Calculo de $\tau$

In [1]:

    

import sys
sys.path.append('../')

import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font', **{'family':'sans-serif', 'sans-serif':['Helvetica']})
rc('text', usetex=True)

%matplotlib inline

import ten


    

In [2]:

    

# Propiedades de la NP
r = 5000
R_Forster = 2.29
L_D = 100
tau_D = 0.333
epsilon = 1
num_exc = 20000
num_acceptors = 0

nano_particle = ten.NanoParticle(r,
                                 0, 
                                num_acceptors,
                                tau_D,
                                R_Forster,
                                L_D,
                                epsilon,
                                'vol')

# Inicialización del exiton
simu = ten.Exciton(nano_particle,
                    num_exc,
                    'laser')

# Calulamos
out = simu.single_count()


    

In [3]:

    

# Función con la cual voy a realizar el ajuste
def func(x, a, b, c):
    return a * np.exp(-b * x) + c

num_bins = 50
# the histogram of the data
plt.figure(figsize=(14, 5))
n, bins, patches = plt.hist(out*nano_particle.delta_t, num_bins, normed=1, facecolor='green', alpha=0.5, 
                            label="Original Noised Data")

popt, pcov = curve_fit(func, bins[:-1], n)

plt.plot(bins, func(bins, popt[0], popt[1], popt[2]), 'r--', linewidth=4, label="Fitted Curve")
plt.xlabel('Bins',fontsize=20)
plt.ylabel(r'$N*\Delta t$',fontsize=20)
plt.title(r'$a*exp^{-bx}+c:$ $a=%f$, $b=%f$, $c=%f$' %(popt[0], popt[1], popt[2]),fontsize=30)
plt.text(2.5, 2, r'$\tau\; original = %f$' %(tau_D), fontsize=20)
plt.text(2.5, 1.6, r'$\tau\; calculado = %f$' %(1/popt[1]), fontsize=20)
plt.legend()

plt.show()


    

In [4]:

    

#Este css esta basado en el de @LorenaABarba y su grupo
from IPython.core.display import HTML
css_file = 'css/personal.css'
HTML(open(css_file, "r").read())


    

    
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