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import pandas as pd |
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import numpy as np |
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import csv |
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import matplotlib.pyplot as plt |
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import warnings |
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import matplotlib.cbook |
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#split forward and backward sweping data, to make it easier for processing. |
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def split(vector): |
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""" |
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This function takes an array and splits it into two half. |
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""" |
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split = int(len(vector)/2) |
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end = int(len(vector)) |
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vector1 = np.array(vector)[0:split] |
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vector2 = np.array(vector)[split:end] |
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return vector1, vector2 |
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View Code Duplication |
def critical_idx(x, y): ## Finds index where data set is no longer linear |
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""" |
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22
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This function takes x and y values callculate the derrivative of x and y, and calculate moving average of 5 and 15 points. |
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Finds intercepts of different moving average curves and return the indexs of the first intercepts. |
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""" |
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25
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k = np.diff(y)/(np.diff(x)) #calculated slops of x and y |
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## Calculate moving average for 5 and 15 points. |
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## This two arbitrary number can be tuned to get better fitting. |
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ave10 = [] |
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ave15 = [] |
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for i in range(len(k)-10): # The reason to minus 5 is to prevent j from running out of index. |
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31
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a = 0 |
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32
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for j in range(0,5): |
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a = a + k[i+j] |
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ave10.append(round(a/5, 5)) # keeping 9 desimal points for more accuracy |
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35
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36
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for i in range(len(k)-15): |
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b = 0 |
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38
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for j in range(0,10): |
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b = b + k[i+j] |
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40
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ave15.append(round(b/10, 5)) |
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41
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ave10i = np.asarray(ave5) |
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print(ave10i) |
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ave15i = np.asarray(ave15) |
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print(ave15i) |
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## Find intercepts of different moving average curves |
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idx = np.argwhere(np.diff(np.sign(ave15i - ave10i[:len(ave15i)])!= 0)).reshape(-1)+0 #reshape into one row. |
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return idx[1] |
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48
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49
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# This is based on the method 1 where user can't choose the baseline. |
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# If wanted to add that, choose method2. |
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def sum_mean(vector): |
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""" |
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This function returns the mean values. |
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""" |
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a = 0 |
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56
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for i in vector: |
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a = a + i |
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return [a,a/len(vector)] |
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60
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61
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def multiplica(vetor_x, vetor_y): |
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a = 0 |
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63
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for x,y in zip(vetor_x, vetor_y): |
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64
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a = a + (x * y) |
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return a |
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67
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68
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def linear_coeff(x, y): |
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""" |
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70
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This function returns the inclination coeffecient and y axis interception coeffecient m and b. |
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""" |
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m = (multiplica(x,y) - sum_mean(x)[0] * sum_mean(y)[1]) / (multiplica(x,x) - sum_mean(x)[0] * sum_mean(x)[1]) |
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b = sum_mean(y)[1] - m * sum_mean(x)[1] |
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return m, b |
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76
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77
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def y_fitted_line(m, b, x): |
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78
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y_base = [] |
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for i in x: |
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80
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y = m * i + b |
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81
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y_base.append(y) |
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return y_base |
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83
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84
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85
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def linear_background(x, y): |
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86
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idx = critical_idx(x, y) + 5 #this is also arbitrary number we can play with. |
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87
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m, b = linear_coeff(x[(idx - int(0.5 * idx)) : (idx + int(0.5 * idx))], y[(idx - int(0.5 * idx)) : (idx + int(0.5 * idx))]) |
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88
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y_base = y_fitted_line(m, b, x) |
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return y_base |
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90
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sabiharustam /
voltcycle
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