Filtering and Signal Processing

FIR filter design, filtering helpers, and window functions.

eegprep.dftfilt(length, maxfreq, cycle, ...)	Return legacy discrete Fourier filters.
eegprep.dftfilt2(freqs, cycles, srate[, ...])	Return EEGLAB dftfilt2 wavelet filters.
eegprep.dftfilt3(freqs, cycles, srate, *[, ...])	Return EEGLAB dftfilt3-style complex wavelet filters.
eegprep.fir_filterdcpadded(b, a, data[, ...])	Pad data with endpoint constants and filter along the first axis.
eegprep.firfiltreport(*args, **kwargs)	Return an EEGLAB-style FIR filter report.
eegprep.firfiltsplit(EEG, b[, causal, ...])	Filter continuous chunks split at boundaries or each epoch independently.
eegprep.firgauss(fc[, fs])	Return a Gaussian low-pass FIR coefficient vector.
eegprep.firws(m, f[, t, w])	Designs windowed sinc type I linear phase FIR filter.
eegprep.firwsord(wintype, fs, df[, dev])	Estimate windowed sinc FIR filter order depending on window type and requested transition band width.
eegprep.invfirwsord(wintype, fs, m[, dev])	Estimate transition bandwidth and ripple for a windowed-sinc FIR order.
eegprep.invkaiserbeta(beta)	Estimate maximum passband deviation from a Kaiser window beta.
eegprep.kaiserbeta(dev)	Estimate the Kaiser window beta from maximum passband deviation.
eegprep.minphaserceps(b, *[, ...])	Convert FIR coefficients to a minimum-phase impulse response.
eegprep.plotfresp(b[, a, nfft, fs, dir, show])	Plot impulse, step, magnitude, and phase response like FIRFilt.
eegprep.windows(t, m[, a])	Return a symmetric window vector.