Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
population-genetics
demographic-inference
effective-population-size
coalescent
msprime
simulation
License:
File size: 3,623 Bytes
ee5b44c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | 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"]
|