sabiharustam /
voltcycle
| @@ 482-508 (lines=27) @@ | ||
| 479 | return half_wave_pot |
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| 480 | ||
| 481 | ||
| 482 | def peak_heights(dataframe_x, dataframe_y): |
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| 483 | """Outputs heights of minimum peak and maximum |
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| 484 | peak from cyclic voltammetry data. |
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| 485 | ||
| 486 | Parameters |
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| 487 | ---------- |
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| 488 | DataFrame_x : should be in the form of a pandas DataFrame column. |
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| 489 | For example, df['potentials'] could be input as the column of x |
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| 490 | data. |
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| 491 | ||
| 492 | DataFrame_y : should be in the form of a pandas DataFrame column. |
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| 493 | For example, df['currents'] could be input as the column of y |
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| 494 | data. |
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| 495 | ||
| 496 | Returns |
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| 497 | ------- |
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| 498 | Results: height of maximum peak, height of minimum peak |
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| 499 | in that order in the form of a list.""" |
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| 500 | current_max = peak_values(dataframe_x, dataframe_y)[1] |
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| 501 | current_min = peak_values(dataframe_x, dataframe_y)[3] |
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| 502 | col_x1, col_x2 = split(dataframe_x) |
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| 503 | col_y1, col_y2 = split(dataframe_y) |
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| 504 | line_at_min = linear_background(col_x1, col_y1)[peak_detection_fxn(dataframe_y)[1]] |
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| 505 | line_at_max = linear_background(col_x2, col_y2)[peak_detection_fxn(dataframe_y)[0]] |
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| 506 | height_of_max = current_max - line_at_max |
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| 507 | height_of_min = abs(current_min - line_at_min) |
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| 508 | return [height_of_max, height_of_min] |
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| 509 | ||
| 510 | ||
| 511 | def peak_ratio(dataframe_x, dataframe_y): |
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| @@ 82-108 (lines=27) @@ | ||
| 79 | return half_wave_pot |
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| 80 | ||
| 81 | ||
| 82 | 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. |
|
| 89 | For example, df['potentials'] could be input as the column of x |
|
| 90 | data. |
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| 91 | ||
| 92 | DataFrame_y : should be in the form of a pandas DataFrame column. |
|
| 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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