:mod:`gfas_frp`
===============

.. py:module:: gfas_frp

.. autoapi-nested-parse::

   The dataset class to be used with fwi-forcings and gfas-frp data.



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

.. py:class:: ModelDataset(out_var=None, out_mean=None, forecast_dir=None, forcings_dir=None, reanalysis_dir=None, frp_dir=None, hparams=None, **kwargs)

   Bases: :class:`dataloader.base_loader.ModelDataset`

   .. autoapi-inheritance-diagram:: gfas_frp.ModelDataset
      :parts: 1

   The dataset class responsible for loading the data and providing the samples for
   training.

   .. method:: generate_isolated_mask(self, x)


      Generate the mask for value which have no fire occurrences for the day before and after.

      :param x: The numpy array to create the mask for
      :type x: ndarray
      :return: Mask for isolated values
      :rtype: ndarray


   .. method:: training_step(self, model, batch)


      Called inside the training loop with the data from the training dataloader passed in as `batch`.

      :param model: The chosen model
      :type model: Model
      :param batch: Batch of input and ground truth variables
      :type batch: int
      :return: Loss and logs
      :rtype: dict


   .. method:: validation_step(self, model, batch)


      Called inside the validation loop with the data from the validation dataloader passed in as `batch`.

      :param model: The chosen model
      :type model: Model
      :param batch: Batch of input and ground truth variables
      :type batch: int
      :return: Loss and logs
      :rtype: dict


   .. method:: test_step(self, model, batch)


      Called inside the testing loop with the data from the testing dataloader passed in as `batch`.

      :param model: The chosen model
      :type model: Model
      :param batch: Batch of input and ground truth variables
      :type batch: int
      :return: Loss and logs
      :rtype: dict