pycrostates.cluster.AAHCluster#

class pycrostates.cluster.AAHCluster(n_clusters, normalize_input=False)[source]#

Atomize and Agglomerate Hierarchical Clustering (AAHC) algorithm.

See Murray et al.[1] for additional information.

Parameters:

n_clustersint: The number of clusters, i.e. the number of microstates.
normalize_inputbool: If set, the input data is normalized along the channel dimension.

Attributes:

GEV_: Global Explained Variance.
cluster_centers_: Fitted clusters (the microstates maps).
cluster_names: Name of the clusters.
compensation_grade: The current gradient compensation grade.
fitted: Fitted state.
fitted_data: Data array used to fit the clustering algorithm.
info: Info instance with the channel information used to fit the instance.
labels_: Microstate label attributed to each sample of the fitted data.
n_clusters: Number of clusters (number of microstates).
normalize_input: If set, the input data is normalized along the channel dimension.

Methods

`copy`([deep])	Return a copy of the instance.
`fit`(inst[, picks, tmin, tmax, ...])	Compute cluster centers.
`get_channel_types`([picks, unique, only_data_chs])	Get a list of channel type for each channel.
`get_montage`()	Get a DigMontage from instance.
`invert_polarity`(invert)	Invert map polarities.
`plot`([axes, show_gradient, gradient_kwargs, ...])	Plot cluster centers as topographic maps.
`predict`(inst[, picks, factor, ...])	Segment `Raw` or `Epochs` into microstate sequence.
`rename_clusters`([mapping, new_names])	Rename the clusters.
`reorder_clusters`([mapping, order, template])	Reorder the clusters of the fitted model.
`save`(fname)	Save clustering solution to disk.
`set_montage`(montage[, match_case, ...])	Set EEG/sEEG/ECoG/DBS/fNIRS channel positions and digitization points.

References

__contains__(ch_type)#

Check channel type membership.

Parameters:

ch_typestr: Channel type to check for. Can be e.g. 'meg', 'eeg', 'stim', etc.

Returns:

inbool: Whether or not the instance contains the given channel type.

Examples

Channel type membership can be tested as:

>>> 'meg' in inst  
True
>>> 'seeg' in inst  
False

copy(deep=True)#

Return a copy of the instance.

Parameters:

deepbool: If True, deepcopy is used instead of copy.

fit(inst, picks='eeg', tmin=None, tmax=None, reject_by_annotation=True, *, verbose=None)[source]#

Compute cluster centers.

Parameters:

instRaw | Epochs | ChData

MNE Raw, Epochs or ChData object from which to extract cluster centers.

picksstr | list | slice | None

Channels to include. Note that all channels selected must have the same type. Slices and lists of integers will be interpreted as channel indices. In lists, channel name strings (e.g. ['Fp1', 'Fp2']) will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. "eeg" (default) will pick all eeg channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

tminfloat

Start time of the raw data to use in seconds (must be >= 0).

tmaxfloat | None

End time of the raw data to use in seconds (cannot exceed data duration). If None (default), the current end of the data is used.

reject_by_annotationbool

Whether to omit bad segments from the data before fitting. If True (default), annotated segments whose description begins with 'bad' are omitted. If False, no rejection based on annotations is performed.

Has no effect if inst is not a mne.io.Raw object.

verboseint | str | bool | None

Sets the verbosity level. The verbosity increases gradually between "CRITICAL", "ERROR", "WARNING", "INFO" and "DEBUG". If None is provided, the verbosity is set to "WARNING". If a bool is provided, the verbosity is set to "WARNING" for False and to "INFO" for True.

get_channel_types(picks=None, unique=False, only_data_chs=False)#

Get a list of channel type for each channel.

Parameters:

picksstr | array_like | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values 'all' to pick all channels, or 'data' to pick data channels. None (default) will pick all channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
uniquebool: Whether to return only unique channel types. Default is False.
only_data_chsbool: Whether to ignore non-data channels. Default is False.

Returns:

channel_typeslist: The channel types.

get_montage()#

Get a DigMontage from instance.

Returns:

montageNone | DigMontage: A copy of the channel positions, if available, otherwise None.

invert_polarity(invert)#

Invert map polarities.

Parameters:

invertbool | list of bool | array of bool: List of bool of length n_clusters. True will invert map polarity, while False will have no effect. If a bool is provided, it is applied to all maps.

Notes

Operates in-place.

Inverting polarities has no effect on the other steps of the analysis as polarity is ignored in the current methodology. This function is only used for tuning visualization (i.e. for visual inspection and/or to generate figure for an article).

plot(axes=None, show_gradient=False, gradient_kwargs={'color': 'black', 'linestyle': '-', 'marker': 'P'}, *, block=False, show=None, verbose=None, **kwargs)#

Plot cluster centers as topographic maps.

Parameters:

axesAxes | None: Either None to create a new figure or axes (or an array of axes) on which the topographic map should be plotted. If the number of microstates maps to plot is ≥ 1, an array of axes of size n_clusters should be provided.
show_gradientbool: If True, plot a line between channel locations with highest and lowest values.
gradient_kwargsdict: Additional keyword arguments passed to matplotlib.axes.Axes.plot() to plot gradient line.
blockbool: Whether to halt program execution until the figure is closed.
showbool | None: If True, the figure is shown. If None, the figure is shown if the matplotlib backend is interactive.
verboseint | str | bool | None: Sets the verbosity level. The verbosity increases gradually between "CRITICAL", "ERROR", "WARNING", "INFO" and "DEBUG". If None is provided, the verbosity is set to "WARNING". If a bool is provided, the verbosity is set to "WARNING" for False and to "INFO" for True.
**kwargs: Additional keyword arguments are passed to mne.viz.plot_topomap().

Returns:

fFigure: Matplotlib figure containing the topographic plots.

predict(inst, picks=None, factor=0, half_window_size=1, tol=1e-05, min_segment_length=0, reject_edges=True, reject_by_annotation=True, *, verbose=None)#

Segment Raw or Epochs into microstate sequence.

Segment instance into microstate sequence using the segmentation smoothing algorithm[2].

Parameters:

instRaw | Epochs

MNE Raw or Epochs object containing the data to use for prediction.

picksstr | list | slice | None

Channels to include. Note that all channels selected must have the same type. Slices and lists of integers will be interpreted as channel indices. In lists, channel name strings (e.g. ['Fp1', 'Fp2']) will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels used during fitting (e.g., self.info['ch_names']). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

factorint

Factor used for label smoothing. 0 means no smoothing. Default to 0.

half_window_sizeint

Number of samples used for the half window size while smoothing labels. The half window size is defined as window_size = 2 * half_window_size + 1. It has no effect if factor=0 (default). Default to 1.

tolfloat

Convergence tolerance.

min_segment_lengthint

Minimum segment length (in samples). If a segment is shorter than this value, it will be recursively reasigned to neighbouring segments based on absolute spatial correlation.

reject_edgesbool

If True, set first and last segments to unlabeled.

reject_by_annotationbool

Whether to omit bad segments from the data before fitting. If True (default), annotated segments whose description begins with 'bad' are omitted. If False, no rejection based on annotations is performed.

Has no effect if inst is not a mne.io.Raw object.

verboseint | str | bool | None

Sets the verbosity level. The verbosity increases gradually between "CRITICAL", "ERROR", "WARNING", "INFO" and "DEBUG". If None is provided, the verbosity is set to "WARNING". If a bool is provided, the verbosity is set to "WARNING" for False and to "INFO" for True.

Returns:

segmentationRawSegmentation | EpochsSegmentation: Microstate sequence derivated from instance data. Timepoints are labeled according to cluster centers number: 0 for the first center, 1 for the second, etc.. -1 is used for unlabeled time points.

References

rename_clusters(mapping=None, new_names=None)#

Rename the clusters.

Parameters:

mappingdict: Mapping from the old names to the new names. The keys are the old names and the values are the new names.
new_nameslist | tuple: 1D iterable containing the new cluster names. The length of the iterable should match the number of clusters.

Notes

Operates in-place.

reorder_clusters(mapping=None, order=None, template=None)#

Reorder the clusters of the fitted model.

Specify one of the following arguments to change the current order:

mapping: a dictionary that maps old cluster positions to new positions,
order: a 1D iterable containing the new order,
template: a fitted clustering algorithm used as a reference to match the order.

Only one argument can be set at a time.

Parameters:

mappingdict: Mapping from the old order to the new order. key: old position, value: new position.
orderlist of int | tuple of int | array of int: 1D iterable containing the new order. Positions are 0-indexed.
templateCluster: Fitted clustering algorithm use as template for ordering optimization. For more details about the current implementation, check the pycrostates.cluster.utils.optimize_order() documentation.

Notes

Operates in-place.

save(fname)[source]#

Save clustering solution to disk.

Parameters:

fnamepath-like: Path to the .fif file where the clustering solution is saved.

set_montage(montage, match_case=True, match_alias=False, on_missing='raise', verbose=None)#

Set EEG/sEEG/ECoG/DBS/fNIRS channel positions and digitization points.

Parameters:

montageNone | str | DigMontage: A montage containing channel positions. If a string or DigMontage is specified, the existing channel information will be updated with the channel positions from the montage. Valid strings are the names of the built-in montages that ship with MNE-Python; you can list those via mne.channels.get_builtin_montages(). If None (default), the channel positions will be removed from the Info.
match_casebool: If True (default), channel name matching will be case sensitive.

Added in version 0.20.
match_aliasbool | dict: Whether to use a lookup table to match unrecognized channel location names to their known aliases. If True, uses the mapping in mne.io.constants.CHANNEL_LOC_ALIASES. If a dict is passed, it will be used instead, and should map from non-standard channel names to names in the specified montage. Default is False.

Added in version 0.23.
on_missing‘raise’ | ‘warn’ | ‘ignore’: Can be 'raise' (default) to raise an error, 'warn' to emit a warning, or 'ignore' to ignore when channels have missing coordinates.

Added in version 0.20.1.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

instinstance of Raw | Epochs | Evoked: The instance, modified in-place.