Plot Probability Density of an Histogram

[Python_TLS]

Hi,

Please =i have a code wich plot a histogram for each iteration
and i want to plot the Probability Density of each histogram
how can i do this please ?

you find attached the code

import matplotlib.pyplot as plt
import numpy as np
import pylab 

importdata=open('Hmz_OT_MULC_Discrete5_TOPO.dens','r+')
data=importdata.readlines()
importdata.close()



tri=[]
j=0
for i in range(len(data)):
    if 'iter' in data[i]:
        l=i
        nbre_iter=[]        
        while l+1 < len(data) and 'iter' not in data [l+1]:    
            l+=1
            nbre_iter.append(data[l])  

        tri.append(nbre_iter)
        j+=1


tri_final=[]    
for i in range(len(tri)):
    extract_density=[]   
    for j in range(len(tri[0])):
        extract_density.append(tri[i][j][12 :])
    tri_final.append(extract_density) 
       

convert_into_float=[]    
for i in range(len(tri_final)):
    convert=[]   
    for j in range(len(tri_final[0])):
        convert.append(float(tri_final[i][j]))
    convert_into_float.append(convert)        
 
for i in range(len(convert_into_float)):
    nbre_elements=np.zeros(10)
    for j in range(len(convert_into_float[0])):       
        if 0<= convert_into_float[i][j]< 0.1:
            nbre_elements[0]+=1
        elif 0.1<= convert_into_float[i][j] <0.2:
            nbre_elements[1]+=1
        elif 0.2<= convert_into_float[i][j] <0.3:
            nbre_elements[2]+=1
        elif 0.3<= convert_into_float[i][j] <0.4:
            nbre_elements[3]+=1                         
        elif 0.4<= convert_into_float[i][j] <0.5:
            nbre_elements[4]+=1                               
        elif 0.5<= convert_into_float[i][j] <0.6:
            nbre_elements[5]+=1                
        elif 0.6<= convert_into_float[i][j] <0.7:
            nbre_elements[6]+=1       
        elif 0.7<= convert_into_float[i][j] <0.8:
            nbre_elements[7]+=1
        elif 0.8<= convert_into_float[i][j] <0.9:
            nbre_elements[8]+=1
        elif 0.9<= convert_into_float[i][j] <=1:
            nbre_elements[9]+=1

                                         
    fig, ax = plt.subplots()
    x=[1,2,3,4,5,6,7,8,9,10]
    width = 1.0
    BarName = ['0.0-0.1','0.1-0.2','0.2-0.3','0.3-0.4','0.4-0.5','0.5-0.6','0.6-0.7','0.7-0.8','0.8-0.9','0.9-1']
    rects=plt.bar(x, nbre_elements, width, color='b' )
 
    def autolabel(rects):
        # attach some text labels
        for rect in rects:
            height = rect.get_height()
            ax.text(rect.get_x()+rect.get_width()/2., 1.0*height, '%d'%int(height),
                    ha='center', va='bottom')
    plt.xlabel('Densite')        
    plt.ylabel('Nombre d elements')
    plt.title('NNombre d elements en fonction de leur densite_ITER_'+str(i))
    nom_figure='Histogramme'+str(i)+ '.png'
    pylab.xticks(x, BarName, rotation=40)
    autolabel(rects)
    plt.savefig(nom_figure)
    plt.show()
 

[scidam]

To plot probability density distribution from empirical data you need to estimate probability density first.
One of the ways to do it is to use the kernel density estimation approach (see scipy's kde density estimator).
Also, you can use ready-made function from seaborn package.

import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
x=np.random.randn(10000)                                                                                                                                                                              
sns.distplot(x) #plots histogram and kde-estimation of the pdf.
plt.show()