def powspecSN(st, stnoise, SNrat=1.5, win=None):
"""
Return masked arrays of power spectrum, masking where SNratio is greater than SNrat
USAGE
freqs, amps, ampmask = powspecSN(st, stnoise, SNrat=1.5, freqlim=(0, 25), win=None)
INPUTS
st = obspy stream object, or trace object
stnoise = obspy stream object from time period just before st (or whatever noise window desired, but needs to have same sample rate)
win = tuple of time window in seconds (e.g. win=(3., 20.)) over which to compute mean squared frequency, None computes for entire time window in each trace of st
OUTPUTS
freqs = list of np.arrays of frequency vectors (Hz)
amps = list of np.arrays of amplitudes
ampmaks = list of np.arrays of masked amplitudes
"""
import obspy.signal.spectral_estimation as spec
st = Stream(st) # turn into a stream object in case st is a trace
stnoise = Stream(stnoise)
freqs = [] # preallocate
amps = []
ampmask = []
for i, trace in enumerate(st):
if trace.stats.sampling_rate != stnoise[i].stats.sampling_rate:
print 'Signal and noise sample rates are different. Abort!'
return
Fs = trace.stats.sampling_rate # Time vector
dat = trace.data
pFs = stnoise[i].stats.sampling_rate
pdat = stnoise[i].data
tvec = maketvec(trace) # Time vector
if win is not None:
if win[1] > tvec.max() or win[0] < tvec.min():
print 'Time window specified not compatible with length of time series'
return
dat = dat[(tvec >= win[0]) & (tvec <= win[1])]
trace = trace.trim(trace.stats.starttime + win[0],
trace.stats.starttime + win[1])
# Find max nfft of the two and use that for both so they line up
maxnfft = np.max((nextpow2(len(dat)), nextpow2(len(pdat))))
Pxx, f = spec.psd(dat, NFFT=maxnfft, Fs=Fs)
pPxx, pf = spec.psd(pdat, NFFT=maxnfft, Fs=pFs)
idx = (Pxx / pPxx < SNrat) # good values
amps.append(Pxx)
freqs.append(f)
ampmask.append(ma.array(Pxx, mask=idx))
return freqs, amps, ampmask
def powspecSN(st, stnoise, SNrat=1.5, win=None):
"""
Return masked arrays of power spectrum, masking where SNratio is greater than SNrat
USAGE
freqs, amps, ampmask = powspecSN(st, stnoise, SNrat=1.5, freqlim=(0, 25), win=None)
INPUTS
st = obspy stream object, or trace object
stnoise = obspy stream object from time period just before st (or whatever noise window desired, but needs to have same sample rate)
win = tuple of time window in seconds (e.g. win=(3., 20.)) over which to compute mean squared frequency, None computes for entire time window in each trace of st
OUTPUTS
freqs = list of np.arrays of frequency vectors (Hz)
amps = list of np.arrays of amplitudes
ampmaks = list of np.arrays of masked amplitudes
"""
import obspy.signal.spectral_estimation as spec
st = Stream(st) # turn into a stream object in case st is a trace
stnoise = Stream(stnoise)
freqs = [] # preallocate
amps = []
ampmask = []
for i, trace in enumerate(st):
if trace.stats.sampling_rate != stnoise[i].stats.sampling_rate:
print 'Signal and noise sample rates are different. Abort!'
return
Fs = trace.stats.sampling_rate # Time vector
dat = trace.data
pFs = stnoise[i].stats.sampling_rate
pdat = stnoise[i].data
tvec = maketvec(trace) # Time vector
if win is not None:
if win[1] > tvec.max() or win[0] < tvec.min():
print 'Time window specified not compatible with length of time series'
return
dat = dat[(tvec >= win[0]) & (tvec <= win[1])]
trace = trace.trim(trace.stats.starttime+win[0], trace.stats.starttime+win[1])
# Find max nfft of the two and use that for both so they line up
maxnfft = np.max((nextpow2(len(dat)), nextpow2(len(pdat))))
Pxx, f = spec.psd(dat, NFFT=maxnfft, Fs=Fs)
pPxx, pf = spec.psd(pdat, NFFT=maxnfft, Fs=pFs)
idx = (Pxx/pPxx < SNrat) # good values
amps.append(Pxx)
freqs.append(f)
ampmask.append(ma.array(Pxx, mask=idx))
return freqs, amps, ampmask
def test_obspy_psd_vs_pitsa(self):
"""
Test to compare results of PITSA's psd routine to the
:func:`matplotlib.mlab.psd` routine wrapped in
:func:`obspy.signal.spectral_estimation.psd`.
The test works on 8192 samples long Gaussian noise with a standard
deviation of 0.1 generated with PITSA, sampling rate for processing in
PITSA was 100.0 Hz, length of nfft 512 samples. The overlap in PITSA
cannot be controlled directly, instead only the number of overlapping
segments can be specified. Therefore the test works with zero overlap
to have full control over the data segments used in the psd.
It seems that PITSA has one frequency entry more, i.e. the psd is one
point longer. I dont know were this can come from, for now this last
sample in the psd is ignored.
"""
SAMPLING_RATE = 100.0
NFFT = 512
NOVERLAP = 0
file_noise = os.path.join(self.path, "pitsa_noise.npy")
fn_psd_pitsa = "pitsa_noise_psd_samprate_100_nfft_512_noverlap_0.npy"
file_psd_pitsa = os.path.join(self.path, fn_psd_pitsa)
noise = np.load(file_noise)
# in principle to mimic PITSA's results detrend should be specified as
# some linear detrending (e.g. from matplotlib.mlab.detrend_linear)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
psd_obspy, _ = psd(noise,
NFFT=NFFT,
Fs=SAMPLING_RATE,
window=welch_taper,
noverlap=NOVERLAP)
self.assertEqual(len(w), 1)
self.assertTrue(
'This wrapper is no longer necessary.' in str(w[0].message))
psd_pitsa = np.load(file_psd_pitsa)
# mlab's psd routine returns Nyquist frequency as last entry, PITSA
# seems to omit it and returns a psd one frequency sample shorter.
psd_obspy = psd_obspy[:-1]
# test results. first couple of frequencies match not as exactly as all
# the rest, test them separately with a little more allowance..
np.testing.assert_array_almost_equal(psd_obspy[:3],
psd_pitsa[:3],
decimal=4)
np.testing.assert_array_almost_equal(psd_obspy[1:5],
psd_pitsa[1:5],
decimal=5)
np.testing.assert_array_almost_equal(psd_obspy[5:],
psd_pitsa[5:],
decimal=6)
def test_obspy_psd_vs_pitsa(self):
"""
Test to compare results of PITSA's psd routine to the
:func:`matplotlib.mlab.psd` routine wrapped in
:func:`obspy.signal.spectral_estimation.psd`.
The test works on 8192 samples long Gaussian noise with a standard
deviation of 0.1 generated with PITSA, sampling rate for processing in
PITSA was 100.0 Hz, length of nfft 512 samples. The overlap in PITSA
cannot be controlled directly, instead only the number of overlapping
segments can be specified. Therefore the test works with zero overlap
to have full control over the data segments used in the psd.
It seems that PITSA has one frequency entry more, i.e. the psd is one
point longer. I dont know were this can come from, for now this last
sample in the psd is ignored.
"""
SAMPLING_RATE = 100.0
NFFT = 512
NOVERLAP = 0
file_noise = os.path.join(self.path, "pitsa_noise.npy")
fn_psd_pitsa = "pitsa_noise_psd_samprate_100_nfft_512_noverlap_0.npy"
file_psd_pitsa = os.path.join(self.path, fn_psd_pitsa)
noise = np.load(file_noise)
# in principle to mimic PITSA's results detrend should be specified as
# some linear detrending (e.g. from matplotlib.mlab.detrend_linear)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
psd_obspy, _ = psd(noise, NFFT=NFFT, Fs=SAMPLING_RATE,
window=welch_taper, noverlap=NOVERLAP)
self.assertEqual(len(w), 1)
self.assertTrue('This wrapper is no longer necessary.' in
str(w[0].message))
psd_pitsa = np.load(file_psd_pitsa)
# mlab's psd routine returns Nyquist frequency as last entry, PITSA
# seems to omit it and returns a psd one frequency sample shorter.
psd_obspy = psd_obspy[:-1]
# test results. first couple of frequencies match not as exactly as all
# the rest, test them separately with a little more allowance..
np.testing.assert_array_almost_equal(psd_obspy[:3], psd_pitsa[:3],
decimal=4)
np.testing.assert_array_almost_equal(psd_obspy[1:5], psd_pitsa[1:5],
decimal=5)
np.testing.assert_array_almost_equal(psd_obspy[5:], psd_pitsa[5:],
decimal=6)
