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"""This module consists of all the functions requried |
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to calculate the baselines.""" |
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# This module is to fit baseline to calculate peak current |
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# values from cyclic voltammetry data. |
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# If you wish to choose best fitted baseline, |
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# checkout branch baseline_old method2. |
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# If have any questions contact [email protected] |
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import pandas as pd |
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import numpy as np |
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#split forward and backward sweping data, to make it easier for processing. |
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View Code Duplication |
def split(vector): |
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""" |
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This function takes an array and splits it into equal two half. |
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---------- |
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Parameters |
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---------- |
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vector : Can be in any form of that can be turned into numpy array. |
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Normally, for the use of this function, it expects pandas DataFrame column. |
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For example, df['potentials'] could be input as the column of x data. |
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------- |
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Returns |
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------- |
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This function returns two equally splited vector. |
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The output then can be used to ease the implementation of peak detection and baseline finding. |
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""" |
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assert isinstance(vector, pd.core.series.Series), "Input should be pandas series" |
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split_top = 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_top:end] |
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return vector1, vector2 |
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View Code Duplication |
def critical_idx(arr_x, arr_y): ## Finds index where data set is no longer linear |
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""" |
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This function takes x and y values callculate the derrivative of x and y, |
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and calculate moving average of 5 and 15 points. Finds intercepts of different |
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moving average curves and return the indexs of the first intercepts. |
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---------- |
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Parameters |
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---------- |
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x : Numpy array. |
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y : Numpy array. |
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Normally, for the use of this function, it expects numpy array |
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that came out from split function. For example, output of |
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split.df['potentials'] could be input for this function as x. |
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------- |
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Returns |
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------- |
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This function returns 5th index of the intercepts of different moving average curves. |
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User can change this function according to baseline |
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branch method 2 to get various indexes.. |
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""" |
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assert isinstance(arr_x, np.ndarray), "Input should be numpy array" |
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assert isinstance(arr_y == np.ndarray), "Input should be numpy array" |
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if arr_x.shape[0] != arr_y.shape[0]: |
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raise ValueError("x and y must have same first dimension, but " |
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"have shapes {} and {}".format(arr_x.shape, arr_y.shape)) |
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k_val = np.diff(arr_y)/(np.diff(arr_x)) #calculated slops of x and y |
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## Calculate moving average for 10 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_val)-10): |
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# The reason to minus 10 is to prevent j from running out of index. |
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a_val = 0 |
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for j in range(0, 5): |
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a_val = a_val + k_val[i+j] |
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ave10.append(round(a_val/10, 5)) |
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# keeping 5 desimal points for more accuracy |
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# This numbers affect how sensitive to noise. |
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for i in range(len(k_val)-15): |
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b_val = 0 |
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for j in range(0, 15): |
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b_val = b_val + k_val[i+j] |
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ave15.append(round(b_val/15, 5)) |
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ave10i = np.asarray(ave10) |
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ave15i = np.asarray(ave15) |
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## Find intercepts of different moving average curves |
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#reshape into one row. |
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idx = np.argwhere(np.diff(np.sign(ave15i - ave10i[:len(ave15i)]) != 0)).reshape(-1)+0 |
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return idx[5] |
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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 and sum of the given vector. |
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---------- |
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Parameters |
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---------- |
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vector : Can be in any form of that can be turned into numpy array. |
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Normally, for the use of this function, it expects pandas DataFrame column. |
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For example, df['potentials'] could be input as the column of x data. |
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""" |
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assert isinstance(vector == np.ndarray), "Input should be numpy array" |
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a_val = 0 |
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for i in vector: |
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a_val = a_val + i |
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return [a_val, a_val/len(vector)] |
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View Code Duplication |
def multiplica(vector_x, vector_y): |
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""" |
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This function returns the sum of the multilica of two given vector. |
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---------- |
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Parameters |
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---------- |
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vector_x, vector_y : Output of the split vector function. |
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Two inputs can be the same vector or different vector with same length. |
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------- |
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Returns |
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------- |
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This function returns a number that is the sum of multiplicity of given two vector. |
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""" |
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assert isinstance(vector_x == np.ndarray), "Input should be numpy array" |
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assert isinstance(vector_y == np.ndarray), "Input should be numpy array" |
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a_val = 0 |
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for vec_x, vec_y in zip(vector_x, vector_y): |
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a_val = a_val + (vec_x * vec_y) |
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return a_val |
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View Code Duplication |
def linear_coeff(vec_x, vec_y): |
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""" |
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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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Parameters |
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---------- |
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x : Output of the split vector function. |
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y : Output of the split vector function. |
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------- |
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Returns |
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------- |
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float number of m and b. |
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""" |
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m_val = ((multiplica(vec_x, vec_y) - sum_mean(vec_x)[0] * sum_mean(vec_y)[1])/ |
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(multiplica(vec_x, vec_x) - sum_mean(vec_x)[0] * sum_mean(vec_x)[1])) |
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b_val = sum_mean(vec_y)[1] - m_val * sum_mean(vec_x)[1] |
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return m_val, b_val |
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def y_fitted_line(m_val, b_val, vec_x): |
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""" |
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This function returns the fitted baseline constructed by coeffecient m and b and x values. |
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---------- |
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Parameters |
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---------- |
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x : Output of the split vector function. x value of the input. |
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m : inclination of the baseline. |
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b : y intercept of the baseline. |
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------- |
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Returns |
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------- |
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List of constructed y_labels. |
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""" |
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y_base = [] |
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for i in vec_x: |
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y_val = m_val * i + b_val |
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y_base.append(y_val) |
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return y_base |
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View Code Duplication |
def linear_background(vec_x, vec_y): |
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""" |
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This function is wrapping function for calculating linear fitted line. |
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It takes x and y values of the cv data, returns the fitted baseline. |
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---------- |
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Parameters |
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---------- |
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x : Output of the split vector function. x value of the cyclic voltammetry data. |
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y : Output of the split vector function. y value of the cyclic voltammetry data. |
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------- |
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Returns |
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------- |
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List of constructed y_labels. |
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""" |
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assert isinstance(vec_x, np.ndarray), "Input of the function should be numpy array" |
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assert isinstance(vec_y, np.ndarray), "Input of the function should be numpy array" |
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idx = critical_idx(vec_x, vec_y) + 5 #this is also arbitrary number we can play with. |
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m_val, b_val = (linear_coeff(vec_x[(idx - int(0.5 * idx)) : (idx + int(0.5 * idx))], |
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vec_y[(idx - int(0.5 * idx)) : (idx + int(0.5 * idx))])) |
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y_base = y_fitted_line(m_val, b_val, vec_x) |
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return y_base |
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sabiharustam /
voltcycle
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