def test_model_nan_policy(self):
x = np.linspace(0, 10, 201)
np.random.seed(0)
y = gaussian(x, 10.0, 6.15, 0.8)
y += gaussian(x, 8.0, 6.35, 1.1)
y += gaussian(x, 0.25, 6.00, 7.5)
y += np.random.normal(size=len(x), scale=0.5)
y[55] = y[91] = np.nan
mod = PseudoVoigtModel()
params = mod.make_params(amplitude=20, center=5.5,
sigma=1, fraction=0.25)
params['fraction'].vary = False
# with raise, should get a ValueError
result = lambda: mod.fit(y, params, x=x, nan_policy='raise')
self.assertRaises(ValueError, result)
# with propagate, should get no error, but bad results
result = mod.fit(y, params, x=x, nan_policy='propagate')
self.assertTrue(result.success)
self.assertTrue(np.isnan(result.chisqr))
self.assertTrue(np.isnan(result.aic))
self.assertFalse(result.errorbars)
self.assertTrue(result.params['amplitude'].stderr is None)
self.assertTrue(abs(result.params['amplitude'].value - 20.0) < 0.001)
# with omit, should get good results
result = mod.fit(y, params, x=x, nan_policy='omit')
self.assertTrue(result.success)
self.assertTrue(result.chisqr > 2.0)
self.assertTrue(result.aic < -100)
self.assertTrue(result.errorbars)
self.assertTrue(result.params['amplitude'].stderr > 0.1)
self.assertTrue(abs(result.params['amplitude'].value - 20.0) < 5.0)
self.assertTrue(abs(result.params['center'].value - 6.0) < 0.5)
def __init__(self, type):
self.peak = [None] * (defPar.NumPeaks)
if type == 0:
for i in range(0, defPar.NumPeaks):
self.peak[i] = PseudoVoigtModel(prefix="p" + str(i) + "_")
self.typec = "PseudoVoigt"
elif type == 1:
for i in range(0, defPar.NumPeaks):
self.peak[i] = GaussianModel(prefix="p" + str(i) + "_")
self.typec = "Gauss"
elif type == 2:
for i in range(0, defPar.NumPeaks):
self.peak[i] = LorentzianModel(prefix="p" + str(i) + "_")
self.typec = "Lorentz"
elif type == 3:
for i in range(0, defPar.NumPeaks):
self.peak[i] = VoigtModel(prefix="p" + str(i) + "_")
self.typec = "Voigt"
else:
print("Warning: type undefined. Using PseudoVoigt")
for i in range(0, defPar.NumPeaks):
self.peak[i] = PseudoVoigtModel(prefix="p" + str(i) + "_")
self.typec = "PVoigt"
def calc_center_pseudoVoigt(vx,vy):
mod = PseudoVoigtModel()
y = vy
pars = mod.guess(y, x=vx)
out = mod.fit(y, pars, x=vx)
center = out.best_values['center']
return center
def make_model(peak_positions,
fwhm=0.05,
max_fwhm=0.5,
pos_range=0.5,
amplitude=1000.):
n_peaks = len(peak_positions)
pars = Parameters()
bg = LinearModel(prefix='bg_')
pars.update(bg.make_params(slope=0, intercept=0))
mod = bg
#pars['bg_intercept'].set(vary=True)
#pars['bg_slope'].set(vary=True)
for i in range(n_peaks):
prefix = 'pk{}_'.format(i)
peak = PseudoVoigtModel(prefix=prefix)
# Set this zero
pars.update(peak.make_params())
pars[prefix + 'center'].set(peak_positions[i],
min=peak_positions[i] - pos_range,
max=peak_positions[i] + pos_range,
vary=True)
pars[prefix + 'sigma'].set(fwhm, min=0., max=max_fwhm, vary=True)
pars[prefix + 'amplitude'].set(amplitude, min=0., vary=True)
pars[prefix + 'fraction'].set(0.0, min=0., max=1., vary=True)
mod += peak
return mod, pars
def guess_params(self, peak):
"""Guesses parameters for a new peak and adds it. See add_peak()."""
if peak.region is not self._region:
logger.error("peak with ID {} does not belong to region ID {}"
"".format(peak.ID, self._region.ID))
raise ValueError("Peak does not belong to Region")
if peak.model_name == "PseudoVoigt":
model = PseudoVoigtModel(prefix=peak.prefix)
# model.set_param_hint("fraction", vary=False)
else:
raise NotImplementedError("Only PseudoVoigt models supported")
other_models_cps = [0] * len(self._region.energy)
for other_peak in self._region.peaks:
if other_peak == peak:
continue
other_models_cps += self.get_peak_cps(other_peak, peak.energy)
y = self._region.cps - self._region.background - other_models_cps
params = model.guess(y, x=peak.energy)
self._params += params
fwhmname = "{}fwhm".format(peak.prefix)
sigmaname = "{}sigma".format(peak.prefix)
ampname = "{}amplitude".format(peak.prefix)
centername = "{}center".format(peak.prefix)
params[fwhmname].set(value=params[fwhmname].value, vary=True, min=0)
params[sigmaname].set(expr="{}/2".format(fwhmname))
params[ampname].set(min=0)
params[centername].set(min=0)
self._single_models[peak.prefix] = model
def voigt_mod(N):
'''
Returns a model consisting of N pseudo-voigt curves
'''
# initialize model
model = PseudoVoigtModel(prefix='voi1_')
# Add N-1 pseudo-voigts
for i in range(N - 1):
model += PseudoVoigtModel(prefix='voi' + str(i + 2) + '_')
return model
def fit_three_peaks(peak_data,
pv_1_cent=3.43,
pv_1_min=3.36,
pv_1_max=3.44,
pv_2_cent=3.52,
pv_2_min=3.42,
pv_2_max=3.55,
pv_3_cent=3.59,
pv_3_min=3.54,
pv_3_max=3.6,
initParams=None):
""" Pseudo-Voigt fit to the lattice plane peak intensity for three peaks (adjust function to include more).
Parameters for each peak also require limits and centres to be set in the case of overlapping peaks.
Return results of the fit as an lmfit class, which contains the fitted parameters (amplitude, fwhm, etc.)
and the fit line calculated using the fit parameters and 100x two-theta points.
"""
ttheta = peak_data[:, 0]
intensity = peak_data[:, 1]
PV_1 = PseudoVoigtModel(prefix='pv_1')
PV_2 = PseudoVoigtModel(prefix='pv_2')
PV_3 = PseudoVoigtModel(prefix='pv_3')
model = PV_1 + PV_2 + PV_3 + Model(line)
if initParams is None:
# if reflection == '(0002),(110),(10-11)': use starting centres 3.43(min=3.36,max=3.44), 3.54(min=3.42,max=3.55), 3.59(min=3.54,max=3.6)...
#if reflection == '(20-20),(11-22),(20-21)': use starting centres 6.32(min=6.23,max=6.33), 6.45(min=6.37,max=6.47), 6.54(min=6.46,max=6.56)...
pars_1 = PV_1.guess(intensity, x=ttheta)
#note, min and max values are not the same as bounds around the peak, but values that they can go up to!
pars_1['pv_1center'].set(pv_1_cent, min=pv_1_min, max=pv_1_max)
#pars_1['pv_1sigma'].set(min=0.001, max=0.1)
#pars_1['pv_1amplitude'].set(min=0)
#pars_1['pv_1height'].set(min=10)
#pars_1['pv_1fwhm'].set(min=0.02, max=0.2)
pars_2 = PV_2.guess(intensity, x=ttheta)
pars_2['pv_2center'].set(pv_2_cent, min=pv_2_min, max=pv_2_max)
#pars_2['pv_2sigma'].set(min=0.001, max=0.1)
#pars_2['pv_2amplitude'].set(min=0)
#pars_2['pv_2height'].set(min=10, max = 5000)
#pars_2['pv_2fwhm'].set(min=0.02, max=0.2)
pars_3 = PV_3.guess(intensity, x=ttheta)
pars_3['pv_3center'].set(pv_3_cent, min=pv_3_min, max=pv_3_max)
#pars_3['pv_3sigma'].set(min=0.001, max=0.1)
#pars_3['pv_3amplitude'].set(min=1)
#pars_3['pv_3height'].set(min=10)
#pars_3['pv_3fwhm'].set(min=0.02, max=0.2)
pars = pars_1 + pars_2 + pars_3
pars.add("constBG", 0)
else:
pars = initParams
fit_results = model.fit(intensity, pars, x=ttheta)
fit_ttheta = np.linspace(ttheta[0], ttheta[-1], 100)
fit_line = [fit_ttheta, model.eval(fit_results.params, x=fit_ttheta)]
return fit_results, fit_line
def set_params(peaks):
"""
This module takes in the list of peaks from the peak detection modules, and then uses
that to initialize parameters for a set of Pseudo-Voigt models that are not yet fit.
There is a single model for every peak.
Args:
peaks (list): A list containing the x and y-values (in tuples) of the peaks.
Returns:
mod (lmfit.models.PseudoVoigtModel or lmfit.model.CompositeModel): This is an array of
the initialized Pseudo-Voigt models. The array contains all of the values
that are found in `pars` that are fed to an lmfit lorentzian model class.
pars (lmfit.parameter.Parameters): An array containing the parameters for each peak
that were generated through the use of a Lorentzian fit. The pars
array contains a center value, a height, a sigma, and an amplitude
value. The center value is allowed to vary +- 10 wavenumber from
the peak max that was detected in scipy. Some wiggle room was allowed
to help mitigate problems from slight issues in the peakdetect
algorithm for peaks that might have relatively flat maxima. The height
value was allowed to vary between 0 and 1, as it is assumed the y-values
are normalized. Sigma is set to a maximum of 500, as we found that
giving it an unbound maximum led to a number of peaks that were
unrealistic for Raman spectra (ie, they were far too broad, and shallow,
to correspond to real data. Finally, the amplitude for the peak was set
to a minimum of 0, to prevent negatives.
"""
# handling errors in inputs
if not isinstance(peaks, list):
raise TypeError('Passed value of `peaks` is not a list! Instead, it is: '
+ str(type(peaks)))
for i, _ in enumerate(peaks):
if not isinstance(peaks[i], tuple):
raise TypeError("""Passed value of `peaks[{}]` is not a tuple.
Instead, it is: """.format(i) + str(type(peaks[i])))
peak_list = []
for i, _ in enumerate(peaks):
prefix = 'p{}_'.format(i+1)
peak = PseudoVoigtModel(prefix=prefix)
if i == 0:
pars = peak.make_params()
else:
pars.update(peak.make_params())
pars[prefix+'center'].set(peaks[i][0], vary=False)
pars[prefix+'height'].set(peaks[i][1], vary=False)
pars[prefix+'sigma'].set(50, min=0, max=500)
pars[prefix+'amplitude'].set(min=0)
peak_list.append(peak)
if i == 0:
mod = peak_list[i]
else:
mod = mod + peak_list[i]
return mod, pars
def voigt(x, y):
# Voigt fit to a curve
x_shifted = x - x.min() # Shifting to 0
y_shifted = y - y.min() # Shifting to 0
mod = PseudoVoigtModel() # Setting model type
pars = mod.guess(y_shifted, x=x_shifted) # Estimating fit
out = mod.fit(y_shifted, pars, x=x_shifted) # Fitting fit
# print(out.fit_report(min_correl=0.25)) # Outputting best fit results
print("Voigt FWHM = ", out.params['fwhm'].value) # Outputting only FWHM
out.plot() # Plotting fit
def fit_one_Psudo_Voigt(x_lst,y_lst):
'''
Fits one Pseudo Voigt returns the
results object
'''
mod = PseudoVoigtModel(independent_vars=['x'],nan_policy='raise')
x_lst = np.asarray(x_lst)
y_lst = np.asarray(y_lst)
# Guess good values computer
mod.guess(y_lst,x = x_lst)
result = mod.fit(y_lst, x = x_lst)
return result
def test_figure_title_using_title_keyword_argument(peakdata):
"""Test setting figure title using title keyword argument."""
x, y = peakdata
pvmodel = PseudoVoigtModel()
params = pvmodel.guess(y, x=x)
result = pvmodel.fit(y, params, x=x)
ax = result.plot_fit(title='test')
assert ax.axes.get_title() == 'test'
ax = result.plot_residuals(title='test')
assert ax.axes.get_title() == 'test'
fig, _ = result.plot(title='test')
assert fig.axes[0].get_title() == 'test'
assert fig.axes[1].get_title() == '' # no title for fit subplot
def test_figure_default_title(peakdata):
"""Test default figure title."""
x, y = peakdata
pvmodel = PseudoVoigtModel()
params = pvmodel.guess(y, x=x)
result = pvmodel.fit(y, params, x=x)
ax = result.plot_fit()
assert ax.axes.get_title() == 'Model(pvoigt)'
ax = result.plot_residuals()
assert ax.axes.get_title() == 'Model(pvoigt)'
fig, _ = result.plot()
assert fig.axes[0].get_title() == 'Model(pvoigt)' # default model.name
assert fig.axes[1].get_title() == '' # no title for fit subplot
def center_psi(file_name):
#print(file_name)
psi, vx, vy = read_file(file_name)
vy = processing_of_data(psi,vx,vy)
legenda = file_name.split('/')[-1]
#plt.grid()
#plt.legend(loc=0)
#import pdb; pdb.set_trace()
plt.plot(vx,vy,label=legenda)
mod = PseudoVoigtModel()
y=vy
pars = mod.guess(y, x=vx)
out = mod.fit(y, pars, x=vx)
center =out.best_values['center']
print('center: {} <--> psi: {}'.format(center,psi))
return psi, center
def set_params(peaks):
"""
This function takes in the list of peaks from the peak detection modules, and then uses
that to initialize parameters for a set of Pseudo-Voigt models that are not yet fit.
There is a single model for every peak.
Args:
peaks (list): A list containing tuples of the x_data (wavenumber) and y_data (counts)
values of the peaks.
Returns:
mod (lmfit.models.PseudoVoigtModel or lmfit.model.CompositeModel): This is an array of
the initialized pseudo-Voigt models. The array contains all of the values
that are found in `pars` that are fed to an lmfit lorentzian model class.
pars (lmfit.parameter.Parameters): An array containing the parameters for each peak
that were generated through the use of a Lorentzian fit. The pars
array contains values for fraction, center, height, sigma, the full width
at half maximum (fwhm = 2*sigma), and amplitude.
"""
# handling errors in inputs
if not isinstance(peaks, list):
raise TypeError(
'Passed value of `peaks` is not a list! Instead, it is: ' +
str(type(peaks)))
for i, peak in enumerate(peaks):
if not isinstance(peak, tuple):
raise TypeError("""The {} value of `peaks` is not a tuple.
Instead, it is: """.format(i) + str(type(peak)))
peak_list = []
for i, value in enumerate(peaks):
prefix = 'p{}_'.format(i + 1)
peak = PseudoVoigtModel(prefix=prefix)
if i == 0:
pars = peak.make_params()
else:
pars.update(peak.make_params())
# constraints on profile desciptors
pars[prefix + 'center'].set(value[0], vary=False)
pars[prefix + 'height'].set(min=0.1 * value[1])
pars[prefix + 'sigma'].set(10, min=1, max=100)
pars[prefix + 'amplitude'].set(100 * value[1], min=0)
peak_list.append(peak)
if i == 0:
mod = peak_list[i]
else:
mod = mod + peak_list[i]
return mod, pars
def fit_data(x, y, xmin=None, xmax=None):
if y is None:
return "No spectrum! Fit not performed!"
else:
if xmin is None:
xmin = x.min()
elif xmin < x.min():
xmin = x.min()
if xmax is None:
xmax = x.max()
elif xmax < x.max():
xmax = x.max()
y = np.copy(y[np.logical_and(x > xmin, x < xmax)])
x = np.copy(x[np.logical_and(x > xmin, x < xmax)])
x, y = remove_spike(y, x=x)
mod = (ConstantModel() + PseudoVoigtModel(prefix="peak1_") +
PseudoVoigtModel(prefix="peak2_"))
params = mod.make_params()
ymax_ind = np.argmax(y)
xmax = x[ymax_ind]
params["c"].set(0)
params.add("psplit", value=1.5, vary=True, min=0.5, max=2.0)
params["peak2_center"].set(xmax, min=xmax - 0.5, max=x.max())
params["peak2_sigma"].set(0.5, min=0.01)
params["peak2_amplitude"].set(y[ymax_ind] * 0.5 * np.sqrt(2 * np.pi),
min=0)
params["peak2_fraction"].set(0.5, min=0, max=1)
params["peak1_center"].set(vary=False, expr="peak2_center-psplit")
params["peak1_sigma"].set(0.5, min=0.01)
params["peak1_amplitude"].set(y[ymax_ind] * 0.5 * np.sqrt(2 * np.pi) /
2,
min=0)
params["peak1_fraction"].set(0.5, min=0, max=1)
fit = mod.fit(y, params, x=x)
return fit
def fit_experimental_data_gauss(exp_x,
exp_y,
expected_peak_pos,
deg_of_bck_poly=5,
maxfev=25000):
num_of_peaks = len(expected_peak_pos)
mod = PolynomialModel(deg_of_bck_poly, prefix='poly_')
for c in range(num_of_peaks):
mod = mod + PseudoVoigtModel(prefix='p{}_'.format(c))
params = mod.make_params()
center = 0
sigma = 0
amplitude = 0
fraction = 0
for param in params:
if 'center' in param:
params[param].set(value=expected_peak_pos[center])
params[param].set(min=expected_peak_pos[center] - 0.5)
params[param].set(max=expected_peak_pos[center] + 0.5)
center += 1
if 'poly' in param:
if param == 'poly_c0':
params[param].set(value=50)
params[param].set(min=-100)
params[param].set(max=100)
continue
if param == 'poly_c1':
params[param].set(value=-1)
params[param].set(min=-100)
params[param].set(max=100)
continue
params[param].set(value=0)
## params[param].set(min = 3e-1)
## params[param].set(max = 3e-1)
if 'sigma' in param:
params[param].set(value=0.5)
params[param].set(min=0.0001)
params[param].set(max=0.8)
sigma += 1
if 'amplitude' in param:
params[param].set(value=5.5)
params[param].set(min=0.0001)
amplitude += 1
if 'fraction' in param:
params[param].set(value=0.0)
params[param].set(min=0.000)
params[param].set(max=0.000001)
fraction += 1
result = mod.fit(np.asarray(exp_y),
params,
x=np.asarray(exp_x),
fit_kws={'maxfev': maxfev})
print(result.fit_report())
return result
def methodfunciont(key):
dic = {
"VoigtModel": VoigtModel(),
"PseudoVoigtModel": PseudoVoigtModel(),
"GaussianModel": GaussianModel()
}
return dic[key]
def prepare_for_fitting(self, poly_order, maxwidth, centerrange):
"""
:param x_center: numpy array of initial x values at picked centers
:param y_center: numpy array of initial y values at picked centers
:param fwhm: single float number for initial fwhm value
"""
self.set_baseline(poly_order)
baseline_mod = PolynomialModel(poly_order, prefix='b_')
mod = baseline_mod
pars = baseline_mod.make_params()
peakinfo = {}
for i in range(poly_order + 1):
prefix = "b_c{0:d}".format(i)
pars[prefix].set(value=self.baseline_in_queue[i]['value'],
vary=self.baseline_in_queue[i]['vary'])
i = 0
for peak in self.peaks_in_queue:
prefix = "p{0:d}_".format(i)
peak_mod = PseudoVoigtModel(prefix=prefix, )
pars.update(peak_mod.make_params())
pars[prefix + 'center'].set(value=peak['center'],
min=peak['center'] - centerrange,
max=peak['center'] + centerrange,
vary=peak['center_vary'])
pars[prefix + 'sigma'].set(value=peak['sigma'],
min=0.0,
vary=peak['sigma_vary'],
max=maxwidth)
pars[prefix + 'amplitude'].set(value=peak['amplitude'],
min=0,
vary=peak['amplitude_vary'])
pars[prefix + 'fraction'].set(value=peak['fraction'],
min=0.,
max=1.,
vary=peak['fraction_vary'])
peakinfo[prefix + 'phasename'] = peak['phasename']
peakinfo[prefix + 'h'] = peak['h']
peakinfo[prefix + 'k'] = peak['k']
peakinfo[prefix + 'l'] = peak['l']
mod += peak_mod
i += 1
self.parameters = pars
self.peakinfo = peakinfo
self.fit_model = mod
def fit_peak(peak_data, initParams=None):
""" Pseudo-Voigt fit to the lattice plane peak intensity.
Return results of the fit as an lmfit class, which contains the fitted parameters (amplitude, fwhm, etc.)
and the fit line calculated using the fit parameters and 100x two-theta points.
"""
ttheta = peak_data[:, 0]
intensity = peak_data[:, 1]
pvModel = PseudoVoigtModel()
model = pvModel + Model(line)
if initParams is None:
pars = pvModel.guess(intensity, x=ttheta)
pars['sigma'].set(min=0.01, max=1) #sigma is the width?
pars['amplitude'].set(min=0.05)
pars.add("constBG", 0)
else:
pars = initParams
fit_results = model.fit(intensity, pars, x=ttheta)
fit_ttheta = np.linspace(ttheta[0], ttheta[-1], 100)
fit_line = [fit_ttheta, model.eval(fit_results.params, x=fit_ttheta)]
return fit_results, fit_line
def test_figure_title_using_title_to_ax_kws(peakdata):
"""Test setting figure title by supplying ax_{fit,res}_kws."""
x, y = peakdata
pvmodel = PseudoVoigtModel()
params = pvmodel.guess(y, x=x)
result = pvmodel.fit(y, params, x=x)
ax = result.plot_fit(ax_kws={'title': 'ax_kws'})
assert ax.axes.get_title() == 'ax_kws'
ax = result.plot_residuals(ax_kws={'title': 'ax_kws'})
assert ax.axes.get_title() == 'ax_kws'
fig, _ = result.plot(ax_res_kws={'title': 'ax_res_kws'})
assert fig.axes[0].get_title() == 'ax_res_kws'
assert fig.axes[1].get_title() == ''
fig, _ = result.plot(ax_fit_kws={'title': 'ax_fit_kws'})
assert fig.axes[0].get_title() == 'Model(pvoigt)' # default model.name
assert fig.axes[1].get_title() == '' # no title for fit subplot
def test_priority_setting_figure_title(peakdata):
"""Test for setting figure title: title keyword argument has priority."""
x, y = peakdata
pvmodel = PseudoVoigtModel()
params = pvmodel.guess(y, x=x)
result = pvmodel.fit(y, params, x=x)
ax = result.plot_fit(ax_kws={'title': 'ax_kws'}, title='test')
assert ax.axes.get_title() == 'test'
ax = result.plot_residuals(ax_kws={'title': 'ax_kws'}, title='test')
assert ax.axes.get_title() == 'test'
fig, _ = result.plot(ax_res_kws={'title': 'ax_res_kws'}, title='test')
assert fig.axes[0].get_title() == 'test'
assert fig.axes[1].get_title() == ''
fig, _ = result.plot(ax_fit_kws={'title': 'ax_fit_kws'}, title='test')
assert fig.axes[0].get_title() == 'test'
assert fig.axes[1].get_title() == ''
def double_voigt(self, sepPt=None, pars: list = None, bounds: list = None):
if sepPt == None: sepPt = find_separation_point(self.x, self.y)
x1 = self.x[self.x < sepPt]
x2 = self.x[self.x > sepPt]
y1 = self.y[self.x < sepPt]
y2 = self.y[self.x > sepPt]
mod1 = PseudoVoigtModel(prefix='v1_')
mod2 = PseudoVoigtModel(prefix='v2_')
if pars == None:
pars1 = self.guess_pars(self, mod1, x1, y1, prefix='v1_')
pars2 = self.guess_pars(self, mod2, x2, y2, prefix='v2_')
mod = mod1 + mod2
pars = mod.make_params()
pars.update(pars1)
pars.update(pars2)
return self.finish_fit(self, mod, pars)
def test_model_nan_policy(self):
x = np.linspace(0, 10, 201)
np.random.seed(0)
y = gaussian(x, 10.0, 6.15, 0.8)
y += gaussian(x, 8.0, 6.35, 1.1)
y += gaussian(x, 0.25, 6.00, 7.5)
y += np.random.normal(size=len(x), scale=0.5)
y[55] = y[91] = np.nan
mod = PseudoVoigtModel()
params = mod.make_params(amplitude=20, center=5.5,
sigma=1, fraction=0.25)
params['fraction'].vary = False
# with raise, should get a ValueError
result = lambda: mod.fit(y, params, x=x, nan_policy='raise')
self.assertRaises(ValueError, result)
# with propagate, should get no error, but bad results
result = mod.fit(y, params, x=x, nan_policy='propagate')
self.assertTrue(result.success)
self.assertTrue(np.isnan(result.chisqr))
self.assertTrue(np.isnan(result.aic))
self.assertFalse(result.errorbars)
self.assertTrue(result.params['amplitude'].stderr is None)
self.assertTrue(abs(result.params['amplitude'].value - 20.0) < 0.001)
# with omit, should get good results
result = mod.fit(y, params, x=x, nan_policy='omit')
self.assertTrue(result.success)
self.assertTrue(result.chisqr > 2.0)
self.assertTrue(result.aic < -100)
self.assertTrue(result.errorbars)
self.assertTrue(result.params['amplitude'].stderr > 0.1)
self.assertTrue(abs(result.params['amplitude'].value - 20.0) < 5.0)
self.assertTrue(abs(result.params['center'].value - 6.0) < 0.5)
def test_model_nan_policy(self):
"""Tests for nan_policy with NaN values in the input data."""
x = np.linspace(0, 10, 201)
np.random.seed(0)
y = gaussian(x, 10.0, 6.15, 0.8)
y += gaussian(x, 8.0, 6.35, 1.1)
y += gaussian(x, 0.25, 6.00, 7.5)
y += np.random.normal(size=len(x), scale=0.5)
# with NaN values in the input data
y[55] = y[91] = np.nan
mod = PseudoVoigtModel()
params = mod.make_params(amplitude=20,
center=5.5,
sigma=1,
fraction=0.25)
params['fraction'].vary = False
# with raise, should get a ValueError
result = lambda: mod.fit(y, params, x=x, nan_policy='raise')
msg = (
'NaN values detected in your input data or the output of your '
'objective/model function - fitting algorithms cannot handle this!'
)
self.assertRaisesRegex(ValueError, msg, result)
# with propagate, should get no error, but bad results
result = mod.fit(y, params, x=x, nan_policy='propagate')
self.assertTrue(result.success)
self.assertTrue(np.isnan(result.chisqr))
self.assertTrue(np.isnan(result.aic))
self.assertFalse(result.errorbars)
self.assertTrue(result.params['amplitude'].stderr is None)
self.assertTrue(abs(result.params['amplitude'].value - 20.0) < 0.001)
# with omit, should get good results
result = mod.fit(y, params, x=x, nan_policy='omit')
self.assertTrue(result.success)
self.assertTrue(result.chisqr > 2.0)
self.assertTrue(result.aic < -100)
self.assertTrue(result.errorbars)
self.assertTrue(result.params['amplitude'].stderr > 0.1)
self.assertTrue(abs(result.params['amplitude'].value - 20.0) < 5.0)
self.assertTrue(abs(result.params['center'].value - 6.0) < 0.5)
# with 'wrong_argument', should get a ValueError
err_msg = r"nan_policy must be 'propagate', 'omit', or 'raise'."
with pytest.raises(ValueError, match=err_msg):
mod.fit(y, params, x=x, nan_policy='wrong_argument')
def fitXY(self):
#self.analyze ()
#self.amplitude1 = self.maxval - self.minval
#self.amplitude2 = .6 * self.amplitude1
peak1 = PseudoVoigtModel(prefix='p1_')
peak2 = PseudoVoigtModel(prefix='p2_')
basef = LinearModel()
model = peak1 + peak2 + basef
params = model.make_params(p1_center=self.sample_position,
p2_center=self.sample_position - 1.5,
p1_amplitude=self.amplitude1,
p2_amplitude=self.amplitude1 * 0.6,
p1_sigma=0.25,
p2_sigma=0.25,
p1_fraction=0.8,
p2_fraction=0.8,
slope=0,
intercept=self.minval)
result = model.fit(self.ydata, x=self.wave, **params)
self.modelFit = result.best_fit
bfd = result.best_values
self.fitParams = [
bfd['p1_amplitude'], bfd['p1_center'], bfd['p1_sigma'],
bfd['p1_fraction'], bfd['p2_amplitude'], bfd['p2_center'],
bfd['p2_sigma'], bfd['p2_fraction'], bfd['slope'], bfd['intercept']
]
self.oparams1[0] = bfd['p1_amplitude']
self.oparams1[1] = bfd['p1_center']
self.oparams1[2] = bfd['p1_sigma']
self.oparams1[3] = bfd['p1_fraction']
self.oparams2[0] = bfd['p2_amplitude']
self.oparams2[1] = bfd['p2_center']
self.oparams2[2] = bfd['p2_sigma']
self.oparams2[3] = bfd['p2_fraction']
self.oparams3[0] = bfd['slope']
self.oparams3[1] = bfd['intercept']
self.fitDone.emit()
def add_peak(self, peak):
"""Adds a new Peak to the Model list."""
if peak.region is not self._region:
logger.error("peak with ID {} does not belong to region ID {}"
"".format(peak.ID, self._region.ID))
raise ValueError("Peak does not belong to Region")
if peak.model_name == "PseudoVoigt":
model = PseudoVoigtModel(prefix=peak.prefix)
model.set_param_hint("fraction", vary=False, value=0.2)
params = model.make_params()
else:
raise NotImplementedError("Only PseudoVoigt models supported")
self._params += params
fwhmname = "{}fwhm".format(peak.prefix)
sigmaname = "{}sigma".format(peak.prefix)
ampname = "{}amplitude".format(peak.prefix)
centername = "{}center".format(peak.prefix)
params[fwhmname].set(value=params[fwhmname].value, vary=True, min=0)
params[sigmaname].set(expr="{}/2".format(fwhmname))
params[ampname].set(min=0)
params[centername].set(min=0)
self._single_models[peak.prefix] = model
def fit_voigt(xp: XPS_experiment,
region: str,
prefix: str = 'v_',
pars: list = None,
bounds: list = None,
ax=None,
flag_plot: bool = True):
"""General method for fitting voigt model
Input
----------
xp : class XPS_experiment
XPS data
region : str
core level name
pars, bounds : list
initial guess of the fit parameters and bounds. If unspecified, guessed automatically
Returns
-----------
fitv : lmfit.model
fit result to Voigt model
"""
from lmfit.models import PseudoVoigtModel, GaussianModel
x = xp.dfx[region].dropna().energy
y = xp.dfx[region].dropna().counts
mod = PseudoVoigtModel(prefix=prefix)
if pars == None:
pars = mod.guess(y, x=x)
pars[prefix + 'sigma'].set(value=1) # Usually guessed wrong anyway
pars[prefix + 'fraction'].set(value=0.2, min=0.15, max=0.20)
fitv = mod.fit(y, pars, x=x)
xp.fit.update({region: fitv})
if flag_plot:
hatchplot_fit(xp, region, fitv, ax=ax, plot_comps=True)
return fitv
def prepareFittingModels(roiCoordsList, modelType):
modelList = []
paramList = []
index = 1
for region in roiCoordsList:
individualModelsList = []
individualParamsList = []
if isinstance(region, dict):
# If the region is just a single region, make it a list so the for loops pulls a dict rather than a dict entry
region = [region]
for entry in region:
prefixName = 'v' + str(index) + '_'
index += 1
# pull info out of region dict
selectedXVals = entry['x']
selectedYVals = entry['y']
mod = None
if modelType.lower() == 'voigt':
mod = VoigtModel(prefix=prefixName)
elif modelType.lower() == 'psuedovoigt':
mod = PseudoVoigtModel(prefix=prefixName)
elif modelType.lower() == 'lorentzian':
mod = LorentzianModel(prefix=prefixName)
elif modelType.lower() == 'gaussian':
mod = GaussianModel(prefix=prefixName)
elif modelType.lower() == 'pearsonvii':
mod = Pearson7Model(prefix=prefixName)
assert mod, "Entered model type is not supported"
individualModelsList.append(mod)
pars = mod.guess(selectedYVals, x=selectedXVals, negative=False)
pars[prefixName + 'center'].set(min=min(selectedXVals), max=max(selectedXVals))
pars[prefixName + 'amplitude'].set(min=0)
pars[prefixName + 'sigma'].set(min=0)
if modelType.lower() == 'voigt':
pars[prefixName + 'gamma'].set(value=0.3, vary=True, expr='', min=0)
individualParamsList.append(pars)
combinedModel = individualModelsList[0]
combinedParams = individualParamsList[0]
if len(individualModelsList) > 1:
for model, params in zip(individualModelsList[1:], individualParamsList[1:]):
combinedModel += model
combinedParams += params
modelList.append(combinedModel)
paramList.append(combinedParams)
return modelList, paramList
def test_param_hint_explicit_value(self):
# tests Github Issue 384
pmod = PseudoVoigtModel()
params = pmod.make_params(sigma=2, fraction=0.77)
assert_allclose(params['fraction'].value, 0.77, rtol=0.01)
def fitMo3d(filename, groupNo, regionNo, E_b_min, E_b_max, doplot=False):
"""Fit a Moly 3d signal using elemental and oxide components (two for each peak, 3/2 and 5/2).
A shirley background is subtracted first.
The elemental Mo components are fittet to the PseudoVoigt line shape.
Oxide components are fitted to a Gaussian line shape.
Parameters
----------
filename : string
location of the data file, expects an SpecsLab xml file.
groupNo : int
index of the group of spectra (from 0)
regionNo : int
index of the spectrum that should be fitted (from 0)
E_b_min : float
region boundary
E_b_max : float
region boundary
doplot : bool
wether or not to plot the fit and components (default False)
"""
#load data from SpecsLab xml file and select region
region = get_region(filename, groupNo, regionNo)
# determine index of left and right boundary
index1 = np.where(abs(region.x_be - (E_b_min)) < 1e-10)
index2 = np.where(abs(region.x_be - (E_b_max)) < 1e-10)
E = region.x_be[index1[0][0]:index2[0][0]] #binding energy scale
s = region.y_avg_counts_mcd[index1[0][0]:index2[0][0]] #signal
#signal with background substacted
epsilon, sb, B = remove_shirley_background(s, E, 3, 1e-6)
######################
# set up model components and set constraints for the fit
######################
pre11 = 'Mo3d_32_1_'
mod11 = PseudoVoigtModel(prefix=pre11)
pars = mod11.make_params()
pars.add('delta', value=3, min=2, max=4)
pars[pre11 + 'amplitude'].set(9000, min=500, max=100000)
pars[pre11 + 'center'].set(-231.2, min=-231.4, max=-231)
pars[pre11 + 'sigma'].set(0.3, min=0.1, max=0.4)
pars[pre11 + 'fraction'].set(0.5, min=0.1, max=1)
mod21 = PseudoVoigtModel(prefix='Mo3d_52_1_')
pars.update(mod21.make_params())
pars['Mo3d_52_1_amplitude'].set(
expr='3/2*Mo3d_32_1_amplitude') #(12000, min=500, max=100000)
pars['Mo3d_52_1_center'].set(expr='Mo3d_32_1_center+delta'
) #same distance between 3/2 and 5/2 peaks
#pars['Mo3d_52_1_sigma' ].set(expr='1.0*Mo3d_32_1_sigma') #all have same sigma value
pars['Mo3d_52_1_fraction'].set(
expr='1.0*Mo3d_32_1_fraction') #(0.5, min=0.2, max=1)
pre12 = 'MoO23d_32_2_'
mod12 = GaussianModel(prefix=pre12)
pars.update(mod12.make_params())
pars[pre12 + 'amplitude'].set(2050, min=500, max=100000)
pars[pre12 + 'center'].set(-234.5, min=-236, max=-234)
pars[pre12 + 'sigma'].set(0.3, min=0.2, max=2)
pre22 = 'MoO23d_52_2_'
mod22 = GaussianModel(prefix=pre22)
pars.update(mod22.make_params())
pars[pre22 + 'amplitude'].set(
expr='3/2*MoO23d_32_2_amplitude') #(1050, min=500, max=100000)
pars[pre22 + 'center'].set(expr='MoO23d_32_2_center+delta'
) #same distance between 3/2 and 5/2 peaks
pars[pre22 + 'sigma'].set(
expr='1.0*MoO23d_32_2_sigma') #all have same sigma value
pre13 = 'MoO23d_32_3_'
mod13 = GaussianModel(prefix=pre13)
pars.update(mod13.make_params())
pars[pre13 + 'amplitude'].set(2050, min=500, max=100000)
pars[pre13 + 'center'].set(-235.5, min=-237, max=-234)
pars[pre13 + 'sigma'].set(0.3, min=0.2, max=2)
pre23 = 'MoO23d_52_3_'
mod23 = GaussianModel(prefix=pre23)
pars.update(mod23.make_params())
pars[pre23 + 'amplitude'].set(
expr='3/2*MoO23d_32_3_amplitude') #(1050, min=500, max=100000)
pars[pre23 + 'center'].set(expr='MoO23d_32_3_center+delta'
) #same distance between 3/2 and 5/2 peaks
pars[pre23 + 'sigma'].set(
expr='1.0*MoO23d_32_3_sigma') #all have same sigma value
#composite model is a sum of the components
mod = mod11 + mod21 + mod12 + mod22 + mod13 + mod23
#perform the fit
out = mod.fit(sb, pars, x=E)
print(out.fit_report(min_correl=0.25))
if (doplot):
fig = plt.figure(None, figsize=(6, 4))
#plot individual components
comps = mod.eval_components(params=out.params, x=E)
labels = [
'Mo', 'Mo', 'MoO$_3$', 'MoO$_3$', 'MoO$_2$', 'MoO$_2$', 'MoO$_2$',
'MoO$_2$'
]
for n, key in enumerate(comps):
if ((n % 2) == 0):
plt.plot(-E,
comps[key] / 10**3,
color=colors[n],
label=labels[n])
else:
plt.plot(-E, comps[key] / 10**3, color=colors[n - 1])
#plt.fill_between(-E, 0.0, comps[key]/10**3, facecolor=colors[int(i/2)], alpha=0.5)
#plot measured signal (dots) and best fit (sum of components)
plt.plot(-E, sb / 10**3, 'k.', label='measurement')
#plt.plot( -E , s/10**3 , 'k-')
#plt.plot( -E , B/10**3 , 'k--')
plt.plot(-E, out.best_fit / 10**3, color=colors[1], label='total fit')
ax = mpl.pyplot.gca()
ax.set_xlim(241, -E_b_max)
plt.xlabel('binding energy (eV)')
plt.ylabel('counts (arb. u.)')
print(out.best_values['MoO23d_32_2_center'] -
out.best_values['Mo3d_32_1_center'])
print(out.best_values['MoO23d_32_3_center'] -
out.best_values['Mo3d_32_1_center'])
## reference : also plot pure Mo signal
#load data from SpecsLab xml file and select region
region = get_region(filename, 3, 2)
# determine index of left and right boundary
index1 = np.where(abs(region.x_be - (-235)) < 1e-10)
index2 = np.where(abs(region.x_be - (E_b_max)) < 1e-10)
E = region.x_be[index1[0][0]:index2[0][0]] #binding energy scale
s = region.y_avg_counts_mcd[index1[0][0]:index2[0][0]] #signal
epsilon, sb, B = remove_shirley_background(s, E, 3, 1e-6)
plt.plot(-E, sb / (6 * 10**3), 'k--', label='pure Mo')
plt.legend()
fig.tight_layout()
def fitpureMo3d(filename, groupNo, regionNo, E_b_min, E_b_max, doplot=False):
"""Fit a Moly 3d signal using elemental and oxide components (two for each peak, 3/2 and 5/2).
A shirley background is subtracted first.
The elemental Mo components are fittet to the PseudoVoigt line shape.
Oxide components are fitted to a Gaussian line shape.
Parameters
----------
filename : string
location of the data file, expects an SpecsLab xml file.
groupNo : int
index of the group of spectra (from 0)
regionNo : int
index of the spectrum that should be fitted (from 0)
E_b_min : float
region boundary
E_b_max : float
region boundary
doplot : bool
wether or not to plot the fit and components (default False)
"""
#load data from SpecsLab xml file and select region
region = get_region(filename, groupNo, regionNo)
#Sb = VAMAS.VAMASExperiment('P006_Sb_surveyf.vms');
#regions = [data[0][2], data[1][1], data[2][1]]
print(region.x_be)
# determine index of left and right boundary
index1 = np.where(abs(region.x_be - (E_b_min)) < 1e-10)
index2 = np.where(abs(region.x_be - (E_b_max)) < 1e-10)
E = region.x_be[index1[0][0]:index2[0][0]] #binding energy scale
s = region.y_avg_counts_mcd[index1[0][0]:index2[0][0]] #signal
#signal with background substacted
epsilon, sb, B = remove_shirley_background(s, E, 3, 1e-6)
######################
# set up model components and set constraints for the fit
######################
pre11 = 'Mo3d_32_1_'
mod11 = PseudoVoigtModel(prefix=pre11)
pars = mod11.make_params()
pars.add('delta', value=3, min=2, max=5)
pars[pre11 + 'amplitude'].set(9000, min=500, max=100000)
pars[pre11 + 'center'].set(-231, min=-231, max=-230)
pars[pre11 + 'sigma'].set(0.3, min=0.2, max=1)
pars[pre11 + 'fraction'].set(0.5, min=0.2, max=1)
mod21 = PseudoVoigtModel(prefix='Mo3d_52_1_')
pars.update(mod21.make_params())
pars['Mo3d_52_1_amplitude'].set(
expr='3/2*Mo3d_32_1_amplitude') #(12000, min=500, max=100000)
pars['Mo3d_52_1_center'].set(expr='Mo3d_32_1_center+delta'
) #same distance between 3/2 and 5/2 peaks
#pars['Mo3d_52_1_sigma' ].set(expr='1.0*Mo3d_32_1_sigma') #all have same sigma value
pars['Mo3d_52_1_fraction'].set(
expr='1.0*Mo3d_32_1_fraction') #(0.5, min=0.2, max=1)
#composite model is a sum of the components
mod = mod11 + mod21
#perform the fit
out = mod.fit(sb, pars, x=E)
if (doplot):
plt.figure()
#plot individual components
comps = mod.eval_components(params=out.params, x=E)
i = 0
for key in comps:
plt.plot(E, comps[key] / 10**3, color=colors[i], label=key)
plt.fill_between(E,
0.0,
comps[key] / 10**3,
facecolor=colors[i],
alpha=0.5)
i = i + 1
#plot measured signal (dots) and best fit (sum of components)
plt.plot(E, sb / 10**3, 'k.')
plt.plot(E, s / 10**3, 'k-')
plt.plot(E, B / 10**3, 'k--')
plt.plot(E, out.best_fit / 10**3, color=colors[1])
plt.legend()
ax = mpl.pyplot.gca()
ax.set_xlim(E_b_min, E_b_max)
plt.xlabel('binding energy (eV)')
plt.ylabel('cps ($10^3$)')
print(out.fit_report(min_correl=0.25))
def test_param_hint_explicit_value(self):
# tests Github Issue 384
pmod = PseudoVoigtModel()
params = pmod.make_params(sigma=2, fraction=0.77)
assert_allclose(params['fraction'].value, 0.77, rtol=0.01)
