def _read_single_event(event_file, locate_dir, units, local_mag_ph):
"""
Parse an event file from QuakeMigrate into an obspy Event object.
Parameters
----------
event_file : `pathlib.Path` object
Path to .event file to read.
locate_dir : `pathlib.Path` object
Path to locate directory (contains "events", "picks" etc. directories).
units : {"km", "m"}
Grid projection coordinates for QM LUT (determines units of depths and
uncertainties in the .event files).
local_mag_ph : {"S", "P"}
Amplitude measurement used to calculate local magnitudes.
Returns
-------
event : `obspy.Event` object
Event object populated with all available information output by
:class:`~quakemigrate.signal.scan.locate()`, including event locations
and uncertainties, picks, and amplitudes and magnitudes if available.
"""
# Parse information from event file
event_info = pd.read_csv(event_file).iloc[0]
event_uid = str(event_info["EventID"])
# Set distance conversion factor (from units of QM LUT projection units).
if units == "km":
factor = 1e3
elif units == "m":
factor = 1
else:
raise AttributeError(f"units must be 'km' or 'm'; not {units}")
# Create event object to store origin and pick information
event = Event()
event.extra = AttribDict()
event.resource_id = str(event_info["EventID"])
event.creation_info = CreationInfo(author="QuakeMigrate",
version=quakemigrate.__version__)
# Add COA info to extra
event.extra.coa = {"value": event_info["COA"], "namespace": ns}
event.extra.coa_norm = {"value": event_info["COA_NORM"], "namespace": ns}
event.extra.trig_coa = {"value": event_info["TRIG_COA"], "namespace": ns}
event.extra.dec_coa = {"value": event_info["DEC_COA"], "namespace": ns}
event.extra.dec_coa_norm = {
"value": event_info["DEC_COA_NORM"],
"namespace": ns
}
# Determine location of cut waveform data - add to event object as a
# custom extra attribute.
mseed = locate_dir / "raw_cut_waveforms" / event_uid
event.extra.cut_waveforms_file = {
"value": str(mseed.with_suffix(".m").resolve()),
"namespace": ns
}
if (locate_dir / "real_cut_waveforms").exists():
mseed = locate_dir / "real_cut_waveforms" / event_uid
event.extra.real_cut_waveforms_file = {
"value": str(mseed.with_suffix(".m").resolve()),
"namespace": ns
}
if (locate_dir / "wa_cut_waveforms").exists():
mseed = locate_dir / "wa_cut_waveforms" / event_uid
event.extra.wa_cut_waveforms_file = {
"value": str(mseed.with_suffix(".m").resolve()),
"namespace": ns
}
# Create origin with spline location and set to preferred event origin.
origin = Origin()
origin.method_id = "spline"
origin.longitude = event_info["X"]
origin.latitude = event_info["Y"]
origin.depth = event_info["Z"] * factor
origin.time = UTCDateTime(event_info["DT"])
event.origins = [origin]
event.preferred_origin_id = origin.resource_id
# Create origin with gaussian location and associate with event
origin = Origin()
origin.method_id = "gaussian"
origin.longitude = event_info["GAU_X"]
origin.latitude = event_info["GAU_Y"]
origin.depth = event_info["GAU_Z"] * factor
origin.time = UTCDateTime(event_info["DT"])
event.origins.append(origin)
ouc = OriginUncertainty()
ce = ConfidenceEllipsoid()
ce.semi_major_axis_length = event_info["COV_ErrY"] * factor
ce.semi_intermediate_axis_length = event_info["COV_ErrX"] * factor
ce.semi_minor_axis_length = event_info["COV_ErrZ"] * factor
ce.major_axis_plunge = 0
ce.major_axis_azimuth = 0
ce.major_axis_rotation = 0
ouc.confidence_ellipsoid = ce
ouc.preferred_description = "confidence ellipsoid"
# Set uncertainties for both as the gaussian uncertainties
for origin in event.origins:
origin.longitude_errors.uncertainty = kilometer2degrees(
event_info["GAU_ErrX"] * factor / 1e3)
origin.latitude_errors.uncertainty = kilometer2degrees(
event_info["GAU_ErrY"] * factor / 1e3)
origin.depth_errors.uncertainty = event_info["GAU_ErrZ"] * factor
origin.origin_uncertainty = ouc
# Add OriginQuality info to each origin?
for origin in event.origins:
origin.origin_type = "hypocenter"
origin.evaluation_mode = "automatic"
# --- Handle picks file ---
pick_file = locate_dir / "picks" / event_uid
if pick_file.with_suffix(".picks").is_file():
picks = pd.read_csv(pick_file.with_suffix(".picks"))
else:
return None
for _, pickline in picks.iterrows():
station = str(pickline["Station"])
phase = str(pickline["Phase"])
wid = WaveformStreamID(network_code="", station_code=station)
for method in ["modelled", "autopick"]:
pick = Pick()
pick.extra = AttribDict()
pick.waveform_id = wid
pick.method_id = method
pick.phase_hint = phase
if method == "autopick" and str(pickline["PickTime"]) != "-1":
pick.time = UTCDateTime(pickline["PickTime"])
pick.time_errors.uncertainty = float(pickline["PickError"])
pick.extra.snr = {
"value": float(pickline["SNR"]),
"namespace": ns
}
elif method == "modelled":
pick.time = UTCDateTime(pickline["ModelledTime"])
else:
continue
event.picks.append(pick)
# --- Handle amplitudes file ---
amps_file = locate_dir / "amplitudes" / event_uid
if amps_file.with_suffix(".amps").is_file():
amps = pd.read_csv(amps_file.with_suffix(".amps"))
i = 0
for _, ampsline in amps.iterrows():
wid = WaveformStreamID(seed_string=ampsline["id"])
noise_amp = ampsline["Noise_amp"] / 1000 # mm to m
for phase in ["P_amp", "S_amp"]:
amp = Amplitude()
if pd.isna(ampsline[phase]):
continue
amp.generic_amplitude = ampsline[phase] / 1000 # mm to m
amp.generic_amplitude_errors.uncertainty = noise_amp
amp.unit = "m"
amp.type = "AML"
amp.method_id = phase
amp.period = 1 / ampsline[f"{phase[0]}_freq"]
amp.time_window = TimeWindow(
reference=UTCDateTime(ampsline[f"{phase[0]}_time"]))
# amp.pick_id = ?
amp.waveform_id = wid
# amp.filter_id = ?
amp.magnitude_hint = "ML"
amp.evaluation_mode = "automatic"
amp.extra = AttribDict()
try:
amp.extra.filter_gain = {
"value": ampsline[f"{phase[0]}_filter_gain"],
"namespace": ns
}
amp.extra.avg_amp = {
"value": ampsline[f"{phase[0]}_avg_amp"] / 1000, # m
"namespace": ns
}
except KeyError:
pass
if phase[0] == local_mag_ph and not pd.isna(ampsline["ML"]):
i += 1
stat_mag = StationMagnitude()
stat_mag.extra = AttribDict()
# stat_mag.origin_id = ? local_mag_loc
stat_mag.mag = ampsline["ML"]
stat_mag.mag_errors.uncertainty = ampsline["ML_Err"]
stat_mag.station_magnitude_type = "ML"
stat_mag.amplitude_id = amp.resource_id
stat_mag.extra.picked = {
"value": ampsline["is_picked"],
"namespace": ns
}
stat_mag.extra.epi_dist = {
"value": ampsline["epi_dist"],
"namespace": ns
}
stat_mag.extra.z_dist = {
"value": ampsline["z_dist"],
"namespace": ns
}
event.station_magnitudes.append(stat_mag)
event.amplitudes.append(amp)
mag = Magnitude()
mag.extra = AttribDict()
mag.mag = event_info["ML"]
mag.mag_errors.uncertainty = event_info["ML_Err"]
mag.magnitude_type = "ML"
# mag.origin_id = ?
mag.station_count = i
mag.evaluation_mode = "automatic"
mag.extra.r2 = {"value": event_info["ML_r2"], "namespace": ns}
event.magnitudes = [mag]
event.preferred_magnitude_id = mag.resource_id
return event
def write_qml(config, sourcepar):
if not config.options.qml_file:
return
qml_file = config.options.qml_file
cat = read_events(qml_file)
evid = config.hypo.evid
try:
ev = [e for e in cat if evid in str(e.resource_id)][0]
except Exception:
logging.warning('Unable to find evid "{}" in QuakeML file. '
'QuakeML output will not be written.'.format(evid))
origin = ev.preferred_origin()
if origin is None:
origin = ev.origins[0]
origin_id = origin.resource_id
origin_id_strip = origin_id.id.split('/')[-1]
origin_id_strip = origin_id_strip.replace(config.smi_strip_from_origin_id,
'')
# Common parameters
ssp_version = get_versions()['version']
method_id = config.smi_base + '/sourcespec/' + ssp_version
cr_info = CreationInfo()
cr_info.agency_id = config.agency_id
if config.author is None:
author = '{}@{}'.format(getuser(), gethostname())
else:
author = config.author
cr_info.author = author
cr_info.creation_time = UTCDateTime()
means = sourcepar.means_weight
errors = sourcepar.errors_weight
stationpar = sourcepar.station_parameters
# Magnitude
mag = Magnitude()
_id = config.smi_magnitude_template.replace('$SMI_BASE', config.smi_base)
_id = _id.replace('$ORIGIN_ID', origin_id_strip)
mag.resource_id = ResourceIdentifier(id=_id)
mag.method_id = ResourceIdentifier(id=method_id)
mag.origin_id = origin_id
mag.magnitude_type = 'Mw'
mag.mag = means['Mw']
mag_err = QuantityError()
mag_err.uncertainty = errors['Mw']
mag_err.confidence_level = 68.2
mag.mag_errors = mag_err
mag.station_count = len([_s for _s in stationpar.keys()])
mag.evaluation_mode = 'automatic'
mag.creation_info = cr_info
# Seismic moment -- It has to be stored in a MomentTensor object
# which, in turn, is part of a FocalMechanism object
mt = MomentTensor()
_id = config.smi_moment_tensor_template.replace('$SMI_BASE',
config.smi_base)
_id = _id.replace('$ORIGIN_ID', origin_id_strip)
mt.resource_id = ResourceIdentifier(id=_id)
mt.derived_origin_id = origin_id
mt.moment_magnitude_id = mag.resource_id
mt.scalar_moment = means['Mo']
mt_err = QuantityError()
mt_err.lower_uncertainty = errors['Mo'][0]
mt_err.upper_uncertainty = errors['Mo'][1]
mt_err.confidence_level = 68.2
mt.scalar_moment_errors = mt_err
mt.method_id = method_id
mt.creation_info = cr_info
# And here is the FocalMechanism object
fm = FocalMechanism()
_id = config.smi_focal_mechanism_template.replace('$SMI_BASE',
config.smi_base)
_id = _id.replace('$ORIGIN_ID', origin_id_strip)
fm.resource_id = ResourceIdentifier(id=_id)
fm.triggering_origin_id = origin_id
fm.method_id = ResourceIdentifier(id=method_id)
fm.moment_tensor = mt
fm.creation_info = cr_info
ev.focal_mechanisms.append(fm)
# Station magnitudes
for statId in sorted(stationpar.keys()):
par = stationpar[statId]
st_mag = StationMagnitude()
seed_id = statId.split()[0]
_id = config.smi_station_magnitude_template.replace(
'$SMI_MAGNITUDE_TEMPLATE', config.smi_magnitude_template)
_id = _id.replace('$ORIGIN_ID', origin_id_strip)
_id = _id.replace('$SMI_BASE', config.smi_base)
_id = _id.replace('$WAVEFORM_ID', seed_id)
st_mag.resource_id = ResourceIdentifier(id=_id)
st_mag.origin_id = origin_id
st_mag.mag = par['Mw']
st_mag.station_magnitude_type = 'Mw'
st_mag.method_id = mag.method_id
st_mag.creation_info = cr_info
st_mag.waveform_id = WaveformStreamID(seed_string=seed_id)
st_mag.extra = SSPExtra()
st_mag.extra.moment = SSPTag(par['Mo'])
st_mag.extra.corner_frequency = SSPTag(par['fc'])
st_mag.extra.t_star = SSPTag(par['t_star'])
ev.station_magnitudes.append(st_mag)
st_mag_contrib = StationMagnitudeContribution()
st_mag_contrib.station_magnitude_id = st_mag.resource_id
mag.station_magnitude_contributions.append(st_mag_contrib)
ev.magnitudes.append(mag)
# Write other average parameters as custom tags
ev.extra = SSPExtra()
ev.extra.corner_frequency = SSPContainerTag()
ev.extra.corner_frequency.value.value = SSPTag(means['fc'])
ev.extra.corner_frequency.value.lower_uncertainty =\
SSPTag(errors['fc'][0])
ev.extra.corner_frequency.value.upper_uncertainty =\
SSPTag(errors['fc'][1])
ev.extra.corner_frequency.value.confidence_level = SSPTag(68.2)
ev.extra.t_star = SSPContainerTag()
ev.extra.t_star.value.value = SSPTag(means['t_star'])
ev.extra.t_star.value.uncertainty = SSPTag(errors['t_star'])
ev.extra.t_star.value.confidence_level = SSPTag(68.2)
ev.extra.source_radius = SSPContainerTag()
ev.extra.source_radius.value.value = SSPTag(means['ra'])
ev.extra.source_radius.value.lower_uncertainty =\
SSPTag(errors['ra'][0])
ev.extra.source_radius.value.upper_uncertainty =\
SSPTag(errors['ra'][1])
ev.extra.source_radius.value.confidence_level = SSPTag(68.2)
ev.extra.stress_drop = SSPContainerTag()
ev.extra.stress_drop.value.value = SSPTag(means['bsd'])
ev.extra.stress_drop.value.lower_uncertainty =\
SSPTag(errors['bsd'][0])
ev.extra.stress_drop.value.upper_uncertainty =\
SSPTag(errors['bsd'][1])
ev.extra.stress_drop.value.confidence_level = SSPTag(68.2)
if config.set_preferred_magnitude:
ev.preferred_magnitude_id = mag.resource_id.id
qml_file_out = os.path.join(config.options.outdir, evid + '.xml')
ev.write(qml_file_out, format='QUAKEML')
logging.info('QuakeML file written to: ' + qml_file_out)
