pyriemann.channelselection.FlatChannelRemover¶
- class pyriemann.channelselection.FlatChannelRemover¶
Flat channel removal.
- Attributes:
- channels_ndarray, shape (n_good_channels,)
Indices of the non-flat channels.
- __init__(*args, **kwargs)¶
- fit(X, y=None)¶
Find flat channels.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series.
- yNone
Not used, here for compatibility with sklearn API.
- Returns:
- selfFlatChannelRemover instance
The FlatChannelRemover instance.
- fit_transform(X, y=None)¶
Fit and transform in a single function.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series.
- yNone
Not used, here for compatibility with sklearn API.
- Returns:
- X_newndarray, shape (n_matrices, n_good_channels, n_times)
Multi-channel time-series without flat channels.
- get_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequestencapsulating routing information.
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- set_output(*, transform=None)¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”, “polars”}, default=None
Configure output of transform and fit_transform.
“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged
Added in version 1.4: “polars” option was added.
- Returns:
- selfestimator instance
Estimator instance.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- transform(X)¶
Remove flat channels.
- Parameters:
- Xndarray, shape (n_matrices, n_channels, n_times)
Multi-channel time-series.
- Returns:
- X_newndarray, shape (n_matrices, n_good_channels, n_times)
Multi-channel time-series without flat channels.