Datasets:
Formats:
parquet
Size:
100K - 1M
Tags:
population-genetics
demographic-inference
effective-population-size
coalescent
msprime
simulation
License:
File size: 8,563 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 | from __future__ import annotations
import dataclasses
import numpy as np
import pytest
from simulator.config import (
DEMOGRAPHY_PROB_FIELDS,
FLOAT32_MIN_POSITIVE,
INT16_MAX,
SOURCE_PROB_FIELDS,
UINT32_MAX,
Config,
apply_preset,
validate_config,
)
from simulator.noise import flat_index_dtype
def valid_config() -> Config:
return apply_preset(Config(preset="mini"))
@pytest.mark.parametrize("preset", ["mini", "core_v01", "full_v01"])
def test_builtin_presets_are_valid(preset: str) -> None:
validate_config(apply_preset(Config(preset=preset)))
@pytest.mark.parametrize("preset", ["mini", "core_v01", "full_v01"])
def test_builtin_presets_target_64_bins(preset: str) -> None:
cfg = apply_preset(Config(preset=preset))
assert cfg.time_bins == 64
assert cfg.demography_resolution_log10 > 0.0
@pytest.mark.parametrize(
("changes", "message"),
[
({"n_samples": 0}, "n_samples"),
({"n_samples": True}, "n_samples"),
({"shard_size": 0}, "shard_size"),
({"compute_chunk_size": 0}, "compute_chunk_size"),
({"seq_len": 0}, "seq_len"),
({"time_bins": 0}, "time_bins"),
({"demography_resolution_log10": 0.0}, "demography_resolution_log10"),
({"demography_resolution_log10": -1.0}, "demography_resolution_log10"),
({"n_haplotypes": 1}, "at least 2"),
({"n_haplotypes": 3}, "even"),
({"n_haplotypes": INT16_MAX + 1}, "int16"),
({"min_time": 0.0}, "min_time"),
({"min_time": 500.0, "max_time": 500.0}, "max_time"),
({"min_time": float("nan")}, "min_time"),
({"baseline_mu": 0.0}, "baseline_mu"),
({"baseline_rec": -1.0e-8}, "baseline_rec"),
({"baseline_mu": FLOAT32_MIN_POSITIVE / 2.0}, "float32"),
({"baseline_mu": 5.1e-7}, "synthetic maps"),
({"baseline_rec": 1.1e-4}, "baseline_rec"),
({"p_clean_constant": -0.01}, "p_clean_constant"),
({"genotype_error_min": -0.01}, "genotype_error_min"),
({"genotype_error_max": 1.01}, "genotype_error_max"),
({"genotype_error_min": 0.2, "genotype_error_max": 0.1}, "must not exceed"),
({"missing_rate_min": -0.01}, "missing_rate_min"),
({"missing_rate_max": 1.01}, "missing_rate_max"),
({"missing_rate_min": 0.2, "missing_rate_max": 0.1}, "must not exceed"),
({"phase_switch_rate_per_het_min": -0.1}, "phase_switch_rate_per_het_min"),
(
{"phase_switch_rate_per_het_min": 0.2, "phase_switch_rate_per_het_max": 0.1},
"must not exceed",
),
({"hotspot_missing_prob": -0.1}, "hotspot_missing_prob"),
({"hotspot_missing_prob": 1.1}, "hotspot_missing_prob"),
({"observed_map_noise": -0.1}, "observed_map_noise"),
({"observed_map_noise": float("inf")}, "observed_map_noise"),
({"empirical_ne_template_log10_shift_sd": -0.1}, "log10_shift"),
({"empirical_ne_template_contrast_sd": -0.1}, "contrast"),
({"empirical_ne_template_time_warp_sd": -0.1}, "time_warp"),
({"empirical_ne_template_bin_noise_sd": -0.1}, "bin_noise"),
({"map_segments_min": 0}, "map_segments_min"),
({"map_segments_min": 10, "map_segments_max": 9}, "map_segments_min"),
({"max_retries": 0}, "max_retries"),
({"workers": 0}, "workers"),
({"workers": -2}, "workers"),
({"compression_level": -1}, "compression_level"),
({"compression_level": 10}, "compression_level"),
({"compression_level": True}, "compression_level"),
({"seed": 1.5}, "seed"),
({"dataset_name": ""}, "dataset_name"),
({"preset": "unknown"}, "preset"),
({"out_dir": ""}, "out_dir"),
({"out_dir": "."}, "dedicated dataset directory"),
({"demography_mixture_version": ""}, "demography_mixture_version"),
({"empirical_ne_template_dir": ""}, "empirical_ne_template_dir"),
({"stdpopsim_species": ""}, "stdpopsim_species"),
({"stdpopsim_contig_length": -1}, "stdpopsim_contig_length"),
({"recomb_map_unit": "centimorgans"}, "recomb_map_unit"),
({"mut_map_unit": "unknown"}, "mut_map_unit"),
],
)
def test_invalid_scalar_and_range_settings_are_rejected(changes: dict, message: str) -> None:
with pytest.raises(ValueError, match=message):
validate_config(dataclasses.replace(valid_config(), **changes))
def test_source_mixture_requires_a_positive_weight() -> None:
cfg = dataclasses.replace(valid_config(), **{name: 0.0 for name in SOURCE_PROB_FIELDS})
with pytest.raises(ValueError, match="source probability"):
validate_config(cfg)
def test_disabled_stdpopsim_weight_is_not_an_enabled_source() -> None:
source_weights = {name: 0.0 for name in SOURCE_PROB_FIELDS}
source_weights["p_stdpopsim_anchor"] = 1.0
cfg = dataclasses.replace(valid_config(), **source_weights, enable_stdpopsim=False)
with pytest.raises(ValueError, match="enabled source probability"):
validate_config(cfg)
validate_config(dataclasses.replace(cfg, enable_stdpopsim=True))
def test_demography_mixture_requires_a_positive_weight() -> None:
cfg = dataclasses.replace(valid_config(), **{name: 0.0 for name in DEMOGRAPHY_PROB_FIELDS})
with pytest.raises(ValueError, match="demography probability"):
validate_config(cfg)
def test_sequence_positions_must_fit_uint32_storage() -> None:
with pytest.raises(ValueError, match="uint32 position"):
validate_config(dataclasses.replace(valid_config(), seq_len=UINT32_MAX + 1))
@pytest.mark.parametrize(
("seq_len", "n_haplotypes"),
[(60_000_000, 64), (80_000_000, 128), (80_000_000, 256), (40_000_000, 256)],
)
def test_a_long_sequence_with_many_haplotypes_is_accepted(
seq_len: int, n_haplotypes: int
) -> None:
"""The sparse missing index addresses variants, not bases.
Every configuration here was rejected by a precondition that multiplied
``seq_len`` by ``n_haplotypes``, which is the size of the matrix only if
every base is segregating. Variants are three orders of magnitude rarer,
and the width that is actually used is chosen per row from the matrix that
was built.
"""
validate_config(
dataclasses.replace(
valid_config(), seq_len=seq_len, n_haplotypes=n_haplotypes
)
)
def test_the_flat_index_width_follows_the_matrix_it_addresses() -> None:
limit = np.iinfo(np.uint32).max
assert flat_index_dtype(0) == np.uint32
assert flat_index_dtype(limit) == np.uint32
assert flat_index_dtype(limit + 1) == np.uint64
assert flat_index_dtype(10 * limit) == np.uint64
def test_stdpopsim_contig_positions_must_fit_uint32_storage() -> None:
cfg = dataclasses.replace(valid_config(), stdpopsim_contig_length=UINT32_MAX + 1)
with pytest.raises(ValueError, match="uint32 position"):
validate_config(cfg)
def test_a_long_stdpopsim_contig_is_accepted() -> None:
validate_config(
dataclasses.replace(
valid_config(),
n_haplotypes=256,
stdpopsim_contig_length=UINT32_MAX // 2 + 1,
)
)
def test_probability_fields_are_weights_and_need_not_sum_to_one() -> None:
source_weights = {name: 0.0 for name in SOURCE_PROB_FIELDS}
source_weights["p_custom_hetero"] = 4.0
demography_weights = {name: 0.0 for name in DEMOGRAPHY_PROB_FIELDS}
demography_weights["p_constant"] = 2.0
validate_config(dataclasses.replace(valid_config(), **source_weights, **demography_weights))
@pytest.mark.parametrize(
"source_mix",
[
"",
", ,",
"smcpp_1kg:",
":1",
"smcpp_1kg:not-a-number",
"smcpp_1kg:-0.1",
"smcpp_1kg:nan",
"smcpp_1kg:0,phlash_unified:0",
"smcpp_1kg:1,smcpp_1kg:0",
"smcpp_1kg:1e308,phlash_unified:1e308",
"smcpp_typo:1",
],
)
def test_invalid_empirical_ne_source_mix_is_rejected(source_mix: str) -> None:
cfg = dataclasses.replace(valid_config(), empirical_ne_template_source_mix=source_mix)
with pytest.raises(ValueError, match="empirical_ne_template_source_mix"):
validate_config(cfg)
@pytest.mark.parametrize(
"name",
[
"force",
"compression",
"include_truth",
"enable_stdpopsim",
"mut_map_scale_to_baseline",
],
)
def test_boolean_settings_reject_integer_substitutes(name: str) -> None:
with pytest.raises(ValueError, match=name):
validate_config(dataclasses.replace(valid_config(), **{name: 1}))
|