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comane
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Mar 26, 2026
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| # filter from integrability outliers | ||
| log.info("Filtering out non-integrable replicas") | ||
| log.info(f"Integrability threshold: {integrability_threshold}") | ||
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These run unconditionally even if filter_integrability=False
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| v_grid = pdf_array[ | ||
| :, FLAVOUR_TO_ID_MAPPING["V"], : | ||
| ] # extract V flavour from all replicas | ||
| mask = ( | ||
| np.abs(v_grid[:, :20].sum(axis=1)) <= integrability_threshold | ||
| ) # Sum first 20 x-points | ||
| # Count how many replicas are being discarded | ||
| n_discarded = (~mask).sum() # Count False values | ||
| n_kept = mask.sum() # Count True values | ||
| log.info( | ||
| f"Filtering V integrability: discarded {n_discarded} replicas, kept {n_kept}" | ||
| ) | ||
| pdf_array = pdf_array[mask, :, :] # keep only replicas where mask is 'true' | ||
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| v3_grid = pdf_array[:, FLAVOUR_TO_ID_MAPPING["V3"], :] | ||
| mask = np.abs(v3_grid[:, :20].sum(axis=1)) <= integrability_threshold | ||
| n_discarded = (~mask).sum() | ||
| n_kept = mask.sum() |
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The inner loop in here could be factored in something like:
for flavour in ["V", "V3", "V8", "T3", "T8"]:
grid = pdf_array[:, FLAVOUR_TO_ID_MAPPING[flavour], :]
mask = np.abs(grid[:, :20].sum(axis=1)) <= integrability_threshold
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| filter_integrability: bool = True, | ||
| integrability_threshold: float = 0.5, | ||
| ): |
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This new parameters should be documented in the docstring
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| mask = ( | ||
| np.abs(v_grid[:, :20].sum(axis=1)) <= integrability_threshold | ||
| ) # Sum first 20 x-points |
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20 is a number that we might want to not keep hardcoded? If we want to keep it hardcoded, instead, then it needs explaining (eg 1e-7 ...)
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