def get_model(model_name, model_prefix=''):
if model_name == 'voigt':
mdl = models.VoigtModel(prefix=model_prefix)
elif model_name == 'gauss':
mdl = models.GaussianModel(prefix=model_prefix)
elif model_name == 'constant':
mdl = models.ConstantModel(prefix=model_prefix)
elif model_name == 'linear':
mdl = models.LinearModel(prefix=model_prefix)
elif model_name == 'exp':
mdl = models.ExponentialModel(prefix=model_prefix)
elif model_name == 'logistic':
mdl = models.StepModel(prefix=model_prefix, form='logistic')
elif model_name == 'sine':
mdl = models.SineModel(prefix=model_prefix)
else:
raise ValueError('Model name not recognized.')
return mdl
def test_height_and_fwhm_expression_evalution_in_builtin_models():
"""Assert models do not throw an ZeroDivisionError."""
mod = models.GaussianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.LorentzianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.SplitLorentzianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, sigma_r=1.0)
params.update_constraints()
mod = models.VoigtModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=1.0)
params.update_constraints()
mod = models.PseudoVoigtModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, fraction=0.5)
params.update_constraints()
mod = models.MoffatModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, beta=0.0)
params.update_constraints()
mod = models.Pearson7Model()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, expon=1.0)
params.update_constraints()
mod = models.StudentsTModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.BreitWignerModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, q=0.0)
params.update_constraints()
mod = models.LognormalModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.DampedOscillatorModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.DampedHarmonicOscillatorModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.ExponentialGaussianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.SkewedGaussianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.SkewedVoigtModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0,
skew=0.0)
params.update_constraints()
mod = models.DoniachModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.StepModel()
for f in ('linear', 'arctan', 'erf', 'logistic'):
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, form=f)
params.update_constraints()
mod = models.RectangleModel()
for f in ('linear', 'arctan', 'erf', 'logistic'):
params = mod.make_params(amplitude=1.0, center1=0.0, sigma1=0.0,
center2=0.0, sigma2=0.0, form=f)
params.update_constraints()
def fit_peak(self,
roi=None,
xpeak=None,
sigma_guess=None,
model_name='gauss-erf-const',
**kwargs):
"""
Main routine
"""
# Exit if no roi
if roi is None:
self.fit = None
self.model_name = None
else:
self.model_name = model_name
# Start timer
tic = time.time()
# ---------
# Setup ROI
# ---------
self.set_roi(roi)
x = self.get_x_roi()
y = self.get_y_roi()
y_sig = self.get_y_sig_roi()
x_widths = self.get_x_widths_roi()
# ---------------------------
# Guesses based on input data
# ---------------------------
# Set peak center to center of ROI if not given
if xpeak is None:
xpeak = (x[0] + x[-1]) / 2.
# Guess sigma if not provided
if sigma_guess is None:
fwhm_guess = self.guess_fwhm(xpeak)
sigma_guess = fwhm_guess / FWHM_SIG_RATIO
# Heights at the sides of the ROI
left_shelf_height = y[0]
right_shelf_height = y[-1]
# Line guess
lin_slope = (y[-1] - y[0]) / (x[-1] - x[0])
lin_intercept = y[0] - lin_slope * x[0]
# Two peaks guess (33 and 66 percent through ROI)
xpeak0 = x[0] + (x[-1] - x[0]) * 0.33
xpeak1 = x[0] + (x[-1] - x[0]) * 0.66
# Index of at the ROI center
ix_half = int(round(float(len(x)) / 2.))
# -------------------
# Setup fitting model
# -------------------
if model_name == 'gauss-erf-const':
# Models
erf_mod = models.StepModel(form='erf', prefix='erf_')
gauss_mod = models.GaussianModel(prefix='gauss_')
bk_mod = models.ConstantModel(prefix='bk_')
# Initialize parameters
pars = erf_mod.make_params()
pars.update(gauss_mod.make_params())
pars.update(bk_mod.make_params())
# Erfc (sigma and center are locked to gauss below)
pars['erf_amplitude'].set(right_shelf_height -
left_shelf_height,
max=0.)
# Gauss
pars['gauss_center'].set(
xpeak
) # , min=xpeak - 2 * fwhm_guess, max=xpeak + 2 * fwhm_guess)
pars['gauss_sigma'].set(sigma_guess)
pars['gauss_amplitude'].set(np.sum(y * x_widths), min=0)
# Background
pars['bk_c'].set(left_shelf_height, min=0.)
# Same center and sigma
pars.add('erf_center', expr='gauss_center')
pars.add('erf_sigma',
expr='gauss_sigma * {}'.format(FWHM_SIG_RATIO))
self.model = gauss_mod + erf_mod + bk_mod
elif model_name == 'double-gauss-line':
# Models
lin_mod = models.LinearModel(prefix='lin_')
g0_mod = models.GaussianModel(prefix='gauss0_')
g1_mod = models.GaussianModel(prefix='gauss1_')
# Initialize parameters
pars = lin_mod.make_params()
pars.update(g0_mod.make_params())
pars.update(g1_mod.make_params())
# Line (background)
pars['lin_slope'].set(lin_slope, max=0.)
pars['lin_intercept'].set(lin_intercept)
# Gauss 0 (left)
pars['gauss0_center'].set(
xpeak0
) # , min=xpeak - 2 * fwhm_guess, max=xpeak + 2 * fwhm_guess)
pars['gauss0_sigma'].set(sigma_guess)
pars['gauss0_amplitude'].set(np.sum(y[:ix_half] *
x_widths[:ix_half]),
min=0)
# Gauss 1 (right)
pars['gauss1_center'].set(
xpeak1
) # , min=xpeak - 2 * fwhm_guess, max=xpeak + 2 * fwhm_guess)
pars['gauss1_sigma'].set(sigma_guess)
pars['gauss1_amplitude'].set(np.sum(y[ix_half:] *
x_widths[ix_half:]),
min=0)
self.model = lin_mod + g0_mod + g1_mod
else:
raise NotImplementedError(
'Model ({}) not recognized'.format(model_name))
# -----------
# Perform fit
# -----------
try:
self.fit = self.model.fit(y, pars, x=x, weights=1. / y_sig)
except:
print("[ERROR] Couldn't fit peak")
self.fit = None
if self.verbosity > 0:
print('Fit time: {:.3f} seconds'.format(time.time() - tic))