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1 | 1 | """describe class""" |
| 2 | + |
2 | 3 | import copy |
3 | 4 | import warnings |
4 | 5 |
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@@ -512,21 +513,39 @@ def generate_sample_correlated( |
512 | 513 | if len(corr_x[i]) == len(u_x[i].ravel()): |
513 | 514 | # cov_x = cm.convert_corr_to_cov(corr_x[i], u_x[i]) |
514 | 515 | MC_data = generate_sample_corr( |
515 | | - MCsteps, x[i], u_x[i], corr_x[i], dtype=dtype |
| 516 | + MCsteps, |
| 517 | + x[i], |
| 518 | + u_x[i], |
| 519 | + corr_x[i], |
| 520 | + dtype=dtype, |
| 521 | + pdf_shape=pdf_shape, |
| 522 | + pdf_params=pdf_params, |
516 | 523 | ) |
517 | 524 | elif len(corr_x[i]) == len(u_x[i]): |
518 | 525 | MC_data = np.zeros((MCsteps,) + (u_x[i].shape)) |
519 | 526 | for j in range(len(u_x[i][0])): |
520 | 527 | # cov_x = cm.convert_corr_to_cov(corr_x[i], u_x[i][:, j]) |
521 | 528 | MC_data[:, :, j] = generate_sample_corr( |
522 | | - MCsteps, x[i][:, j], u_x[i][:, j], corr_x[i], dtype=dtype |
| 529 | + MCsteps, |
| 530 | + x[i][:, j], |
| 531 | + u_x[i][:, j], |
| 532 | + corr_x[i], |
| 533 | + dtype=dtype, |
| 534 | + pdf_shape=pdf_shape, |
| 535 | + pdf_params=pdf_params, |
523 | 536 | ) |
524 | 537 | elif u_x[i].ndim > 1 and len(corr_x[i]) == len(u_x[i][0]): |
525 | 538 | MC_data = np.zeros((MCsteps,) + (u_x[i].shape)) |
526 | 539 | for j in range(len(u_x[i][:, 0])): |
527 | 540 | # cov_x = cm.convert_corr_to_cov(corr_x[i], u_x[i][j]) |
528 | 541 | MC_data[:, j, :] = generate_sample_corr( |
529 | | - MCsteps, x[i][j], u_x[i][j], corr_x[i], dtype=dtype |
| 542 | + MCsteps, |
| 543 | + x[i][j], |
| 544 | + u_x[i][j], |
| 545 | + corr_x[i], |
| 546 | + dtype=dtype, |
| 547 | + pdf_shape=pdf_shape, |
| 548 | + pdf_params=pdf_params, |
530 | 549 | ) |
531 | 550 | else: |
532 | 551 | raise NotImplementedError( |
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