Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
population-genetics
demographic-inference
effective-population-size
coalescent
msprime
simulation
License:
| from __future__ import annotations | |
| import gzip | |
| import json | |
| from pathlib import Path | |
| import numpy as np | |
| from simulator.config import Config, DEMOGRAPHY_PROB_FIELDS, validate_config | |
| from simulator.empirical_maps import EmpiricalMapStore, PiecewiseRateMap | |
| from simulator.shards import generate_dataset | |
| def _empirical_store() -> EmpiricalMapStore: | |
| rec_map = PiecewiseRateMap( | |
| chrom="chr1", | |
| position=np.array([0.0, 500.0, 1_500.0, 3_000.0], dtype=np.float64), | |
| rate=np.array([1.0e-8, 2.0e-8, 1.5e-8], dtype=np.float64), | |
| ) | |
| mut_map = PiecewiseRateMap( | |
| chrom="chr1", | |
| position=np.array([0.0, 750.0, 2_000.0, 3_000.0], dtype=np.float64), | |
| rate=np.array([1.2e-8, 0.8e-8, 1.6e-8], dtype=np.float64), | |
| ) | |
| return EmpiricalMapStore( | |
| rec_maps={"chr1": rec_map}, | |
| mut_maps={"chr1": mut_map}, | |
| metadata={"fixture": {"shared": True}}, | |
| ) | |
| def _config(out_dir: Path, workers: int) -> Config: | |
| cfg = Config( | |
| dataset_name="parallel-shared-map-test", | |
| out_dir=str(out_dir), | |
| n_samples=2, | |
| shard_size=1, | |
| seq_len=1_000, | |
| n_haplotypes=4, | |
| time_bins=4, | |
| min_time=50.0, | |
| max_time=5_000.0, | |
| p_clean_constant=0.0, | |
| p_random_global=0.0, | |
| p_custom_hetero=0.0, | |
| p_empirical_slice=1.0, | |
| p_stdpopsim_anchor=0.0, | |
| recomb_map="must-not-be-loaded-in-workers.bed", | |
| mut_map="must-not-be-loaded-in-workers.bed", | |
| observed_map_noise=0.0, | |
| workers=workers, | |
| compression=False, | |
| ) | |
| for field in DEMOGRAPHY_PROB_FIELDS: | |
| setattr(cfg, field, 0.0) | |
| cfg.p_constant = 1.0 | |
| validate_config(cfg) | |
| return cfg | |
| def _assert_shards_equal(left: Path, right: Path) -> None: | |
| left_shards = sorted((left / "samples").glob("shard_*.npz")) | |
| right_shards = sorted((right / "samples").glob("shard_*.npz")) | |
| assert [path.name for path in left_shards] == [path.name for path in right_shards] | |
| for left_path, right_path in zip(left_shards, right_shards, strict=True): | |
| with np.load(left_path, allow_pickle=False) as left_data: | |
| with np.load(right_path, allow_pickle=False) as right_data: | |
| assert set(left_data.files) == set(right_data.files) | |
| for key in left_data.files: | |
| np.testing.assert_array_equal(left_data[key], right_data[key], err_msg=key) | |
| def _read_shard_metadata(dataset: Path) -> list[dict]: | |
| rows: list[dict] = [] | |
| for path in sorted((dataset / "samples").glob("shard_*.jsonl.gz")): | |
| with gzip.open(path, "rt", encoding="utf-8") as handle: | |
| rows.extend(json.loads(line) for line in handle) | |
| return rows | |
| def test_parallel_workers_share_maps_and_match_single_worker(tmp_path: Path) -> None: | |
| parallel_dir = tmp_path / "parallel" | |
| single_dir = tmp_path / "single" | |
| store = _empirical_store() | |
| parallel_manifest = generate_dataset(_config(parallel_dir, workers=2), store) | |
| single_manifest = generate_dataset(_config(single_dir, workers=1), store) | |
| assert parallel_manifest["source_counts"] == {"empirical_map_slice": 2} | |
| assert single_manifest["source_counts"] == {"empirical_map_slice": 2} | |
| _assert_shards_equal(parallel_dir, single_dir) | |
| metadata = _read_shard_metadata(parallel_dir) | |
| assert len(metadata) == 2 | |
| for row in metadata: | |
| assert row["slice_alignment"] == "shared_chromosomal_interval" | |
| assert row["rec_slice"]["slice_start_bp"] == row["mut_slice"]["slice_start_bp"] | |
| assert row["rec_slice"]["slice_end_bp"] == row["mut_slice"]["slice_end_bp"] | |