def test_spectra( self ):
"""
Tests that signals show in the right bin with the
right amplitude
"""
# Make the window the size of the sample rate
window_length = self.fs
# test with 1.0 overlap
test1 = spectrogram( self.data, window_length,overlap=1.0 )
self.assertAlmostEquals( test1[ 0, self.f1 ], self.a1,
places=2 )
self.assertAlmostEquals( test1[ 8,self.f2 ], self.a2,
places=2 )
self.assertAlmostEquals( test1[ 6, self.f3 ], self.a3,
places=2 )
# test with 0.5 overlap
test2 = spectrogram( self.data, window_length,overlap=0.5 )
self.assertAlmostEquals( test2[ 0, self.f1 ],
self.a1, places=2 )
self.assertAlmostEquals( test2[ 10, self.f2], self.a2,
places=2 )
self.assertAlmostEquals( test2[ 15, self.f3 ], self.a3,
places=2 )
def test_spectra(self):
"""
Tests that signals show in the right bin with the
right amplitude.
"""
# Make the window the size of the sample rate
window_length = self.fs
# Test with 1.0 overlap.
test1 = spectrogram(self.data, window_length, overlap=1.0)
self.assertAlmostEquals(test1[0, self.f1], self.a1, places=2)
self.assertAlmostEquals(test1[8, self.f2], self.a2, places=2)
self.assertAlmostEquals(test1[6, self.f3], self.a3, places=2)
# Test with 0.5 overlap.
test2 = spectrogram(self.data, window_length, overlap=0.5)
self.assertAlmostEquals(test2[0, self.f1], self.a1, places=2)
self.assertAlmostEquals(test2[10, self.f2], self.a2, places=2)
self.assertAlmostEquals(test2[15, self.f3], self.a3, places=2)
def spectraldecomp(data,
f=(0.1, 0.25, 0.4),
window_length=32,
dt=1,
window_type='hann',
overlap=1,
normalize=False):
"""
Uses the STFT to decompose traces into normalized spectra. Only
frequencies defined by the user will be output. Using 3
frequencies will work for RGB color plots.
:param data: A 1/2D array (samples, traces) of data that will
be decomposed.
:keyword f: A list of frequencies to select from the spectra.
:keyword window_length: The length of fft window to use for
the STFTs. Defaults to 32. Can be
provided in seconds if dt is specified.
:keyword dt: The time sample interval of the traces.
:keyword window_type: The type of window to use for the STFT. The
same input as scipy.signal.get_window.
:keyword overlap: The fraction of overlap between adjacent
STFT windows
:keyword normalize: Normalize the energy in each STFT window
:returns: an array of shape (samples, traces, f)
"""
# Do the 1D case
if len(data.shape) == 1:
ntraces = 1
else:
ntraces = data.shape[-1]
if overlap > 1:
overlap = 1
zp = 4 * window_length
# TODO We should think about removing these for loops
for i in range(ntraces):
if (ntraces == 1):
spect = spectrogram(data,
window_length,
dt=dt,
window_type=window_type,
overlap=overlap,
normalize=normalize,
zero_padding=zp)
if (i == 0):
output = np.zeros((spect.shape[0], len(f)))
else:
spect = spectrogram(data[:, i],
window_length,
dt=dt,
window_type=window_type,
overlap=overlap,
normalize=normalize,
zero_padding=zp)
if (i == 0):
output = np.zeros((spect.shape[0], ntraces, len(f)))
res = ((1. / dt) / 2.) / spect.shape[-1]
# TODO again, we should think about removing this loop
for j in range(len(f)):
index = int(f[j] / res)
if (ntraces == 1):
output[:, j] = spect[:, index]
else:
output[:, i, j] = spect[:, index]
return (output)
def spectraldecomp(data, f=(.1, .25, .4),
window_length=32, dt=1,
window_type='hann',
overlap=1,
normalize=False):
"""
Uses the STFT to decompose traces into normalized spectra. Only
frequencies defined by the user will be output. Using 3
frequencies will work for RGB color plots.
:param data: A 1/2D array (samples, traces) of data that will
be decomposed.
:keyword f: A list of frequencies to select from the spectra.
:keyword window_length: The length of fft window to use for
the STFTs. Defaults to 32. Can be
provided in seconds if dt is specified.
:keyword dt: The time sample interval of the traces.
:keyword window_type: The type of window to use for the STFT. The
same input as scipy.signal.get_window.
:keyword overlap: The fraction of overlap between adjacent
STFT windows
:keyword normalize: Normalize the energy in each STFT window
:returns: an array of shape (samples, traces, f)
"""
# Do the 1D case
if len(data.shape) == 1:
ntraces = 1
else:
ntraces = data.shape[-1]
if overlap > 1: overlap = 1
zp = 4*window_length
# TODO We should think about removing these for loops
for i in range(ntraces):
if(ntraces == 1):
spect = spectrogram(data, window_length, dt=dt,
window_type=window_type, overlap=overlap,
normalize=normalize, zero_padding=zp)
if( i == 0 ): output = np.zeros( (spect.shape[0], len(f)))
else:
spect = spectrogram(data[:,i], window_length, dt=dt,
window_type=window_type, overlap=overlap,
normalize=normalize, zero_padding=zp)
if( i == 0 ):
output = np.zeros( (spect.shape[0],ntraces,
len(f) ))
res = ((1. / dt) /2.) / spect.shape[-1]
# TODO again, we should think about removing this loop
for j in range(len(f)):
index = int(f[j] / res)
if( ntraces == 1 ): output[:,j] = spect[:,index]
else: output[:,i, j] = spect[:,index]
return( output )
