PythonicDISORT
The facts below are what the maker publishes about PythonicDISORT, checked and dated. Our editors have not written about it yet.
The facts
Each one read from PythonicDISORT's own pages, with the date it was checked.
What it does
- 3D visuals
- Nochecked 24 Sep 2026
- Additional calculations
- The package includes a subroutine for computing actinic fluxes and supports integration with respect to optical depth.checked 12 Oct 2026
- Boundary conditions
- Optional inputs include bidirectional reflectance Fourier modes, boundary sources, and isotropic internal sources.checked 5 Oct 2026
- Dependencies
- The listed runtime requirements are Python 3.8 or later, NumPy 1.8.0 or later, and SciPy 1.8.0 or later.checked 5 Oct 2026
- Differentiation
- When autograd is installed and compatibility is enabled, it can compute tau derivatives of output functions, with slightly lower efficiency.checked 5 Oct 2026
- Documentation and audience
- The documentation recommends that new users read the non-optional parts of sections 1 and 2, which cover parameter choices and modules and outputs.checked 5 Oct 2026
- Implementation
- It is implemented entirely in Python 3 as a reimplementation of DISORT, rather than as a wrapper.checked 12 Oct 2026
- Installation
- The package can be installed from PyPI or Conda-Forge, or by cloning its repository and running pip install ., and requires Python 3.8 or later.checked 12 Oct 2026
- Integrations
- The installation instructions list PyPI and Conda-Forge as package distribution channels; no other integrations are stated on that page.checked 12 Oct 2026
- License
- PyPI lists the package license as MIT License.checked 12 Oct 2026
- Notable limit
- Only the first NFourier bidirectional reflectance Fourier modes are used; additional modes are ignored.checked 5 Oct 2026
- Numerical limitation
- The documentation says computational cost scales approximately with the cube of the number of quadrature points and warns that around 300 or more streams can cause numerical instability and inaccurate intensity at some angles.checked 12 Oct 2026
- Optional dependencies
- Autograd can optionally compute gradients of output functions; pytest is optional for running tests, and Jupyter, Notebook, and Matplotlib are listed for the comprehensive notebook.checked 12 Oct 2026
- Optional integrations
- Autograd is optional for derivatives; Jupyter, Notebook, and Matplotlib are listed as optional requirements for the comprehensive notebook.checked 5 Oct 2026
- Outputs
- Its solver computes fluxes and can optionally compute intensity.checked 5 Oct 2026
- Performance limitation
- The project says it is optimized for speed but naturally slower than similar Fortran algorithms.checked 12 Oct 2026
- Purpose
- PythonicDISORT solves the one-dimensional radiative transfer equation for a plane-parallel, horizontally homogeneous atmosphere.checked 12 Oct 2026
- Radiative transfer features
- The documented solver supports optional delta-M scaling and Nakajima-Tanaka intensity corrections.checked 5 Oct 2026
- Solver features
- Features include a multilayer solver, delta-M scaling, Nakajima-Tanaka corrections, flux-only mode, direct beam and isotropic internal sources, diffuse-flux boundary sources, surface reflection using a bidirectional reflectance distribution function, and polar-angle interpolation.checked 12 Oct 2026
- Support
- The author invites feature requests and feedback and provides the contact address [email protected].checked 12 Oct 2026
- Verification
- The project provides notebook verification tests and recreated DISORT test problems whose solutions are compared with a F2PY-wrapped Stamnes DISORT version 4.0.99.checked 12 Oct 2026
Where it runs
- Platform compatibility
- The project says it should be system agnostic because it has minimal dependencies and pure Python code, and says the repository was built and tested on Windows 11.checked 12 Oct 2026
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