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 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_two_Psudo_Voigt(x_lst,y_lst,x_min_flt,x_max_flt,print_all_fits_bool,place_to_save_str):
'''
x_lst = x axis
y_lst = spectra to fit
first = beginning of fitting regions
last = end of fitting region
print_all_fits = Bool, do you want to save all plots
place_to_save = string that is the filename where we're saving the data
This takes the spectra and fits two Lorentzian curves to it.
Returns dictionary of fit values
Parameters have prefixes "one" for first V, "two" for second V, "c" for constant
'''
import numpy as np
# for smoothing the curves
import scipy.interpolate as interp #import splev
from lmfit.models import PseudoVoigtModel, ConstantModel
# Restrict the fit
x_fit = []
y_fit = []
for x,y in zip(x_lst, y_lst):
if x_min_flt < x < x_max_flt:
x_fit.append(float(x))
y_fit.append(float(y))
x_fit = np.asarray(x_fit)
y_fit = np.asarray(y_fit)
# now we find the parameters using the - d^2/dx^2
ysmooth = interp.interp1d(x_fit, y_fit, kind='cubic')
# differentiate x 2
yp = np.gradient(ysmooth(x_fit))
ypp = np.gradient(yp)
# we want the peaks of -d2/dx2
ypp = np.asarray([-x for x in ypp])
'''
*******************************************************
Section of bad code that it'd take too long to do right
*******************************************************
'''
# % of wavelength you want the peak centers to move
wiggle_room = .05
w_guess = 3 # sigma
pref1 = 'one'
pref2 = 'two'
prefo = 'off'
# if the fancy shit doesn't work, this is how far in index
# we shift the 2nd peak and max over
doesnt_work_shift = 10
'''
*******************************************************
Section of bad code that it'd take too long to do right
*******************************************************
'''
# this is the money
# defines the model that'll be fit
peak1 = PseudoVoigtModel(prefix = pref1, independent_vars=['x'],nan_policy='raise')
peak2 = PseudoVoigtModel(prefix = pref2, independent_vars=['x'],nan_policy='raise')
offset = ConstantModel(prefix=prefo, independent_vars=['x'],nan_policy='raise')
mod = peak1 + peak2 + offset
# guess parameters
x_max = x_fit[np.argmax(ypp)]
y_max = y_fit[np.argmax(ypp)]
# peak #1
# here we set up the peak fitting guess. Then the peak fitter will make a parameter object out of them
mod.set_param_hint(pref1+'amplitude', value = 5*y_max, min=y_max*.8,max = y_max*6,vary=True)
mod.set_param_hint(pref1+'center', value = x_max, min = x_max*(1-wiggle_room), max = x_max*(1+wiggle_room),vary=True)
mod.set_param_hint(pref1+'sigma', value = w_guess, min = 0, max = 5*w_guess,vary=True)
# Set Fraction
mod.set_param_hint(pref1+'fraction', value = .5, min = 0, max = 1,vary=True)
# Change gama maybe
# mod.set_param_hint(pref1+'gamma', value = 1, vary=True)
# peak #2
x_trunk = []
y_trunk = []
ypp_trunk = []
try:
for a,b,c in zip(x_fit.tolist(),y_fit.tolist(),ypp.tolist()):
'''
BAD CODE MAKE THIS BETTER
'''
if x_max + 8 < a < x_max + 12:
x_trunk.append(a)
y_trunk.append(b)
ypp_trunk.append(c)
x_trunk = np.asarray(x_trunk)
y_trunk = np.asarray(y_trunk)
ypp_trunk = np.asarray(ypp_trunk)
x_max_2 = x_trunk[np.argmax(ypp_trunk)]
y_max_2 = y_trunk[np.argmax(ypp_trunk)]
except ValueError:
x_max_2 = x_trunk[np.argmax(ypp) + doesnt_work_shift]
y_max_2 = y_trunk[np.argmax(ypp) + doesnt_work_shift]
# add peak 2 paramaters
mod.set_param_hint(pref2+'amplitude', value = 4*y_max_2, min=y_max_2*.8,max = y_max_2*6,vary=True)
# changed the bounds to be near other peak
mod.set_param_hint(pref2+'center', value = x_max_2, min = x_max+8, max = x_max+14,vary=True)
mod.set_param_hint(pref2+'sigma', value = w_guess/2, min = 0, max = w_guess ,vary=True)
#mod.set_param_hint(pref2+'sigma', pref1 + 'sigma' < expr < pref1 + 'sigma')
# Set Fraction
mod.set_param_hint(pref2+'fraction', value = .5, min = 0, max = 1,vary=True)
# Change gama maybe
# mod.set_param_hint(pref2+'gamma', value = 1, vary=False)
# constant offest
mod.set_param_hint(prefo+'c', value = y_fit[-1], min = 0, max = 5*y_fit[-1],vary=False)
# this does the fitting
# the params = mod.ma... is what initializes the parameters
result = mod.fit(y_fit, x=x_fit, params = mod.make_params())
# If print all fits ...
if print_all_fits_bool:
x_dense = np.arange(x_min_flt,x_max_flt,(x_max_flt-x_min_flt)/300.0).tolist()
result.plot_fit(xlabel='Inv Cm', ylabel='counts',datafmt = 'xb', numpoints=len(x_fit)*10)
'''
Here we make paramaters for peak 1 and 2
'''
for x in result.best_values:
if pref1 in x:
peak1.set_param_hint(x, value = result.best_values[str(x)])
elif pref2 in x:
peak2.set_param_hint(x, value = result.best_values[str(x)])
else:
peak1.set_param_hint(x, value = result.best_values[str(x)])
peak2.set_param_hint(x, value = result.best_values[str(x)])
comp = [peak1.eval(x=yy, params=peak1.make_params()) for yy in x_dense]
plt.plot(x_dense,comp, 'green', label = None)
comp = [peak2.eval(x=yy, params=peak2.make_params()) for yy in x_dense]
plt.plot(x_dense, comp, 'green', label= None)
plt.title("Fit vs Data")
plt.ylim(0, 1.1*np.max(y_fit))
plt.legend()
plt.savefig(place_to_save_str)
plt.clf()
return result.best_values
