Source code for hail.vds.combiner.variant_dataset_combiner

import collections
import hashlib
import json
import os
import sys
import uuid

from math import floor, log
from typing import Collection, Dict, List, NamedTuple, Optional, Union

import hail as hl

from hail.expr import HailType, tmatrix
from hail.utils import Interval
from import info, warning
from hail.experimental.vcf_combiner.vcf_combiner import calculate_even_genome_partitioning, \
from .combine import combine_variant_datasets, transform_gvcf, defined_entry_fields

[docs]class VDSMetadata(NamedTuple): """The path to a Variant Dataset and the number of samples within. Parameters ---------- path : :class:`str` Path to the variant dataset. n_samples : :class:`int` Number of samples contained within the Variant Dataset at `path`. """ path: str n_samples: int
class CombinerOutType(NamedTuple): """A container for the types of a VDS""" reference_type: tmatrix variant_type: tmatrix FAST_CODEC_SPEC = """{ "name": "LEB128BufferSpec", "child": { "name": "BlockingBufferSpec", "blockSize": 32768, "child": { "name": "LZ4FastBlockBufferSpec", "blockSize": 32768, "child": { "name": "StreamBlockBufferSpec" } } } }"""
[docs]class VariantDatasetCombiner: # pylint: disable=too-many-instance-attributes """A restartable and failure-tolerant method for combining one or more GVCFs and Variant Datasets. Examples -------- A Variant Dataset comprises one or more sequences. A new Variant Dataset is constructed from GVCF files and/or extant Variant Datasets. For example, the following produces a new Variant Dataset from four GVCF files containing whole genome sequences :: gvcfs = [ 'gs://bucket/sample_10123.g.vcf.bgz', 'gs://bucket/sample_10124.g.vcf.bgz', 'gs://bucket/sample_10125.g.vcf.bgz', 'gs://bucket/sample_10126.g.vcf.bgz', ] combiner = hl.vds.new_combiner( output_path='gs://bucket/dataset.vds', temp_path='gs://1-day-temp-bucket/', gvcf_paths=gvcfs, use_genome_default_intervals=True, ) vds = hl.read_vds('gs://bucket/dataset.vds') The following combines four new samples from GVCFs with multiple extant Variant Datasets:: gvcfs = [ 'gs://bucket/sample_10123.g.vcf.bgz', 'gs://bucket/sample_10124.g.vcf.bgz', 'gs://bucket/sample_10125.g.vcf.bgz', 'gs://bucket/sample_10126.g.vcf.bgz', ] vdses = [ 'gs://bucket/hgdp.vds', 'gs://bucket/1kg.vds' ] combiner = hl.vds.new_combiner( output_path='gs://bucket/dataset.vds', temp_path='gs://1-day-temp-bucket/', save_path='gs://1-day-temp-bucket/', gvcf_paths=gvcfs, vds_paths=vdses, use_genome_default_intervals=True, ) vds = hl.read_vds('gs://bucket/dataset.vds') The speed of the Variant Dataset Combiner critically depends on data partitioning. Although the partitioning is fully customizable, two high-quality partitioning strategies are available by default, one for exomes and one for genomes. These partitioning strategies can be enabled, respectively, with the parameters: ``use_exome_default_intervals=True`` and ``use_genome_default_intervals=True``. The combiner serializes itself to `save_path` so that it can be restarted after failure. Parameters ---------- save_path : :class:`str` The location to store this VariantDatasetCombiner plan. A failed execution can be restarted using this plan. output_path : :class:`str` The location to store the new VariantDataset. temp_path : :class:`str` The location to store temporary intermediates. We recommend using a bucket with an automatic deletion or lifecycle policy. reference_genome : :class:`.ReferenceGenome` The reference genome to which all inputs (GVCFs and Variant Datasets) are aligned. branch_factor : :class:`int` The number of Variant Datasets to combine at once. target_records : :class:`int` The target number of variants per partition. gvcf_batch_size : :class:`int` The number of GVCFs to combine into a Variant Dataset at once. contig_recoding : :class:`dict` mapping :class:`str` to :class:`str` or :obj:`None` This mapping is applied to GVCF contigs before importing them into Hail. This is used to handle GVCFs containing invalid contig names. For example, GRCh38 GVCFs which contain the contig "1" instead of the correct "chr1". vdses : :class:`list` of :class:`.VDSMetadata` A list of Variant Datasets to combine. Each dataset is identified by a :class:`.VDSMetadata`, which is a pair of a path and the number of samples in said Variant Dataset. gvcfs : :class:`list` of :class:`.str` A list of paths of GVCF files to combine. gvcf_sample_names : :class:`list` of :class:`str` or :obj:`None` List of names to use for the samples from the GVCF files. Must be the same length as `gvcfs`. Must be specified if `gvcf_external_header` is specified. gvcf_external_header : :class:`str` or :obj:`None` A path to a file containing a VCF header which is applied to all GVCFs. Must be specified if `gvcf_sample_names` is specified. gvcf_import_intervals : :class:`list` of :class:`.Interval` A list of intervals defining how to partition the GVCF files. The same partitioning is used for all GVCF files. Finer partitioning yields more parallelism but less work per task. gvcf_info_to_keep : :class:`list` of :class:`str` or :obj:`None` GVCF ``INFO`` fields to keep in the ``gvcf_info`` entry field. By default, all fields are kept except ``END`` and ``DP`` are kept. gvcf_reference_entry_fields_to_keep : :class:`list` of :class:`str` or :obj:`None` Genotype fields to keep in the reference table. If empty, the first 10,000 reference block rows of ``mt`` will be sampled and all fields found to be defined other than ``GT``, ``AD``, and ``PL`` will be entry fields in the resulting reference matrix in the dataset. """ _default_gvcf_batch_size = 50 _default_branch_factor = 100 _default_target_records = 24_000 _gvcf_merge_task_limit = 150_000 # These are used to calculate intervals for reading GVCFs in the combiner # The genome interval size results in 2568 partitions for GRCh38. The exome # interval size assumes that they are around 2% the size of a genome and # result in 65 partitions for GRCh38. default_genome_interval_size = 1_200_000 "A reasonable partition size in basepairs given the density of genomes." default_exome_interval_size = 60_000_000 "A reasonable partition size in basepairs given the density of exomes." __serialized_slots__ = [ '_save_path', '_output_path', '_temp_path', '_reference_genome', '_dataset_type', '_gvcf_type', '_branch_factor', '_target_records', '_gvcf_batch_size', '_contig_recoding', '_vdses', '_gvcfs', '_gvcf_external_header', '_gvcf_sample_names', '_gvcf_import_intervals', '_gvcf_info_to_keep', '_gvcf_reference_entry_fields_to_keep', ] __slots__ = tuple(__serialized_slots__ + ['_uuid', '_job_id', '__intervals_cache']) def __init__(self, *, save_path: str, output_path: str, temp_path: str, reference_genome: hl.ReferenceGenome, dataset_type: CombinerOutType, gvcf_type: Optional[tmatrix] = None, branch_factor: int = _default_branch_factor, target_records: int = _default_target_records, gvcf_batch_size: int = _default_gvcf_batch_size, contig_recoding: Optional[Dict[str, str]] = None, vdses: List[VDSMetadata], gvcfs: List[str], gvcf_sample_names: Optional[List[str]] = None, gvcf_external_header: Optional[str] = None, gvcf_import_intervals: List[Interval], gvcf_info_to_keep: Optional[Collection[str]] = None, gvcf_reference_entry_fields_to_keep: Optional[Collection[str]] = None, ): if not (vdses or gvcfs): raise ValueError("one of 'vdses' or 'gvcfs' must be nonempty") if not gvcf_import_intervals: raise ValueError('gvcf import intervals must be nonempty') interval = gvcf_import_intervals[0] if not isinstance(interval.point_type, hl.tlocus): raise ValueError(f'intervals point type must be a locus, found {interval.point_type}') if interval.point_type.reference_genome != reference_genome: raise ValueError(f'mismatch in intervals ({interval.point_type.reference_genome}) ' f'and reference genome ({reference_genome}) types') if (gvcf_sample_names is None) != (gvcf_external_header is None): raise ValueError("both 'gvcf_sample_names' and 'gvcf_external_header' must be set or unset") if gvcf_sample_names is not None and len(gvcf_sample_names) != len(gvcfs): raise ValueError("'gvcf_sample_names' and 'gvcfs' must have the same length " f'{len(gvcf_sample_names)} != {len(gvcfs)}') if branch_factor < 2: raise ValueError(f"'branch_factor' must be at least 2, found {branch_factor}") if gvcf_batch_size < 1: raise ValueError(f"'gvcf_batch_size' must be at least 1, found {gvcf_batch_size}") self._save_path = save_path self._output_path = output_path self._temp_path = temp_path self._reference_genome = reference_genome self._dataset_type = dataset_type self._gvcf_type = gvcf_type self._branch_factor = branch_factor self._target_records = target_records self._contig_recoding = contig_recoding self._vdses = collections.defaultdict(list) for vds in vdses: self._vdses[max(1, floor(log(vds.n_samples, self._branch_factor)))].append(vds) self._gvcfs = gvcfs self._gvcf_sample_names = gvcf_sample_names self._gvcf_external_header = gvcf_external_header self._gvcf_import_intervals = gvcf_import_intervals self._gvcf_info_to_keep = set(gvcf_info_to_keep) if gvcf_info_to_keep is not None \ else None self._gvcf_reference_entry_fields_to_keep = set(gvcf_reference_entry_fields_to_keep) \ if gvcf_reference_entry_fields_to_keep is not None else None self._uuid = uuid.uuid4() self._job_id = 1 self.__intervals_cache = {} self._gvcf_batch_size = gvcf_batch_size @property def gvcf_batch_size(self): """The number of GVCFs to combine into a Variant Dataset at once.""" return self._gvcf_batch_size @gvcf_batch_size.setter def gvcf_batch_size(self, value: int): if value * len(self._gvcf_import_intervals) > VariantDatasetCombiner._gvcf_merge_task_limit: old_value = value value = VariantDatasetCombiner._gvcf_merge_task_limit // len(self._gvcf_import_intervals) warning(f'gvcf_batch_size of {old_value} would produce too many tasks ' f'using {value} instead') self._gvcf_batch_size = value
[docs] def __eq__(self, other): if other.__class__ != VariantDatasetCombiner: return False for slot in self.__serialized_slots__: if getattr(self, slot) != getattr(other, slot): return False return True
@property def finished(self) -> bool: """Have all GVCFs and input Variant Datasets been combined?""" return not self._gvcfs and not self._vdses
[docs] def save(self): """Save a :class:`.VariantDatasetCombiner` to its `save_path`.""" fs = hl.current_backend().fs try: backup_path = self._save_path + '.bak' if fs.exists(self._save_path): fs.copy(self._save_path, backup_path) with, 'w') as out: json.dump(self, out, indent=2, cls=Encoder) if fs.exists(backup_path): fs.remove(backup_path) except OSError as e: # these messages get printed, because there is absolutely no guarantee # that the hail context is in a sane state if any of the above operations # fail print(f'Failed saving {self.__class__.__name__} state at {self._save_path}') print(f'An attempt was made to copy {self._save_path} to {backup_path}') print('An old version of this state may be there.') print('Dumping current state as json to standard output, you may wish ' 'to save this output in order to resume the combiner.') json.dump(self, sys.stdout, indent=2, cls=Encoder) print() raise e
[docs] def run(self): """Combine the specified GVCFs and Variant Datasets.""" flagname = 'no_ir_logging' prev_flag_value = hl._get_flags(flagname).get(flagname) hl._set_flags(**{flagname: '1'}) vds_samples = sum(vds.n_samples for vdses in self._vdses.values() for vds in vdses) info('Running VDS combiner:\n' f' VDS arguments: {self._num_vdses} datasets with {vds_samples} samples\n' f' GVCF arguments: {len(self._gvcfs)} inputs/samples\n' f' Branch factor: {self._branch_factor}\n' f' GVCF merge batch size: {self._gvcf_batch_size}') while not self.finished: self.step() info('Finished VDS combiner!') hl._set_flags(**{flagname: prev_flag_value})
[docs] @staticmethod def load(path) -> 'VariantDatasetCombiner': """Load a :class:`.VariantDatasetCombiner` from `path`.""" fs = hl.current_backend().fs with as stream: combiner = json.load(stream, cls=Decoder) if combiner._save_path != path: warning('path/save_path mismatch in loaded VariantDatasetCombiner, using ' f'{path} as the new save_path for this combiner') combiner._save_path = path return combiner
[docs] def to_dict(self) -> dict: """A serializable representation of this combiner.""" intervals_typ = hl.tarray(hl.tinterval(hl.tlocus(self._reference_genome))) return {'name': self.__class__.__name__, 'save_path': self._save_path, 'output_path': self._output_path, 'temp_path': self._temp_path, 'reference_genome': str(self._reference_genome), 'dataset_type': self._dataset_type, 'gvcf_type': self._gvcf_type, 'branch_factor': self._branch_factor, 'target_records': self._target_records, 'gvcf_batch_size': self._gvcf_batch_size, 'gvcf_external_header': self._gvcf_external_header, # put this here for humans 'contig_recoding': self._contig_recoding, 'gvcf_info_to_keep': None if self._gvcf_info_to_keep is None else list(self._gvcf_info_to_keep), 'gvcf_reference_entry_fields_to_keep': None if self._gvcf_reference_entry_fields_to_keep is None else list(self._gvcf_reference_entry_fields_to_keep), 'vdses': [md for i in sorted(self._vdses, reverse=True) for md in self._vdses[i]], 'gvcfs': self._gvcfs, 'gvcf_sample_names': self._gvcf_sample_names, 'gvcf_import_intervals': intervals_typ._convert_to_json(self._gvcf_import_intervals), }
@property def _num_vdses(self): return sum(len(v) for v in self._vdses.values())
[docs] def step(self): """Run one layer of combinations. :meth:`.run` effectively runs :meth:`.step` until all GVCFs and Variant Datasets have been combined. """ if self.finished: return if self._gvcfs: self._step_gvcfs() else: self._step_vdses() if not self.finished: self._job_id += 1
def _step_vdses(self): current_bin = original_bin = min(self._vdses) files_to_merge = self._vdses[current_bin][:self._branch_factor] if len(files_to_merge) == len(self._vdses[current_bin]): del self._vdses[current_bin] else: self._vdses[current_bin] = self._vdses[current_bin][self._branch_factor:] remaining = self._branch_factor - len(files_to_merge) while self._num_vdses > 0 and remaining > 0: current_bin = min(self._vdses) extra = self._vdses[current_bin][-remaining:] if len(extra) == len(self._vdses[current_bin]): del self._vdses[current_bin] else: self._vdses[current_bin] = self._vdses[current_bin][:-remaining] files_to_merge = extra + files_to_merge remaining = self._branch_factor - len(files_to_merge) new_n_samples = sum(f.n_samples for f in files_to_merge) info(f'VDS Combine (job {self._job_id}): merging {len(files_to_merge)} datasets with {new_n_samples} samples') temp_path = self._temp_out_path(f'vds-combine_job{self._job_id}') largest_vds = max(files_to_merge, key=lambda vds: vds.n_samples) vds = hl.vds.read_vds(largest_vds.path, _assert_reference_type=self._dataset_type.reference_type, _assert_variant_type=self._dataset_type.variant_type) interval_bin = floor(log(new_n_samples, self._branch_factor)) intervals = self.__intervals_cache.get(interval_bin) if intervals is None: # we use the reference data since it generally has more rows than the variant data intervals, _ = calculate_new_intervals(vds.reference_data, self._target_records, os.path.join(temp_path, '')) self.__intervals_cache[interval_bin] = intervals paths = [f.path for f in files_to_merge] vdss = self._read_variant_datasets(paths, intervals) combined = combine_variant_datasets(vdss) if self.finished: combined.write(self._output_path) return new_path = os.path.join(temp_path, 'dataset.vds') combined.write(new_path, overwrite=True, _codec_spec=FAST_CODEC_SPEC) new_bin = floor(log(new_n_samples, self._branch_factor)) # this ensures that we don't somehow stick a vds at the end of # the same bin, ending up with a weird ordering issue if new_bin <= original_bin: new_bin = original_bin + 1 self._vdses[new_bin].append(VDSMetadata(path=new_path, n_samples=new_n_samples)) def _step_gvcfs(self): step = self._branch_factor files_to_merge = self._gvcfs[:self._gvcf_batch_size * step] self._gvcfs = self._gvcfs[self._gvcf_batch_size * step:] info(f'GVCF combine (job {self._job_id}): merging {len(files_to_merge)} GVCFs into ' f'{(len(files_to_merge) + step - 1) // step} datasets') if self._gvcf_external_header is not None: sample_names = self._gvcf_sample_names[:self._gvcf_batch_size * step] self._gvcf_sample_names = self._gvcf_sample_names[self._gvcf_batch_size * step:] else: sample_names = None merge_vds = [] merge_n_samples = [] vcfs = [transform_gvcf(vcf, reference_entry_fields_to_keep=self._gvcf_reference_entry_fields_to_keep, info_to_keep=self._gvcf_info_to_keep) for vcf in hl.import_gvcfs(files_to_merge, self._gvcf_import_intervals, array_elements_required=False, _external_header=self._gvcf_external_header, _external_sample_ids=[[name] for name in sample_names] if sample_names is not None else None, reference_genome=self._reference_genome, contig_recoding=self._contig_recoding)] while vcfs: merging, vcfs = vcfs[:step], vcfs[step:] merge_vds.append(combine_variant_datasets(merging)) merge_n_samples.append(len(merging)) if self.finished and len(merge_vds) == 1: merge_vds[0].write(self._output_path) return temp_path = self._temp_out_path(f'gvcf-combine_job{self._job_id}/dataset_') pad = len(str(len(merge_vds) - 1)) merge_metadata = [VDSMetadata(path=temp_path + str(count).rjust(pad, '0') + '.vds', n_samples=n_samples) for count, n_samples in enumerate(merge_n_samples)] paths = [md.path for md in merge_metadata] hl.vds.write_variant_datasets(merge_vds, paths, overwrite=True, codec_spec=FAST_CODEC_SPEC) for md in merge_metadata: self._vdses[max(1, floor(log(md.n_samples, self._branch_factor)))].append(md) def _temp_out_path(self, extra): return os.path.join(self._temp_path, 'combiner-intermediates', f'{self._uuid}_{extra}') def _read_variant_datasets(self, inputs: List[str], intervals: List[Interval]): reference_type = self._dataset_type.reference_type variant_type = self._dataset_type.variant_type return [hl.vds.read_vds(path, intervals=intervals, _assert_reference_type=reference_type, _assert_variant_type=variant_type) for path in inputs]
[docs]def new_combiner(*, output_path: str, temp_path: str, save_path: Optional[str] = None, gvcf_paths: Optional[List[str]] = None, vds_paths: Optional[List[str]] = None, vds_sample_counts: Optional[List[int]] = None, intervals: Optional[List[Interval]] = None, import_interval_size: Optional[int] = None, use_genome_default_intervals: bool = False, use_exome_default_intervals: bool = False, gvcf_external_header: Optional[str] = None, gvcf_sample_names: Optional[List[str]] = None, gvcf_info_to_keep: Optional[Collection[str]] = None, gvcf_reference_entry_fields_to_keep: Optional[Collection[str]] = None, branch_factor: int = VariantDatasetCombiner._default_branch_factor, target_records: int = VariantDatasetCombiner._default_target_records, gvcf_batch_size: Optional[int] = None, batch_size: Optional[int] = None, reference_genome: Union[str, hl.ReferenceGenome] = 'default', contig_recoding: Optional[Dict[str, str]] = None, force: bool = False, ) -> VariantDatasetCombiner: """Create a new :class:`.VariantDatasetCombiner` or load one from `save_path`.""" if not (gvcf_paths or vds_paths): raise ValueError("at least one of 'gvcf_paths' or 'vds_paths' must be nonempty") if gvcf_paths is None: gvcf_paths = [] if vds_paths is None: vds_paths = [] if vds_sample_counts is not None and len(vds_paths) != len(vds_sample_counts): raise ValueError("'vds_paths' and 'vds_sample_counts' (if present) must have the same length " f'{len(vds_paths)} != {len(vds_sample_counts)}') if (gvcf_sample_names is None) != (gvcf_external_header is None): raise ValueError("both 'gvcf_sample_names' and 'gvcf_external_header' must be set or unset") if gvcf_sample_names is not None and len(gvcf_sample_names) != len(gvcf_paths): raise ValueError("'gvcf_sample_names' and 'gvcf_paths' must have the same length " f'{len(gvcf_sample_names)} != {len(gvcf_paths)}') if batch_size is None: if gvcf_batch_size is None: gvcf_batch_size = VariantDatasetCombiner._default_gvcf_batch_size else: pass else: if gvcf_batch_size is None: warning('The batch_size parameter is deprecated. ' 'The batch_size parameter will be removed in a future version of Hail. ' 'Please use gvcf_batch_size instead.') gvcf_batch_size = batch_size else: raise ValueError('Specify only one of batch_size and gvcf_batch_size. ' f'Received {batch_size} and {gvcf_batch_size}.') del batch_size n_partition_args = (int(intervals is not None) + int(import_interval_size is not None) + int(use_genome_default_intervals) + int(use_exome_default_intervals)) if n_partition_args == 0: raise ValueError("'new_combiner': require one argument from 'intervals', 'import_interval_size', " "'use_genome_default_intervals', or 'use_exome_default_intervals' to choose GVCF partitioning") def maybe_load_from_saved_path(save_path: str) -> Optional[VariantDatasetCombiner]: if force: return None fs = hl.current_backend().fs if fs.exists(save_path): try: combiner = load_combiner(save_path) warning(f'found existing combiner plan at {save_path}, using it') # we overwrite these values as they are serialized, but not part of the # hash for an autogenerated name and we want users to be able to overwrite # these when resuming a combine (a common reason to need to resume a combine # is a failure due to branch factor being too large) combiner._branch_factor = branch_factor combiner._target_records = target_records combiner._gvcf_batch_size = gvcf_batch_size return combiner except (ValueError, TypeError, OSError, KeyError): warning(f'file exists at {save_path}, but it is not a valid combiner plan, overwriting') return None # We do the first save_path check now after validating the arguments if save_path is not None: saved_combiner = maybe_load_from_saved_path(save_path) if saved_combiner is not None: return saved_combiner if n_partition_args > 1: warning("'run_combiner': multiple colliding arguments found from 'intervals', 'import_interval_size', " "'use_genome_default_intervals', or 'use_exome_default_intervals'." "\n The argument found first in the list in this warning will be used, and others ignored.") if intervals is not None: pass elif import_interval_size is not None: intervals = calculate_even_genome_partitioning(reference_genome, import_interval_size) elif use_genome_default_intervals: size = VariantDatasetCombiner.default_genome_interval_size intervals = calculate_even_genome_partitioning(reference_genome, size) elif use_exome_default_intervals: size = VariantDatasetCombiner.default_exome_interval_size intervals = calculate_even_genome_partitioning(reference_genome, size) assert intervals is not None if isinstance(reference_genome, str): reference_genome = hl.get_reference(reference_genome) # we need to compute the type that the combiner will have, this will allow us to read matrix # tables quickly, especially in an asynchronous environment like query on batch where typing # a read uses a blocking round trip. vds = None gvcf_type = None if vds_paths: vds = hl.vds.read_vds(vds_paths[0]) ref_entry_tmp = set(vds.reference_data.entry) - {'END'} if gvcf_reference_entry_fields_to_keep is not None and ref_entry_tmp != gvcf_reference_entry_fields_to_keep: warning("Mismatch between 'gvcf_reference_entry_fields' to keep and VDS reference data " "entry types. Overwriting with types from supplied VDS.") gvcf_reference_entry_fields_to_keep = ref_entry_tmp if gvcf_paths: mt = hl.import_vcf(gvcf_paths[0], header_file=gvcf_external_header, force_bgz=True, array_elements_required=False, reference_genome=reference_genome, contig_recoding=contig_recoding) gvcf_type = mt._type if gvcf_reference_entry_fields_to_keep is None: rmt = mt.filter_rows(hl.is_defined( gvcf_reference_entry_fields_to_keep = defined_entry_fields(rmt, 100_000) - {'GT', 'PGT', 'PL'} if vds is None: vds = transform_gvcf(mt._key_rows_by_assert_sorted('locus'), gvcf_reference_entry_fields_to_keep, gvcf_info_to_keep) dataset_type = CombinerOutType(reference_type=vds.reference_data._type, variant_type=vds.variant_data._type) if save_path is None: sha = hashlib.sha256() sha.update(output_path.encode()) sha.update(temp_path.encode()) sha.update(str(reference_genome).encode()) sha.update(str(dataset_type).encode()) if gvcf_type is not None: sha.update(str(gvcf_type).encode()) for path in vds_paths: sha.update(path.encode()) for path in gvcf_paths: sha.update(path.encode()) if gvcf_external_header is not None: sha.update(gvcf_external_header.encode()) if gvcf_sample_names is not None: for name in gvcf_sample_names: sha.update(name.encode()) if gvcf_info_to_keep is not None: for kept_info in sorted(gvcf_info_to_keep): sha.update(kept_info.encode()) if gvcf_reference_entry_fields_to_keep is not None: for field in sorted(gvcf_reference_entry_fields_to_keep): sha.update(field.encode()) if contig_recoding is not None: for key, value in sorted(contig_recoding.items()): sha.update(key.encode()) sha.update(value.encode()) for interval in intervals: sha.update(str(interval).encode()) digest = sha.hexdigest() name = f'vds-combiner-plan_{digest}_{hl.__pip_version__}.json' save_path = os.path.join(temp_path, 'combiner-plans', name) saved_combiner = maybe_load_from_saved_path(save_path) if saved_combiner is not None: return saved_combiner warning(f'generated combiner save path of {save_path}') if vds_sample_counts: vdses = [VDSMetadata(path, n_samples) for path, n_samples in zip(vds_paths, vds_sample_counts)] else: vdses = [] for path in vds_paths: vds = hl.vds.read_vds(path, _assert_reference_type=dataset_type.reference_type, _assert_variant_type=dataset_type.variant_type) n_samples = vds.n_samples() vdses.append(VDSMetadata(path, n_samples)) vdses.sort(key=lambda x: x.n_samples, reverse=True) return VariantDatasetCombiner(save_path=save_path, output_path=output_path, temp_path=temp_path, reference_genome=reference_genome, dataset_type=dataset_type, branch_factor=branch_factor, target_records=target_records, gvcf_batch_size=gvcf_batch_size, contig_recoding=contig_recoding, vdses=vdses, gvcfs=gvcf_paths, gvcf_import_intervals=intervals, gvcf_external_header=gvcf_external_header, gvcf_sample_names=gvcf_sample_names, gvcf_info_to_keep=gvcf_info_to_keep, gvcf_reference_entry_fields_to_keep=gvcf_reference_entry_fields_to_keep)
[docs]def load_combiner(path: str) -> VariantDatasetCombiner: """Load a :class:`.VariantDatasetCombiner` from `path`.""" return VariantDatasetCombiner.load(path)
class Encoder(json.JSONEncoder): def default(self, o): if isinstance(o, VariantDatasetCombiner): return o.to_dict() if isinstance(o, HailType): return str(o) if isinstance(o, tmatrix): return o.to_dict() return json.JSONEncoder.default(self, o) class Decoder(json.JSONDecoder): def __init__(self, **kwargs): super().__init__(object_hook=Decoder._object_hook, **kwargs) @staticmethod def _object_hook(obj): if 'name' not in obj: return obj name = obj['name'] if name == VariantDatasetCombiner.__name__: del obj['name'] obj['vdses'] = [VDSMetadata(*x) for x in obj['vdses']] obj['dataset_type'] = CombinerOutType(*(tmatrix._from_json(ty) for ty in obj['dataset_type'])) if 'gvcf_type' in obj and obj['gvcf_type']: obj['gvcf_type'] = tmatrix._from_json(obj['gvcf_type']) rg = hl.get_reference(obj['reference_genome']) obj['reference_genome'] = rg intervals_type = hl.tarray(hl.tinterval(hl.tlocus(rg))) intervals = intervals_type._convert_from_json(obj['gvcf_import_intervals']) obj['gvcf_import_intervals'] = intervals return VariantDatasetCombiner(**obj) return obj