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ForeFire Adapter
================

Overview
--------

``forefire`` is a deterministic raster adapter that approximates simulator-style front propagation through fixed diffusion kernels.

At a Glance
-----------

.. grid:: 1 2 4 4
   :gutter: 2
   :class-container: catalog-grid

   .. grid-item-card:: Hazard Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Wildfire

      .. container:: catalog-stat-note

         Public catalog grouping used for this model.

   .. grid-item-card:: Maturity
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         Implemented

      .. container:: catalog-stat-note

         Catalog maturity label used on the index page.

   .. grid-item-card:: Tasks
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         1

      .. container:: catalog-stat-note

         Spread

   .. grid-item-card:: Benchmark Family
      :class-card: catalog-stat-card

      .. container:: catalog-stat-value

         :doc:`Wildfire Benchmark </benchmarks/wildfire_benchmark>`

      .. container:: catalog-stat-note

         Primary benchmark-family link used for compatible evaluation coverage.


Description
-----------

``forefire`` is a deterministic raster adapter that approximates simulator-style front propagation through fixed diffusion kernels.

PyHazards exposes it as a benchmarkable baseline through the standard model registry.

Benchmark Compatibility
-----------------------

**Primary benchmark family:** :doc:`Wildfire Benchmark </benchmarks/wildfire_benchmark>`

**Mapped benchmark ecosystems:** :doc:`WildfireSpreadTS </benchmarks/wildfirespreadts_ecosystem>`

External References
-------------------

**Paper:** `ForeFire: A Modular, Scriptable C++ Simulation Engine and Library for Wildland-Fire Spread <https://doi.org/10.21105/joss.08680>`_ | **Repo:** `Repository <https://github.com/forefireAPI/forefire>`__

Registry Name
-------------

Primary entrypoint: ``forefire``

Supported Tasks
---------------

- Spread

Programmatic Use
----------------

.. code-block:: python

   import torch
   from pyhazards.models import build_model

   model = build_model(name="forefire", task="segmentation", in_channels=12)
   logits = model(torch.randn(2, 12, 16, 16))
   print(logits.shape)

Notes
-----

- This adapter is deterministic and does not learn parameters during the smoke test.