def _parseRecordAH(self, line, event):
"""
Parses the 'additional hypocenter' record AH
"""
date = line[2:10]
time = line[11:20]
#unused: hypocenter_quality = line[20]
latitude = self._float(line[21:27])
lat_type = line[27]
longitude = self._float(line[29:36])
lon_type = line[36]
#unused: preliminary_flag = line[37]
depth = self._float(line[38:43])
#unused: depth_quality = line[43]
standard_dev = self._floatUnused(line[44:48])
station_number = self._intUnused(line[48:51])
phase_number = self._intUnused(line[51:55])
source_code = line[56:60].strip()
evid = event.resource_id.id.split('/')[-1]
origin = Origin()
res_id = '/'.join((res_id_prefix, 'origin', evid, source_code.lower()))
origin.resource_id = ResourceIdentifier(id=res_id)
origin.creation_info = CreationInfo(agency_id=source_code)
origin.time = UTCDateTime(date + time)
origin.latitude = latitude * self._coordinateSign(lat_type)
origin.longitude = longitude * self._coordinateSign(lon_type)
origin.depth = depth * 1000
origin.depth_type = 'from location'
origin.quality = OriginQuality()
origin.quality.standard_error = standard_dev
origin.quality.used_station_count = station_number
origin.quality.used_phase_count = phase_number
origin.type = 'hypocenter'
event.origins.append(origin)
def _block2event(block, seed_map, id_default, ph2comp, eventid_map):
"""
Read HypoDD event block
"""
lines = block.strip().splitlines()
yr, mo, dy, hr, mn, sc, la, lo, dp, mg, eh, ez, rms, id_ = lines[0].split()
if eventid_map is not None and id_ in eventid_map:
id_ = eventid_map[id_]
time = UTCDateTime(int(yr),
int(mo),
int(dy),
int(hr),
int(mn),
float(sc),
strict=False)
laterr = None if float(eh) == 0 else float(eh) / DEG2KM
lonerr = (None if laterr is None or float(la) > 89 else laterr /
cos(deg2rad(float(la))))
ez = None if float(ez) == 0 else float(ez) * 1000
rms = None if float(rms) == 0 else float(rms)
picks = []
arrivals = []
for line in lines[1:]:
sta, reltime, weight, phase = line.split()
comp = ph2comp.get(phase, '')
wid = seed_map.get(sta, id_default)
_waveform_id = WaveformStreamID(seed_string=wid.format(sta, comp))
pick = Pick(waveform_id=_waveform_id,
phase_hint=phase,
time=time + float(reltime))
arrival = Arrival(phase=phase,
pick_id=pick.resource_id,
time_weight=float(weight))
picks.append(pick)
arrivals.append(arrival)
qu = OriginQuality(associated_phase_count=len(picks), standard_error=rms)
origin = Origin(arrivals=arrivals,
resource_id="smi:local/origin/" + id_,
quality=qu,
latitude=float(la),
longitude=float(lo),
depth=1000 * float(dp),
latitude_errors=laterr,
longitude_errors=lonerr,
depth_errors=ez,
time=time)
if mg.lower() == 'nan':
magnitudes = []
preferred_magnitude_id = None
else:
magnitude = Magnitude(mag=mg, resource_id="smi:local/magnitude/" + id_)
magnitudes = [magnitude]
preferred_magnitude_id = magnitude.resource_id
event = Event(resource_id="smi:local/event/" + id_,
picks=picks,
origins=[origin],
magnitudes=magnitudes,
preferred_origin_id=origin.resource_id,
preferred_magnitude_id=preferred_magnitude_id)
return event
def _parse_record_hy(self, line):
"""
Parses the 'hypocenter' record HY
"""
date = line[2:10]
time = line[11:20]
# unused: location_quality = line[20]
latitude = self._float(line[21:27])
lat_type = line[27]
longitude = self._float(line[29:36])
lon_type = line[36]
depth = self._float(line[38:43])
# unused: depth_quality = line[43]
standard_dev = self._float(line[44:48])
station_number = self._int(line[48:51])
# unused: version_flag = line[51]
fe_region_number = line[52:55]
fe_region_name = self._decode_fe_region_number(fe_region_number)
source_code = line[55:60].strip()
event = Event()
# FIXME: a smarter way to define evid?
evid = date + time
res_id = '/'.join((res_id_prefix, 'event', evid))
event.resource_id = ResourceIdentifier(id=res_id)
description = EventDescription(
type='region name',
text=fe_region_name)
event.event_descriptions.append(description)
description = EventDescription(
type='Flinn-Engdahl region',
text=fe_region_number)
event.event_descriptions.append(description)
origin = Origin()
res_id = '/'.join((res_id_prefix, 'origin', evid))
origin.resource_id = ResourceIdentifier(id=res_id)
origin.creation_info = CreationInfo()
if source_code:
origin.creation_info.agency_id = source_code
else:
origin.creation_info.agency_id = 'USGS-NEIC'
res_id = '/'.join((res_id_prefix, 'earthmodel/ak135'))
origin.earth_model_id = ResourceIdentifier(id=res_id)
origin.time = UTCDateTime(date + time)
origin.latitude = latitude * self._coordinate_sign(lat_type)
origin.longitude = longitude * self._coordinate_sign(lon_type)
origin.depth = depth * 1000
origin.depth_type = 'from location'
origin.quality = OriginQuality()
origin.quality.associated_station_count = station_number
origin.quality.standard_error = standard_dev
# associated_phase_count can be incremented in records 'P ' and 'S '
origin.quality.associated_phase_count = 0
# depth_phase_count can be incremented in record 'S '
origin.quality.depth_phase_count = 0
origin.origin_type = 'hypocenter'
origin.region = fe_region_name
event.origins.append(origin)
return event
def ORNL_events_to_cat(ornl_file):
"""Make Catalog from ORNL locations"""
cat = Catalog()
loc_df = pd.read_csv(ornl_file, infer_datetime_format=True)
loc_df = loc_df.set_index('event_datetime')
eid = 0
for dt, row in loc_df.iterrows():
ot = UTCDateTime(dt)
hmc_east = row['x(m)']
hmc_north = row['y(m)']
hmc_elev = row['z(m)']
errX = row['error_x (m)']
errY = row['error_y (m)']
errZ = row['error_z (m)']
rms = row['rms (millisecond)']
converter = SURF_converter()
lon, lat, elev = converter.to_lonlat((hmc_east, hmc_north,
hmc_elev))
o = Origin(time=ot, latitude=lat, longitude=lon, depth=130 - elev)
o.origin_uncertainty = OriginUncertainty()
o.quality = OriginQuality()
ou = o.origin_uncertainty
oq = o.quality
ou.max_horizontal_uncertainty = np.max([errX, errY])
ou.min_horizontal_uncertainty = np.min([errX, errY])
o.depth_errors.uncertainty = errZ
oq.standard_error = rms * 1e3
extra = AttribDict({
'hmc_east': {
'value': hmc_east,
'namespace': 'smi:local/hmc'
},
'hmc_north': {
'value': hmc_north,
'namespace': 'smi:local/hmc'
},
'hmc_elev': {
'value': hmc_elev,
'namespace': 'smi:local/hmc'
},
'hmc_eid': {
'value': eid,
'namespace': 'smi:local/hmc'
}
})
o.extra = extra
rid = ResourceIdentifier(id=ot.strftime('%Y%m%d%H%M%S%f'))
# Dummy magnitude of 1. for all events until further notice
mag = Magnitude(mag=1., mag_errors=QuantityError(uncertainty=1.))
ev = Event(origins=[o], magnitudes=[mag], resource_id=rid)
ev.preferred_origin_id = o.resource_id.id
cat.events.append(ev)
eid += 1
return cat
def _block2event(block, seed_map, id_default, ph2comp):
"""
Read HypoDD event block
"""
lines = block.strip().splitlines()
yr, mo, dy, hr, mn, sc, la, lo, dp, mg, eh, ez, rms, id_ = lines[0].split()
time = UTCDateTime(int(yr),
int(mo),
int(dy),
int(hr),
int(mn),
float(sc),
strict=False)
picks = []
arrivals = []
for line in lines[1:]:
sta, reltime, weight, phase = line.split()
comp = ph2comp.get(phase, '')
wid = seed_map.get(sta, id_default)
_waveform_id = WaveformStreamID(seed_string=wid.format(sta, comp))
pick = Pick(waveform_id=_waveform_id,
phase_hint=phase,
time=time + float(reltime))
arrival = Arrival(phase=phase,
pick_id=pick.resource_id,
time_weight=float(weight))
picks.append(pick)
arrivals.append(arrival)
qu = None if rms == '0.0' else OriginQuality(standard_error=float(rms))
origin = Origin(arrivals=arrivals,
resource_id="smi:local/origin/" + id_,
quality=qu,
latitude=float(la),
longitude=float(lo),
depth=1000 * float(dp),
time=time)
if mg.lower() == 'nan':
magnitudes = []
preferred_magnitude_id = None
else:
magnitude = Magnitude(mag=mg, resource_id="smi:local/magnitude/" + id_)
magnitudes = [magnitude]
preferred_magnitude_id = magnitude.resource_id
event = Event(resource_id="smi:local/event/" + id_,
picks=picks,
origins=[origin],
magnitudes=magnitudes,
preferred_origin_id=origin.resource_id,
preferred_magnitude_id=preferred_magnitude_id)
return event
def read_header_line(string_line):
new_event = Event()
line = string_line
param_event = line.split()[1:]
### check if line as required number of arguments
if len(param_event) != 14:
return new_event
### Get parameters
year, month, day = [int(x) for x in param_event[0:3]]
hour, minu = [int(x) for x in param_event[3:5]]
sec = float(param_event[5])
if sec >= 60:
sec = 59.999
lat, lon, z = [float(x) for x in param_event[6:9]]
mag = float(param_event[9])
errh, errz, rms = [float(x) for x in param_event[10:13]]
_time = UTCDateTime(year, month, day, hour, minu, sec)
_origin_quality = OriginQuality(standard_error=rms)
# change what's next to handle origin with no errors estimates
origin = Origin(time=_time,
longitude=lon,
latitude=lat,
depth=z,
longitude_errors=QuantityError(uncertainty=errh),
latitude_errors=QuantityError(uncertainty=errh),
depth_errors=QuantityError(uncertainty=errz),
quality=_origin_quality)
magnitude = Magnitude(mag=mag, origin_id=origin.resource_id)
### Return
new_event.origins.append(origin)
new_event.magnitudes.append(magnitude)
new_event.preferred_origin_id = origin.resource_id
new_event.preferred_magnitude_id = magnitude.resource_id
return new_event
def _phase_to_event(event_text):
"""
Function to convert the text for one event in hypoDD phase format to \
event object.
:type event_text: dict
:param event_text: dict of two elements, header and picks, header is a \
str, picks is a list of str.
:returns: obspy.core.event.Event
"""
from obspy.core.event import Event, Origin, Magnitude
from obspy.core.event import Pick, WaveformStreamID, Arrival, OriginQuality
from obspy import UTCDateTime
ph_event = Event()
# Extract info from header line
# YR, MO, DY, HR, MN, SC, LAT, LON, DEP, MAG, EH, EZ, RMS, ID
header = event_text['header'].split()
ph_event.origins.append(Origin())
ph_event.origins[0].time =\
UTCDateTime(year=int(header[1]), month=int(header[2]),
day=int(header[3]), hour=int(header[4]),
minute=int(header[5]), second=int(header[6].split('.')[0]),
microsecond=int(float(('0.' + header[6].split('.')[1])) *
1000000))
ph_event.origins[0].latitude = float(header[7])
ph_event.origins[0].longitude = float(header[8])
ph_event.origins[0].depth = float(header[9]) * 1000
ph_event.origins[0].quality = OriginQuality(
standard_error=float(header[13]))
ph_event.magnitudes.append(Magnitude())
ph_event.magnitudes[0].mag = float(header[10])
ph_event.magnitudes[0].magnitude_type = 'M'
# Extract arrival info from picks!
for i, pick_line in enumerate(event_text['picks']):
pick = pick_line.split()
_waveform_id = WaveformStreamID(station_code=pick[0])
pick_time = ph_event.origins[0].time + float(pick[1])
ph_event.picks.append(
Pick(waveform_id=_waveform_id, phase_hint=pick[3], time=pick_time))
ph_event.origins[0].arrivals.append(
Arrival(phase=ph_event.picks[i],
pick_id=ph_event.picks[i].resource_id))
ph_event.origins[0].arrivals[i].time_weight = float(pick[2])
return ph_event
def read_origin(event_str):
"""
Read the origin information from the REST file string
:param event_str: Contents of file as list of str
:type event_str: list
:returns: :class:`obspy.core.event.Event`
"""
event = Event()
head = event_str[0].split()
try:
gap = float(head[17])
except IndexError:
gap = None
origin = Origin(
time=UTCDateTime(
year=int(head[0]), julday=int(head[1]), hour=int(head[2]),
minute=int(head[3])) + float(head[4]),
latitude=float(head[5]), longitude=float(head[6]),
depth=float(head[7]) * 1000, origin_quality=OriginQuality(
standard_error=float(head[9]),
azimuthal_gap=gap,
used_phase_count=int(head[17])),
longitude_errors=QuantityError(
uncertainty=kilometer2degrees(float(head[12]))),
latitude_errors=QuantityError(
uncertainty=kilometer2degrees(float(head[11]))),
depth_errors=QuantityError(uncertainty=float(head[13]) * 1000),
method_id=ResourceIdentifier("smi:local/REST"),
evaluation_mode="automatic")
event.origins.append(origin)
try:
event.magnitudes.append(Magnitude(
mag=float(head[19]), magnitude_type="M"))
except IndexError:
pass
return event
def read_nlloc_hyp(filename, coordinate_converter=None, picks=None, **kwargs):
"""
Reads a NonLinLoc Hypocenter-Phase file to a
:class:`~obspy.core.event.Catalog` object.
.. note::
Coordinate conversion from coordinate frame of NonLinLoc model files /
location run to WGS84 has to be specified explicitly by the user if
necessary.
.. note::
An example can be found on the :mod:`~obspy.io.nlloc` submodule front
page in the documentation pages.
:param filename: File or file-like object in text mode.
:type coordinate_converter: func
:param coordinate_converter: Function to convert (x, y, z)
coordinates of NonLinLoc output to geographical coordinates and depth
in meters (longitude, latitude, depth in kilometers).
If left ``None``, NonLinLoc (x, y, z) output is left unchanged (e.g. if
it is in geographical coordinates already like for NonLinLoc in
global mode).
The function should accept three arguments x, y, z (each of type
:class:`numpy.ndarray`) and return a tuple of three
:class:`numpy.ndarray` (lon, lat, depth in kilometers).
:type picks: list of :class:`~obspy.core.event.Pick`
:param picks: Original picks used to generate the NonLinLoc location.
If provided, the output event will include the original picks and the
arrivals in the output origin will link to them correctly (with their
``pick_id`` attribute). If not provided, the output event will include
(the rather basic) pick information that can be reconstructed from the
NonLinLoc hypocenter-phase file.
:rtype: :class:`~obspy.core.event.Catalog`
"""
if not hasattr(filename, "read"):
# Check if it exists, otherwise assume its a string.
try:
with open(filename, "rt") as fh:
data = fh.read()
except:
try:
data = filename.decode()
except:
data = str(filename)
data = data.strip()
else:
data = filename.read()
if hasattr(data, "decode"):
data = data.decode()
lines = data.splitlines()
# remember picks originally used in location, if provided
original_picks = picks
if original_picks is None:
original_picks = []
# determine indices of block start/end of the NLLOC output file
indices_hyp = [None, None]
indices_phases = [None, None]
for i, line in enumerate(lines):
if line.startswith("NLLOC "):
indices_hyp[0] = i
elif line.startswith("END_NLLOC"):
indices_hyp[1] = i
elif line.startswith("PHASE "):
indices_phases[0] = i
elif line.startswith("END_PHASE"):
indices_phases[1] = i
if any([i is None for i in indices_hyp]):
msg = ("NLLOC HYP file seems corrupt,"
" could not detect 'NLLOC' and 'END_NLLOC' lines.")
raise RuntimeError(msg)
# strip any other lines around NLLOC block
lines = lines[indices_hyp[0]:indices_hyp[1]]
# extract PHASES lines (if any)
if any(indices_phases):
if not all(indices_phases):
msg = ("NLLOC HYP file seems corrupt, 'PHASE' block is corrupt.")
raise RuntimeError(msg)
i1, i2 = indices_phases
lines, phases_lines = lines[:i1] + lines[i2 + 1:], lines[i1 + 1:i2]
else:
phases_lines = []
lines = dict([line.split(None, 1) for line in lines])
line = lines["SIGNATURE"]
line = line.rstrip().split('"')[1]
signature, version, date, time = line.rsplit(" ", 3)
creation_time = UTCDateTime().strptime(date + time, str("%d%b%Y%Hh%Mm%S"))
# maximum likelihood origin location info line
line = lines["HYPOCENTER"]
x, y, z = map(float, line.split()[1:7:2])
if coordinate_converter:
x, y, z = coordinate_converter(x, y, z)
# origin time info line
line = lines["GEOGRAPHIC"]
year, month, day, hour, minute = map(int, line.split()[1:6])
seconds = float(line.split()[6])
time = UTCDateTime(year, month, day, hour, minute, seconds)
# distribution statistics line
line = lines["STATISTICS"]
covariance_xx = float(line.split()[7])
covariance_yy = float(line.split()[13])
covariance_zz = float(line.split()[17])
stats_info_string = str(
"Note: Depth/Latitude/Longitude errors are calculated from covariance "
"matrix as 1D marginal (Lon/Lat errors as great circle degrees) "
"while OriginUncertainty min/max horizontal errors are calculated "
"from 2D error ellipsoid and are therefore seemingly higher compared "
"to 1D errors. Error estimates can be reconstructed from the "
"following original NonLinLoc error statistics line:\nSTATISTICS " +
lines["STATISTICS"])
# goto location quality info line
line = lines["QML_OriginQuality"].split()
(assoc_phase_count, used_phase_count, assoc_station_count,
used_station_count, depth_phase_count) = map(int, line[1:11:2])
stderr, az_gap, sec_az_gap = map(float, line[11:17:2])
gt_level = line[17]
min_dist, max_dist, med_dist = map(float, line[19:25:2])
# goto location quality info line
line = lines["QML_OriginUncertainty"]
hor_unc, min_hor_unc, max_hor_unc, hor_unc_azim = \
map(float, line.split()[1:9:2])
# assign origin info
event = Event()
cat = Catalog(events=[event])
o = Origin()
event.origins = [o]
o.origin_uncertainty = OriginUncertainty()
o.quality = OriginQuality()
ou = o.origin_uncertainty
oq = o.quality
o.comments.append(Comment(text=stats_info_string))
cat.creation_info.creation_time = UTCDateTime()
cat.creation_info.version = "ObsPy %s" % __version__
event.creation_info = CreationInfo(creation_time=creation_time,
version=version)
event.creation_info.version = version
o.creation_info = CreationInfo(creation_time=creation_time,
version=version)
# negative values can appear on diagonal of covariance matrix due to a
# precision problem in NLLoc implementation when location coordinates are
# large compared to the covariances.
o.longitude = x
try:
o.longitude_errors.uncertainty = kilometer2degrees(sqrt(covariance_xx))
except ValueError:
if covariance_xx < 0:
msg = ("Negative value in XX value of covariance matrix, not "
"setting longitude error (epicentral uncertainties will "
"still be set in origin uncertainty).")
warnings.warn(msg)
else:
raise
o.latitude = y
try:
o.latitude_errors.uncertainty = kilometer2degrees(sqrt(covariance_yy))
except ValueError:
if covariance_yy < 0:
msg = ("Negative value in YY value of covariance matrix, not "
"setting longitude error (epicentral uncertainties will "
"still be set in origin uncertainty).")
warnings.warn(msg)
else:
raise
o.depth = z * 1e3 # meters!
o.depth_errors.uncertainty = sqrt(covariance_zz) * 1e3 # meters!
o.depth_errors.confidence_level = 68
o.depth_type = str("from location")
o.time = time
ou.horizontal_uncertainty = hor_unc
ou.min_horizontal_uncertainty = min_hor_unc
ou.max_horizontal_uncertainty = max_hor_unc
# values of -1 seem to be used for unset values, set to None
for field in ("horizontal_uncertainty", "min_horizontal_uncertainty",
"max_horizontal_uncertainty"):
if ou.get(field, -1) == -1:
ou[field] = None
else:
ou[field] *= 1e3 # meters!
ou.azimuth_max_horizontal_uncertainty = hor_unc_azim
ou.preferred_description = str("uncertainty ellipse")
ou.confidence_level = 68 # NonLinLoc in general uses 1-sigma (68%) level
oq.standard_error = stderr
oq.azimuthal_gap = az_gap
oq.secondary_azimuthal_gap = sec_az_gap
oq.used_phase_count = used_phase_count
oq.used_station_count = used_station_count
oq.associated_phase_count = assoc_phase_count
oq.associated_station_count = assoc_station_count
oq.depth_phase_count = depth_phase_count
oq.ground_truth_level = gt_level
oq.minimum_distance = kilometer2degrees(min_dist)
oq.maximum_distance = kilometer2degrees(max_dist)
oq.median_distance = kilometer2degrees(med_dist)
# go through all phase info lines
for line in phases_lines:
line = line.split()
arrival = Arrival()
o.arrivals.append(arrival)
station = str(line[0])
phase = str(line[4])
arrival.phase = phase
arrival.distance = kilometer2degrees(float(line[21]))
arrival.azimuth = float(line[23])
arrival.takeoff_angle = float(line[24])
arrival.time_residual = float(line[16])
arrival.time_weight = float(line[17])
pick = Pick()
wid = WaveformStreamID(station_code=station)
date, hourmin, sec = map(str, line[6:9])
t = UTCDateTime().strptime(date + hourmin, "%Y%m%d%H%M") + float(sec)
pick.waveform_id = wid
pick.time = t
pick.time_errors.uncertainty = float(line[10])
pick.phase_hint = phase
pick.onset = ONSETS.get(line[3].lower(), None)
pick.polarity = POLARITIES.get(line[5].lower(), None)
# try to determine original pick for each arrival
for pick_ in original_picks:
wid = pick_.waveform_id
if station == wid.station_code and phase == pick_.phase_hint:
pick = pick_
break
else:
# warn if original picks were specified and we could not associate
# the arrival correctly
if original_picks:
msg = ("Could not determine corresponding original pick for "
"arrival. "
"Falling back to pick information in NonLinLoc "
"hypocenter-phase file.")
warnings.warn(msg)
event.picks.append(pick)
arrival.pick_id = pick.resource_id
return cat
def _read_single_hypocenter(lines, coordinate_converter, original_picks):
"""
Given a list of lines (starting with a 'NLLOC' line and ending with a
'END_NLLOC' line), parse them into an Event.
"""
try:
# some paranoid checks..
assert lines[0].startswith("NLLOC ")
assert lines[-1].startswith("END_NLLOC")
for line in lines[1:-1]:
assert not line.startswith("NLLOC ")
assert not line.startswith("END_NLLOC")
except Exception:
msg = ("This should not have happened, please report this as a bug at "
"https://github.com/obspy/obspy/issues.")
raise Exception(msg)
indices_phases = [None, None]
for i, line in enumerate(lines):
if line.startswith("PHASE "):
indices_phases[0] = i
elif line.startswith("END_PHASE"):
indices_phases[1] = i
# extract PHASES lines (if any)
if any(indices_phases):
if not all(indices_phases):
msg = ("NLLOC HYP file seems corrupt, 'PHASE' block is corrupt.")
raise RuntimeError(msg)
i1, i2 = indices_phases
lines, phases_lines = lines[:i1] + lines[i2 + 1:], lines[i1 + 1:i2]
else:
phases_lines = []
lines = dict([line.split(None, 1) for line in lines[:-1]])
line = lines["SIGNATURE"]
line = line.rstrip().split('"')[1]
signature, version, date, time = line.rsplit(" ", 3)
# new NLLoc > 6.0 seems to add prefix 'run:' before date
if date.startswith('run:'):
date = date[4:]
signature = signature.strip()
creation_time = UTCDateTime.strptime(date + time, str("%d%b%Y%Hh%Mm%S"))
if coordinate_converter:
# maximum likelihood origin location in km info line
line = lines["HYPOCENTER"]
x, y, z = coordinate_converter(*map(float, line.split()[1:7:2]))
else:
# maximum likelihood origin location lon lat info line
line = lines["GEOGRAPHIC"]
y, x, z = map(float, line.split()[8:13:2])
# maximum likelihood origin time info line
line = lines["GEOGRAPHIC"]
year, mon, day, hour, min = map(int, line.split()[1:6])
seconds = float(line.split()[6])
time = UTCDateTime(year, mon, day, hour, min, seconds, strict=False)
# distribution statistics line
line = lines["STATISTICS"]
covariance_xx = float(line.split()[7])
covariance_yy = float(line.split()[13])
covariance_zz = float(line.split()[17])
stats_info_string = str(
"Note: Depth/Latitude/Longitude errors are calculated from covariance "
"matrix as 1D marginal (Lon/Lat errors as great circle degrees) "
"while OriginUncertainty min/max horizontal errors are calculated "
"from 2D error ellipsoid and are therefore seemingly higher compared "
"to 1D errors. Error estimates can be reconstructed from the "
"following original NonLinLoc error statistics line:\nSTATISTICS " +
lines["STATISTICS"])
# goto location quality info line
line = lines["QML_OriginQuality"].split()
(assoc_phase_count, used_phase_count, assoc_station_count,
used_station_count, depth_phase_count) = map(int, line[1:11:2])
stderr, az_gap, sec_az_gap = map(float, line[11:17:2])
gt_level = line[17]
min_dist, max_dist, med_dist = map(float, line[19:25:2])
# goto location quality info line
line = lines["QML_OriginUncertainty"]
if "COMMENT" in lines:
comment = lines["COMMENT"].strip()
comment = comment.strip('\'"')
comment = comment.strip()
hor_unc, min_hor_unc, max_hor_unc, hor_unc_azim = \
map(float, line.split()[1:9:2])
# assign origin info
event = Event()
o = Origin()
event.origins = [o]
event.preferred_origin_id = o.resource_id
o.origin_uncertainty = OriginUncertainty()
o.quality = OriginQuality()
ou = o.origin_uncertainty
oq = o.quality
o.comments.append(Comment(text=stats_info_string, force_resource_id=False))
event.comments.append(Comment(text=comment, force_resource_id=False))
# SIGNATURE field's first item is LOCSIG, which is supposed to be
# 'Identification of an individual, institiution or other entity'
# according to
# http://alomax.free.fr/nlloc/soft6.00/control.html#_NLLoc_locsig_
# so use it as author in creation info
event.creation_info = CreationInfo(creation_time=creation_time,
version=version,
author=signature)
o.creation_info = CreationInfo(creation_time=creation_time,
version=version,
author=signature)
# negative values can appear on diagonal of covariance matrix due to a
# precision problem in NLLoc implementation when location coordinates are
# large compared to the covariances.
o.longitude = x
try:
o.longitude_errors.uncertainty = kilometer2degrees(sqrt(covariance_xx))
except ValueError:
if covariance_xx < 0:
msg = ("Negative value in XX value of covariance matrix, not "
"setting longitude error (epicentral uncertainties will "
"still be set in origin uncertainty).")
warnings.warn(msg)
else:
raise
o.latitude = y
try:
o.latitude_errors.uncertainty = kilometer2degrees(sqrt(covariance_yy))
except ValueError:
if covariance_yy < 0:
msg = ("Negative value in YY value of covariance matrix, not "
"setting longitude error (epicentral uncertainties will "
"still be set in origin uncertainty).")
warnings.warn(msg)
else:
raise
o.depth = z * 1e3 # meters!
o.depth_errors.uncertainty = sqrt(covariance_zz) * 1e3 # meters!
o.depth_errors.confidence_level = 68
o.depth_type = str("from location")
o.time = time
ou.horizontal_uncertainty = hor_unc
ou.min_horizontal_uncertainty = min_hor_unc
ou.max_horizontal_uncertainty = max_hor_unc
# values of -1 seem to be used for unset values, set to None
for field in ("horizontal_uncertainty", "min_horizontal_uncertainty",
"max_horizontal_uncertainty"):
if ou.get(field, -1) == -1:
ou[field] = None
else:
ou[field] *= 1e3 # meters!
ou.azimuth_max_horizontal_uncertainty = hor_unc_azim
ou.preferred_description = str("uncertainty ellipse")
ou.confidence_level = 68 # NonLinLoc in general uses 1-sigma (68%) level
oq.standard_error = stderr
oq.azimuthal_gap = az_gap
oq.secondary_azimuthal_gap = sec_az_gap
oq.used_phase_count = used_phase_count
oq.used_station_count = used_station_count
oq.associated_phase_count = assoc_phase_count
oq.associated_station_count = assoc_station_count
oq.depth_phase_count = depth_phase_count
oq.ground_truth_level = gt_level
oq.minimum_distance = kilometer2degrees(min_dist)
oq.maximum_distance = kilometer2degrees(max_dist)
oq.median_distance = kilometer2degrees(med_dist)
# go through all phase info lines
for line in phases_lines:
line = line.split()
arrival = Arrival()
o.arrivals.append(arrival)
station = str(line[0])
phase = str(line[4])
arrival.phase = phase
arrival.distance = kilometer2degrees(float(line[21]))
arrival.azimuth = float(line[23])
arrival.takeoff_angle = float(line[24])
arrival.time_residual = float(line[16])
arrival.time_weight = float(line[17])
pick = Pick()
# network codes are not used by NonLinLoc, so they can not be known
# when reading the .hyp file.. to conform with QuakeML standard set an
# empty network code
wid = WaveformStreamID(network_code="", station_code=station)
# have to split this into ints for overflow to work correctly
date, hourmin, sec = map(str, line[6:9])
ymd = [int(date[:4]), int(date[4:6]), int(date[6:8])]
hm = [int(hourmin[:2]), int(hourmin[2:4])]
t = UTCDateTime(*(ymd + hm), strict=False) + float(sec)
pick.waveform_id = wid
pick.time = t
pick.time_errors.uncertainty = float(line[10])
pick.phase_hint = phase
pick.onset = ONSETS.get(line[3].lower(), None)
pick.polarity = POLARITIES.get(line[5].lower(), None)
# try to determine original pick for each arrival
for pick_ in original_picks:
wid = pick_.waveform_id
if station == wid.station_code and phase == pick_.phase_hint:
pick = pick_
break
else:
# warn if original picks were specified and we could not associate
# the arrival correctly
if original_picks:
msg = ("Could not determine corresponding original pick for "
"arrival. "
"Falling back to pick information in NonLinLoc "
"hypocenter-phase file.")
warnings.warn(msg)
event.picks.append(pick)
arrival.pick_id = pick.resource_id
event.scope_resource_ids()
return event
def setEventData(eventParser, arrivals, count):
global originCount
global eventCount
global pickCount
creation_info = CreationInfo(
author='niket_engdahl_parser',
creation_time=UTCDateTime(),
agency_uri=ResourceIdentifier(id='smi:engdahl.ga.gov.au/ga-engdahl'),
agency_id='ga-engdahl')
# magnitudeSurface = Magnitude(resource_id=ResourceIdentifier(id='smi:engdahl.ga.gov.au/origin/'+str(originCount)+'#netMag.Ms'),
# mag=eventParser.ms,
# magnitude_type='Ms',
# origin_id=ResourceIdentifier(id='smi:engdahl.ga.gov.au/origin/'+str(originCount)),
# azimuthal_gap=eventParser.openaz2,
# creation_info=creation_info)
origin = Origin(
resource_id=ResourceIdentifier(id='smi:engdahl.ga.gov.au/origin/' +
str(originCount)),
time=UTCDateTime(int(str(2000 + int(eventParser.iyr))),
int(eventParser.mon), int(eventParser.iday),
int(eventParser.ihr), int(eventParser.min),
int(eventParser.sec.split('.')[0]),
int(eventParser.sec.split('.')[1] + '0')),
longitude=eventParser.glon,
latitude=eventParser.glat,
depth=float(eventParser.depth) *
1000, # engdahl files report kms, obspy expects m
depth_errors=eventParser.sedep,
method_id=ResourceIdentifier(id='EHB'),
earth_model_id=ResourceIdentifier(id='ak135'),
quality=OriginQuality(associated_phase_count=len(arrivals),
used_phase_count=len(arrivals),
standard_error=eventParser.se,
azimuthal_gap=eventParser.openaz2),
evaluation_mode='automatic',
creation_info=creation_info)
magnitude = Magnitude(
resource_id=ResourceIdentifier(id='smi:engdahl.ga.gov.au/origin/' +
str(originCount) + '#netMag.Mb'),
mag=eventParser.mb,
magnitude_type='Mb',
origin_id=ResourceIdentifier(id='smi:engdahl.ga.gov.au/origin/' +
str(originCount)),
azimuthal_gap=eventParser.openaz1,
creation_info=creation_info)
originCount += 1
pickList = []
arrivalList = []
pPhaseArrival = None
for arrParser in arrivals:
pickOnset = None
pol = None
if arrParser.year and arrParser.month and arrParser.day and arrParser.station:
pPhaseArrival = arrParser
else:
arrParser.year = pPhaseArrival.year
arrParser.day = pPhaseArrival.day
arrParser.month = pPhaseArrival.month
arrParser.station = pPhaseArrival.station
arrParser.delta = pPhaseArrival.delta
arrParser.dtdd = pPhaseArrival.dtdd
arrParser.backaz = pPhaseArrival.backaz
arrParser.focalDip = pPhaseArrival.focalDip
arrParser.angleAzimuth = pPhaseArrival.angleAzimuth
if arrParser.phase1 == 'LR' or arrParser.phase2 == 'LR' or arrParser.hour == '24':
continue
if arrParser.phase1.startswith('i'):
pickOnset = PickOnset.impulsive
if arrParser.fm == '+':
pol = PickPolarity.positive
elif arrParser.fm == '-':
pol = PickPolarity.negative
elif arrParser.phase1.startswith('e'):
pickOnset = PickOnset.emergent
pick = Pick(
resource_id=ResourceIdentifier(id='smi:engdahl.ga.gov.au/pick/' +
str(pickCount)),
time=UTCDateTime(int(str(2000 + int(arrParser.year))),
int(arrParser.month), int(arrParser.day),
int(arrParser.hour), int(arrParser.minute),
int(arrParser.second.split('.')[0]),
int(arrParser.second.split('.')[1] + '0')),
waveform_id=WaveformStreamID(network_code='',
station_code=arrParser.station,
channel_code='BHZ'),
methodID=ResourceIdentifier('STA/LTA'),
backazimuth=arrParser.backaz if arrParser.backaz else None,
onset=pickOnset,
phase_hint=arrParser.phase,
polarity=pol,
evaluation_mode='automatic',
# TO-DO
comment='populate all the remaining fields here as key value',
creation_info=creation_info)
if not arrParser.backaz:
print "arrParser.backaz is empty. printing the arrParser for debugging"
pickCount += 1
pickList.append(pick)
arrival = Arrival(
pick_id=ResourceIdentifier(id='smi:engdahl.ga.gov.au/pick/' +
str(pickCount - 1)),
phase=arrParser.phase if arrParser.phase else None,
azimuth=arrParser.backaz if arrParser.backaz else None,
distance=arrParser.delta if arrParser.delta else None,
# if the * has some significance, it should be accounted for. ignoring for now.
time_residual=arrParser.residual.rstrip('*'),
time_weight=arrParser.wgt if arrParser.wgt else None,
backazimuth_weight=arrParser.wgt if arrParser.wgt else None)
arrivalList.append(arrival)
if not arrParser.wgt:
print "arrParser.wgt is empty. printing the arrParser for debugging"
# pprint.pprint(arrParser)
origin.arrivals = arrivalList
event = Event(resource_id=ResourceIdentifier(
id='smi:engdahl.ga.gov.au/event/' + str(eventCount)),
creation_info=creation_info,
event_type='earthquake')
eventCount += 1
event.picks = pickList
event.origins = [
origin,
]
event.magnitudes = [
magnitude,
]
event.preferred_origin_id = origin.resource_id
event.preferred_magnitude_id = magnitude.resource_id
return event
def full_test_event():
"""
Function to generate a basic, full test event
"""
test_event = Event()
test_event.origins.append(
Origin(time=UTCDateTime("2012-03-26") + 1.2,
latitude=45.0,
longitude=25.0,
depth=15000))
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.magnitudes.append(
Magnitude(mag=0.1,
magnitude_type='ML',
creation_info=CreationInfo('TES'),
origin_id=test_event.origins[0].resource_id))
test_event.magnitudes.append(
Magnitude(mag=0.5,
magnitude_type='Mc',
creation_info=CreationInfo('TES'),
origin_id=test_event.origins[0].resource_id))
test_event.magnitudes.append(
Magnitude(mag=1.3,
magnitude_type='Ms',
creation_info=CreationInfo('TES'),
origin_id=test_event.origins[0].resource_id))
# Define the test pick
_waveform_id_1 = WaveformStreamID(station_code='FOZ',
channel_code='SHZ',
network_code='NZ')
_waveform_id_2 = WaveformStreamID(station_code='WTSZ',
channel_code='BH1',
network_code=' ')
# Pick to associate with amplitude
test_event.picks.append(
Pick(waveform_id=_waveform_id_1,
phase_hint='IAML',
polarity='undecidable',
time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"))
# Need a second pick for coda
test_event.picks.append(
Pick(waveform_id=_waveform_id_1,
onset='impulsive',
phase_hint='PN',
polarity='positive',
time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2,
onset='impulsive',
phase_hint='SG',
polarity='undecidable',
time=UTCDateTime("2012-03-26") + 1.72,
evaluation_mode="manual"))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2,
onset='impulsive',
phase_hint='PN',
polarity='undecidable',
time=UTCDateTime("2012-03-26") + 1.62,
evaluation_mode="automatic"))
# Test a generic local magnitude amplitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=2.0,
period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id,
unit='m',
magnitude_hint='ML',
category='point',
type='AML'))
# Test a coda magnitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=10,
pick_id=test_event.picks[1].resource_id,
waveform_id=test_event.picks[1].waveform_id,
type='END',
category='duration',
unit='s',
magnitude_hint='Mc',
snr=2.3))
test_event.origins[0].arrivals.append(
Arrival(time_weight=0,
phase=test_event.picks[1].phase_hint,
pick_id=test_event.picks[1].resource_id))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2,
phase=test_event.picks[2].phase_hint,
pick_id=test_event.picks[2].resource_id,
backazimuth_residual=5,
time_residual=0.2,
distance=15,
azimuth=25))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2,
phase=test_event.picks[3].phase_hint,
pick_id=test_event.picks[3].resource_id,
backazimuth_residual=5,
time_residual=0.2,
distance=15,
azimuth=25))
# Add in error info (line E)
test_event.origins[0].quality = OriginQuality(standard_error=0.01,
azimuthal_gap=36)
# Origin uncertainty in Seisan is output as long-lat-depth, quakeML has
# semi-major and semi-minor
test_event.origins[0].origin_uncertainty = OriginUncertainty(
confidence_ellipsoid=ConfidenceEllipsoid(
semi_major_axis_length=3000,
semi_minor_axis_length=1000,
semi_intermediate_axis_length=2000,
major_axis_plunge=20,
major_axis_azimuth=100,
major_axis_rotation=4))
test_event.origins[0].time_errors = QuantityError(uncertainty=0.5)
# Add in fault-plane solution info (line F) - Note have to check program
# used to determine which fields are filled....
test_event.focal_mechanisms.append(
FocalMechanism(nodal_planes=NodalPlanes(
nodal_plane_1=NodalPlane(strike=180,
dip=20,
rake=30,
strike_errors=QuantityError(10),
dip_errors=QuantityError(10),
rake_errors=QuantityError(20))),
method_id=ResourceIdentifier(
"smi:nc.anss.org/focalMechanism/FPFIT"),
creation_info=CreationInfo(agency_id="NC"),
misfit=0.5,
station_distribution_ratio=0.8))
# Need to test high-precision origin and that it is preferred origin.
# Moment tensor includes another origin
test_event.origins.append(
Origin(time=UTCDateTime("2012-03-26") + 1.2,
latitude=45.1,
longitude=25.2,
depth=14500))
test_event.magnitudes.append(
Magnitude(mag=0.1,
magnitude_type='MW',
creation_info=CreationInfo('TES'),
origin_id=test_event.origins[-1].resource_id))
# Moment tensors go with focal-mechanisms
test_event.focal_mechanisms.append(
FocalMechanism(moment_tensor=MomentTensor(
derived_origin_id=test_event.origins[-1].resource_id,
moment_magnitude_id=test_event.magnitudes[-1].resource_id,
scalar_moment=100,
tensor=Tensor(
m_rr=100, m_tt=100, m_pp=10, m_rt=1, m_rp=20, m_tp=15),
method_id=ResourceIdentifier(
'smi:nc.anss.org/momentTensor/BLAH'))))
return test_event
def _parseRecordDp(self, line, event):
"""
Parses the 'source parameter data - primary' record Dp
"""
source_contributor = line[2:6].strip()
computation_type = line[6]
exponent = self._intZero(line[7])
scale = math.pow(10, exponent)
centroid_origin_time = line[8:14] + "." + line[14]
orig_time_stderr = line[15:17]
if orig_time_stderr == "FX":
orig_time_stderr = "Fixed"
else:
orig_time_stderr = self._floatWithFormat(orig_time_stderr, "2.1", scale)
centroid_latitude = self._floatWithFormat(line[17:21], "4.2")
lat_type = line[21]
if centroid_latitude is not None:
centroid_latitude *= self._coordinateSign(lat_type)
lat_stderr = line[22:25]
if lat_stderr == "FX":
lat_stderr = "Fixed"
else:
lat_stderr = self._floatWithFormat(lat_stderr, "3.2", scale)
centroid_longitude = self._floatWithFormat(line[25:30], "5.2")
lon_type = line[30]
if centroid_longitude is not None:
centroid_longitude *= self._coordinateSign(lon_type)
lon_stderr = line[31:34]
if lon_stderr == "FX":
lon_stderr = "Fixed"
else:
lon_stderr = self._floatWithFormat(lon_stderr, "3.2", scale)
centroid_depth = self._floatWithFormat(line[34:38], "4.1")
depth_stderr = line[38:40]
if depth_stderr == "FX" or depth_stderr == "BD":
depth_stderr = "Fixed"
else:
depth_stderr = self._floatWithFormat(depth_stderr, "2.1", scale)
station_number = self._intZero(line[40:43])
component_number = self._intZero(line[43:46])
station_number2 = self._intZero(line[46:48])
component_number2 = self._intZero(line[48:51])
# unused: half_duration = self._floatWithFormat(line[51:54], '3.1')
moment = self._floatWithFormat(line[54:56], "2.1")
moment_stderr = self._floatWithFormat(line[56:58], "2.1")
moment_exponent = self._int(line[58:60])
if (moment is not None) and (moment_exponent is not None):
moment *= math.pow(10, moment_exponent)
if (moment_stderr is not None) and (moment_exponent is not None):
moment_stderr *= math.pow(10, moment_exponent)
evid = event.resource_id.id.split("/")[-1]
# Create a new origin only if centroid time is defined:
origin = None
if centroid_origin_time.strip() != ".":
origin = Origin()
res_id = "/".join(
(res_id_prefix, "origin", evid, source_contributor.lower(), "mw" + computation_type.lower())
)
origin.resource_id = ResourceIdentifier(id=res_id)
origin.creation_info = CreationInfo(agency_id=source_contributor)
date = event.origins[0].time.strftime("%Y%m%d")
origin.time = UTCDateTime(date + centroid_origin_time)
# Check if centroid time is on the next day:
if origin.time < event.origins[0].time:
origin.time += timedelta(days=1)
self._storeUncertainty(origin.time_errors, orig_time_stderr)
origin.latitude = centroid_latitude
origin.longitude = centroid_longitude
origin.depth = centroid_depth * 1000
if lat_stderr == "Fixed" and lon_stderr == "Fixed":
origin.epicenter_fixed = True
else:
self._storeUncertainty(origin.latitude_errors, self._latErrToDeg(lat_stderr))
self._storeUncertainty(origin.longitude_errors, self._lonErrToDeg(lon_stderr, origin.latitude))
if depth_stderr == "Fixed":
origin.depth_type = "operator assigned"
else:
origin.depth_type = "from location"
self._storeUncertainty(origin.depth_errors, depth_stderr, scale=1000)
quality = OriginQuality()
quality.used_station_count = station_number + station_number2
quality.used_phase_count = component_number + component_number2
origin.quality = quality
origin.type = "centroid"
event.origins.append(origin)
focal_mechanism = FocalMechanism()
res_id = "/".join(
(res_id_prefix, "focalmechanism", evid, source_contributor.lower(), "mw" + computation_type.lower())
)
focal_mechanism.resource_id = ResourceIdentifier(id=res_id)
focal_mechanism.creation_info = CreationInfo(agency_id=source_contributor)
moment_tensor = MomentTensor()
if origin is not None:
moment_tensor.derived_origin_id = origin.resource_id
else:
# this is required for QuakeML validation:
res_id = "/".join((res_id_prefix, "no-origin"))
moment_tensor.derived_origin_id = ResourceIdentifier(id=res_id)
for mag in event.magnitudes:
if mag.creation_info.agency_id == source_contributor:
moment_tensor.moment_magnitude_id = mag.resource_id
res_id = "/".join(
(res_id_prefix, "momenttensor", evid, source_contributor.lower(), "mw" + computation_type.lower())
)
moment_tensor.resource_id = ResourceIdentifier(id=res_id)
moment_tensor.scalar_moment = moment
self._storeUncertainty(moment_tensor.scalar_moment_errors, moment_stderr)
data_used = DataUsed()
data_used.station_count = station_number + station_number2
data_used.component_count = component_number + component_number2
if computation_type == "C":
res_id = "/".join((res_id_prefix, "methodID=CMT"))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
# CMT algorithm uses long-period body waves,
# very-long-period surface waves and
# intermediate period surface waves (since 2004
# for shallow and intermediate-depth earthquakes
# --Ekstrom et al., 2012)
data_used.wave_type = "combined"
if computation_type == "M":
res_id = "/".join((res_id_prefix, "methodID=moment_tensor"))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
# FIXME: not sure which kind of data is used by
# "moment tensor" algorithm.
data_used.wave_type = "unknown"
elif computation_type == "B":
res_id = "/".join((res_id_prefix, "methodID=broadband_data"))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
# FIXME: is 'combined' correct here?
data_used.wave_type = "combined"
elif computation_type == "F":
res_id = "/".join((res_id_prefix, "methodID=P-wave_first_motion"))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
data_used.wave_type = "P waves"
elif computation_type == "S":
res_id = "/".join((res_id_prefix, "methodID=scalar_moment"))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
# FIXME: not sure which kind of data is used
# for scalar moment determination.
data_used.wave_type = "unknown"
moment_tensor.data_used = data_used
focal_mechanism.moment_tensor = moment_tensor
event.focal_mechanisms.append(focal_mechanism)
return focal_mechanism
def full_test_event():
"""
Function to generate a basic, full test event
"""
test_event = Event()
test_event.origins.append(Origin())
test_event.origins[0].time = UTCDateTime("2012-03-26") + 1.2
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.origins[0].latitude = 45.0
test_event.origins[0].longitude = 25.0
test_event.origins[0].depth = 15000
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.origins[0].quality = OriginQuality(standard_error=0.01)
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[0].mag = 0.1
test_event.magnitudes[0].magnitude_type = 'ML'
test_event.magnitudes[0].creation_info = CreationInfo('TES')
test_event.magnitudes[0].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[1].mag = 0.5
test_event.magnitudes[1].magnitude_type = 'Mc'
test_event.magnitudes[1].creation_info = CreationInfo('TES')
test_event.magnitudes[1].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[2].mag = 1.3
test_event.magnitudes[2].magnitude_type = 'Ms'
test_event.magnitudes[2].creation_info = CreationInfo('TES')
test_event.magnitudes[2].origin_id = test_event.origins[0].resource_id
# Define the test pick
_waveform_id_1 = WaveformStreamID(station_code='FOZ', channel_code='SHZ',
network_code='NZ')
_waveform_id_2 = WaveformStreamID(station_code='WTSZ', channel_code='BH1',
network_code=' ')
# Pick to associate with amplitude - 0
test_event.picks = [
Pick(waveform_id=_waveform_id_1, phase_hint='IAML',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_1, onset='impulsive', phase_hint='PN',
polarity='positive', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_1, phase_hint='IAML',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='SG',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.72,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='PN',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.62,
evaluation_mode="automatic")]
# Test a generic local magnitude amplitude pick
test_event.amplitudes = [
Amplitude(generic_amplitude=2.0, period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id, unit='m',
magnitude_hint='ML', category='point', type='AML'),
Amplitude(generic_amplitude=10,
pick_id=test_event.picks[1].resource_id,
waveform_id=test_event.picks[1].waveform_id, type='END',
category='duration', unit='s', magnitude_hint='Mc',
snr=2.3),
Amplitude(generic_amplitude=5.0, period=0.6,
pick_id=test_event.picks[2].resource_id,
waveform_id=test_event.picks[0].waveform_id, unit='m',
category='point', type='AML')]
test_event.origins[0].arrivals = [
Arrival(time_weight=0, phase=test_event.picks[1].phase_hint,
pick_id=test_event.picks[1].resource_id),
Arrival(time_weight=2, phase=test_event.picks[3].phase_hint,
pick_id=test_event.picks[3].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25),
Arrival(time_weight=2, phase=test_event.picks[4].phase_hint,
pick_id=test_event.picks[4].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25)]
return test_event
def _parse_origin(self, line):
# 1-10 i4,a1,i2,a1,i2 epicenter date (yyyy/mm/dd)
# 12-22 i2,a1,i2,a1,f5.2 epicenter time (hh:mm:ss.ss)
time = UTCDateTime.strptime(line[:17], '%Y/%m/%d %H:%M:')
time += float(line[17:22])
# 23 a1 fixed flag (f = fixed origin time solution, blank if
# not a fixed origin time)
time_fixed = fixed_flag(line[22])
# 25-29 f5.2 origin time error (seconds; blank if fixed origin time)
time_error = float_or_none(line[24:29])
time_error = time_error and QuantityError(uncertainty=time_error)
# 31-35 f5.2 root mean square of time residuals (seconds)
rms = float_or_none(line[30:35])
# 37-44 f8.4 latitude (negative for South)
latitude = float_or_none(line[36:44])
# 46-54 f9.4 longitude (negative for West)
longitude = float_or_none(line[45:54])
# 55 a1 fixed flag (f = fixed epicenter solution, blank if not
# a fixed epicenter solution)
epicenter_fixed = fixed_flag(line[54])
# 56-60 f5.1 semi-major axis of 90% ellipse or its estimate
# (km, blank if fixed epicenter)
_uncertainty_major_m = float_or_none(line[55:60], multiplier=1e3)
# 62-66 f5.1 semi-minor axis of 90% ellipse or its estimate
# (km, blank if fixed epicenter)
_uncertainty_minor_m = float_or_none(line[61:66], multiplier=1e3)
# 68-70 i3 strike (0 <= x <= 360) of error ellipse clock-wise from
# North (degrees)
_uncertainty_major_azimuth = float_or_none(line[67:70])
# 72-76 f5.1 depth (km)
depth = float_or_none(line[71:76], multiplier=1e3)
# 77 a1 fixed flag (f = fixed depth station, d = depth phases,
# blank if not a fixed depth)
epicenter_fixed = fixed_flag(line[76])
# 79-82 f4.1 depth error 90% (km; blank if fixed depth)
depth_error = float_or_none(line[78:82], multiplier=1e3)
# 84-87 i4 number of defining phases
used_phase_count = int_or_none(line[83:87])
# 89-92 i4 number of defining stations
used_station_count = int_or_none(line[88:92])
# 94-96 i3 gap in azimuth coverage (degrees)
azimuthal_gap = float_or_none(line[93:96])
# 98-103 f6.2 distance to closest station (degrees)
minimum_distance = float_or_none(line[97:103])
# 105-110 f6.2 distance to furthest station (degrees)
maximum_distance = float_or_none(line[104:110])
# 112 a1 analysis type: (a = automatic, m = manual, g = guess)
evaluation_mode, evaluation_status = \
evaluation_mode_and_status(line[111])
# 114 a1 location method: (i = inversion, p = pattern
# recognition, g = ground truth, o =
# other)
location_method = LOCATION_METHODS[line[113].strip().lower()]
# 116-117 a2 event type:
# XXX event type and event type certainty is specified per origin,
# XXX not sure how to bset handle this, for now only use it if
# XXX information on the individual origins do not clash.. not sure yet
# XXX how to identify the preferred origin..
event_type, event_type_certainty = \
EVENT_TYPE_CERTAINTY[line[115:117].strip().lower()]
# 119-127 a9 author of the origin
author = line[118:127].strip()
# 129-136 a8 origin identification
origin_id = self._construct_id(['origin', line[128:136].strip()])
# do some combinations
depth_error = depth_error and dict(uncertainty=depth_error,
confidence_level=90)
if all(v is not None
for v in (_uncertainty_major_m, _uncertainty_minor_m,
_uncertainty_major_azimuth)):
origin_uncertainty = OriginUncertainty(
min_horizontal_uncertainty=_uncertainty_minor_m,
max_horizontal_uncertainty=_uncertainty_major_m,
azimuth_max_horizontal_uncertainty=_uncertainty_major_azimuth,
preferred_description='uncertainty ellipse',
confidence_level=90)
# event init always sets an empty QuantityError, even when
# specifying None, which is strange
for key in ['confidence_ellipsoid']:
setattr(origin_uncertainty, key, None)
else:
origin_uncertainty = None
origin_quality = OriginQuality(standard_error=rms,
used_phase_count=used_phase_count,
used_station_count=used_station_count,
azimuthal_gap=azimuthal_gap,
minimum_distance=minimum_distance,
maximum_distance=maximum_distance)
comments = []
if location_method:
comments.append(
self._make_comment('location method: ' + location_method))
if author:
creation_info = CreationInfo(author=author)
else:
creation_info = None
# assemble whole event
origin = Origin(time=time,
resource_id=origin_id,
longitude=longitude,
latitude=latitude,
depth=depth,
depth_errors=depth_error,
origin_uncertainty=origin_uncertainty,
time_fixed=time_fixed,
epicenter_fixed=epicenter_fixed,
origin_quality=origin_quality,
comments=comments,
creation_info=creation_info)
# event init always sets an empty QuantityError, even when specifying
# None, which is strange
for key in ('time_errors', 'longitude_errors', 'latitude_errors',
'depth_errors'):
setattr(origin, key, None)
return origin, event_type, event_type_certainty
def surf_events_to_cat(loc_file, pick_file):
"""
Take location files (hypoinverse formatted) and picks (format TBD)
and creates a single obspy catalog for later use and dissemination.
:param loc_file: File path
:param pick_file: File path
:return: obspy.core.Catalog
"""
# Read/parse location file and create Events for each
surf_cat = Catalog()
# Parse the pick file to a dictionary
pick_dict = parse_picks(pick_file)
with open(loc_file, 'r') as f:
next(f)
for ln in f:
ln = ln.strip('\n')
line = ln.split(',')
eid = line[0]
if eid not in pick_dict:
print('No picks for this location, skipping for now.')
continue
ot = UTCDateTime(line[1])
hmc_east = float(line[2])
hmc_north = float(line[3])
hmc_elev = float(line[4])
gap = float(line[-5])
rms = float(line[-3])
errXY = float(line[-2])
errZ = float(line[-1])
converter = SURF_converter()
lon, lat, elev = converter.to_lonlat((hmc_east, hmc_north,
hmc_elev))
o = Origin(time=ot, longitude=lon, latitude=lat, depth=130 - elev)
o.origin_uncertainty = OriginUncertainty()
o.quality = OriginQuality()
ou = o.origin_uncertainty
oq = o.quality
ou.horizontal_uncertainty = errXY * 1e3
ou.preferred_description = "horizontal uncertainty"
o.depth_errors.uncertainty = errZ * 1e3
oq.standard_error = rms
oq.azimuthal_gap = gap
extra = AttribDict({
'hmc_east': {
'value': hmc_east,
'namespace': 'smi:local/hmc'
},
'hmc_north': {
'value': hmc_north,
'namespace': 'smi:local/hmc'
},
'hmc_elev': {
'value': hmc_elev,
'namespace': 'smi:local/hmc'
},
'hmc_eid': {
'value': eid,
'namespace': 'smi:local/hmc'
}
})
o.extra = extra
rid = ResourceIdentifier(id=ot.strftime('%Y%m%d%H%M%S%f'))
# Dummy magnitude of 1. for all events until further notice
mag = Magnitude(mag=1., mag_errors=QuantityError(uncertainty=1.))
ev = Event(origins=[o], magnitudes=[mag],
picks=pick_dict[eid], resource_id=rid)
surf_cat.append(ev)
return surf_cat
def __toOrigin(parser, origin_el):
"""
Parses a given origin etree element.
:type parser: :class:`~obspy.core.util.xmlwrapper.XMLParser`
:param parser: Open XMLParser object.
:type origin_el: etree.element
:param origin_el: origin element to be parsed.
:return: A ObsPy :class:`~obspy.core.event.Origin` object.
"""
global CURRENT_TYPE
origin = Origin()
origin.resource_id = ResourceIdentifier(
prefix="/".join([RESOURCE_ROOT, "origin"]))
# I guess setting the program used as the method id is fine.
origin.method_id = "%s/location_method/%s/1" % (
RESOURCE_ROOT, parser.xpath2obj('program', origin_el))
if str(origin.method_id).lower().endswith("none"):
origin.method_id = None
# Standard parameters.
origin.time, origin.time_errors = \
__toTimeQuantity(parser, origin_el, "time")
origin.latitude, origin_latitude_error = \
__toFloatQuantity(parser, origin_el, "latitude")
origin.longitude, origin_longitude_error = \
__toFloatQuantity(parser, origin_el, "longitude")
origin.depth, origin.depth_errors = \
__toFloatQuantity(parser, origin_el, "depth")
if origin_longitude_error:
origin_longitude_error = origin_longitude_error["uncertainty"]
if origin_latitude_error:
origin_latitude_error = origin_latitude_error["uncertainty"]
# Figure out the depth type.
depth_type = parser.xpath2obj("depth_type", origin_el)
# Map Seishub specific depth type to the QuakeML depth type.
if depth_type == "from location program":
depth_type = "from location"
if depth_type is not None:
origin.depth_type = depth_type
# XXX: CHECK DEPTH ORIENTATION!!
if CURRENT_TYPE == "seiscomp3":
origin.depth *= 1000
if origin.depth_errors.uncertainty:
origin.depth_errors.uncertainty *= 1000
else:
# Convert to m.
origin.depth *= -1000
if origin.depth_errors.uncertainty:
origin.depth_errors.uncertainty *= 1000
# Earth model.
earth_mod = parser.xpath2obj('earth_mod', origin_el, str)
if earth_mod:
earth_mod = earth_mod.split()
earth_mod = ",".join(earth_mod)
origin.earth_model_id = "%s/earth_model/%s/1" % (RESOURCE_ROOT,
earth_mod)
if (origin_latitude_error is None or origin_longitude_error is None) and \
CURRENT_TYPE not in ["seiscomp3", "toni"]:
print "AAAAAAAAAAAAA"
raise Exception
if origin_latitude_error and origin_latitude_error:
if CURRENT_TYPE in ["baynet", "obspyck"]:
uncert = OriginUncertainty()
if origin_latitude_error > origin_longitude_error:
uncert.azimuth_max_horizontal_uncertainty = 0
else:
uncert.azimuth_max_horizontal_uncertainty = 90
uncert.min_horizontal_uncertainty, \
uncert.max_horizontal_uncertainty = \
sorted([origin_longitude_error, origin_latitude_error])
uncert.min_horizontal_uncertainty *= 1000.0
uncert.max_horizontal_uncertainty *= 1000.0
uncert.preferred_description = "uncertainty ellipse"
origin.origin_uncertainty = uncert
elif CURRENT_TYPE == "earthworm":
uncert = OriginUncertainty()
uncert.horizontal_uncertainty = origin_latitude_error
uncert.horizontal_uncertainty *= 1000.0
uncert.preferred_description = "horizontal uncertainty"
origin.origin_uncertainty = uncert
elif CURRENT_TYPE in ["seiscomp3", "toni"]:
pass
else:
raise Exception
# Parse the OriginQuality if applicable.
if not origin_el.xpath("originQuality"):
return origin
origin_quality_el = origin_el.xpath("originQuality")[0]
origin.quality = OriginQuality()
origin.quality.associated_phase_count = \
parser.xpath2obj("associatedPhaseCount", origin_quality_el, int)
# QuakeML does apparently not distinguish between P and S wave phase
# count. Some Seishub event files do.
p_phase_count = parser.xpath2obj("P_usedPhaseCount", origin_quality_el,
int)
s_phase_count = parser.xpath2obj("S_usedPhaseCount", origin_quality_el,
int)
# Use both in case they are set.
if p_phase_count is not None and s_phase_count is not None:
phase_count = p_phase_count + s_phase_count
# Also add two Seishub element file specific elements.
origin.quality.p_used_phase_count = p_phase_count
origin.quality.s_used_phase_count = s_phase_count
# Otherwise the total usedPhaseCount should be specified.
else:
phase_count = parser.xpath2obj("usedPhaseCount", origin_quality_el,
int)
if p_phase_count is not None:
origin.quality.setdefault("extra", AttribDict())
origin.quality.extra.usedPhaseCountP = {
'value': p_phase_count,
'namespace': NAMESPACE
}
if s_phase_count is not None:
origin.quality.setdefault("extra", AttribDict())
origin.quality.extra.usedPhaseCountS = {
'value': s_phase_count,
'namespace': NAMESPACE
}
origin.quality.used_phase_count = phase_count
associated_station_count = \
parser.xpath2obj("associatedStationCount", origin_quality_el, int)
used_station_count = parser.xpath2obj("usedStationCount",
origin_quality_el, int)
depth_phase_count = parser.xpath2obj("depthPhaseCount", origin_quality_el,
int)
standard_error = parser.xpath2obj("standardError", origin_quality_el,
float)
azimuthal_gap = parser.xpath2obj("azimuthalGap", origin_quality_el, float)
secondary_azimuthal_gap = \
parser.xpath2obj("secondaryAzimuthalGap", origin_quality_el, float)
ground_truth_level = parser.xpath2obj("groundTruthLevel",
origin_quality_el, str)
minimum_distance = parser.xpath2obj("minimumDistance", origin_quality_el,
float)
maximum_distance = parser.xpath2obj("maximumDistance", origin_quality_el,
float)
median_distance = parser.xpath2obj("medianDistance", origin_quality_el,
float)
if minimum_distance is not None:
minimum_distance = kilometer2degrees(minimum_distance)
if maximum_distance is not None:
maximum_distance = kilometer2degrees(maximum_distance)
if median_distance is not None:
median_distance = kilometer2degrees(median_distance)
if associated_station_count is not None:
origin.quality.associated_station_count = associated_station_count
if used_station_count is not None:
origin.quality.used_station_count = used_station_count
if depth_phase_count is not None:
origin.quality.depth_phase_count = depth_phase_count
if standard_error is not None and not math.isnan(standard_error):
origin.quality.standard_error = standard_error
if azimuthal_gap is not None:
origin.quality.azimuthal_gap = azimuthal_gap
if secondary_azimuthal_gap is not None:
origin.quality.secondary_azimuthal_gap = secondary_azimuthal_gap
if ground_truth_level is not None:
origin.quality.ground_truth_level = ground_truth_level
if minimum_distance is not None:
origin.quality.minimum_distance = minimum_distance
if maximum_distance is not None:
origin.quality.maximum_distance = maximum_distance
if median_distance is not None and not math.isnan(median_distance):
origin.quality.median_distance = median_distance
return origin
def _parse_first_line_origin(self, line, event, magnitudes):
"""
Parse the first line of origin data.
:type line: str
:param line: Line to parse.
:type event: :class:`~obspy.core.event.event.Event`
:param event: Event of the origin.
:type magnitudes: list of
:class:`~obspy.core.event.magnitude.Magnitude`
:param magnitudes: Store magnitudes in a list to keep
their positions.
:rtype: :class:`~obspy.core.event.origin.Origin`,
:class:`~obspy.core.event.resourceid.ResourceIdentifier`
:returns: Parsed origin or None, resource identifier of the
origin.
"""
magnitude_types = []
magnitude_values = []
magnitude_station_counts = []
fields = self.fields['line_1']
time_origin = line[fields['time']].strip()
time_fixed_flag = line[fields['time_fixf']].strip()
latitude = line[fields['lat']].strip()
longitude = line[fields['lon']].strip()
epicenter_fixed_flag = line[fields['epicenter_fixf']].strip()
depth = line[fields['depth']].strip()
depth_fixed_flag = line[fields['depth_fixf']].strip()
phase_count = line[fields['n_def']].strip()
station_count = line[fields['n_sta']].strip()
azimuthal_gap = line[fields['gap']].strip()
magnitude_types.append(line[fields['mag_type_1']].strip())
magnitude_values.append(line[fields['mag_1']].strip())
magnitude_station_counts.append(line[fields['mag_n_sta_1']].strip())
magnitude_types.append(line[fields['mag_type_2']].strip())
magnitude_values.append(line[fields['mag_2']].strip())
magnitude_station_counts.append(line[fields['mag_n_sta_2']].strip())
magnitude_types.append(line[fields['mag_type_3']].strip())
magnitude_values.append(line[fields['mag_3']].strip())
magnitude_station_counts.append(line[fields['mag_n_sta_3']].strip())
author = line[fields['author']].strip()
origin_id = line[fields['id']].strip()
origin = Origin()
origin.quality = OriginQuality()
try:
origin.time = UTCDateTime(time_origin.replace('/', '-'))
origin.latitude = float(latitude)
origin.longitude = float(longitude)
except (TypeError, ValueError):
self._warn('Missing origin data, skipping event')
return None, None
origin.time_fixed = time_fixed_flag.lower() == 'f'
origin.epicenter_fixed = epicenter_fixed_flag.lower() == 'f'
try:
# Convert value from km to m
origin.depth = float(depth) * 1000
except ValueError:
pass
try:
origin.depth_type = DEPTH_TYPES[depth_fixed_flag]
except KeyError:
origin.depth_type = OriginDepthType('from location')
try:
origin.quality.used_phase_count = int(phase_count)
origin.quality.associated_phase_count = int(phase_count)
except ValueError:
pass
try:
origin.quality.used_station_count = int(station_count)
origin.quality.associated_station_count = int(station_count)
except ValueError:
pass
try:
origin.quality.azimuthal_gap = float(azimuthal_gap)
except ValueError:
pass
self.author = author
origin.creation_info = self._get_creation_info()
public_id = "origin/%s" % origin_id
origin_res_id = self._get_res_id(public_id)
for i in range(3):
try:
magnitude = Magnitude()
magnitude.creation_info = self._get_creation_info()
magnitude.magnitude_type = magnitude_types[i]
magnitude.mag = float(magnitude_values[i])
magnitude.station_count = int(magnitude_station_counts[i])
magnitude.origin_id = origin_res_id
magnitudes.append(magnitude)
event.magnitudes.append(magnitude)
except ValueError:
# Magnitude can be empty but we need to keep the
# position between mag1, mag2 or mag3.
magnitudes.append(None)
return origin, origin_res_id
'distance (deg)': ('distance', float),
'theo. azimuth (deg)': ('azimuth', float),
'weight': ('time_weight', float),
},
'origin': {
'origin time': ('time', to_utcdatetime),
'latitude': ('latitude', float),
'longitude': ('longitude', float),
'depth (km)': ('depth', _km2m),
'error in origin time': ('time_errors', float),
'error in latitude (km)': ('latitude_errors', _km2deg),
'error in longitude (km)': ('longitude_errors', _km2deg),
# (correction for lat in _read_evt)
'error in depth (km)': ('depth_errors', _km2m),
'no. of stations used': (
'quality', lambda x: OriginQuality(used_station_count=int(x))),
'source region': ('region', str)
},
'origin_uncertainty': {
'error ellipse major': ('max_horizontal_uncertainty', _km2m),
'error ellipse minor': ('min_horizontal_uncertainty', _km2m),
'error ellipse strike': ('azimuth_max_horizontal_uncertainty', float)
},
'event': {
'event type': ('event_type', _event_type)
}
# no dict for magnitudes, these are handled by function _mag
}
# define supported keys just for documentation
def sdxtoquakeml(sdx_dir,
out_xml,
time_uncertainties=[0.1, 0.2, 0.5, 0.8, 1.5],
catalog_description="",
catalog_version="",
agency_id="",
author="",
vel_mod_id=""):
"""
Convert SDX to QuakeML format using ObsPy inventory structure.
SDX filename prefix is stored under event description.
Input parameters:
- sdx_dir: directory containing sdx files (required)
- out_xml: Filename of quakeML file (required)
- time_uncertainties: List containing time uncertainities in seconds
for mapping from weights 0-4, respectively (optional)
- catalog_description (optional)
- cat_agency_id (optional)
- author (optional)
- vel_mod_id (optional)
Output:
- xml catalog in QuakeML format.
"""
# Prepare catalog
cat = Catalog(description=catalog_description,
creation_info=CreationInfo(author=author,
agency_id=agency_id,
version=catalog_version))
# Read in sdx files in directory, recursively
files = glob.glob("{:}/**/*.sdx".format(sdx_dir), recursive=True)
if len(files) == 0:
print("No SDX files found in path. Exiting")
for sdx_file_path in files:
print("Working on ", sdx_file_path.split('/')[-1])
# Set-up event
evt_id = (sdx_file_path.split('/')[-1])[:-4]
event = Event(event_type="earthquake",
creation_info=CreationInfo(author=author,
agency_id=agency_id),
event_descriptions=[EventDescription(text=evt_id)])
# Get station details, append to arrays
sdx_file = open(sdx_file_path, "r")
stations = []
for line in sdx_file:
if line.rstrip() == "station":
sdxstation = list(islice(sdx_file, 5))
stations.append([
sdxstation[1].split()[0],
float(sdxstation[2].split()[0]),
float(sdxstation[3].split()[0]),
float(sdxstation[4].split()[0])
])
sdx_file.close()
# Find origin details, append to origin object
sdx_file = open(sdx_file_path, "r")
found_origin = False
for line in sdx_file:
if line.rstrip() == "origin":
found_origin = True
sdxorigin = list(islice(sdx_file, 17))
orig_time = ("{:}T{:}".format(
sdxorigin[1][0:10].replace(".", "-"), sdxorigin[1][11:23]))
evt_lat = float(sdxorigin[2].split()[0])
evt_lon = float(sdxorigin[3].split()[0])
evt_depth = float(sdxorigin[4].split()[0])
creation_time = UTCDateTime("{:}T{:}".format(
sdxorigin[16].split()[6][0:10].replace(".", "-"),
sdxorigin[16].split()[6][11:23]))
num_arrivals = int(sdxorigin[12].split()[0])
num_arrivals_p = (int(sdxorigin[12].split()[0]) -
int(sdxorigin[12].split()[1]))
min_dist = float(sdxorigin[12].split()[9])
max_dist = float(sdxorigin[12].split()[10])
med_dist = float(sdxorigin[12].split()[11])
max_az_gap = float(sdxorigin[12].split()[6])
origin = Origin(time=UTCDateTime(orig_time),
longitude=evt_lon,
latitude=evt_lat,
depth=evt_depth * -1000,
earth_model_id=vel_mod_id,
origin_type="hypocenter",
evaluation_mode="manual",
evaluation_status="confirmed",
method_id=ResourceIdentifier(id="SDX_hypo71"),
creation_info=CreationInfo(
creation_time=creation_time,
author=author,
agency_id=agency_id),
quality=OriginQuality(
associated_phase_count=num_arrivals,
used_phase_count=num_arrivals,
associated_station_count=num_arrivals_p,
used_station_count=num_arrivals_p,
azimuthal_gap=max_az_gap,
minimum_distance=min_dist,
maximum_distance=max_dist,
median_distance=med_dist))
event.origins.append(origin)
sdx_file.close()
# Skip event if no computed origin
if found_origin is False:
print("No origin found ... skipping event")
continue
# Get pick details, append to pick and arrival objects
sdx_file = open(sdx_file_path, "r")
found_pick = False
for line in sdx_file:
if line.rstrip() == "pick":
found_pick = True
sdxpick = list(islice(sdx_file, 15))
pick_time = UTCDateTime("{:}T{:}".format(
sdxpick[1][0:10].replace(".", "-"), sdxpick[1][11:23]))
network = sdxpick[2].split()[0]
station = sdxpick[2].split()[1]
location = sdxpick[2].split()[2]
if "NOT_SET" in location:
location = ""
channel = sdxpick[2].split()[3]
onset = sdxpick[8].split()[0]
if onset == "0":
pickonset = "emergent"
elif onset == "1":
pickonset = "impulsive"
elif onset == "2":
pickonset = "questionable"
phase = sdxpick[9].split()[0]
polarity = sdxpick[10].split()[0]
if polarity == "0":
pol = "positive"
elif polarity == "1":
pol = "negative"
elif polarity == "2":
pol = "undecidable"
weight = int(sdxpick[11].split()[0])
creation_time = UTCDateTime("{:}T{:}".format(
sdxpick[14].split()[6][0:10].replace(".", "-"),
sdxpick[14].split()[6][11:23]))
pick = Pick(
time=pick_time,
waveform_id=WaveformStreamID(network_code=network,
station_code=station,
location_code=location,
channel_code=channel),
time_errors=time_uncertainties[weight],
evaluation_mode="manual",
evaluation_status="confirmed",
onset=pickonset,
phase_hint=phase,
polarity=pol,
method_id=ResourceIdentifier(id="SDX"),
creation_info=CreationInfo(creation_time=creation_time))
event.picks.append(pick)
# Compute azimuth, distance, append to arrival object
for i in range(0, len(stations)):
if stations[i][0] == station:
azimuth = (gps2dist_azimuth(evt_lat, evt_lon,
stations[i][1],
stations[i][2])[1])
dist_deg = locations2degrees(evt_lat, evt_lon,
stations[i][1],
stations[i][2])
arrival = Arrival(phase=phase,
pick_id=pick.resource_id,
azimuth=azimuth,
distance=dist_deg,
time_weight=1.00)
event.origins[0].arrivals.append(arrival)
# Skip event if no picks
if found_pick is False:
print("No picks found ... skipping event")
continue
# Set preferred origin and append event to catalogue
event.preferred_origin_id = event.origins[0].resource_id
cat.events.append(event)
sdx_file.close()
cat.write(out_xml, format="QUAKEML")
def _readheader(f):
"""
Internal header reader.
:type f: file
:param f: File open in read-mode.
:returns: :class:`~obspy.core.event.event.Event`
"""
f.seek(0)
# Base populate to allow for empty parts of file
new_event = Event()
topline = _get_headline(f=f)
if not topline:
raise NordicParsingError('No header found, or incorrect '
'formatting: corrupt s-file')
try:
sfile_seconds = int(topline[16:18])
if sfile_seconds == 60:
sfile_seconds = 0
add_seconds = 60
else:
add_seconds = 0
new_event.origins.append(Origin())
new_event.origins[0].time = UTCDateTime(int(topline[1:5]),
int(topline[6:8]),
int(topline[8:10]),
int(topline[11:13]),
int(topline[13:15]),
sfile_seconds,
int(topline[19:20]) *
100000)\
+ add_seconds
except Exception:
NordicParsingError("Couldn't read a date from sfile")
# new_event.loc_mod_ind=topline[20]
new_event.event_descriptions.append(EventDescription())
new_event.event_descriptions[0].text = topline[21:23]
# new_event.ev_id=topline[22]
try:
new_event.origins[0].latitude = float(topline[23:30])
new_event.origins[0].longitude = float(topline[31:38])
new_event.origins[0].depth = float(topline[39:43]) * 1000
except ValueError:
# The origin 'requires' a lat & long
new_event.origins[0].latitude = None
new_event.origins[0].longitude = None
new_event.origins[0].depth = None
# new_event.depth_ind = topline[44]
# new_event.loc_ind = topline[45]
new_event.creation_info = CreationInfo(agency_id=topline[45:48].strip())
ksta = Comment(text='Number of stations=' + topline[49:51].strip())
new_event.origins[0].comments.append(ksta)
if _float_conv(topline[51:55]) is not None:
new_event.origins[0].quality = OriginQuality(
standard_error=_float_conv(topline[51:55]))
# Read in magnitudes if they are there.
for index in [59, 67, 75]:
if not topline[index].isspace():
new_event.magnitudes.append(Magnitude())
new_event.magnitudes[-1].mag = _float_conv(topline[index -
3:index])
new_event.magnitudes[-1].magnitude_type = \
_nortoevmag(topline[index])
new_event.magnitudes[-1].creation_info = \
CreationInfo(agency_id=topline[index + 1:index + 4].strip())
new_event.magnitudes[-1].origin_id = new_event.origins[0].\
resource_id
# Set the useful things like preferred magnitude and preferred origin
new_event.preferred_origin_id = new_event.origins[0].resource_id
try:
# Select moment first, then local, then
mag_filter = [
'MW', 'Mw', 'ML', 'Ml', 'MB', 'Mb', 'MS', 'Ms', 'MC', 'Mc'
]
_magnitudes = [(m.magnitude_type, m.resource_id)
for m in new_event.magnitudes]
preferred_magnitude = sorted(_magnitudes,
key=lambda x: mag_filter.index(x[0]))[0]
new_event.preferred_magnitude_id = preferred_magnitude[1]
except (ValueError, IndexError):
# If there is a magnitude not specified in filter
try:
new_event.preferred_magnitude_id = new_event.magnitudes[0].\
resource_id
except IndexError:
pass
return new_event
def _parseRecordDp(self, line, event):
"""
Parses the 'source parameter data - primary' record Dp
"""
source_contributor = line[2:6].strip()
computation_type = line[6]
exponent = self._intZero(line[7])
scale = math.pow(10, exponent)
centroid_origin_time = line[8:14] + '.' + line[14]
orig_time_stderr = line[15:17]
if orig_time_stderr == 'FX':
orig_time_stderr = 'Fixed'
else:
orig_time_stderr =\
self._floatWithFormat(orig_time_stderr, '2.1', scale)
centroid_latitude = self._floatWithFormat(line[17:21], '4.2')
lat_type = line[21]
if centroid_latitude is not None:
centroid_latitude *= self._coordinateSign(lat_type)
lat_stderr = line[22:25]
if lat_stderr == 'FX':
lat_stderr = 'Fixed'
else:
lat_stderr = self._floatWithFormat(lat_stderr, '3.2', scale)
centroid_longitude = self._floatWithFormat(line[25:30], '5.2')
lon_type = line[30]
if centroid_longitude is not None:
centroid_longitude *= self._coordinateSign(lon_type)
lon_stderr = line[31:34]
if lon_stderr == 'FX':
lon_stderr = 'Fixed'
else:
lon_stderr = self._floatWithFormat(lon_stderr, '3.2', scale)
centroid_depth = self._floatWithFormat(line[34:38], '4.1')
depth_stderr = line[38:40]
if depth_stderr == 'FX' or depth_stderr == 'BD':
depth_stderr = 'Fixed'
else:
depth_stderr = self._floatWithFormat(depth_stderr, '2.1', scale)
station_number = self._intZero(line[40:43])
component_number = self._intZero(line[43:46])
station_number2 = self._intZero(line[46:48])
component_number2 = self._intZero(line[48:51])
#unused: half_duration = self._floatWithFormat(line[51:54], '3.1')
moment = self._floatWithFormat(line[54:56], '2.1')
moment_stderr = self._floatWithFormat(line[56:58], '2.1')
moment_exponent = self._int(line[58:60])
if (moment is not None) and (moment_exponent is not None):
moment *= math.pow(10, moment_exponent)
if (moment_stderr is not None) and (moment_exponent is not None):
moment_stderr *= math.pow(10, moment_exponent)
evid = event.resource_id.id.split('/')[-1]
#Create a new origin only if centroid time is defined:
origin = None
if centroid_origin_time.strip() != '.':
origin = Origin()
res_id = '/'.join(
(res_id_prefix, 'origin', evid, source_contributor.lower(),
'mw' + computation_type.lower()))
origin.resource_id = ResourceIdentifier(id=res_id)
origin.creation_info =\
CreationInfo(agency_id=source_contributor)
date = event.origins[0].time.strftime('%Y%m%d')
origin.time = UTCDateTime(date + centroid_origin_time)
#Check if centroid time is on the next day:
if origin.time < event.origins[0].time:
origin.time += timedelta(days=1)
self._storeUncertainty(origin.time_errors, orig_time_stderr)
origin.latitude = centroid_latitude
origin.longitude = centroid_longitude
origin.depth = centroid_depth * 1000
if lat_stderr == 'Fixed' and lon_stderr == 'Fixed':
origin.epicenter_fixed = True
else:
self._storeUncertainty(origin.latitude_errors,
self._latErrToDeg(lat_stderr))
self._storeUncertainty(
origin.longitude_errors,
self._lonErrToDeg(lon_stderr, origin.latitude))
if depth_stderr == 'Fixed':
origin.depth_type = 'operator assigned'
else:
origin.depth_type = 'from location'
self._storeUncertainty(origin.depth_errors,
depth_stderr,
scale=1000)
quality = OriginQuality()
quality.used_station_count =\
station_number + station_number2
quality.used_phase_count =\
component_number + component_number2
origin.quality = quality
origin.type = 'centroid'
event.origins.append(origin)
focal_mechanism = FocalMechanism()
res_id = '/'.join(
(res_id_prefix, 'focalmechanism', evid, source_contributor.lower(),
'mw' + computation_type.lower()))
focal_mechanism.resource_id = ResourceIdentifier(id=res_id)
focal_mechanism.creation_info =\
CreationInfo(agency_id=source_contributor)
moment_tensor = MomentTensor()
if origin is not None:
moment_tensor.derived_origin_id = origin.resource_id
else:
#this is required for QuakeML validation:
res_id = '/'.join((res_id_prefix, 'no-origin'))
moment_tensor.derived_origin_id =\
ResourceIdentifier(id=res_id)
for mag in event.magnitudes:
if mag.creation_info.agency_id == source_contributor:
moment_tensor.moment_magnitude_id = mag.resource_id
res_id = '/'.join(
(res_id_prefix, 'momenttensor', evid, source_contributor.lower(),
'mw' + computation_type.lower()))
moment_tensor.resource_id = ResourceIdentifier(id=res_id)
moment_tensor.scalar_moment = moment
self._storeUncertainty(moment_tensor.scalar_moment_errors,
moment_stderr)
data_used = DataUsed()
data_used.station_count = station_number + station_number2
data_used.component_count = component_number + component_number2
if computation_type == 'C':
res_id = '/'.join((res_id_prefix, 'methodID=CMT'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
#CMT algorithm uses long-period body waves,
#very-long-period surface waves and
#intermediate period surface waves (since 2004
#for shallow and intermediate-depth earthquakes
# --Ekstrom et al., 2012)
data_used.wave_type = 'combined'
if computation_type == 'M':
res_id = '/'.join((res_id_prefix, 'methodID=moment_tensor'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
#FIXME: not sure which kind of data is used by
#"moment tensor" algorithm.
data_used.wave_type = 'unknown'
elif computation_type == 'B':
res_id = '/'.join((res_id_prefix, 'methodID=broadband_data'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
#FIXME: is 'combined' correct here?
data_used.wave_type = 'combined'
elif computation_type == 'F':
res_id = '/'.join((res_id_prefix, 'methodID=P-wave_first_motion'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
data_used.wave_type = 'P waves'
elif computation_type == 'S':
res_id = '/'.join((res_id_prefix, 'methodID=scalar_moment'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
#FIXME: not sure which kind of data is used
#for scalar moment determination.
data_used.wave_type = 'unknown'
moment_tensor.data_used = data_used
focal_mechanism.moment_tensor = moment_tensor
event.focal_mechanisms.append(focal_mechanism)
return focal_mechanism
def _parse_record_dp(self, line, event):
"""
Parses the 'source parameter data - primary' record Dp
"""
source_contributor = line[2:6].strip()
computation_type = line[6]
exponent = self._int_zero(line[7])
scale = math.pow(10, exponent)
centroid_origin_time = line[8:14] + '.' + line[14]
orig_time_stderr = line[15:17]
if orig_time_stderr == 'FX':
orig_time_stderr = 'Fixed'
else:
orig_time_stderr = \
self._float_with_format(orig_time_stderr, '2.1', scale)
centroid_latitude = self._float_with_format(line[17:21], '4.2')
lat_type = line[21]
if centroid_latitude is not None:
centroid_latitude *= self._coordinate_sign(lat_type)
lat_stderr = line[22:25]
if lat_stderr == 'FX':
lat_stderr = 'Fixed'
else:
lat_stderr = self._float_with_format(lat_stderr, '3.2', scale)
centroid_longitude = self._float_with_format(line[25:30], '5.2')
lon_type = line[30]
if centroid_longitude is not None:
centroid_longitude *= self._coordinate_sign(lon_type)
lon_stderr = line[31:34]
if lon_stderr == 'FX':
lon_stderr = 'Fixed'
else:
lon_stderr = self._float_with_format(lon_stderr, '3.2', scale)
centroid_depth = self._float_with_format(line[34:38], '4.1')
depth_stderr = line[38:40]
if depth_stderr == 'FX' or depth_stderr == 'BD':
depth_stderr = 'Fixed'
else:
depth_stderr = self._float_with_format(depth_stderr, '2.1', scale)
station_number = self._int_zero(line[40:43])
component_number = self._int_zero(line[43:46])
station_number2 = self._int_zero(line[46:48])
component_number2 = self._int_zero(line[48:51])
# unused: half_duration = self._float_with_format(line[51:54], '3.1')
moment = self._float_with_format(line[54:56], '2.1')
moment_stderr = self._float_with_format(line[56:58], '2.1')
moment_exponent = self._int(line[58:60])
if (moment is not None) and (moment_exponent is not None):
moment *= math.pow(10, moment_exponent)
if (moment_stderr is not None) and (moment_exponent is not None):
moment_stderr *= math.pow(10, moment_exponent)
evid = event.resource_id.id.split('/')[-1]
# Create a new origin only if centroid time is defined:
origin = None
if centroid_origin_time.strip() != '.':
origin = Origin()
res_id = '/'.join((res_id_prefix, 'origin',
evid, source_contributor.lower(),
'mw' + computation_type.lower()))
origin.resource_id = ResourceIdentifier(id=res_id)
origin.creation_info = \
CreationInfo(agency_id=source_contributor)
date = event.origins[0].time.strftime('%Y%m%d')
origin.time = UTCDateTime(date + centroid_origin_time)
# Check if centroid time is on the next day:
if origin.time < event.origins[0].time:
origin.time += timedelta(days=1)
self._store_uncertainty(origin.time_errors, orig_time_stderr)
origin.latitude = centroid_latitude
origin.longitude = centroid_longitude
origin.depth = centroid_depth * 1000
if lat_stderr == 'Fixed' and lon_stderr == 'Fixed':
origin.epicenter_fixed = True
else:
self._store_uncertainty(origin.latitude_errors,
self._lat_err_to_deg(lat_stderr))
self._store_uncertainty(origin.longitude_errors,
self._lon_err_to_deg(lon_stderr,
origin.latitude))
if depth_stderr == 'Fixed':
origin.depth_type = 'operator assigned'
else:
origin.depth_type = 'from location'
self._store_uncertainty(origin.depth_errors,
depth_stderr, scale=1000)
quality = OriginQuality()
quality.used_station_count = \
station_number + station_number2
quality.used_phase_count = \
component_number + component_number2
origin.quality = quality
origin.origin_type = 'centroid'
event.origins.append(origin)
focal_mechanism = FocalMechanism()
res_id = '/'.join((res_id_prefix, 'focalmechanism',
evid, source_contributor.lower(),
'mw' + computation_type.lower()))
focal_mechanism.resource_id = ResourceIdentifier(id=res_id)
focal_mechanism.creation_info = \
CreationInfo(agency_id=source_contributor)
moment_tensor = MomentTensor()
if origin is not None:
moment_tensor.derived_origin_id = origin.resource_id
else:
# this is required for QuakeML validation:
res_id = '/'.join((res_id_prefix, 'no-origin'))
moment_tensor.derived_origin_id = \
ResourceIdentifier(id=res_id)
for mag in event.magnitudes:
if mag.creation_info.agency_id == source_contributor:
moment_tensor.moment_magnitude_id = mag.resource_id
res_id = '/'.join((res_id_prefix, 'momenttensor',
evid, source_contributor.lower(),
'mw' + computation_type.lower()))
moment_tensor.resource_id = ResourceIdentifier(id=res_id)
moment_tensor.scalar_moment = moment
self._store_uncertainty(moment_tensor.scalar_moment_errors,
moment_stderr)
data_used = DataUsed()
data_used.station_count = station_number + station_number2
data_used.component_count = component_number + component_number2
if computation_type == 'C':
res_id = '/'.join((res_id_prefix, 'methodID=CMT'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
# CMT algorithm uses long-period body waves,
# very-long-period surface waves and
# intermediate period surface waves (since 2004
# for shallow and intermediate-depth earthquakes
# --Ekstrom et al., 2012)
data_used.wave_type = 'combined'
if computation_type == 'M':
res_id = '/'.join((res_id_prefix, 'methodID=moment_tensor'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
# FIXME: not sure which kind of data is used by
# "moment tensor" algorithm.
data_used.wave_type = 'unknown'
elif computation_type == 'B':
res_id = '/'.join((res_id_prefix, 'methodID=broadband_data'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
# FIXME: is 'combined' correct here?
data_used.wave_type = 'combined'
elif computation_type == 'F':
res_id = '/'.join((res_id_prefix, 'methodID=P-wave_first_motion'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
data_used.wave_type = 'P waves'
elif computation_type == 'S':
res_id = '/'.join((res_id_prefix, 'methodID=scalar_moment'))
focal_mechanism.method_id = ResourceIdentifier(id=res_id)
# FIXME: not sure which kind of data is used
# for scalar moment determination.
data_used.wave_type = 'unknown'
moment_tensor.data_used = [data_used]
focal_mechanism.moment_tensor = moment_tensor
event.focal_mechanisms.append(focal_mechanism)
return focal_mechanism
