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fix: Error handling for fits and root finding with numpy instead of autograd.numpy

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improved. Tests added.
This commit is contained in:
Fabian Joswig 2022-03-02 15:10:07 +00:00
parent 7f5989dfb9
commit b14314b424
4 changed files with 53 additions and 10 deletions
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10
pyerrors/fits.py
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@ -260,7 +260,10 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
output.chisquare_by_expected_chisquare)
fitp = out.beta
hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel()))))
try:
hess_inv = np.linalg.pinv(jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel()))))
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
def odr_chisquare_compact_x(d):
model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
@ -537,7 +540,10 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
output.chisquare_by_expected_chisquare)
fitp = fit_result.x
hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(fitp))
try:
hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(fitp))
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
if kwargs.get('correlated_fit') is True:
def chisqfunc_compact(d):