def fit_two_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
'''
# Follows
# http://lmfit.github.io/lmfit-py/builtin_models.html#example-1-fit-peaked-data-to-gaussian-lorentzian-and-voigt-profiles
# and
# http://cars9.uchicago.edu/software/python/lmfit_MinimizerResult/builtin_models.html
# and
# the alpha version of this code
# Figure out Composit Model
import numpy as np
# for smoothing the curves
import scipy.interpolate as interp #import splev
from lmfit.models import VoigtModel, 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 = 1 # 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 = VoigtModel(prefix = pref1, independent_vars=['x'],nan_policy='raise')
peak2 = VoigtModel(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 = 4*y_max, min=y_max*.8,max = y_max*9,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)
# 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*9,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, min = 0, max = 5*w_guess,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:
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
def fit_Voigt_and_step(x_lst,y_lst,x_min_flt,x_max_flt, pre, width_1, width_2, 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
returns result object
'''
# Restrict the fit
x_bkp = x_lst
y_bkp = y_lst
x_lst, y_lst, y_p, ypp = smooth_and_remove_step(x_lst, y_lst, x_min_flt, x_max_flt,True)
'''
*******************************************************
Section of bad code that it'd take too long to do right
*******************************************************
'''
step_at = 95
step_width = 10
prefp = pre
prefs = "stp"
prefc = 'c'
w_guess = 3 # sigma
'''
*******************************************************
Section of bad code that it'd take too long to do right
*******************************************************
'''
# this is the money
# defines the model that'll be fit
peak = VoigtModel(prefix = prefp, independent_vars=['x'],nan_policy='raise')
step = StepModel(prefix = prefs, independent_vars=['x'],form='logistic')
const = ConstantModel(prefix = prefc,independent_vars=['x'], nan_policy='raise', form ='logistic')
mod = peak + step + const
#mod = peak + const
# guess parameters
x_max = x_lst[np.argmax(y_lst)]
y_max = y_lst[np.argmax(y_lst)]
# Peak
# here we set up the peak fitting guess. Then the peak fitter will make a parameter object out of them
mod.set_param_hint(prefp+'amplitude', value = value_max*y_max, min = .6*value_max_min*y_max,max = 4*value_max_max*y_max, vary=True)
# mod.set_param_hint(prefp+'center', value = x_max, min = x_max*(1-wiggle_room), max = x_max*(1+wiggle_room),vary=True)
mod.set_param_hint(prefp+'center', value = x_max,min = x_max*.97, max = x_max*1.03, vary=True)
# Basically FWHM/3.6
if pre =='one': # fitting with only one peak
mod.set_param_hint(prefp+'sigma', value = width_1, min = .25*width_2, max = 2*width_1,vary=True)
else: # fitting with two peaks
mod.set_param_hint(prefp+'sigma', value = width_2, min = 0, max = width_1,vary=True)
# Constant
top = []
bottom = []
for a,b in zip(x_lst,y_lst):
if a > 135:
top.append(b)
elif a < 93:
bottom.append(b)
top = np.mean(np.asarray(top))
bottom = np.mean(np.asarray(bottom))
mod.set_param_hint(prefc+'c', value = bottom, min = -3*bottom, max = 3*bottom,vary=True)
# restrict the fit again
x_fit = []
y_fit = []
for a,b in zip(x_lst,y_lst):
if 80 < a < 135:
x_fit.append(a)
y_fit.append(b)
top = y_fit[0]
bottom = y_fit[-1]
# Step
# Step height
delta = 2*abs(top - bottom)
if delta == 0:
delta = 1
mod.set_param_hint(prefs+'amplitude', value = delta, min = -3*delta, max = 3*delta, vary=True)
# Charastic width
mod.set_param_hint(prefs+'sigma', value = 3,min = 1, max = 3, vary=False)
# The half way point...
mod.set_param_hint(prefs+'center', value = step_at, min = step_at-step_width, max = step_at+step_width, vary = False)
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()
# each component
for x in result.best_values:
if prefp in x: # Get peak
peak.set_param_hint(x, value = result.best_values[str(x)])
elif prefs in x: # Get step
step.set_param_hint(x, value = result.best_values[str(x)])
# Data - 'background'
y_m_background = []
for a,b in zip(x_lst,y_lst):
y_m_background.append(b - result.eval(x=a) + peak.eval(x=a, params=peak.make_params()))
peak_only = [peak.eval(x=yy, params=peak.make_params()) for yy in x_dense]
#stp_only = [result.best_values['stpamplitude'] + result.best_values['cc']]*len(x_dense)
# sum of them
#y_fit = [a+b for a,b in zip(peak_only,stp_only)]
y_fit = [a+b for a in peak_only]
plt.plot(x_dense,peak_only, 'g', label = 'Peak Only')
#plt.plot(x_dense,stp_only, 'g--', label = None)
#plt.plot(x_dense, y_fit, 'g', label = "Fit Result")
plt.plot(x_lst,y_lst,'bx', label= "Data")
plt.plot(x_lst,y_m_background,'ko', label= "Data-Background")
plt.title("Fit vs Data")
plt.xlabel("Inv Cm")
plt.ylabel("counts")
plt.legend()
plt.savefig(place_to_save_str+"Voigt&Step")
plt.clf()
return result
def fit_Voigt_and_step(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
'''
import numpy as np
# for smoothing the curves
import scipy.interpolate as interp #import splev
from lmfit.models import VoigtModel, StepModel, ConstantModel
from lmfit import CompositeModel
# 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
*******************************************************
'''
step_at = 100
step_width = 3
prefp = "one"
prefs = "stp"
prefc = 'c'
w_guess = 3 # sigma
'''
*******************************************************
Section of bad code that it'd take too long to do right
*******************************************************
'''
# this is the money
# defines the model that'll be fit
peak = VoigtModel(prefix = prefp, independent_vars=['x'],nan_policy='raise')
step = StepModel(prefix = prefs, independent_vars=['x'], nan_policy='raise')
const = ConstantModel(prefix = prefc,independent_vars=['x'], nan_policy='raise', form ='logistic')
mod = peak + step + const
# guess parameters
x_max = x_fit[np.argmax(y_fit)]
y_max = y_fit[np.argmax(y_fit)]
# Peak
# here we set up the peak fitting guess. Then the peak fitter will make a parameter object out of them
mod.set_param_hint(prefp+'amplitude', value = 4*y_max, min = y_max,max = 30*y_max, vary=True)
# mod.set_param_hint(prefp+'center', value = x_max, min = x_max*(1-wiggle_room), max = x_max*(1+wiggle_room),vary=True)
mod.set_param_hint(prefp+'center', value = x_max, vary=True)
# Basically FWHM/3.6
mod.set_param_hint(prefp+'sigma', value = w_guess, min = 0, max = 5*w_guess,vary=True)
# Step
# Step height
delta = abs(y_fit[-1]-y_fit[0])
mod.set_param_hint(prefs+'amplitude', value = delta, min = delta*.9, max = delta*1.1, vary=True)
# Charastic width
mod.set_param_hint(prefs+'sigma', value = 2,min = 1, max = 3, vary=True)
# The half way point...
mod.set_param_hint(prefs+'center', value = step_at, min = step_at-step_width, max = step_at+step_width, vary = True)
# Constant
mod.set_param_hint(prefc+'c', value = y_fit[-1], min = 0, max = 2*y_fit[0],vary=True)
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)
for x in result.best_values:
if prefp in x: # Get peak
peak.set_param_hint(x, value = result.best_values[str(x)])
elif prefs in x: # Get step
step.set_param_hint(x, value = result.best_values[str(x)])
comp = [result.best_values['cc'] + peak.eval(x=yy, params=peak.make_params()) for yy in x_dense]
plt.plot(x_dense,comp, 'green', label = None)
comp = [result.best_values['stpamplitude'] + result.best_values['cc']]*len(x_dense)
plt.plot(x_dense, comp, 'green', label= None)
# comp = [result.best_values['cc'] + step.eval(x=yy, params=step.make_params()) for yy in x_dense]
# plt.plot(x_dense, comp, 'green', label= None)
plt.title("Fit vs Data")
plt.legend()
plt.savefig(place_to_save_str)
plt.clf()
return result.best_values
