Add error propagation estimates for BoxcarExtract and HorneExtract #286
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Also currently working on issue #281 as well |
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## main #286 +/- ##
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- Coverage 87.11% 86.68% -0.44%
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Files 15 15
Lines 1281 1419 +138
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+ Hits 1116 1230 +114
- Misses 165 189 +24 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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| @@ -194,6 +194,139 @@ class BoxcarExtract(SpecreduceOperation): | |||
| def spectrum(self): | |||
| return self.__call__() | |||
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| def _variance2d_from_image(self, image): | |||
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You can simplify uncertainty parsing because the _ImageParser._parse_image method always assures that the image.uncertainty attribute always exists, and is a subclass of astropy.nddata.NDUncertainty.
specreduce/extract.py
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| @@ -673,6 +919,7 @@ def __call__( | |||
| bkgrd_prof = models.Polynomial1D(2) | |||
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| self.image = self._parse_image(image, variance, mask, unit, disp_axis) | |||
| var2d_q = self._var2d_as_quantity() | |||
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var2d_q is not used later in the code.
specreduce/extract.py
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| @@ -565,6 +783,34 @@ def _fit_spatial_profile( | |||
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| return RectBivariateSpline(x=bin_centers, y=np.arange(nrows), z=samples, kx=kx, ky=ky) | |||
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| def _var2d_as_quantity(self) -> u.Quantity: | |||
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This utility method is not really needed. The HorneExtract._parse_image method already ensures that the image.uncertainty attribute exists and contains a VarianceUncertainty uncertainty.
This changes how uncertainties are handled in:
BoxcarExtract.__call__HorneExtract.__call__HorneExtract._fit_gaussian_spatial_profileas described in #282. Now they:
Also, I have added test scripts under the
testsdirectory, as well as a .ipynb file for the Horne case with some examples.