Source code for hail.experimental.vcf_combiner.densify

import hail as hl

[docs]def densify(sparse_mt): """Convert sparse matrix table to a dense VCF-like representation by expanding reference blocks. Parameters ---------- sparse_mt : :class:`.MatrixTable` Sparse MatrixTable to densify. The first row key field must be named ``locus`` and have type ``locus``. Must have an ``END`` entry field of type ``int32``. Returns ------- :class:`.MatrixTable` The densified MatrixTable. The ``END`` entry field is dropped. While computationally expensive, this operation is necessary for many downstream analyses, and should be thought of as roughly costing as much as reading a matrix table created by importing a dense project VCF. """ if list(sparse_mt.row_key)[0] != 'locus' or not isinstance(, hl.tlocus): raise ValueError("first row key field must be named 'locus' and have type 'locus'") if 'END' not in sparse_mt.entry or sparse_mt.END.dtype != hl.tint32: raise ValueError("'densify' requires 'END' entry field of type 'int32'") col_key_fields = list(sparse_mt.col_key) contigs = contig_idx_map = hl.literal({contigs[i]: i for i in range(len(contigs))}, 'dict<str, int32>') mt = sparse_mt.annotate_rows(__contig_idx=contig_idx_map[]) mt = mt.annotate_entries(__contig=mt.__contig_idx) t = mt._localize_entries('__entries', '__cols') t = t.annotate( __entries=hl.rbind( hl.scan.array_agg( lambda entry: hl.scan._prev_nonnull(hl.or_missing(hl.is_defined(entry.END), entry)), t.__entries), lambda prev_entries: lambda i: hl.rbind( prev_entries[i], t.__entries[i], lambda prev_entry, entry: hl.cond( (~hl.is_defined(entry) & (prev_entry.END >= & (prev_entry.__contig == t.__contig_idx)), prev_entry, entry)), hl.range(0, hl.len(t.__entries))))) mt = t._unlocalize_entries('__entries', '__cols', col_key_fields) mt = mt.drop('__contig_idx', '__contig', 'END') return mt