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.

Functions that operate on mne-python objects:

`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.
`psd_welch`(inst[, fmin, fmax, tmin, tmax, ...])	Compute the power spectral density (PSD) using Welch's method.
`psd_multitaper`(inst[, fmin, fmax, tmin, ...])	Compute the power spectral density (PSD) using multitapers.
`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])	Read TFR datasets from hdf5 file.
`write_tfrs`(fname, tfr[, overwrite, verbose])	Write a TFR dataset to hdf5.

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[, low_bias, ...])	Compute Discrete Prolate Spheroidal Sequences.
`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`(epoch_data, sfreq, freqs[, ...])	Compute Time-Frequency Representation (TFR) using Morlet wavelets.
`tfr_array_multitaper`(epoch_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 freq decomposition with continuous wavelet transform.
`morlet`(sfreq, freqs[, n_cycles, sigma, ...])	Compute Morlet wavelets for the given frequency range.