Finding max value on raw experimental data [duplicate]

I'm working on an investigation proyect, where I have to plot some experimetal data, and then find the max value (critical point), like on this image:

I was trying to adjust a fit curve (using a cuadratic equation) in order to find that max value, and I am looking to optimize that curve right now. I've tried with maxarray, but there is an specific value that doesn´t fix to the tendence.

This is what I've done until now:

import matplotlib.pyplot as plt from scipy.optimize import curve_fit import numpy as np import scipy # Subo los datos experimentales # datos=np.loadtxt("OFET2.txt", delimiter="\t") x=datos[:,1] y=datos[:,4] n=len(x) dr=np.diff(y)/np.diff(x) dx=datos[:n-1,1] def func(x, a, b, c): return -(a*x+b*x**2-c) # Este x no tiene nada que ver con el dato de x plt.plot(dx, dr, label='experimental-data') popt, pcov = curve_fit(func, dx, dr) print(popt) #x values for the fitted function xFit = np.arange(-100, 400, 0.01) #Plot the fitted function plt.plot(xFit, func(xFit, *popt), 'r--', label='fit params: a=%5.3f, b=%5.3f, c=%5.3f' % tuple(popt)) plt.xlabel('x') plt.ylabel('y') plt.grid() plt.legend() plt.show() 

And here there is an image of that plot:

An idea was to fit the curve just to the "y" values greater than 0.008 (y<0.008), but actually I don't know how to do it. After that, I would need to (somehow) point the max value of the curve. I should try to get something like this I would be really thankful if you could help me to solve this,either like on the first image, or fixing my script. I am also keen on any advice or correction you might have about my script or some techniques, since I'm kinda new on Python.

Greetins!