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import numpy as np |
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import pandas as pd |
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def peak_detection_fxn(Dataframe_y): |
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list = [0, 1] |
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return list |
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def split(vector): |
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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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def linear_background(x, y): |
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fake_line_list = [1, 2, 3, 4] |
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fake_line_array = np.array(fake_line_list) |
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return fake_line_array |
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def peak_values(DataFrame_x, DataFrame_y): |
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"""Outputs x (potentials) and y (currents) values from data indices |
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given by peak_detection function. |
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---------- |
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Parameters |
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---------- |
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DataFrame_x : should be in the form of a pandas DataFrame column. |
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For example, df['potentials'] could be input as the column of x |
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data. |
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DataFrame_y : should be in the form of a pandas DataFrame column. |
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For example, df['currents'] could be input as the column of y |
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data. |
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Returns |
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------- |
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Result : numpy array of coordinates at peaks in the following order: |
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potential of peak on top curve, current of peak on top curve, |
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potential of peak on bottom curve, current of peak on bottom curve""" |
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index = peak_detection_fxn(DataFrame_y) |
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potential1, potential2 = split(DataFrame_x) |
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current1, current2 = split(DataFrame_y) |
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Peak_values = [] |
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Peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is |
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# the first part of DataFrame) |
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Peak_values.append(current2[(index[0])]) # TOPY |
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Peak_values.append(potential1[(index[1])]) # BOTTOMX |
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Peak_values.append(current1[(index[1])]) # BOTTOMY |
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Peak_array = np.array(Peak_values) |
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return Peak_array |
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def del_potential(DataFrame_x, DataFrame_y): |
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"""Outputs the difference in potentials between anoidc and |
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cathodic peaks in cyclic voltammetry data. |
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Parameters |
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---------- |
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DataFrame_x : should be in the form of a pandas DataFrame column. |
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For example, df['potentials'] could be input as the column of x |
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data. |
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DataFrame_y : should be in the form of a pandas DataFrame column. |
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For example, df['currents'] could be input as the column of y |
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data. |
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Returns |
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------- |
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Results: difference in peak potentials.""" |
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del_potentials = (peak_values(DataFrame_x, DataFrame_y)[0] - |
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peak_values(DataFrame_x, DataFrame_y)[2]) |
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return del_potentials |
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def half_wave_potential(DataFrame_x, DataFrame_y): |
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"""Outputs the half wave potential(redox potential) from cyclic |
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voltammetry data. |
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Parameters |
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---------- |
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DataFrame_x : should be in the form of a pandas DataFrame column. |
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For example, df['potentials'] could be input as the column of x |
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data. |
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DataFrame_y : should be in the form of a pandas DataFrame column. |
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For example, df['currents'] could be input as the column of y |
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data. |
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Returns |
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------- |
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Results : the half wave potential.""" |
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half_wave_potential = (del_potential(DataFrame_x, DataFrame_y))/2 |
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return half_wave_potential |
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def peak_heights(DataFrame_x, DataFrame_y): |
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"""Outputs heights of minimum peak and maximum |
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peak from cyclic voltammetry data. |
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Parameters |
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---------- |
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DataFrame_x : should be in the form of a pandas DataFrame column. |
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For example, df['potentials'] could be input as the column of x |
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data. |
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DataFrame_y : should be in the form of a pandas DataFrame column. |
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For example, df['currents'] could be input as the column of y |
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data. |
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Returns |
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------- |
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Results: height of maximum peak, height of minimum peak |
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in that order in the form of a list.""" |
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current_max = peak_values(DataFrame_x, DataFrame_y)[1] |
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current_min = peak_values(DataFrame_x, DataFrame_y)[3] |
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x1, x2 = split(DataFrame_x) |
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y1, y2 = split(DataFrame_y) |
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line_at_min = linear_background(x1, y1)[peak_detection_fxn(DataFrame_y)[1]] |
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line_at_max = linear_background(x2, y2)[peak_detection_fxn(DataFrame_y)[0]] |
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height_of_max = current_max - line_at_max |
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height_of_min = abs(current_min - line_at_min) |
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return [height_of_max, height_of_min] |
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def peak_ratio(DataFrame_x, DataFrame_y): |
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"""Outputs the peak ratios from cyclic voltammetry data. |
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Parameters |
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---------- |
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DataFrame_x : should be in the form of a pandas DataFrame column. |
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For example, df['potentials'] could be input as the column of x |
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data. |
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DataFrame_y : should be in the form of a pandas DataFrame column. |
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For example, df['currents'] could be input as the column of y |
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data. |
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Returns |
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------- |
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Result : returns a the peak ratio.""" |
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ratio = (peak_heights(DataFrame_x, DataFrame_y)[0] / |
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peak_heights(DataFrame_x, DataFrame_y)[1]) |
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return ratio |
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sabiharustam /
voltcycle
