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Simon Kuberski 2021-11-15 16:46:22 +01:00
parent 5a347e0506
commit 9839eb7f0c
1 changed files with 8 additions and 8 deletions
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16
pyerrors/fits.py
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@ -110,7 +110,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
This can take a while as the full correlation matrix
has to be calculated (default False).
correlated_fit : bool
If true, use the full correlation matrix in the definition of the chisquare
If true, use the full correlation matrix in the definition of the chisquare
(only works for prior==None and when no method is given, at the moment).
const_par : list, optional
List of N Obs that are used to constrain the last N fit parameters of func.
@ -181,7 +181,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
const_par = kwargs['const_par']
if isinstance(const_par, Obs):
const_par = [const_par]
def func(p, x):
return func_aug(np.concatenate((p, [o.value for o in const_par])), x)
else:
@ -511,23 +511,23 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
chol = np.linalg.cholesky(corr)
chol_inv = np.linalg.inv(chol)
chol_inv = np.dot(chol_inv, covdiag)
def chisqfunc(p):
model = func(p, x)
chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
return chisq
def chisqfunc_aug(p):
model = func_aug(np.concatenate((p, [o.value for o in const_par])), x)
chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
return chisq
else:
def chisqfunc(p):
model = func(p, x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
def chisqfunc_aug(p):
model = func_aug(np.concatenate((p, [o.value for o in const_par])), x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
@ -557,7 +557,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
model = func(p, x)
chisq = anp.dot(chol_inv, (y_f - model))
return chisq
else:
def chisqfunc_residuals(p):
model = func(p, x)
@ -606,7 +606,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
model = func_aug(d[:n_parms_aug], x)
chisq = anp.sum(anp.dot(chol_inv, (d[n_parms_aug:] - model)) ** 2)
return chisq
else:
def chisqfunc_compact(d):
model = func_aug(d[:n_parms_aug], x)