Source code for yass.detect.run

"""
Detection pipeline
"""
from yass.util import file_loader
from yass.detect import threshold


def run(standarized_path, standarized_params, whiten_filter,
        if_file_exists='skip', save_results=False, function=threshold.run):
    """Execute detect step

    Parameters
    ----------
    standarized_path: str or pathlib.Path
        Path to standarized data binary file

    standarized_params: dict, str or pathlib.Path
        Dictionary with standarized data parameters or path to a yaml file

    whiten_filter: numpy.ndarray, str or pathlib.Path
        Whiten matrix or path to a npy file

    if_file_exists: str, optional
      One of 'overwrite', 'abort', 'skip'. Control de behavior for every
      generated file. If 'overwrite' it replaces the files if any exist,
      if 'abort' it raises a ValueError exception if any file exists,
      if 'skip' if skips the operation if any file exists

    save_results: bool, optional
        Whether to save results to disk, defaults to False

    Returns
    -------
    clear_scores: numpy.ndarray (n_spikes, n_features, n_channels)
        3D array with the scores for the clear spikes, first simension is
        the number of spikes, second is the nymber of features and third the
        number of channels

    spike_index_clear: numpy.ndarray (n_clear_spikes, 2)
        2D array with indexes for clear spikes, first column contains the
        spike location in the recording and the second the main channel
        (channel whose amplitude is maximum)

    spike_index_all: numpy.ndarray (n_collided_spikes, 2)
        2D array with indexes for all spikes, first column contains the
        spike location in the recording and the second the main channel
        (channel whose amplitude is maximum)

    Notes
    -----
    Running the preprocessor will generate the followiing files in
    CONFIG.data.root_folder/output_directory/ (if save_results is
    True):

    * ``spike_index_clear.npy`` - Same as spike_index_clear returned
    * ``spike_index_all.npy`` - Same as spike_index_collision returned
    * ``rotation.npy`` - Rotation matrix for dimensionality reduction
    * ``scores_clear.npy`` - Scores for clear spikes

    Threshold detector runs on CPU, neural network detector runs CPU and GPU,
    depending on how tensorflow is configured.

    Examples
    --------

    .. literalinclude:: ../../examples/pipeline/detect.py
    """
    # load files in case they are strings or Path objects
    standarized_params = file_loader(standarized_params)
    whiten_filter = file_loader(whiten_filter)

    return function(standarized_path,
                    standarized_params,
                    whiten_filter,
                    if_file_exists,
                    save_results)