sabiharustam /
voltcycle
| @@ 404-434 (lines=31) @@ | ||
| 401 | return index_list |
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| 402 | ||
| 403 | ||
| 404 | def peak_values(DataFrame_x, DataFrame_y): |
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| 405 | """Outputs x (potentials) and y (currents) values from data indices |
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| 406 | given by peak_detection function. |
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| 407 | ||
| 408 | ---------- |
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| 409 | Parameters |
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| 410 | ---------- |
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| 411 | DataFrame_x : should be in the form of a pandas DataFrame column. |
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| 412 | For example, df['potentials'] could be input as the column of x |
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| 413 | data. |
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| 414 | ||
| 415 | DataFrame_y : should be in the form of a pandas DataFrame column. |
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| 416 | For example, df['currents'] could be input as the column of y |
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| 417 | data. |
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| 418 | ||
| 419 | Returns |
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| 420 | ------- |
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| 421 | Result : numpy array of coordinates at peaks in the following order: |
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| 422 | potential of peak on top curve, current of peak on top curve, |
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| 423 | potential of peak on bottom curve, current of peak on bottom curve""" |
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| 424 | index = peak_detection_fxn(DataFrame_y) |
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| 425 | potential1, potential2 = split(DataFrame_x) |
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| 426 | current1, current2 = split(DataFrame_y) |
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| 427 | Peak_values = [] |
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| 428 | Peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is |
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| 429 | # the first part of DataFrame) |
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| 430 | Peak_values.append(current2[(index[0])]) # TOPY |
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| 431 | Peak_values.append(potential1[(index[1])]) # BOTTOMX |
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| 432 | Peak_values.append(current1[(index[1])]) # BOTTOMY |
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| 433 | Peak_array = np.array(Peak_values) |
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| 434 | return Peak_array |
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| 435 | ||
| 436 | ||
| 437 | def del_potential(DataFrame_x, DataFrame_y): |
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| @@ 24-54 (lines=31) @@ | ||
| 21 | return fake_line_array |
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| 22 | ||
| 23 | ||
| 24 | def peak_values(DataFrame_x, DataFrame_y): |
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| 25 | """Outputs x (potentials) and y (currents) values from data indices |
|
| 26 | given by peak_detection function. |
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| 27 | ||
| 28 | ---------- |
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| 29 | Parameters |
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| 30 | ---------- |
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| 31 | DataFrame_x : should be in the form of a pandas DataFrame column. |
|
| 32 | For example, df['potentials'] could be input as the column of x |
|
| 33 | data. |
|
| 34 | ||
| 35 | DataFrame_y : should be in the form of a pandas DataFrame column. |
|
| 36 | For example, df['currents'] could be input as the column of y |
|
| 37 | data. |
|
| 38 | ||
| 39 | Returns |
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| 40 | ------- |
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| 41 | Result : numpy array of coordinates at peaks in the following order: |
|
| 42 | potential of peak on top curve, current of peak on top curve, |
|
| 43 | potential of peak on bottom curve, current of peak on bottom curve""" |
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| 44 | index = peak_detection_fxn(DataFrame_y) |
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| 45 | potential1, potential2 = split(DataFrame_x) |
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| 46 | current1, current2 = split(DataFrame_y) |
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| 47 | Peak_values = [] |
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| 48 | Peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is |
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| 49 | # the first part of DataFrame) |
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| 50 | Peak_values.append(current2[(index[0])]) # TOPY |
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| 51 | Peak_values.append(potential1[(index[1])]) # BOTTOMX |
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| 52 | Peak_values.append(current1[(index[1])]) # BOTTOMY |
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| 53 | Peak_array = np.array(Peak_values) |
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| 54 | return Peak_array |
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| 55 | ||
| 56 | ||
| 57 | def del_potential(DataFrame_x, DataFrame_y): |
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