sabiharustam /
voltcycle
| @@ 316-346 (lines=31) @@ | ||
| 313 | return index_list |
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| 314 | ||
| 315 | ||
| 316 | def peak_values(DataFrame_x, DataFrame_y): |
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| 317 | """Outputs x (potentials) and y (currents) values from data indices |
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| 318 | given by peak_detection function. |
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| 319 | ||
| 320 | ---------- |
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| 321 | Parameters |
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| 322 | ---------- |
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| 323 | DataFrame_x : should be in the form of a pandas DataFrame column. |
|
| 324 | For example, df['potentials'] could be input as the column of x |
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| 325 | data. |
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| 326 | ||
| 327 | DataFrame_y : should be in the form of a pandas DataFrame column. |
|
| 328 | For example, df['currents'] could be input as the column of y |
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| 329 | data. |
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| 330 | ||
| 331 | Returns |
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| 332 | ------- |
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| 333 | Result : numpy array of coordinates at peaks in the following order: |
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| 334 | potential of peak on top curve, current of peak on top curve, |
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| 335 | potential of peak on bottom curve, current of peak on bottom curve""" |
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| 336 | index = peak_detection_fxn(DataFrame_y) |
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| 337 | potential1, potential2 = split(DataFrame_x) |
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| 338 | current1, current2 = split(DataFrame_y) |
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| 339 | Peak_values = [] |
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| 340 | Peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is |
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| 341 | # the first part of DataFrame) |
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| 342 | Peak_values.append(current2[(index[0])]) # TOPY |
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| 343 | Peak_values.append(potential1[(index[1])]) # BOTTOMX |
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| 344 | Peak_values.append(current1[(index[1])]) # BOTTOMY |
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| 345 | Peak_array = np.array(Peak_values) |
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| 346 | return Peak_array |
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| 347 | ||
| 348 | ||
| 349 | def del_potential(DataFrame_x, DataFrame_y): |
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| @@ 316-346 (lines=31) @@ | ||
| 313 | return index_list |
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| 314 | ||
| 315 | ||
| 316 | def peak_values(DataFrame_x, DataFrame_y): |
|
| 317 | """Outputs x (potentials) and y (currents) values from data indices |
|
| 318 | given by peak_detection function. |
|
| 319 | ||
| 320 | ---------- |
|
| 321 | Parameters |
|
| 322 | ---------- |
|
| 323 | DataFrame_x : should be in the form of a pandas DataFrame column. |
|
| 324 | For example, df['potentials'] could be input as the column of x |
|
| 325 | data. |
|
| 326 | ||
| 327 | DataFrame_y : should be in the form of a pandas DataFrame column. |
|
| 328 | For example, df['currents'] could be input as the column of y |
|
| 329 | data. |
|
| 330 | ||
| 331 | Returns |
|
| 332 | ------- |
|
| 333 | Result : numpy array of coordinates at peaks in the following order: |
|
| 334 | potential of peak on top curve, current of peak on top curve, |
|
| 335 | potential of peak on bottom curve, current of peak on bottom curve""" |
|
| 336 | index = peak_detection_fxn(DataFrame_y) |
|
| 337 | potential1, potential2 = split(DataFrame_x) |
|
| 338 | current1, current2 = split(DataFrame_y) |
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| 339 | Peak_values = [] |
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| 340 | Peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is |
|
| 341 | # the first part of DataFrame) |
|
| 342 | Peak_values.append(current2[(index[0])]) # TOPY |
|
| 343 | Peak_values.append(potential1[(index[1])]) # BOTTOMX |
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| 344 | Peak_values.append(current1[(index[1])]) # BOTTOMY |
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| 345 | Peak_array = np.array(Peak_values) |
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| 346 | return Peak_array |
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| 347 | ||
| 348 | ||
| 349 | def del_potential(DataFrame_x, DataFrame_y): |
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