sabiharustam /
voltcycle
| Total Complexity | 5 |
| Total Lines | 130 |
| Duplicated Lines | 44.62 % |
| Changes | 0 | ||
Duplicate code is one of the most pungent code smells. A rule that is often used is to re-structure code once it is duplicated in three or more places.
Common duplication problems, and corresponding solutions are:
| 1 | """This module consists of all the functions used |
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| 2 | to calculate the pertinent values. """ |
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| 3 | import numpy as np |
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| 4 | import core |
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| 5 | |||
| 6 | View Code Duplication | def peak_values(dataframe_x, dataframe_y): |
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Duplication
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| 7 | """Outputs x (potentials) and y (currents) values from data indices |
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| 8 | given by peak_detection function. |
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| 9 | |||
| 10 | ---------- |
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| 11 | Parameters |
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| 12 | ---------- |
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| 13 | DataFrame_x : should be in the form of a pandas DataFrame column. |
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| 14 | For example, df['potentials'] could be input as the column of x |
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| 15 | data. |
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| 16 | |||
| 17 | DataFrame_y : should be in the form of a pandas DataFrame column. |
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| 18 | For example, df['currents'] could be input as the column of y |
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| 19 | data. |
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| 20 | |||
| 21 | Returns |
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| 22 | ------- |
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| 23 | Result : numpy array of coordinates at peaks in the following order: |
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| 24 | potential of peak on top curve, current of peak on top curve, |
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| 25 | potential of peak on bottom curve, current of peak on bottom curve""" |
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| 26 | index = core.peak_detection_fxn(dataframe_y) |
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| 27 | potential1, potential2 = core.split(dataframe_x) |
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| 28 | current1, current2 = core.split(dataframe_y) |
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| 29 | peak_values = [] |
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| 30 | peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is |
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| 31 | # the first part of DataFrame) |
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| 32 | peak_values.append(current2[(index[0])]) # TOPY |
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| 33 | peak_values.append(potential1[(index[1])]) # BOTTOMX |
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| 34 | peak_values.append(current1[(index[1])]) # BOTTOMY |
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| 35 | peak_array = np.array(peak_values) |
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| 36 | return peak_array |
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| 37 | |||
| 38 | |||
| 39 | def del_potential(dataframe_x, dataframe_y): |
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| 40 | """Outputs the difference in potentials between anoidc and |
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| 41 | cathodic peaks in cyclic voltammetry data. |
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| 42 | |||
| 43 | Parameters |
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| 44 | ---------- |
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| 45 | DataFrame_x : should be in the form of a pandas DataFrame column. |
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| 46 | For example, df['potentials'] could be input as the column of x |
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| 47 | data. |
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| 48 | |||
| 49 | DataFrame_y : should be in the form of a pandas DataFrame column. |
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| 50 | For example, df['currents'] could be input as the column of y |
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| 51 | data. |
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| 52 | |||
| 53 | Returns |
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| 54 | ------- |
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| 55 | Results: difference in peak potentials.""" |
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| 56 | del_potentials = (peak_values(dataframe_x, dataframe_y)[0] - |
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| 57 | peak_values(dataframe_x, dataframe_y)[2]) |
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| 58 | return del_potentials |
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| 59 | |||
| 60 | |||
| 61 | def half_wave_potential(dataframe_x, dataframe_y): |
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| 62 | """Outputs the half wave potential(redox potential) from cyclic |
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| 63 | voltammetry data. |
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| 64 | |||
| 65 | Parameters |
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| 66 | ---------- |
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| 67 | DataFrame_x : should be in the form of a pandas DataFrame column. |
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| 68 | For example, df['potentials'] could be input as the column of x |
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| 69 | data. |
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| 70 | |||
| 71 | DataFrame_y : should be in the form of a pandas DataFrame column. |
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| 72 | For example, df['currents'] could be input as the column of y |
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| 73 | data. |
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| 74 | |||
| 75 | Returns |
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| 76 | ------- |
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| 77 | Results : the half wave potential.""" |
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| 78 | half_wave_pot = (del_potential(dataframe_x, dataframe_y))/2 |
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| 79 | return half_wave_pot |
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| 80 | |||
| 81 | |||
| 82 | View Code Duplication | def peak_heights(dataframe_x, dataframe_y): |
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| 83 | """Outputs heights of minimum peak and maximum |
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| 84 | peak from cyclic voltammetry data. |
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| 85 | |||
| 86 | Parameters |
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| 87 | ---------- |
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| 88 | DataFrame_x : should be in the form of a pandas DataFrame column. |
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| 89 | For example, df['potentials'] could be input as the column of x |
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| 90 | data. |
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| 91 | |||
| 92 | DataFrame_y : should be in the form of a pandas DataFrame column. |
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| 93 | For example, df['currents'] could be input as the column of y |
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| 94 | data. |
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| 95 | |||
| 96 | Returns |
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| 97 | ------- |
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| 98 | Results: height of maximum peak, height of minimum peak |
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| 99 | in that order in the form of a list.""" |
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| 100 | current_max = peak_values(dataframe_x, dataframe_y)[1] |
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| 101 | current_min = peak_values(dataframe_x, dataframe_y)[3] |
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| 102 | col_x1, col_x2 = core.split(dataframe_x) |
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| 103 | col_y1, col_y2 = core.split(dataframe_y) |
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| 104 | line_at_min = core.linear_background(col_x1, col_y1)[core.peak_detection_fxn(dataframe_y)[1]] |
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| 105 | line_at_max = core.linear_background(col_x2, col_y2)[core.peak_detection_fxn(dataframe_y)[0]] |
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| 106 | height_of_max = current_max - line_at_max |
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| 107 | height_of_min = abs(current_min - line_at_min) |
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| 108 | return [height_of_max, height_of_min] |
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| 109 | |||
| 110 | |||
| 111 | def peak_ratio(dataframe_x, dataframe_y): |
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| 112 | """Outputs the peak ratios from cyclic voltammetry data. |
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| 113 | |||
| 114 | Parameters |
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| 115 | ---------- |
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| 116 | DataFrame_x : should be in the form of a pandas DataFrame column. |
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| 117 | For example, df['potentials'] could be input as the column of x |
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| 118 | data. |
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| 119 | |||
| 120 | DataFrame_y : should be in the form of a pandas DataFrame column. |
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| 121 | For example, df['currents'] could be input as the column of y |
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| 122 | data. |
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| 123 | |||
| 124 | Returns |
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| 125 | ------- |
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| 126 | Result : returns a the peak ratio.""" |
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| 127 | ratio = (peak_heights(dataframe_x, dataframe_y)[0] / |
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| 128 | peak_heights(dataframe_x, dataframe_y)[1]) |
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| 129 | return ratio |
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| 130 |
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