deepextractor.api

Top-level convenience functions for DeepExtractor inference.

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

Module Contents

deepextractor.api.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[source]

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 DeepExtractorModel directly to avoid reloading weights on each call.

Parameters:

  • noisy_input (np.ndarray) – 1-D array of shape (T,) or 2-D batch of shape (N, T).

  • checkpoint (str) – Model name. Default "DeepExtractor_257".

  • checkpoint_filename (str) – Checkpoint filename. Defaults to the bilby-noise checkpoint.

  • checkpoint_dir (str | None) – Local checkpoint directory. Falls back to HuggingFace Hub if None.

  • device (str | None) – Torch device string. Auto-detected if None.

  • scaler_path (str | None) – Path to scaler .pkl. Uses bundled asset if None.

Returns:

Reconstructed signal, same shape as noisy_input.

Return type:

np.ndarray

deepextractor.api.extract[source]