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
