def _read_common_header(lines):
"""
Read given data into an :class:`~obspy.core.event.Event` object.
Parses the first few common header lines and sets creation time and some
other basic info.
:type lines: list
:param lines: List of decoded unicode strings with data from a FOCMEC out
file.
"""
event = Event()
# parse time.. too much bother to mess around with switching locales, so do
# it manually.. example:
# " Fri Sep 8 14:54:58 2017 for program Focmec"
month, day, time_of_day, year = lines[0].split()[1:5]
year = int(year)
day = int(day)
month = int(MONTHS[month.lower()])
hour, minute, second = [int(x) for x in time_of_day.split(':')]
event.creation_info = CreationInfo()
event.creation_info.creation_time = UTCDateTime(year, month, day, hour,
minute, second)
# get rid of those common lines already parsed
lines = lines[4:]
return event, lines
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 brightness(stations, nodes, lags, stream, threshold, thresh_type,
template_length, template_saveloc, coherence_thresh,
coherence_stations=['all'], coherence_clip=False,
gap=2.0, clip_level=100, instance=0, pre_pick=0.2,
plotsave=True, cores=1):
r"""Function to calculate the brightness function in terms of energy for \
a day of data over the entire network for a given grid of nodes.
Note data in stream must be all of the same length and have the same
sampling rates.
:type stations: list
:param stations: List of station names from in the form where stations[i] \
refers to nodes[i][:] and lags[i][:]
:type nodes: list, tuple
:param nodes: List of node points where nodes[i] referes to stations[i] \
and nodes[:][:][0] is latitude in degrees, nodes[:][:][1] is \
longitude in degrees, nodes[:][:][2] is depth in km.
:type lags: :class: 'numpy.array'
:param lags: Array of arrays where lags[i][:] refers to stations[i]. \
lags[i][j] should be the delay to the nodes[i][j] for stations[i] in \
seconds.
:type stream: :class: `obspy.Stream`
:param data: Data through which to look for detections.
:type threshold: float
:param threshold: Threshold value for detection of template within the \
brightness function
:type thresh_type: str
:param thresh_type: Either MAD or abs where MAD is the Median Absolute \
Deviation and abs is an absoulte brightness.
:type template_length: float
:param template_length: Length of template to extract in seconds
:type template_saveloc: str
:param template_saveloc: Path of where to save the templates.
:type coherence_thresh: tuple of floats
:param coherence_thresh: Threshold for removing incoherant peaks in the \
network response, those below this will not be used as templates. \
Must be in the form of (a,b) where the coherence is given by: \
a-kchan/b where kchan is the number of channels used to compute \
the coherence
:type coherence_stations: list
:param coherence_stations: List of stations to use in the coherance \
thresholding - defaults to 'all' which uses all the stations.
:type coherence_clip: float
:param coherence_clip: tuple
:type coherence_clip: Start and end in seconds of data to window around, \
defaults to False, which uses all the data given.
:type pre_pick: float
:param pre_pick: Seconds before the detection time to include in template
:type plotsave: bool
:param plotsave: Save or show plots, if False will try and show the plots \
on screen - as this is designed for bulk use this is set to \
True to save any plots rather than show them if you create \
them - changes the backend of matplotlib, so if is set to \
False you will see NO PLOTS!
:type cores: int
:param core: Number of cores to use, defaults to 1.
:type clip_level: float
:param clip_level: Multiplier applied to the mean deviation of the energy \
as an upper limit, used to remove spikes (earthquakes, \
lightning, electircal spikes) from the energy stack.
:type gap: float
:param gap: Minimum inter-event time in seconds for detections
:return: list of templates as :class: `obspy.Stream` objects
"""
from eqcorrscan.core.template_gen import _template_gen
if plotsave:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ioff()
# from joblib import Parallel, delayed
from multiprocessing import Pool, cpu_count
from copy import deepcopy
from obspy import read as obsread
from obspy.core.event import Catalog, Event, Pick, WaveformStreamID, Origin
from obspy.core.event import EventDescription, CreationInfo, Comment
import obspy.Stream
import matplotlib.pyplot as plt
from eqcorrscan.utils import plotting
# Check that we actually have the correct stations
realstations = []
for station in stations:
st = stream.select(station=station)
if st:
realstations += station
del st
stream_copy = stream.copy()
# Force convert to int16
for tr in stream_copy:
# int16 max range is +/- 32767
if max(abs(tr.data)) > 32767:
tr.data = 32767 * (tr.data / max(abs(tr.data)))
# Make sure that the data aren't clipped it they are high gain
# scale the data
tr.data = tr.data.astype(np.int16)
# The internal _node_loop converts energy to int16 too to converse memory,
# to do this it forces the maximum of a single energy trace to be 500 and
# normalises to this level - this only works for fewer than 65 channels of
# data
if len(stream_copy) > 130:
raise OverflowError('Too many streams, either re-code and cope with' +
'either more memory usage, or less precision, or' +
'reduce data volume')
detections = []
detect_lags = []
parallel = True
plotvar = True
mem_issue = False
# Loop through each node in the input
# Linear run
print('Computing the energy stacks')
if not parallel:
for i in range(0, len(nodes)):
print(i)
if not mem_issue:
j, a = _node_loop(stations, lags[:, i], stream, plot=True)
if 'energy' not in locals():
energy = a
else:
energy = np.concatenate((energy, a), axis=0)
print('energy: ' + str(np.shape(energy)))
else:
j, filename = _node_loop(stations, lags[:, i], stream, i,
mem_issue)
energy = np.array(energy)
print(np.shape(energy))
else:
# Parallel run
num_cores = cores
if num_cores > len(nodes):
num_cores = len(nodes)
if num_cores > cpu_count():
num_cores = cpu_count()
pool = Pool(processes=num_cores)
results = [pool.apply_async(_node_loop, args=(stations, lags[:, i],
stream, i, clip_level,
mem_issue, instance))
for i in range(len(nodes))]
pool.close()
if not mem_issue:
print('Computing the cumulative network response from memory')
energy = [p.get() for p in results]
pool.join()
energy.sort(key=lambda tup: tup[0])
energy = [node[1] for node in energy]
energy = np.concatenate(energy, axis=0)
print(energy.shape)
else:
pool.join()
# Now compute the cumulative network response and then detect possible
# events
if not mem_issue:
print(energy.shape)
indeces = np.argmax(energy, axis=0) # Indeces of maximum energy
print(indeces.shape)
cum_net_resp = np.array([np.nan] * len(indeces))
cum_net_resp[0] = energy[indeces[0]][0]
peak_nodes = [nodes[indeces[0]]]
for i in range(1, len(indeces)):
cum_net_resp[i] = energy[indeces[i]][i]
peak_nodes.append(nodes[indeces[i]])
del energy, indeces
else:
print('Reading the temp files and computing network response')
node_splits = int(len(nodes) // num_cores)
indeces = [range(node_splits)]
for i in range(1, num_cores - 1):
indeces.append(range(node_splits * i, node_splits * (i + 1)))
indeces.append(range(node_splits * (i + 1), len(nodes)))
pool = Pool(processes=num_cores)
results = [pool.apply_async(_cum_net_resp, args=(indeces[i], instance))
for i in range(num_cores)]
pool.close()
results = [p.get() for p in results]
pool.join()
responses = [result[0] for result in results]
print(np.shape(responses))
node_indeces = [result[1] for result in results]
cum_net_resp = np.array(responses)
indeces = np.argmax(cum_net_resp, axis=0)
print(indeces.shape)
print(cum_net_resp.shape)
cum_net_resp = np.array([cum_net_resp[indeces[i]][i]
for i in range(len(indeces))])
peak_nodes = [nodes[node_indeces[indeces[i]][i]]
for i in range(len(indeces))]
del indeces, node_indeces
if plotvar:
cum_net_trace = deepcopy(stream[0])
cum_net_trace.data = cum_net_resp
cum_net_trace.stats.station = 'NR'
cum_net_trace.stats.channel = ''
cum_net_trace.stats.network = 'Z'
cum_net_trace.stats.location = ''
cum_net_trace.stats.starttime = stream[0].stats.starttime
cum_net_trace = obspy.Stream(cum_net_trace)
cum_net_trace += stream.select(channel='*N')
cum_net_trace += stream.select(channel='*1')
cum_net_trace.sort(['network', 'station', 'channel'])
# np.save('cum_net_resp.npy',cum_net_resp)
# cum_net_trace.plot(size=(800,600), equal_scale=False,\
# outfile='NR_timeseries.eps')
# Find detection within this network response
print('Finding detections in the cumulatve network response')
detections = _find_detections(cum_net_resp, peak_nodes, threshold,
thresh_type, stream[0].stats.sampling_rate,
realstations, gap)
del cum_net_resp
templates = []
nodesout = []
good_detections = []
if detections:
print('Converting detections in to templates')
# Generate a catalog of detections
detections_cat = Catalog()
for j, detection in enumerate(detections):
print('Converting for detection ' + str(j) + ' of ' +
str(len(detections)))
# Create an event for each detection
event = Event()
# Set up some header info for the event
event.event_descriptions.append(EventDescription())
event.event_descriptions[0].text = 'Brightness detection'
event.creation_info = CreationInfo(agency_id='EQcorrscan')
copy_of_stream = deepcopy(stream_copy)
# Convert detections to obspy.core.event type -
# name of detection template is the node.
node = (detection.template_name.split('_')[0],
detection.template_name.split('_')[1],
detection.template_name.split('_')[2])
print(node)
# Look up node in nodes and find the associated lags
index = nodes.index(node)
detect_lags = lags[:, index]
ksta = Comment(text='Number of stations=' + len(detect_lags))
event.origins.append(Origin())
event.origins[0].comments.append(ksta)
event.origins[0].time = copy_of_stream[0].stats.starttime +\
detect_lags[0] + detection.detect_time
event.origins[0].latitude = node[0]
event.origins[0].longitude = node[1]
event.origins[0].depth = node[2]
for i, detect_lag in enumerate(detect_lags):
station = stations[i]
st = copy_of_stream.select(station=station)
if len(st) != 0:
for tr in st:
_waveform_id = WaveformStreamID(station_code=tr.stats.
station,
channel_code=tr.stats.
channel,
network_code='NA')
event.picks.append(Pick(waveform_id=_waveform_id,
time=tr.stats.starttime +
detect_lag +
detection.detect_time +
pre_pick,
onset='emergent',
evalutation_mode='automatic'))
print('Generating template for detection: ' + str(j))
template = (_template_gen(event.picks, copy_of_stream,
template_length, 'all'))
template_name = template_saveloc + '/' +\
str(template[0].stats.starttime) + '.ms'
# In the interests of RAM conservation we write then read
# Check coherancy here!
temp_coher, kchan = coherence(template, coherence_stations,
coherence_clip)
coh_thresh = float(coherence_thresh[0]) - kchan / \
float(coherence_thresh[1])
if temp_coher > coh_thresh:
template.write(template_name, format="MSEED")
print('Written template as: ' + template_name)
print('---------------------------------coherence LEVEL: ' +
str(temp_coher))
coherant = True
else:
print('Template was incoherant, coherence level: ' +
str(temp_coher))
coherant = False
del copy_of_stream, tr, template
if coherant:
templates.append(obsread(template_name))
nodesout += [node]
good_detections.append(detection)
else:
print('No template for you')
if plotvar:
all_detections = [(cum_net_trace[-1].stats.starttime +
detection.detect_time).datetime
for detection in detections]
good_detections = [(cum_net_trace[-1].stats.starttime +
detection.detect_time).datetime
for detection in good_detections]
if not plotsave:
plotting.NR_plot(cum_net_trace[0:-1],
obspy.Stream(cum_net_trace[-1]),
detections=good_detections,
size=(18.5, 10),
title='Network response')
# cum_net_trace.plot(size=(800,600), equal_scale=False)
else:
savefile = 'plots/' +\
cum_net_trace[0].stats.starttime.datetime.strftime('%Y%m%d') +\
'_NR_timeseries.pdf'
plotting.NR_plot(cum_net_trace[0:-1],
obspy.Stream(cum_net_trace[-1]),
detections=good_detections,
size=(18.5, 10), save=savefile,
title='Network response')
nodesout = list(set(nodesout))
return templates, nodesout
def cross_net(stream, env=False, debug=0, master=False):
"""
Generate picks using a simple envelope cross-correlation.
Picks are made for each channel based on optimal moveout \
defined by maximum cross-correlation with master trace. Master trace \
will be the first trace in the stream.
:type stream: :class: obspy.Stream
:param stream: Stream to pick
:type env: bool
:param env: To compute cross-correlations on the envelope or not.
:type debug: int
:param debug: Debug level from 0-5
:type master: obspy.Trace
:param master: Trace to use as master, if False, will use the first trace \
in stream.
:returns: obspy.core.event.Event
.. rubric:: Example
>>> from obspy import read
>>> from eqcorrscan.utils.picker import cross_net
>>> st = read()
>>> event = cross_net(st, env=True)
>>> event.creation_info.author
'EQcorrscan'
"""
from obspy.signal.cross_correlation import xcorr
from obspy.signal.filter import envelope
from obspy import UTCDateTime
from obspy.core.event import Event, Pick, WaveformStreamID
from obspy.core.event import CreationInfo, Comment, Origin
import matplotlib.pyplot as plt
import numpy as np
event = Event()
event.origins.append(Origin())
event.creation_info = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
event.comments.append(Comment(text='cross_net'))
samp_rate = stream[0].stats.sampling_rate
if not env:
if debug > 2:
print('Using the raw data')
st = stream.copy()
st.resample(samp_rate)
else:
st = stream.copy()
if debug > 2:
print('Computing envelope')
for tr in st:
tr.resample(samp_rate)
tr.data = envelope(tr.data)
if debug > 2:
st.plot(equal_scale=False, size=(800, 600))
if not master:
master = st[0]
else:
master = master
master.data = np.nan_to_num(master.data)
for i, tr in enumerate(st):
tr.data = np.nan_to_num(tr.data)
if debug > 2:
msg = ' '.join(['Comparing', tr.stats.station, tr.stats.channel,
'with the master'])
print(msg)
shift_len = int(0.3 * len(tr))
if debug > 2:
print('Shift length is set to ' + str(shift_len) + ' samples')
if debug > 3:
index, cc, cc_vec = xcorr(master, tr, shift_len, full_xcorr=True)
cc_vec = np.nan_to_num(cc_vec)
if debug > 4:
print(cc_vec)
fig = plt.figure()
ax1 = fig.add_subplot(211)
x = np.linspace(0, len(master) / samp_rate,
len(master))
ax1.plot(x, master.data / float(master.data.max()), 'k',
label='Master')
ax1.plot(x + (index / samp_rate), tr.data / float(tr.data.max()),
'r', label='Slave shifted')
ax1.legend(loc="lower right", prop={'size': "small"})
ax1.set_xlabel("time [s]")
ax1.set_ylabel("norm. amplitude")
ax2 = fig.add_subplot(212)
print(len(cc_vec))
x = np.linspace(0, len(cc_vec) / samp_rate, len(cc_vec))
ax2.plot(x, cc_vec, label='xcorr')
# ax2.set_ylim(-1, 1)
# ax2.set_xlim(0, len(master))
plt.show()
index, cc = xcorr(master, tr, shift_len)
wav_id = WaveformStreamID(station_code=tr.stats.station,
channel_code=tr.stats.channel,
network_code=tr.stats.network)
event.picks.append(Pick(time=tr.stats.starttime + (index / tr.stats.sampling_rate),
waveform_id=wav_id,
phase_hint='S',
onset='emergent'))
if debug > 2:
print(event.picks[i])
event.origins[0].time = min([pick.time for pick in event.picks]) - 1
event.origins[0].latitude = float('nan')
event.origins[0].longitude = float('nan')
# Set arbitrary origin time
del st
return event
def _read_single_hypocenter(lines, coordinate_converter, original_picks):
"""
Given a list of lines (starting with a 'NLLOC' line and ending with a
'END_NLLOC' line), parse them into an Event.
"""
try:
# some paranoid checks..
assert lines[0].startswith("NLLOC ")
assert lines[-1].startswith("END_NLLOC")
for line in lines[1:-1]:
assert not line.startswith("NLLOC ")
assert not line.startswith("END_NLLOC")
except Exception:
msg = ("This should not have happened, please report this as a bug at "
"https://github.com/obspy/obspy/issues.")
raise Exception(msg)
indices_phases = [None, None]
for i, line in enumerate(lines):
if line.startswith("PHASE "):
indices_phases[0] = i
elif line.startswith("END_PHASE"):
indices_phases[1] = i
# extract PHASES lines (if any)
if any(indices_phases):
if not all(indices_phases):
msg = ("NLLOC HYP file seems corrupt, 'PHASE' block is corrupt.")
raise RuntimeError(msg)
i1, i2 = indices_phases
lines, phases_lines = lines[:i1] + lines[i2 + 1:], lines[i1 + 1:i2]
else:
phases_lines = []
lines = dict([line.split(None, 1) for line in lines[:-1]])
line = lines["SIGNATURE"]
line = line.rstrip().split('"')[1]
signature, version, date, time = line.rsplit(" ", 3)
# new NLLoc > 6.0 seems to add prefix 'run:' before date
if date.startswith('run:'):
date = date[4:]
signature = signature.strip()
creation_time = UTCDateTime.strptime(date + time, str("%d%b%Y%Hh%Mm%S"))
if coordinate_converter:
# maximum likelihood origin location in km info line
line = lines["HYPOCENTER"]
x, y, z = coordinate_converter(*map(float, line.split()[1:7:2]))
else:
# maximum likelihood origin location lon lat info line
line = lines["GEOGRAPHIC"]
y, x, z = map(float, line.split()[8:13:2])
# maximum likelihood origin time info line
line = lines["GEOGRAPHIC"]
year, mon, day, hour, min = map(int, line.split()[1:6])
seconds = float(line.split()[6])
time = UTCDateTime(year, mon, day, hour, min, seconds, strict=False)
# distribution statistics line
line = lines["STATISTICS"]
covariance_xx = float(line.split()[7])
covariance_yy = float(line.split()[13])
covariance_zz = float(line.split()[17])
stats_info_string = str(
"Note: Depth/Latitude/Longitude errors are calculated from covariance "
"matrix as 1D marginal (Lon/Lat errors as great circle degrees) "
"while OriginUncertainty min/max horizontal errors are calculated "
"from 2D error ellipsoid and are therefore seemingly higher compared "
"to 1D errors. Error estimates can be reconstructed from the "
"following original NonLinLoc error statistics line:\nSTATISTICS " +
lines["STATISTICS"])
# goto location quality info line
line = lines["QML_OriginQuality"].split()
(assoc_phase_count, used_phase_count, assoc_station_count,
used_station_count, depth_phase_count) = map(int, line[1:11:2])
stderr, az_gap, sec_az_gap = map(float, line[11:17:2])
gt_level = line[17]
min_dist, max_dist, med_dist = map(float, line[19:25:2])
# goto location quality info line
line = lines["QML_OriginUncertainty"]
if "COMMENT" in lines:
comment = lines["COMMENT"].strip()
comment = comment.strip('\'"')
comment = comment.strip()
hor_unc, min_hor_unc, max_hor_unc, hor_unc_azim = \
map(float, line.split()[1:9:2])
# assign origin info
event = Event()
o = Origin()
event.origins = [o]
event.preferred_origin_id = o.resource_id
o.origin_uncertainty = OriginUncertainty()
o.quality = OriginQuality()
ou = o.origin_uncertainty
oq = o.quality
o.comments.append(Comment(text=stats_info_string, force_resource_id=False))
event.comments.append(Comment(text=comment, force_resource_id=False))
# SIGNATURE field's first item is LOCSIG, which is supposed to be
# 'Identification of an individual, institiution or other entity'
# according to
# http://alomax.free.fr/nlloc/soft6.00/control.html#_NLLoc_locsig_
# so use it as author in creation info
event.creation_info = CreationInfo(creation_time=creation_time,
version=version,
author=signature)
o.creation_info = CreationInfo(creation_time=creation_time,
version=version,
author=signature)
# negative values can appear on diagonal of covariance matrix due to a
# precision problem in NLLoc implementation when location coordinates are
# large compared to the covariances.
o.longitude = x
try:
o.longitude_errors.uncertainty = kilometer2degrees(sqrt(covariance_xx))
except ValueError:
if covariance_xx < 0:
msg = ("Negative value in XX value of covariance matrix, not "
"setting longitude error (epicentral uncertainties will "
"still be set in origin uncertainty).")
warnings.warn(msg)
else:
raise
o.latitude = y
try:
o.latitude_errors.uncertainty = kilometer2degrees(sqrt(covariance_yy))
except ValueError:
if covariance_yy < 0:
msg = ("Negative value in YY value of covariance matrix, not "
"setting longitude error (epicentral uncertainties will "
"still be set in origin uncertainty).")
warnings.warn(msg)
else:
raise
o.depth = z * 1e3 # meters!
o.depth_errors.uncertainty = sqrt(covariance_zz) * 1e3 # meters!
o.depth_errors.confidence_level = 68
o.depth_type = str("from location")
o.time = time
ou.horizontal_uncertainty = hor_unc
ou.min_horizontal_uncertainty = min_hor_unc
ou.max_horizontal_uncertainty = max_hor_unc
# values of -1 seem to be used for unset values, set to None
for field in ("horizontal_uncertainty", "min_horizontal_uncertainty",
"max_horizontal_uncertainty"):
if ou.get(field, -1) == -1:
ou[field] = None
else:
ou[field] *= 1e3 # meters!
ou.azimuth_max_horizontal_uncertainty = hor_unc_azim
ou.preferred_description = str("uncertainty ellipse")
ou.confidence_level = 68 # NonLinLoc in general uses 1-sigma (68%) level
oq.standard_error = stderr
oq.azimuthal_gap = az_gap
oq.secondary_azimuthal_gap = sec_az_gap
oq.used_phase_count = used_phase_count
oq.used_station_count = used_station_count
oq.associated_phase_count = assoc_phase_count
oq.associated_station_count = assoc_station_count
oq.depth_phase_count = depth_phase_count
oq.ground_truth_level = gt_level
oq.minimum_distance = kilometer2degrees(min_dist)
oq.maximum_distance = kilometer2degrees(max_dist)
oq.median_distance = kilometer2degrees(med_dist)
# go through all phase info lines
for line in phases_lines:
line = line.split()
arrival = Arrival()
o.arrivals.append(arrival)
station = str(line[0])
phase = str(line[4])
arrival.phase = phase
arrival.distance = kilometer2degrees(float(line[21]))
arrival.azimuth = float(line[23])
arrival.takeoff_angle = float(line[24])
arrival.time_residual = float(line[16])
arrival.time_weight = float(line[17])
pick = Pick()
# network codes are not used by NonLinLoc, so they can not be known
# when reading the .hyp file.. to conform with QuakeML standard set an
# empty network code
wid = WaveformStreamID(network_code="", station_code=station)
# have to split this into ints for overflow to work correctly
date, hourmin, sec = map(str, line[6:9])
ymd = [int(date[:4]), int(date[4:6]), int(date[6:8])]
hm = [int(hourmin[:2]), int(hourmin[2:4])]
t = UTCDateTime(*(ymd + hm), strict=False) + float(sec)
pick.waveform_id = wid
pick.time = t
pick.time_errors.uncertainty = float(line[10])
pick.phase_hint = phase
pick.onset = ONSETS.get(line[3].lower(), None)
pick.polarity = POLARITIES.get(line[5].lower(), None)
# try to determine original pick for each arrival
for pick_ in original_picks:
wid = pick_.waveform_id
if station == wid.station_code and phase == pick_.phase_hint:
pick = pick_
break
else:
# warn if original picks were specified and we could not associate
# the arrival correctly
if original_picks:
msg = ("Could not determine corresponding original pick for "
"arrival. "
"Falling back to pick information in NonLinLoc "
"hypocenter-phase file.")
warnings.warn(msg)
event.picks.append(pick)
arrival.pick_id = pick.resource_id
event.scope_resource_ids()
return event
def read_nlloc_hyp(filename, coordinate_converter=None, picks=None, **kwargs):
"""
Reads a NonLinLoc Hypocenter-Phase file to a
:class:`~obspy.core.event.Catalog` object.
.. note::
Coordinate conversion from coordinate frame of NonLinLoc model files /
location run to WGS84 has to be specified explicitly by the user if
necessary.
.. note::
An example can be found on the :mod:`~obspy.io.nlloc` submodule front
page in the documentation pages.
:param filename: File or file-like object in text mode.
:type coordinate_converter: func
:param coordinate_converter: Function to convert (x, y, z)
coordinates of NonLinLoc output to geographical coordinates and depth
in meters (longitude, latitude, depth in kilometers).
If left ``None``, NonLinLoc (x, y, z) output is left unchanged (e.g. if
it is in geographical coordinates already like for NonLinLoc in
global mode).
The function should accept three arguments x, y, z (each of type
:class:`numpy.ndarray`) and return a tuple of three
:class:`numpy.ndarray` (lon, lat, depth in kilometers).
:type picks: list of :class:`~obspy.core.event.Pick`
:param picks: Original picks used to generate the NonLinLoc location.
If provided, the output event will include the original picks and the
arrivals in the output origin will link to them correctly (with their
``pick_id`` attribute). If not provided, the output event will include
(the rather basic) pick information that can be reconstructed from the
NonLinLoc hypocenter-phase file.
:rtype: :class:`~obspy.core.event.Catalog`
"""
if not hasattr(filename, "read"):
# Check if it exists, otherwise assume its a string.
try:
with open(filename, "rt") as fh:
data = fh.read()
except:
try:
data = filename.decode()
except:
data = str(filename)
data = data.strip()
else:
data = filename.read()
if hasattr(data, "decode"):
data = data.decode()
lines = data.splitlines()
# remember picks originally used in location, if provided
original_picks = picks
if original_picks is None:
original_picks = []
# determine indices of block start/end of the NLLOC output file
indices_hyp = [None, None]
indices_phases = [None, None]
for i, line in enumerate(lines):
if line.startswith("NLLOC "):
indices_hyp[0] = i
elif line.startswith("END_NLLOC"):
indices_hyp[1] = i
elif line.startswith("PHASE "):
indices_phases[0] = i
elif line.startswith("END_PHASE"):
indices_phases[1] = i
if any([i is None for i in indices_hyp]):
msg = ("NLLOC HYP file seems corrupt,"
" could not detect 'NLLOC' and 'END_NLLOC' lines.")
raise RuntimeError(msg)
# strip any other lines around NLLOC block
lines = lines[indices_hyp[0]:indices_hyp[1]]
# extract PHASES lines (if any)
if any(indices_phases):
if not all(indices_phases):
msg = ("NLLOC HYP file seems corrupt, 'PHASE' block is corrupt.")
raise RuntimeError(msg)
i1, i2 = indices_phases
lines, phases_lines = lines[:i1] + lines[i2 + 1:], lines[i1 + 1:i2]
else:
phases_lines = []
lines = dict([line.split(None, 1) for line in lines])
line = lines["SIGNATURE"]
line = line.rstrip().split('"')[1]
signature, version, date, time = line.rsplit(" ", 3)
creation_time = UTCDateTime().strptime(date + time, str("%d%b%Y%Hh%Mm%S"))
# maximum likelihood origin location info line
line = lines["HYPOCENTER"]
x, y, z = map(float, line.split()[1:7:2])
if coordinate_converter:
x, y, z = coordinate_converter(x, y, z)
# origin time info line
line = lines["GEOGRAPHIC"]
year, month, day, hour, minute = map(int, line.split()[1:6])
seconds = float(line.split()[6])
time = UTCDateTime(year, month, day, hour, minute, seconds)
# distribution statistics line
line = lines["STATISTICS"]
covariance_xx = float(line.split()[7])
covariance_yy = float(line.split()[13])
covariance_zz = float(line.split()[17])
stats_info_string = str(
"Note: Depth/Latitude/Longitude errors are calculated from covariance "
"matrix as 1D marginal (Lon/Lat errors as great circle degrees) "
"while OriginUncertainty min/max horizontal errors are calculated "
"from 2D error ellipsoid and are therefore seemingly higher compared "
"to 1D errors. Error estimates can be reconstructed from the "
"following original NonLinLoc error statistics line:\nSTATISTICS " +
lines["STATISTICS"])
# goto location quality info line
line = lines["QML_OriginQuality"].split()
(assoc_phase_count, used_phase_count, assoc_station_count,
used_station_count, depth_phase_count) = map(int, line[1:11:2])
stderr, az_gap, sec_az_gap = map(float, line[11:17:2])
gt_level = line[17]
min_dist, max_dist, med_dist = map(float, line[19:25:2])
# goto location quality info line
line = lines["QML_OriginUncertainty"]
hor_unc, min_hor_unc, max_hor_unc, hor_unc_azim = \
map(float, line.split()[1:9:2])
# assign origin info
event = Event()
cat = Catalog(events=[event])
o = Origin()
event.origins = [o]
o.origin_uncertainty = OriginUncertainty()
o.quality = OriginQuality()
ou = o.origin_uncertainty
oq = o.quality
o.comments.append(Comment(text=stats_info_string))
cat.creation_info.creation_time = UTCDateTime()
cat.creation_info.version = "ObsPy %s" % __version__
event.creation_info = CreationInfo(creation_time=creation_time,
version=version)
event.creation_info.version = version
o.creation_info = CreationInfo(creation_time=creation_time,
version=version)
# negative values can appear on diagonal of covariance matrix due to a
# precision problem in NLLoc implementation when location coordinates are
# large compared to the covariances.
o.longitude = x
try:
o.longitude_errors.uncertainty = kilometer2degrees(sqrt(covariance_xx))
except ValueError:
if covariance_xx < 0:
msg = ("Negative value in XX value of covariance matrix, not "
"setting longitude error (epicentral uncertainties will "
"still be set in origin uncertainty).")
warnings.warn(msg)
else:
raise
o.latitude = y
try:
o.latitude_errors.uncertainty = kilometer2degrees(sqrt(covariance_yy))
except ValueError:
if covariance_yy < 0:
msg = ("Negative value in YY value of covariance matrix, not "
"setting longitude error (epicentral uncertainties will "
"still be set in origin uncertainty).")
warnings.warn(msg)
else:
raise
o.depth = z * 1e3 # meters!
o.depth_errors.uncertainty = sqrt(covariance_zz) * 1e3 # meters!
o.depth_errors.confidence_level = 68
o.depth_type = str("from location")
o.time = time
ou.horizontal_uncertainty = hor_unc
ou.min_horizontal_uncertainty = min_hor_unc
ou.max_horizontal_uncertainty = max_hor_unc
# values of -1 seem to be used for unset values, set to None
for field in ("horizontal_uncertainty", "min_horizontal_uncertainty",
"max_horizontal_uncertainty"):
if ou.get(field, -1) == -1:
ou[field] = None
else:
ou[field] *= 1e3 # meters!
ou.azimuth_max_horizontal_uncertainty = hor_unc_azim
ou.preferred_description = str("uncertainty ellipse")
ou.confidence_level = 68 # NonLinLoc in general uses 1-sigma (68%) level
oq.standard_error = stderr
oq.azimuthal_gap = az_gap
oq.secondary_azimuthal_gap = sec_az_gap
oq.used_phase_count = used_phase_count
oq.used_station_count = used_station_count
oq.associated_phase_count = assoc_phase_count
oq.associated_station_count = assoc_station_count
oq.depth_phase_count = depth_phase_count
oq.ground_truth_level = gt_level
oq.minimum_distance = kilometer2degrees(min_dist)
oq.maximum_distance = kilometer2degrees(max_dist)
oq.median_distance = kilometer2degrees(med_dist)
# go through all phase info lines
for line in phases_lines:
line = line.split()
arrival = Arrival()
o.arrivals.append(arrival)
station = str(line[0])
phase = str(line[4])
arrival.phase = phase
arrival.distance = kilometer2degrees(float(line[21]))
arrival.azimuth = float(line[23])
arrival.takeoff_angle = float(line[24])
arrival.time_residual = float(line[16])
arrival.time_weight = float(line[17])
pick = Pick()
wid = WaveformStreamID(station_code=station)
date, hourmin, sec = map(str, line[6:9])
t = UTCDateTime().strptime(date + hourmin, "%Y%m%d%H%M") + float(sec)
pick.waveform_id = wid
pick.time = t
pick.time_errors.uncertainty = float(line[10])
pick.phase_hint = phase
pick.onset = ONSETS.get(line[3].lower(), None)
pick.polarity = POLARITIES.get(line[5].lower(), None)
# try to determine original pick for each arrival
for pick_ in original_picks:
wid = pick_.waveform_id
if station == wid.station_code and phase == pick_.phase_hint:
pick = pick_
break
else:
# warn if original picks were specified and we could not associate
# the arrival correctly
if original_picks:
msg = ("Could not determine corresponding original pick for "
"arrival. "
"Falling back to pick information in NonLinLoc "
"hypocenter-phase file.")
warnings.warn(msg)
event.picks.append(pick)
arrival.pick_id = pick.resource_id
return cat
def 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
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 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
def _readheader(f):
"""
Internal header reader.
:type f: file
:param f: File open in read-mode.
:returns: :class:`~obspy.core.event.event.Event`
"""
f.seek(0)
# Base populate to allow for empty parts of file
new_event = Event()
topline = _get_headline(f=f)
if not topline:
raise NordicParsingError('No header found, or incorrect '
'formatting: corrupt s-file')
try:
sfile_seconds = int(topline[16:18])
if sfile_seconds == 60:
sfile_seconds = 0
add_seconds = 60
else:
add_seconds = 0
new_event.origins.append(Origin())
new_event.origins[0].time = UTCDateTime(int(topline[1:5]),
int(topline[6:8]),
int(topline[8:10]),
int(topline[11:13]),
int(topline[13:15]),
sfile_seconds,
int(topline[19:20]) *
100000)\
+ add_seconds
except Exception:
NordicParsingError("Couldn't read a date from sfile")
# new_event.loc_mod_ind=topline[20]
new_event.event_descriptions.append(EventDescription())
new_event.event_descriptions[0].text = topline[21:23]
# new_event.ev_id=topline[22]
try:
new_event.origins[0].latitude = float(topline[23:30])
new_event.origins[0].longitude = float(topline[31:38])
new_event.origins[0].depth = float(topline[39:43]) * 1000
except ValueError:
# The origin 'requires' a lat & long
new_event.origins[0].latitude = None
new_event.origins[0].longitude = None
new_event.origins[0].depth = None
# new_event.depth_ind = topline[44]
# new_event.loc_ind = topline[45]
new_event.creation_info = CreationInfo(agency_id=topline[45:48].strip())
ksta = Comment(text='Number of stations=' + topline[49:51].strip())
new_event.origins[0].comments.append(ksta)
if _float_conv(topline[51:55]) is not None:
new_event.origins[0].quality = OriginQuality(
standard_error=_float_conv(topline[51:55]))
# Read in magnitudes if they are there.
for index in [59, 67, 75]:
if not topline[index].isspace():
new_event.magnitudes.append(Magnitude())
new_event.magnitudes[-1].mag = _float_conv(topline[index -
3:index])
new_event.magnitudes[-1].magnitude_type = \
_nortoevmag(topline[index])
new_event.magnitudes[-1].creation_info = \
CreationInfo(agency_id=topline[index + 1:index + 4].strip())
new_event.magnitudes[-1].origin_id = new_event.origins[0].\
resource_id
# Set the useful things like preferred magnitude and preferred origin
new_event.preferred_origin_id = new_event.origins[0].resource_id
try:
# Select moment first, then local, then
mag_filter = [
'MW', 'Mw', 'ML', 'Ml', 'MB', 'Mb', 'MS', 'Ms', 'MC', 'Mc'
]
_magnitudes = [(m.magnitude_type, m.resource_id)
for m in new_event.magnitudes]
preferred_magnitude = sorted(_magnitudes,
key=lambda x: mag_filter.index(x[0]))[0]
new_event.preferred_magnitude_id = preferred_magnitude[1]
except (ValueError, IndexError):
# If there is a magnitude not specified in filter
try:
new_event.preferred_magnitude_id = new_event.magnitudes[0].\
resource_id
except IndexError:
pass
return new_event
def full_test_event():
"""
Function to generate a basic, full test event
"""
test_event = Event()
test_event.origins.append(Origin())
test_event.origins[0].time = UTCDateTime("2012-03-26") + 1.2
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.origins[0].latitude = 45.0
test_event.origins[0].longitude = 25.0
test_event.origins[0].depth = 15000
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.origins[0].quality = OriginQuality(standard_error=0.01)
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[0].mag = 0.1
test_event.magnitudes[0].magnitude_type = 'ML'
test_event.magnitudes[0].creation_info = CreationInfo('TES')
test_event.magnitudes[0].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[1].mag = 0.5
test_event.magnitudes[1].magnitude_type = 'Mc'
test_event.magnitudes[1].creation_info = CreationInfo('TES')
test_event.magnitudes[1].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[2].mag = 1.3
test_event.magnitudes[2].magnitude_type = 'Ms'
test_event.magnitudes[2].creation_info = CreationInfo('TES')
test_event.magnitudes[2].origin_id = test_event.origins[0].resource_id
# Define the test pick
_waveform_id_1 = WaveformStreamID(station_code='FOZ', channel_code='SHZ',
network_code='NZ')
_waveform_id_2 = WaveformStreamID(station_code='WTSZ', channel_code='BH1',
network_code=' ')
# Pick to associate with amplitude
test_event.picks.append(
Pick(waveform_id=_waveform_id_1, phase_hint='IAML',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"))
# Need a second pick for coda
test_event.picks.append(
Pick(waveform_id=_waveform_id_1, onset='impulsive', phase_hint='PN',
polarity='positive', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='SG',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.72,
evaluation_mode="manual"))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='PN',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.62,
evaluation_mode="automatic"))
# Test a generic local magnitude amplitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=2.0, period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id, unit='m',
magnitude_hint='ML', category='point', type='AML'))
# Test a coda magnitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=10,
pick_id=test_event.picks[1].resource_id,
waveform_id=test_event.picks[1].waveform_id, type='END',
category='duration', unit='s', magnitude_hint='Mc',
snr=2.3))
test_event.origins[0].arrivals.append(
Arrival(time_weight=0, phase=test_event.picks[1].phase_hint,
pick_id=test_event.picks[1].resource_id))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2, phase=test_event.picks[2].phase_hint,
pick_id=test_event.picks[2].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2, phase=test_event.picks[3].phase_hint,
pick_id=test_event.picks[3].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25))
return test_event
def test_nortoevmag(self):
self.assertEqual(_nortoevmag('b'), 'mB')
# raises "UserWarning: bob is not convertible"
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always', UserWarning)
self.assertEqual(_nortoevmag('bob'), '')
self.assertEquals(len(w), 1)
self.assertEquals('bob is not convertible', w[0].messages)
def test_evmagtonor(self):
self.assertEqual(_evmagtonor('mB'), 'B')
self.assertEqual(_evmagtonor('M'), 'W')
# raises "UserWarning: bob is not convertible"
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always', UserWarning)
self.assertEqual(_evmagtonor('bob'), '')
self.assertEquals(len(w), 1)
self.assertEquals('bob is not convertible', w[0].messages)
def basic_test_event():
"""
Function to generate a basic, full test event
"""
from obspy.core.event import Pick, WaveformStreamID, Arrival, Amplitude
from obspy.core.event import Event, Origin, Magnitude
from obspy.core.event import EventDescription, CreationInfo
from obspy import UTCDateTime
test_event = Event()
test_event.origins.append(Origin())
test_event.origins[0].time = UTCDateTime("2012-03-26") + 1
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.origins[0].latitude = 45.0
test_event.origins[0].longitude = 25.0
test_event.origins[0].depth = 15000
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.origins[0].time_errors['Time_Residual_RMS'] = 0.01
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[0].mag = 0.1
test_event.magnitudes[0].magnitude_type = 'ML'
test_event.magnitudes[0].creation_info = CreationInfo('TES')
test_event.magnitudes[0].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[1].mag = 0.5
test_event.magnitudes[1].magnitude_type = 'Mc'
test_event.magnitudes[1].creation_info = CreationInfo('TES')
test_event.magnitudes[1].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[2].mag = 1.3
test_event.magnitudes[2].magnitude_type = 'Ms'
test_event.magnitudes[2].creation_info = CreationInfo('TES')
test_event.magnitudes[2].origin_id = test_event.origins[0].resource_id
# Define the test pick
_waveform_id = WaveformStreamID(station_code='FOZ', channel_code='SHZ',
network_code='NZ')
test_event.picks.append(Pick(waveform_id=_waveform_id,
onset='impulsive', phase_hint='PN',
polarity='positive',
time=UTCDateTime("2012-03-26") + 1.68,
horizontal_slowness=12, backazimuth=20))
test_event.amplitudes.append(Amplitude(generic_amplitude=2.0,
period=0.4,
pick_id=test_event.picks[0].
resource_id,
waveform_id=test_event.picks[0].
waveform_id,
unit='m'))
test_event.origins[0].arrivals.append(Arrival(time_weight=2,
phase=test_event.
picks[0].
phase_hint,
pick_id=test_event.
picks[0].
resource_id,
backazimuth_residual=5,
time_residual=0.2,
distance=15,
azimuth=25))
return test_event
def test_read_write():
"""
Function to test the read and write capabilities of Sfile_util.
"""
import os
from obspy.core.event import Pick, WaveformStreamID, Arrival, Amplitude
from obspy.core.event import Catalog, Event, Origin, Magnitude
from obspy.core.event import EventDescription, CreationInfo
import obspy
if int(obspy.__version__.split('.')[0]) >= 1:
from obspy.core.event import read_events
else:
from obspy.core.event import readEvents as read_events
from obspy import UTCDateTime
# Set-up a test event
test_event = Event()
test_event.origins.append(Origin())
test_event.origins[0].time = UTCDateTime("2012-03-26") + 1
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.origins[0].latitude = 45.0
test_event.origins[0].longitude = 25.0
test_event.origins[0].depth = 15.0
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.origins[0].time_errors['Time_Residual_RMS'] = 0.01
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[0].mag = 0.1
test_event.magnitudes[0].magnitude_type = 'ML'
test_event.magnitudes[0].creation_info = CreationInfo('TES')
test_event.magnitudes[0].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[1].mag = 0.5
test_event.magnitudes[1].magnitude_type = 'Mc'
test_event.magnitudes[1].creation_info = CreationInfo('TES')
test_event.magnitudes[1].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[2].mag = 1.3
test_event.magnitudes[2].magnitude_type = 'Ms'
test_event.magnitudes[2].creation_info = CreationInfo('TES')
test_event.magnitudes[2].origin_id = test_event.origins[0].resource_id
# Define the test pick
_waveform_id = WaveformStreamID(station_code='FOZ',
channel_code='SHZ',
network_code='NZ')
test_event.picks.append(
Pick(waveform_id=_waveform_id,
onset='impulsive',
phase_hint='PN',
polarity='positive',
time=UTCDateTime("2012-03-26") + 1.68,
horizontal_slowness=12,
backazimuth=20))
test_event.amplitudes.append(
Amplitude(generic_amplitude=2.0,
period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id,
unit='m'))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2,
phase=test_event.picks[0].phase_hint,
pick_id=test_event.picks[0].resource_id,
backazimuth_residual=5,
time_residual=0.2,
distance=15,
azimuth=25))
# Add the event to a catalogue which can be used for QuakeML testing
test_cat = Catalog()
test_cat += test_event
# Write the catalog
test_cat.write("Test_catalog.xml", format='QUAKEML')
# Read and check
read_cat = read_events("Test_catalog.xml")
os.remove("Test_catalog.xml")
assert read_cat[0].resource_id == test_cat[0].resource_id
assert read_cat[0].picks == test_cat[0].picks
assert read_cat[0].origins[0].resource_id ==\
test_cat[0].origins[0].resource_id
assert read_cat[0].origins[0].time == test_cat[0].origins[0].time
# Note that time_residuel_RMS is not a quakeML format
assert read_cat[0].origins[0].longitude == test_cat[0].origins[0].longitude
assert read_cat[0].origins[0].latitude == test_cat[0].origins[0].latitude
assert read_cat[0].origins[0].depth == test_cat[0].origins[0].depth
assert read_cat[0].magnitudes == test_cat[0].magnitudes
assert read_cat[0].event_descriptions == test_cat[0].event_descriptions
assert read_cat[0].amplitudes[0].resource_id ==\
test_cat[0].amplitudes[0].resource_id
assert read_cat[0].amplitudes[0].period == test_cat[0].amplitudes[0].period
assert read_cat[0].amplitudes[0].unit == test_cat[0].amplitudes[0].unit
assert read_cat[0].amplitudes[0].generic_amplitude ==\
test_cat[0].amplitudes[0].generic_amplitude
assert read_cat[0].amplitudes[0].pick_id ==\
test_cat[0].amplitudes[0].pick_id
assert read_cat[0].amplitudes[0].waveform_id ==\
test_cat[0].amplitudes[0].waveform_id
# Check the read-write s-file functionality
sfile = eventtoSfile(test_cat[0],
userID='TEST',
evtype='L',
outdir='.',
wavefiles='test',
explosion=True,
overwrite=True)
del read_cat
assert readwavename(sfile) == ['test']
read_cat = Catalog()
read_cat += readpicks(sfile)
os.remove(sfile)
assert read_cat[0].picks[0].time == test_cat[0].picks[0].time
assert read_cat[0].picks[0].backazimuth == test_cat[0].picks[0].backazimuth
assert read_cat[0].picks[0].onset == test_cat[0].picks[0].onset
assert read_cat[0].picks[0].phase_hint == test_cat[0].picks[0].phase_hint
assert read_cat[0].picks[0].polarity == test_cat[0].picks[0].polarity
assert read_cat[0].picks[0].waveform_id.station_code ==\
test_cat[0].picks[0].waveform_id.station_code
assert read_cat[0].picks[0].waveform_id.channel_code[-1] ==\
test_cat[0].picks[0].waveform_id.channel_code[-1]
# assert read_cat[0].origins[0].resource_id ==\
# test_cat[0].origins[0].resource_id
assert read_cat[0].origins[0].time == test_cat[0].origins[0].time
# Note that time_residuel_RMS is not a quakeML format
assert read_cat[0].origins[0].longitude == test_cat[0].origins[0].longitude
assert read_cat[0].origins[0].latitude == test_cat[0].origins[0].latitude
assert read_cat[0].origins[0].depth == test_cat[0].origins[0].depth
assert read_cat[0].magnitudes[0].mag == test_cat[0].magnitudes[0].mag
assert read_cat[0].magnitudes[1].mag == test_cat[0].magnitudes[1].mag
assert read_cat[0].magnitudes[2].mag == test_cat[0].magnitudes[2].mag
assert read_cat[0].magnitudes[0].creation_info ==\
test_cat[0].magnitudes[0].creation_info
assert read_cat[0].magnitudes[1].creation_info ==\
test_cat[0].magnitudes[1].creation_info
assert read_cat[0].magnitudes[2].creation_info ==\
test_cat[0].magnitudes[2].creation_info
assert read_cat[0].magnitudes[0].magnitude_type ==\
test_cat[0].magnitudes[0].magnitude_type
assert read_cat[0].magnitudes[1].magnitude_type ==\
test_cat[0].magnitudes[1].magnitude_type
assert read_cat[0].magnitudes[2].magnitude_type ==\
test_cat[0].magnitudes[2].magnitude_type
assert read_cat[0].event_descriptions == test_cat[0].event_descriptions
# assert read_cat[0].amplitudes[0].resource_id ==\
# test_cat[0].amplitudes[0].resource_id
assert read_cat[0].amplitudes[0].period == test_cat[0].amplitudes[0].period
assert read_cat[0].amplitudes[0].snr == test_cat[0].amplitudes[0].snr
del read_cat
# assert read_cat[0].amplitudes[0].pick_id ==\
# test_cat[0].amplitudes[0].pick_id
# assert read_cat[0].amplitudes[0].waveform_id ==\
# test_cat[0].amplitudes[0].waveform_id
# Test the wrappers for PICK and EVENTINFO classes
picks, evinfo = eventtopick(test_cat)
# Test the conversion back
conv_cat = Catalog()
conv_cat.append(picktoevent(evinfo, picks))
assert conv_cat[0].picks[0].time == test_cat[0].picks[0].time
assert conv_cat[0].picks[0].backazimuth == test_cat[0].picks[0].backazimuth
assert conv_cat[0].picks[0].onset == test_cat[0].picks[0].onset
assert conv_cat[0].picks[0].phase_hint == test_cat[0].picks[0].phase_hint
assert conv_cat[0].picks[0].polarity == test_cat[0].picks[0].polarity
assert conv_cat[0].picks[0].waveform_id.station_code ==\
test_cat[0].picks[0].waveform_id.station_code
assert conv_cat[0].picks[0].waveform_id.channel_code[-1] ==\
test_cat[0].picks[0].waveform_id.channel_code[-1]
# assert read_cat[0].origins[0].resource_id ==\
# test_cat[0].origins[0].resource_id
assert conv_cat[0].origins[0].time == test_cat[0].origins[0].time
# Note that time_residuel_RMS is not a quakeML format
assert conv_cat[0].origins[0].longitude == test_cat[0].origins[0].longitude
assert conv_cat[0].origins[0].latitude == test_cat[0].origins[0].latitude
assert conv_cat[0].origins[0].depth == test_cat[0].origins[0].depth
assert conv_cat[0].magnitudes[0].mag == test_cat[0].magnitudes[0].mag
assert conv_cat[0].magnitudes[1].mag == test_cat[0].magnitudes[1].mag
assert conv_cat[0].magnitudes[2].mag == test_cat[0].magnitudes[2].mag
assert conv_cat[0].magnitudes[0].creation_info ==\
test_cat[0].magnitudes[0].creation_info
assert conv_cat[0].magnitudes[1].creation_info ==\
test_cat[0].magnitudes[1].creation_info
assert conv_cat[0].magnitudes[2].creation_info ==\
test_cat[0].magnitudes[2].creation_info
assert conv_cat[0].magnitudes[0].magnitude_type ==\
test_cat[0].magnitudes[0].magnitude_type
assert conv_cat[0].magnitudes[1].magnitude_type ==\
test_cat[0].magnitudes[1].magnitude_type
assert conv_cat[0].magnitudes[2].magnitude_type ==\
test_cat[0].magnitudes[2].magnitude_type
assert conv_cat[0].event_descriptions == test_cat[0].event_descriptions
# assert read_cat[0].amplitudes[0].resource_id ==\
# test_cat[0].amplitudes[0].resource_id
assert conv_cat[0].amplitudes[0].period == test_cat[0].amplitudes[0].period
assert conv_cat[0].amplitudes[0].snr == test_cat[0].amplitudes[0].snr
return True
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 _detect(detector,
st,
threshold,
trig_int,
moveout=0,
min_trig=0,
process=True,
extract_detections=False,
debug=0):
"""
Detect within continuous data using the subspace method.
Not to be called directly, use the detector.detect method.
:type detector: eqcorrscan.core.subspace.Detector
:param detector: Detector to use.
:type st: obspy.core.stream.Stream
:param st: Un-processed stream to detect within using the subspace \
detector
:type threshold: float
:param threshold: Threshold value for detections between 0-1
:type trig_int: float
:param trig_int: Minimum trigger interval in seconds.
:type moveout: float
:param moveout: Maximum allowable moveout window for non-multiplexed,
network detection. See note.
:type min_trig: int
:param min_trig: Minimum number of stations exceeding threshold for \
non-multiplexed, network detection. See note.
:type process: bool
:param process: Whether or not to process the stream according to the \
parameters defined by the detector. Default is to process the \
data (True).
:type extract_detections: bool
:param extract_detections: Whether to extract waveforms for each \
detection or not, if true will return detections and streams.
:type debug: int
:param debug: Debug output level from 0-5.
:return: list of detections
:rtype: list of eqcorrscan.core.match_filter.DETECTION
"""
from eqcorrscan.core import subspace_statistic
detections = []
# First process the stream
if process:
if debug > 0:
print('Processing Stream')
stream, stachans = _subspace_process(
streams=[st.copy()],
lowcut=detector.lowcut,
highcut=detector.highcut,
filt_order=detector.filt_order,
sampling_rate=detector.sampling_rate,
multiplex=detector.multiplex,
stachans=detector.stachans,
parallel=True,
align=False,
shift_len=None,
reject=False)
else:
# Check the sampling rate at the very least
for tr in st:
if not tr.stats.sampling_rate == detector.sampling_rate:
raise ValueError('Sampling rates do not match.')
stream = [st]
stachans = detector.stachans
outtic = time.clock()
if debug > 0:
print('Computing detection statistics')
stats = np.zeros(
(len(stream[0]), len(stream[0][0]) - len(detector.data[0][0]) + 1),
dtype=np.float32)
for det_channel, in_channel, i in zip(detector.data, stream[0],
np.arange(len(stream[0]))):
stats[i] = subspace_statistic.\
det_statistic(detector=det_channel.astype(np.float32),
data=in_channel.data.astype(np.float32))
if debug > 0:
print(stats[i].shape)
if debug > 3:
plt.plot(stats[i])
plt.show()
# Hard typing in Cython loop requires float32 type.
# statistics
if detector.multiplex:
trig_int_samples = (len(detector.stachans) * detector.sampling_rate *
trig_int)
else:
trig_int_samples = detector.sampling_rate * trig_int
if debug > 0:
print('Finding peaks')
peaks = []
for i in range(len(stream[0])):
peaks.append(
findpeaks.find_peaks2_short(arr=stats[i],
thresh=threshold,
trig_int=trig_int_samples,
debug=debug))
if not detector.multiplex:
# Conduct network coincidence triggering
peaks = findpeaks.coin_trig(peaks=peaks,
samp_rate=detector.sampling_rate,
moveout=moveout,
min_trig=min_trig,
stachans=stachans,
trig_int=trig_int)
else:
peaks = peaks[0]
if len(peaks) > 0:
for peak in peaks:
if detector.multiplex:
detecttime = st[0].stats.starttime + (
peak[1] /
(detector.sampling_rate * len(detector.stachans)))
else:
detecttime = st[0].stats.starttime + (peak[1] /
detector.sampling_rate)
rid = ResourceIdentifier(id=detector.name + '_' + str(detecttime),
prefix='smi:local')
ev = Event(resource_id=rid)
cr_i = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
ev.creation_info = cr_i
# All detection info in Comments for lack of a better idea
thresh_str = 'threshold=' + str(threshold)
ccc_str = 'detect_val=' + str(peak[0])
used_chans = 'channels used: ' +\
' '.join([str(pair) for pair in detector.stachans])
ev.comments.append(Comment(text=thresh_str))
ev.comments.append(Comment(text=ccc_str))
ev.comments.append(Comment(text=used_chans))
for stachan in detector.stachans:
tr = st.select(station=stachan[0], channel=stachan[1])
if tr:
net_code = tr[0].stats.network
else:
net_code = ''
pick_tm = detecttime
wv_id = WaveformStreamID(network_code=net_code,
station_code=stachan[0],
channel_code=stachan[1])
ev.picks.append(Pick(time=pick_tm, waveform_id=wv_id))
detections.append(
DETECTION(detector.name,
detecttime,
len(detector.stachans),
peak[0],
threshold,
'subspace',
detector.stachans,
event=ev))
outtoc = time.clock()
print('Detection took %s seconds' % str(outtoc - outtic))
if extract_detections:
detection_streams = extract_from_stream(st, detections)
return detections, detection_streams
return detections
def match_filter(template_names, template_list, st, threshold,
threshold_type, trig_int, plotvar, plotdir='.', cores=1,
tempdir=False, debug=0, plot_format='png',
output_cat=False, extract_detections=False,
arg_check=True):
"""
Main matched-filter detection function.
Over-arching code to run the correlations of given templates with a \
day of seismic data and output the detections based on a given threshold.
For a functional example see the tutorials.
:type template_names: list
:param template_names: List of template names in the same order as \
template_list
:type template_list: list
:param template_list: A list of templates of which each template is a \
Stream of obspy traces containing seismic data and header information.
:type st: obspy.core.stream.Stream
:param st: An obspy.Stream object containing all the data available and \
required for the correlations with templates given. For efficiency \
this should contain no excess traces which are not in one or more of \
the templates. This will now remove excess traces internally, but \
will copy the stream and work on the copy, leaving your input stream \
untouched.
:type threshold: float
:param threshold: A threshold value set based on the threshold_type
:type threshold_type: str
:param threshold_type: The type of threshold to be used, can be MAD, \
absolute or av_chan_corr. MAD threshold is calculated as the \
threshold*(median(abs(cccsum))) where cccsum is the cross-correlation \
sum for a given template. absolute threhsold is a true absolute \
threshold based on the cccsum value av_chan_corr is based on the mean \
values of single-channel cross-correlations assuming all data are \
present as required for the template, \
e.g. av_chan_corr_thresh=threshold*(cccsum/len(template)) where \
template is a single template from the input and the length is the \
number of channels within this template.
:type trig_int: float
:param trig_int: Minimum gap between detections in seconds.
:type plotvar: bool
:param plotvar: Turn plotting on or off
:type plotdir: str
:param plotdir: Path to plotting folder, plots will be output here, \
defaults to run location.
:type tempdir: str
:param tempdir: Directory to put temporary files, or False
:type cores: int
:param cores: Number of cores to use
:type debug: int
:param debug: Debug output level, the bigger the number, the more the \
output.
:type plot_format: str
:param plot_format: Specify format of output plots if saved
:type output_cat: bool
:param output_cat: Specifies if matched_filter will output an \
obspy.Catalog class containing events for each detection. Default \
is False, in which case matched_filter will output a list of \
detection classes, as normal.
:type extract_detections: bool
:param extract_detections: Specifies whether or not to return a list of \
streams, one stream per detection.
:type arg_check: bool
:param arg_check: Check arguments, defaults to True, but if running in \
bulk, and you are certain of your arguments, then set to False.
:return: :class: 'DETECTIONS' detections for each channel formatted as \
:class: 'obspy.UTCDateTime' objects.
:return: :class: obspy.Catalog containing events for each detection.
:return: list of :class: obspy.Stream objects for each detection.
.. note:: Plotting within the match-filter routine uses the Agg backend \
with interactive plotting turned off. This is because the function \
is designed to work in bulk. If you wish to turn interactive \
plotting on you must import matplotlib in your script first, when you \
them import match_filter you will get the warning that this call to \
matplotlib has no effect, which will mean that match_filter has not \
changed the plotting behaviour.
.. note:: The output_cat flag will create an :class: obspy.Catalog \
containing one event for each :class: 'DETECTIONS' generated by \
match_filter. Each event will contain a number of comments dealing \
with correlation values and channels used for the detection. Each \
channel used for the detection will have a corresponding :class: Pick \
which will contain time and waveform information. HOWEVER, the user \
should note that, at present, the pick times do not account for the \
prepick times inherent in each template. For example, if a template \
trace starts 0.1 seconds before the actual arrival of that phase, \
then the pick time generated by match_filter for that phase will be \
0.1 seconds early. We are looking towards a solution which will \
involve saving templates alongside associated metadata.
"""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ioff()
import copy
from eqcorrscan.utils import plotting
from eqcorrscan.utils import findpeaks
from obspy import Trace, Catalog, UTCDateTime, Stream
from obspy.core.event import Event, Pick, CreationInfo, ResourceIdentifier
from obspy.core.event import Comment, WaveformStreamID
import time
if arg_check:
# Check the arguments to be nice - if arguments wrong type the parallel
# output for the error won't be useful
if not type(template_names) == list:
raise IOError('template_names must be of type: list')
if not type(template_list) == list:
raise IOError('templates must be of type: list')
for template in template_list:
if not type(template) == Stream:
msg = 'template in template_list must be of type: ' +\
'obspy.core.stream.Stream'
raise IOError(msg)
if not type(st) == Stream:
msg = 'st must be of type: obspy.core.stream.Stream'
raise IOError(msg)
if str(threshold_type) not in [str('MAD'), str('absolute'),
str('av_chan_corr')]:
msg = 'threshold_type must be one of: MAD, absolute, av_chan_corr'
raise IOError(msg)
# Copy the stream here because we will muck about with it
stream = st.copy()
templates = copy.deepcopy(template_list)
# Debug option to confirm that the channel names match those in the
# templates
if debug >= 2:
template_stachan = []
data_stachan = []
for template in templates:
for tr in template:
template_stachan.append(tr.stats.station + '.' +
tr.stats.channel)
for tr in stream:
data_stachan.append(tr.stats.station + '.' + tr.stats.channel)
template_stachan = list(set(template_stachan))
data_stachan = list(set(data_stachan))
if debug >= 3:
print('I have template info for these stations:')
print(template_stachan)
print('I have daylong data for these stations:')
print(data_stachan)
# Perform a check that the daylong vectors are all the same length
min_start_time = min([tr.stats.starttime for tr in stream])
max_end_time = max([tr.stats.endtime for tr in stream])
longest_trace_length = stream[0].stats.sampling_rate * (max_end_time -
min_start_time)
for tr in stream:
if not tr.stats.npts == longest_trace_length:
msg = 'Data are not equal length, padding short traces'
warnings.warn(msg)
start_pad = np.zeros(int(tr.stats.sampling_rate *
(tr.stats.starttime - min_start_time)))
end_pad = np.zeros(int(tr.stats.sampling_rate *
(max_end_time - tr.stats.endtime)))
tr.data = np.concatenate([start_pad, tr.data, end_pad])
# Perform check that all template lengths are internally consistent
for i, temp in enumerate(template_list):
if len(set([tr.stats.npts for tr in temp])) > 1:
msg = 'Template %s contains traces of differing length!! THIS \
WILL CAUSE ISSUES' % template_names[i]
raise ValueError(msg)
# Call the _template_loop function to do all the correlation work
outtic = time.clock()
# Edit here from previous, stable, but slow match_filter
# Would be worth testing without an if statement, but with every station in
# the possible template stations having data, but for those without real
# data make the data NaN to return NaN ccc_sum
# Note: this works
if debug >= 2:
print('Ensuring all template channels have matches in long data')
template_stachan = []
for template in templates:
for tr in template:
template_stachan += [(tr.stats.station, tr.stats.channel)]
template_stachan = list(set(template_stachan))
# Copy this here to keep it safe
for stachan in template_stachan:
if not stream.select(station=stachan[0], channel=stachan[1]):
# Remove template traces rather than adding NaN data
for template in templates:
if template.select(station=stachan[0], channel=stachan[1]):
for tr in template.select(station=stachan[0],
channel=stachan[1]):
template.remove(tr)
# Remove un-needed channels
for tr in stream:
if not (tr.stats.station, tr.stats.channel) in template_stachan:
stream.remove(tr)
# Also pad out templates to have all channels
for template, template_name in zip(templates, template_names):
if len(template) == 0:
msg = ('No channels matching in continuous data for ' +
'template' + template_name)
warnings.warn(msg)
templates.remove(template)
template_names.remove(template_name)
continue
for stachan in template_stachan:
if not template.select(station=stachan[0], channel=stachan[1]):
nulltrace = Trace()
nulltrace.stats.station = stachan[0]
nulltrace.stats.channel = stachan[1]
nulltrace.stats.sampling_rate = template[0].stats.sampling_rate
nulltrace.stats.starttime = template[0].stats.starttime
nulltrace.data = np.array([np.NaN] * len(template[0].data),
dtype=np.float32)
template += nulltrace
if debug >= 2:
print('Starting the correlation run for this day')
[cccsums, no_chans, chans] = _channel_loop(templates=templates,
stream=stream,
cores=cores,
debug=debug)
if len(cccsums[0]) == 0:
raise ValueError('Correlation has not run, zero length cccsum')
outtoc = time.clock()
print(' '.join(['Looping over templates and streams took:',
str(outtoc - outtic), 's']))
if debug >= 2:
print(' '.join(['The shape of the returned cccsums is:',
str(np.shape(cccsums))]))
print(' '.join(['This is from', str(len(templates)), 'templates']))
print(' '.join(['Correlated with', str(len(stream)),
'channels of data']))
detections = []
if output_cat:
det_cat = Catalog()
for i, cccsum in enumerate(cccsums):
template = templates[i]
if str(threshold_type) == str('MAD'):
rawthresh = threshold * np.median(np.abs(cccsum))
elif str(threshold_type) == str('absolute'):
rawthresh = threshold
elif str(threshold_type) == str('av_chan_corr'):
rawthresh = threshold * no_chans[i]
# Findpeaks returns a list of tuples in the form [(cccsum, sample)]
print(' '.join(['Threshold is set at:', str(rawthresh)]))
print(' '.join(['Max of data is:', str(max(cccsum))]))
print(' '.join(['Mean of data is:', str(np.mean(cccsum))]))
if np.abs(np.mean(cccsum)) > 0.05:
warnings.warn('Mean is not zero! Check this!')
# Set up a trace object for the cccsum as this is easier to plot and
# maintains timing
if plotvar:
stream_plot = copy.deepcopy(stream[0])
# Downsample for plotting
stream_plot.decimate(int(stream[0].stats.sampling_rate / 10))
cccsum_plot = Trace(cccsum)
cccsum_plot.stats.sampling_rate = stream[0].stats.sampling_rate
# Resample here to maintain shape better
cccsum_hist = cccsum_plot.copy()
cccsum_hist = cccsum_hist.decimate(int(stream[0].stats.
sampling_rate / 10)).data
cccsum_plot = plotting.chunk_data(cccsum_plot, 10,
'Maxabs').data
# Enforce same length
stream_plot.data = stream_plot.data[0:len(cccsum_plot)]
cccsum_plot = cccsum_plot[0:len(stream_plot.data)]
cccsum_hist = cccsum_hist[0:len(stream_plot.data)]
plotting.triple_plot(cccsum_plot, cccsum_hist,
stream_plot, rawthresh, True,
plotdir + '/cccsum_plot_' +
template_names[i] + '_' +
stream[0].stats.starttime.
datetime.strftime('%Y-%m-%d') +
'.' + plot_format)
if debug >= 4:
print(' '.join(['Saved the cccsum to:', template_names[i],
stream[0].stats.starttime.datetime.
strftime('%Y%j')]))
np.save(template_names[i] +
stream[0].stats.starttime.datetime.strftime('%Y%j'),
cccsum)
tic = time.clock()
if debug >= 4:
np.save('cccsum_' + str(i) + '.npy', cccsum)
if debug >= 3 and max(cccsum) > rawthresh:
peaks = findpeaks.find_peaks2_short(cccsum, rawthresh,
trig_int * stream[0].stats.
sampling_rate, debug,
stream[0].stats.starttime,
stream[0].stats.sampling_rate)
elif max(cccsum) > rawthresh:
peaks = findpeaks.find_peaks2_short(cccsum, rawthresh,
trig_int * stream[0].stats.
sampling_rate, debug)
else:
print('No peaks found above threshold')
peaks = False
toc = time.clock()
if debug >= 1:
print(' '.join(['Finding peaks took:', str(toc - tic), 's']))
if peaks:
for peak in peaks:
detecttime = stream[0].stats.starttime +\
peak[1] / stream[0].stats.sampling_rate
# Detect time must be valid QuakeML uri within resource_id.
# This will write a formatted string which is still
# readable by UTCDateTime
rid = ResourceIdentifier(id=template_names[i] + '_' +
str(detecttime.
strftime('%Y%m%dT%H%M%S.%f')),
prefix='smi:local')
ev = Event(resource_id=rid)
cr_i = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
ev.creation_info = cr_i
# All detection info in Comments for lack of a better idea
thresh_str = 'threshold=' + str(rawthresh)
ccc_str = 'detect_val=' + str(peak[0])
used_chans = 'channels used: ' +\
' '.join([str(pair) for pair in chans[i]])
ev.comments.append(Comment(text=thresh_str))
ev.comments.append(Comment(text=ccc_str))
ev.comments.append(Comment(text=used_chans))
min_template_tm = min([tr.stats.starttime for tr in template])
for tr in template:
if (tr.stats.station, tr.stats.channel) not in chans[i]:
continue
else:
pick_tm = detecttime + (tr.stats.starttime -
min_template_tm)
wv_id = WaveformStreamID(network_code=tr.stats.network,
station_code=tr.stats.station,
channel_code=tr.stats.channel)
ev.picks.append(Pick(time=pick_tm, waveform_id=wv_id))
detections.append(DETECTION(template_names[i],
detecttime,
no_chans[i], peak[0], rawthresh,
'corr', chans[i], event=ev))
if output_cat:
det_cat.append(ev)
if extract_detections:
detection_streams = extract_from_stream(stream, detections)
del stream, templates
if output_cat and not extract_detections:
return detections, det_cat
elif not extract_detections:
return detections
elif extract_detections and not output_cat:
return detections, detection_streams
else:
return detections, det_cat, detection_streams
def match_filter(template_names, template_list, st, threshold,
threshold_type, trig_int, plotvar, plotdir='.', cores=1,
debug=0, plot_format='png', output_cat=False,
extract_detections=False, arg_check=True):
"""
Main matched-filter detection function.
Over-arching code to run the correlations of given templates with a \
day of seismic data and output the detections based on a given threshold.
For a functional example see the tutorials.
:type template_names: list
:param template_names: List of template names in the same order as \
template_list
:type template_list: list
:param template_list: A list of templates of which each template is a \
Stream of obspy traces containing seismic data and header information.
:type st: obspy.core.stream.Stream
:param st: A Stream object containing all the data available and \
required for the correlations with templates given. For efficiency \
this should contain no excess traces which are not in one or more of \
the templates. This will now remove excess traces internally, but \
will copy the stream and work on the copy, leaving your input stream \
untouched.
:type threshold: float
:param threshold: A threshold value set based on the threshold_type
:type threshold_type: str
:param threshold_type: The type of threshold to be used, can be MAD, \
absolute or av_chan_corr. See Note on thresholding below.
:type trig_int: float
:param trig_int: Minimum gap between detections in seconds.
:type plotvar: bool
:param plotvar: Turn plotting on or off
:type plotdir: str
:param plotdir: Path to plotting folder, plots will be output here, \
defaults to run location.
:type cores: int
:param cores: Number of cores to use
:type debug: int
:param debug: Debug output level, the bigger the number, the more the \
output.
:type plot_format: str
:param plot_format: Specify format of output plots if saved
:type output_cat: bool
:param output_cat: Specifies if matched_filter will output an \
obspy.Catalog class containing events for each detection. Default \
is False, in which case matched_filter will output a list of \
detection classes, as normal.
:type extract_detections: bool
:param extract_detections: Specifies whether or not to return a list of \
streams, one stream per detection.
:type arg_check: bool
:param arg_check: Check arguments, defaults to True, but if running in \
bulk, and you are certain of your arguments, then set to False.\n
.. rubric::
If neither `output_cat` or `extract_detections` are set to `True`,
then only the list of :class:`eqcorrscan.core.match_filter.DETECTION`'s
will be output:
:return: :class:`eqcorrscan.core.match_filter.DETECTION`'s detections for
each detection made.
:rtype: list
.. rubric::
If `output_cat` is set to `True`, then the
:class:`obspy.core.event.Catalog` will also be output:
:return: Catalog containing events for each detection, see above.
:rtype: :class:`obspy.core.event.Catalog`
.. rubric::
If `extract_detections` is set to `True` then the list of
:class:`obspy.core.stream.Stream`'s will also be output.
:return:
list of :class:`obspy.core.stream.Stream`'s for each detection, see
above.
:rtype: list
.. warning::
Plotting within the match-filter routine uses the Agg backend
with interactive plotting turned off. This is because the function
is designed to work in bulk. If you wish to turn interactive
plotting on you must import matplotlib in your script first, when you
them import match_filter you will get the warning that this call to
matplotlib has no effect, which will mean that match_filter has not
changed the plotting behaviour.
.. note::
**Thresholding:**
**MAD** threshold is calculated as the:
.. math::
threshold {\\times} (median(abs(cccsum)))
where :math:`cccsum` is the cross-correlation sum for a given template.
**absolute** threshold is a true absolute threshold based on the
cccsum value.
**av_chan_corr** is based on the mean values of single-channel
cross-correlations assuming all data are present as required for the
template, e.g:
.. math::
av\_chan\_corr\_thresh=threshold \\times (cccsum / len(template))
where :math:`template` is a single template from the input and the
length is the number of channels within this template.
.. note::
The output_cat flag will create an :class:`obspy.core.eventCatalog`
containing one event for each
:class:`eqcorrscan.core.match_filter.DETECTION`'s generated by
match_filter. Each event will contain a number of comments dealing
with correlation values and channels used for the detection. Each
channel used for the detection will have a corresponding
:class:`obspy.core.event.Pick` which will contain time and
waveform information. **HOWEVER**, the user should note that, at
present, the pick times do not account for the
prepick times inherent in each template. For example, if a template
trace starts 0.1 seconds before the actual arrival of that phase,
then the pick time generated by match_filter for that phase will be
0.1 seconds early. We are working on a solution that will involve
saving templates alongside associated metadata.
"""
import matplotlib
matplotlib.use('Agg')
if arg_check:
# Check the arguments to be nice - if arguments wrong type the parallel
# output for the error won't be useful
if not type(template_names) == list:
raise MatchFilterError('template_names must be of type: list')
if not type(template_list) == list:
raise MatchFilterError('templates must be of type: list')
if not len(template_list) == len(template_names):
raise MatchFilterError('Not the same number of templates as names')
for template in template_list:
if not type(template) == Stream:
msg = 'template in template_list must be of type: ' +\
'obspy.core.stream.Stream'
raise MatchFilterError(msg)
if not type(st) == Stream:
msg = 'st must be of type: obspy.core.stream.Stream'
raise MatchFilterError(msg)
if str(threshold_type) not in [str('MAD'), str('absolute'),
str('av_chan_corr')]:
msg = 'threshold_type must be one of: MAD, absolute, av_chan_corr'
raise MatchFilterError(msg)
# Copy the stream here because we will muck about with it
stream = st.copy()
templates = copy.deepcopy(template_list)
_template_names = copy.deepcopy(template_names)
# Debug option to confirm that the channel names match those in the
# templates
if debug >= 2:
template_stachan = []
data_stachan = []
for template in templates:
for tr in template:
if isinstance(tr.data, np.ma.core.MaskedArray):
raise MatchFilterError('Template contains masked array,'
' split first')
template_stachan.append(tr.stats.station + '.' +
tr.stats.channel)
for tr in stream:
data_stachan.append(tr.stats.station + '.' + tr.stats.channel)
template_stachan = list(set(template_stachan))
data_stachan = list(set(data_stachan))
if debug >= 3:
print('I have template info for these stations:')
print(template_stachan)
print('I have daylong data for these stations:')
print(data_stachan)
# Perform a check that the continuous data are all the same length
min_start_time = min([tr.stats.starttime for tr in stream])
max_end_time = max([tr.stats.endtime for tr in stream])
longest_trace_length = stream[0].stats.sampling_rate * (max_end_time -
min_start_time)
for tr in stream:
if not tr.stats.npts == longest_trace_length:
msg = 'Data are not equal length, padding short traces'
warnings.warn(msg)
start_pad = np.zeros(int(tr.stats.sampling_rate *
(tr.stats.starttime - min_start_time)))
end_pad = np.zeros(int(tr.stats.sampling_rate *
(max_end_time - tr.stats.endtime)))
tr.data = np.concatenate([start_pad, tr.data, end_pad])
# Perform check that all template lengths are internally consistent
for i, temp in enumerate(template_list):
if len(set([tr.stats.npts for tr in temp])) > 1:
msg = ('Template %s contains traces of differing length, this is '
'not currently supported' % _template_names[i])
raise MatchFilterError(msg)
outtic = time.clock()
if debug >= 2:
print('Ensuring all template channels have matches in long data')
template_stachan = {}
# Work out what station-channel pairs are in the templates, including
# duplicate station-channel pairs. We will use this information to fill
# all templates with the same station-channel pairs as required by
# _template_loop.
for template in templates:
stachans_in_template = []
for tr in template:
stachans_in_template.append((tr.stats.network, tr.stats.station,
tr.stats.location, tr.stats.channel))
stachans_in_template = dict(Counter(stachans_in_template))
for stachan in stachans_in_template.keys():
if stachan not in template_stachan.keys():
template_stachan.update({stachan:
stachans_in_template[stachan]})
elif stachans_in_template[stachan] > template_stachan[stachan]:
template_stachan.update({stachan:
stachans_in_template[stachan]})
# Remove un-matched channels from templates.
_template_stachan = copy.deepcopy(template_stachan)
for stachan in template_stachan.keys():
if not stream.select(network=stachan[0], station=stachan[1],
location=stachan[2], channel=stachan[3]):
# Remove stachan from list of dictionary of template_stachans
_template_stachan.pop(stachan)
# Remove template traces rather than adding NaN data
for template in templates:
if template.select(network=stachan[0], station=stachan[1],
location=stachan[2], channel=stachan[3]):
for tr in template.select(network=stachan[0],
station=stachan[1],
location=stachan[2],
channel=stachan[3]):
template.remove(tr)
template_stachan = _template_stachan
# Remove un-needed channels from continuous data.
for tr in stream:
if not (tr.stats.network, tr.stats.station,
tr.stats.location, tr.stats.channel) in \
template_stachan.keys():
stream.remove(tr)
# Check for duplicate channels
stachans = [(tr.stats.network, tr.stats.station,
tr.stats.location, tr.stats.channel) for tr in stream]
c_stachans = Counter(stachans)
for key in c_stachans.keys():
if c_stachans[key] > 1:
msg = ('Multiple channels for %s.%s.%s.%s, likely a data issue'
% (key[0], key[1], key[2], key[3]))
raise MatchFilterError(msg)
# Pad out templates to have all channels
for template, template_name in zip(templates, _template_names):
if len(template) == 0:
msg = ('No channels matching in continuous data for ' +
'template' + template_name)
warnings.warn(msg)
templates.remove(template)
_template_names.remove(template_name)
continue
for stachan in template_stachan.keys():
number_of_channels = len(template.select(network=stachan[0],
station=stachan[1],
location=stachan[2],
channel=stachan[3]))
if number_of_channels < template_stachan[stachan]:
missed_channels = template_stachan[stachan] -\
number_of_channels
nulltrace = Trace()
nulltrace.stats.update(
{'network': stachan[0], 'station': stachan[1],
'location': stachan[2], 'channel': stachan[3],
'sampling_rate': template[0].stats.sampling_rate,
'starttime': template[0].stats.starttime})
nulltrace.data = np.array([np.NaN] * len(template[0].data),
dtype=np.float32)
for dummy in range(missed_channels):
template += nulltrace
template.sort()
# Quick check that this has all worked
if len(template) != max([len(t) for t in templates]):
raise MatchFilterError('Internal error forcing same template '
'lengths, report this error.')
if debug >= 2:
print('Starting the correlation run for this day')
if debug >= 4:
for template in templates:
print(template)
print(stream)
[cccsums, no_chans, chans] = _channel_loop(templates=templates,
stream=stream,
cores=cores,
debug=debug)
if len(cccsums[0]) == 0:
raise MatchFilterError('Correlation has not run, zero length cccsum')
outtoc = time.clock()
print(' '.join(['Looping over templates and streams took:',
str(outtoc - outtic), 's']))
if debug >= 2:
print(' '.join(['The shape of the returned cccsums is:',
str(np.shape(cccsums))]))
print(' '.join(['This is from', str(len(templates)), 'templates']))
print(' '.join(['Correlated with', str(len(stream)),
'channels of data']))
detections = []
if output_cat:
det_cat = Catalog()
for i, cccsum in enumerate(cccsums):
template = templates[i]
if str(threshold_type) == str('MAD'):
rawthresh = threshold * np.median(np.abs(cccsum))
elif str(threshold_type) == str('absolute'):
rawthresh = threshold
elif str(threshold_type) == str('av_chan_corr'):
rawthresh = threshold * no_chans[i]
# Findpeaks returns a list of tuples in the form [(cccsum, sample)]
print(' '.join(['Threshold is set at:', str(rawthresh)]))
print(' '.join(['Max of data is:', str(max(cccsum))]))
print(' '.join(['Mean of data is:', str(np.mean(cccsum))]))
if np.abs(np.mean(cccsum)) > 0.05:
warnings.warn('Mean is not zero! Check this!')
# Set up a trace object for the cccsum as this is easier to plot and
# maintains timing
if plotvar:
_match_filter_plot(stream=stream, cccsum=cccsum,
template_names=_template_names,
rawthresh=rawthresh, plotdir=plotdir,
plot_format=plot_format, i=i)
if debug >= 4:
print(' '.join(['Saved the cccsum to:', _template_names[i],
stream[0].stats.starttime.datetime.
strftime('%Y%j')]))
np.save(_template_names[i] +
stream[0].stats.starttime.datetime.strftime('%Y%j'),
cccsum)
tic = time.clock()
if max(cccsum) > rawthresh:
peaks = findpeaks.find_peaks2_short(
arr=cccsum, thresh=rawthresh,
trig_int=trig_int * stream[0].stats.sampling_rate, debug=debug,
starttime=stream[0].stats.starttime,
samp_rate=stream[0].stats.sampling_rate)
else:
print('No peaks found above threshold')
peaks = False
toc = time.clock()
if debug >= 1:
print(' '.join(['Finding peaks took:', str(toc - tic), 's']))
if peaks:
for peak in peaks:
detecttime = stream[0].stats.starttime +\
peak[1] / stream[0].stats.sampling_rate
# Detect time must be valid QuakeML uri within resource_id.
# This will write a formatted string which is still
# readable by UTCDateTime
rid = ResourceIdentifier(id=_template_names[i] + '_' +
str(detecttime.
strftime('%Y%m%dT%H%M%S.%f')),
prefix='smi:local')
ev = Event(resource_id=rid)
cr_i = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
ev.creation_info = cr_i
# All detection info in Comments for lack of a better idea
thresh_str = 'threshold=' + str(rawthresh)
ccc_str = 'detect_val=' + str(peak[0])
used_chans = 'channels used: ' +\
' '.join([str(pair) for pair in chans[i]])
ev.comments.append(Comment(text=thresh_str))
ev.comments.append(Comment(text=ccc_str))
ev.comments.append(Comment(text=used_chans))
min_template_tm = min([tr.stats.starttime for tr in template])
for tr in template:
if (tr.stats.station, tr.stats.channel) not in chans[i]:
continue
else:
pick_tm = detecttime + (tr.stats.starttime -
min_template_tm)
wv_id = WaveformStreamID(network_code=tr.stats.network,
station_code=tr.stats.station,
channel_code=tr.stats.channel)
ev.picks.append(Pick(time=pick_tm, waveform_id=wv_id))
detections.append(DETECTION(_template_names[i],
detecttime,
no_chans[i], peak[0], rawthresh,
'corr', chans[i], event=ev))
if output_cat:
det_cat.append(ev)
if extract_detections:
detection_streams = extract_from_stream(stream, detections)
del stream, templates
if output_cat and not extract_detections:
return detections, det_cat
elif not extract_detections:
return detections
elif extract_detections and not output_cat:
return detections, detection_streams
else:
return detections, det_cat, detection_streams
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 cross_net(stream, env=False, debug=0, master=False):
"""
Generate picks using a simple envelope cross-correlation.
Picks are made for each channel based on optimal moveout \
defined by maximum cross-correlation with master trace. Master trace \
will be the first trace in the stream.
:type stream: :class: obspy.Stream
:param stream: Stream to pick
:type env: bool
:param env: To compute cross-correlations on the envelope or not.
:type debug: int
:param debug: Debug level from 0-5
:type master: obspy.Trace
:param master: Trace to use as master, if False, will use the first trace \
in stream.
:returns: obspy.core.event.Event
.. rubric:: Example
>>> from obspy import read
>>> from eqcorrscan.utils.picker import cross_net
>>> st = read()
>>> event = cross_net(st, env=True)
>>> event.creation_info.author
'EQcorrscan'
"""
from obspy.signal.cross_correlation import xcorr
from obspy.signal.filter import envelope
from obspy import UTCDateTime
from obspy.core.event import Event, Pick, WaveformStreamID
from obspy.core.event import CreationInfo, Comment, Origin
import matplotlib.pyplot as plt
import numpy as np
event = Event()
event.origins.append(Origin())
event.creation_info = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
event.comments.append(Comment(text='cross_net'))
samp_rate = stream[0].stats.sampling_rate
if not env:
if debug > 2:
print('Using the raw data')
st = stream.copy()
st.resample(samp_rate)
else:
st = stream.copy()
if debug > 2:
print('Computing envelope')
for tr in st:
tr.resample(samp_rate)
tr.data = envelope(tr.data)
if debug > 2:
st.plot(equal_scale=False, size=(800, 600))
if not master:
master = st[0]
else:
master = master
master.data = np.nan_to_num(master.data)
for i, tr in enumerate(st):
tr.data = np.nan_to_num(tr.data)
if debug > 2:
msg = ' '.join([
'Comparing', tr.stats.station, tr.stats.channel,
'with the master'
])
print(msg)
shift_len = int(0.3 * len(tr))
if debug > 2:
print('Shift length is set to ' + str(shift_len) + ' samples')
if debug > 3:
index, cc, cc_vec = xcorr(master, tr, shift_len, full_xcorr=True)
cc_vec = np.nan_to_num(cc_vec)
if debug > 4:
print(cc_vec)
fig = plt.figure()
ax1 = fig.add_subplot(211)
x = np.linspace(0, len(master) / samp_rate, len(master))
ax1.plot(x,
master.data / float(master.data.max()),
'k',
label='Master')
ax1.plot(x + (index / samp_rate),
tr.data / float(tr.data.max()),
'r',
label='Slave shifted')
ax1.legend(loc="lower right", prop={'size': "small"})
ax1.set_xlabel("time [s]")
ax1.set_ylabel("norm. amplitude")
ax2 = fig.add_subplot(212)
print(len(cc_vec))
x = np.linspace(0, len(cc_vec) / samp_rate, len(cc_vec))
ax2.plot(x, cc_vec, label='xcorr')
# ax2.set_ylim(-1, 1)
# ax2.set_xlim(0, len(master))
plt.show()
index, cc = xcorr(master, tr, shift_len)
wav_id = WaveformStreamID(station_code=tr.stats.station,
channel_code=tr.stats.channel,
network_code=tr.stats.network)
event.picks.append(
Pick(time=tr.stats.starttime + (index / tr.stats.sampling_rate),
waveform_id=wav_id,
phase_hint='S',
onset='emergent'))
if debug > 2:
print(event.picks[i])
event.origins[0].time = min([pick.time for pick in event.picks]) - 1
event.origins[0].latitude = float('nan')
event.origins[0].longitude = float('nan')
# Set arbitrary origin time
del st
return event
def readheader(sfile):
"""
Read header information from a seisan nordic format S-file.
Returns an obspy.core.event.Catalog type: note this changed for version \
0.1.0 from the inbuilt class types.
:type sfile: str
:param sfile: Path to the s-file
:returns: :class: obspy.core.event.Event
>>> event = readheader('eqcorrscan/tests/test_data/REA/TEST_/' +
... '01-0411-15L.S201309')
>>> print(event.origins[0].time)
2013-09-01T04:11:15.700000Z
"""
import warnings
from obspy.core.event import Event, Origin, Magnitude, Comment
from obspy.core.event import EventDescription, CreationInfo
f = open(sfile, 'r')
# Base populate to allow for empty parts of file
new_event = Event()
topline = f.readline()
if not len(topline.rstrip()) == 80:
raise IOError('s-file has a corrupt header, not 80 char long')
f.seek(0)
for line in f:
if line[79] in [' ', '1']:
topline = line
break
if line[79] == '7':
raise IOError('No header found, corrupt s-file?')
try:
sfile_seconds = int(topline[16:18])
if sfile_seconds == 60:
sfile_seconds = 0
add_seconds = 60
else:
add_seconds = 0
new_event.origins.append(Origin())
new_event.origins[0].time = UTCDateTime(int(topline[1:5]),
int(topline[6:8]),
int(topline[8:10]),
int(topline[11:13]),
int(topline[13:15]),
sfile_seconds,
int(topline[19:20]) *
100000)\
+ add_seconds
except:
warnings.warn("Couldn't read a date from sfile: " + sfile)
new_event.origins.append(Origin(time=UTCDateTime(0)))
# new_event.loc_mod_ind=topline[20]
new_event.event_descriptions.append(EventDescription())
new_event.event_descriptions[0].text = topline[21:23]
# new_event.ev_id=topline[22]
if not _float_conv(topline[23:30]) == 999:
new_event.origins[0].latitude = _float_conv(topline[23:30])
new_event.origins[0].longitude = _float_conv(topline[31:38])
new_event.origins[0].depth = _float_conv(topline[39:43]) * 1000
else:
# The origin 'requires' a lat & long
new_event.origins[0].latitude = float('NaN')
new_event.origins[0].longitude = float('NaN')
new_event.origins[0].depth = float('NaN')
# new_event.depth_ind = topline[44]
# new_event.loc_ind = topline[45]
new_event.creation_info = CreationInfo(agency_id=topline[45:48].
strip())
ksta = Comment(text='Number of stations=' +
topline[49:51].strip())
new_event.origins[0].comments.append(ksta)
# new_event.origins[0].nsta??? = _int_conv(topline[49:51])
if not _float_conv(topline[51:55]) == 999:
new_event.origins[0].time_errors['Time_Residual_RMS'] = \
_float_conv(topline[51:55])
# Read in magnitudes if they are there.
if len(topline[59].strip()) > 0:
new_event.magnitudes.append(Magnitude())
new_event.magnitudes[0].mag = _float_conv(topline[56:59])
new_event.magnitudes[0].magnitude_type = topline[59]
new_event.magnitudes[0].creation_info = \
CreationInfo(agency_id=topline[60:63].strip())
new_event.magnitudes[0].origin_id = new_event.origins[0].\
resource_id
if len(topline[67].strip()) > 0:
new_event.magnitudes.append(Magnitude())
new_event.magnitudes[1].mag = _float_conv(topline[64:67])
new_event.magnitudes[1].magnitude_type = topline[67]
new_event.magnitudes[1].creation_info = \
CreationInfo(agency_id=topline[68:71].strip())
new_event.magnitudes[1].origin_id = new_event.origins[0].\
resource_id
if len(topline[75].strip()) > 0:
new_event.magnitudes.append(Magnitude())
new_event.magnitudes[2].mag = _float_conv(topline[72:75])
new_event.magnitudes[2].magnitude_type = topline[75]
new_event.magnitudes[2].creation_info = \
CreationInfo(agency_id=topline[76:79].strip())
new_event.magnitudes[2].origin_id = new_event.origins[0].\
resource_id
f.close()
# convert the nordic notation of magnitude to more general notation
for _magnitude in new_event.magnitudes:
_magnitude.magnitude_type = _nortoevmag(_magnitude.magnitude_type)
# Set the useful things like preferred magnitude and preferred origin
new_event.preferred_origin_id = str(new_event.origins[0].resource_id)
if len(new_event.magnitudes) > 1:
try:
# Select moment first, then local, then
mag_filter = ['MW', 'Mw', 'ML', 'Ml', 'MB', 'Mb',
'MS', 'Ms', 'Mc', 'MC']
_magnitudes = [(m.magnitude_type, m.resource_id)
for m in new_event.magnitudes]
preferred_magnitude = sorted(_magnitudes,
key=lambda x: mag_filter.index(x[0]))
new_event.preferred_magnitude_id = str(preferred_magnitude[0][1])
except ValueError:
# If there is a magnitude not specified in filter
new_event.preferred_magnitude_id =\
str(new_event.magnitudes[0].resource_id)
elif len(new_event.magnitudes) == 1:
new_event.preferred_magnitude_id =\
str(new_event.magnitudes[0].resource_id)
return new_event
def full_test_event():
"""
Function to generate a basic, full test event
"""
test_event = Event()
test_event.origins.append(Origin())
test_event.origins[0].time = UTCDateTime("2012-03-26") + 1.2
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.origins[0].latitude = 45.0
test_event.origins[0].longitude = 25.0
test_event.origins[0].depth = 15000
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.origins[0].time_errors['Time_Residual_RMS'] = 0.01
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[0].mag = 0.1
test_event.magnitudes[0].magnitude_type = 'ML'
test_event.magnitudes[0].creation_info = CreationInfo('TES')
test_event.magnitudes[0].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[1].mag = 0.5
test_event.magnitudes[1].magnitude_type = 'Mc'
test_event.magnitudes[1].creation_info = CreationInfo('TES')
test_event.magnitudes[1].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[2].mag = 1.3
test_event.magnitudes[2].magnitude_type = 'Ms'
test_event.magnitudes[2].creation_info = CreationInfo('TES')
test_event.magnitudes[2].origin_id = test_event.origins[0].resource_id
# Define the test pick
_waveform_id_1 = WaveformStreamID(station_code='FOZ', channel_code='SHZ',
network_code='NZ')
_waveform_id_2 = WaveformStreamID(station_code='WTSZ', channel_code='BH1',
network_code=' ')
# Pick to associate with amplitude
test_event.picks.append(
Pick(waveform_id=_waveform_id_1, phase_hint='IAML',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"))
# Need a second pick for coda
test_event.picks.append(
Pick(waveform_id=_waveform_id_1, onset='impulsive', phase_hint='PN',
polarity='positive', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='SG',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.72,
evaluation_mode="manual"))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='PN',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.62,
evaluation_mode="automatic"))
# Test a generic local magnitude amplitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=2.0, period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id, unit='m',
magnitude_hint='ML', category='point', type='AML'))
# Test a coda magnitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=10,
pick_id=test_event.picks[1].resource_id,
waveform_id=test_event.picks[1].waveform_id, type='END',
category='duration', unit='s', magnitude_hint='Mc',
snr=2.3))
test_event.origins[0].arrivals.append(
Arrival(time_weight=0, phase=test_event.picks[1].phase_hint,
pick_id=test_event.picks[1].resource_id))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2, phase=test_event.picks[2].phase_hint,
pick_id=test_event.picks[2].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2, phase=test_event.picks[3].phase_hint,
pick_id=test_event.picks[3].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25))
return test_event
def full_test_event():
"""
Function to generate a basic, full test event
"""
test_event = Event()
test_event.origins.append(Origin())
test_event.origins[0].time = UTCDateTime("2012-03-26") + 1.2
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.origins[0].latitude = 45.0
test_event.origins[0].longitude = 25.0
test_event.origins[0].depth = 15000
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.origins[0].quality = OriginQuality(standard_error=0.01)
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[0].mag = 0.1
test_event.magnitudes[0].magnitude_type = 'ML'
test_event.magnitudes[0].creation_info = CreationInfo('TES')
test_event.magnitudes[0].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[1].mag = 0.5
test_event.magnitudes[1].magnitude_type = 'Mc'
test_event.magnitudes[1].creation_info = CreationInfo('TES')
test_event.magnitudes[1].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[2].mag = 1.3
test_event.magnitudes[2].magnitude_type = 'Ms'
test_event.magnitudes[2].creation_info = CreationInfo('TES')
test_event.magnitudes[2].origin_id = test_event.origins[0].resource_id
# Define the test pick
_waveform_id_1 = WaveformStreamID(station_code='FOZ', channel_code='SHZ',
network_code='NZ')
_waveform_id_2 = WaveformStreamID(station_code='WTSZ', channel_code='BH1',
network_code=' ')
# Pick to associate with amplitude - 0
test_event.picks = [
Pick(waveform_id=_waveform_id_1, phase_hint='IAML',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_1, onset='impulsive', phase_hint='PN',
polarity='positive', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_1, phase_hint='IAML',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='SG',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.72,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='PN',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.62,
evaluation_mode="automatic")]
# Test a generic local magnitude amplitude pick
test_event.amplitudes = [
Amplitude(generic_amplitude=2.0, period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id, unit='m',
magnitude_hint='ML', category='point', type='AML'),
Amplitude(generic_amplitude=10,
pick_id=test_event.picks[1].resource_id,
waveform_id=test_event.picks[1].waveform_id, type='END',
category='duration', unit='s', magnitude_hint='Mc',
snr=2.3),
Amplitude(generic_amplitude=5.0, period=0.6,
pick_id=test_event.picks[2].resource_id,
waveform_id=test_event.picks[0].waveform_id, unit='m',
category='point', type='AML')]
test_event.origins[0].arrivals = [
Arrival(time_weight=0, phase=test_event.picks[1].phase_hint,
pick_id=test_event.picks[1].resource_id),
Arrival(time_weight=2, phase=test_event.picks[3].phase_hint,
pick_id=test_event.picks[3].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25),
Arrival(time_weight=2, phase=test_event.picks[4].phase_hint,
pick_id=test_event.picks[4].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25)]
return test_event
def match_filter(template_names, template_list, st, threshold,
threshold_type, trig_int, plotvar, plotdir='.', cores=1,
tempdir=False, debug=0, plot_format='png',
output_cat=False, extract_detections=False,
arg_check=True):
"""
Main matched-filter detection function.
Over-arching code to run the correlations of given templates with a \
day of seismic data and output the detections based on a given threshold.
For a functional example see the tutorials.
:type template_names: list
:param template_names: List of template names in the same order as \
template_list
:type template_list: list
:param template_list: A list of templates of which each template is a \
Stream of obspy traces containing seismic data and header information.
:type st: obspy.core.stream.Stream
:param st: An obspy.Stream object containing all the data available and \
required for the correlations with templates given. For efficiency \
this should contain no excess traces which are not in one or more of \
the templates. This will now remove excess traces internally, but \
will copy the stream and work on the copy, leaving your input stream \
untouched.
:type threshold: float
:param threshold: A threshold value set based on the threshold_type
:type threshold_type: str
:param threshold_type: The type of threshold to be used, can be MAD, \
absolute or av_chan_corr. MAD threshold is calculated as the \
threshold*(median(abs(cccsum))) where cccsum is the cross-correlation \
sum for a given template. absolute threhsold is a true absolute \
threshold based on the cccsum value av_chan_corr is based on the mean \
values of single-channel cross-correlations assuming all data are \
present as required for the template, \
e.g. av_chan_corr_thresh=threshold*(cccsum/len(template)) where \
template is a single template from the input and the length is the \
number of channels within this template.
:type trig_int: float
:param trig_int: Minimum gap between detections in seconds.
:type plotvar: bool
:param plotvar: Turn plotting on or off
:type plotdir: str
:param plotdir: Path to plotting folder, plots will be output here, \
defaults to run location.
:type tempdir: str
:param tempdir: Directory to put temporary files, or False
:type cores: int
:param cores: Number of cores to use
:type debug: int
:param debug: Debug output level, the bigger the number, the more the \
output.
:type plot_format: str
:param plot_format: Specify format of output plots if saved
:type output_cat: bool
:param output_cat: Specifies if matched_filter will output an \
obspy.Catalog class containing events for each detection. Default \
is False, in which case matched_filter will output a list of \
detection classes, as normal.
:type extract_detections: bool
:param extract_detections: Specifies whether or not to return a list of \
streams, one stream per detection.
:type arg_check: bool
:param arg_check: Check arguments, defaults to True, but if running in \
bulk, and you are certain of your arguments, then set to False.
:return: :class: 'DETECTIONS' detections for each channel formatted as \
:class: 'obspy.UTCDateTime' objects.
:return: :class: obspy.Catalog containing events for each detection.
:return: list of :class: obspy.Stream objects for each detection.
.. note:: Plotting within the match-filter routine uses the Agg backend \
with interactive plotting turned off. This is because the function \
is designed to work in bulk. If you wish to turn interactive \
plotting on you must import matplotlib in your script first, when you \
them import match_filter you will get the warning that this call to \
matplotlib has no effect, which will mean that match_filter has not \
changed the plotting behaviour.
.. note:: The output_cat flag will create an :class: obspy.Catalog \
containing one event for each :class: 'DETECTIONS' generated by \
match_filter. Each event will contain a number of comments dealing \
with correlation values and channels used for the detection. Each \
channel used for the detection will have a corresponding :class: Pick \
which will contain time and waveform information. HOWEVER, the user \
should note that, at present, the pick times do not account for the \
prepick times inherent in each template. For example, if a template \
trace starts 0.1 seconds before the actual arrival of that phase, \
then the pick time generated by match_filter for that phase will be \
0.1 seconds early. We are looking towards a solution which will \
involve saving templates alongside associated metadata.
"""
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ioff()
import copy
from eqcorrscan.utils import plotting
from eqcorrscan.utils import findpeaks
from obspy import Trace, Catalog, UTCDateTime, Stream
from obspy.core.event import Event, Pick, CreationInfo, ResourceIdentifier
from obspy.core.event import Comment, WaveformStreamID
import time
if arg_check:
# Check the arguments to be nice - if arguments wrong type the parallel
# output for the error won't be useful
if not type(template_names) == list:
raise IOError('template_names must be of type: list')
if not type(template_list) == list:
raise IOError('templates must be of type: list')
for template in template_list:
if not type(template) == Stream:
msg = 'template in template_list must be of type: ' +\
'obspy.core.stream.Stream'
raise IOError(msg)
if not type(st) == Stream:
msg = 'st must be of type: obspy.core.stream.Stream'
raise IOError(msg)
if threshold_type not in ['MAD', 'absolute', 'av_chan_corr']:
msg = 'threshold_type must be one of: MAD, absolute, av_chan_corr'
raise IOError(msg)
# Copy the stream here because we will muck about with it
stream = st.copy()
templates = copy.deepcopy(template_list)
# Debug option to confirm that the channel names match those in the
# templates
if debug >= 2:
template_stachan = []
data_stachan = []
for template in templates:
for tr in template:
template_stachan.append(tr.stats.station + '.' +
tr.stats.channel)
for tr in stream:
data_stachan.append(tr.stats.station + '.' + tr.stats.channel)
template_stachan = list(set(template_stachan))
data_stachan = list(set(data_stachan))
if debug >= 3:
print('I have template info for these stations:')
print(template_stachan)
print('I have daylong data for these stations:')
print(data_stachan)
# Perform a check that the daylong vectors are daylong
for tr in stream:
if not tr.stats.sampling_rate * 86400 == tr.stats.npts:
msg = ' '.join(['Data are not daylong for', tr.stats.station,
tr.stats.channel])
raise ValueError(msg)
# Perform check that all template lengths are internally consistent
for i, temp in enumerate(template_list):
if len(set([tr.stats.npts for tr in temp])) > 1:
msg = 'Template %s contains traces of differing length!! THIS \
WILL CAUSE ISSUES' % template_names[i]
raise ValueError(msg)
# Call the _template_loop function to do all the correlation work
outtic = time.clock()
# Edit here from previous, stable, but slow match_filter
# Would be worth testing without an if statement, but with every station in
# the possible template stations having data, but for those without real
# data make the data NaN to return NaN ccc_sum
# Note: this works
if debug >= 2:
print('Ensuring all template channels have matches in daylong data')
template_stachan = []
for template in templates:
for tr in template:
template_stachan += [(tr.stats.station, tr.stats.channel)]
template_stachan = list(set(template_stachan))
# Copy this here to keep it safe
for stachan in template_stachan:
if not stream.select(station=stachan[0], channel=stachan[1]):
# Remove template traces rather than adding NaN data
for template in templates:
if template.select(station=stachan[0], channel=stachan[1]):
for tr in template.select(station=stachan[0],
channel=stachan[1]):
template.remove(tr)
# Remove un-needed channels
for tr in stream:
if not (tr.stats.station, tr.stats.channel) in template_stachan:
stream.remove(tr)
# Also pad out templates to have all channels
for template, template_name in zip(templates, template_names):
if len(template) == 0:
msg = ('No channels matching in continuous data for ' +
'template' + template_name)
warnings.warn(msg)
templates.remove(template)
template_names.remove(template_name)
continue
for stachan in template_stachan:
if not template.select(station=stachan[0], channel=stachan[1]):
nulltrace = Trace()
nulltrace.stats.station = stachan[0]
nulltrace.stats.channel = stachan[1]
nulltrace.stats.sampling_rate = template[0].stats.sampling_rate
nulltrace.stats.starttime = template[0].stats.starttime
nulltrace.data = np.array([np.NaN] * len(template[0].data),
dtype=np.float32)
template += nulltrace
if debug >= 2:
print('Starting the correlation run for this day')
[cccsums, no_chans, chans] = _channel_loop(templates, stream, cores, debug)
if len(cccsums[0]) == 0:
raise ValueError('Correlation has not run, zero length cccsum')
outtoc = time.clock()
print(' '.join(['Looping over templates and streams took:',
str(outtoc - outtic), 's']))
if debug >= 2:
print(' '.join(['The shape of the returned cccsums is:',
str(np.shape(cccsums))]))
print(' '.join(['This is from', str(len(templates)), 'templates']))
print(' '.join(['Correlated with', str(len(stream)),
'channels of data']))
detections = []
if output_cat:
det_cat = Catalog()
for i, cccsum in enumerate(cccsums):
template = templates[i]
if threshold_type == 'MAD':
rawthresh = threshold * np.median(np.abs(cccsum))
elif threshold_type == 'absolute':
rawthresh = threshold
elif threshold_type == 'av_chan_corr':
rawthresh = threshold * no_chans[i]
# Findpeaks returns a list of tuples in the form [(cccsum, sample)]
print(' '.join(['Threshold is set at:', str(rawthresh)]))
print(' '.join(['Max of data is:', str(max(cccsum))]))
print(' '.join(['Mean of data is:', str(np.mean(cccsum))]))
if np.abs(np.mean(cccsum)) > 0.05:
warnings.warn('Mean is not zero! Check this!')
# Set up a trace object for the cccsum as this is easier to plot and
# maintains timing
if plotvar:
stream_plot = copy.deepcopy(stream[0])
# Downsample for plotting
stream_plot.decimate(int(stream[0].stats.sampling_rate / 10))
cccsum_plot = Trace(cccsum)
cccsum_plot.stats.sampling_rate = stream[0].stats.sampling_rate
# Resample here to maintain shape better
cccsum_hist = cccsum_plot.copy()
cccsum_hist = cccsum_hist.decimate(int(stream[0].stats.
sampling_rate / 10)).data
cccsum_plot = plotting.chunk_data(cccsum_plot, 10,
'Maxabs').data
# Enforce same length
stream_plot.data = stream_plot.data[0:len(cccsum_plot)]
cccsum_plot = cccsum_plot[0:len(stream_plot.data)]
cccsum_hist = cccsum_hist[0:len(stream_plot.data)]
plotting.triple_plot(cccsum_plot, cccsum_hist,
stream_plot, rawthresh, True,
plotdir + '/cccsum_plot_' +
template_names[i] + '_' +
stream[0].stats.starttime.
datetime.strftime('%Y-%m-%d') +
'.' + plot_format)
if debug >= 4:
print(' '.join(['Saved the cccsum to:', template_names[i],
stream[0].stats.starttime.datetime.
strftime('%Y%j')]))
np.save(template_names[i] +
stream[0].stats.starttime.datetime.strftime('%Y%j'),
cccsum)
tic = time.clock()
if debug >= 4:
np.save('cccsum_' + str(i) + '.npy', cccsum)
if debug >= 3 and max(cccsum) > rawthresh:
peaks = findpeaks.find_peaks2_short(cccsum, rawthresh,
trig_int * stream[0].stats.
sampling_rate, debug,
stream[0].stats.starttime,
stream[0].stats.sampling_rate)
elif max(cccsum) > rawthresh:
peaks = findpeaks.find_peaks2_short(cccsum, rawthresh,
trig_int * stream[0].stats.
sampling_rate, debug)
else:
print('No peaks found above threshold')
peaks = False
toc = time.clock()
if debug >= 1:
print(' '.join(['Finding peaks took:', str(toc - tic), 's']))
if peaks:
for peak in peaks:
detecttime = stream[0].stats.starttime +\
peak[1] / stream[0].stats.sampling_rate
# Detect time must be valid QuakeML uri within resource_id.
# This will write a formatted string which is still readable by UTCDateTime
rid = ResourceIdentifier(id=template_names[i] + '_' +
str(detecttime.strftime('%Y%m%dT%H%M%S.%f')),
prefix='smi:local')
ev = Event(resource_id=rid)
cr_i = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
ev.creation_info = cr_i
# All detection info in Comments for lack of a better idea
thresh_str = 'threshold=' + str(rawthresh)
ccc_str = 'detect_val=' + str(peak[0])
used_chans = 'channels used: ' +\
' '.join([str(pair) for pair in chans[i]])
ev.comments.append(Comment(text=thresh_str))
ev.comments.append(Comment(text=ccc_str))
ev.comments.append(Comment(text=used_chans))
min_template_tm = min([tr.stats.starttime for tr in template])
for tr in template:
if (tr.stats.station, tr.stats.channel) not in chans[i]:
continue
else:
pick_tm = detecttime + (tr.stats.starttime - min_template_tm)
wv_id = WaveformStreamID(network_code=tr.stats.network,
station_code=tr.stats.station,
channel_code=tr.stats.channel)
ev.picks.append(Pick(time=pick_tm, waveform_id=wv_id))
detections.append(DETECTION(template_names[i],
detecttime,
no_chans[i], peak[0], rawthresh,
'corr', chans[i], event=ev))
if output_cat:
det_cat.append(ev)
if extract_detections:
detection_streams = extract_from_stream(stream, detections)
del stream, templates
if output_cat and not extract_detections:
return detections, det_cat
elif not extract_detections:
return detections
elif extract_detections and not output_cat:
return detections, detection_streams
else:
return detections, det_cat, detection_streams
def _calculate_event(self,
template=None,
template_st=None,
estimate_origin=True,
correct_prepick=True):
"""
Calculate an event for this detection using a given template.
:type template: Template
:param template: The template that made this detection
:type template_st: `obspy.core.stream.Stream`
:param template_st:
Template stream, used to calculate pick times, not needed if
template is given.
:type estimate_origin: bool
:param estimate_origin:
Whether to include an estimate of the origin based on the template
origin.
:type correct_prepick: bool
:param correct_prepick:
Whether to apply the prepick correction defined in the template.
Only applicable if template is not None
.. rubric:: Note
Works in place on Detection - over-writes previous events.
Corrects for prepick if template given.
"""
if template is not None and template.name != self.template_name:
Logger.info("Template names do not match: {0}: {1}".format(
template.name, self.template_name))
return
# Detect time must be valid QuakeML uri within resource_id.
# This will write a formatted string which is still
# readable by UTCDateTime
det_time = str(self.detect_time.strftime('%Y%m%dT%H%M%S.%f'))
ev = Event(resource_id=ResourceIdentifier(
id=self.template_name + '_' + det_time, prefix='smi:local'))
ev.creation_info = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
ev.comments.append(
Comment(text="Template: {0}".format(self.template_name)))
ev.comments.append(
Comment(text='threshold={0}'.format(self.threshold)))
ev.comments.append(
Comment(text='detect_val={0}'.format(self.detect_val)))
if self.chans is not None:
ev.comments.append(
Comment(text='channels used: {0}'.format(' '.join(
[str(pair) for pair in self.chans]))))
if template is not None:
template_st = template.st
if correct_prepick:
template_prepick = template.prepick
else:
template_prepick = 0
try:
template_picks = template.event.picks
except AttributeError:
template_picks = []
else:
template_prepick = 0
template_picks = []
min_template_tm = min([tr.stats.starttime for tr in template_st])
for tr in template_st:
if (tr.stats.station, tr.stats.channel) \
not in self.chans:
continue
elif tr.stats.__contains__("not_in_original"):
continue
elif np.all(np.isnan(tr.data)):
continue # The channel contains no data and was not used.
else:
pick_time = self.detect_time + (tr.stats.starttime -
min_template_tm)
pick_time += template_prepick
new_pick = Pick(time=pick_time,
waveform_id=WaveformStreamID(
network_code=tr.stats.network,
station_code=tr.stats.station,
channel_code=tr.stats.channel,
location_code=tr.stats.location))
template_pick = [
p for p in template_picks
if p.waveform_id.get_seed_string() ==
new_pick.waveform_id.get_seed_string()
]
if len(template_pick) == 0:
new_pick.phase_hint = None
elif len(template_pick) == 1:
new_pick.phase_hint = template_pick[0].phase_hint
else:
# Multiple picks for this trace in template
similar_traces = template_st.select(id=tr.id)
similar_traces.sort()
_index = similar_traces.traces.index(tr)
try:
new_pick.phase_hint = sorted(
template_pick,
key=lambda p: p.time)[_index].phase_hint
except IndexError:
Logger.error(f"No pick for trace: {tr.id}")
ev.picks.append(new_pick)
if estimate_origin and template is not None\
and template.event is not None:
try:
template_origin = (template.event.preferred_origin()
or template.event.origins[0])
except IndexError:
template_origin = None
if template_origin:
for pick in ev.picks:
comparison_pick = [
p for p in template.event.picks
if p.waveform_id.get_seed_string() ==
pick.waveform_id.get_seed_string()
]
comparison_pick = [
p for p in comparison_pick
if p.phase_hint == pick.phase_hint
]
if len(comparison_pick) > 0:
break
else:
Logger.error("Could not compute relative origin: no picks")
self.event = ev
return
origin_time = pick.time - (comparison_pick[0].time -
template_origin.time)
# Calculate based on difference between pick and origin?
_origin = Origin(
ResourceIdentifier(id="EQcorrscan/{0}_{1}".format(
self.template_name, det_time),
prefix="smi:local"),
time=origin_time,
evaluation_mode="automatic",
evaluation_status="preliminary",
creation_info=CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime()),
comments=[
Comment(
text=
"Origin automatically assigned based on template"
" origin: use with caution.")
],
latitude=template_origin.latitude,
longitude=template_origin.longitude,
depth=template_origin.depth,
time_errors=template_origin.time_errors,
latitude_errors=template_origin.latitude_errors,
longitude_errors=template_origin.longitude_errors,
depth_errors=template_origin.depth_errors,
depth_type=template_origin.depth_type,
time_fixed=False,
epicenter_fixed=template_origin.epicenter_fixed,
reference_system_id=template_origin.reference_system_id,
method_id=template_origin.method_id,
earth_model_id=template_origin.earth_model_id,
origin_type=template_origin.origin_type,
origin_uncertainty=template_origin.origin_uncertainty,
region=template_origin.region)
ev.origins = [_origin]
self.event = ev
return self
def full_test_event():
"""
Function to generate a basic, full test event
"""
test_event = Event()
test_event.origins.append(Origin(
time=UTCDateTime("2012-03-26") + 1.2, latitude=45.0, longitude=25.0,
depth=15000))
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.magnitudes.append(Magnitude(
mag=0.1, magnitude_type='ML', creation_info=CreationInfo('TES'),
origin_id=test_event.origins[0].resource_id))
test_event.magnitudes.append(Magnitude(
mag=0.5, magnitude_type='Mc', creation_info=CreationInfo('TES'),
origin_id=test_event.origins[0].resource_id))
test_event.magnitudes.append(Magnitude(
mag=1.3, magnitude_type='Ms', creation_info=CreationInfo('TES'),
origin_id=test_event.origins[0].resource_id))
# Define the test pick
_waveform_id_1 = WaveformStreamID(station_code='FOZ', channel_code='SHZ',
network_code='NZ')
_waveform_id_2 = WaveformStreamID(station_code='WTSZ', channel_code='BH1',
network_code=' ')
# Pick to associate with amplitude - 0
test_event.picks = [
Pick(waveform_id=_waveform_id_1, phase_hint='IAML',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_1, onset='impulsive', phase_hint='PN',
polarity='positive', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_1, phase_hint='IAML',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='SG',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.72,
evaluation_mode="manual"),
Pick(waveform_id=_waveform_id_2, onset='impulsive', phase_hint='PN',
polarity='undecidable', time=UTCDateTime("2012-03-26") + 1.62,
evaluation_mode="automatic")]
# Test a generic local magnitude amplitude pick
test_event.amplitudes = [
Amplitude(generic_amplitude=2.0, period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id, unit='m',
magnitude_hint='ML', category='point', type='AML'),
Amplitude(generic_amplitude=10,
pick_id=test_event.picks[1].resource_id,
waveform_id=test_event.picks[1].waveform_id, type='END',
category='duration', unit='s', magnitude_hint='Mc',
snr=2.3),
Amplitude(generic_amplitude=5.0, period=0.6,
pick_id=test_event.picks[2].resource_id,
waveform_id=test_event.picks[0].waveform_id, unit='m',
category='point', type='AML')]
test_event.origins[0].arrivals = [
Arrival(time_weight=0, phase=test_event.picks[1].phase_hint,
pick_id=test_event.picks[1].resource_id),
Arrival(time_weight=2, phase=test_event.picks[3].phase_hint,
pick_id=test_event.picks[3].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25),
Arrival(time_weight=2, phase=test_event.picks[4].phase_hint,
pick_id=test_event.picks[4].resource_id,
backazimuth_residual=5, time_residual=0.2, distance=15,
azimuth=25)]
# Add in error info (line E)
test_event.origins[0].quality = OriginQuality(
standard_error=0.01, azimuthal_gap=36)
# Origin uncertainty in Seisan is output as long-lat-depth, quakeML has
# semi-major and semi-minor
test_event.origins[0].origin_uncertainty = OriginUncertainty(
confidence_ellipsoid=ConfidenceEllipsoid(
semi_major_axis_length=3000, semi_minor_axis_length=1000,
semi_intermediate_axis_length=2000, major_axis_plunge=20,
major_axis_azimuth=100, major_axis_rotation=4))
test_event.origins[0].time_errors = QuantityError(uncertainty=0.5)
# Add in fault-plane solution info (line F) - Note have to check program
# used to determine which fields are filled....
test_event.focal_mechanisms.append(FocalMechanism(
nodal_planes=NodalPlanes(nodal_plane_1=NodalPlane(
strike=180, dip=20, rake=30, strike_errors=QuantityError(10),
dip_errors=QuantityError(10), rake_errors=QuantityError(20))),
method_id=ResourceIdentifier("smi:nc.anss.org/focalMechanism/FPFIT"),
creation_info=CreationInfo(agency_id="NC"), misfit=0.5,
station_distribution_ratio=0.8))
# Need to test high-precision origin and that it is preferred origin.
# Moment tensor includes another origin
test_event.origins.append(Origin(
time=UTCDateTime("2012-03-26") + 1.2, latitude=45.1, longitude=25.2,
depth=14500))
test_event.magnitudes.append(Magnitude(
mag=0.1, magnitude_type='MW', creation_info=CreationInfo('TES'),
origin_id=test_event.origins[-1].resource_id))
# Moment tensors go with focal-mechanisms
test_event.focal_mechanisms.append(FocalMechanism(
moment_tensor=MomentTensor(
derived_origin_id=test_event.origins[-1].resource_id,
moment_magnitude_id=test_event.magnitudes[-1].resource_id,
scalar_moment=100, tensor=Tensor(
m_rr=100, m_tt=100, m_pp=10, m_rt=1, m_rp=20, m_tp=15),
method_id=ResourceIdentifier(
'smi:nc.anss.org/momentTensor/BLAH'))))
return test_event
def cross_net(stream, env=False, master=False):
"""
Generate picks using a simple envelope cross-correlation.
Picks are made for each channel based on optimal moveout defined by
maximum cross-correlation with master trace. Master trace will be the
first trace in the stream if not set. Requires good inter-station
coherance.
:type stream: obspy.core.stream.Stream
:param stream: Stream to pick
:type env: bool
:param env: To compute cross-correlations on the envelope or not.
:type master: obspy.core.trace.Trace
:param master:
Trace to use as master, if False, will use the first trace in stream.
:returns: :class:`obspy.core.event.event.Event`
.. rubric:: Example
>>> from obspy import read
>>> from eqcorrscan.utils.picker import cross_net
>>> st = read()
>>> event = cross_net(st, env=True)
>>> print(event.creation_info.author)
EQcorrscan
.. warning::
This routine is not designed for accurate picking, rather it can be
used for a first-pass at picks to obtain simple locations. Based on
the waveform-envelope cross-correlation method.
"""
event = Event()
event.origins.append(Origin())
event.creation_info = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
event.comments.append(Comment(text='cross_net'))
samp_rate = stream[0].stats.sampling_rate
if not env:
Logger.info('Using the raw data')
st = stream.copy()
st.resample(samp_rate)
else:
st = stream.copy()
Logger.info('Computing envelope')
for tr in st:
tr.resample(samp_rate)
tr.data = envelope(tr.data)
if not master:
master = st[0]
else:
master = master
master.data = np.nan_to_num(master.data)
for i, tr in enumerate(st):
tr.data = np.nan_to_num(tr.data)
Logger.debug('Comparing {0} with the master'.format(tr.id))
shift_len = int(0.3 * len(tr))
Logger.debug('Shift length is set to ' + str(shift_len) + ' samples')
index, cc = xcorr(master, tr, shift_len)
wav_id = WaveformStreamID(station_code=tr.stats.station,
channel_code=tr.stats.channel,
network_code=tr.stats.network)
event.picks.append(
Pick(time=tr.stats.starttime + (index / tr.stats.sampling_rate),
waveform_id=wav_id,
phase_hint='S',
onset='emergent'))
Logger.debug(event.picks[i])
event.origins[0].time = min([pick.time for pick in event.picks]) - 1
# event.origins[0].latitude = float('nan')
# event.origins[0].longitude = float('nan')
# Set arbitrary origin time
del st
return event
def full_test_event():
"""
Function to generate a basic, full test event
"""
from obspy.core.event import Pick, WaveformStreamID, Arrival, Amplitude
from obspy.core.event import Event, Origin, Magnitude
from obspy.core.event import EventDescription, CreationInfo
from obspy import UTCDateTime
test_event = Event()
test_event.origins.append(Origin())
test_event.origins[0].time = UTCDateTime("2012-03-26") + 1
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.origins[0].latitude = 45.0
test_event.origins[0].longitude = 25.0
test_event.origins[0].depth = 15000
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.origins[0].time_errors['Time_Residual_RMS'] = 0.01
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[0].mag = 0.1
test_event.magnitudes[0].magnitude_type = 'ML'
test_event.magnitudes[0].creation_info = CreationInfo('TES')
test_event.magnitudes[0].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[1].mag = 0.5
test_event.magnitudes[1].magnitude_type = 'Mc'
test_event.magnitudes[1].creation_info = CreationInfo('TES')
test_event.magnitudes[1].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[2].mag = 1.3
test_event.magnitudes[2].magnitude_type = 'Ms'
test_event.magnitudes[2].creation_info = CreationInfo('TES')
test_event.magnitudes[2].origin_id = test_event.origins[0].resource_id
# Define the test pick
_waveform_id_1 = WaveformStreamID(station_code='FOZ',
channel_code='SHZ',
network_code='NZ')
_waveform_id_2 = WaveformStreamID(station_code='WTSZ',
channel_code='BH1',
network_code=' ')
# Pick to associate with amplitude
test_event.picks.append(
Pick(waveform_id=_waveform_id_1,
phase_hint='IAML',
polarity='undecidable',
time=UTCDateTime("2012-03-26") + 1.68))
# Need a second pick for coda
test_event.picks.append(
Pick(waveform_id=_waveform_id_1,
onset='impulsive',
phase_hint='PN',
polarity='positive',
time=UTCDateTime("2012-03-26") + 1.68))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2,
onset='impulsive',
phase_hint='SG',
polarity='undecidable',
time=UTCDateTime("2012-03-26") + 1.72))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2,
onset='impulsive',
phase_hint='PN',
polarity='undecidable',
time=UTCDateTime("2012-03-26") + 1.62))
# Test a generic local magnitude amplitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=2.0,
period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id,
unit='m',
magnitude_hint='Ml'))
# Test a coda magnitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=10,
pick_id=test_event.picks[1].resource_id,
waveform_id=test_event.picks[1].waveform_id,
type='END',
category='duration',
unit='s',
magnitude_hint='Mc',
snr=2.3))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2,
phase=test_event.picks[2].phase_hint,
pick_id=test_event.picks[2].resource_id,
backazimuth_residual=5,
time_residual=0.2,
distance=15,
azimuth=25))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2,
phase=test_event.picks[3].phase_hint,
pick_id=test_event.picks[3].resource_id,
backazimuth_residual=5,
time_residual=0.2,
distance=15,
azimuth=25))
return test_event
def stalta_pick(stream, stalen, ltalen, trig_on, trig_off, freqmin=False,
freqmax=False, debug=0, show=False):
"""
Basic sta/lta picker, suggest using alternative in obspy.
Simple sta-lta (short-term average/long-term average) picker, using \
obspy's stalta routine to generate the characteristic function.
Currently very basic quick wrapper, there are many other (better) options \
in obspy, found \
`here <http://docs.obspy.org/packages/autogen/obspy.signal.trigger.html>`_.
:type stream: obspy.Stream
:param stream: The stream to pick on, can be any number of channels.
:type stalen: float
:param stalen: Length of the short-term average window in seconds.
:type ltalen: float
:param ltalen: Length of the long-term average window in seconds.
:type trig_on: float
:param trig_on: sta/lta ratio to trigger a detection/pick
:type trig_off: float
:param trig_off: sta/lta ratio to turn the trigger off - no further picks\
will be made between exceeding trig_on until trig_off is reached.
:type freqmin: float
:param freqmin: Low-cut frequency in Hz for bandpass filter
:type freqmax: float
:param freqmax: High-cut frequency in Hz for bandpass filter
:type debug: int
:param debug: Debug output level from 0-5.
:type show: bool
:param show: Show picks on waveform.
:returns: obspy.core.event.Event
.. rubric:: Example
>>> from obspy import read
>>> from eqcorrscan.utils.picker import stalta_pick
>>> st = read()
>>> event = stalta_pick(st, stalen=0.2, ltalen=4, trig_on=10,
... trig_off=1, freqmin=3.0, freqmax=20.0)
>>> event.creation_info.author
'EQcorrscan'
"""
from obspy.signal.trigger import classic_sta_lta, trigger_onset
from obspy.signal.trigger import plot_trigger
from obspy import UTCDateTime
from obspy.core.event import Event, Pick, WaveformStreamID
from obspy.core.event import CreationInfo, Comment, Origin
import eqcorrscan.utils.plotting as plotting
event = Event()
event.origins.append(Origin())
event.creation_info = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
event.comments.append(Comment(text='stalta'))
picks = []
for tr in stream:
# We are going to assume, for now, that if the pick is made on the
# horizontal channel then it is an S, otherwise we will assume it is
# a P-phase: obviously a bad assumption...
if tr.stats.channel[-1] == 'Z':
phase = 'P'
else:
phase = 'S'
if freqmin and freqmax:
tr.detrend('simple')
tr.filter('bandpass', freqmin=freqmin, freqmax=freqmax,
corners=3, zerophase=True)
df = tr.stats.sampling_rate
cft = classic_sta_lta(tr.data, int(stalen * df), int(ltalen * df))
if debug > 3:
plot_trigger(tr, cft, trig_on, trig_off)
triggers = trigger_onset(cft, trig_on, trig_off)
for trigger in triggers:
on = tr.stats.starttime + (trigger[0] / df)
# off = tr.stats.starttime + (trigger[1] / df)
wav_id = WaveformStreamID(station_code=tr.stats.station,
channel_code=tr.stats.channel,
network_code=tr.stats.network)
pick = Pick(waveform_id=wav_id, phase_hint=phase, time=on)
if debug > 2:
print('Pick made:')
print(pick)
picks.append(pick)
# QC picks
del pick
pick_stations = list(set([pick.waveform_id.station_code for pick in picks]))
for pick_station in pick_stations:
station_picks = [pick for pick in picks if
pick.waveform_id.station_code == pick_station]
# If P-pick is after S-picks, remove it.
p_time = [pick.time for pick in station_picks if pick.phase_hint == 'P']
s_time = [pick.time for pick in station_picks if pick.phase_hint == 'S']
if p_time > s_time:
p_pick = [pick for pick in station_picks if pick.phase_hint == 'P']
for pick in p_pick:
print('P pick after S pick, removing P pick')
picks.remove(pick)
if show:
plotting.pretty_template_plot(stream, picks=picks, title='Autopicks',
size=(8, 9))
event.picks = picks
event.origins[0].time = min([pick.time for pick in event.picks]) - 1
event.origins[0].latitude = float('nan')
event.origins[0].longitude = float('nan')
# Set arbitrary origin time
return event
def _detect(detector,
st,
threshold,
trig_int,
moveout=0,
min_trig=0,
process=True,
extract_detections=False,
cores=1):
"""
Detect within continuous data using the subspace method.
Not to be called directly, use the detector.detect method.
:type detector: eqcorrscan.core.subspace.Detector
:param detector: Detector to use.
:type st: obspy.core.stream.Stream
:param st: Un-processed stream to detect within using the subspace \
detector
:type threshold: float
:param threshold: Threshold value for detections between 0-1
:type trig_int: float
:param trig_int: Minimum trigger interval in seconds.
:type moveout: float
:param moveout: Maximum allowable moveout window for non-multiplexed,
network detection. See note.
:type min_trig: int
:param min_trig: Minimum number of stations exceeding threshold for \
non-multiplexed, network detection. See note.
:type process: bool
:param process: Whether or not to process the stream according to the \
parameters defined by the detector. Default is to process the \
data (True).
:type extract_detections: bool
:param extract_detections: Whether to extract waveforms for each \
detection or not, if true will return detections and streams.
:return: list of detections
:rtype: list of eqcorrscan.core.match_filter.Detection
"""
detections = []
# First process the stream
if process:
Logger.info('Processing Stream')
stream, stachans = _subspace_process(
streams=[st.copy()],
lowcut=detector.lowcut,
highcut=detector.highcut,
filt_order=detector.filt_order,
sampling_rate=detector.sampling_rate,
multiplex=detector.multiplex,
stachans=detector.stachans,
parallel=True,
align=False,
shift_len=None,
reject=False,
cores=cores)
else:
# Check the sampling rate at the very least
for tr in st:
if not tr.stats.sampling_rate == detector.sampling_rate:
raise ValueError('Sampling rates do not match.')
stream = [st]
stachans = detector.stachans
outtic = time.clock()
# If multiplexed, how many samples do we increment by?
if detector.multiplex:
Nc = len(detector.stachans)
else:
Nc = 1
# Here do all ffts
fft_vars = _do_ffts(detector, stream, Nc)
Logger.info('Computing detection statistics')
Logger.info('Preallocating stats matrix')
stats = np.zeros(
(len(stream[0]), (len(stream[0][0]) // Nc) - (fft_vars[4] // Nc) + 1))
for det_freq, data_freq_sq, data_freq, i in zip(fft_vars[0], fft_vars[1],
fft_vars[2],
np.arange(len(stream[0]))):
# Calculate det_statistic in frequency domain
stats[i] = _det_stat_freq(det_freq, data_freq_sq, data_freq,
fft_vars[3], Nc, fft_vars[4], fft_vars[5])
Logger.info('Stats matrix is shape %s' % str(stats[i].shape))
trig_int_samples = detector.sampling_rate * trig_int
Logger.info('Finding peaks')
peaks = []
for i in range(len(stream[0])):
peaks.append(
findpeaks.find_peaks2_short(arr=stats[i],
thresh=threshold,
trig_int=trig_int_samples))
if not detector.multiplex:
# Conduct network coincidence triggering
peaks = findpeaks.coin_trig(peaks=peaks,
samp_rate=detector.sampling_rate,
moveout=moveout,
min_trig=min_trig,
stachans=stachans,
trig_int=trig_int)
else:
peaks = peaks[0]
if len(peaks) > 0:
for peak in peaks:
detecttime = st[0].stats.starttime + \
(peak[1] / detector.sampling_rate)
rid = ResourceIdentifier(id=detector.name + '_' + str(detecttime),
prefix='smi:local')
ev = Event(resource_id=rid)
cr_i = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
ev.creation_info = cr_i
# All detection info in Comments for lack of a better idea
thresh_str = 'threshold=' + str(threshold)
ccc_str = 'detect_val=' + str(peak[0])
used_chans = 'channels used: ' +\
' '.join([str(pair) for pair in detector.stachans])
ev.comments.append(Comment(text=thresh_str))
ev.comments.append(Comment(text=ccc_str))
ev.comments.append(Comment(text=used_chans))
for stachan in detector.stachans:
tr = st.select(station=stachan[0], channel=stachan[1])
if tr:
net_code = tr[0].stats.network
else:
net_code = ''
pick_tm = detecttime
wv_id = WaveformStreamID(network_code=net_code,
station_code=stachan[0],
channel_code=stachan[1])
ev.picks.append(Pick(time=pick_tm, waveform_id=wv_id))
detections.append(
Detection(template_name=detector.name,
detect_time=detecttime,
no_chans=len(detector.stachans),
detect_val=peak[0],
threshold=threshold,
typeofdet='subspace',
threshold_type='abs',
threshold_input=threshold,
chans=detector.stachans,
event=ev))
outtoc = time.clock()
Logger.info('Detection took %s seconds' % str(outtoc - outtic))
if extract_detections:
detection_streams = extract_from_stream(st, detections)
return detections, detection_streams
return detections
def readheader(sfile):
"""
Read header information from a seisan nordic format S-file.
Returns an obspy.core.event.Catalog type: note this changed for version \
0.1.0 from the inbuilt class types.
:type sfile: str
:param sfile: Path to the s-file
:returns: :class: obspy.core.event.Event
>>> event = readheader('eqcorrscan/tests/test_data/REA/TEST_/' +
... '01-0411-15L.S201309')
>>> print(event.origins[0].time)
2013-09-01T04:11:15.700000Z
"""
import warnings
from obspy.core.event import Event, Origin, Magnitude, Comment
from obspy.core.event import EventDescription, CreationInfo
f = open(sfile, 'r')
# Base populate to allow for empty parts of file
new_event = Event()
topline = f.readline()
if not len(topline.rstrip()) == 80:
raise IOError('s-file has a corrupt header, not 80 char long')
f.seek(0)
for line in f:
if line[79] in [' ', '1']:
topline = line
break
if line[79] == '7':
raise IOError('No header found, corrupt s-file?')
try:
sfile_seconds = int(topline[16:18])
if sfile_seconds == 60:
sfile_seconds = 0
add_seconds = 60
else:
add_seconds = 0
new_event.origins.append(Origin())
new_event.origins[0].time = UTCDateTime(int(topline[1:5]),
int(topline[6:8]),
int(topline[8:10]),
int(topline[11:13]),
int(topline[13:15]),
sfile_seconds,
int(topline[19:20]) *
100000)\
+ add_seconds
except:
warnings.warn("Couldn't read a date from sfile: " + sfile)
new_event.origins.append(Origin(time=UTCDateTime(0)))
# new_event.loc_mod_ind=topline[20]
new_event.event_descriptions.append(EventDescription())
new_event.event_descriptions[0].text = topline[21:23]
# new_event.ev_id=topline[22]
if not _float_conv(topline[23:30]) == 999:
new_event.origins[0].latitude = _float_conv(topline[23:30])
new_event.origins[0].longitude = _float_conv(topline[31:38])
new_event.origins[0].depth = _float_conv(topline[39:43]) * 1000
# else:
# # The origin 'requires' a lat & long
# new_event.origins[0].latitude = float('NaN')
# new_event.origins[0].longitude = float('NaN')
# new_event.origins[0].depth = float('NaN')
# new_event.depth_ind = topline[44]
# new_event.loc_ind = topline[45]
new_event.creation_info = CreationInfo(agency_id=topline[45:48].strip())
ksta = Comment(text='Number of stations=' + topline[49:51].strip())
new_event.origins[0].comments.append(ksta)
# new_event.origins[0].nsta??? = _int_conv(topline[49:51])
if not _float_conv(topline[51:55]) == 999:
new_event.origins[0].time_errors['Time_Residual_RMS'] = \
_float_conv(topline[51:55])
# Read in magnitudes if they are there.
if len(topline[59].strip()) > 0:
new_event.magnitudes.append(Magnitude())
new_event.magnitudes[0].mag = _float_conv(topline[56:59])
new_event.magnitudes[0].magnitude_type = topline[59]
new_event.magnitudes[0].creation_info = \
CreationInfo(agency_id=topline[60:63].strip())
new_event.magnitudes[0].origin_id = new_event.origins[0].\
resource_id
if len(topline[67].strip()) > 0:
new_event.magnitudes.append(Magnitude())
new_event.magnitudes[1].mag = _float_conv(topline[64:67])
new_event.magnitudes[1].magnitude_type = topline[67]
new_event.magnitudes[1].creation_info = \
CreationInfo(agency_id=topline[68:71].strip())
new_event.magnitudes[1].origin_id = new_event.origins[0].\
resource_id
if len(topline[75].strip()) > 0:
new_event.magnitudes.append(Magnitude())
new_event.magnitudes[2].mag = _float_conv(topline[72:75])
new_event.magnitudes[2].magnitude_type = topline[75]
new_event.magnitudes[2].creation_info = \
CreationInfo(agency_id=topline[76:79].strip())
new_event.magnitudes[2].origin_id = new_event.origins[0].\
resource_id
f.close()
# convert the nordic notation of magnitude to more general notation
for _magnitude in new_event.magnitudes:
_magnitude.magnitude_type = _nortoevmag(_magnitude.magnitude_type)
# Set the useful things like preferred magnitude and preferred origin
new_event.preferred_origin_id = str(new_event.origins[0].resource_id)
if len(new_event.magnitudes) > 1:
try:
# Select moment first, then local, then
mag_filter = [
'MW', 'Mw', 'ML', 'Ml', 'MB', 'Mb', 'MS', 'Ms', 'Mc', 'MC'
]
_magnitudes = [(m.magnitude_type, m.resource_id)
for m in new_event.magnitudes]
preferred_magnitude = sorted(_magnitudes,
key=lambda x: mag_filter.index(x[0]))
new_event.preferred_magnitude_id = str(preferred_magnitude[0][1])
except ValueError:
# If there is a magnitude not specified in filter
new_event.preferred_magnitude_id =\
str(new_event.magnitudes[0].resource_id)
elif len(new_event.magnitudes) == 1:
new_event.preferred_magnitude_id =\
str(new_event.magnitudes[0].resource_id)
return new_event
def stalta_pick(stream,
stalen,
ltalen,
trig_on,
trig_off,
freqmin=False,
freqmax=False,
show=False):
"""
Basic sta/lta picker, suggest using alternative in obspy.
Simple sta/lta (short-term average/long-term average) picker, using
obspy's :func:`obspy.signal.trigger.classic_sta_lta` routine to generate
the characteristic function.
Currently very basic quick wrapper, there are many other (better) options
in obspy in the :mod:`obspy.signal.trigger` module.
:type stream: obspy.core.stream.Stream
:param stream: The stream to pick on, can be any number of channels.
:type stalen: float
:param stalen: Length of the short-term average window in seconds.
:type ltalen: float
:param ltalen: Length of the long-term average window in seconds.
:type trig_on: float
:param trig_on: sta/lta ratio to trigger a detection/pick
:type trig_off: float
:param trig_off: sta/lta ratio to turn the trigger off - no further picks\
will be made between exceeding trig_on until trig_off is reached.
:type freqmin: float
:param freqmin: Low-cut frequency in Hz for bandpass filter
:type freqmax: float
:param freqmax: High-cut frequency in Hz for bandpass filter
:type show: bool
:param show: Show picks on waveform.
:returns: :class:`obspy.core.event.event.Event`
.. rubric:: Example
>>> from obspy import read
>>> from eqcorrscan.utils.picker import stalta_pick
>>> st = read()
>>> event = stalta_pick(st, stalen=0.2, ltalen=4, trig_on=10,
... trig_off=1, freqmin=3.0, freqmax=20.0)
>>> print(event.creation_info.author)
EQcorrscan
.. warning::
This function is not designed for accurate picking, rather it can give
a first idea of whether picks may be possible. Proceed with caution.
"""
event = Event()
event.origins.append(Origin())
event.creation_info = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
event.comments.append(Comment(text='stalta'))
picks = []
for tr in stream:
# We are going to assume, for now, that if the pick is made on the
# horizontal channel then it is an S, otherwise we will assume it is
# a P-phase: obviously a bad assumption...
if tr.stats.channel[-1] == 'Z':
phase = 'P'
else:
phase = 'S'
if freqmin and freqmax:
tr.detrend('simple')
tr.filter('bandpass',
freqmin=freqmin,
freqmax=freqmax,
corners=3,
zerophase=True)
df = tr.stats.sampling_rate
cft = classic_sta_lta(tr.data, int(stalen * df), int(ltalen * df))
triggers = trigger_onset(cft, trig_on, trig_off)
for trigger in triggers:
on = tr.stats.starttime + (trigger[0] / df)
# off = tr.stats.starttime + (trigger[1] / df)
wav_id = WaveformStreamID(station_code=tr.stats.station,
channel_code=tr.stats.channel,
network_code=tr.stats.network)
p = Pick(waveform_id=wav_id, phase_hint=phase, time=on)
Logger.info('Pick made: {0}'.format(p))
picks.append(p)
# QC picks
pick_stations = list(set([pick.waveform_id.station_code
for pick in picks]))
for pick_station in pick_stations:
station_picks = [
pick for pick in picks
if pick.waveform_id.station_code == pick_station
]
# If P-pick is after S-picks, remove it.
p_time = [
pick.time for pick in station_picks if pick.phase_hint == 'P'
]
s_time = [
pick.time for pick in station_picks if pick.phase_hint == 'S'
]
if p_time > s_time:
p_pick = [pick for pick in station_picks if pick.phase_hint == 'P']
for pick in p_pick:
Logger.info('P pick after S pick, removing P pick')
picks.remove(pick)
event.picks = picks
if show:
plotting.pretty_template_plot(stream,
event=event,
title='Autopicks',
size=(8, 9))
if len(event.picks) > 0:
event.origins[0].time = min([pick.time for pick in event.picks]) - 1
# event.origins[0].latitude = float('nan')
# event.origins[0].longitude = float('nan')
# Set arbitrary origin time
return event
def brightness(stations,
nodes,
lags,
stream,
threshold,
thresh_type,
template_length,
template_saveloc,
coherence_thresh,
coherence_stations=['all'],
coherence_clip=False,
gap=2.0,
clip_level=100,
instance=0,
pre_pick=0.2,
plotvar=False,
plotsave=True,
cores=1,
debug=0,
mem_issue=False):
"""
Calculate the brightness function for a single day.
Written to calculate the brightness function for a single day of data,
using moveouts from a 3D travel-time grid.
.. Note::
Data in stream must be all of the same length and have the same
sampling rates, see :func:`eqcorrscan.utils.pre_processing.dayproc`
:type stations: list
:param stations:
List of station names from in the form where stations[i] refers to
nodes[i][:] and lags[i][:]
:type nodes: list
:param nodes:
List of node points where nodes[i] refers to stations[i] and
nodes[:][:][0] is latitude in degrees, nodes[:][:][1] is longitude in
degrees, nodes[:][:][2] is depth in km.
:type lags: numpy.ndarray
:param lags:
Array of arrays where lags[i][:] refers to stations[i]. lags[i][j]
should be the delay to the nodes[i][j] for stations[i] in seconds.
:type stream: obspy.core.stream.Stream
:param stream: Data through which to look for detections.
:type threshold: float
:param threshold:
Threshold value for detection of template within the brightness
function.
:type thresh_type: str
:param thresh_type:
Either MAD or abs where MAD is the Median Absolute Deviation and abs
is an absolute brightness.
:type template_length: float
:param template_length: Length of template to extract in seconds
:type template_saveloc: str
:param template_saveloc: Path of where to save the templates.
:type coherence_thresh: tuple
:param coherence_thresh:
Threshold for removing incoherent peaks in the network response,
those below this will not be used as templates. Must be in the
form of (a,b) where the coherence is given by: :math:`a-kchan/b`
where kchan is the number of channels used to compute the
coherence.
:type coherence_stations: list
:param coherence_stations:
List of stations to use in the coherence thresholding - defaults to
`all` which uses all the stations.
:type coherence_clip: tuple
:param coherence_clip:
Start and end in seconds of data to window around, defaults to False,
which uses all the data given.
:type gap: float
:param gap: Minimum inter-event time in seconds for detections.
:type clip_level: float
:param clip_level:
Multiplier applied to the mean deviation of the energy as an upper
limit, used to remove spikes (earthquakes, lightning, electrical
spikes) from the energy stack.
:type instance: int
:param instance:
Optional, used for tracking when using a distributed computing system.
:type pre_pick: float
:param pre_pick: Seconds before the detection time to include in template
:type plotvar: bool
:param plotvar: Turn plotting on or off
:type plotsave: bool
:param plotsave:
Save or show plots, if `False` will try and show the plots on screen -
as this is designed for bulk use this is set to `True` to save any
plots rather than show them if you create them - changes the backend
of matplotlib, so if is set to `False` you will see NO PLOTS!
:type cores: int
:param cores: Number of cores to use, defaults to 1.
:type debug: int
:param debug: Debug level from 0-5, higher is more output.
:type mem_issue: bool
:param mem_issue:
Set to True to write temporary variables to disk rather than store in
memory, slow.
:return: list of templates as :class:`obspy.core.stream.Stream` objects
:rtype: list
"""
if plotsave:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ioff()
from eqcorrscan.utils import plotting
from eqcorrscan.utils.debug_log import debug_print
# Check that we actually have the correct stations
realstations = []
for station in stations:
st = stream.select(station=station)
if st:
realstations += station
del st
stream_copy = stream.copy()
# Force convert to int16
for tr in stream_copy:
# int16 max range is +/- 32767
if max(abs(tr.data)) > 32767:
tr.data = 32767 * (tr.data / max(abs(tr.data)))
# Make sure that the data aren't clipped it they are high gain
# scale the data
tr.data = tr.data.astype(np.int16)
# The internal _node_loop converts energy to int16 too to conserve memory,
# to do this it forces the maximum of a single energy trace to be 500 and
# normalises to this level - this only works for fewer than 65 channels of
# data
if len(stream_copy) > 130:
raise BrightnessError(
'Too many streams, either re-code and cope with either more memory'
' usage, or less precision, or reduce data volume')
# Loop through each node in the input
# Linear run
print('Computing the energy stacks')
# Parallel run
num_cores = cores
if num_cores > len(nodes):
num_cores = len(nodes)
if num_cores > cpu_count():
num_cores = cpu_count()
if mem_issue and not os.path.isdir('tmp' + str(instance)):
os.makedirs('tmp' + str(instance))
pool = Pool(processes=num_cores)
results = [
pool.apply_async(
_node_loop, (stations, ), {
'lags': lags[:, i],
'stream': stream,
'i': i,
'clip_level': clip_level,
'mem_issue': mem_issue,
'instance': instance
}) for i in range(len(nodes))
]
pool.close()
if not mem_issue:
print('Computing the cumulative network response from memory')
energy = [p.get() for p in results]
pool.join()
energy.sort(key=lambda tup: tup[0])
energy = [node[1] for node in energy]
energy = np.concatenate(energy, axis=0)
print(energy.shape)
else:
pool.join()
del results
# Now compute the cumulative network response and then detect possible
# events
if not mem_issue:
print(energy.shape)
indices = np.argmax(energy, axis=0) # Indices of maximum energy
print(indices.shape)
cum_net_resp = np.array([np.nan] * len(indices))
cum_net_resp[0] = energy[indices[0]][0]
peak_nodes = [nodes[indices[0]]]
for i in range(1, len(indices)):
cum_net_resp[i] = energy[indices[i]][i]
peak_nodes.append(nodes[indices[i]])
del energy, indices
else:
print('Reading the temp files and computing network response')
node_splits = int(len(nodes) // num_cores)
print(node_splits)
indices = []
for i in range(num_cores):
indices.append(
list(np.arange(node_splits * i, node_splits * (i + 1))))
indices[-1] += list(np.arange(node_splits * (i + 1), len(nodes)))
# results = [_cum_net_resp(node_lis=indices[i], instance=instance)
# for i in range(num_cores)]
pool = Pool(processes=num_cores)
results = [
pool.apply_async(_cum_net_resp, args=(indices[i], instance))
for i in range(num_cores)
]
pool.close()
results = [p.get() for p in results]
pool.join()
responses = [result[0] for result in results]
print(np.shape(responses))
node_indices = [result[1] for result in results]
cum_net_resp = np.array(responses)
indices = np.argmax(cum_net_resp, axis=0)
print(indices.shape)
print(cum_net_resp.shape)
cum_net_resp = np.array(
[cum_net_resp[indices[i]][i] for i in range(len(indices))])
peak_nodes = [
nodes[node_indices[indices[i]][i]] for i in range(len(indices))
]
del indices, node_indices
if plotvar:
cum_net_trace = Stream(
Trace(data=cum_net_resp,
header=Stats({
'station': 'NR',
'channel': '',
'network': 'Z',
'location': '',
'starttime': stream[0].stats.starttime,
'sampling_rate': stream[0].stats.sampling_rate
})))
cum_net_trace += stream.select(channel='*N')
cum_net_trace += stream.select(channel='*1')
cum_net_trace.sort(['network', 'station', 'channel'])
# Find detection within this network response
print('Finding detections in the cumulative network response')
detections = _find_detections(cum_net_resp, peak_nodes, threshold,
thresh_type, stream[0].stats.sampling_rate,
realstations, gap)
del cum_net_resp
templates = []
nodesout = []
good_detections = []
if detections:
print('Converting detections into templates')
# Generate a catalog of detections
# detections_cat = Catalog()
for j, detection in enumerate(detections):
debug_print(
'Converting for detection %i of %i' % (j, len(detections)), 3,
debug)
# Create an event for each detection
event = Event()
# Set up some header info for the event
event.event_descriptions.append(EventDescription())
event.event_descriptions[0].text = 'Brightness detection'
event.creation_info = CreationInfo(agency_id='EQcorrscan')
copy_of_stream = deepcopy(stream_copy)
# Convert detections to obspy.core.event type -
# name of detection template is the node.
node = (detection.template_name.split('_')[0],
detection.template_name.split('_')[1],
detection.template_name.split('_')[2])
# Look up node in nodes and find the associated lags
index = nodes.index(
(float(node[0]), float(node[1]), float(node[2])))
detect_lags = lags[:, index]
ksta = Comment(text='Number of stations=' + str(len(detect_lags)))
event.origins.append(Origin())
event.origins[0].comments.append(ksta)
event.origins[0].time = copy_of_stream[0].stats.starttime +\
detect_lags[0] + detection.detect_time
event.origins[0].latitude = node[0]
event.origins[0].longitude = node[1]
event.origins[0].depth = node[2]
for i, detect_lag in enumerate(detect_lags):
station = stations[i]
st = copy_of_stream.select(station=station)
if len(st) != 0:
for tr in st:
_waveform_id = WaveformStreamID(
station_code=tr.stats.station,
channel_code=tr.stats.channel,
network_code=tr.stats.network)
event.picks.append(
Pick(waveform_id=_waveform_id,
time=tr.stats.starttime + detect_lag +
detection.detect_time + pre_pick,
onset='emergent',
evalutation_mode='automatic'))
debug_print('Generating template for detection: %i' % j, 0, debug)
template = template_gen(picks=event.picks,
st=copy_of_stream,
length=template_length,
swin='all')
template_name = template_saveloc + '/' +\
str(template[0].stats.starttime) + '.ms'
# In the interests of RAM conservation we write then read
# Check coherency here!
temp_coher, kchan = coherence(template, coherence_stations,
coherence_clip)
coh_thresh = float(coherence_thresh[0]) - kchan / \
float(coherence_thresh[1])
coherent = False
if temp_coher > coh_thresh:
template.write(template_name, format="MSEED")
print('Written template as: ' + template_name)
print('---------------------------------coherence LEVEL: ' +
str(temp_coher))
coherent = True
debug_print(
'Template was incoherent, coherence level: ' +
str(temp_coher), 0, debug)
coherent = False
del copy_of_stream, tr, template
if coherent:
templates.append(obsread(template_name))
nodesout += [node]
good_detections.append(detection)
debug_print('No template for you', 0, debug)
# detections_cat += event
if plotvar:
good_detections = [(cum_net_trace[-1].stats.starttime +
detection.detect_time).datetime
for detection in good_detections]
if not plotsave:
plotting.NR_plot(cum_net_trace[0:-1],
Stream(cum_net_trace[-1]),
detections=good_detections,
size=(18.5, 10),
title='Network response')
# cum_net_trace.plot(size=(800,600), equal_scale=False)
else:
savefile = 'plots/' +\
cum_net_trace[0].stats.starttime.datetime.strftime('%Y%m%d') +\
'_NR_timeseries.pdf'
plotting.NR_plot(cum_net_trace[0:-1],
Stream(cum_net_trace[-1]),
detections=good_detections,
size=(18.5, 10),
save=True,
savefile=savefile,
title='Network response')
nodesout = list(set(nodesout))
return templates, nodesout
def _readheader(f):
"""
Internal header reader.
:type f: file
:param f: File open in read-mode.
:returns: :class:`~obspy.core.event.event.Event`
"""
f.seek(0)
# Base populate to allow for empty parts of file
new_event = Event()
topline = _get_headline(f=f)
if not topline:
raise NordicParsingError('No header found, or incorrect '
'formatting: corrupt s-file')
try:
sfile_seconds = int(topline[16:18])
if sfile_seconds == 60:
sfile_seconds = 0
add_seconds = 60
else:
add_seconds = 0
new_event.origins.append(Origin())
new_event.origins[0].time = UTCDateTime(int(topline[1:5]),
int(topline[6:8]),
int(topline[8:10]),
int(topline[11:13]),
int(topline[13:15]),
sfile_seconds,
int(topline[19:20]) *
100000)\
+ add_seconds
except:
NordicParsingError("Couldn't read a date from sfile")
# new_event.loc_mod_ind=topline[20]
new_event.event_descriptions.append(EventDescription())
new_event.event_descriptions[0].text = topline[21:23]
# new_event.ev_id=topline[22]
try:
new_event.origins[0].latitude = float(topline[23:30])
new_event.origins[0].longitude = float(topline[31:38])
new_event.origins[0].depth = float(topline[39:43]) * 1000
except ValueError:
# The origin 'requires' a lat & long
new_event.origins[0].latitude = None
new_event.origins[0].longitude = None
new_event.origins[0].depth = None
# new_event.depth_ind = topline[44]
# new_event.loc_ind = topline[45]
new_event.creation_info = CreationInfo(agency_id=topline[45:48].strip())
ksta = Comment(text='Number of stations=' + topline[49:51].strip())
new_event.origins[0].comments.append(ksta)
if _float_conv(topline[51:55]) is not None:
new_event.origins[0].time_errors['Time_Residual_RMS'] = \
_float_conv(topline[51:55])
# Read in magnitudes if they are there.
for index in [59, 67, 75]:
if not topline[index].isspace():
new_event.magnitudes.append(Magnitude())
new_event.magnitudes[-1].mag = _float_conv(
topline[index - 3:index])
new_event.magnitudes[-1].magnitude_type = \
_nortoevmag(topline[index])
new_event.magnitudes[-1].creation_info = \
CreationInfo(agency_id=topline[index + 1:index + 4].strip())
new_event.magnitudes[-1].origin_id = new_event.origins[0].\
resource_id
# Set the useful things like preferred magnitude and preferred origin
new_event.preferred_origin_id = new_event.origins[0].resource_id
try:
# Select moment first, then local, then
mag_filter = ['MW', 'Mw', 'ML', 'Ml', 'MB', 'Mb',
'MS', 'Ms', 'MC', 'Mc']
_magnitudes = [(m.magnitude_type, m.resource_id)
for m in new_event.magnitudes]
preferred_magnitude = sorted(_magnitudes,
key=lambda x: mag_filter.index(x[0]))[0]
new_event.preferred_magnitude_id = preferred_magnitude[1]
except (ValueError, IndexError):
# If there is a magnitude not specified in filter
try:
new_event.preferred_magnitude_id = new_event.magnitudes[0].\
resource_id
except IndexError:
pass
return new_event
def full_test_event():
"""
Function to generate a basic, full test event
"""
test_event = Event()
test_event.origins.append(
Origin(time=UTCDateTime("2012-03-26") + 1.2,
latitude=45.0,
longitude=25.0,
depth=15000))
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.magnitudes.append(
Magnitude(mag=0.1,
magnitude_type='ML',
creation_info=CreationInfo('TES'),
origin_id=test_event.origins[0].resource_id))
test_event.magnitudes.append(
Magnitude(mag=0.5,
magnitude_type='Mc',
creation_info=CreationInfo('TES'),
origin_id=test_event.origins[0].resource_id))
test_event.magnitudes.append(
Magnitude(mag=1.3,
magnitude_type='Ms',
creation_info=CreationInfo('TES'),
origin_id=test_event.origins[0].resource_id))
# Define the test pick
_waveform_id_1 = WaveformStreamID(station_code='FOZ',
channel_code='SHZ',
network_code='NZ')
_waveform_id_2 = WaveformStreamID(station_code='WTSZ',
channel_code='BH1',
network_code=' ')
# Pick to associate with amplitude
test_event.picks.append(
Pick(waveform_id=_waveform_id_1,
phase_hint='IAML',
polarity='undecidable',
time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"))
# Need a second pick for coda
test_event.picks.append(
Pick(waveform_id=_waveform_id_1,
onset='impulsive',
phase_hint='PN',
polarity='positive',
time=UTCDateTime("2012-03-26") + 1.68,
evaluation_mode="manual"))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2,
onset='impulsive',
phase_hint='SG',
polarity='undecidable',
time=UTCDateTime("2012-03-26") + 1.72,
evaluation_mode="manual"))
# Unassociated pick
test_event.picks.append(
Pick(waveform_id=_waveform_id_2,
onset='impulsive',
phase_hint='PN',
polarity='undecidable',
time=UTCDateTime("2012-03-26") + 1.62,
evaluation_mode="automatic"))
# Test a generic local magnitude amplitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=2.0,
period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id,
unit='m',
magnitude_hint='ML',
category='point',
type='AML'))
# Test a coda magnitude pick
test_event.amplitudes.append(
Amplitude(generic_amplitude=10,
pick_id=test_event.picks[1].resource_id,
waveform_id=test_event.picks[1].waveform_id,
type='END',
category='duration',
unit='s',
magnitude_hint='Mc',
snr=2.3))
test_event.origins[0].arrivals.append(
Arrival(time_weight=0,
phase=test_event.picks[1].phase_hint,
pick_id=test_event.picks[1].resource_id))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2,
phase=test_event.picks[2].phase_hint,
pick_id=test_event.picks[2].resource_id,
backazimuth_residual=5,
time_residual=0.2,
distance=15,
azimuth=25))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2,
phase=test_event.picks[3].phase_hint,
pick_id=test_event.picks[3].resource_id,
backazimuth_residual=5,
time_residual=0.2,
distance=15,
azimuth=25))
# Add in error info (line E)
test_event.origins[0].quality = OriginQuality(standard_error=0.01,
azimuthal_gap=36)
# Origin uncertainty in Seisan is output as long-lat-depth, quakeML has
# semi-major and semi-minor
test_event.origins[0].origin_uncertainty = OriginUncertainty(
confidence_ellipsoid=ConfidenceEllipsoid(
semi_major_axis_length=3000,
semi_minor_axis_length=1000,
semi_intermediate_axis_length=2000,
major_axis_plunge=20,
major_axis_azimuth=100,
major_axis_rotation=4))
test_event.origins[0].time_errors = QuantityError(uncertainty=0.5)
# Add in fault-plane solution info (line F) - Note have to check program
# used to determine which fields are filled....
test_event.focal_mechanisms.append(
FocalMechanism(nodal_planes=NodalPlanes(
nodal_plane_1=NodalPlane(strike=180,
dip=20,
rake=30,
strike_errors=QuantityError(10),
dip_errors=QuantityError(10),
rake_errors=QuantityError(20))),
method_id=ResourceIdentifier(
"smi:nc.anss.org/focalMechanism/FPFIT"),
creation_info=CreationInfo(agency_id="NC"),
misfit=0.5,
station_distribution_ratio=0.8))
# Need to test high-precision origin and that it is preferred origin.
# Moment tensor includes another origin
test_event.origins.append(
Origin(time=UTCDateTime("2012-03-26") + 1.2,
latitude=45.1,
longitude=25.2,
depth=14500))
test_event.magnitudes.append(
Magnitude(mag=0.1,
magnitude_type='MW',
creation_info=CreationInfo('TES'),
origin_id=test_event.origins[-1].resource_id))
# Moment tensors go with focal-mechanisms
test_event.focal_mechanisms.append(
FocalMechanism(moment_tensor=MomentTensor(
derived_origin_id=test_event.origins[-1].resource_id,
moment_magnitude_id=test_event.magnitudes[-1].resource_id,
scalar_moment=100,
tensor=Tensor(
m_rr=100, m_tt=100, m_pp=10, m_rt=1, m_rp=20, m_tp=15),
method_id=ResourceIdentifier(
'smi:nc.anss.org/momentTensor/BLAH'))))
return test_event
def basic_test_event():
"""
Function to generate a basic, full test event
"""
from obspy.core.event import Pick, WaveformStreamID, Arrival, Amplitude
from obspy.core.event import Event, Origin, Magnitude
from obspy.core.event import EventDescription, CreationInfo
from obspy import UTCDateTime
test_event = Event()
test_event.origins.append(Origin())
test_event.origins[0].time = UTCDateTime("2012-03-26") + 1
test_event.event_descriptions.append(EventDescription())
test_event.event_descriptions[0].text = 'LE'
test_event.origins[0].latitude = 45.0
test_event.origins[0].longitude = 25.0
test_event.origins[0].depth = 15000
test_event.creation_info = CreationInfo(agency_id='TES')
test_event.origins[0].time_errors['Time_Residual_RMS'] = 0.01
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[0].mag = 0.1
test_event.magnitudes[0].magnitude_type = 'ML'
test_event.magnitudes[0].creation_info = CreationInfo('TES')
test_event.magnitudes[0].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[1].mag = 0.5
test_event.magnitudes[1].magnitude_type = 'Mc'
test_event.magnitudes[1].creation_info = CreationInfo('TES')
test_event.magnitudes[1].origin_id = test_event.origins[0].resource_id
test_event.magnitudes.append(Magnitude())
test_event.magnitudes[2].mag = 1.3
test_event.magnitudes[2].magnitude_type = 'Ms'
test_event.magnitudes[2].creation_info = CreationInfo('TES')
test_event.magnitudes[2].origin_id = test_event.origins[0].resource_id
# Define the test pick
_waveform_id = WaveformStreamID(station_code='FOZ',
channel_code='SHZ',
network_code='NZ')
test_event.picks.append(
Pick(waveform_id=_waveform_id,
onset='impulsive',
phase_hint='PN',
polarity='positive',
time=UTCDateTime("2012-03-26") + 1.68,
horizontal_slowness=12,
backazimuth=20))
test_event.amplitudes.append(
Amplitude(generic_amplitude=2.0,
period=0.4,
pick_id=test_event.picks[0].resource_id,
waveform_id=test_event.picks[0].waveform_id,
unit='m'))
test_event.origins[0].arrivals.append(
Arrival(time_weight=2,
phase=test_event.picks[0].phase_hint,
pick_id=test_event.picks[0].resource_id,
backazimuth_residual=5,
time_residual=0.2,
distance=15,
azimuth=25))
return test_event
def stalta_pick(stream,
stalen,
ltalen,
trig_on,
trig_off,
freqmin=False,
freqmax=False,
debug=0,
show=False):
"""
Basic sta/lta picker, suggest using alternative in obspy.
Simple sta-lta (short-term average/long-term average) picker, using \
obspy's stalta routine to generate the characteristic function.
Currently very basic quick wrapper, there are many other (better) options \
in obspy, found \
`here <http://docs.obspy.org/packages/autogen/obspy.signal.trigger.html>`_.
:type stream: obspy.Stream
:param stream: The stream to pick on, can be any number of channels.
:type stalen: float
:param stalen: Length of the short-term average window in seconds.
:type ltalen: float
:param ltalen: Length of the long-term average window in seconds.
:type trig_on: float
:param trig_on: sta/lta ratio to trigger a detection/pick
:type trig_off: float
:param trig_off: sta/lta ratio to turn the trigger off - no further picks\
will be made between exceeding trig_on until trig_off is reached.
:type freqmin: float
:param freqmin: Low-cut frequency in Hz for bandpass filter
:type freqmax: float
:param freqmax: High-cut frequency in Hz for bandpass filter
:type debug: int
:param debug: Debug output level from 0-5.
:type show: bool
:param show: Show picks on waveform.
:returns: obspy.core.event.Event
.. rubric:: Example
>>> from obspy import read
>>> from eqcorrscan.utils.picker import stalta_pick
>>> st = read()
>>> event = stalta_pick(st, stalen=0.2, ltalen=4, trig_on=10,
... trig_off=1, freqmin=3.0, freqmax=20.0)
>>> event.creation_info.author
'EQcorrscan'
"""
from obspy.signal.trigger import classic_sta_lta, trigger_onset
from obspy.signal.trigger import plot_trigger
from obspy import UTCDateTime
from obspy.core.event import Event, Pick, WaveformStreamID
from obspy.core.event import CreationInfo, Comment, Origin
import eqcorrscan.utils.plotting as plotting
event = Event()
event.origins.append(Origin())
event.creation_info = CreationInfo(author='EQcorrscan',
creation_time=UTCDateTime())
event.comments.append(Comment(text='stalta'))
picks = []
for tr in stream:
# We are going to assume, for now, that if the pick is made on the
# horizontal channel then it is an S, otherwise we will assume it is
# a P-phase: obviously a bad assumption...
if tr.stats.channel[-1] == 'Z':
phase = 'P'
else:
phase = 'S'
if freqmin and freqmax:
tr.detrend('simple')
tr.filter('bandpass',
freqmin=freqmin,
freqmax=freqmax,
corners=3,
zerophase=True)
df = tr.stats.sampling_rate
cft = classic_sta_lta(tr.data, int(stalen * df), int(ltalen * df))
if debug > 3:
plot_trigger(tr, cft, trig_on, trig_off)
triggers = trigger_onset(cft, trig_on, trig_off)
for trigger in triggers:
on = tr.stats.starttime + (trigger[0] / df)
# off = tr.stats.starttime + (trigger[1] / df)
wav_id = WaveformStreamID(station_code=tr.stats.station,
channel_code=tr.stats.channel,
network_code=tr.stats.network)
pick = Pick(waveform_id=wav_id, phase_hint=phase, time=on)
if debug > 2:
print('Pick made:')
print(pick)
picks.append(pick)
# QC picks
del pick
pick_stations = list(set([pick.waveform_id.station_code
for pick in picks]))
for pick_station in pick_stations:
station_picks = [
pick for pick in picks
if pick.waveform_id.station_code == pick_station
]
# If P-pick is after S-picks, remove it.
p_time = [
pick.time for pick in station_picks if pick.phase_hint == 'P'
]
s_time = [
pick.time for pick in station_picks if pick.phase_hint == 'S'
]
if p_time > s_time:
p_pick = [pick for pick in station_picks if pick.phase_hint == 'P']
for pick in p_pick:
print('P pick after S pick, removing P pick')
picks.remove(pick)
if show:
plotting.pretty_template_plot(stream,
picks=picks,
title='Autopicks',
size=(8, 9))
event.picks = picks
event.origins[0].time = min([pick.time for pick in event.picks]) - 1
event.origins[0].latitude = float('nan')
event.origins[0].longitude = float('nan')
# Set arbitrary origin time
return event
def brightness(stations,
nodes,
lags,
stream,
threshold,
thresh_type,
template_length,
template_saveloc,
coherence_thresh,
coherence_stations=['all'],
coherence_clip=False,
gap=2.0,
clip_level=100,
instance=0,
pre_pick=0.2,
plotsave=True,
cores=1):
r"""Function to calculate the brightness function in terms of energy for \
a day of data over the entire network for a given grid of nodes.
Note data in stream must be all of the same length and have the same
sampling rates.
:type stations: list
:param stations: List of station names from in the form where stations[i] \
refers to nodes[i][:] and lags[i][:]
:type nodes: list, tuple
:param nodes: List of node points where nodes[i] referes to stations[i] \
and nodes[:][:][0] is latitude in degrees, nodes[:][:][1] is \
longitude in degrees, nodes[:][:][2] is depth in km.
:type lags: :class: 'numpy.array'
:param lags: Array of arrays where lags[i][:] refers to stations[i]. \
lags[i][j] should be the delay to the nodes[i][j] for stations[i] in \
seconds.
:type stream: :class: `obspy.Stream`
:param data: Data through which to look for detections.
:type threshold: float
:param threshold: Threshold value for detection of template within the \
brightness function
:type thresh_type: str
:param thresh_type: Either MAD or abs where MAD is the Median Absolute \
Deviation and abs is an absoulte brightness.
:type template_length: float
:param template_length: Length of template to extract in seconds
:type template_saveloc: str
:param template_saveloc: Path of where to save the templates.
:type coherence_thresh: tuple of floats
:param coherence_thresh: Threshold for removing incoherant peaks in the \
network response, those below this will not be used as templates. \
Must be in the form of (a,b) where the coherence is given by: \
a-kchan/b where kchan is the number of channels used to compute \
the coherence
:type coherence_stations: list
:param coherence_stations: List of stations to use in the coherance \
thresholding - defaults to 'all' which uses all the stations.
:type coherence_clip: float
:param coherence_clip: tuple
:type coherence_clip: Start and end in seconds of data to window around, \
defaults to False, which uses all the data given.
:type pre_pick: float
:param pre_pick: Seconds before the detection time to include in template
:type plotsave: bool
:param plotsave: Save or show plots, if False will try and show the plots \
on screen - as this is designed for bulk use this is set to \
True to save any plots rather than show them if you create \
them - changes the backend of matplotlib, so if is set to \
False you will see NO PLOTS!
:type cores: int
:param core: Number of cores to use, defaults to 1.
:type clip_level: float
:param clip_level: Multiplier applied to the mean deviation of the energy \
as an upper limit, used to remove spikes (earthquakes, \
lightning, electircal spikes) from the energy stack.
:type gap: float
:param gap: Minimum inter-event time in seconds for detections
:return: list of templates as :class: `obspy.Stream` objects
"""
from eqcorrscan.core.template_gen import _template_gen
if plotsave:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ioff()
# from joblib import Parallel, delayed
from multiprocessing import Pool, cpu_count
from copy import deepcopy
from obspy import read as obsread
from obspy.core.event import Catalog, Event, Pick, WaveformStreamID, Origin
from obspy.core.event import EventDescription, CreationInfo, Comment
import obspy.Stream
import matplotlib.pyplot as plt
from eqcorrscan.utils import EQcorrscan_plotting as plotting
# Check that we actually have the correct stations
realstations = []
for station in stations:
st = stream.select(station=station)
if st:
realstations += station
del st
stream_copy = stream.copy()
# Force convert to int16
for tr in stream_copy:
# int16 max range is +/- 32767
if max(abs(tr.data)) > 32767:
tr.data = 32767 * (tr.data / max(abs(tr.data)))
# Make sure that the data aren't clipped it they are high gain
# scale the data
tr.data = tr.data.astype(np.int16)
# The internal _node_loop converts energy to int16 too to converse memory,
# to do this it forces the maximum of a single energy trace to be 500 and
# normalises to this level - this only works for fewer than 65 channels of
# data
if len(stream_copy) > 130:
raise OverflowError('Too many streams, either re-code and cope with' +
'either more memory usage, or less precision, or' +
'reduce data volume')
detections = []
detect_lags = []
parallel = True
plotvar = True
mem_issue = False
# Loop through each node in the input
# Linear run
print('Computing the energy stacks')
if not parallel:
for i in range(0, len(nodes)):
print(i)
if not mem_issue:
j, a = _node_loop(stations, lags[:, i], stream, plot=True)
if 'energy' not in locals():
energy = a
else:
energy = np.concatenate((energy, a), axis=0)
print('energy: ' + str(np.shape(energy)))
else:
j, filename = _node_loop(stations, lags[:, i], stream, i,
mem_issue)
energy = np.array(energy)
print(np.shape(energy))
else:
# Parallel run
num_cores = cores
if num_cores > len(nodes):
num_cores = len(nodes)
if num_cores > cpu_count():
num_cores = cpu_count()
pool = Pool(processes=num_cores)
results = [
pool.apply_async(_node_loop,
args=(stations, lags[:, i], stream, i, clip_level,
mem_issue, instance))
for i in range(len(nodes))
]
pool.close()
if not mem_issue:
print('Computing the cumulative network response from memory')
energy = [p.get() for p in results]
pool.join()
energy.sort(key=lambda tup: tup[0])
energy = [node[1] for node in energy]
energy = np.concatenate(energy, axis=0)
print(energy.shape)
else:
pool.join()
# Now compute the cumulative network response and then detect possible
# events
if not mem_issue:
print(energy.shape)
indeces = np.argmax(energy, axis=0) # Indeces of maximum energy
print(indeces.shape)
cum_net_resp = np.array([np.nan] * len(indeces))
cum_net_resp[0] = energy[indeces[0]][0]
peak_nodes = [nodes[indeces[0]]]
for i in range(1, len(indeces)):
cum_net_resp[i] = energy[indeces[i]][i]
peak_nodes.append(nodes[indeces[i]])
del energy, indeces
else:
print('Reading the temp files and computing network response')
node_splits = len(nodes) // num_cores
indeces = [range(node_splits)]
for i in range(1, num_cores - 1):
indeces.append(range(node_splits * i, node_splits * (i + 1)))
indeces.append(range(node_splits * (i + 1), len(nodes)))
pool = Pool(processes=num_cores)
results = [
pool.apply_async(_cum_net_resp, args=(indeces[i], instance))
for i in range(num_cores)
]
pool.close()
results = [p.get() for p in results]
pool.join()
responses = [result[0] for result in results]
print(np.shape(responses))
node_indeces = [result[1] for result in results]
cum_net_resp = np.array(responses)
indeces = np.argmax(cum_net_resp, axis=0)
print(indeces.shape)
print(cum_net_resp.shape)
cum_net_resp = np.array(
[cum_net_resp[indeces[i]][i] for i in range(len(indeces))])
peak_nodes = [
nodes[node_indeces[indeces[i]][i]] for i in range(len(indeces))
]
del indeces, node_indeces
if plotvar:
cum_net_trace = deepcopy(stream[0])
cum_net_trace.data = cum_net_resp
cum_net_trace.stats.station = 'NR'
cum_net_trace.stats.channel = ''
cum_net_trace.stats.network = 'Z'
cum_net_trace.stats.location = ''
cum_net_trace.stats.starttime = stream[0].stats.starttime
cum_net_trace = obspy.Stream(cum_net_trace)
cum_net_trace += stream.select(channel='*N')
cum_net_trace += stream.select(channel='*1')
cum_net_trace.sort(['network', 'station', 'channel'])
# np.save('cum_net_resp.npy',cum_net_resp)
# cum_net_trace.plot(size=(800,600), equal_scale=False,\
# outfile='NR_timeseries.eps')
# Find detection within this network response
print('Finding detections in the cumulatve network response')
detections = _find_detections(cum_net_resp, peak_nodes, threshold,
thresh_type, stream[0].stats.sampling_rate,
realstations, gap)
del cum_net_resp
templates = []
nodesout = []
good_detections = []
if detections:
print('Converting detections in to templates')
# Generate a catalog of detections
detections_cat = Catalog()
for j, detection in enumerate(detections):
print('Converting for detection ' + str(j) + ' of ' +
str(len(detections)))
# Create an event for each detection
event = Event()
# Set up some header info for the event
event.event_descriptions.append(EventDescription())
event.event_descriptions[0].text = 'Brightness detection'
event.creation_info = CreationInfo(agency_id='EQcorrscan')
copy_of_stream = deepcopy(stream_copy)
# Convert detections to obspy.core.event type -
# name of detection template is the node.
node = (detection.template_name.split('_')[0],
detection.template_name.split('_')[1],
detection.template_name.split('_')[2])
print(node)
# Look up node in nodes and find the associated lags
index = nodes.index(node)
detect_lags = lags[:, index]
ksta = Comment(text='Number of stations=' + len(detect_lags))
event.origins.append(Origin())
event.origins[0].comments.append(ksta)
event.origins[0].time = copy_of_stream[0].stats.starttime +\
detect_lags[0] + detection.detect_time
event.origins[0].latitude = node[0]
event.origins[0].longitude = node[1]
event.origins[0].depth = node[2]
for i, detect_lag in enumerate(detect_lags):
station = stations[i]
st = copy_of_stream.select(station=station)
if len(st) != 0:
for tr in st:
_waveform_id = WaveformStreamID(
station_code=tr.stats.station,
channel_code=tr.stats.channel,
network_code='NA')
event.picks.append(
Pick(waveform_id=_waveform_id,
time=tr.stats.starttime + detect_lag +
detection.detect_time + pre_pick,
onset='emergent',
evalutation_mode='automatic'))
print('Generating template for detection: ' + str(j))
template = (_template_gen(event.picks, copy_of_stream,
template_length, 'all'))
template_name = template_saveloc + '/' +\
str(template[0].stats.starttime) + '.ms'
# In the interests of RAM conservation we write then read
# Check coherancy here!
temp_coher, kchan = coherence(template, coherence_stations,
coherence_clip)
coh_thresh = float(coherence_thresh[0]) - kchan / \
float(coherence_thresh[1])
if temp_coher > coh_thresh:
template.write(template_name, format="MSEED")
print('Written template as: ' + template_name)
print('---------------------------------coherence LEVEL: ' +
str(temp_coher))
coherant = True
else:
print('Template was incoherant, coherence level: ' +
str(temp_coher))
coherant = False
del copy_of_stream, tr, template
if coherant:
templates.append(obsread(template_name))
nodesout += [node]
good_detections.append(detection)
else:
print('No template for you')
if plotvar:
all_detections = [(cum_net_trace[-1].stats.starttime +
detection.detect_time).datetime
for detection in detections]
good_detections = [(cum_net_trace[-1].stats.starttime +
detection.detect_time).datetime
for detection in good_detections]
if not plotsave:
plotting.NR_plot(cum_net_trace[0:-1],
obspy.Stream(cum_net_trace[-1]),
detections=good_detections,
size=(18.5, 10),
title='Network response')
# cum_net_trace.plot(size=(800,600), equal_scale=False)
else:
savefile = 'plots/' +\
cum_net_trace[0].stats.starttime.datetime.strftime('%Y%m%d') +\
'_NR_timeseries.pdf'
plotting.NR_plot(cum_net_trace[0:-1],
obspy.Stream(cum_net_trace[-1]),
detections=good_detections,
size=(18.5, 10),
save=savefile,
title='Network response')
nodesout = list(set(nodesout))
return templates, nodesout
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
