py2sess 0.4.4

Standalone Python implementation of the optimized 2S-ESS forward model.

py2sess

py2sess is a Python implementation of the optimized 2S-ESS radiative-transfer model. It supports solar and thermal forward calculations with NumPy and optional torch backends. It does not call the original Fortran code.

Install

python3 -m pip install .
python3 -m pip install -e ".[torch,dev]"

The optional native backend requires PyTorch:

python3 -m pip install "py2sess[native]"

For source-tree runs without installation, set PYTHONPATH=src.

Build

py2sess uses CMake through scikit-build-core. By default, source builds prepare the Python package without compiling the optional native backend.

cmake -S . -B build
cmake --build build
python3 -m build

To build a local wheel with the native backend, install PyTorch and build without PEP 517 isolation so CMake can find Torch:

python3 -m pip install build scikit-build-core setuptools-scm torch
python3 -m build --wheel --no-isolation -Ccmake.define.PY2SESS_BUILD_NATIVE=ON

Native wheels link against the PyTorch shared libraries supplied by the installed torch package.

Releases are tagged from merged PRs by GitHub Actions. Add a release:major, release:minor, or release:patch label to the PR before merging; the default is release:patch.

PyPI publication uses Trusted Publishing. Configure PyPI to trust happysky19/py2sess, workflow .github/workflows/release.yml, and the pypi GitHub environment, then run the Publish to PyPI GitHub Actions workflow for the tag. The release workflow currently publishes macOS arm64 native wheels and the source distribution.

Quick Start

Solar:

import numpy as np
from py2sess import TwoStreamEss, TwoStreamEssOptions

solver = TwoStreamEss(TwoStreamEssOptions(nlyr=3, mode="solar"))
result = solver.forward(
    tau=np.full(3, 0.02),
    ssa=np.full(3, 0.2),
    g=np.full(3, 0.1),
    z=np.array([3.0, 2.0, 1.0, 0.0]),
    angles=[30.0, 20.0, 0.0],  # sza, vza, relative azimuth
    albedo=0.3,
)
print(result.radiance)

Thermal:

solver = TwoStreamEss(TwoStreamEssOptions(nlyr=3, mode="thermal"))
result = solver.forward(
    tau=np.full(3, 0.1),
    ssa=np.zeros(3),
    g=np.zeros(3),
    z=np.array([3.0, 2.0, 1.0, 0.0]),
    angles=20.0,
    planck=np.array([1.0, 1.1, 1.2, 1.3]),
    surface_planck=1.4,
    emissivity=1.0,
)

Batched wavelengths use leading dimensions:

solver = TwoStreamEss(TwoStreamEssOptions(nlyr=3, mode="thermal"))
tau = np.full((100, 3), 0.02)
result = solver.forward(
    tau=tau,
    ssa=np.zeros_like(tau),
    g=np.zeros_like(tau),
    z=np.array([3.0, 2.0, 1.0, 0.0]),
    angles=20.0,
    planck=np.ones((100, 4)),
    surface_planck=np.ones(100),
    emissivity=np.ones(100),
)
print(result.radiance.shape)  # (100,)

Level fluxes use the final axis for TOA-to-BOA levels. This clear absorbing solar case has an analytic Beer-Lambert flux solution:

import numpy as np
from py2sess import TwoStreamEss, TwoStreamEssOptions

sza = 30.0
mu0 = np.cos(np.deg2rad(sza))
fbeam = 1.0
tau = np.array([0.1, 0.2])
z = np.array([2.0, 1.0, 0.0])

solver = TwoStreamEss(
    TwoStreamEssOptions(
        nlyr=tau.size,
        mode="solar",
        plane_parallel=True,
        delta_scaling=False,
        downwelling=True,
        output_levels=True,
        output_fluxes=True,
        fo_flux_n_mu=8,
    )
)
result = solver.forward(
    tau=tau,
    ssa=np.zeros_like(tau),  # pure absorption
    g=np.zeros_like(tau),
    z=z,
    angles=[sza, 0.0, 0.0],
    fbeam=fbeam,
    albedo=0.0,  # black surface: no upward reflected flux
    delta_m_truncation_factor=np.zeros_like(tau),
    include_fo=True,
)

level_tau = np.concatenate(([0.0], np.cumsum(tau)))
analytic_down = fbeam * mu0 * np.exp(-level_tau / mu0)

np.testing.assert_allclose(result.flux_down[0], analytic_down, atol=1.0e-9)
np.testing.assert_allclose(result.flux_up[0], 0.0, atol=1.0e-8)
np.testing.assert_allclose(result.flux_net, result.flux_up - result.flux_down)

print(result.flux_down[0])

Torch CPU float64:

solver = TwoStreamEss(
    TwoStreamEssOptions(nlyr=3, mode="solar", backend="torch", torch_dtype="float64")
)

API Notes

Core inputs are tau, ssa, g, z, angles, and the surface/source terms needed by the selected mode. Solar angles are [sza, vza, raz] in degrees; thermal angles are viewing zenith angles. Heights are in km, ordered top to bottom.

See docs/api_arguments.md for the full argument table and conventions. Level-flux conventions are summarized in docs/level_fluxes.md.

Examples

python3 examples/level_flux_beer_lambert.py
python3 examples/build_thermal_source_from_temperature.py
python3 examples/retrieve_synthetic_spectra.py --case uv --noise-level 0

Scene/profile runs:

from py2sess.scene import load_scene

scene = load_scene(profile="profile.txt", config="scene.yaml")
result = scene.forward(backend="numpy", include_fo=True)

Full-spectrum benchmark details are in docs/full_spectrum_benchmarks.md.

Tests

python3 -m unittest discover -s tests -v
python3 -m ruff check .
python3 -m ruff format --check .

Full-spectrum benchmarks use profile text plus scene YAML inputs and Python optical preprocessing. Keep large local cross-section tables, benchmark bundles, and generated outputs out of git.

References

• Fortran repository: vnatraj1/2S-ESS
• Paper: Natraj et al., 2022, JQSRT