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06_Simple_data_visualization.py
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import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import re
from matplotlib.ticker import FuncFormatter
import numpy as np
import yaml
from pathlib import Path
import argparse
import os
def create_e_dict_from_sum_csv(csv_file_path, cfgs):
lat_sign = cfgs['HypoInverse']['lat_sign']
lon_sign = cfgs['HypoInverse']['lon_sign']
csv_e_list = list()
f = open(csv_file_path,'r')
e_id = 0
for line in f.readlines():
e_id += 1
codes = line.split()
date, hrmn, sec = codes[0:3]
dtime = date + hrmn + sec.zfill(5)
time = dtime
is_loc = 1 # whether loc reliable
if '-' in codes or '#' in codes: is_loc = 0
npha = float(line[52:55])
azm = float(line[56:59])
rms = float(line[64:69])
erh = float(line[70:74])
erz = float(line[75:79])
lat_deg = float(line[20:22])
lat_min = float(line[23:28])
lat = lat_deg + lat_min/60
lat *= lat_sign
lon_deg = float(line[29:32])
lon_min = float(line[33:38])
lon = lon_deg + lon_min/60
lon *= lon_sign
dep = float(line[38:44])
temp_list = [e_id,time,lat,lon,dep,is_loc,npha,azm,rms,erh,erz]
csv_e_list.append(temp_list)
f.close()
return csv_e_list
def visualize_profile(cfgs, s_lat, s_lon, e_lat, e_lon, lat_list, lon_list, dep_list, name_flag = 'S_EqT'):
dist_list = list()
A = e_lat - s_lat
B = s_lon - e_lon
C = e_lon*s_lat - s_lon*e_lat
MAX_Range = cfgs['VIS']['profile_range']
dep_list_in_profile = list()
MAX_X = np.sqrt( (e_lat - s_lat)**2 + (e_lon- s_lon)**2 ) * 111.0
for idx in range(len(lat_list)):
t_lon = lon_list[idx]
t_lat = lat_list[idx]
project_lon = (B*B*t_lon - A*B*t_lat - A*C)/(A*A + B*B)
project_lat = (A*A*t_lat - A*B*t_lon - B*C)/(A*A + B*B)
project_dist = np.sqrt((project_lon - t_lon)**2 + (project_lat - t_lat)**2 ) * 111.0
if project_dist > MAX_Range:
continue
t_dist = np.sqrt( (project_lat -s_lat)**2 + (project_lon-s_lon)**2 ) * 111.0
dist_list.append(t_dist)
dep_list_in_profile.append( dep_list[idx] )
vis_key = cfgs['TASKID']
MAXDEP = cfgs['VIS']['MAXDEP']
ratio = 1
plt.figure(figsize=(12,4))
plt.scatter(dist_list,dep_list_in_profile)
plt.title('{} Profile {} earthquakes TASK {}'.format(name_flag, len(dist_list),vis_key))
plt.xlim([0,MAX_X])
plt.ylim([MAXDEP,0.0])
plt.xlabel('Distance (km)')
plt.ylabel('Depth (km)')
plt.gca().set_aspect(ratio)
plt.savefig('./VISRes/{}_Profile.png'.format(name_flag),dpi=300)
plt.close()
return
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='05_simple_visualization')
parser.add_argument('--config-file', dest='config_file',
type=str, help='Configuration file path',default='./default_pipline_config.yaml')
args = parser.parse_args()
cfgs = yaml.load(open(args.config_file,'r'),Loader=yaml.SafeLoader)
task_dir = './' + cfgs['TASKID'] + '/'
os.chdir(task_dir)
if os.path.exists('./VISRes'):
pass
else:
os.makedirs('./VISRes')
# visualize EqT results
vis_key = cfgs['TASKID']
e_list = create_e_dict_from_sum_csv('{}/eqt_hypoInverse.sum'.format(cfgs['REAL']['eqt_catalog_dir']), cfgs)
MINLAT = cfgs['VIS']['MINLAT']
MAXLAT = cfgs['VIS']['MAXLAT']
MINLON = cfgs['VIS']['MINLON']
MAXLON = cfgs['VIS']['MAXLON']
MAXERH = cfgs['VIS']['MAXERH']
MAXERZ = cfgs['VIS']['MAXERZ']
MAXAZM = cfgs['VIS']['MAXAZM']
MAXDEP = cfgs['VIS']['MAXDEP']
PRO_SX = cfgs['VIS']['profile_start_lon']
PRO_SY = cfgs['VIS']['profile_start_lat']
PRO_EX = cfgs['VIS']['profile_end_lon']
PRO_EY = cfgs['VIS']['profile_end_lat']
e_good_list = list()
for e in e_list:
if e[2] > MINLAT and e[2] < MAXLAT and e[3] > MINLON and e[3] < MAXLON:
pass
else:
continue
if e[4] > MAXDEP:
continue
if e[-2] > MAXERH or e[-1] > MAXERZ or e[-4] > MAXAZM:
continue
e_good_list.append(e)
e_lat = list()
e_lon = list()
e_dep = list()
for e in e_good_list:
e_lat.append(e[2])
e_lon.append(e[3])
e_dep.append(e[4])
plt.figure(figsize=(10,10))
m = Basemap(llcrnrlon=MINLON,llcrnrlat=MINLAT,urcrnrlon=MAXLON,urcrnrlat=MAXLAT)
m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1000, verbose= True)
plt.title('EqT {} earthquakes TASK {}'.format(len(e_lon), vis_key))
plt.plot([PRO_SX,PRO_EX],[PRO_SY,PRO_EY],color='b', linewidth=1.5)
plt.scatter(e_lon,e_lat,color='r',label='Detected Earthquakes')
plt.xlim([MINLON,MAXLON])
plt.ylim([MINLAT,MAXLAT])
m.drawparallels(np.arange(2,8,1.0),labels=[0,1,0,0],fontsize=14)
m.drawmeridians(np.arange(114,120,1.0),labels=[0,0,0,1],fontsize=14)
plt.legend()
plt.savefig('./VISRes/eqt_HypoInverse_Horizontal.png',dpi=300)
plt.close()
ratio = 1/111.0
plt.figure(figsize=(12,4))
plt.scatter(e_lon,e_dep)
plt.title('EqT {} earthquakes TASK {}'.format(len(e_lon), vis_key))
plt.xlim([MINLON,MAXLON])
plt.ylim([MAXDEP,0.0])
plt.xlabel('Longitude')
plt.ylabel('Depth (km)')
plt.gca().set_aspect(ratio)
plt.savefig('./VISRes/eqt_HypoInverse_W-E_Vertical.png',dpi=300)
plt.close()
plt.figure(figsize=(12,4))
plt.scatter(e_lat,e_dep)
plt.title('EqT {} earthquakes TASK {}'.format(len(e_lat),vis_key))
plt.xlim([MAXLAT,MINLAT])
plt.ylim([MAXDEP,0.0])
plt.xlabel('Latitude')
plt.ylabel('Depth (km)')
plt.gca().set_aspect(ratio)
plt.savefig('./VISRes/eqt_HypoInverse_N-S_Vertical.png',dpi=300)
plt.close()
visualize_profile(cfgs, PRO_SY, PRO_SX, PRO_EY, PRO_EX, e_lat, e_lon, e_dep, name_flag = 'EqT')
# visualize S-EqT results
e_list = create_e_dict_from_sum_csv('{}/seqt_hypoInverse.sum'.format(cfgs['REAL']['seqt_catalog_dir']), cfgs)
e_good_list = list()
for e in e_list:
if e[2] > MINLAT and e[2] < MAXLAT and e[3] > MINLON and e[3] < MAXLON:
pass
else:
continue
if e[4] > MAXDEP:
continue
if e[-2] > MAXERH or e[-1] > MAXERZ or e[-4] > MAXAZM:
continue
e_good_list.append(e)
e_lat = list()
e_lon = list()
e_dep = list()
for e in e_good_list:
e_lat.append(e[2])
e_lon.append(e[3])
e_dep.append(e[4])
plt.figure(figsize=(10,10))
m = Basemap(llcrnrlon=MINLON,llcrnrlat=MINLAT,urcrnrlon=MAXLON,urcrnrlat=MAXLAT)
m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1000, verbose= True)
plt.title('S-EqT {} earthquakes TASK {}'.format(len(e_lon), vis_key))
plt.plot([PRO_SX,PRO_EX],[PRO_SY,PRO_EY],color='b', linewidth=1.5)
plt.scatter(e_lon,e_lat,color='r',label='Detected Earthquakes')
plt.xlim([MINLON,MAXLON])
plt.ylim([MINLAT,MAXLAT])
m.drawparallels(np.arange(2,8,1.0),labels=[0,1,0,0],fontsize=14)
m.drawmeridians(np.arange(114,120,1.0),labels=[0,0,0,1],fontsize=14)
plt.legend()
plt.savefig('./VISRes/seqt_HypoInverse_Horizontal.png',dpi=300)
plt.close()
ratio = 1/111.0
plt.figure(figsize=(12,4))
plt.scatter(e_lon,e_dep)
plt.title('S-EqT {} earthquakes TASK {}'.format(len(e_lon), vis_key))
plt.xlim([MINLON,MAXLON])
plt.ylim([MAXDEP,0.0])
plt.xlabel('Longitude')
plt.ylabel('Depth (km)')
plt.gca().set_aspect(ratio)
plt.savefig('./VISRes/seqt_HypoInverse_W-E_Vertical.png',dpi=300)
plt.close()
plt.figure(figsize=(12,4))
plt.scatter(e_lat,e_dep)
plt.title('S-EqT {} earthquakes TASK {}'.format(len(e_lat),vis_key))
plt.xlim([MAXLAT,MINLAT])
plt.ylim([MAXDEP,0.0])
plt.xlabel('Latitude')
plt.ylabel('Depth (km)')
plt.gca().set_aspect(ratio)
plt.savefig('./VISRes/seqt_HypoInverse_N-S_Vertical.png',dpi=300)
plt.close()
visualize_profile(cfgs, PRO_SY, PRO_SX, PRO_EY, PRO_EX, e_lat, e_lon, e_dep, name_flag = 'S_EqT')
# visualize PickNet results
e_list = create_e_dict_from_sum_csv('{}/picknet_hypoInverse.sum'.format(cfgs['REAL']['picknet_catalog_dir']), cfgs)
e_good_list = list()
for e in e_list:
if e[2] > MINLAT and e[2] < MAXLAT and e[3] > MINLON and e[3] < MAXLON:
pass
else:
continue
if e[4] > MAXDEP:
continue
if e[-2] > MAXERH or e[-1] > MAXERZ or e[-4] > MAXAZM:
continue
e_good_list.append(e)
e_lat = list()
e_lon = list()
e_dep = list()
for e in e_good_list:
e_lat.append(e[2])
e_lon.append(e[3])
e_dep.append(e[4])
plt.figure(figsize=(10,10))
m = Basemap(llcrnrlon=MINLON,llcrnrlat=MINLAT,urcrnrlon=MAXLON,urcrnrlat=MAXLAT)
m.arcgisimage(service='ESRI_Imagery_World_2D', xpixels = 1000, verbose= True)
plt.title('PickNet {} earthquakes TASK {}'.format(len(e_lon), vis_key))
plt.plot([PRO_SX,PRO_EX],[PRO_SY,PRO_EY],color='b', linewidth=1.5)
plt.scatter(e_lon,e_lat,color='r',label='Detected Earthquakes')
plt.xlim([MINLON,MAXLON])
plt.ylim([MINLAT,MAXLAT])
m.drawparallels(np.arange(2,8,1.0),labels=[0,1,0,0],fontsize=14)
m.drawmeridians(np.arange(114,120,1.0),labels=[0,0,0,1],fontsize=14)
plt.legend()
plt.savefig('./VISRes/picknet_HypoInverse_Horizontal.png',dpi=300)
plt.close()
ratio = 1/111.0
plt.figure(figsize=(12,4))
plt.scatter(e_lon,e_dep)
plt.title('PickNet {} earthquakes TASK {}'.format(len(e_lon), vis_key))
plt.xlim([MINLON,MAXLON])
plt.ylim([MAXDEP,0.0])
plt.xlabel('Longitude')
plt.ylabel('Depth (km)')
plt.gca().set_aspect(ratio)
plt.savefig('./VISRes/picknet_HypoInverse_W-E_Vertical.png',dpi=300)
plt.close()
plt.figure(figsize=(12,4))
plt.scatter(e_lat,e_dep)
plt.title('PickNet {} earthquakes TASK {}'.format(len(e_lat),vis_key))
plt.xlim([MAXLAT,MINLAT])
plt.ylim([MAXDEP,0.0])
plt.xlabel('Latitude')
plt.ylabel('Depth (km)')
plt.gca().set_aspect(ratio)
plt.savefig('./VISRes/picknet_HypoInverse_N-S_Vertical.png',dpi=300)
plt.close()
visualize_profile(cfgs, PRO_SY, PRO_SX, PRO_EY, PRO_EX, e_lat, e_lon, e_dep, name_flag = 'PickNet')