sabiharustam /
voltcycle
| @@ 532-573 (lines=42) @@ | ||
| 529 | return ratio |
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| 530 | ||
| 531 | ||
| 532 | def data_analysis(data): |
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| 533 | """This function returns a dictionary consisting of |
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| 534 | the relevant values. This can be seen in the user |
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| 535 | interface (Dash) as well.""" |
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| 536 | results_dict = {} |
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| 537 | ||
| 538 | # df = main.data_frame(dict_1,1) |
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| 539 | x_val = data['Potential'] |
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| 540 | y_val = data['Current'] |
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| 541 | # Peaks are here [list] |
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| 542 | peak_index = peak_detection_fxn(y_val) |
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| 543 | # Split x,y to get baselines |
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| 544 | col_x1, col_x2 = split(x_val) |
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| 545 | col_y1, col_y2 = split(y_val) |
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| 546 | y_base1 = linear_background(col_x1, col_y1) |
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| 547 | y_base2 = linear_background(col_x2, col_y2) |
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| 548 | # Calculations based on baseline and peak |
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| 549 | values = peak_values(x_val, y_val) |
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| 550 | esub_t = values[0] |
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| 551 | esub_b = values[2] |
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| 552 | dof_e = del_potential(x_val, y_val) |
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| 553 | half_e = min(esub_t, esub_b) + half_wave_potential(x_val, y_val) |
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| 554 | ipa = peak_heights(x_val, y_val)[0] |
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| 555 | ipc = peak_heights(x_val, y_val)[1] |
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| 556 | ratio_i = peak_ratio(x_val, y_val) |
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| 557 | results_dict['Peak Current Ratio'] = ratio_i |
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| 558 | results_dict['Ipc (A)'] = ipc |
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| 559 | results_dict['Ipa (A)'] = ipa |
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| 560 | results_dict['Epc (V)'] = esub_b |
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| 561 | results_dict['Epa (V)'] = esub_t |
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| 562 | results_dict['∆E (V)'] = dof_e |
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| 563 | results_dict['Redox Potential (V)'] = half_e |
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| 564 | if dof_e > 0.3: |
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| 565 | results_dict['Reversible'] = 'No' |
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| 566 | else: |
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| 567 | results_dict['Reversible'] = 'Yes' |
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| 568 | ||
| 569 | if half_e > 0 and 'Yes' in results_dict.values(): |
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| 570 | results_dict['Type'] = 'Catholyte' |
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| 571 | elif 'Yes' in results_dict.values(): |
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| 572 | results_dict['Type'] = 'Anolyte' |
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| 573 | return results_dict, col_x1, col_x2, col_y1, col_y2, y_base1, y_base2, peak_index |
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| 574 | #return results_dict |
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| 575 | ||
| @@ 151-192 (lines=42) @@ | ||
| 148 | return df |
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| 149 | ||
| 150 | ||
| 151 | def data_analysis(data): |
|
| 152 | """This function returns a dictionary consisting of |
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| 153 | the relevant values. This can be seen in the user |
|
| 154 | interface (Dash) as well.""" |
|
| 155 | results_dict = {} |
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| 156 | ||
| 157 | # df = main.data_frame(dict_1,1) |
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| 158 | x_val = data['Potential'] |
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| 159 | y_val = data['Current'] |
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| 160 | # Peaks are here [list] |
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| 161 | peak_index = peak_detection_fxn(y_val) |
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| 162 | # Split x,y to get baselines |
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| 163 | col_x1, col_x2 = split(x_val) |
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| 164 | col_y1, col_y2 = split(y_val) |
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| 165 | y_base1 = linear_background(col_x1, col_y1) |
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| 166 | y_base2 = linear_background(col_x2, col_y2) |
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| 167 | # Calculations based on baseline and peak |
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| 168 | values = peak_values(x_val, y_val) |
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| 169 | esub_t = values[0] |
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| 170 | esub_b = values[2] |
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| 171 | dof_e = del_potential(x_val, y_val) |
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| 172 | half_e = min(esub_t, esub_b) + half_wave_potential(x_val, y_val) |
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| 173 | ipa = peak_heights(x_val, y_val)[0] |
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| 174 | ipc = peak_heights(x_val, y_val)[1] |
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| 175 | ratio_i = peak_ratio(x_val, y_val) |
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| 176 | results_dict['Peak Current Ratio'] = ratio_i |
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| 177 | results_dict['Ipc (A)'] = ipc |
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| 178 | results_dict['Ipa (A)'] = ipa |
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| 179 | results_dict['Epc (V)'] = esub_b |
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| 180 | results_dict['Epa (V)'] = esub_t |
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| 181 | results_dict['∆E (V)'] = dof_e |
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| 182 | results_dict['Redox Potential (V)'] = half_e |
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| 183 | if dof_e > 0.3: |
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| 184 | results_dict['Reversible'] = 'No' |
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| 185 | else: |
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| 186 | results_dict['Reversible'] = 'Yes' |
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| 187 | ||
| 188 | if half_e > 0 and 'Yes' in results_dict.values(): |
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| 189 | results_dict['Type'] = 'Catholyte' |
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| 190 | elif 'Yes' in results_dict.values(): |
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| 191 | results_dict['Type'] = 'Anolyte' |
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| 192 | return results_dict, col_x1, col_x2, col_y1, col_y2, y_base1, y_base2, peak_index |
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| 193 | #return results_dict |
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| 194 | ||
| 195 | ||
