def butter(N, Wn, btype='<STR_LIT>', analog=False, output='<STR_LIT>'):
return iirfilter(N, Wn, btype=btype, analog=analog,<EOL>output=output, ftype='<STR_LIT>')<EOL>
Butterworth digital and analog filter design.

Design an Nth order digital or analog Butterworth filter and return
the filter coefficients in (B,A) or (Z,P,K) form.

Parameters
----------
N : int
    The order of the filter.
Wn : array_like
    A scalar or length-2 sequence giving the critical frequencies.
    For a Butterworth filter, this is the point at which the gain
    drops to 1/sqrt(2) that of the passband (the "-3 dB point").
    For digital filters, `Wn` is normalized from 0 to 1, where 1 is the
    Nyquist frequency, pi radians/sample.  (`Wn` is thus in
    half-cycles / sample.)
    For analog filters, `Wn` is an angular frequency (e.g. rad/s).
btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional
    The type of filter.  Default is 'lowpass'.
analog : bool, optional
    When True, return an analog filter, otherwise a digital filter is
    returned.
output : {'ba', 'zpk'}, optional
    Type of output:  numerator/denominator ('ba') or pole-zero ('zpk').
    Default is 'ba'.

Returns
-------
b, a : ndarray, ndarray
    Numerator (`b`) and denominator (`a`) polynomials of the IIR filter.
    Only returned if ``output='ba'``.
z, p, k : ndarray, ndarray, float
    Zeros, poles, and system gain of the IIR filter transfer
    function.  Only returned if ``output='zpk'``.

See also
--------
buttord

Notes
-----
The Butterworth filter has maximally flat frequency response in the
passband.

Examples
--------
Plot the filter's frequency response, showing the critical points:

>>> from scipy import signal
>>> import matplotlib.pyplot as plt

>>> b, a = signal.butter(4, 100, 'low', analog=True)
>>> w, h = signal.freqs(b, a)
>>> plt.semilogx(w, 20 * np.log10(abs(h)))
>>> plt.title('Butterworth filter frequency response')
>>> plt.xlabel('Frequency [radians / second]')
>>> plt.ylabel('Amplitude [dB]')
>>> plt.margins(0, 0.1)
>>> plt.grid(which='both', axis='both')
>>> plt.axvline(100, color='green') # cutoff frequency
>>> plt.show()