def getCatData(date, opt):
"""
Download data from IRIS or Earthworm waveserver with padding and filter it. This is
a specialized version getData() for catalog events, pulling a smaller amount of time
around a known event.
date: UTCDateTime of known catalog event
opt: Options object describing station/run parameters
Returns ObsPy stream objects, one for cutting and the other for triggering
"""
nets = opt.network.split(',')
stas = opt.station.split(',')
locs = opt.location.split(',')
chas = opt.channel.split(',')
if opt.server == "IRIS":
client = Client("IRIS")
else:
client = EWClient(opt.server, opt.port)
st = Stream()
for n in range(len(stas)):
try:
stmp = client.get_waveforms(nets[n], stas[n], locs[n], chas[n],
date - opt.atrig, date + 3*opt.atrig)
stmp = stmp.filter("bandpass", freqmin=opt.fmin, freqmax=opt.fmax,
corners=2, zerophase=True)
stmp = stmp.merge(method=1, fill_value='interpolate')
except (obspy.fdsn.header.FDSNException):
try: # try again
stmp = client.get_waveforms(nets[n], stas[n], locs[n], chas[n],
date - opt.atrig, date + 3*opt.atrig)
stmp = stmp.filter("bandpass", freqmin=opt.fmin, freqmax=opt.fmax,
corners=2, zerophase=True)
stmp = stmp.merge(method=1, fill_value='interpolate')
except (obspy.fdsn.header.FDSNException):
print('No data found for {0}.{1}'.format(stas[n],nets[n]))
trtmp = Trace()
trtmp.stats.sampling_rate = opt.samprate
trtmp.stats.station = stas[n]
stmp = Stream().extend([trtmp.copy()])
# Resample to ensure all traces are same length
if stmp[0].stats.sampling_rate != opt.samprate:
stmp = stmp.resample(opt.samprate)
st.extend(stmp.copy())
st = st.trim(starttime=date-opt.atrig, endtime=date+3*opt.atrig, pad=True,
fill_value=0)
stC = st.copy()
return st, stC
def elab(self):
print('tremorStart ' + UTCDateTime.now().strftime("%Y%m%d %H%M%S"))
s = np.asarray(self.get_all_nslc())
appTrace = Stream()
stTrace = Stream()
self._elRunning = True
for network in np.unique(s[:, 0]):
for station in np.unique(s[:, 1]):
stTrace = self._traces.select(network, station)
elab = {
'ts': np.long(self._tEnd.strftime("%Y%m%d%H%M%S"))
}
# TREMOR
for tr in stTrace:
rms = {}
id = tr.get_id()
spl = id.split('.')
channel = spl[3]
elab[channel] = {}
tStart = self._tEnd - 60
appTrace = tr.copy()
appTrace.trim(tStart, self._tEnd)
appTrace.remove_response(self._inv)
for b in band:
bb = band[b]
trF = appTrace.copy()
trF.filter('bandpass', freqmin=bb[0], freqmax=bb[1], corners=2, zerophase=True)
rms[b] = np.sqrt(np.mean(trF.data ** 2))
# print(id+' '+str(b)+' '+str(rms))
elab[channel]['rms_' + b] = rms[b]
nTr=network + '_' + station
try:
self._elab[nTr][elab['ts']]=elab
self._elabHyst[nTr][elab['ts']] = elab
except:
self._elab[nTr]={}
self._elab[nTr][elab['ts']] = elab
self._elabHyst[nTr] = {}
self._elabHyst[nTr][elab['ts']] = elab
#pulisco e slavo
m=np.long((self._tEnd-1440*60).strftime("%Y%m%d%H%M%S"))
mm=np.min(list(self._elab[nTr].keys()))
if mm<m:
self._elab[nTr].pop(mm)
for e in self._elab:
filename = basePath + 'RT/ELAB_' + e + '.json'
with open(filename, 'w') as fp:
json.dump(list(self._elab[e].values()), fp)
fp.close()
#np.savez('elSave',h=self._elabHyst,e=self._elab)
self._elRunning = False
def copy(orig):
"""
True-copy a stream by creating a new stream and copying old attributes to it.
This is necessary because the old stream accumulates *something* that causes
CPU usage to increase over time as more data is added. This is a bug in obspy
that I intend to find--or at the very least report--but until then this hack
works fine and is plenty fast enough.
In this example, we make a stream object with some RS 1Dv7 data and then copy it to a new stream:
.. code-block:: python
>>> import rsudp.raspberryshake as rs
>>> from obspy.core.stream import Stream
>>> rs.initRSlib(dport=8888, rsstn='R3BCF')
>>> s = Stream()
>>> d = rs.getDATA()
>>> t = rs.make_trace(d)
>>> s = rs.update_stream(s, d)
>>> s
1 Trace(s) in Stream:
AM.R3BCF.00.EHZ | 2020-02-21T19:58:50.292000Z - 2020-02-21T19:58:50.532000Z | 100.0 Hz, 25 samples
>>> s = rs.copy(s)
>>> s
1 Trace(s) in Stream:
AM.R3BCF.00.EHZ | 2020-02-21T19:58:50.292000Z - 2020-02-21T19:58:50.532000Z | 100.0 Hz, 25 samples
:param obspy.core.stream.Stream orig: The data stream to copy information from
:rtype: obspy.core.stream.Stream
:return: A low-memory copy of the passed data stream
"""
stream = Stream()
for t in range(len(orig)):
trace = Trace(data=orig[t].data)
trace.stats.network = orig[t].stats.network
trace.stats.location = orig[t].stats.location
trace.stats.station = orig[t].stats.station
trace.stats.channel = orig[t].stats.channel
trace.stats.sampling_rate = orig[t].stats.sampling_rate
trace.stats.starttime = orig[t].stats.starttime
stream.append(trace).merge(fill_value=None)
return stream.copy()
def copy(orig):
"""
True copy a stream by creating a new stream and copying old attributes to it.
This is necessary because the old stream accumulates *something* that causes
CPU usage to increase over time as more data is added. This is a bug in obspy
that I intend to find--or at the very least report--but until then this hack
works fine.
"""
stream = Stream()
for t in range(len(orig)):
trace = Trace(data=orig[t].data)
trace.stats.network = orig[t].stats.network
trace.stats.location = orig[t].stats.location
trace.stats.station = orig[t].stats.station
trace.stats.channel = orig[t].stats.channel
trace.stats.sampling_rate = orig[t].stats.sampling_rate
trace.stats.starttime = orig[t].stats.starttime
stream.append(trace).merge(fill_value=None)
return stream.copy()
def getData(tstart, tend, opt):
"""
Download data from files in a folder, from IRIS, or a Earthworm waveserver
A note on SAC/miniSEED files: as this makes no assumptions about the naming scheme of
your data files, please ensure that your headers contain the correct SCNL information!
tstart: UTCDateTime of beginning of period of interest
tend: UTCDateTime of end of period of interest
opt: Options object describing station/run parameters
Returns ObsPy stream objects, one for cutting and the other for triggering
"""
nets = opt.network.split(',')
stas = opt.station.split(',')
locs = opt.location.split(',')
chas = opt.channel.split(',')
st = Stream()
if opt.server == 'file':
# Generate list of files
if opt.server == 'file':
flist = list(itertools.chain.from_iterable(glob.iglob(os.path.join(
root,opt.filepattern)) for root, dirs, files in os.walk(opt.searchdir)))
# Determine which subset of files to load based on start and end times and
# station name; we'll fully deal with stations below
flist_sub = []
for f in flist:
# Load header only
stmp = obspy.read(f, headonly=True)
# Check if station is contained in the stas list
if stmp[0].stats.station in stas:
# Check if contains either start or end time
ststart = stmp[0].stats.starttime
stend = stmp[-1].stats.endtime
if (ststart<=tstart and tstart<=stend) or (ststart<=tend and
tend<=stend) or (tstart<=stend and ststart<=tend):
flist_sub.append(f)
# Fully load data from file
stmp = Stream()
for f in flist_sub:
tmp = obspy.read(f, starttime=tstart, endtime=tend+opt.maxdt)
if len(tmp) > 0:
stmp = stmp.extend(tmp)
# Filter and merge
stmp = stmp.filter('bandpass', freqmin=opt.fmin, freqmax=opt.fmax, corners=2,
zerophase=True)
stmp = stmp.taper(0.05,type='hann',max_length=opt.mintrig)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != opt.samprate:
stmp[m] = stmp[m].resample(opt.samprate)
stmp = stmp.merge(method=1, fill_value=0)
# Only grab stations/channels that we want and in order
netlist = []
stalist = []
chalist = []
loclist = []
for s in stmp:
stalist.append(s.stats.station)
chalist.append(s.stats.channel)
netlist.append(s.stats.network)
loclist.append(s.stats.location)
# Find match of SCNL in header or fill empty
for n in range(len(stas)):
for m in range(len(stalist)):
if (stas[n] in stalist[m] and chas[n] in chalist[m] and nets[n] in
netlist[m] and locs[n] in loclist[m]):
st = st.append(stmp[m])
if len(st) == n:
print("Couldn't find "+stas[n]+'.'+chas[n]+'.'+nets[n]+'.'+locs[n])
trtmp = Trace()
trtmp.stats.sampling_rate = opt.samprate
trtmp.stats.station = stas[n]
st = st.append(trtmp.copy())
else:
if '.' not in opt.server:
client = Client(opt.server)
else:
client = EWClient(opt.server, opt.port)
for n in range(len(stas)):
try:
stmp = client.get_waveforms(nets[n], stas[n], locs[n], chas[n],
tstart, tend+opt.maxdt)
for m in range(len(stmp)):
stmp[m].data = np.where(stmp[m].data == -2**31, 0, stmp[m].data) # replace -2**31 (Winston NaN token) w 0
stmp = stmp.filter('bandpass', freqmin=opt.fmin, freqmax=opt.fmax,
corners=2, zerophase=True)
stmp = stmp.taper(0.05,type='hann',max_length=opt.mintrig)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != opt.samprate:
stmp[m] = stmp[m].resample(opt.samprate)
stmp = stmp.merge(method=1, fill_value=0)
except (obspy.clients.fdsn.header.FDSNException):
try: # try again
stmp = client.get_waveforms(nets[n], stas[n], locs[n], chas[n],
tstart, tend+opt.maxdt)
for m in range(len(stmp)):
stmp[m].data = np.where(stmp[m].data == -2**31, 0, stmp[m].data) # replace -2**31 (Winston NaN token) w 0
stmp = stmp.filter('bandpass', freqmin=opt.fmin, freqmax=opt.fmax,
corners=2, zerophase=True)
stmp = stmp.taper(0.05,type='hann',max_length=opt.mintrig)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != opt.samprate:
stmp[m] = stmp[m].resample(opt.samprate)
stmp = stmp.merge(method=1, fill_value=0)
except (obspy.clients.fdsn.header.FDSNException):
print('No data found for {0}.{1}'.format(stas[n],nets[n]))
trtmp = Trace()
trtmp.stats.sampling_rate = opt.samprate
trtmp.stats.station = stas[n]
stmp = Stream().extend([trtmp.copy()])
# Last check for length; catches problem with empty waveserver
if len(stmp) != 1:
print('No data found for {0}.{1}'.format(stas[n],nets[n]))
trtmp = Trace()
trtmp.stats.sampling_rate = opt.samprate
trtmp.stats.station = stas[n]
stmp = Stream().extend([trtmp.copy()])
st.extend(stmp.copy())
# Edit 'start' time if using offset option
if opt.maxdt:
dts = np.fromstring(opt.offset, sep=',')
for n, tr in enumerate(st):
tr.stats.starttime = tr.stats.starttime-dts[n]
st = st.trim(starttime=tstart, endtime=tend, pad=True, fill_value=0)
stC = st.copy()
return st, stC
def get_PGMs(tr,args):
ta=Stream()
ta=tr.copy()
ts=tr.copy()
for i in range(len(ta)):
m_dis=0
m_vel=0
m_acc=0
#### Displacement
if abs(max(ta[i])) >= abs(min(ta[i])):
m_dis=abs(max(ta[i]))
else:
m_dis=abs(min(ta[i]))
#### Velocity
ta[i].data = np.gradient(ta[i].data,ta[i].stats['delta'])
if abs(max(ta[i])) >= abs(min(ta[i])):
m_vel=abs(max(ta[i]))
else:
m_vel=abs(min(ta[i]))
ts[i].data = ta[i].data
#### Acceleration
ta[i].data = np.gradient(ta[i].data,ta[i].stats['delta'])
if abs(max(ta[i])) >= abs(min(ta[i])):
m_acc=abs(max(ta[i]))
else:
m_acc=abs(min(ta[i]))
#store obtained pgms
tr[i].stats['max_dis'] = m_dis
tr[i].stats['max_vel'] = m_vel
tr[i].stats['max_acc'] = m_acc
#define vectrors for Hz, T and G
sa=args.sa.split(' ')
spa=[]
for l in range(len(sa)-1):
spa.append(0)
per=[]
for l in range(len(sa)-1):
per.append(0)
ges=[]
for l in range(len(sa)-1):
ges.append(0)
#now for each value of sa convolve with response of pendulum
for j in range(len(sa)):
#apply convolution
if j >= 1:
tu=ta.copy()
T=eval(sa[j])*1.0
D=eval(sa[0])
Ts = '%5.3f' % (1/T)
omega = (2 * 3.14159 * T)**2
paz_sa=cornFreq2Paz(T,damp=D)
paz_sa['sensitivity'] =omega
paz_sa['zeros'] = []
for n in range(len(tu)):
tu[n].simulate(paz_remove=None,paz_simulate=paz_sa,taper=True, simulate_sensitivity=True, taper_fraction=0.050000000000000003)
per[j-1] = Ts
# #now measure for each i
for i in range(len(tu)):
if abs(max(tu[i])) >= abs(min(tu[i])):
val=abs(max(tu[i]))
else:
val=abs(min(tu[i]))
g=val/9.80665*100
g='%10.3e' % (g)
val='%10.3e' % (val)
#here give spectral acceleration in standard units m/s^2
# and not in g (suitable only for shakemap, can be
# later converted
tr[i]=UpdatePsaHeader(tr[i],j,val)
for i in range(len(tr)):
tr[i].stats['Tsa'] = per
return tr
# Data features
# fs_dat = 100.0
fs_dat = check_stream_sampling_rate(HNx_st) # 100Hz
npts_dat = check_stream_npts(HNx_st) # Variable
t_dat = check_stream_t(HNx_st) # Variable
# Calculated values based on data and targets
new_npts = npts_dat
""" Check out the different tapers that can be used for Lanczos interpolation
for a particular value of 'a' plot_lanczos_windows(a=a)
"""
plot_lanczos_windows(a=a)
# plot_lanczos_windows(a=a, filename=fig_path+"lanczos_windows_a="+str(a)+".png")
# Is this easier to do in a loop (check values for each trace?)
HNx_resamp = HNx_st.copy()
HNx_resamp = HNx_resamp.interpolate(fs_dat,
"lanczos",
a=a,
window=windows[0])
#%% Plot this
plots = False
if plots:
HNx_resamp.plot()
for tr in HNx_resamp:
tr.plot()
#%% Check stream histograms
def get_stream(datasource,
scnl,
tstart,
tend,
fill_value=0,
filepattern='*',
filter=None,
samprate=100,
verbose=False):
"""
Generalized (and more robust) way to retrieve waveform data through ObsPy
Download data from files in a folder, from IRIS, or a Earthworm waveserver
A note on SAC/miniSEED files: as this makes no assumptions about the naming scheme of
your data files, please ensure that your headers contain the correct SCNL information!
tstart: UTCDateTime of beginning of period of interest
tend: UTCDateTime of end of period of interest
filepattern='*'
You can specify a pattern for your files to reduce the files within the directory
searched. For example, filepattern=2019.06.*.mseed if your files are miniSEED files
named by date and you only want those from June 2019. Simple wildcarding is supported
(i.e., * and ?, [] for ranges of values or lists) but not full regular expressions.
samprate=100
Resamples all waveforms to the same sample rate.
Returns ObsPy stream objects
Based on code by Alicia Hotovec-Ellis and Aaron Wech.
Example:
>>> get_stream(['vdap.org', 16024], ['HSR.EHZ.CC.--'], '2004-09-28T00:00:00', '2004-09-28T01:00:00')
>>> get_stream(['file', '/Users/vdapseismo/data/'], ['HSR.EHZ.CC.--'], '2004-09-28T00:00:00', '2004-09-28T01:00:00')
>>> get_stream(['IRIS'], ['HSR.EHZ.CC.--'], '2004-09-28T00:00:00', '2004-09-28T01:00:00')
"""
from obspy import UTCDateTime
import obspy
from obspy.clients.fdsn import Client
from obspy.clients.earthworm import Client as EWClient
from obspy.core.trace import Trace
from obspy.core.stream import Stream
from obspy.signal.trigger import coincidence_trigger
import numpy as np
from scipy import stats
from scipy.fftpack import fft
import glob, os, itertools
#print(datasource)
#print(scnl)
#print(tstart)
#print(tend)
tstart = UTCDateTime(tstart)
tend = UTCDateTime(tend)
nets = []
stas = []
locs = []
chas = []
for s in scnl:
#print(s)
nets.append(s.split('.')[2])
stas.append(s.split('.')[0])
locs.append(s.split('.')[3])
chas.append(s.split('.')[1])
st = Stream()
if '/' in datasource:
# Retrieve data from file structure
flist = list(
itertools.chain.from_iterable(
glob.iglob(os.path.join(root, filepattern))
for root, dirs, files in os.walk(datasource)))
# Determine which subset of files to load based on start and end times and
# station name; we'll fully deal with stations below
flist_sub = []
for f in flist:
# Load header only
stmp = obspy.read(f, headonly=True)
# Check if station is contained in the stas list
if stmp[0].stats.station in stas:
# Check if contains either start or end time
ststart = stmp[0].stats.starttime
stend = stmp[0].stats.endtime
if (ststart <= tstart and tstart <= stend) or (
ststart <= tend
and tend <= stend) or (tstart <= stend
and ststart <= tend):
flist_sub.append(f)
# Fully load data from file
stmp = Stream()
for f in flist_sub:
tmp = obspy.read(f, starttime=tstart, endtime=tend)
if len(tmp) > 0:
stmp = stmp.extend(tmp)
# merge
stmp = stmp.taper(max_percentage=0.01)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != samprate:
stmp[m] = stmp[m].resample(samprate)
stmp = stmp.merge(method=1, fill_value=fill_value)
# Only grab stations/channels that we want and in order
netlist = []
stalist = []
chalist = []
loclist = []
for s in stmp:
stalist.append(s.stats.station)
chalist.append(s.stats.channel)
netlist.append(s.stats.network)
loclist.append(s.stats.location)
# Find match of SCNL in header or fill empty
for n in range(len(stas)):
for m in range(len(stalist)):
if (stas[n] in stalist[m] and chas[n] in chalist[m]
and nets[n] in netlist[m] and locs[n] in loclist[m]):
st = st.append(stmp[m])
if len(st) == n:
print('No data found for {}.{}.{}.{}'.format(
stas[n], chas[n], nets[n], locs[n]))
trtmp = Trace()
trtmp.stats.sampling_rate = samprate
trtmp.stats.station = stas[n]
st = st.append(trtmp.copy())
else:
# retrieve data from server
if '.' not in datasource:
client = Client(datasource)
else:
datasource = datasource.split(':')
client = EWClient(datasource[0], int(datasource[1]))
for n in range(len(stas)):
try:
stmp = client.get_waveforms(nets[n], stas[n], locs[n], chas[n],
tstart, tend)
for m in range(len(stmp)):
#stmp[m].data = np.ma.masked_where(stmp[m].data == -2**31, stmp[m].data) # masks out all values of -2**31 (Winston NaN Token)
#stmp[m] = stmp[m].split().merge(method=0, fill_value='interpolate')[0] # splits trace at masked values; then re-merges using linear interpolation
stmp[m].data = np.where(stmp[m].data == -2**31, 0,
stmp[m].data)
if stmp[m].stats.sampling_rate != samprate:
stmp[m] = stmp[m].resample(samprate)
stmp = stmp.taper(max_percentage=0.01)
stmp = stmp.merge(method=1, fill_value=fill_value)
except (obspy.clients.fdsn.header.FDSNException):
try: # try again
stmp = client.get_waveforms(nets[n], stas[n], locs[n],
chas[n], tstart, tend)
for m in range(len(stmp)):
#stmp[m].data = np.ma.masked_where(stmp[m].data == -2**31, stmp[m].data) # masks out all values of -2**31 (Winston NaN Token)
#stmp[m] = stmp[m].split().merge(method=0, fill_value='interpolate')[0] # splits trace at masked values; then re-merges using linear interpolation
stmp[m].data = np.where(stmp[m].data == -2**31, 0,
stmp[m].data)
if stmp[m].stats.sampling_rate != samprate:
stmp[m] = stmp[m].resample(samprate)
stmp = stmp.taper(max_percentage=0.01)
stmp = stmp.merge(method=1, fill_value=fill_value)
except (obspy.clients.fdsn.header.FDSNException):
print('No data found for {0}.{1}'.format(stas[n], nets[n]))
trtmp = Trace()
trtmp.stats.sampling_rate = samprate
trtmp.stats.station = stas[n]
stmp = Stream().extend([trtmp.copy()])
# Last check for length; catches problem with empty waveserver
if len(stmp) != 1:
print('No data found for {}.{}.{}.{}'.format(
stas[n], chas[n], nets[n], locs[n]))
trtmp = Trace()
trtmp.stats.sampling_rate = samprate
trtmp.stats.station = stas[n]
stmp = Stream().extend([trtmp.copy()])
st.extend(stmp.copy())
st = st.trim(starttime=tstart,
endtime=tend,
pad=True,
fill_value=fill_value)
return st
print(event.event_descriptions[0]['type'], ': ',event.event_descriptions[0]['text'])
c = arclinkClient(user='******')
start = event.origins[0].time
print('Origin time: ', start)
end = start + 3600
RLAS = c.get_waveforms(network='BW', station='RLAS', location='', channel='BJZ', starttime=start, endtime=end)
BHE = c.get_waveforms(network='GR', station='WET', location='', channel='BHE', starttime=start, endtime=end)
BHN = c.get_waveforms(network='GR', station='WET', location='', channel='BHN', starttime=start, endtime=end)
BHZ = c.get_waveforms(network='GR', station='WET', location='', channel='BHZ', starttime=start, endtime=end)
AC = Stream(traces=[BHE[0],BHN[0],BHZ[0]])
ac = AC.copy()
# **Remove the instrument responses of the instruments from the recordings + convert units**
# - convert Ring Laser recordings to nrad/s units using a conversion factor
# - remove the seismometer response using poles and zeros + convert from velocity to acceleration [nm/s^2] in one step
# - trim the traces to make sure start- and endtimes match for both instruments
# In[3]:
RLAS.detrend(type='linear')
RLAS[0].data = RLAS[0].data * 1/6.3191 * 1e-3
AC.detrend(type='linear')
AC.taper(max_percentage=0.05)
def getData(tstart, tend, opt):
"""
Download data from files in a folder, from IRIS, or a Earthworm waveserver
A note on SAC/miniSEED files: as this makes no assumptions about the naming scheme of
your data files, please ensure that your headers contain the correct SCNL information!
tstart: UTCDateTime of beginning of period of interest
tend: UTCDateTime of end of period of interest
opt: Options object describing station/run parameters
Returns ObsPy stream objects, one for cutting and the other for triggering
"""
nets = opt.network.split(',')
stas = opt.station.split(',')
locs = opt.location.split(',')
chas = opt.channel.split(',')
st = Stream()
if opt.server == 'SAC' or opt.server == 'miniSEED':
# Generate list of files
if opt.server == 'SAC':
flist = glob.glob(opt.sacdir + '*.sac') + glob.glob(opt.sacdir +
'*.SAC')
elif opt.server == 'miniSEED':
flist = glob.glob(opt.mseeddir +
'*.mseed') + glob.glob(opt.mseeddir + '*.MSEED')
# Load data from file
stmp = Stream()
for f in flist:
tmp = obspy.read(f, starttime=tstart, endtime=tend)
if len(tmp) > 0:
stmp = stmp.extend(tmp)
# Filter and merge
stmp = stmp.filter('bandpass',
freqmin=opt.fmin,
freqmax=opt.fmax,
corners=2,
zerophase=True)
stmp = stmp.taper(0.05, type='hann', max_length=opt.mintrig)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != opt.samprate:
stmp[m] = stmp[m].resample(opt.samprate)
stmp = stmp.merge(method=1, fill_value=0)
# Only grab stations/channels that we want and in order
netlist = []
stalist = []
chalist = []
loclist = []
for s in stmp:
stalist.append(s.stats.station)
chalist.append(s.stats.channel)
netlist.append(s.stats.network)
loclist.append(s.stats.location)
# Find match of SCNL in header or fill empty
for n in range(len(stas)):
for m in range(len(stalist)):
if (stas[n] in stalist[m] and chas[n] in chalist[m]
and nets[n] in netlist[m] and locs[n] in loclist[m]):
st = st.append(stmp[m])
if len(st) == n:
print("Couldn't find " + stas[n] + '.' + chas[n] + '.' +
nets[n] + '.' + locs[n])
trtmp = Trace()
trtmp.stats.sampling_rate = opt.samprate
trtmp.stats.station = stas[n]
st = st.append(trtmp.copy())
else:
if '.' not in opt.server:
client = Client(opt.server)
else:
client = EWClient(opt.server, opt.port)
for n in range(len(stas)):
try:
stmp = client.get_waveforms(nets[n], stas[n], locs[n], chas[n],
tstart, tend)
stmp = stmp.filter('bandpass',
freqmin=opt.fmin,
freqmax=opt.fmax,
corners=2,
zerophase=True)
stmp = stmp.taper(0.05, type='hann', max_length=opt.mintrig)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != opt.samprate:
stmp[m] = stmp[m].resample(opt.samprate)
stmp = stmp.merge(method=1, fill_value=0)
except (obspy.fdsn.header.FDSNException):
try: # try again
stmp = client.get_waveforms(nets[n], stas[n], locs[n],
chas[n], tstart, tend)
stmp = stmp.filter('bandpass',
freqmin=opt.fmin,
freqmax=opt.fmax,
corners=2,
zerophase=True)
stmp = stmp.taper(0.05,
type='hann',
max_length=opt.mintrig)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != opt.samprate:
stmp[m] = stmp[m].resample(opt.samprate)
stmp = stmp.merge(method=1, fill_value=0)
except (obspy.fdsn.header.FDSNException):
print('No data found for {0}.{1}'.format(stas[n], nets[n]))
trtmp = Trace()
trtmp.stats.sampling_rate = opt.samprate
trtmp.stats.station = stas[n]
stmp = Stream().extend([trtmp.copy()])
st.extend(stmp.copy())
st = st.trim(starttime=tstart, endtime=tend, pad=True, fill_value=0)
stC = st.copy()
return st, stC
def getData(tstart, tend, opt):
"""
Download data from files in a folder, from IRIS, or a Earthworm waveserver
A note on SAC/miniSEED files: as this makes no assumptions about the naming scheme of
your data files, please ensure that your headers contain the correct SCNL information!
tstart: UTCDateTime of beginning of period of interest
tend: UTCDateTime of end of period of interest
opt: Options object describing station/run parameters
Returns ObsPy stream objects, one for cutting and the other for triggering
"""
nets = opt.network.split(',')
stas = opt.station.split(',')
locs = opt.location.split(',')
chas = opt.channel.split(',')
st = Stream()
if opt.server == 'SAC' or opt.server == 'miniSEED':
# Generate list of files
if opt.server == 'SAC':
flist = list(itertools.chain.from_iterable(glob.iglob(os.path.join(
root,'*.sac')) for root, dirs, files in os.walk(opt.sacdir)))+list(
itertools.chain.from_iterable(glob.iglob(os.path.join(
root,'*.SAC')) for root, dirs, files in os.walk(opt.sacdir)))
elif opt.server == 'miniSEED':
flist = list(itertools.chain.from_iterable(glob.iglob(os.path.join(
root,'*.mseed')) for root, dirs, files in os.walk(opt.mseeddir)))+list(
itertools.chain.from_iterable(glob.iglob(os.path.join(
root,'*.MSEED')) for root, dirs, files in os.walk(opt.mseeddir)))
# Determine which subset of files to load based on start and end times and
# station name; we'll fully deal with stations below
flist_sub = []
for f in flist:
# Load header only
stmp = obspy.read(f, headonly=True)
# Check if station is contained in the stas list
if stmp[0].stats.station in stas:
# Check if contains either start or end time
ststart = stmp[0].stats.starttime
stend = stmp[0].stats.endtime
if (ststart<=tstart and tstart<=stend) or (ststart<=tend and
tend<=stend) or (tstart<=stend and ststart<=tend):
flist_sub.append(f)
# Fully load data from file
stmp = Stream()
for f in flist_sub:
tmp = obspy.read(f, starttime=tstart, endtime=tend+opt.maxdt)
if len(tmp) > 0:
stmp = stmp.extend(tmp)
# Filter and merge
stmp = stmp.filter('bandpass', freqmin=opt.fmin, freqmax=opt.fmax, corners=2,
zerophase=True)
stmp = stmp.taper(0.05,type='hann',max_length=opt.mintrig)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != opt.samprate:
stmp[m] = stmp[m].resample(opt.samprate)
stmp = stmp.merge(method=1, fill_value=0)
# Only grab stations/channels that we want and in order
netlist = []
stalist = []
chalist = []
loclist = []
for s in stmp:
stalist.append(s.stats.station)
chalist.append(s.stats.channel)
netlist.append(s.stats.network)
loclist.append(s.stats.location)
# Find match of SCNL in header or fill empty
for n in range(len(stas)):
for m in range(len(stalist)):
if (stas[n] in stalist[m] and chas[n] in chalist[m] and nets[n] in
netlist[m] and locs[n] in loclist[m]):
st = st.append(stmp[m])
if len(st) == n:
print("Couldn't find "+stas[n]+'.'+chas[n]+'.'+nets[n]+'.'+locs[n])
trtmp = Trace()
trtmp.stats.sampling_rate = opt.samprate
trtmp.stats.station = stas[n]
st = st.append(trtmp.copy())
else:
if '.' not in opt.server:
client = Client(opt.server)
else:
client = EWClient(opt.server, opt.port)
for n in range(len(stas)):
try:
stmp = client.get_waveforms(nets[n], stas[n], locs[n], chas[n],
tstart, tend+opt.maxdt)
stmp = stmp.filter('bandpass', freqmin=opt.fmin, freqmax=opt.fmax,
corners=2, zerophase=True)
stmp = stmp.taper(0.05,type='hann',max_length=opt.mintrig)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != opt.samprate:
stmp[m] = stmp[m].resample(opt.samprate)
stmp = stmp.merge(method=1, fill_value=0)
except (obspy.clients.fdsn.header.FDSNException):
try: # try again
stmp = client.get_waveforms(nets[n], stas[n], locs[n], chas[n],
tstart, tend+opt.maxdt)
stmp = stmp.filter('bandpass', freqmin=opt.fmin, freqmax=opt.fmax,
corners=2, zerophase=True)
stmp = stmp.taper(0.05,type='hann',max_length=opt.mintrig)
for m in range(len(stmp)):
if stmp[m].stats.sampling_rate != opt.samprate:
stmp[m] = stmp[m].resample(opt.samprate)
stmp = stmp.merge(method=1, fill_value=0)
except (obspy.clients.fdsn.header.FDSNException):
print('No data found for {0}.{1}'.format(stas[n],nets[n]))
trtmp = Trace()
trtmp.stats.sampling_rate = opt.samprate
trtmp.stats.station = stas[n]
stmp = Stream().extend([trtmp.copy()])
# Last check for length; catches problem with empty waveserver
if len(stmp) != 1:
print('No data found for {0}.{1}'.format(stas[n],nets[n]))
trtmp = Trace()
trtmp.stats.sampling_rate = opt.samprate
trtmp.stats.station = stas[n]
stmp = Stream().extend([trtmp.copy()])
st.extend(stmp.copy())
# Edit 'start' time if using offset option
if opt.maxdt:
dts = np.fromstring(opt.offset, sep=',')
for n, tr in enumerate(st):
tr.stats.starttime = tr.stats.starttime-dts[n]
st = st.trim(starttime=tstart, endtime=tend, pad=True, fill_value=0)
stC = st.copy()
return st, stC
