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clean-up

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ppetrak 2022-12-16 18:55:43 +01:00
parent 9a05fc916a
commit 500c5234cf
3 changed files with 81 additions and 369 deletions
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18
pyerrors/fits.py
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@ -106,11 +106,11 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
Do not need to use ordered dictionaries: python version >= 3.7: Dictionary order is guaranteed to be insertion order.
(https://docs.python.org/3/library/stdtypes.html#dict-views) Ensures that x, y and func values are mapped correctly.
x : ordered dict
x : dict
dict of lists.
y : ordered dict
y : dict
dict of lists of Obs.
funcs : ordered dict
funcs : dict
dict of objects
fit functions have to be of the form (here a[0] is the common fit parameter)
```python
@ -141,7 +141,7 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
can be used to choose an alternative method for the minimization of chisquare.
The possible methods are the ones which can be used for scipy.optimize.minimize and
migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
Reliable alternatives are migrad, Powell and Nelder-Mead.
Reliable alternatives are migrad (default for combined fit), Powell and Nelder-Mead.
correlated_fit : bool
If True, use the full inverse covariance matrix in the definition of the chisquare cost function.
For details about how the covariance matrix is estimated see `pyerrors.obs.covariance`.
@ -744,6 +744,10 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
else:
x0 = [0.1] * n_parms
output.method = kwargs.get('method', 'migrad')
if not silent:
print('Method:', output.method)
def chisqfunc(p):
chisq = 0.0
@ -756,10 +760,6 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
C_inv = np.diag(np.diag(np.ones((len(x_array),len(x_array)))))/dy_f/dy_f
chisq += anp.sum((y_f - model)@ C_inv @(y_f - model))
return chisq
output.method = kwargs.get('method', 'Levenberg-Marquardt')
if not silent:
print('Method:', output.method)
if output.method == 'migrad':
tolerance = 1e-4
@ -816,7 +816,7 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
for key in func.keys():
x_array = np.asarray(x[key])
if (len(x_array)!= 0):
hat_vector.append(anp.array(jacobian(func[key])(fit_result.x, x_array)))
hat_vector.append(jacobian(func[key])(fit_result.x, x_array))
hat_vector = [item for sublist in hat_vector for item in sublist]
return hat_vector