fir1: Window-based 'FIR' filter design

In ravetools: Signal and Image Processing Toolbox for Analyzing Intracranial Electroencephalography Data

Window-based FIR filter design

Description

Generate a fir1 filter that is checked against Matlab fir1 function.

Usage

fir1(
  n,
  w,
  type = c("low", "high", "stop", "pass", "DC-0", "DC-1"),
  window = hamming,
  scale = TRUE,
  hilbert = FALSE
)

Arguments

n	filter order
w	band edges, non-decreasing vector in the range 0 to 1, where 1 is the Nyquist frequency. A scalar for high-pass or low-pass filters, a vector pair for band-pass or band-stop, or a vector for an alternating pass/stop filter.
type	type of the filter, one of "low" for a low-pass filter, "high" for a high-pass filter, "stop" for a stop-band (band-reject) filter, "pass" for a pass-band filter, "DC-0" for a band-pass as the first band of a multi-band filter, or "DC-1" for a band-stop as the first band of a multi-band filter; default "low"
window	smoothing window function or a numerical vector. The filter is the same shape as the smoothing window. When window is a function, window(n+1) will be called, otherwise the length of the window vector needs to have length of n+1; default: hamming
scale	whether to scale the filter; default is true
hilbert	whether to use 'Hilbert' transformer; default is false

Value

The FIR filter coefficients with class 'Arma'. The moving average coefficient is a vector of length n+1.

ravetools documentation built on Sept. 11, 2024, 9:06 p.m.