Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
population-genetics
demographic-inference
effective-population-size
coalescent
msprime
simulation
License:
File size: 10,303 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 | from __future__ import annotations
import dataclasses
import csv
import gzip
import json
import subprocess
import sys
import zipfile
from pathlib import Path
import numpy as np
from scripts.dataset_admin import check_shard
from simulator.config import (
DEMOGRAPHY_PROB_FIELDS,
DEMOGRAPHY_TYPE_IDS,
GENERATOR_RECIPE_IDS,
Config,
validate_config,
)
from simulator.dataset_files import write_dataset_files
from simulator.dataset_state import SAMPLE_GENERATOR_SCHEMA_VERSION
from simulator.empirical_maps import EmpiricalMapStore
from simulator.shards import _write_npz_arrays, generate_dataset
def _config(
out_dir: Path,
*,
workers: int,
compute_chunk_size: int,
) -> Config:
cfg = Config(
dataset_name="chunked-io-test",
out_dir=str(out_dir),
seed=271828,
n_samples=4,
shard_size=2,
compute_chunk_size=compute_chunk_size,
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=workers,
compression=True,
compression_level=1,
include_truth=True,
)
for field in DEMOGRAPHY_PROB_FIELDS:
setattr(cfg, field, 0.0)
cfg.p_constant = 1.0
validate_config(cfg)
return cfg
def _assert_logical_outputs_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 left_data.files == right_data.files
for key in left_data.files:
assert left_data[key].dtype == right_data[key].dtype, key
assert left_data[key].shape == right_data[key].shape, key
np.testing.assert_array_equal(left_data[key], right_data[key], err_msg=key)
assert (
left_data[key].tobytes(order="C")
== right_data[key].tobytes(order="C")
), key
with gzip.open(left_path.with_suffix(".jsonl.gz"), "rb") as handle:
left_metadata = handle.read()
with gzip.open(right_path.with_suffix(".jsonl.gz"), "rb") as handle:
right_metadata = handle.read()
assert left_metadata == right_metadata
with gzip.open(left / "metadata" / "samples.jsonl.gz", "rb") as handle:
left_aggregate = handle.read()
with gzip.open(right / "metadata" / "samples.jsonl.gz", "rb") as handle:
right_aggregate = handle.read()
assert left_aggregate == right_aggregate
assert (left / "metadata" / "samples.csv").read_bytes() == (
right / "metadata" / "samples.csv"
).read_bytes()
def _sidecar_bytes(dataset: Path) -> bytes:
parts = []
for path in sorted((dataset / "samples").glob("shard_*.jsonl.gz")):
with gzip.open(path, "rb") as handle:
parts.append(handle.read())
return b"".join(parts)
def test_npz_writer_honors_compression_level_and_preserves_arrays(tmp_path: Path) -> None:
arrays = {
"zeros": np.zeros(256_000, dtype=np.uint8),
"values": np.arange(128, dtype=np.float32),
"labels": np.array(["alpha", "beta"], dtype="U8"),
}
base = Config(out_dir=str(tmp_path / "unused"))
level_zero = tmp_path / "level-zero.npz"
level_nine = tmp_path / "level-nine.npz"
stored = tmp_path / "stored.npz"
_write_npz_arrays(
level_zero,
arrays,
dataclasses.replace(base, compression=True, compression_level=0),
)
_write_npz_arrays(
level_nine,
arrays,
dataclasses.replace(base, compression=True, compression_level=9),
)
_write_npz_arrays(
stored,
arrays,
dataclasses.replace(base, compression=False, compression_level=1),
)
for path in (level_zero, level_nine, stored):
with np.load(path, allow_pickle=False) as data:
assert data.files == list(arrays)
for key, expected in arrays.items():
np.testing.assert_array_equal(data[key], expected)
with zipfile.ZipFile(level_zero) as archive:
assert {entry.compress_type for entry in archive.infolist()} == {
zipfile.ZIP_DEFLATED
}
with zipfile.ZipFile(level_nine) as archive:
assert {entry.compress_type for entry in archive.infolist()} == {
zipfile.ZIP_DEFLATED
}
with zipfile.ZipFile(stored) as archive:
assert {entry.compress_type for entry in archive.infolist()} == {
zipfile.ZIP_STORED
}
assert level_nine.stat().st_size < level_zero.stat().st_size
def test_compute_chunks_and_worker_count_are_lossless(tmp_path: Path) -> None:
baseline_dir = tmp_path / "baseline"
chunked_dir = tmp_path / "chunked"
parallel_dir = tmp_path / "parallel"
baseline = generate_dataset(
_config(baseline_dir, workers=1, compute_chunk_size=2),
EmpiricalMapStore(),
)
chunked = generate_dataset(
_config(chunked_dir, workers=1, compute_chunk_size=1),
EmpiricalMapStore(),
)
parallel = generate_dataset(
_config(parallel_dir, workers=2, compute_chunk_size=1),
EmpiricalMapStore(),
)
assert baseline["shards"] == chunked["shards"] == parallel["shards"]
assert baseline["demography_counts"] == {"constant": 4}
assert baseline["generator_recipe_counts"] == {"constant": 4}
_assert_logical_outputs_equal(baseline_dir, chunked_dir)
_assert_logical_outputs_equal(chunked_dir, parallel_dir)
assert not (baseline_dir / "samples" / ".fragments").exists()
assert not (chunked_dir / "samples" / ".fragments").exists()
assert not (parallel_dir / "samples" / ".fragments").exists()
for dataset in (baseline_dir, chunked_dir, parallel_dir):
for shard_path in sorted((dataset / "samples").glob("shard_*.npz")):
assert len(check_shard(shard_path)) == 2
with np.load(shard_path, allow_pickle=False) as data:
assert data["demography_type_id"].dtype == np.int16
assert data["generator_recipe_id"].dtype == np.int16
np.testing.assert_array_equal(
data["generator_schema_version"],
np.array([SAMPLE_GENERATOR_SCHEMA_VERSION], dtype=np.int16),
)
np.testing.assert_array_equal(
data["demography_type_id"],
np.full(
len(data["sample_id"]),
DEMOGRAPHY_TYPE_IDS["constant"],
),
)
np.testing.assert_array_equal(
data["generator_recipe_id"],
np.full(
len(data["sample_id"]),
GENERATOR_RECIPE_IDS["constant"],
),
)
with gzip.open(dataset / "metadata" / "samples.jsonl.gz", "rb") as handle:
aggregate = handle.read()
assert aggregate == _sidecar_bytes(dataset)
with (dataset / "metadata" / "samples.csv").open(
newline="",
encoding="utf-8",
) as handle:
rows = list(csv.DictReader(handle))
assert {row["demography_type"] for row in rows} == {"constant"}
assert {row["generator_recipe"] for row in rows} == {"constant"}
assert {int(row["demography_type_id"]) for row in rows} == {
DEMOGRAPHY_TYPE_IDS["constant"]
}
assert {int(row["generator_recipe_id"]) for row in rows} == {
GENERATOR_RECIPE_IDS["constant"]
}
assert all("log10_ne_range" in row for row in rows)
baseline_cfg = _config(baseline_dir, workers=1, compute_chunk_size=2)
write_dataset_files(baseline_cfg, baseline)
config_json = json.loads((baseline_dir / "config.json").read_text(encoding="utf-8"))
manifest_json = json.loads((baseline_dir / "manifest.json").read_text(encoding="utf-8"))
for payload in (config_json, manifest_json):
assert payload["demography_type_ids"] == DEMOGRAPHY_TYPE_IDS
assert payload["generator_recipe_ids"] == GENERATOR_RECIPE_IDS
assert (
payload["generator_schema_version"]
== SAMPLE_GENERATOR_SCHEMA_VERSION
)
subprocess.run(
[
sys.executable,
str(baseline_dir / "scripts" / "validate_dataset.py"),
str(baseline_dir),
],
check=True,
)
def test_fragment_metadata_honors_compression_level(tmp_path: Path) -> None:
level_zero_dir = tmp_path / "metadata-level-zero"
level_nine_dir = tmp_path / "metadata-level-nine"
level_zero = dataclasses.replace(
_config(level_zero_dir, workers=2, compute_chunk_size=1),
compression_level=0,
)
level_nine = dataclasses.replace(
_config(level_nine_dir, workers=2, compute_chunk_size=1),
compression_level=9,
)
generate_dataset(level_zero, EmpiricalMapStore())
generate_dataset(level_nine, EmpiricalMapStore())
zero_sidecars = sorted((level_zero_dir / "samples").glob("shard_*.jsonl.gz"))
nine_sidecars = sorted((level_nine_dir / "samples").glob("shard_*.jsonl.gz"))
assert [path.name for path in zero_sidecars] == [
path.name for path in nine_sidecars
]
for zero_path, nine_path in zip(zero_sidecars, nine_sidecars, strict=True):
with gzip.open(zero_path, "rb") as handle:
zero_metadata = handle.read()
with gzip.open(nine_path, "rb") as handle:
nine_metadata = handle.read()
assert zero_metadata == nine_metadata
assert sum(path.stat().st_size for path in nine_sidecars) < sum(
path.stat().st_size for path in zero_sidecars
)
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