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fix: fits and root now work when the value of the zeroth input is

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exactly zero. Tests extended.
This commit is contained in:
Fabian Joswig 2021-12-07 18:40:36 +00:00
parent 968cdf3181
commit 9ddadaf6b3
3 changed files with 33 additions and 4 deletions
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6
pyerrors/fits.py
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@ -301,7 +301,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
result = []
for i in range(n_parms):
result.append(derived_observable(lambda my_var, **kwargs: my_var[0] / x.ravel()[0].value * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
output.fit_parameters = result + const_par
@ -418,7 +418,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
result = []
for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: x[0] / y[0].value * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
output.fit_parameters = result
output.chisquare = chisqfunc(np.asarray(params))
@ -612,7 +612,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
result = []
for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: x[0] / y[0].value * fit_result.x[i], list(y), man_grad=list(deriv[i])))
result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
output.fit_parameters = result + const_par

3
pyerrors/roots.py
View file

@ -1,3 +1,4 @@
import numpy as np
import scipy.optimize
from autograd import jacobian
from .obs import derived_observable
@ -33,5 +34,5 @@ def find_root(d, func, guess=1.0, **kwargs):
da = jacobian(lambda u, v: func(v, u))(d.value, root[0])
deriv = - da / dx
res = derived_observable(lambda x, **kwargs: x[0] / d.value * root[0], [d], man_grad=[deriv])
res = derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (d.value + np.finfo(np.float64).eps) * root[0], [d], man_grad=[deriv])
return res