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[Fix] Correct type hints in fits.py

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Fabian Joswig 2025-01-05 16:30:42 +01:00
parent 6a990c147b
commit 9c960ae24c
2 changed files with 41 additions and 20 deletions
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57
pyerrors/fits.py
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@ -36,13 +36,31 @@ class Fit_result(Sequence):
Hotelling t-squared p-value for correlated fits. Hotelling t-squared p-value for correlated fits.
""" """
def __init__(self): def __init__(self) -> None:
self.fit_parameters = None self.fit_parameters: Optional[list] = None
self.fit_function: Optional[Union[Callable, dict[str, Callable]]] = None
self.priors: Optional[Union[list[Obs], dict[int, Obs]]] = None
self.method: Optional[str] = None
self.iterations: Optional[int] = None
self.chisquare: Optional[float] = None
self.odr_chisquare: Optional[float] = None
self.dof: Optional[int] = None
self.p_value: Optional[float] = None
self.message: Optional[str] = None
self.t2_p_value: Optional[float] = None
self.chisquare_by_dof: Optional[float] = None
self.chisquare_by_expected_chisquare: Optional[float] = None
self.residual_variance: Optional[float] = None
self.xplus: Optional[float] = None
def __getitem__(self, idx: int) -> Obs: def __getitem__(self, idx: int) -> Obs:
if self.fit_parameters is None:
raise ValueError('No fit parameters available.')
return self.fit_parameters[idx] return self.fit_parameters[idx]
def __len__(self) -> int: def __len__(self) -> int:
if self.fit_parameters is None:
raise ValueError('No fit parameters available.')
return len(self.fit_parameters) return len(self.fit_parameters)
def gamma_method(self, **kwargs): def gamma_method(self, **kwargs):
@ -64,6 +82,8 @@ class Fit_result(Sequence):
if hasattr(self, 't2_p_value'): if hasattr(self, 't2_p_value'):
my_str += 't\u00B2p-value = ' + f'{self.t2_p_value:2.4f}' + '\n' my_str += 't\u00B2p-value = ' + f'{self.t2_p_value:2.4f}' + '\n'
my_str += 'Fit parameters:\n' my_str += 'Fit parameters:\n'
if self.fit_parameters is None:
raise ValueError('No fit parameters available.')
for i_par, par in enumerate(self.fit_parameters): for i_par, par in enumerate(self.fit_parameters):
my_str += str(i_par) + '\t' + ' ' * int(par >= 0) + str(par).rjust(int(par < 0.0)) + '\n' my_str += str(i_par) + '\t' + ' ' * int(par >= 0) + str(par).rjust(int(par < 0.0)) + '\n'
return my_str return my_str
@ -338,9 +358,8 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
p_f = dp_f = np.array([]) p_f = dp_f = np.array([])
prior_mask = [] prior_mask = []
loc_priors = [] loc_priors = []
x0 = kwargs.get('initial_guess')
if 'initial_guess' in kwargs: if x0 is not None:
x0 = kwargs.get('initial_guess')
if len(x0) != n_parms: if len(x0) != n_parms:
raise ValueError('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms)) raise ValueError('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
else: else:
@ -359,8 +378,8 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
return anp.sum(general_chisqfunc_uncorr(p, y_f, p_f) ** 2) return anp.sum(general_chisqfunc_uncorr(p, y_f, p_f) ** 2)
if kwargs.get('correlated_fit') is True: if kwargs.get('correlated_fit') is True:
if 'inv_chol_cov_matrix' in kwargs: chol_inv = kwargs.get('inv_chol_cov_matrix')
chol_inv = kwargs.get('inv_chol_cov_matrix') if chol_inv is not None:
if (chol_inv[0].shape[0] != len(dy_f)): if (chol_inv[0].shape[0] != len(dy_f)):
raise TypeError('The number of columns of the inverse covariance matrix handed over needs to be equal to the number of y errors.') raise TypeError('The number of columns of the inverse covariance matrix handed over needs to be equal to the number of y errors.')
if (chol_inv[0].shape[0] != chol_inv[0].shape[1]): if (chol_inv[0].shape[0] != chol_inv[0].shape[1]):
@ -389,17 +408,17 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
if output.method != 'Levenberg-Marquardt': if output.method != 'Levenberg-Marquardt':
if output.method == 'migrad': if output.method == 'migrad':
tolerance = 1e-4 # default value of 1e-1 set by iminuit can be problematic tolerance = kwargs.get('tol')
if 'tol' in kwargs: if tolerance is None:
tolerance = kwargs.get('tol') tolerance = 1e-4 # default value of 1e-1 set by iminuit can be problematic
fit_result = iminuit.minimize(chisqfunc_uncorr, x0, tol=tolerance) # Stopping criterion 0.002 * tol * errordef fit_result = iminuit.minimize(chisqfunc_uncorr, x0, tol=tolerance) # Stopping criterion 0.002 * tol * errordef
if kwargs.get('correlated_fit') is True: if kwargs.get('correlated_fit') is True:
fit_result = iminuit.minimize(chisqfunc, fit_result.x, tol=tolerance) fit_result = iminuit.minimize(chisqfunc, fit_result.x, tol=tolerance)
output.iterations = fit_result.nfev output.iterations = fit_result.nfev
else: else:
tolerance = 1e-12 tolerance = kwargs.get('tol')
if 'tol' in kwargs: if tolerance is None:
tolerance = kwargs.get('tol') tolerance = 1e-12
fit_result = scipy.optimize.minimize(chisqfunc_uncorr, x0, method=kwargs.get('method'), tol=tolerance) fit_result = scipy.optimize.minimize(chisqfunc_uncorr, x0, method=kwargs.get('method'), tol=tolerance)
if kwargs.get('correlated_fit') is True: if kwargs.get('correlated_fit') is True:
fit_result = scipy.optimize.minimize(chisqfunc, fit_result.x, method=kwargs.get('method'), tol=tolerance) fit_result = scipy.optimize.minimize(chisqfunc, fit_result.x, method=kwargs.get('method'), tol=tolerance)
@ -429,8 +448,8 @@ def least_squares(x: Any, y: Union[dict[str, ndarray], list[Obs], ndarray, dict[
if not fit_result.success: if not fit_result.success:
raise Exception('The minimization procedure did not converge.') raise Exception('The minimization procedure did not converge.')
output.chisquare = chisquare output.chisquare = float(chisquare)
output.dof = y_all.shape[-1] - n_parms + len(loc_priors) output.dof = int(y_all.shape[-1] - n_parms + len(loc_priors))
output.p_value = 1 - scipy.stats.chi2.cdf(output.chisquare, output.dof) output.p_value = 1 - scipy.stats.chi2.cdf(output.chisquare, output.dof)
if output.dof > 0: if output.dof > 0:
output.chisquare_by_dof = output.chisquare / output.dof output.chisquare_by_dof = output.chisquare / output.dof
@ -603,8 +622,8 @@ def total_least_squares(x: list[Obs], y: list[Obs], func: Callable, silent: bool
if np.any(np.asarray(dy_f) <= 0.0): if np.any(np.asarray(dy_f) <= 0.0):
raise Exception('No y errors available, run the gamma method first.') raise Exception('No y errors available, run the gamma method first.')
if 'initial_guess' in kwargs: x0 = kwargs.get('initial_guess')
x0 = kwargs.get('initial_guess') if x0 is not None:
if len(x0) != n_parms: if len(x0) != n_parms:
raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms)) raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
else: else:
@ -890,6 +909,8 @@ def _construct_prior_obs(i_prior: Union[Obs, str], i_n: int) -> Obs:
return i_prior return i_prior
elif isinstance(i_prior, str): elif isinstance(i_prior, str):
loc_val, loc_dval = _extract_val_and_dval(i_prior) loc_val, loc_dval = _extract_val_and_dval(i_prior)
return cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}") prior_obs = cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}")
assert isinstance(prior_obs, Obs)
return prior_obs
else: else:
raise TypeError("Prior entries need to be 'Obs' or 'str'.") raise TypeError("Prior entries need to be 'Obs' or 'str'.")

4
pyerrors/obs.py
View file

@ -1435,8 +1435,8 @@ def reweight(weight: Obs, obs: Union[ndarray, list[Obs]], **kwargs) -> list[Obs]
for name in obs[i].names: for name in obs[i].names:
if not set(obs[i].idl[name]).issubset(weight.idl[name]): if not set(obs[i].idl[name]).issubset(weight.idl[name]):
raise ValueError('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name)) raise ValueError('obs[%d] has to be defined on a subset of the configs in weight.idl[%s]!' % (i, name))
new_samples = [] new_samples: list = []
w_deltas = {} w_deltas: dict[str, ndarray] = {}
for name in sorted(obs[i].names): for name in sorted(obs[i].names):
w_deltas[name] = _reduce_deltas(weight.deltas[name], weight.idl[name], obs[i].idl[name]) w_deltas[name] = _reduce_deltas(weight.deltas[name], weight.idl[name], obs[i].idl[name])
new_samples.append((w_deltas[name] + weight.r_values[name]) * (obs[i].deltas[name] + obs[i].r_values[name])) new_samples.append((w_deltas[name] + weight.r_values[name]) * (obs[i].deltas[name] + obs[i].r_values[name]))