def main(argv=None):
parser = ArgumentParser(prog='obspy-print', description=__doc__.strip())
parser.add_argument('-V', '--version', action='version',
version='%(prog)s ' + __version__)
parser.add_argument('-f', '--format', choices=ENTRY_POINTS['waveform'],
help='Waveform format (slightly faster if specified).')
parser.add_argument('-n', '--no-merge', action='store_false',
dest='merge', help='Switch off cleanup merge.')
parser.add_argument('--no-sorting', action='store_false',
dest='sort', help='Switch off sorting of traces.')
parser.add_argument('-g', '--print-gaps', action='store_true',
help='Switch on printing of gap information.')
parser.add_argument('files', nargs='+',
help='Files to process.')
# Deprecated arguments
action = _get_deprecated_argument_action(
'--nomerge', '--no-merge', real_action='store_false')
parser.add_argument('--nomerge', nargs=0, action=action, dest='merge',
help=SUPPRESS)
args = parser.parse_args(argv)
st = Stream()
for f in args.files:
st += read(f, format=args.format)
if args.merge:
st.merge(-1)
if args.sort:
st.sort()
print(st.__str__(extended=True))
if args.print_gaps:
print()
st.printGaps()
def main(argv=None):
parser = ArgumentParser(prog='obspy-print', description=__doc__.strip())
parser.add_argument('-V', '--version', action='version',
version='%(prog)s ' + __version__)
parser.add_argument('-f', '--format', choices=ENTRY_POINTS['waveform'],
help='Waveform format (slightly faster if specified).')
parser.add_argument('-n', '--no-merge', action='store_false',
dest='merge', help='Switch off cleanup merge.')
parser.add_argument('--no-sorting', action='store_false',
dest='sort', help='Switch off sorting of traces.')
parser.add_argument('-g', '--print-gaps', action='store_true',
help='Switch on printing of gap information.')
parser.add_argument('files', nargs='+',
help='Files to process.')
args = parser.parse_args(argv)
st = Stream()
for f in args.files:
st += read(f, format=args.format)
if args.merge:
st.merge(-1)
if args.sort:
st.sort()
print(st.__str__(extended=True))
if args.print_gaps:
print()
st.print_gaps()
def main(argv=None):
parser = ArgumentParser(prog="obspy-print", description=__doc__.strip())
parser.add_argument("-V", "--version", action="version", version="%(prog)s " + __version__)
parser.add_argument(
"-f", "--format", choices=ENTRY_POINTS["waveform"], help="Waveform format (slightly faster if specified)."
)
parser.add_argument("-n", "--no-merge", action="store_false", dest="merge", help="Switch off cleanup merge.")
parser.add_argument("--no-sorting", action="store_false", dest="sort", help="Switch off sorting of traces.")
parser.add_argument("-g", "--print-gaps", action="store_true", help="Switch on printing of gap information.")
parser.add_argument("files", nargs="+", help="Files to process.")
args = parser.parse_args(argv)
st = Stream()
for f in args.files:
st += read(f, format=args.format)
if args.merge:
st.merge(-1)
if args.sort:
st.sort()
print(st.__str__(extended=True))
if args.print_gaps:
print()
st.print_gaps()
tr.trim(starttime=s_t, endtime=e_t)
# deduct theoretical traveltime and start_buf from starttime
if rel_time == 1:
tr.stats.starttime = tr.stats.starttime - atime
st_pickalign2 += tr
except:
pass
print('After alignment and range selection - event 1: ' +
str(len(st_pickalign1)) + ' traces')
print('After alignment and range selection - event 2: ' +
str(len(st_pickalign2)) + ' traces')
#%%
#print(st) # at length
if verbose:
print(st1.__str__(extended=True))
print(st2.__str__(extended=True))
if rel_time == 1:
print(st_pickalign1.__str__(extended=True))
print(st_pickalign2.__str__(extended=True))
#%% detrend, taper, filter
st_pickalign1.detrend(type='simple')
st_pickalign2.detrend(type='simple')
st_pickalign1.taper(taper_frac)
st_pickalign2.taper(taper_frac)
st_pickalign1.filter('bandpass',
freqmin=freq_min,
freqmax=freq_max,
corners=2,
zerophase=True)
def read_local(data_dir,
coord_file,
network,
station,
location,
channel,
starttime,
endtime,
merge=True):
"""
Read in waveforms from "local" 1-hour, IRIS-compliant miniSEED files, and
output a Stream object with station/element coordinates attached.
NOTE 1:
The expected naming convention for the miniSEED files is:
<network>.<station>.<location>.<channel>.<year>.<julian_day>.<hour>
NOTE 2:
This function assumes that the response has been removed from the
waveforms in the input miniSEED files.
Args:
data_dir: Directory containing miniSEED files
coord_file: JSON file containing coordinates for local stations (full
path required)
network: SEED network code [wildcards (*, ?) accepted]
station: SEED station code [wildcards (*, ?) accepted]
location: SEED location code [wildcards (*, ?) accepted]
channel: SEED channel code [wildcards (*, ?) accepted]
starttime: Start time for data request (UTCDateTime)
endtime: End time for data request (UTCDateTime)
merge: Toggle merging of Traces with identical IDs (default: True)
Returns:
st_out: Stream containing gathered waveforms
"""
print('-----------------------------')
print('GATHERING LOCAL MINISEED DATA')
print('-----------------------------')
# Take (hour) floor of starttime
starttime_hr = UTCDateTime(starttime.year, starttime.month, starttime.day,
starttime.hour)
# Take (hour) floor of endtime - this ensures we check this miniSEED file
endtime_hr = UTCDateTime(endtime.year, endtime.month, endtime.day,
endtime.hour)
# Define filename template
template = f'{network}.{station}.{location}.{channel}.{{}}.{{}}.{{}}'
# Initialize Stream object
st_out = Stream()
# Initialize the starting hour
tmp_time = starttime_hr
# Cycle forward in time, advancing hour by hour through miniSEED files
while tmp_time <= endtime_hr:
pattern = template.format(tmp_time.strftime('%Y'),
tmp_time.strftime('%j'),
tmp_time.strftime('%H'))
files = glob.glob(os.path.join(data_dir, pattern))
for file in files:
st_out += read(file)
tmp_time += HR2SEC # Add an hour!
if merge:
st_out.merge() # Merge Traces with the same ID
st_out.sort()
# If the Stream is empty, then we can stop here
if st_out.count() == 0:
print('No data downloaded.')
return st_out
# Otherwise, show what the Stream contains
print(st_out.__str__(extended=True)) # This syntax prints the WHOLE Stream
# Add zeros to ensure all Traces have same length
st_out.trim(starttime, endtime, pad=True, fill_value=0)
print('Assigning coordinates...')
# Assign coordinates by searching through user-supplied JSON file
local_coords = load_json_file(coord_file)
for tr in st_out:
try:
tr.stats.latitude, tr.stats.longitude,\
tr.stats.elevation = local_coords[tr.stats.station]
except KeyError:
print(f'No coordinates available for {tr.id}. Stopping.')
raise
print('Done')
# Return the Stream with coordinates attached
return st_out
def gather_waveforms(source,
network,
station,
location,
channel,
starttime,
endtime,
time_buffer=0,
merge_fill_value=0,
trim_fill_value=0,
remove_response=False,
return_failed_stations=False,
watc_url=None,
watc_username=None,
watc_password=None):
"""
Gather seismic/infrasound waveforms from IRIS or WATC FDSN, or AVO Winston,
and output a :class:`~obspy.core.stream.Stream` with station/element
coordinates attached. Optionally remove the sensitivity.
**NOTE**
Usual RTM usage is to specify a starttime/endtime that brackets the
estimated source origin time. Then time_buffer is used to download enough
extra data to account for the time required for an infrasound signal to
propagate to the farthest station.
Args:
source (str): Which source to gather waveforms from. Options are:
* `'IRIS'` – IRIS FDSN
* `'WATC'` – WATC FDSN
* `'AVO'` – AVO Winston
network (str): SEED network code [wildcards (``*``, ``?``) accepted]
station (str): SEED station code [wildcards (``*``, ``?``) accepted]
location (str): SEED location code [wildcards (``*``, ``?``) accepted]
channel (str): SEED channel code [wildcards (``*``, ``?``) accepted]
starttime (:class:`~obspy.core.utcdatetime.UTCDateTime`): Start time for
data request
endtime (:class:`~obspy.core.utcdatetime.UTCDateTime`): End time for
data request
time_buffer (int or float): Extra amount of data to download after
`endtime` [s]
merge_fill_value (bool, int, float, str, or None): Controls merging of
:class:`~obspy.core.trace.Trace` objects with identical IDs. If
`False`, no merging is performed. Otherwise, a merge is performed
with the ``fill_value`` provided to this parameter. For details,
see the docstring of :meth:`obspy.core.stream.Stream.trim`
trim_fill_value (bool, int, float, or None): Controls trimming of the
output :class:`~obspy.core.stream.Stream`, useful if precisely
uniform start and end times are desired. If `False`, no trimming is
performed. Otherwise, a trim is performed with the ``fill_value``
provided to this parameter. For details, see the docstring of
:meth:`obspy.core.stream.Stream.merge`
remove_response (bool): Toggle response removal via
:meth:`~obspy.core.trace.Trace.remove_sensitivity` or a simple
scalar multiplication
return_failed_stations (bool): If `True`, returns a list of station
codes that were requested but not downloaded. This disables the
standard failed station warning message
watc_url (str): URL for WATC FDSN server
watc_username (str): Username for WATC FDSN server
watc_password (str): Password for WATC FDSN server
Returns:
:class:`~obspy.core.stream.Stream` containing gathered waveforms. If
`return_failed_stations` is `True`, additionally returns a list
containing station codes that were requested but not downloaded
"""
# Check for issues with fill value args
if merge_fill_value is True or trim_fill_value is True:
raise ValueError('Cannot provide True to fill value parameters.')
print('--------------')
print('GATHERING DATA')
print('--------------')
# IRIS FDSN
if source == 'IRIS':
client = FDSN_Client('IRIS')
print('Reading data from IRIS FDSN...')
try:
st_out = client.get_waveforms(network,
station,
location,
channel,
starttime,
endtime + time_buffer,
attach_response=True)
except FDSNNoDataException:
st_out = Stream() # Just create an empty Stream object
# WATC FDSN
elif source == 'WATC':
print('Connecting to WATC FDSN...')
client = FDSN_Client(base_url=watc_url,
user=watc_username,
password=watc_password)
print('Successfully connected. Reading data from WATC FDSN...')
try:
st_out = client.get_waveforms(network,
station,
location,
channel,
starttime,
endtime + time_buffer,
attach_response=True)
except FDSNNoDataException:
st_out = Stream() # Just create an empty Stream object
# AVO Winston
elif source == 'AVO':
client = EW_Client('pubavo1.wr.usgs.gov',
port=16023) # 16023 is long-term
print('Reading data from AVO Winston...')
st_out = Stream() # Make empty Stream object to populate
# Brute-force "dynamic grid search" over network/station/channel/location codes
for nw in _restricted_matching('network', network, client):
for sta in _restricted_matching('station',
station,
client,
network=nw):
for cha in _restricted_matching('channel',
channel,
client,
network=nw,
station=sta):
for loc in _restricted_matching('location',
location,
client,
network=nw,
station=sta,
channel=cha):
try:
st_out += client.get_waveforms(
nw, sta, loc, cha, starttime,
endtime + time_buffer)
except KeyError:
pass
else:
raise ValueError('Unrecognized source. Valid options are \'IRIS\', '
'\'WATC\', or \'AVO\'.')
# Merge, if specified
if merge_fill_value is not False:
st_out.merge(fill_value=merge_fill_value) # Merge Traces with same ID
warnings.warn(f'Merging with "fill_value={merge_fill_value}"',
CollectionWarning)
st_out.sort()
# Check that all requested stations are present in Stream
requested_stations = station.split(',')
downloaded_stations = [tr.stats.station for tr in st_out]
failed_stations = []
for sta in requested_stations:
# The below check works with wildcards, but obviously cannot detect if
# ALL stations corresponding to a given wildcard (e.g., O??K) were
# downloaded. Thus, if careful station selection is desired, specify
# each station explicitly and the below check will then be effective.
if not fnmatch.filter(downloaded_stations, sta):
if not return_failed_stations:
# If we're not returning the failed stations, then show this
# warning message to alert the user
warnings.warn(
f'Station {sta} not downloaded from {source} '
'server for this time period.', CollectionWarning)
failed_stations.append(sta)
# If the Stream is empty, then we can stop here
if st_out.count() == 0:
print('No data downloaded.')
if return_failed_stations:
return st_out, failed_stations
else:
return st_out
# Otherwise, show what the Stream contains
print(st_out.__str__(extended=True)) # This syntax prints the WHOLE Stream
# Trim, if specified
if trim_fill_value is not False:
st_out.trim(starttime,
endtime + time_buffer,
pad=True,
fill_value=trim_fill_value)
warnings.warn(f'Trimming with "fill_value={trim_fill_value}"',
CollectionWarning)
print('Assigning coordinates...')
# Use IRIS inventory info for AVO data source
if source == 'AVO':
client = FDSN_Client('IRIS')
try:
inv = client.get_stations(network=network,
station=station,
location=location,
channel=channel,
starttime=starttime,
endtime=endtime + time_buffer,
level='channel')
except FDSNNoDataException:
inv = Inventory() # Make an empty inv
warnings.warn('Creating empty inventory.', CollectionWarning)
for tr in st_out:
try:
coords = inv.get_coordinates(tr.id)
tr.stats.longitude = coords['longitude']
tr.stats.latitude = coords['latitude']
tr.stats.elevation = coords['elevation']
except Exception as e:
if str(e) == 'No matching channel metadata found.':
warnings.warn(f'No metadata for {tr.id} found in inventory.',
CollectionWarning)
else:
raise
# Check if any Trace did NOT get coordinates assigned, and try to use JSON
# coordinates if available
for tr in st_out:
try:
tr.stats.longitude, tr.stats.latitude, tr.stats.elevation
except AttributeError:
try:
tr.stats.latitude, tr.stats.longitude,\
tr.stats.elevation = AVO_COORDS[tr.id]
warnings.warn(f'Using coordinates from JSON file for {tr.id}.',
CollectionWarning)
except KeyError:
print(f'No coordinates available for {tr.id}. Stopping.')
raise
# Remove sensitivity
if remove_response:
print('Removing sensitivity...')
for tr in st_out:
try:
# Just removing sensitivity for now. remove_response() can lead
# to errors. This should be sufficient for now. Plus some
# IRIS-AVO responses are wonky.
tr.remove_sensitivity()
except ValueError: # No response information found
# This is only set up for infrasound calibration values
try:
calib = AVO_INFRA_CALIBS[tr.id]
tr.data = tr.data * calib
warnings.warn(
'Using calibration value from JSON file for '
f'{tr.id}.', CollectionWarning)
except KeyError:
print(f'No calibration value available for {tr.id}. '
'Stopping.')
raise
print('Done')
# Return the Stream with coordinates attached (and responses removed if
# specified)
if return_failed_stations:
return st_out, failed_stations
else:
return st_out
def pro3pair(eq_num1,
eq_num2,
stat_corr=1,
simple_taper=0,
skip_SNR=0,
dphase='PKIKP',
dphase2='PKiKP',
dphase3='PKIKP',
dphase4='PKiKP',
rel_time=1,
start_buff=-200,
end_buff=500,
plot_scale_fac=0.05,
qual_threshold=0,
corr_threshold=0.5,
freq_min=1,
freq_max=3,
min_dist=0,
max_dist=180,
auto_dist=True,
alt_statics=0,
statics_file='nothing',
ARRAY=0,
ref_loc=False,
ref_rad=0.4,
max_taper_length=5.,
no_plots=False,
taper_frac=0.05):
#%% Import functions
from obspy import UTCDateTime
from obspy import Stream
from obspy import read
from obspy.geodetics import gps2dist_azimuth
import numpy as np
import os
from obspy.taup import TauPyModel
import matplotlib.pyplot as plt
import time
model = TauPyModel(model='iasp91')
import sys # don't show any warnings
import warnings
from termcolor import colored
if not sys.warnoptions:
warnings.simplefilter("ignore")
print(colored('Running pro3a_sort_plot_pair', 'cyan'))
start_time_wc = time.time()
#%% Set some parameters
verbose = 0 # more output
# rel_time = 1 # timing is relative to a chosen phase, otherwise relative to OT
# taper_frac = 0.05 # Fraction of window tapered on both ends
signal_dur = 5. # signal length used in SNR calculation
plot_tt = 1 # plot the traveltimes?
do_decimate = 0 # 0 if no decimation desired
# if ref_loc ==true, use ref_rad to filter station distance
# if ref_loc ==false, use earthquake loc to filter station distance
# ref_rad = 0.4 # ° radius (°) set by input or at top
if ARRAY == 0:
ref_lat = 36.3 # °N, around middle of Japan
ref_lon = 138.5 # °E
if ARRAY == 1:
ref_lat = 46.7 # °N keep only inner rings A-D if radius is 0.4°
ref_lon = -106.22 # °E
if ARRAY == 2:
ref_lat = 38 # °N
ref_lon = 104.5 # °E
if rel_time == 0: # SNR requirement not implemented for unaligned traces
qual_threshold = 0
# Plot with reduced velocity?
red_plot = 0
red_dist = 55
red_time = 300
red_slow = 7.2 # seconds per degree
#%% Get saved event info, also used to name files
# event 2016-05-28T09:47:00.000 -56.241 -26.935 78
print('Opening locations for events ' + str(eq_num1) + ' and ' +
str(eq_num2))
fname1 = '/Users/vidale/Documents/Research/IC/EvLocs/event' + str(
eq_num1) + '.txt'
fname2 = '/Users/vidale/Documents/Research/IC/EvLocs/event' + str(
eq_num2) + '.txt'
file1 = open(fname1, 'r')
file2 = open(fname2, 'r')
lines1 = file1.readlines()
lines2 = file2.readlines()
split_line1 = lines1[0].split()
split_line2 = lines2[0].split()
# ids.append(split_line[0]) ignore label for now
t1 = UTCDateTime(split_line1[1])
t2 = UTCDateTime(split_line2[1])
date_label1 = split_line1[1][0:10]
date_label2 = split_line2[1][0:10]
year1 = split_line1[1][0:4]
year2 = split_line2[1][0:4]
ev_lat1 = float(split_line1[2])
ev_lat2 = float(split_line2[2])
ev_lon1 = float(split_line1[3])
ev_lon2 = float(split_line2[3])
ev_depth1 = float(split_line1[4])
ev_depth2 = float(split_line2[4])
print('1st event: date_label ' + date_label1 + ' time ' + str(t1) +
' lat ' + str(ev_lat1) + ' lon ' + str(ev_lon1) + ' depth ' +
str(ev_depth1))
print('2nd event: date_label ' + date_label2 + ' time ' + str(t2) +
' lat ' + str(ev_lat2) + ' lon ' + str(ev_lon2) + ' depth ' +
str(ev_depth2))
#%% Get station location file
if stat_corr == 1: # load static terms, only applies to Hinet and LASA
if ARRAY == 0:
if alt_statics == 0: # standard set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_statics_hinet.txt'
else: # custom set made by this event for this event
print('probably needs fixing')
sta_file = (
'/Users/vidale/Documents/PyCode/Hinet/Array_codes/Files/' +
'HA' + date_label1[:10] + 'pro4_' + dphase + '.statics')
elif ARRAY == 1:
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_statics_LASA.txt'
elif ARRAY == 2:
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_statics_ch.txt'
with open(sta_file, 'r') as file:
lines = file.readlines()
print(str(len(lines)) + ' stations read from ' + sta_file)
# Load station coords into arrays
station_index = range(len(lines))
st_names = []
st_dist = []
st_lats = []
st_lons = []
st_shift = []
st_corr = []
for ii in station_index:
line = lines[ii]
split_line = line.split()
st_names.append(split_line[0])
st_dist.append(split_line[1])
st_lats.append(split_line[2])
st_lons.append(split_line[3])
st_shift.append(split_line[4])
st_corr.append(split_line[5])
else: # no static terms, always true for NORSAR
if ARRAY == 0: # Hinet set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_hinet.txt'
elif ARRAY == 1: # LASA set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_LASA.txt'
elif ARRAY == 2: # LASA set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_ch.txt'
else: # NORSAR set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_NORSAR.txt'
with open(sta_file, 'r') as file:
lines = file.readlines()
print(str(len(lines)) + ' stations read from ' + sta_file)
# Load station coords into arrays
station_index = range(len(lines))
st_names = []
st_lats = []
st_lons = []
for ii in station_index:
line = lines[ii]
split_line = line.split()
st_names.append(split_line[0])
st_lats.append(split_line[1])
st_lons.append(split_line[2])
if ARRAY == 0: # shorten and make upper case Hi-net station names to match station list
for ii in station_index:
this_name = st_names[ii]
this_name_truc = this_name[0:5]
st_names[ii] = this_name_truc.upper()
#%% Is taper too long compared to noise estimation window?
totalt = end_buff - start_buff
noise_time_skipped = taper_frac * totalt
noise_time_skipped = min(noise_time_skipped,
10.0) # set max of 10s to taper length
if simple_taper == 0:
if noise_time_skipped >= 0.5 * (-start_buff):
print(
'Specified taper of ' + str(taper_frac * totalt) +
' is not big enough compared to available noise estimation window '
+ str(-start_buff - noise_time_skipped) +
'. May not work well.')
old_taper_frac = taper_frac
taper_frac = -0.5 * start_buff / totalt
print('Taper reset from ' + str(old_taper_frac * totalt) + ' to ' +
str(taper_frac * totalt) + ' seconds.')
#%% Load waveforms and decimate to 10 sps
st1 = Stream()
st2 = Stream()
fname1 = '/Users/vidale/Documents/GitHub/LASA_data/HD' + date_label1 + '.mseed'
fname2 = '/Users/vidale/Documents/GitHub/LASA_data/HD' + date_label2 + '.mseed'
st1 = read(fname1)
st2 = read(fname2)
if do_decimate != 0:
st1.decimate(do_decimate, no_filter=True)
st2.decimate(do_decimate, no_filter=True)
print(
f'1st trace for event 1 has {len(st1[0].data)} time pts which is {len(st1[0].data)*st1[0].stats.delta:.1f} s'
)
print(
f'1st trace for event 2 has {len(st2[0].data)} time pts which is {len(st2[0].data)*st2[0].stats.delta:.1f} s'
)
print('st1 has ' + str(len(st1)) + ' traces')
print('st2 has ' + str(len(st2)) + ' traces')
print('1st trace starts at ' + str(st1[0].stats.starttime) +
', event at ' + str(t1))
print('2nd trace starts at ' + str(st2[0].stats.starttime) +
', event at ' + str(t2))
#%% Select by distance, window and adjust start time to align picked times
st_pickalign1 = Stream()
st_pickalign2 = Stream()
tra1_in_range = 0
tra1_sta_found = 0
nodata1 = 0
tra2_in_range = 0
tra2_sta_found = 0
nodata2 = 0
min_dist_auto = 180
max_dist_auto = 0
min_time_plot = 1000000
max_time_plot = -1000000
# not used in all cases, but printed out below
# only used if rel_slow == 1, preserves 0 slowness, otherwise 0 is set to phase slowness
ref_distance = gps2dist_azimuth(ref_lat, ref_lon, ev_lat1, ev_lon1)
ref1_dist = ref_distance[0] / (1000 * 111)
dist_minus = ref1_dist - 0.5
dist_plus = ref1_dist + 0.5
arrivals_ref = model.get_travel_times(source_depth_in_km=ev_depth1,
distance_in_degree=ref1_dist,
phase_list=[dphase])
arrivals_minus = model.get_travel_times(source_depth_in_km=ev_depth1,
distance_in_degree=dist_minus,
phase_list=[dphase])
arrivals_plus = model.get_travel_times(source_depth_in_km=ev_depth1,
distance_in_degree=dist_plus,
phase_list=[dphase])
atime_ref = arrivals_ref[
0].time # phase arrival time at reference distance
ref_slow = arrivals_plus[0].time - arrivals_minus[
0].time # dt over 1 degree at ref distance
for tr in st1: # find lat-lon from list, chop, statics, traces one by one
if float(
year1
) < 1970: # fix the damn 1969 -> 2069 bug in Gibbon's LASA data
temp_t = str(tr.stats.starttime)
temp_tt = '19' + temp_t[2:]
tr.stats.starttime = UTCDateTime(temp_tt)
if tr.stats.station in st_names: # find station in station list
ii = st_names.index(tr.stats.station)
tra1_sta_found += 1
if stat_corr != 1 or float(
st_corr[ii]
) > corr_threshold: # if using statics, reject low correlations
stalat = float(
st_lats[ii]) # look up lat & lon again to find distance
stalon = float(st_lons[ii])
distance = gps2dist_azimuth(
stalat, stalon, ev_lat1,
ev_lon1) # Get traveltimes again, hard to store
tr.stats.distance = distance[0] # distance in km
dist = distance[0] / (1000 * 111)
in_range = 0 # flag for whether this trace goes into stack
if ref_loc == False: # check whether trace is in distance range from earthquake
if min_dist < dist and dist < max_dist:
in_range = 1
tra1_in_range += 1
elif ref_loc == True: # alternately, check whether trace is close enough to ref_location
ref_distance = gps2dist_azimuth(ref_lat, ref_lon, stalat,
stalon)
ref2_dist = ref_distance[0] / (1000 * 111)
if ref2_dist < ref_rad:
in_range = 1
tra1_in_range += 1
if in_range == 1: # trace fulfills the specified criteria for being in range
s_t = t1 + start_buff
e_t = t1 + end_buff
if stat_corr == 1: # apply static station corrections
tr.stats.starttime -= float(st_shift[ii])
if rel_time == 0: # don't adjust absolute time
tr.trim(starttime=s_t, endtime=e_t)
else: # shift relative to a chosen phase
arrivals_each = model.get_travel_times(
source_depth_in_km=ev_depth1,
distance_in_degree=dist,
phase_list=[dphase])
atime_each = arrivals_each[0].time
if rel_time == 1: # each window has a shift proportional to ref_dist at phase slowness at ref_dist
s_t += atime_each
e_t += atime_each
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_each - (
dist - ref1_dist) * ref_slow
elif rel_time == 2: # each window has a distinct shift, but offset is common to all stations
s_t += atime_each
e_t += atime_each
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_ref
elif rel_time == 3: # each station has an individual, chosen-phase shift, phase arrival set to zero
s_t += atime_each
e_t += atime_each
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_each
elif rel_time == 4: # use same window around chosen phase for all stations, phase arrival set to zero
s_t += atime_ref
e_t += atime_ref
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_ref
else:
print('invalid rel_time, must be integer 0 to 4')
sys.exit()
if len(tr.data) > 0:
st_pickalign1 += tr
else:
nodata1 += 1
else:
print(tr.stats.station + ' not found in station list ')
# sys.exit()
for tr in st2: # find lat-lon from list, chop, statics, traces one by one
if float(
year2
) < 1970: # fix the damn 1969 -> 2069 bug in Gibbon's LASA data
temp_t = str(tr.stats.starttime)
temp_tt = '19' + temp_t[2:]
tr.stats.starttime = UTCDateTime(temp_tt)
if tr.stats.station in st_names: # find station in station list
ii = st_names.index(tr.stats.station)
tra2_sta_found += 1
if stat_corr != 1 or float(
st_corr[ii]
) > corr_threshold: # if using statics, reject low correlations
stalat = float(
st_lats[ii]) # look up lat & lon again to find distance
stalon = float(st_lons[ii])
distance = gps2dist_azimuth(
stalat, stalon, ev_lat2,
ev_lon2) # Get traveltimes again, hard to store
tr.stats.distance = distance[0] # distance in km
dist = distance[0] / (1000 * 111)
in_range = 0 # flag for whether this trace goes into stack
if ref_loc == False: # check whether trace is in distance range from earthquake
if min_dist < dist and dist < max_dist:
in_range = 1
tra2_in_range += 1
elif ref_loc == True: # alternately, check whether trace is close enough to ref_location
ref_distance = gps2dist_azimuth(ref_lat, ref_lon, stalat,
stalon)
ref2_dist = ref_distance[0] / (1000 * 111)
if ref2_dist < ref_rad:
in_range = 1
tra2_in_range += 1
if in_range == 1: # trace fulfills the specified criteria for being in range
s_t = t2 + start_buff
e_t = t2 + end_buff
if stat_corr == 1: # apply static station corrections
tr.stats.starttime -= float(st_shift[ii])
if rel_time == 0: # don't adjust absolute time
tr.trim(starttime=s_t, endtime=e_t)
else: # shift relative to a chosen phase
arrivals_each = model.get_travel_times(
source_depth_in_km=ev_depth2,
distance_in_degree=dist,
phase_list=[dphase])
atime_each = arrivals_each[0].time
if rel_time == 1: # each window has a shift proportional to ref_dist at phase slowness at ref_dist
s_t += atime_each
e_t += atime_each
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_each - (
dist - ref1_dist) * ref_slow
elif rel_time == 2: # each window has a distinct shift, but offset is common to all stations
s_t += atime_each
e_t += atime_each
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_ref
elif rel_time == 3: # each station has an individual, chosen-phase shift, phase arrival set to zero
s_t += atime_each
e_t += atime_each
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_each
elif rel_time == 4: # use same window around chosen phase for all stations, phase arrival set to zero
s_t += atime_ref
e_t += atime_ref
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_ref
else:
print('invalid rel_time, must be integer 0 to 4')
sys.exit()
if len(tr.data) > 0:
st_pickalign2 += tr
else:
nodata2 += 1
else:
print(tr.stats.station + ' not found in station list')
print('After alignment + range and correlation selection')
print('1st event, Traces found: ' + str(tra1_sta_found) +
' Traces in range: ' + str(tra1_in_range) +
' Traces with no data: ' + str(nodata1))
print('2nd event, Traces found: ' + str(tra2_sta_found) +
' Traces in range: ' + str(tra2_in_range) +
' Traces with no data: ' + str(nodata2))
print(
f'ref1_distance {ref1_dist:.3f} relative start time {atime_ref:.3f}'
)
if ref_loc == True:
print(
f'ref2_distance {ref2_dist:.3f} relative start time {atime_ref:.3f}'
)
print('ref_loc == True, ref_lat: ' + str(ref_lat) + ' ref_lon: ' +
str(ref_lon))
print(
f'last station: distance {dist:.3f} last station lat: {stalat:.3f} last station lon: {stalon:.3f}'
)
#%%
#print(st) # at length
if verbose:
print(st1.__str__(extended=True))
print(st2.__str__(extended=True))
if rel_time == 1:
print(st_pickalign1.__str__(extended=True))
print(st_pickalign2.__str__(extended=True))
#%% Detrend, taper, filter
print('Taper fraction is ' + str(taper_frac) + ' bandpass is ' +
str(freq_min) + ' to ' + str(freq_max))
st_pickalign1.detrend(type='simple')
st_pickalign2.detrend(type='simple')
st_pickalign1.taper(taper_frac, max_length=max_taper_length)
st_pickalign2.taper(taper_frac, max_length=max_taper_length)
st_pickalign1.filter('bandpass',
freqmin=freq_min,
freqmax=freq_max,
corners=4,
zerophase=True)
st_pickalign2.filter('bandpass',
freqmin=freq_min,
freqmax=freq_max,
corners=4,
zerophase=True)
st_pickalign1.taper(taper_frac, max_length=max_taper_length)
st_pickalign2.taper(taper_frac, max_length=max_taper_length)
#%% Cull further by imposing SNR threshold on both traces
st1good = Stream()
st2good = Stream()
for tr1 in st_pickalign1:
for tr2 in st_pickalign2:
if ((tr1.stats.network == tr2.stats.network) &
(tr1.stats.station == tr2.stats.station)):
if skip_SNR == 1:
st1good += tr1
st2good += tr2
else:
# estimate median noise
t_noise1_start = int(len(tr1.data) * taper_frac)
t_noise2_start = int(len(tr2.data) * taper_frac)
t_noise1_end = int(
len(tr1.data) * (-start_buff) /
(end_buff - start_buff))
t_noise2_end = int(
len(tr2.data) * (-start_buff) /
(end_buff - start_buff))
noise1 = np.median(
abs(tr1.data[t_noise1_start:t_noise1_end]))
noise2 = np.median(
abs(tr2.data[t_noise2_start:t_noise2_end]))
# estimate median signal
t_signal1_start = int(
len(tr1.data) * (-start_buff) /
(end_buff - start_buff))
t_signal2_start = int(
len(tr2.data) * (-start_buff) /
(end_buff - start_buff))
t_signal1_end = t_signal1_start + int(
len(tr1.data) * signal_dur / (end_buff - start_buff))
t_signal2_end = t_signal2_start + int(
len(tr2.data) * signal_dur / (end_buff - start_buff))
signal1 = np.median(
abs(tr1.data[t_signal1_start:t_signal1_end]))
signal2 = np.median(
abs(tr2.data[t_signal2_start:t_signal2_end]))
# test SNR
SNR1 = signal1 / noise1
SNR2 = signal2 / noise2
if (SNR1 > qual_threshold and SNR2 > qual_threshold):
st1good += tr1
st2good += tr2
if skip_SNR == 1:
print('Matches (no SNR test): ' + str(len(st1good)) + ' traces')
else:
print('Match and above SNR threshold: ' + str(len(st1good)) +
' traces')
#%% get station lat-lon, compute distance for plot
min_dist_auto = 180
max_dist_auto = 0
min_time_plot = 1000000
max_time_plot = -1000000
for tr in st1good:
if tr.stats.station in st_names: # find station in station list
ii = st_names.index(tr.stats.station)
stalon = float(
st_lons[ii]) # look up lat & lon again to find distance
stalat = float(st_lats[ii])
distance = gps2dist_azimuth(stalat, stalon, ev_lat1, ev_lon1)
tr.stats.distance = distance[0] / (1000 * 111) # distance in km
if tr.stats.distance < min_dist_auto:
min_dist_auto = tr.stats.distance
if tr.stats.distance > max_dist_auto:
max_dist_auto = tr.stats.distance
if tr.stats.starttime - t1 < min_time_plot:
min_time_plot = tr.stats.starttime - t1
if ((tr.stats.starttime - t1) +
((len(tr.data) - 1) * tr.stats.delta)) > max_time_plot:
max_time_plot = ((tr.stats.starttime - t1) +
((len(tr.data) - 1) * tr.stats.delta))
for tr in st2good:
if tr.stats.station in st_names: # find station in station list
ii = st_names.index(tr.stats.station)
stalon = float(
st_lons[ii]) # look up lat & lon again to find distance
stalat = float(st_lats[ii])
distance = gps2dist_azimuth(stalat, stalon, ev_lat2, ev_lon2)
tr.stats.distance = distance[0] / (1000 * 111) # distance in km
print(
f'Min distance is {min_dist_auto:.3f} Max distance is {max_dist_auto:.3f}'
)
print(
f'Min time is {min_time_plot:.2f} Max time is {max_time_plot:.2f}')
if min_time_plot > start_buff:
print(f'Min time {min_time_plot:.2f} > start_buff {start_buff:.2f}')
print(
colored('Write zero-filling into pro3 for this code to work',
'red'))
sys.exit(-1)
if max_time_plot < end_buff:
print(f'Max time {max_time_plot:.2f} < end_buff {end_buff:.2f}')
print(
colored('Write zero-filling into pro3 for this code to work',
'red'))
sys.exit(-1)
#%%
# plot traces
fig_index = 3
plt.close(fig_index)
plt.figure(fig_index, figsize=(8, 8))
plt.xlim(start_buff, end_buff)
if auto_dist == True:
dist_diff = max_dist_auto - min_dist_auto # add space at extremes
plt.ylim(min_dist_auto - 0.1 * dist_diff,
max_dist_auto + 0.1 * dist_diff)
else:
plt.ylim(min_dist, max_dist)
for tr in st1good:
dist_offset = tr.stats.distance # trying for approx degrees
ttt = np.arange(len(tr.data)) * tr.stats.delta + (tr.stats.starttime -
t1)
if red_plot == 1:
shift = red_time + (dist_offset - red_dist) * red_slow
ttt = ttt - shift
plt.plot(ttt, (tr.data - np.median(tr.data)) * plot_scale_fac /
(tr.data.max() - tr.data.min()) + dist_offset,
color='green')
for tr in st2good:
dist_offset = tr.stats.distance # trying for approx degrees
ttt = np.arange(len(tr.data)) * tr.stats.delta + (tr.stats.starttime -
t2)
if red_plot == 1:
shift = red_time + (dist_offset - red_dist) * red_slow
ttt = ttt - shift
ttt = ttt
plt.plot(ttt, (tr.data - np.median(tr.data)) * plot_scale_fac /
(tr.data.max() - tr.data.min()) + dist_offset,
color='red')
print('And made it to here.')
#%% Plot traveltime curves
if rel_time != 1:
if plot_tt:
# first traveltime curve
line_pts = 50
dist_vec = np.arange(min_dist_auto, max_dist_auto,
(max_dist_auto - min_dist_auto) /
line_pts) # distance grid
time_vec1 = np.arange(
min_dist_auto, max_dist_auto, (max_dist_auto - min_dist_auto) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth1,
distance_in_degree=dist_vec[i],
phase_list=[dphase])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase:
time_vec1[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec1[i] = np.nan
# second traveltime curve
if dphase2 != 'no':
time_vec2 = np.arange(
min_dist_auto, max_dist_auto,
(max_dist_auto - min_dist_auto) / line_pts
) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth1,
distance_in_degree=dist_vec[i],
phase_list=[dphase2])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase2:
time_vec2[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec2[i] = np.nan
if rel_time == 3 or rel_time == 4:
time_vec2 = time_vec2 - time_vec1
elif rel_time == 2:
time_vec2 = time_vec2 - atime_ref
plt.plot(time_vec2, dist_vec, color='orange')
# third traveltime curve
if dphase3 != 'no':
time_vec3 = np.arange(
min_dist_auto, max_dist_auto,
(max_dist_auto - min_dist_auto) / line_pts
) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth1,
distance_in_degree=dist_vec[i],
phase_list=[dphase3])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase3:
time_vec3[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec3[i] = np.nan
if rel_time == 3 or rel_time == 4:
time_vec2 = time_vec2 - time_vec1
elif rel_time == 2:
time_vec2 = time_vec2 - atime_ref
plt.plot(time_vec3, dist_vec, color='yellow')
# fourth traveltime curve
# if dphase4 != 'no':
# time_vec4 = np.arange(min_dist, max_dist_auto, (max_dist_auto - min_dist)/line_pts) # empty time grid of same length (filled with -1000)
# for i in range(0,line_pts):
# arrivals = model.get_travel_times(source_depth_in_km=ev_depth1,distance_in_degree
# =dist_vec[i],phase_list=[dphase4])
# num_arrivals = len(arrivals)
# found_it = 0
# for j in range(0,num_arrivals):
# if arrivals[j].name == dphase4:
# time_vec4[i] = arrivals[j].time
# found_it = 1
# if found_it == 0:
# time_vec4[i] = np.nan
# if rel_time == 3 or rel_time == 4:
# time_vec2 = time_vec2 - time_vec1
# elif rel_time == 2:
# time_vec2 = time_vec2 - atime_ref
# plt.plot(time_vec4,dist_vec, color = 'purple')
# if rel_time == 3 or rel_time == 4:
# time_vec1 = time_vec1 - time_vec1
# elif rel_time == 2:
# time_vec1 = time_vec1 - atime_ref
# plt.plot(time_vec1,dist_vec, color = 'blue')
# if no_plots == False:
# plt.show()
plt.xlabel('Time (s)')
plt.ylabel('Epicentral distance from event (°)')
plt.title(dphase + ' for ' + fname1[43:53] + ' vs ' + fname2[43:53])
if no_plots == False:
plt.show()
#%% Save processed files
fname1 = '/Users/vidale/Documents/Research/IC/Pro_Files/HD' + date_label1 + 'sel.mseed'
fname2 = '/Users/vidale/Documents/Research/IC/Pro_Files/HD' + date_label2 + 'sel.mseed'
st1good.write(fname1, format='MSEED')
st2good.write(fname2, format='MSEED')
elapsed_time_wc = time.time() - start_time_wc
print(f'This job took {elapsed_time_wc:.1f} seconds')
os.system('say "Done"')
ev_time = init_time + det_times[kk]
start_time = ev_time - wtime_before
end_time = ev_time + wtime_after
print ev_time, start_time, end_time
if (diff_times[kk] > wtime_after): # special case: unusually long delay between start and end times
end_time = ev_time + diff_times[kk] + wtime_after
jday_start = start_time.julday
jday_end = end_time.julday
if (jday_start != jday_end):
print "Warning: start and end day not equal", kk, jday_start, jday_end
stalist = []
st =Stream()
if int(peaksum[kk])<150:continue
for s, sta in zip(ss[:,kk], stations):
if np.isnan(s):continue
else:st += read(ts_dir+'%03d/*%s*.mseed'%(jday_start, sta), format='MSEED')
#st = read(ts_dir+'%03d/*.mseed'%jday_start, format='MSEED')
print len(st)
print st.__str__(extended=True)
st_slice = st.slice(start_time, end_time)
out_file = 'event_rank'+format(kk,'05d')+'_nsta'+str(int(num_sta[kk]))\
+'_peaksum'+str(int(peaksum[kk]))+'_ind'+str(int(det_start_ind[kk]))+'_time'\
+str(det_times[kk])+'_'+ev_time.strftime('%Y-%m-%dT%H:%M:%S.%f')
st_slice.write(os.path.join(output_eq_dir, out_file+'.mseed'), format='MSEED' )
if plot:
st_slice.plot(equal_scale=False, size=(out_width,out_height), outfile=out_file)
def pro3singlet(eq_file,
stat_corr=0,
rel_time=1,
rel_slow=1,
simple_taper=0,
skip_SNR=0,
dphase='PKiKP',
dphase2='PKKP',
dphase3='PKIKP',
dphase4='PPP',
start_buff=-10,
end_buff=30,
plot_scale_fac=0.05,
qual_threshold=0,
corr_threshold=0,
freq_min=0.25,
freq_max=1,
do_filt=1,
min_dist=0,
max_dist=180,
auto_dist=0,
do_decimate=0,
alt_statics=0,
statics_file='nothing',
ARRAY=0,
ref_loc=0,
ref_rad=0.4,
verbose=0,
fig_index=101,
event_no=0):
# 0 is Hinet, 1 is LASA, 2 is NORSAR
#%% Import functions
from obspy import UTCDateTime
from obspy import Stream
from obspy import read
from obspy.geodetics import gps2dist_azimuth
import numpy as np
import os
from obspy.taup import TauPyModel
import matplotlib.pyplot as plt
import time
model = TauPyModel(model='iasp91')
# import sys # don't show any warnings
# import warnings
#
# if not sys.warnoptions:
# warnings.simplefilter("ignore")
print('Running pro3b_sort_plot_singlet')
start_time_wc = time.time()
#%% Get saved event info, also used to name files
# input event data with 1-line file of format
# event 2016-05-28T09:47:00.000 -56.241 -26.935 78
if ARRAY == 0:
file = open(eq_file, 'r')
elif ARRAY == 1:
file = open('EvLocs/' + eq_file, 'r')
lines = file.readlines()
split_line = lines[0].split()
# ids.append(split_line[0]) ignore label for now
t = UTCDateTime(split_line[1])
date_label = split_line[1][0:10]
year = split_line[1][0:4]
ev_lat = float(split_line[2])
ev_lon = float(split_line[3])
ev_depth = float(split_line[4])
print('date_label ' + date_label + ' time ' + str(t) + ' lat ' +
str(ev_lat) + ' lon ' + str(ev_lon) + ' depth ' + str(ev_depth))
#%% Get station location file
if stat_corr == 1: # load static terms, only applies to Hinet and LASA
if ARRAY == 0:
if alt_statics == 0: # standard set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/hinet_sta_statics.txt'
else: # custom set made by this event for this event
sta_file = (
'/Users/vidale/Documents/PyCode/Hinet/Array_codes/Files/' +
'HA' + date_label[:10] + 'pro4_' + dphase + '.statics')
elif ARRAY == 1:
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/L_sta_statics.txt'
with open(sta_file, 'r') as file:
lines = file.readlines()
print(str(len(lines)) + ' stations read from ' + sta_file)
# Load station coords into arrays
station_index = range(len(lines))
st_names = []
st_dist = []
st_lats = []
st_lons = []
st_shift = []
st_corr = []
for ii in station_index:
line = lines[ii]
split_line = line.split()
st_names.append(split_line[0])
st_dist.append(split_line[1])
st_lats.append(split_line[2])
st_lons.append(split_line[3])
st_shift.append(split_line[4])
st_corr.append(split_line[5])
else: # no static terms, always true for NORSAR
if ARRAY == 0: # Hinet set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/hinet_sta.txt'
elif ARRAY == 1: # LASA set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/LASA_sta.txt'
else: # NORSAR set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/NORSAR_sta.txt'
with open(sta_file, 'r') as file:
lines = file.readlines()
print(str(len(lines)) + ' stations read from ' + sta_file)
# Load station coords into arrays
station_index = range(len(lines))
st_names = []
st_lats = []
st_lons = []
for ii in station_index:
line = lines[ii]
split_line = line.split()
st_names.append(split_line[0])
st_lats.append(split_line[1])
st_lons.append(split_line[2])
#%% Set some parameters
# fig_index = 101
# stat_corr = 1 # apply station static corrections
# rel_time = 1 # timing is relative to a chosen phase, otherwise relative to OT
# dphase = 'PKIKP' # phase to be aligned
# dphase2 = 'PKiKP' # another phase to have traveltime plotted
# dphase3 = 'PKP' # another phase to have traveltime plotted
# dphase4 = 'pP' # another phase to have traveltime plotted
taper_frac = .05 #Fraction of window tapered on both ends
signal_dur = 10. # signal length used in SNR calculation
# plot_scale_fac = 0.5 # Bigger numbers make each trace amplitude bigger on plot
# qual_threshold = 2 # minimum SNR
# corr_threshold = 0.7 # minimum correlation in measuring shift to use station
plot_tt = 1 # plot the traveltimes?
# ref_loc = 0 # 1 if selecting stations within ref_rad of ref_lat and ref_lon
# 0 if selecting stations by distance from earthquake
if ARRAY == 0:
ref_lat = 36.3 # °N, around middle of Japan
ref_lon = 138.5 # °E
ref_rad = 1.5 # ° radius (°)
elif ARRAY == 1:
ref_lat = 46.7 # °N keep only inner rings A-D
ref_lon = -106.22 # °E
# ref_rad = 0.4 # ° radius (°) set by input or at top
#%% Is taper too long compared to noise estimation window?
totalt = end_buff - start_buff
noise_time_skipped = taper_frac * totalt
if simple_taper == 0:
if noise_time_skipped >= -0.5 * start_buff:
print(
'Specified taper of ' + str(taper_frac * totalt) +
' is not big enough compared to available noise estimation window '
+ str(-start_buff - noise_time_skipped) +
'. May not work well.')
old_taper_frac = taper_frac
taper_frac = -0.5 * start_buff / totalt
if start_buff > 0:
taper_frac = 0.05 # pick random minimal window if there is no leader
print('Taper reset from ' + str(old_taper_frac * totalt) + ' to ' +
str(taper_frac * totalt) + ' seconds.')
if rel_time == 0: # SNR requirement not implemented for unaligned traces
qual_threshold = 0
# Plot with reduced velocity?
red_plot = 0
red_dist = 55
red_time = 300
red_slow = 7.2 # seconds per degree
#%% In case one wants to manually enter data here
# date_label = '2018-04-02' # date for filename
# ev_lon = -63.006
# ev_lat = -20.659
# ev_depth = 559
# t = UTCDateTime('2018-04-02T13:40:34.840')
#%% Load waveforms and decimate to 10 sps
st = Stream()
if ARRAY == 0:
fname = 'HD' + date_label + '.mseed'
elif ARRAY == 1:
fname = 'Mseed/HD' + date_label + '.mseed'
st = read(fname)
if do_decimate != 0:
st.decimate(do_decimate)
print('Read in: ' + str(len(st)) + ' traces')
print('First trace has : ' + str(len(st[0].data)) + ' time pts ')
print('Start time : ' + str(st[0].stats.starttime) + ' event time : ' +
str(t))
print('After decimation: ' + str(len(st)) + ' traces')
nt = len(st[0].data)
dt = st[0].stats.delta
print('First trace has : ' + str(nt) + ' time pts, time sampling of ' +
str(dt) + ' and thus duration of ' + str((nt - 1) * dt))
# print(f'Sta lat-lon {stalat:.4f} {stalon:.4f}')
#%% Select by distance, window and adjust start time to align picked times
st_pickalign = Stream()
tra_located = 0
tra_in_range = 0
tra_sta_found = 0
if auto_dist != 0: # set plot limit automatically
min_dist_auto = 180
max_dist_auto = 0
# for ii in station_index:
# print('Station name of index ' + str(ii) + ' is ' + str(st_names[ii])) # enumerate stations
# for tr in st: # traces one by one, find lat-lon by searching entire inventory. Inefficient
# print('Station name of tr ' + str(tr.stats.station)) # enumerate stations
for tr in st: # traces one by one, find lat-lon by searching entire inventory. Inefficient
if float(
year
) < 1970: # fix the damn 1969 -> 2069 bug in Gibbon's LASA data
temp_t = str(tr.stats.starttime)
temp_tt = '19' + temp_t[2:]
tr.stats.starttime = UTCDateTime(temp_tt)
for ii in station_index:
if ARRAY == 0: # have to chop off last letter, always 'h'
this_name = st_names[ii]
this_name_truc = this_name[0:5]
name_truc_cap = this_name_truc.upper()
elif ARRAY == 1:
name_truc_cap = st_names[ii]
if (tr.stats.station == name_truc_cap
): # find station in inventory
tra_sta_found += 1
if stat_corr != 1 or float(
st_corr[ii]
) > corr_threshold: # if using statics, reject low correlations
stalat = float(st_lats[ii])
stalon = float(
st_lons[ii]
) # look up lat & lon again to find distance
# only used if ref_slow == 1:
ref_distance = gps2dist_azimuth(ref_lat, ref_lon, ev_lat,
ev_lon)
ref1_dist = ref_distance[0] / (1000 * 111)
distance = gps2dist_azimuth(
stalat, stalon, ev_lat,
ev_lon) # Get traveltimes again, hard to store
tr.stats.distance = distance[0] # distance in km
dist = distance[0] / (1000 * 111)
if ref_loc != 1:
tra_located += 1
if min_dist < dist and dist < max_dist: # select distance range from earthquake
tra_in_range += 1
try:
if rel_slow == 0:
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist,
phase_list=[dphase])
else:
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=ref1_dist,
phase_list=[dphase])
atime = arrivals[0].time
if stat_corr == 1: # apply static station corrections
tr.stats.starttime -= float(st_shift[ii])
if rel_time == 1:
s_t = t + atime + start_buff
e_t = t + atime + end_buff
else:
s_t = t + start_buff
e_t = t + end_buff
tr.trim(starttime=s_t, endtime=e_t)
# deduct theoretical traveltime and start_buf from starttime
if rel_time == 1:
tr.stats.starttime -= atime
st_pickalign += tr
except:
pass
elif ref_loc == 1:
# only used if ref_loc == 1:
ref_distance = gps2dist_azimuth(
ref_lat, ref_lon, stalat, stalon)
ref2_dist = ref_distance[0] / (1000 * 111)
# print('ref_rad ' + str(ref_rad) + ' ref2_dist ' + str(ref2_dist))
if ref2_dist < ref_rad: # alternatively, select based on distance from ref location
try:
if rel_slow == 0:
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist,
phase_list=[dphase])
else:
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=ref1_dist,
phase_list=[dphase])
atime = arrivals[0].time
if stat_corr == 1: # apply static station corrections
tr.stats.starttime -= float(st_shift[ii])
if rel_time == 1:
s_t = t + atime + start_buff
e_t = t + atime + end_buff
else:
s_t = t + start_buff
e_t = t + end_buff
tr.trim(starttime=s_t, endtime=e_t)
# deduct theoretical traveltime and start_buf from starttime
if rel_time == 1:
tr.stats.starttime -= atime
st_pickalign += tr
except:
pass
print('After alignment + range and correlation selection - event: ' +
str(len(st_pickalign)) + ' traces')
print('Traces found: ' + str(tra_sta_found) +
' traces with range examined: ' + str(tra_located) +
' traces in range: ' + str(tra_in_range))
print('distance: ' + str(dist) + ' ref1_distance: ' + str(ref1_dist) +
' atime: ' + str(atime))
print('ref_lat: ' + str(ref_lat) + ' ref_lon: ' + str(ref_lon))
print('ref_lat: ' + str(stalat) + ' ref_lon: ' + str(stalon))
#print(st) # at length
if verbose:
print(st.__str__(extended=True))
if rel_time == 1:
print(st_pickalign.__str__(extended=True))
#%% Detrend, taper, filter
st_pickalign.detrend(type='simple')
print('taper_frac is ' + str(taper_frac))
st_pickalign.taper(taper_frac)
if do_filt == 1:
st_pickalign.filter('bandpass',
freqmin=freq_min,
freqmax=freq_max,
corners=4,
zerophase=True)
st_pickalign.taper(taper_frac)
#%% Cull further by imposing SNR threshold on both traces
if skip_SNR == 1:
stgood = st_pickalign.copy()
else:
stgood = Stream()
for tr in st_pickalign:
# estimate median noise
t_noise_start = int(len(tr.data) * taper_frac)
t_noise_end = int(
len(tr.data) * start_buff / (start_buff - end_buff))
noise = np.median(abs(tr.data[t_noise_start:t_noise_end]))
# estimate median signal
t_signal_start = int(
len(tr.data) * start_buff / (start_buff - end_buff))
t_signal_end = t_signal_start + int(
len(tr.data) * signal_dur / (end_buff - start_buff))
signal = np.median(abs(tr.data[t_signal_start:t_signal_end]))
# test SNR
SNR = signal / noise
if (SNR > qual_threshold):
stgood += tr
print('Above SNR threshold: ' + str(len(stgood)) + ' traces')
if verbose:
for tr in stgood:
print('Distance is ' + str(tr.stats.distance / (1000 * 111)) +
' for station ' + tr.stats.station)
#%% get station lat-lon, compute distance for plot
for tr in stgood:
for ii in station_index:
if (tr.stats.station == st_names[ii]): # find station in inventory
stalon = float(
st_lons[ii]) # look up lat & lon again to find distance
stalat = float(st_lats[ii])
distance = gps2dist_azimuth(stalat, stalon, ev_lat, ev_lon)
tr.stats.distance = distance[0] / (1000 * 111
) # distance in degrees
if auto_dist != 0:
if tr.stats.distance < min_dist_auto:
min_dist_auto = tr.stats.distance
if tr.stats.distance > max_dist_auto:
max_dist_auto = tr.stats.distance
#%% This section causes a crash in Spyder
# plot traces
plt.close(fig_index)
plt.figure(fig_index, figsize=(10, 10))
plt.xlim(start_buff, end_buff)
if auto_dist == 1:
dist_diff = max_dist_auto - min_dist_auto # add space at extremes
plt.ylim(min_dist_auto - 0.1 * dist_diff,
max_dist_auto + 0.1 * dist_diff)
else:
plt.ylim(min_dist, max_dist)
for tr in stgood:
dist_offset = tr.stats.distance
ttt = np.arange(len(tr.data)) * tr.stats.delta + (tr.stats.starttime -
t)
if red_plot == 1:
shift = red_time + (dist_offset - red_dist) * red_slow
ttt = ttt - shift
plt.plot(ttt, (tr.data - np.median(tr.data)) * plot_scale_fac /
(tr.data.max() - tr.data.min()) + dist_offset,
color='black')
#%% Plot traveltime curves
if plot_tt:
# first traveltime curve
line_pts = 50
dist_vec = np.arange(min_dist, max_dist,
(max_dist - min_dist) / line_pts) # distance grid
time_vec1 = np.arange(
min_dist, max_dist, (max_dist - min_dist) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list=[dphase])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase:
time_vec1[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec1[i] = np.nan
# second traveltime curve
if dphase2 != 'no':
time_vec2 = np.arange(
min_dist, max_dist, (max_dist - min_dist) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list=[dphase2])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase2:
time_vec2[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec2[i] = np.nan
if rel_time == 1:
time_vec2 = time_vec2 - time_vec1
plt.plot(time_vec2, dist_vec, color='orange')
# third traveltime curve
if dphase3 != 'no':
time_vec3 = np.arange(
min_dist, max_dist, (max_dist - min_dist) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list=[dphase3])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase3:
time_vec3[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec3[i] = np.nan
if rel_time == 1:
time_vec3 = time_vec3 - time_vec1
plt.plot(time_vec3, dist_vec, color='yellow')
# fourth traveltime curve
if dphase4 != 'no':
time_vec4 = np.arange(
min_dist, max_dist, (max_dist - min_dist) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list=[dphase4])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase4:
time_vec4[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec4[i] = np.nan
if rel_time == 1:
time_vec4 = time_vec4 - time_vec1
plt.plot(time_vec4, dist_vec, color='purple')
if rel_time == 1:
time_vec1 = time_vec1 - time_vec1
plt.plot(time_vec1, dist_vec, color='blue')
plt.show()
plt.xlabel('Time (s)')
plt.ylabel('Epicentral distance from event (°)')
if ARRAY == 0:
plt.title(dphase + ' for ' + fname)
elif ARRAY == 1:
plt.title(dphase + ' for ' + fname[8:18] + ' event # ' + str(event_no))
os.chdir('/Users/vidale/Documents/PyCode/LASA/Quake_results/Plots')
# plt.savefig(date_label + '_' + str(event_no) + '_raw.png')
plt.show()
#%% Save processed files
if ARRAY == 0:
fname3 = '/Users/vidale/Documents/PyCode/LASA/HD' + date_label + 'sel.mseed'
elif ARRAY == 1:
fname3 = '/Users/vidale/Documents/PyCode/LASA/Pro_Files/HD' + date_label + 'sel.mseed'
stgood.write(fname3, format='MSEED')
elapsed_time_wc = time.time() - start_time_wc
print('This job took ' + str(elapsed_time_wc) + ' seconds')
os.system('say "Done"')
"latitude"] = station.latitude
st[y].stats["coordinates"][
"longitude"] = station.longitude
distance = locations2degrees(EQLAT, EQLON,
station.latitude,
station.longitude)
st[y].stats["distance"] = distance
# sort the traces by distance and subsample them, leaving at least SEPARATION between them and copying the desired traces into st2
st.sort(keys=['distance']) # sort the traces by distance from the epicentre
st2 = Stream() # set up a blank stream object for data
last_distance = -10 # variable to record the last distance copied to the stream for the plot
for trace in st:
if last_distance + SEPARATION <= trace.stats.distance: # only copy traces that are >= SEPARATION from the previous copied trace
st2 += trace.copy()
last_distance = trace.stats.distance
print(st2.__str__(extended=True))
# filter
st2.filter("bandpass", freqmin=F1, freqmax=F2, corners=2, zerophase=True)
# Create the section plot
fig = plt.figure(figsize=(16, 12), dpi=80)
plt.title('Section plot for ' + EQNAME + " " + str(START_TIME.date) + " " +
str(START_TIME.time) + " lat:" + str(EQLAT) + " lon:" + str(EQLON),
fontsize=12,
y=1.07)
# plot the data
st2.plot(size=(960, 720),
type='section',
recordlength=DURATION,
linewidth=1.5,
grid_linewidth=.5,
show=False,
def pro3singlet(eq_num,
stat_corr=1,
rel_time=1,
max_taper_length=5.,
simple_taper=0,
skip_SNR=0,
dphase='P',
dphase2='',
dphase3='',
dphase4='',
start_buff=-10,
end_buff=10,
start_beam=0,
end_beam=0,
plot_scale_fac=0.2,
qual_threshold=0,
corr_threshold=0,
freq_min=0.25,
freq_max=1,
do_filt=1,
min_dist=0,
max_dist=180,
auto_dist=True,
do_decimate=0,
alt_statics=0,
statics_file='nothing',
ARRAY=0,
JST=0,
ref_loc=0,
ref_rad=0.4,
verbose=0,
fig_index=101,
event_no=0):
# 0 is Hinet, 1 is LASA, 2 is NORSAR
#%% Import functions
from obspy import UTCDateTime
from obspy import Stream
from obspy import read
from obspy.geodetics import gps2dist_azimuth
import numpy as np
import os
import sys
from obspy.taup import TauPyModel
import matplotlib.pyplot as plt
import time
from termcolor import colored
model = TauPyModel(model='iasp91')
# import sys # don't show any warnings
# import warnings
#
# if not sys.warnoptions:
# warnings.simplefilter("ignore")
print(colored('Running pro3b_sort_plot_singlet', 'cyan'))
start_time_wc = time.time()
#%% Get saved event info, also used to name files
# input event data with 1-line file of format
# event 2016-05-28T09:47:00.000 -56.241 -26.935 78
fname = '/Users/vidale/Documents/Research/IC/EvLocs/event' + str(
eq_num) + '.txt'
print('Opening ' + fname)
file = open(fname, 'r')
lines = file.readlines()
split_line = lines[0].split()
# ids.append(split_line[0]) ignore label for now
t = UTCDateTime(split_line[1])
date_label = split_line[1][0:10]
year_label = split_line[1][0:4]
year_short_label = split_line[1][2:4]
month_label = split_line[1][5:7]
day_label = split_line[1][8:10]
hour_label = split_line[1][11:13]
minute_label = split_line[1][14:16]
print(date_label + ' year_label ' + year_label + ' hour_label ' +
hour_label + ' min_label ' + minute_label)
ev_lat = float(split_line[2])
ev_lon = float(split_line[3])
ev_depth = float(split_line[4])
print(' date_label ' + date_label + ' time ' + str(t) + ' lat ' +
str(ev_lat) + ' lon ' + str(ev_lon) + ' depth ' + str(ev_depth))
#%% Get station location file
if stat_corr == 1: # load static terms, only applies to Hinet, LASA, and China
if ARRAY == 0:
if alt_statics == 0: # standard set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_statics_hinet.txt'
else: # custom set made by this event for this event
sta_file = (
'/Users/vidale/Documents/PyCode/Hinet/Array_codes/Files/' +
'HA' + date_label[:10] + 'pro4_' + dphase + '.statics')
elif ARRAY == 1:
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_statics_LASA.txt'
elif ARRAY == 2:
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_statics_ch.txt'
with open(sta_file, 'r') as file:
lines = file.readlines()
print(' ' + str(len(lines)) + ' coarse station statics read from ' +
sta_file)
# Load station coords into arrays
station_index = range(len(lines))
st_names = []
st_dist = []
st_lats = []
st_lons = []
st_shift = []
st_corr = []
for ii in station_index:
line = lines[ii]
split_line = line.split()
st_names.append(split_line[0])
if ARRAY == 0 or ARRAY == 1:
st_dist.append(split_line[1])
st_lats.append(split_line[2])
st_lons.append(split_line[3])
st_shift.append(split_line[4])
st_corr.append(split_line[5])
elif ARRAY == 2:
st_lats.append(split_line[1])
st_lons.append(split_line[2])
st_shift.append(split_line[3])
st_corr.append(split_line[4]) # but really std dev
else: # no static terms, always true for NORSAR
if ARRAY == 0: # Hinet set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_hinet.txt'
elif ARRAY == 1: # LASA set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_LASA.txt'
elif ARRAY == 2: # China set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_ch.txt'
else: # NORSAR set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/sta_NORSAR.txt'
with open(sta_file, 'r') as file:
lines = file.readlines()
print(' ' + str(len(lines)) + ' stations read from ' + sta_file)
# Load station coords into arrays
station_index = range(len(lines))
st_names = []
st_lats = []
st_lons = []
for ii in station_index:
line = lines[ii]
split_line = line.split()
st_names.append(split_line[0])
st_lats.append(split_line[1])
st_lons.append(split_line[2])
if ARRAY == 0: # shorten and make upper case Hi-net station names to match station list
for ii in station_index:
this_name = st_names[ii]
this_name_truc = this_name[0:5]
st_names[ii] = this_name_truc.upper()
#%% Set some parameters
# fig_index = 101
# stat_corr = 1 # apply station static corrections
# rel_time = 1 # timing is relative to a chosen phase, otherwise relative to OT
# dphase = 'PKIKP' # phase to be aligned
# dphase2 = 'PKiKP' # another phase to have traveltime plotted
# dphase3 = 'PKP' # another phase to have traveltime plotted
# dphase4 = 'pP' # another phase to have traveltime plotted
taper_frac = .05 # Fraction of window tapered on both ends
noise_win_max = 20 # maximum length of noise window for SNR estimation, seconds
# plot_scale_fac = 0.5 # Bigger numbers make each trace amplitude bigger on plot
# qual_threshold = 2 # minimum SNR
# corr_threshold = 0.7 # minimum correlation in measuring shift to use station
plot_tt = 1 # plot the traveltimes?
# if ref_loc ==true, use ref_rad to filter station distance
# if ref_loc ==false, use earthquake loc to filter station distance
# ref_rad = 0.4 # ° radius (°) set by input or at top
if ARRAY == 0:
ref_lat = 36.3 # °N, around middle of Japan
ref_lon = 138.5 # °E
if ARRAY == 1:
ref_lat = 46.7 # °N keep only inner rings A-D if radius is 0.4°
ref_lon = -106.22 # °E
if ARRAY == 2:
ref_lat = 38 # °N
ref_lon = 104.5 # °E
# ref_rad = 0.4 # ° radius (°) set by input or at top
#%% Is taper too long compared to noise estimation window?
totalt = end_buff - start_buff
noise_time_skipped = taper_frac * totalt
noise_time_skipped = min(noise_time_skipped,
10.0) # set max of 10s to taper length
if simple_taper == 0:
if noise_time_skipped >= -0.5 * start_buff:
print(
' ' + 'Specified taper of ' + str(taper_frac * totalt) +
' is not big enough compared to available noise estimation window '
+ str(-start_buff - noise_time_skipped) +
'. May not work well.')
old_taper_frac = taper_frac
taper_frac = -0.5 * start_buff / totalt
if start_buff > 0:
taper_frac = 0.05 # pick random minimal window if there is no leader
print(' ' + 'Taper reset from ' +
str(old_taper_frac * totalt) + ' to ' +
str(taper_frac * totalt) + ' seconds.')
if rel_time == 0: # SNR requirement not implemented for unaligned traces
qual_threshold = 0
# Plot with reduced velocity?
red_plot = 0
red_dist = 55
red_time = 300
red_slow = 7.2 # seconds per degree
#%% Load waveforms and decimate to 10 sps, if not already decimated
st = Stream()
# fname = '/Users/vidale/Documents/GitHub/LASA_data/HD' + date_label + '.mseed'
mseed_name = year_short_label + month_label + day_label + '_' + hour_label + minute_label
fname = '/Users/vidale/Documents/Research/IC/Mseed/L' + mseed_name + '.mseed'
print('file name attempt: ' + fname)
st = read(fname)
if do_decimate != 0:
st.decimate(do_decimate, no_filter=True)
print(' ' + fname)
print(' ' + str(len(st)) + ' traces read in')
if (len(st) == 0):
exit('No traces is a failure')
print(' First trace has : ' + str(len(st[0].data)) + ' time pts ')
print(' Start time : ' + str(st[0].stats.starttime) +
' event time : ' + str(t))
# print(' ' + str(len(st)) + ' traces after decimation')
nt = len(st[0].data)
dt = st[0].stats.delta
print(' First trace has : ' + str(nt) +
' time pts, time sampling of ' + str(dt) + ' and thus duration of ' +
str((nt - 1) * dt))
#%% Select by distance, window and adjust start time to align picked times
st_pickalign = Stream()
tra_in_range = 0
tra_sta_found = 0
nodata = 0
min_dist_auto = 180
max_dist_auto = 0
min_time_plot = 1000000
max_time_plot = -1000000
# not used in all cases, but printed out below
# only used if rel_slow == 1, preserves 0 slowness, otherwise 0 is set to phase slowness
ref_distance = gps2dist_azimuth(ref_lat, ref_lon, ev_lat, ev_lon)
ref1_dist = ref_distance[0] / (1000 * 111)
dist_minus = ref1_dist - 0.5
dist_plus = ref1_dist + 0.5
arrivals_ref = model.get_travel_times(source_depth_in_km=ev_depth,
distance_in_degree=ref1_dist,
phase_list=[dphase])
if (len(arrivals_ref) == 0 and ref1_dist < 10 and dphase
== 'P'): # in case first arrival is upgoing P, which is 'p'
arrivals_ref = model.get_travel_times(source_depth_in_km=ev_depth,
distance_in_degree=ref1_dist,
phase_list='p')
arrivals_minus = model.get_travel_times(source_depth_in_km=ev_depth,
distance_in_degree=dist_minus,
phase_list=[dphase])
if (len(arrivals_minus) == 0 and dist_minus < 10 and dphase == 'P'):
arrivals_minus = model.get_travel_times(source_depth_in_km=ev_depth,
distance_in_degree=dist_minus,
phase_list='p')
arrivals_plus = model.get_travel_times(source_depth_in_km=ev_depth,
distance_in_degree=dist_plus,
phase_list=[dphase])
if (len(arrivals_plus) == 0 and dist_plus < 10 and dphase == 'P'):
arrivals_plus = model.get_travel_times(source_depth_in_km=ev_depth,
distance_in_degree=dist_plus,
phase_list='p')
if (len(arrivals_ref) == 0 or len(arrivals_minus) == 0
or len(arrivals_plus) == 0):
print('model.get_travel_times failed: dist, phase ' + str(ref1_dist) +
' ' + dphase)
atime_ref = arrivals_ref[
0].time # phase arrival time at reference distance
ref_slow = arrivals_plus[0].time - arrivals_minus[
0].time # dt over 1 degree at ref distance
for tr in st: # traces one by one, find lat-lon
if float(
year_label
) < 1970: # fix the damn 1969 -> 2069 bug in Gibbon's LASA data
temp_t = str(tr.stats.starttime)
temp_tt = '19' + temp_t[2:]
tr.stats.starttime = UTCDateTime(temp_tt)
if JST == 1: # if necessary, convert JST -> UTC, time in Greenwich 9 hours earlier than Japan
tr.stats.starttime = tr.stats.starttime - 9 * 60 * 60
# temp_t = str(tr.stats.starttime)
# temp_tt = '19' + temp_t[2:]
# tr.stats.starttime = UTCDateTime(temp_tt)
# times.append( tr.stats.starttime.strptime(split_line[1], dtformat) - dt.timedelta(seconds=offset))
# timesUTC.append(dt.datetime.strptime(split_line[1], dtformat)) # keep UTC version for output
if tr.stats.station in st_names: # find station in station list
ii = st_names.index(tr.stats.station)
tra_sta_found += 1
corr = 1
if stat_corr == 1:
corr = float(st_corr[ii])
if corr > corr_threshold: # if using statics, reject low correlations
stalat = float(
st_lats[ii]) # look up lat & lon again to find distance
stalon = float(st_lons[ii])
distance = gps2dist_azimuth(
stalat, stalon, ev_lat,
ev_lon) # Get traveltimes again, hard to store
tr.stats.distance = distance[0] # distance in km
dist = distance[0] / (1000 * 111)
in_range = 0 # flag for whether this trace goes into stack
rejector = False # flag in case model.get_travel_times fails
if ref_loc == False: # check whether trace is in distance range from earthquake
if min_dist < dist and dist < max_dist:
in_range = 1
tra_in_range += 1
elif ref_loc == True: # alternately, check whether trace is close enough to ref_location
ref_distance = gps2dist_azimuth(ref_lat, ref_lon, stalat,
stalon)
ref2_dist = ref_distance[0] / (1000 * 111)
if ref2_dist < ref_rad:
in_range = 1
tra_in_range += 1
if in_range == 1: # trace fulfills the specified criteria for being in range
s_t = t + start_buff
e_t = t + end_buff
if stat_corr == 1: # apply static station corrections
tr.stats.starttime -= float(st_shift[ii])
if rel_time == 0: # don't adjust absolute time
tr.trim(starttime=s_t, endtime=e_t)
else: # shift relative to a chosen phase
arrivals_each = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist,
phase_list=[dphase])
if (len(arrivals_each) == 0):
if (
dist < 10 and dphase == 'P'
): # in case first arrival is upgoing P, try 'p'
arrivals_each = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist,
phase_list='p')
if (len(arrivals_each) == 0): # did it still fail?
print(
'model.get_travel_times failed: dist, depth, phase '
+ tr.stats.station + ' ' + str(ref1_dist) +
' ' + ' ' + str(ev_depth) + ' ' + dphase)
tra_in_range -= 1 # don't count this trace after all
rejector = True
else:
atime_each = arrivals_each[0].time
if rel_time == 1: # each window has a shift proportional to ref_dist at phase slowness at ref_dist
s_t += atime_each
e_t += atime_each
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_each - (
dist - ref1_dist) * ref_slow
elif rel_time == 2: # each window has a distinct shift, but offset is common to all stations
s_t += atime_each
e_t += atime_each
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_ref
elif rel_time == 3: # each station has an individual, chosen-phase shift, phase arrival set to zero
s_t += atime_each
e_t += atime_each
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_each
elif rel_time == 4: # use same window around chosen phase for all stations, phase arrival set to zero
s_t += atime_ref
e_t += atime_ref
tr.trim(starttime=s_t, endtime=e_t)
tr.stats.starttime -= atime_ref
else:
sys.exit(
'Invalid rel_time parameter, must be integer 0 to 4'
)
if len(tr.data) > 0 and rejector == False:
st_pickalign += tr
else:
nodata += 1
else:
print(tr.stats.station + ' not found in station list with statics')
print(' ' + str(tra_in_range) + ' traces in range')
print(' ' + str(len(st_pickalign)) +
' traces after alignment and correlation selection')
print(' ' + str(nodata) + ' traces with no data')
#print(st) # at length
if verbose:
print(st.__str__(extended=True))
if rel_time == 1:
print(st_pickalign.__str__(extended=True))
#%% Detrend, taper, filter
st_pickalign.detrend(type='simple')
st_pickalign.taper(taper_frac, max_length=max_taper_length)
if do_filt == 1:
st_pickalign.filter('bandpass',
freqmin=freq_min,
freqmax=freq_max,
corners=4,
zerophase=True)
st_pickalign.taper(taper_frac, max_length=max_taper_length)
#%% Cull further by imposing SNR threshold
if skip_SNR == 1:
stgood = st_pickalign.copy()
else:
stgood = Stream()
for tr in st_pickalign:
# estimate median noise
time_to_beam_start = (start_beam - start_buff)
if time_to_beam_start - taper_frac * (
end_buff -
start_buff) < noise_win_max: # noise window < max length
t_noise_start = int(len(tr.data) *
taper_frac) # start just after taper
t_noise_end = int(
len(tr.data) * time_to_beam_start /
(end_buff - start_buff)) # end at beam start
else: # plenty of leader, set noise window to max length
time_to_noise_start = time_to_beam_start - noise_win_max
t_noise_start = int(
len(tr.data) * time_to_noise_start /
(end_buff - start_buff)) # start just after taper
t_noise_end = int(
len(tr.data) * time_to_beam_start /
(end_buff - start_buff)) # end at beam start
noise = np.median(abs(tr.data[t_noise_start:t_noise_end]))
# estimate median signal
t_signal_start = t_noise_end
t_signal_end = int(
len(tr.data) * (end_beam - start_buff) /
(end_buff - start_buff))
# old t_signal_start = int(len(tr.data) * start_buff/(start_buff-end_buff))
# old t_signal_end = t_signal_start + int(len(tr.data) * signal_dur/(end_buff - start_buff))
signal = np.median(abs(tr.data[t_signal_start:t_signal_end]))
# test SNR
SNR = signal / noise
# print('set noise window to max length: ' + str(t_noise_start) + ' start ' + str(t_noise_end) + ' end')
# print('set signal window: ' + str(t_signal_start) + ' start ' + str(t_signal_end) + ' end')
# print('SNR: ' + str(SNR))
if (SNR > qual_threshold):
stgood += tr
print(' ' + str(len(stgood)) + ' traces above SNR threshold')
if verbose:
for tr in stgood:
print(' Distance is ' + str(tr.stats.distance /
(1000 * 111)) +
' for station ' + tr.stats.station)
#%% get station lat-lon, compute distance and time limits for plot
for tr in stgood:
if tr.stats.station in st_names: # find station in station list
ii = st_names.index(tr.stats.station)
stalon = float(
st_lons[ii]) # look up lat & lon again to find distance
stalat = float(st_lats[ii])
distance = gps2dist_azimuth(stalat, stalon, ev_lat, ev_lon)
tr.stats.distance = distance[0] / (1000 * 111
) # distance in degrees
if tr.stats.distance < min_dist_auto:
min_dist_auto = tr.stats.distance
if tr.stats.distance > max_dist_auto:
max_dist_auto = tr.stats.distance
if tr.stats.starttime - t < min_time_plot:
min_time_plot = tr.stats.starttime - t
if ((tr.stats.starttime - t) +
((len(tr.data) - 1) * tr.stats.delta)) > max_time_plot:
max_time_plot = ((tr.stats.starttime - t) +
((len(tr.data) - 1) * tr.stats.delta))
print(
f' Min distance is {min_dist_auto:.3f} Max distance is {max_dist_auto:.3f}'
)
print(
f' Min time is {min_time_plot:.2f} Max time is {max_time_plot:.2f}'
)
if min_time_plot > start_buff:
print(f'Min time {min_time_plot:.2f} > start_buff {start_buff:.2f}')
print(
colored('Write zero-filling into pro3 for this code to work',
'red'))
sys.exit(-1)
if max_time_plot < end_buff:
print(f'Max time {max_time_plot:.2f} < end_buff {end_buff:.2f}')
print(
colored('Write zero-filling into pro3 for this code to work',
'red'))
sys.exit(-1)
#%% Plot traces
plt.close(fig_index)
plt.figure(fig_index, figsize=(10, 10))
plt.xlim(min_time_plot, max_time_plot)
if auto_dist == True:
dist_diff = max_dist_auto - min_dist_auto # add space at extremes
plt.ylim(min_dist_auto - 0.1 * dist_diff,
max_dist_auto + 0.1 * dist_diff)
max_dist = max_dist_auto
min_dist = min_dist_auto
else:
plt.ylim(min_dist, max_dist)
for tr in stgood:
dist_offset = tr.stats.distance
ttt = np.arange(len(tr.data)) * tr.stats.delta + (tr.stats.starttime -
t)
if red_plot == 1:
shift = red_time + (dist_offset - red_dist) * red_slow
ttt = ttt - shift
if len(tr.data) > 0:
if tr.data.max() - tr.data.min() > 0:
plt.plot(ttt, (tr.data - np.median(tr.data)) * plot_scale_fac /
(tr.data.max() - tr.data.min()) + dist_offset,
color='black')
else:
print('Max ' + str(tr.data.max()) + ' equals min ' +
str(tr.data.min()) + ', skip plotting')
else:
nodata += 1
print('Trace ' + tr.stats.station + ' has : ' + str(len(tr.data)) +
' time pts, skip plotting')
#%% Plot traveltime curves
if rel_time != 100:
if plot_tt:
# first traveltime curve
line_pts = 50
dist_vec = np.arange(min_dist_auto, max_dist_auto,
(max_dist_auto - min_dist_auto) /
line_pts) # distance grid
time_vec1 = np.arange(
min_dist_auto, max_dist_auto, (max_dist_auto - min_dist_auto) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list=[dphase])
if (len(arrivals) == 0 and dist_vec[i] < 10 and dphase == 'P'
): # in case first arrival is upgoing P, which is 'p'
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list='p')
if (len(arrivals) == 0):
print('model.get_travel_times failed: dist, phase ' +
str(dist_vec[i]) + ' ' + dphase)
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase:
time_vec1[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec1[i] = np.nan
# second traveltime curve
if dphase2 != 'no':
time_vec2 = np.arange(
min_dist_auto, max_dist_auto,
(max_dist_auto - min_dist_auto) / line_pts
) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list=[dphase2])
if (len(arrivals) == 0 and dist_vec[i] < 10
and dphase2 == 'P'
): # in case first arrival is upgoing P, which is 'p'
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list='p')
if (len(arrivals) == 0):
print(
'model.get_travel_times failed: dist, phase '
+ str(dist_vec[i]) + ' ' + dphase2)
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase2:
time_vec2[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec2[i] = np.nan
if rel_time == 3 or rel_time == 4:
time_vec2 = time_vec2 - time_vec1
elif rel_time == 2:
time_vec2 = time_vec2 - atime_ref
plt.plot(time_vec2, dist_vec, color='orange')
# third traveltime curve
if dphase3 != 'no':
time_vec3 = np.arange(
min_dist_auto, max_dist_auto,
(max_dist_auto - min_dist_auto) / line_pts
) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list=[dphase3])
if (len(arrivals) == 0 and dist_vec[i] < 10
and dphase3 == 'P'
): # in case first arrival is upgoing P, which is 'p'
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list='p')
if (len(arrivals) == 0):
print(
'model.get_travel_times failed: dist, phase '
+ str(dist_vec[i]) + ' ' + dphase3)
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase3:
time_vec3[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec3[i] = np.nan
if rel_time == 3 or rel_time == 4:
time_vec2 = time_vec2 - time_vec1
elif rel_time == 2:
time_vec2 = time_vec2 - atime_ref
plt.plot(time_vec3, dist_vec, color='yellow')
# fourth traveltime curve
if dphase4 != 'no':
time_vec4 = np.arange(
min_dist_auto, max_dist_auto,
(max_dist_auto - min_dist_auto) / line_pts
) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list=[dphase4])
if (len(arrivals) == 0 and dist_vec[i] < 10
and dphase4 == 'P'
): # in case first arrival is upgoing P, which is 'p'
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth,
distance_in_degree=dist_vec[i],
phase_list='p')
if (len(arrivals) == 0):
print(
'model.get_travel_times failed: dist, phase '
+ str(dist_vec[i]) + ' ' + dphase4)
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase4:
time_vec4[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec4[i] = np.nan
if rel_time == 3 or rel_time == 4:
time_vec2 = time_vec2 - time_vec1
elif rel_time == 2:
time_vec2 = time_vec2 - atime_ref
plt.plot(time_vec4, dist_vec, color='purple')
if rel_time == 3 or rel_time == 4:
time_vec1 = time_vec1 - time_vec1
elif rel_time == 2:
time_vec1 = time_vec1 - atime_ref
plt.plot(time_vec1, dist_vec, color='blue')
plt.xlabel('Time (s)')
plt.ylabel('Epicentral distance from event (°)')
plt.title(date_label + ' event #' + str(eq_num))
# os.chdir('/Users/vidale/Documents/PyCode/Plots')
# plt.savefig(date_label + '_' + str(event_no) + '_raw.png')
plt.show()
#%% Save processed files
fname3 = '/Users/vidale/Documents/Research/IC/Pro_Files/HD' + date_label + 'sel.mseed'
stgood.write(fname3, format='MSEED')
elapsed_time_wc = time.time() - start_time_wc
print(f' This job took {elapsed_time_wc:.1f} seconds')
os.system('say "Done"')
def gather_waveforms(source,
network,
station,
location,
channel,
starttime,
endtime,
time_buffer=0,
merge=True,
remove_response=False,
return_failed_stations=False,
watc_url=None,
watc_username=None,
watc_password=None):
"""
Gather seismic/infrasound waveforms from IRIS or WATC FDSN, or AVO Winston,
and output a Stream object with station/element coordinates attached.
Optionally remove the sensitivity.
NOTE 1:
Usual RTM usage is to specify a starttime/endtime that brackets the
estimated source origin time. Then time_buffer is used to download
enough extra data to account for the time required for an infrasound
signal to propagate to the farthest station.
Args:
source: Which source to gather waveforms from - options are:
'IRIS' <-- IRIS FDSN
'WATC' <-- WATC FDSN
'AVO' <-- AVO Winston
network: SEED network code
station: SEED station code
location: SEED location code
channel: SEED channel code
starttime: Start time for data request (UTCDateTime)
endtime: End time for data request (UTCDateTime)
time_buffer: [s] Extra amount of data to download after endtime
(default: 0)
merge: Toggle merging of Traces with identical IDs (default: True)
remove_response: Toggle response removal via remove_sensitivity() or a
simple scalar multiplication (default: False)
return_failed_stations: If True, returns a list of station codes that
were requested but not downloaded. This
disables the standard failed station warning
message (default: False)
watc_url: URL for WATC FDSN server (default: None)
watc_username: Username for WATC FDSN server (default: None)
watc_password: Password for WATC FDSN server (default: None)
Returns:
st_out: Stream containing gathered waveforms
failed_stations: (Optional) List containing station codes that were
requested but not downloaded
"""
print('--------------')
print('GATHERING DATA')
print('--------------')
# IRIS FDSN
if source == 'IRIS':
client = FDSN_Client('IRIS')
print('Reading data from IRIS FDSN...')
try:
st_out = client.get_waveforms(network,
station,
location,
channel,
starttime,
endtime + time_buffer,
attach_response=True)
except FDSNNoDataException:
st_out = Stream() # Just create an empty Stream object
# WATC FDSN
elif source == 'WATC':
print('Connecting to WATC FDSN...')
client = FDSN_Client(base_url=watc_url,
user=watc_username,
password=watc_password)
print('Successfully connected. Reading data from WATC FDSN...')
try:
st_out = client.get_waveforms(network,
station,
location,
channel,
starttime,
endtime + time_buffer,
attach_response=True)
except FDSNNoDataException:
st_out = Stream() # Just create an empty Stream object
# AVO Winston
elif source == 'AVO':
client = EW_Client('pubavo1.wr.usgs.gov',
port=16023) # 16023 is long-term
print('Reading data from AVO Winston...')
st_out = Stream() # Make empty Stream object to populate
# Brute-force "dynamic grid search" over network/station/channel/location codes
for nw in _restricted_matching('network', network, client):
for sta in _restricted_matching('station',
station,
client,
network=nw):
for cha in _restricted_matching('channel',
channel,
client,
network=nw,
station=sta):
for loc in _restricted_matching('location',
location,
client,
network=nw,
station=sta,
channel=cha):
try:
st_out += client.get_waveforms(
nw, sta, loc, cha, starttime,
endtime + time_buffer)
except KeyError:
pass
else:
raise ValueError('Unrecognized source. Valid options are \'IRIS\', '
'\'WATC\', or \'AVO\'.')
if merge:
st_out.merge() # Merge Traces with the same ID
st_out.sort()
# Check that all requested stations are present in Stream
requested_stations = station.split(',')
downloaded_stations = [tr.stats.station for tr in st_out]
failed_stations = []
for sta in requested_stations:
# The below check works with wildcards, but obviously cannot detect if
# ALL stations corresponding to a given wildcard (e.g., O??K) were
# downloaded. Thus, if careful station selection is desired, specify
# each station explicitly and the below check will then be effective.
if not fnmatch.filter(downloaded_stations, sta):
if not return_failed_stations:
# If we're not returning the failed stations, then show this
# warning message to alert the user
warnings.warn(
f'Station {sta} not downloaded from {source} '
'server for this time period.', CollectionWarning)
failed_stations.append(sta)
# If the Stream is empty, then we can stop here
if st_out.count() == 0:
print('No data downloaded.')
if return_failed_stations:
return st_out, failed_stations
else:
return st_out
# Otherwise, show what the Stream contains
print(st_out.__str__(extended=True)) # This syntax prints the WHOLE Stream
# Add zeros to ensure all Traces have same length
st_out.trim(starttime, endtime + time_buffer, pad=True, fill_value=0)
print('Assigning coordinates...')
# Use IRIS inventory info for AVO data source
if source == 'AVO':
client = FDSN_Client('IRIS')
try:
inv = client.get_stations(network=network,
station=station,
location=location,
channel=channel,
starttime=starttime,
endtime=endtime + time_buffer,
level='channel')
except FDSNNoDataException:
inv = []
for tr in st_out:
for nw in inv:
for sta in nw:
for cha in sta:
# Being very thorough to check if everything matches!
if (tr.stats.network == nw.code
and tr.stats.station == sta.code
and tr.stats.location == cha.location_code
and tr.stats.channel == cha.code):
tr.stats.longitude = cha.longitude
tr.stats.latitude = cha.latitude
tr.stats.elevation = cha.elevation
# Check if any Trace did NOT get coordinates assigned, and try to use JSON
# coordinates if available
for tr in st_out:
try:
tr.stats.longitude, tr.stats.latitude, tr.stats.elevation
except AttributeError:
try:
tr.stats.latitude, tr.stats.longitude,\
tr.stats.elevation = AVO_COORDS[tr.id]
warnings.warn(f'Using coordinates from JSON file for {tr.id}.',
CollectionWarning)
except KeyError:
print(f'No coordinates available for {tr.id}. Stopping.')
raise
# Remove sensitivity
if remove_response:
print('Removing sensitivity...')
for tr in st_out:
try:
# Just removing sensitivity for now. remove_response() can lead
# to errors. This should be sufficient for now. Plus some
# IRIS-AVO responses are wonky.
tr.remove_sensitivity()
except ValueError: # No response information found
# This is only set up for infrasound calibration values
try:
calib = AVO_INFRA_CALIBS[tr.id]
tr.data = tr.data * calib
warnings.warn(
'Using calibration value from JSON file for '
f'{tr.id}.', CollectionWarning)
except KeyError:
print(f'No calibration value available for {tr.id}. '
'Stopping.')
raise
print('Done')
# Return the Stream with coordinates attached (and responses removed if
# specified)
if return_failed_stations:
return st_out, failed_stations
else:
return st_out
def calculate_onsets(self, data, log=True, timespan=None):
"""
Calculate onset functions for the requested stations and phases.
Returns a stacked array of onset functions for the requested phases,
and an :class:`~quakemigrate.signal.onsets.base.OnsetData` object
containing all outputs from the onset function calculation: a dict of
the onset functions, a Stream containing the pre-processed input
waveforms, and a dict of availability info describing which of the
requested onset functions could be calculated (depending on data
availability and data quality checks).
Parameters
----------
data : :class:`~quakemigrate.io.data.WaveformData` object
Light class encapsulating data returned by an archive query.
log : bool
Calculate log(onset) if True, otherwise calculate the raw onset.
timespan : float or None, optional
If the timespan for which the onsets are being generated is
provided, this will be used to calculate the tapered window of data
at the start and end of the onset function which should be
disregarded. This is necessary to accurately set the pick threshold
in GaussianPicker, for example.
Returns
-------
onsets : `numpy.ndarray` of float
Stacked onset functions served up for migration,
shape(nonsets, nsamples).
onset_data : :class:`~quakemigrate.signal.onsets.base.OnsetData` object
Light class encapsulating data generated during onset calculation.
"""
onsets = []
onsets_dict = {}
filtered_waveforms = Stream()
availability = {}
# Loop through phases, pre-process data, and calculate onsets.
for phase in self.phases:
# Select traces based on channel map for this phase
phase_waveforms = data.waveforms.select(
channel=self.channel_maps[phase])
# Convert sta window, lta window lengths from seconds to samples.
stw, ltw = self.sta_lta_windows[phase]
stw = util.time2sample(stw, self.sampling_rate) + 1
ltw = util.time2sample(ltw, self.sampling_rate) + 1
# Pre-process the data. The ObsPy functions operate by trace, so
# will not break on gappy data (we haven't checked availability
# yet)
filtered_phase_waveforms = pre_process(
phase_waveforms, self.sampling_rate, data.resample,
data.upfactor, self.bandpass_filters[phase], data.starttime,
data.endtime)
# Loop through stations, check data availability for this phase,
# and store this info, filtered waveforms and calculated onsets
for station in data.stations:
waveforms = filtered_phase_waveforms.select(station=station)
available, av_dict = data.check_availability(
waveforms,
all_channels=self.all_channels,
n_channels=self.channel_counts[phase],
allow_gaps=self.allow_gaps,
full_timespan=self.full_timespan,
check_sampling_rate=True,
sampling_rate=self.sampling_rate)
availability[f"{station}_{phase}"] = available
# If no data available, skip
if available == 0:
logging.info(f"\t\tNo {phase} onset for {station}.")
continue
# Check that all channels met the availability critera. If
# not, remove this channel from the stream.
for key, available in av_dict.items():
if available == 0:
to_remove = waveforms.select(id=key)
[waveforms.remove(tr) for tr in to_remove]
# Pad with tiny floats so onset will be the correct length.
# Note: this will only have an effect if allow_gaps=True or
# full_timespan=False. Otherwise, there will be no gaps to pad.
if self.allow_gaps or not self.full_timespan:
# Square root to avoid floating point errors when value
# is squared to compute the energy trace
tiny = np.sqrt(np.finfo(float).tiny)
# Apply another taper to remove transients from filtering -
# this is within the pre- and post-pad for continuous data
waveforms.taper(type="cosine", max_percentage=0.05)
# Fill gaps
waveforms.merge(method=1, fill_value=tiny)
# Pad start/end; delta of +/-0.00001 is to avoid
# occasional obspy weirdness. `nearest_sample` is
# appropriate as data is at uniform sampling rate with
# off-sample data corrected by util.shift_to_sample()
waveforms.trim(starttime=data.starttime - 0.00001,
endtime=data.endtime + 0.00001,
pad=True,
fill_value=tiny,
nearest_sample=False)
# Calculate onset and add to WaveForm data object; add filtered
# waveforms that have passed the availability check to
# WaveformData.filtered_waveforms
onsets_dict.setdefault(station, {}).update(
{phase: self._onset(waveforms, stw, ltw, log, timespan)})
onsets.append(onsets_dict[station][phase])
filtered_waveforms += waveforms
logging.debug(filtered_waveforms.__str__(extended=True))
onsets = np.stack(onsets, axis=0)
onset_data = OnsetData(onsets_dict, self.phases, self.channel_maps,
filtered_waveforms, availability,
data.starttime, data.endtime,
self.sampling_rate)
return onsets, onset_data
s_t = t - start_buff
e_t = t + end_buff
tr.trim(starttime=s_t, endtime=e_t)
# deduct theoretical traveltime and start_buf from starttime
if rel_time == 1:
tr.stats.starttime = tr.stats.starttime - atime
st_pickalign += tr
except:
pass
print('After alignment and range selection - event: ' +
str(len(st_pickalign)) + ' traces')
#%%
#print(st) # at length
if verbose:
print(st.__str__(extended=True))
if rel_time == 1:
print(st_pickalign.__str__(extended=True))
#%% detrend, taper, filter
st_pickalign.detrend(type='simple')
st_pickalign.taper(taper_frac)
st_pickalign.filter('bandpass',
freqmin=freq_min,
freqmax=freq_max,
corners=2,
zerophase=True)
st_pickalign.taper(taper_frac)
#%% Cull further by imposing SNR threshold on both traces
stgood = Stream()
def pro3pair(eq_file1,
eq_file2,
stat_corr=1,
simple_taper=0,
skip_SNR=0,
dphase='PKIKP',
dphase2='PKiKP',
dphase3='PKIKP',
dphase4='PKiKP',
rel_time=1,
start_buff=-200,
end_buff=500,
plot_scale_fac=0.05,
qual_threshold=0,
corr_threshold=0.5,
freq_min=1,
freq_max=3,
min_dist=0,
max_dist=180,
alt_statics=0,
statics_file='nothing',
ARRAY=0,
ref_loc=0):
# Parameters
# ARRAY 0 is Hinet, 1 is LASA, 2 is NORSAR
# start_buff = -50 # plots start Xs after PKIKP
# end_buff = 200 # plots end Xs after PKIKP
# plot_scale_fac = 0.5 # Bigger numbers make each trace amplitude bigger on plot
# stat_corr = 1 # apply station static corrections
# qual_threshold = 0.2 # minimum SNR
# corr_threshold = 0.7 # minimum correlation in measuring shift to use station in static construction
# dphase = 'PKIKP' # phase to be aligned
# dphase2 = 'PKiKP' # another phase to have traveltime plotted
# dphase3 = 'pPKiKP' # another phase to have traveltime plotted
# dphase4 = 'pPKIKP' # another phase to have traveltime plotted
#%% Set some parameters
verbose = 0 # more output
# rel_time = 1 # timing is relative to a chosen phase, otherwise relative to OT
taper_frac = .05 #Fraction of window tapered on both ends
signal_dur = 5. # signal length used in SNR calculation
plot_tt = 1 # plot the traveltimes?
do_decimate = 0 # 0 if no decimation desired
#ref_loc = 0 # 1 if selecting stations within ref_rad of ref_lat and ref_lon
# 0 if selecting stations by distance from earthquake
if ref_loc == 1:
if ARRAY == 0:
ref_lat = 36.3 # °N, around middle of Japan
ref_lon = 138.5 # °E
ref_rad = 1.5 # ° radius (°)
elif ARRAY == 1:
ref_lat = 46.7 # °N keep only inner rings A-D
ref_lon = -106.22 # °E
ref_rad = 0.4 # ° radius (°)
if rel_time == 0: # SNR requirement not implemented for unaligned traces
qual_threshold = 0
# Plot with reduced velocity?
red_plot = 0
red_dist = 55
red_time = 300
red_slow = 7.2 # seconds per degree
#%% Import functions
from obspy import UTCDateTime
from obspy import Stream
from obspy import read
from obspy.geodetics import gps2dist_azimuth
import numpy as np
import os
from obspy.taup import TauPyModel
import matplotlib.pyplot as plt
import time
model = TauPyModel(model='iasp91')
import sys # don't show any warnings
import warnings
if not sys.warnoptions:
warnings.simplefilter("ignore")
print('Running pro3a_sort_plot_pair')
start_time_wc = time.time()
#%% Get saved event info, also used to name files
# event 2016-05-28T09:47:00.000 -56.241 -26.935 78
print('Opening ' + eq_file1)
if ARRAY == 0:
file = open(eq_file1, 'r')
elif ARRAY == 1:
file = open('EvLocs/' + eq_file1, 'r')
lines = file.readlines()
split_line = lines[0].split()
# ids.append(split_line[0]) ignore label for now
t1 = UTCDateTime(split_line[1])
date_label1 = split_line[1][0:10]
year1 = split_line[1][0:4]
ev_lat1 = float(split_line[2])
ev_lon1 = float(split_line[3])
ev_depth1 = float(split_line[4])
print('1st event: date_label ' + date_label1 + ' time ' + str(t1) +
' lat ' + str(ev_lat1) + ' lon ' + str(ev_lon1) + ' depth ' +
str(ev_depth1))
print('Opening ' + eq_file2)
if ARRAY == 0:
file = open(eq_file2, 'r')
elif ARRAY == 1:
file = open('EvLocs/' + eq_file2, 'r')
lines = file.readlines()
split_line = lines[0].split()
# ids.append(split_line[0]) ignore label for now
t2 = UTCDateTime(split_line[1])
date_label2 = split_line[1][0:10]
year2 = split_line[1][0:4]
ev_lat2 = float(split_line[2])
ev_lon2 = float(split_line[3])
ev_depth2 = float(split_line[4])
print('2nd event: date_label ' + date_label2 + ' time ' + str(t2) +
' lat ' + str(ev_lat2) + ' lon ' + str(ev_lon2) + ' depth ' +
str(ev_depth2))
#%% Get station location file
if stat_corr == 1: # load static terms, only applies to Hinet and LASA
if ARRAY == 0:
if alt_statics == 0: # standard set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/hinet_sta_statics.txt'
else: # custom set made by this event for this event
sta_file = (
'/Users/vidale/Documents/GitHub/Array_codes/Files/' +
'HA' + date_label1[:10] + 'pro4_' + dphase + '.statics')
elif ARRAY == 1:
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/L_sta_statics.txt'
with open(sta_file, 'r') as file:
lines = file.readlines()
print(str(len(lines)) + ' stations read from ' + sta_file)
# Load station coords into arrays
station_index = range(len(lines))
st_names = []
st_dist = []
st_lats = []
st_lons = []
st_shift = []
st_corr = []
for ii in station_index:
line = lines[ii]
split_line = line.split()
st_names.append(split_line[0])
st_dist.append(split_line[1])
st_lats.append(split_line[2])
st_lons.append(split_line[3])
st_shift.append(split_line[4])
st_corr.append(split_line[5])
else: # no static terms, always true for LASA or NORSAR
if ARRAY == 0: # Hinet set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/hinet_sta.txt'
elif ARRAY == 1: # LASA set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/LASA_sta.txt'
else: # NORSAR set
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/NORSAR_sta.txt'
with open(sta_file, 'r') as file:
lines = file.readlines()
print(str(len(lines)) + ' stations read from ' + sta_file)
# Load station coords into arrays
station_index = range(len(lines))
st_names = []
st_lats = []
st_lons = []
for ii in station_index:
line = lines[ii]
split_line = line.split()
st_names.append(split_line[0])
st_lats.append(split_line[1])
st_lons.append(split_line[2])
#%% Is taper too long compared to noise estimation window?
totalt = end_buff - start_buff
noise_time_skipped = taper_frac * totalt
if simple_taper == 0:
if noise_time_skipped >= 0.5 * (-start_buff):
print(
'Specified taper of ' + str(taper_frac * totalt) +
' is not big enough compared to available noise estimation window '
+ str(-start_buff - noise_time_skipped) +
'. May not work well.')
old_taper_frac = taper_frac
taper_frac = -0.5 * start_buff / totalt
print('Taper reset from ' + str(old_taper_frac * totalt) + ' to ' +
str(taper_frac * totalt) + ' seconds.')
#%% Load waveforms and decimate to 10 sps
st1 = Stream()
st2 = Stream()
if ARRAY == 0:
fname1 = 'HD' + date_label1 + '.mseed'
fname2 = 'HD' + date_label2 + '.mseed'
elif ARRAY == 1:
fname1 = 'Mseed/HD' + date_label1 + '.mseed'
fname2 = 'Mseed/HD' + date_label2 + '.mseed'
st1 = read(fname1)
st2 = read(fname2)
if do_decimate != 0:
st1.decimate(do_decimate)
st2.decimate(do_decimate)
print('1st trace has : ' + str(len(st1[0].data)) + ' time pts ')
print('st1 has ' + str(len(st1)) + ' traces')
print('st2 has ' + str(len(st2)) + ' traces')
print('1st trace starts at ' + str(st1[0].stats.starttime) +
', event at ' + str(t1))
print('2nd trace starts at ' + str(st2[0].stats.starttime) +
', event at ' + str(t2))
#%% Select by distance, window and adjust start time to align picked times
st_pickalign1 = Stream()
st_pickalign2 = Stream()
for tr in st1: # traces one by one, find lat-lon by searching entire inventory. Inefficient
if float(
year1
) < 1970: # fix the damn 1969 -> 2069 bug in Gibbon's LASA data
temp_t = str(tr.stats.starttime)
temp_tt = '19' + temp_t[2:]
tr.stats.starttime = UTCDateTime(temp_tt)
for ii in station_index:
if ARRAY == 0: # have to chop off last letter, always 'h'
this_name = st_names[ii]
this_name_truc = this_name[0:5]
name_truc_cap = this_name_truc.upper()
elif ARRAY == 1:
name_truc_cap = st_names[ii]
if (tr.stats.station == name_truc_cap
): # find station in inventory
# if (tr.stats.station == st_names[ii]): # find station in inventory
if stat_corr != 1 or float(
st_corr[ii]
) > corr_threshold: # if using statics, reject low correlations
stalat = float(st_lats[ii])
stalon = float(
st_lons[ii]
) # look up lat & lon again to find distance
if ref_loc == 1:
ref_distance = gps2dist_azimuth(
stalat, stalon, ref_lat, ref_lon)
ref_dist = ref_distance[0] / (1000 * 111)
distance = gps2dist_azimuth(
stalat, stalon, ev_lat1,
ev_lon1) # Get traveltimes again, hard to store
tr.stats.distance = distance[0] # distance in km
dist = distance[0] / (1000 * 111)
if ref_loc != 1 and min_dist < dist and dist < max_dist: # select distance range from earthquake
try:
# print('Phase ' + dphase + ', depth ' + str(ev_depth1) + ' distance ' + str(dist))
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth1,
distance_in_degree=dist,
phase_list=[dphase])
atime = arrivals[0].time
# print(dphase + ' arrival time is ' + str(atime))
if stat_corr == 1: # apply static station corrections
tr.stats.starttime -= float(st_shift[ii])
if rel_time == 1:
s_t = t1 + atime + start_buff
e_t = t1 + atime + end_buff
else:
s_t = t1 + start_buff
e_t = t1 + end_buff
tr.trim(starttime=s_t, endtime=e_t)
# deduct theoretical traveltime and start_buf from starttime
if rel_time == 1:
tr.stats.starttime -= atime
st_pickalign1 += tr
except:
pass
elif ref_loc == 1:
if ref_dist < ref_rad: # alternatively, select based on distance from ref location
try:
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth1,
distance_in_degree=dist,
phase_list=[dphase])
atime = arrivals[0].time
if stat_corr == 1: # apply static station corrections
tr.stats.starttime -= float(st_shift[ii])
if rel_time == 1:
s_t = t1 + atime + start_buff
e_t = t1 + atime + end_buff
else:
s_t = t1 + start_buff
e_t = t1 + end_buff
tr.trim(starttime=s_t, endtime=e_t)
# deduct theoretical traveltime and start_buf from starttime
if rel_time == 1:
tr.stats.starttime -= atime
st_pickalign1 += tr
except:
pass
# if len(tr.data) == 0:
# print('Event 1 - empty window. Trace starts at ' + str(tr.stats.starttime) + ', event at ' + str(t1))
for tr in st2: # traces one by one
if float(
year2
) < 1970: # fix the damn 1969 -> 2069 bug in Gibbon's LASA data
temp_t = str(tr.stats.starttime)
temp_tt = '19' + temp_t[2:]
tr.stats.starttime = UTCDateTime(temp_tt)
for ii in station_index:
if ARRAY == 0: # have to chop off last letter, always 'h'
this_name = st_names[ii]
this_name_truc = this_name[0:5]
name_truc_cap = this_name_truc.upper()
elif ARRAY == 1:
name_truc_cap = st_names[ii]
if (tr.stats.station == name_truc_cap
): # find station in inventory
# if (tr.stats.station == st_names[ii]): # find station in inventory
if stat_corr != 1 or float(
st_corr[ii]
) > corr_threshold: # if using statics, reject low correlations
stalat = float(st_lats[ii])
stalon = float(st_lons[ii])
if ref_loc == 1:
ref_distance = gps2dist_azimuth(
stalat, stalon, ref_lat, ref_lon)
ref_dist = ref_distance[0] / (1000 * 111)
distance = gps2dist_azimuth(
stalat, stalon, ev_lat2,
ev_lon2) # Get traveltimes again, hard to store
tr.stats.distance = distance[0] # distance in km
dist = distance[0] / (1000 * 111)
if ref_loc != 1 and min_dist < dist and dist < max_dist: # select distance range from earthquake
try:
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth2,
distance_in_degree=dist,
phase_list=[dphase])
atime = arrivals[0].time
if stat_corr == 1: # apply static station corrections
tr.stats.starttime -= float(st_shift[ii])
if rel_time == 1:
s_t = t2 + atime + start_buff
e_t = t2 + atime + end_buff
else:
s_t = t2 + start_buff
e_t = t2 + end_buff
tr.trim(starttime=s_t, endtime=e_t)
# deduct theoretical traveltime and start_buf from starttime
if rel_time == 1:
tr.stats.starttime -= atime
st_pickalign2 += tr
except:
pass
elif ref_loc == 1:
if ref_dist < ref_rad: # alternatively, select based on distance from ref location
try:
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth2,
distance_in_degree=dist,
phase_list=[dphase])
atime = arrivals[0].time
if stat_corr == 1: # apply static station corrections
tr.stats.starttime -= float(st_shift[ii])
if rel_time == 1:
s_t = t2 + atime + start_buff
e_t = t2 + atime + end_buff
else:
s_t = t2 + start_buff
e_t = t2 + end_buff
tr.trim(starttime=s_t, endtime=e_t)
# deduct theoretical traveltime and start_buf from starttime
if rel_time == 1:
tr.stats.starttime -= atime
st_pickalign2 += tr
except:
pass
# if len(tr.data) == 0:
# print('Event 2 - empty window. Trace starts at ' + str(tr.stats.starttime) + ', event at ' + str(t2))
print('After alignment and range selection: ' + str(len(st_pickalign1)) +
' traces')
#%%
#print(st) # at length
if verbose:
print(st1.__str__(extended=True))
print(st2.__str__(extended=True))
if rel_time == 1:
print(st_pickalign1.__str__(extended=True))
print(st_pickalign2.__str__(extended=True))
#%% Detrend, taper, filter
st_pickalign1.detrend(type='simple')
st_pickalign2.detrend(type='simple')
st_pickalign1.taper(taper_frac)
st_pickalign2.taper(taper_frac)
st_pickalign1.filter('bandpass',
freqmin=freq_min,
freqmax=freq_max,
corners=4,
zerophase=True)
st_pickalign2.filter('bandpass',
freqmin=freq_min,
freqmax=freq_max,
corners=4,
zerophase=True)
st_pickalign1.taper(taper_frac)
st_pickalign2.taper(taper_frac)
#%% Cull further by imposing SNR threshold on both traces
st1good = Stream()
st2good = Stream()
for tr1 in st_pickalign1:
for tr2 in st_pickalign2:
if ((tr1.stats.network == tr2.stats.network) &
(tr1.stats.station == tr2.stats.station)):
if skip_SNR == 1:
st1good += tr1
st2good += tr2
else:
# estimate median noise
t_noise1_start = int(len(tr1.data) * taper_frac)
t_noise2_start = int(len(tr2.data) * taper_frac)
t_noise1_end = int(
len(tr1.data) * (-start_buff) /
(end_buff - start_buff))
t_noise2_end = int(
len(tr2.data) * (-start_buff) /
(end_buff - start_buff))
noise1 = np.median(
abs(tr1.data[t_noise1_start:t_noise1_end]))
noise2 = np.median(
abs(tr2.data[t_noise2_start:t_noise2_end]))
# estimate median signal
t_signal1_start = int(
len(tr1.data) * (-start_buff) /
(end_buff - start_buff))
t_signal2_start = int(
len(tr2.data) * (-start_buff) /
(end_buff - start_buff))
t_signal1_end = t_signal1_start + int(
len(tr1.data) * signal_dur / (end_buff - start_buff))
t_signal2_end = t_signal2_start + int(
len(tr2.data) * signal_dur / (end_buff - start_buff))
signal1 = np.median(
abs(tr1.data[t_signal1_start:t_signal1_end]))
signal2 = np.median(
abs(tr2.data[t_signal2_start:t_signal2_end]))
# test SNR
SNR1 = signal1 / noise1
SNR2 = signal2 / noise2
if (SNR1 > qual_threshold and SNR2 > qual_threshold):
st1good += tr1
st2good += tr2
if skip_SNR == 1:
print('Matches (no SNR test): ' + str(len(st1good)) + ' traces')
else:
print('Match and above SNR threshold: ' + str(len(st1good)) +
' traces')
#%% get station lat-lon, compute distance for plot
for tr in st1good:
for ii in station_index:
if (tr.stats.station == st_names[ii]): # find station in inventory
stalon = float(
st_lons[ii]) # look up lat & lon again to find distance
stalat = float(st_lats[ii])
distance = gps2dist_azimuth(stalat, stalon, ev_lat1, ev_lon1)
tr.stats.distance = distance[0] # distance in km
for tr in st2good:
for ii in station_index:
if (tr.stats.station == st_names[ii]): # find station in inventory
stalon = float(
st_lons[ii]) # look up lat & lon again to find distance
stalat = float(st_lats[ii])
distance = gps2dist_azimuth(stalat, stalon, ev_lat2, ev_lon2)
tr.stats.distance = distance[0] # distance in km
print('Made it to here.')
#%%
# plot traces
fig_index = 3
plt.close(fig_index)
plt.figure(fig_index, figsize=(8, 8))
plt.xlim(start_buff, end_buff)
plt.ylim(min_dist, max_dist)
for tr in st1good:
dist_offset = tr.stats.distance / (1000 * 111
) # trying for approx degrees
ttt = np.arange(len(tr.data)) * tr.stats.delta + (tr.stats.starttime -
t1)
if red_plot == 1:
shift = red_time + (dist_offset - red_dist) * red_slow
ttt = ttt - shift
# These lines used to cause a crash in Spyder
plt.plot(ttt, (tr.data - np.median(tr.data)) * plot_scale_fac /
(tr.data.max() - tr.data.min()) + dist_offset,
color='green')
#plt.title(fname1)
print('And made it to here?')
for tr in st2good:
dist_offset = tr.stats.distance / (1000 * 111
) # trying for approx degrees
ttt = np.arange(len(tr.data)) * tr.stats.delta + (tr.stats.starttime -
t2)
if red_plot == 1:
shift = red_time + (dist_offset - red_dist) * red_slow
ttt = ttt - shift
ttt = ttt
# These lines used to cause a crash in Spyder
plt.plot(ttt, (tr.data - np.median(tr.data)) * plot_scale_fac /
(tr.data.max() - tr.data.min()) + dist_offset,
color='red')
print('And made it to here.')
#%% Plot traveltime curves
if plot_tt:
# first traveltime curve
line_pts = 50
dist_vec = np.arange(min_dist, max_dist,
(max_dist - min_dist) / line_pts) # distance grid
time_vec1 = np.arange(
min_dist, max_dist, (max_dist - min_dist) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(source_depth_in_km=ev_depth1,
distance_in_degree=dist_vec[i],
phase_list=[dphase])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase:
time_vec1[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec1[i] = np.nan
# second traveltime curve
if dphase2 != 'no':
time_vec2 = np.arange(
min_dist, max_dist, (max_dist - min_dist) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth1,
distance_in_degree=dist_vec[i],
phase_list=[dphase2])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase2:
time_vec2[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec2[i] = np.nan
if rel_time == 1:
time_vec2 = time_vec2 - time_vec1
plt.plot(time_vec2, dist_vec, color='orange')
# third traveltime curve
if dphase3 != 'no':
time_vec3 = np.arange(
min_dist, max_dist, (max_dist - min_dist) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth1,
distance_in_degree=dist_vec[i],
phase_list=[dphase3])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase3:
time_vec3[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec3[i] = np.nan
if rel_time == 1:
time_vec3 = time_vec3 - time_vec1
plt.plot(time_vec3, dist_vec, color='yellow')
# fourth traveltime curve
if dphase4 != 'no':
time_vec4 = np.arange(
min_dist, max_dist, (max_dist - min_dist) /
line_pts) # empty time grid of same length (filled with -1000)
for i in range(0, line_pts):
arrivals = model.get_travel_times(
source_depth_in_km=ev_depth1,
distance_in_degree=dist_vec[i],
phase_list=[dphase4])
num_arrivals = len(arrivals)
found_it = 0
for j in range(0, num_arrivals):
if arrivals[j].name == dphase4:
time_vec4[i] = arrivals[j].time
found_it = 1
if found_it == 0:
time_vec4[i] = np.nan
if rel_time == 1:
time_vec4 = time_vec4 - time_vec1
plt.plot(time_vec4, dist_vec, color='purple')
if rel_time == 1:
time_vec1 = time_vec1 - time_vec1
plt.plot(time_vec1, dist_vec, color='blue')
plt.xlabel('Time (s)')
plt.ylabel('Epicentral distance from event (°)')
if ARRAY == 0:
plt.title(dphase + ' for ' + fname1 + ' vs ' + fname2)
elif ARRAY == 1:
plt.title(dphase + ' for ' + fname1[8:18] + ' vs ' + fname2[8:18])
plt.show()
#%% Save processed files
if ARRAY == 0:
fname1 = 'HD' + date_label1 + 'sel.mseed'
fname2 = 'HD' + date_label2 + 'sel.mseed'
elif ARRAY == 1:
fname1 = 'Pro_Files/HD' + date_label1 + 'sel.mseed'
fname2 = 'Pro_Files/HD' + date_label2 + 'sel.mseed'
st1good.write(fname1, format='MSEED')
st2good.write(fname2, format='MSEED')
elapsed_time_wc = time.time() - start_time_wc
print('This job took ' + str(elapsed_time_wc) + ' seconds')
os.system('say "Done"')
def read_local(data_dir,
coord_file,
network,
station,
location,
channel,
starttime,
endtime,
merge=True):
"""
Read in waveforms from "local" 1-hour, IRIS-compliant miniSEED files, and
output a :class:`~obspy.core.stream.Stream` with station/element coordinates
attached.
**NOTE 1**
The expected naming convention for the miniSEED files is
``<network>.<station>.<location>.<channel>.<year>.<julian_day>.<hour>``
**NOTE 2**
This function assumes that the response has been removed from the waveforms
in the input miniSEED files.
Args:
data_dir (str): Directory containing miniSEED files
coord_file (str): JSON file containing coordinates for local stations
(full path required)
network (str): SEED network code [wildcards (``*``, ``?``) accepted]
station (str): SEED station code [wildcards (``*``, ``?``) accepted]
location (str): SEED location code [wildcards (``*``, ``?``) accepted]
channel (str): SEED channel code [wildcards (``*``, ``?``) accepted]
starttime (:class:`~obspy.core.utcdatetime.UTCDateTime`): Start time for
data request
endtime (:class:`~obspy.core.utcdatetime.UTCDateTime`): End time for
data request
merge (bool): Toggle merging of :class:`~obspy.core.trace.Trace` objects
with identical IDs
Returns:
:class:`~obspy.core.stream.Stream` containing gathered waveforms
"""
print('-----------------------------')
print('GATHERING LOCAL MINISEED DATA')
print('-----------------------------')
# Take (hour) floor of starttime
starttime_hr = UTCDateTime(starttime.year, starttime.month, starttime.day,
starttime.hour)
# Take (hour) floor of endtime - this ensures we check this miniSEED file
endtime_hr = UTCDateTime(endtime.year, endtime.month, endtime.day,
endtime.hour)
# Define filename template
template = f'{network}.{station}.{location}.{channel}.{{}}.{{}}.{{}}'
# Initialize Stream object
st_out = Stream()
# Initialize the starting hour
tmp_time = starttime_hr
# Cycle forward in time, advancing hour by hour through miniSEED files
while tmp_time <= endtime_hr:
pattern = template.format(tmp_time.strftime('%Y'),
tmp_time.strftime('%j'),
tmp_time.strftime('%H'))
files = glob.glob(os.path.join(data_dir, pattern))
for file in files:
st_out += read(file)
tmp_time += HR2SEC # Add an hour!
if merge:
st_out.merge() # Merge Traces with the same ID
st_out.sort()
# If the Stream is empty, then we can stop here
if st_out.count() == 0:
print('No data downloaded.')
return st_out
# Otherwise, show what the Stream contains
print(st_out.__str__(extended=True)) # This syntax prints the WHOLE Stream
# Add zeros to ensure all Traces have same length
st_out.trim(starttime, endtime, pad=True, fill_value=0)
# Replace numerical outliers with zeroes
for tr in st_out:
d0 = np.where(tr.data > OUTLIER_THRESHOLD)[0]
if d0.any():
print(f'{len(d0)} data points in {tr.id} were outliers with '
f' values > {OUTLIER_THRESHOLD} and are now set to 0')
tr.data[d0] = 0
print('Assigning coordinates...')
# Assign coordinates by searching through user-supplied JSON file
local_coords = load_json_file(coord_file)
for tr in st_out:
try:
tr.stats.latitude, tr.stats.longitude,\
tr.stats.elevation = local_coords[tr.stats.station]
except KeyError:
print(f'No coordinates available for {tr.id}. Stopping.')
raise
print('Done')
# Return the Stream with coordinates attached
return st_out