def test_corner_frequencies():
# Default config has 'constant' corner frequency method, so the need
# here is to force the 'snr' method.
data_files, origin = read_data_dir('geonet', 'us1000778i', '*.V1A')
streams = []
for f in data_files:
streams += read_data(f)
sc = StreamCollection(streams)
config = get_config()
window_conf = config['windows']
processed_streams = sc.copy()
for st in processed_streams:
if st.passed:
# Estimate noise/signal split time
event_time = origin.time
event_lon = origin.longitude
event_lat = origin.latitude
st = signal_split(st, origin)
# Estimate end of signal
end_conf = window_conf['signal_end']
event_mag = origin.magnitude
print(st)
st = signal_end(
st,
event_time=event_time,
event_lon=event_lon,
event_lat=event_lat,
event_mag=event_mag,
**end_conf
)
wcheck_conf = window_conf['window_checks']
st = window_checks(
st,
min_noise_duration=wcheck_conf['min_noise_duration'],
min_signal_duration=wcheck_conf['min_signal_duration']
)
pconfig = config['processing']
# Run SNR check
# I think we don't do this anymore.
test = [
d for d in pconfig if list(d.keys())[0] == 'compute_snr'
]
snr_config = test[0]['compute_snr']
for stream in processed_streams:
stream = compute_snr(
stream,
**snr_config
)
# Run get_corner_frequencies
test = [
d for d in pconfig if list(d.keys())[0] == 'get_corner_frequencies'
]
cf_config = test[0]['get_corner_frequencies']
snr_config = cf_config['snr']
lp = []
hp = []
for stream in processed_streams:
if not stream.passed:
continue
stream = get_corner_frequencies(
stream,
method="snr",
snr=snr_config
)
if stream[0].hasParameter('corner_frequencies'):
cfdict = stream[0].getParameter('corner_frequencies')
lp.append(cfdict['lowpass'])
hp.append(cfdict['highpass'])
np.testing.assert_allclose(
np.sort(hp),
[0.00751431, 0.01354455, 0.04250735],
atol=1e-6
)
def process_streams(streams, origin, config=None):
"""
Run processing steps from the config file.
This method looks in the 'processing' config section and loops over those
steps and hands off the config options to the appropriate prcessing method.
Streams that fail any of the tests are kepth in the StreamCollection but
the parameter 'passed_checks' is set to False and subsequent processing
steps are not applied once a check has failed.
Args:
streams (list):
A StreamCollection object.
origin (ScalarEvent):
ScalarEvent object.
config (dict):
Configuration dictionary (or None). See get_config().
Returns:
A StreamCollection object.
"""
if not isinstance(streams, StreamCollection):
raise ValueError('streams must be a StreamCollection instance.')
if config is None:
config = get_config()
logging.info('Processing streams...')
event_time = origin.time
event_lon = origin.longitude
event_lat = origin.latitude
# -------------------------------------------------------------------------
# Compute a travel-time matrix for interpolation later in the
# trim_multiple events step
if any('trim_multiple_events' in dict for dict in config['processing']):
travel_time_df, catalog = create_travel_time_dataframe(
streams, **config['travel_time'])
# -------------------------------------------------------------------------
# Begin noise/signal window steps
logging.info('Windowing noise and signal...')
window_conf = config['windows']
model = TauPyModel(config['pickers']['travel_time']['model'])
for st in streams:
logging.info('Checking stream %s...' % st.get_id())
# Estimate noise/signal split time
st = signal_split(st,
origin,
model,
picker_config=config['pickers'],
config=config)
# Estimate end of signal
end_conf = window_conf['signal_end']
event_mag = origin.magnitude
st = signal_end(st,
event_time=event_time,
event_lon=event_lon,
event_lat=event_lat,
event_mag=event_mag,
**end_conf)
wcheck_conf = window_conf['window_checks']
if wcheck_conf['do_check']:
st = window_checks(
st,
min_noise_duration=wcheck_conf['min_noise_duration'],
min_signal_duration=wcheck_conf['min_signal_duration'])
# -------------------------------------------------------------------------
# Begin processing steps
logging.info('Starting processing...')
processing_steps = config['processing']
# Loop over streams
for stream in streams:
logging.info('Stream: %s' % stream.get_id())
for processing_step_dict in processing_steps:
key_list = list(processing_step_dict.keys())
if len(key_list) != 1:
raise ValueError(
'Each processing step must contain exactly one key.')
step_name = key_list[0]
logging.info('Processing step: %s' % step_name)
step_args = processing_step_dict[step_name]
# Using globals doesn't seem like a great solution here, but it
# works.
if step_name not in globals():
raise ValueError('Processing step %s is not valid.' %
step_name)
# Origin is required by some steps and has to be handled specially.
# There must be a better solution for this...
if step_name == 'fit_spectra':
step_args = {'origin': origin}
elif step_name in REQ_ORIGIN:
step_args['origin'] = origin
elif step_name == 'trim_multiple_events':
step_args['catalog'] = catalog
step_args['travel_time_df'] = travel_time_df
elif step_name == 'compute_snr':
step_args['mag'] = origin.magnitude
if step_args is None:
stream = globals()[step_name](stream)
else:
stream = globals()[step_name](stream, **step_args)
# -------------------------------------------------------------------------
# Begin colocated instrument selection
colocated_conf = config['colocated']
streams.select_colocated(**colocated_conf)
for st in streams:
for tr in st:
tr.stats.standard.process_level = PROCESS_LEVELS['V2']
logging.info('Finished processing streams.')
return streams
def process_streams(streams, origin, config=None):
"""
Run processing steps from the config file.
This method looks in the 'processing' config section and loops over those
steps and hands off the config options to the appropriate prcessing method.
Streams that fail any of the tests are kepth in the StreamCollection but
the parameter 'passed_checks' is set to False and subsequent processing
steps are not applied once a check has failed.
Args:
streams (list):
A StreamCollection object.
origin (ScalarEvent):
ScalarEvent object.
config (dict): Configuration dictionary (or None). See get_config().
Returns:
A StreamCollection object.
"""
if not isinstance(streams, StreamCollection):
raise ValueError('streams must be a StreamCollection instance.')
if config is None:
config = get_config()
logging.info('Processing streams...')
event_time = origin.time
event_lon = origin.longitude
event_lat = origin.latitude
# -------------------------------------------------------------------------
# Begin noise/signal window steps
logging.info('Windowing noise and signal...')
window_conf = config['windows']
processed_streams = streams.copy()
for st in processed_streams:
logging.info('Checking stream %s...' % st.get_id())
# Estimate noise/signal split time
st = signal_split(st, origin)
# Estimate end of signal
end_conf = window_conf['signal_end']
event_mag = origin.magnitude
st = signal_end(st,
event_time=event_time,
event_lon=event_lon,
event_lat=event_lat,
event_mag=event_mag,
**end_conf)
wcheck_conf = window_conf['window_checks']
if wcheck_conf['do_check']:
st = window_checks(
st,
min_noise_duration=wcheck_conf['min_noise_duration'],
min_signal_duration=wcheck_conf['min_signal_duration'])
# -------------------------------------------------------------------------
# Begin processing steps
logging.info('Starting processing...')
processing_steps = config['processing']
# Loop over streams
for stream in processed_streams:
logging.info('Stream: %s' % stream.get_id())
for processing_step_dict in processing_steps:
key_list = list(processing_step_dict.keys())
if len(key_list) != 1:
raise ValueError(
'Each processing step must contain exactly one key.')
step_name = key_list[0]
logging.info('Processing step: %s' % step_name)
step_args = processing_step_dict[step_name]
# Using globals doesn't seem like a great solution here, but it
# works.
if step_name not in globals():
raise ValueError('Processing step %s is not valid.' %
step_name)
# Origin is required by some steps and has to be handled specially.
# There must be a better solution for this...
if step_name == 'fit_spectra':
step_args = {'origin': origin}
elif step_name in REQ_ORIGIN:
step_args['origin'] = origin
if step_args is None:
stream = globals()[step_name](stream)
else:
stream = globals()[step_name](stream, **step_args)
# Build the summary report?
build_conf = config['build_report']
if build_conf['run']:
build_report(processed_streams,
build_conf['directory'],
origin,
config=config)
logging.info('Finished processing streams.')
return processed_streams
def test_corner_frequencies():
# Default config has 'constant' corner frequency method, so the need
# here is to force the 'snr' method.
data_files, origin = read_data_dir('geonet', 'us1000778i', '*.V1A')
streams = []
for f in data_files:
streams += read_data(f)
sc = StreamCollection(streams)
config = get_config()
window_conf = config['windows']
processed_streams = sc.copy()
for st in processed_streams:
if st.passed:
# Estimate noise/signal split time
event_time = origin.time
event_lon = origin.longitude
event_lat = origin.latitude
st = signal_split(st, origin)
# Estimate end of signal
end_conf = window_conf['signal_end']
event_mag = origin.magnitude
print(st)
st = signal_end(st,
event_time=event_time,
event_lon=event_lon,
event_lat=event_lat,
event_mag=event_mag,
**end_conf)
wcheck_conf = window_conf['window_checks']
st = window_checks(
st,
min_noise_duration=wcheck_conf['min_noise_duration'],
min_signal_duration=wcheck_conf['min_signal_duration'])
pconfig = config['processing']
# Run SNR check
# I think we don't do this anymore.
test = [d for d in pconfig if list(d.keys())[0] == 'compute_snr']
snr_config = test[0]['compute_snr']
for stream in processed_streams:
stream = compute_snr(stream, **snr_config)
# Run get_corner_frequencies
test = [
d for d in pconfig if list(d.keys())[0] == 'get_corner_frequencies'
]
cf_config = test[0]['get_corner_frequencies']
snr_config = cf_config['snr']
# With same_horiz False
snr_config['same_horiz'] = False
lp = []
hp = []
for stream in processed_streams:
if not stream.passed:
continue
stream = get_corner_frequencies(stream, method="snr", snr=snr_config)
if stream[0].hasParameter('corner_frequencies'):
cfdict = stream[0].getParameter('corner_frequencies')
lp.append(cfdict['lowpass'])
hp.append(cfdict['highpass'])
np.testing.assert_allclose(np.sort(hp),
[0.00751431, 0.01354455, 0.04250735],
atol=1e-6)
st = processed_streams.select(station='HSES')[0]
lps = [tr.getParameter('corner_frequencies')['lowpass'] for tr in st]
hps = [tr.getParameter('corner_frequencies')['highpass'] for tr in st]
np.testing.assert_allclose(np.sort(lps), [100., 100., 100.], atol=1e-6)
np.testing.assert_allclose(np.sort(hps),
[0.00305176, 0.00751431, 0.02527502],
atol=1e-6)
# With same_horiz True
snr_config['same_horiz'] = True
lp = []
hp = []
for stream in processed_streams:
if not stream.passed:
continue
stream = get_corner_frequencies(stream, method="snr", snr=snr_config)
if stream[0].hasParameter('corner_frequencies'):
cfdict = stream[0].getParameter('corner_frequencies')
lp.append(cfdict['lowpass'])
hp.append(cfdict['highpass'])
np.testing.assert_allclose(np.sort(hp),
[0.00751431, 0.01354455, 0.04882812],
atol=1e-6)
st = processed_streams.select(station='HSES')[0]
lps = [tr.getParameter('corner_frequencies')['lowpass'] for tr in st]
hps = [tr.getParameter('corner_frequencies')['highpass'] for tr in st]
np.testing.assert_allclose(np.sort(lps), [100., 100., 100.], atol=1e-6)
np.testing.assert_allclose(np.sort(hps),
[0.00751431, 0.00751431, 0.02527502],
atol=1e-6)
def process_streams(streams, origin, config=None):
"""
Run processing steps from the config file.
This method looks in the 'processing' config section and loops over those
steps and hands off the config options to the appropriate prcessing method.
Streams that fail any of the tests are kepth in the StreamCollection but
the parameter 'passed_checks' is set to False and subsequent processing
steps are not applied once a check has failed.
Args:
streams (list):
A StreamCollection object.
origin (ScalarEvent):
ScalarEvent object.
config (dict): Configuration dictionary (or None). See get_config().
Returns:
A StreamCollection object.
"""
if not isinstance(streams, StreamCollection):
raise ValueError('streams must be a StreamCollection instance.')
if config is None:
config = get_config()
logging.info('Processing streams...')
event_time = origin.time
event_lon = origin.longitude
event_lat = origin.latitude
# -------------------------------------------------------------------------
# Begin noise/signal window steps
logging.info('Windowing noise and signal...')
window_conf = config['windows']
processed_streams = streams.copy()
for st in processed_streams:
logging.info('Checking stream %s...' % st.get_id())
# Estimate noise/signal split time
st = signal_split(
st,
origin)
# Estimate end of signal
end_conf = window_conf['signal_end']
event_mag = origin.magnitude
st = signal_end(
st,
event_time=event_time,
event_lon=event_lon,
event_lat=event_lat,
event_mag=event_mag,
**end_conf
)
wcheck_conf = window_conf['window_checks']
if wcheck_conf['do_check']:
st = window_checks(
st,
min_noise_duration=wcheck_conf['min_noise_duration'],
min_signal_duration=wcheck_conf['min_signal_duration']
)
# -------------------------------------------------------------------------
# Begin processing steps
logging.info('Starting processing...')
processing_steps = config['processing']
# Loop over streams
for stream in processed_streams:
logging.info('Stream: %s' % stream.get_id())
for processing_step_dict in processing_steps:
key_list = list(processing_step_dict.keys())
if len(key_list) != 1:
raise ValueError(
'Each processing step must contain exactly one key.')
step_name = key_list[0]
logging.info('Processing step: %s' % step_name)
step_args = processing_step_dict[step_name]
# Using globals doesn't seem like a great solution here, but it
# works.
if step_name not in globals():
raise ValueError(
'Processing step %s is not valid.' % step_name)
# Origin is required by some steps and has to be handled specially.
# There must be a better solution for this...
if step_name == 'fit_spectra':
step_args = {
'origin': origin
}
elif step_name in REQ_ORIGIN:
step_args['origin'] = origin
if step_args is None:
stream = globals()[step_name](stream)
else:
stream = globals()[step_name](stream, **step_args)
# Build the summary report?
build_conf = config['build_report']
if build_conf['run']:
build_report(processed_streams,
build_conf['directory'],
origin, config=config)
logging.info('Finished processing streams.')
return processed_streams