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peak fitting #68

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usov_i merged 6 commits from JakHolzer-fit_peak into det1d 2020-09-15 14:23:49 +02:00
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pyzebra/fit2.py
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@ -1,12 +1,9 @@
from lmfit import minimize, Parameters, Model
from lmfit.models import LinearModel, LorentzianModel, GaussianModel
import matplotlib.pyplot as plt
from scipy.integrate import simps
import scipy as sc
from scipy import integrate
import numpy as np
from time import sleep
def fitccl(data, keys, guess, vary, constraints_min, constraints_max, numfit_min=None, numfit_max=None):
"""Made for fitting of ccl date where 1 peak is expected. Allows for combination of gaussian, lorentzian and linear model combination
@ -113,26 +110,6 @@ def fitccl(data, keys, guess, vary, constraints_min, constraints_max, numfit_min
print(result.params['g_cen'].value)
num_int_area = simps(y[numfit_min:numfit_max], x[numfit_min:numfit_max])
num_int_bacground = integrate.quad(background, numfit_min, numfit_max, args=(result.params['slope'].value,result.params['intercept'].value))
plt.plot(x, y, 'b', label='Original data')
plt.plot(x, comps['gaussian'], 'r--', label='Gaussian component')
plt.fill_between(x, comps['gaussian'], facecolor="red", alpha=0.4)
plt.plot(x, comps['lorentzian'], 'b--', label='Lorentzian component')
plt.fill_between(x, comps['lorentzian'], facecolor="blue", alpha=0.4)
plt.plot(x, comps['background'], 'g--', label='Line component')
plt.fill_between(x, comps['background'], facecolor="green", alpha=0.4)
#plt.plot(x[numfit_min:numfit_max],y[numfit_min:numfit_max], 'vy', markersize=7)
plt.fill_between(x[numfit_min:numfit_max], y[numfit_min:numfit_max], facecolor="yellow", alpha=0.4, label='Integrated area')
#plt.plot(x, result.init_fit, 'k--', label='initial fit')
plt.plot(x, result.best_fit, 'k-', label='Best fit')
plt.title('%s \n Gaussian: centre = %9.4f, width = %9.4f, amp = %9.4f \n'
'Lorentzian: centre, %9.4f, width = %9.4f, amp = %9.4f \n'
'background: slope = %9.4f, intercept = %9.4f, int_area %9.4f' % (keys, result.params['g_cen'].value, result.params['g_width'].value,
result.params['g_amp'].value, result.params['l_cen'].value, result.params['l_width'].value,
result.params['l_amp'].value, result.params['slope'].value, result.params['intercept'].value, num_int_area))
plt.legend(loc='best')
plt.show()
d = {}
for pars in result.params:
d[str(pars)] = (result.params[str(pars)].value, result.params[str(pars)].vary)