def realTimeDrumPlot(self):
print('start ' + UTCDateTime.now().strftime("%Y%m%d %H%M%S"))
appTrace = Stream()
self._rtRunning = True
for tr in self._traces:
id = tr.get_id()
spl = id.split('.')
network = spl[0]
station = spl[1]
channel = spl[3]
l = int(self._tEnd - tr.stats['endtime'])
self._status[station] = {}
self._status[station]["Noise Level"] = "---"
self._status[station]["Latency"] = str(l) + 's'
self._status[station]["Voltage"] = "---"
self._status[station]["Color"] = "#FF0000"
for b in band:
fileNameRT = 'RT_' + network + '_' + station + '_' + channel + '_' + str(b) + '.png'
appTrace = tr.copy()
bb = band[b]
appTrace.trim(self._tEnd - self._rTWindow * 60, self._tEnd, pad=True, fill_value=0)
appTrace.filter('bandpass', freqmin=bb[0], freqmax=bb[1], corners=2, zerophase=True)
self.plotDrum(appTrace, basePath + 'RT/' + fileNameRT)
with open(basePath + 'RT/geophone_network_status.json', 'w') as fp:
json.dump(self._status, fp)
fp.close()
print('end ' + UTCDateTime.now().strftime("%Y%m%d %H%M%S"))
self._rtRunning = False
def hystDrumPlot(self):
print('Hyststart ' + UTCDateTime.now().strftime("%Y%m%d %H%M%S"))
appTrace = Stream()
self._hyRunning = True
for tr in self._traces:
id = tr.get_id()
# print('hyst '+id)
spl = id.split('.')
network = spl[0]
station = spl[1]
channel = spl[3]
for h in hystType:
if self._tEnd.hour % int(h / 60) == 0:
for b in band:
tStart = self._tEnd - h * 60
p = network + '/' + station + '/' + channel + '/' + str(self._tEnd.year) + '/' + str(
self._tEnd.month) + '/' + str(
self._tEnd.day) + '/' + str(h) + '/' + str(b)
fileName = p + '/' + tStart.strftime("%Y%m%d%H%M") + '_' + self._tEnd.strftime(
"%Y%m%d%H%M") + '.png'
appTrace = tr.copy()
bb = band[b]
appTrace.trim(tStart, self._tEnd, pad=True, fill_value=0)
appTrace.filter('bandpass', freqmin=bb[0], freqmax=bb[1], corners=2, zerophase=True)
self.plotDrum(appTrace, basePath + fileName)
print('Hystend ' + UTCDateTime.now().strftime("%Y%m%d %H%M%S"))
self._hyRunning = False
def write_st(st: Stream,
event: Event,
outfolder: str,
statxml: Inventory,
resample: bool = True):
"""
Write raw waveform data to an asdf file. This includes the corresponding
(teleseismic) event and the station inventory (i.e., response information).
:param st: The stream holding the raw waveform data.
:type st: Stream
:param event: The seismic event associated to the recorded data.
:type event: Event
:param outfolder: Output folder to write the asdf file to.
:type outfolder: str
:param statxml: The station inventory
:type statxml: Inventory
:param resample: Resample the data to 10Hz sampling rate? Defaults to True.
:type resample: bool, optional
"""
fname = '%s.%s.h5' % (st[0].stats.network, st[0].stats.station)
if resample:
st.filter('lowpass_cheby_2', freq=4, maxorder=12)
st = resample_or_decimate(st, 10, filter=False)
with ASDFDataSet(os.path.join(outfolder, fname)) as ds:
# Events should not be added because it will read the whole
# catalogue every single time!
ds.add_waveforms(st, tag='raw_recording')
ds.add_stationxml(statxml) # If there are still problems, we will have
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
'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)
AC.trim(startaim, endtaim, nearest_sample=True)
RLAS.trim(startaim, endtaim, nearest_sample=True)
# **Resample, Filter and Rotate**
# In[4]:
RLAS.decimate(factor=4)
AC.decimate(factor=4)
f_cutoff = 1.0
RLAS.filter('lowpass', freq=f_cutoff, corners=2, zerophase=True)
AC.filter('lowpass', freq=f_cutoff, corners=2, zerophase=True)
# event location from event info
source_latitude = event.origins[0].latitude
source_longitude = event.origins[0].longitude
# station location (Wettzell)
station_latitude = 49.144001
station_longitude = 12.8782
# theoretical backazimuth and distance
baz = gps2dist_azimuth(source_latitude, source_longitude, station_latitude, station_longitude)
print('Epicentral distance [m]: ',baz[0])
print('Theoretical azimuth [deg]: ', baz[1])
print('Theoretical backazimuth [deg]: ', baz[2])
end = S_arr + 3
day_wf = glob.glob(stn + '/' + '*' + str(origin_time.year) + '.' + str(origin_time.julday))
if len(day_wf) == 1:
fmt_file_name = pwd + '/' + day_wf[0]
else:
print('Error finding correct file')
t = read(fmt_file_name)[0]
traces += read(fmt_file_name,
starttime = begin,
endtime = end)
st = Stream(traces)
# store prefilter data
if len(st) > 1:
st.write('/auto/home/talongi/Cascadia/Code/NC_PI_events/MSEED/' + stn + '.SAC')
st.filter('bandpass', freqmin = 1, freqmax = 10, zerophase = True)
st.sort(['starttime'])
plot_waveforms(st)
wf_similarity = waveform_similarity(st, vmin = 0.4)
#save all figures to a PDF
multipage('/auto/home/talongi/Cascadia/Figures/06_waveforms/waveforms_cc_matrix.pdf')
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
fig.tight_layout()
plt.show()
# -
# ### Resample, Filter and Rotate
# Resample seismograms using **decimate** in order to reduce the size of the arrays (speeds up processing).
# The seismograms are high-cut and low-cut filtered, depending on the frequency range of interest and the resolution of the instruments.
# +
RLAS.decimate(factor=4)
AC.decimate(factor=4)
high_cut = 1.0
low_cut = 0.005
RLAS.filter('bandpass', freqmax=high_cut, freqmin=low_cut, corners=2, zerophase=True)
AC.filter('bandpass', freqmax=high_cut, freqmin=low_cut, corners=2, zerophase=True)
# -
# In order to align the seismometer recordings with the event direction, we need to rotate the horizontal components<br>
# of the acceleration to transverse and radial.<br>
# <br>
# We can determine the theoretical rotation/direction angle (= backazimuth) from station and event location using **gps2dist_azimuth**.<br>
# This function also yields the epicentral distance and the azimuth angle.
# +
from obspy.geodetics.base import gps2dist_azimuth
# event location from event info
source_latitude = event.origins[0].latitude
source_longitude = event.origins[0].longitude
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)
def calculate_rf(data,
filter_config=config.FILTER_FREQ,
time_from=config.RF_TIME_FROM,
time_to=config.RF_TIME_TO,
zero_shift=0.):
"""
input:
[data] - obspy.core.stream.Stream object with event in LQT format.
After function input [data] may be changed.
[filter_config] (optional) - Python dictionary with keys 'FREQMIN' and 'FREQMAX',
used in ObsPy 'boundpass' filter function.
[time_from, time_to] (floats, optional)- result will contain data between
[time_from] and [time_to] seconds. Time is measured from time 0.
[zero_shift] (float, optional) - time 0 is set [zero_shift] seconds after theoretical
time 0 (maximum in deconvolved L trace).
Default value is 0. (float)
[zero_date] (obspy.core.utcdatetime.UTCDateTime or convertable, optional)
- time 0 is represented as [zero_date] time
output: obspy.core.stream.Stream object containing
calculated reveival function
"""
assert time_from < time_to
zero_date = _get_zero_date(data)
# filtering
data = data.filter('bandpass',
freqmin=filter_config['FREQMIN'],
freqmax=filter_config['FREQMAX'])
# normalization
if config.NORMALIZE_BEFORE:
mx = max([np.max(t.data) for t in data.traces])
mn = min([np.min(t.data) for t in data.traces])
for tr in data.traces:
tr.data /= mx - mn
# counting receival function
stL = data.select(component='L')
stQ = data.select(component='Q')
stT = data.select(component='T')
rfQ, rfT, rfL = deconvolve(
[stQ.traces[0].data, stT.traces[0].data, stL.traces[0].data],
stL.traces[0].data)
if config.REVERSE_QRF: rfQ = -rfQ
if config.REVERSE_TRF: rfT = -rfT
# setting "zero" moment
freq = int(1 / stL.traces[0].stats['delta'])
zero_pos = np.argmax(rfL) + int(freq * zero_shift)
from_pos = zero_pos + int(time_from * freq)
to_pos = zero_pos + int(time_to * freq)
if from_pos < 0:
rfQ = np.concatenate((np.zeros(-from_pos), rfQ))
rfT = np.concatenate((np.zeros(-from_pos), rfT))
to_pos += -from_pos
from_pos = 0
if to_pos > len(rfQ):
rfQ = np.concatenate((rfQ, np.zeros(to_pos - len(rfQ))))
rfT = np.concatenate((rfT, np.zeros(to_pos - len(rfT))))
stQ.traces[0].data = rfQ[from_pos:to_pos]
stT.traces[0].data = rfT[from_pos:to_pos]
stQ.traces[0].stats.starttime = zero_date + time_from
stT.traces[0].stats.starttime = zero_date + time_from
stQ.traces[0].stats.channel = 'RFQ'
stT.traces[0].stats.channel = 'RFT'
data = Stream(stQ.traces + stT.traces)
# filtering
data = data.filter('bandpass',
freqmin=filter_config['FREQMIN'],
freqmax=filter_config['FREQMAX'])
# normalization
if config.NORMALIZE_AFTER:
mx = max([np.max(t.data) for t in data.traces])
mn = min([np.min(t.data) for t in data.traces])
for tr in data.traces:
tr.data /= mx - mn
return data
