def _deserialize(self, zmap_str):
catalog = Catalog()
for row in zmap_str.split('\n'):
if len(row) == 0:
continue
origin = Origin()
event = Event(origins=[origin])
event.preferred_origin_id = origin.resource_id.id
# Begin value extraction
columns = row.split('\t', 13)[:13] # ignore extra columns
values = dict(zip(_STD_ZMAP_COLUMNS + _EXT_ZMAP_COLUMNS, columns))
# Extract origin
origin.longitude = self._str2num(values.get('lon'))
origin.latitude = self._str2num(values.get('lat'))
depth = self._str2num(values.get('depth'))
if depth is not None:
origin.depth = depth * 1000.0
z_err = self._str2num(values.get('z_err'))
if z_err is not None:
origin.depth_errors.uncertainty = z_err * 1000.0
h_err = self._str2num(values.get('h_err'))
if h_err is not None:
ou = OriginUncertainty()
ou.horizontal_uncertainty = h_err
ou.preferred_description = 'horizontal uncertainty'
origin.origin_uncertainty = ou
year = self._str2num(values.get('year'))
if year is not None:
t_fields = ['year', 'month', 'day', 'hour', 'minute', 'second']
comps = [self._str2num(values.get(f)) for f in t_fields]
if year % 1 != 0:
origin.time = self._decyear2utc(year)
elif any(v > 0 for v in comps[1:]):
# no seconds involved
if len(comps) < 6:
utc_args = [int(v) for v in comps if v is not None]
# we also have to handle seconds
else:
utc_args = [
int(v) if v is not None else 0 for v in comps[:-1]
]
# just leave float seconds as is
utc_args.append(comps[-1])
origin.time = UTCDateTime(*utc_args)
mag = self._str2num(values.get('mag'))
# Extract magnitude
if mag is not None:
magnitude = Magnitude(mag=mag)
m_err = self._str2num(values.get('m_err'))
magnitude.mag_errors.uncertainty = m_err
event.magnitudes.append(magnitude)
event.preferred_magnitude_id = magnitude.resource_id.id
event.scope_resource_ids()
catalog.append(event)
return catalog
def _deserialize(self, zmap_str):
catalog = Catalog()
for row in zmap_str.split('\n'):
if len(row) == 0:
continue
origin = Origin()
event = Event(origins=[origin])
event.preferred_origin_id = origin.resource_id.id
# Begin value extraction
columns = row.split('\t', 13)[:13] # ignore extra columns
values = dict(zip(_STD_ZMAP_COLUMNS + _EXT_ZMAP_COLUMNS, columns))
# Extract origin
origin.longitude = self._str2num(values.get('lon'))
origin.latitude = self._str2num(values.get('lat'))
depth = self._str2num(values.get('depth'))
if depth is not None:
origin.depth = depth * 1000.0
z_err = self._str2num(values.get('z_err'))
if z_err is not None:
origin.depth_errors.uncertainty = z_err * 1000.0
h_err = self._str2num(values.get('h_err'))
if h_err is not None:
ou = OriginUncertainty()
ou.horizontal_uncertainty = h_err
ou.preferred_description = 'horizontal uncertainty'
origin.origin_uncertainty = ou
year = self._str2num(values.get('year'))
if year is not None:
t_fields = ['year', 'month', 'day', 'hour', 'minute', 'second']
comps = [self._str2num(values.get(f)) for f in t_fields]
if year % 1 != 0:
origin.time = self._decyear2utc(year)
elif any(v > 0 for v in comps[1:]):
# no seconds involved
if len(comps) < 6:
utc_args = [int(v) for v in comps if v is not None]
# we also have to handle seconds
else:
utc_args = [int(v) if v is not None else 0
for v in comps[:-1]]
# just leave float seconds as is
utc_args.append(comps[-1])
origin.time = UTCDateTime(*utc_args)
mag = self._str2num(values.get('mag'))
# Extract magnitude
if mag is not None:
magnitude = Magnitude(mag=mag)
m_err = self._str2num(values.get('m_err'))
magnitude.mag_errors.uncertainty = m_err
event.magnitudes.append(magnitude)
event.preferred_magnitude_id = magnitude.resource_id.id
event.scope_resource_ids()
catalog.append(event)
return catalog
def _parse_record_l(self, line, event):
"""
Parses the '90 percent error ellipse' record L
"""
origin = event.origins[0]
semi_major_axis_azimuth = self._float(line[2:8])
if semi_major_axis_azimuth is None:
return
semi_major_axis_plunge = self._float(line[8:13])
semi_major_axis_length = self._float(line[13:21])
intermediate_axis_azimuth = self._float(line[21:27])
intermediate_axis_plunge = self._float(line[27:32])
# This is called "intermediate_axis_length",
# but it is definitively a "semi_intermediate_axis_length",
# since in most cases:
# (intermediate_axis_length / 2) < semi_minor_axis_length
intermediate_axis_length = self._float(line[32:40])
semi_minor_axis_azimuth = self._float(line[40:46])
semi_minor_axis_plunge = self._float(line[46:51])
semi_minor_axis_length = self._float(line[51:59])
if (semi_minor_axis_azimuth ==
semi_minor_axis_plunge ==
semi_minor_axis_length == 0):
semi_minor_axis_azimuth = intermediate_axis_azimuth
semi_minor_axis_plunge = intermediate_axis_plunge
semi_minor_axis_length = intermediate_axis_length
origin.depth_type = 'operator assigned'
# FIXME: The following code needs to be double-checked!
semi_major_axis_unit_vect = \
self._spherical_to_cartesian((1,
semi_major_axis_azimuth,
semi_major_axis_plunge))
semi_minor_axis_unit_vect = \
self._spherical_to_cartesian((1,
semi_minor_axis_azimuth,
semi_minor_axis_plunge))
major_axis_rotation = \
self._angle_between(semi_major_axis_unit_vect,
semi_minor_axis_unit_vect)
origin.origin_uncertainty = OriginUncertainty()
origin.origin_uncertainty.preferred_description = \
'confidence ellipsoid'
origin.origin_uncertainty.confidence_level = 90
confidence_ellipsoid = ConfidenceEllipsoid()
confidence_ellipsoid.semi_major_axis_length = \
semi_major_axis_length * 1000
confidence_ellipsoid.semi_minor_axis_length = \
semi_minor_axis_length * 1000
confidence_ellipsoid.semi_intermediate_axis_length = \
intermediate_axis_length * 1000
confidence_ellipsoid.major_axis_plunge = semi_major_axis_plunge
confidence_ellipsoid.major_axis_azimuth = semi_major_axis_azimuth
# We need to add 90 to match NEIC QuakeML format,
# but I don't understand why...
confidence_ellipsoid.major_axis_rotation = \
major_axis_rotation + 90
origin.origin_uncertainty.confidence_ellipsoid = confidence_ellipsoid
def _deserialize(self, zmap_str):
catalog = Catalog()
for row in zmap_str.split("\n"):
if len(row) == 0:
continue
origin = Origin()
event = Event(origins=[origin])
event.preferred_origin_id = origin.resource_id.id
# Begin value extraction
columns = row.split("\t", 13)[:13] # ignore extra columns
values = dict(zip(_STD_ZMAP_COLUMNS + _EXT_ZMAP_COLUMNS, columns))
# Extract origin
origin.longitude = self._str2num(values.get("lon"))
origin.latitude = self._str2num(values.get("lat"))
depth = self._str2num(values.get("depth"))
if depth is not None:
origin.depth = depth * 1000.0
z_err = self._str2num(values.get("z_err"))
if z_err is not None:
origin.depth_errors.uncertainty = z_err * 1000.0
h_err = self._str2num(values.get("h_err"))
if h_err is not None:
ou = OriginUncertainty()
ou.horizontal_uncertainty = h_err
ou.preferred_description = "horizontal uncertainty"
origin.origin_uncertainty = ou
year = self._str2num(values.get("year"))
if year is not None:
t_fields = ["year", "month", "day", "hour", "minute", "second"]
comps = [self._str2num(values.get(f)) for f in t_fields]
if year % 1 != 0:
origin.time = self._decyear2utc(year)
elif any(v > 0 for v in comps[1:]):
utc_args = [int(v) for v in comps if v is not None]
origin.time = UTCDateTime(*utc_args)
mag = self._str2num(values.get("mag"))
# Extract magnitude
if mag is not None:
magnitude = Magnitude(mag=mag)
m_err = self._str2num(values.get("m_err"))
magnitude.mag_errors.uncertainty = m_err
event.magnitudes.append(magnitude)
event.preferred_magnitude_id = magnitude.resource_id.id
catalog.append(event)
return catalog
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 __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 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 _parse_second_line_origin(self, line, event, origin, magnitudes):
magnitude_errors = []
fields = self.fields['line_2']
standard_error = line[fields['rms']].strip()
time_uncertainty = line[fields['ot_error']].strip()
max_horizontal_uncertainty = line[fields['s_major']].strip()
min_horizontal_uncertainty = line[fields['s_minor']].strip()
azimuth_max_horizontal_uncertainty = line[fields['az']].strip()
depth_uncertainty = line[fields['depth_err']].strip()
min_distance = line[fields['min_dist']].strip()
max_distance = line[fields['max_dist']].strip()
magnitude_errors.append(line[fields['mag_err_1']].strip())
magnitude_errors.append(line[fields['mag_err_2']].strip())
magnitude_errors.append(line[fields['mag_err_3']].strip())
analysis_type = line[fields['antype']].strip().lower()
location_method = line[fields['loctype']].strip().lower()
event_type = line[fields['evtype']].strip().lower()
try:
origin.quality.standard_error = float(standard_error)
except ValueError:
pass
try:
origin.time_errors.uncertainty = float(time_uncertainty)
except ValueError:
pass
try:
uncertainty = OriginUncertainty()
# Convert values from km to m
min_value = float(min_horizontal_uncertainty) * 1000
max_value = float(max_horizontal_uncertainty) * 1000
azimuth_value = float(azimuth_max_horizontal_uncertainty)
description = OriginUncertaintyDescription('uncertainty ellipse')
uncertainty.min_horizontal_uncertainty = min_value
uncertainty.max_horizontal_uncertainty = max_value
uncertainty.azimuth_max_horizontal_uncertainty = azimuth_value
uncertainty.preferred_description = description
origin.origin_uncertainty = uncertainty
except ValueError:
pass
try:
# Convert value from km to m
origin.depth_errors.uncertainty = float(depth_uncertainty) * 1000
except ValueError:
pass
try:
origin.quality.minimum_distance = float(min_distance)
origin.quality.maximum_distance = float(max_distance)
except ValueError:
self._warn('Missing minimum/maximum distance')
for i in range(2):
try:
mag_errors = magnitudes[i].mag_errors
mag_errors.uncertainty = float(magnitude_errors[i])
except (AttributeError, ValueError):
pass
# No match for 'g' (guess)
# We map 'g' to 'manual' and create a comment for origin
try:
origin.evaluation_mode = EVALUATION_MODES[analysis_type]
if analysis_type == 'g':
# comment: 'GSE2.0:antype=g'
text = 'GSE2.0:antype=g'
comment = self._comment(text)
origin.comments.append(comment)
except KeyError:
self._warn('Wrong analysis type')
if location_method not in LOCATION_METHODS.keys():
location_method = 'o'
method = LOCATION_METHODS[location_method]
method_id = "method/%s" % method
origin.method_id = self._get_res_id(method_id)
if event_type not in EVENT_TYPES.keys():
event_type = 'uk'
self._warn('Wrong or unknown event type')
event_data = EVENT_TYPES[event_type]
event.event_type_certainty, event.event_type = event_data
# comment: 'GSE2.0:evtype=<evtype>'
if event_type:
text = 'GSE2.0:evtype=%s' % event_type
comment = self._comment(text)
event.comments.append(comment)
def _read_single_event(event_file, locate_dir, units, local_mag_ph):
"""
Parse an event file from QuakeMigrate into an obspy Event object.
Parameters
----------
event_file : `pathlib.Path` object
Path to .event file to read.
locate_dir : `pathlib.Path` object
Path to locate directory (contains "events", "picks" etc. directories).
units : {"km", "m"}
Grid projection coordinates for QM LUT (determines units of depths and
uncertainties in the .event files).
local_mag_ph : {"S", "P"}
Amplitude measurement used to calculate local magnitudes.
Returns
-------
event : `obspy.Event` object
Event object populated with all available information output by
:class:`~quakemigrate.signal.scan.locate()`, including event locations
and uncertainties, picks, and amplitudes and magnitudes if available.
"""
# Parse information from event file
event_info = pd.read_csv(event_file).iloc[0]
event_uid = str(event_info["EventID"])
# Set distance conversion factor (from units of QM LUT projection units).
if units == "km":
factor = 1e3
elif units == "m":
factor = 1
else:
raise AttributeError(f"units must be 'km' or 'm'; not {units}")
# Create event object to store origin and pick information
event = Event()
event.extra = AttribDict()
event.resource_id = str(event_info["EventID"])
event.creation_info = CreationInfo(author="QuakeMigrate",
version=quakemigrate.__version__)
# Add COA info to extra
event.extra.coa = {"value": event_info["COA"], "namespace": ns}
event.extra.coa_norm = {"value": event_info["COA_NORM"], "namespace": ns}
event.extra.trig_coa = {"value": event_info["TRIG_COA"], "namespace": ns}
event.extra.dec_coa = {"value": event_info["DEC_COA"], "namespace": ns}
event.extra.dec_coa_norm = {
"value": event_info["DEC_COA_NORM"],
"namespace": ns
}
# Determine location of cut waveform data - add to event object as a
# custom extra attribute.
mseed = locate_dir / "raw_cut_waveforms" / event_uid
event.extra.cut_waveforms_file = {
"value": str(mseed.with_suffix(".m").resolve()),
"namespace": ns
}
if (locate_dir / "real_cut_waveforms").exists():
mseed = locate_dir / "real_cut_waveforms" / event_uid
event.extra.real_cut_waveforms_file = {
"value": str(mseed.with_suffix(".m").resolve()),
"namespace": ns
}
if (locate_dir / "wa_cut_waveforms").exists():
mseed = locate_dir / "wa_cut_waveforms" / event_uid
event.extra.wa_cut_waveforms_file = {
"value": str(mseed.with_suffix(".m").resolve()),
"namespace": ns
}
# Create origin with spline location and set to preferred event origin.
origin = Origin()
origin.method_id = "spline"
origin.longitude = event_info["X"]
origin.latitude = event_info["Y"]
origin.depth = event_info["Z"] * factor
origin.time = UTCDateTime(event_info["DT"])
event.origins = [origin]
event.preferred_origin_id = origin.resource_id
# Create origin with gaussian location and associate with event
origin = Origin()
origin.method_id = "gaussian"
origin.longitude = event_info["GAU_X"]
origin.latitude = event_info["GAU_Y"]
origin.depth = event_info["GAU_Z"] * factor
origin.time = UTCDateTime(event_info["DT"])
event.origins.append(origin)
ouc = OriginUncertainty()
ce = ConfidenceEllipsoid()
ce.semi_major_axis_length = event_info["COV_ErrY"] * factor
ce.semi_intermediate_axis_length = event_info["COV_ErrX"] * factor
ce.semi_minor_axis_length = event_info["COV_ErrZ"] * factor
ce.major_axis_plunge = 0
ce.major_axis_azimuth = 0
ce.major_axis_rotation = 0
ouc.confidence_ellipsoid = ce
ouc.preferred_description = "confidence ellipsoid"
# Set uncertainties for both as the gaussian uncertainties
for origin in event.origins:
origin.longitude_errors.uncertainty = kilometer2degrees(
event_info["GAU_ErrX"] * factor / 1e3)
origin.latitude_errors.uncertainty = kilometer2degrees(
event_info["GAU_ErrY"] * factor / 1e3)
origin.depth_errors.uncertainty = event_info["GAU_ErrZ"] * factor
origin.origin_uncertainty = ouc
# Add OriginQuality info to each origin?
for origin in event.origins:
origin.origin_type = "hypocenter"
origin.evaluation_mode = "automatic"
# --- Handle picks file ---
pick_file = locate_dir / "picks" / event_uid
if pick_file.with_suffix(".picks").is_file():
picks = pd.read_csv(pick_file.with_suffix(".picks"))
else:
return None
for _, pickline in picks.iterrows():
station = str(pickline["Station"])
phase = str(pickline["Phase"])
wid = WaveformStreamID(network_code="", station_code=station)
for method in ["modelled", "autopick"]:
pick = Pick()
pick.extra = AttribDict()
pick.waveform_id = wid
pick.method_id = method
pick.phase_hint = phase
if method == "autopick" and str(pickline["PickTime"]) != "-1":
pick.time = UTCDateTime(pickline["PickTime"])
pick.time_errors.uncertainty = float(pickline["PickError"])
pick.extra.snr = {
"value": float(pickline["SNR"]),
"namespace": ns
}
elif method == "modelled":
pick.time = UTCDateTime(pickline["ModelledTime"])
else:
continue
event.picks.append(pick)
# --- Handle amplitudes file ---
amps_file = locate_dir / "amplitudes" / event_uid
if amps_file.with_suffix(".amps").is_file():
amps = pd.read_csv(amps_file.with_suffix(".amps"))
i = 0
for _, ampsline in amps.iterrows():
wid = WaveformStreamID(seed_string=ampsline["id"])
noise_amp = ampsline["Noise_amp"] / 1000 # mm to m
for phase in ["P_amp", "S_amp"]:
amp = Amplitude()
if pd.isna(ampsline[phase]):
continue
amp.generic_amplitude = ampsline[phase] / 1000 # mm to m
amp.generic_amplitude_errors.uncertainty = noise_amp
amp.unit = "m"
amp.type = "AML"
amp.method_id = phase
amp.period = 1 / ampsline[f"{phase[0]}_freq"]
amp.time_window = TimeWindow(
reference=UTCDateTime(ampsline[f"{phase[0]}_time"]))
# amp.pick_id = ?
amp.waveform_id = wid
# amp.filter_id = ?
amp.magnitude_hint = "ML"
amp.evaluation_mode = "automatic"
amp.extra = AttribDict()
try:
amp.extra.filter_gain = {
"value": ampsline[f"{phase[0]}_filter_gain"],
"namespace": ns
}
amp.extra.avg_amp = {
"value": ampsline[f"{phase[0]}_avg_amp"] / 1000, # m
"namespace": ns
}
except KeyError:
pass
if phase[0] == local_mag_ph and not pd.isna(ampsline["ML"]):
i += 1
stat_mag = StationMagnitude()
stat_mag.extra = AttribDict()
# stat_mag.origin_id = ? local_mag_loc
stat_mag.mag = ampsline["ML"]
stat_mag.mag_errors.uncertainty = ampsline["ML_Err"]
stat_mag.station_magnitude_type = "ML"
stat_mag.amplitude_id = amp.resource_id
stat_mag.extra.picked = {
"value": ampsline["is_picked"],
"namespace": ns
}
stat_mag.extra.epi_dist = {
"value": ampsline["epi_dist"],
"namespace": ns
}
stat_mag.extra.z_dist = {
"value": ampsline["z_dist"],
"namespace": ns
}
event.station_magnitudes.append(stat_mag)
event.amplitudes.append(amp)
mag = Magnitude()
mag.extra = AttribDict()
mag.mag = event_info["ML"]
mag.mag_errors.uncertainty = event_info["ML_Err"]
mag.magnitude_type = "ML"
# mag.origin_id = ?
mag.station_count = i
mag.evaluation_mode = "automatic"
mag.extra.r2 = {"value": event_info["ML_r2"], "namespace": ns}
event.magnitudes = [mag]
event.preferred_magnitude_id = mag.resource_id
return event
def _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 _read_evt(filename, encoding='utf-8', **kwargs):
"""
Read a SeismicHandler EVT file and returns an ObsPy Catalog object.
.. warning::
This function should NOT be called directly, it registers via the
ObsPy :func:`~obspy.core.event.read_events` function, call this
instead.
:param str encoding: encoding used (default: utf-8)
:rtype: :class:`~obspy.core.event.Catalog`
:return: An ObsPy Catalog object.
.. note::
The following fields are supported by this function: %s.
Compare with http://www.seismic-handler.org/wiki/ShmDocFileEvt
"""
with io.open(filename, 'r', encoding=encoding) as f:
temp = f.read()
# first create phases and phases_o dictionaries for different phases
# and phases with origin information
phases = defaultdict(list)
phases_o = {}
phase = {}
evid = None
for line in temp.splitlines():
if 'End of Phase' in line:
if 'origin time' in phase.keys():
if evid in phases_o:
# found more than one origin
pass
phases_o[evid] = phase
phases[evid].append(phase)
phase = {}
evid = None
elif line.strip() != '':
try:
key, value = line.split(':', 1)
except ValueError:
continue
key = key.strip().lower()
value = value.strip()
if key == 'event id':
evid = value
elif value != '':
phase[key] = value
assert evid is None
# now create obspy Events from phases and phases_o dictionaries
events = []
for evid in phases:
picks = []
arrivals = []
stamags = []
origins = []
po = None
magnitudes = []
pm = None
for p in phases[evid]:
sta = p.get('station code', '')
comp = p.get('component', '')
pick_kwargs = _kw(p, 'pick')
widargs = _resolve_seedid(sta,
comp,
time=pick_kwargs['time'],
**kwargs)
wid = WaveformStreamID(*widargs)
pick = Pick(waveform_id=wid, **pick_kwargs)
arrival = Arrival(pick_id=pick.resource_id, **_kw(p, 'arrival'))
picks.append(pick)
arrivals.append(arrival)
stamags_temp, _ = _mags(p, evid, stamag=True, wid=wid)
stamags.extend(stamags_temp)
if evid in phases_o:
o = phases_o[evid]
uncertainty = OriginUncertainty(**_kw(o, 'origin_uncertainty'))
origin = Origin(arrivals=arrivals,
origin_uncertainty=uncertainty,
**_kw(o, 'origin'))
if origin.latitude is None or origin.longitude is None:
warn('latitude or longitude not set for event %s' % evid)
else:
if origin.longitude_errors.uncertainty is not None:
origin.longitude_errors.uncertainty *= cos(
origin.latitude / 180 * pi)
origins = [origin]
po = origin.resource_id
magnitudes, pm = _mags(o, evid)
else:
o = p
event = Event(resource_id=ResourceIdentifier(evid),
picks=picks,
origins=origins,
magnitudes=magnitudes,
station_magnitudes=stamags,
preferred_origin_id=po,
preferred_magnitude_id=pm,
**_kw(o, 'event'))
events.append(event)
return Catalog(events,
description='Created from SeismicHandler EVT format')
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 _map_join2origin(self, db):
"""
Return an Origin instance from an dict of CSS key/values
Inputs
======
db : dict of key/values of CSS fields related to the origin (see Join)
Returns
=======
obspy.core.event.Origin
Notes
=====
Any object that supports the dict 'get' method can be passed as
input, e.g. OrderedDict, custom classes, etc.
Join
----
origin <- origerr (outer)
"""
#-- Basic location ------------------------------------------
origin = Origin()
origin.latitude = db.get('lat')
origin.longitude = db.get('lon')
origin.depth = _km2m(db.get('depth'))
origin.time = _utc(db.get('time'))
origin.extra = {}
#-- Quality -------------------------------------------------
quality = OriginQuality(
associated_phase_count = db.get('nass'),
used_phase_count = db.get('ndef'),
standard_error = db.get('sdobs'),
)
origin.quality = quality
#-- Solution Uncertainties ----------------------------------
# in CSS the ellipse is projected onto the horizontal plane
# using the covariance matrix
uncertainty = OriginUncertainty()
a = _km2m(db.get('smajax'))
b = _km2m(db.get('sminax'))
s = db.get('strike')
dep_u = _km2m(db.get('sdepth'))
time_u = db.get('stime')
uncertainty.max_horizontal_uncertainty = a
uncertainty.min_horizontal_uncertainty = b
uncertainty.azimuth_max_horizontal_uncertainty = s
uncertainty.horizontal_uncertainty = a
uncertainty.preferred_description = "horizontal uncertainty"
if db.get('conf') is not None:
uncertainty.confidence_level = db.get('conf') * 100.
if uncertainty.horizontal_uncertainty is not None:
origin.origin_uncertainty = uncertainty
#-- Parameter Uncertainties ---------------------------------
if all([a, b, s]):
n, e = _get_NE_on_ellipse(a, b, s)
lat_u = _m2deg_lat(n)
lon_u = _m2deg_lon(e, lat=origin.latitude)
origin.latitude_errors = {'uncertainty': lat_u}
origin.longitude_errors = {'uncertainty': lon_u}
if dep_u:
origin.depth_errors = {'uncertainty': dep_u}
if time_u:
origin.time_errors = {'uncertainty': time_u}
#-- Analyst-determined Status -------------------------------
posted_author = _str(db.get('auth'))
mode, status = self.get_event_status(posted_author)
origin.evaluation_mode = mode
origin.evaluation_status = status
# Save etype per origin due to schema differences...
css_etype = _str(db.get('etype'))
# Compatible with future patch rename "_namespace" -> "namespace"
origin.extra['etype'] = {
'value': css_etype,
'namespace': CSS_NAMESPACE
}
origin.creation_info = CreationInfo(
creation_time = _utc(db.get('lddate')),
agency_id = self.agency,
version = db.get('orid'),
author = posted_author,
)
origin.resource_id = self._rid(origin)
return origin
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 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 computeOriginErrors(org):
"""
Given a NLL's event build the Confidence Ellipsoid from Covariance Matrix
:param evt: NLL's QML Event
:return: Dictionary containing computed errors
"""
# WARNING: QuakeML uses meter for origin depth, origin uncertainty and confidence ellipsoid, SC3ML uses kilometers.
d = {}
confidenceLevel = 0.90 # Conficence level
kp1 = np.sqrt(chi2.ppf(confidenceLevel, 1)) # 1D confidence coefficient
kp2 = np.sqrt(chi2.ppf(confidenceLevel, 2)) # 2D confidence coefficient
kp3 = np.sqrt(chi2.ppf(confidenceLevel, 3)) # 3D confidence coefficient
# Covariance matrix is given in the NLL's "STATISTICS" line of *.grid0.loc.hyp file and in the Origin's comments parsed by ObsPy
comments = org['comments'][0].text
stats = comments.split('STATISTICS')[-1].split()
cvm = [float(i) for i in stats[1::2]][3:9] # Covariance matrix
# Code adapted from IGN's computation of ConfidenceEllipsoid in "locsat.cpp" program
cvxx = cvm[0]
cvxy = cvm[1]
cvxz = cvm[2]
cvyy = cvm[3]
cvyz = cvm[4]
cvzz = cvm[5]
nll3d = np.array([[cvxx, cvxy, cvxz],
[cvxy, cvyy, cvyz],
[cvxz, cvyz, cvzz]
])
# 1D confidence intervals at confidenceLevel
errx = kp1 * np.sqrt(cvxx)
qe = QuantityError(uncertainty=errx, confidence_level=confidenceLevel * 100.0)
d['longitude_errors'] = qe
erry = kp1 * np.sqrt(cvyy)
qe = QuantityError(uncertainty=erry, confidence_level=confidenceLevel * 100.0)
d['latitude_errors'] = qe
errz = kp1 * np.sqrt(cvzz)
qe = QuantityError(uncertainty=errz, confidence_level=confidenceLevel * 100.0)
d['depth_errors'] = qe
#NLL kp1=1 because is up to 1 sigma 68.3%, LocSAT kp1=2.71 because is up to 90% (one dim)
#LocSAT np.sqrt(cvzz)/2.71 = NLL np.sqrt(cvzz)
# 2D confidence intervals at confidenceLevel
nll2d = np.array(nll3d[:2, :2])
eigval2d, eigvec2d = np.linalg.eig(nll2d) # XY (horizontal) plane
# indexes are not necessarily ordered. Sort them by eigenvalues
idx = eigval2d.argsort()
eigval2d = eigval2d[idx]
eigvec2d = eigvec2d[:, idx]
# sminax = kp2 * np.sqrt(eigval2d[0]) * 1.0e3 # QML in meters
# smajax = kp2 * np.sqrt(eigval2d[1]) * 1.0e3 # QML in meters
sminax = kp2 * np.sqrt(eigval2d[0]) # SC3ML in kilometers
smajax = kp2 * np.sqrt(eigval2d[1]) # SC3ML in kilometers
strike = 90.0 - np.rad2deg(np.arctan(eigvec2d[1, 1] / eigvec2d[0, 1])) # calculate and refer it to North
# horizontalUncertainty = np.sqrt((errx ** 2) + (erry ** 2)) * 1.0e3 # QML in meters
horizontalUncertainty = np.sqrt((errx ** 2) + (erry ** 2)) # SC3ML in kilometers
# 3D confidence intervals at confidenceLevel
eigval3d, eigvec3d = np.linalg.eig(nll3d)
idx = eigval3d.argsort()
eigval3d = eigval3d[idx]
eigvec3d = eigvec3d[:, idx]
# s3dminax = kp3 * np.sqrt(eigval3d[0]) * 1.0e3 # QML in meters
# s3dintax = kp3 * np.sqrt(eigval3d[1]) * 1.0e3 # QML in meters
# s3dmaxax = kp3 * np.sqrt(eigval3d[2]) * 1.0e3 # QML in meters
s3dminax = kp3 * np.sqrt(eigval3d[0]) # SC3ML in kilometers
s3dintax = kp3 * np.sqrt(eigval3d[1]) # SC3ML in kilometers
s3dmaxax = kp3 * np.sqrt(eigval3d[2]) # SCEML in kilometers
majaxplunge = normalizeAngle(
np.rad2deg(np.arctan(eigvec3d[2, 2] / np.sqrt((eigvec3d[2, 0] ** 2) + (eigvec3d[2, 1] ** 2)))))
majaxazimuth = normalizeAngle(np.rad2deg(np.arctan(eigvec3d[2, 1] / eigvec3d[2, 0])))
majaxrotation = normalizeAngle(
np.rad2deg(np.arctan(eigvec3d[0, 2] / np.sqrt((eigvec3d[0, 0] ** 2) + (eigvec3d[0, 1] ** 2)))))
# print('2D sminax:\t{}\tsmajax:\t{}\tstrike:\t{}'.format(sminax, smajax, strike))
# print('3D sminax:\t{}\tsmajax:\t{}\tsintax:\t{}'.format(s3dminax, s3dmaxax, s3dintax))
# print(' plunge:\t{}\tazim:\t{}\trotat:\t{}'.format(majaxplunge, majaxazimuth, majaxrotation))
# print('-' * 144)
ce = ConfidenceEllipsoid(semi_major_axis_length=s3dmaxax,
semi_minor_axis_length=s3dminax,
semi_intermediate_axis_length=s3dintax,
major_axis_plunge=majaxplunge,
major_axis_azimuth=majaxazimuth,
major_axis_rotation=majaxrotation)
ou = OriginUncertainty(horizontal_uncertainty=horizontalUncertainty,
min_horizontal_uncertainty=sminax,
max_horizontal_uncertainty=smajax,
azimuth_max_horizontal_uncertainty=strike,
confidence_ellipsoid=ce,
preferred_description='confidence ellipsoid',
confidence_level=confidenceLevel * 100.0)
d['origin_uncertainty'] = ou
return d
def _parse_second_line_origin(self, line, event, origin, magnitudes):
magnitude_errors = []
fields = self.fields['line_2']
standard_error = line[fields['rms']].strip()
time_uncertainty = line[fields['ot_error']].strip()
max_horizontal_uncertainty = line[fields['s_major']].strip()
min_horizontal_uncertainty = line[fields['s_minor']].strip()
azimuth_max_horizontal_uncertainty = line[fields['az']].strip()
depth_uncertainty = line[fields['depth_err']].strip()
min_distance = line[fields['min_dist']].strip()
max_distance = line[fields['max_dist']].strip()
magnitude_errors.append(line[fields['mag_err_1']].strip())
magnitude_errors.append(line[fields['mag_err_2']].strip())
magnitude_errors.append(line[fields['mag_err_3']].strip())
analysis_type = line[fields['antype']].strip().lower()
location_method = line[fields['loctype']].strip().lower()
event_type = line[fields['evtype']].strip().lower()
try:
origin.quality.standard_error = float(standard_error)
except ValueError:
pass
try:
origin.time_errors.uncertainty = float(time_uncertainty)
except ValueError:
pass
try:
uncertainty = OriginUncertainty()
# Convert values from km to m
min_value = float(min_horizontal_uncertainty) * 1000
max_value = float(max_horizontal_uncertainty) * 1000
azimuth_value = float(azimuth_max_horizontal_uncertainty)
description = OriginUncertaintyDescription('uncertainty ellipse')
uncertainty.min_horizontal_uncertainty = min_value
uncertainty.max_horizontal_uncertainty = max_value
uncertainty.azimuth_max_horizontal_uncertainty = azimuth_value
uncertainty.preferred_description = description
origin.origin_uncertainty = uncertainty
except ValueError:
pass
try:
# Convert value from km to m
origin.depth_errors.uncertainty = float(depth_uncertainty) * 1000
except ValueError:
pass
try:
origin.quality.minimum_distance = float(min_distance)
origin.quality.maximum_distance = float(max_distance)
except ValueError:
self._warn('Missing minimum/maximum distance')
for i in range(2):
try:
mag_errors = magnitudes[i].mag_errors
mag_errors.uncertainty = float(magnitude_errors[i])
except (AttributeError, ValueError):
pass
# No match for 'g' (guess)
# We map 'g' to 'manual' and create a comment for origin
try:
origin.evaluation_mode = EVALUATION_MODES[analysis_type]
if analysis_type == 'g':
# comment: 'GSE2.0:antype=g'
text = 'GSE2.0:antype=g'
comment = self._comment(text)
origin.comments.append(comment)
except KeyError:
self._warn('Wrong analysis type')
if location_method not in LOCATION_METHODS.keys():
location_method = 'o'
method = LOCATION_METHODS[location_method]
method_id = "method/%s" % method
origin.method_id = self._get_res_id(method_id)
if event_type not in EVENT_TYPES.keys():
event_type = 'uk'
self._warn('Wrong or unknown event type')
event_data = EVENT_TYPES[event_type]
event.event_type_certainty, event.event_type = event_data
# comment: 'GSE2.0:evtype=<evtype>'
if event_type:
text = 'GSE2.0:evtype=%s' % event_type
comment = self._comment(text)
event.comments.append(comment)
def _read_evt(filename, inventory=None, id_map=None, id_default='.{}..{}',
encoding='utf-8'):
"""
Read a SeismicHandler EVT file and returns an ObsPy Catalog object.
.. warning::
This function should NOT be called directly, it registers via the
ObsPy :func:`~obspy.core.event.read_events` function, call this
instead.
:type filename: str
:param filename: File or file-like object in text mode.
:type inventory: :class:`~obspy.core.inventory.inventory.Inventory`
:param inventory: Inventory used to retrieve network code, location code
and channel code of stations (SEED id).
:type id_map: dict
:param id_map: If channel information was not found in inventory,
it will be looked up in this dictionary
(example: `id_map={'MOX': 'GR.{}..HH{}'`).
The values must contain three dots and two `{}` which are
substituted by station code and component.
:type id_default: str
:param id_default: Default SEED id expression.
The value must contain three dots and two `{}` which are
substituted by station code and component.
:param str encoding: encoding used (default: utf-8)
:rtype: :class:`~obspy.core.event.Catalog`
:return: An ObsPy Catalog object.
.. note::
The following fields are supported by this function: %s.
Compare with http://www.seismic-handler.org/wiki/ShmDocFileEvt
"""
seed_map = _seed_id_map(inventory, id_map, id_default)
with io.open(filename, 'r', encoding=encoding) as f:
temp = f.read()
# first create phases and phases_o dictionaries for different phases
# and phases with origin information
phases = defaultdict(list)
phases_o = {}
phase = {}
evid = None
for line in temp.splitlines():
if 'End of Phase' in line:
if 'origin time' in phase.keys():
if evid in phases_o:
# found more than one origin
pass
phases_o[evid] = phase
phases[evid].append(phase)
phase = {}
evid = None
elif line.strip() != '':
try:
key, value = line.split(':', 1)
except ValueError:
continue
key = key.strip().lower()
value = value.strip()
if key == 'event id':
evid = value
elif value != '':
phase[key] = value
assert evid is None
# now create obspy Events from phases and phases_o dictionaries
events = []
for evid in phases:
picks = []
arrivals = []
stamags = []
origins = []
po = None
magnitudes = []
pm = None
for p in phases[evid]:
try:
sta = p['station code']
except KeyError:
sta = ''
try:
comp = p['component']
except KeyError:
comp = ''
try:
wid = seed_map[sta]
except KeyError:
wid = id_default
wid = WaveformStreamID(seed_string=wid.format(sta, comp))
pick = Pick(waveform_id=wid, **_kw(p, 'pick'))
arrival = Arrival(pick_id=pick.resource_id, **_kw(p, 'arrival'))
picks.append(pick)
arrivals.append(arrival)
stamags_temp, _ = _mags(p, evid, stamag=True, wid=wid)
stamags.extend(stamags_temp)
if evid in phases_o:
o = phases_o[evid]
uncertainty = OriginUncertainty(**_kw(o, 'origin_uncertainty'))
origin = Origin(arrivals=arrivals, origin_uncertainty=uncertainty,
**_kw(o, 'origin'))
if origin.latitude is None or origin.longitude is None:
warn('latitude or longitude not set for event %s' % evid)
else:
if origin.longitude_errors.uncertainty is not None:
origin.longitude_errors.uncertainty *= cos(
origin.latitude / 180 * pi)
origins = [origin]
po = origin.resource_id
magnitudes, pm = _mags(o, evid)
else:
o = p
event = Event(resource_id=ResourceIdentifier(evid),
picks=picks,
origins=origins,
magnitudes=magnitudes,
station_magnitudes=stamags,
preferred_origin_id=po,
preferred_magnitude_id=pm,
**_kw(o, 'event')
)
events.append(event)
return Catalog(events,
description='Created from SeismicHandler EVT format')
