lattice/pyerrors
1
0
Fork 0
mirror of https://github.com/fjosw/pyerrors.git synced 2025-06-30 00:39:27 +02:00
Code Issues Projects Releases Packages Wiki Activity

fix: flak8 & pytest

Browse source
This commit is contained in:
ppetrak 2022-12-19 14:03:45 +01:00
parent 500c5234cf
commit 3d6ec7b397
3 changed files with 54 additions and 59 deletions
Download patch file Download diff file Expand all files Collapse all files

BIN
examples/my_db.sqlite Normal file
View file

Binary file not shown.

1
pyerrors/__init__.py
View file

@ -457,7 +457,6 @@ from .obs import *
from .correlators import *
from .fits import *
from .misc import *
from . import combined_fits
from . import dirac
from . import input
from . import linalg

6
pyerrors/fits.py
View file

@ -70,7 +70,6 @@ class Fit_result(Sequence):
def least_squares(x, y, func, priors=None, silent=False, **kwargs):
r'''Performs a non-linear fit to y = func(x).
```
Parameters
@ -688,6 +687,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
return output
def _combined_fit(x, y, func, silent=False, **kwargs):
if kwargs.get('correlated_fit') is True:
@ -803,7 +803,6 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
c2 += len(x_array)
model = anp.array(func[key](d[:n_parms], x_array))
y_obs = y[key]
y_f = [o.value for o in y_obs]
dy_f = [o.dvalue for o in y_obs]
C_inv = np.diag(np.diag(np.ones((len(x_array), len(x_array))))) / dy_f / dy_f
list_tmp.append(anp.sum((d[n_parms + c1:n_parms + c2] - model) @ C_inv @ (d[n_parms + c1:n_parms + c2] - model)))
@ -840,7 +839,6 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
if kwargs.get('expected_chisquare') is True:
if kwargs.get('correlated_fit') is not True:
W = np.diag(1 / np.asarray(dy_f))
@ -853,7 +851,6 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
if not silent:
print('chisquare/expected_chisquare:', output.chisquare_by_expected_chisquare)
result = []
for i in range(n_parms):
result.append(derived_observable(lambda x_all, **kwargs: (x_all[0] + np.finfo(np.float64).eps) / (y_all[0].value + np.finfo(np.float64).eps) * fitp[i], list(y_all), man_grad=list(deriv_y[i])))
@ -863,7 +860,6 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
return output
def fit_lin(x, y, **kwargs):
"""Performs a linear fit to y = n + m * x and returns two Obs n, m.