deepextractor.data.datasets
===========================

.. py:module:: deepextractor.data.datasets




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

.. py:class:: TimeSeriesDataset(input_npy, target_npy, transform=None)

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


   An abstract class representing a :class:`Dataset`.

   All datasets that represent a map from keys to data samples should subclass
   it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
   data sample for a given key. Subclasses could also optionally overwrite
   :meth:`__len__`, which is expected to return the size of the dataset by many
   :class:`~torch.utils.data.Sampler` implementations and the default options
   of :class:`~torch.utils.data.DataLoader`. Subclasses could also
   optionally implement :meth:`__getitems__`, for speedup batched samples
   loading. This method accepts list of indices of samples of batch and returns
   list of samples.

   .. note::
     :class:`~torch.utils.data.DataLoader` by default constructs an index
     sampler that yields integral indices.  To make it work with a map-style
     dataset with non-integral indices/keys, a custom sampler must be provided.


   .. py:attribute:: inputs


   .. py:attribute:: targets


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



.. py:class:: SpectrogramDataset(input_npy, target_npy, transform=None)

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


   An abstract class representing a :class:`Dataset`.

   All datasets that represent a map from keys to data samples should subclass
   it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
   data sample for a given key. Subclasses could also optionally overwrite
   :meth:`__len__`, which is expected to return the size of the dataset by many
   :class:`~torch.utils.data.Sampler` implementations and the default options
   of :class:`~torch.utils.data.DataLoader`. Subclasses could also
   optionally implement :meth:`__getitems__`, for speedup batched samples
   loading. This method accepts list of indices of samples of batch and returns
   list of samples.

   .. note::
     :class:`~torch.utils.data.DataLoader` by default constructs an index
     sampler that yields integral indices.  To make it work with a map-style
     dataset with non-integral indices/keys, a custom sampler must be provided.


   .. py:attribute:: input_path


   .. py:attribute:: target_path


   .. py:attribute:: input_shape


   .. py:attribute:: target_shape


   .. py:attribute:: input_channels_needed


   .. py:attribute:: target_channels_needed


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



.. py:class:: HDF5Dataset(hdf5_path, input_key, background_key, signal_key, input_scaler=None, target_signal_only=False, transform=None)

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


   HDF5-backed dataset for time-domain two-detector signal/glitch separation.

   Lazy-opens the HDF5 file per worker process. Use shuffle=False in DataLoader
   — data is pre-shuffled at generation time; random HDF5 seeks are expensive.

   :param hdf5_path: Path to the HDF5 file.
   :param input_key: Dataset key for the 2-channel (H1+L1) strain inputs.
   :param background_key: Dataset key for the background (noise) targets.
   :param signal_key: Dataset key for the signal targets.
   :param input_scaler: Optional sklearn-compatible scaler (must expose mean_ and
                        scale_ attributes, shaped (n_channels,)). Applied to inputs only;
                        targets are assumed to be whitened already.
   :param target_signal_only: If True, return only the signal targets (2-channel).
                              If False (default), concatenate [background, signal] → 4-channel target.
   :param transform: Optional callable with signature
                     transform(input_ts=..., target_ts=...) → dict with same keys.


   .. py:attribute:: hdf5_path


   .. py:attribute:: input_key


   .. py:attribute:: background_key


   .. py:attribute:: signal_key


   .. py:attribute:: input_scaler
      :value: None



   .. py:attribute:: target_signal_only
      :value: False



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