# add the inventory for all components + all time of this tation
if (not ds.waveforms.list()):
ds.add_stationxml(sta_inv)
try:
# get data
t0 = time.time()
tr = client.get_waveforms(network=net[ista],station=sta[ista],\
channel=chan[ista],location=location[ista],starttime=s1,endtime=s2)
t1 = time.time()
except Exception as e:
print(e)
continue
# preprocess to clean data
tr = noise_module.preprocess_raw(tr, sta_inv, prepro_para,
date_info)
t2 = time.time()
if len(tr):
new_tags = '{0:s}_{1:s}'.format(chan[ista].lower(),
location[ista].lower())
print(new_tags)
ds.add_waveforms(tr, tag=new_tags)
if flag:
print(ds)
print('downloading data %6.2f s; pre-process %6.2f s' %
((t1 - t0), (t2 - t1)))
tt1 = time.time()
print('downloading step takes %6.2f s' % (tt1 - tt0))
comm.barrier()
source = obspy.Stream()
for ifile in tttfiles:
try:
tr = obspy.read(ifile)
for ttr in tr:
source.append(ttr)
except Exception as inst:
print(inst);continue
# jump if no good data left
if not len(source):continue
# make inventory to save into ASDF file
t1=time.time()
inv1 = noise_module.stats2inv(source[0].stats,prepro_para,locs=locs)
tr = noise_module.preprocess_raw(source,inv1,prepro_para,date_info)
if np.all(tr[0].data==0):continue
t2 = time.time()
if flag:print('pre-processing takes %6.2fs'%(t2-t1))
# jump if no good data left
if not len(tr):continue
# ready for output
ff=os.path.join(DATADIR,all_chunk[ick]+'T'+all_chunk[ick+1]+'.h5')
if not os.path.isfile(ff):
with pyasdf.ASDFDataSet(ff,mpi=False,compression="gzip-3",mode='w') as ds:
pass
with pyasdf.ASDFDataSet(ff,mpi=False,compression="gzip-3",mode='a') as ds:
# add the inventory for all components + all time of this tation
if flag:
print("working on trace " + all_tags[itag])
source = ds.waveforms[temp[0]][all_tags[itag]]
comp = source[0].stats.channel
if prepro:
if all_tags[itag].split('_')[0] != 'raw':
#raise ValueError('it appears pre-processing has been performed!')
print(
'warning! it appears pre-processing has been performed!'
)
t0 = time.time()
source = noise_module.preprocess_raw(
source, inv1, downsamp_freq, checkt, pre_filt,
resp, resp_dir)
t1 = time.time()
if flag:
print("prepro takes %f s" % (t1 - t0))
#----------variables to define days with earthquakes----------
all_madS = noise_module.mad(source[0].data)
all_stdS = np.std(source[0].data)
if all_madS == 0 or all_stdS == 0 or np.isnan(
all_madS) or np.isnan(all_stdS):
print("continue! madS or stdS equeals to 0 for %s" %
source)
continue
trace_madS = []
try:
# get data
t0 = time.time()
tr = client.get_waveforms(network=K.code, station=sta.code, channel=chan.code, location='*', \
starttime = t1, endtime=t2)
t1 = time.time()
except Exception as e:
print(e)
continue
if len(tr):
# clean up data
t2 = time.time()
tr = noise_module.preprocess_raw(
tr, NewFreq, checkt, pre_filt, resp, respdir)
t3 = time.time()
# only keep the one with good data after processing
if len(tr) > 0:
if len(tr) == 1:
new_tags = tags+'_{0:04d}_{1:02d}_{2:02d}_{3}'.format(tr[0].stats.starttime.year,\
tr[0].stats.starttime.month,tr[0].stats.starttime.day,chan.code.lower())
ds.add_waveforms(tr, tag=new_tags)
else:
for ii in range(len(tr)):
new_tags = tags+'_{0:04d}_{1:02d}_{2:02d}_{3}'.format(tr[ii].stats.starttime.year,\
tr[ii].stats.starttime.month,tr[ii].stats.starttime.day,chan.code.lower())
ds.add_waveforms(tr[ii], tag=new_tags)
if flag:
if len(bigsource) == 0:
continue
print(bigsource)
#---------make an inventory---------
inv1 = noise_module.stats2inv(bigsource[0].stats,
locs=locs)
#------------Pre-Processing-----------
#source = obspy.Stream()
bigsource = bigsource.merge(method=1,
fill_value='interpolate') #[0]
bigsource = noise_module.preprocess_raw(bigsource,
downsamp_freq,
clean_time=True,
pre_filt=None,
resp=None,
respdir=None)
print(bigsource)
with pyasdf.ASDFDataSet(data_h5,
mpi=False,
compression=None) as fft_ds:
fft_ds.add_stationxml(inv1)
for ii in range(len(bigsource)):
print((bigsource[ii]))
if isinstance(bigsource[ii], np.float32):
continue
fft_ds.add_waveforms(bigsource[ii],
tag='waveforms')
print(bigsource[ii].stats.starttime)