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pro13_PKiKP_comp.py
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pro13_PKiKP_comp.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat March 21 2020
Compares predicted and observed PKiKP slownesses
@author: vidale
"""
def map_slo_plot(min_dist = 0, max_dist = 180):
import numpy as np
import matplotlib.pyplot as plt
from obspy.taup import TauPyModel
print('Starting')
model = TauPyModel(model='iasp91')
dphase = 'PKiKP'
sta_file = '/Users/vidale/Documents/GitHub/Array_codes/Files/events_best_PKiKP.txt'
with open(sta_file, 'r') as file:
lines = file.readlines()
event_count = len(lines)
print(str(event_count) + ' lines read from ' + sta_file)
# Load station coords into arrays
station_index = range(event_count)
event_names = []
event_year = np.zeros(event_count)
event_mo = np.zeros(event_count)
event_day = np.zeros(event_count)
event_hr = np.zeros(event_count)
event_min = np.zeros(event_count)
event_sec = np.zeros(event_count)
event_lat = np.zeros(event_count)
event_lon = np.zeros(event_count)
event_dep = np.zeros(event_count)
event_mb = np.zeros(event_count)
event_ms = np.zeros(event_count)
event_tstart = np.zeros(event_count)
event_tend = np.zeros(event_count)
event_gcdist = np.zeros(event_count)
event_dist = np.zeros(event_count)
event_baz = np.zeros(event_count)
event_SNR = np.zeros(event_count)
event_Sflag = np.zeros(event_count)
event_PKiKPflag = np.zeros(event_count)
event_ICSflag = np.zeros(event_count)
event_PKiKP_radslo = np.zeros(event_count)
event_PKiKP_traslo = np.zeros(event_count)
event_PKiKP_qual = np.zeros(event_count)
for ii in station_index: # read file
line = lines[ii]
split_line = line.split()
event_names.append(split_line[0])
event_year[ii] = float(split_line[1])
event_mo[ii] = float(split_line[2])
event_day[ii] = float(split_line[3])
event_hr[ii] = float(split_line[4])
event_min[ii] = float(split_line[5])
event_sec[ii] = float(split_line[6])
event_lat[ii] = float(split_line[7])
event_lon[ii] = float(split_line[8])
event_dep[ii] = float(split_line[9])
event_mb[ii] = float(split_line[10])
event_ms[ii] = float(split_line[11])
event_tstart[ii] = float(split_line[12])
event_tend[ii] = float(split_line[13])
event_gcdist[ii] = float(split_line[14])
event_dist[ii] = float(split_line[15])
event_baz[ii] = float(split_line[16])
event_SNR[ii] = float(split_line[17])
event_Sflag[ii] = float(split_line[18])
event_PKiKPflag[ii] = float(split_line[19])
event_ICSflag[ii] = float(split_line[20])
event_PKiKP_radslo[ii] = float(split_line[21])
event_PKiKP_traslo[ii] = float(split_line[22])
event_PKiKP_qual[ii] = float(split_line[23])
event_pred_bazi = np.zeros(event_count)
event_pred_slo = np.zeros(event_count)
event_obs_bazi = np.zeros(event_count)
event_obs_slo = np.zeros(event_count)
print(str(event_dep[0]))
print(str(event_PKiKP_radslo[1]))
# for ii in station_index: # read file
for ii in range(event_count): # read file
# find predicted slowness
arrivals1 = model.get_travel_times(source_depth_in_km=event_dep[ii],distance_in_degree=event_gcdist[ii]-0.5,phase_list=[dphase])
arrivals2 = model.get_travel_times(source_depth_in_km=event_dep[ii],distance_in_degree=event_gcdist[ii]+0.5,phase_list=[dphase])
dtime = arrivals2[0].time - arrivals1[0].time
event_pred_slo[ii] = dtime/111. # s/km
# find observed slowness
rad2 = event_PKiKP_radslo[ii]*event_PKiKP_radslo[ii]
tra2 = event_PKiKP_traslo[ii]*event_PKiKP_traslo[ii]
event_obs_slo[ii] = np.sqrt(rad2 + tra2)
# find observed back-azimuth
bazi_rad = np.arctan(event_PKiKP_traslo[ii]/event_PKiKP_radslo[ii])
event_obs_bazi[ii] = event_baz[ii] + (bazi_rad * 180 / np.pi)
# predicted back-azimuth
event_pred_bazi[ii] = event_baz[ii]
# print('t1 t2 dtime: ' + str(arrivals1[0].time) + ' ' + str(arrivals2[0].time) + ' ' + str(dtime))
# print('rslo tslo in_bazi : ' + str(event_PKiKP_radslo[ii]) + ' ' + str(event_PKiKP_traslo[ii]) + ' ' + str(event_baz[ii]))
# print('pred: slo bazi obs: slo bazi : ' + str(event_pred_slo[ii]) + ' ' + str(event_pred_bazi[ii]) + ' ' +
# str(event_obs_slo[ii]) + ' ' + str(event_obs_bazi[ii]))
# fig_index = 3
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='polar')
# c = ax.scatter(np.pi * event_baz / 180, event_dist, c=event_PKiKP_qual, s=100, cmap='brg', alpha=0.75)
# plt.title('PKiKP quality - polar plot')
# ax.set_theta_zero_location("N") # theta=0 at the top
# ax.set_theta_direction(-1) # theta increasing clockwise
# plt.show()
# fig_index = 4
fig = plt.figure()
ax = fig.add_subplot(111, projection='polar')
event_pred_bazi = event_pred_bazi * np.pi / 180.
event_obs_bazi = event_obs_bazi * np.pi / 180.
c = ax.scatter(event_pred_bazi, event_pred_slo, color='blue', s=100, alpha=0.75)
c = ax.scatter(event_obs_bazi, event_obs_slo, color='red', s=100, alpha=0.75)
for ii in range(event_count): # read file
if event_PKiKP_qual[ii] == 2:
c = ax.plot([event_pred_bazi[ii], event_obs_bazi[ii]], [event_pred_slo[ii], event_obs_slo[ii]], color='black')
elif event_PKiKP_qual[ii] == 1:
c = ax.plot([event_pred_bazi[ii], event_obs_bazi[ii]], [event_pred_slo[ii], event_obs_slo[ii]], color='gray')
else:
c = ax.plot([event_pred_bazi[ii], event_obs_bazi[ii]], [event_pred_slo[ii], event_obs_slo[ii]], color='pink')
ax.set_rmax(0.025)
ax.set_rmin(0.0)
ax.grid(True)
plt.title('Predicted vs observed slowness of PKiKP')
ax.set_theta_zero_location("N") # theta=0 at the top
ax.set_theta_direction(-1) # theta increasing clockwise
plt.show()