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⚡️ NATAL Core

Numerical Aggregation Toolkit for Analysis of Lifecycles

GitHub PyPI Python NumPy Docs License

NATAL logo

NATAL Core is a high-performance forward-time population genetics simulation engine that supports configurable lifecycles of species. Rather than tracking individuals one by one, it adopts a numerical aggregation approach: individuals are grouped by age, sex, and genotype, their dynamics computed mathematically, and randomness introduced through probability distributions — avoiding the overhead of per-individual iteration. It supports age-structured and discrete-generation populations, sperm storage, genetic presets, hook-based interventions, and a Rust-native computation core as its only execution engine. NATAL Core is especially useful for modeling gene drive systems in insect populations, but its flexible architecture allows it to be applied to a wide range of population genetics scenarios.

NATAL Core is part of the NATAL project. The full project also includes NATAL Inferencer, a toolkit for parameter inference in population genetics models based on NATAL Core.

Key Features

  • 🪲 Forward-time configurable population modeling (age-structured and discrete-generation).
  • 🧬 Genetic architecture definition with chromosomes, loci, and alleles.
  • 🚀 Numerical aggregation engine, Rust-native.
  • 🧩 Built-in genetic presets, especially for homing drives and toxin-antidote drives.
  • 🪝 Hook system for custom interventions during simulation.
  • 🔍 Observation and filtering utilities for downstream analysis.
  • 🗺️ Spatial simulation support across multiple demes.

Installation

1. Create a virtual environment

It is strongly recommended to use a virtual environment to manage dependencies.

Choose one of the following commands. Python 3.12 is recommended, but any Python version >= 3.10 should work.

uv venv --python 3.12 .venv            # uv (recommended)
python -m venv .venv                   # venv (please ensure Python >= 3.10 is used)
conda create -n natal-env python=3.12  # conda

On Windows, you can run py -3.12 -m venv .venv to specify Python 3.12 as the interpreter for the virtual environment.

2. Activate the virtual environment

Linux / macOS:

source .venv/bin/activate    # uv / venv
conda activate natal-env     # conda

Windows:

.venv\Scripts\activate       # uv / venv
conda activate natal-env     # conda

3. Install NATAL Core

uv pip install natal-core
# or
pip install natal-core

A Minimal Example

import natal as nt
from natal import launch_vue

# 1. Define the genetics architecture of a species
sp = nt.Species.from_dict(
    name="TestSpecies",
    structure={
        "chr1": {"loc1": ["WT", "Dr", "R2", "R1"]}
    },
    gamete_labels=["default", "cas9_deposited"]
)

# 2. Define a drive using built-in presets
drive = nt.HomingDrive(
    name="TestHoming",
    drive_allele="Dr",
    cas9_allele="Dr",
    target_allele="WT",
    resistance_allele="R2",
    functional_resistance_allele="R1",
    drive_conversion_rate=0.95,
    late_germline_resistance_formation_rate=0.9,
    functional_resistance_ratio=0.001,
    embryo_resistance_formation_rate=0.0,
    viability_scaling=1.0,
    fecundity_scaling={"female": 0.0},
    fecundity_mode="recessive",
    cas9_deposition_glab="cas9_deposited"
)

# 3. Build a panmictic population and declare the release event with an Op
pop = (nt.DiscreteGenerationPopulation
    .setup(
        species=sp,
        name="TestPop",
        stochastic=True
    )
    .initial_state(
        individual_count={
            "male": {"WT|WT": 50000}, "female": {"WT|WT": 50000}
        }
    )
    .reproduction(
        eggs_per_female=100
    )
    .competition(
        low_density_growth_rate=6.0,
        carrying_capacity=100000,
        juvenile_growth_mode="beverton_holt"
    )
    .presets(drive)
    .hooks(
        nt.Op.add(genotypes="WT|Dr", ages=1, sex="male", delta=500, when="tick == 10"),
        event="first",
    )
    .build())

# 4. Launch interactive WebUI and run simulation
launch_vue(pop)

For more ready-to-run examples, see the demos directory in the GitHub repository.

Development Checks

Use Python 3.10 or later, a Rust toolchain with rustfmt/clippy, Node.js 24 with Corepack, and a virtual environment:

python -m pip install -e ".[dev]"
python scripts/ci_full.py

Local checks and GitHub Actions share this entry point. Use --only lint types stubs, --only tests, --only baseline, or --only rust to run selected stages. The default run also builds a fresh release wheel and installs it in a temporary, isolated environment to run the complex genetic, spatial, and runtime-update tests, and request the bundled dashboard HTML, assets, and API. End users installing a release wheel do not need Node.js; it is required only to build the dashboard. This requires access to the package index for dependencies. Passing locally verifies the current environment; GitHub checks Python 3.10–3.13 across the supported wheel platforms. See development and release checks for details.

License

This project is licensed under the MIT License.

Metadata

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