def get_waveforms():
events = get_events()
client = ArcClient()
wforms = Stream()
for event in events:
t = event.preferred_origin().time
args = seed_id.split('.') + [t + 5 * 60, t + 14 * 60]
wforms.extend(client.getWaveform(*args))
wforms.decimate(int(round(wforms[0].stats.sampling_rate)) // 5,
no_filter=True)
wforms.write(wavname, wavformat)
fig = plt.figure(1, figsize=(12, 12))
plt.subplots_adjust(hspace=0.001)
for idx, chan in enumerate(chans):
st = Stream()
for s in range(1, 9):
if debug:
print('On ' + str(s) + ' ' + chan)
st += read(string + str(s) + '/' + chan + '.D/XX*')
stime = UTCDateTime('2017-05-03T01:10:00.0')
etime = UTCDateTime('2017-05-03T02:10:00.0')
st.trim(starttime=stime, endtime=etime)
nsHF, psHF, fHF = selfnoise(st)
st.decimate(5)
st.decimate(2)
st.decimate(5)
print(st)
ns, ps, f = selfnoise(st)
#st.plot()
if chan == 'HHE':
sts2chan = 'BH2'
elif chan == 'HHN':
sts2chan = 'BH1'
elif chan == 'HHZ':
sts2chan = 'BH0'
#st= read(curfile)
#if idx == 0:
#ppsd = PPSD(st[0].stats, paz)
#ppsd.add(st)
#ppsd.save_npz(st[0].id + 'PDF.npz')
debug = True
string = 'newdata/output_raw/2017/XX/FF'
pazNEW = corn_freq_2_paz(4.9, .96)
pazNEW['sensitivity'] = 75900.
chans = ['HHZ', 'HHE', 'HHN']
for idx, chan in enumerate(chans):
st = Stream()
for s in range(1, 9):
if debug:
print('On ' + str(s) + ' ' + chan)
st += read(string + str(s) + '/' + chan + '.D/XX*')
stime = UTCDateTime('2017-05-03T01:10:00.0')
etime = UTCDateTime('2017-05-04T01:10:00.0')
st.trim(starttime=stime, endtime=etime)
st.decimate(4)
st.decimate(2)
print(st)
ppsd = PPSD(st[0].stats, pazNEW)
ppsd.add(st)
ppsd.save_npz(st[0].id + 'PDF.npz')
# Create a stream just with winds that overlap SP data
st_windoverlap = Stream()
for tr in st_SP:
stime = tr.stats[
'starttime'] + 2. # strip out first two seconds with transient
etime = tr.stats['endtime']
st_temp = read(windsdatafile, starttime=stime,
endtime=etime).select(channel='VWS')
st_windoverlap += st_temp
st_windoverlap = st_windoverlap.merge().split()
# (st_windoverlap+st_SP).plot(method='full', equal_scale=False)
# One more plot with original
# (st_windoverlap+st_SP_IR).plot(method='full', equal_scale=False)
st_windoverlap.decimate(15) #reduce sampling to 30 seconds
# (st_windoverlap+st_SP).plot(method='full', equal_scale=False)
print(st_windoverlap)
# Test comparison
# Initiate figure instance and clear csv file for output
fig = plt.figure()
csvfile = 'WS_SPviking_rms.csv'
with open(csvfile, 'w') as f: # This should empty the file and add headers
writer = csv.writer(f)
writer.writerow(["RMS wind speed", "RMS SP Viking output"])
for tr in tqdm(st_windoverlap):
stime = tr.stats['starttime']
etime = tr.stats['endtime']
st_SPtemp = st_SP.copy().trim(starttime=stime, endtime=etime)
ctime = stime
st = Stream()
#while ctime <= etime:
# string = '/tr1/telemetry_days/IU_' + sta + '/' + str(ctime.year) + '/' + \
# str(ctime.year) + '_' + str(ctime.julday).zfill(3) + '/*'
# st += read(string + chans + '_LH*')
# st += read(string + presloc + '_LDO*')
# ctime += 24.*60.*60.
st = read('cordata')
#st += client.get_waveforms(net, sta, "00","LH*", stime, etime)
#st += client.get_waveforms(net, sta, "30","LDO", stime, etime)
st.detrend('constant')
st.merge(fill_value=0)
st.decimate(5)
st.decimate(2)
st.decimate(6)
st.decimate(6)
# Convert to velocity
st.attach_response(inv)
# We now have the data and the metadata so we should rotate it and do particle motion
st.rotate(method="->ZNE", inventory=inv)
st.select(channel="LH*").remove_response(output='ACC')
st.filter('bandpass', freqmin=0.03 / 1000., freqmax=0.04 / 1000.)
st.taper(0.05)
if debug:
print(st)
