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
o[imid:] = 1.0
return o
def convolve(arr, kernel):
# simple convolution of two arrays
npts = min(len(arr), len(kernel))
pad = np.ones(npts)
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:
print('Names of parameters:', model.param_names)
print('Independent variable(s):', model.independent_vars)
# Define boundaries for parameters to be refined
model.set_param_hint('scale', min=0, max=100)
model.set_param_hint('center', min=-0.1, max=0.1)
model.set_param_hint('D', min=0.05, max=0.25)
model.set_param_hint('resTime', min=0, max=1)
model.set_param_hint('radius', min=0.9, max=1.1)
model.set_param_hint('DR', min=0, max=1)
# Fix some of the parameters
model.set_param_hint('q', vary=False)
model.set_param_hint('spectrum_nb', vary=False)
params = model.make_params()
# Plot of the fitting models without and convoluted with the resolution function
# The values of the parameters are specified below.
# Therefore they could be different from those used in the fitting.
fig, ax = plt.subplots(1, 2)
# First subplot
for i in range(nb_q_values):
xx = f_5A['x'][i]
ax[0].plot(xx,
QENSmodels.sqwWaterTeixeira(xx,
data_5A['q'][i],
scale=1,
center=0,
D=1,
def convolve(arr, kernel):
# simple convolution of two arrays
npts = min(len(arr), len(kernel))
pad = np.ones(npts)
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:
return o
def convolve(arr, kernel):
"""Simple convolution of two arrays."""
npts = min(arr.size, kernel.size)
pad = np.ones(npts)
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=.01, center=0, sigma=.5, mid=0.5)
# '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())
# generate components
comps = result.eval_components(x=x)
# plot results
fig, axes = plt.subplots(1, 2, figsize=(12.8, 4.8))