def test_exceptions():
ddir = os.path.join("data", "testdata", "geonet")
homedir = pkg_resources.resource_filename("gmprocess", ddir)
datafile_v2 = os.path.join(homedir, "us1000778i",
"20161113_110259_WTMC_20.V2A")
stream_v2 = read_geonet(datafile_v2)[0]
# Check for origin Error
passed = True
try:
m = MetricsController("pga",
"radial_transverse",
stream_v2,
config=config)
except PGMException as e:
passed = False
assert passed == False
# -------- Horizontal Channel Errors -----------
# Check for horizontal passthrough gm
st2 = stream_v2.select(component="[N1]")
st3 = stream_v2.select(component="Z")
st1 = StationStream([st2[0], st3[0]])
passed = True
m = MetricsController("pga", "geometric_mean", st1, config=config)
pgm = m.pgms
result = pgm["Result"].tolist()[0]
assert np.isnan(result)
# Check for horizontal passthrough rotd50
m = MetricsController("pga", "rotd50", st1, config=config)
pgm = m.pgms
result = pgm["Result"].tolist()[0]
assert np.isnan(result)
# Check for horizontal passthrough gmrotd50
m = MetricsController("pga", "gmrotd50", st1, config=config)
pgm = m.pgms
result = pgm["Result"].tolist()[0]
assert np.isnan(result)
# No horizontal channels
try:
m = MetricsController("sa3.0", "channels", st3, config=config)
except PGMException as e:
passed = False
assert passed == False
def test_exceptions():
ddir = os.path.join('data', 'testdata', 'geonet')
homedir = pkg_resources.resource_filename('gmprocess', ddir)
datafile_v2 = os.path.join(homedir, 'us1000778i',
'20161113_110259_WTMC_20.V2A')
stream_v2 = read_geonet(datafile_v2)[0]
# Check for origin Error
passed = True
try:
m = MetricsController('pga',
'radial_transverse',
stream_v2,
config=config)
except PGMException as e:
passed = False
assert passed == False
# -------- Horizontal Channel Errors -----------
# Check for horizontal passthrough gm
st2 = stream_v2.select(component='[N1]')
st3 = stream_v2.select(component='Z')
st1 = StationStream([st2[0], st3[0]])
passed = True
m = MetricsController('pga', 'geometric_mean', st1, config=config)
pgm = m.pgms
result = pgm['Result'].tolist()[0]
assert np.isnan(result)
# Check for horizontal passthrough rotd50
m = MetricsController('pga', 'rotd50', st1, config=config)
pgm = m.pgms
result = pgm['Result'].tolist()[0]
assert np.isnan(result)
# Check for horizontal passthrough gmrotd50
m = MetricsController('pga', 'gmrotd50', st1, config=config)
pgm = m.pgms
result = pgm['Result'].tolist()[0]
assert np.isnan(result)
# No horizontal channels
try:
m = MetricsController('sa3.0', 'channels', st3, config=config)
except PGMException as e:
passed = False
assert passed == False
def test_end_to_end():
datafiles, _ = read_data_dir('geonet', 'us1000778i',
'20161113_110259_WTMC_20.V2A')
datafile = datafiles[0]
target_imcs = np.sort(
np.asarray([
'GREATER_OF_TWO_HORIZONTALS', 'H1', 'H2', 'Z', 'ROTD50.0',
'ROTD100.0'
]))
target_imts = np.sort(np.asarray(['SA(1.0)', 'PGA', 'PGV']))
stream = read_geonet(datafile)[0]
input_imcs = [
'greater_of_two_horizontals', 'channels', 'rotd50', 'rotd100',
'invalid'
]
input_imts = ['sa1.0', 'PGA', 'pgv', 'invalid']
m = MetricsController(input_imts, input_imcs, stream)
test_pgms = [('PGV', 'ROTD(100.0)', 114.24894584734818),
('PGV', 'ROTD(50.0)', 81.55436750525355),
('PGV', 'Z', 37.47740000000001),
('PGV', 'H1', 100.81460000000004), ('PGV', 'H2', 68.4354),
('PGV', 'GREATER_OF_TWO_HORIZONTALS', 100.81460000000004),
('PGA', 'ROTD(100.0)', 100.73875535385548),
('PGA', 'ROTD(50.0)', 91.40178541935455),
('PGA', 'Z', 183.7722361866693),
('PGA', 'H1', 99.24999872535474),
('PGA', 'H2', 81.23467239067368),
('PGA', 'GREATER_OF_TWO_HORIZONTALS', 99.24999872535474),
('SA(1.0)', 'ROTD(100.0)', 146.9023350124098),
('SA(1.0)', 'ROTD(50.0)', 106.03202302692158),
('SA(1.0)', 'Z', 27.74118995438756),
('SA(1.0)', 'H1', 136.25041187387063),
('SA(1.0)', 'H2', 84.69296738413021),
('SA(1.0)', 'GREATER_OF_TWO_HORIZONTALS', 136.25041187387063)]
pgms = m.pgms
assert len(pgms['IMT'].tolist()) == len(test_pgms)
for target in test_pgms:
target_imt = target[0]
target_imc = target[1]
value = target[2]
sub_imt = pgms.loc[pgms['IMT'] == target_imt]
df = sub_imt.loc[sub_imt['IMC'] == target_imc]
assert len(df['IMT'].tolist()) == 1
np.testing.assert_array_almost_equal(df['Result'].tolist()[0],
value,
decimal=10)
def from_config(cls, stream, config=None, event=None):
"""
Args:
stream (obspy.core.stream.Stream): Strong motion timeseries
for one station.
event (ScalarEvent):
Object containing latitude, longitude, depth, and magnitude.
config (dictionary): Configuration dictionary.
Note:
Assumes a processed stream with units of gal (1 cm/s^2).
No processing is done by this class.
"""
if config is None:
config = get_config()
station = cls()
damping = config['metrics']['sa']['damping']
smoothing = config['metrics']['fas']['smoothing']
bandwidth = config['metrics']['fas']['bandwidth']
station._damping = damping
station._smoothing = smoothing
station._bandwidth = bandwidth
station._stream = stream
station.event = event
station.set_metadata()
metrics = MetricsController.from_config(stream,
config=config,
event=event)
pgms = metrics.pgms
if pgms is None:
station._components = metrics.imcs
station._imts = metrics.imts
station.pgms = pd.DataFrame.from_dict({
'IMT': [],
'IMC': [],
'Result': []
})
else:
station._components = set(pgms['IMC'].tolist())
station._imts = set(pgms['IMT'].tolist())
station.pgms = pgms
station._summary = station.get_summary()
return station
def test_end_to_end():
datafiles, _ = read_data_dir("geonet", "us1000778i",
"20161113_110259_WTMC_20.V2A")
datafile = datafiles[0]
stream = read_geonet(datafile)[0]
input_imcs = [
"greater_of_two_horizontals",
"channels",
"rotd50",
"rotd100",
"invalid",
]
input_imts = ["sa1.0", "PGA", "pgv", "invalid"]
m = MetricsController(input_imts, input_imcs, stream, config=config)
test_pgms = [
("PGV", "ROTD(100.0)", 114.24894584734818),
("PGV", "ROTD(50.0)", 81.55436750525355),
("PGV", "Z", 37.47740000000001),
("PGV", "H1", 100.81460000000004),
("PGV", "H2", 68.4354),
("PGV", "GREATER_OF_TWO_HORIZONTALS", 100.81460000000004),
("PGA", "ROTD(100.0)", 100.73875535385548),
("PGA", "ROTD(50.0)", 91.40178541935455),
("PGA", "Z", 183.7722361866693),
("PGA", "H1", 99.24999872535474),
("PGA", "H2", 81.23467239067368),
("PGA", "GREATER_OF_TWO_HORIZONTALS", 99.24999872535474),
("SA(1.000)", "ROTD(100.0)", 146.9023350124098),
("SA(1.000)", "ROTD(50.0)", 106.03202302692158),
("SA(1.000)", "Z", 27.74118995438756),
("SA(1.000)", "H1", 136.25041187387063),
("SA(1.000)", "H2", 84.69296738413021),
("SA(1.000)", "GREATER_OF_TWO_HORIZONTALS", 136.25041187387063),
]
pgms = m.pgms
assert len(pgms) == len(test_pgms)
for target in test_pgms:
target_imt = target[0]
target_imc = target[1]
value = target[2]
df = pgms.loc[target_imt, target_imc]
assert len(df) == 1
np.testing.assert_array_almost_equal(df["Result"], value, decimal=10)
def from_config(cls, stream, config=None, event=None):
"""
Args:
stream (obspy.core.stream.Stream): Strong motion timeseries
for one station.
event (ScalarEvent):
Object containing latitude, longitude, depth, and magnitude.
config (dictionary): Configuration dictionary.
Note:
Assumes a processed stream with units of gal (1 cm/s^2).
No processing is done by this class.
"""
if config is None:
config = get_config()
station = cls()
damping = config['metrics']['sa']['damping']
smoothing = config['metrics']['fas']['smoothing']
bandwidth = config['metrics']['fas']['bandwidth']
station._damping = damping
station._smoothing = smoothing
station._bandwidth = bandwidth
station._stream = stream
station.event = event
station.set_metadata()
metrics = MetricsController.from_config(stream, config=config,
event=event)
pgms = metrics.pgms
if pgms is None:
station._components = metrics.imcs
station._imts = metrics.imts
station.pgms = pd.DataFrame.from_dict({
'IMT': [],
'IMC': [],
'Result': []
})
else:
station._components = set(pgms['IMC'].tolist())
station._imts = set(pgms['IMT'].tolist())
station.pgms = pgms
station._summary = station.get_summary()
return station
def from_stream(cls,
stream,
components,
imts,
event=None,
damping=None,
smoothing=None,
bandwidth=None,
config=None):
"""
Args:
stream (obspy.core.stream.Stream): Strong motion timeseries
for one station.
components (list): List of requested components (str).
imts (list): List of requested imts (str).
event (ScalarEvent):
Origin/magnitude for the event containing time, latitude,
longitude, depth, and magnitude.
damping (float): Damping of oscillator. Default is None.
smoothing (float): Smoothing method. Default is None.
bandwidth (float): Bandwidth of smoothing. Default is None.
config (dictionary): Configuration dictionary.
Note:
Assumes a processed stream with units of gal (1 cm/s^2).
No processing is done by this class.
"""
if config is None:
config = get_config()
station = cls()
imts = np.sort(imts)
components = np.sort(components)
if damping is None:
damping = config['metrics']['sa']['damping']
if smoothing is None:
smoothing = config['metrics']['fas']['smoothing']
if bandwidth is None:
bandwidth = config['metrics']['fas']['bandwidth']
station._damping = damping
station._smoothing = smoothing
station._bandwidth = bandwidth
station._stream = stream
station.event = event
station.set_metadata()
metrics = MetricsController(imts,
components,
stream,
bandwidth=bandwidth,
damping=damping,
event=event,
smooth_type=smoothing)
pgms = metrics.pgms
if pgms is None:
station._components = metrics.imcs
station._imts = metrics.imts
station.pgms = pd.DataFrame.from_dict({
'IMT': [],
'IMC': [],
'Result': []
})
else:
station._components = set(pgms['IMC'].tolist())
station._imts = set(pgms['IMT'].tolist())
station.pgms = pgms
station._summary = station.get_summary()
return station
def test_controller():
datafiles, event = read_data_dir('geonet', 'us1000778i',
'20161113_110259_WTMC_20.V2A')
datafile = datafiles[0]
input_imts = [
'pgv', 'pga', 'sa2', 'sa1.0', 'sa0.3', 'fas2', 'fas1.0', 'fas0.3',
'arias', 'invalid'
]
input_imcs = [
'rotd50', 'rotd100.0', 'gmrotd50', 'gmrotd100.0', 'radial_transverse',
'geometric_mean', 'arithmetic_mean', 'channels',
'greater_of_two_horizontals', 'invalid', 'quadratic_mean'
]
stream_v2 = read_geonet(datafile)[0]
# Testing for acceleration --------------------------
m1 = MetricsController(input_imts, input_imcs, stream_v2, event=event)
pgms = m1.pgms
# testing for pga, pgv, sa
target_imcs = [
'ROTD(50.0)', 'ROTD(100.0)', 'GMROTD(50.0)', 'GMROTD(100.0)', 'HNR',
'HNT', 'GEOMETRIC_MEAN', 'ARITHMETIC_MEAN', 'H1', 'H2', 'Z',
'GREATER_OF_TWO_HORIZONTALS', 'QUADRATIC_MEAN'
]
for col in ['PGA', 'PGV', 'SA(1.000)', 'SA(2.000)', 'SA(0.300)']:
imt = pgms.loc[pgms['IMT'] == col]
imcs = imt['IMC'].tolist()
assert len(imcs) == len(target_imcs)
np.testing.assert_array_equal(np.sort(imcs), np.sort(target_imcs))
# testing for fas
for col in ['FAS(1.000)', 'FAS(2.000)', 'FAS(0.300)']:
imt = pgms.loc[pgms['IMT'] == col]
imcs = imt['IMC'].tolist()
assert len(imcs) == 3
np.testing.assert_array_equal(
np.sort(imcs),
['ARITHMETIC_MEAN', 'GEOMETRIC_MEAN', 'QUADRATIC_MEAN'])
# testing for arias
imt = pgms.loc[pgms['IMT'] == 'ARIAS']
imcs = imt['IMC'].tolist()
assert len(imcs) == 1
np.testing.assert_array_equal(np.sort(imcs), ['ARITHMETIC_MEAN'])
_validate_steps(m1.step_sets, 'acc')
# Testing for Velocity --------------------------
for trace in stream_v2:
trace.stats.standard.units = 'vel'
m = MetricsController(input_imts, input_imcs, stream_v2, event=event)
pgms = m.pgms
# testing for pga, pgv, sa
target_imcs = [
'ROTD(50.0)', 'ROTD(100.0)', 'GMROTD(50.0)', 'GMROTD(100.0)', 'HNR',
'HNT', 'GEOMETRIC_MEAN', 'ARITHMETIC_MEAN', 'QUADRATIC_MEAN', 'H1',
'H2', 'Z', 'GREATER_OF_TWO_HORIZONTALS'
]
for col in ['PGA', 'PGV', 'SA(1.000)', 'SA(2.000)', 'SA(0.300)']:
imt = pgms.loc[pgms['IMT'] == col]
imcs = imt['IMC'].tolist()
assert len(imcs) == len(target_imcs)
np.testing.assert_array_equal(np.sort(imcs), np.sort(target_imcs))
# testing for fas
for col in ['FAS(1.000)', 'FAS(2.000)', 'FAS(0.300)']:
imt = pgms.loc[pgms['IMT'] == col]
imcs = imt['IMC'].tolist()
assert len(imcs) == 3
np.testing.assert_array_equal(
np.sort(imcs),
['ARITHMETIC_MEAN', 'GEOMETRIC_MEAN', 'QUADRATIC_MEAN'])
# testing for arias
imt = pgms.loc[pgms['IMT'] == 'ARIAS']
imcs = imt['IMC'].tolist()
assert len(imcs) == 1
np.testing.assert_array_equal(np.sort(imcs), ['ARITHMETIC_MEAN'])
_validate_steps(m.step_sets, 'vel')
def from_stream(cls,
stream,
components,
imts,
event=None,
damping=None,
smoothing=None,
bandwidth=None,
allow_nans=None,
config=None,
calc_waveform_metrics=True,
calc_station_metrics=True,
rupture=None,
vs30_grids=None):
"""
Args:
stream (obspy.core.stream.Stream): Strong motion timeseries
for one station.
components (list): List of requested components (str).
imts (list): List of requested imts (str).
event (ScalarEvent):
Origin/magnitude for the event containing time, latitude,
longitude, depth, and magnitude.
damping (float): Damping of oscillator. Default is None.
smoothing (float): Smoothing method. Default is None.
bandwidth (float): Bandwidth of smoothing. Default is None.
config (dictionary): Configuration dictionary.
calc_waveform_metrics (bool):
Whether to calculate waveform metrics. Default is True.
calc_station_metrics (bool):
Whether to calculate station metrics. Default is True.
rupture (PointRupture or QuadRupture):
impactutils rupture object. Default is None.
vs30_grids (dict):
A dictionary containing the vs30 grid files, names, and
descriptions (see config).
Note:
Assumes a processed stream with units of gal (1 cm/s^2).
No processing is done by this class.
"""
if config is None:
config = get_config()
station = cls()
imts = np.sort(imts)
components = np.sort(components)
if damping is None:
damping = config['metrics']['sa']['damping']
if smoothing is None:
smoothing = config['metrics']['fas']['smoothing']
if bandwidth is None:
bandwidth = config['metrics']['fas']['bandwidth']
if allow_nans is None:
allow_nans = config['metrics']['fas']['allow_nans']
station._damping = damping
station._smoothing = smoothing
station._bandwidth = bandwidth
station._stream = stream
station.event = event
station.set_metadata()
if stream.passed and calc_waveform_metrics:
metrics = MetricsController(imts,
components,
stream,
bandwidth=bandwidth,
allow_nans=allow_nans,
damping=damping,
event=event,
smooth_type=smoothing)
station.channel_dict = metrics.channel_dict.copy()
pgms = metrics.pgms
if pgms.empty:
station._components = metrics.imcs
station._imts = metrics.imts
station.pgms = pd.DataFrame.from_dict({
'IMT': [],
'IMC': [],
'Result': []
})
else:
station._components = set(pgms.index.get_level_values('IMC'))
station._imts = set(pgms.index.get_level_values('IMT'))
station.pgms = pgms
if calc_station_metrics:
station.compute_station_metrics(rupture, vs30_grids)
return station
def from_config(cls,
stream,
config=None,
event=None,
calc_waveform_metrics=True,
calc_station_metrics=True,
rupture=None,
vs30_grids=None):
"""
Args:
stream (obspy.core.stream.Stream): Strong motion timeseries
for one station.
config (dictionary): Configuration dictionary.
event (ScalarEvent):
Object containing latitude, longitude, depth, and magnitude.
calc_waveform_metrics (bool):
Whether to calculate waveform metrics. Default is True.
calc_station_metrics (bool):
Whether to calculate station metrics. Default is True.
rupture (PointRupture or QuadRupture):
impactutils rupture object. Default is None.
vs30_grids (dict):
A dictionary containing the vs30 grid files, names, and
descriptions (see config).
Note:
Assumes a processed stream with units of gal (1 cm/s^2).
No processing is done by this class.
"""
if config is None:
config = get_config()
station = cls()
damping = config['metrics']['sa']['damping']
smoothing = config['metrics']['fas']['smoothing']
bandwidth = config['metrics']['fas']['bandwidth']
station._damping = damping
station._smoothing = smoothing
station._bandwidth = bandwidth
station._stream = stream
station.event = event
station.set_metadata()
if stream.passed and calc_waveform_metrics:
metrics = MetricsController.from_config(stream,
config=config,
event=event)
station.channel_dict = metrics.channel_dict.copy()
pgms = metrics.pgms
if pgms is None:
station._components = metrics.imcs
station._imts = metrics.imts
station.pgms = pd.DataFrame.from_dict({
'IMT': [],
'IMC': [],
'Result': []
})
else:
station._components = set(pgms.index.get_level_values('IMC'))
station._imts = set(pgms.index.get_level_values('IMT'))
station.pgms = pgms
if calc_station_metrics:
station.compute_station_metrics(rupture, vs30_grids)
return station
def test_controller():
datafiles, event = read_data_dir("geonet", "us1000778i",
"20161113_110259_WTMC_20.V2A")
datafile = datafiles[0]
input_imts = [
"pgv",
"pga",
"sa2",
"sa1.0",
"sa0.3",
"fas2",
"fas1.0",
"fas0.3",
"arias",
"invalid",
]
input_imcs = [
"rotd50",
"rotd100.0",
"gmrotd50",
"gmrotd100.0",
"radial_transverse",
"geometric_mean",
"arithmetic_mean",
"channels",
"greater_of_two_horizontals",
"invalid",
"quadratic_mean",
]
stream_v2 = read_geonet(datafile)[0]
# Testing for acceleration --------------------------
m1 = MetricsController(input_imts,
input_imcs,
stream_v2,
event=event,
config=config)
pgms = m1.pgms
# testing for pga, pgv, sa
target_imcs = [
"ROTD(50.0)",
"ROTD(100.0)",
"GMROTD(50.0)",
"GMROTD(100.0)",
"HNR",
"HNT",
"GEOMETRIC_MEAN",
"ARITHMETIC_MEAN",
"H1",
"H2",
"Z",
"GREATER_OF_TWO_HORIZONTALS",
"QUADRATIC_MEAN",
]
for col in ["PGA", "PGV", "SA(1.000)", "SA(2.000)", "SA(0.300)"]:
imcs = pgms.loc[col].index.tolist()
assert len(imcs) == len(target_imcs)
np.testing.assert_array_equal(np.sort(imcs), np.sort(target_imcs))
# testing for fas
for col in ["FAS(1.000)", "FAS(2.000)", "FAS(0.300)"]:
imcs = pgms.loc[col].index.tolist()
assert len(imcs) == 9
np.testing.assert_array_equal(
np.sort(imcs),
[
"ARITHMETIC_MEAN",
"GEOMETRIC_MEAN",
"GREATER_OF_TWO_HORIZONTALS",
"H1",
"H2",
"HNR",
"HNT",
"QUADRATIC_MEAN",
"Z",
],
)
# testing for arias
imcs = pgms.loc["ARIAS"].index.tolist()
assert len(imcs) == 9
np.testing.assert_array_equal(
np.sort(imcs),
[
"ARITHMETIC_MEAN",
"GEOMETRIC_MEAN",
"GREATER_OF_TWO_HORIZONTALS",
"H1",
"H2",
"HNR",
"HNT",
"QUADRATIC_MEAN",
"Z",
],
)
_validate_steps(m1.step_sets, "acc")
# Testing for Velocity --------------------------
for trace in stream_v2:
trace.stats.standard.units = "vel"
m = MetricsController(input_imts,
input_imcs,
stream_v2,
event=event,
config=config)
pgms = m.pgms
# testing for pga, pgv, sa
target_imcs = [
"ROTD(50.0)",
"ROTD(100.0)",
"GMROTD(50.0)",
"GMROTD(100.0)",
"HNR",
"HNT",
"GEOMETRIC_MEAN",
"ARITHMETIC_MEAN",
"QUADRATIC_MEAN",
"H1",
"H2",
"Z",
"GREATER_OF_TWO_HORIZONTALS",
]
for col in ["PGA", "PGV", "SA(1.000)", "SA(2.000)", "SA(0.300)"]:
imcs = pgms.loc[col].index.tolist()
assert len(imcs) == len(target_imcs)
np.testing.assert_array_equal(np.sort(imcs), np.sort(target_imcs))
# testing for fas
for col in ["FAS(1.000)", "FAS(2.000)", "FAS(0.300)"]:
imcs = pgms.loc[col].index.tolist()
assert len(imcs) == 9
np.testing.assert_array_equal(
np.sort(imcs),
[
"ARITHMETIC_MEAN",
"GEOMETRIC_MEAN",
"GREATER_OF_TWO_HORIZONTALS",
"H1",
"H2",
"HNR",
"HNT",
"QUADRATIC_MEAN",
"Z",
],
)
# testing for arias
imcs = pgms.loc["ARIAS"].index.tolist()
assert len(imcs) == 9
np.testing.assert_array_equal(
np.sort(imcs),
[
"ARITHMETIC_MEAN",
"GEOMETRIC_MEAN",
"GREATER_OF_TWO_HORIZONTALS",
"H1",
"H2",
"HNR",
"HNT",
"QUADRATIC_MEAN",
"Z",
],
)
_validate_steps(m.step_sets, "vel")