Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
population-genetics
demographic-inference
effective-population-size
coalescent
msprime
simulation
License:
File size: 10,623 Bytes
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import dataclasses
from pathlib import Path
import numpy as np
import pytest
from simulator import shards
from simulator.config import DEMOGRAPHY_PROB_FIELDS, Config, validate_config
from simulator.dataset_state import DatasetConfigMismatchError
from simulator.empirical_maps import EmpiricalMapStore
from simulator.shards import (
DatasetGenerationLockedError,
DatasetSourceUnavailableError,
UnsafeOutputDirectoryError,
dataset_output_lock,
generate_dataset,
reuse_completed_dataset_if_compatible,
)
from simulator.stdpopsim_support import StdpopsimSimulationError
def _config(out_dir: Path, *, force: bool = False) -> Config:
cfg = Config(
dataset_name="recovery-test",
out_dir=str(out_dir),
force=force,
n_samples=1,
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=1.0,
p_random_global=0.0,
p_custom_hetero=0.0,
p_empirical_slice=0.0,
p_stdpopsim_anchor=0.0,
recomb_map="",
mut_map="",
workers=1,
compression=False,
)
for field in DEMOGRAPHY_PROB_FIELDS:
setattr(cfg, field, 0.0)
cfg.p_constant = 1.0
validate_config(cfg)
return cfg
def test_no_done_cleans_stale_shards_before_regeneration(tmp_path: Path) -> None:
out_dir = tmp_path / "dataset"
samples = out_dir / "samples"
metadata = out_dir / "metadata"
samples.mkdir(parents=True)
metadata.mkdir()
(samples / "shard_99999.npz").write_bytes(b"stale")
(samples / "shard_99999.jsonl.gz").write_bytes(b"stale")
(samples / "shard_00000.tmp.npz").write_bytes(b"partial")
(metadata / "samples.csv").write_text("stale", encoding="utf-8")
(out_dir / "manifest.json").write_text("stale", encoding="utf-8")
manifest = generate_dataset(_config(out_dir), EmpiricalMapStore())
assert manifest["n_samples"] == 1
assert [path.name for path in samples.glob("shard_*.npz")] == ["shard_00000.npz"]
assert [path.name for path in samples.glob("shard_*.jsonl.gz")] == [
"shard_00000.jsonl.gz"
]
assert not (out_dir / "manifest.json").exists()
assert reuse_completed_dataset_if_compatible(_config(out_dir))["n_samples"] == 1
def test_completed_dataset_with_missing_or_extra_shards_is_rejected(tmp_path: Path) -> None:
out_dir = tmp_path / "dataset"
cfg = _config(out_dir)
generate_dataset(cfg, EmpiricalMapStore())
missing = out_dir / "samples" / "shard_00000.jsonl.gz"
missing.unlink()
with pytest.raises(DatasetConfigMismatchError, match=r"metadata files.*--force"):
generate_dataset(cfg, EmpiricalMapStore())
missing.write_bytes(b"not-a-valid-gzip")
(out_dir / "samples" / "shard_99999.npz").write_bytes(b"stale")
with pytest.raises(DatasetConfigMismatchError, match=r"NPZ shard files.*--force"):
generate_dataset(cfg, EmpiricalMapStore())
def test_completed_dataset_missing_recipe_ids_is_rejected(tmp_path: Path) -> None:
out_dir = tmp_path / "dataset"
cfg = _config(out_dir)
generate_dataset(cfg, EmpiricalMapStore())
shard = out_dir / "samples" / "shard_00000.npz"
with np.load(shard, allow_pickle=False) as data:
arrays = {
key: np.asarray(data[key])
for key in data.files
if key != "generator_recipe_id"
}
np.savez(shard, **arrays)
with pytest.raises(
DatasetConfigMismatchError,
match=r"missing arrays: generator_recipe_id.*--force",
):
generate_dataset(cfg, EmpiricalMapStore())
def test_completed_dataset_with_misaligned_valid_type_id_is_rejected(
tmp_path: Path,
) -> None:
out_dir = tmp_path / "dataset"
cfg = _config(out_dir)
generate_dataset(cfg, EmpiricalMapStore())
shard = out_dir / "samples" / "shard_00000.npz"
with np.load(shard, allow_pickle=False) as data:
arrays = {key: np.asarray(data[key]) for key in data.files}
arrays["demography_type_id"] = np.ones_like(arrays["demography_type_id"])
np.savez(shard, **arrays)
with pytest.raises(
DatasetConfigMismatchError,
match=r"metadata demography_type_id differs.*--force",
):
generate_dataset(cfg, EmpiricalMapStore())
def test_force_invalidates_done_before_failed_regeneration(
tmp_path: Path,
monkeypatch: pytest.MonkeyPatch,
) -> None:
out_dir = tmp_path / "dataset"
cfg = _config(out_dir)
generate_dataset(cfg, EmpiricalMapStore())
(out_dir / "manifest.json").write_text("previous complete dataset", encoding="utf-8")
def fail_generation(_task):
raise RuntimeError("simulated worker failure")
monkeypatch.setattr(shards, "_generate_shard", fail_generation)
with pytest.raises(RuntimeError, match="simulated worker failure"):
generate_dataset(dataclasses.replace(cfg, force=True), EmpiricalMapStore())
assert not (out_dir / "samples" / "DONE.json").exists()
assert not (out_dir / "manifest.json").exists()
assert not list((out_dir / "samples").glob("shard_*"))
def test_failed_chunk_generation_retains_fragments_until_safe_retry_cleanup(
tmp_path: Path,
monkeypatch: pytest.MonkeyPatch,
) -> None:
out_dir = tmp_path / "dataset"
cfg = dataclasses.replace(
_config(out_dir),
n_samples=2,
shard_size=1,
compute_chunk_size=1,
)
original_generate_shard = shards._generate_shard
calls = 0
def fail_after_first_fragment(task):
nonlocal calls
calls += 1
if calls == 2:
raise RuntimeError("simulated chunk failure")
return original_generate_shard(task)
monkeypatch.setattr(shards, "_generate_shard", fail_after_first_fragment)
with pytest.raises(RuntimeError, match="simulated chunk failure"):
generate_dataset(cfg, EmpiricalMapStore())
fragment_dir = out_dir / "samples" / ".fragments"
assert not (out_dir / "samples" / "DONE.json").exists()
assert [path.name for path in fragment_dir.glob("fragment_*.npz")] == [
"fragment_00000000.npz"
]
assert [path.name for path in fragment_dir.glob("fragment_*.jsonl.gz")] == [
"fragment_00000000.jsonl.gz"
]
resumed_chunk_ids: list[int] = []
def record_resumed_generation(task):
resumed_chunk_ids.append(task[0])
return original_generate_shard(task)
monkeypatch.setattr(shards, "_generate_shard", record_resumed_generation)
manifest = generate_dataset(cfg, EmpiricalMapStore())
assert resumed_chunk_ids == [1]
assert manifest["n_samples"] == 2
assert manifest["shards"] == [
"samples/shard_00000.npz",
"samples/shard_00001.npz",
]
assert not fragment_dir.exists()
def test_invalid_force_config_is_rejected_before_existing_done_is_touched(
tmp_path: Path,
) -> None:
out_dir = tmp_path / "dataset"
cfg = _config(out_dir)
generate_dataset(cfg, EmpiricalMapStore())
done = out_dir / "samples" / "DONE.json"
original = done.read_bytes()
with pytest.raises(ValueError, match="n_samples"):
generate_dataset(
dataclasses.replace(cfg, force=True, n_samples=0),
EmpiricalMapStore(),
)
assert done.read_bytes() == original
def test_output_lock_rejects_a_concurrent_writer(tmp_path: Path) -> None:
with dataset_output_lock(tmp_path):
with pytest.raises(DatasetGenerationLockedError, match="already writing"):
with dataset_output_lock(tmp_path):
pass
def test_nonempty_unmarked_output_directory_is_rejected(tmp_path: Path) -> None:
out_dir = tmp_path / "unrelated"
out_dir.mkdir()
note = out_dir / "keep-me.txt"
note.write_text("user data", encoding="utf-8")
with pytest.raises(UnsafeOutputDirectoryError, match="non-empty directory"):
generate_dataset(_config(out_dir), EmpiricalMapStore())
assert note.read_text(encoding="utf-8") == "user data"
assert not (out_dir / ".dlcoalsim.lock").exists()
def test_empirical_only_demography_requires_available_templates(tmp_path: Path) -> None:
out_dir = tmp_path / "dataset"
cfg = _config(out_dir)
for field in DEMOGRAPHY_PROB_FIELDS:
setattr(cfg, field, 0.0)
cfg.p_empirical_human_ne_template = 1.0
cfg.empirical_ne_template_dir = str(tmp_path / "missing-templates")
validate_config(cfg)
with pytest.raises(DatasetSourceUnavailableError, match="only enabled demography"):
generate_dataset(cfg, EmpiricalMapStore())
assert not (out_dir / "samples" / "DONE.json").exists()
def test_enabled_empirical_map_source_fails_closed_when_maps_are_unavailable(
tmp_path: Path,
) -> None:
out_dir = tmp_path / "dataset"
cfg = _config(out_dir)
cfg.p_empirical_slice = 0.1
validate_config(cfg)
with pytest.raises(
DatasetSourceUnavailableError,
match="refusing to disable the requested empirical source",
):
generate_dataset(cfg, EmpiricalMapStore())
assert not (out_dir / "samples" / "DONE.json").exists()
def test_unavailable_optional_demography_is_disabled_with_a_warning(
tmp_path: Path,
capsys: pytest.CaptureFixture[str],
) -> None:
out_dir = tmp_path / "dataset"
cfg = _config(out_dir)
cfg.p_constant = 1.0
cfg.p_empirical_human_ne_template = 1.0
cfg.empirical_ne_template_dir = str(tmp_path / "missing-templates")
validate_config(cfg)
manifest = generate_dataset(cfg, EmpiricalMapStore())
assert manifest["demography_counts"] == {"constant": 1}
assert manifest["generator_recipe_counts"] == {"constant": 1}
assert "templates are unavailable" in capsys.readouterr().out
def test_requested_stdpopsim_setup_failure_preserves_existing_dataset(
tmp_path: Path,
monkeypatch: pytest.MonkeyPatch,
) -> None:
out_dir = tmp_path / "dataset"
cfg = _config(out_dir)
generate_dataset(cfg, EmpiricalMapStore())
done = out_dir / "samples" / "DONE.json"
original = done.read_bytes()
requested = dataclasses.replace(
cfg,
force=True,
enable_stdpopsim=True,
p_stdpopsim_anchor=1.0,
)
def fail_setup(_cfg: Config) -> None:
raise StdpopsimSimulationError("dependency missing")
monkeypatch.setattr(shards, "validate_stdpopsim_setup", fail_setup)
with pytest.raises(DatasetSourceUnavailableError, match="dependency missing"):
generate_dataset(requested, EmpiricalMapStore())
assert done.read_bytes() == original
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