scipy.signal.

firwin#

scipy.signal.firwin(numtaps, cutoff, *, width=None, window='hamming', pass_zero=True, scale=True, fs=None)[source]#

FIR filter design using the window method.

This function computes the coefficients of a finite impulse response filter. The filter will have linear phase; it will be Type I if numtaps is odd and Type II if numtaps is even.

Type II filters always have zero response at the Nyquist frequency, so a ValueError exception is raised if firwin is called with numtaps even and having a passband whose right end is at the Nyquist frequency.

Parameters:

numtapsint

Length of the filter (number of coefficients, i.e. the filter order + 1). numtaps must be odd if a passband includes the Nyquist frequency.

cutofffloat or 1-D array_like

Cutoff frequency of filter (expressed in the same units as fs) OR an array of cutoff frequencies (that is, band edges). In the former case, as a float, the cutoff frequency should correspond with the half-amplitude point, where the attenuation will be -6dB. In the latter case, the frequencies in cutoff should be positive and monotonically increasing between 0 and fs/2. The values 0 and fs/2 must not be included in cutoff. It should be noted that this is different than the behavior of scipy.signal.iirdesign, where the cutoff is the half-power point (-3dB).

widthfloat or None, optional

If width is not None, then assume it is the approximate width of the transition region (expressed in the same units as fs) for use in Kaiser FIR filter design. In this case, the window argument is ignored.

windowstring or tuple of string and parameter values, optional

Desired window to use. See scipy.signal.get_window for a list of windows and required parameters.

pass_zero{True, False, ‘bandpass’, ‘lowpass’, ‘highpass’, ‘bandstop’}, optional

If True, the gain at the frequency 0 (i.e., the “DC gain”) is 1. If False, the DC gain is 0. Can also be a string argument for the desired filter type (equivalent to btype in IIR design functions).

Added in version 1.3.0: Support for string arguments.

scalebool, optional

Set to True to scale the coefficients so that the frequency response is exactly unity at a certain frequency. That frequency is either:

0 (DC) if the first passband starts at 0 (i.e. pass_zero is True)
fs/2 (the Nyquist frequency) if the first passband ends at fs/2 (i.e the filter is a single band highpass filter); center of first passband otherwise

fsfloat, optional

The sampling frequency of the signal. Each frequency in cutoff must be between 0 and fs/2. Default is 2.

Returns:

h(numtaps,) ndarray: Coefficients of length numtaps FIR filter.

Raises:

ValueError: If any value in cutoff is less than or equal to 0 or greater than or equal to fs/2, if the values in cutoff are not strictly monotonically increasing, or if numtaps is even but a passband includes the Nyquist frequency.

See also

firwin2
firls
minimum_phase
remez

Examples

Low-pass from 0 to f:

>>> from scipy import signal
>>> numtaps = 3
>>> f = 0.1
>>> signal.firwin(numtaps, f)
array([ 0.06799017,  0.86401967,  0.06799017])

Use a specific window function:

>>> signal.firwin(numtaps, f, window='nuttall')
array([  3.56607041e-04,   9.99286786e-01,   3.56607041e-04])

High-pass (‘stop’ from 0 to f):

>>> signal.firwin(numtaps, f, pass_zero=False)
array([-0.00859313,  0.98281375, -0.00859313])

Band-pass:

>>> f1, f2 = 0.1, 0.2
>>> signal.firwin(numtaps, [f1, f2], pass_zero=False)
array([ 0.06301614,  0.88770441,  0.06301614])

Band-stop:

>>> signal.firwin(numtaps, [f1, f2])
array([-0.00801395,  1.0160279 , -0.00801395])

Multi-band (passbands are [0, f1], [f2, f3] and [f4, 1]):

>>> f3, f4 = 0.3, 0.4
>>> signal.firwin(numtaps, [f1, f2, f3, f4])
array([-0.01376344,  1.02752689, -0.01376344])

Multi-band (passbands are [f1, f2] and [f3,f4]):

>>> signal.firwin(numtaps, [f1, f2, f3, f4], pass_zero=False)
array([ 0.04890915,  0.91284326,  0.04890915])