Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
population-genetics
demographic-inference
effective-population-size
coalescent
msprime
simulation
License:
File size: 9,901 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 | from __future__ import annotations
import csv
import hashlib
import re
from dataclasses import dataclass
from pathlib import Path
import numpy as np
from .assets import asset_path
from .config import Config
@dataclass(frozen=True)
class NeTemplate:
source_key: str
method: str
population: str
generations_ago: np.ndarray
ne: np.ndarray
path: str
time_unit_note: str
@dataclass
class EmpiricalNeTemplateStore:
templates: list[NeTemplate]
source_counts: dict[str, int]
def choose(self, rng: np.random.Generator, cfg: Config) -> NeTemplate:
weights = _parse_source_mix(cfg.empirical_ne_template_source_mix)
available = [key for key in weights if self.source_counts.get(key, 0) > 0 and weights[key] > 0]
if not available:
available = [key for key, count in self.source_counts.items() if count > 0]
if not available:
raise RuntimeError("no empirical Ne templates are available")
p = np.asarray([weights.get(key, 1.0) for key in available], dtype=np.float64)
source_key = str(rng.choice(available, p=p / p.sum()))
choices = [t for t in self.templates if t.source_key == source_key]
return choices[int(rng.integers(0, len(choices)))]
_STORE_CACHE: dict[str, EmpiricalNeTemplateStore] = {}
def empirical_ne_templates_available(cfg: Config) -> bool:
return len(load_empirical_ne_templates(cfg).templates) > 0
def empirical_ne_template_provenance(
cfg: Config,
*,
include_source_files: bool = True,
) -> dict:
"""Fingerprint the exact parsed template curves and their source files."""
store = load_empirical_ne_templates(cfg)
digest = hashlib.sha256()
ordered = sorted(
store.templates,
key=lambda template: (
template.source_key,
template.method,
template.population,
template.path,
),
)
def update_text(value: str) -> None:
encoded = value.encode("utf-8")
digest.update(len(encoded).to_bytes(8, "little"))
digest.update(encoded)
for template in ordered:
for value in (
template.source_key,
template.method,
template.population,
template.time_unit_note,
):
update_text(value)
for array in (template.generations_ago, template.ne):
values = np.asarray(array, dtype="<f8")
digest.update(values.size.to_bytes(8, "little"))
digest.update(values.tobytes(order="C"))
files: list[dict[str, int | str]] = []
if include_source_files:
for raw_path in sorted({template.path for template in ordered}):
path = Path(raw_path).expanduser().resolve(strict=True)
before = path.stat()
file_digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
file_digest.update(chunk)
after = path.stat()
before_identity = (
before.st_dev,
before.st_ino,
before.st_size,
before.st_mtime_ns,
)
after_identity = (
after.st_dev,
after.st_ino,
after.st_size,
after.st_mtime_ns,
)
if before_identity != after_identity:
raise ValueError(
"Empirical Ne template changed while recording provenance: "
f"{path}"
)
files.append(
{
"path": str(path),
"source_size_bytes": int(after.st_size),
"source_sha256": file_digest.hexdigest(),
}
)
return {
"n_templates": len(ordered),
"source_counts": {
str(key): int(value)
for key, value in sorted(store.source_counts.items())
},
"parsed_templates_sha256": digest.hexdigest(),
"source_files": files,
}
def load_empirical_ne_templates(cfg: Config) -> EmpiricalNeTemplateStore:
root = _resolve_template_dir(cfg.empirical_ne_template_dir)
key = str(root.resolve()) if root.exists() else str(root)
cached = _STORE_CACHE.get(key)
if cached is not None:
return cached
templates: list[NeTemplate] = []
templates.extend(_load_smcpp_1kg(root / "smcpp_popsizes_1kg.csv"))
templates.extend(
_load_normalized(
root / "phlash_unified_populations_normalized.csv",
source_key="phlash_unified",
method="PHLaSH",
time_unit_note="generations; normalized from phlash_paper fig7a",
)
)
if not any(t.source_key == "phlash_unified" for t in templates):
templates.extend(
_load_phlash_raw(
root / "phlash_fig7a_unified_populations.csv",
source_key="phlash_unified",
method="PHLaSH",
)
)
templates.extend(
_load_normalized(
root / "phlash_superpopulations_normalized.csv",
source_key="phlash_super",
method="PHLaSH",
time_unit_note="generations; normalized from phlash_paper fig7b",
)
)
if not any(t.source_key == "phlash_super" for t in templates):
templates.extend(
_load_phlash_raw(
root / "phlash_fig7b_superpopulations.csv",
source_key="phlash_super",
method="PHLaSH",
)
)
templates.extend(
_load_normalized(
root / "msmc2_yoruba_french_within_ne_normalized.csv",
source_key="msmc2_example",
method="MSMC2",
time_unit_note="generations; converted from MSMC2 scaled time using mu=1.25e-8",
)
)
templates.extend(
_load_normalized(
root / "msmc_im_yoruba_french_ne_normalized.csv",
source_key="msmc_im_example",
method="MSMC-IM",
time_unit_note="generations; MSMC-IM example output",
)
)
counts: dict[str, int] = {}
for template in templates:
counts[template.source_key] = counts.get(template.source_key, 0) + 1
store = EmpiricalNeTemplateStore(templates=templates, source_counts=counts)
_STORE_CACHE[key] = store
return store
def _resolve_template_dir(path: str) -> Path:
return asset_path(path)
def _parse_source_mix(text: str) -> dict[str, float]:
out: dict[str, float] = {}
for token in text.split(","):
token = token.strip()
if not token:
continue
if ":" in token:
key, value = token.split(":", 1)
out[key.strip()] = max(0.0, float(value))
else:
out[token] = 1.0
return out
def _read_csv(path: Path) -> list[dict[str, str]]:
if not path.exists():
return []
with path.open("r", encoding="utf-8", newline="") as f:
return list(csv.DictReader(f))
def _load_smcpp_1kg(path: Path) -> list[NeTemplate]:
rows = _read_csv(path)
if not rows:
return []
grouped: dict[str, list[tuple[float, float]]] = {}
for row in rows:
if row.get("plot_type", "path") != "path":
continue
pop = row["label"]
generations = float(row["x"]) / 29.0
grouped.setdefault(pop, []).append((generations, float(row["y"])))
return _templates_from_groups(
grouped,
source_key="smcpp_1kg",
method="SMC++",
path=path,
time_unit_note="years converted to generations using generation_time=29",
)
def _load_normalized(path: Path, source_key: str, method: str, time_unit_note: str) -> list[NeTemplate]:
rows = _read_csv(path)
if not rows:
return []
grouped: dict[str, list[tuple[float, float]]] = {}
for row in rows:
pop = row["population"]
grouped.setdefault(pop, []).append((float(row["generations_ago"]), float(row["Ne"])))
return _templates_from_groups(grouped, source_key, method, path, time_unit_note)
def _load_phlash_raw(path: Path, source_key: str, method: str) -> list[NeTemplate]:
rows = _read_csv(path)
if not rows:
return []
grouped: dict[str, list[tuple[float, float]]] = {}
for row in rows:
pop = _clean_phlash_population(row["pop"])
grouped.setdefault(pop, []).append((float(row["years"]), float(row["Ne"])))
return _templates_from_groups(
grouped,
source_key=source_key,
method=method,
path=path,
time_unit_note="generations; phlash_paper CSV column is named years but generated from T in generations",
)
def _clean_phlash_population(value: str) -> str:
match = re.search(r"unified/(.*?)/phlash/estimates\.pkl", value)
return match.group(1) if match else value
def _templates_from_groups(
grouped: dict[str, list[tuple[float, float]]],
source_key: str,
method: str,
path: Path,
time_unit_note: str,
) -> list[NeTemplate]:
out: list[NeTemplate] = []
for pop, pairs in grouped.items():
arr = np.asarray(sorted(pairs), dtype=np.float64)
arr = arr[np.isfinite(arr).all(axis=1)]
arr = arr[(arr[:, 0] >= 0) & (arr[:, 1] > 0)]
if arr.shape[0] < 2:
continue
_, unique_idx = np.unique(arr[:, 0], return_index=True)
arr = arr[np.sort(unique_idx)]
out.append(
NeTemplate(
source_key=source_key,
method=method,
population=pop,
generations_ago=arr[:, 0].astype(np.float64),
ne=arr[:, 1].astype(np.float64),
path=str(path),
time_unit_note=time_unit_note,
)
)
return out
|