IPKM/nmreval
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Make FitResults from all fixed parameters; closes #151

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This commit is contained in:
Dominik Demuth 2023-11-30 18:16:07 +01:00
parent 12726e6f56
commit aa0d14a322
1 changed files with 10 additions and 5 deletions
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11
src/nmreval/fit/minimizer.py
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@ -227,6 +227,8 @@ class FitRoutine(object):
if mode is None: if mode is None:
mode = self.fitmethod mode = self.fitmethod
print('run')
fit_groups, linked_parameter = self.prepare_links() fit_groups, linked_parameter = self.prepare_links()
for data_groups in fit_groups: for data_groups in fit_groups:
if len(data_groups) == 1 and not self.linked: if len(data_groups) == 1 and not self.linked:
@ -234,6 +236,8 @@ class FitRoutine(object):
# get variable parameter for fitter # get variable parameter for fitter
p0_k, lb_k, ub_k, var_pars_k = self._prep_data(data) p0_k, lb_k, ub_k, var_pars_k = self._prep_data(data)
print(p0_k, var_pars_k)
if mode == 'lsq': if mode == 'lsq':
self._least_squares_single(data, p0_k, lb_k, ub_k, var_pars_k) self._least_squares_single(data, p0_k, lb_k, ub_k, var_pars_k)
@ -248,6 +252,7 @@ class FitRoutine(object):
data_pars, p0, lb, ub, var_pars = self._prep_global(data_groups, linked_parameter) data_pars, p0, lb, ub, var_pars = self._prep_global(data_groups, linked_parameter)
if mode == 'lsq': if mode == 'lsq':
print(data_pars, p0,var_pars)
self._least_squares_global(data_groups, p0, lb, ub, var_pars, data_pars) self._least_squares_global(data_groups, p0, lb, ub, var_pars, data_pars)
elif mode == 'nm': elif mode == 'nm':
@ -287,8 +292,8 @@ class FitRoutine(object):
vals = [] vals = []
var_pars = [] var_pars = []
for p_k, v_k in parameter.items(): for p_k, v_k in parameter.items():
if v_k.var:
vals.append([v_k.scaled_value, v_k.lb / v_k.scale, v_k.ub / v_k.scale]) vals.append([v_k.scaled_value, v_k.lb / v_k.scale, v_k.ub / v_k.scale])
if v_k.var:
var_pars.append(p_k) var_pars.append(p_k)
pp, lb, ub = zip(*vals) pp, lb, ub = zip(*vals)
@ -318,10 +323,10 @@ class FitRoutine(object):
actual_pars.append(p_k_used) actual_pars.append(p_k_used)
# parameter is variable and was not found before as shared parameter # parameter is variable and was not found before as shared parameter
if v_k_used.var and p_k_used not in var:
p0.append(v_k_used.scaled_value) p0.append(v_k_used.scaled_value)
lb.append(v_k_used.lb / v_k_used.scale) lb.append(v_k_used.lb / v_k_used.scale)
ub.append(v_k_used.ub / v_k_used.scale) ub.append(v_k_used.ub / v_k_used.scale)
if v_k_used.var and p_k_used not in var:
var.append(p_k_used) var.append(p_k_used)
data_pars.append(actual_pars) data_pars.append(actual_pars)
@ -472,7 +477,7 @@ class FitRoutine(object):
pass pass
# reshape the correlation matrices # reshape the correlation matrices
if corr is None: if corr is None or not corr_idx:
actual_corr = None actual_corr = None
actual_pcorr = None actual_pcorr = None
else: else: