Normal Fit from experimental data

user_14155670 0
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Hi all I want to obtain a normal fit from a set of data obtained from experimental results. Since i am starting with python, I dont know where to start. Here's my experimental data. Its a particle size distribution. I want to obtain mean and std. x is the size and y the frequency.

Thank you in advance for any help!

import matplotlib.pyplot as plt
import numpy as np



x=([0.251839516,0.490440575,0.744647994,0.990643452,1.244142316,1.488611658,1.741274792,1.986416351,2.232538986,2.495993944,2.736393641,2.985059803,3.241792581,3.497435276,3.744829674,3.991788039,4.23860106])
y=([0.271164269,0.492366389,1.256781226,2.468772142,4.479769871,8.376708554,11.85803482,14.57231794,15.56056321,14.05547313,11.11227252,7.625604845,3.947070401,2.186355791,0.937144587,0.455061317,0.228687358])
plt.scatter(x,y,color='red',label='Experiment')
normal-distribution python
4 Answers

If you want to use SciPy you have scipy.stats.norm:

from scipy.stats import norm
mu, std = norm.fit(data)
user_82787000

use this code

from scipy.stats import norm as normalDist
x=([0.251839516,0.490440575,0.744647994,0.990643452,1.244142316,1.488611658,1.741274792,1.986416351,2.232538986,2.495993944,2.736393641,2.985059803,3.241792581,3.497435276,3.744829674,3.991788039,4.23860106])
y=([0.271164269,0.492366389,1.256781226,2.468772142,4.479769871,8.376708554,11.85803482,14.57231794,15.56056321,14.05547313,11.11227252,7.625604845,3.947070401,2.186355791,0.937144587,0.455061317,0.228687358])
points = list(zip(*(x, y)))
mu, std = normalDist.fit(points)
user_137178510
user_107807150

Using Gaussion Distribution can solve your question.

from sklearn.mixture import GaussianMixture
gmm.means_
gmm.covariances_
user_171908750
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