MatDat_SSFR/Database.py at main · sashi1994/MatDat_SSFR · GitHub

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# -*- coding: utf-8 -*-
"""
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SAFRAN SEAT FRANCE
% sashi
% version 1
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Version 01 - XXXX
Created on %(date)
@author: Sashi.madugula - Simulation Engineer - SSFR
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import streamlit as st

xls = pd.ExcelFile('Data.xlsx')
D1000 = pd.read_excel(xls,0)
D1000.head()

def JC (x,A,B,n):
    return A+B*(np.power(x,n))

xdata = D1000["Strain"].values #
ydata = D1000["Stress (Mpa)"].values #

g = [0,0,0.9]
c, cov = curve_fit(f = JC,xdata = xdata,ydata = ydata,p0 = g, bounds=((0, 0, 0), (np.inf,np.inf,np.inf)))
print(c)

A = c[0]
B = c[1]
n = c[2]

fun1000 = A+B*(np.power(D1000["Strain"],n))

plt.plot(D1000["Strain"],D1000["Stress (Mpa)"],'r-',label="raw")
plt.plot(D1000["Strain"],fun1000,'k.',label='fit A= %.1f B= %.1f n= %.2f' %(A,B,n))
plt.grid()
plt.legend(loc="upper right")
plt.xlim([0,0.35])
plt.xlabel("True Strain [mm/mm]")
plt.ylabel("True Stress (MPa)")
plt.show()
xls.close()