 4, -np.pi / 4]), np.array([0.25, -0.25, 0.35355339])) | ||
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| sph_car( | ||
| np.array([0.5, -np.pi / 4, np.pi / 4]), np.array([-0.25, -0.25, 0.35355339]) | ||
| ) | ||
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| result = spherical_to_cartesian(np.array([0.5, -np.pi / 4, np.pi / 4])) | ||
| expected = np.array([-0.25, -0.25, 0.35355339]) | ||
| assert np.allclose(expected, result) | ||
| sph_car( | ||
| np.array([[0.5, np.pi / 4, -np.pi / 4], [0.5, -np.pi / 4, np.pi / 4]]), | ||
| np.array([[0.25, -0.25, 0.35355339], [-0.25, -0.25, 0.35355339]]), | ||
| ) | ||
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| result = spherical_to_cartesian( | ||
| np.array([[0.5, np.pi / 4, -np.pi / 4], [0.5, -np.pi / 4, np.pi / 4]]) | ||
| sph_car( | ||
| np.array([2.60564963, 1.75305207, 4.4458828]), | ||
| np.array([-0.674864797187, -2.472029259161, -0.472269863940]), |
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I suggest we use pytest.mark.parametrize for these, what do you think?
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I have no experience with pytest so I cannot give an opinion. But these particular values were chosen because they trigger corner cases due to the use of arctan2 and arccos. There are probably nicer ways to test the corner cases but I didn't have much time to think about them.
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In fact, if there was a way to sample the full range of values in terms of positive/negative directions, that would be even better.
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Right! I'll have a look at this and try a version with Hypothesis + pytest then
MLopez-Ibanez
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