# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin, check_array, clone
from sklearn.preprocessing import RobustScaler, StandardScaler
from sklearn.utils import check_X_y
from sklearn.utils.validation import check_is_fitted, validate_data
from .._fiff.pick import (
_pick_data_channels,
_picks_by_type,
_picks_to_idx,
pick_info,
)
from ..cov import _check_scalings_user
from ..epochs import BaseEpochs
from ..filter import filter_data
from ..time_frequency import psd_array_multitaper
from ..utils import _check_option, _validate_type, fill_doc
class MNETransformerMixin(TransformerMixin):
"""TransformerMixin plus some helpers."""
def _check_data(
self,
epochs_data,
*,
y=None,
atleast_3d=True,
fit=False,
return_y=False,
multi_output=False,
check_n_features=True,
):
# Sklearn calls asarray under the hood which works, but elsewhere they check for
# __len__ then look at the size of obj[0]... which is an epoch of shape (1, ...)
# rather than what they expect (shape (...)). So we explicitly get the NumPy
# array to make everyone happy.
if isinstance(epochs_data, BaseEpochs):
epochs_data = epochs_data.get_data(copy=False)
kwargs = dict(dtype=np.float64, allow_nd=True, order="C", force_writeable=True)
if hasattr(self, "n_features_in_") and check_n_features:
if y is None:
epochs_data = validate_data(
self,
epochs_data,
**kwargs,
reset=fit,
)
else:
epochs_data, y = validate_data(
self,
epochs_data,
y,
**kwargs,
reset=fit,
)
elif y is None:
epochs_data = check_array(epochs_data, **kwargs)
else:
epochs_data, y = check_X_y(
X=epochs_data, y=y, multi_output=multi_output, **kwargs
)
if fit:
self.n_features_in_ = epochs_data.shape[1]
if atleast_3d:
epochs_data = np.atleast_3d(epochs_data)
return (epochs_data, y) if return_y else epochs_data
class _ConstantScaler:
"""Scale channel types using constant values."""
def __init__(self, info, scalings, do_scaling=True):
self._scalings = scalings
self._info = info
self._do_scaling = do_scaling
def fit(self, X, y=None):
scalings = _check_scalings_user(self._scalings)
picks_by_type = _picks_by_type(
pick_info(self._info, _pick_data_channels(self._info, exclude=()))
)
std = np.ones(sum(len(p[1]) for p in picks_by_type))
if X.shape[1] != len(std):
raise ValueError(
f"info had {len(std)} data channels but X has {len(X)} channels"
)
if self._do_scaling: # this is silly, but necessary for completeness
for kind, picks in picks_by_type:
std[picks] = 1.0 / scalings[kind]
self.std_ = std
self.mean_ = np.zeros_like(std)
return self
def transform(self, X):
return X / self.std_
def inverse_transform(self, X, y=None):
return X * self.std_
def fit_transform(self, X, y=None):
return self.fit(X, y).transform(X)
def _sklearn_reshape_apply(func, return_result, X, *args, **kwargs):
"""Reshape epochs and apply function."""
_validate_type(X, np.ndarray, "X")
if X.size == 0:
return X.copy() if return_result else None
orig_shape = X.shape
X = np.reshape(X.transpose(0, 2, 1), (-1, orig_shape[1]))
X = func(X, *args, **kwargs)
if return_result:
X.shape = (orig_shape[0], orig_shape[2], orig_shape[1])
X = X.transpose(0, 2, 1)
return X
@fill_doc
class Scaler(MNETransformerMixin, BaseEstimator):
"""Standardize channel data.
This class scales data for each channel. It differs from scikit-learn
classes (e.g., :class:`sklearn.preprocessing.StandardScaler`) in that
it scales each *channel* by estimating μ and σ using data from all
time points and epochs, as opposed to standardizing each *feature*
(i.e., each time point for each channel) by estimating using μ and σ
using data from all epochs.
Parameters
----------
%(info)s Only necessary if ``scalings`` is a dict or None.
scalings : dict, str, default None
Scaling method to be applied to data channel wise.
* if scalings is None (default), scales mag by 1e15, grad by 1e13,
and eeg by 1e6.
* if scalings is :class:`dict`, keys are channel types and values
are scale factors.
* if ``scalings=='median'``,
:class:`sklearn.preprocessing.RobustScaler`
is used (requires sklearn version 0.17+).
* if ``scalings=='mean'``,
:class:`sklearn.preprocessing.StandardScaler`
is used.
with_mean : bool, default True
If True, center the data using mean (or median) before scaling.
Ignored for channel-type scaling.
with_std : bool, default True
If True, scale the data to unit variance (``scalings='mean'``),
quantile range (``scalings='median``), or using channel type
if ``scalings`` is a dict or None).
"""
def __init__(self, info=None, scalings=None, with_mean=True, with_std=True):
self.info = info
self.with_mean = with_mean
self.with_std = with_std
self.scalings = scalings
def fit(self, epochs_data, y=None):
"""Standardize data across channels.
Parameters
----------
epochs_data : array, shape (n_epochs, n_channels, n_times)
The data to concatenate channels.
y : array, shape (n_epochs,)
The label for each epoch.
Returns
-------
self : instance of Scaler
The modified instance.
"""
epochs_data = self._check_data(epochs_data, y=y, fit=True, multi_output=True)
assert epochs_data.ndim == 3, epochs_data.shape
_validate_type(self.scalings, (dict, str, type(None)), "scalings")
if isinstance(self.scalings, str):
_check_option(
"scalings", self.scalings, ["mean", "median"], extra="when str"
)
if self.scalings is None or isinstance(self.scalings, dict):
if self.info is None:
raise ValueError(
f'Need to specify "info" if scalings is {type(self.scalings)}'
)
self.scaler_ = _ConstantScaler(self.info, self.scalings, self.with_std)
elif self.scalings == "mean":
self.scaler_ = StandardScaler(
with_mean=self.with_mean, with_std=self.with_std
)
else: # scalings == 'median':
self.scaler_ = RobustScaler(
with_centering=self.with_mean, with_scaling=self.with_std
)
_sklearn_reshape_apply(self.scaler_.fit, False, epochs_data, y=y)
return self
def transform(self, epochs_data):
"""Standardize data across channels.
Parameters
----------
epochs_data : array, shape (n_epochs, n_channels[, n_times])
The data.
Returns
-------
X : array, shape (n_epochs, n_channels, n_times)
The data concatenated over channels.
Notes
-----
This function makes a copy of the data before the operations and the
memory usage may be large with big data.
"""
check_is_fitted(self, "scaler_")
epochs_data = self._check_data(epochs_data, atleast_3d=False)
if epochs_data.ndim == 2: # can happen with SlidingEstimator
if self.info is not None:
assert len(self.info["ch_names"]) == epochs_data.shape[1]
epochs_data = epochs_data[..., np.newaxis]
assert epochs_data.ndim == 3, epochs_data.shape
return _sklearn_reshape_apply(self.scaler_.transform, True, epochs_data)
def fit_transform(self, epochs_data, y=None):
"""Fit to data, then transform it.
Fits transformer to epochs_data and y and returns a transformed version
of epochs_data.
Parameters
----------
epochs_data : array, shape (n_epochs, n_channels, n_times)
The data.
y : None | array, shape (n_epochs,)
The label for each epoch.
Defaults to None.
Returns
-------
X : array, shape (n_epochs, n_channels, n_times)
The data concatenated over channels.
Notes
-----
This function makes a copy of the data before the operations and the
memory usage may be large with big data.
"""
return self.fit(epochs_data, y).transform(epochs_data)
def inverse_transform(self, epochs_data):
"""Invert standardization of data across channels.
Parameters
----------
epochs_data : array, shape ([n_epochs, ]n_channels, n_times)
The data.
Returns
-------
X : array, shape (n_epochs, n_channels, n_times)
The data concatenated over channels.
Notes
-----
This function makes a copy of the data before the operations and the
memory usage may be large with big data.
"""
epochs_data = self._check_data(epochs_data, atleast_3d=False)
squeeze = False
# Can happen with CSP
if epochs_data.ndim == 2:
squeeze = True
epochs_data = epochs_data[..., np.newaxis]
assert epochs_data.ndim == 3, epochs_data.shape
out = _sklearn_reshape_apply(self.scaler_.inverse_transform, True, epochs_data)
if squeeze:
out = out[..., 0]
return out
class Vectorizer(MNETransformerMixin, BaseEstimator):
"""Transform n-dimensional array into 2D array of n_samples by n_features.
This class reshapes an n-dimensional array into an n_samples * n_features
array, usable by the estimators and transformers of scikit-learn.
Attributes
----------
features_shape_ : tuple
Stores the original shape of data.
Examples
--------
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> clf = make_pipeline(Vectorizer(), StandardScaler(), LogisticRegression())
"""
def fit(self, X, y=None):
"""Store the shape of the features of X.
Parameters
----------
X : array-like
The data to fit. Can be, for example a list, or an array of at
least 2d. The first dimension must be of length n_samples, where
samples are the independent samples used by the estimator
(e.g. n_epochs for epoched data).
y : None | array, shape (n_samples,)
Used for scikit-learn compatibility.
Returns
-------
self : instance of Vectorizer
Return the modified instance.
"""
X = self._check_data(X, y=y, atleast_3d=False, fit=True, check_n_features=False)
self.features_shape_ = X.shape[1:]
return self
def transform(self, X):
"""Convert given array into two dimensions.
Parameters
----------
X : array-like
The data to fit. Can be, for example a list, or an array of at
least 2d. The first dimension must be of length n_samples, where
samples are the independent samples used by the estimator
(e.g. n_epochs for epoched data).
Returns
-------
X : array, shape (n_samples, n_features)
The transformed data.
"""
X = self._check_data(X, atleast_3d=False)
if X.shape[1:] != self.features_shape_:
raise ValueError("Shape of X used in fit and transform must be same")
return X.reshape(len(X), -1)
def fit_transform(self, X, y=None):
"""Fit the data, then transform in one step.
Parameters
----------
X : array-like
The data to fit. Can be, for example a list, or an array of at
least 2d. The first dimension must be of length n_samples, where
samples are the independent samples used by the estimator
(e.g. n_epochs for epoched data).
y : None | array, shape (n_samples,)
Used for scikit-learn compatibility.
Returns
-------
X : array, shape (n_samples, -1)
The transformed data.
"""
return self.fit(X).transform(X)
def inverse_transform(self, X):
"""Transform 2D data back to its original feature shape.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Data to be transformed back to original shape.
Returns
-------
X : array
The data transformed into shape as used in fit. The first
dimension is of length n_samples.
"""
X = self._check_data(X, atleast_3d=False, check_n_features=False)
if X.ndim not in (2, 3):
raise ValueError(
f"X should be of 2 or 3 dimensions but has shape {X.shape}"
)
return X.reshape(X.shape[:-1] + self.features_shape_)
@fill_doc
class PSDEstimator(MNETransformerMixin, BaseEstimator):
"""Compute power spectral density (PSD) using a multi-taper method.
Parameters
----------
sfreq : float
The sampling frequency.
fmin : float
The lower frequency of interest.
fmax : float
The upper frequency of interest.
bandwidth : float
The bandwidth of the multi taper windowing function in Hz.
adaptive : bool
Use adaptive weights to combine the tapered spectra into PSD
(slow, use n_jobs >> 1 to speed up computation).
low_bias : bool
Only use tapers with more than 90%% spectral concentration within
bandwidth.
n_jobs : int
Number of parallel jobs to use (only used if adaptive=True).
%(normalization)s
See Also
--------
mne.time_frequency.psd_array_multitaper
mne.io.Raw.compute_psd
mne.Epochs.compute_psd
mne.Evoked.compute_psd
"""
def __init__(
self,
sfreq=2 * np.pi,
fmin=0,
fmax=np.inf,
bandwidth=None,
adaptive=False,
low_bias=True,
n_jobs=None,
normalization="length",
):
self.sfreq = sfreq
self.fmin = fmin
self.fmax = fmax
self.bandwidth = bandwidth
self.adaptive = adaptive
self.low_bias = low_bias
self.n_jobs = n_jobs
self.normalization = normalization
def fit(self, epochs_data, y=None):
"""Compute power spectral density (PSD) using a multi-taper method.
Parameters
----------
epochs_data : array, shape (n_epochs, n_channels, n_times)
The data.
y : array, shape (n_epochs,)
The label for each epoch.
Returns
-------
self : instance of PSDEstimator
The modified instance.
"""
self._check_data(epochs_data, y=y, fit=True)
self.fitted_ = True # sklearn compliance
return self
def transform(self, epochs_data):
"""Compute power spectral density (PSD) using a multi-taper method.
Parameters
----------
epochs_data : array, shape (n_epochs, n_channels, n_times)
The data.
Returns
-------
psd : array, shape (n_signals, n_freqs) or (n_freqs,)
The computed PSD.
"""
epochs_data = self._check_data(epochs_data)
psd, _ = psd_array_multitaper(
epochs_data,
sfreq=self.sfreq,
fmin=self.fmin,
fmax=self.fmax,
bandwidth=self.bandwidth,
adaptive=self.adaptive,
low_bias=self.low_bias,
normalization=self.normalization,
n_jobs=self.n_jobs,
)
return psd
@fill_doc
class FilterEstimator(MNETransformerMixin, BaseEstimator):
"""Estimator to filter RtEpochs.
Applies a zero-phase low-pass, high-pass, band-pass, or band-stop
filter to the channels selected by "picks".
l_freq and h_freq are the frequencies below which and above which,
respectively, to filter out of the data. Thus the uses are:
- l_freq < h_freq: band-pass filter
- l_freq > h_freq: band-stop filter
- l_freq is not None, h_freq is None: low-pass filter
- l_freq is None, h_freq is not None: high-pass filter
If n_jobs > 1, more memory is required as "len(picks) * n_times"
additional time points need to be temporarily stored in memory.
Parameters
----------
%(info_not_none)s
%(l_freq)s
%(h_freq)s
%(picks_good_data)s
%(filter_length)s
%(l_trans_bandwidth)s
%(h_trans_bandwidth)s
n_jobs : int | str
Number of jobs to run in parallel.
Can be 'cuda' if ``cupy`` is installed properly and method='fir'.
method : str
'fir' will use overlap-add FIR filtering, 'iir' will use IIR filtering.
iir_params : dict | None
Dictionary of parameters to use for IIR filtering.
See mne.filter.construct_iir_filter for details. If iir_params
is None and method="iir", 4th order Butterworth will be used.
%(fir_design)s
See Also
--------
TemporalFilter
Notes
-----
This is primarily meant for use in realtime applications.
In general it is not recommended in a normal processing pipeline as it may result
in edge artifacts. Use with caution.
"""
def __init__(
self,
info,
l_freq,
h_freq,
picks=None,
filter_length="auto",
l_trans_bandwidth="auto",
h_trans_bandwidth="auto",
n_jobs=None,
method="fir",
iir_params=None,
fir_design="firwin",
):
self.info = info
self.l_freq = l_freq
self.h_freq = h_freq
self.picks = picks
self.filter_length = filter_length
self.l_trans_bandwidth = l_trans_bandwidth
self.h_trans_bandwidth = h_trans_bandwidth
self.n_jobs = n_jobs
self.method = method
self.iir_params = iir_params
self.fir_design = fir_design
def fit(self, epochs_data, y):
"""Filter data.
Parameters
----------
epochs_data : array, shape (n_epochs, n_channels, n_times)
The data.
y : array, shape (n_epochs,)
The label for each epoch.
Returns
-------
self : instance of FilterEstimator
The modified instance.
"""
self.picks_ = _picks_to_idx(self.info, self.picks)
self._check_data(epochs_data, y=y, fit=True)
if self.l_freq == 0:
self.l_freq = None
if self.info["lowpass"] is None or (
self.h_freq is not None
and (self.l_freq is None or self.l_freq < self.h_freq)
and self.h_freq < self.info["lowpass"]
):
with self.info._unlock():
self.info["lowpass"] = self.h_freq
if self.info["highpass"] is None or (
self.l_freq is not None
and (self.h_freq is None or self.l_freq < self.h_freq)
and self.l_freq > self.info["highpass"]
):
with self.info._unlock():
self.info["highpass"] = self.l_freq
return self
def transform(self, epochs_data):
"""Filter data.
Parameters
----------
epochs_data : array, shape (n_epochs, n_channels, n_times)
The data.
Returns
-------
X : array, shape (n_epochs, n_channels, n_times)
The data after filtering.
"""
return filter_data(
self._check_data(epochs_data),
self.info["sfreq"],
self.l_freq,
self.h_freq,
self.picks_,
self.filter_length,
self.l_trans_bandwidth,
self.h_trans_bandwidth,
method=self.method,
iir_params=self.iir_params,
n_jobs=self.n_jobs,
copy=False,
fir_design=self.fir_design,
verbose=False,
)
class UnsupervisedSpatialFilter(MNETransformerMixin, BaseEstimator):
"""Use unsupervised spatial filtering across time and samples.
Parameters
----------
estimator : instance of sklearn.base.BaseEstimator
Estimator using some decomposition algorithm.
average : bool, default False
If True, the estimator is fitted on the average across samples
(e.g. epochs).
"""
def __init__(self, estimator, average=False):
self.estimator = estimator
self.average = average
def fit(self, X, y=None):
"""Fit the spatial filters.
Parameters
----------
X : array, shape (n_epochs, n_channels, n_times)
The data to be filtered.
y : None | array, shape (n_samples,)
Used for scikit-learn compatibility.
Returns
-------
self : instance of UnsupervisedSpatialFilter
Return the modified instance.
"""
# sklearn.utils.estimator_checks.check_estimator(self.estimator) is probably
# too strict for us, given that we don't fully adhere yet, so just check attrs
for attr in ("fit", "transform", "fit_transform"):
if not hasattr(self.estimator, attr):
raise ValueError(
"estimator must be a scikit-learn "
f"transformer, missing {attr} method"
)
_validate_type(self.average, bool, "average")
X = self._check_data(X, y=y, fit=True)
if self.average:
X = np.mean(X, axis=0).T
else:
n_epochs, n_channels, n_times = X.shape
# trial as time samples
X = np.transpose(X, (1, 0, 2)).reshape((n_channels, n_epochs * n_times)).T
self.estimator_ = clone(self.estimator)
self.estimator_.fit(X)
return self
def fit_transform(self, X, y=None):
"""Transform the data to its filtered components after fitting.
Parameters
----------
X : array, shape (n_epochs, n_channels, n_times)
The data to be filtered.
y : None | array, shape (n_samples,)
Used for scikit-learn compatibility.
Returns
-------
X : array, shape (n_epochs, n_channels, n_times)
The transformed data.
"""
return self.fit(X).transform(X)
def transform(self, X):
"""Transform the data to its spatial filters.
Parameters
----------
X : array, shape (n_epochs, n_channels, n_times)
The data to be filtered.
Returns
-------
X : array, shape (n_epochs, n_channels, n_times)
The transformed data.
"""
check_is_fitted(self.estimator_)
X = self._check_data(X)
return self._apply_method(X, "transform")
def inverse_transform(self, X):
"""Inverse transform the data to its original space.
Parameters
----------
X : array, shape (n_epochs, n_components, n_times)
The data to be inverted.
Returns
-------
X : array, shape (n_epochs, n_channels, n_times)
The transformed data.
"""
return self._apply_method(X, "inverse_transform")
def _apply_method(self, X, method):
"""Vectorize time samples as trials, apply method and reshape back.
Parameters
----------
X : array, shape (n_epochs, n_dims, n_times)
The data to be inverted.
Returns
-------
X : array, shape (n_epochs, n_dims, n_times)
The transformed data.
"""
n_epochs, n_channels, n_times = X.shape
# trial as time samples
X = np.transpose(X, [1, 0, 2])
X = np.reshape(X, [n_channels, n_epochs * n_times]).T
# apply method
method = getattr(self.estimator_, method)
X = method(X)
# put it back to n_epochs, n_dimensions
X = np.reshape(X.T, [-1, n_epochs, n_times]).transpose([1, 0, 2])
return X
@fill_doc
class TemporalFilter(MNETransformerMixin, BaseEstimator):
"""Estimator to filter data array along the last dimension.
Applies a zero-phase low-pass, high-pass, band-pass, or band-stop
filter to the channels.
l_freq and h_freq are the frequencies below which and above which,
respectively, to filter out of the data. Thus the uses are:
- l_freq < h_freq: band-pass filter
- l_freq > h_freq: band-stop filter
- l_freq is not None, h_freq is None: low-pass filter
- l_freq is None, h_freq is not None: high-pass filter
See :func:`mne.filter.filter_data`.
Parameters
----------
l_freq : float | None
Low cut-off frequency in Hz. If None the data are only low-passed.
h_freq : float | None
High cut-off frequency in Hz. If None the data are only
high-passed.
sfreq : float, default 1.0
Sampling frequency in Hz.
filter_length : str | int, default 'auto'
Length of the FIR filter to use (if applicable):
* int: specified length in samples.
* 'auto' (default in 0.14): the filter length is chosen based
on the size of the transition regions (7 times the reciprocal
of the shortest transition band).
* str: (default in 0.13 is "10s") a human-readable time in
units of "s" or "ms" (e.g., "10s" or "5500ms") will be
converted to that number of samples if ``phase="zero"``, or
the shortest power-of-two length at least that duration for
``phase="zero-double"``.
l_trans_bandwidth : float | str
Width of the transition band at the low cut-off frequency in Hz
(high pass or cutoff 1 in bandpass). Can be "auto"
(default in 0.14) to use a multiple of ``l_freq``::
min(max(l_freq * 0.25, 2), l_freq)
Only used for ``method='fir'``.
h_trans_bandwidth : float | str
Width of the transition band at the high cut-off frequency in Hz
(low pass or cutoff 2 in bandpass). Can be "auto"
(default in 0.14) to use a multiple of ``h_freq``::
min(max(h_freq * 0.25, 2.), info['sfreq'] / 2. - h_freq)
Only used for ``method='fir'``.
n_jobs : int | str, default 1
Number of jobs to run in parallel.
Can be 'cuda' if ``cupy`` is installed properly and method='fir'.
method : str, default 'fir'
'fir' will use overlap-add FIR filtering, 'iir' will use IIR
forward-backward filtering (via filtfilt).
iir_params : dict | None, default None
Dictionary of parameters to use for IIR filtering.
See mne.filter.construct_iir_filter for details. If iir_params
is None and method="iir", 4th order Butterworth will be used.
fir_window : str, default 'hamming'
The window to use in FIR design, can be "hamming", "hann",
or "blackman".
fir_design : str
Can be "firwin" (default) to use :func:`scipy.signal.firwin`,
or "firwin2" to use :func:`scipy.signal.firwin2`. "firwin" uses
a time-domain design technique that generally gives improved
attenuation using fewer samples than "firwin2".
.. versionadded:: 0.15
See Also
--------
FilterEstimator
Vectorizer
mne.filter.filter_data
"""
def __init__(
self,
l_freq=None,
h_freq=None,
sfreq=1.0,
filter_length="auto",
l_trans_bandwidth="auto",
h_trans_bandwidth="auto",
n_jobs=None,
method="fir",
iir_params=None,
fir_window="hamming",
fir_design="firwin",
):
self.l_freq = l_freq
self.h_freq = h_freq
self.sfreq = sfreq
self.filter_length = filter_length
self.l_trans_bandwidth = l_trans_bandwidth
self.h_trans_bandwidth = h_trans_bandwidth
self.n_jobs = n_jobs
self.method = method
self.iir_params = iir_params
self.fir_window = fir_window
self.fir_design = fir_design
def fit(self, X, y=None):
"""Do nothing (for scikit-learn compatibility purposes).
Parameters
----------
X : array, shape ([n_epochs, ]n_channels, n_times)
The data to be filtered over the last dimension. The channels
dimension can be zero when passing a 2D array.
y : None
Not used, for scikit-learn compatibility issues.
Returns
-------
self : instance of TemporalFilter
The modified instance.
"""
self.fitted_ = True # sklearn compliance
self._check_data(X, y=y, atleast_3d=False, fit=True)
return self
def transform(self, X):
"""Filter data along the last dimension.
Parameters
----------
X : array, shape ([n_epochs, ]n_channels, n_times)
The data to be filtered over the last dimension. The channels
dimension can be zero when passing a 2D array.
Returns
-------
X : array
The data after filtering.
""" # noqa: E501
X = self._check_data(X, atleast_3d=False)
X = np.atleast_2d(X)
if X.ndim > 3:
raise ValueError(
"Array must be of at max 3 dimensions instead "
f"got {X.ndim} dimensional matrix"
)
shape = X.shape
X = X.reshape(-1, shape[-1])
X = filter_data(
X,
self.sfreq,
self.l_freq,
self.h_freq,
filter_length=self.filter_length,
l_trans_bandwidth=self.l_trans_bandwidth,
h_trans_bandwidth=self.h_trans_bandwidth,
n_jobs=self.n_jobs,
method=self.method,
iir_params=self.iir_params,
copy=False,
fir_window=self.fir_window,
fir_design=self.fir_design,
)
return X.reshape(shape)
Datasets
Models
