def align_phases(stream, event, inventory, phase_name, method="simple"):
"""
Aligns the waveforms with the theoretical travel times for some phase. The
theoretical travel times are calculated with obspy.taup.
:param stream: Waveforms for the array processing.
:type stream: :class:`obspy.core.stream.Stream`
:param event: The event for which to calculate phases.
:type event: :class:`obspy.core.event.Event`
:param inventory: Station metadata.
:type inventory: :class:`obspy.station.inventory.Inventory`
:param phase_name: The name of the phase you want to align. Must be
contained in all traces. Otherwise the behaviour is undefined.
:type phase_name: str
:param method: Method is either `simple` or `fft`. Simple will just shift
the starttime of Trace, while 'fft' will do the shift in the frequency
domain. Defaults to `simple`.
:type method: str
"""
method = method.lower()
if method not in ['simple', 'fft']:
msg = "method must be 'simple' or 'fft'"
raise ValueError(msg)
stream = stream.copy()
attach_coordinates_to_traces(stream, inventory, event)
stream.traces = sorted(stream.traces, key=lambda x: x.stats.distance)[::-1]
tr_1 = stream[-1]
tt_1 = getTravelTimes(tr_1.stats.distance, event.origins[0].depth / 1000.0,
"ak135")
cont = 0.
for tt in tt_1:
if tt["phase_name"] != phase_name:
continue
if tt["phase_name"] == phase_name:
cont = 1.
tt_1 = tt["time"]
break
if cont == 0:
msg = "The selected phase is not present in your seismograms!!!"
raise ValueError(msg)
for tr in stream:
tt = getTravelTimes(tr.stats.distance, event.origins[0].depth / 1000.0,
"ak135")
for t in tt:
if t["phase_name"] != phase_name:
continue
tt = t["time"]
break
if method == "simple":
tr.stats.starttime -= (tt - tt_1)
else:
shifttrace_freq(Stream(traces=[tr]), [-((tt - tt_1))])
return stream
def align_phases(stream, event, inventory, phase_name, method="simple"):
"""
Aligns the waveforms with the theoretical travel times for some phase. The
theoretical travel times are calculated with obspy.taup.
:param stream: Waveforms for the array processing.
:type stream: :class:`obspy.core.stream.Stream`
:param event: The event for which to calculate phases.
:type event: :class:`obspy.core.event.Event`
:param inventory: Station metadata.
:type inventory: :class:`obspy.station.inventory.Inventory`
:param phase_name: The name of the phase you want to align. Must be
contained in all traces. Otherwise the behaviour is undefined.
:type phase_name: str
:param method: Method is either `simple` or `fft`. Simple will just shift
the starttime of Trace, while 'fft' will do the shift in the frequency
domain. Defaults to `simple`.
:type method: str
"""
method = method.lower()
if method not in ["simple", "fft"]:
msg = "method must be 'simple' or 'fft'"
raise ValueError(msg)
stream = stream.copy()
attach_coordinates_to_traces(stream, inventory, event)
stream.traces = sorted(stream.traces, key=lambda x: x.stats.distance)[::-1]
tr_1 = stream[-1]
tt_1 = getTravelTimes(tr_1.stats.distance, event.origins[0].depth / 1000.0, "ak135")
cont = 0.0
for tt in tt_1:
if tt["phase_name"] != phase_name:
continue
if tt["phase_name"] == phase_name:
cont = 1.0
tt_1 = tt["time"]
break
if cont == 0:
msg = "The selected phase is not present in your seismograms!!!"
raise ValueError(msg)
for tr in stream:
tt = getTravelTimes(tr.stats.distance, event.origins[0].depth / 1000.0, "ak135")
for t in tt:
if t["phase_name"] != phase_name:
continue
tt = t["time"]
break
if method == "simple":
tr.stats.starttime -= tt - tt_1
else:
shifttrace_freq(Stream(traces=[tr]), [-((tt - tt_1))])
return stream
def calculate_time_phase(event, sta):
"""
calculate arrival time of the requested phase to use in retrieving
waveforms.
:param event:
:param sta:
:return:
"""
ev_lat = event['latitude']
ev_lon = event['longitude']
ev_dp = abs(float(event['depth']))
sta_lat = float(sta[4])
sta_lon = float(sta[5])
delta = locations2degrees(ev_lat, ev_lon, sta_lat, sta_lon)
tt = getTravelTimes(delta, ev_dp)
phase_list = ['P', 'Pdiff', 'PKIKP']
time_ph = 0
flag = False
for ph in phase_list:
for i in range(len(tt)):
if tt[i]['phase_name'] == ph:
flag = True
time_ph = tt[i]['time']
break
else:
continue
if flag:
print 'Phase: %s' % ph
break
t_start = event['t1'] + time_ph
t_end = event['t2'] + time_ph
return t_start, t_end
def calculate_time_phase(event, sta):
"""
calculate arrival time of the requested phase to use in retrieving
waveforms.
:param event:
:param sta:
:return:
"""
ev_lat = event['latitude']
ev_lon = event['longitude']
ev_dp = abs(float(event['depth']))
sta_lat = float(sta[4])
sta_lon = float(sta[5])
delta = locations2degrees(ev_lat, ev_lon, sta_lat, sta_lon)
tt = getTravelTimes(delta, ev_dp)
phase_list = ['P', 'Pdiff', 'PKIKP']
time_ph = 0
flag = False
for ph in phase_list:
for i in range(len(tt)):
if tt[i]['phase_name'] == ph:
flag = True
time_ph = tt[i]['time']
break
else:
continue
if flag:
print 'Phase: %s' % ph
break
t_start = event['t1'] + time_ph
t_end = event['t2'] + time_ph
return t_start, t_end
def calculate_time_phase(event, sta, bg_model='iasp91'):
"""
calculate arrival time of the requested phase
:param event:
:param sta:
:param bg_model:
:return:
"""
phase_list = ['P', 'Pdiff', 'PKIKP']
time_ph = 0
ev_lat = event['latitude']
ev_lon = event['longitude']
evdp = abs(float(event['depth']))
sta_lat = float(sta[4])
sta_lon = float(sta[5])
dist = locations2degrees(ev_lat, ev_lon, sta_lat, sta_lon)
try:
from obspy.taup import tau
tau_bg = tau.TauPyModel(model=bg_model)
except:
tau_bg = False
if not tau_bg:
try:
tt = getTravelTimes(dist, evdp)
flag = False
for ph in phase_list:
for i in range(len(tt)):
if tt[i]['phase_name'] == ph:
flag = True
time_ph = tt[i]['time']
break
else:
continue
if not flag:
time_ph = 0
except:
time_ph = 0
else:
try:
for ph in phase_list:
tt = tau_bg.get_travel_times(evdp, dist,
phase_list=[ph])[0].time
if not tt:
time_ph = 0
continue
else:
time_ph = tt
break
except:
time_ph = 0
t_start = event['t1'] + time_ph
t_end = event['t2'] + time_ph
return t_start, t_end
def calculate_ttimes(self):
"""
Calculate theoretical travel times. Only call if station and event
information is available!
"""
dist_in_deg = geodetics.locations2degrees(
self.station.latitude, self.station.longitude,
self.event.latitude, self.event.longitude)
tts = getTravelTimes(dist_in_deg, self.event.depth_in_m / 1000.0,
model=self.config.earth_model)
self.ttimes = sorted(tts, key=lambda x: x["time"])
logger.info("Calculated travel times.")
def calculate_ttimes(self):
"""
Calculate theoretical travel times. Only call if station and event
information is available!
"""
dist_in_deg = geodetics.locations2degrees(self.station.latitude,
self.station.longitude,
self.event.latitude,
self.event.longitude)
tts = getTravelTimes(dist_in_deg,
self.event.depth_in_m / 1000.0,
model=self.config.earth_model)
self.ttimes = sorted(tts, key=lambda x: x["time"])
logger.info("Calculated travel times.")
def travel_time_calc(evla, evlo, stla, stlo, evdp, bg_model):
"""
calculate arrival time of different seismic phases
:param evla:
:param evlo:
:param stla:
:param stlo:
:param evdp:
:param bg_model:
:return:
"""
# --------------- TAUP
dist = locations2degrees(evla, evlo, stla, stlo)
try:
tt = [_i for _i in getTravelTimes(dist, evdp, bg_model)
if 'Pdiff' == _i['phase_name']][0]['time']
except Exception, e:
tt = False
def travel_time_calc(evla, evlo, stla, stlo, evdp, bg_model):
"""
calculate arrival time of different seismic phases
:param evla:
:param evlo:
:param stla:
:param stlo:
:param evdp:
:param bg_model:
:return:
"""
# --------------- TAUP
dist = locations2degrees(evla, evlo, stla, stlo)
try:
tt = [
_i for _i in getTravelTimes(dist, evdp, bg_model)
if 'Pdiff' == _i['phase_name']
][0]['time']
except Exception, e:
tt = False
import numpy as np
from obspy import taup
seismic_phase = ['P', 'Pdiff']
min_degree = 90.
max_degree = 180.
step_degree = 1.
evdepth = 0.
dist_pdiff = []
dist_all = []
take_off = []
for dist in np.arange(min_degree, max_degree, step_degree):
flag = False
tt = taup.getTravelTimes(delta=dist, depth=evdepth)
for i in range(len(tt)):
if tt[i]['phase_name'] in seismic_phase:
flag = True
take_off_tmp = tt[i]['take-off angle']
time_tmp = tt[i]['time']
if tt[i]['phase_name'] == 'Pdiff':
dist_pdiff.append(dist)
break
if flag:
take_off.append(take_off_tmp)
dist_all.append(dist)
plt.plot(dist_all, take_off, lw=3)
plt.xlabel('Distance (deg)', size='large', weight='bold')
plt.ylabel('Take-off angle', size='large', weight='bold')
def select_windows(data_trace, synthetic_trace, ev_lat, ev_lng, ev_depth_in_km,
st_lat, st_lng, minimum_period, maximum_period):
"""
Window selection algorithm for picking windows suitable for misfit
calculation based on phase differences.
:param data_trace:
:param synthetic_trace:
:param ev_lat:
:param ev_lng:
:param ev_depth_in_km:
:param st_lat:
:param st_lng:
:param minimum_period:
:param maximum_period:
"""
print "* ---------------------------"
print "* autoselect " + data_trace.stats.channel
# =========================================================================
# set a couple of selection parameters - might become part of the input in
# future versions
# =========================================================================
# Minimum normalised correlation coefficient of the complete traces.
min_cc = 0.0
# Maximum relative noise level for the whole trace. Measured from maximum
# amplitudes before and after the first arrival.
max_noise = 0.3
# Maximum relative noise level for individual windows.
max_noise_window = 0.4
# All arrivals later than those corresponding to the threshold velocity
# [km/s] will be excluded.
threshold_velocity = 2.4
# Maximum allowable time shift within a window, as a fraction of the
# minimum period.
threshold_shift = 0.2
# Minimum normalised correlation coeficient within a window.
threshold_correlation = 0.5
# Minimum length of the time windows relative to the minimum period.
min_length_period = 1.5
# Minimum number of extreme in an individual time window (excluding the
# edges).
min_peaks_troughs = 2
# Maximum energy ratio between data and synthetics within a time window.
max_energy_ratio = 3.0
# =========================================================================
# initialisations
# =========================================================================
dt = synthetic_trace.stats.delta
npts = synthetic_trace.stats.npts
dist_in_deg = geodetics.locations2degrees(st_lat, st_lng, ev_lat, ev_lng)
dist_in_km = geodetics.calcVincentyInverse(
st_lat, st_lng, ev_lat, ev_lng)[0] / 1000.0
tts = getTravelTimes(dist_in_deg, ev_depth_in_km, model="ak135")
first_tt_arrival = min([_i["time"] for _i in tts])
# Number of samples in the sliding window. Currently, the length of the
# window is set to a multiple of the dominant period of the synthetics.
# Make sure it is an uneven number; just to have an easy midpoint
# definition.
window_length = int(round(float(2 * minimum_period) / dt))
if not window_length % 2:
window_length += 1
# Allocate arrays to collect the time dependent values.
sliding_time_shift = np.zeros(npts, dtype="float32")
max_cc_coeff = np.zeros(npts, dtype="float32")
taper = np.hanning(window_length)
# =========================================================================
# check if whole seismograms are sufficiently correlated and estimate noise
# level
# =========================================================================
synth = synthetic_trace.data
data = data_trace.data
# compute correlation coefficient
norm = np.sqrt(np.sum(data ** 2)) * np.sqrt(np.sum(synth ** 2))
cc = np.sum(data * synth) / norm
print "** correlation coefficient: " + str(cc)
# estimate noise level from waveforms prior to the first arrival
idx = int(np.ceil((first_tt_arrival - minimum_period * 0.5) / dt))
noise_absolute = data[50:idx].ptp()
noise_relative = noise_absolute / data.ptp()
print "** absolute noise level: " + str(noise_absolute) + " m/s"
print "** relative noise level: " + str(noise_relative)
# rejection criteria
accept = True
if cc < min_cc:
print "** no windows selected, correlation " + str(cc) + \
" is below threshold value of " + str(min_cc)
accept = False
if noise_relative > max_noise:
print "** no windows selected, noise level " + str(noise_relative) + \
" is above threshold value of " + str(max_noise)
accept = False
if accept is False:
print "* autoselect done"
return []
# =========================================================================
# compute sliding time shifts and correlation coefficients
# =========================================================================
for start_idx, end_idx, midpoint_idx in _window_generator(npts,
window_length):
# Slice windows. Create a copy to be able to taper without affecting
# the original time series.
data_window = data_trace.data[start_idx: end_idx].copy() * taper
synthetic_window = \
synthetic_trace.data[start_idx: end_idx].copy() * taper
# Skip windows that have essentially no energy to avoid instabilities.
if synthetic_window.ptp() < synthetic_trace.data.ptp() * 0.001:
continue
# Calculate the time shift. Here this is defined as the shift of the
# synthetics relative to the data. So a value of 2, for instance, means
# that the synthetics are 2 timesteps later then the data.
cc = np.correlate(data_window, synthetic_window, mode="full")
time_shift = cc.argmax() - window_length + 1
# Express the time shift in fraction of the minimum period.
sliding_time_shift[midpoint_idx] = (time_shift * dt) / minimum_period
# Normalized cross correlation.
max_cc_value = cc.max() / np.sqrt((synthetic_window ** 2).sum() *
(data_window ** 2).sum())
max_cc_coeff[midpoint_idx] = max_cc_value
# =========================================================================
# compute the initial mask, i.e. intervals (windows) where no measurements
# are made.
# =========================================================================
# Step 1: Initialise masked arrays. The mask will be set to True where no
# windows are chosen.
time_windows = np.ma.ones(npts)
time_windows.mask = np.zeros(npts)
# Step 2: Mark everything more then half a dominant period before the first
# theoretical arrival as positive.
time_windows.mask[:int(np.ceil(
(first_tt_arrival - minimum_period * 0.5) / dt))] = True
# Step 3: Mark everything more then half a dominant period after the
# threshold arrival time - computed from the threshold velocity - as
# negative.
time_windows.mask[int(np.floor(dist_in_km / threshold_velocity / dt)):] = \
True
# Step 4: Mark everything with an absolute travel time shift of more than
# threshold_shift times the dominant period as negative
time_windows.mask[np.abs(sliding_time_shift) > threshold_shift] = True
# Step 5: Mark the area around every "travel time shift jump" (based on
# the traveltime time difference) negative. The width of the area is
# currently chosen to be a tenth of a dominant period to each side.
sample_buffer = int(np.ceil(minimum_period / dt * 0.1))
indices = np.ma.where(np.abs(np.diff(sliding_time_shift)) > 0.1)[0]
for index in indices:
time_windows.mask[index - sample_buffer: index + sample_buffer] = True
# Step 6: Mark all areas where the normalized cross correlation coefficient
# is under threshold_correlation as negative
time_windows.mask[max_cc_coeff < threshold_correlation] = True
# =========================================================================
# Make the final window selection.
# =========================================================================
min_length = min(
minimum_period / dt * min_length_period, maximum_period / dt)
final_windows = []
# loop through all the time windows
for i in np.ma.flatnotmasked_contiguous(time_windows):
window_npts = i.stop - i.start
synthetic_window = synthetic_trace.data[i.start: i.stop]
data_window = data_trace.data[i.start: i.stop]
# Step 7: Throw away all windows with a length of less then
# min_length_period the dominant period.
if (i.stop - i.start) < min_length:
continue
# Step 8: Exclude windows without a real peak or trough (except for the
# edges).
data_p, data_t, data_extrema = find_local_extrema(data_window, 0)
synth_p, synth_t, synth_extrema = find_local_extrema(synthetic_window,
0)
if np.min([len(synth_p), len(synth_t), len(data_p), len(data_t)]) < \
min_peaks_troughs:
continue
# Step 9: Peak and trough matching algorithm
window_mask = np.ones(window_npts, dtype="bool")
closest_peaks = find_closest(data_p, synth_p)
diffs = np.diff(closest_peaks)
for idx in np.where(diffs == 1)[0]:
if idx > 0:
start = synth_p[idx - 1]
else:
start = 0
if idx < (len(synth_p) - 1):
end = synth_p[idx + 1]
else:
end = -1
window_mask[start: end] = False
closest_troughs = find_closest(data_t, synth_t)
diffs = np.diff(closest_troughs)
for idx in np.where(diffs == 1)[0]:
if idx > 0:
start = synth_t[idx - 1]
else:
start = 0
if idx < (len(synth_t) - 1):
end = synth_t[idx + 1]
else:
end = -1
window_mask[start: end] = False
window_mask = np.ma.masked_array(window_mask, mask=window_mask)
if window_mask.mask.all():
continue
# Step 10: Check if the time windows have sufficiently similar energy
# and are above the noise
for j in np.ma.flatnotmasked_contiguous(window_mask):
# Again assert a certain minimal length.
if (j.stop - j.start) < min_length:
continue
# Compare the energy in the data window and the synthetic window.
data_energy = (data_window[j.start: j.stop] ** 2).sum()
synth_energy = (synthetic_window[j.start: j.stop] ** 2).sum()
energies = sorted([data_energy, synth_energy])
if energies[1] > max_energy_ratio * energies[0]:
continue
# Check that amplitudes in the data are above the noise
if noise_absolute / data_window[j.start: j.stop].ptp() > \
max_noise_window:
continue
final_windows.append((i.start + j.start, i.start + j.stop))
print "* autoselect done"
return final_windows
def vespagram(stream, ev, inv, method, frqlow, frqhigh, baz, scale, nthroot=4,
filter=True, static3D=False, vel_corr=4.8, sl=(0.0, 10.0, 0.5),
align=False, align_phase=['P', 'Pdiff'], plot_trace=True):
"""
vespagram wrapper routine for MESS 2014.
:param stream: Waveforms for the array processing.
:type stream: :class:`obspy.core.stream.Stream`
:param inventory: Station metadata for waveforms
:type inventory: :class:`obspy.station.inventory.Inventory`
:param method: Method used for the array analysis
(one of "DLS": Delay and Sum, "PWS": Phase Weighted Stack).
:type method: str
:param frqlow: Low corner of frequency range for array analysis
:type frqlow: float
:param frqhigh: High corner of frequency range for array analysis
:type frqhigh: float
:param baz: pre-defined (theoretical or calculated) backazimuth used for calculation
:type baz_plot: float
:param scale: scale for plotting
:type scale: float
:param nthroot: estimating the nthroot for calculation of the beam
:type nthroot: int
:param filter: Whether to bandpass data to selected frequency range
:type filter: bool
:param static3D: static correction of topography using `vel_corr` as
velocity (slow!)
:type static3D: bool
:param vel_corr: Correction velocity for static topography correction in
km/s.
:type vel_corr: float
:param sl: Min/Max and stepwidthslowness for analysis
:type sl: (float, float,float)
:param align: whether to align the vespagram to a certain phase
:type align: bool
:param align_phase: phase to be aligned with (might be a list if simulateneous arivials are expected (P,PcP,Pdif)
:type align: str
:param plot_trace: if True plot the vespagram as wiggle plot, if False as density map
:type align: bool
"""
starttime = max([tr.stats.starttime for tr in stream])
endtime = min([tr.stats.endtime for tr in stream])
stream.trim(starttime, endtime)
org = ev.preferred_origin() or ev.origins[0]
ev_lat = org.latitude
ev_lon = org.longitude
ev_depth = org.depth/1000. # in km
ev_otime = org.time
sll, slm, sls = sl
sll /= KM_PER_DEG
slm /= KM_PER_DEG
sls /= KM_PER_DEG
center_lon = 0.
center_lat = 0.
center_elv = 0.
seismo = stream
seismo.attach_response(inv)
seismo.merge()
sz = Stream()
i = 0
for tr in seismo:
for station in inv[0].stations:
if tr.stats.station == station.code:
tr.stats.coordinates = \
AttribDict({'latitude': station.latitude,
'longitude': station.longitude,
'elevation': station.elevation})
center_lon += station.longitude
center_lat += station.latitude
center_elv += station.elevation
i += 1
sz.append(tr)
center_lon /= float(i)
center_lat /= float(i)
center_elv /= float(i)
starttime = max([tr.stats.starttime for tr in stream])
stt = starttime
endtime = min([tr.stats.endtime for tr in stream])
e = endtime
stream.trim(starttime, endtime)
#nut = 0
max_amp = 0.
sz.trim(stt, e)
sz.detrend('simple')
print sz
fl, fh = frqlow, frqhigh
if filter:
sz.filter('bandpass', freqmin=fl, freqmax=fh, zerophase=True)
if align:
deg = []
shift = []
res = gps2DistAzimuth(center_lat, center_lon, ev_lat, ev_lon)
deg.append(kilometer2degrees(res[0]/1000.))
tt = getTravelTimes(deg[0], ev_depth, model='ak135')
for item in tt:
phase = item['phase_name']
if phase in align_phase:
try:
travel = item['time']
travel = ev_otime.timestamp + travel
dtime = travel - stt.timestamp
shift.append(dtime)
except:
break
for i, tr in enumerate(sz):
res = gps2DistAzimuth(tr.stats.coordinates['latitude'],
tr.stats.coordinates['longitude'],
ev_lat, ev_lon)
deg.append(kilometer2degrees(res[0]/1000.))
tt = getTravelTimes(deg[i+1], ev_depth, model='ak135')
for item in tt:
phase = item['phase_name']
if phase in align_phase:
try:
travel = item['time']
travel = ev_otime.timestamp + travel
dtime = travel - stt.timestamp
shift.append(dtime)
except:
break
shift = np.asarray(shift)
shift -= shift[0]
AA.shifttrace_freq(sz, -shift)
baz += 180.
nbeam = int((slm - sll)/sls + 0.5) + 1
kwargs = dict(
# slowness grid: X min, X max, Y min, Y max, Slow Step
sll=sll, slm=slm, sls=sls, baz=baz, stime=stt, method=method,
nthroot=nthroot, etime=e, correct_3dplane=False, static_3D=static3D,
vel_cor=vel_corr)
start = UTCDateTime()
slow, beams, max_beam, beam_max = AA.vespagram_baz(sz, **kwargs)
print "Total time in routine: %f\n" % (UTCDateTime() - start)
df = sz[0].stats.sampling_rate
# Plot the seismograms
npts = len(beams[0])
print npts
T = np.arange(0, npts/df, 1/df)
sll *= KM_PER_DEG
slm *= KM_PER_DEG
sls *= KM_PER_DEG
slow = np.arange(sll, slm, sls)
max_amp = np.max(beams[:, :])
#min_amp = np.min(beams[:, :])
scale *= sls
fig = plt.figure(figsize=(12, 8))
if plot_trace:
ax1 = fig.add_axes([0.1, 0.1, 0.85, 0.85])
for i in xrange(nbeam):
if i == max_beam:
ax1.plot(T, sll + scale*beams[i]/max_amp + i*sls, 'r',
zorder=1)
else:
ax1.plot(T, sll + scale*beams[i]/max_amp + i*sls, 'k',
zorder=-1)
ax1.set_xlabel('Time [s]')
ax1.set_ylabel('slowness [s/deg]')
ax1.set_xlim(T[0], T[-1])
data_minmax = ax1.yaxis.get_data_interval()
minmax = [min(slow[0], data_minmax[0]), max(slow[-1], data_minmax[1])]
ax1.set_ylim(*minmax)
#####
else:
#step = (max_amp - min_amp)/100.
#level = np.arange(min_amp, max_amp, step)
#beams = beams.transpose()
#cmap = cm.hot_r
cmap = cm.rainbow
ax1 = fig.add_axes([0.1, 0.1, 0.85, 0.85])
#ax1.contour(slow,T,beams,level)
#extent = (slow[0], slow[-1], \
# T[0], T[-1])
extent = (T[0], T[-1], slow[0] - sls * 0.5, slow[-1] + sls * 0.5)
ax1.set_ylabel('slowness [s/deg]')
ax1.set_xlabel('T [s]')
beams = np.flipud(beams)
ax1.imshow(beams, cmap=cmap, interpolation="nearest",
extent=extent, aspect='auto')
####
result = "BAZ: %.2f Time %s" % (baz-180., stt)
ax1.set_title(result)
plt.show()
return slow, beams, max_beam, beam_max
def show_distance_plot(stream, event, inventory, starttime, endtime,
plot_travel_times=True):
"""
Plots distance dependent seismogramm sections.
:param stream: The waveforms.
:type stream: :class:`obspy.core.stream.Stream`
:param event: The event.
:type event: :class:`obspy.core.event.Event`
:param inventory: The station information.
:type inventory: :class:`obspy.station.inventory.Inventory`
:param starttime: starttime of traces to be plotted
:type starttime: UTCDateTime
:param endttime: endttime of traces to be plotted
:type endttime: UTCDateTime
:param plot_travel_times: flag whether phases are marked as traveltime plots
in the section obspy.taup is used to calculate the phases
:type pot_travel_times: bool
"""
stream = stream.slice(starttime=starttime, endtime=endtime).copy()
event_depth_in_km = event.origins[0].depth / 1000.0
event_time = event.origins[0].time
attach_coordinates_to_traces(stream, inventory, event=event)
cm = plt.cm.jet
stream.traces = sorted(stream.traces, key=lambda x: x.stats.distance)[::-1]
# One color for each trace.
colors = [cm(_i) for _i in np.linspace(0, 1, len(stream))]
# Relative event times.
times_array = stream[0].times() + (stream[0].stats.starttime - event_time)
distances = [tr.stats.distance for tr in stream]
min_distance = min(distances)
max_distance = max(distances)
distance_range = max_distance - min_distance
stream_range = distance_range / 10.0
# Normalize data and "shift to distance".
stream.normalize()
for tr in stream:
tr.data *= stream_range
tr.data += tr.stats.distance
plt.figure(figsize=(18, 10))
for _i, tr in enumerate(stream):
plt.plot(times_array, tr.data, label="%s.%s" % (tr.stats.network,
tr.stats.station), color=colors[_i])
plt.grid()
plt.ylabel("Distance in degree to event")
plt.xlabel("Time in seconds since event")
plt.legend()
dist_min, dist_max = plt.ylim()
if plot_travel_times:
distances = defaultdict(list)
ttimes = defaultdict(list)
for i in np.linspace(dist_min, dist_max, 1000):
tts = getTravelTimes(i, event_depth_in_km, "ak135")
for phase in tts:
name = phase["phase_name"]
distances[name].append(i)
ttimes[name].append(phase["time"])
for key in distances.iterkeys():
min_distance = min(distances[key])
max_distance = max(distances[key])
min_tt_time = min(ttimes[key])
max_tt_time = max(ttimes[key])
if min_tt_time >= times_array[-1] or \
max_tt_time <= times_array[0] or \
(max_distance - min_distance) < 0.8 * (dist_max - dist_min):
continue
ttime = ttimes[key]
dist = distances[key]
if max(ttime) > times_array[0] + 0.9 * times_array.ptp():
continue
plt.scatter(ttime, dist, s=0.5, zorder=-10, color="black", alpha=0.8)
plt.text(max(ttime) + 0.005 * times_array.ptp(),
dist_max - 0.02 * (dist_max - dist_min),
key)
plt.ylim(dist_min, dist_max)
plt.xlim(times_array[0], times_array[-1])
plt.title(event.short_str())
plt.show()
import numpy as np
from obspy import taup
seismic_phase = ['P', 'Pdiff']
min_degree = 90.
max_degree = 180.
step_degree = 1.
evdepth = 0.
dist_pdiff = []
dist_all = []
take_off = []
for dist in np.arange(min_degree, max_degree, step_degree):
flag = False
tt = taup.getTravelTimes(delta=dist, depth=evdepth)
for i in range(len(tt)):
if tt[i]['phase_name'] in seismic_phase:
flag = True
take_off_tmp = tt[i]['take-off angle']
time_tmp = tt[i]['time']
if tt[i]['phase_name'] == 'Pdiff':
dist_pdiff.append(dist)
break
if flag:
take_off.append(take_off_tmp)
dist_all.append(dist)
plt.plot(dist_all, take_off, lw=3)
plt.xlabel('Distance (deg)', size='large', weight='bold')
plt.ylabel('Take-off angle', size='large', weight='bold')
def show_distance_plot(stream,
event,
inventory,
starttime,
endtime,
plot_travel_times=True):
"""
Plots distance dependent seismogramm sections.
:param stream: The waveforms.
:type stream: :class:`obspy.core.stream.Stream`
:param event: The event.
:type event: :class:`obspy.core.event.Event`
:param inventory: The station information.
:type inventory: :class:`obspy.station.inventory.Inventory`
:param starttime: starttime of traces to be plotted
:type starttime: UTCDateTime
:param endttime: endttime of traces to be plotted
:type endttime: UTCDateTime
:param plot_travel_times: flag whether phases are marked as traveltime plots
in the section obspy.taup is used to calculate the phases
:type pot_travel_times: bool
"""
stream = stream.slice(starttime=starttime, endtime=endtime).copy()
event_depth_in_km = event.origins[0].depth / 1000.0
event_time = event.origins[0].time
attach_coordinates_to_traces(stream, inventory, event=event)
cm = plt.cm.jet
stream.traces = sorted(stream.traces, key=lambda x: x.stats.distance)[::-1]
# One color for each trace.
colors = [cm(_i) for _i in np.linspace(0, 1, len(stream))]
# Relative event times.
times_array = stream[0].times() + (stream[0].stats.starttime - event_time)
distances = [tr.stats.distance for tr in stream]
min_distance = min(distances)
max_distance = max(distances)
distance_range = max_distance - min_distance
stream_range = distance_range / 10.0
# Normalize data and "shift to distance".
stream.normalize()
for tr in stream:
tr.data *= stream_range
tr.data += tr.stats.distance
plt.figure(figsize=(18, 10))
for _i, tr in enumerate(stream):
plt.plot(times_array,
tr.data,
label="%s.%s" % (tr.stats.network, tr.stats.station),
color=colors[_i])
plt.grid()
plt.ylabel("Distance in degree to event")
plt.xlabel("Time in seconds since event")
plt.legend()
dist_min, dist_max = plt.ylim()
if plot_travel_times:
distances = defaultdict(list)
ttimes = defaultdict(list)
for i in np.linspace(dist_min, dist_max, 1000):
tts = getTravelTimes(i, event_depth_in_km, "ak135")
for phase in tts:
name = phase["phase_name"]
distances[name].append(i)
ttimes[name].append(phase["time"])
for key in distances.iterkeys():
min_distance = min(distances[key])
max_distance = max(distances[key])
min_tt_time = min(ttimes[key])
max_tt_time = max(ttimes[key])
if min_tt_time >= times_array[-1] or \
max_tt_time <= times_array[0] or \
(max_distance - min_distance) < 0.8 * (dist_max - dist_min):
continue
ttime = ttimes[key]
dist = distances[key]
if max(ttime) > times_array[0] + 0.9 * times_array.ptp():
continue
plt.scatter(ttime,
dist,
s=0.5,
zorder=-10,
color="black",
alpha=0.8)
plt.text(
max(ttime) + 0.005 * times_array.ptp(),
dist_max - 0.02 * (dist_max - dist_min), key)
plt.ylim(dist_min, dist_max)
plt.xlim(times_array[0], times_array[-1])
plt.title(event.short_str())
plt.show()
