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_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_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_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')
# 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, 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
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 = 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 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_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)
workspace.calcStationMetrics(event.id, labels=['processed'])
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,
'H1': 0.3869,
'H2': 0.6787,
'Z': 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_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_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 = 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_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_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_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 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 _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 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_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)
