sabiharustam /
voltcycle
| @@ 327-347 (lines=21) @@ | ||
| 324 | return y_base |
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| 325 | ||
| 326 | ||
| 327 | def linear_background(vec_x, vec_y): |
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| 328 | """ |
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| 329 | This function is wrapping function for calculating linear fitted line. |
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| 330 | It takes x and y values of the cv data, returns the fitted baseline. |
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| 331 | ---------- |
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| 332 | Parameters |
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| 333 | ---------- |
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| 334 | x : Output of the split vector function. x value of the cyclic voltammetry data. |
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| 335 | y : Output of the split vector function. y value of the cyclic voltammetry data. |
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| 336 | ------- |
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| 337 | Returns |
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| 338 | ------- |
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| 339 | List of constructed y_labels. |
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| 340 | """ |
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| 341 | assert isinstance(vec_x, np.ndarray), "Input of the function should be numpy array" |
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| 342 | assert isinstance(vec_y, np.ndarray), "Input of the function should be numpy array" |
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| 343 | idx = critical_idx(vec_x, vec_y) + 5 #this is also arbitrary number we can play with. |
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| 344 | m_val, b_val = (linear_coeff(vec_x[(idx - int(0.5 * idx)) : (idx + int(0.5 * idx))], |
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| 345 | vec_y[(idx - int(0.5 * idx)) : (idx + int(0.5 * idx))])) |
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| 346 | y_base = y_fitted_line(m_val, b_val, vec_x) |
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| 347 | return y_base |
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| 348 | ||
| 349 | ||
| 350 | def peak_detection_fxn(data_y): |
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| @@ 168-188 (lines=21) @@ | ||
| 165 | return y_base |
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| 166 | ||
| 167 | ||
| 168 | def linear_background(vec_x, vec_y): |
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| 169 | """ |
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| 170 | This function is wrapping function for calculating linear fitted line. |
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| 171 | It takes x and y values of the cv data, returns the fitted baseline. |
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| 172 | ---------- |
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| 173 | Parameters |
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| 174 | ---------- |
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| 175 | x : Output of the split vector function. x value of the cyclic voltammetry data. |
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| 176 | y : Output of the split vector function. y value of the cyclic voltammetry data. |
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| 177 | ------- |
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| 178 | Returns |
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| 179 | ------- |
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| 180 | List of constructed y_labels. |
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| 181 | """ |
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| 182 | assert isinstance(vec_x, np.ndarray), "Input of the function should be numpy array" |
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| 183 | assert isinstance(vec_y, np.ndarray), "Input of the function should be numpy array" |
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| 184 | idx = critical_idx(vec_x, vec_y) + 5 #this is also arbitrary number we can play with. |
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| 185 | m_val, b_val = (linear_coeff(vec_x[(idx - int(0.5 * idx)) : (idx + int(0.5 * idx))], |
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| 186 | vec_y[(idx - int(0.5 * idx)) : (idx + int(0.5 * idx))])) |
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| 187 | y_base = y_fitted_line(m_val, b_val, vec_x) |
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| 188 | return y_base |
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| 189 | ||
