deepextractor.api
=================

.. py:module:: deepextractor.api

.. autoapi-nested-parse::

   Top-level convenience functions for DeepExtractor inference.

   For one-shot use. For repeated inference on many signals, instantiate
   :class:`DeepExtractorModel` directly to amortise the model load cost.







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

.. py:function:: reconstruct(noisy_input: numpy.ndarray, checkpoint: str = 'DeepExtractor_257', checkpoint_filename: str = CHECKPOINT_BILBY, checkpoint_dir: str | None = None, device: str | None = None, scaler_path: str | None = None) -> numpy.ndarray

   Extract the transient signal from a noisy gravitational-wave strain.

   Loads a DeepExtractor model, runs inference, and returns the reconstructed
   signal. For repeated calls, prefer instantiating :class:`DeepExtractorModel`
   directly to avoid reloading weights on each call.

   :param noisy_input: 1-D array of shape ``(T,)`` or 2-D batch of shape ``(N, T)``.
   :type noisy_input: np.ndarray
   :param checkpoint: Model name. Default ``"DeepExtractor_257"``.
   :type checkpoint: str
   :param checkpoint_filename: Checkpoint filename. Defaults to the bilby-noise checkpoint.
   :type checkpoint_filename: str
   :param checkpoint_dir: Local checkpoint directory. Falls back to HuggingFace Hub if None.
   :type checkpoint_dir: str | None
   :param device: Torch device string. Auto-detected if None.
   :type device: str | None
   :param scaler_path: Path to scaler .pkl. Uses bundled asset if None.
   :type scaler_path: str | None

   :returns: Reconstructed signal, same shape as ``noisy_input``.
   :rtype: np.ndarray


.. py:data:: extract