# Execute sonic
arguments = dict(
# slowness grid: X min, X max, Y min, Y max, Slow Step
sll_x=-3.0, slm_x=3.0, sll_y=-3.0, slm_y=3.0, sl_s=0.03,
# sliding window propertieds
win_len=1.0, win_frac=0.05,
# frequency properties
frqlow=1.0, frqhigh=8.0, prewhiten=0,
# restrict output
semb_thres=-1e9, vel_thres=-1e9, verbose=True, timestamp='mlabhour',
stime=UTCDateTime("20080217110515"),
etime=UTCDateTime("20080217110545")
)
# Perform beamforming with previously set arguments.
out = sonic(stream, **arguments)
#import matplotlib.pyplot as plt
#
#labels = 'rel.power abs.power baz slow'.split()
#
#fig = plt.figure()
#for i, lab in enumerate(labels):
# ax = fig.add_subplot(4,1,i+1)
# ax.scatter(out[:,0], out[:,i+1], c=out[:,1], alpha=0.6,
# edgecolors='none')
# ax.set_ylabel(lab)
# ax.xaxis_date()
#
#fig.autofmt_xdate()
#fig.subplots_adjust(top=0.95, right=0.95, bottom=0.2, hspace=0)
# sliding window propertieds
win_len=1.0,
win_frac=0.05,
# frequency properties
frqlow=1.0,
frqhigh=8.0,
prewhiten=0,
# restrict output
semb_thres=-1e9,
vel_thres=-1e9,
verbose=True,
timestamp='mlabhour',
stime=UTCDateTime("20080217110515"),
etime=UTCDateTime("20080217110545"))
# Perform beamforming with previously set arguments.
out = sonic(stream, **arguments)
#import matplotlib.pyplot as plt
#
#labels = 'rel.power abs.power baz slow'.split()
#
#fig = plt.figure()
#for i, lab in enumerate(labels):
# ax = fig.add_subplot(4,1,i+1)
# ax.scatter(out[:,0], out[:,i+1], c=out[:,1], alpha=0.6,
# edgecolors='none')
# ax.set_ylabel(lab)
# ax.xaxis_date()
#
#fig.autofmt_xdate()
#fig.subplots_adjust(top=0.95, right=0.95, bottom=0.2, hspace=0)
paz1hz = cornFreq2Paz(1.0, damp=0.707)
st.simulate(paz_remove='self', paz_simulate=paz1hz)
# Execute sonic
kwargs = dict(
# slowness grid: X min, X max, Y min, Y max, Slow Step
sll_x=-3.0, slm_x=3.0, sll_y=-3.0, slm_y=3.0, sl_s=0.03,
# sliding window properties
win_len=1.0, win_frac=0.05,
# frequency properties
frqlow=1.0, frqhigh=8.0, prewhiten=0,
# restrict output
semb_thres=-1e9, vel_thres=-1e9, verbose=True, timestamp='mlabday',
stime=UTCDateTime("20080217110515"), etime=UTCDateTime("20080217110545")
)
out = sonic(st, **kwargs)
# Plot
from matplotlib.colorbar import ColorbarBase
from matplotlib.colors import Normalize
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
cmap = cm.hot_r
pi = np.pi
#
# make output human readable, adjust backazimuth to values between 0 and 360
t, rel_power, abs_power, baz, slow = out.T
baz[baz < 0.0] += 360
def call(self):
try:
from obspy.core import UTCDateTime, stream
from obspy.signal import array_analysis
from obspy.imaging.cm import obspy_sequential as cmap
except ImportError as _import_error:
self.fail('ImportError:\n%s' % _import_error)
from matplotlib.colorbar import ColorbarBase
from matplotlib.colors import Normalize
import matplotlib.dates as mdates
self.cleanup()
viewer = self.get_viewer()
if viewer.lowpass is None or viewer.highpass is None:
self.fail('highpass and lowpass in viewer must be set!')
traces = []
for trs in self.chopper_selected_traces(fallback=True):
for tr in trs:
tr.lowpass(2, viewer.lowpass)
tr.highpass(2, viewer.highpass)
traces.extend(trs)
if not traces:
self.fail('no traces selected')
if self.downresample == 'resample':
dt_want = min([t.deltat for t in traces])
for t in traces:
t.resample(dt_want)
elif self.downresample == 'downsample':
dt_want = max([t.deltat for t in traces])
for t in traces:
t.downsample_to(dt_want)
elif self.downresample == 'downsample to "target dt"':
for t in traces:
t.downsample_to(float(self.target_dt))
tmin = max([t.tmin for t in traces])
tmax = min([t.tmax for t in traces])
try:
obspy_traces = [
p2o_trace(tr, viewer.get_station(viewer.station_key(tr)))
for tr in traces
]
except KeyError:
self.fail('station information missing')
st = stream.Stream(traces=obspy_traces)
center = array_analysis.get_geometry(st, return_center=True)
center_lon, center_lat, center_ele = center[len(center) - 1]
# Execute sonic
kwargs = dict(sll_x=-self.smax,
slm_x=self.smax,
sll_y=-self.smax,
slm_y=self.smax,
sl_s=self.smax / self.divisor,
win_len=self.window_lenth,
win_frac=self.win_frac,
frqlow=viewer.highpass,
frqhigh=viewer.lowpass,
prewhiten=0,
semb_thres=-1.0e9,
vel_thres=-1.0e9,
verbose=True,
timestamp='mlabday',
stime=UTCDateTime(tmin),
etime=UTCDateTime(tmax))
try:
out = array_analysis.array_processing(st, **kwargs)
except AttributeError:
from obspy.signal.array_analysis import sonic
out = sonic(st, **kwargs)
pi = num.pi
# make output human readable, adjust backazimuth to values between 0
# and 360
t, rel_power, abs_power, baz, slow = out.T
baz[baz < 0.0] += 360.
# choose number of fractions in plot (desirably 360 degree/N is an
# integer!)
N = int(self.numberOfFraction)
abins = num.arange(N + 1) * 360. / N
sbins = num.linspace(0., self.smax, N + 1)
# sum rel power in bins given by abins and sbins
hist, baz_edges, sl_edges = num.histogram2d(baz,
slow,
bins=[abins, sbins],
weights=rel_power)
# transform to gradient
baz_edges = baz_edges / 180. * pi
fig = self.pylab(get='figure')
cax = fig.add_axes([0.85, 0.2, 0.05, 0.5])
ax = fig.add_axes([0.10, 0.1, 0.70, 0.7], polar=True)
ax.grid(False)
dh = abs(sl_edges[1] - sl_edges[0])
dw = abs(baz_edges[1] - baz_edges[0])
# circle through backazimuth
for i, row in enumerate(hist):
ax.bar(left=(pi / 2 - (i + 1) * dw) * num.ones(N),
height=dh * num.ones(N),
width=dw,
bottom=dh * num.arange(N),
color=cmap(row / hist.max()))
ax.set_xticks([pi / 2, 0, 3. / 2 * pi, pi])
ax.set_xticklabels(['N', 'E', 'S', 'W'])
ax.set_ylim(0., self.smax)
ColorbarBase(cax,
cmap=cmap,
norm=Normalize(vmin=hist.min(), vmax=hist.max()))
fig2 = self.pylab(get='figure')
labels = ['rel.power', 'abs.power', 'baz', 'slow']
xlocator = mdates.AutoDateLocator()
ax = None
for i, lab in enumerate(labels):
ax = fig2.add_subplot(4, 1, i + 1, sharex=ax)
ax.scatter(out[:, 0],
out[:, i + 1],
c=out[:, 1],
alpha=0.6,
edgecolors='none',
cmap=cmap)
ax.set_ylabel(lab)
ax.set_xlim(out[0, 0], out[-1, 0])
ax.set_ylim(out[:, i + 1].min(), out[:, i + 1].max())
ax.xaxis.set_tick_params(which='both', direction='in')
ax.xaxis.set_major_locator(xlocator)
ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(xlocator))
if i != 3:
ax.set_xticklabels([])
fig2.subplots_adjust(hspace=0.)
fig2.canvas.draw()
fig.canvas.draw()
print('Center of Array at latitude %s and longitude %s' %
(center_lat, center_lon))
sl_s=0.03,
# sliding window properties
win_len=1.0,
win_frac=0.05,
# frequency properties
frqlow=1.0,
frqhigh=8.0,
prewhiten=0,
# restrict output
semb_thres=-1e9,
vel_thres=-1e9,
verbose=True,
timestamp='mlabday',
stime=UTCDateTime("20080217110515"),
etime=UTCDateTime("20080217110545"))
out = sonic(st, **kwargs)
# Plot
from matplotlib.colorbar import ColorbarBase
from matplotlib.colors import Normalize
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
cmap = cm.hot_r
pi = np.pi
#
# make output human readable, adjust backazimuth to values between 0 and 360
t, rel_power, abs_power, baz, slow = out.T
baz[baz < 0.0] += 360
def call(self):
try:
from obspy.core import UTCDateTime, stream
from obspy.signal import array_analysis
from obspy.imaging.cm import obspy_sequential as cmap
except ImportError as _import_error:
self.fail('ImportError:\n%s' % _import_error)
from matplotlib.colorbar import ColorbarBase
from matplotlib.colors import Normalize
import matplotlib.dates as mdates
self.cleanup()
viewer = self.get_viewer()
if viewer.lowpass is None or viewer.highpass is None:
self.fail('highpass and lowpass in viewer must be set!')
traces = []
for trs in self.chopper_selected_traces(fallback=True):
for tr in trs:
tr.lowpass(2, viewer.lowpass)
tr.highpass(2, viewer.highpass)
traces.extend(trs)
if not traces:
self.fail('no traces selected')
if self.downresample == 'resample':
dt_want = min([t.deltat for t in traces])
for t in traces:
t.resample(dt_want)
elif self.downresample == 'downsample':
dt_want = max([t.deltat for t in traces])
for t in traces:
t.downsample_to(dt_want)
elif self.downresample == 'downsample to "target dt"':
for t in traces:
t.downsample_to(float(self.target_dt))
tmin = max([t.tmin for t in traces])
tmax = min([t.tmax for t in traces])
try:
obspy_traces = [p2o_trace(
tr, viewer.get_station(viewer.station_key(tr)))
for tr in traces]
except KeyError:
self.fail('station information missing')
st = stream.Stream(traces=obspy_traces)
center = array_analysis.get_geometry(st, return_center=True)
center_lon, center_lat, center_ele = center[len(center)-1]
# Execute sonic
kwargs = dict(
sll_x=-self.smax, slm_x=self.smax, sll_y=-self.smax,
slm_y=self.smax, sl_s=self.smax/self.divisor,
win_len=self.window_lenth, win_frac=self.win_frac,
frqlow=viewer.highpass, frqhigh=viewer.lowpass, prewhiten=0,
semb_thres=-1.0e9, vel_thres=-1.0e9, verbose=True,
timestamp='mlabday', stime=UTCDateTime(tmin),
etime=UTCDateTime(tmax)
)
try:
out = array_analysis.array_processing(st, **kwargs)
except AttributeError:
from obspy.signal.array_analysis import sonic
out = sonic(st, **kwargs)
pi = num.pi
# make output human readable, adjust backazimuth to values between 0
# and 360
t, rel_power, abs_power, baz, slow = out.T
baz[baz < 0.0] += 360.
# choose number of fractions in plot (desirably 360 degree/N is an
# integer!)
N = int(self.numberOfFraction)
abins = num.arange(N + 1) * 360. / N
sbins = num.linspace(0., self.smax, N + 1)
# sum rel power in bins given by abins and sbins
hist, baz_edges, sl_edges = num.histogram2d(
baz, slow, bins=[abins, sbins], weights=rel_power)
# transform to gradient
baz_edges = baz_edges / 180. * pi
fig = self.pylab(get='figure')
cax = fig.add_axes([0.85, 0.2, 0.05, 0.5])
ax = fig.add_axes([0.10, 0.1, 0.70, 0.7], polar=True)
ax.grid(False)
dh = abs(sl_edges[1] - sl_edges[0])
dw = abs(baz_edges[1] - baz_edges[0])
# circle through backazimuth
for i, row in enumerate(hist):
ax.bar(left=(pi / 2 - (i + 1) * dw) * num.ones(N),
height=dh * num.ones(N), width=dw,
bottom=dh * num.arange(N), color=cmap(row / hist.max()))
ax.set_xticks([pi / 2, 0, 3. / 2 * pi, pi])
ax.set_xticklabels(['N', 'E', 'S', 'W'])
ax.set_ylim(0., self.smax)
ColorbarBase(cax, cmap=cmap,
norm=Normalize(vmin=hist.min(), vmax=hist.max()))
fig2 = self.pylab(get='figure')
labels = ['rel.power', 'abs.power', 'baz', 'slow']
xlocator = mdates.AutoDateLocator()
ax = None
for i, lab in enumerate(labels):
ax = fig2.add_subplot(4, 1, i + 1, sharex=ax)
ax.scatter(out[:, 0], out[:, i + 1], c=out[:, 1], alpha=0.6,
edgecolors='none', cmap=cmap)
ax.set_ylabel(lab)
ax.set_xlim(out[0, 0], out[-1, 0])
ax.set_ylim(out[:, i + 1].min(), out[:, i + 1].max())
ax.xaxis.set_tick_params(which='both', direction='in')
ax.xaxis.set_major_locator(xlocator)
ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(xlocator))
if i != 3:
ax.set_xticklabels([])
fig2.subplots_adjust(hspace=0.)
fig2.canvas.draw()
fig.canvas.draw()
print('Center of Array at latitude %s and longitude %s' %
(center_lat, center_lon))
# sliding window propertieds
win_len=1.0,
win_frac=0.05,
# frequency properties
frqlow=1.0,
frqhigh=8.0,
prewhiten=0,
# restrict output
semb_thres=-1e9,
vel_thres=-1e9,
verbose=True,
timestamp='mlabhour',
stime=UTCDateTime("20080217110515"),
etime=UTCDateTime("20080217110545"))
# Perform beamforming with previously set arguments.
out = sonic(stream, **kwargs)
cmap = cm.hot_r
pi = np.pi
#
# make output human readable, adjust backazimuth to values between 0 and 360
t, rel_power, abs_power, baz, slow = out.T
baz[baz < 0.0] += 360
# choose number of fractions in plot (desirably 360/N is an integer!)
N = 30
abins = np.arange(N + 1) * 360. / N
sbins = np.linspace(0, 3, N + 1)
# sum rel power in bins given by abins and sbins
def test_sonic(self):
# for i in xrange(100):
np.random.seed(2348)
geometry = np.array([[0.0, 0.0, 0.0],
[-5.0, 7.0, 0.0],
[5.0, 7.0, 0.0],
[10.0, 0.0, 0.0],
[5.0, -7.0, 0.0],
[-5.0, -7.0, 0.0],
[-10.0, 0.0, 0.0]])
geometry /= 100 # in km
slowness = 1.3 # in s/km
baz_degree = 20.0 # 0.0 > source in x direction
baz = baz_degree * np.pi / 180.
df = 100 # samplerate
# SNR = 100. # signal to noise ratio
amp = .00001 # amplitude of coherent wave
length = 500 # signal length in samples
coherent_wave = amp * np.random.randn(length)
# time offsets in samples
dt = df * slowness * (np.cos(baz) * geometry[:, 1] + np.sin(baz) *
geometry[:, 0])
dt = np.round(dt)
dt = dt.astype('int32')
max_dt = np.max(dt) + 1
min_dt = np.min(dt) - 1
trl = list()
for i in xrange(len(geometry)):
tr = Trace(coherent_wave[-min_dt + dt[i]:-max_dt + dt[i]].copy())
# + amp / SNR * \
# np.random.randn(length - abs(min_dt) - abs(max_dt)))
tr.stats.sampling_rate = df
tr.stats.coordinates = AttribDict()
tr.stats.coordinates.x = geometry[i, 0]
tr.stats.coordinates.y = geometry[i, 1]
tr.stats.coordinates.elevation = geometry[i, 2]
# lowpass random signal to f_nyquist / 2
tr.filter("lowpass", freq=df / 4.)
trl.append(tr)
st = Stream(trl)
stime = UTCDateTime(1970, 1, 1, 0, 0)
etime = UTCDateTime(1970, 1, 1, 0, 0) + \
(length - abs(min_dt) - abs(max_dt)) / df
win_len = 2.
step_frac = 0.2
sll_x = -3.0
slm_x = 3.0
sll_y = -3.0
slm_y = 3.0
sl_s = 0.1
frqlow = 1.0
frqhigh = 8.0
prewhiten = 0
semb_thres = -1e99
vel_thres = -1e99
# out returns: rel. power, abs. power, backazimuth, slowness
out = sonic(st, win_len, step_frac, sll_x, slm_x, sll_y, slm_y, sl_s,
semb_thres, vel_thres, frqlow, frqhigh, stime, etime,
prewhiten, coordsys='xy', verbose=False)
# returns baz
np.testing.assert_almost_equal(out[:, 3].mean(), 18.434948822922024)
# slowness ~= 1.3
np.testing.assert_almost_equal(out[:, 4].mean(), 1.26491106407)
# Execute sonic
kwargs = dict(
# slowness grid: X min, X max, Y min, Y max, Slow Step
sll_x=-3.0, slm_x=3.0, sll_y=-3.0, slm_y=3.0, sl_s=0.03,
# sliding window propertieds
win_len=1.0, win_frac=0.05,
# frequency properties
frqlow=1.0, frqhigh=8.0, prewhiten=0,
# restrict output
semb_thres=-1e9, vel_thres=-1e9, verbose=True, timestamp='mlabhour',
stime=UTCDateTime("20080217110515"),
etime=UTCDateTime("20080217110545")
)
# Perform beamforming with previously set arguments.
out = sonic(stream, **kwargs)
cmap = cm.hot_r
pi = np.pi
#
# make output human readable, adjust backazimuth to values between 0 and 360
t, rel_power, abs_power, baz, slow = out.T
baz[baz < 0.0] += 360
# choose number of fractions in plot (desirably 360/N is an integer!)
N = 30
abins = np.arange(N+1)*360./N
sbins = np.linspace(0, 3, N+1)
# sum rel power in bins given by abins and sbins
def test_sonic(self):
# for i in xrange(100):
np.random.seed(2348)
geometry = np.array(
[
[0.0, 0.0, 0.0],
[-5.0, 7.0, 0.0],
[5.0, 7.0, 0.0],
[10.0, 0.0, 0.0],
[5.0, -7.0, 0.0],
[-5.0, -7.0, 0.0],
[-10.0, 0.0, 0.0],
]
)
geometry /= 100 # in km
slowness = 1.3 # in s/km
baz_degree = 20.0 # 0.0 > source in x direction
baz = baz_degree * np.pi / 180.0
df = 100 # samplerate
# SNR = 100. # signal to noise ratio
amp = 0.00001 # amplitude of coherent wave
length = 500 # signal length in samples
coherent_wave = amp * np.random.randn(length)
# time offsets in samples
dt = df * slowness * (np.cos(baz) * geometry[:, 1] + np.sin(baz) * geometry[:, 0])
dt = np.round(dt)
dt = dt.astype("int32")
max_dt = np.max(dt) + 1
min_dt = np.min(dt) - 1
trl = list()
for i in xrange(len(geometry)):
tr = Trace(coherent_wave[-min_dt + dt[i] : -max_dt + dt[i]].copy())
# + amp / SNR * \
# np.random.randn(length - abs(min_dt) - abs(max_dt)))
tr.stats.sampling_rate = df
tr.stats.coordinates = AttribDict()
tr.stats.coordinates.x = geometry[i, 0]
tr.stats.coordinates.y = geometry[i, 1]
tr.stats.coordinates.elevation = geometry[i, 2]
# lowpass random signal to f_nyquist / 2
tr.filter("lowpass", freq=df / 4.0)
trl.append(tr)
st = Stream(trl)
stime = UTCDateTime(1970, 1, 1, 0, 0)
etime = UTCDateTime(1970, 1, 1, 0, 0) + (length - abs(min_dt) - abs(max_dt)) / df
win_len = 2.0
step_frac = 0.2
sll_x = -3.0
slm_x = 3.0
sll_y = -3.0
slm_y = 3.0
sl_s = 0.1
frqlow = 1.0
frqhigh = 8.0
prewhiten = 0
semb_thres = -1e99
vel_thres = -1e99
# out returns: rel. power, abs. power, backazimuth, slowness
out = sonic(
st,
win_len,
step_frac,
sll_x,
slm_x,
sll_y,
slm_y,
sl_s,
semb_thres,
vel_thres,
frqlow,
frqhigh,
stime,
etime,
prewhiten,
coordsys="xy",
verbose=False,
)
# returns baz
np.testing.assert_almost_equal(out[:, 3].mean(), 18.434948822922024)
# slowness ~= 1.3
np.testing.assert_almost_equal(out[:, 4].mean(), 1.26491106407)