test function for get_angle_index #191
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #191 +/- ##
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- Coverage 96.53% 94.19% -2.35%
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Files 8 8
Lines 289 310 +21
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+ Hits 279 292 +13
- Misses 10 18 +8
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| the index of the value in the array | ||
| """ | ||
| if self.on_xtype(xtype) is None: | ||
| raise ValueError(_xtype_wmsg(xtype)) |
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I think I can remove this if I let on_xtype() to raise an error for invalid xtypes. Will do that in the other PR.
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@sbillinge ready for review! |
tests/test_diffraction_objects.py
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| # UC1: empty array | ||
| ( | ||
| [0.71, np.array([]), np.array([]), "tth", "tth", 10], | ||
| [IndexError, "WARNING: no matching value 10 found in the tth array."], |
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is this an error or a warning?
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Should be an error. I've edited the error message.
tests/test_diffraction_objects.py
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| test = DiffractionObject( | ||
| wavelength=0.71, xarray=np.array([30, 60, 90]), yarray=np.array([1, 2, 3]), xtype="tth" | ||
| ) | ||
| actual_index = test.get_array_index(xtype="tth", value=30) |
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we need more cases. Here everything is integers and there is a match. What do we want to happen if the value lies between two other values? Return nearest and a warning? What if it lies outside the range of values? Return nearest and a warning? What if it is really far away? Have a threshold after which we raise and error?
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@sbillinge I addressed this in the new commit. please review.
| xtype = self.input_xtype | ||
| if self.on_xtype(xtype) is None or len(self.on_xtype(xtype)[0]) == 0: | ||
| raise ValueError( | ||
| f"The '{xtype}' array is empty. " "Please ensure it is initialized and the correct xtype is used." |
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The current on_xtype function does not raise an error when the specified xtype is invalid. I was thinking about letting that function raise an error in those cases, but it looks like we can handle invalid xtype together with empty array.
| i = (np.abs(array - value)).argmin() | ||
| nearest_value = np.abs(array[i] - value) | ||
| distance = min(np.abs(value - array.min()), np.abs(value - array.max())) | ||
| threshold = 0.5 * (array.max() - array.min()) |
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I am not super sure about the threshold here. I'm thinking about a dynamic threshold within the reasonable range of each array, but this here can cause a bit of problem if the array is sparse.
| distance = min(np.abs(value - array.min()), np.abs(value - array.max())) | ||
| threshold = 0.5 * (array.max() - array.min()) | ||
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| if nearest_value != 0 and (array.min() <= value <= array.max() or distance <= threshold): |
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I am rethinking all this. I wonder if we should just return i and be done with it. Raise an error if the array is empty as you do, but then just return i. what do you think? Then it is up to the user to make sure the result makes sense.
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this needs a news too. |
issue #186: test function for
get_angle_index@sbillinge ready for review