credit.datasets.les_singlestep
==============================

.. py:module:: credit.datasets.les_singlestep

.. autoapi-nested-parse::

   les_dataset.py
   -------------------------------------------------------
   Content:
       - LESDataset
       - LESPredict



Classes
-------

.. autoapisummary::

   credit.datasets.les_singlestep.LESDataset
   credit.datasets.les_singlestep.LESPredict


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

.. py:class:: LESDataset(param_interior, transform=None, seed=42)

   Bases: :py:obj:`torch.utils.data.Dataset`


   LES model Pytorch Dataset class


   .. py:attribute:: list_upper_ds


   .. py:attribute:: list_surf_ds
      :value: []



   .. py:attribute:: list_dyn_forcing_ds
      :value: []



   .. py:attribute:: list_diag_ds
      :value: []



   .. py:attribute:: history_len


   .. py:attribute:: forecast_len


   .. py:attribute:: total_seq_len


   .. py:attribute:: LES_file_indices


   .. py:attribute:: filename_forcing


   .. py:attribute:: filename_static


   .. py:attribute:: transform
      :value: None



   .. py:attribute:: size_list


   .. py:attribute:: size_full


   .. py:attribute:: rng


   .. py:attribute:: total_len
      :value: 0



   .. py:method:: __post_init__()


   .. py:method:: __len__()


   .. py:method:: __getitem__(index)


.. py:class:: LESPredict(param_interior, data_lookup, rank, world_size, transform=None)

   Bases: :py:obj:`torch.utils.data.IterableDataset`


   An iterable Dataset.

   All datasets that represent an iterable of data samples should subclass it.
   Such form of datasets is particularly useful when data come from a stream.

   All subclasses should overwrite :meth:`__iter__`, which would return an
   iterator of samples in this dataset.

   When a subclass is used with :class:`~torch.utils.data.DataLoader`, each
   item in the dataset will be yielded from the :class:`~torch.utils.data.DataLoader`
   iterator. When :attr:`num_workers > 0`, each worker process will have a
   different copy of the dataset object, so it is often desired to configure
   each copy independently to avoid having duplicate data returned from the
   workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker
   process, returns information about the worker. It can be used in either the
   dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's
   :attr:`worker_init_fn` option to modify each copy's behavior.

   Example 1: splitting workload across all workers in :meth:`__iter__`::

       >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
       >>> # xdoctest: +SKIP("Fails on MacOS12")
       >>> class MyIterableDataset(torch.utils.data.IterableDataset):
       ...     def __init__(self, start, end):
       ...         super(MyIterableDataset).__init__()
       ...         assert end > start, "this example only works with end >= start"
       ...         self.start = start
       ...         self.end = end
       ...
       ...     def __iter__(self):
       ...         worker_info = torch.utils.data.get_worker_info()
       ...         if worker_info is None:  # single-process data loading, return the full iterator
       ...             iter_start = self.start
       ...             iter_end = self.end
       ...         else:  # in a worker process
       ...             # split workload
       ...             per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
       ...             worker_id = worker_info.id
       ...             iter_start = self.start + worker_id * per_worker
       ...             iter_end = min(iter_start + per_worker, self.end)
       ...         return iter(range(iter_start, iter_end))
       ...
       >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
       >>> ds = MyIterableDataset(start=3, end=7)

       >>> # Single-process loading
       >>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
       [tensor([3]), tensor([4]), tensor([5]), tensor([6])]

       >>> # xdoctest: +REQUIRES(POSIX)
       >>> # Multi-process loading with two worker processes
       >>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
       >>> # xdoctest: +IGNORE_WANT("non deterministic")
       >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
       [tensor([3]), tensor([5]), tensor([4]), tensor([6])]

       >>> # With even more workers
       >>> # xdoctest: +IGNORE_WANT("non deterministic")
       >>> print(list(torch.utils.data.DataLoader(ds, num_workers=12)))
       [tensor([3]), tensor([5]), tensor([4]), tensor([6])]

   Example 2: splitting workload across all workers using :attr:`worker_init_fn`::

       >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DATALOADER)
       >>> class MyIterableDataset(torch.utils.data.IterableDataset):
       ...     def __init__(self, start, end):
       ...         super(MyIterableDataset).__init__()
       ...         assert end > start, "this example only works with end >= start"
       ...         self.start = start
       ...         self.end = end
       ...
       ...     def __iter__(self):
       ...         return iter(range(self.start, self.end))
       ...
       >>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
       >>> ds = MyIterableDataset(start=3, end=7)

       >>> # Single-process loading
       >>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
       [3, 4, 5, 6]
       >>>
       >>> # Directly doing multi-process loading yields duplicate data
       >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
       [3, 3, 4, 4, 5, 5, 6, 6]

       >>> # Define a `worker_init_fn` that configures each dataset copy differently
       >>> def worker_init_fn(worker_id):
       ...     worker_info = torch.utils.data.get_worker_info()
       ...     dataset = worker_info.dataset  # the dataset copy in this worker process
       ...     overall_start = dataset.start
       ...     overall_end = dataset.end
       ...     # configure the dataset to only process the split workload
       ...     per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
       ...     worker_id = worker_info.id
       ...     dataset.start = overall_start + worker_id * per_worker
       ...     dataset.end = min(dataset.start + per_worker, overall_end)
       ...

       >>> # Mult-process loading with the custom `worker_init_fn`
       >>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
       >>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
       [3, 5, 4, 6]

       >>> # With even more workers
       >>> print(list(torch.utils.data.DataLoader(ds, num_workers=12, worker_init_fn=worker_init_fn)))
       [3, 4, 5, 6]


   .. py:attribute:: list_upper_ds


   .. py:attribute:: list_surf_ds
      :value: []



   .. py:attribute:: list_dyn_forcing_ds
      :value: []



   .. py:attribute:: list_diag_ds
      :value: []



   .. py:attribute:: filenames


   .. py:attribute:: filename_surface


   .. py:attribute:: filename_dyn_forcing


   .. py:attribute:: filename_forcing


   .. py:attribute:: filename_static


   .. py:attribute:: filename_diagnostic


   .. py:attribute:: rank


   .. py:attribute:: world_size


   .. py:attribute:: transform
      :value: None



   .. py:attribute:: history_len


   .. py:attribute:: data_lookup


   .. py:method:: load_zarr_as_input(i_file, i_init_start, i_init_end, mode='input')


   .. py:method:: __len__()


   .. py:method:: __iter__()