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pro5a_stack junk.py
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pro5a_stack junk.py
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#!/usr/bin/env python
# 1D Slant stack for a single event
# Input is set of selected traces "*sel.mseed"
# traces have already been aligned and corrected for near-vertical statics
# to have specified phase start at the earthquake origin time
# plots either traces or envelopes of traces
# saves 1D stack "_1Dstack.mseed"
# John Vidale 2/2019
def pro5stack(eq_file, plot_scale_fac = 0.05, slowR_lo = -0.1, slowR_hi = 0.1,
slow_delta = 0.0005, start_buff = -50, end_buff = 50,
ref_lat = 36.3, ref_lon = 138.5, envelope = 1, plot_dyn_range = 1000,
log_plot = 1, norm = 1, global_norm_plot = 1, color_plot = 1, fig_index = 401, ARRAY = 0):
#%% Import functions
import obspy
import obspy.signal
from obspy import UTCDateTime
from obspy import Stream, Trace
from obspy import read
from obspy.geodetics import gps2dist_azimuth
import numpy as np
import os
from obspy.taup import TauPyModel
import obspy.signal as sign
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
model = TauPyModel(model='iasp91')
from scipy.signal import hilbert
import math
import time
# import sys # don't show any warnings
# import warnings
print('Running pro5a_stack')
#%% Get saved event info, also used to name files
start_time_wc = time.time()
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]
ev_lat = float( split_line[2])
ev_lon = float( split_line[3])
ev_depth = float( split_line[4])
#if not sys.warnoptions:
# warnings.simplefilter("ignore")
#%% Get station location file
if ARRAY == 0: # Hinet set and center
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/hinet_sta.txt'
ref_lat = 36.3
ref_lon = 138.5
elif ARRAY == 1: # LASA set and center
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/LASA_sta.txt'
ref_lat = 46.69
ref_lon = -106.22
else: # NORSAR set and center if 2
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/NORSAR_sta.txt'
ref_lat = 61
ref_lon = 11
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])
#%% Name file, read data
# date_label = '2018-04-02' # date for filename
if ARRAY == 0:
fname = 'HD' + date_label + 'sel.mseed'
elif ARRAY == 1:
fname = 'Pro_Files/HD' + date_label + 'sel.mseed'
st = Stream()
print('reading ' + fname)
st = read(fname)
print('Read in: ' + 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))
#%% Build Stack arrays
stack = Stream()
tr = Trace()
tr.stats.delta = dt
tr.stats.network = 'stack'
tr.stats.channel = 'BHZ'
slow_n = int(1 + (slowR_hi - slowR_lo)/slow_delta) # number of slownesses
stack_nt = int(1 + ((end_buff - start_buff)/dt)) # number of time points
# In English, stack_slows = range(slow_n) * slow_delta - slowR_lo
a1 = range(slow_n)
stack_slows = [(x * slow_delta + slowR_lo) for x in a1]
print(str(slow_n) + ' slownesses.')
tr.stats.starttime = t + start_buff
tr.data = np.zeros(stack_nt)
done = 0
for stack_one in stack_slows:
tr1 = tr.copy()
tr1.stats.station = str(int(done))
stack.extend([tr1])
done += 1
# stack.append([tr])
# stack += tr
# Only need to compute ref location to event distance once
ref_distance = gps2dist_azimuth(ev_lat,ev_lon,ref_lat,ref_lon)
#%% Select traces by distance, window and adjust start time to align picked times
done = 0
for tr in st: # traces one by one, find lat-lon by searching entire inventory. Inefficient but cheap
for ii in station_index:
if ARRAY == 0: # for hi-net, 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 norm == 1:
tr.normalize()
# tr.normalize(norm= -len(st)) # mystery command or error
stalat = float(st_lats[ii])
stalon = float(st_lons[ii]) # look up lat & lon again to find distance
distance = gps2dist_azimuth(stalat,stalon,ev_lat,ev_lon) # Get traveltimes again, hard to store
tr.stats.distance=distance[0] # distance in m
del_dist = (ref_distance[0] - distance[0])/(1000) # in km
# ALSO NEEDS distance station - hypocenter calculation
#isolate components of distance in radial and transverse directions, ref_distR & ref_distT
# FIX ref_distR = distance*cos(azi-backazi)
# FIX ref_distT = distance*sin(azi-backazi)
# for(k=0;k<nslow;k++){
# slow = 110.*(LOWSLOW + k*DELTASLOW);
for slow_i in range(slow_n): # for this station, loop over slownesses
time_lag = -del_dist * stack_slows[slow_i] # time shift due to slowness, flipped to match 2D
# start_offset = tr.stats.starttime - t
# time_correction = (start_buff - (start_offset + time_lag))/dt
time_correction = ((t-tr.stats.starttime) + (time_lag + start_buff))/dt
# print('Time lag ' + str(time_lag) + ' for slowness ' + str(stack_slows[slow_i]) + ' and distance ' + str(del_dist) + ' time sample correction is ' + str(time_correction))
for it in range(stack_nt): # check points one at a time
it_in = int(it + time_correction)
if it_in >= 0 and it_in < nt - 1: # does data lie within seismogram?
stack[slow_i].data[it] += tr[it_in]
done += 1
if done%50 == 0:
print('Done stacking ' + str(done) + ' out of ' + str(len(st)) + ' stations.')
#%% Plot traces
global_max = 0
for slow_i in range(slow_n): # find global max, and if requested, take envelope
if len(stack[slow_i].data) == 0:
print('%d data has zero length ' % (slow_i))
if envelope == 1 or color_plot == 1:
stack[slow_i].data = np.abs(hilbert(stack[slow_i].data))
local_max = max(abs(stack[slow_i].data))
if local_max > global_max:
global_max = local_max
if global_max <= 0:
print('global_max ' + str(global_max) + ' slow_n ' + str(slow_n))
# create time axis (x-axis), use of slow_i here is arbitrary, oops
ttt = (np.arange(len(stack[slow_i].data)) * stack[slow_i].stats.delta +
(stack[slow_i].stats.starttime - t)) # in units of seconds
# Plotting
if color_plot == 1: # 2D color plot
stack_array = np.zeros((slow_n,stack_nt))
# stack_array = np.random.rand(int(slow_n),int(stack_nt)) # test with random numbers
min_allowed = global_max/plot_dyn_range
if log_plot == 1:
for it in range(stack_nt): # check points one at a time
for slow_i in range(slow_n): # for this station, loop over slownesses
num_val = stack[slow_i].data[it]
if num_val < min_allowed:
num_val = min_allowed
stack_array[slow_i, it] = math.log10(num_val) - math.log10(min_allowed)
else:
for it in range(stack_nt): # check points one at a time
for slow_i in range(slow_n): # for this station, loop over slownesses
stack_array[slow_i, it] = stack[slow_i].data[it]/global_max
y, x = np.mgrid[slice(stack_slows[0], stack_slows[-1] + slow_delta, slow_delta),
slice(ttt[0], ttt[-1] + dt, dt)] # make underlying x-y grid for plot
# y, x = np.mgrid[ stack_slows , time ] # make underlying x-y grid for plot
plt.close(fig_index)
fig, ax = plt.subplots(1, figsize=(9,2))
fig.subplots_adjust(bottom=0.3)
# c = ax.pcolormesh(x, y, stack_array, cmap=plt.cm.gist_yarg)
# c = ax.pcolormesh(x, y, stack_array, cmap=plt.cm.gist_rainbow_r)
c = ax.pcolormesh(x, y, stack_array, cmap=plt.cm.binary)
ax.axis([x.min(), x.max(), y.min(), y.max()])
fig.colorbar(c, ax=ax)
plt.figure(fig_index,figsize=(6,8))
plt.close(fig_index)
else: # line plot
for slow_i in range(slow_n):
dist_offset = stack_slows[slow_i] # in units of slowness
if global_norm_plot != 1:
plt.plot(ttt, stack[slow_i].data*plot_scale_fac / (stack[slow_i].data.max()
- stack[slow_i].data.min()) + dist_offset, color = 'black')
else:
plt.plot(ttt, stack[slow_i].data*plot_scale_fac / (global_max
- stack[slow_i].data.min()) + dist_offset, color = 'black')
plt.ylim(slowR_lo,slowR_hi)
plt.xlim(start_buff,end_buff)
plt.xlabel('Time (s)')
plt.ylabel('Slowness (s/km)')
plt.title(date_label)
plt.show()
#%% Save processed files
print('Stack has ' + str(len(stack)) + ' traces')
if ARRAY == 0:
fname = 'HD' + date_label + '_1dstack.mseed'
elif ARRAY == 1:
fname = 'Pro_Files/HD' + date_label + '_1dstack.mseed'
stack.write(fname,format = 'MSEED')
elapsed_time_wc = time.time() - start_time_wc
print('This job took ' + str(elapsed_time_wc) + ' seconds')
os.system('say "Done"')