audit_barcodes
Check an index set you ALREADY have, rather than designing a new one: the minimum distance between any two, how many errors that guarantees correcting, the full distance matrices, and — optionally — the subset that survives pruning to a stricter error budget. This is the question asked of a set inherited from a kit or a previous experiment.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| _files | No | The project: record references shaped like _subject. Their records become library documents a tool names in sequences/parts/document; a trace or alignment is found by file name. | |
| metric | No | error model to audit against — a set safe under substitutions may be unsafe under indels. Values: hamming (Hamming (substitutions)), seqlev (Sequence-Levenshtein (indels)), levenshtein (Levenshtein). | seqlev |
| _subject | No | The record to act on: exactly one of sequence, accession (+source), sample (an id from list_samples) or file ({name, text} or {name, base64}); record_index picks one record of several. | |
| barcodes | Yes | the index sequences to audit. | |
| prune_to_errors | No | also report the subset that would survive pruning to a free code correcting this many errors. 0 = do not prune. Advanced. | |
| required_distance | No | the minimum distance the set is supposed to have — the audit says whether it does. |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| kept | Yes | the barcodes surviving `prune_to_errors` (the whole set when pruning is off) | |
| audit | Yes | the verdict: the set's real minimum distance, what it guarantees, and every pair that falls short | |
| dropped | Yes | how many the prune removed | |
| analysis | Yes | per-metric analysis of the whole set | |
| matrices | Yes | the pairwise distance matrix under each metric | |
| distanceMap | Yes | the pairwise distance matrix under the chosen metric, as a heatmap |