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fix: no-autograd exception in fits works again.

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This commit is contained in:
Fabian Joswig 2022-05-25 14:51:46 +01:00
parent 9ad0146d96
commit 967ddb0ecd
1 changed files with 8 additions and 6 deletions
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10
pyerrors/fits.py
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@ -263,14 +263,15 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
output.chisquare_by_expected_chisquare) output.chisquare_by_expected_chisquare)
fitp = out.beta fitp = out.beta
try:
hess = jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel()))) hess = 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
condn = np.linalg.cond(hess) condn = np.linalg.cond(hess)
if condn > 1e8: if condn > 1e8:
warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.".format(condn), RuntimeWarning) warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.".format(condn), RuntimeWarning)
try: try:
hess_inv = np.linalg.inv(hess) hess_inv = np.linalg.inv(hess)
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
except np.linalg.LinAlgError: except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.") raise Exception("Cannot invert hessian matrix.")
except Exception: except Exception:
@ -549,14 +550,15 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
output.chisquare_by_expected_chisquare) output.chisquare_by_expected_chisquare)
fitp = fit_result.x fitp = fit_result.x
try:
hess = jacobian(jacobian(chisqfunc))(fitp) hess = 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
condn = np.linalg.cond(hess) condn = np.linalg.cond(hess)
if condn > 1e8: if condn > 1e8:
warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.".format(condn), RuntimeWarning) warnings.warn("Hessian matrix might be ill-conditioned ({0:1.2e}), error propagation might be unreliable.".format(condn), RuntimeWarning)
try: try:
hess_inv = np.linalg.inv(hess) hess_inv = np.linalg.inv(hess)
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
except np.linalg.LinAlgError: except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.") raise Exception("Cannot invert hessian matrix.")
except Exception: except Exception: