def do_SDS_processing_setup_and_run(opdict):
"""
Does all the processing for an SDS archive. All options are given within a
*WavlocOptions* object. Steps are:
* reading data
* filtering
* resampling if requested
* applying kurtosis processing
* calculating kurtosis gradient if requested
* convolving with gaussian if requested
* writing processed files at each stage
:param opdict: Dictionary of options. Refers the the opdict attribute of
*WavelocOptions* objects.
:type opdict: dictionary
"""
base_path = opdict['base_path']
data_dir = os.path.join(base_path, 'data', opdict['datadir'])
filter_c1 = opdict['c1']
filter_c2 = opdict['c2']
kurt_window = opdict['kwin']
dataglob = opdict['dataglob']
kurtglob = opdict['kurtglob']
gradglob = opdict['gradglob']
if opdict['gauss']:
gaussglob = opdict['gaussglob']
# start and end time to process
start_time = utcdatetime.UTCDateTime(opdict['starttime'])
end_time = utcdatetime.UTCDateTime(opdict['endtime'])
# if have a channel file then read it
if 'channel_file' in opdict:
fname = os.path.join(base_path, 'lib', opdict['channel_file'])
triplet_list = read_channel_file(fname)
else:
# else make triplet list from net, sta, comp lists
triplet_list = []
net_list = opdict['net_list'].split(',')
sta_list = opdict['sta_list'].split(',')
comp_list = opdict['comp_list'].split(',')
for net in net_list:
for sta in sta_list:
for comp in comp_list:
triplet_list.append((net, sta, comp))
# loop over data
for net, sta, comp in triplet_list:
full_path = os.path.join(data_dir, net, sta, "%s.D" % comp)
logging.debug("Full path : %s" % full_path)
if os.path.exists(full_path):
# construct the base filename for output and create the wf object
filt_filename = os.path.join(data_dir,
"%s.%s.%s.%s.%s" %
(start_time.isoformat(),
net, sta, comp, dataglob[1:]))
logging.debug("Processing to create %s" % (filt_filename))
wf = Waveform()
# read and process the data
try:
# read and filter data
wf.read_from_SDS(data_dir, net, sta, comp,
starttime=start_time, endtime=end_time)
wf.bp_filter(filter_c1, filter_c2, rmean=True, taper=True)
if opdict['resample']:
wf.resample(opdict['fs'])
wf.write_to_file_filled(filt_filename, format='MSEED',
fill_value=0)
# do kurtosis processing
kurt_filename = os.path.join(data_dir,
"%s.%s.%s.%s.%s" %
(start_time.isoformat(),
net, sta, comp, kurtglob[1:]))
logging.debug("Processing to create %s" % (kurt_filename))
wf.process_kurtosis(kurt_window, recursive=opdict['krec'],
pre_taper=True, post_taper=True)
wf.write_to_file_filled(kurt_filename, format='MSEED',
fill_value=0)
# calculate kurtosis gradient if requested
if opdict['kderiv']:
kurt_grad_filename = os.path.join(data_dir,
"%s.%s.%s.%s.%s" %
(start_time.isoformat(),
net, sta, comp,
gradglob[1:]))
logging.debug("Processing to create %s" %
(kurt_grad_filename))
wf.take_positive_derivative(pre_taper=True,
post_taper=True)
wf.write_to_file_filled(kurt_grad_filename, format='MSEED',
fill_value=0)
# do convolution with gaussian if requested
if opdict['gauss']:
thres = opdict['gthreshold']
mu = opdict['mu']
sigma = opdict['sigma']
gauss_filename = os.path.join(data_dir,
"%s.%s.%s.%s.%s" %
(start_time.isoformat(),
net, sta, comp,
gaussglob[1:]))
logging.debug("Processing to create %s" % (gauss_filename))
wf.process_gaussian(thres, mu, sigma)
wf.write_to_file_filled(gauss_filename, format='MSEED',
fill_value=0)
except UserWarning:
logging.info('No data within time limits for %s %s %s' %
(net, sta, comp))
def do_SDS_processing_setup_and_run(opdict):
"""
Does all the processing for an SDS archive. All options are given within a
*WavlocOptions* object. Steps are:
* reading data
* filtering
* resampling if requested
* applying kurtosis processing
* calculating kurtosis gradient if requested
* convolving with gaussian if requested
* writing processed files at each stage
:param opdict: Dictionary of options. Refers the the opdict attribute of
*WavelocOptions* objects.
:type opdict: dictionary
"""
base_path = opdict['base_path']
data_dir = os.path.join(base_path, 'data', opdict['datadir'])
filter_c1 = opdict['c1']
filter_c2 = opdict['c2']
kurt_window = opdict['kwin']
dataglob = opdict['dataglob']
kurtglob = opdict['kurtglob']
gradglob = opdict['gradglob']
if opdict['gauss']:
gaussglob = opdict['gaussglob']
# start and end time to process
start_time = utcdatetime.UTCDateTime(opdict['starttime'])
end_time = utcdatetime.UTCDateTime(opdict['endtime'])
# if have a channel file then read it
if 'channel_file' in opdict:
fname = os.path.join(base_path, 'lib', opdict['channel_file'])
triplet_list = read_channel_file(fname)
else:
# else make triplet list from net, sta, comp lists
triplet_list = []
net_list = opdict['net_list'].split(',')
sta_list = opdict['sta_list'].split(',')
comp_list = opdict['comp_list'].split(',')
for net in net_list:
for sta in sta_list:
for comp in comp_list:
triplet_list.append((net, sta, comp))
# loop over data
for net, sta, comp in triplet_list:
full_path = os.path.join(data_dir, net, sta, "%s.D" % comp)
logging.debug("Full path : %s" % full_path)
if os.path.exists(full_path):
# construct the base filename for output and create the wf object
filt_filename = os.path.join(
data_dir, "%s.%s.%s.%s.%s" %
(start_time.isoformat(), net, sta, comp, dataglob[1:]))
logging.debug("Processing to create %s" % (filt_filename))
wf = Waveform()
# read and process the data
try:
# read and filter data
wf.read_from_SDS(data_dir,
net,
sta,
comp,
starttime=start_time,
endtime=end_time)
wf.bp_filter(filter_c1, filter_c2, rmean=True, taper=True)
if opdict['resample']:
wf.resample(opdict['fs'])
wf.write_to_file_filled(filt_filename,
format='MSEED',
fill_value=0)
# do kurtosis processing
kurt_filename = os.path.join(
data_dir, "%s.%s.%s.%s.%s" %
(start_time.isoformat(), net, sta, comp, kurtglob[1:]))
logging.debug("Processing to create %s" % (kurt_filename))
wf.process_kurtosis(kurt_window,
recursive=opdict['krec'],
pre_taper=True,
post_taper=True)
wf.write_to_file_filled(kurt_filename,
format='MSEED',
fill_value=0)
# calculate kurtosis gradient if requested
if opdict['kderiv']:
kurt_grad_filename = os.path.join(
data_dir, "%s.%s.%s.%s.%s" %
(start_time.isoformat(), net, sta, comp, gradglob[1:]))
logging.debug("Processing to create %s" %
(kurt_grad_filename))
wf.take_positive_derivative(pre_taper=True,
post_taper=True)
wf.write_to_file_filled(kurt_grad_filename,
format='MSEED',
fill_value=0)
# do convolution with gaussian if requested
if opdict['gauss']:
thres = opdict['gthreshold']
mu = opdict['mu']
sigma = opdict['sigma']
gauss_filename = os.path.join(
data_dir,
"%s.%s.%s.%s.%s" % (start_time.isoformat(), net, sta,
comp, gaussglob[1:]))
logging.debug("Processing to create %s" % (gauss_filename))
wf.process_gaussian(thres, mu, sigma)
wf.write_to_file_filled(gauss_filename,
format='MSEED',
fill_value=0)
except UserWarning:
logging.info('No data within time limits for %s %s %s' %
(net, sta, comp))
