# DLCoalSim Coalescent simulation of demographic histories, for training and evaluating methods that infer effective population size through time, Ne(t), from sequence data. A row is one simulated sample: bit-packed haplotypes, variant positions, the recombination and mutation maps *as a method would receive them* rather than as they truly were, and a 64-bin log-time target. Nothing is precomputed into summary statistics — the point is that a method computes its own. ```bash pip install -e . dlcoalsim --preset mini --out-dir runs/first ``` That writes 512 rows in a few minutes. `--preset core_v01` is 20,000 rows at 10 Mb; `--preset full_v01` is 100,000 rows at 20 Mb. ## Reading a dataset ``` runs/first/ samples/shard_00000.npz numerical arrays, 128 rows a shard samples/shard_00000.jsonl.gz one metadata object per row metadata/samples.csv the same metadata, whole dataset config.json manifest.json what produced it scripts/loader.py a minimal reader, written beside the data ``` ```python import numpy as np shard = np.load("runs/first/samples/shard_00000.npz") row = 0 lo, hi = shard["variant_offsets"][row], shard["variant_offsets"][row + 1] target = shard["target_log10_ne"][row] # (64,) log10 Ne per time bin positions = shard["variant_positions_bp"][lo:hi] # uint32 packed = shard["genotype_packed"][lo:hi] # (variants, 8) uint8 n_haplotypes = int(shard["n_haplotypes"][0]) haplotypes = np.unpackbits(packed, axis=1, bitorder="little")[:, :n_haplotypes] ``` Time bins are log-spaced from 50 to 500,000 generations; `time_edges` in `manifest.json` gives the boundaries. Genotypes are packed one bit per haplotype, `n_haplotypes` to a row. Missing calls are a sparse flat index (`missing_flat_idx`, `missing_offsets`) rather than a mask. A map stores one offsets array, not two. Its `*_pos` array holds the breakpoints and `*_rate` the rates *between* them, so a row holds one fewer rate than positions — none of either where a row has no map: ```python offsets = shard["obs_rec_offsets"] lengths = np.diff(offsets.astype(np.int64)) rate_at = np.concatenate(([0], np.cumsum(np.maximum(lengths - 1, 0)))) breakpoints = shard["obs_rec_pos"][offsets[row]:offsets[row + 1]] rates = shard["obs_rec_rate"][rate_at[row]:rate_at[row + 1]] ``` The maps come in two forms. `obs_rec_*` and `obs_mut_*` are what a method is allowed to see: a published map's resolution, its error, and its overall scale offset. `true_rec_*` and `true_mut_*` are what the sequence was actually simulated under, written for `--truth-fraction` of rows — 2% by default — so the gap between the two can be measured on a subsample. **Training on the true maps is cheating.** ## What a row can be Two mixtures are drawn independently, so any history can appear under any map. ### Demographic history | recipe | weight | what it is | | --- | --- | --- | | `spectral_walk` | 0.200 | Band-limited Fourier series on continuous log time. Spectral slope sets roughness, an explicit log10 range sets amplitude, an envelope tilt sets the recent–ancient asymmetry. | | `empirical_human_ne_template` | 0.190 | 191 curves inferred from real human data by SMC++, phlash, MSMC2 and MSMC-IM, with time warp, level shift, contrast and smoothed noise. | | `size_event` | 0.100 | One size change: time, duration, signed depth, and the level it settles at. A duration running past the window is a permanent step. | | `bottleneck` | 0.100 | A drop of 0.4 to 3 decades that ends and recovers. | | `event_on_background` | 0.086 | A quieter spectral walk with one or two sharp events on it. | | `multi_event` | 0.080 | Two to four events with independent signs. | | `ooa_chain` | 0.080 | Ancestral size, ancient expansion, a bottleneck held open below the ancestral level, then recent exponential growth — ordered. | | `constant` | 0.040 | A single effective size. The control. | | `exponential` | 0.040 | A signed rate through msprime's own `growth_rate`, so the trajectory is exponential in linear time. | | `sawtooth` | 0.030 | Periodic growth and decline, with strength, placement, extent, regularity and amplitude drift all drawn. | | `literature_sawtooth` | 0.030 | The Zigzag_1S14 character: a drawn number of half cycles, spaced about fourfold, each about a decade deep. | | `stdpopsim_single_pop` | 0.024 | All 18 single-population stdpopsim models across 8 species, verbatim. Rows carry `held_out_evaluation`. | Sizes span 102 to 106, and the level prior is near-uniform across that range — the bottom decade is real (a vaquita's effective size is under three thousand) and a method that has never seen it cannot be trusted there. ### Maps | source | weight | what it is | | --- | --- | --- | | `custom_heterogeneous` | 0.55 | Synthetic: hotspots at 60 per Mb, median width 498 bp, carrying 2.0% of sequence above ten times the mean, on an Ornstein–Uhlenbeck background. Calibrated against deCODE 2019 measured on chr20. | | `empirical_map_slice` | 0.20 | A random interval of the real deCODE and Roulette maps, taken at the same chromosomal coordinates so the two stay in register. | | `random_global_mu_r` | 0.15 | Flat maps, recombination drawn 0.3–3× baseline and mutation 0.5–2×. | | `clean_constant_map` | 0.10 | Flat at baseline, no observation noise. The control. | Observation error is a *fraction of each map's own spread*, split between a regional component correlated over 80 kb–1.5 Mb and a per-cell one. The regional part is what makes the error survive averaging, which is what a real map's disagreement does: two published maps of one region correlate 0.687 at a kilobase and 0.890 at a hundred. ## Configuration Every field of `simulator.config.Config` is a command-line flag with the underscores turned into dashes. `--preset` sets a coherent group of them: | preset | rows | length | haplotypes | | --- | --- | --- | --- | | `mini` | 512 | 1 Mb | 24 | | `core_v01` | 20,000 | 10 Mb | 64 | | `full_v01` | 100,000 | 20 Mb | 64 | Useful ones beyond the preset: - `--truth-fraction` — what share of rows carry the true maps - `--arg-detail` — `none`, `summary` (default: pair coalescences and branch length per time bin, 512 bytes a row) or `full` (the tree sequence itself, which runs 0.8–28 MB a row) - `--workers`, `--shard-size`, `--compute-chunk-size` — parallelism and layout Generation is resumable and content-addressed: an interrupted run picks up from its receipts rather than starting over, and a completed dataset is verified against them before it is trusted. ## Data that ships with the package Everything the simulator needs is inside `simulator/assets`, resolved against the package rather than the working directory, so an installed copy works from anywhere. | | source | | --- | --- | | `maps/decode2019_avg_hg38.bed` | deCODE 2019, Halldórsson et al., *Science* 363:eaau1043. 4,531,535 localised crossovers, 682 bp average resolution, native GRCh38. A pedigree map, so it carries no demographic assumption — the LD-based alternatives are inferred *under* one, and this project infers demography from linkage. | | `maps/roulette_2023_...bed` | Roulette, Seplyarskiy et al., *Nature Genetics* 2023. 1 kb bins. | | `templates/human_ne/` | 191 inferred human curves: SMC++ on 1000 Genomes, phlash, MSMC2 and MSMC-IM. | | `templates/stdpopsim_single_pop/` | The 18 single-population models in the stdpopsim catalogue, across 8 species. | `simulator/assets/SOURCES.md` carries the full provenance. The build scripts that produced each frozen file sit beside it; rebuilding needs `pip install -e ".[build-assets]"`. ## Tests ```bash pip install -e ".[dev]" pytest ``` The suite is calibration as much as correctness. Where a number in the generator was measured from real data, a test pins it to that measurement and says which one — the synthetic map's hotspot width against deCODE chr20, the demography mixture's turning points against the 191 inferred curves, the observed map's correlation with the truth against two published maps of one region. ## License MIT. The maps and templates under `simulator/assets` are third-party data redistributed under their own terms; see `SOURCES.md`.