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There is still a fair amount of cleaning up that would make this easier to follow/modify:
Create a method for verifying Gaussian parameters, verify_gaussian(FitResult, height, center, sigma) which would use np.testing.assert_allclose to check the raw peak parameters.
Create a method for verifying pseudo-Voigt parameters, verify_pseudovoigt(FitResult, intensity, center, fwhm, mixing) to check the raw parameters
The checks for native vs effective parameters as that is already covered by more complete tests in test_peak_collection that are part of this.
The checks for cost can also dropped
This is a follow-on to #260, but should not be started until #292 is merged in.
The text was updated successfully, but these errors were encountered:
Some peaks were being used with a height of .2 and noise of +-2. This
reduces the noise to be smaller than the peak height which makes the
tests pass more regularly. Also remove checks for the chisq.
Refs #291
Some peaks were being used with a height of .2 and noise of +-2. This
reduces the noise to be smaller than the peak height which makes the
tests pass more regularly. Also remove checks for the chisq.
Refs #291
There is still a fair amount of cleaning up that would make this easier to follow/modify:
verify_gaussian(FitResult, height, center, sigma)
which would usenp.testing.assert_allclose
to check the raw peak parameters.verify_pseudovoigt(FitResult, intensity, center, fwhm, mixing)
to check the raw parameterstest_peak_collection
that are part of this.This is a follow-on to #260, but should not be started until #292 is merged in.
The text was updated successfully, but these errors were encountered: