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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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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 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 |
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of peak detection and baseline finding. |
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""" |
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(assert type(vector) == pd.core.series.Series, |
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"Input of the function should be pandas series") |
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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 critical_idx(x, 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 |
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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 |
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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 |
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numpy array that came out from split function. |
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For example, output of split.df['potentials'] |
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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 |
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of different moving average curves. |
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User can change this function according to |
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baseline branch method 2 to get various indexes.. |
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""" |
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(assert type(x) == np.ndarray, |
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"Input of the function should be numpy array") |
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(assert type(y) == np.ndarray, |
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"Input of the function should be numpy array") |
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if x.shape[0] != 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(x.shape, y.shape)) |
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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 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)-10): |
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# The reason to minus 10 is to prevent j from running out of index. |
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a = 0 |
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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/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)-15): |
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b = 0 |
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for j in range(0, 15): |
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b = b + k[i+j] |
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ave15.append(round(b/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 - |
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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 type(vector) == np.ndarray, |
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"Input of the function should be numpy array") |
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a = 0 |
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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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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 |
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of multiplicity of given two vector. |
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""" |
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(assert type(vector_x) == np.ndarray, |
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"Input of the function should be numpy array") |
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(assert type(vector_y) == np.ndarray, |
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"Input of the function should be numpy array") |
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a = 0 |
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for x, y in zip(vector_x, vector_y): |
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a = a + (x * y) |
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return a |
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def linear_coeff(x, y): |
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""" |
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This function returns the inclination coeffecient and |
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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 = {(multiplica(x, y) - sum_mean(x)[0] * sum_mean(y)[1]) / |
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(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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def y_fitted_line(m, b, x): |
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""" |
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This function returns the fitted baseline constructed |
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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 x: |
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y = m * i + b |
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y_base.append(y) |
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return y_base |
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def linear_background(x, 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 |
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of the cyclic voltammetry data. |
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y : Output of the split vector function. y value |
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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 type(x) == np.ndarray, "Input of the function should be numpy array" |
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assert type(y) == np.ndarray, "Input of the function should be numpy array" |
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idx = critical_idx(x, y) + 5 |
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# this is also arbitrary number we can play with. |
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m, b = {linear_coeff(x[(idx - int(0.5 * idx)): (idx + int(0.5 * idx))], |
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y[(idx - int(0.5 * idx)): (idx + int(0.5 * idx))])} |
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y_base = y_fitted_line(m, b, x) |
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return y_base |
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sabiharustam /
voltcycle
