def get_event_object(self):
'''
update events otime,lat,lon and mag with IRIS (or any other clients) catalog
'''
# get parameters from the cataog
if self.use_catalog == 1:
print("WARNING using event data from the IRIS catalog")
cat = self.client.get_events(
starttime=self.otime - self.sec_before_after_event,
endtime=self.otime + self.sec_before_after_event)
self.ev = cat[0]
# use catalog parameters
self.otime = self.ev.origins[0].time
self.elat = self.ev.origins[0].latitude
self.elon = self.ev.origins[0].longitude
self.edep = self.ev.origins[0].depth
self.emag = self.ev.magnitudes[0].mag
# use parameters from the input file
else:
print("WARNING using event data from user-defined catalog")
#self.ev = Event()
org = Origin()
org.latitude = self.elat
org.longitude = self.elon
org.depth = self.edep
org.time = self.otime
mag = Magnitude()
mag.mag = self.emag
mag.magnitude_type = "Mw"
self.ev.origins.append(org)
self.ev.magnitudes.append(mag)
def read_cat_ref(cat_file):
"""
Parses a given refrence catalogue (in ascii format,see the header for details)
output is Obspy catalogue object
"""
cat_ref = np.loadtxt(cat_file,delimiter=',',skiprows=1)
cat = Catalog()
for i,e in enumerate(cat_ref):
event = Event(resource_id='smi:local/='+str(i),creation_info='HG')
origin = Origin()
origin.time = UTCDateTime(int(e[2]),int(e[3]),int(e[4]),
int(e[7]),int(e[8]),e[9])
origin.longitude = e[0]
origin.latitude = e[1]
origin.depth = e[6] * 1000. #in meters
event.origins.append(origin)
if ~(np.isnan(e[10])):
mag = Magnitude(creation_info='HER')
mag.mag = e[10]
mag.magnitude_type = 'Mw'
event.magnitudes.append(mag)
if ~(np.isnan(e[11])):
mag = Magnitude(creation_info='MAR')
mag.mag = e[11]
mag.magnitude_type = 'Mw'
event.magnitudes.append(mag)
if ~(np.isnan(e[12])):
mag = Magnitude(creation_info='SIP')
mag.mag = e[12]
mag.magnitude_type = 'Mw'
event.magnitudes.append(mag)
cat.append(event)
return cat
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 _parse_record_ah(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._float_unused(line[44:48])
station_number = self._int_unused(line[48:51])
phase_number = self._int_unused(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._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.standard_error = standard_dev
origin.quality.used_station_count = station_number
origin.quality.used_phase_count = phase_number
origin.origin_type = 'hypocenter'
event.origins.append(origin)
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 _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 _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 _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 event_to_quakeml(event, filename):
"""
Write one of those events to QuakeML.
"""
# Create all objects.
cat = Catalog()
ev = Event()
org = Origin()
mag = Magnitude()
fm = FocalMechanism()
mt = MomentTensor()
t = Tensor()
# Link them together.
cat.append(ev)
ev.origins.append(org)
ev.magnitudes.append(mag)
ev.focal_mechanisms.append(fm)
fm.moment_tensor = mt
mt.tensor = t
# Fill values
ev.resource_id = "smi:inversion/%s" % str(event["identifier"])
org.time = event["time"]
org.longitude = event["longitude"]
org.latitude = event["latitude"]
org.depth = event["depth_in_km"] * 1000
mag.mag = event["Mw"]
mag.magnitude_type = "Mw"
t.m_rr = event["Mrr"]
t.m_tt = event["Mpp"]
t.m_pp = event["Mtt"]
t.m_rt = event["Mrt"]
t.m_rp = event["Mrp"]
t.m_tp = event["Mtp"]
cat.write(filename, format="quakeml")
def event_to_quakeml(event, filename):
"""
Write one of those events to QuakeML.
"""
# Create all objects.
cat = Catalog()
ev = Event()
org = Origin()
mag = Magnitude()
fm = FocalMechanism()
mt = MomentTensor()
t = Tensor()
# Link them together.
cat.append(ev)
ev.origins.append(org)
ev.magnitudes.append(mag)
ev.focal_mechanisms.append(fm)
fm.moment_tensor = mt
mt.tensor = t
# Fill values
ev.resource_id = "smi:inversion/%s" % str(event["identifier"])
org.time = event["time"]
org.longitude = event["longitude"]
org.latitude = event["latitude"]
org.depth = event["depth_in_km"] * 1000
mag.mag = event["Mw"]
mag.magnitude_type = "Mw"
t.m_rr = event["Mrr"]
t.m_tt = event["Mpp"]
t.m_pp = event["Mtt"]
t.m_rt = event["Mrt"]
t.m_rp = event["Mrp"]
t.m_tp = event["Mtp"]
cat.write(filename, format="quakeml")
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 _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
# + from obspy import UTCDateTime from obspy.core.event import Catalog, Event, Origin, Magnitude from obspy.geodetics import FlinnEngdahl # cat = Catalog() cat.description = "Just a fictitious toy example catalog built from scratch" e = Event() e.event_type = "not existing" o = Origin() o.time = UTCDateTime(2014, 2, 23, 18, 0, 0) o.latitude = 47.6 o.longitude = 12.0 o.depth = 10000 o.depth_type = "operator assigned" o.evaluation_mode = "manual" o.evaluation_status = "preliminary" o.region = FlinnEngdahl().get_region(o.longitude, o.latitude) m = Magnitude() m.mag = 7.2 m.magnitude_type = "Mw" m2 = Magnitude() m2.mag = 7.4 m2.magnitude_type = "Ms" # also included could be: custom picks, amplitude measurements, station magnitudes, # focal mechanisms, moment tensors, ...
def get_results(self):
cids = []
clusters = []
results_file = "{}/{}".format(self.hypoDD_control.control_directory,
self.hypoDD_control.relocated_hypocenters_output
)
residuals_file = "{}/{}".format(self.hypoDD_control.control_directory,
self.hypoDD_control.data_residual_output
)
with open(results_file, "r") as f:
for line in f:
num = line.split()
evid = num[0]
lat = float(num[1])
lon = float(num[2])
dep = 1000 * float(num[3]) # km to m
errx = num[7]
erry = num[8]
errz = num[9]
yr = int(num[10])
mo = int(num[11])
dy = int(num[12])
hr = int(num[13])
mi = int(num[14])
sc = float(num[15])
mag = num[16]
nccp = num[17]
nccs = num[18]
nctp = num[19]
ncts = num[20]
rcc = num[21]
rct = num[22]
cid = num[23]
if cid not in cids:
cids.append(cid)
clusters.append(Cluster())
clusters[-1].hypoDD_id=cid
clusters[-1].successful_relocation=True
clusters[-1].catalog=Catalog()
clusters[-1].event_ids=[]
origin=Origin()
isec = int ( math.floor( sc ))
micsec = int ( ( sc - isec) * 1000000 )
origin.time = UTCDateTime(yr, mo, dy, hr, mi, isec, micsec)
origin.longitude = lon
origin.latitude = lat
origin.depth = dep
origin.method_id = "hypoDD"
# TODO (@ogalanis): Add time/location errors (when
# appropriate. Add quality and origin_uncertainty. Add arrivals.
event=Event()
event.creation_info=CreationInfo()
event.creation_info.author = __package__
event.creation_info.version = info.__version__
event.origins=[origin]
event.magnitude=Magnitude()
event.magnitude.mag=mag
idx=cids.index(cid)
clusters[idx].catalog.events.append(event)
clusters[idx].event_ids.append(evid)
if self.hypoDD_control.cid != 0 :
my_list = []
clusters[0].connectedness = Connectedness()
with open(residuals_file, "r") as f:
for line in f:
num = line.split()
evid_1 = num[2]
evid_2 = num[3]
obs_type = num[4]
if obs_type == "1":
my_list = clusters[0].connectedness.cross_corr_P
elif obs_type == "2":
my_list = clusters[0].connectedness.cross_corr_S
elif obs_type == "3":
my_list = clusters[0].connectedness.catalog_P
elif obs_type == "4":
my_list = clusters[0].connectedness.catalog_S
else:
continue
in_list = [x for x in my_list if (( x[0] == evid_1 and
x[1] == evid_2
) or
( x[0] == evid_2 and
x[1] == evid_1
))]
if in_list:
for x in my_list:
if (( x[0] == evid_1 and
x[1] == evid_2
) or
( x[0] == evid_2 and
x[1] == evid_1
)):
x[2] += 1
else:
my_list.append([evid_1,evid_2,1])
return clusters
def outputOBSPY(hp, event=None, only_fm_picks=False):
"""
Make an Event which includes the current focal mechanism information from HASH
Use the 'only_fm_picks' flag to only include the picks HASH used for the FocalMechanism.
This flag will replace the 'picks' and 'arrivals' lists of existing events with new ones.
Inputs
-------
hp : hashpy.HashPype instance
event : obspy.core.event.Event
only_fm_picks : bool of whether to overwrite the picks/arrivals lists
Returns
-------
obspy.core.event.Event
Event will be new if no event was input, FocalMech added to existing event
"""
# Returns new (or updates existing) Event with HASH solution
n = hp.npol
if event is None:
event = Event(focal_mechanisms=[], picks=[], origins=[])
origin = Origin(arrivals=[])
origin.time = UTCDateTime(hp.tstamp)
origin.latitude = hp.qlat
origin.longitude = hp.qlon
origin.depth = hp.qdep
origin.creation_info = CreationInfo(version=hp.icusp)
origin.resource_id = ResourceIdentifier('smi:hash/Origin/{0}'.format(
hp.icusp))
for _i in range(n):
p = Pick()
p.creation_info = CreationInfo(version=hp.arid[_i])
p.resource_id = ResourceIdentifier('smi:nsl/Pick/{0}'.format(
p.creation_info.version))
p.waveform_id = WaveformStreamID(network_code=hp.snet[_i],
station_code=hp.sname[_i],
channel_code=hp.scomp[_i])
if hp.p_pol[_i] > 0:
p.polarity = 'positive'
else:
p.polarity = 'negative'
a = Arrival()
a.creation_info = CreationInfo(version=hp.arid[_i])
a.resource_id = ResourceIdentifier('smi:nsl/Arrival/{0}'.format(
p.creation_info.version))
a.azimuth = hp.p_azi_mc[_i, 0]
a.takeoff_angle = 180. - hp.p_the_mc[_i, 0]
a.pick_id = p.resource_id
origin.arrivals.append(a)
event.picks.append(p)
event.origins.append(origin)
event.preferred_origin_id = origin.resource_id.resource_id
else: # just update the changes
origin = event.preferred_origin()
picks = []
arrivals = []
for _i in range(n):
ind = hp.p_index[_i]
a = origin.arrivals[ind]
p = a.pick_id.getReferredObject()
a.takeoff_angle = hp.p_the_mc[_i, 0]
picks.append(p)
arrivals.append(a)
if only_fm_picks:
origin.arrivals = arrivals
event.picks = picks
# Use me double couple calculator and populate planes/axes etc
x = hp._best_quality_index
# Put all the mechanisms into the 'focal_mechanisms' list, mark "best" as preferred
for s in range(hp.nmult):
dc = DoubleCouple([hp.str_avg[s], hp.dip_avg[s], hp.rak_avg[s]])
ax = dc.axis
focal_mech = FocalMechanism()
focal_mech.creation_info = CreationInfo(creation_time=UTCDateTime(),
author=hp.author)
focal_mech.triggering_origin_id = origin.resource_id
focal_mech.resource_id = ResourceIdentifier(
'smi:hash/FocalMechanism/{0}/{1}'.format(hp.icusp, s + 1))
focal_mech.method_id = ResourceIdentifier('HASH')
focal_mech.nodal_planes = NodalPlanes()
focal_mech.nodal_planes.nodal_plane_1 = NodalPlane(*dc.plane1)
focal_mech.nodal_planes.nodal_plane_2 = NodalPlane(*dc.plane2)
focal_mech.principal_axes = PrincipalAxes()
focal_mech.principal_axes.t_axis = Axis(azimuth=ax['T']['azimuth'],
plunge=ax['T']['dip'])
focal_mech.principal_axes.p_axis = Axis(azimuth=ax['P']['azimuth'],
plunge=ax['P']['dip'])
focal_mech.station_polarity_count = n
focal_mech.azimuthal_gap = hp.magap
focal_mech.misfit = hp.mfrac[s]
focal_mech.station_distribution_ratio = hp.stdr[s]
focal_mech.comments.append(
Comment(
hp.qual[s],
resource_id=ResourceIdentifier(
focal_mech.resource_id.resource_id + '/comment/quality')))
#----------------------------------------
event.focal_mechanisms.append(focal_mech)
if s == x:
event.preferred_focal_mechanism_id = focal_mech.resource_id.resource_id
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 getwf_iris_ncedc_llnl(origin0, client_pick):
print("Running function getwf_iris_ncedc_llnl")
# parameters for waveform request
tbefore_sec = 100
tafter_sec = 600
# DEFAULT SETTINGS (see getwaveform_iris.py)
rotateRTZ = True
rotateUVW = False # works only if 'rotateRTZ = True'
output_cap_weight_file = True
detrend = True
demean = True
output_event_info = True
taper = False
ifplot_spectrogram = False
# for CAP all waveforms need to have the same sample rate
resample_TF = True
resample_freq = 20.0 # 0 for no resampling
scale_factor = 10**2 # for CAP use 10**2 (to convert m/s to cm/s)
# event parameters
sec_before_after_event = 10 # time window to search for a target event in a catalog
min_dist = 0
max_dist = 1200
# station parameters
# 20170321 [email protected] -- I disabled retrieving data for the
# networks listed below
# reason 1 some are dense/local and don't improve station coverage
# reason 2 many have crappy data (noisy)
# reason 3 some have very large amplitudes. Bad response?
network = '*,-XK,-XM,-XS,-XC,-XU,-XT,-XE'
station = '*,-PURD,-NV33,-GPO' # all stations
channel = 'BH?,LH?'
overwrite_ddir = 1 # 1 = delete data directory if it already exists
icreateNull = 0 # create Null traces so that rotation can work (obsby stream.rotate require 3 traces)
# filter
# set ipre_filt = 0 to prevent extra filtering
ifFilter = False
filter_type = 'bandpass'
# LLNL filter 10-50 sec is most stable. Some waveforms >= 100 sec show
# oscillations. Use factor of 2 limit = 200 sec for prefilter
f1 = 1 / 200
f2 = 1 / 10
zerophase = True # False = causal, True = acausal
corners = 4 # Is corner in Obspy same as Pole in SAC?
remove_response = True
iplot_response = False
ipre_filt = 2 # 0 No pre_filter
# 1 default pre_filter (see getwaveform_iris.py)
# 2 user-defined pre_filter
f0 = 0.5 * f1
f3 = 2.0 * f2
# The following are for the FMTU paper
f0 = 0.005
f1 = 0.006
f3 = 10
f4 = 15
pre_filt = (f0, f1, f2, f3) # applies for ipre_filt = 2 only
# NOTE event data from user-defined catalog!
# initialize objects
ev = Event()
org = Origin()
mag = Magnitude()
# build objects
org.time = UTCDateTime(origin0[0])
org.longitude = origin0[1]
org.latitude = origin0[2]
org.depth = origin0[3]
mag.mag = origin0[5]
mag.magnitude_type = origin0[6] # Mw, ml, mb, ...
ev.origins.append(org)
ev.magnitudes.append(mag)
# Delete existing data directory
eid = util_helpers.otime2eid(ev.origins[0].time)
ddir = './' + eid
if os.path.exists('RAW'):
print("WARNING. %s already exists. Deleting ..." % ddir)
shutil.rmtree('RAW')
if overwrite_ddir and os.path.exists(ddir):
print("WARNING. %s already exists. Deleting ..." % ddir)
shutil.rmtree(ddir)
if client_pick is "IRIS":
print('Using client %s' % client_pick)
idb = 1
client_list = ["IRIS", "NCEDC"]
print("WARNING using event data from user-defined catalog")
# LLNL
if client_pick is "LLNL":
print('Using client %s' % client_pick)
idb = 3
client_list = ["LLNL"]
client = llnl_db_client.LLNLDBClient(
"/store/raw/LLNL/UCRL-MI-222502/westernus.wfdisc")
# get event time and event ID
otime = obspy.UTCDateTime(origin0[0])
cat = client.get_catalog()
mintime_str = "time > %s" % (otime - sec_before_after_event)
maxtime_str = "time < %s" % (otime + sec_before_after_event)
print(mintime_str + "\n" + maxtime_str)
ev = cat.filter(mintime_str, maxtime_str)
nev = len(ev)
if nev == 1:
ev = ev[0] # doesn't have magnitude (ATRISCO)
elif nev > 1:
ev = ev[1] # [0] may not include magnitude. [1] may (LLNL)
else:
print("No events in the catalog for the given time period. Stop.")
# The IRIS requests include BK data, but it has to be requested through
# the NCEDC client
for iclient in client_list:
if iclient is "IRIS":
network = network
client = Client(iclient)
elif iclient is "NCEDC":
network = 'BK'
station = '*' # doesn't like "-staX"
client = Client(iclient)
try:
gw.run_get_waveform(c=client,
event=ev,
idb=idb,
ref_time_place=ev,
min_dist=min_dist,
max_dist=max_dist,
before=tbefore_sec,
after=tafter_sec,
network=network,
station=station,
channel=channel,
resample_freq=resample_freq,
ifrotateRTZ=rotateRTZ,
ifrotateUVW=rotateUVW,
ifCapInp=output_cap_weight_file,
ifRemoveResponse=remove_response,
ifDetrend=detrend,
ifDemean=demean,
Taper=taper,
ifEvInfo=output_event_info,
scale_factor=scale_factor,
icreateNull=icreateNull,
ipre_filt=ipre_filt,
pre_filt=pre_filt,
ifFilter=ifFilter,
fmin=f1,
fmax=f2,
filter_type=filter_type,
zerophase=zerophase,
corners=corners,
iplot_response=iplot_response,
ifplot_spectrogram=ifplot_spectrogram)
except:
print("~~~~~ SOMETHING HAPPENED ~~~~~~~~~~")
print(ev, client)
print("Continuing")
continue
def par2quakeml(Par_filename, QuakeML_filename, rotation_axis=[0.0, 1.0, 0.0],
rotation_angle=-57.5, origin_time="2000-01-01 00:00:00.0",
event_type="other event"):
# initialise event
ev = Event()
# open and read Par file
fid = open(Par_filename, 'r')
fid.readline()
fid.readline()
fid.readline()
fid.readline()
lat_old = 90.0 - float(fid.readline().strip().split()[0])
lon_old = float(fid.readline().strip().split()[0])
depth = float(fid.readline().strip().split()[0])
fid.readline()
Mtt_old = float(fid.readline().strip().split()[0])
Mpp_old = float(fid.readline().strip().split()[0])
Mrr_old = float(fid.readline().strip().split()[0])
Mtp_old = float(fid.readline().strip().split()[0])
Mtr_old = float(fid.readline().strip().split()[0])
Mpr_old = float(fid.readline().strip().split()[0])
# rotate event into physical domain
lat, lon = rot.rotate_lat_lon(lat_old, lon_old, rotation_axis,
rotation_angle)
Mrr, Mtt, Mpp, Mtr, Mpr, Mtp = rot.rotate_moment_tensor(
Mrr_old, Mtt_old, Mpp_old, Mtr_old, Mpr_old, Mtp_old, lat_old, lon_old,
rotation_axis, rotation_angle)
# populate event origin data
ev.event_type = event_type
ev_origin = Origin()
ev_origin.time = UTCDateTime(origin_time)
ev_origin.latitude = lat
ev_origin.longitude = lon
ev_origin.depth = depth
ev.origins.append(ev_origin)
# populte event moment tensor
ev_tensor = Tensor()
ev_tensor.m_rr = Mrr
ev_tensor.m_tt = Mtt
ev_tensor.m_pp = Mpp
ev_tensor.m_rt = Mtr
ev_tensor.m_rp = Mpr
ev_tensor.m_tp = Mtp
ev_momenttensor = MomentTensor()
ev_momenttensor.tensor = ev_tensor
ev_momenttensor.scalar_moment = np.sqrt(Mrr ** 2 + Mtt ** 2 + Mpp ** 2 +
Mtr ** 2 + Mpr ** 2 + Mtp ** 2)
ev_focalmechanism = FocalMechanism()
ev_focalmechanism.moment_tensor = ev_momenttensor
ev_focalmechanism.nodal_planes = NodalPlanes().setdefault(0, 0)
ev.focal_mechanisms.append(ev_focalmechanism)
# populate event magnitude
ev_magnitude = Magnitude()
ev_magnitude.mag = 0.667 * (np.log10(ev_momenttensor.scalar_moment) - 9.1)
ev_magnitude.magnitude_type = 'Mw'
ev.magnitudes.append(ev_magnitude)
# write QuakeML file
cat = Catalog()
cat.append(ev)
cat.write(QuakeML_filename, format="quakeml")
# clean up
fid.close()
def build(self):
"""
Build an obspy moment tensor focal mech event
This makes the tensor output into an Event containing:
1) a FocalMechanism with a MomentTensor, NodalPlanes, and PrincipalAxes
2) a Magnitude of the Mw from the Tensor
Which is what we want for outputting QuakeML using
the (slightly modified) obspy code.
Input
-----
filehandle => open file OR str from filehandle.read()
Output
------
event => instance of Event() class as described above
"""
p = self.parser
event = Event(event_type='earthquake')
origin = Origin()
focal_mech = FocalMechanism()
nodal_planes = NodalPlanes()
moment_tensor = MomentTensor()
principal_ax = PrincipalAxes()
magnitude = Magnitude()
data_used = DataUsed()
creation_info = CreationInfo(agency_id='NN')
ev_mode = 'automatic'
ev_stat = 'preliminary'
evid = None
orid = None
# Parse the entire file line by line.
for n,l in enumerate(p.line):
if 'REVIEWED BY NSL STAFF' in l:
ev_mode = 'manual'
ev_stat = 'reviewed'
if 'Event ID' in l:
evid = p._id(n)
if 'Origin ID' in l:
orid = p._id(n)
if 'Ichinose' in l:
moment_tensor.category = 'regional'
if re.match(r'^\d{4}\/\d{2}\/\d{2}', l):
ev = p._event_info(n)
if 'Depth' in l:
derived_depth = p._depth(n)
if 'Mw' in l:
magnitude.mag = p._mw(n)
magnitude.magnitude_type = 'Mw'
if 'Mo' in l and 'dyne' in l:
moment_tensor.scalar_moment = p._mo(n)
if 'Percent Double Couple' in l:
moment_tensor.double_couple = p._percent(n)
if 'Percent CLVD' in l:
moment_tensor.clvd = p._percent(n)
if 'Epsilon' in l:
moment_tensor.variance = p._epsilon(n)
if 'Percent Variance Reduction' in l:
moment_tensor.variance_reduction = p._percent(n)
if 'Major Double Couple' in l and 'strike' in p.line[n+1]:
np = p._double_couple(n)
nodal_planes.nodal_plane_1 = NodalPlane(*np[0])
nodal_planes.nodal_plane_2 = NodalPlane(*np[1])
nodal_planes.preferred_plane = 1
if 'Spherical Coordinates' in l:
mt = p._mt_sphere(n)
moment_tensor.tensor = Tensor(
m_rr = mt['Mrr'],
m_tt = mt['Mtt'],
m_pp = mt['Mff'],
m_rt = mt['Mrt'],
m_rp = mt['Mrf'],
m_tp = mt['Mtf'],
)
if 'Eigenvalues and eigenvectors of the Major Double Couple' in l:
ax = p._vectors(n)
principal_ax.t_axis = Axis(ax['T']['trend'], ax['T']['plunge'], ax['T']['ev'])
principal_ax.p_axis = Axis(ax['P']['trend'], ax['P']['plunge'], ax['P']['ev'])
principal_ax.n_axis = Axis(ax['N']['trend'], ax['N']['plunge'], ax['N']['ev'])
if 'Number of Stations' in l:
data_used.station_count = p._number_of_stations(n)
if 'Maximum' in l and 'Gap' in l:
focal_mech.azimuthal_gap = p._gap(n)
if re.match(r'^Date', l):
creation_info.creation_time = p._creation_time(n)
# Creation Time
creation_info.version = orid
# Fill in magnitude values
magnitude.evaluation_mode = ev_mode
magnitude.evaluation_status = ev_stat
magnitude.creation_info = creation_info.copy()
magnitude.resource_id = self._rid(magnitude)
# Stub origin
origin.time = ev.get('time')
origin.latitude = ev.get('lat')
origin.longitude = ev.get('lon')
origin.depth = derived_depth * 1000.
origin.depth_type = "from moment tensor inversion"
origin.creation_info = creation_info.copy()
# Unique from true origin ID
_oid = self._rid(origin)
origin.resource_id = ResourceIdentifier(str(_oid) + '/mt')
del _oid
# Make an id for the MT that references this origin
ogid = str(origin.resource_id)
doid = ResourceIdentifier(ogid, referred_object=origin)
# Make an id for the moment tensor mag which references this mag
mrid = str(magnitude.resource_id)
mmid = ResourceIdentifier(mrid, referred_object=magnitude)
# MT todo: could check/use URL for RID if parsing the php file
moment_tensor.evaluation_mode = ev_mode
moment_tensor.evaluation_status = ev_stat
moment_tensor.data_used = data_used
moment_tensor.moment_magnitude_id = mmid
moment_tensor.derived_origin_id = doid
moment_tensor.creation_info = creation_info.copy()
moment_tensor.resource_id = self._rid(moment_tensor)
# Fill in focal_mech values
focal_mech.nodal_planes = nodal_planes
focal_mech.moment_tensor = moment_tensor
focal_mech.principal_axes = principal_ax
focal_mech.creation_info = creation_info.copy()
focal_mech.resource_id = self._rid(focal_mech)
# add mech and new magnitude to event
event.focal_mechanisms = [focal_mech]
event.magnitudes = [magnitude]
event.origins = [origin]
event.creation_info = creation_info.copy()
# If an MT was done, that's the preferred mag/mech
event.preferred_magnitude_id = str(magnitude.resource_id)
event.preferred_focal_mechanism_id = str(focal_mech.resource_id)
if evid:
event.creation_info.version = evid
event.resource_id = self._rid(event)
self.event = event
def main(phases):
from obspy.io.xseed import Parser
from obspy.core.event import Event, Origin, Magnitude, NodalPlane
from obspy.core import read, UTCDateTime
from obspy.clients.fdsn import Client
from obspy import read_events
stas_templates = {
'ALL': [
'POHA', 'PET', 'MAJO', 'BILL', 'YSS', 'MA2', 'MDJ', 'YAK', 'INCN',
'TIXI', 'AIS', 'CASY', 'QSPA', 'DRV', 'SBA', 'DZM', 'RAR', 'MSVF',
'AFI', 'HNR', 'WMQ', 'BRVK', 'KMI', 'AAK', 'CHTO', 'ABKT', 'FURI',
'KMBO', 'MSEY'
]
}
sta_adjtime = {}
stas = {}
for phase in phases:
stas[phase] = stas_templates['ALL']
#stalocs = {'KDAK':'10'}
stalocs = {}
mseed_filename = '../data/2004-11-11-mw75-timor-region-2.miniseed'
client = Client('IRIS')
#client = Client('http://localhost:8080')
#client = Client('http://compute2.rses.anu.edu.au:8080')
DO_SYNTH = True
if DO_SYNTH and ('R' in phases or 'L' in phases):
print 'Initialize surface wave calculations'
surfw_excite('./java ')
readhrv(source_dir + '/surfw/ETL-JGR97 ')
read_gdm52(source_dir + '/surfw/GDM52/')
read_de06(source_dir + '/surfw/DE06a/')
event_name = 'Alor_20041111'
FAULT_FROM_PKL = False
if os.path.isfile('event.pkl'):
event = pickle.load(open('event.pkl', 'r'))
print "SAYA DI SANA!"
else:
event = read_events('./quakeml.xml')[0]
pickle.dump(event, open('event.pkl', 'w'))
print "SAYA DI SINI!"
org = event.preferred_origin()
if org is None:
org = event.origins[0]
if FAULT_FROM_PKL:
fault = pickle.load(open('fault_neic.pkl', 'r'))
else:
fault = neic2fault('p000d85g.param',
org,
xdr=-0.375,
velmod=read_velmod('./velmod.txt'),
NEIC_potency=True)
# Set parameters for deconvolution and time windowing
windows = {
'R': (2.75, 4.5),
'L': (3.25, 5.5), # Group velocity windows for surface waves
'P': (5, 75.),
'SH': (5, 85.)
} # Times before and after body wave phases
frq4 = {}
for key in ['R', 'L']:
frq4[key] = [0.002, 0.004, 0.008, 0.0125] # Surface wave filter
for key in ['P', 'SH']:
frq4[key] = [0.004, 0.01, .25, 0.5] # Body wave filter
origin = Origin()
origin.time = UTCDateTime(org.time)
origin.latitude = fault.hypo.y
origin.depth = fault.hypo.z
origin.longitude = fault.hypo.x
fault.ntw = 1
#stobs = getobs_surf(client,org,phases,frq4,gvel_win,stas,stalocs)
print 'WAKTU=', origin.time
stobs, inv = getobs(mseed_filename,
client,
event,
phases,
frq4,
windows,
stas,
stalocs,
picks=get_isctimes('isc.txt', org.time),
taper=0.2,
adjtime=sta_adjtime)
#return stobs,inv
# write out the station inventory
inv.write('%s.inv' % event_name, format="STATIONXML")
# Calculate the subfault bodywave Green's functions
for phase in ['P', 'SH']:
if not phase in phases:
continue
if phase in phases:
for sbf in fault.subfaults:
sft1, sft2 = np.diff(sbf.ruptimes()[0:3])
bodywave(sbf,
stobs[phase],
phase,
sft1,
sft2,
frq4=frq4[phase])
if not FAULT_FROM_PKL:
pickle.dump(fault, open('fault_neic.pkl', 'w'))
figs = {}
ntrc = {'P': 0, 'SH': 0, 'R': 0, 'L': 0}
if 'R' in stobs.keys() or 'L' in stobs.keys():
stobs_path = './stobs_surf_%s.pkl' % event_name
print 'Pickling stream to %s\n' % stobs_path
stobs_surf = {'params': stobs['params']}
for key in stobs.keys():
if key == 'R' or key == 'L':
ntrc[key] = len(stobs[key])
stobs_surf[key] = stobs[key]
pickle.dump(stobs_surf, open(stobs_path, "wb"))
figs['Surf'] = plt.figure(figsize=(12., 12.))
if 'P' in stobs.keys() or 'SH' in stobs.keys():
stobs_path = './stobs_body_%s.pkl' % event_name
print 'Pickling stream to %s\n' % stobs_path
stobs_body = {'params': stobs['params']}
for key in stobs.keys():
if key == 'P' or key == 'SH':
ntrc[key] = len(stobs[key])
stobs_body[key] = stobs[key]
pickle.dump(stobs_body, open(stobs_path, "wb"))
figs['Body'] = plt.figure(figsize=(12., 12.))
# """
### Plot the data
npl_col = 2 # One for body and one for surface waves
ax_wvf = {}
th = 10.
print windows['P']
if 'P' in stobs.keys() or 'SH' in stobs.keys():
print "WINDOWS[P][0]", windows['P'][0]
stref = bodysynth_fault(fault, stobs, windows['P'][0])
else:
stref = {}
##stref,d5 = surfsynth_fault(fault,th,org.time,stobs['R'],frq4,gvel_win['R'])
mxtrc = max(ntrc['R'], ntrc['L'])
ncol = mxtrc / 11 + 1
nrow = min(11, mxtrc)
print mxtrc, nrow, ncol
if 'R' in stobs.keys():
plt.figure(figs['Surf'].number)
phs = 'R'
#.return stobs
stref[phs], d5 = surfsynth_fault(fault, th, org.time, stobs[phs],
frq4[phs], windows[phs])
return stobs, stref
ax_wvf['R'] = plt.subplot2grid((2, npl_col), (0, 0),
rowspan=2,
colspan=1)
plot_datacomp(ax_wvf['R'],
stobs, ['R'],
stref,
origin,
rowcol=(nrow, ncol),
yscale=0.25,
ttick=500.)
plt.show()
if 'L' in stobs.keys():
plt.figure(figs['Surf'].number)
phs = 'L'
stref[phs], d5 = surfsynth_fault(fault, th, org.time, stobs[phs],
frq4[phs], windows[phs])
ax_wvf['L'] = plt.subplot2grid((2, npl_col), (0, 1),
rowspan=2,
colspan=1)
plot_datacomp(ax_wvf['L'],
stobs, ['L'],
stref,
origin,
rowcol=(nrow, ncol, 5),
yscale=0.25,
ttick=500.)
mxtrc = max(ntrc['P'], ntrc['SH'])
ncol = mxtrc / 11 + 1
nrow = min(11, mxtrc)
print mxtrc, nrow, ncol
sys.exit(0)
if 'P' in stobs.keys():
plt.figure(figs['Body'].number)
#stref = None
ax_wvf['P'] = plt.subplot2grid((2, npl_col), (0, 0),
rowspan=2,
colspan=1)
plot_datacomp(ax_wvf['P'],
stobs, ['P'],
stref,
origin,
rowcol=(nrow, ncol),
yscale=0.25,
ttick=100.)
if 'SH' in stobs.keys():
plt.figure(figs['Body'].number)
ax_wvf['SH'] = plt.subplot2grid((2, npl_col), (0, 1),
rowspan=2,
colspan=1)
plot_datacomp(ax_wvf['SH'],
stobs, ['SH'],
stref,
origin,
rowcol=(nrow, ncol),
yscale=0.25,
ttick=100.)
if 'P' in stobs.keys() or 'SH' in stobs.keys():
plt.figure(figs['Body'].number)
plt.tight_layout(pad=5., w_pad=0.5, h_pad=.0)
plt.savefig('getdata_PSH.pdf')
if 'R' in stobs.keys() or 'L' in stobs.keys():
plt.figure(figs['Surf'].number)
plt.tight_layout(pad=5., w_pad=0.5, h_pad=.0)
plt.savefig('getdata_RL.pdf')
plt.show()
# """
return (fault, stref, stobs) #,d0,M
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
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 outputOBSPY(hp, event=None, only_fm_picks=False):
"""
Make an Event which includes the current focal mechanism information from HASH
Use the 'only_fm_picks' flag to only include the picks HASH used for the FocalMechanism.
This flag will replace the 'picks' and 'arrivals' lists of existing events with new ones.
Inputs
-------
hp : hashpy.HashPype instance
event : obspy.core.event.Event
only_fm_picks : bool of whether to overwrite the picks/arrivals lists
Returns
-------
obspy.core.event.Event
Event will be new if no event was input, FocalMech added to existing event
"""
# Returns new (or updates existing) Event with HASH solution
n = hp.npol
if event is None:
event = Event(focal_mechanisms=[], picks=[], origins=[])
origin = Origin(arrivals=[])
origin.time = UTCDateTime(hp.tstamp)
origin.latitude = hp.qlat
origin.longitude = hp.qlon
origin.depth = hp.qdep
origin.creation_info = CreationInfo(version=hp.icusp)
origin.resource_id = ResourceIdentifier('smi:hash/Origin/{0}'.format(hp.icusp))
for _i in range(n):
p = Pick()
p.creation_info = CreationInfo(version=hp.arid[_i])
p.resource_id = ResourceIdentifier('smi:hash/Pick/{0}'.format(p.creation_info.version))
p.waveform_id = WaveformStreamID(network_code=hp.snet[_i], station_code=hp.sname[_i], channel_code=hp.scomp[_i])
if hp.p_pol[_i] > 0:
p.polarity = 'positive'
else:
p.polarity = 'negative'
a = Arrival()
a.creation_info = CreationInfo(version=hp.arid[_i])
a.resource_id = ResourceIdentifier('smi:hash/Arrival/{0}'.format(p.creation_info.version))
a.azimuth = hp.p_azi_mc[_i,0]
a.takeoff_angle = 180. - hp.p_the_mc[_i,0]
a.pick_id = p.resource_id
origin.arrivals.append(a)
event.picks.append(p)
event.origins.append(origin)
event.preferred_origin_id = str(origin.resource_id)
else: # just update the changes
origin = event.preferred_origin()
picks = []
arrivals = []
for _i in range(n):
ind = hp.p_index[_i]
a = origin.arrivals[ind]
p = a.pick_id.getReferredObject()
a.takeoff_angle = hp.p_the_mc[_i,0]
picks.append(p)
arrivals.append(a)
if only_fm_picks:
origin.arrivals = arrivals
event.picks = picks
# Use me double couple calculator and populate planes/axes etc
x = hp._best_quality_index
# Put all the mechanisms into the 'focal_mechanisms' list, mark "best" as preferred
for s in range(hp.nmult):
dc = DoubleCouple([hp.str_avg[s], hp.dip_avg[s], hp.rak_avg[s]])
ax = dc.axis
focal_mech = FocalMechanism()
focal_mech.creation_info = CreationInfo(creation_time=UTCDateTime(), author=hp.author)
focal_mech.triggering_origin_id = origin.resource_id
focal_mech.resource_id = ResourceIdentifier('smi:hash/FocalMechanism/{0}/{1}'.format(hp.icusp, s+1))
focal_mech.method_id = ResourceIdentifier('HASH')
focal_mech.nodal_planes = NodalPlanes()
focal_mech.nodal_planes.nodal_plane_1 = NodalPlane(*dc.plane1)
focal_mech.nodal_planes.nodal_plane_2 = NodalPlane(*dc.plane2)
focal_mech.principal_axes = PrincipalAxes()
focal_mech.principal_axes.t_axis = Axis(azimuth=ax['T']['azimuth'], plunge=ax['T']['dip'])
focal_mech.principal_axes.p_axis = Axis(azimuth=ax['P']['azimuth'], plunge=ax['P']['dip'])
focal_mech.station_polarity_count = n
focal_mech.azimuthal_gap = hp.magap
focal_mech.misfit = hp.mfrac[s]
focal_mech.station_distribution_ratio = hp.stdr[s]
focal_mech.comments.append(
Comment(hp.qual[s], resource_id=ResourceIdentifier(str(focal_mech.resource_id) + '/comment/quality'))
)
#----------------------------------------
event.focal_mechanisms.append(focal_mech)
if s == x:
event.preferred_focal_mechanism_id = str(focal_mech.resource_id)
return event
print evnum, ev['datetime']
# convert datetime object to UTCdatetime
dt = utcdatetime.UTCDateTime(ev['datetime'])
#start_time=utcdatetime.UTCDateTime(yr,mon,day,hr,mn,int(sec),int((sec-int(sec))*100000))
# Build event object
evnt = Event(resource_id='GG_cat_' + str(evnum + 1),
creation_info='AU')
origin = Origin()
origin.time = ev['datetime']
origin.longitude = ev['lon']
origin.latitude = ev['lat']
origin.depth = ev['dep']
evnt.origins.append(origin)
mag = Magnitude(creation_info='GG_cat')
mag.mag = ev['prefmag']
mag.magnitude_type = ev['prefmagtype']
evnt.magnitudes.append(mag)
''' the time window to request the data will be 20 minutes, check maximum travel time and increase this value accordingly '''
#end_time=start_time+960 # 16 minutes
start_time = dt - datetime.timedelta(seconds=60)
end_time = dt + datetime.timedelta(seconds=960) # 16 minutes
#end_time = dt + datetime.timedelta(seconds=600) # 5 minutes
''' get all waveform data available, use wildcards to reduce the data volume and speedup the process,
unfortunately we need to request few times for every number of characters that forms the station name '''
cat = client.get_events(starttime = otime - sec_before_after_event,\
endtime = otime + sec_before_after_event)
ev = cat[0]
ref_time_place = ev
if rlat != dummyval:
ref_time_place.origins[0].latitude = rlat
ref_time_place.origins[0].longitude = rlon
ref_time_place.origins[0].time = rtime
else:
print("WARNING using event data from user-defined catalog")
ev = Event()
org = Origin()
org.latitude = elat
org.longitude = elon
org.depth = edep
org.time = otime
mag = Magnitude()
mag.mag = emag
mag.magnitude_type = "Mw"
ev.origins.append(org)
ev.magnitudes.append(mag)
if rlat == dummyval:
# By default this should be the event time and location unless we want to grab stations centered at another location
rlat = elat
rlon = elon
rtime = otime
ref_time_place = Event()
ref_org = Origin()
# ''' correct the time if not UTC '''
# Is this really needed??? Looks like may already be converted??
# if code=="AEST" :
# start_time-=36000
# if code=="WITA" or code=="AWST":
# start_time-=28800
# Build event object
event = Event(resource_id='GG_cat_' + str(event_num),
creation_info='JG')
event_num += 1
origin = Origin()
origin.time = start_time
origin.longitude = lon
origin.latitude = lat
origin.depth = dep
event.origins.append(origin)
mag = Magnitude(creation_info='GG_cat')
mag.mag = mag_pref
mag.magnitude_type = mag_type
event.magnitudes.append(mag)
''' the time window to request the data will be 20 minutes, check maximum travel time and increase this value accordingly '''
end_time = start_time + 1200 # 20 minutes - to catch ~Rayleigh waves at 20 degrees distance (assume v>=3km/s)
''' get all waveform data available, use wildcards to reduce the data volume and speedup the process,
unfortunately we need to request few times for every number of characters that forms the station name '''
st_3 = client.get_waveforms("AU", "???", "", "[BS]?[ENZ]", start_time,
end_time)
st_4 = client.get_waveforms("AU", "????", "", "[BS]?[ENZ]", start_time,
end_time)
if len(st_4) > 0:
st = st_3 + st_4
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
def par2quakeml(Par_filename,
QuakeML_filename,
rotation_axis=[0.0, 1.0, 0.0],
rotation_angle=-57.5,
origin_time="2000-01-01 00:00:00.0",
event_type="other event"):
# initialise event
ev = Event()
# open and read Par file
fid = open(Par_filename, 'r')
fid.readline()
fid.readline()
fid.readline()
fid.readline()
lat_old = 90.0 - float(fid.readline().strip().split()[0])
lon_old = float(fid.readline().strip().split()[0])
depth = float(fid.readline().strip().split()[0])
fid.readline()
Mtt_old = float(fid.readline().strip().split()[0])
Mpp_old = float(fid.readline().strip().split()[0])
Mrr_old = float(fid.readline().strip().split()[0])
Mtp_old = float(fid.readline().strip().split()[0])
Mtr_old = float(fid.readline().strip().split()[0])
Mpr_old = float(fid.readline().strip().split()[0])
# rotate event into physical domain
lat, lon = rot.rotate_lat_lon(lat_old, lon_old, rotation_axis,
rotation_angle)
Mrr, Mtt, Mpp, Mtr, Mpr, Mtp = rot.rotate_moment_tensor(
Mrr_old, Mtt_old, Mpp_old, Mtr_old, Mpr_old, Mtp_old, lat_old, lon_old,
rotation_axis, rotation_angle)
# populate event origin data
ev.event_type = event_type
ev_origin = Origin()
ev_origin.time = UTCDateTime(origin_time)
ev_origin.latitude = lat
ev_origin.longitude = lon
ev_origin.depth = depth
ev.origins.append(ev_origin)
# populte event moment tensor
ev_tensor = Tensor()
ev_tensor.m_rr = Mrr
ev_tensor.m_tt = Mtt
ev_tensor.m_pp = Mpp
ev_tensor.m_rt = Mtr
ev_tensor.m_rp = Mpr
ev_tensor.m_tp = Mtp
ev_momenttensor = MomentTensor()
ev_momenttensor.tensor = ev_tensor
ev_momenttensor.scalar_moment = np.sqrt(Mrr**2 + Mtt**2 + Mpp**2 + Mtr**2 +
Mpr**2 + Mtp**2)
ev_focalmechanism = FocalMechanism()
ev_focalmechanism.moment_tensor = ev_momenttensor
ev_focalmechanism.nodal_planes = NodalPlanes().setdefault(0, 0)
ev.focal_mechanisms.append(ev_focalmechanism)
# populate event magnitude
ev_magnitude = Magnitude()
ev_magnitude.mag = 0.667 * (np.log10(ev_momenttensor.scalar_moment) - 9.1)
ev_magnitude.magnitude_type = 'Mw'
ev.magnitudes.append(ev_magnitude)
# write QuakeML file
cat = Catalog()
cat.append(ev)
cat.write(QuakeML_filename, format="quakeml")
# clean up
fid.close()
eid = line_elements[-1] # Fix to take microseconds
otime = uh.eid2otime(eid)
elat = line_elements[7]
elon = line_elements[6]
edep = float(line_elements[8]) * 1000.0 # in meters
emag = line_elements[16]
#for line in lines:
print(otime, elat, elon, edep, emag)
# Create event object
client = Client("IRIS")
ev = Event()
org = Origin()
org.latitude = elat
org.longitude = elon
org.depth = edep
org.time = otime
mag = Magnitude()
mag.mag = emag
mag.magnitude_type = "Mw"
ev.origins.append(org)
ev.magnitudes.append(mag)
# Extract waveforms
getwaveform_iris.run_get_waveform(c = client, event = ev,
min_dist = min_dist, max_dist = max_dist,
before = tbefore_sec, after = tafter_sec,
network = network, station = station, channel = channel,
resample_freq = resample_freq, ifrotate = rotate,
ifCapInp = output_cap_weight_file,
ifRemoveResponse = remove_response,
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.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 and return a
tuple of three values (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
# NOTE this loop sends a request for each line in the catalog (63 events)
for line in lines:
line_elements = line.split()
eid = line_elements[-1]
otime = uh.eid2otime(eid)
elon = line_elements[6]
elat = line_elements[7]
edep = float(line_elements[8]) * 1000.0 # meters
emag = line_elements[16]
# create event object
orig = Origin()
orig.longitude = elon
orig.latitude = elat
orig.depth = edep
orig.time = otime
mag = Magnitude()
mag.mag = emag
mag.magnitude_type = "Mw"
ev = Event()
ev.origins.append(orig)
ev.magnitudes.append(mag)
if send_request:
# get waveforms
client = Client("IRIS")
getwaveform_iris.run_get_waveform(c=client,
event=ev,
min_dist=min_dist,
max_dist=max_dist,
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.
"""
origin = Origin()
# I guess setting the program used as the method id is fine.
origin.method_id = parser.xpath2obj('program', origin_el)
# Standard parameters.
origin.time, origin.time_errors = \
__toTimeQuantity(parser, origin_el, "time")
origin.latitude, origin.latitude_errors = \
__toFloatQuantity(parser, origin_el, "latitude")
origin.longitude, origin.longitude_errors = \
__toFloatQuantity(parser, origin_el, "longitude")
origin.depth, origin.depth_errors = \
__toFloatQuantity(parser, origin_el, "depth")
# Figure out the depth type.
depth_type = parser.xpath2obj("depth_type", origin_el, str)
# Map Seishub specific depth type to the QuakeML depth type.
if depth_type == "from location program":
depth_type == "from location"
origin.depth_type = "from location"
# Earth model.
origin.earth_model_id = parser.xpath2obj("earth_mod", origin_el, str)
# Parse th origin uncertainty. Rather verbose but should cover all cases.
pref_desc = parser.xpath2obj("originUncertainty/preferredDescription",
origin_el, str)
hor_uncert = parser.xpath2obj("originUncertainty/horizontalUncertainty",
origin_el, float)
min_hor_uncert = parser.xpath2obj(\
"originUncertainty/minHorizontalUncertainty", origin_el, float)
max_hor_uncert = parser.xpath2obj(\
"originUncertainty/maxHorizontalUncertainty", origin_el, float)
azi_max_hor_uncert = parser.xpath2obj(\
"originUncertainty/azimuthMaxHorizontalUncertainty", origin_el, float)
origin_uncert = {}
if pref_desc:
origin_uncert["preferred_description"] = pref_desc
if hor_uncert:
origin_uncert["horizontal_uncertainty"] = hor_uncert
if min_hor_uncert:
origin_uncert["min_horizontal_uncertainty"] = min_hor_uncert
if max_hor_uncert:
origin_uncert["max_horizontal_uncertainty"] = max_hor_uncert
if azi_max_hor_uncert:
origin_uncert["azimuth_max_horizontal_uncertainty"] = \
azi_max_hor_uncert
if origin_uncert:
origin.origin_uncertainty = origin_uncert
# 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 and s_phase_count:
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)
origin.quality.used_phase_count = phase_count
origin.quality.associated_station_count = \
parser.xpath2obj("associatedStationCount", origin_quality_el, int)
origin.quality.used_station_count = \
parser.xpath2obj("usedStationCount", origin_quality_el, int)
origin.quality.depth_phase_count = \
parser.xpath2obj("depthPhaseCount", origin_quality_el, int)
origin.quality.standard_error = \
parser.xpath2obj("standardError", origin_quality_el, float)
origin.quality.azimuthal_gap = \
parser.xpath2obj("azimuthalGap", origin_quality_el, float)
origin.quality.secondary_azimuthal_gap = \
parser.xpath2obj("secondaryAzimuthalGap", origin_quality_el, float)
origin.quality.ground_truth_level = \
parser.xpath2obj("groundTruthLevel", origin_quality_el, float)
origin.quality.minimum_distance = \
parser.xpath2obj("minimumDistance", origin_quality_el, float)
origin.quality.maximum_distance = \
parser.xpath2obj("maximumDistance", origin_quality_el, float)
origin.quality.median_distance = \
parser.xpath2obj("medianDistance", origin_quality_el, float)
return origin
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 request_gcmt(starttime,
endtime,
minmagnitude=None,
mindepth=None,
maxdepth=None,
minlatitude=None,
maxlatitude=None,
minlongitude=None,
maxlongitude=None):
from mechanize import Browser
import re
"""
Description
I am using mechanize. My attempt is just preliminary, for the current
globalcmt.org site. It is possible to store Moment Tensor information
in the catalog file.
"""
# Split numbers and text
r = re.compile("([a-zA-Z]+)([0-9]+)")
br = Browser()
br.open('http://www.globalcmt.org/CMTsearch.html')
# Site has just one form
br.select_form(nr=0)
br.form['yr'] = str(starttime.year)
br.form['mo'] = str(starttime.month)
br.form['day'] = str(starttime.day)
br.form['oyr'] = str(endtime.year)
br.form['omo'] = str(endtime.month)
br.form['oday'] = str(endtime.day)
br.form['list'] = ['4']
br.form['itype'] = ['ymd']
br.form['otype'] = ['ymd']
if minmagnitude:
br.form['lmw'] = str(minmagnitude)
if minlatitude:
br.form['llat'] = str(minlatitude)
if maxlatitude:
br.form['ulat'] = str(maxlatitude)
if minlongitude:
br.form['llon'] = str(minlongitude)
if maxlongitude:
br.form['ulon'] = str(maxlongitude)
if mindepth:
br.form['lhd'] = str(mindepth)
if maxdepth:
br.form['uhd'] = str(maxdepth)
print("Submitting parameters to globalcmt.org ")
req = br.submit()
print("Retrieving data, creating catalog.")
data = []
for line in req:
data.append(line)
data_chunked = _chunking_list(keyword='\n', list=data)
origins = []
magnitudes = []
tensor = []
for line in data_chunked:
for element in line:
if 'event name' in element:
try:
org = line[1].split()
year = int(r.match(org[0]).groups()[1])
mon = int(org[1])
day = int(org[2])
hour = int(org[3])
minute = int(org[4])
sec_temp = int(org[5].split('.')[0])
msec_temp = int(org[5].split('.')[1])
except:
org = line[1].split()
year = int(org[1])
mon = int(org[2])
day = int(org[3])
hour = int(org[4])
minute = int(org[5])
sec_temp = int(org[6].split('.')[0])
msec_temp = int(org[6].split('.')[1])
origins_temp = UTCDateTime(year, mon, day, hour, minute,
sec_temp, msec_temp)
# adding time shift located in line[3]
origin = origins_temp + float(line[3].split()[2])
magnitude = float(line[1].split()[10])
latitude = float(line[5].split()[1])
longitude = float(line[6].split()[1])
depth = 1000. * float(line[7].split()[1])
m_rr = float(line[8].split()[1])
m_tt = float(line[9].split()[1])
m_pp = float(line[10].split()[1])
m_rt = float(line[11].split()[1])
m_rp = float(line[12].split()[1])
m_tp = float(line[13].split()[1])
magnitudes.append(("Mw", magnitude))
origins.append((latitude, longitude, depth, origin))
tensor.append((m_rr, m_tt, m_pp, m_rt, m_rp, m_tp))
cat = Catalog()
for mag, org, ten in zip(magnitudes, origins, tensor):
# Create magnitude object.
magnitude = Magnitude()
magnitude.magnitude_type = mag[0]
magnitude.mag = mag[1]
# Write origin object.
origin = Origin()
origin.latitude = org[0]
origin.longitude = org[1]
origin.depth = org[2]
origin.time = org[3]
# Create event object and append to catalog object.
event = Event()
event.magnitudes.append(magnitude)
event.origins.append(origin)
event.MomentTensor = MomentTensor()
event.MomentTensor.m_rr = ten[0]
event.MomentTensor.m_tt = ten[1]
event.MomentTensor.m_pp = ten[2]
event.MomentTensor.m_rt = ten[3]
event.MomentTensor.m_rp = ten[4]
event.MomentTensor.m_tp = ten[5]
cat.append(event)
return cat
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 _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 _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 request_gcmt(starttime, endtime, minmagnitude=None, mindepth=None, maxdepth=None, minlatitude=None, maxlatitude=None, minlongitude=None, maxlongitude=None):
import mechanize
from mechanize import Browser
import re
"""
Description
I am using mechanize. My attempt is just preliminary, for the current globalcmt.org site.
"""
#Split numbers and text
r = re.compile("([a-zA-Z]+)([0-9]+)")
br = Browser()
br.open('http://www.globalcmt.org/CMTsearch.html')
#Site has just one form
br.select_form(nr=0)
br.form['yr'] = str(starttime.year)
br.form['mo'] = str(starttime.month)
br.form['day'] = str(starttime.day)
br.form['oyr'] = str(endtime.year)
br.form['omo'] = str(endtime.month)
br.form['oday'] = str(endtime.day)
br.form['list'] = ['4']
br.form['itype'] = ['ymd']
br.form['otype'] = ['ymd']
if minmagnitude: br.form['lmw'] = str(minmagnitude)
if minlatitude : br.form['llat'] = str(minlatitude)
if maxlatitude : br.form['ulat'] = str(maxlatitude)
if minlongitude: br.form['llon'] = str(minlongitude)
if maxlongitude: br.form['ulon'] = str(maxlongitude)
if mindepth : br.form['lhd'] = str(mindepth)
if maxdepth : br.form['uhd'] = str(maxdepth)
print("Submitting parameters to globalcmt.")
req = br.submit()
print("Retrieving data, creating catalog.")
data = []
for line in req:
data.append(line)
data_chunked = _chunking_list(keyword='\n', list=data)
origins = []
magnitudes = []
tensor = []
for line in data_chunked:
for element in line:
if 'event name' in element:
for content in element:
org = line[1].split()
year = int(r.match(org[0]).groups()[1])
mon = int(org[1])
day = int(org[2])
hour = int(org[3])
minute = int(org[4])
sec_temp = int(org[5].split('.')[0])
msec_temp = int(org[5].split('.')[1])
origins_temp = UTCDateTime(year, mon, day, hour, minute, sec_temp, msec_temp)
#adding time shift located in line[3]
origin = origins_temp + float(line[3].split()[2])
magnitude = float(line[1].split()[10])
latitude = float(line[5].split()[1])
longitude = float(line[6].split()[1])
depth = 1000. * float(line[7].split()[1])
m_rr = float(line[8].split()[1])
m_tt = float(line[9].split()[1])
m_pp = float(line[10].split()[1])
m_rt = float(line[11].split()[1])
m_rp = float(line[12].split()[1])
m_tp = float(line[13].split()[1])
magnitudes.append( ("Mw", magnitude) )
origins.append( (latitude, longitude, depth, origin) )
tensor.append( (m_rr, m_tt, m_pp, m_rt, m_rp, m_tp) )
cat = Catalog()
for mag, org, ten in zip(magnitudes, origins, tensor):
# Create magnitude object.
magnitude = Magnitude()
magnitude.magnitude_type = mag[0]
magnitude.mag = mag[1]
# Write origin object.
origin = Origin()
origin.latitude = org[0]
origin.longitude = org[1]
origin.depth = org[2]
origin.time = org[3]
# Create event object and append to catalog object.
event = Event()
event.magnitudes.append(magnitude)
event.origins.append(origin)
event.MomentTensor = MomentTensor()
event.MomentTensor.m_rr = ten[0]
event.MomentTensor.m_tt = ten[1]
event.MomentTensor.m_pp = ten[2]
event.MomentTensor.m_rt = ten[3]
event.MomentTensor.m_rp = ten[4]
event.MomentTensor.m_tp = ten[5]
cat.append(event)
return cat
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
