def multitaper(st, number_of_tapers=None, time_bandwidth=4., sine=False):
"""
Output multitaper for stream st
RETURNS POWER SPECTRAL DENSITY, units = input units **2 per Hz
# TO DO add statistics and optional_output options
:param st: obspy stream or trace containing data to plot
:param number_of_tapers: Number of tapers to use, Defaults to int(2*time_bandwidth) - 1.
This is maximum senseful amount. More tapers will have no great influence on the final spectrum but increase the
calculation time. Use fewer tapers for a faster calculation.
:param time_bandwidth_div: Smoothing amount, should be between 1 and Nsamps/2.
:param sine: if True, will use sine_psd instead of multitaper, sine method should be used for sharp cutoffs or
deep valleys, or small sample sizes
"""
from mtspec import mtspec, sine_psd
st = Stream(st) # turn into a stream object in case st is a trace
amps = []
freqs = []
for st1 in st:
if sine is False:
nfft = int(nextpow2((st1.stats.endtime - st1.stats.starttime) * st1.stats.sampling_rate))
amp, freq = mtspec(st1.data, 1./st1.stats.sampling_rate, time_bandwidth=time_bandwidth,
number_of_tapers=number_of_tapers, nfft=nfft)
else:
st1.taper(max_percentage=0.05, type='cosine')
amp, freq = sine_psd(st1.data, 1./st1.stats.sampling_rate)
amps.append(amp)
freqs.append(freq)
return freqs, amps
def test_figure3(self):
"""
Recreate Figure 2
"""
data = load_mtdata('PASC.dat.gz')
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax1.plot(data, color='black')
ax1.set_xlim(0, len(data))
spec, freq = mtspec(data, 1.0, 1.5, number_of_tapers=1)
ax2 = fig.add_subplot(3, 2, 3)
ax2.set_yscale('log')
ax2.set_xscale('log')
ax2.plot(freq, spec, color='black')
ax2.set_xlim(freq[0], freq[-1])
spec, freq = mtspec(data, 1.0, 4.5, number_of_tapers=5)
ax3 = fig.add_subplot(3, 2, 4)
ax3.set_yscale('log')
ax3.set_xscale('log')
ax3.plot(freq, spec, color='black')
ax3.set_xlim(freq[0], freq[-1])
spec, freq = sine_psd(data, 1.0)
ax4 = fig.add_subplot(3, 2, 5)
ax4.set_yscale('log')
ax4.set_xscale('log')
ax4.plot(freq, spec, color='black')
ax4.set_xlim(freq[0], freq[-1])
spec, freq = mtspec(data, 1.0, 4.5, number_of_tapers=5, quadratic=True)
ax5 = fig.add_subplot(3, 2, 6)
ax5.set_yscale('log')
ax5.set_xscale('log')
ax5.plot(freq, spec, color='black')
ax5.set_xlim(freq[0], freq[-1])
outfile = os.path.join(self.outpath, 'fig3.pdf')
fig.savefig(outfile)
stat = os.stat(outfile)
self.assertTrue(abs(stat.st_mtime - time.time()) < 3)
def test_figure3(self):
"""
Recreate Figure 2
"""
data = load_mtdata('PASC.dat.gz')
fig = plt.figure()
ax1 = fig.add_subplot(3, 1, 1)
ax1.plot(data, color='black')
ax1.set_xlim(0, len(data))
spec, freq = mtspec(data, 1.0, 1.5, number_of_tapers=1)
ax2 = fig.add_subplot(3, 2, 3)
ax2.set_yscale('log')
ax2.set_xscale('log')
ax2.plot(freq, spec, color='black')
ax2.set_xlim(freq[0], freq[-1])
spec, freq = mtspec(data, 1.0, 4.5, number_of_tapers=5)
ax3 = fig.add_subplot(3, 2, 4)
ax3.set_yscale('log')
ax3.set_xscale('log')
ax3.plot(freq, spec, color='black')
ax3.set_xlim(freq[0], freq[-1])
spec, freq = sine_psd(data, 1.0)
ax4 = fig.add_subplot(3, 2, 5)
ax4.set_yscale('log')
ax4.set_xscale('log')
ax4.plot(freq, spec, color='black')
ax4.set_xlim(freq[0], freq[-1])
spec, freq = mtspec(data, 1.0, 4.5, number_of_tapers=5, quadratic=True)
ax5 = fig.add_subplot(3, 2, 6)
ax5.set_yscale('log')
ax5.set_xscale('log')
ax5.plot(freq, spec, color='black')
ax5.set_xlim(freq[0], freq[-1])
outfile = os.path.join(self.outpath, 'fig3.pdf')
fig.savefig(outfile)
stat = os.stat(outfile)
self.assertTrue(abs(stat.st_mtime - time.time()) < 3)
def test_sinePSD(self):
"""
Test for the sine_psd spectra. The result is compared to the output of
test_recreatePaperFigures.py in the same directory. This is assumed to
be correct because they are identical to the figures in the paper on
the machine that created these.
"""
data = load_mtdata('PASC.dat.gz')
# Calculate the spectra.
spec, freq = sine_psd(data, 1.0)
# No NaNs are supposed to be in the output.
self.assertEqual(np.isnan(spec).any(), False)
self.assertEqual(np.isnan(freq).any(), False)
# Load the good data.
datafile = os.path.join(os.path.dirname(__file__), 'data',
'sine_psd.npz')
spec2 = np.load(datafile)['spec']
freq2 = np.arange(43201) * 1.15740741e-05
# Compare.
np.testing.assert_almost_equal(freq, freq2)
np.testing.assert_almost_equal(spec / spec, spec2 / spec, 2)
def test_sinePSD(self):
"""
Test for the sine_psd spectra. The result is compared to the output of
test_recreatePaperFigures.py in the same directory. This is assumed to
be correct because they are identical to the figures in the paper on
the machine that created these.
"""
data = load_mtdata('PASC.dat.gz')
# Calculate the spectra.
spec, freq = sine_psd(data, 1.0)
# No NaNs are supposed to be in the output.
self.assertEqual(np.isnan(spec).any(), False)
self.assertEqual(np.isnan(freq).any(), False)
# Load the good data.
datafile = os.path.join(os.path.dirname(__file__), 'data',
'sine_psd.npz')
spec2 = np.load(datafile)['spec']
freq2 = np.arange(43201) * 1.15740741e-05
# Compare.
np.testing.assert_almost_equal(freq, freq2)
np.testing.assert_almost_equal(spec / spec, spec2 / spec, 2)
def test_sinePSDStatistics(self):
"""
Test for the sine_psd spectra with optional output. The result is
compared to the output of test_recreatePaperFigures.py in the same
directory. This is assumed to be correct because they are identical
to the figures in the paper on the machine that created these.
"""
data = load_mtdata('PASC.dat.gz')
# Calculate the spectra.
spec, freq, errors, tapers = sine_psd(data, 1.0, statistics=True)
# No NaNs are supposed to be in the output.
self.assertEqual(np.isnan(spec).any(), False)
self.assertEqual(np.isnan(freq).any(), False)
self.assertEqual(np.isnan(errors).any(), False)
self.assertEqual(np.isnan(tapers).any(), False)
#XXX: assert for errors and tapers is missing
# Load the good data.
datafile = os.path.join(os.path.dirname(__file__), 'data',
'sine_psd.npz')
spec2 = np.load(datafile)['spec']
freq2 = np.arange(43201) * 1.15740741e-05
# Compare #XXX really bad precision for spec (linux 64bit)
np.testing.assert_almost_equal(freq, freq2)
np.testing.assert_almost_equal(spec / spec, spec2 / spec, 2)
def test_sinePSDStatistics(self):
"""
Test for the sine_psd spectra with optional output. The result is
compared to the output of test_recreatePaperFigures.py in the same
directory. This is assumed to be correct because they are identical
to the figures in the paper on the machine that created these.
"""
data = load_mtdata('PASC.dat.gz')
# Calculate the spectra.
spec, freq, errors, tapers = sine_psd(data, 1.0, statistics=True)
# No NaNs are supposed to be in the output.
self.assertEqual(np.isnan(spec).any(), False)
self.assertEqual(np.isnan(freq).any(), False)
self.assertEqual(np.isnan(errors).any(), False)
self.assertEqual(np.isnan(tapers).any(), False)
#XXX: assert for errors and tapers is missing
# Load the good data.
datafile = os.path.join(os.path.dirname(__file__), 'data',
'sine_psd.npz')
spec2 = np.load(datafile)['spec']
freq2 = np.arange(43201) * 1.15740741e-05
# Compare #XXX really bad precision for spec (linux 64bit)
np.testing.assert_almost_equal(freq, freq2)
np.testing.assert_almost_equal(spec / spec, spec2 / spec, 2)
def calculateHVSR(stream, intervals, window_length, method, options,
master_method, cutoff_value, smoothing=None,
smoothing_count=1, smoothing_constant=40,
message_function=None):
"""
Calculates the HVSR curve.
"""
# Some arithmetics.
length = len(intervals)
good_length = window_length // 2 + 1
# Create the matrix that will be used to store the single spectra.
hvsr_matrix = np.empty((length, good_length))
# The stream that will be used.
# XXX: Add option to use the raw data stream.
if method == 'multitaper':
if options['nfft']:
good_length = options['nfft']// 2 + 1
# Create the matrix that will be used to store the single
# spectra.
hvsr_matrix = np.empty((length, good_length))
# Loop over each interval
for _i, interval in enumerate(intervals):
if message_function:
message_function('Calculating HVSR %i of %i...' % \
(_i+1, length))
# Figure out which traces are vertical and which are horizontal.
v = [_j for _j, trace in enumerate(stream) if \
trace.stats.orientation == 'vertical']
h = [_j for _j, trace in enumerate(stream) if \
trace.stats.orientation == 'horizontal']
v = stream[v[0]].data[interval[0]: interval[0] + \
window_length]
h1 = stream[h[0]].data[interval[0]: interval[0] + \
window_length]
h2 = stream[h[1]].data[interval[0]: interval[0] + \
window_length]
# Calculate the spectra.
v_spec, v_freq = mtspec(v, stream[0].stats.delta,
options['time_bandwidth'],
nfft=options['nfft'],
number_of_tapers=options['number_of_tapers'],
quadratic=options['quadratic'],
adaptive=options['adaptive'])
h1_spec, h1_freq = mtspec(h1, stream[0].stats.delta,
options['time_bandwidth'], nfft=options['nfft'],
number_of_tapers=options['number_of_tapers'],
quadratic=options['quadratic'],
adaptive=options['adaptive'])
h2_spec, h2_freq = mtspec(h2, stream[0].stats.delta,
options['time_bandwidth'],
nfft=options['nfft'],
number_of_tapers=options['number_of_tapers'],
quadratic=options['quadratic'],
adaptive=options['adaptive'])
# Apply smoothing.
if smoothing:
if 'konno-ohmachi' in smoothing.lower():
if _i == 0:
sm_matrix = calculate_smoothing_matrix(v_freq,
smoothing_constant)
for _j in xrange(smoothing_count):
v_spec = np.dot(v_spec, sm_matrix)
h1_spec = np.dot(h1_spec, sm_matrix)
h2_spec = np.dot(h2_spec, sm_matrix)
hv_spec = np.sqrt(h1_spec * h2_spec) / v_spec
if _i == 0:
good_freq = v_freq
hvsr_matrix[_i, :] = hv_spec
# Cut the hvsr matrix.
hvsr_matrix = hvsr_matrix[0:length, :]
elif method == 'sine multitaper':
for _i, interval in enumerate(intervals):
if message_function:
message_function('Calculating HVSR %i of %i...' % \
(_i+1, length))
# Figure out which traces are vertical and which are horizontal.
v = [_j for _j, trace in enumerate(stream) if \
trace.stats.orientation == 'vertical']
h = [_j for _j, trace in enumerate(stream) if \
trace.stats.orientation == 'horizontal']
v = stream[v[0]].data[interval[0]: interval[0] + \
window_length]
h1 = stream[h[0]].data[interval[0]: interval[0] + \
window_length]
h2 = stream[h[1]].data[interval[0]: interval[0] + \
window_length]
# Calculate the spectra.
v_spec, v_freq = sine_psd(v, stream[0].stats.delta,
number_of_tapers=options['number_of_tapers'],
number_of_iterations=options['number_of_iterations'],
degree_of_smoothing=options['degree_of_smoothing'])
h1_spec, h1_freq = sine_psd(h1, stream[0].stats.delta,
number_of_tapers=options['number_of_tapers'],
number_of_iterations=options['number_of_iterations'],
degree_of_smoothing=options['degree_of_smoothing'])
h2_spec, h2_freq = sine_psd(h2, stream[0].stats.delta,
number_of_tapers=options['number_of_tapers'],
number_of_iterations=options['number_of_iterations'],
degree_of_smoothing=options['degree_of_smoothing'])
# Apply smoothing.
if smoothing:
if 'konno-ohmachi' in smoothing.lower():
if _i == 0:
sm_matrix = calculate_smoothing_matrix(v_freq,
smoothing_constant)
for _j in xrange(smoothing_count):
v_spec = np.dot(v_spec, sm_matrix)
h1_spec = np.dot(h1_spec, sm_matrix)
h2_spec = np.dot(h2_spec, sm_matrix)
hv_spec = np.sqrt(h1_spec * h2_spec) / v_spec
if _i == 0:
good_freq = v_freq
# Store it into the matrix if it has the correct length.
hvsr_matrix[_i,:] = hv_spec
# Cut the hvsr matrix.
hvsr_matrix = hvsr_matrix[0:length, :]
# Use a single taper spectrum with different available tapers.
elif method == 'single taper':
for _i, interval in enumerate(intervals):
if message_function:
message_function('Calculating HVSR %i of %i...' % \
(_i+1, length))
v = [_j for _j, trace in enumerate(stream) if \
trace.stats.orientation == 'vertical']
h = [_j for _j, trace in enumerate(stream) if \
trace.stats.orientation == 'horizontal']
v = stream[v[0]].data[interval[0]: interval[0] + \
window_length]
h1 = stream[h[0]].data[interval[0]: interval[0] + \
window_length]
h2 = stream[h[1]].data[interval[0]: interval[0] + \
window_length]
# Calculate the spectra.
v_spec, v_freq = single_taper_spectrum(v,
stream[0].stats.delta, options['taper'])
h1_spec, h1_freq = single_taper_spectrum(h1,
stream[0].stats.delta, options['taper'])
h2_spec, h2_freq = single_taper_spectrum(h2,
stream[0].stats.delta, options['taper'])
# Apply smoothing.
if smoothing:
if 'konno-ohmachi' in smoothing.lower():
if _i == 0:
sm_matrix = calculate_smoothing_matrix(v_freq,
smoothing_constant)
for _j in xrange(smoothing_count):
v_spec = np.dot(v_spec, sm_matrix)
h1_spec = np.dot(h1_spec, sm_matrix)
h2_spec = np.dot(h2_spec, sm_matrix)
hv_spec = np.sqrt(h1_spec * h2_spec) / v_spec
if _i == 0:
good_freq = v_freq
# Store it into the matrix if it has the correct length.
hvsr_matrix[_i, :] = hv_spec
# Cut the hvsr matrix.
hvsr_matrix = hvsr_matrix[0:length, :]
# Should never happen.
else:
msg = 'Something went wrong.'
raise Exception(msg)
# Copy once to be able to calculate standard deviations.
original_matrix = deepcopy(hvsr_matrix)
# Sort it for quantile operations.
hvsr_matrix.sort(axis=0)
# Only senseful for mean calculations. Omitted for the median.
if cutoff_value != 0.0 and master_method != 'median':
hvsr_matrix = hvsr_matrix[int(length * cutoff_value):
ceil(length * (1 - cutoff_value)), :]
length = len(hvsr_matrix)
# Mean.
if master_method == 'mean':
master_curve = hvsr_matrix.mean(axis=0)
# Geometric average.
elif master_method == 'geometric average':
master_curve = hvsr_matrix.prod(axis=0) ** (1.0 / length)
# Median.
elif master_method == 'median':
# Use another method because interpolation might be necessary.
master_curve = np.empty(len(hvsr_matrix[0, :]))
error = np.empty((len(master_curve), 2))
for _i in xrange(len(master_curve)):
cur_row = hvsr_matrix[:, _i]
master_curve[_i] = quantile(cur_row, 50)
error[_i, 0] = quantile(cur_row, 25)
error[_i, 1] = quantile(cur_row, 75)
# Calculate the standard deviation for the two mean methods.
if master_method != 'median':
error = np.empty((len(master_curve), 2))
std = (hvsr_matrix[:][:] - master_curve) ** 2
std = std.sum(axis=0)
std /= float(length)
std **= 0.5
error[:, 0] = master_curve - std
error[:, 1] = master_curve + std
return original_matrix, good_freq, length, master_curve, error
plt.subplot(323)
plt.loglog(freq, spec, color='black')
plt.xlim(freq[0], freq[-1])
plt.text(x=0.5, y=0.85, s="Single Taper",
transform=plt.gca().transAxes, ha="center")
spec, freq = mtspec(data, 1.0, 4.5, number_of_tapers=5)
plt.subplot(324)
plt.loglog(freq, spec, color='black')
plt.xlim(freq[0], freq[-1])
plt.text(x=0.5, y=0.85, s="5 Tapers Multitaper",
transform=plt.gca().transAxes, ha="center")
spec, freq = sine_psd(data, 1.0)
plt.subplot(325)
plt.loglog(freq, spec, color='black')
plt.xlim(freq[0], freq[-1])
plt.text(x=0.5, y=0.85, s="Sine Multitaper",
transform=plt.gca().transAxes, ha="center")
spec, freq = mtspec(data, 1.0, 4.5, number_of_tapers=5,
quadratic=True)
plt.subplot(326)
plt.loglog(freq, spec, color='black')
plt.xlim(freq[0], freq[-1])
plt.text(x=0.5, y=0.85, s="Quadratic Multitaper",
transform=plt.gca().transAxes, ha="center")