""" Contact Binners ~~~~~~~~~~~~~~~ Binners are iterators that convert input data of various flavors into a properly sorted, chunked stream of binned contacts. """ from __future__ import annotations import itertools import warnings from bisect import bisect_left from collections import Counter, OrderedDict from collections.abc import Iterator from functools import partial from typing import Any, Callable import h5py import numpy as np import pandas as pd from pandas.api.types import is_integer_dtype from .._logging import get_logger from .._typing import MapFunctor from ..util import ( GenomeSegmentation, balanced_partition, check_bins, get_chromsizes, partition, rlencode, ) logger = get_logger("cooler.create") class BadInputError(ValueError): pass SANITIZE_PRESETS = { "bg2": { "decode_chroms": True, "is_one_based": False, "tril_action": "reflect", "chrom_field": "chrom", "anchor_field": "start", "sided_fields": ("chrom", "start", "end"), "suffixes": ("1", "2"), "sort": True, "validate": True, }, "pairs": { "decode_chroms": True, "is_one_based": False, "tril_action": "reflect", "chrom_field": "chrom", "anchor_field": "pos", "sided_fields": ("chrom", "pos"), "suffixes": ("1", "2"), "sort": False, "validate": True, }, } def _sanitize_records( chunk, gs, decode_chroms, is_one_based, tril_action, chrom_field, anchor_field, sided_fields, suffixes, sort, validate, ): # Get integer contig IDs if decode_chroms: # Unspecified chroms get assigned category = NaN and integer code = -1 chrom1_ids = np.array( pd.Categorical(chunk["chrom1"], gs.contigs, ordered=True).codes ) chrom2_ids = np.array( pd.Categorical(chunk["chrom2"], gs.contigs, ordered=True).codes ) else: chrom1_ids = chunk["chrom1"].values chrom2_ids = chunk["chrom2"].values if validate: for col, dt in [("chrom1", chrom1_ids.dtype), ("chrom2", chrom2_ids.dtype)]: if not is_integer_dtype(dt): raise BadInputError( f"`{col}` column is non-integer. " + "If string, use `decode_chroms=True` to convert to enum" ) # Drop records from non-requested chromosomes to_drop = (chrom1_ids < 0) | (chrom2_ids < 0) if np.any(to_drop): mask = ~to_drop chrom1_ids = chrom1_ids[mask] chrom2_ids = chrom2_ids[mask] chunk = chunk[mask].copy() # Handle empty case if not len(chunk): chunk["bin1_id"] = [] chunk["bin2_id"] = [] return chunk # Find positional anchor columns, convert to zero-based if needed anchor1 = chunk[anchor_field + suffixes[0]].to_numpy(copy=True) anchor2 = chunk[anchor_field + suffixes[1]].to_numpy(copy=True) if is_one_based: anchor1 -= 1 anchor2 -= 1 # Check types and bounds if validate: for dt in [anchor1.dtype, anchor2.dtype]: if not is_integer_dtype(dt): raise BadInputError("Found a non-integer anchor column") is_neg = (anchor1 < 0) | (anchor2 < 0) if np.any(is_neg): err = chunk[is_neg] raise BadInputError( "Found an anchor position with negative value. Make sure your " "coordinates are 1-based or use the --zero-based option " "when loading. \n{}".format(err.head().to_csv(sep="\t")) ) chromsizes1 = gs.chromsizes.values[chrom1_ids] chromsizes2 = gs.chromsizes.values[chrom2_ids] is_excess = (anchor1 > chromsizes1) | (anchor2 > chromsizes2) if np.any(is_excess): err = chunk[is_excess] raise BadInputError( "Found an anchor position exceeding chromosome length:\n{}".format( err.head().to_csv(sep="\t") ) ) # Handle lower triangle records # Note: Swap assignment works as desired because boolean masks create copies # See https://github.com/open2c/cooler/pull/229 if tril_action is not None: is_tril = (chrom1_ids > chrom2_ids) | ( (chrom1_ids == chrom2_ids) & (anchor1 > anchor2) ) if np.any(is_tril): if tril_action == "reflect": (chrom1_ids[is_tril], chrom2_ids[is_tril]) = ( chrom2_ids[is_tril], chrom1_ids[is_tril], ) anchor1[is_tril], anchor2[is_tril] = anchor2[is_tril], anchor1[is_tril] for field in sided_fields: ( chunk.loc[is_tril, field + suffixes[0]], chunk.loc[is_tril, field + suffixes[1]], ) = ( chunk.loc[is_tril, field + suffixes[1]], chunk.loc[is_tril, field + suffixes[0]], ) elif tril_action == "drop": mask = ~is_tril chrom1_ids = chrom1_ids[mask] chrom2_ids = chrom2_ids[mask] anchor1 = anchor1[mask] anchor2 = anchor2[mask] chunk = chunk[mask].copy() elif tril_action == "raise": err = chunk[is_tril] raise BadInputError( "Found lower triangle pairs:\n{}".format( err.head().to_csv(sep="\t") ) ) else: raise ValueError(f"Unknown tril_action value: '{tril_action}'") # Assign bin IDs from bin table chrom_binoffset = gs.chrom_binoffset binsize = gs.binsize if binsize is None: chrom_abspos = gs.chrom_abspos start_abspos = gs.start_abspos bin1_ids = [] bin2_ids = [] for cid1, pos1, cid2, pos2 in zip(chrom1_ids, anchor1, chrom2_ids, anchor2): lo = chrom_binoffset[cid1] hi = chrom_binoffset[cid1 + 1] bin1_ids.append( lo + np.searchsorted( start_abspos[lo:hi], chrom_abspos[cid1] + pos1, side="right" ) - 1 ) lo = chrom_binoffset[cid2] hi = chrom_binoffset[cid2 + 1] bin2_ids.append( lo + np.searchsorted( start_abspos[lo:hi], chrom_abspos[cid2] + pos2, side="right" ) - 1 ) chunk["bin1_id"] = bin1_ids chunk["bin2_id"] = bin2_ids else: chunk["bin1_id"] = chrom_binoffset[chrom1_ids] + anchor1 // binsize chunk["bin2_id"] = chrom_binoffset[chrom2_ids] + anchor2 // binsize # Sort by bin IDs if sort: chunk = chunk.sort_values(["bin1_id", "bin2_id"]) # TODO: check for duplicate records and warn return chunk def sanitize_records( bins: pd.DataFrame, schema: str | None = None, **kwargs ) -> Callable[[pd.DataFrame], pd.DataFrame]: """ Builds a funtion to sanitize and assign bin IDs to a data frame of paired genomic positions based on a provided genomic bin segmentation. Parameters ---------- bins : DataFrame Bin table to compare records against. schema : str, optional Use pre-defined parameters for a particular format. Any options can be overriden via kwargs. If not provided, values for all the options below must be given. decode_chroms : bool Convert string chromosome names to integer IDs based on the order given in the bin table. Set to False if the chromosomes are already given as an enumeration, starting at 0. Records with either chrom ID < 0 are dropped. is_one_based : bool Whether the input anchor coordinates are one-based, rather than zero-based. They will be converted to zero-based. tril_action : 'reflect', 'drop', 'raise' or None How to handle lower triangle ("tril") records. If set to 'reflect', tril records will be flipped or "reflected" to their mirror image: "sided" column pairs will have their values swapped. If set to 'drop', tril records will be discarded. This is useful if your input data is symmetric, i.e. contains mirror duplicates of every record. If set to 'raise', an exception will be raised if any tril record is encountered. chrom_field : str Base name of the two chromosome/scaffold/contig columns. anchor_field : str Base name of the positional anchor columns. sided_fields : sequence of str Base names of column pairs to swap values between when mirror-reflecting records. suffixes : pair of str Suffixes used to identify pairs of sided columns. e.g.: ('1', '2'), ('_x', '_y'), etc. sort : bool Whether to sort the output dataframe by bin_id and bin2_id. validate : bool Whether to do type- and bounds-checking on the anchor position columns. Raises BadInputError. Returns ------- callable : Function of one argument that takes a raw dataframe and returns a sanitized dataframe with bin IDs assigned. """ if schema is not None: try: options = SANITIZE_PRESETS[schema] except KeyError: raise ValueError(f"Unknown schema: '{schema}'") from None else: options = {} options = options.copy() options.update(**kwargs) chromsizes = get_chromsizes(bins) options["gs"] = GenomeSegmentation(chromsizes, bins) return partial(_sanitize_records, **options) def _sanitize_pixels( chunk, gs, is_one_based=False, tril_action="reflect", bin1_field="bin1_id", bin2_field="bin2_id", sided_fields=(), suffixes=("1", "2"), sort=True, ): if is_one_based: chunk[bin1_field] -= 1 chunk[bin2_field] -= 1 # Note: Swap assignment syntax works as desired because boolean mask # selection produces array copies rather than views. # See https://github.com/open2c/cooler/pull/229. if tril_action is not None: is_tril = chunk[bin1_field] > chunk[bin2_field] # boolean mask if np.any(is_tril): if tril_action == "reflect": ( chunk.loc[is_tril, bin1_field], chunk.loc[is_tril, bin2_field], ) = ( chunk.loc[is_tril, bin2_field], chunk.loc[is_tril, bin1_field], ) for field in sided_fields: ( chunk.loc[is_tril, field + suffixes[0]], chunk.loc[is_tril, field + suffixes[1]], ) = ( chunk.loc[is_tril, field + suffixes[1]], chunk.loc[is_tril, field + suffixes[0]], ) elif tril_action == "drop": chunk = chunk[~is_tril] elif tril_action == "raise": raise BadInputError("Found bin1_id greater than bin2_id") else: raise ValueError(f"Unknown tril_action value: '{tril_action}'") return chunk.sort_values([bin1_field, bin2_field]) if sort else chunk def sanitize_pixels( bins: pd.DataFrame, **kwargs, ) -> Callable[[pd.DataFrame], pd.DataFrame]: """ Builds a function to sanitize an already-binned genomic data with genomic bin assignments. Parameters ---------- bins : DataFrame Bin table to compare pixel records against. is_one_based : bool, optional Whether the input bin IDs are one-based, rather than zero-based. They will be converted to zero-based. tril_action : 'reflect', 'drop', 'raise' or None How to handle lower triangle ("tril") pixels. If set to 'reflect' [default], tril pixels will be flipped or "reflected" to their mirror image: "sided" column pairs will have their values swapped. If set to 'drop', tril pixels will be discarded. This is useful if your input data is duplexed, i.e. contains mirror duplicates of every record. If set to 'raise', an exception will be raised if any tril record is encountered. bin1_field : str Name of the column representing ith (row) axis of the matrix. Default is 'bin1_id'. bin2_field : str Name of the column representing jth (col) axis of the matrix. Default is 'bin2_id'. sided_fields : sequence of str Base names of column pairs to swap values between when mirror-reflecting pixels. suffixes : pair of str Suffixes used to identify pairs of sided columns. e.g.: ('1', '2'), ('_x', '_y'), etc. sort : bool Whether to sort the output dataframe by bin_id and bin2_id. Returns ------- callable : Function of one argument that takes a raw dataframe and returns a sanitized dataframe. """ chromsizes = get_chromsizes(bins) kwargs["gs"] = GenomeSegmentation(chromsizes, bins) return partial(_sanitize_pixels, **kwargs) def _validate_pixels(chunk, n_bins, boundscheck, triucheck, dupcheck, ensure_sorted): if boundscheck: is_neg = (chunk["bin1_id"] < 0) | (chunk["bin2_id"] < 0) if np.any(is_neg): raise BadInputError("Found bin ID < 0") is_excess = (chunk["bin1_id"] >= n_bins) | (chunk["bin2_id"] >= n_bins) if np.any(is_excess): raise BadInputError( "Found a bin ID that exceeds the declared number of bins. " "Check whether your bin table is correct." ) if triucheck: is_tril = chunk["bin1_id"] > chunk["bin2_id"] if np.any(is_tril): raise BadInputError("Found bin1_id greater than bin2_id") if not isinstance(chunk, pd.DataFrame): chunk = pd.DataFrame(chunk) if dupcheck: is_dup = chunk.duplicated(["bin1_id", "bin2_id"]) if is_dup.any(): err = chunk[is_dup] raise BadInputError( "Found duplicate pixels:\n{}".format(err.head().to_csv(sep="\t")) ) if ensure_sorted: chunk = chunk.sort_values(["bin1_id", "bin2_id"]) return chunk def validate_pixels( n_bins: pd.DataFrame, boundscheck: bool, triucheck: bool, dupcheck: bool, ensure_sorted: bool ) -> Callable[[pd.DataFrame], pd.DataFrame]: return partial( _validate_pixels, n_bins=n_bins, boundscheck=boundscheck, triucheck=triucheck, dupcheck=dupcheck, ensure_sorted=ensure_sorted, ) def aggregate_records( sort: bool = True, count: bool = True, agg: dict[str, Any] | None = None, rename: dict[str, str] | None = None ) -> Callable[[pd.DataFrame], pd.DataFrame]: """ Generates a function that aggregates bin-assigned records by pixel. Parameters ---------- sort : bool, optional Sort group keys. Get better performance by turning this off. Note that this does not influence the order of observations within each group. count : bool, optional Output the number of records per pixel. Default is True. agg : dict, optional Dict of column names -> functions or names. rename : dict, optional Dict to rename columns after aggregating. Returns ------- Function that takes a dataframe of records with bin IDs assigned, groups them by pixel, counts them, and optionally aggregates other value columns. Notes ----- The GroupBy 'count' method ignores NaNs within groups, as opposed to 'size'. """ if agg is None: agg = {} if rename is None: rename = {} # We use one of the grouper columns to count the number of pairs per pixel. # We always do count, even if 'count' isn't requested as output. if count and "count" not in agg: agg["bin1_id"] = "size" rename["bin1_id"] = "count" def _aggregate_records(chunk): return ( chunk.groupby(["bin1_id", "bin2_id"], sort=sort) .aggregate(agg) .rename(columns=rename) .reset_index() ) return _aggregate_records class ContactBinner: """ Base class for iterable contact binners. """ def __getstate__(self) -> dict: d = self.__dict__.copy() d.pop("_map", None) return d def __iter__(self) -> Iterator[dict[str, np.ndarray]]: """Iterator over chunks of binned contacts (i.e., nonzero pixels) Chunks are expected to have the following format: * dict of 1D arrays * keys `bin1_id`, `bin2_id`, `count` * arrays lexically sorted by `bin_id` then `bin2_id` """ raise NotImplementedError class HDF5Aggregator(ContactBinner): """ Aggregate contacts from a hiclib-style HDF5 contacts file. """ def __init__( self, h5pairs: h5py.Group, chromsizes: pd.Series, bins: pd.DataFrame, chunksize: int, **kwargs ): self.h5 = h5pairs self.C1 = kwargs.pop("C1", "chrms1") self.P1 = kwargs.pop("P1", "cuts1") self.C2 = kwargs.pop("C2", "chrms2") self.P2 = kwargs.pop("P2", "cuts2") self.gs = GenomeSegmentation(chromsizes, bins) self.chunksize = chunksize self.partition = self._index_chroms() def _index_chroms(self) -> dict[str, tuple[int, int]]: # index extents of chromosomes on first axis of contact list starts, lengths, values = rlencode(self.h5[self.C1], self.chunksize) if len(set(values)) != len(values): raise ValueError("Read pair coordinates are not sorted on the first axis") return dict(zip(values, zip(starts, starts + lengths))) def _load_chunk(self, lo, hi) -> dict[str, np.ndarray]: data = OrderedDict( [ ("chrom_id1", self.h5[self.C1][lo:hi]), ("cut1", self.h5[self.P1][lo:hi]), ("chrom_id2", self.h5[self.C2][lo:hi]), ("cut2", self.h5[self.P2][lo:hi]), ] ) return pd.DataFrame(data) def aggregate(self, chrom: str) -> pd.DataFrame: # pragma: no cover h5pairs = self.h5 C1, P1, C2, P2 = self.C1, self.P1, self.C2, self.P2 chunksize = self.chunksize bins = self.gs.bins binsize = self.gs.binsize chrom_binoffset = self.gs.chrom_binoffset chrom_abspos = self.gs.chrom_abspos start_abspos = self.gs.start_abspos cid = self.gs.idmap[chrom] chrom_lo, chrom_hi = self.partition.get(cid, (-1, -1)) lo = chrom_lo hi = lo while hi < chrom_hi: # update `hi` to make sure our selection doesn't split a bin1 lo, hi = hi, min(hi + chunksize, chrom_hi) abspos = chrom_abspos[cid] + h5pairs[P1][hi - 1] bin_id = int(np.searchsorted(start_abspos, abspos, side="right")) - 1 bin_end = bins["end"][bin_id] hi = bisect_left(h5pairs[P1], bin_end, lo, chrom_hi) if lo == hi: hi = chrom_hi logger.info(f"{lo} {hi}") # load chunk and assign bin IDs to each read side table = self._load_chunk(lo, hi) abspos1 = chrom_abspos[h5pairs[C1][lo:hi]] + h5pairs[P1][lo:hi] abspos2 = chrom_abspos[h5pairs[C2][lo:hi]] + h5pairs[P2][lo:hi] if np.any(abspos1 > abspos2): raise ValueError( "Found a read pair that maps to the lower triangle of the " "contact map (side1 > side2). Check that the provided " "chromosome ordering and read pair file are consistent " "such that all pairs map to the upper triangle with " "respect to the given chromosome ordering." ) if binsize is None: table["bin1_id"] = ( np.searchsorted(start_abspos, abspos1, side="right") - 1 ) table["bin2_id"] = ( np.searchsorted(start_abspos, abspos2, side="right") - 1 ) else: rel_bin1 = np.floor(table["cut1"] / binsize).astype(np.int64) rel_bin2 = np.floor(table["cut2"] / binsize).astype(np.int64) table["bin1_id"] = chrom_binoffset[table["chrom_id1"].values] + rel_bin1 table["bin2_id"] = chrom_binoffset[table["chrom_id2"].values] + rel_bin2 # reduce gby = table.groupby(["bin1_id", "bin2_id"]) agg = ( gby["chrom_id1"] .count() .reset_index() .rename(columns={"chrom_id1": "count"}) ) yield agg def size(self) -> int: return len(self.h5["chrms1"]) def __iter__(self) -> Iterator[dict[str, np.ndarray]]: for chrom in self.gs.contigs: for df in self.aggregate(chrom): yield {k: v.values for k, v in df.items()} class TabixAggregator(ContactBinner): """ Aggregate contacts from a sorted, BGZIP-compressed and tabix-indexed tab-delimited text file. """ def __init__( self, filepath: str, chromsizes: pd.Series, bins: pd.DataFrame, map: MapFunctor = map, n_chunks: int = 1, is_one_based: bool = False, **kwargs, ): try: import pysam except ImportError: raise ImportError("pysam is required to read tabix files") from None import pickle import dill dill.settings["protocol"] = pickle.HIGHEST_PROTOCOL self._map = map self.n_chunks = n_chunks self.is_one_based = bool(is_one_based) self.C2 = kwargs.pop("C2", 3) self.P2 = kwargs.pop("P2", 4) # all requested contigs will be placed in the output matrix self.gs = GenomeSegmentation(chromsizes, bins) # find available contigs in the contact list self.filepath = filepath self.n_records = None with pysam.TabixFile(filepath, "r", encoding="ascii") as f: try: self.file_contigs = [c.decode("ascii") for c in f.contigs] except AttributeError: self.file_contigs = f.contigs if not len(self.file_contigs): raise RuntimeError("No reference sequences found.") # warn about requested contigs not seen in the contact list for chrom in self.gs.contigs: if chrom not in self.file_contigs: warnings.warn( "Did not find contig " + f" '{chrom}' in contact list file.", stacklevel=2, ) warnings.warn( "NOTE: When using the Tabix aggregator, make sure the order of " "chromosomes in the provided chromsizes agrees with the chromosome " "ordering of read ends in the contact list file.", stacklevel=2, ) def aggregate( self, grange: tuple[str, int, int] ) -> pd.DataFrame | None: # pragma: no cover chrom1, start, end = grange import pysam filepath = self.filepath binsize = self.gs.binsize idmap = self.gs.idmap # chromsizes = self.gs.chromsizes chrom_binoffset = self.gs.chrom_binoffset chrom_abspos = self.gs.chrom_abspos start_abspos = self.gs.start_abspos decr = int(self.is_one_based) C2 = self.C2 P2 = self.P2 logger.info(f"Binning {chrom1}:{start}-{end}|*") these_bins = self.gs.fetch((chrom1, start, end)) rows = [] with pysam.TabixFile(filepath, "r", encoding="ascii") as f: parser = pysam.asTuple() accumulator = Counter() for bin1_id, bin1 in these_bins.iterrows(): for line in f.fetch(chrom1, bin1.start, bin1.end, parser=parser): chrom2 = line[C2] pos2 = int(line[P2]) - decr try: cid2 = idmap[chrom2] except KeyError: # this chrom2 is not requested continue if binsize is None: lo = chrom_binoffset[cid2] hi = chrom_binoffset[cid2 + 1] bin2_id = ( lo + np.searchsorted( start_abspos[lo:hi], chrom_abspos[cid2] + pos2, side="right", ) - 1 ) else: bin2_id = chrom_binoffset[cid2] + (pos2 // binsize) accumulator[bin2_id] += 1 if not accumulator: continue rows.append( pd.DataFrame( { "bin1_id": bin1_id, "bin2_id": list(accumulator.keys()), "count": list(accumulator.values()), }, columns=["bin1_id", "bin2_id", "count"], ).sort_values("bin2_id") ) accumulator.clear() logger.info(f"Finished {chrom1}:{start}-{end}|*") return pd.concat(rows, axis=0) if len(rows) else None def __iter__(self) -> Iterator[dict[str, np.ndarray]]: granges = balanced_partition(self.gs, self.n_chunks, self.file_contigs) for df in self._map(self.aggregate, granges): if df is not None: yield {k: v.values for k, v in df.items()} class PairixAggregator(ContactBinner): """ Aggregate contacts from a sorted, BGZIP-compressed and pairix-indexed tab-delimited text file. """ def __init__( self, filepath: str, chromsizes: pd.Series, bins: pd.DataFrame, map: MapFunctor = map, n_chunks: int = 1, is_one_based: bool = False, block_char: str = "|", **kwargs, ): try: import pypairix except ImportError: raise ImportError( "pypairix is required to read pairix-indexed files" ) from None import pickle import dill dill.settings["protocol"] = pickle.HIGHEST_PROTOCOL self._map = map self.n_chunks = n_chunks self.is_one_based = bool(is_one_based) self.block_char = block_char f = pypairix.open(filepath, "r") self.C1 = f.get_chr1_col() self.C2 = f.get_chr2_col() self.P1 = f.get_startpos1_col() self.P2 = f.get_startpos2_col() blocknames = f.get_blocknames() if block_char not in blocknames[0]: raise ValueError( f"The contig separator character `{block_char}` does not " f"appear in the first block name `{blocknames[0]}`. Please " "specify the correct character as `block_char`." ) self.file_contigs = set( itertools.chain.from_iterable([ blockname.split(block_char) for blockname in blocknames ]) ) if not len(self.file_contigs): raise RuntimeError("No reference sequences found.") for c1, c2 in itertools.combinations(self.file_contigs, 2): if f.exists2(c1, c2) and f.exists2(c2, c1): raise RuntimeError( "Pairs are not triangular: found blocks " + f"'{c1}{block_char}{c2}'' and '{c2}{block_char}{c1}'" ) # dumb heuristic to prevent excessively large chunks on one worker if hasattr(f, "get_linecount"): n_lines = f.get_linecount() if n_lines < 0: # correct int32 overflow bug MAXINT32 = 2147483647 n_lines = MAXINT32 + MAXINT32 + n_lines max_chunk = int(100e6) n_chunks = n_lines // 2 // max_chunk old_n = self.n_chunks self.n_chunks = max(self.n_chunks, n_chunks) if self.n_chunks > old_n: logger.info( f"Pairs file has {n_lines} lines. " f"Increasing max-split to {self.n_chunks}." ) # all requested contigs will be placed in the output matrix self.gs = GenomeSegmentation(chromsizes, bins) # find available contigs in the contact list self.filepath = filepath self.n_records = None # warn about requested contigs not seen in the contact list for chrom in self.gs.contigs: if chrom not in self.file_contigs: warnings.warn( "Did not find contig " + f" '{chrom}' in contact list file.", stacklevel=2, ) def aggregate( self, grange: tuple[str, int, int] ) -> pd.DataFrame | None: # pragma: no cover chrom1, start, end = grange import pypairix filepath = self.filepath binsize = self.gs.binsize chromsizes = self.gs.chromsizes chrom_binoffset = self.gs.chrom_binoffset chrom_abspos = self.gs.chrom_abspos start_abspos = self.gs.start_abspos decr = int(self.is_one_based) # C1 = self.C1 # C2 = self.C2 P1 = self.P1 P2 = self.P2 logger.info(f"Binning {chrom1}:{start}-{end}|*") f = pypairix.open(filepath, "r") these_bins = self.gs.fetch((chrom1, start, end)) remaining_chroms = self.gs.idmap[chrom1:] # cid1 = self.gs.idmap[chrom1] accumulator = Counter() rows = [] for bin1_id, bin1 in these_bins.iterrows(): for chrom2, cid2 in remaining_chroms.items(): chrom2_size = chromsizes[chrom2] if chrom1 != chrom2 and f.exists2(chrom2, chrom1): # flipped iterator = f.query2D( chrom2, 0, chrom2_size, chrom1, bin1.start, bin1.end ) pos2_col = P1 else: iterator = f.query2D( chrom1, bin1.start, bin1.end, chrom2, 0, chrom2_size ) pos2_col = P2 for line in iterator: pos2 = int(line[pos2_col]) - decr if binsize is None: lo = chrom_binoffset[cid2] hi = chrom_binoffset[cid2 + 1] bin2_id = ( lo + np.searchsorted( start_abspos[lo:hi], chrom_abspos[cid2] + pos2, side="right", ) - 1 ) else: bin2_id = chrom_binoffset[cid2] + (pos2 // binsize) accumulator[bin2_id] += 1 if not accumulator: continue rows.append( pd.DataFrame( { "bin1_id": bin1_id, "bin2_id": list(accumulator.keys()), "count": list(accumulator.values()), }, columns=["bin1_id", "bin2_id", "count"], ).sort_values("bin2_id") ) accumulator.clear() logger.info(f"Finished {chrom1}:{start}-{end}|*") return pd.concat(rows, axis=0) if len(rows) else None def __iter__(self) -> Iterator[dict[str, np.ndarray]]: granges = balanced_partition(self.gs, self.n_chunks, self.file_contigs) for df in self._map(self.aggregate, granges): if df is not None: yield {k: v.values for k, v in df.items()} class SparseBlockLoader(ContactBinner): # pragma: no cover """ """ def __init__(self, chromsizes, bins, mapping, chunksize): bins = check_bins(bins, chromsizes) self.bins = bins self.chromosomes = list(chromsizes.index) self.chunksize = chunksize n_chroms = len(chromsizes) n_bins = len(bins) chrom_ids = bins["chrom"].cat.codes self.offsets = np.zeros(n_chroms + 1, dtype=int) curr_val = 0 for start, _length, value in zip(*rlencode(chrom_ids)): self.offsets[curr_val : value + 1] = start curr_val = value + 1 self.offsets[curr_val:] = n_bins self.mapping = mapping def select_block(self, chrom1, chrom2): try: block = self.mapping[chrom1, chrom2] except KeyError: try: block = self.mapping[chrom2, chrom1].T except KeyError: warnings.warn( f"Block for {{{chrom1}, {chrom2}}} not found", stacklevel=2, ) raise return block def __iter__(self) -> Iterator[dict[str, np.ndarray]]: # n_bins = len(self.bins) import scipy.sparse chromosomes = self.chromosomes for cid1, chrom1 in enumerate(chromosomes): offset = self.offsets[cid1] chrom1_nbins = self.offsets[cid1 + 1] - offset spans = partition(0, chrom1_nbins, self.chunksize) for lo, hi in spans: chunks = [] for chrom2 in chromosomes[cid1:]: try: block = self.select_block(chrom1, chrom2) except KeyError: continue chunks.append(block.tocsr()[lo:hi, :]) X = scipy.sparse.hstack(chunks).tocsr().tocoo() i, j, v = X.row, X.col, X.data mask = (offset + i) <= (offset + j) triu_i, triu_j, triu_v = i[mask], j[mask], v[mask] yield { "bin1_id": offset + triu_i, "bin2_id": offset + triu_j, "count": triu_v, } class ArrayLoader(ContactBinner): """ Load a dense genome-wide numpy array contact matrix. Works with array-likes such as h5py.Dataset and memmapped arrays See Also -------- numpy.save, numpy.load (mmap_mode) """ def __init__( self, bins: pd.DataFrame, array: np.ndarray | h5py.Dataset, chunksize: int, ): if len(bins) != array.shape[0]: raise ValueError("Number of bins must equal the dimenion of the matrix") self.array = array self.chunksize = chunksize def __iter__(self) -> Iterator[dict[str, np.ndarray]]: n_bins = self.array.shape[0] spans = partition(0, n_bins, self.chunksize) # TRIU sparsify the matrix for lo, hi in spans: X = self.array[lo:hi, :] i, j = np.nonzero(X) mask = (lo + i) <= j triu_i, triu_j = i[mask], j[mask] yield { "bin1_id": lo + triu_i, "bin2_id": triu_j, "count": X[triu_i, triu_j], } class ArrayBlockLoader(ContactBinner): # pragma: no cover """ """ def __init__(self, chromsizes, bins, mapping, chunksize): bins = check_bins(bins, chromsizes) self.bins = bins self.chromosomes = list(chromsizes.index) self.chunksize = chunksize n_chroms = len(chromsizes) n_bins = len(bins) chrom_ids = bins["chrom"].cat.codes self.offsets = np.zeros(n_chroms + 1, dtype=int) curr_val = 0 for start, _length, value in zip(*rlencode(chrom_ids)): self.offsets[curr_val : value + 1] = start curr_val = value + 1 self.offsets[curr_val:] = n_bins self.mapping = mapping def select_block(self, chrom1, chrom2): try: block = self.mapping[chrom1, chrom2] except KeyError: try: block = self.mapping[chrom2, chrom1].T except KeyError: warnings.warn( f"Block for {{{chrom1}, {chrom2}}} not found", stacklevel=2, ) raise return block def __iter__(self): # n_bins = len(self.bins) chromosomes = self.chromosomes for cid1, chrom1 in enumerate(chromosomes): offset = self.offsets[cid1] chrom1_nbins = self.offsets[cid1 + 1] - offset spans = partition(0, chrom1_nbins, self.chunksize) for lo, hi in spans: chunks = [] for chrom2 in chromosomes[cid1:]: try: block = self.select_block(chrom1, chrom2) except KeyError: continue chunks.append(block[lo:hi, :]) X = np.concatenate(chunks, axis=1) i, j = np.nonzero(X) mask = (offset + i) <= (offset + j) triu_i, triu_j = i[mask], j[mask] yield { "bin1_id": offset + triu_i, "bin2_id": offset + triu_j, "count": X[triu_i, triu_j], }