badObs = []
for ob in iagaCodes:
dist_stream = dist_factory.get_timeseries(
observatory=ob,
starttime=out_start,
endtime=out_end,
channels=['Xdt', 'Ydt', 'Zdt', 'Fdt'])
sq_stream = sq_factory.get_timeseries(
observatory=ob,
starttime=out_start,
endtime=out_end,
channels=['Xsq', 'Ysq', 'Zsq', 'Fsq'])
if dist_stream.count() == 0 or sq_stream.count() == 0:
print ob, 'data could not be read; skipping...'
badObs.append(ob) # remove bad iagaCodes after for-loop
continue
else:
print ob, 'data read in successfully'
dist_X = dist_stream.select(channel="Xdt")[0]
dist_Y = dist_stream.select(channel="Ydt")[0]
dist_Z = dist_stream.select(channel="Zdt")[0]
sq_X = sq_stream.select(channel="Xsq")[0]
sq_Y = sq_stream.select(channel="Ysq")[0]
sq_Z = sq_stream.select(channel="Zsq")[0]
#
if os.path.isfile(my_file):
st += read(my_file)
my_file = ('/auto/proj/Cascadia/data_nobackup/NC/KCS/KCS.NC.EHZ.%s.%s' % (yr, days))
if os.path.isfile(my_file):
st += read(my_file)
my_file = ('/auto/proj/Cascadia/data_nobackup/NC/KCO/KCO.NC.EHZ.%s.%s' % (yr, days))
if os.path.isfile(my_file):
st += read(my_file)
my_file = ('/auto/proj/Cascadia/data_nobackup/NC/KMR/KMR.NC.HHZ.%s.%s' % (yr, days))
if os.path.isfile(my_file):
st += read(my_file)
my_file = ('/auto/proj/Cascadia/data_nobackup/NC/KPP/KPP.NC.HHZ.%s.%s' % (yr, days))
if os.path.isfile(my_file):
st += read(my_file)
if st.count() > 0: # need waveforms to continue
std = Stream()
for tr in st:
num = tr.stats.npts
samp = tr.stats.sampling_rate
if num >= (samp*86400)*.8:
std.append(tr)
print('number of good waveforms ', std.count())
if std.count() < 3: # want 3 or more waveforms for templates
print('skipping event not enough good waveforms')
else:
std.sort(['starttime'])
std.merge(fill_value="interpolate")
st1=std.copy()
def read_from_SDS(self,
sds_root,
net_name,
sta_name,
comp_name,
starttime=None,
endtime=None,
rmean=False,
taper=False,
pad_value=None):
"""
Read waveform data from an SDS structured archive. Simple overlaps and
adjacent traces are merged if possile.
:param sds_root: root of the SDS archive
:param net_name: network name
:param sta_name: station name
:param comp_name: component name
:param starttime: Start time of data to be read.
:param endtime: End time of data to be read.
:param rmean: If ``True`` removes the mean from the data upon reading.
If data are segmented, the mean will be removed from all segments
individually.
:param taper: If ``True`` applies a cosine taper to the data upon
reading. If data are segmented, tapers are applied to all segments
individually.
:param pad_value: If this parameter is set, points between
``starttime`` and the first point in the file, and points between
the last point in the file and ``endtime``, will be set to
``pad_value``. You may want to also use the ``rmean`` and
``taper`` parameters, depending on the nature of the data.
:type sds_root: string
:type net_name: string
:type sta_name: string
:type comp_name: string
:type starttime: ``obspy.core.utcdatetime.UTCDateTime`` object,
optional
:type endtime: ``obspy.core.utcdatetime.UTCDateTime`` object, optional
:type rmean: boolean, optional
:type taper: boolean, optional
:type pad_value: float, optional
:raises UserWarning: If there are no data between ``starttime`` and
``endtime``
"""
logging.info("Reading from SDS structure %s %s %s ..." %
(net_name, sta_name, comp_name))
# Get the complete file list. If a directory, get all the filenames.
filename = os.path.join(sds_root, net_name, sta_name,
"%s.D" % comp_name, "*")
logging.debug("Reading %s between %s and %s" %
(filename, starttime.isoformat(), endtime.isoformat()))
if os.path.isdir(glob.glob(filename)[0]):
filename = os.path.join(filename, "*")
file_glob = glob.glob(filename)
# read header from all files to keep only those within the time limits
fnames_within_times = []
for fname in file_glob:
st_head = stream.read(fname, headonly=True)
# retrieve first_start and last_end time for the stream
# without making any assumptions on order of traces
first_start = st_head[0].stats.starttime
last_end = st_head[0].stats.endtime
# find earliest start time and latest end time in stream
for tr in st_head:
if tr.stats.starttime < first_start:
first_start = tr.stats.starttime
if tr.stats.endtime > last_end:
last_end = tr.stats.endtime
# add to list if start or end time are within our requested limits
if (first_start < endtime and last_end > starttime):
fnames_within_times.append(fname)
logging.debug("Found %d files to read" % len(fnames_within_times))
# now read the full data only for the relevant files
st = Stream()
for fname in fnames_within_times:
st_tmp = read(fname, starttime=starttime, endtime=endtime)
for tr in st_tmp:
st.append(tr)
# and merge nicely
st.merge(method=-1)
if st.count() > 1: # There are gaps after sensible cleanup merging
logging.info("File contains gaps:")
st.printGaps()
# apply rmean if requested
if rmean:
logging.info("Removing the mean from single traces.")
st = stream_rmean(st)
# apply rmean if requested
if taper:
logging.info("Tapering single traces.")
st = stream_taper(st)
if not pad_value is None:
try:
first_tr = st.traces[0]
# save delta (to save typing)
delta = first_tr.stats.delta
if (not starttime is None) and \
((first_tr.stats.starttime - starttime) > delta):
logging.debug("Padding with value %f from %s to first\
point in file at %s." %
(pad_value, starttime.isoformat(),
first_tr.stats.starttime.isoformat()))
# find the number of points from starttime to
# end of the first trace
npts_full_trace = \
int(np.floor((first_tr.stats.endtime -
starttime) / delta))+1
# find the number of points of the padding section
n_pad = npts_full_trace - first_tr.stats.npts
# fill the full time range with padd value
tr_pad = np.zeros(npts_full_trace) + pad_value
# substitute in the data
tr_pad[n_pad:] = first_tr.data[:]
first_tr.data = tr_pad
first_tr.stats.starttime = starttime
first_tr.stats.npts = npts_full_trace
st.traces[0] = first_tr
last_tr = st.traces[-1]
# save delta (to save typing)
delta = last_tr.stats.delta
if (not endtime is None) and \
((endtime - last_tr.stats.endtime) > delta):
logging.debug(
"Padding with value %f from last point\
in file at %s to %s." %
(pad_value, last_tr.stats.endtime.isoformat(),
endtime.isoformat()))
# find the number of points from endtime to
# start of the last trace
npts_full_trace = \
int(np.floor((endtime -
last_tr.stats.starttime) / delta))+1
# fill the full time range with padd value
tr_pad = np.zeros(npts_full_trace) + pad_value
# substitute in the data
tr_pad[0:last_tr.stats.npts] = last_tr.data[:]
last_tr.data = tr_pad
last_tr.stats.npts = npts_full_trace
st.traces[-1] = last_tr
except IndexError:
logging.warning('No data within time limits requested')
raise UserWarning('No data within time limits requested.')
try:
self.stream = st
self.trace = st.traces[0]
self.proc = "None"
except IndexError:
raise UserWarning('No data within time limits requested.')
in_factory = EdgeFactory(host=edge_url,
port=edge_port,
interval='minute',
type='adjusted')
in_stream += in_factory.get_timeseries(starttime=in_start,
endtime=in_end,
observatory=ob,
channels=ch)
print('Retrieved from Edge: %s-%s' % (ob, ch), end="")
print(' from', in_start, 'to', in_end)
else:
print("Decreasing interval requested (", end="")
print(out_start, 'to', out_end, ")", end="")
print(" skipping %s-%s..." % (ob, ch))
if in_stream.count() is not len(channels):
# if any channel was not read in, STOP PROCESSING
print("No inputs processed or written...")
pass
else:
# channels are processed separately from input retrieval in order to
# guarantee and maintain synchronization
chan_dt = []
chan_sq = []
chan_sv = []
chan_sd = []
for ch in channels:
# process time series with SqDistAlgorithm
out_stream += svsqdist[ch].process(
def read_from_SDS(self, sds_root, net_name, sta_name, comp_name,
starttime=None, endtime=None, rmean=False, taper=False,
pad_value=None):
"""
Read waveform data from an SDS structured archive. Simple overlaps and
adjacent traces are merged if possile.
:param sds_root: root of the SDS archive
:param net_name: network name
:param sta_name: station name
:param comp_name: component name
:param starttime: Start time of data to be read.
:param endtime: End time of data to be read.
:param rmean: If ``True`` removes the mean from the data upon reading.
If data are segmented, the mean will be removed from all segments
individually.
:param taper: If ``True`` applies a cosine taper to the data upon
reading. If data are segmented, tapers are applied to all segments
individually.
:param pad_value: If this parameter is set, points between
``starttime`` and the first point in the file, and points between
the last point in the file and ``endtime``, will be set to
``pad_value``. You may want to also use the ``rmean`` and
``taper`` parameters, depending on the nature of the data.
:type sds_root: string
:type net_name: string
:type sta_name: string
:type comp_name: string
:type starttime: ``obspy.core.utcdatetime.UTCDateTime`` object,
optional
:type endtime: ``obspy.core.utcdatetime.UTCDateTime`` object, optional
:type rmean: boolean, optional
:type taper: boolean, optional
:type pad_value: float, optional
:raises UserWarning: If there are no data between ``starttime`` and
``endtime``
"""
logging.info("Reading from SDS structure %s %s %s ..." %
(net_name, sta_name, comp_name))
# Get the complete file list. If a directory, get all the filenames.
filename = os.path.join(sds_root, net_name, sta_name,
"%s.D" % comp_name, "*")
logging.debug("Reading %s between %s and %s" %
(filename, starttime.isoformat(), endtime.isoformat()))
if os.path.isdir(glob.glob(filename)[0]):
filename = os.path.join(filename, "*")
file_glob = glob.glob(filename)
# read header from all files to keep only those within the time limits
fnames_within_times = []
for fname in file_glob:
st_head = stream.read(fname, headonly=True)
# retrieve first_start and last_end time for the stream
# without making any assumptions on order of traces
first_start = st_head[0].stats.starttime
last_end = st_head[0].stats.endtime
# find earliest start time and latest end time in stream
for tr in st_head:
if tr.stats.starttime < first_start:
first_start = tr.stats.starttime
if tr.stats.endtime > last_end:
last_end = tr.stats.endtime
# add to list if start or end time are within our requested limits
if (first_start < endtime and last_end > starttime):
fnames_within_times.append(fname)
logging.debug("Found %d files to read" % len(fnames_within_times))
# now read the full data only for the relevant files
st = Stream()
for fname in fnames_within_times:
st_tmp = read(fname, starttime=starttime, endtime=endtime)
for tr in st_tmp:
st.append(tr)
# and merge nicely
st.merge(method=-1)
if st.count() > 1: # There are gaps after sensible cleanup merging
logging.info("File contains gaps:")
st.printGaps()
# apply rmean if requested
if rmean:
logging.info("Removing the mean from single traces.")
st = stream_rmean(st)
# apply rmean if requested
if taper:
logging.info("Tapering single traces.")
st = stream_taper(st)
if not pad_value is None:
try:
first_tr = st.traces[0]
# save delta (to save typing)
delta = first_tr.stats.delta
if (not starttime is None) and \
((first_tr.stats.starttime - starttime) > delta):
logging.debug("Padding with value %f from %s to first\
point in file at %s." %
(pad_value,
starttime.isoformat(),
first_tr.stats.starttime.isoformat()))
# find the number of points from starttime to
# end of the first trace
npts_full_trace = \
int(np.floor((first_tr.stats.endtime -
starttime) / delta))+1
# find the number of points of the padding section
n_pad = npts_full_trace-first_tr.stats.npts
# fill the full time range with padd value
tr_pad = np.zeros(npts_full_trace)+pad_value
# substitute in the data
tr_pad[n_pad:] = first_tr.data[:]
first_tr.data = tr_pad
first_tr.stats.starttime = starttime
first_tr.stats.npts = npts_full_trace
st.traces[0] = first_tr
last_tr = st.traces[-1]
# save delta (to save typing)
delta = last_tr.stats.delta
if (not endtime is None) and \
((endtime - last_tr.stats.endtime) > delta):
logging.debug("Padding with value %f from last point\
in file at %s to %s." %
(pad_value,
last_tr.stats.endtime.isoformat(),
endtime.isoformat()))
# find the number of points from endtime to
# start of the last trace
npts_full_trace = \
int(np.floor((endtime -
last_tr.stats.starttime) / delta))+1
# fill the full time range with padd value
tr_pad = np.zeros(npts_full_trace)+pad_value
# substitute in the data
tr_pad[0:last_tr.stats.npts] = last_tr.data[:]
last_tr.data = tr_pad
last_tr.stats.npts = npts_full_trace
st.traces[-1] = last_tr
except IndexError:
logging.warning('No data within time limits requested')
raise UserWarning('No data within time limits requested.')
try:
self.stream = st
self.trace = st.traces[0]
self.proc = "None"
except IndexError:
raise UserWarning('No data within time limits requested.')
