lattice/pyerrors
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add some typecasts

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This commit is contained in:
Justus Kuhlmann 2025-11-04 09:04:39 +00:00
commit c9cef8ff5f
1 changed files with 9 additions and 6 deletions
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15
pyerrors/fits.py
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@ -776,7 +776,7 @@ def fit_lin(x: Sequence[Union[Obs, int, float]], y: Sequence[Obs], **kwargs) ->
Returns Returns
------- -------
fit_parameters : list[Obs] fit_parameters : list[Obs]
LIist of fitted observables. List of fitted observables.
""" """
def f(a, x): def f(a, x):
@ -879,7 +879,10 @@ def error_band(x: list[int], func: Callable, beta: Union[Fit_result, list[Obs]])
err : np.array(Obs) err : np.array(Obs)
Error band for an array of sample values x Error band for an array of sample values x
""" """
cov = covariance(beta) if isinstance(beta, Fit_result):
cov = covariance(np.array(beta.fit_parameters))
else:
cov = covariance(beta)
if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps): if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning) warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
@ -890,9 +893,9 @@ def error_band(x: list[int], func: Callable, beta: Union[Fit_result, list[Obs]])
err = [] err = []
for i, item in enumerate(x): for i, item in enumerate(x):
err.append(np.sqrt(deriv[i] @ cov @ deriv[i])) err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
err = np.array(err) err_array = np.array(err)
return err return err_array
def ks_test(objects: Optional[list[Fit_result]]=None): def ks_test(objects: Optional[list[Fit_result]]=None):
@ -916,7 +919,7 @@ def ks_test(objects: Optional[list[Fit_result]]=None):
else: else:
obs_list = objects obs_list = objects
p_values = [o.p_value for o in obs_list] p_values = np.asarray([o.p_value for o in obs_list])
bins = len(p_values) bins = len(p_values)
x = np.arange(0, 1.001, 0.001) x = np.arange(0, 1.001, 0.001)
@ -928,7 +931,7 @@ def ks_test(objects: Optional[list[Fit_result]]=None):
plt.title(str(bins) + ' p-values') plt.title(str(bins) + ' p-values')
n = np.arange(1, bins + 1) / np.float64(bins) n = np.arange(1, bins + 1) / np.float64(bins)
Xs = np.sort(p_values) Xs: ndarray[float] = np.sort(p_values)
plt.step(Xs, n) plt.step(Xs, n)
diffs = n - Xs diffs = n - Xs
loc_max_diff = np.argmax(np.abs(diffs)) loc_max_diff = np.argmax(np.abs(diffs))