sabiharustam /
voltcycle
| @@ 406-436 (lines=31) @@ | ||
| 403 | # first value is the top, second value is the bottom |
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| 404 | return index_list |
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| 405 | ||
| 406 | def peak_values(dataframe_x, dataframe_y): |
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| 407 | """Outputs x (potentials) and y (currents) values from data indices |
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| 408 | given by peak_detection function. |
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| 409 | ||
| 410 | ---------- |
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| 411 | Parameters |
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| 412 | ---------- |
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| 413 | DataFrame_x : should be in the form of a pandas DataFrame column. |
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| 414 | For example, df['potentials'] could be input as the column of x |
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| 415 | data. |
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| 416 | ||
| 417 | DataFrame_y : should be in the form of a pandas DataFrame column. |
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| 418 | For example, df['currents'] could be input as the column of y |
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| 419 | data. |
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| 420 | ||
| 421 | Returns |
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| 422 | ------- |
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| 423 | Result : numpy array of coordinates at peaks in the following order: |
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| 424 | potential of peak on top curve, current of peak on top curve, |
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| 425 | potential of peak on bottom curve, current of peak on bottom curve""" |
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| 426 | index = peak_detection_fxn(dataframe_y) |
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| 427 | potential1, potential2 = split(dataframe_x) |
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| 428 | current1, current2 = split(dataframe_y) |
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| 429 | peak_values = [] |
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| 430 | peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is |
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| 431 | # the first part of DataFrame) |
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| 432 | peak_values.append(current2[(index[0])]) # TOPY |
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| 433 | peak_values.append(potential1[(index[1])]) # BOTTOMX |
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| 434 | peak_values.append(current1[(index[1])]) # BOTTOMY |
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| 435 | peak_array = np.array(peak_values) |
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| 436 | return peak_array |
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| 437 | ||
| 438 | ||
| 439 | def del_potential(dataframe_x, dataframe_y): |
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| @@ 6-36 (lines=31) @@ | ||
| 3 | import numpy as np |
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| 4 | from . import core |
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| 5 | ||
| 6 | def peak_values(dataframe_x, dataframe_y): |
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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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