Foton

Hardware-adaptive, backend-neutral daylight analysis engine for progressive daylight coefficients, daylight factor, daylight autonomy, and static sDA.

Install

The product and import name is Foton. The PyPI distribution is foton-daylight because the shorter foton project name is owned by another publisher.

python -m pip install foton-daylight

from foton import Engine

engine = Engine()
print(engine.capabilities())

See installation, backend requirements, Python API, and benchmarking.

Implemented

  • Typed Rust scene contracts for instanced triangle meshes, materials, and sensors.
  • Canonical Tregenza 146 and Reinhart MF:2 578 sky mapping including ground.
  • Exact MF:2-to-Tregenza parent aggregation and deterministic sample keys.
  • Lambertian transport and Radiance-compatible visible-transmittance conversion with angular thin-glass interface effects.
  • Streamed annual metrics with explicit weighted occupancy schedules.
  • Native Metal BLAS/TLAS construction, direct ray-query visibility, and tiled GPU annual reduction through runtime-compiled MSL.
  • Vulkan 1.3 BLAS/TLAS construction and SPIR-V ray-query compute pipelines for NVIDIA, AMD, and Intel GPUs on Linux and Windows.
  • Resident scene handles that reuse acceleration structures across analyses and revisioned instance updates.
  • Async PyO3 jobs with cancellation, supersession, copied NumPy ownership, snapshots, GIL release while waiting, and opt-in coefficient export.
  • Canonical shoebox and generated 1,000-room/25,000-sensor fixtures.

Direct visibility, multi-bounce diffuse and thin-glass transport, and annual metric reduction execute on Metal or Vulkan. The deterministic CPU backend and Radiance remain validation oracles.

Development

cargo test -p daylight-core -p daylight-metal -p daylight-vulkan -p daylight-cli
cargo check -p daylight-python --features extension-module
cargo run -p daylight-cli -- hardware
cargo run -p daylight-cli -- fixture --output tests/fixtures/shoebox.json
python -m unittest discover -s validation

Build the Python extension from source:

python -m pip install -e .

Compare a Honeybee shaded shoebox against native Radiance direct visibility:

python scripts/compare_honeybee_shoebox.py --auto-grid

Hardware Benchmarks

Run the reproducible Honeybee/Radiance benchmark after installing the Honeybee extra and making Radiance oconv and rcontrib available on PATH (or set RADIANCE_BIN). Bundled OpenStudio installations under /Applications are detected automatically:

python -m pip install -e '.[honeybee]'
python scripts/benchmark_hardware.py --backend auto --append-readme

Each run writes a self-contained folder under benchmarks/results/ with command logs, benchmark.json, and benchmark.md. It executes four versioned stages:

Stage Fixture Validation
Direct visibility Honeybee shaded shoebox, aperture, overhang, fins, 216 sensors Binary patch-center ray mismatches and weighted visible energy against Radiance
Full transport Same room with 0.6 visible-transmittance glass and diffuse bounces Coefficient NMBE and CV(RMSE) against rcontrib
Annual metrics Both coefficient matrices multiplied by the same deterministic 8,760-hour Tregenza sky and 08:00–18:00 schedule Occupied illuminance NMBE/CV(RMSE), DA, area-weighted sDA, and GPU/CPU reduction agreement
Large scene One resident shoebox mesh instanced into 1,000 rooms with 25,000 sensors Cold and cached scene, tracing, annual-reduction, and wall-clock timings

Use --quick for a 256-sample, one-bounce smoke benchmark. Use an explicit --backend metal or --backend vulkan to require that device; the run fails rather than silently benchmarking the CPU reference. --append-readme adds one hardware row and five fixture rows below, while the JSON report remains the detailed artifact.

Hardware

Run Date (UTC) Host OS Model CPU Cores RAM GPU Backend Engine
20260731T054146Z-CM-GHHXPN239T 2026-07-31 05:42:00Z CM-GHHXPN239T macOS 26.5.2 MacBook Pro Apple M4 Pro 14 24 GB Apple M4 Pro metal 0.1.0

Results

Run Fixture Scale Samples / bounces Accuracy vs Radiance Scene / AS Trace Annual Wall Radiance
20260731T054146Z-CM-GHHXPN239T Honeybee direct visibility 216 sensors × 146 patches 0 / 0 13 mismatches; 2.477% energy 0.00 ms 0.17 ms 0.06 ms n/a 66.27 ms
20260731T054146Z-CM-GHHXPN239T Diffuse + thin glass coefficients 216 sensors × 146 patches 4096 / 2 NMBE 5.295%; CV(RMSE) 344.379% 0.00 ms 5.79 ms 7.81 ms 30.95 ms 3574.31 ms
20260731T054146Z-CM-GHHXPN239T Annual illuminance + DA/sDA 216 sensors × 8760 hours 4096 / 2 NMBE 5.193%; CV(RMSE) 26.955%; sDA Δ 0.00 pp 0.00 ms 5.79 ms 7.81 ms 30.95 ms 3583.38 ms
20260731T054146Z-CM-GHHXPN239T 1,000-room resident scene 1000 rooms / 25000 sensors 64 / 1 performance fixture 5.00 ms 18.28 ms 2.10 ms 63.86 ms 4621.42 ms
20260731T054146Z-CM-GHHXPN239T 1,000-room resident scene (cached) 1000 rooms / 25000 sensors 64 / 1 resident BLAS/TLAS reuse 0.00 ms 4.24 ms 2.10 ms 39.17 ms 4431.31 ms

Local Three.js viewer

Install the native extension and local viewer dependencies:

python -m pip install -e '.[viewer]'
cd viewer && npm install

Run the Python service and Vite frontend together:

cd viewer && npm run dev

Open http://127.0.0.1:5173, upload an hourly EPW, and edit the parametric shoebox. The viewer runs a 64-sample Tregenza preview during interaction and a 4,096-sample Reinhart MF:2 refinement after the controls are idle. Radiance gendaymtx must be on PATH or under RADIANCE_BIN; daylight transport remains native Metal or Vulkan, selected from the GPU vendor.

For a single-process local build:

cd viewer && npm run build
foton-viewer

Release maintainers should also read the release process.

Project links

Data verified by PyPI on Jul 31, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ssanjay from gravatar.com ssanjay

GitHub Statistics

Data verified by PyPI on Jul 31, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python >=3.10

Provides Extra

honeybee test viewer

Tags

daylight radiance honeybee metal vulkan ray-tracing

Classifiers

Report project as malware

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Publisher: release.yml on snjsomnath/foton

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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0.2.0

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This release

0.1.0 This release

17 files

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