am.grid
=======

.. py:module:: am.grid


Classes
-------

.. autoapisummary::

   am.grid.ModelGrid


Module Contents
---------------

.. py:class:: ModelGrid(path: pathlib.Path, params: xarray.Dataset, args_fn: Callable[Ellipsis, list[str]], max_workers: int | None = None)

   Run an am model over a grid of parameter values.

   Parameters
   ----------
   path:
       Path to the ``.amc`` template file.
   params:
       xarray Dataset defining the parameter space.  Two forms are supported:

       * **Regular (Cartesian) grid** — coordinates on independent dimensions::

           xr.Dataset(coords={"elevation": [30, 45, 60], "pwv": [0.5, 1.0, 2.0]})

       * **Irregular grid** — variables sharing one dimension::

           xr.Dataset({"elevation": ("profile", elev), "pwv": ("profile", pwv)})

   args_fn:
       Callable that maps parameter values to an am args list.  Keyword
       argument names must match coordinate/variable names in *params*::

           args_fn=lambda elevation, pwv: [
               "0", "GHz", "350", "GHz", "0.5", "GHz",
               str(elevation), "deg", str(pwv),
           ]

   max_workers:
       Number of worker processes (default: ``os.cpu_count()``).


   .. py:method:: compute() -> xarray.Dataset

      Run all models and return an xarray Dataset.

      The output Dataset has the same dimensions and coordinates as *params*,
      plus a ``frequency`` dimension.  Each requested am output (e.g.
      ``tb_rj``, ``transmittance``) becomes a data variable.