def __toAmplitude(parser, el):
"""
"""
amp = Amplitude()
amp.resource_id = ResourceIdentifier(
prefix="/".join([RESOURCE_ROOT, "amplitude"]))
if CURRENT_TYPE == "obspyck":
amp.method_id = "%s/amplitude_method/obspyck/1" % RESOURCE_ROOT
else:
msg = "encountered non-obspyck amplitude!"
raise Exception(msg)
amp.generic_amplitude, amp.generic_amplitude_errors = \
__toFloatQuantity(parser, el, "genericAmplitude")
amp.unit = parser.xpath2obj('unit', el, str)
waveform = el.xpath("waveform")[0]
network = waveform.get("networkCode")
station = fix_station_name(waveform.get("stationCode"))
# Map some station names.
if station in STATION_DICT:
station = STATION_DICT[station]
if not network:
network = NETWORK_DICT[station]
location = waveform.get("locationCode") or ""
channel = waveform.get("channelCode") or ""
amp.waveform_id = WaveformStreamID(network_code=network,
station_code=station,
channel_code=channel,
location_code=location)
# Amplitudes without generic_amplitude are not quakeml conform
if amp.generic_amplitude is None:
print(
"Amplitude has no generic_amplitude value and is "
"ignored: %s" % station)
return None
# generate time_window
amp.time_window = TimeWindow()
t_min = parser.xpath2obj('timeWindow/reference', el, UTCDateTime)
t_max = t_min + parser.xpath2obj('timeWindow/end', el, float)
dt_abs = abs(t_max - t_min)
amp.time_window.reference = t_min
if t_min < t_max:
amp.time_window.begin = 0.0
amp.time_window.end = dt_abs
else:
amp.time_window.begin = dt_abs
amp.time_window.end = 0.0
# we have so far saved frequency in Hz as "period" tag
# use two times dt instead
##amp.period = 1.0 / parser.xpath2obj('period', el, float)
amp.period = 2.0 * dt_abs
t = ("PGV; reference time is time of minimum amplitude. if minimum comes "
"before maximum, start is 0 and end is relative time of maximum. "
"otherwise end is 0, start is relative time of minimum.")
comment = Comment(force_resource_id=False, resource_id=None, text=t)
amp.comments.append(comment)
return amp
def __toAmplitude(parser, el):
"""
"""
amp = Amplitude()
amp.resource_id = ResourceIdentifier(
prefix="/".join([RESOURCE_ROOT, "amplitude"]))
if CURRENT_TYPE == "obspyck":
amp.method_id = "%s/amplitude_method/obspyck/1" % RESOURCE_ROOT
else:
msg = "encountered non-obspyck amplitude!"
raise Exception(msg)
amp.generic_amplitude, amp.generic_amplitude_errors = \
__toFloatQuantity(parser, el, "genericAmplitude")
amp.unit = parser.xpath2obj('unit', el, str)
waveform = el.xpath("waveform")[0]
network = waveform.get("networkCode")
station = fix_station_name(waveform.get("stationCode"))
# Map some station names.
if station in STATION_DICT:
station = STATION_DICT[station]
if not network:
network = NETWORK_DICT[station]
location = waveform.get("locationCode") or ""
channel = waveform.get("channelCode") or ""
amp.waveform_id = WaveformStreamID(
network_code=network,
station_code=station,
channel_code=channel,
location_code=location)
# Amplitudes without generic_amplitude are not quakeml conform
if amp.generic_amplitude is None:
print ("Amplitude has no generic_amplitude value and is "
"ignored: %s" % station)
return None
# generate time_window
amp.time_window = TimeWindow()
t_min = parser.xpath2obj('timeWindow/reference', el, UTCDateTime)
t_max = t_min + parser.xpath2obj('timeWindow/end', el, float)
dt_abs = abs(t_max - t_min)
amp.time_window.reference = t_min
if t_min < t_max:
amp.time_window.begin = 0.0
amp.time_window.end = dt_abs
else:
amp.time_window.begin = dt_abs
amp.time_window.end = 0.0
# we have so far saved frequency in Hz as "period" tag
# use two times dt instead
##amp.period = 1.0 / parser.xpath2obj('period', el, float)
amp.period = 2.0 * dt_abs
t = ("PGV; reference time is time of minimum amplitude. if minimum comes "
"before maximum, start is 0 and end is relative time of maximum. "
"otherwise end is 0, start is relative time of minimum.")
comment = Comment(force_resource_id=False, resource_id=None, text=t)
amp.comments.append(comment)
return amp
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
