pyparticle 0.1.2

Describing and analyzing aerosol particles and particle populations

pyparticle

A Python library for constructing aerosol particle populations, attaching species-level physical properties, building per-particle morphologies, and aggregating to population-level aerosol properties. The package uses factory/builder discovery, so new population types, aerosol species, and morphologies can be added by dropping small modules into factory/ folders.

Note: The distribution and import name are both pyparticle (PEP 8). If you previously imported PyParticle, switch to pyparticle.

Install

Create a dev environment

conda env create -f environment.yml -n pyparticle
conda activate pyparticle

Editable install

pip install -e .

Optional extras (used by some examples/tests)

• PyMieScatt (used for optics calculations)
• netCDF4 (used to construct populations from aerosol model output)

Install them in the same environment if you need those features:

pip install PyMieScatt netCDF4

Quickstart

Build a population → attach morphology (if needed) → query an aerosol property.

Example (optics shown here; the same pattern applies to freezing):

from PyParticle.population.builder import build_population
from PyParticle.optics.builder import build_optical_population

# 1) Build a simple binned lognormal population (single species: SO4)
pop_cfg = {
    "type": "binned_lognormals",
    "GMD": [100e-9],               # meters
    "GSD": [1.6],
    "N":   [1e8],                  # m^-3
    "aero_spec_names": [["SO4"]],
    "aero_spec_fracs": [[1.0]],
    "N_bins": 60,
    "species_modifications": {"SO4": {"density": 1770, "n_550": 1.45, "k_550": 0.0}}
}
pop = build_population(pop_cfg)

# 2) Build optics (homogeneous spheres) on an RH/λ grid
opt_cfg = {"type": "homogeneous", "wvl_grid": [550e-9], "rh_grid": [0.0]}
opt_pop = build_optical_population(pop, opt_cfg)

# 3) Query scattering coefficient at RH=0, λ=550 nm (SI units inside: meters)
b_scat = opt_pop.get_optical_coeff("b_scat", rh=0.0)  # numpy array or float depending on wvl_grid defined in opt_cfg
print(b_scat)

Concepts & Architecture (brief)

• Particle: lightweight object holding species and per-species masses; exposes helpers like dry/wet diameters and κ.

• ParticlePopulation: container for many Particle items with number concentrations and IDs; carries species_modifications: Dict[str, dict] for runtime overrides.

• Derived properties:

  • OpticalParticle / OpticalPopulation: wraps base particles to compute per-particle optical cross-sections (Csca, Cabs, Cext, g) and aggregates to optical coefficients (b_scat, b_abs, etc.).
  • CCN (cloud condensation nuclei): water uptake and activation are computed on the base Particle / ParticlePopulation.
  • FreezingParticle / FreezingPopulation: wraps base particles to evaluate heterogeneous ice nucleation properties (e.g., Jhet/IN metrics) and aligns results with population IDs.

Discovery / extension points

• Population types: add a module under src/PyParticle/population/factory/ exposing a build(config) callable. The population builder auto-discovers modules in that folder.
• Species default: define default species in src/PyParticle/species/factory.py.
• Optics morphologies: add a module under src/PyParticle/optics/factory/ and register a build callable (the registry or module-level build will be discovered).
• Freezing morphologies: add a module under src/PyParticle/freezing/factory/ and register a build callable (the registry or module-level build will be discovered).

Developer guidance and templates are available in docs/developer/factories.md.

Repository layout (high level)

• src/PyParticle/ — core library

  • aerosol_particle.py
  • population/ (builder, base, factories)
  • optics/ (builder, base, refractive_index, factories)
  • species/ (registry and data readers)
  • freezing/ (builder, base, factories)
  • analysis/ (particle- and population-level)
  • viz/ (plotting helpers)

• examples/

• datasets/ (species_data/, model_data/)

• tests/

• docs/

• environment.yml, setup.py, pyproject.toml, tools/

Testing

Run unit tests locally with the conda env active:

pytest -q

Contributing

1. Fork or branch from main/develop.
2. Add tests (unit tests for new behavior; integration tests when optional deps apply).
3. Run the test suite locally and ensure examples still run.
4. Submit a PR with a clear description and rationale.

License

See LICENSE in the repository root.

Acknowledgments

The PyParticle architecture was developed under the Integrated Cloud, Land-surface, and Aerosol System Study (ICLASS) project with support from the U.S. Department of Energy's Atmospheric System Research. Development and optics work were supported in part by Pacific Northwest National Laboratory.