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"]
snr_config["check"]["min_freq"] = 0.2
for stream in processed_streams:
stream = compute_snr(stream, mag=origin.magnitude, **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),
[0.00467919, 0.00584742, 0.01026485],
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), [100.0, 100.0, 100.0], atol=1e-6)
np.testing.assert_allclose(np.sort(hps),
[0.00542478, 0.01026485, 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),
[0.00467919, 0.01026485, 0.01787214],
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), [100.0, 100.0, 100.0], atol=1e-6)
np.testing.assert_allclose(np.sort(hps), [0.010265, 0.010265, 0.025275],
atol=1e-6)
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']
snr_config['check']['min_freq'] = 0.2
for stream in processed_streams:
stream = compute_snr(stream, mag=origin.magnitude, **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), [0.003052, 0.003052, 0.010265],
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), [0.003052, 0.010265, 0.025275],
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), [0.003052, 0.010265, 0.017872],
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), [0.010265, 0.010265, 0.025275],
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()
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'])
window_conf = config['windows']
model = TauPyModel(config['pickers']['travel_time']['model'])
for st in streams:
logging.debug('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
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.debug('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 in REQ_ORIGIN:
step_args['origin'] = origin
if step_name == 'trim_multiple_events':
step_args['catalog'] = catalog
step_args['travel_time_df'] = travel_time_df
if 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, old_streams=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 (StreamCollection):
A StreamCollection object of unprocessed streams.
origin (ScalarEvent):
ScalarEvent object.
config (dict):
Configuration dictionary (or None). See get_config().
old_streams (StreamCollection):
A StreamCollection object of previously processed streams that contain
manually reviewed information. None if not reprocessing.
Returns:
A StreamCollection object.
"""
if not isinstance(streams, (StreamCollection, StreamArray)):
raise ValueError("streams must be a StreamCollection instance.")
if config is None:
config = get_config()
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"])
window_conf = config["windows"]
model = TauPyModel(config["pickers"]["travel_time"]["model"])
for st in streams:
logging.debug(f"Checking stream {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
processing_steps = config["processing"]
# Loop over streams
for i, stream in enumerate(streams):
logging.info(f"Stream: {stream.get_id()}")
# Check if we are reprocessing (indicated by presence of old_streams)
if old_streams is not None:
old_stream = old_streams[i]
for j in range(len(old_stream)):
tr_old = old_stream[j]
# Check if old_streams have review parameters because it is not
# guaranteed
if tr_old.hasParameter("review"):
review_dict = tr_old.getParameter("review")
# Transfer review parameter from old stream to new
stream[j].setParameter("review", review_dict)
# Was it failed via manual review?
if "accepted" in review_dict:
if not review_dict["accepted"]:
stream[j].fail("Manual review")
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.debug(f"Processing step: {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(f"Processing step {step_name} is not valid.")
# Origin is required by some steps and has to be handled specially.
# There must be a better solution for this...
if step_name in REQ_ORIGIN:
step_args["origin"] = origin
if step_name == "trim_multiple_events":
step_args["catalog"] = catalog
step_args["travel_time_df"] = travel_time_df
if step_name == "compute_snr":
step_args["mag"] = origin.magnitude
if step_args is None:
stream = globals()[step_name](stream, config)
else:
stream = globals()[step_name](stream,
**step_args,
config=config)
# -------------------------------------------------------------------------
# Begin colocated instrument selection
if "colocated" in config:
colocated_conf = config["colocated"]
if isinstance(streams, StreamCollection):
streams.select_colocated(**colocated_conf, origin=origin)
for st in streams:
for tr in st:
tr.stats.standard.process_level = PROCESS_LEVELS["V2"]
logging.info("Finished processing streams.")
return streams
def test_corner_frequencies_magnitude():
# Default config has 'constant' corner frequency method, so the need
# here is to force the 'magnitude' 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 get_corner_frequencies
test = [d for d in pconfig if list(d.keys())[0] == "get_corner_frequencies"]
cf_config = test[0]["get_corner_frequencies"]
mag_config = cf_config["magnitude"]
lp = []
hp = []
for stream in processed_streams:
if not stream.passed:
continue
stream = get_corner_frequencies(
stream, origin, method="magnitude", magnitude=mag_config
)
if stream[0].hasParameter("corner_frequencies"):
cfdict = stream[0].getParameter("corner_frequencies")
lp.append(cfdict["lowpass"])
hp.append(cfdict["highpass"])