def model(self):
"""Returns the sum of all peak models."""
model = ConstantModel(prefix="BASE_")
model.set_param_hint("c", vary=False, value=0)
self.params += model.make_params()
for peak in self._peaks:
model += peak.model
return model
class CModel(Model):
"""docstring for CModel"""
def __init__(self, nexp=0, moddef=" "):
self.definition = moddef
self.model = ConstantModel()
self.nexp = 0
self.errors = None
self.params = None
self.res = None
for _ in range(nexp):
self.add_exp()
def add_exp(self):
self.nexp += 1
self.model += ExponentialModel(prefix=Prefixes[self.nexp])
def prep_params(self):
amp = '{}amplitude'
dec = '{}decay'
cntrl_expr = 'c'
self.model.set_param_hint('c', value=1.0, min=0)
for i in range(1, self.nexp + 1):
currA = amp.format(Prefixes[i])
currT = dec.format(Prefixes[i])
self.model.set_param_hint(currA, value=1.0, min=0)
self.model.set_param_hint(currT, value=1.0, min=0)
cntrl_expr += ' + ' + currA
self.params = self.model.make_params()
self.params.add('cntrl', value=1, min=1 - 1e-5, max=1 + 1e-5)
self.params['cntrl'].vary = True
self.params['cntrl'].expr = cntrl_expr
def fit(self, *args, **kwargs):
try:
self.prep_params()
self.res = self.model.fit(params=self.params, *args, **kwargs)
except Exception as e:
print("Undefined exception while fit: {} {}".format(type(e), e),
file=sys.stderr)
raise
return self.res
def has_covar(self):
covar = self.res.covar
if covar is None or np.isnan(np.sum(covar)):
return False
else:
return True
def report(self):
return self.res.fit_report(), self.res.covar
def FitSpectrumInit(self, label):
"""
Fit the spectrum with the fit params of another spectrum (given by label) as initial values. Useful when you fit big number of similar spectra.
"""
borders = np.genfromtxt(label + '/spectrumborders_' + label + '.txt',
unpack=True)
np.savetxt(self.label + '/spectrumborders_' + self.label + '.txt',
borders)
self.y = self.y[(self.x > borders[0]) & (self.x < borders[-1])]
self.x = self.x[(self.x > borders[0]) & (self.x < borders[-1])]
FitData = np.load(label + '/fitparams_' + label + '.npz')
baseline = FitData['c'] / self.maxyvalue
ctr = FitData['x0']
sigma = FitData['sigma']
gamma = FitData['gamma']
ramanmodel = ConstantModel()
ramanmodel.set_param_hint('c', value=baseline[0], min=0)
for i in range(len(sigma)):
prefix = 'p' + str(i + 1)
tempvoigt = Model(func=voigt, prefix=prefix)
tempvoigt.set_param_hint(prefix + 'x0', value=ctr[i], min=0)
tempvoigt.set_param_hint(prefix + 'sigma', value=sigma[i], min=0)
tempvoigt.set_param_hint(prefix + 'gamma', value=gamma[i], min=0)
tempvoigt.set_param_hint(prefix + 'height',
expr='wofz(((0) + 1j*' + prefix +
'gamma) / ' + prefix +
'sigma / sqrt(2)).real')
tempvoigt.set_param_hint(prefix + 'fwhm',
expr='0.5346 * 2 *' + prefix +
'gamma + sqrt(0.2166 * (2*' + prefix +
'gamma)**2 + (2 * ' + prefix +
'sigma * sqrt(2 * log(2) ) )**2 )')
ramanmodel += tempvoigt
pars = ramanmodel.make_params()
fitresult = ramanmodel.fit(self.y, pars, x=self.x, scale_covar=True)
plt.clf()
comps = fitresult.eval_components()
xplot = np.linspace(self.x[0], self.x[-1], 1000)
plt.plot(self.x, self.y * self.maxyvalue, 'r-')
plt.plot(self.x, fitresult.best_fit * self.maxyvalue)
for i in range(0, len(sigma)):
plt.plot(
self.x, comps['p' + str(i + 1)] * self.maxyvalue +
comps['constant'] * self.maxyvalue, 'k-')
plt.savefig(self.label + '/rawplot_' + self.label + '.pdf')
save_modelresult(fitresult,
self.label + '/modelresult_' + self.label + '.sav')
plt.clf()
def FitSpectrum(self):
"""
Fit Spectrum with initial values provided by SelectBaseline() and SelectPeaks()
"""
polyparams = self.Fitbaseline(self)
base = polyparams[0].n
ramanmodel = ConstantModel()
ramanmodel.set_param_hint('c', value=base, min=0)
globwidth = 1
xpeak, ypeak = np.genfromtxt(self.peakfile, unpack=True)
if type(xpeak) == np.float64:
xpeak = [xpeak]
ypeak = [ypeak]
for i in range(0, len(xpeak)):
prefix = 'p' + str(i + 1)
tempvoigt = Model(func=voigt, prefix=prefix)
tempvoigt.set_param_hint(prefix + 'x0', value=xpeak[i], min=0)
tempvoigt.set_param_hint(prefix + 'sigma', value=globwidth, min=0)
tempvoigt.set_param_hint(prefix + 'gamma', value=globwidth, min=0)
tempvoigt.set_param_hint(prefix + 'height',
value=ypeak[i],
expr='wofz(((0) + 1j*' + prefix +
'gamma) / ' + prefix +
'sigma / sqrt(2)).real')
tempvoigt.set_param_hint(prefix + 'fwhm',
expr='0.5346 * 2 *' + prefix +
'gamma + sqrt(0.2166 * (2*' + prefix +
'gamma)**2 + (2 * ' + prefix +
'sigma * sqrt(2 * log(2) ) )**2 )')
ramanmodel += tempvoigt
pars = ramanmodel.make_params()
fitresult = ramanmodel.fit(self.y, pars, x=self.x, scale_covar=True)
print(fitresult.fit_report(min_correl=0.5))
comps = fitresult.eval_components()
xplot = np.linspace(self.x[0], self.x[-1], 1000)
plt.plot(self.x, self.y * self.maxyvalue, 'rx')
plt.plot(self.x, fitresult.best_fit * self.maxyvalue)
for i in range(0, len(xpeak)):
plt.plot(
self.x, comps['p' + str(i + 1)] * self.maxyvalue +
comps['constant'] * self.maxyvalue, 'k-')
plt.show()
plt.savefig(self.label + '/rawplot_' + self.label + '.pdf')
save_modelresult(fitresult,
self.label + '/modelresult_' + self.label + '.sav')
class CModel(Model):
"""docstring for CModel"""
def __init__(self, nexp=0, sum_one=False):
self.sum_one_flag = sum_one
self.model = ConstantModel()
self.nexp = 0
self.errors = None
self.params = None
self.res = None
for _ in range(nexp):
self.add_exp()
def add_exp(self):
self.nexp += 1
self.model += ExponentialModel(prefix=Prefixes[self.nexp])
def prep_params(self, tcf_type):
if tcf_type == 'acf':
self.prep_acfParams()
elif tcf_type == 'ccf':
self.prep_ccfParams()
def prep_ccfParams(self):
amp = '{}amplitude'
dec = '{}decay'
self.model.set_param_hint('c', value=1.0)
for i in range(1, self.nexp + 1):
currA = amp.format(Prefixes[i])
currT = dec.format(Prefixes[i])
self.model.set_param_hint(currA, value=1, min=-1.5, max=1.5)
self.model.set_param_hint(currT, value=1, min=0)
self.params = self.model.make_params()
def prep_acfParams(self):
amp = '{}amplitude'
dec = '{}decay'
cntrl_expr = ' '
if self.sum_one_flag:
cntrl_expr = 'c'
self.model.set_param_hint('c', value=1.0, min=0)
for i in range(1, self.nexp + 1):
currA = amp.format(Prefixes[i])
currT = dec.format(Prefixes[i])
self.model.set_param_hint(currA, value=1.0, min=0.0)
self.model.set_param_hint(currT, value=1.0, min=0.0)
if self.sum_one_flag:
cntrl_expr += ' + ' + currA
self.params = self.model.make_params()
if self.sum_one_flag:
self.params.add('cntrl', value= 1, max= 1+1e-5, min= 1-1e-5)
self.params['cntrl'].vary = True
self.params['cntrl'].expr = cntrl_expr
def fit(self, tcf_type='acf', *args, **kwargs):
try:
self.prep_params(tcf_type)
self.res = self.model.fit(params=self.params, *args, **kwargs)
except Exception as e:
print("Undefined exception while fit: {} {}".format(type(e), e), file=sys.stderr)
raise
return self.res
def has_covar(self):
covar = self.res.covar
if covar is None or np.isnan(np.sum(covar)):
return False
else:
return True
def check_errors(self):
for ivar, name in enumerate(self.res.var_names):
par = self.res.params[name]
if par.stderr / par.value > 1:
return False
return True
def report(self):
return self.res.fit_report(), self.res.covar
