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"""This module consists of all the functions used |
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to calculate the pertinent values. """ |
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import numpy as np |
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import core |
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View Code Duplication |
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 = core.peak_detection_fxn(dataframe_y) |
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potential1, potential2 = core.split(dataframe_x) |
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current1, current2 = core.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_pot = (del_potential(dataframe_x, dataframe_y))/2 |
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return half_wave_pot |
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View Code Duplication |
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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col_x1, col_x2 = core.split(dataframe_x) |
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col_y1, col_y2 = core.split(dataframe_y) |
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line_at_min = core.linear_background(col_x1, col_y1)[core.peak_detection_fxn(dataframe_y)[1]] |
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line_at_max = core.linear_background(col_x2, col_y2)[core.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
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