def _plotResults(self):
"""
Plots original, filtered original and real time processed traces into
a single plot.
"""
# plot only if test is started manually
if __name__ != '__main__':
return
# create empty stream
st = Stream()
st.label = self._testMethodName
# original trace
self.orig_trace.label = "Original Trace"
st += self.orig_trace
# use header information of original trace with filtered trace data
tr = self.orig_trace.copy()
tr.data = self.filt_trace_data
tr.label = "Filtered original Trace"
st += tr
# real processed chunks
for i, tr in enumerate(self.rt_appended_traces):
tr.label = "RT Chunk %02d" % (i + 1)
st += tr
# real time processed trace
self.rt_trace.label = "RT Trace"
st += self.rt_trace
st.plot(automerge=False, color='blue', equal_scale=False)
def _plot_results(self):
"""
Plots original, filtered original and real time processed traces into
a single plot.
"""
# plot only if test is started manually
if __name__ != '__main__':
return
# create empty stream
st = Stream()
st.label = self._testMethodName
# original trace
self.orig_trace.label = "Original Trace"
st += self.orig_trace
# use header information of original trace with filtered trace data
tr = self.orig_trace.copy()
tr.data = self.filt_trace_data
tr.label = "Filtered original Trace"
st += tr
# real processed chunks
for i, tr in enumerate(self.rt_appended_traces):
tr.label = "RT Chunk %02d" % (i + 1)
st += tr
# real time processed trace
self.rt_trace.label = "RT Trace"
st += self.rt_trace
st.plot(automerge=False, color='blue', equal_scale=False)
def merger(noise_trace, st_events_poisson, samp_rate, delta):
""" Merges the noise with the events """
# Creates the stream with the noise and events
newnoise_t_formerging = noise_trace.slice(0, (st_events_poisson[-1].stats.endtime)+100)
seis = Stream()
seis += newnoise_t_formerging
seis += st_events_poisson
figs = plt.figure(figsize=(14,5))
seis.plot(fig = figs)
figs.suptitle("Noise and events", fontsize = 12)
ylab=figs.text(0.05, 0.5, 'Amplitude of signal', va='center',
rotation='vertical', fontsize=12)
figs.text(0.5, 0, 'Timestamp', ha='center', fontsize=12)
seis.merge()
seis += st_events_poisson
seis.merge(method = 1, interpolation_samples=-1)
figs = plt.figure(figsize=(14,5))
seis.plot(fig = figs)
plt.title("Synthetic seismogram", fontsize = 12)
ylab=figs.text(0.05, 0.5, 'Amplitude of signal', va='center',
rotation='vertical', fontsize=12)
figs.text(0.5, 0, 'Timestamp', ha='center', fontsize=12)
syn_seis = seis[0]
syn_seis.stats.sampling_rate = samp_rate
syn_seis.stats.delta = delta
return syn_seis
model_Pangles = [25]
model_Sangles = [26]
n = 0
for a in model_Pangles:
print('P')
stP = stP_og.copy()
hhQ, hhL = rotate(stP[1].data, stP[2].data, a)
t1, t2, t3 = Trace(stP[0].data, header=headerP), Trace(
hhQ, header=headerP), Trace(hhL, header=headerP)
stP_LQ = Stream(traces=[t1, t2, t3])
stP_LQ[0].stats.component = 'T'
stP_LQ[1].stats.component = 'Q'
stP_LQ[2].stats.component = 'L'
stP_LQ.plot(equal_scale=True)
n += 1
#S-wave
n = 0
for a in model_Sangles:
print('S')
stS = stS_og.copy()
hhQ, hhL = rotate(stS[1].data, stS[2].data, a)
t1, t2, t3 = Trace(stS[0].data, header=headerS), Trace(
hhQ, header=headerS), Trace(hhL, header=headerS)
stS_LQ = Stream(traces=[t1, t2, t3])
stS_LQ[0].stats.component = 'T'
stS_LQ[1].stats.component = 'Q'
stS_LQ[2].stats.component = 'L'
mseed_storage=data_path + "waveforms/USC/",
stationxml_storage=data_path + "stations/")
#%% Read in a couple test waveforms
plots = False
HNx_st = Stream()
LNx_st = Stream()
# High Broad Band (H??)
HNx_st += read(data_path + "waveforms/USC/*HN*.mseed")
# Long Period (L??)
LNx_st += read(data_path + "waveforms/USC/*LN*.mseed")
if plots:
HNx_st.plot()
LNx_st.plot()
#%% Try to get Arias intensity ...
HNx_st = HNx_st.detrend()
LNx_st = LNx_st.detrend()
HNx_arias = get_arias(HNx_st)
LNx_arias = get_arias(LNx_st)
#%% Plot Arias
HNx_arias.plot()
LNx_arias.plot()
# ... Not what I expected ...
# Derp, needed to detrend at the very least
def plot(self):
stream = Stream()
stream += self.BW_obs.BW_stream.traces[0]
# stream += self.BW_obs.S_stream.traces[1]
# stream += self.BW_obs.S_stream.traces[2]
#
#
stream += self.BW_syn.BW_stream.traces[0]
# stream += self.BW_syn.S_stream.traces[1]
# stream += self.BW_syn.S_stream.traces[2]
#
stream.plot()
fig = plt.figure(figsize=(10, 12))
delta = self.BW_obs.P_stream.traces[0].meta.delta
p_time_array = np.arange(len(self.BW_obs.P_stream.traces[0].data)) * delta
s_time_array = np.arange(len(self.BW_obs.S_stream.traces[0].data)) * delta
start_P = int((self.BW_obs.start_P.timestamp - self.or_time.timestamp - 10) / delta)
end_P = int((self.BW_obs.start_P.timestamp - self.or_time.timestamp + 30) / delta)
start_S = int((self.BW_obs.start_S.timestamp - self.or_time.timestamp - 20) / delta)
end_S = int((self.BW_obs.start_S.timestamp - self.or_time.timestamp + 100) / delta)
ax1 = plt.subplot2grid((5, 1), (0, 0))
plt.plot(p_time_array[start_P:end_P], self.BW_obs.P_stream.traces[0].data[start_P:end_P], 'b', label='Observed')
plt.plot(p_time_array[start_P:end_P], self.BW_syn.P_stream.traces[0].data[start_P:end_P], 'r',
label='Synthetic')
ymin, ymax = ax1.get_ylim()
xmin, xmax = ax1.get_xlim()
plt.text(xmax - 5, ymax / 1.7, "P-Z", fontsize=20, color='b')
ax1.ticklabel_format(style="sci", axis='y', scilimits=(-2, 2))
ax1.tick_params(axis='x', labelsize=18)
ax1.tick_params(axis='y', labelsize=18)
plt.tight_layout()
plt.legend(loc='lower left', fontsize=15)
ax2 = plt.subplot2grid((5, 1), (1, 0))
plt.plot(p_time_array[start_P:end_P], self.BW_obs.P_stream.traces[1].data[start_P:end_P], 'b')
plt.plot(p_time_array[start_P:end_P], self.BW_syn.P_stream.traces[1].data[start_P:end_P], 'r')
ymin, ymax = ax2.get_ylim()
xmin, xmax = ax2.get_xlim()
plt.text(xmax - 5, ymax / 1.7, "P-R", fontsize=20, color='b')
ax2.ticklabel_format(style="sci", axis='y', scilimits=(-2, 2))
ax2.tick_params(axis='x', labelsize=18)
ax2.tick_params(axis='y', labelsize=18)
plt.tight_layout()
ax3 = plt.subplot2grid((5, 1), (2, 0))
plt.plot(s_time_array[start_S:end_S], self.BW_obs.S_stream.traces[0].data[start_S:end_S], 'b')
plt.plot(s_time_array[start_S:end_S], self.BW_syn.S_stream.traces[0].data[start_S:end_S], 'r')
ymin, ymax = ax3.get_ylim()
xmin, xmax = ax3.get_xlim()
plt.text(xmax - 10, ymax / 1.7, "S-Z", fontsize=20, color='b')
ax3.ticklabel_format(style="sci", axis='y', scilimits=(-2, 2))
ax3.tick_params(axis='x', labelsize=18)
ax3.tick_params(axis='y', labelsize=18)
plt.tight_layout()
ax4 = plt.subplot2grid((5, 1), (3, 0))
plt.plot(s_time_array[start_S:end_S], self.BW_obs.S_stream.traces[1].data[start_S:end_S], 'b')
plt.plot(s_time_array[start_S:end_S], self.BW_syn.S_stream.traces[1].data[start_S:end_S], 'r')
ymin, ymax = ax4.get_ylim()
xmin, xmax = ax4.get_xlim()
plt.text(xmax - 10, ymax / 1.7, "S-R", fontsize=20, color='b')
ax4.ticklabel_format(style="sci", axis='y', scilimits=(-2, 2))
ax4.tick_params(axis='x', labelsize=18)
ax4.tick_params(axis='y', labelsize=18)
plt.tight_layout()
ax5 = plt.subplot2grid((5, 1), (4, 0))
plt.plot(s_time_array[start_S:end_S], self.BW_obs.S_stream.traces[2].data[start_S:end_S], 'b')
plt.plot(s_time_array[start_S:end_S], self.BW_syn.S_stream.traces[2].data[start_S:end_S], 'r')
ymin, ymax = ax5.get_ylim()
xmin, xmax = ax5.get_xlim()
plt.text(xmax - 10, ymax / 1.9, "S-T", fontsize=20, color='b')
ax5.ticklabel_format(style="sci", axis='y', scilimits=(-2, 2))
ax5.tick_params(axis='x', labelsize=18)
ax5.tick_params(axis='y', labelsize=18)
ax5.set_xlabel(self.BW_obs.start_P.strftime('From P arrival: %Y-%m-%dT%H:%M:%S + [sec]'), fontsize=18)
plt.tight_layout()
# plt.show()
plt.savefig(self.prior['save_dir'] + '/plots/%s_%i.png' % (self.prior['save_name'], self.i))
# plt.show()
plt.close()
'TOK.2011.328.21.10.54.OKR07.HHN.inv',
'TOK.2011.328.21.10.54.OKR08.HHN.inv',
'TOK.2011.328.21.10.54.OKR09.HHN.inv',
'TOK.2011.328.21.10.54.OKR10.HHN.inv'
]
# Earthquakes' epicenter
eq_lat = 35.565
eq_lon = -96.792
# Reading the waveforms
st = Stream()
for waveform in files:
st += read(host + waveform)
# Calculating distance from SAC headers lat/lon
# (trace.stats.sac.stla and trace.stats.sac.stlo)
for tr in st:
tr.stats.distance = gps2DistAzimuth(tr.stats.sac.stla, tr.stats.sac.stlo,
eq_lat, eq_lon)[0]
# Setting Network name for plot title
tr.stats.network = 'TOK'
st.filter('bandpass', freqmin=0.1, freqmax=10)
# Plot
st.plot(type='section',
plot_dx=20e3,
recordlength=100,
time_down=True,
linewidth=.25,
grid_linewidth=.25)
axs.append(fig.add_axes([0.83, 0.1, 0.03, 0.8]))
#fig, axs = plt.subplots(n_chans)
st = Stream()
for i, channel in enumerate(channels):
channel_path = os.path.join(station_path, channel)
chan = read(channel_path)
st.append(chan[0])
ax2 = chan.spectrogram(title='Canal: %s' % (channel),
show=False,
axes=axs[i],
cmap=j)
#print(ax2[0].images[0])
#canvas = FigureCanvas(fig2)
#image = np.fromstring(canvas.tostring_rgb(), dtype='uint8')
#print(list(axs[0]y.get_images()))
mappable = ax2[0].images[0]
plt.colorbar(mappable=mappable, cax=axs[-1])
try:
if not path.exists(
os.path.join(station_path, 'seismograms_plot.png')):
st.plot(
outfile=os.path.join(station_path, 'seismograms_plot.png'))
if path.exists(os.path.join(station_path,
'spectrograms_plot.png')):
os.remove(os.path.join(station_path, 'spectrograms_plot.png'))
plt.savefig(os.path.join(station_path, 'spectrograms_plot.png'))
except IndexError:
print("No data for station %s" % (station))
plt.clf()
plt.close()
from obspy.core.util import gps2DistAzimuth
host = 'http://examples.obspy.org/'
# Files (fmt: SAC)
files = ['TOK.2011.328.21.10.54.OKR01.HHN.inv',
'TOK.2011.328.21.10.54.OKR02.HHN.inv', 'TOK.2011.328.21.10.54.OKR03.HHN.inv',
'TOK.2011.328.21.10.54.OKR04.HHN.inv', 'TOK.2011.328.21.10.54.OKR05.HHN.inv',
'TOK.2011.328.21.10.54.OKR06.HHN.inv', 'TOK.2011.328.21.10.54.OKR07.HHN.inv',
'TOK.2011.328.21.10.54.OKR08.HHN.inv', 'TOK.2011.328.21.10.54.OKR09.HHN.inv',
'TOK.2011.328.21.10.54.OKR10.HHN.inv']
# Earthquakes' epicenter
eq_lat = 35.565
eq_lon = -96.792
# Reading the waveforms
st = Stream()
for waveform in files:
st += read(host + waveform)
# Calculating distance from SAC headers lat/lon
# (trace.stats.sac.stla and trace.stats.sac.stlo)
for tr in st:
tr.stats.distance = gps2DistAzimuth(tr.stats.sac.stla,
tr.stats.sac.stlo, eq_lat, eq_lon)[0]
# Setting Network name for plot title
tr.stats.network = 'TOK'
st.filter('bandpass', freqmin=0.1, freqmax=10)
# Plot
st.plot(type='section', plot_dx=20e3, recordlength=100,
time_down=True, linewidth=.25, grid_linewidth=.25)