def getStreamMetrics(self, eventid, station, label):
"""Extract a StationSummary object from the ASDF file for a given input Stream.
Args:
eventid (str):
ID of event to search for in ASDF file.
station (str):
Station to return metrics from.
label (str):
Processing label to return metrics from.
Returns:
StationSummary: Object containing all stream metrics.
"""
if 'WaveFormMetrics' not in self.dataset.auxiliary_data:
logging.warning('Waveform metrics not found in workspace, '
'cannot get stream metrics.')
auxholder = self.dataset.auxiliary_data.WaveFormMetrics
stream_path = '%s_%s_%s' % (eventid, station.lower(), label)
if stream_path not in auxholder:
logging.warning(
'Stream path (%s) not in WaveFormMetrics auxiliary_data.' %
stream_path)
return
bytelist = auxholder[stream_path].data[:].tolist()
xml_stream = ''.join([chr(b) for b in bytelist])
xml_stream = xml_stream.encode('utf-8')
if 'StationMetrics' not in self.dataset.auxiliary_data:
raise KeyError('Station metrics not found in workspace.')
auxholder = self.dataset.auxiliary_data.StationMetrics
stream_path = '%s_%s_%s' % (eventid, station.lower(), label)
if stream_path not in auxholder:
logging.warning(
'Stream path (%s) not in StationMetrics auxiliary_data.' %
stream_path)
return
bytelist = auxholder[stream_path].data[:].tolist()
xml_station = ''.join([chr(b) for b in bytelist])
xml_station = xml_station.encode('utf-8')
summary = StationSummary.from_xml(xml_stream, xml_station)
return summary
def getStreamMetrics(self, eventid, network, station, label, streams=None,
stream_label=None, config=None):
"""Extract a StationSummary object from the ASDF file for a given
input Stream.
Args:
eventid (str):
ID of event to search for in ASDF file.
network (str):
Network to return metrics from.
station (str):
Station to return metrics from.
label (str):
Processing label to return metrics from.
streams (StreamCollection):
Optional StreamCollection object to get metrics for.
stream_label (str):
Label to be used in the metrics path when providing a
StreamCollection.
config (dict):
Configuration options.
Returns:
StationSummary: Object containing all stream metrics or None.
"""
if 'WaveFormMetrics' not in self.dataset.auxiliary_data:
msg = ('Waveform metrics not found in workspace, '
'cannot get stream metrics.')
logging.warning(msg)
return None
auxholder = self.dataset.auxiliary_data.WaveFormMetrics
# get the stream matching the eventid, station, and label
if streams is None:
streams = self.getStreams(eventid, stations=[station],
labels=[label], config=config)
# Only get streams that passed and match network
streams = [st for st in streams if
(st.passed and st[0].stats.network == network)]
if not len(streams):
fmt = ('Stream matching event ID %s, '
'station ID %s, and processing label %s not found in '
'workspace.')
msg = fmt % (eventid, station, label)
logging.warning(msg)
return None
if stream_label is not None:
stream_tag = '%s_%s' % (eventid, stream_label)
else:
stream_tag = streams[0].tag
metricpath = format_nslit(streams[0][0].stats,
streams[0].get_inst(),
stream_tag)
top = format_netsta(streams[0][0].stats)
if top in auxholder:
tauxholder = auxholder[top]
if metricpath not in tauxholder:
fmt = ('Stream metrics path (%s) not in WaveFormMetrics '
'auxiliary_data.')
logging.warning(fmt % metricpath)
return None
bytelist = tauxholder[metricpath].data[:].tolist()
xml_stream = ''.join([chr(b) for b in bytelist])
xml_stream = xml_stream.encode('utf-8')
else:
return
if 'StationMetrics' not in self.dataset.auxiliary_data:
logging.warning('Station metrics not found in workspace.')
return None
auxholder = self.dataset.auxiliary_data.StationMetrics
station_path = format_nslit(
streams[0][0].stats, streams[0].get_inst(), eventid)
if top in auxholder:
tauxholder = auxholder[top]
if station_path not in tauxholder:
logging.warning(
'Stream path (%s) not in StationMetrics auxiliary_data.'
% station_path)
return
bytelist = tauxholder[station_path].data[:].tolist()
xml_station = ''.join([chr(b) for b in bytelist])
xml_station = xml_station.encode('utf-8')
else:
return
summary = StationSummary.from_xml(xml_stream, xml_station)
return summary
def test_stationsummary():
datafiles, _ = read_data_dir(
'geonet', 'us1000778i', '20161113_110259_WTMC_20.V2A')
datafile = datafiles[0]
origin = Origin(latitude=42.6925, longitude=173.021944)
target_imcs = np.sort(np.asarray(['GREATER_OF_TWO_HORIZONTALS',
'H1', 'H2', 'Z', 'ROTD(50.0)',
'ROTD(100.0)']))
target_imts = np.sort(np.asarray(['SA(1.0)', 'PGA', 'PGV']))
stream = read_geonet(datafile)[0]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
stream_summary = StationSummary.from_stream(
stream,
['greater_of_two_horizontals',
'channels',
'rotd50',
'rotd100',
'invalid'],
['sa1.0', 'PGA', 'pgv', 'invalid'], origin)
original_stream = stream_summary.stream
stream_summary.stream = []
final_stream = stream_summary.stream
assert original_stream == final_stream
original_code = stream_summary.station_code
np.testing.assert_array_equal(np.sort(stream_summary.components),
target_imcs)
np.testing.assert_array_equal(np.sort(stream_summary.imts),
target_imts)
np.testing.assert_almost_equal(stream_summary.get_pgm('PGA', 'H1'),
99.3173469387755, decimal=1)
target_available = np.sort(np.asarray([
'greater_of_two_horizontals', 'geometric_mean', 'arithmetic_mean',
'channels', 'gmrotd', 'rotd', 'quadratic_mean',
'radial_transverse']))
imcs = stream_summary.available_imcs
np.testing.assert_array_equal(np.sort(imcs), target_available)
target_available = np.sort(np.asarray(['pga',
'pgv',
'sa',
'arias',
'fas']))
imts = stream_summary.available_imts
np.testing.assert_array_equal(np.sort(imts), target_available)
test_pgms = {
'PGV': {
'ROTD(100.0)': 114.24894584734818,
'ROTD(50.0)': 81.55436750525355,
'Z': 37.47740000000001,
'H1': 100.81460000000004,
'H2': 68.4354,
'GREATER_OF_TWO_HORIZONTALS': 100.81460000000004},
'PGA': {
'ROTD(100.0)': 100.73875535385548,
'ROTD(50.0)': 91.40178541935455,
'Z': 183.7722361866693,
'H1': 99.24999872535474,
'H2': 81.23467239067368,
'GREATER_OF_TWO_HORIZONTALS': 99.24999872535474},
'SA(1.0)': {
'ROTD(100.0)': 146.9023350124098,
'ROTD(50.0)': 106.03202302692158,
'Z': 27.74118995438756,
'H1': 136.25041187387063,
'H2': 84.69296738413021,
'GREATER_OF_TWO_HORIZONTALS': 136.25041187387063}
}
pgms = stream_summary.pgms
for imt_str in test_pgms:
for imc_str in test_pgms[imt_str]:
imt = pgms.loc[pgms['IMT'] == imt_str]
imc = imt.loc[imt['IMC'] == imc_str]
results = imc.Result.tolist()
assert len(results) == 1
np.testing.assert_almost_equal(results[0], test_pgms[imt_str][imc_str],
decimal=10)
# Test with fas
stream = read_geonet(datafile)[0]
stream_summary = StationSummary.from_stream(
stream,
['greater_of_two_horizontals',
'channels',
'geometric_mean'],
['sa1.0', 'PGA', 'pgv', 'fas2.0'])
target_imcs = np.sort(np.asarray(['GREATER_OF_TWO_HORIZONTALS',
'H1', 'H2', 'Z',
'GEOMETRIC_MEAN']))
target_imts = np.sort(np.asarray(['SA(1.0)',
'PGA', 'PGV', 'FAS(2.0)']))
np.testing.assert_array_equal(np.sort(stream_summary.components),
target_imcs)
np.testing.assert_array_equal(np.sort(stream_summary.imts),
target_imts)
# Test config use
stream = read_geonet(datafile)[0]
stream_summary = StationSummary.from_config(stream)
target_imcs = np.sort(np.asarray(['GREATER_OF_TWO_HORIZONTALS',
'H1', 'H2', 'Z']))
target_imts = np.sort(np.asarray(['SA(1.0)', 'SA(2.0)', 'SA(3.0)',
'SA(0.3)', 'PGA', 'PGV', 'FAS(1.0)', 'FAS(2.0)',
'FAS(3.0)', 'FAS(0.3)']))
assert(stream_summary.smoothing == 'konno_ohmachi')
assert(stream_summary.bandwidth == 20.0)
assert(stream_summary.damping == 0.05)
# test XML output
stream = read_geonet(datafile)[0]
imclist = ['greater_of_two_horizontals',
'channels',
'rotd50.0',
'rotd100.0']
imtlist = ['sa1.0', 'PGA', 'pgv', 'fas2.0', 'arias']
stream_summary = StationSummary.from_stream(stream, imclist, imtlist)
xmlstr = stream_summary.get_metric_xml()
print(xmlstr)
xml_station = stream_summary.get_station_xml()
stream2 = StationSummary.from_xml(xmlstr, xml_station)
cmp1 = np.sort(['GREATER_OF_TWO_HORIZONTALS', 'H1', 'H2', 'Z',
'ROTD100.0', 'ROTD50.0'])
cmp2 = np.sort(stream2.components)
np.testing.assert_array_equal(cmp1, cmp2)
imt1 = np.sort(stream_summary.imts)
imt2 = np.sort(stream2.imts)
np.testing.assert_array_equal(imt1, imt2)
def getStreamMetrics(self, eventid, network, station, label):
"""Extract a StationSummary object from the ASDF file for a given input Stream.
Args:
eventid (str):
ID of event to search for in ASDF file.
network (str):
Network to return metrics from.
station (str):
Station to return metrics from.
label (str):
Processing label to return metrics from.
Returns:
StationSummary: Object containing all stream metrics or None.
"""
if 'WaveFormMetrics' not in self.dataset.auxiliary_data:
logging.warning('Waveform metrics not found in workspace, '
'cannot get stream metrics.')
auxholder = self.dataset.auxiliary_data.WaveFormMetrics
# get the stream matching the eventid, station, and label
streams = self.getStreams(eventid, stations=[station],
labels=[label])
# Only get streams that passed and match network
streams = [st for st in streams if
(st.passed and st[0].stats.network == network)]
if not len(streams):
fmt = '''Stream matching event ID %s,
station ID %s, and processing label %s not found in workspace.'''
msg = fmt % (eventid, station, label)
logging.warning(msg)
return None
metricpath = format_nslit(streams[0][0].stats,
streams[0].get_inst(),
streams[0].tag)
top = format_netsta(streams[0][0].stats)
if top in auxholder:
tauxholder = auxholder[top]
if metricpath not in tauxholder:
fmt = 'Stream metrics path (%s) not in WaveFormMetrics auxiliary_data.'
logging.warning(fmt % metricpath)
return None
bytelist = tauxholder[metricpath].data[:].tolist()
xml_stream = ''.join([chr(b) for b in bytelist])
xml_stream = xml_stream.encode('utf-8')
if 'StationMetrics' not in self.dataset.auxiliary_data:
raise KeyError('Station metrics not found in workspace.')
auxholder = self.dataset.auxiliary_data.StationMetrics
station_path = format_nslit(streams[0][0].stats, streams[0].get_inst(), eventid)
if top in auxholder:
tauxholder = auxholder[top]
if station_path not in tauxholder:
logging.warning(
'Stream path (%s) not in StationMetrics auxiliary_data.'
% station_path)
return
bytelist = tauxholder[station_path].data[:].tolist()
xml_station = ''.join([chr(b) for b in bytelist])
xml_station = xml_station.encode('utf-8')
summary = StationSummary.from_xml(xml_stream, xml_station)
return summary
def test_stationsummary():
datafiles, _ = read_data_dir("geonet", "us1000778i",
"20161113_110259_WTMC_20.V2A")
datafile = datafiles[0]
origin = Origin(latitude=42.6925, longitude=173.021944)
target_imcs = np.sort(
np.asarray([
"GREATER_OF_TWO_HORIZONTALS", "H1", "H2", "Z", "ROTD(50.0)",
"ROTD(100.0)"
]))
target_imts = np.sort(np.asarray(["SA(1.000)", "PGA", "PGV"]))
stream = read_geonet(datafile)[0]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
stream_summary = StationSummary.from_stream(
stream,
[
"greater_of_two_horizontals", "channels", "rotd50", "rotd100",
"invalid"
],
["sa1.0", "PGA", "pgv", "invalid"],
origin,
)
original_stream = stream_summary.stream
stream_summary.stream = []
final_stream = stream_summary.stream
assert original_stream == final_stream
original_code = stream_summary.station_code
np.testing.assert_array_equal(np.sort(stream_summary.components),
target_imcs)
np.testing.assert_array_equal(np.sort(stream_summary.imts),
target_imts)
np.testing.assert_almost_equal(stream_summary.get_pgm("PGA", "H1"),
99.3173469387755,
decimal=1)
target_available = np.sort(
np.asarray([
"greater_of_two_horizontals",
"geometric_mean",
"arithmetic_mean",
"channels",
"gmrotd",
"rotd",
"quadratic_mean",
"radial_transverse",
]))
imcs = stream_summary.available_imcs
np.testing.assert_array_equal(np.sort(imcs), target_available)
target_available = np.sort(
np.asarray([
"pga", "pgv", "sa", "arias", "fas", "duration",
"sorted_duration"
]))
imts = stream_summary.available_imts
np.testing.assert_array_equal(np.sort(imts), target_available)
test_pgms = {
"PGV": {
"ROTD(100.0)": 114.24894584734818,
"ROTD(50.0)": 81.55436750525355,
"Z": 37.47740000000001,
"H1": 100.81460000000004,
"H2": 68.4354,
"GREATER_OF_TWO_HORIZONTALS": 100.81460000000004,
},
"PGA": {
"ROTD(100.0)": 100.73875535385548,
"ROTD(50.0)": 91.40178541935455,
"Z": 183.7722361866693,
"H1": 99.24999872535474,
"H2": 81.23467239067368,
"GREATER_OF_TWO_HORIZONTALS": 99.24999872535474,
},
"SA(1.000)": {
"ROTD(100.0)": 146.9023350124098,
"ROTD(50.0)": 106.03202302692158,
"Z": 27.74118995438756,
"H1": 136.25041187387063,
"H2": 84.69296738413021,
"GREATER_OF_TWO_HORIZONTALS": 136.25041187387063,
},
}
pgms = stream_summary.pgms
for imt_str in test_pgms:
for imc_str in test_pgms[imt_str]:
result = pgms.loc[imt_str, imc_str].Result
np.testing.assert_almost_equal(result,
test_pgms[imt_str][imc_str],
decimal=10)
# Test with fas
stream = read_geonet(datafile)[0]
stream_summary = StationSummary.from_stream(
stream,
["greater_of_two_horizontals", "channels", "geometric_mean"],
["sa1.0", "PGA", "pgv", "fas2.0"],
)
target_imcs = np.sort(
np.asarray(
["GEOMETRIC_MEAN", "GREATER_OF_TWO_HORIZONTALS", "H1", "H2", "Z"]))
target_imts = np.sort(np.asarray(["SA(1.000)", "PGA", "PGV",
"FAS(2.000)"]))
np.testing.assert_array_equal(np.sort(stream_summary.components),
target_imcs)
np.testing.assert_array_equal(np.sort(stream_summary.imts), target_imts)
# Test config use
stream = read_geonet(datafile)[0]
stream_summary = StationSummary.from_config(stream)
target_imcs = np.sort(
np.asarray(["GREATER_OF_TWO_HORIZONTALS", "H1", "H2", "Z"]))
target_imts = np.sort(
np.asarray([
"SA(1.000)",
"SA(2.000)",
"SA(3.000)",
"SA(0.300)",
"PGA",
"PGV",
"FAS(1.000)",
"FAS(2.000)",
"FAS(3.000)",
"FAS(0.300)",
]))
assert stream_summary.smoothing == "konno_ohmachi"
assert stream_summary.bandwidth == 20.0
assert stream_summary.damping == 0.05
# test XML output
stream = read_geonet(datafile)[0]
imclist = [
"greater_of_two_horizontals", "channels", "rotd50.0", "rotd100.0"
]
imtlist = ["sa1.0", "PGA", "pgv", "fas2.0", "arias"]
stream_summary = StationSummary.from_stream(stream, imclist, imtlist)
xmlstr = stream_summary.get_metric_xml()
xml_station = stream_summary.get_station_xml()
stream2 = StationSummary.from_xml(xmlstr, xml_station)
cmp1 = np.sort([
"GREATER_OF_TWO_HORIZONTALS", "H1", "H2", "ROTD100.0", "ROTD50.0", "Z"
])
cmp2 = np.sort(stream2.components)
np.testing.assert_array_equal(cmp1, cmp2)
imt1 = np.sort(stream_summary.imts)
imt2 = np.sort(stream2.imts)
np.testing.assert_array_equal(imt1, imt2)
