Datasets:

Larrivhan
/

NeCast-simulations

from __future__ import annotations

import math

import msprime
import numpy as np

from .config import MAX_MSPRIME_RATE, MAX_SYNTHETIC_RATE_MULTIPLIER, Config
from .empirical_maps import EmpiricalMapStore, slice_piecewise_map
from .utils import loguniform


def constant_map(length: int, rate: float) -> tuple[np.ndarray, np.ndarray]:
    return np.array([0.0, float(length)], dtype=np.float64), np.array([float(rate)], dtype=np.float64)


def make_rate_map(pos: np.ndarray, rates: np.ndarray) -> msprime.RateMap:
    pos = np.asarray(pos, dtype=np.float64).copy()
    rates = np.asarray(rates, dtype=np.float64)
    if len(pos) == 0 or len(rates) == 0:
        raise ValueError("RateMap requires at least one interval")
    pos[0] = 0.0
    if len(pos) != len(rates) + 1:
        pos = pos[: len(rates) + 1]
        if len(pos) < len(rates) + 1:
            pos = np.concatenate([pos, [pos[-1] + 1.0]])
    rates = np.clip(rates, 0.0, None)
    if not np.all(np.isfinite(pos)) or not np.all(np.isfinite(rates)):
        raise ValueError("RateMap contains non-finite positions or rates")
    if np.any(np.diff(pos) <= 0):
        raise ValueError("RateMap positions must be strictly increasing")
    if np.nanmax(rates) > MAX_MSPRIME_RATE:
        raise ValueError("RateMap rates are too large; expected per-bp per-generation probabilities")
    return msprime.RateMap(position=pos, rate=rates)


def _sample_bp_width(rng: np.random.Generator, length: int, lo: float, hi: float) -> float:
    upper = max(1.0, min(float(hi), float(length)))
    lower = max(1.0, min(float(lo), upper))
    return loguniform(rng, lower, upper)


# Calibration targets, all measured from the maps in ``maps/`` rather than
# chosen.  Reference windows: chr1 20-40 Mb and chr20 10-26 Mb, both clear of
# a centromere.
#
# Recombination, log-rate standard deviation of the length-weighted mean on a
# regular grid:
#
#                    1 kb    10 kb   100 kb    1 Mb
#   deCODE 2019      3.821   3.472    1.898   0.712
#   HapMap II        2.088   1.964    1.381   0.615
#   pyrho CEU        1.321   1.371    1.137   0.688
#
# deCODE runs high at fine scale because 18.4% of its 1 kb cells are exactly
# zero -- no crossover was observed there in 4.5M meioses -- which is counting
# sparsity rather than biology, so the fine-scale target comes from the two LD
# maps and the megabase target from all three, which agree there.
#
# Autocorrelation of log rate on 10 kb cells is 0.65 at one cell, 0.50 at two,
# 0.27 at ten, and then flattens near 0.08 out to megabases.  That plateau is
# the reason the background here is a separate slow component rather than a
# single decaying one: a map is a megabase-scale landscape with kilobase-scale
# hotspots on it, not one process.
#
# Hotspots: chr20 between 10 and 26 Mb carries 393 runs above ten times the
# mean, a median run width of 466 bp, and 1.76% of sequence in them.
BACKGROUND_CELL_BP = 2_000
# 24.6 runs per megabase were measured, but a run merges hotspots that abut,
# so the generating density is higher than the counted one.
HOTSPOT_PER_MB = 60.0
HOTSPOT_WIDTH_BP = (200.0, 1_200.0)
HOTSPOT_FACTOR = (15.0, 200.0)
COLDSPOT_COUNT_RANGE = (0, 4)   # rng.integers is half-open
COLDSPOT_WIDTH_BP = (50_000.0, 500_000.0)
COLDSPOT_FACTOR = (0.1, 0.5)
# Drawn per row rather than fixed.  The measured amplitude differs more
# between maps (1.30 for pyrho against 1.76 for HapMap II, at 10 kb) than
# between windows of one map (1.72 to 2.02 within HapMap II), so a single
# constant would be both wrong and a fingerprint: every synthetic row would
# carry the same log-rate spread to nine decimal places, which is a label the
# model can condition on.  These brackets straddle the measured range.
RECOMBINATION_BROAD_SD = (0.50, 1.50)
RECOMBINATION_CORRELATION_BP = (200_000.0, 3_000_000.0)
#
# Mutation, Roulette on chr1 20-60 Mb in 1 kb bins: log-rate sd 0.132, with a
# megabase p90/p10 of 1.12, and autocorrelation 0.39 at one kilobase falling
# to a 0.08 plateau beyond 200 kb.  So the variance is mostly local -- that is
# sequence context, which is uncorrelated between kilobases -- sitting on a
# very mild megabase drift.  There are no mutation hotspots: fine-scale
# mutation variation is a property of single sites, which a binary mutation
# model cannot represent at all.
MUTATION_BROAD_SD = (0.03, 0.07)
MUTATION_CORRELATION_BP = (1_000_000.0, 10_000_000.0)
MUTATION_LOCAL_SD = (0.08, 0.16)
# Not white noise: Roulette's autocorrelation is 0.39 at one kilobase and
# 0.25 at ten, so the local term carries a correlation length of its own.
MUTATION_LOCAL_CORRELATION_BP = (1_500.0, 12_000.0)
MUTATION_CELL_BP = 1_000
# A published map reports the average rate in bins of its own, which both
# hides the true breakpoints and partly smooths a hotspot it did not resolve.
# How a published map is wrong.  The noise here used to be one lognormal draw
# per cell, independent of its neighbours, and that has a consequence at scale:
# independent error averages away.  Measured against the truth, the map handed
# to the model correlated 0.720 in log rate at a kilobase -- close to the 0.687
# two real published maps of the same region manage -- but 0.984 at a hundred
# kilobases where the real pair manage 0.890.  Zoomed out, our map was almost
# perfect, and a model can learn to trust the coarse map completely.  On real
# data that is false: a published map is wrong in patches, because what makes
# it wrong is how much data went into a region and what the method assumed
# there, and neither of those changes cell by cell.
#
# So the error has two parts, and they are given separate amplitudes rather
# than a shared one split by a fraction, because they are calibrated against
# different measurements.  ``observed_map_noise`` sets the per-cell part and is
# pinned by the kilobase-scale correlation; the regional part is pinned by the
# hundred-kilobase one, where averaging has already removed everything else.
#
# The correlation length has to sit near the scale it is meant to spoil, not
# far above it.  A field correlated over many megabases is, across one row, a
# constant -- and a constant multiplier does not change a correlation at all,
# because it shifts the log rate rather than reshaping it.  What makes two real
# maps disagree at a hundred kilobases is error that varies on that scale:
# regions with different amounts of data behind them, hundreds of kilobases
# across.
# Relative to the map, not absolute.  A recombination map's log rate has a
# standard deviation near 0.5 across a row and a mutation map's near 0.04, so
# one absolute amplitude cannot serve both: measured at an absolute 0.25, the
# observed recombination map correlated 0.851 with the truth while the observed
# mutation map correlated 0.163 -- five to eight times more noise than signal,
# which is not a noisy map, it is no map.  The error is now a fraction of
# whatever spread the true map has, so it lands in the same place on both.
# Per-row jitter around whatever ratio the config asks for.  The config value
# is the ratio itself -- error log-spread over the true map's log-spread -- not
# an absolute amplitude, so this multiplies rather than replaces it.
OBSERVED_NOISE_RATIO = (0.75, 1.30)
# Share of that error which is regional rather than per-cell.  Regional error
# survives averaging and per-cell error does not, and it is the surviving part
# that decides how wrong a map still is once a model has zoomed out.
OBSERVED_NOISE_REGIONAL_SHARE = (0.55, 0.90)
OBSERVED_NOISE_REGIONAL_CORRELATION_BP = (80_000.0, 1_500_000.0)
# A separate, absolute error on the map's overall level, in natural log.  This
# one is not relative, because it stands for something else: published rate
# estimates disagree about the total, not only about where the rate sits.  It
# matters more than its size suggests -- the observed integral is what a
# segregating count gets divided by, so this is a direct scale error on the
# effective size a model reads off the row.
OBSERVED_NOISE_SCALE_SIGMA = (0.03, 0.18)
OBSERVED_CELL_BP = (1_000.0, 20_000.0)


def observed_noise_multiplier(
    rng: np.random.Generator,
    n_cells: int,
    cell_bp: float,
    sigma: float,
    true_rates: np.ndarray | None = None,
) -> np.ndarray:
    """The multiplicative error between the true map and the published one.
    Two components with separate amplitudes: a regional field correlated over
    megabases, and independent per-cell noise.  The regional part is what makes
    the error survive averaging, which is what a real map's disagreement does
    and an independent draw's does not -- and it is the part that decides how
    wrong the map still is once a model has zoomed out far enough for the
    per-cell noise to have cancelled.
    Centred so the multiplier has mean one.  A lognormal with a zero location
    has mean ``exp(sigma**2 / 2)``, and the observed integral is what the
    segregating count gets divided by, so an uncentred draw biases every row
    in the same direction.
    """

    if n_cells <= 0:
        return np.ones(0, dtype=np.float64)
    if sigma <= 0.0:
        return np.ones(n_cells, dtype=np.float64)

    # How much the true map itself moves, in natural log.  Everything except
    # the overall-level error is measured against this.
    spread = 0.0
    if true_rates is not None:
        positive = np.asarray(true_rates, dtype=np.float64)
        positive = positive[positive > 0.0]
        if positive.size > 2:
            spread = float(np.std(np.log(positive)))
    if spread <= 0.0:
        # A flat map has no structure to get wrong; only its level can be.
        scale_sigma = float(rng.uniform(*OBSERVED_NOISE_SCALE_SIGMA))
        offset = float(rng.normal(0.0, scale_sigma))
        return np.full(
            n_cells, math.exp(offset - 0.5 * scale_sigma**2), dtype=np.float64
        )

    ratio = float(rng.uniform(*OBSERVED_NOISE_RATIO)) * float(sigma)
    share = float(rng.uniform(*OBSERVED_NOISE_REGIONAL_SHARE))
    correlation = float(loguniform(rng, *OBSERVED_NOISE_REGIONAL_CORRELATION_BP))
    structural = ratio * spread
    regional = (
        structural
        * math.sqrt(share)
        * _ornstein_uhlenbeck(rng, n_cells, cell_bp, correlation)
    )
    per_cell = (
        structural * math.sqrt(1.0 - share) * rng.normal(0.0, 1.0, size=n_cells)
    )
    scale_sigma = float(rng.uniform(*OBSERVED_NOISE_SCALE_SIGMA))
    offset = float(rng.normal(0.0, scale_sigma))
    total_variance = structural**2 + scale_sigma**2
    return np.exp(regional + per_cell + offset - 0.5 * total_variance)


def _ornstein_uhlenbeck(
    rng: np.random.Generator, n_cells: int, cell_bp: float, correlation_bp: float
) -> np.ndarray:
    """A unit-variance process whose correlation decays over a real distance.
    The previous background was a random walk indexed by segment number, and
    segment widths were spacings of uniform draws.  Measured on that version,
    the log rate moved by 0.0105 across a segment at the 25th width percentile
    and 0.0097 across one at the 75th -- the same jump over four times the
    sequence -- so the correlation length in base pairs was an artefact of the
    segmentation rather than a property of the map.
    Nothing standardises the result: the realised amplitude has to vary from
    row to row, because forcing it constant is what turned the old generator's
    spread into a fingerprint.
    """

    if n_cells <= 0:
        raise ValueError("a background needs at least one cell")
    retention = math.exp(-float(cell_bp) / float(correlation_bp))
    innovation = math.sqrt(max(1.0 - retention * retention, 0.0))
    noise = rng.normal(0.0, 1.0, size=n_cells)
    out = np.empty(n_cells, dtype=np.float64)
    out[0] = noise[0]
    for index in range(1, n_cells):
        out[index] = retention * out[index - 1] + innovation * noise[index]
    return out


def _apply_spans(
    edges: np.ndarray, rates: np.ndarray, spans: list[tuple[float, float, float]]
) -> np.ndarray:
    """Multiply exactly the sequence each span covers.
    The edges are already in the grid, so a span multiplies the cells it was
    drawn over and no others.  Applying a multiplier to whole pre-existing
    segments instead made a feature as wide as the segment containing it: 39
    to 156 kb for one drawn at 1 to 20 kb.
    """

    out = rates.copy()
    for left, right, factor in spans:
        covered = (edges[:-1] >= left) & (edges[1:] <= right)
        out = np.where(covered, out * factor, out)
    return out


def _observed_view(
    rng: np.random.Generator,
    edges: np.ndarray,
    rates: np.ndarray,
    baseline: float,
    obs_noise_sigma: float,
    hotspot_missing_prob: float,
    has_hotspots: bool,
) -> tuple[np.ndarray, np.ndarray]:
    """What a published map of this region would look like.
    Re-binned onto a grid of its own, so the true breakpoints are not visible
    in it.  That mattered once the feature edges entered the true grid: the
    observed map was returned on the same edges, which handed over every
    hotspot position exactly and noised only the intensities.  A real
    published map is uncertain about where the hotspots are -- PRDM9 alleles
    differ between populations and move them -- not just about how strong they
    are.
    The lognormal multiplier is centred so its mean is one.  With a zero
    location it has mean ``exp(sigma^2/2)``, so the observed integral sat 3.2%
    above the truth in every row at the default sigma, and ``nw_log10``
    divides the segregating count by exactly that integral.
    """

    length = float(edges[-1])
    cell = float(loguniform(rng, *OBSERVED_CELL_BP))
    obs_edges = np.arange(0.0, length + cell, cell)
    obs_edges[-1] = length
    if len(obs_edges) < 2:
        obs_edges = np.array([0.0, length])

    cumulative = np.concatenate(([0.0], np.cumsum(rates * np.diff(edges))))
    integrated = np.interp(obs_edges, edges, cumulative)
    obs = np.diff(integrated) / np.diff(obs_edges)

    obs = obs * observed_noise_multiplier(
        rng, obs.shape[0], cell, obs_noise_sigma, true_rates=obs
    )
    # Only a map with hotspots can miss one.  Applying this to the mutation
    # map, which has none, rewrote the top ventile of a smooth drift to model
    # a phenomenon that does not exist there.
    if has_hotspots and hotspot_missing_prob > 0 and len(obs) > 20:
        high = obs > np.quantile(obs, 0.95)
        miss = high & (rng.random(obs.shape) < hotspot_missing_prob)
        if miss.any():
            obs[miss] = baseline * rng.uniform(0.5, 2.0, size=int(miss.sum()))
            # Replacing the top ventile with something near the baseline
            # removes rate, so without this the observed integral lands
            # systematically low -- 0.9679 of the truth at the default
            # dropout, against 1.0016 with it switched off.  The lognormal
            # above was centred for exactly this reason; missing a hotspot
            # should change where a published map puts its rate, not how much
            # of it there is in total.
            widths = np.diff(obs_edges)
            scale = (rates * np.diff(edges)).sum() / (obs * widths).sum()
            obs = obs * scale
    obs = np.clip(obs, baseline * 1e-6, baseline * 500)
    return obs_edges, obs


def _length_weighted_mean(rates: np.ndarray, pos: np.ndarray) -> float:
    """The mean that decides how many events actually occur.
    Normalising by the unweighted segment mean instead leaves the realised
    rate off by however much rate and segment width correlate, and they do:
    a feature applied by interval overlap preferentially hits wide segments.
    Measured on the previous generator, the realised recombination rate
    averaged 1.655 times the configured baseline, spread 1.06 to 2.36.
    """

    widths = np.diff(pos)
    total = widths.sum()
    if total <= 0:
        raise ValueError("a rate map must span a positive length")
    return float((np.asarray(rates) * widths).sum() / total)


def synthetic_heterogeneous_map(
    rng: np.random.Generator,
    cfg: Config,
    baseline: float,
    is_mut: bool,
    obs_noise_sigma: float,
    hotspot_missing_prob: float,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, dict]:
    length = int(cfg.seq_len)
    cell = MUTATION_CELL_BP if is_mut else BACKGROUND_CELL_BP
    cell = float(min(cell, max(1.0, length)))

    broad_sd = float(loguniform(rng, *(MUTATION_BROAD_SD if is_mut else RECOMBINATION_BROAD_SD)))
    correlation_bp = float(
        loguniform(
            rng,
            *(MUTATION_CORRELATION_BP if is_mut else RECOMBINATION_CORRELATION_BP),
        )
    )

    n_cells = max(1, int(math.ceil(length / cell)))
    edges = np.concatenate((np.arange(n_cells, dtype=np.float64) * cell, [float(length)]))
    log_rate = broad_sd * _ornstein_uhlenbeck(rng, n_cells, cell, correlation_bp)

    local_sd = 0.0
    if is_mut:
        # Sequence context: real at the scale of single sites, and between
        # kilobases it is nearly independent -- Roulette's autocorrelation is
        # 0.39 at one kilobase.  A binary mutation model has no sequence to
        # condition on, so it enters as an uncorrelated cell-level term.
        local_sd = float(loguniform(rng, *MUTATION_LOCAL_SD))
        local_correlation = float(loguniform(rng, *MUTATION_LOCAL_CORRELATION_BP))
        log_rate = log_rate + local_sd * _ornstein_uhlenbeck(
            rng, n_cells, cell, local_correlation
        )

    spans: list[tuple[float, float, float]] = []
    hot_widths: list[float] = []
    cold_widths: list[float] = []
    if not is_mut:
        for _ in range(int(rng.poisson(HOTSPOT_PER_MB * length / 1_000_000.0))):
            width = _sample_bp_width(rng, length, *HOTSPOT_WIDTH_BP)
            centre = float(rng.uniform(0, length))
            hot_widths.append(width)
            spans.append(
                (
                    max(0.0, centre - width / 2.0),
                    min(float(length), centre + width / 2.0),
                    float(loguniform(rng, *HOTSPOT_FACTOR)),
                )
            )
        for _ in range(int(rng.integers(*COLDSPOT_COUNT_RANGE))):
            width = _sample_bp_width(rng, length, *COLDSPOT_WIDTH_BP)
            centre = float(rng.uniform(0, length))
            cold_widths.append(width)
            spans.append(
                (
                    max(0.0, centre - width / 2.0),
                    min(float(length), centre + width / 2.0),
                    float(rng.uniform(*COLDSPOT_FACTOR)),
                )
            )

    # Feature edges join the grid so each span multiplies exactly what it
    # covers.  The background is carried across by locating each refined cell
    # in the regular grid it came from, which leaves it a function of physical
    # position rather than of the refinement.
    pos = np.unique(
        np.concatenate([edges] + [np.array([a, b]) for a, b, _ in spans])
    ) if spans else edges
    pos = pos[(pos >= 0.0) & (pos <= float(length))]
    owner = np.clip(
        np.searchsorted(edges, 0.5 * (pos[:-1] + pos[1:]), side="right") - 1,
        0,
        n_cells - 1,
    )
    rates = baseline * np.exp(log_rate[owner])
    rates = _apply_spans(pos, rates, spans)

    # Clip first, then normalise.  The other order lets the ceiling remove
    # rate the normalisation has already counted, which left the realised mean
    # at 0.810 of the baseline.
    rates = np.clip(rates, baseline * 1e-5, baseline * MAX_SYNTHETIC_RATE_MULTIPLIER)
    rates = rates * (baseline / _length_weighted_mean(rates, pos))

    obs_pos, obs = _observed_view(
        rng,
        pos,
        rates,
        baseline,
        obs_noise_sigma,
        hotspot_missing_prob,
        has_hotspots=bool(hot_widths),
    )
    meta = {
        "synthetic_map_segments": int(len(rates)),
        "background_cell_bp": float(cell),
        "map_is_mutation": bool(is_mut),
        "hotspot_model": "physical_grid_exact_span",
        "hotspot_count": int(len(hot_widths)),
        "coldspot_count": int(len(cold_widths)),
        "hotspot_width_bp_mean": float(np.mean(hot_widths)) if hot_widths else 0.0,
        "coldspot_width_bp_mean": float(np.mean(cold_widths)) if cold_widths else 0.0,
        "observed_noise_sigma": float(obs_noise_sigma),
        "broad_sd": broad_sd,
        "local_sd": local_sd,
        "correlation_bp": correlation_bp,
        "observed_segments": int(len(obs)),
        "realised_rate_over_baseline": float(
            _length_weighted_mean(rates, pos) / baseline
        ),
    }
    return pos, rates.astype(np.float64), obs_pos, obs.astype(np.float64), meta


def choose_source(rng: np.random.Generator, cfg: Config, empirical_available: bool) -> str:
    probs = {
        "clean_constant_map": cfg.p_clean_constant,
        "random_global_mu_r": cfg.p_random_global,
        "custom_heterogeneous": cfg.p_custom_hetero,
        "empirical_map_slice": cfg.p_empirical_slice if empirical_available else 0.0,
        "stdpopsim_anchor": cfg.p_stdpopsim_anchor if cfg.enable_stdpopsim else 0.0,
    }
    total = sum(probs.values())
    if total <= 0:
        return "custom_heterogeneous"
    keys = list(probs.keys())
    p = np.array([probs[k] for k in keys], dtype=float)
    return str(rng.choice(keys, p=p / p.sum()))


def sample_maps_for_source(
    rng: np.random.Generator,
    cfg: Config,
    source: str,
    empirical: EmpiricalMapStore,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, dict]:
    obs_noise = cfg.observed_map_noise
    empirical_noise = getattr(cfg, "empirical_map_noise", obs_noise)
    miss_prob = cfg.hotspot_missing_prob

    meta: dict = {"source_type": source}
    if source == "clean_constant_map":
        rec_pos, rec_rate = constant_map(cfg.seq_len, cfg.baseline_rec)
        mut_pos, mut_rate = constant_map(cfg.seq_len, cfg.baseline_mu)
        obs_rec_pos, obs_rec_rate = rec_pos.copy(), rec_rate.copy()
        obs_mut_pos, obs_mut_rate = mut_pos.copy(), mut_rate.copy()
        meta.update({
            "map_type": "constant_clean",
            "map_mode": "constant_clean",
            "recomb_mode": "constant",
            "mu_mode": "constant",
            "global_rec_rate": cfg.baseline_rec,
            "global_mu_rate": cfg.baseline_mu,
        })
    elif source == "random_global_mu_r":
        rec = cfg.baseline_rec * loguniform(rng, 0.3, 3.0)
        mu = cfg.baseline_mu * loguniform(rng, 0.5, 2.0)
        rec_pos, rec_rate = constant_map(cfg.seq_len, rec)
        mut_pos, mut_rate = constant_map(cfg.seq_len, mu)
        obs_rec_pos, obs_rec_rate = rec_pos.copy(), rec_rate.copy()
        obs_mut_pos, obs_mut_rate = mut_pos.copy(), mut_rate.copy()
        meta.update({
            "map_type": "constant_random_global",
            "map_mode": "constant_random_global",
            "recomb_mode": "constant_random_global",
            "mu_mode": "constant_random_global",
            "global_rec_rate": rec,
            "global_mu_rate": mu,
        })
    elif source == "empirical_map_slice" and empirical.is_available(cfg.seq_len):
        chrom = str(rng.choice(empirical.common_chroms(cfg.seq_len)))
        start_min, start_max = empirical.common_slice_start_bounds(chrom, cfg.seq_len)
        shared_start = start_min if start_min == start_max else float(rng.uniform(start_min, start_max))
        shared_end = shared_start + cfg.seq_len
        rec_pos, rec_rate, obs_rec_pos, obs_rec_rate, rec_meta = slice_piecewise_map(
            rng,
            empirical.rec_maps[chrom],
            cfg.seq_len,
            empirical_noise,
            cfg.baseline_rec,
            start=shared_start,
            validate_input=False,
        )
        mut_pos, mut_rate, obs_mut_pos, obs_mut_rate, mut_meta = slice_piecewise_map(
            rng,
            empirical.mut_maps[chrom],
            cfg.seq_len,
            empirical_noise * 0.6,
            cfg.baseline_mu,
            start=shared_start,
            validate_input=False,
        )
        meta.update({
            "map_type": "empirical_slice",
            "map_mode": "empirical_slice",
            "recomb_mode": "empirical_slice",
            "mu_mode": "empirical_slice",
            "observed_noise_sigma": obs_noise,
            "slice_alignment": "shared_chromosomal_interval",
            "slice_chrom": chrom,
            "slice_start_bp": shared_start,
            "slice_end_bp": shared_end,
            "rec_slice": rec_meta,
            "mut_slice": mut_meta,
        })
    else:
        source = "custom_heterogeneous"
        rec_pos, rec_rate, obs_rec_pos, obs_rec_rate, rec_meta = synthetic_heterogeneous_map(
            rng, cfg, cfg.baseline_rec, is_mut=False, obs_noise_sigma=obs_noise, hotspot_missing_prob=miss_prob
        )
        mut_pos, mut_rate, obs_mut_pos, obs_mut_rate, mut_meta = synthetic_heterogeneous_map(
            rng, cfg, cfg.baseline_mu, is_mut=True, obs_noise_sigma=obs_noise * 0.6, hotspot_missing_prob=miss_prob * 0.5
        )
        meta.update({
            "source_type": source,
            "map_type": "synthetic_heterogeneous",
            "map_mode": "synthetic_heterogeneous",
            "recomb_mode": "synthetic_heterogeneous",
            "mu_mode": "synthetic_heterogeneous",
            "rec_map_meta": rec_meta,
            "mut_map_meta": mut_meta,
        })

    return rec_pos, rec_rate, obs_rec_pos, obs_rec_rate, mut_pos, mut_rate, obs_mut_pos, obs_mut_rate, meta