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"')