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# Author: Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
from .mixin import TransformerMixin
from .base import BaseEstimator
from ..time_frequency.tfr import _compute_tfr, _check_tfr_param
class TimeFrequency(TransformerMixin, BaseEstimator):
"""Time frequency transformer.
Time-frequency transform of times series along the last axis.
Parameters
----------
freqs : array-like of floats, shape (n_freqs,)
The frequencies.
sfreq : float | int, defaults to 1.0
Sampling frequency of the data.
method : 'multitaper' | 'morlet', defaults to 'morlet'
The time-frequency method. 'morlet' convolves a Morlet wavelet.
'multitaper' uses Morlet wavelets windowed with multiple DPSS
multitapers.
n_cycles : float | array of float, defaults to 7.0
Number of cycles in the Morlet wavelet. Fixed number
or one per frequency.
time_bandwidth : float, defaults to None
If None and method=multitaper, will be set to 4.0 (3 tapers).
Time x (Full) Bandwidth product. Only applies if
method == 'multitaper'. The number of good tapers (low-bias) is
chosen automatically based on this to equal floor(time_bandwidth - 1).
use_fft : bool, defaults to True
Use the FFT for convolutions or not.
decim : int | slice, defaults to 1
To reduce memory usage, decimation factor after time-frequency
decomposition.
If `int`, returns tfr[..., ::decim].
If `slice`, returns tfr[..., decim].
.. note:: Decimation may create aliasing artifacts, yet decimation
is done after the convolutions.
output : str, defaults to 'complex'
* 'complex' : single trial complex.
* 'power' : single trial power.
* 'phase' : single trial phase.
n_jobs : int, defaults to 1
The number of epochs to process at the same time. The parallelization
is implemented across channels.
verbose : bool, str, int, or None, defaults to None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
See Also
--------
mne.time_frequency.tfr_morlet
mne.time_frequency.tfr_multitaper
"""
def __init__(self, freqs, sfreq=1.0, method='morlet', n_cycles=7.0,
time_bandwidth=None, use_fft=True, decim=1, output='complex',
n_jobs=1, verbose=None): # noqa: D102
"""Init TimeFrequency transformer."""
freqs, sfreq, _, n_cycles, time_bandwidth, decim = \
_check_tfr_param(freqs, sfreq, method, True, n_cycles,
time_bandwidth, use_fft, decim, output)
self.freqs = freqs
self.sfreq = sfreq
self.method = method
self.n_cycles = n_cycles
self.time_bandwidth = time_bandwidth
self.use_fft = use_fft
self.decim = decim
# Check that output is not an average metric (e.g. ITC)
if output not in ['complex', 'power', 'phase']:
raise ValueError("output must be 'complex', 'power', 'phase'. "
"Got %s instead." % output)
self.output = output
self.n_jobs = n_jobs
self.verbose = verbose
def fit_transform(self, X, y=None):
"""Time-frequency transform of times series along the last axis.
Parameters
----------
X : array, shape (n_samples, n_channels, n_times)
The training data samples. The channel dimension can be zero- or
1-dimensional.
y : None
For scikit-learn compatibility purposes.
Returns
-------
Xt : array, shape (n_samples, n_channels, n_freqs, n_times)
The time-frequency transform of the data, where n_channels can be
zero- or 1-dimensional.
"""
return self.fit(X, y).transform(X)
def fit(self, X, y=None): # noqa: D401
"""Do nothing (for scikit-learn compatibility purposes).
Parameters
----------
X : array, shape (n_samples, n_channels, n_times)
The training data.
y : array | None
The target values.
Returns
-------
self : object
Return self.
"""
return self
def transform(self, X):
"""Time-frequency transform of times series along the last axis.
Parameters
----------
X : array, shape (n_samples, n_channels, n_times)
The training data samples. The channel dimension can be zero- or
1-dimensional.
Returns
-------
Xt : array, shape (n_samples, n_channels, n_freqs, n_times)
The time-frequency transform of the data, where n_channels can be
zero- or 1-dimensional.
"""
# Ensure 3-dimensional X
shape = X.shape[1:-1]
if not shape:
X = X[:, np.newaxis, :]
# Compute time-frequency
Xt = _compute_tfr(X, self.freqs, self.sfreq, self.method,
self.n_cycles, True, self.time_bandwidth,
self.use_fft, self.decim, self.output, self.n_jobs,
self.verbose)
# Back to original shape
if not shape:
Xt = Xt[:, 0, :]
return Xt
|
