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): |
|
| 482 | """Outputs heights of minimum peak and maximum |
|
| 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. |
|
| 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. |
|
| 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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| @@ 393-419 (lines=27) @@ | ||
| 390 | return half_wave_potential |
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| 391 | ||
| 392 | ||
| 393 | def peak_heights(DataFrame_x, DataFrame_y): |
|
| 394 | """Outputs heights of minimum peak and maximum |
|
| 395 | peak from cyclic voltammetry data. |
|
| 396 | ||
| 397 | Parameters |
|
| 398 | ---------- |
|
| 399 | DataFrame_x : should be in the form of a pandas DataFrame column. |
|
| 400 | For example, df['potentials'] could be input as the column of x |
|
| 401 | data. |
|
| 402 | ||
| 403 | DataFrame_y : should be in the form of a pandas DataFrame column. |
|
| 404 | For example, df['currents'] could be input as the column of y |
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| 405 | data. |
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| 406 | ||
| 407 | Returns |
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| 408 | ------- |
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| 409 | Results: height of maximum peak, height of minimum peak |
|
| 410 | in that order in the form of a list.""" |
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| 411 | current_max = peak_values(DataFrame_x, DataFrame_y)[1] |
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| 412 | current_min = peak_values(DataFrame_x, DataFrame_y)[3] |
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| 413 | x1, x2 = split(DataFrame_x) |
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| 414 | y1, y2 = split(DataFrame_y) |
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| 415 | line_at_min = linear_background(x1, y1)[peak_detection_fxn(DataFrame_y)[1]] |
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| 416 | line_at_max = linear_background(x2, y2)[peak_detection_fxn(DataFrame_y)[0]] |
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| 417 | height_of_max = current_max - line_at_max |
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| 418 | height_of_min = abs(current_min - line_at_min) |
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| 419 | return [height_of_max, height_of_min] |
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| 420 | ||
| 421 | ||
| 422 | def peak_ratio(DataFrame_x, DataFrame_y): |
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| @@ 393-419 (lines=27) @@ | ||
| 390 | return half_wave_potential |
|
| 391 | ||
| 392 | ||
| 393 | def peak_heights(DataFrame_x, DataFrame_y): |
|
| 394 | """Outputs heights of minimum peak and maximum |
|
| 395 | peak from cyclic voltammetry data. |
|
| 396 | ||
| 397 | Parameters |
|
| 398 | ---------- |
|
| 399 | DataFrame_x : should be in the form of a pandas DataFrame column. |
|
| 400 | For example, df['potentials'] could be input as the column of x |
|
| 401 | data. |
|
| 402 | ||
| 403 | DataFrame_y : should be in the form of a pandas DataFrame column. |
|
| 404 | For example, df['currents'] could be input as the column of y |
|
| 405 | data. |
|
| 406 | ||
| 407 | Returns |
|
| 408 | ------- |
|
| 409 | Results: height of maximum peak, height of minimum peak |
|
| 410 | in that order in the form of a list.""" |
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| 411 | current_max = peak_values(DataFrame_x, DataFrame_y)[1] |
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| 412 | current_min = peak_values(DataFrame_x, DataFrame_y)[3] |
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| 413 | x1, x2 = split(DataFrame_x) |
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| 414 | y1, y2 = split(DataFrame_y) |
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| 415 | line_at_min = linear_background(x1, y1)[peak_detection_fxn(DataFrame_y)[1]] |
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| 416 | line_at_max = linear_background(x2, y2)[peak_detection_fxn(DataFrame_y)[0]] |
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| 417 | height_of_max = current_max - line_at_max |
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| 418 | height_of_min = abs(current_min - line_at_min) |
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| 419 | return [height_of_max, height_of_min] |
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| 420 | ||
| 421 | ||
| 422 | def peak_ratio(DataFrame_x, DataFrame_y): |
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| @@ 81-107 (lines=27) @@ | ||
| 78 | return half_wave_potential |
|
| 79 | ||
| 80 | ||
| 81 | def peak_heights(DataFrame_x, DataFrame_y): |
|
| 82 | """Outputs heights of minimum peak and maximum |
|
| 83 | peak from cyclic voltammetry data. |
|
| 84 | ||
| 85 | Parameters |
|
| 86 | ---------- |
|
| 87 | DataFrame_x : should be in the form of a pandas DataFrame column. |
|
| 88 | For example, df['potentials'] could be input as the column of x |
|
| 89 | data. |
|
| 90 | ||
| 91 | DataFrame_y : should be in the form of a pandas DataFrame column. |
|
| 92 | For example, df['currents'] could be input as the column of y |
|
| 93 | data. |
|
| 94 | ||
| 95 | Returns |
|
| 96 | ------- |
|
| 97 | Results: height of maximum peak, height of minimum peak |
|
| 98 | in that order in the form of a list.""" |
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| 99 | current_max = peak_values(DataFrame_x, DataFrame_y)[1] |
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| 100 | current_min = peak_values(DataFrame_x, DataFrame_y)[3] |
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| 101 | x1, x2 = split(DataFrame_x) |
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| 102 | y1, y2 = split(DataFrame_y) |
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| 103 | line_at_min = linear_background(x1, y1)[peak_detection(DataFrame_y)[1]] |
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| 104 | line_at_max = linear_background(x2, y2)[peak_detection(DataFrame_y)[0]] |
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| 105 | height_of_max = current_max - line_at_max |
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| 106 | height_of_min = abs(current_min - line_at_min) |
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| 107 | return [height_of_max, height_of_min] |
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| 108 | ||
| 109 | ||
| 110 | def peak_ratio(DataFrame_x, DataFrame_y): |
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