def test_get_travel_time_df():
datapath = os.path.join('data', 'testdata', 'travel_times')
datadir = pkg_resources.resource_filename('gmprocess', datapath)
sc1 = StreamCollection.from_directory(os.path.join(datadir, 'ci37218996'))
sc2 = StreamCollection.from_directory(os.path.join(datadir, 'ci38461735'))
scs = [sc1, sc2]
df1, catalog = create_travel_time_dataframe(
sc1, os.path.join(datadir, 'catalog_test_traveltimes.csv'), 5, 0.1,
'iasp91')
df2, catalog = create_travel_time_dataframe(
sc2, os.path.join(datadir, 'catalog_test_traveltimes.csv'), 5, 0.1,
'iasp91')
model = TauPyModel('iasp91')
for dfidx, df in enumerate([df1, df2]):
for staidx, sta in enumerate(df):
for eqidx, time in enumerate(df[sta]):
sta_coords = scs[dfidx][staidx][0].stats.coordinates
event = catalog[eqidx]
dist = locations2degrees(sta_coords['latitude'],
sta_coords['longitude'],
event.latitude, event.longitude)
if event.depth_km < 0:
depth = 0
else:
depth = event.depth_km
travel_time = model.get_travel_times(depth, dist,
['p', 'P', 'Pn'])[0].time
abs_time = event.time + travel_time
np.testing.assert_almost_equal(abs_time, time, decimal=1)
def test_trim_multiple_events():
datapath = os.path.join('data', 'testdata', 'multiple_events')
datadir = pkg_resources.resource_filename('gmprocess', datapath)
sc = StreamCollection.from_directory(os.path.join(datadir, 'ci38457511'))
origin = get_event_object('ci38457511')
df, catalog = create_travel_time_dataframe(
sc, os.path.join(datadir, 'catalog.csv'), 5, 0.1, 'iasp91')
for st in sc:
st.detrend('demean')
remove_response(st, None, None)
st = corner_frequencies.constant(st)
lowpass_filter(st)
highpass_filter(st)
signal_split(st, origin)
signal_end(st,
origin.time,
origin.longitude,
origin.latitude,
origin.magnitude,
method='model',
model='AS16')
cut(st, 2)
trim_multiple_events(st, origin, catalog, df, 0.2, 0.7, 'B14',
{'vs30': 760}, {'rake': 0})
num_failures = sum([1 if not st.passed else 0 for st in sc])
assert num_failures == 1
failure = sc.select(station='WRV2')[0][0].getParameter('failure')
assert failure['module'] == 'trim_multiple_events'
assert failure['reason'] == ('A significant arrival from another event '
'occurs within the first 70.0 percent of the '
'signal window')
for tr in sc.select(station='JRC2')[0]:
np.testing.assert_almost_equal(
tr.stats.endtime, UTCDateTime('2019-07-06T03:20:38.7983Z'))
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