Does cool analysis stuff

analyze.py

#!/bin/env python3
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import numpy as np
from math import log

with open('resilience.txt','r') as res_file:
    data = list(map(str.rstrip, res_file.readlines()))

data = list(map(lambda line: tuple(map(float, line.split(',')))[1], data))

min_denominator = 2
max_denominator = len(data)

data = data[min_denominator - 1:max_denominator + 1]

x = list(range(min_denominator, max_denominator + 1))
y = data

#Clear data from mem
del data


"""
Plot your data
"""
plt.plot(x, y, 'ro',label="Original Data")

"""
brutal force to avoid errors
""" 
x = np.array(x, dtype=float) #transform your data in a numpy array of floats 
y = np.array(y, dtype=float) #so the curve_fit can work
"""
create a function to fit with your data. a, b, c and d are the coefficients
that curve_fit will calculate for you. 
In this part you need to guess and/or use mathematical knowledge to find
a function that resembles your data
"""
"""
def func(x, a, b, c):
    return a * np.log(x - b) + c
"""
def func(x, a, b, c):
    return a / (x - b) + c

"""
make the curve_fit
"""
popt, pcov = curve_fit(func, x, y)

"""
The result is:
popt[0] = a , popt[1] = b, popt[2] = c and popt[3] = d of the function,
so f(x) = popt[0]*x**3 + popt[1]*x**2 + popt[2]*x + popt[3].
"""
print("a = %s , b = %s, c = %s" % (popt[0], popt[1], popt[2]))


"""
Print the coefficients and plot the funcion.
"""

plt.plot(x, func(x, *popt), label="Fitted Curve") #same as line above \/
#plt.plot(x, popt[0]*x**3 + popt[1]*x**2 + popt[2]*x + popt[3], label="Fitted Curve") 

plt.legend(loc='upper left')
plt.show()