def fit_composit(seq, *params, report=False):
x = np.arange(len(seq))
y = seq
amp, cen, wid = params
cmodel = CompositeModel(Model(gaussian), Model(LognormalModel),
ExponentialGaussianModel)
pars = cmodel.make_params(amplitude=amp, center=cen, sigma=wid, mid=cen)
# 'mid' and 'center' should be completely correlated, and 'mid' is
# used as an integer index, so a very poor fit variable:
pars['mid'].vary = False
# fit this model to data array y
result = cmodel.fit(y, params=pars, x=x)
# limit the amplitude to be in 0-256
cmodel.set_param_hint('amp', min=0)
cmodel.set_param_hint('amp', max=256)
result = gmodel.fit(y, x=x, amp=amp, cen=cen, wid=wid)
if result.redchi >= 1e3:
# if report:
plt.figure()
plt.plot(x, y, 'bo')
# plt.plot(x, result.init_fit, 'k--')
plt.plot(x, np.ceil(result.best_fit), 'r-')
plt.title('chi2: {0:.2e} - red_chi2: {0:.2e}'.format(
result.chisqr, result.redchi))
plt.show()
return result
def fit_gaussian_peak_step_background_2(x, y):
# create data from broadened step
npts = 201
x = np.linspace(0, 10, npts)
y = step(x, amplitude=12.5, center=4.5, sigma=0.88, form='erf')
y = y + np.random.normal(size=npts, scale=0.35)
# create Composite Model using the custom convolution operator
mod = CompositeModel(Model(jump), Model(gaussian), convolve)
pars = mod.make_params(amplitude=1, center=3.5, sigma=1.5, mid=5.0)
# 'mid' and 'center' should be completely correlated, and 'mid' is
# used as an integer index, so a very poor fit variable:
pars['mid'].vary = False
# fit this model to data array y
result = mod.fit(y, params=pars, x=x)
print(result.fit_report())
plot_components = True
# plot results
plt.plot(x, y, 'bo')
if plot_components:
# generate components
comps = result.eval_components(x=x)
plt.plot(x, 10 * comps['jump'], 'k--')
plt.plot(x, 10 * comps['gaussian'], 'r-')
else:
plt.plot(x, result.init_fit, 'k--')
plt.plot(x, result.best_fit, 'r-')
plt.show()
# #<end examples/model_doc3.py>
return fit_fwhm, fit_center, fwhm_err
# Create model for the fit
gmodel = CompositeModel(Model(irf_gate), Model(model_2lorentzians), convolve)
print('Names of parameters:', gmodel.param_names)
print('Independent variable(s):', gmodel.independent_vars)
# Load reference data - extract x and y values
two_lorentzians_iris = np.loadtxt(path_to_data + 'data_2lorentzians.dat')
xx = two_lorentzians_iris[:, 0]
yy = two_lorentzians_iris[:, 1]
# Fit
result = gmodel.fit(yy,
x=xx,
scale1=1.,
center1=0.,
hwhm1=0.25,
scale2=1.,
center2=1.,
hwhm2=0.25)
fig1, ax1 = plt.subplots()
ax1.plot(xx, yy, '+', label='experimental data')
ax1.plot(xx, result.init_fit, 'k--', label='model with initial guesses')
ax1.legend()
ax1.set(
xlabel='Energy transfer (meV)',
title='Plot before fitting: experimental data and mode with initial guesses'
)
ax1.grid()
# display result
tmp = np.concatenate((pad*arr[0], arr, pad*arr[-1]))
out = np.convolve(tmp, kernel, mode='valid')
noff = int((len(out) - npts)/2)
return out[noff:noff+npts]
#
# create Composite Model using the custom convolution operator
mod = CompositeModel(Model(jump), Model(gaussian), convolve)
pars = mod.make_params(amplitude=1, center=3.5, sigma=1.5, mid=5.0)
# 'mid' and 'center' should be completely correlated, and 'mid' is
# used as an integer index, so a very poor fit variable:
pars['mid'].vary = False
# fit this model to data array y
result = mod.fit(y, params=pars, x=x)
print(result.fit_report())
plot_components = False
# plot results
plt.plot(x, y, 'bo')
if plot_components:
# generate components
comps = result.eval_components(x=x)
plt.plot(x, 10*comps['jump'], 'k--')
plt.plot(x, 10*comps['gaussian'], 'r-')
else:
plt.plot(x, result.init_fit, 'k--')
plt.plot(x, result.best_fit, 'r-')
radius=ini_values['radius'],
DR=ini_values['DR'])
# Q-independent parameters
if i == 0:
D_value = params['D'].value
resTime_value = params['resTime'].value
radius_value = params['radius'].value
DR_value = params['DR'].value
else:
params['D'].set(value=D_value)
params['resTime'].set(value=resTime_value)
params['radius'].set(value=radius_value)
params['DR'].set(value=DR_value)
result_fit[i] = model.fit(f_5A['y'][i], params, w=f_5A['x'][i])
# display result
for i in range(nb_q_values):
print(f'Result of fit {i}:\n', result_fit[i].fit_report())
# plot results using lmfit's features
for i in range(nb_q_values):
result_fit[i].plot()
# other option to plot: experimental data, initial fitting model and fitted model for each spectrum
for indx in range(nb_q_values):
fig1, ax1 = plt.subplots()
ax1.plot(f_5A['x'][indx], f_5A['y'][indx], 'bo', label='exp')
ax1.plot(f_5A['x'][indx], result_fit[indx].init_fit, 'k--', label='ini')
noff = int((len(out) - npts) / 2)
return out[noff:noff + npts]
#
# create Composite Model using the custom convolution operator
mod = CompositeModel(Model(jump), Model(gaussian), convolve)
pars = mod.make_params(amplitude=1, center=3.5, sigma=1.5, mid=5.0)
# 'mid' and 'center' should be completely correlated, and 'mid' is
# used as an integer index, so a very poor fit variable:
pars['mid'].vary = False
# fit this model to data array y
result = mod.fit(y, params=pars, x=x)
print(result.fit_report())
plot_components = False
# plot results
plt.plot(x, y, 'bo')
if plot_components:
# generate components
comps = result.eval_components(x=x)
plt.plot(x, 10 * comps['jump'], 'k--')
plt.plot(x, 10 * comps['gaussian'], 'r-')
else:
plt.plot(x, result.init_fit, 'k--')
plt.plot(x, result.best_fit, 'r-')