neurone_loader.mne_export¶
Classes¶
neurone_loader.mne_export.MneExportable |
A metaclass that provides a function allowing objects that expose data, events, channels and sampling_rate properties to be converted to an mne.io.RawArray. |
Exceptions¶
neurone_loader.mne_export.UnknownChannelException |
Raised if data contains a channel name that is neither in a list of well-known channels nor in an (optional) list of user supplied channel name to channel type mappings. |
Provides the metaclass MneExportable that allows subclasses implementing all the metaclass’s properties to be converted to a mne.io.RawArray.
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class
neurone_loader.mne_export.MneExportable[source]¶ A metaclass that provides a function allowing objects that expose data, events, channels and sampling_rate properties to be converted to an mne.io.RawArray.
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channels¶ Abstract Property
Returns: should contain the names of channels, matching the sequence used in the data property Return type: list[str]
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data¶ Abstract Property
Returns: should contain data in (n_samples, n_channels) shape Return type: numpy.ndarray
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events¶ Abstract Property
Returns: should contain the events as a DataFrame, required fields are StartSampleIndex, StopSampleIndex and Code. Additional fields are ignored. Return type: pandas.DataFrame
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to_mne(substitute_zero_events_with=None, copy=False, channel_type_mappings=None)[source]¶ Convert loaded data to a mne.io.RawArray
Parameters: - substitute_zero_events_with (None or int) – None. events with code = 0 are not supported by MNE, if this parameter is set, the event code 0 will be substituted with this parameter
- copy (bool) – False. If False (default), the original data will be removed from memory to save space while creating the mne.io.RawArray. If the data is needed again it must be reloaded from disk
- channel_type_mappings (None or dict) – Optional. You can provide a dictionary of channel name to type mappings. If the
data contains any channel not in the list of well-known channel names and not in
this mapping the conversion will raise UnknownChannelException. You can choose
to map any unknown channel to one specific type e.g. {‘#unknown’: ‘eeg’}. For a
list of available types see the documentation of
mne.pick_types(). This setting takes precedence over the built-in list of common channel names.
Returns: the converted data
Return type: Raises: - ImportError – if the mne package is not installed
- UnknownChannelException – if a unknown channel name is encountered (see channel_type_mappings parameter)
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