import numpy as np
import matplotlib.pyplot as plt

a=np.loadtxt("data2.txt",dtype=float) 
nbins=100
xmax=a.max()
xmin=a.min()
mu=a.mean()
sigma = a.std()
textstr = '$\sigma=%.2f$'+str(round(sigma,2))+'    $\mu=%.2f$'+str(round(mu,2))
x=np.linspace(0,xmax)
y=(1/mu*np.exp(-1/mu*x))
fig,ax=plt.subplots()
ax.plot(x,y,c="red")
ax.hist(a,bins=nbins,density=True,histtype='step',range=[xmin,xmax])
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
ax.text(0.7, 0.95, textstr, transform=ax.transAxes, fontsize=8,
        verticalalignment='top', bbox=props)


b=np.loadtxt("data1.txt",dtype=float) 
nbins=200
xmax=b.max()
xmin=b.min()
mu=b.mean()
p=(2+1/mu) 
sigma = b.std()
if p>3 or p<1 : textstr = '$\sigma=%.2f$'+str(round(sigma,2))+'   $\mu=%.2f$'+str(round(mu,2))
elif 3>p>1 : textstr = '$\sigma=%.2f$'+'inf'+'   $\mu=%.2f$'+str(round(mu,2))
x=np.linspace(0,20)
y=((p+(-1))/(x+1)**p)
fig,bx=plt.subplots()
bx.plot(x,y,c="red")
bx.hist(b,bins=nbins,density=True, histtype='step',range=[0,20]) 
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
bx.text(0.7, 0.95, textstr, transform=bx.transAxes, fontsize=8,
        verticalalignment='top', bbox=props)
plt.show()