Time-Frequency

mne.time_frequency:

Time frequency analysis tools.

AverageTFR([info, data, times, freqs, nave, ...])	Data object for spectrotemporal representations of averaged data.
AverageTFRArray(info, data, times, freqs, *)	Data object for precomputed spectrotemporal representations of averaged data.
BaseTFR(inst, method, freqs, tmin, tmax, ...)	Base class for RawTFR, EpochsTFR, and AverageTFR (for type checking only).
EpochsTFR([info, data, times, freqs, inst, ...])	Data object for spectrotemporal representations of epoched data.
EpochsTFRArray(info, data, times, freqs, *)	Data object for precomputed spectrotemporal representations of epoched data.
RawTFR(inst[, method, freqs, tmin, tmax, ...])	Data object for spectrotemporal representations of continuous data.
RawTFRArray(info, data, times, freqs, *[, ...])	Data object for precomputed spectrotemporal representations of continuous data.
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[, ...])	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, ...])
tfr_multitaper(inst, freqs, n_cycles[, ...])
tfr_stockwell(inst[, fmin, fmax, n_fft, ...])
read_tfrs(fname[, condition, verbose])	Load a TFR object from disk.
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.