def fftfilt( d, fs, band=(58,62), mode='bandstop', axis=0, dB=20 ): """ Filter brutally by weighting the fft and inverse-transforming. """### if not isinstance( mode, ( tuple, list ) ): mode = [ mode ] if not isinstance( band[ 0 ], ( tuple, list ) ): band = [ band ] if not isinstance( dB, ( tuple, list ) ): dB = [ dB ] if len( mode ) == 1: mode = mode * len( band ) if len( dB ) == 1: dB = dB * len( band ) D = fft( d, axis=axis ) f = numpy.abs( fftfreqs( d.shape[ axis ], fs ) ) shape = [ 1 for x in d.shape ] shape[ axis ] = len( f ) f.shape = shape w = f * 0 + 1 for mode, band, dB in zip( mode, band, dB ): band = list( band ) depth = 10.0 ** ( -abs( dB ) / 20.0 ) if mode == 'highpass': w = w * ( depth + ( 1.0 - depth ) * shoulder( f, band + [ fs, fs ] ) ) elif mode == 'lowpass': w = w * ( depth + ( 1.0 - depth ) * shoulder( f, [ -fs, -fs ] + band ) ) elif mode == 'bandpass': w = w * ( depth + ( 1.0 - depth ) * shoulder( f, band ) ) elif mode == 'bandstop': w = w * ( 1.0 - ( 1.0 - depth ) * shoulder( f, band ) ) else: raise ValueError( 'unrecognized filtering mode "%s"' % mode ) d = numpy.real( ifft( D * w, axis=axis ) ) return d
def plotsig(x, samplingfreq_hz=None, hold=False, axis=0, welch=0, **kwargs):
"""
Makes two subplots, showing time-series <x> in the upper panel and its
amplitude spectrum in the lower panel. Set <hold> in order to re-use
a previous figure.
Any additional keyword arguments are passed through to pylab.plot for
both subplots.
"""###
fs = getfs(samplingfreq_hz)
if fs == None: fs = getfs(x, 2.0)
if hasattr(x, 'x'): x = x.x
elif hasattr(x, 'y'): x = x.y
if not isnumpyarray(x):
axis = 0
if isinstance(x[0], list) or isinstance(x[0], tuple): axis = 1
x = numpy.array(x, dtype='float')
xwin = x = project(x, axis).swapaxes(0, axis)
nsamp = x.shape[0]
class Unfinished(Exception):
pass
if welch == 1: xwin = x * project(hanning(nsamp), len(x.shape) - 1)
elif welch > 0: raise Unfinished, "Welch periodogram not yet implemented"
t = numpy.arange(0, nsamp) / float(fs)
ap = fft2ap(fft(xwin, axis=0), samplingfreq_hz=fs, axis=0)
f = ap['freq_hz']
a = 20.0 * numpy.log10(ap['amplitude'])
pylab = load_pylab()
if not hold: pylab.clf()
pylab.subplot(2, 1, 1)
h1 = pylab.plot(t, x, **kwargs)
ax = pylab.gca()
ax.set_xlim(t[0], t[-1])
ax.xaxis.grid(True)
ax.yaxis.grid(True)
pylab.subplot(2, 1, 2)
a[numpy.isinf(
a
)] = numpy.nan # crude workaround---pylab.plot can't cope with infinite values
h2 = pylab.plot(f, a, **kwargs)
ax = pylab.gca()
ax.set_xlim(f[0], f[-1])
ax.xaxis.grid(True)
ax.yaxis.grid(True)
pylab.draw()
def plotsig(x, samplingfreq_hz=None, hold=False, axis=0, welch=0, **kwargs): """ Makes two subplots, showing time-series <x> in the upper panel and its amplitude spectrum in the lower panel. Set <hold> in order to re-use a previous figure. Any additional keyword arguments are passed through to pylab.plot for both subplots. """### fs = getfs(samplingfreq_hz) if fs==None: fs = getfs(x,2.0) if hasattr(x, 'x'): x = x.x elif hasattr(x, 'y'): x = x.y if not isnumpyarray(x): axis = 0 if isinstance(x[0], list) or isinstance(x[0], tuple): axis = 1 x = numpy.array(x,dtype='float') xwin = x = project(x,axis).swapaxes(0, axis) nsamp = x.shape[0] class Unfinished(Exception): pass if welch==1: xwin = x * project(hanning(nsamp),len(x.shape)-1) elif welch > 0: raise Unfinished, "Welch periodogram not yet implemented" t = numpy.arange(0, nsamp) / float(fs) ap = fft2ap(fft(xwin,axis=0),samplingfreq_hz=fs,axis=0) f = ap['freq_hz'] a = 20.0 * numpy.log10(ap['amplitude']) pylab = load_pylab() if not hold: pylab.clf() pylab.subplot(2,1,1) h1 = pylab.plot(t,x,**kwargs) ax = pylab.gca() ax.set_xlim(t[0], t[-1]) ax.xaxis.grid(True) ax.yaxis.grid(True) pylab.subplot(2,1,2) a[numpy.isinf(a)] = numpy.nan # crude workaround---pylab.plot can't cope with infinite values h2 = pylab.plot(f,a,**kwargs) ax = pylab.gca() ax.set_xlim(f[0], f[-1]) ax.xaxis.grid(True) ax.yaxis.grid(True) pylab.draw()
