neurotic.datasets.data

The neurotic.datasets.data module implements a function for loading a dataset from selected metadata.

neurotic.datasets.data.load_dataset(metadata, blk=None, lazy=False, signal_group_mode='split-all', filter_events_from_epochs=False)

Load a dataset.

metadata may be a MetadataSelector or a simple dictionary containing the appropriate data.

The data_file in metadata is read into a Neo Block using an automatically detected neo.io class if lazy=False or a neo.rawio class if lazy=True. If data_file is unspecified, an empty Neo Block is created instead. If a Neo Block is passed as blk, data_file is ignored.

Epochs and events loaded from annotations_file and epoch_encoder_file and spike trains loaded from tridesclous_file are added to the Neo Block.

If lazy=False, parameters given in metadata are used to apply filters to the signals, to detect spikes using amplitude discriminators, to calculate smoothed firing rates from spike trains, to detect bursts of spikes, and to calculate the rectified area under the curve (RAUC) for each signal.