def bandPass(df , fmin = None, fmax = None , unit = "Hz" ) :
"""
Return filtered signal
"""
from scipy.fftpack import rfft, irfft, rfftfreq #Warning convention of scipy.fftpack != numpy.fft !!!
if type(df) == pd.Series :
df = pd.DataFrame(df)
ise = True
else :
ise = False
filtered = pd.DataFrame( index = df.index )
W = rfftfreq( df.index.size, d = dx( df ) )
for col in df.columns :
tmp = rfft(df[col])
if fmin is not None :
tmp[ ( W < fmin) ] = 0
if fmax is not None :
tmp[ ( W > fmax) ] = 0
filtered[col] = irfft(tmp)
if ise :
return filtered.iloc[:,0]
else :
return filtered
python类fftpack()的实例源码
def arfft(x):
if use_numpy_arfft:
y = numpy.fft.rfft(x)
y = abs(y)
return y
else:
assert (len(x) % 2) == 0
y = scipy.fftpack.rfft(x)
if type(y[0]) == numpy.float32:
cty = numpy.complex64
elif type(y[0]) == numpy.float64:
cty = numpy.complex128
else:
assert False
# y = [ Re0, Re1, Im1, Re2, Im2, ..., ReN ]
#y1 = numpy.sqrt(numpy.add(numpy.square(y[1:-1:2]),
# numpy.square(y[2:-1:2])))
y0 = abs(y[0])
yn = abs(y[-1])
y = abs(y[1:-1].view(cty))
y = numpy.concatenate(([y0], y, [yn]))
return y
def rfft(x):
if use_numpy_rfft:
y = numpy.fft.rfft(x)
return y
else:
assert (len(x) % 2) == 0
y = scipy.fftpack.rfft(x)
if type(y[0]) == numpy.float32:
cty = numpy.complex64
elif type(y[0]) == numpy.float64:
cty = numpy.complex128
else:
assert False
# y = [ Re0, Re1, Im1, Re2, Im2, ..., ReN ]
y1 = y[1:-1].view(cty)
y = numpy.concatenate((y[0:1], y1, y[-1:]))
return y
# apply automatic gain control.
# causes each winlen window of samples
# to have average absolute value of 1.0.
# winlen is in units of samples.
def DST(x):
"""
Converts Scipy's DST output to Matlab's DST (scaling).
"""
X = scipy.fftpack.dst(x,type=1,axis=0)
return X/2.0
def IDST(X):
"""
Inverse DST. Python -> Matlab
"""
n = X.shape[0]
x = np.real(scipy.fftpack.idst(X,type=1,axis=0))
return x/(n+1.0)
def make_wave(self):
"""Transforms to the time domain.
returns: Wave
"""
ys = scipy.fftpack.dct(self.amps, type=3) / 2
return Wave(ys, self.framerate)
def make_dct(self):
amps = scipy.fftpack.dct(self.ys, type=2)
return Dct(amps, self.framerate)
def make_wave(self):
"""Transforms to the time domain.
returns: Wave
"""
N = len(self.hs)
ys = scipy.fftpack.idct(self.hs, type=2) / 2 / N
#NOTE: whatever the start time was, we lose it when
# we transform back
#ts = self.start + np.arange(len(ys)) / self.framerate
return Wave(ys, framerate=self.framerate)
def make_dct(self):
"""Computes the DCT of this wave.
"""
N = len(self.ys)
hs = scipy.fftpack.dct(self.ys, type=2)
fs = (0.5 + np.arange(N)) / 2
return Dct(hs, fs, self.framerate)
