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207-noncomplex-fits #244

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dominik merged 4 commits from 207-noncomplex-fits into master 2024-02-11 17:40:51 +00:00
Conversation 0 Commits 4 Files Changed 5 +132 -115
3 changed files with 132 additions and 115 deletions
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40
src/nmreval/fit/minimizer.py
View File

@ -361,7 +361,7 @@ class FitRoutine(object):
with np.errstate(all='ignore'):
res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=500 * len(p0))
err, corr, partial_corr = self._calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
err, corr, partial_corr = _calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
self.make_results(data, res.x, var, data.para_keys, res.jac.shape,
err=err, corr=corr, partial_corr=partial_corr)
@ -375,7 +375,7 @@ class FitRoutine(object):
with np.errstate(all='ignore'):
res = optimize.least_squares(cost, p0, bounds=(lb, ub), max_nfev=500 * len(p0))
err, corr, partial_corr = self._calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
err, corr, partial_corr = _calc_error(res.jac, np.sum(res.fun**2), *res.jac.shape)
for v, var_pars_k in zip(data, data_pars):
self.make_results(v, res.x, var, var_pars_k, res.jac.shape,
err=err, corr=corr, partial_corr=partial_corr)
@ -458,9 +458,18 @@ class FitRoutine(object):
self.make_results(v, res.beta, var, var_pars_k, (sum(len(d) for d in data), len(p0)),
err=res.sd_beta, corr=corr, partial_corr=partial_corr)
def make_results(self, data, p, var_pars, used_pars, shape,
err=None, corr=None, partial_corr=None):
def make_results(
self,
data: Data,
p: list[float],
var_pars: list[str],
used_pars: list[str],
shape: tuple[int, int],
err: list[float] = None,
corr: np.ndarray = None,
partial_corr: np.ndarray = None,
):
print(data.complex_type)
if err is None:
err = [0] * len(p)
@ -498,22 +507,23 @@ class FitRoutine(object):
model = data.get_model()
self.result[idx] = FitResultCreator.make_with_model(
model,
data.x,
data.y,
actual_parameters,
data.fun_kwargs,
data.we_string,
data.idx,
*shape,
model=model,
x_orig=data.x,
y_orig=data.y,
p=actual_parameters,
fun_kwargs=data.fun_kwargs,
we=data.we_string,
idx=data.idx,
nobs=shape[0],
nvar=shape[1],
corr=actual_corr,
pcorr=actual_pcorr,
data_mode=data.complex_type,
)
return self.result
@staticmethod
def _calc_error(jac, chi, nobs, nvars):
def _calc_error(jac, chi, nobs, nvars):
# copy of scipy.curve_fit to calculate covariance
# noinspection PyTupleAssignmentBalance
try:

2
src/nmreval/fit/model.py
View File

@ -9,7 +9,7 @@ from ._meta import MultiModel
from .parameter import Parameters, Parameter
class Model(object):
class Model:
def __init__(self, model, *args, **kwargs):
self.idx = kwargs.pop('idx', None)

145
src/nmreval/fit/result.py
View File

@ -11,6 +11,7 @@ from scipy.stats import f as fdist
from scipy.interpolate import interp1d
from ._meta import MultiModel
from .model import Model
from .parameter import Parameter
from ..data.points import Points
from ..data.signals import Signal
@ -36,17 +37,30 @@ class FitResultCreator:
else:
resid = kwargs['y'] - y_orig
stats = FitResultCreator.calc_statistics(resid, _y)
stats = calc_statistics(resid, _y)
return FitResult(kwargs['x'], kwargs['y'], x_orig, y_orig, params, dict(kwargs['choice']), resid, 0, 0,
kwargs['name'], stats, idx)
return FitResult(
x=kwargs['x'],
y=kwargs['y'],
x_data=x_orig,
y_data=y_orig,
params=params,
fun_kwargs=dict(kwargs['choice']),
resid=resid,
nobs=0,
nvar=0,
we='',
name=kwargs['name'],
stats=stats,
idx=idx,
)
@staticmethod
def make_with_model(
model: 'Model',
x_orig: np.ndarray,
y_orig: np.ndarray,
p: 'Parameters',
p: list,
fun_kwargs: dict,
we: str,
idx: str | None,
@ -54,7 +68,7 @@ class FitResultCreator:
nvar: int,
corr: np.ndarray,
pcorr: np.ndarray,
data_mode: int = 1,
data_mode: int,
) -> FitResult:
if np.all(x_orig > 0) and (np.max(x_orig) > 100 * np.min(x_orig)):
islog = True
@ -84,20 +98,12 @@ class FitResultCreator:
actual_mode = fun_kwargs['complex_mode']
fun_kwargs['complex_mode'] = 0
_y = model.func(p_final, _x, **fun_kwargs)
if not actual_mode < 0:
if actual_mode == 1:
_Y = _y.real
# _y.imag = 0
elif actual_mode == 2:
# if data_mode ==
_y = _y.imag
# _y.real = 0
_y = check_complex(model.func(p_final, _x, **fun_kwargs), actual_mode, data_mode)
print(_y)
fun_kwargs['complex_mode'] = actual_mode
stats = FitResultCreator.calc_statistics(_y, resid, nobs, nvar)
stats = calc_statistics(_y, resid, nobs, nvar)
varied = [p.var for p in parameters.values()]
if corr is None:
@ -138,38 +144,9 @@ class FitResultCreator:
pcorr=partial_correlation,
islog=islog,
func=model,
data_complex=data_mode,
)
@staticmethod
def calc_statistics(y, residual, nobs=None, nvar=None):
chi = (residual**2).sum()
try:
r = 1 - chi/((y-np.mean(y))**2).sum()
except RuntimeWarning:
r = -9999
if nobs is None:
nobs = 1
if nvar is None:
nvar = 0
dof = nobs - nvar
loglikehood = nobs * np.log(chi / nobs)
stats = {
'chi^2': chi,
'R^2': r,
'AIC': loglikehood + 2 * nvar,
'BIC': loglikehood + np.log(nobs) * nvar,
'adj. R^2': 1 - (nobs-1) / (dof+1e-13) * (1-r),
'red. chi^2': chi / (dof + 1e-13),
}
stats['AICc'] = stats['AIC'] + 2*(nvar+1)*nvar / (dof - 1 + 1e-13)
return stats
class FitResult(Points):
@ -192,7 +169,8 @@ class FitResult(Points):
pcorr: np.ndarray = None,
islog: bool = False,
func=None,
**kwargs
data_complex: int = 1,
**kwargs,
):
self.parameter, name = self._prepare_names(params, name)
@ -214,6 +192,7 @@ class FitResult(Points):
self.y_data = y_data
self._model_name = name
self._func = func
self._data_complex = data_complex
@staticmethod
def _prepare_names(parameter: dict, modelname: str):
@ -422,20 +401,9 @@ class FitResult(Points):
if self.func is None:
raise ValueError('no fit function available to calculate new y values')
actual_mode = -1
if 'complex_mode' in self.fun_kwargs:
actual_mode = self.fun_kwargs['complex_mode']
self.fun_kwargs['complex_mode'] = 0
new_fit = self.copy()
y_values = self.func.func(self.p_final, x_values, **self.fun_kwargs)
if not actual_mode < 0:
if actual_mode == 1:
y_values.imag = 0
elif actual_mode == 2:
y_values.real = 0
self.fun_kwargs['complex_mode'] = actual_mode
y_values = check_complex(y_values, self.fun_kwargs.get('complex_mode', -1), self._data_complex)
new_fit.set_data(x_values, y_values, y_err=0.0)
@ -446,20 +414,13 @@ class FitResult(Points):
raise ValueError('no fit function available to calculate new y values')
part_functions = []
actual_mode = -1
if 'complex_mode' in self.fun_kwargs:
actual_mode = self.fun_kwargs['complex_mode']
self.fun_kwargs['complex_mode'] = 0
actual_mode = self.fun_kwargs.get('complex_mode', -1)
for sub_name, sub_y in zip(self.func.sub_name(), self.func.sub(self.p_final, x_values, **self.fun_kwargs)):
if not actual_mode < 0:
if actual_mode == 1:
sub_y.imag = 0
elif actual_mode == 2:
sub_y.real = 0
sub_y = check_complex(sub_y, actual_mode, self._data_complex)
if np.iscomplexobj(sub_y):
part_functions.append(Signal(x_values, sub_y, name=sub_name))
else:
part_functions.append(Points(x_values, sub_y, name=sub_name))
@ -467,3 +428,49 @@ class FitResult(Points):
self.fun_kwargs['complex_mode'] = actual_mode
return part_functions
def check_complex(y, model_complex, data_complex):
if not np.iscomplexobj(y):
return y
if model_complex == 1:
y.imag = 0
if data_complex == 1:
y = y.real
elif model_complex == 2:
y.real = 0
if data_complex == 1:
y = y.imag
return y
def calc_statistics(y, residual, nobs=None, nvar=None):
chi = (residual**2).sum()
try:
r = 1 - chi/((y-np.mean(y))**2).sum()
except RuntimeWarning:
r = -9999
if nobs is None:
nobs = 1
if nvar is None:
nvar = 0
dof = nobs - nvar
loglikehood = nobs * np.log(chi / nobs)
stats = {
'chi^2': chi,
'R^2': r,
'AIC': loglikehood + 2 * nvar,
'BIC': loglikehood + np.log(nobs) * nvar,
'adj. R^2': 1 - (nobs-1) / (dof+1e-13) * (1-r),
'red. chi^2': chi / (dof + 1e-13),
}
stats['AICc'] = stats['AIC'] + 2*(nvar+1)*nvar / (dof - 1 + 1e-13)
return stats