%matplotlib inline
import csv, math, os
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = (16.0, 10.0)

# Function to read in the data
def read_data(filename):
    # Read in data
    a = np.loadtxt(filename, delimiter=";", skiprows=2)
    # Sort on the 5th column, return the result
    return a[a[:,4].argsort()]

# Some constants that can be used in the remainder of the code
area = (3.5**2)*math.pi # cross section area of the sample
cutoff_peak = 0.005   # we assume the load peak is at a strain (x-value) lower than this value
cutoff_min = 0.05     # we assume the minimum after the peak is at a strain lower than this value
a = "hej";

# Function to calculate the strain and load from the loaded data
def calc_strain_load(data):
    strain = data[:,4]
    load = data[:,2]
    return strain,load

# Function to compute the peak strain and return the result
def find_peak(strain, load):
    i = -1
    peak_y = 0
    while peak_y < 0.005:
        i = i+1
        peak_y = strain[i]
    peak_x = load[i-1]
    peak_y = strain[i-1]
    print(strain[i-1])
    return peak_x,peak_y

# Function to compute E-modul and return the result
def find_gradient(strain, load):
    e_modul = (strain.max()-strain.min())/(load[strain.argmax()]-load[strain.argmin()])
    return e_modul

# Function to compute R_el and return the result
def find_strain_limit(strain, load):
    # .....
    return limit_x,limit_y

def process_file(filename):
    data = read_data(filename)
    strain,load = calc_strain_load(data)
    peak_x,peak_y = find_peak(strain,load)
    gradient = find_gradient(strain,load)
    #limit_x,limit_y = find_limit(strain,load)
    
    # print gradient
    print(gradient)

    # plot strain and load
    plt.plot()
    plt.text(1, 1, r'$\mu=100,\ \sigma=15$')

    # mark peak and limit on the graph
process_file("Specimen_RawData_1.csv")

0.00498
5.67671114978e-05