def test_metrics2():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = get_config()
config['metrics']['output_imts'].append('Arias')
config['metrics']['output_imcs'].append('arithmetic_mean')
# turn off sta/lta check and snr checks
newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label='processed')
workspace.calcMetrics(event.id, labels=['processed'])
etable, imc_tables1 = workspace.getTables('processed')
etable2, imc_tables2 = workspace.getTables('processed', config=config)
assert 'ARITHMETIC_MEAN' not in imc_tables1
assert 'ARITHMETIC_MEAN' in imc_tables2
assert 'ARIAS' in imc_tables2['ARITHMETIC_MEAN']
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def test_stream_params():
eventid = 'us1000778i'
datafiles, event = read_data_dir(
'geonet',
eventid,
'20161113_110259_WTMC_20.V1A'
)
tdir = tempfile.mkdtemp()
streams = []
try:
streams += read_data(datafiles[0])
statsdict = {'name': 'Fred', 'age': 34}
streams[0].setStreamParam('stats', statsdict)
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, streams, label='stats')
outstreams = workspace.getStreams(event.id, labels=['stats'])
cmpdict = outstreams[0].getStreamParam('stats')
assert cmpdict == statsdict
workspace.close()
except Exception as e:
raise(e)
finally:
shutil.rmtree(tdir)
def get_sample_data(volume):
thisdir = pathlib.Path(__file__).parent
datafile = (thisdir / ".." / ".." / ".." / ".." / "gmprocess" / "data" /
"testdata" / "asdf" / "nc71126864" / "workspace.h5")
workspace = StreamWorkspace.open(datafile)
t1 = time.time()
eventid = workspace.getEventIds()[0]
t2 = time.time()
print(f"{t2-t1:.2f} seconds to read eventid")
scalar_event = workspace.getEvent(eventid)
station = "CE.79435"
labels = workspace.getLabels()
if volume == Volume.RAW:
labels.remove("default")
elif volume == Volume.CONVERTED:
labels.remove("default")
else:
labels.remove("unprocessed")
plabel = labels[0]
streams = workspace.getStreams(eventid,
stations=[station],
labels=[plabel])
gmprocess_version = workspace.getGmprocessVersion()
idx = gmprocess_version.find(".dev")
gmprocess_version = gmprocess_version[0:idx]
stream = streams[0]
trace = stream[0]
workspace.close()
return (trace, eventid, scalar_event, stream, gmprocess_version)
def generate_workspace():
"""Generate simple HDF5 with ASDF layout for testing.
"""
PCOMMANDS = [
'assemble',
'process',
]
EVENTID = 'us1000778i'
LABEL = 'ptest'
datafiles, event = read_data_dir('geonet', EVENTID, '*.V1A')
tdir = tempfile.mkdtemp()
tfilename = os.path.join(tdir, 'workspace.h5')
raw_data = []
for dfile in datafiles:
raw_data += read_data(dfile)
write_asdf(tfilename, raw_data, event, label="unprocessed")
del raw_data
config = get_config()
workspace = StreamWorkspace.open(tfilename)
raw_streams = workspace.getStreams(EVENTID, labels=['unprocessed'])
pstreams = process_streams(raw_streams, event, config=config)
workspace.addStreams(event, pstreams, label=LABEL)
workspace.calcMetrics(event.id, labels=[LABEL], config=config)
return tfilename
def _process_event(self, event):
event_dir = os.path.join(self.gmrecords.data_path, event.id)
workname = os.path.join(event_dir, WORKSPACE_NAME)
if not os.path.isfile(workname):
logging.info('No workspace file found for event %s. Please run '
'subcommand \'assemble\' to generate workspace file.')
logging.info('Continuing to next event.')
return event.id
workspace = StreamWorkspace.open(workname)
rstreams = workspace.getStreams(event.id,
labels=['unprocessed'],
config=self.gmrecords.conf)
if len(rstreams):
logging.info('Processing \'%s\' streams for event %s...' %
('unprocessed', event.id))
pstreams = process_streams(rstreams,
event,
config=self.gmrecords.conf)
workspace.addStreams(event, pstreams, label=self.process_tag)
else:
logging.info('No streams found. Nothing to do. Goodbye.')
workspace.close()
return event.id
def get_eq_full_status_df(wdir, eqid, imc):
ws = StreamWorkspace.open(os.path.join(wdir, eqid, 'workspace.h5'))
labels = ws.getLabels()
labels.remove('unprocessed')
processed_label = labels[0]
sc = ws.getStreams(ws.getEventIds()[0], labels=[processed_label])
ws.close()
rows = []
for st in sc:
coords = st[0].stats.coordinates
row = [st.id, coords.latitude, coords.longitude]
if st.passed:
row.append('Passed')
else:
for tr in st:
if tr.hasParameter('failure'):
row.append(tr.getParameter('failure')['reason'])
break
rows.append(row)
df = pd.DataFrame(rows,
columns=[
'StationID', 'StationLatitude', 'StationLongitude',
'Failure reason'
])
return df
def generate_workspace():
"""Generate simple HDF5 with ASDF layout for testing."""
PCOMMANDS = [
"assemble",
"process",
]
EVENTID = "us1000778i"
LABEL = "ptest"
datafiles, event = read_data_dir("geonet", EVENTID, "*.V1A")
tdir = tempfile.mkdtemp()
tfilename = os.path.join(tdir, "workspace.h5")
raw_data = []
for dfile in datafiles:
raw_data += read_data(dfile)
write_asdf(tfilename, raw_data, event, label="unprocessed")
del raw_data
config = update_config(os.path.join(datadir, "config_min_freq_0p2.yml"))
workspace = StreamWorkspace.open(tfilename)
raw_streams = workspace.getStreams(EVENTID,
labels=["unprocessed"],
config=config)
pstreams = process_streams(raw_streams, event, config=config)
workspace.addStreams(event, pstreams, label=LABEL)
workspace.calcMetrics(event.id, labels=[LABEL], config=config)
return tfilename
def __init__(self,
cosmos_directory,
h5_filename,
volume=Volume.PROCESSED,
label=None):
if volume == Volume.PROCESSED and label is None:
raise Exception("Must supply label for processed data")
self._workspace = StreamWorkspace.open(h5_filename)
self._cosmos_directory = pathlib.Path(cosmos_directory)
self._volume = volume
self._label = label
def main(self, gmrecords):
"""Export provenance tables.
Args:
gmrecords:
GMrecordsApp instance.
"""
logging.info('Running subcommand \'%s\'' % self.command_name)
self.gmrecords = gmrecords
self._check_arguments()
self._get_events()
for event in self.events:
self.eventid = event.id
logging.info('Creating provenance tables for event %s...' %
self.eventid)
event_dir = os.path.join(gmrecords.data_path, self.eventid)
workname = os.path.join(event_dir, WORKSPACE_NAME)
if not os.path.isfile(workname):
logging.info(
'No workspace file found for event %s. Please run '
'subcommand \'assemble\' to generate workspace file.' %
self.eventid)
logging.info('Continuing to next event.')
continue
self.workspace = StreamWorkspace.open(workname)
self._get_pstreams()
if not (hasattr(self, 'pstreams') and len(self.pstreams) > 0):
logging.info('No processed waveforms available. No provenance '
'tables created.')
self.workspace.close()
continue
provdata = self.workspace.getProvenance(
self.eventid, labels=self.gmrecords.args.label)
self.workspace.close()
basename = '%s_%s_provenance' % (gmrecords.project,
gmrecords.args.label)
if gmrecords.args.output_format == 'csv':
csvfile = os.path.join(event_dir, '%s.csv' % basename)
self.append_file('Provenance', csvfile)
provdata.to_csv(csvfile, index=False)
else:
excelfile = os.path.join(event_dir, '%s.xlsx' % basename)
self.append_file('Provenance', excelfile)
provdata.to_excel(excelfile, index=False)
self._summarize_files_created()
def main(self, gmrecords):
"""Export files for ShakeMap input.
Args:
gmrecords:
GMrecordsApp instance.
"""
logging.info('Running subcommand \'%s\'' % self.command_name)
self.gmrecords = gmrecords
self._get_events()
for event in self.events:
self.eventid = event.id
logging.info('Creating shakemap files for event %s...' %
self.eventid)
event_dir = os.path.join(gmrecords.data_path, event.id)
workname = os.path.join(event_dir, WORKSPACE_NAME)
if not os.path.isfile(workname):
logging.info(
'No workspace file found for event %s. Please run '
'subcommand \'assemble\' to generate workspace file.' %
event.id)
logging.info('Continuing to next event.')
continue
self.workspace = StreamWorkspace.open(workname)
self._get_labels()
self._get_pstreams()
if not hasattr(self, 'pstreams'):
logging.info('No processed waveforms available. No shakemap '
'files created.')
return
# TODO: re-write this so that it uses the already computer values
# in self.workspace.dataset.auxiliary_data.WaveFormMetrics
# rather than recomputing the metrics from self.pstreams.
# shakemap_file, jsonfile = save_shakemap_amps(
# self.pstreams, event, event_dir)
jsonfile, stationfile, _ = create_json(self.workspace, event,
event_dir,
self.gmrecords.args.label)
self.workspace.close()
self.append_file('shakemap', jsonfile)
self.append_file('shakemap', stationfile)
self._summarize_files_created()
def _compute_event_waveforms(self, event):
self.eventid = event.id
logging.info(
'Computing waveform metrics for event %s...' % self.eventid)
event_dir = os.path.join(self.gmrecords.data_path, self.eventid)
workname = os.path.join(event_dir, WORKSPACE_NAME)
if not os.path.isfile(workname):
logging.info(
'No workspace file found for event %s. Please run '
'subcommand \'assemble\' to generate workspace file.'
% self.eventid)
logging.info('Continuing to next event.')
return event.id
self.workspace = StreamWorkspace.open(workname)
self._get_pstreams()
if not hasattr(self, 'pstreams'):
logging.info('No processed waveforms available. No waveform '
'metrics computed.')
self.workspace.close()
return event.id
for stream in self.pstreams:
if stream.passed:
logging.info(
'Calculating waveform metrics for %s...'
% stream.get_id()
)
summary = StationSummary.from_config(
stream, event=event, config=self.gmrecords.conf,
calc_waveform_metrics=True,
calc_station_metrics=False
)
xmlstr = summary.get_metric_xml()
tag = stream.tag
metricpath = '/'.join([
format_netsta(stream[0].stats),
format_nslit(stream[0].stats, stream.get_inst(), tag)
])
self.workspace.insert_aux(
xmlstr, 'WaveFormMetrics', metricpath,
overwrite=self.gmrecords.args.overwrite)
logging.info('Added waveform metrics to workspace files '
'with tag \'%s\'.' % self.gmrecords.args.label)
self.workspace.close()
return event.id
def download(event, event_dir, config, directory):
"""Download data or load data from local directory, turn into Streams.
Args:
event (ScalarEvent):
Object containing basic event hypocenter, origin time, magnitude.
event_dir (str):
Path where raw directory should be created (if downloading).
config (dict):
Dictionary with gmprocess configuration information.
directory (str):
Path where raw data already exists.
Returns:
tuple:
- StreamWorkspace: Contains the event and raw streams.
- str: Name of workspace HDF file.
- StreamCollection: Raw data StationStreams.
"""
# generate the raw directory
rawdir = get_rawdir(event_dir)
if directory is None:
tcollection, terrors = fetch_data(
event.time.datetime,
event.latitude,
event.longitude,
event.depth_km,
event.magnitude,
config=config,
rawdir=rawdir)
# create an event.json file in each event directory,
# in case user is simply downloading for now
create_event_file(event, event_dir)
else:
streams, bad, terrors = directory_to_streams(directory)
tcollection = StreamCollection(streams)
# plot the raw waveforms
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
pngfiles = glob.glob(os.path.join(rawdir, '*.png'))
if not len(pngfiles):
plot_raw(rawdir, tcollection, event)
# create the workspace file and put the unprocessed waveforms in it
workname = os.path.join(event_dir, 'workspace.hdf')
if os.path.isfile(workname):
os.remove(workname)
workspace = StreamWorkspace(workname)
workspace.addEvent(event)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=H5pyDeprecationWarning)
workspace.addStreams(event, tcollection, label='unprocessed')
return (workspace, workname, tcollection)
def _test_stream_params():
eventid = "us1000778i"
datafiles, event = read_data_dir("geonet", eventid,
"20161113_110259_WTMC_20.V1A")
tdir = tempfile.mkdtemp()
streams = []
try:
streams += read_data(datafiles[0])
statsdict = {"name": "Fred", "age": 34}
streams[0].setStreamParam("stats", statsdict)
tfile = os.path.join(tdir, "test.hdf")
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, streams, label="stats")
outstreams = workspace.getStreams(event.id, labels=["stats"])
cmpdict = outstreams[0].getStreamParam("stats")
assert cmpdict == statsdict
workspace.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def main(self, gmrecords):
"""Export files for ShakeMap input.
Args:
gmrecords:
GMrecordsApp instance.
"""
logging.info('Running subcommand \'%s\'' % self.command_name)
self.gmrecords = gmrecords
self._check_arguments()
self._get_events()
for event in self.events:
self.eventid = event.id
logging.info('Creating shakemap files for event %s...' %
self.eventid)
event_dir = os.path.join(gmrecords.data_path, event.id)
workname = os.path.join(event_dir, WORKSPACE_NAME)
if not os.path.isfile(workname):
logging.info(
'No workspace file found for event %s. Please run '
'subcommand \'assemble\' to generate workspace file.' %
event.id)
logging.info('Continuing to next event.')
continue
self.workspace = StreamWorkspace.open(workname)
self._get_labels()
expanded_imts = self.gmrecords.args.expand_imts
jsonfile, stationfile, _ = create_json(self.workspace,
event,
event_dir,
self.gmrecords.args.label,
config=self.gmrecords.conf,
expanded_imts=expanded_imts)
self.workspace.close()
if jsonfile is not None:
self.append_file('shakemap', jsonfile)
if stationfile is not None:
self.append_file('shakemap', stationfile)
self._summarize_files_created()
def test_stream_params():
eventid = 'us1000778i'
datafiles, event = read_data_dir('geonet',
eventid,
'20161113_110259_WTMC_20.V1A')
tdir = tempfile.mkdtemp()
streams = []
try:
streams += read_data(datafiles[0])
statsdict = {'name': 'Fred', 'age': 34}
streams[0].setStreamParam('stats', statsdict)
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, streams, label='stats')
outstreams = workspace.getStreams(event.id, labels=['stats'])
cmpdict = outstreams[0].getStreamParam('stats')
assert cmpdict == statsdict
workspace.close()
except Exception as e:
raise(e)
finally:
shutil.rmtree(tdir)
def test_get_shakemap():
tdir = tempfile.mkdtemp()
try:
thisdir = pathlib.Path(__file__).parent
datadir = (thisdir / '..' / '..' / '..' / 'gmprocess' / 'data' /
'testdata')
datafile = datadir / 'workspace_ci38457511.hdf'
workspace = StreamWorkspace.open(datafile)
eventid = workspace.getEventIds()[0]
event = workspace.getEvent(eventid)
label = '20201209195000'
processed = workspace.getStreams(eventid, labels=[label])
excelfile, jsonfile = save_shakemap_amps(processed, event, tdir)
with open(jsonfile, 'rt', encoding='utf-8') as fp:
jdict = json.load(fp)
assert jdict['features'][0]['id'] == 'CJ.T001230'
except Exception as e:
raise AssertionError(str(e))
finally:
shutil.rmtree(tdir)
def main(self, gmrecords):
"""Export metric tables.
Args:
gmrecords:
GMrecordsApp instance.
"""
logging.info('Running subcommand \'%s\'' % self.command_name)
self.gmrecords = gmrecords
self._get_events()
for event in self.events:
self.eventid = event.id
logging.info('Creating tables for event %s...' % self.eventid)
event_dir = os.path.join(gmrecords.data_path, self.eventid)
workname = os.path.join(event_dir, WORKSPACE_NAME)
if not os.path.isfile(workname):
logging.info(
'No workspace file found for event %s. Please run '
'subcommand \'assemble\' to generate workspace file.' %
self.eventid)
logging.info('Continuing to next event.')
continue
self.workspace = StreamWorkspace.open(workname)
self._get_pstreams()
if not hasattr(self, 'pstreams'):
logging.info('No processed waveforms available. No metric '
'tables created.')
self.workspace.close()
return
event_table, imc_tables, readmes = self.workspace.getTables(
self.gmrecords.args.label, streams=self.pstreams)
ev_fit_spec, fit_readme = self.workspace.getFitSpectraTable(
self.eventid, self.gmrecords.args.label, self.pstreams)
self.workspace.close()
outdir = gmrecords.data_path
# Set the precisions for the imc tables, event table, and
# fit_spectra table before writing
imc_tables_formatted = {}
for imc, imc_table in imc_tables.items():
imc_tables_formatted[imc] = set_precisions(imc_table)
event_table_formatted = set_precisions(event_table)
df_fit_spectra_formatted = set_precisions(ev_fit_spec)
imc_list = [
'%s_%s_metrics_%s' %
(gmrecords.project, gmrecords.args.label, imc.lower())
for imc in imc_tables_formatted.keys()
]
readme_list = [
'%s_%s_metrics_%s_README' %
(gmrecords.project, gmrecords.args.label, imc.lower())
for imc in readmes.keys()
]
proj_lab = (gmrecords.project, gmrecords.args.label)
event_filename = ['%s_%s_events' % proj_lab]
filenames = event_filename + imc_list + readme_list + [
'%s_%s_fit_spectra_parameters' % proj_lab,
'%s_%s_fit_spectra_parameters_README' % proj_lab
]
files = [event_table_formatted] + list(
imc_tables_formatted.values()) + list(
readmes.values()) + [df_fit_spectra_formatted, fit_readme]
output_format = gmrecords.args.output_format
if output_format != 'csv':
output_format = 'xlsx'
for filename, df in dict(zip(filenames, files)).items():
filepath = os.path.join(outdir,
filename + '.%s' % output_format)
if os.path.exists(filepath):
if 'README' in filename:
continue
else:
if self.gmrecords.args.overwrite:
logging.warning('File exists: %s' % filename)
logging.warning('Overwriting file: %s' % filename)
mode = 'w'
header = True
else:
logging.warning('File exists: %s' % filename)
logging.warning('Appending to file: %s' % filename)
mode = 'a'
header = False
else:
mode = 'w'
header = True
if output_format == 'csv':
df.to_csv(filepath,
index=False,
float_format=DEFAULT_FLOAT_FORMAT,
na_rep=DEFAULT_NA_REP,
mode=mode,
header=header)
if mode == "w":
self.append_file('Metric tables', filepath)
else:
df.to_excel(filepath,
index=False,
float_format=DEFAULT_FLOAT_FORMAT,
na_rep=DEFAULT_NA_REP,
mode=mode,
header=header)
if mode == "w":
self.append_file('Metric tables', filepath)
self._summarize_files_created()
def download(event, event_dir, config, directory):
"""Download data or load data from local directory, turn into Streams.
Args:
event (ScalarEvent):
Object containing basic event hypocenter, origin time, magnitude.
event_dir (str):
Path where raw directory should be created (if downloading).
config (dict):
Dictionary with gmprocess configuration information.
directory (str):
Path where data already exists. Must be organized in a 'raw'
directory, within directories with names as the event ids. For
example, if `directory` is 'proj_dir' and you have data for
event id 'abc123' then the raw data to be read in should be
located in `proj_dir/abc123/raw/`.
Returns:
tuple:
- StreamWorkspace: Contains the event and raw streams.
- str: Name of workspace HDF file.
- StreamCollection: Raw data StationStreams.
"""
# Make raw directory
rawdir = get_rawdir(event_dir)
if directory is None:
tcollection, terrors = fetch_data(event.time.datetime,
event.latitude,
event.longitude,
event.depth_km,
event.magnitude,
config=config,
rawdir=rawdir)
# create an event.json file in each event directory,
# in case user is simply downloading for now
create_event_file(event, event_dir)
else:
# Make raw directory
in_event_dir = os.path.join(directory, event.id)
in_raw_dir = get_rawdir(in_event_dir)
streams, bad, terrors = directory_to_streams(in_raw_dir)
tcollection = StreamCollection(streams, **config['duplicate'])
create_event_file(event, event_dir)
# Plot the raw waveforms
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
pngfiles = glob.glob(os.path.join(rawdir, '*.png'))
if not len(pngfiles):
plot_raw(rawdir, tcollection, event)
# Create the workspace file and put the unprocessed waveforms in it
workname = os.path.join(event_dir, 'workspace.hdf')
# Remove any existing workspace file
if os.path.isfile(workname):
os.remove(workname)
workspace = StreamWorkspace(workname)
workspace.addEvent(event)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=H5pyDeprecationWarning)
workspace.addStreams(event, tcollection, label='unprocessed')
return (workspace, workname, tcollection)
def _event_station_metrics(self, event):
self.eventid = event.id
logging.info('Computing station metrics for event %s...' %
self.eventid)
event_dir = os.path.join(self.gmrecords.data_path, self.eventid)
workname = os.path.join(event_dir, WORKSPACE_NAME)
if not os.path.isfile(workname):
logging.info(
'No workspace file found for event %s. Please run '
'subcommand \'assemble\' to generate workspace file.' %
self.eventid)
logging.info('Continuing to next event.')
return event.id
self.workspace = StreamWorkspace.open(workname)
self._get_pstreams()
if not (hasattr(self, 'pstreams') and len(self.pstreams) > 0):
logging.info('No streams found. Nothing to do. Goodbye.')
self.workspace.close()
return event.id
rupture_file = get_rupture_file(event_dir)
origin = Origin({
'id': self.eventid,
'netid': '',
'network': '',
'lat': event.latitude,
'lon': event.longitude,
'depth': event.depth_km,
'locstring': '',
'mag': event.magnitude,
'time': event.time
})
self.origin = origin
rupture = get_rupture(origin, rupture_file)
sta_lats = []
sta_lons = []
sta_elev = []
self.sta_repi = []
self.sta_rhyp = []
self.sta_baz = []
for st in self.pstreams:
sta_lats.append(st[0].stats.coordinates.latitude)
sta_lons.append(st[0].stats.coordinates.longitude)
sta_elev.append(st[0].stats.coordinates.elevation)
geo_tuple = gps2dist_azimuth(st[0].stats.coordinates.latitude,
st[0].stats.coordinates.longitude,
origin.lat, origin.lon)
self.sta_repi.append(geo_tuple[0] / M_PER_KM)
self.sta_baz.append(geo_tuple[1])
self.sta_rhyp.append(
distance(st[0].stats.coordinates.longitude,
st[0].stats.coordinates.latitude,
-st[0].stats.coordinates.elevation / M_PER_KM,
origin.lon, origin.lat, origin.depth))
if isinstance(rupture, PointRupture):
self._get_ps2ff_splines()
rjb_hat = self.rjb_spline(self.sta_repi)
rjb_mean = rjb_hat[0]
rjb_var = rjb_hat[1]
rrup_hat = self.rrup_spline(self.sta_repi)
rrup_mean = rrup_hat[0]
rrup_var = rrup_hat[1]
gc2_rx = np.full_like(rjb_mean, np.nan)
gc2_ry = np.full_like(rjb_mean, np.nan)
gc2_ry0 = np.full_like(rjb_mean, np.nan)
gc2_U = np.full_like(rjb_mean, np.nan)
gc2_T = np.full_like(rjb_mean, np.nan)
else:
logging.info('******************************')
logging.info('* Found rupture *')
logging.info('******************************')
sta_lons = np.array(sta_lons)
sta_lats = np.array(sta_lats)
elev = np.full_like(sta_lons, ELEVATION_FOR_DISTANCE_CALCS)
rrup_mean, rrup_var = rupture.computeRrup(sta_lons, sta_lats, elev)
rjb_mean, rjb_var = rupture.computeRjb(sta_lons, sta_lats, elev)
rrup_var = np.full_like(rrup_mean, np.nan)
rjb_var = np.full_like(rjb_mean, np.nan)
gc2_dict = rupture.computeGC2(sta_lons, sta_lats, elev)
gc2_rx = gc2_dict['rx']
gc2_ry = gc2_dict['ry']
gc2_ry0 = gc2_dict['ry0']
gc2_U = gc2_dict['U']
gc2_T = gc2_dict['T']
# If we don't have a point rupture, then back azimuth needs
# to be calculated to the closest point on the rupture
self.sta_baz = []
for i in range(len(self.pstreams)):
dists = []
bazs = []
for quad in rupture._quadrilaterals:
P0, P1, P2, P3 = quad
for point in [P0, P1]:
dist, az, baz = gps2dist_azimuth(
point.y, point.x, sta_lats[i], sta_lons[i])
dists.append(dist)
bazs.append(baz)
self.sta_baz.append(bazs[np.argmin(dists)])
for i, stream in enumerate(self.pstreams):
logging.info('Calculating station metrics for %s...' %
stream.get_id())
summary = StationSummary.from_config(stream,
event=event,
config=self.gmrecords.conf,
calc_waveform_metrics=False,
calc_station_metrics=False,
rupture=rupture,
vs30_grids=self.vs30_grids)
summary._distances = {
'epicentral': self.sta_repi[i],
'hypocentral': self.sta_rhyp[i],
'rupture': rrup_mean[i],
'rupture_var': rrup_var[i],
'joyner_boore': rjb_mean[i],
'joyner_boore_var': rjb_var[i],
'gc2_rx': gc2_rx[i],
'gc2_ry': gc2_ry[i],
'gc2_ry0': gc2_ry0[i],
'gc2_U': gc2_U[i],
'gc2_T': gc2_T[i]
}
summary._back_azimuth = self.sta_baz[i]
if self.vs30_grids is not None:
for vs30_name in self.vs30_grids.keys():
tmpgrid = self.vs30_grids[vs30_name]
summary._vs30[vs30_name] = {
'value':
tmpgrid['grid_object'].getValue(
float(sta_lats[i]), float(sta_lons[i])),
'column_header':
tmpgrid['column_header'],
'readme_entry':
tmpgrid['readme_entry'],
'units':
tmpgrid['units']
}
xmlstr = summary.get_station_xml()
metricpath = '/'.join([
format_netsta(stream[0].stats),
format_nslit(stream[0].stats, stream.get_inst(), self.eventid)
])
self.workspace.insert_aux(xmlstr,
'StationMetrics',
metricpath,
overwrite=self.gmrecords.args.overwrite)
logging.info('Added station metrics to workspace files '
'with tag \'%s\'.' % self.gmrecords.args.label)
self.workspace.close()
return event.id
def assemble(event, config, directory, gmprocess_version):
"""Download data or load data from local directory, turn into Streams.
Args:
event (ScalarEvent):
Object containing basic event hypocenter, origin time, magnitude.
config (dict):
Dictionary with gmprocess configuration information.
directory (str):
Path where data already exists. Must be organized in a 'raw'
directory, within directories with names as the event ids. For
example, if `directory` is 'proj_dir' and you have data for
event id 'abc123' then the raw data to be read in should be
located in `proj_dir/abc123/raw/`.
gmprocess_version (str):
Software version for gmprocess.
Returns:
tuple:
- StreamWorkspace: Contains the event and raw streams.
- str: Name of workspace HDF file.
- StreamCollection: Raw data StationStreams.
- str: Path to the rupture file.
"""
# Make raw directory
in_event_dir = os.path.join(directory, event.id)
in_raw_dir = get_rawdir(in_event_dir)
logging.debug(f"in_raw_dir: {in_raw_dir}")
streams, unprocessed_files, unprocessed_file_errors = directory_to_streams(
in_raw_dir, config=config)
# Write errors to a csv file
failures_file = Path(in_raw_dir) / "read_failures.csv"
colnames = ["File", "Failure"]
with open(failures_file, "w", newline="") as f:
writer = csv.writer(f, delimiter=",", quoting=csv.QUOTE_MINIMAL)
writer.writerow(colnames)
for ufile, uerror in zip(unprocessed_files, unprocessed_file_errors):
writer.writerow([ufile, uerror])
logging.debug("streams:")
logging.debug(streams)
if config["read"]["use_streamcollection"]:
stream_array = StreamCollection(streams, **config["duplicate"])
else:
stream_array = StreamArray(streams)
logging.info("stream_array.describe_string():")
logging.info(stream_array.describe_string())
# Create the workspace file and put the unprocessed waveforms in it
workname = os.path.join(in_event_dir, WORKSPACE_NAME)
# Remove any existing workspace file
if os.path.isfile(workname):
os.remove(workname)
workspace = StreamWorkspace(workname)
workspace.addEvent(event)
logging.debug("workspace.dataset.events:")
logging.debug(workspace.dataset.events)
workspace.addGmprocessVersion(gmprocess_version)
workspace.addConfig()
workspace.addStreams(event,
stream_array,
label="unprocessed",
gmprocess_version=gmprocess_version)
logging.debug("workspace.dataset.waveforms.list():")
logging.debug(workspace.dataset.waveforms.list())
logging.debug("workspace.dataset.config")
return workspace
def test_workspace():
eventid = 'us1000778i'
datafiles, event = read_data_dir('geonet', eventid, '*.V1A')
tdir = tempfile.mkdtemp()
try:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=H5pyDeprecationWarning)
warnings.filterwarnings("ignore", category=YAMLLoadWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
config = get_config()
tfile = os.path.join(tdir, 'test.hdf')
raw_streams = []
for dfile in datafiles:
raw_streams += read_data(dfile)
workspace = StreamWorkspace(tfile)
t1 = time.time()
workspace.addStreams(event, raw_streams, label='raw')
t2 = time.time()
print('Adding %i streams took %.2f seconds' %
(len(raw_streams), (t2 - t1)))
str_repr = workspace.__repr__()
assert str_repr == 'Events: 1 Stations: 3 Streams: 3'
eventobj = workspace.getEvent(eventid)
assert eventobj.origins[0].latitude == event.origins[0].latitude
assert eventobj.magnitudes[0].mag == event.magnitudes[0].mag
stations = workspace.getStations()
assert sorted(stations) == ['hses', 'thz', 'wtmc']
stations = workspace.getStations(eventid=eventid)
assert sorted(stations) == ['hses', 'thz', 'wtmc']
# test retrieving tags for an event that doesn't exist
try:
workspace.getStreamTags('foo')
except KeyError:
assert 1 == 1
# test retrieving event that doesn't exist
try:
workspace.getEvent('foo')
except KeyError:
assert 1 == 1
instream = None
for stream in raw_streams:
if stream[0].stats.station.lower() == 'hses':
instream = stream
break
if instream is None:
assert 1 == 2
outstream = workspace.getStreams(eventid,
stations=['hses'],
labels=['raw'])[0]
compare_streams(instream, outstream)
label_summary = workspace.summarizeLabels()
assert label_summary.iloc[0]['Label'] == 'raw'
assert label_summary.iloc[0]['Software'] == 'gmprocess'
sc = StreamCollection(raw_streams)
processed_streams = process_streams(sc, event, config=config)
workspace.addStreams(event, processed_streams, 'processed')
idlist = workspace.getEventIds()
assert idlist[0] == eventid
event_tags = workspace.getStreamTags(eventid)
assert sorted(event_tags) == ['hses_processed', 'hses_raw',
'thz_processed', 'thz_raw',
'wtmc_processed', 'wtmc_raw']
outstream = workspace.getStreams(eventid,
stations=['hses'],
labels=['processed'])[0]
provenance = workspace.getProvenance(eventid, labels=['processed'])
first_row = pd.Series({'Record': 'NZ.HSES.HN1',
'Processing Step': 'Remove Response',
'Step Attribute': 'input_units',
'Attribute Value': 'counts'})
last_row = pd.Series({'Record': 'NZ.WTMC.HNZ',
'Processing Step': 'Lowpass Filter',
'Step Attribute': 'number_of_passes',
'Attribute Value': 2})
assert provenance.iloc[0].equals(first_row)
assert provenance.iloc[-1].equals(last_row)
# compare the parameters from the input processed stream
# to it's output equivalent
instream = None
for stream in processed_streams:
if stream[0].stats.station.lower() == 'hses':
instream = stream
break
if instream is None:
assert 1 == 2
compare_streams(instream, outstream)
workspace.close()
# read in data from a second event and stash it in the workspace
eventid = 'nz2018p115908'
datafiles, event = read_data_dir('geonet', eventid, '*.V2A')
raw_streams = []
for dfile in datafiles:
raw_streams += read_data(dfile)
workspace = StreamWorkspace.open(tfile)
workspace.addStreams(event, raw_streams, label='foo')
stations = workspace.getStations(eventid)
eventids = workspace.getEventIds()
assert eventids == ['us1000778i', 'nz2018p115908']
instation = raw_streams[0][0].stats.station
this_stream = workspace.getStreams(eventid,
stations=[instation],
labels=['foo'])[0]
assert instation == this_stream[0].stats.station
usid = 'us1000778i'
inventory = workspace.getInventory(usid)
codes = [station.code for station in inventory.networks[0].stations]
assert sorted(codes) == ['HSES', 'THZ', 'WPWS', 'WTMC']
except Exception as e:
raise(e)
finally:
shutil.rmtree(tdir)
def _test_metrics2():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = update_config(os.path.join(datadir, 'config_min_freq_0p2.yml'))
config['metrics']['output_imts'].append('Arias')
config['metrics']['output_imcs'].append('arithmetic_mean')
# Adjust checks so that streams pass checks for this test
newconfig = drop_processing(config, ['check_sta_lta'])
csnr = [s for s in newconfig['processing'] if 'compute_snr' in s.keys()][0]
csnr['compute_snr']['check']['threshold'] = -10.0
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label='processed')
workspace.calcMetrics(event.id, labels=['processed'])
etable, imc_tables1, readmes1 = workspace.getTables('processed')
assert 'ARITHMETIC_MEAN' not in imc_tables1
assert 'ARITHMETIC_MEAN' not in readmes1
del workspace.dataset.auxiliary_data.WaveFormMetrics
del workspace.dataset.auxiliary_data.StationMetrics
workspace.calcMetrics(event.id, labels=['processed'], config=config)
etable2, imc_tables2, readmes2 = workspace.getTables('processed')
assert 'ARITHMETIC_MEAN' in imc_tables2
assert 'ARITHMETIC_MEAN' in readmes2
assert 'ARIAS' in imc_tables2['ARITHMETIC_MEAN']
testarray = readmes2['ARITHMETIC_MEAN']['Column header'].to_numpy()
assert 'ARIAS' in testarray
workspace.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def test_metrics():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet',
eventid,
'*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = get_config()
# turn off sta/lta check and snr checks
newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label='processed')
stream1 = processed_streams[0]
stream2 = processed_streams[1]
summary1 = StationSummary.from_config(stream1)
summary2 = StationSummary.from_config(stream2)
workspace.setStreamMetrics(event.id, 'processed', summary1)
workspace.setStreamMetrics(event.id, 'processed', summary2)
summary1_a = workspace.getStreamMetrics(event.id,
stream1[0].stats.station,
'processed')
s1_df_in = summary1.pgms.sort_values(['IMT', 'IMC'])
s1_df_out = summary1_a.pgms.sort_values(['IMT', 'IMC'])
array1 = s1_df_in['Result'].as_matrix()
array2 = s1_df_out['Result'].as_matrix()
np.testing.assert_almost_equal(array1, array2, decimal=4)
df = workspace.getMetricsTable(event.id)
cmp_series = {'GREATER_OF_TWO_HORIZONTALS': 0.6787,
'HN1': 0.3869,
'HN2': 0.6787,
'HNZ': 0.7663}
pga_dict = df.iloc[0]['PGA'].to_dict()
for key, value in pga_dict.items():
value2 = cmp_series[key]
np.testing.assert_almost_equal(value, value2, decimal=4)
workspace.close()
except Exception as e:
raise(e)
finally:
shutil.rmtree(tdir)
def test_metrics():
eventid = "usb000syza"
datafiles, event = read_data_dir("knet", eventid, "*")
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = update_config(os.path.join(datadir, "config_min_freq_0p2.yml"))
# turn off sta/lta check and snr checks
# newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
# processed_streams = process_streams(raw_streams, event, config=newconfig)
newconfig = config.copy()
newconfig["processing"].append(
{"NNet_QA": {
"acceptance_threshold": 0.5,
"model_name": "CantWell"
}})
processed_streams = process_streams(raw_streams.copy(),
event,
config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, "test.hdf")
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, raw_streams, label="raw")
workspace.addStreams(event, processed_streams, label="processed")
stream1 = raw_streams[0]
# Get metrics from station summary for raw streams
summary1 = StationSummary.from_config(stream1)
s1_df_in = summary1.pgms.sort_values(["IMT", "IMC"])
array1 = s1_df_in["Result"].to_numpy()
# Compare to metrics from getStreamMetrics for raw streams
workspace.calcMetrics(eventid, labels=["raw"])
summary1_a = workspace.getStreamMetrics(event.id,
stream1[0].stats.network,
stream1[0].stats.station,
"raw")
s1_df_out = summary1_a.pgms.sort_values(["IMT", "IMC"])
array2 = s1_df_out["Result"].to_numpy()
np.testing.assert_allclose(array1, array2, atol=1e-6, rtol=1e-6)
workspace.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def _test_workspace():
eventid = "us1000778i"
datafiles, event = read_data_dir("geonet", eventid, "*.V1A")
tdir = tempfile.mkdtemp()
try:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=H5pyDeprecationWarning)
warnings.filterwarnings("ignore", category=YAMLError)
warnings.filterwarnings("ignore", category=FutureWarning)
config = update_config(
os.path.join(datadir, "config_min_freq_0p2.yml"))
tfile = os.path.join(tdir, "test.hdf")
raw_streams = []
for dfile in datafiles:
raw_streams += read_data(dfile)
workspace = StreamWorkspace(tfile)
t1 = time.time()
workspace.addStreams(event, raw_streams, label="raw")
t2 = time.time()
print("Adding %i streams took %.2f seconds" % (len(raw_streams),
(t2 - t1)))
str_repr = workspace.__repr__()
assert str_repr == "Events: 1 Stations: 3 Streams: 3"
eventobj = workspace.getEvent(eventid)
assert eventobj.origins[0].latitude == event.origins[0].latitude
assert eventobj.magnitudes[0].mag == event.magnitudes[0].mag
stations = workspace.getStations()
assert sorted(stations) == ["HSES", "THZ", "WTMC"]
stations = workspace.getStations()
assert sorted(stations) == ["HSES", "THZ", "WTMC"]
# test retrieving event that doesn't exist
with pytest.raises(KeyError):
workspace.getEvent("foo")
instream = None
for stream in raw_streams:
if stream[0].stats.station.lower() == "hses":
instream = stream
break
if instream is None:
raise ValueError("Instream should not be none.")
outstream = workspace.getStreams(eventid,
stations=["HSES"],
labels=["raw"])[0]
compare_streams(instream, outstream)
label_summary = workspace.summarizeLabels()
assert label_summary.iloc[0]["Label"] == "raw"
assert label_summary.iloc[0]["Software"] == "gmprocess"
sc = StreamCollection(raw_streams)
processed_streams = process_streams(sc, event, config=config)
workspace.addStreams(event, processed_streams, "processed")
idlist = workspace.getEventIds()
assert idlist[0] == eventid
outstream = workspace.getStreams(eventid,
stations=["HSES"],
labels=["processed"])[0]
provenance = workspace.getProvenance(eventid, labels=["processed"])
first_row = pd.Series({
"Record": "NZ.HSES.--.HN1_us1000778i_processed",
"Processing Step": "Remove Response",
"Step Attribute": "input_units",
"Attribute Value": "counts",
})
last_row = pd.Series({
"Record": "NZ.WTMC.--.HNZ_us1000778i_processed",
"Processing Step": "Lowpass Filter",
"Step Attribute": "number_of_passes",
"Attribute Value": 2,
})
assert provenance.iloc[0].equals(first_row)
assert provenance.iloc[-1].equals(last_row)
# compare the parameters from the input processed stream
# to it's output equivalent
instream = None
for stream in processed_streams:
if stream[0].stats.station.lower() == "hses":
instream = stream
break
if instream is None:
raise ValueError("Instream should not be none.")
compare_streams(instream, outstream)
workspace.close()
# read in data from a second event and stash it in the workspace
eventid = "nz2018p115908"
datafiles, event = read_data_dir("geonet", eventid, "*.V2A")
raw_streams = []
for dfile in datafiles:
raw_streams += read_data(dfile)
workspace = StreamWorkspace.open(tfile)
workspace.addStreams(event, raw_streams, label="foo")
stations = workspace.getStations()
eventids = workspace.getEventIds()
assert eventids == ["us1000778i", "nz2018p115908"]
instation = raw_streams[0][0].stats.station
this_stream = workspace.getStreams(eventid,
stations=[instation],
labels=["foo"])[0]
assert instation == this_stream[0].stats.station
usid = "us1000778i"
inventory = workspace.getInventory(usid)
workspace.close()
codes = [
station.code for station in inventory.networks[0].stations
]
assert sorted(set(codes)) == ["HSES", "THZ", "WPWS", "WTMC"]
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def _test_colocated():
eventid = 'ci38445975'
datafiles, event = read_data_dir('fdsn', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config_file = os.path.join(datadir, 'test_config.yml')
with open(config_file, 'r', encoding='utf-8') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
processed_streams = process_streams(raw_streams, event, config=config)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
ws = StreamWorkspace(tfile)
ws.addEvent(event)
ws.addStreams(event, raw_streams, label='raw')
ws.addStreams(event, processed_streams, label='processed')
ws.calcMetrics(eventid, labels=['processed'], config=config)
stasum = ws.getStreamMetrics(eventid, 'CI', 'MIKB', 'processed')
np.testing.assert_allclose(
stasum.get_pgm('duration', 'geometric_mean'), 38.94480068)
ws.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def _test_vs30_dist_metrics():
KNOWN_DISTANCES = {
'epicentral': 5.1,
'hypocentral': 10.2,
'rupture': 2.21,
'rupture_var': np.nan,
'joyner_boore': 2.21,
'joyner_boore_var': np.nan,
'gc2_rx': 2.66,
'gc2_ry': 3.49,
'gc2_ry0': 0.00,
'gc2_U': 34.34,
'gc2_T': 2.66
}
KNOWN_BAZ = 239.46
KNOWN_VS30 = 331.47
eventid = 'ci38457511'
datafiles, event = read_data_dir('fdsn', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = update_config(os.path.join(datadir, 'config_min_freq_0p2.yml'))
processed_streams = process_streams(raw_streams, event, config=config)
rupture_file = get_rupture_file(datadir)
grid_file = os.path.join(datadir, 'test_grid.grd')
config['metrics']['vs30'] = {
'vs30': {
'file': grid_file,
'column_header': 'GlobalVs30',
'readme_entry': 'GlobalVs30',
'units': 'm/s'
}
}
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
ws = StreamWorkspace(tfile)
ws.addEvent(event)
ws.addStreams(event, raw_streams, label='raw')
ws.addStreams(event, processed_streams, label='processed')
ws.calcMetrics(event.id,
rupture_file=rupture_file,
labels=['processed'],
config=config)
sta_sum = ws.getStreamMetrics(event.id, 'CI', 'CLC', 'processed')
for dist in sta_sum.distances:
np.testing.assert_allclose(sta_sum.distances[dist],
KNOWN_DISTANCES[dist],
rtol=0.01)
np.testing.assert_allclose(sta_sum._back_azimuth, KNOWN_BAZ, rtol=0.01)
np.testing.assert_allclose(sta_sum._vs30['vs30']['value'],
KNOWN_VS30,
rtol=0.01)
event_df, imc_tables, readme_tables = ws.getTables('processed')
ws.close()
check_cols = set([
'EpicentralDistance', 'HypocentralDistance', 'RuptureDistance',
'RuptureDistanceVar', 'JoynerBooreDistance',
'JoynerBooreDistanceVar', 'GC2_rx', 'GC2_ry', 'GC2_ry0', 'GC2_U',
'GC2_T', 'GlobalVs30', 'BackAzimuth'
])
assert check_cols.issubset(set(readme_tables['Z']['Column header']))
assert check_cols.issubset(set(imc_tables['Z'].columns))
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def _test_vs30_dist_metrics():
KNOWN_DISTANCES = {
"epicentral": 5.1,
"hypocentral": 10.2,
"rupture": 2.21,
"rupture_var": np.nan,
"joyner_boore": 2.21,
"joyner_boore_var": np.nan,
"gc2_rx": 2.66,
"gc2_ry": 3.49,
"gc2_ry0": 0.00,
"gc2_U": 34.34,
"gc2_T": 2.66,
}
KNOWN_BAZ = 239.46
KNOWN_VS30 = 331.47
eventid = "ci38457511"
datafiles, event = read_data_dir("fdsn", eventid, "*")
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = update_config(os.path.join(datadir, "config_min_freq_0p2.yml"))
processed_streams = process_streams(raw_streams, event, config=config)
rupture_file = get_rupture_file(datadir)
grid_file = os.path.join(datadir, "test_grid.grd")
config["metrics"]["vs30"] = {
"vs30": {
"file": grid_file,
"column_header": "GlobalVs30",
"readme_entry": "GlobalVs30",
"units": "m/s",
}
}
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, "test.hdf")
ws = StreamWorkspace(tfile)
ws.addEvent(event)
ws.addStreams(event, raw_streams, label="raw")
ws.addStreams(event, processed_streams, label="processed")
ws.calcMetrics(event.id,
rupture_file=rupture_file,
labels=["processed"],
config=config)
sta_sum = ws.getStreamMetrics(event.id, "CI", "CLC", "processed")
for dist in sta_sum.distances:
np.testing.assert_allclose(sta_sum.distances[dist],
KNOWN_DISTANCES[dist],
rtol=0.01)
np.testing.assert_allclose(sta_sum._back_azimuth, KNOWN_BAZ, rtol=0.01)
np.testing.assert_allclose(sta_sum._vs30["vs30"]["value"],
KNOWN_VS30,
rtol=0.01)
event_df, imc_tables, readme_tables = ws.getTables("processed")
ws.close()
check_cols = set([
"EpicentralDistance",
"HypocentralDistance",
"RuptureDistance",
"RuptureDistanceVar",
"JoynerBooreDistance",
"JoynerBooreDistanceVar",
"GC2_rx",
"GC2_ry",
"GC2_ry0",
"GC2_U",
"GC2_T",
"GlobalVs30",
"BackAzimuth",
])
assert check_cols.issubset(set(readme_tables["Z"]["Column header"]))
assert check_cols.issubset(set(imc_tables["Z"].columns))
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def _test_colocated():
eventid = "ci38445975"
datafiles, event = read_data_dir("fdsn", eventid, "*")
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config_file = os.path.join(datadir, "test_config.yml")
with open(config_file, "r", encoding="utf-8") as f:
yaml = YAML()
yaml.preserve_quotes = True
config = yaml.load(f)
processed_streams = process_streams(raw_streams, event, config=config)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, "test.hdf")
ws = StreamWorkspace(tfile)
ws.addEvent(event)
ws.addStreams(event, raw_streams, label="raw")
ws.addStreams(event, processed_streams, label="processed")
ws.calcMetrics(eventid, labels=["processed"], config=config)
stasum = ws.getStreamMetrics(eventid, "CI", "MIKB", "processed")
np.testing.assert_allclose(
stasum.get_pgm("duration", "geometric_mean"), 38.94480068)
ws.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def test_metrics():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = get_config()
# turn off sta/lta check and snr checks
# newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
# processed_streams = process_streams(raw_streams, event, config=newconfig)
newconfig = config.copy()
newconfig['processing'].append(
{'NNet_QA': {
'acceptance_threshold': 0.5,
'model_name': 'CantWell'
}})
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, raw_streams, label='raw')
workspace.addStreams(event, processed_streams, label='processed')
stream1 = raw_streams[0]
summary1 = StationSummary.from_config(stream1)
s1_df_in = summary1.pgms.sort_values(['IMT', 'IMC'])
array1 = s1_df_in['Result'].as_matrix()
workspace.calcStreamMetrics(eventid, labels=['raw'])
workspace.calcStationMetrics(event.id, labels=['raw'])
pstreams2 = workspace.getStreams(event.id, labels=['processed'])
assert pstreams2[0].getStreamParamKeys() == ['nnet_qa']
summary1_a = workspace.getStreamMetrics(event.id,
stream1[0].stats.network,
stream1[0].stats.station,
'raw')
s1_df_out = summary1_a.pgms.sort_values(['IMT', 'IMC'])
array2 = s1_df_out['Result'].as_matrix()
np.testing.assert_almost_equal(array1, array2, decimal=4)
workspace.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def test_metrics():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = update_config(os.path.join(datadir, 'config_min_freq_0p2.yml'))
# turn off sta/lta check and snr checks
# newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
# processed_streams = process_streams(raw_streams, event, config=newconfig)
newconfig = config.copy()
newconfig['processing'].append(
{'NNet_QA': {
'acceptance_threshold': 0.5,
'model_name': 'CantWell'
}})
processed_streams = process_streams(raw_streams.copy(),
event,
config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, raw_streams, label='raw')
workspace.addStreams(event, processed_streams, label='processed')
stream1 = raw_streams[0]
# Get metrics from station summary for raw streams
summary1 = StationSummary.from_config(stream1)
s1_df_in = summary1.pgms.sort_values(['IMT', 'IMC'])
array1 = s1_df_in['Result'].to_numpy()
# Compare to metrics from getStreamMetrics for raw streams
workspace.calcMetrics(eventid, labels=['raw'])
summary1_a = workspace.getStreamMetrics(event.id,
stream1[0].stats.network,
stream1[0].stats.station,
'raw')
s1_df_out = summary1_a.pgms.sort_values(['IMT', 'IMC'])
array2 = s1_df_out['Result'].to_numpy()
np.testing.assert_allclose(array1, array2, atol=1e-6, rtol=1e-6)
workspace.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def _test_metrics2():
eventid = "usb000syza"
datafiles, event = read_data_dir("knet", eventid, "*")
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = update_config(os.path.join(datadir, "config_min_freq_0p2.yml"))
config["metrics"]["output_imts"].append("Arias")
config["metrics"]["output_imcs"].append("arithmetic_mean")
# Adjust checks so that streams pass checks for this test
newconfig = drop_processing(config, ["check_sta_lta"])
csnr = [s for s in newconfig["processing"] if "compute_snr" in s.keys()][0]
csnr["compute_snr"]["check"]["threshold"] = -10.0
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, "test.hdf")
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label="processed")
workspace.calcMetrics(event.id, labels=["processed"])
etable, imc_tables1, readmes1 = workspace.getTables("processed")
assert "ARITHMETIC_MEAN" not in imc_tables1
assert "ARITHMETIC_MEAN" not in readmes1
del workspace.dataset.auxiliary_data.WaveFormMetrics
del workspace.dataset.auxiliary_data.StationMetrics
workspace.calcMetrics(event.id, labels=["processed"], config=config)
etable2, imc_tables2, readmes2 = workspace.getTables("processed")
assert "ARITHMETIC_MEAN" in imc_tables2
assert "ARITHMETIC_MEAN" in readmes2
assert "ARIAS" in imc_tables2["ARITHMETIC_MEAN"]
testarray = readmes2["ARITHMETIC_MEAN"]["Column header"].to_numpy()
assert "ARIAS" in testarray
workspace.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def _test_workspace():
eventid = 'us1000778i'
datafiles, event = read_data_dir('geonet', eventid, '*.V1A')
tdir = tempfile.mkdtemp()
try:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=H5pyDeprecationWarning)
warnings.filterwarnings("ignore", category=YAMLLoadWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
config = update_config(
os.path.join(datadir, 'config_min_freq_0p2.yml'))
tfile = os.path.join(tdir, 'test.hdf')
raw_streams = []
for dfile in datafiles:
raw_streams += read_data(dfile)
workspace = StreamWorkspace(tfile)
t1 = time.time()
workspace.addStreams(event, raw_streams, label='raw')
t2 = time.time()
print('Adding %i streams took %.2f seconds' % (len(raw_streams),
(t2 - t1)))
str_repr = workspace.__repr__()
assert str_repr == 'Events: 1 Stations: 3 Streams: 3'
eventobj = workspace.getEvent(eventid)
assert eventobj.origins[0].latitude == event.origins[0].latitude
assert eventobj.magnitudes[0].mag == event.magnitudes[0].mag
stations = workspace.getStations()
assert sorted(stations) == ['HSES', 'THZ', 'WTMC']
stations = workspace.getStations(eventid=eventid)
assert sorted(stations) == ['HSES', 'THZ', 'WTMC']
# test retrieving event that doesn't exist
with pytest.raises(KeyError):
workspace.getEvent('foo')
instream = None
for stream in raw_streams:
if stream[0].stats.station.lower() == 'hses':
instream = stream
break
if instream is None:
raise ValueError('Instream should not be none.')
outstream = workspace.getStreams(eventid,
stations=['HSES'],
labels=['raw'])[0]
compare_streams(instream, outstream)
label_summary = workspace.summarizeLabels()
assert label_summary.iloc[0]['Label'] == 'raw'
assert label_summary.iloc[0]['Software'] == 'gmprocess'
sc = StreamCollection(raw_streams)
processed_streams = process_streams(sc, event, config=config)
workspace.addStreams(event, processed_streams, 'processed')
idlist = workspace.getEventIds()
assert idlist[0] == eventid
outstream = workspace.getStreams(eventid,
stations=['HSES'],
labels=['processed'])[0]
provenance = workspace.getProvenance(eventid, labels=['processed'])
first_row = pd.Series({
'Record': 'NZ.HSES.--.HN1_us1000778i_processed',
'Processing Step': 'Remove Response',
'Step Attribute': 'input_units',
'Attribute Value': 'counts'
})
last_row = pd.Series({
'Record': 'NZ.WTMC.--.HNZ_us1000778i_processed',
'Processing Step': 'Lowpass Filter',
'Step Attribute': 'number_of_passes',
'Attribute Value': 2
})
assert provenance.iloc[0].equals(first_row)
assert provenance.iloc[-1].equals(last_row)
# compare the parameters from the input processed stream
# to it's output equivalent
instream = None
for stream in processed_streams:
if stream[0].stats.station.lower() == 'hses':
instream = stream
break
if instream is None:
raise ValueError('Instream should not be none.')
compare_streams(instream, outstream)
workspace.close()
# read in data from a second event and stash it in the workspace
eventid = 'nz2018p115908'
datafiles, event = read_data_dir('geonet', eventid, '*.V2A')
raw_streams = []
for dfile in datafiles:
raw_streams += read_data(dfile)
workspace = StreamWorkspace.open(tfile)
workspace.addStreams(event, raw_streams, label='foo')
stations = workspace.getStations(eventid)
eventids = workspace.getEventIds()
assert eventids == ['us1000778i', 'nz2018p115908']
instation = raw_streams[0][0].stats.station
this_stream = workspace.getStreams(eventid,
stations=[instation],
labels=['foo'])[0]
assert instation == this_stream[0].stats.station
usid = 'us1000778i'
inventory = workspace.getInventory(usid)
workspace.close()
codes = [
station.code for station in inventory.networks[0].stations
]
assert sorted(set(codes)) == ['HSES', 'THZ', 'WPWS', 'WTMC']
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
