sabiharustam /
voltcycle
| @@ 481-507 (lines=27) @@ | ||
| 478 | return half_wave_potential |
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| 479 | ||
| 480 | ||
| 481 | def peak_heights(DataFrame_x, DataFrame_y): |
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| 482 | """Outputs heights of minimum peak and maximum |
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| 483 | peak from cyclic voltammetry data. |
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| 484 | ||
| 485 | Parameters |
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| 486 | ---------- |
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| 487 | DataFrame_x : should be in the form of a pandas DataFrame column. |
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| 488 | For example, df['potentials'] could be input as the column of x |
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| 489 | data. |
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| 490 | ||
| 491 | DataFrame_y : should be in the form of a pandas DataFrame column. |
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| 492 | For example, df['currents'] could be input as the column of y |
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| 493 | data. |
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| 494 | ||
| 495 | Returns |
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| 496 | ------- |
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| 497 | Results: height of maximum peak, height of minimum peak |
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| 498 | in that order in the form of a list.""" |
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| 499 | current_max = peak_values(DataFrame_x, DataFrame_y)[1] |
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| 500 | current_min = peak_values(DataFrame_x, DataFrame_y)[3] |
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| 501 | x1, x2 = split(DataFrame_x) |
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| 502 | y1, y2 = split(DataFrame_y) |
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| 503 | line_at_min = linear_background(x1, y1)[peak_detection_fxn(DataFrame_y)[1]] |
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| 504 | line_at_max = linear_background(x2, y2)[peak_detection_fxn(DataFrame_y)[0]] |
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| 505 | height_of_max = current_max - line_at_max |
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| 506 | height_of_min = abs(current_min - line_at_min) |
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| 507 | return [height_of_max, height_of_min] |
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| 508 | ||
| 509 | ||
| 510 | def peak_ratio(DataFrame_x, DataFrame_y): |
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| @@ 100-126 (lines=27) @@ | ||
| 97 | return half_wave_potential |
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| 98 | ||
| 99 | ||
| 100 | def peak_heights(DataFrame_x, DataFrame_y): |
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| 101 | """Outputs heights of minimum peak and maximum |
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| 102 | peak from cyclic voltammetry data. |
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| 103 | ||
| 104 | Parameters |
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| 105 | ---------- |
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| 106 | DataFrame_x : should be in the form of a pandas DataFrame column. |
|
| 107 | For example, df['potentials'] could be input as the column of x |
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| 108 | data. |
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| 109 | ||
| 110 | DataFrame_y : should be in the form of a pandas DataFrame column. |
|
| 111 | For example, df['currents'] could be input as the column of y |
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| 112 | data. |
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| 113 | ||
| 114 | Returns |
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| 115 | ------- |
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| 116 | Results: height of maximum peak, height of minimum peak |
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| 117 | in that order in the form of a list.""" |
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| 118 | current_max = peak_values(DataFrame_x, DataFrame_y)[1] |
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| 119 | current_min = peak_values(DataFrame_x, DataFrame_y)[3] |
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| 120 | x1, x2 = split(DataFrame_x) |
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| 121 | y1, y2 = split(DataFrame_y) |
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| 122 | line_at_min = linear_background(x1, y1)[peak_detection_fxn(DataFrame_y)[1]] |
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| 123 | line_at_max = linear_background(x2, y2)[peak_detection_fxn(DataFrame_y)[0]] |
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| 124 | height_of_max = current_max - line_at_max |
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| 125 | height_of_min = abs(current_min - line_at_min) |
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| 126 | return [height_of_max, height_of_min] |
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| 127 | ||
| 128 | ||
| 129 | def peak_ratio(DataFrame_x, DataFrame_y): |
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