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 node_catalog_no_picks(node_catalog) -> Tuple[obspy.Catalog, Dict]:
"""return the node catalog with just origins"""
eid_map = {}
cat = Catalog()
for num, eve in enumerate(node_catalog):
eve_out = Event(origins=eve.origins)
for o in eve_out.origins:
o.arrivals = []
eve_out.resource_id = ResourceIdentifier(f"event_{num}")
cat.append(eve_out)
eid_map[eve.resource_id.id] = eve_out.resource_id.id
return cat, eid_map
def _parse_event(self, first_line):
"""
Parse an event.
:type first_line: str
:param first_line: First line of an event block, which contains
the event id.
:rtype: :class:`~obspy.core.event.event.Event`
:return: The parsed event or None.
"""
event_id = first_line[5:].strip()
# Skip event without id
if not event_id:
self._warn('Missing event id')
return None
event = Event()
origin, origin_res_id = self._parse_origin(event)
# Skip event without origin
if not origin:
return None
line = self._skip_empty_lines()
self._parse_region_name(line, event)
self._parse_arrivals(event, origin, origin_res_id)
# Origin ResourceIdentifier should be set at the end, when
# Arrivals are already set.
origin.resource_id = origin_res_id
event.origins.append(origin)
event.preferred_origin_id = origin.resource_id.id
# Must be done after the origin parsing
event.creation_info = self._get_creation_info()
public_id = "event/%s" % event_id
event.resource_id = self._get_res_id(public_id)
event.scope_resource_ids()
return event
def _parse_event(self, first_line):
"""
Parse an event.
:type first_line: str
:param first_line: First line of an event block, which contains
the event id.
:rtype: :class:`~obspy.core.event.event.Event`
:return: The parsed event or None.
"""
event_id = first_line[5:].strip()
# Skip event without id
if not event_id:
self._warn('Missing event id')
return None
event = Event()
origin, origin_res_id = self._parse_origin(event)
# Skip event without origin
if not origin:
return None
line = self._skip_empty_lines()
self._parse_region_name(line, event)
self._parse_arrivals(event, origin, origin_res_id)
# Origin ResourceIdentifier should be set at the end, when
# Arrivals are already set.
origin.resource_id = origin_res_id
event.origins.append(origin)
event.preferred_origin_id = origin.resource_id.id
# Must be done after the origin parsing
event.creation_info = self._get_creation_info()
public_id = "event/%s" % event_id
event.resource_id = self._get_res_id(public_id)
event.scope_resource_ids()
return event
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 _read_ndk(filename, *args, **kwargs): # @UnusedVariable
"""
Reads an NDK file to a :class:`~obspy.core.event.Catalog` object.
:param filename: File or file-like object in text mode.
"""
# Read the whole file at once. While an iterator would be more efficient
# the largest NDK file out in the wild is 13.7 MB so it does not matter
# much.
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()
# Create iterator that yields lines.
def lines_iter():
prev_line = -1
while True:
next_line = data.find("\n", prev_line + 1)
if next_line < 0:
break
yield data[prev_line + 1: next_line]
prev_line = next_line
if len(data) > prev_line + 1:
yield data[prev_line + 1:]
# Use one Flinn Engdahl object for all region determinations.
fe = FlinnEngdahl()
cat = Catalog(resource_id=_get_resource_id("catalog", str(uuid.uuid4())))
# Loop over 5 lines at once.
for _i, lines in enumerate(itertools.zip_longest(*[lines_iter()] * 5)):
if None in lines:
msg = "Skipped last %i lines. Not a multiple of 5 lines." % (
lines.count(None))
warnings.warn(msg, ObsPyNDKWarning)
continue
# Parse the lines to a human readable dictionary.
try:
record = _read_lines(*lines)
except (ValueError, ObsPyNDKException):
exc = traceback.format_exc()
msg = (
"Could not parse event %i (faulty file?). Will be "
"skipped. Lines of the event:\n"
"\t%s\n"
"%s") % (_i + 1, "\n\t".join(lines), exc)
warnings.warn(msg, ObsPyNDKWarning)
continue
# Use one creation info for essentially every item.
creation_info = CreationInfo(
agency_id="GCMT",
version=record["version_code"]
)
# Use the ObsPy Flinn Engdahl region determiner as the region in the
# NDK files is oftentimes trimmed.
region = fe.get_region(record["centroid_longitude"],
record["centroid_latitude"])
# Create an event object.
event = Event(
force_resource_id=False,
event_type="earthquake",
event_type_certainty="known",
event_descriptions=[
EventDescription(text=region, type="Flinn-Engdahl region"),
EventDescription(text=record["cmt_event_name"],
type="earthquake name")
]
)
# Assemble the time for the reference origin.
try:
time = _parse_date_time(record["date"], record["time"])
except ObsPyNDKException:
msg = ("Invalid time in event %i. '%s' and '%s' cannot be "
"assembled to a valid time. Event will be skipped.") % \
(_i + 1, record["date"], record["time"])
warnings.warn(msg, ObsPyNDKWarning)
continue
# Create two origins, one with the reference latitude/longitude and
# one with the centroidal values.
ref_origin = Origin(
force_resource_id=False,
time=time,
longitude=record["hypo_lng"],
latitude=record["hypo_lat"],
# Convert to m.
depth=record["hypo_depth_in_km"] * 1000.0,
origin_type="hypocenter",
comments=[Comment(text="Hypocenter catalog: %s" %
record["hypocenter_reference_catalog"],
force_resource_id=False)]
)
ref_origin.comments[0].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="ref_origin")
ref_origin.resource_id = _get_resource_id(record["cmt_event_name"],
"origin", tag="reforigin")
cmt_origin = Origin(
force_resource_id=False,
longitude=record["centroid_longitude"],
longitude_errors={
"uncertainty": record["centroid_longitude_error"]},
latitude=record["centroid_latitude"],
latitude_errors={
"uncertainty": record["centroid_latitude_error"]},
# Convert to m.
depth=record["centroid_depth_in_km"] * 1000.0,
depth_errors={
"uncertainty": record["centroid_depth_in_km_error"] * 1000},
time=ref_origin["time"] + record["centroid_time"],
time_errors={"uncertainty": record["centroid_time_error"]},
depth_type=record["type_of_centroid_depth"],
origin_type="centroid",
time_fixed=False,
epicenter_fixed=False,
creation_info=creation_info.copy()
)
cmt_origin.resource_id = _get_resource_id(record["cmt_event_name"],
"origin",
tag="cmtorigin")
event.origins = [ref_origin, cmt_origin]
event.preferred_origin_id = cmt_origin.resource_id.id
# Create the magnitude object.
mag = Magnitude(
force_resource_id=False,
mag=round(record["Mw"], 2),
magnitude_type="Mwc",
origin_id=cmt_origin.resource_id,
creation_info=creation_info.copy()
)
mag.resource_id = _get_resource_id(record["cmt_event_name"],
"magnitude", tag="moment_mag")
event.magnitudes = [mag]
event.preferred_magnitude_id = mag.resource_id.id
# Add the reported mb, MS magnitudes as additional magnitude objects.
event.magnitudes.append(Magnitude(
force_resource_id=False,
mag=record["mb"],
magnitude_type="mb",
comments=[Comment(
force_resource_id=False,
text="Reported magnitude in NDK file. Most likely 'mb'."
)]
))
event.magnitudes[-1].comments[-1].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="mb_magnitude")
event.magnitudes[-1].resource_id = _get_resource_id(
record["cmt_event_name"], "magnitude", tag="mb")
event.magnitudes.append(Magnitude(
force_resource_id=False,
mag=record["MS"],
magnitude_type="MS",
comments=[Comment(
force_resource_id=False,
text="Reported magnitude in NDK file. Most likely 'MS'."
)]
))
event.magnitudes[-1].comments[-1].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="MS_magnitude")
event.magnitudes[-1].resource_id = _get_resource_id(
record["cmt_event_name"], "magnitude", tag="MS")
# Take care of the moment tensor.
tensor = Tensor(
m_rr=record["m_rr"],
m_rr_errors={"uncertainty": record["m_rr_error"]},
m_pp=record["m_pp"],
m_pp_errors={"uncertainty": record["m_pp_error"]},
m_tt=record["m_tt"],
m_tt_errors={"uncertainty": record["m_tt_error"]},
m_rt=record["m_rt"],
m_rt_errors={"uncertainty": record["m_rt_error"]},
m_rp=record["m_rp"],
m_rp_errors={"uncertainty": record["m_rp_error"]},
m_tp=record["m_tp"],
m_tp_errors={"uncertainty": record["m_tp_error"]},
creation_info=creation_info.copy()
)
mt = MomentTensor(
force_resource_id=False,
scalar_moment=record["scalar_moment"],
tensor=tensor,
data_used=[DataUsed(**i) for i in record["data_used"]],
inversion_type=record["source_type"],
source_time_function=SourceTimeFunction(
type=record["moment_rate_type"],
duration=record["moment_rate_duration"]
),
derived_origin_id=cmt_origin.resource_id,
creation_info=creation_info.copy()
)
mt.resource_id = _get_resource_id(record["cmt_event_name"],
"momenttensor")
axis = [Axis(**i) for i in record["principal_axis"]]
focmec = FocalMechanism(
force_resource_id=False,
moment_tensor=mt,
principal_axes=PrincipalAxes(
# The ordering is the same as for the IRIS SPUD service and
# from a website of the Saint Louis University Earthquake
# center so it should be correct.
t_axis=axis[0],
p_axis=axis[2],
n_axis=axis[1]
),
nodal_planes=NodalPlanes(
nodal_plane_1=NodalPlane(**record["nodal_plane_1"]),
nodal_plane_2=NodalPlane(**record["nodal_plane_2"])
),
comments=[
Comment(force_resource_id=False,
text="CMT Analysis Type: %s" %
record["cmt_type"].capitalize()),
Comment(force_resource_id=False,
text="CMT Timestamp: %s" %
record["cmt_timestamp"])],
creation_info=creation_info.copy()
)
focmec.comments[0].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="cmt_type")
focmec.comments[1].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="cmt_timestamp")
focmec.resource_id = _get_resource_id(record["cmt_event_name"],
"focal_mechanism")
event.focal_mechanisms = [focmec]
event.preferred_focal_mechanism_id = focmec.resource_id.id
# Set at end to avoid duplicate resource id warning.
event.resource_id = _get_resource_id(record["cmt_event_name"],
"event")
cat.append(event)
if len(cat) == 0:
msg = "No valid events found in NDK file."
raise ObsPyNDKException(msg)
return cat
def __read_single_fnetmt_entry(line, **kwargs):
"""
Reads a single F-net moment tensor solution to a
:class:`~obspy.core.event.Event` object.
:param line: String containing moment tensor information.
:type line: str.
"""
a = line.split()
try:
ot = UTCDateTime().strptime(a[0], '%Y/%m/%d,%H:%M:%S.%f')
except ValueError:
ot = UTCDateTime().strptime(a[0], '%Y/%m/%d,%H:%M:%S')
lat, lon, depjma, magjma = map(float, a[1:5])
depjma *= 1000
region = a[5]
strike = tuple(map(int, a[6].split(';')))
dip = tuple(map(int, a[7].split(';')))
rake = tuple(map(int, a[8].split(';')))
mo = float(a[9])
depmt = float(a[10]) * 1000
magmt = float(a[11])
var_red = float(a[12])
mxx, mxy, mxz, myy, myz, mzz, unit = map(float, a[13:20])
event_name = util.gen_sc3_id(ot)
e = Event(event_type="earthquake")
e.resource_id = _get_resource_id(event_name, 'event')
# Standard JMA solution
o_jma = Origin(time=ot, latitude=lat, longitude=lon,
depth=depjma, depth_type="from location",
region=region)
o_jma.resource_id = _get_resource_id(event_name,
'origin', 'JMA')
m_jma = Magnitude(mag=magjma, magnitude_type='ML',
origin_id=o_jma.resource_id)
m_jma.resource_id = _get_resource_id(event_name,
'magnitude', 'JMA')
# MT solution
o_mt = Origin(time=ot, latitude=lat, longitude=lon,
depth=depmt, region=region,
depth_type="from moment tensor inversion")
o_mt.resource_id = _get_resource_id(event_name,
'origin', 'MT')
m_mt = Magnitude(mag=magmt, magnitude_type='Mw',
origin_id=o_mt.resource_id)
m_mt.resource_id = _get_resource_id(event_name,
'magnitude', 'MT')
foc_mec = FocalMechanism(triggering_origin_id=o_jma.resource_id)
foc_mec.resource_id = _get_resource_id(event_name,
"focal_mechanism")
nod1 = NodalPlane(strike=strike[0], dip=dip[0], rake=rake[0])
nod2 = NodalPlane(strike=strike[1], dip=dip[1], rake=rake[1])
nod = NodalPlanes(nodal_plane_1=nod1, nodal_plane_2=nod2)
foc_mec.nodal_planes = nod
tensor = Tensor(m_rr=mxx, m_tt=myy, m_pp=mzz, m_rt=mxy, m_rp=mxz, m_tp=myz)
cm = Comment(text="Basis system: North,East,Down (Jost and \
Herrmann 1989")
cm.resource_id = _get_resource_id(event_name, 'comment', 'mt')
mt = MomentTensor(derived_origin_id=o_mt.resource_id,
moment_magnitude_id=m_mt.resource_id,
scalar_moment=mo, comments=[cm],
tensor=tensor, variance_reduction=var_red)
mt.resource_id = _get_resource_id(event_name,
'moment_tensor')
foc_mec.moment_tensor = mt
e.origins = [o_jma, o_mt]
e.magnitudes = [m_jma, m_mt]
e.focal_mechanisms = [foc_mec]
e.preferred_magnitude_id = m_mt.resource_id.id
e.preferred_origin_id = o_mt.resource_id.id
e.preferred_focal_mechanism_id = foc_mec.resource_id.id
return e
def __read_single_fnetmt_entry(line, **kwargs):
"""
Reads a single F-net moment tensor solution to a
:class:`~obspy.core.event.Event` object.
:param line: String containing moment tensor information.
:type line: str.
"""
a = line.split()
try:
ot = UTCDateTime().strptime(a[0], '%Y/%m/%d,%H:%M:%S.%f')
except ValueError:
ot = UTCDateTime().strptime(a[0], '%Y/%m/%d,%H:%M:%S')
lat, lon, depjma, magjma = map(float, a[1:5])
depjma *= 1000
region = a[5]
strike = tuple(map(int, a[6].split(';')))
dip = tuple(map(int, a[7].split(';')))
rake = tuple(map(int, a[8].split(';')))
mo = float(a[9])
depmt = float(a[10]) * 1000
magmt = float(a[11])
var_red = float(a[12])
mxx, mxy, mxz, myy, myz, mzz, unit = map(float, a[13:20])
event_name = util.gen_sc3_id(ot)
e = Event(event_type="earthquake")
e.resource_id = _get_resource_id(event_name, 'event')
# Standard JMA solution
o_jma = Origin(time=ot,
latitude=lat,
longitude=lon,
depth=depjma,
depth_type="from location",
region=region)
o_jma.resource_id = _get_resource_id(event_name, 'origin', 'JMA')
m_jma = Magnitude(mag=magjma,
magnitude_type='ML',
origin_id=o_jma.resource_id)
m_jma.resource_id = _get_resource_id(event_name, 'magnitude', 'JMA')
# MT solution
o_mt = Origin(time=ot,
latitude=lat,
longitude=lon,
depth=depmt,
region=region,
depth_type="from moment tensor inversion")
o_mt.resource_id = _get_resource_id(event_name, 'origin', 'MT')
m_mt = Magnitude(mag=magmt,
magnitude_type='Mw',
origin_id=o_mt.resource_id)
m_mt.resource_id = _get_resource_id(event_name, 'magnitude', 'MT')
foc_mec = FocalMechanism(triggering_origin_id=o_jma.resource_id)
foc_mec.resource_id = _get_resource_id(event_name, "focal_mechanism")
nod1 = NodalPlane(strike=strike[0], dip=dip[0], rake=rake[0])
nod2 = NodalPlane(strike=strike[1], dip=dip[1], rake=rake[1])
nod = NodalPlanes(nodal_plane_1=nod1, nodal_plane_2=nod2)
foc_mec.nodal_planes = nod
tensor = Tensor(m_rr=mxx, m_tt=myy, m_pp=mzz, m_rt=mxy, m_rp=mxz, m_tp=myz)
cm = Comment(text="Basis system: North,East,Down (Jost and \
Herrmann 1989")
cm.resource_id = _get_resource_id(event_name, 'comment', 'mt')
mt = MomentTensor(derived_origin_id=o_mt.resource_id,
moment_magnitude_id=m_mt.resource_id,
scalar_moment=mo,
comments=[cm],
tensor=tensor,
variance_reduction=var_red)
mt.resource_id = _get_resource_id(event_name, 'moment_tensor')
foc_mec.moment_tensor = mt
e.origins = [o_jma, o_mt]
e.magnitudes = [m_jma, m_mt]
e.focal_mechanisms = [foc_mec]
e.preferred_magnitude_id = m_mt.resource_id.id
e.preferred_origin_id = o_mt.resource_id.id
e.preferred_focal_mechanism_id = foc_mec.resource_id.id
return e
def _channel_loop(detection,
template,
min_cc,
detection_id,
interpolate,
i,
pre_lag_ccsum=None,
detect_chans=0):
"""
Inner loop for correlating and assigning picks.
Utility function to take a stream of data for the detected event and write
maximum correlation to absolute time as picks in an obspy.core.event.Event
object.
Only outputs picks for picks above min_cc.
:type detection: obspy.core.stream.Stream
:param detection:
Stream of data for the slave event detected using template.
:type template: obspy.core.stream.Stream
:param template: Stream of data as the template for the detection.
:type min_cc: float
:param min_cc: Minimum cross-correlation value to allow a pick to be made.
:type detection_id: str
:param detection_id: Detection ID to associate the event with.
:type interpolate: bool
:param interpolate:
Interpolate the correlation function to achieve sub-sample precision.
:type i: int
:param i:
Used to track which process has occurred when running in parallel.
:type pre_lag_ccsum: float
:param pre_lag_ccsum:
Cross-correlation sum before lag-calc, will check that the
cross-correlation sum is increased by lag-calc (using all channels,
ignoring min_cc)
:type detect_chans: int
:param detect_chans:
Number of channels originally used in detections, must match the number
used here to allow for cccsum checking.
:returns:
Event object containing network, station, channel and pick information.
:rtype: :class:`obspy.core.event.Event`
"""
event = Event()
s_stachans = {}
used_s_sta = []
cccsum = 0
checksum = 0
used_chans = 0
for tr in template:
temp_net = tr.stats.network
temp_sta = tr.stats.station
temp_chan = tr.stats.channel
image = detection.select(station=temp_sta, channel=temp_chan)
if image:
if interpolate:
try:
ccc = normxcorr2(tr.data, image[0].data)
shift, cc_max = _xcorr_interp(ccc=ccc,
dt=image[0].stats.delta)
except IndexError:
log.error('Could not interpolate ccc, not smooth')
ccc = normxcorr2(tr.data, image[0].data)
cc_max = np.amax(ccc)
shift = np.argmax(ccc) * image[0].stats.delta
# Convert the maximum cross-correlation time to an actual time
picktime = image[0].stats.starttime + shift
else:
# Convert the maximum cross-correlation time to an actual time
ccc = normxcorr2(tr.data, image[0].data)
cc_max = np.amax(ccc)
picktime = image[0].stats.starttime + (np.argmax(ccc) *
image[0].stats.delta)
log.debug('********DEBUG: Maximum cross-corr=%s' % cc_max)
checksum += cc_max
used_chans += 1
if cc_max < min_cc:
continue
cccsum += cc_max
# Perhaps weight each pick by the cc val or cc val^2?
# weight = np.amax(ccc) ** 2
if temp_chan[-1:] == 'Z':
phase = 'P'
# Only take the S-pick with the best correlation
elif temp_chan[-1:] in ['E', 'N']:
phase = 'S'
if temp_sta not in s_stachans and np.amax(ccc) > min_cc:
s_stachans[temp_sta] = ((temp_chan, np.amax(ccc),
picktime))
elif temp_sta in s_stachans and np.amax(ccc) > min_cc:
if np.amax(ccc) > s_stachans[temp_sta][1]:
picktime = picktime
else:
picktime = s_stachans[temp_sta][2]
temp_chan = s_stachans[temp_sta][0]
elif np.amax(ccc) < min_cc and temp_sta not in used_s_sta:
used_s_sta.append(temp_sta)
else:
continue
else:
phase = None
_waveform_id = WaveformStreamID(network_code=temp_net,
station_code=temp_sta,
channel_code=temp_chan)
event.picks.append(
Pick(waveform_id=_waveform_id,
time=picktime,
method_id=ResourceIdentifier('EQcorrscan'),
phase_hint=phase,
creation_info='eqcorrscan.core.lag_calc',
comments=[Comment(text='cc_max=%s' % cc_max)]))
event.resource_id = detection_id
ccc_str = ("detect_val=%s" % cccsum)
event.comments.append(Comment(text=ccc_str))
if used_chans == detect_chans:
if pre_lag_ccsum is not None and checksum - pre_lag_ccsum < -0.05:
msg = ('lag-calc has decreased cccsum from %f to %f - '
'report this error' % (pre_lag_ccsum, checksum))
raise LagCalcError(msg)
else:
warnings.warn('Cannot check is cccsum is better, used %i channels '
'for detection, but %i are used here' %
(detect_chans, used_chans))
return i, event
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 xcorr_pick_family(family, stream, shift_len=0.2, min_cc=0.4,
horizontal_chans=['E', 'N', '1', '2'],
vertical_chans=['Z'], cores=1, interpolate=False,
plot=False, plotdir=None):
"""
Compute cross-correlation picks for detections in a family.
:type family: `eqcorrscan.core.match_filter.family.Family`
:param family: Family to calculate correlation picks for.
:type stream: `obspy.core.stream.Stream`
:param stream:
Data stream containing data for all (or a subset of) detections in
the Family
:type shift_len: float
:param shift_len:
Shift length allowed for the pick in seconds, will be plus/minus this
amount - default=0.2
:type min_cc: float
:param min_cc:
Minimum cross-correlation value to be considered a pick, default=0.4.
:type horizontal_chans: list
:param horizontal_chans:
List of channel endings for horizontal-channels, on which S-picks will
be made.
:type vertical_chans: list
:param vertical_chans:
List of channel endings for vertical-channels, on which P-picks will
be made.
:type cores: int
:param cores:
Number of cores to use in parallel processing, defaults to one.
:type interpolate: bool
:param interpolate:
Interpolate the correlation function to achieve sub-sample precision.
:type plot: bool
:param plot:
To generate a plot for every detection or not, defaults to False
:type plotdir: str
:param plotdir:
Path to plotting folder, plots will be output here.
:return: Dictionary of picked events keyed by detection id.
"""
picked_dict = {}
delta = family.template.st[0].stats.delta
detect_streams_dict = _prepare_data(
family=family, detect_data=stream, shift_len=shift_len)
detection_ids = list(detect_streams_dict.keys())
detect_streams = [detect_streams_dict[detection_id]
for detection_id in detection_ids]
if len(detect_streams) == 0:
Logger.warning("No appropriate data found, check your family and "
"detections - make sure seed ids match")
return picked_dict
if len(detect_streams) != len(family):
Logger.warning("Not all detections have matching data. "
"Proceeding anyway. HINT: Make sure SEED IDs match")
# Correlation function needs a list of streams, we need to maintain order.
ccc, chans = _concatenate_and_correlate(
streams=detect_streams, template=family.template.st, cores=cores)
for i, detection_id in enumerate(detection_ids):
detection = [d for d in family.detections if d.id == detection_id][0]
correlations = ccc[i]
picked_chans = chans[i]
detect_stream = detect_streams_dict[detection_id]
checksum, cccsum, used_chans = 0.0, 0.0, 0
event = Event()
for correlation, stachan in zip(correlations, picked_chans):
if not stachan.used:
continue
tr = detect_stream.select(
station=stachan.channel[0], channel=stachan.channel[1])[0]
if interpolate:
shift, cc_max = _xcorr_interp(correlation, dt=delta)
else:
cc_max = np.amax(correlation)
shift = np.argmax(correlation) * delta
if np.isnan(cc_max): # pragma: no cover
Logger.error(
'Problematic trace, no cross correlation possible')
continue
picktime = tr.stats.starttime + shift
checksum += cc_max
used_chans += 1
if cc_max < min_cc:
Logger.debug('Correlation of {0} is below threshold, not '
'using'.format(cc_max))
continue
cccsum += cc_max
phase = None
if stachan.channel[1][-1] in vertical_chans:
phase = 'P'
elif stachan.channel[1][-1] in horizontal_chans:
phase = 'S'
_waveform_id = WaveformStreamID(seed_string=tr.id)
event.picks.append(Pick(
waveform_id=_waveform_id, time=picktime,
method_id=ResourceIdentifier('EQcorrscan'), phase_hint=phase,
creation_info='eqcorrscan.core.lag_calc',
evaluation_mode='automatic',
comments=[Comment(text='cc_max={0}'.format(cc_max))]))
event.resource_id = ResourceIdentifier(detection_id)
event.comments.append(Comment(text="detect_val={0}".format(cccsum)))
# Add template-name as comment to events
event.comments.append(Comment(
text="Detected using template: {0}".format(family.template.name)))
if used_chans == detection.no_chans: # pragma: no cover
if detection.detect_val is not None and\
checksum - detection.detect_val < -(0.3 * detection.detect_val):
msg = ('lag-calc has decreased cccsum from %f to %f - '
% (detection.detect_val, checksum))
Logger.error(msg)
continue
else:
Logger.warning(
'Cannot check if cccsum is better, used {0} channels for '
'detection, but {1} are used here'.format(
detection.no_chans, used_chans))
picked_dict.update({detection_id: event})
if plot: # pragma: no cover
for i, event in enumerate(picked_dict.values()):
if len(event.picks) == 0:
continue
plot_stream = detect_streams[i].copy()
template_plot = family.template.st.copy()
pick_stachans = [(pick.waveform_id.station_code,
pick.waveform_id.channel_code)
for pick in event.picks]
for tr in plot_stream:
if (tr.stats.station, tr.stats.channel) \
not in pick_stachans:
plot_stream.remove(tr)
for tr in template_plot:
if (tr.stats.station, tr.stats.channel) \
not in pick_stachans:
template_plot.remove(tr)
if plotdir is not None:
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
savefile = "{plotdir}/{rid}.png".format(
plotdir=plotdir, rid=event.resource_id.id)
plot_repicked(template=template_plot, picks=event.picks,
det_stream=plot_stream, show=False, save=True,
savefile=savefile)
else:
plot_repicked(template=template_plot, picks=event.picks,
det_stream=plot_stream, show=True)
return picked_dict
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 _channel_loop(detection,
template,
min_cc,
detection_id,
interpolate,
i,
pre_lag_ccsum=None,
detect_chans=0,
horizontal_chans=['E', 'N', '1', '2'],
vertical_chans=['Z'],
debug=0):
"""
Inner loop for correlating and assigning picks.
Utility function to take a stream of data for the detected event and write
maximum correlation to absolute time as picks in an obspy.core.event.Event
object.
Only outputs picks for picks above min_cc.
:type detection: obspy.core.stream.Stream
:param detection:
Stream of data for the slave event detected using template.
:type template: obspy.core.stream.Stream
:param template: Stream of data as the template for the detection.
:type min_cc: float
:param min_cc: Minimum cross-correlation value to allow a pick to be made.
:type detection_id: str
:param detection_id: Detection ID to associate the event with.
:type interpolate: bool
:param interpolate:
Interpolate the correlation function to achieve sub-sample precision.
:type i: int
:param i:
Used to track which process has occurred when running in parallel.
:type pre_lag_ccsum: float
:param pre_lag_ccsum:
Cross-correlation sum before lag-calc, will check that the
cross-correlation sum is increased by lag-calc (using all channels,
ignoring min_cc)
:type detect_chans: int
:param detect_chans:
Number of channels originally used in detections, must match the number
used here to allow for cccsum checking.
:type horizontal_chans: list
:param horizontal_chans:
List of channel endings for horizontal-channels, on which S-picks will
be made.
:type vertical_chans: list
:param vertical_chans:
List of channel endings for vertical-channels, on which P-picks will
be made.
:type debug: int
:param debug: Debug output level 0-5.
:returns:
Event object containing network, station, channel and pick information.
:rtype: :class:`obspy.core.event.Event`
"""
from eqcorrscan.core.match_filter import normxcorr2
event = Event()
s_stachans = {}
cccsum = 0
checksum = 0
used_chans = 0
for tr in template:
temp_net = tr.stats.network
temp_sta = tr.stats.station
temp_chan = tr.stats.channel
debug_print('Working on: %s.%s.%s' % (temp_net, temp_sta, temp_chan),
3, debug)
image = detection.select(station=temp_sta, channel=temp_chan)
if len(image) == 0:
print('No match in image.')
continue
if interpolate:
try:
ccc = normxcorr2(tr.data, image[0].data)
except Exception:
print('Could not calculate cc')
print('Image is %i long' % len(image[0].data))
print('Template is %i long' % len(tr.data))
continue
try:
shift, cc_max = _xcorr_interp(ccc=ccc, dt=image[0].stats.delta)
except IndexError:
print('Could not interpolate ccc, not smooth')
ccc = normxcorr2(tr.data, image[0].data)
cc_max = np.amax(ccc)
shift = np.argmax(ccc) * image[0].stats.delta
# Convert the maximum cross-correlation time to an actual time
picktime = image[0].stats.starttime + shift
else:
# Convert the maximum cross-correlation time to an actual time
try:
ccc = normxcorr2(tr.data, image[0].data)
except Exception:
print('Could not calculate cc')
print('Image is %i long' % len(image[0].data))
print('Template is %i long' % len(tr.data))
continue
cc_max = np.amax(ccc)
picktime = image[0].stats.starttime + (np.argmax(ccc) *
image[0].stats.delta)
debug_print('Maximum cross-corr=%s' % cc_max, 3, debug)
checksum += cc_max
used_chans += 1
if cc_max < min_cc:
debug_print('Correlation below threshold, not used', 3, debug)
continue
cccsum += cc_max
# Perhaps weight each pick by the cc val or cc val^2?
# weight = np.amax(ccc) ** 2
if temp_chan[-1] in vertical_chans:
phase = 'P'
# Only take the S-pick with the best correlation
elif temp_chan[-1] in horizontal_chans:
phase = 'S'
debug_print(
'Making S-pick on: %s.%s.%s' % (temp_net, temp_sta, temp_chan),
4, debug)
if temp_sta not in s_stachans.keys():
s_stachans[temp_sta] = ((temp_chan, np.amax(ccc), picktime))
elif temp_sta in s_stachans.keys():
if np.amax(ccc) > s_stachans[temp_sta][1]:
picktime = picktime
else:
continue
else:
phase = None
_waveform_id = WaveformStreamID(network_code=temp_net,
station_code=temp_sta,
channel_code=temp_chan)
event.picks.append(
Pick(waveform_id=_waveform_id,
time=picktime,
method_id=ResourceIdentifier('EQcorrscan'),
phase_hint=phase,
creation_info='eqcorrscan.core.lag_calc',
comments=[Comment(text='cc_max=%s' % cc_max)]))
event.resource_id = detection_id
ccc_str = ("detect_val=%s" % cccsum)
event.comments.append(Comment(text=ccc_str))
if used_chans == detect_chans:
if pre_lag_ccsum is not None and\
checksum - pre_lag_ccsum < -(0.30 * pre_lag_ccsum):
msg = ('lag-calc has decreased cccsum from %f to %f - ' %
(pre_lag_ccsum, checksum))
# warnings.warn(msg)
raise LagCalcError(msg)
else:
warnings.warn('Cannot check if cccsum is better, used %i channels '
'for detection, but %i are used here' %
(detect_chans, used_chans))
return i, event
def _read_ndk(filename, *args, **kwargs): # @UnusedVariable
"""
Reads an NDK file to a :class:`~obspy.core.event.Catalog` object.
:param filename: File or file-like object in text mode.
"""
# Read the whole file at once. While an iterator would be more efficient
# the largest NDK file out in the wild is 13.7 MB so it does not matter
# much.
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 Exception:
try:
data = filename.decode()
except Exception:
data = str(filename)
data = data.strip()
else:
data = filename.read()
if hasattr(data, "decode"):
data = data.decode()
# Create iterator that yields lines.
def lines_iter():
prev_line = -1
while True:
next_line = data.find("\n", prev_line + 1)
if next_line < 0:
break
yield data[prev_line + 1:next_line]
prev_line = next_line
if len(data) > prev_line + 1:
yield data[prev_line + 1:]
# Use one Flinn Engdahl object for all region determinations.
fe = FlinnEngdahl()
cat = Catalog(resource_id=_get_resource_id("catalog", str(uuid.uuid4())))
# Loop over 5 lines at once.
for _i, lines in enumerate(zip_longest(*[lines_iter()] * 5)):
if None in lines:
msg = "Skipped last %i lines. Not a multiple of 5 lines." % (
lines.count(None))
warnings.warn(msg, ObsPyNDKWarning)
continue
# Parse the lines to a human readable dictionary.
try:
record = _read_lines(*lines)
except (ValueError, ObsPyNDKException):
exc = traceback.format_exc()
msg = ("Could not parse event %i (faulty file?). Will be "
"skipped. Lines of the event:\n"
"\t%s\n"
"%s") % (_i + 1, "\n\t".join(lines), exc)
warnings.warn(msg, ObsPyNDKWarning)
continue
# Use one creation info for essentially every item.
creation_info = CreationInfo(agency_id="GCMT",
version=record["version_code"])
# Use the ObsPy Flinn Engdahl region determiner as the region in the
# NDK files is oftentimes trimmed.
region = fe.get_region(record["centroid_longitude"],
record["centroid_latitude"])
# Create an event object.
event = Event(force_resource_id=False,
event_type="earthquake",
event_type_certainty="known",
event_descriptions=[
EventDescription(text=region,
type="Flinn-Engdahl region"),
EventDescription(text=record["cmt_event_name"],
type="earthquake name")
])
# Assemble the time for the reference origin.
try:
time = _parse_date_time(record["date"], record["time"])
except ObsPyNDKException:
msg = ("Invalid time in event %i. '%s' and '%s' cannot be "
"assembled to a valid time. Event will be skipped.") % \
(_i + 1, record["date"], record["time"])
warnings.warn(msg, ObsPyNDKWarning)
continue
# Create two origins, one with the reference latitude/longitude and
# one with the centroidal values.
ref_origin = Origin(
force_resource_id=False,
time=time,
longitude=record["hypo_lng"],
latitude=record["hypo_lat"],
# Convert to m.
depth=record["hypo_depth_in_km"] * 1000.0,
origin_type="hypocenter",
comments=[
Comment(text="Hypocenter catalog: %s" %
record["hypocenter_reference_catalog"],
force_resource_id=False)
])
ref_origin.comments[0].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="ref_origin")
ref_origin.resource_id = _get_resource_id(record["cmt_event_name"],
"origin",
tag="reforigin")
cmt_origin = Origin(
force_resource_id=False,
longitude=record["centroid_longitude"],
longitude_errors={
"uncertainty": record["centroid_longitude_error"]
},
latitude=record["centroid_latitude"],
latitude_errors={"uncertainty": record["centroid_latitude_error"]},
# Convert to m.
depth=record["centroid_depth_in_km"] * 1000.0,
depth_errors={
"uncertainty": record["centroid_depth_in_km_error"] * 1000
},
time=ref_origin["time"] + record["centroid_time"],
time_errors={"uncertainty": record["centroid_time_error"]},
depth_type=record["type_of_centroid_depth"],
origin_type="centroid",
time_fixed=False,
epicenter_fixed=False,
creation_info=creation_info.copy())
cmt_origin.resource_id = _get_resource_id(record["cmt_event_name"],
"origin",
tag="cmtorigin")
event.origins = [ref_origin, cmt_origin]
event.preferred_origin_id = cmt_origin.resource_id.id
# Create the magnitude object.
mag = Magnitude(force_resource_id=False,
mag=round(record["Mw"], 2),
magnitude_type="Mwc",
origin_id=cmt_origin.resource_id,
creation_info=creation_info.copy())
mag.resource_id = _get_resource_id(record["cmt_event_name"],
"magnitude",
tag="moment_mag")
event.magnitudes = [mag]
event.preferred_magnitude_id = mag.resource_id.id
# Add the reported mb, MS magnitudes as additional magnitude objects.
event.magnitudes.append(
Magnitude(
force_resource_id=False,
mag=record["mb"],
magnitude_type="mb",
comments=[
Comment(
force_resource_id=False,
text="Reported magnitude in NDK file. Most likely 'mb'."
)
]))
event.magnitudes[-1].comments[-1].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="mb_magnitude")
event.magnitudes[-1].resource_id = _get_resource_id(
record["cmt_event_name"], "magnitude", tag="mb")
event.magnitudes.append(
Magnitude(
force_resource_id=False,
mag=record["MS"],
magnitude_type="MS",
comments=[
Comment(
force_resource_id=False,
text="Reported magnitude in NDK file. Most likely 'MS'."
)
]))
event.magnitudes[-1].comments[-1].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="MS_magnitude")
event.magnitudes[-1].resource_id = _get_resource_id(
record["cmt_event_name"], "magnitude", tag="MS")
# Take care of the moment tensor.
tensor = Tensor(m_rr=record["m_rr"],
m_rr_errors={"uncertainty": record["m_rr_error"]},
m_pp=record["m_pp"],
m_pp_errors={"uncertainty": record["m_pp_error"]},
m_tt=record["m_tt"],
m_tt_errors={"uncertainty": record["m_tt_error"]},
m_rt=record["m_rt"],
m_rt_errors={"uncertainty": record["m_rt_error"]},
m_rp=record["m_rp"],
m_rp_errors={"uncertainty": record["m_rp_error"]},
m_tp=record["m_tp"],
m_tp_errors={"uncertainty": record["m_tp_error"]},
creation_info=creation_info.copy())
mt = MomentTensor(
force_resource_id=False,
scalar_moment=record["scalar_moment"],
tensor=tensor,
data_used=[DataUsed(**i) for i in record["data_used"]],
inversion_type=record["source_type"],
source_time_function=SourceTimeFunction(
type=record["moment_rate_type"],
duration=record["moment_rate_duration"]),
derived_origin_id=cmt_origin.resource_id,
creation_info=creation_info.copy())
mt.resource_id = _get_resource_id(record["cmt_event_name"],
"momenttensor")
axis = [Axis(**i) for i in record["principal_axis"]]
focmec = FocalMechanism(
force_resource_id=False,
moment_tensor=mt,
principal_axes=PrincipalAxes(
# The ordering is the same as for the IRIS SPUD service and
# from a website of the Saint Louis University Earthquake
# center so it should be correct.
t_axis=axis[0],
p_axis=axis[2],
n_axis=axis[1]),
nodal_planes=NodalPlanes(
nodal_plane_1=NodalPlane(**record["nodal_plane_1"]),
nodal_plane_2=NodalPlane(**record["nodal_plane_2"])),
comments=[
Comment(force_resource_id=False,
text="CMT Analysis Type: %s" %
record["cmt_type"].capitalize()),
Comment(force_resource_id=False,
text="CMT Timestamp: %s" % record["cmt_timestamp"])
],
creation_info=creation_info.copy())
focmec.comments[0].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="cmt_type")
focmec.comments[1].resource_id = _get_resource_id(
record["cmt_event_name"], "comment", tag="cmt_timestamp")
focmec.resource_id = _get_resource_id(record["cmt_event_name"],
"focal_mechanism")
event.focal_mechanisms = [focmec]
event.preferred_focal_mechanism_id = focmec.resource_id.id
# Set at end to avoid duplicate resource id warning.
event.resource_id = _get_resource_id(record["cmt_event_name"], "event")
cat.append(event)
if len(cat) == 0:
msg = "No valid events found in NDK file."
raise ObsPyNDKException(msg)
return cat
event=Event()
picks=Pick()
origin=Origin()
arrival=Arrival()
e_time=out_3[i][1::33]
sta = out_3[i][2::33]
cha = out_3[i][3::33]
phase = out_3[i][4::33]
pick_time = out_3[i][5::33] #phase pick time
lat = out_3[i][6::33]
lon = out_3[i][7::33]
orid=out_3[i][8::33]
dep = out_3[i][9::33]
ml=out_3[i][10::33]
for x, times in enumerate(pick_time):
event.resource_id=str(out_3[i][0]) #assign evid
rd = str(out_3[i][0])
picks = Pick(resource_id=rd, time=UTCDateTime(pick_time[x]),
waveform_id = WaveformStreamID(network_code="CI", station_code=str(sta[x]), channel_code=str(cha[x])), phase_hint=str(phase[x]))
origin = Origin(resource_id=(str(orid[x])),
time = UTCDateTime(e_time[x]),
longitude= str(lon[x]),
latitude=str(lat[x]),
depth=str(dep[x]))
magnitude = Magnitude(mag = ml[x], magnitude_type = "M", origin_id = (str(orid[x])))
arrival = Arrival(pick_id = rd, phase = str(phase[x]))
event.picks.append(picks)
event.origins.append(origin)
origin.arrivals.append(arrival)
event.magnitudes.append(magnitude)
db_catalog.append(event)
def detections_2_cat(detections, template_dict, stream, temp_prepick, max_lag, cc_thresh,
extract_pre_pick=3.0, extract_post_pick=7.0, write_wav=False, debug=0):
r"""Function to create a catalog from a list of detections, adjusting template pick \
times using cross correlation with data stream at the time of detection.
:type detections: list of DETECTION objects
:param detections: Detections which we want to extract and locate.
:type template_dict: dict
:param template_dict: Dictionary of template name: template stream for the entire \
catalog. Template names must be in the format found in the DETECTION objects.
:type stream: obspy.Stream
:param stream: stream encompassing time span of the detections. Will be used for pick \
refinement by cross correlation. Should be fed a stream processed in the same way \
as the streams in template dict (and in the same way that they were processed \
during matched filtering). The waveforms will not be processed here.
:type write_wav: bool or str
:param write_wav: If false, will not write detection waveforms to miniseed files. \
Otherwise, specify a directory to write the templates to. Will use name \
template_name_detection_time.mseed.
:returns: :class: obspy.Catalog
"""
from obspy import UTCDateTime, Catalog, Stream
from obspy.core.event import ResourceIdentifier, Event, Pick, CreationInfo, Comment, WaveformStreamID
from obspy.signal.cross_correlation import xcorr
from eqcorrscan.utils import plotting
#XXX TODO Scripts havent been saving the actual detection objects so we cannot make
#XXX TODO use of DETECTION.chans. Would be useful.
# Copy stream out of the way
st = stream.copy()
# Create nested dictionary of delays template_name: stachan: delay
# dict.items() works in both python 2 and 3 but is memory inefficient in 2 as both vars are
# read into memory as lists
delays = {}
for name, temp in template_dict.items():
sorted_temp = temp.sort(['starttime'])
stachans = [(tr.stats.station, tr.stats.channel, tr.stats.network)
for tr in sorted_temp]
mintime = sorted_temp[0].stats.starttime
delays[name] = {(tr.stats.station, tr.stats.channel): tr.stats.starttime - mintime
for tr in sorted_temp}
# Loop over all detections, saving each as a new event in a catalog
new_cat = Catalog()
for detection in detections:
if write_wav:
new_stream = Stream()
if hasattr(detection, 'event'):
new_event = detection.event
else:
rid = ResourceIdentifier(id=detection.template_name + '_' +\
detection.detect_time.strftime('%Y%m%dT%H%M%S.%f'),
prefix='smi:local')
new_event = Event(resource_id=rid)
cr_i = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
new_event.creation_info = cr_i
thresh_str = 'threshold=' + str(detection.threshold)
ccc_str = 'detect_val=' + str(detection.detect_val)
det_time_str = 'det_time=%s' % str(detection.detect_time)
if detection.chans:
used_chans = 'channels used: ' + \
' '.join([str(pair) for pair in detection.chans])
new_event.comments.append(Comment(text=used_chans))
new_event.comments.append(Comment(text=thresh_str))
new_event.comments.append(Comment(text=ccc_str))
new_event.comments.append(Comment(text=det_time_str))
template = template_dict[detection.template_name]
temp_len = template[0].stats.npts * template[0].stats.sampling_rate
if template.sort(['starttime'])[0].stats.starttime == detection.detect_time:
print('Template %s detected itself at %s.' % (detection.template_name, str(detection.detect_time)))
new_event.resource_id = ResourceIdentifier(id=detection.template_name + '_self',
prefix='smi:local')
if debug >= 2:
print('Plotting detection for template: %s' % detection.template_name)
plt_st = Stream([st.select(station=tr.stats.station,
channel=tr.stats.channel)[0].slice(detection.detect_time-extract_pre_pick,
detection.detect_time+extract_post_pick)
for tr in template if len(st.select(station=tr.stats.station,
channel=tr.stats.channel)) > 0])
plotting.detection_multiplot(plt_st, template, [detection.detect_time.datetime])
# Loop over each trace in the template, correcting picks for new event if need be
for tr in template:
sta = tr.stats.station
chan = tr.stats.channel
if len(st.select(station=sta, channel=chan)) != 0:
st_tr = st.select(station=sta, channel=chan)[0]
else:
print('No stream for %s: %s' % (sta, chan))
continue
st_tr_pick = detection.detect_time + delays[detection.template_name][(sta, chan)] + temp_prepick
i, absval, full_corr = xcorr(tr, st_tr.slice(st_tr_pick - temp_prepick,
st_tr_pick - temp_prepick + temp_len),
shift_len=max_lag, full_xcorr=True)
ccval = max(full_corr)
index = np.argmax(full_corr) - max_lag
pk_str = 'ccval=' + str(ccval)
if index == 0 or index == max_lag * 2:
msg = 'Correlation correction at max_lag. Consider increasing max_lag.'
warnings.warn(msg)
if debug >= 3:
print('Plotting full correlation function')
print('index: %d' % index)
print('max_ccval: %.2f' % ccval)
plt.plot(full_corr)
plt.show()
plt.close()
if ccval > cc_thresh:
print('Threshold exceeded at %s: %s' % (sta, chan))
pick_tm = st_tr_pick + (index / tr.stats.sampling_rate)
else:
print('Correlation at %s: %s not good enough to correct pick' % (sta, chan))
pick_tm = st_tr_pick
if tr.stats.channel[-1] in ['Z']:
phase_hint = 'P'
elif tr.stats.channel[-1] in ['N', 'E', '1', '2']:
phase_hint = 'S'
wv_id = WaveformStreamID(network_code=tr.stats.network,
station_code=tr.stats.station,
channel_code=tr.stats.channel)
new_event.picks.append(Pick(time=pick_tm, waveform_id=wv_id, phase_hint=phase_hint,
comments=[Comment(text=pk_str)]))
if write_wav:
new_stream.append(st_tr.slice(starttime=pick_tm - extract_pre_pick,
endtime=pick_tm + extract_post_pick))
# Append to new catalog
new_cat += new_event
if write_wav:
filename = '%s%s.mseed' % (write_wav, str(new_event.resource_id))
print('Writing new stream for detection to %s' % filename)
new_stream.write(filename, format='MSEED')
return new_cat