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_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 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 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_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):
"""Generate summary report.
This function generates summary plots and then combines them into a
report with latex. If latex (specifically `pdflatex`) is not found on
the system then the PDF report will not be generated but the
constituent plots will be available.
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
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()
self.workspace.close()
if not hasattr(self, 'pstreams'):
logging.info('No processed waveforms available. No report '
'generated.')
return
logging.info('Creating diagnostic plots for event %s...' %
self.eventid)
plot_dir = os.path.join(event_dir, 'plots')
if not os.path.isdir(plot_dir):
os.makedirs(plot_dir)
for stream in self.pstreams:
summary_plots(stream, plot_dir, event)
mapfile = draw_stations_map(self.pstreams, event, event_dir)
moveoutfile = os.path.join(event_dir, 'moveout_plot.png')
plot_moveout(self.pstreams,
event.latitude,
event.longitude,
file=moveoutfile)
self.append_file('Station map', mapfile)
self.append_file('Moveout plot', moveoutfile)
logging.info('Generating summary report for event %s...' %
self.eventid)
build_conf = gmrecords.conf['build_report']
report_format = build_conf['format']
if report_format == 'latex':
report_file, success = build_report_latex(
self.pstreams,
event_dir,
event,
prefix="%s_%s" % (gmrecords.project, gmrecords.args.label),
config=gmrecords.conf)
else:
report_file = ''
success = False
if os.path.isfile(report_file) and success:
self.append_file('Summary report', report_file)
self._summarize_files_created()
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 _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()
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
})
rupture = get_rupture(origin, rupture_file)
if not hasattr(self, 'pstreams'):
logging.info('No processed waveforms available. No station '
'metrics computed.')
self.workspace.close()
return
for stream in 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=True,
rupture=rupture,
vs30_grids=self.vs30_grids)
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 process_event(event,
outdir,
pcommands,
config,
input_directory,
process_tag,
files_created,
output_format,
status,
recompute_metrics,
export_dir=None):
# setup logging to write to the input logfile
argthing = namedtuple('args', ['debug', 'quiet'])
args = argthing(debug=True, quiet=False)
setup_logger(args)
logger = logging.getLogger()
stream_handler = logger.handlers[0]
logfile = os.path.join(outdir, '%s.log' % event.id)
fhandler = logging.FileHandler(logfile)
logger.removeHandler(stream_handler)
logger.addHandler(fhandler)
event_dir = os.path.join(outdir, event.id)
if not os.path.exists(event_dir):
os.makedirs(event_dir)
workname = os.path.join(event_dir, WORKSPACE_NAME)
workspace_exists = os.path.isfile(workname)
workspace_has_processed = False
workspace = None
processing_done = False
if workspace_exists:
workspace = StreamWorkspace.open(workname)
labels = workspace.getLabels()
if len(labels):
labels.remove('unprocessed')
elif 'assemble' not in pcommands:
print('No data in workspace. Please run assemble.')
sys.exit(1)
if len(labels) == 1:
process_tag = labels[0]
workspace_has_processed = True
else:
if 'process' not in pcommands:
fmt = '\nThere are %i sets of processed data in %s.'
tpl = (len(labels), workname)
print(fmt % tpl)
print(('This software can only handle one set of '
'processed data. Exiting.\n'))
sys.exit(1)
download_done = False
# Need to initialize rstreams/pstreams
rstreams = []
pstreams = []
rupture_file = None
if 'assemble' in pcommands:
logging.info('Downloading/loading raw streams...')
workspace, workspace_file, rstreams, rupture_file = download(
event, event_dir, config, input_directory)
download_done = True
append_file(files_created, 'Workspace', workname)
else:
if not workspace_exists:
print('\nYou opted not to download or process from input.')
print('No previous HDF workspace file could be found.')
print('Try re-running with the assemble command with or ')
print('without the --directory option.\n')
sys.exit(1)
if 'process' in pcommands:
logging.info('Getting raw streams from workspace...')
with warnings.catch_warnings():
warnings.simplefilter("ignore",
category=H5pyDeprecationWarning)
rstreams = workspace.getStreams(event.id,
labels=['unprocessed'])
download_done = True
else:
need_processed = set(['report', 'shakemap'])
need_pstreams = len(need_processed.intersection(pcommands))
if workspace_has_processed:
if need_pstreams:
logging.info('Getting processed streams from workspace...')
with warnings.catch_warnings():
warnings.simplefilter("ignore",
category=H5pyDeprecationWarning)
pstreams = workspace.getStreams(event.id,
labels=[process_tag])
download_done = True
processing_done = True
if ('process' in pcommands and download_done and not processing_done
and len(rstreams)):
logging.info('Processing raw streams for event %s...' % event.id)
pstreams = process_streams(rstreams, event, config=config)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=H5pyDeprecationWarning)
workspace.addStreams(event, pstreams, label=process_tag)
workspace.calcMetrics(event.id,
labels=[process_tag],
config=config,
streams=pstreams,
stream_label=process_tag,
rupture_file=rupture_file)
processing_done = True
if 'export' in pcommands:
if export_dir is not None:
if not os.path.isdir(export_dir):
os.makedirs(export_dir)
outdir = export_dir
labels = workspace.getLabels()
if 'unprocessed' not in labels:
fmt = ('Workspace file "%s" appears to have no unprocessed '
'data. Skipping.')
logging.info(fmt % workspace_file)
else:
labels.remove('unprocessed')
if not labels:
fmt = ('Workspace file "%s" appears to have no processed '
'data. Skipping.')
logging.info(fmt % workspace_file)
else:
logging.info('Creating tables for event %s...', event.id)
with warnings.catch_warnings():
warnings.simplefilter("ignore",
category=H5pyDeprecationWarning)
if recompute_metrics:
del workspace.dataset.auxiliary_data.WaveFormMetrics
del workspace.dataset.auxiliary_data.StationMetrics
workspace.calcMetrics(event.id,
labels=labels,
config=config,
rupture_file=rupture_file)
event_table, imc_tables, readmes = workspace.getTables(
labels[0], streams=pstreams, stream_label=process_tag)
ev_fit_spec, fit_readme = workspace.getFitSpectraTable(
event.id, labels[0], pstreams)
# 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)
if not os.path.isdir(outdir):
os.makedirs(outdir)
filenames = ['events'] + \
[imc.lower() for imc in imc_tables_formatted.keys()] + \
[imc.lower() + '_README' for imc in readmes.keys()] + \
['fit_spectra_parameters', 'fit_spectra_parameters_README']
files = [event_table_formatted] + list(
imc_tables_formatted.values()) + list(readmes.values()) + [
df_fit_spectra_formatted, fit_readme
]
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:
mode = 'a'
header = False
else:
mode = 'w'
header = True
append_file(files_created, 'Tables', filepath)
if output_format == 'csv':
df.to_csv(filepath,
index=False,
float_format=DEFAULT_FLOAT_FORMAT,
na_rep=DEFAULT_NA_REP,
mode=mode,
header=header)
else:
df.to_excel(filepath,
index=False,
float_format=DEFAULT_FLOAT_FORMAT,
na_rep=DEFAULT_NA_REP,
mode=mode,
header=header)
if ('report' in pcommands and processing_done and len(pstreams)):
logging.info('Creating diagnostic plots for event %s...' % event.id)
plot_dir = os.path.join(event_dir, 'plots')
if not os.path.isdir(plot_dir):
os.makedirs(plot_dir)
for stream in pstreams:
summary_plots(stream, plot_dir, event)
mapfile = draw_stations_map(pstreams, event, event_dir)
plot_moveout(pstreams,
event.latitude,
event.longitude,
file=os.path.join(event_dir, 'moveout_plot.png'))
append_file(files_created, 'Station map', mapfile)
append_file(files_created, 'Moveout plot', 'moveout_plot.png')
logging.info('Creating diagnostic report for event %s...' % event.id)
# Build the summary report?
build_conf = config['build_report']
report_format = build_conf['format']
if report_format == 'latex':
report_file, success = build_report_latex(pstreams,
event_dir,
event,
config=config)
else:
report_file = ''
success = False
if os.path.isfile(report_file) and success:
append_file(files_created, 'Summary report', report_file)
if 'provenance' in pcommands and processing_done and len(pstreams):
logging.info('Creating provenance table for event %s...' % event.id)
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=H5pyDeprecationWarning)
provdata = workspace.getProvenance(event.id, labels=[process_tag])
if output_format == 'csv':
csvfile = os.path.join(event_dir, 'provenance.csv')
append_file(files_created, 'Provenance', csvfile)
provdata.to_csv(csvfile)
else:
excelfile = os.path.join(event_dir, 'provenance.xlsx')
append_file(files_created, 'Provenance', excelfile)
provdata.to_excel(excelfile, index=False)
if 'shakemap' in pcommands and processing_done and len(pstreams):
logging.info('Creating shakemap table for event %s...' % event.id)
shakemap_file, jsonfile = save_shakemap_amps(pstreams, event,
event_dir)
append_file(files_created, 'shakemap', shakemap_file)
append_file(files_created, 'shakemap', jsonfile)
if status and processing_done and len(pstreams):
if status == 'short':
index = 'Failure reason'
col = ['Number of records']
elif status == 'long':
index = 'Station ID'
col = ['Failure reason']
elif status == 'net':
index = 'Network'
col = ['Number of passed records', 'Number of failed records']
status_info = pstreams.get_status(status)
status_info.to_csv(os.path.join(event_dir, 'status.csv'),
header=col,
index_label=index)
# since we don't know how many events users will be processing,
# let's guard against memory issues by clearing out the big data
# structures
workspace.close()
logging.info('Finishing event %s' % event.id)
return workname
def get_rec_plot(eqid, stid, wdir):
ws = StreamWorkspace.open(os.path.join(wdir, eqid, 'workspace.h5'))
labels = ws.getLabels()
labels.remove('unprocessed')
station = stid.split('.')[1]
r_st = ws.getStreams(eqid, stations=[station], labels=['unprocessed'])[0]
if labels:
p_st = ws.getStreams(eqid, stations=[station], labels=[labels[0]])[0]
v_st = p_st.copy().integrate()
fig = make_subplots(rows=5, cols=len(r_st))
shapes = []
for tr_idx, r_tr in enumerate(r_st):
p_tr, v_tr = p_st[tr_idx], v_st[tr_idx]
x_start = p_tr.stats.starttime - r_tr.stats.starttime
x_end = p_tr.stats.endtime - r_tr.stats.starttime
sig_dict = p_tr.getCached('signal_spectrum')
noi_dict = p_tr.getCached('noise_spectrum')
sig_dict_s = p_tr.getCached('smooth_signal_spectrum')
noi_dict_s = p_tr.getCached('smooth_noise_spectrum')
snr_dict = p_tr.getCached('snr')
sig_spec, sig_freq = sig_dict['spec'], sig_dict['freq']
noi_spec, noi_freq = noi_dict['spec'], noi_dict['freq']
sig_spec_s, sig_freq_s = sig_dict_s['spec'], sig_dict_s['freq']
noi_spec_s, noi_freq_s = noi_dict_s['spec'], noi_dict_s['freq']
snr, snr_freq = snr_dict['snr'], snr_dict['freq']
snr_conf = p_tr.getParameter('snr_conf')
threshold = snr_conf['threshold']
min_freq, max_freq = snr_conf['min_freq'], snr_conf['max_freq']
lp = p_tr.getProvenance('lowpass_filter')[0]['corner_frequency']
hp = p_tr.getProvenance('highpass_filter')[0]['corner_frequency']
fit_spectra_dict = p_tr.getParameter('fit_spectra')
f0 = fit_spectra_dict['f0']
model_spec = spectrum.model(
(fit_spectra_dict['moment'], fit_spectra_dict['stress_drop']),
freq=np.array(sig_freq_s),
dist=fit_spectra_dict['epi_dist'],
kappa=fit_spectra_dict['kappa'])
scatter_data = [[r_tr.times(), r_tr.data, 'black', None, 1],
[p_tr.times(), p_tr.data, 'black', None, 2],
[v_tr.times(), v_tr.data, 'black', None, 3],
[sig_freq, sig_spec, 'lightblue', None, 4],
[sig_freq_s, sig_spec_s, 'blue', None, 4],
[noi_freq, noi_spec, 'salmon', None, 4],
[noi_freq_s, noi_spec_s, 'red', None, 4],
[sig_freq_s, model_spec, 'black', 'dash', 4],
[snr_freq, snr, 'black', None, 5]]
line_data = [[
x_start,
r_tr.data.min(), x_start,
r_tr.data.max(), 'red', 'dash', 1
], [x_end,
r_tr.data.min(), x_end,
r_tr.data.max(), 'red', 'dash', 1],
[f0, 1e-10, f0,
sig_spec.max(), 'black', 'dash', 4],
[
snr_freq.min(), threshold,
snr_freq.max(), threshold, 'gray', None, 5
], [min_freq, 1e-3, min_freq,
snr.max(), 'gray', None, 5],
[max_freq, 1e-3, max_freq,
snr.max(), 'gray', None, 5],
[lp, 1e-3, lp,
snr.max(), 'black', 'dash', 5],
[hp, 1e-3, hp,
snr.max(), 'black', 'dash', 5]]
for scatter in scatter_data:
fig.append_trace(go.Scatter(x=scatter[0],
y=scatter[1],
line=dict(color=scatter[2],
dash=scatter[3])),
row=scatter[4],
col=tr_idx + 1)
for line in line_data:
i_ref = len(r_st) * (line[6] - 1) + tr_idx + 1
shapes.append({
'type': 'line',
'x0': line[0],
'y0': line[1],
'x1': line[2],
'y1': line[3],
'xref': 'x%s' % i_ref,
'yref': 'y%s' % i_ref,
'line': {
'color': line[4],
'dash': line[5]
}
})
fig.update_xaxes(title_text='Time (s)')
fig.update_yaxes(row=1, col=1, title_text='Raw counts')
fig.update_yaxes(row=2, col=1, title_text='Acceleration (cm/s^2)')
fig.update_yaxes(row=3, col=1, title_text='Velocity (cm/s)')
fig.update_xaxes(row=4, title_text='Frequency (Hz)', type='log')
fig.update_yaxes(row=4, col=1, title_text='Amplitude (cm/s)', type='log')
fig.update_yaxes(row=4, type='log')
fig.update_xaxes(row=5, title_text='Frequency (Hz)', type='log')
fig.update_yaxes(row=5, col=1, title_text='SNR', type='log')
fig.update_yaxes(row=5, type='log')
fig.update_layout(showlegend=False)
fig['layout'].update(shapes=shapes,
margin={
'b': 0,
't': 0,
'l': 0,
'r': 30
})
ws.close()
return fig
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 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_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_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)
def main(self, gmrecords):
"""Export failure tables.
Args:
gmrecords:
GMrecordsApp instance.
"""
logging.info('Running subcommand \'%s\'' % self.command_name)
self.gmrecords = gmrecords
self._get_events()
failures = {}
for event in self.events:
self.eventid = event.id
logging.info('Creating failure tables 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.')
continue
self.workspace = StreamWorkspace.open(workname)
self._get_pstreams()
self.workspace.close()
if not hasattr(self, 'pstreams'):
logging.info('No processed waveforms available. No failure '
'tables created.')
return
status_info = self.pstreams.get_status(self.gmrecords.args.type)
failures[event.id] = status_info
base_file_name = os.path.join(
event_dir, '%s_%s_failure_reasons_%s' %
(gmrecords.project, gmrecords.args.label,
self.gmrecords.args.type))
if self.gmrecords.args.output_format == 'csv':
csvfile = base_file_name + '.csv'
self.append_file('Failure table', csvfile)
status_info.to_csv(csvfile)
else:
excelfile = base_file_name + '.xlsx'
self.append_file('Failure table', excelfile)
status_info.to_excel(excelfile)
if failures:
comp_failures_path = os.path.join(
self.gmrecords.data_path, '%s_%s_complete_failures.csv' %
(gmrecords.project, gmrecords.args.label))
if self.gmrecords.args.type == 'long':
for idx, item in enumerate(failures.items()):
eqid, status = item
status = pd.DataFrame(status)
status['EarthquakeId'] = eqid
if idx == 0:
status.to_csv(comp_failures_path, mode='w')
else:
status.to_csv(comp_failures_path,
mode='a',
header=False)
else:
df_failures = pd.concat(failures.values())
df_failures = df_failures.groupby(df_failures.index).sum()
df_failures.to_csv(comp_failures_path)
self.append_file('Complete failures', comp_failures_path)
self._summarize_files_created()
def test_workspace():
eventid = 'us1000778i'
datafiles, origin = read_data_dir('geonet', eventid, '*.V1A')
event = get_event_object(origin)
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, origin, 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': 'Detrend',
'Step Attribute': 'detrending_method',
'Attribute Value': 'baseline_sixth_order'
})
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, origin = read_data_dir('geonet', eventid, '*.V2A')
raw_streams = []
for dfile in datafiles:
raw_streams += read_data(dfile)
event = get_event_object(origin)
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
# set and retrieve waveform metrics in the file
imclist = [
'greater_of_two_horizontals', 'channels', 'rotd50', 'rotd100'
]
imtlist = ['sa1.0', 'PGA', 'pgv', 'fas2.0', 'arias']
usid = 'us1000778i'
tags = workspace.getStreamTags(usid)
workspace.setStreamMetrics(eventid,
labels=['foo'],
imclist=imclist,
imtlist=imtlist)
summary = workspace.getStreamMetrics(eventid, instation, 'foo')
summary_series = summary.toSeries()['ARIAS']
cmpseries = pd.Series({
'GEOMETRIC_MEAN': np.NaN,
'GREATER_OF_TWO_HORIZONTALS': 0.0005,
'HN1': 0.0001,
'HN2': 0.0005,
'HNZ': 0.0000,
'ROTD100.0': 0.0005,
'ROTD50.0': 0.0003
})
assert cmpseries.equals(summary_series)
workspace.setStreamMetrics(usid, labels=['processed'])
df = workspace.getMetricsTable(usid, labels=['processed'])
cmpdict = {
'GREATER_OF_TWO_HORIZONTALS': [26.8906, 4.9415, 94.6646],
'HN1': [24.5105, 4.9415, 94.6646],
'HN2': [26.8906, 4.0758, 86.7877],
'HNZ': [16.0941, 2.5401, 136.7054]
}
cmpframe = pd.DataFrame(cmpdict)
assert df['PGA'].equals(cmpframe)
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)
