Time-Frequency#

mne.time_frequency:

Time frequency analysis tools.

AverageTFR(info, data, times, freqs, nave[, ...])

Container for Time-Frequency data.

EpochsTFR(info, data, times, freqs[, ...])

Container for Time-Frequency data on epochs.

CrossSpectralDensity(data, ch_names, ...[, ...])

Cross-spectral density.

Spectrum(inst, method, fmin, fmax, tmin, ...)

Data object for spectral representations of continuous data.

SpectrumArray(data, info, freqs, *[, verbose])

Data object for precomputed spectral data (in NumPy array format).

EpochsSpectrum(inst, method, fmin, fmax, ...)

Data object for spectral representations of epoched data.

EpochsSpectrumArray(data, info, freqs[, ...])

Data object for precomputed epoched spectral data (in NumPy array format).

Functions that operate on mne-python objects:

csd_tfr(epochs_tfr[, tmin, tmax, picks, ...])

Compute covariance matrices across frequencies for TFR epochs.

csd_fourier(epochs[, fmin, fmax, tmin, ...])

Estimate cross-spectral density from an array using short-time fourier.

csd_multitaper(epochs[, fmin, fmax, tmin, ...])

Estimate cross-spectral density from epochs using a multitaper method.

csd_morlet(epochs, frequencies[, tmin, ...])

Estimate cross-spectral density from epochs using Morlet wavelets.

pick_channels_csd(csd[, include, exclude, ...])

Pick channels from cross-spectral density matrix.

read_csd(fname)

Read a CrossSpectralDensity object from an HDF5 file.

fit_iir_model_raw(raw[, order, picks, tmin, ...])

Fit an AR model to raw data and creates the corresponding IIR filter.

tfr_morlet(inst, freqs, n_cycles[, use_fft, ...])

Compute Time-Frequency Representation (TFR) using Morlet wavelets.

tfr_multitaper(inst, freqs, n_cycles[, ...])

Compute Time-Frequency Representation (TFR) using DPSS tapers.

tfr_stockwell(inst[, fmin, fmax, n_fft, ...])

Compute Time-Frequency Representation (TFR) using Stockwell Transform.

read_tfrs(fname[, condition, verbose])

Read TFR datasets from hdf5 file.

write_tfrs(fname, tfr[, overwrite, verbose])

Write a TFR dataset to hdf5.

read_spectrum(fname)

Load a mne.time_frequency.Spectrum object from disk.

Functions that operate on np.ndarray objects:

csd_array_fourier(X, sfreq[, t0, fmin, ...])

Estimate cross-spectral density from an array using short-time fourier.

csd_array_multitaper(X, sfreq[, t0, fmin, ...])

Estimate cross-spectral density from an array using a multitaper method.

csd_array_morlet(X, sfreq, frequencies[, ...])

Estimate cross-spectral density from an array using Morlet wavelets.

dpss_windows(N, half_nbw, Kmax, *[, sym, ...])

Compute Discrete Prolate Spheroidal Sequences.

fwhm(freq, n_cycles)

Compute the full-width half maximum of a Morlet wavelet.

morlet(sfreq, freqs[, n_cycles, sigma, ...])

Compute Morlet wavelets for the given frequency range.

stft(x, wsize[, tstep, verbose])

STFT Short-Term Fourier Transform using a sine window.

istft(X[, tstep, Tx])

ISTFT Inverse Short-Term Fourier Transform using a sine window.

stftfreq(wsize[, sfreq])

Compute frequencies of stft transformation.

psd_array_multitaper(x, sfreq[, fmin, fmax, ...])

Compute power spectral density (PSD) using a multi-taper method.

psd_array_welch(x, sfreq[, fmin, fmax, ...])

Compute power spectral density (PSD) using Welch's method.

tfr_array_morlet(data, sfreq, freqs[, ...])

Compute Time-Frequency Representation (TFR) using Morlet wavelets.

tfr_array_multitaper(data, sfreq, freqs[, ...])

Compute Time-Frequency Representation (TFR) using DPSS tapers.

tfr_array_stockwell(data, sfreq[, fmin, ...])

Compute power and intertrial coherence using Stockwell (S) transform.

mne.time_frequency.tfr:

A module which implements the time-frequency estimation.

Morlet code inspired by Matlab code from Sheraz Khan & Brainstorm & SPM

cwt(X, Ws[, use_fft, mode, decim])

Compute time-frequency decomposition with continuous wavelet transform.

morlet(sfreq, freqs[, n_cycles, sigma, ...])

Compute Morlet wavelets for the given frequency range.