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bp_post_process_make_results.py
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bp_post_process_make_results.py
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import sys,os
import obspy
from obspy.taup import TauPyModel
import numpy as np
import matplotlib.pyplot as plt
import pygmt
import pandas as pd
import time
###########
import bp_lib
import math
def calculate_shear_mach_front_angle(super_shear_velocity):
# Calculate the shear Mach front angle using the super-shear velocity
sin_shear_mach_front_angle = 1 / super_shear_velocity
shear_mach_front_angle = math.degrees(math.asin(sin_shear_mach_front_angle))
return shear_mach_front_angle
def moving_average(x, w):
"""
Computes the moving average of a 2D numpy array x with a window size of w.
"""
return np.convolve(x, np.ones(w), 'same') / w
#name='KYR_7.0_EU_13.0km_iasp91_0.2_grid_'
try:
name = sys.argv[1]
print('Input experiment is :',(name))
except:
print('You did not provided experiment name (foleder). Aborting.')
exit()
outdir = name #str(Event)+'_'+str(Exp_name)
path = os.getcwd()
scale=5
peak_scale=7
'''
bp_l=0.4
bp_u=8
smooth_time_window=5
smooth_space_window=4
STF_start=0
STF_end=30
stack_start=30
stack_end=200
#stack_end=100
'''
input = pd.read_csv('./'+name+'/input.csv',header=None)
a=input.to_dict('series')
keys = a[0][:]
values = a[1][:]
res = {}
for i in range(len(keys)):
res[keys[i]] = values[i]
#print(keys[i],values[i])
#################################################################
# bp info
## BP parameters from the input file
try:
bp_l = float(sys.argv[2])
bp_u = float(sys.argv[3])
print('bp_l and bp_u is,',(bp_l,bp_u))
except:
bp_l = float(res['bp_l']) #Hz
bp_u = float(res['bp_u']) #Hz
#bp_l = float(res['bp_l']) #Hz
#bp_u = float(res['bp_u']) #Hz
smooth_time_window = int(res['smooth_time_window']) #seconds
smooth_space_window = int(res['smooth_space_window'])
stack_start = int(res['stack_start']) #in seconds
stack_end = int(res['stack_end']) #in seconds
STF_start = int(res['STF_start'])
STF_end = int(res['STF_end'])
sps = int(res['sps']) #samples per seconds
threshold_correlation=float(res['threshold_correlation'])
SNR=float(res['SNR'])
#smooth_time_window=5
#smooth_space_window=5
#smooth_time_window = int((stack_end-stack_start)/10) #int(res['smooth_time_window']) #seconds
#STF_start=0
#STF_end=40
#stack_start=25
#stack_end=stack_start+STF_end
#bp_l=0.5
#bp_u=1.0
##########################################################################
# Event info
Event=res['Event']
event_lat=float(res['event_lat'])
event_long=float(res['event_long'])
event_depth=float(res['event_depth'])
Array_name=res['Array_name']
#Exp_name=res['Exp_name']
azimuth_min=float(res['azimuth_min'])
azimuth_max=float(res['azimuth_max'])
dist_min=float(res['dist_min'])
dist_max=float(res['dist_max'])
origin_time=obspy.UTCDateTime(int(res['origin_year']),int(res['origin_month']),
int(res['origin_day']),int(res['origin_hour']),int(res['origin_minute']),float(res['origin_seconds']))
print(origin_time)
Focal_mech = dict(strike=float(res['event_strike']), dip=float(res['event_dip']), rake=float(res['event_rake'])
, magnitude=float(res['event_magnitude']))
model = TauPyModel(model=str(res['model']))
sps = int(res['sps']) #samples per seconds
threshold_correlation=float(res['threshold_correlation'])
SNR=float(res['SNR'])
#smooth_time_window = int((stack_end-stack_start)/10) #int(res['smooth_time_window']) #seconds
source_grid_size = float(res['source_grid_size']) #degrees
source_grid_extend = float(res['source_grid_extend']) #degrees
source_depth_size = float(res['source_depth_size']) #km
source_depth_extend = float(res['source_grid_extend']) #km
#stream_for_bp=obspy.read('./Turky_7.6_all/stream.mseed')
slong,slat = bp_lib.make_source_grid(event_long,event_lat,source_grid_extend,source_grid_size)
beam_file='beam_'+str(bp_l)+'_'+str(bp_u)+'_'+str(Array_name)+'.dat'
print('Beam file in :',beam_file)
stations_file = str(res['stations'])
stream_for_bp= obspy.read('./'+name+'/stream.mseed')
beam = np.loadtxt('./'+name+'/'+beam_file)
beam_i = np.load('./'+name+'/beam_info.npy',allow_pickle=True)
stream_info = np.load('./'+name+'/array_bp_info.npy',allow_pickle=True)
print('#############################################################################\n')
print('Exp:',name)
print('Origin time:',origin_time)
print('Long= %f Lat= %f Depth= %f' % (event_long,event_lat,event_depth))
#print('bp_low= %f bp_high= %f Correlation threshold= %f SNR= %f'% (bp_l,bp_u,threshold_correlation,SNR))
print('#############################################################################\n')
sta_name=list(stream_info[:,1])
for t in stream_for_bp:
if len(t.stats['station'].split('.')) > 1:
sta = t.stats.station+str('H')
else:
sta = t.stats.station
#net
if sta in sta_name:
ind = sta_name.index(sta)
t.stats['origin_time'] = origin_time
t.stats['station_longitude'] = float(stream_info[ind,2])
t.stats['station_latitude'] = float(stream_info[ind,3])
t.stats['Dist'] = float(stream_info[ind,4])
t.stats['Azimuth'] = float(stream_info[ind,5])
arrivals = model.get_travel_times(source_depth_in_km=event_depth,distance_in_degree=t.stats.Dist,phase_list=["P"])
arr = arrivals[0]
t_travel = arr.time
t.stats['P_arrival'] = origin_time + t_travel
#t.stats['P_arrival'] = float(stream_info[ind,6])
t.stats['Corr_coeff'] = float(stream_info[ind,7])
t.stats['Corr_shift'] = float(stream_info[ind,8])
t.stats['Corr_sign'] = float(stream_info[ind,9])
else:
pass
#print('Something is not right.')
Ref_station_index=bp_lib.get_ref_station(stream_for_bp)
ref_trace = stream_for_bp[Ref_station_index]
for tr in stream_for_bp:
count=0;
for tr_ in stream_for_bp:
dist=((tr.stats.station_latitude-tr_.stats.station_latitude)**2 +
(tr.stats.station_longitude-tr_.stats.station_longitude)**2 )**0.2;
if ( dist <= 1):
count=count+1;
else:
continue
tr.stats['Station_weight'] = count
print('Done loading data')
# Preparing the beam i.e. getting the power
# Smoothening the beam
#beam_use=np.zeros((len(slat),(stack_end-stack_start)*sps-1))
beam_use=np.zeros_like(beam)
#count=0
for i in range(len(slat)):
#env = obspy.signal.filter.envelope(tr.data)
#env_reshaped=np.array(env).reshape((len(stream_for_bp),-1))
#amp=tr.data[0:(stack_end-stack_start)*sps]
beam_use[i,:]= beam[i]**2 #np.power(tr.data[0:(stack_end-stack_start)*sps],2)
#count=count+1
beam_reshaped=beam_use
beam_smoothened = np.zeros_like(beam_reshaped)
m,n=np.shape(beam_smoothened)
for i in range(m):
beam_smoothened[i][:]=moving_average(beam_reshaped[i][:],smooth_time_window*sps)
beam_smoothened=beam_smoothened/np.max(beam_smoothened)
print('Maximum energy of the beam:',np.max(beam_smoothened))
#plt.plot(moving_average(beam_reshaped[:][10],20*sps))
stf_beam = np.sum(beam_smoothened,axis=0)
print('Size of STF:', np.shape(stf_beam))
#Taking square becaouse we are interested in the power
stf_beam=stf_beam**2
stf_beam=stf_beam[stack_start*sps:(stack_end)*sps]
stf_beam=stf_beam/np.max(stf_beam)
print('Size of STF:', np.shape(stf_beam))
stf_beam=np.column_stack((stf_beam,range(len(stf_beam))))
stf_beam[:,1]=stf_beam[:,1]/sps
#plt.plot(stf_beam[:,1],stf_beam[:,0])
file_save = 'STF_beam_'+str(bp_l)+'_'+str(bp_u)+'_'+str(Array_name)+'_'+str(smooth_time_window)+'_'+str(smooth_space_window)+'.dat'
np.savetxt(outdir+'/'+file_save,stf_beam,header='energy(normalized) time(s) ')
# Cumulative energy
beam_cumulative_use=beam_smoothened.T
temp =np.sum(beam_cumulative_use[stack_start*sps:(stack_end)*sps],axis=0)
#temp =np.sum(beam_smoothened,axis=1)
print('Size of the cumulative energy:',np.shape(temp))
np.size(temp)
m,n=np.shape(beam_smoothened)
cumulative_energy=np.zeros((m,3))
cumulative_energy[:,2]=temp/np.max(temp)
cumulative_energy[:,0]=slong
cumulative_energy[:,1]=slat
cumulative_energy[:,2]=cumulative_energy[:,2]/np.max(cumulative_energy[:,2])
file_save='cumulative_energy_'+str(bp_l)+'_'+str(bp_u)+'_'+str(Array_name)+'_'+str(smooth_time_window)+'_'+str(smooth_space_window)+'.dat'
np.savetxt(outdir+'/'+file_save,cumulative_energy,header='long lat energy(normalized)')
print('Maximum energy of the cumulative energy:',np.max(cumulative_energy[:,2]))
beam_peak_energy_use=beam_smoothened.T[stack_start*sps:(stack_end)*sps]
print('Maximum energy of the beam:',np.max(beam_peak_energy_use))
m,n=np.shape(beam_peak_energy_use)
peak_energy=np.zeros((int(m/sps),4))
print('Size of the peak energy:',np.shape(peak_energy))
for i in range(len(peak_energy)):
ind = np.argmax(beam_peak_energy_use[i*sps])
peak_energy[i,0] = i
peak_energy[i,1] = slong[ind]
peak_energy[i,2] = slat[ind]
#peak_energy[i,3] = beam_smoothened[ind][i]
peak_energy[i,3] = beam_peak_energy_use[i*sps][ind]
#print(beam_peak_energy_use[i][ind])
peak_energy[:,3]=peak_energy[:,3]/np.max(peak_energy[:,3])
print('Maximum energy of the peaks:',np.max(peak_energy[:,3]))
dist_rupture=np.zeros(len(peak_energy))
dist_rupture2=np.zeros(len(peak_energy))
for i in range(len(dist_rupture)-1):
dist_rupture[i]= np.sqrt(((peak_energy[i,1]-event_long)**2 + (peak_energy[i,2]-event_lat)**2))*(111)
dist_rupture2[i]= np.sqrt(((peak_energy[i,1]-peak_energy[0,1])**2 + (peak_energy[i,2]-peak_energy[0,2])**2))*(111)
peak_energy=np.column_stack((peak_energy,dist_rupture))
peak_energy=np.column_stack((peak_energy,dist_rupture2))
file_save='Peak_energy_'+str(bp_l)+'_'+str(bp_u)+'_'+str(Array_name)+'_'+str(smooth_time_window)+'_'+str(smooth_space_window)+'.dat'
np.savetxt(outdir+'/'+file_save,peak_energy,header='time(s) long lat energy(normalized) distance_wrt_epiceter(km) distance_peaks(km)')
#plt.scatter(x=dist_rupture[:], y=peak_energy[:,0],s=peak_energy[:,3]*100,c=peak_energy[:,0])
#plt.scatter(x=dist_rupture2[:], y=peak_energy[:,0],s=peak_energy[:,3]*100,c=peak_energy[:,0])
#plt.title(str(name)+'_'+str(smooth_time_window)+'_'+str(smooth_space_window))
#plt.scatter(x=dist_rupture2[:], y=peak_energy[:,0],s=peak_energy[:,3]*100,c=peak_energy[:,0])
#plt.plot(4.5*peak_energy[:,0],peak_energy[:,0],label='4.5 km/s')
#plt.plot(4*peak_energy[:,0],peak_energy[:,0],label='4 km/s')
#plt.plot(2*peak_energy[:,0],peak_energy[:,0],label='2.5 km/s')
#plt.plot(1*peak_energy[st:end,0],peak_energy[st:end,0],label='1.0 km/s')
#plt.xlabel('Distance (km)')
#plt.ylabel('Time (s)')
#plt.legend()
#plt.colorbar()
#plt.savefig(outdir+'/'+str(name)+'_Rupture_'+str(bp_l)+'_'+str(bp_u)+'_'+str(Array_name)+'.png')
stream_for_plot=stream_for_bp.copy()
#aftershock=pd.read_csv('./data/Melgar_et_al_model/zenodo/aftershocks.xlsx')
try:
aftershocks=pd.read_csv('./data/kyrgyzstan-xinjiang-border_catalog.csv')
#aftershock= aftershock_orig.loc[(aftershock_orig['relative_time'] <= 32835.731) & (aftershock_orig['mag'] >= 0.0) ]
#aftershock_melgar=pd.read_csv('./data/Melgar_et_al_model/zenodo/Aftershocks_Melgar_et_al_2023.csv')
#aftershock_dd=pd.read_csv('./data/Melgar_et_al_model/zenodo/aftershocks.csv')
#faults = geopd.read_file('./data/Turkey_Emergency_EQ_Data/Turkey_Emergency_EQ_Data/simple_fault_2023-03-15/simple_fault_2023-3-15.shp')
except:
pass
#source_grid_extend=1
region=[event_long-source_grid_extend,event_long+source_grid_extend,event_lat-source_grid_extend,event_lat+source_grid_extend]
#region=[35.5,39,36,39]
#region=[35.5,39.5,36,38.5]
title="+t"+name+'_'+str(bp_l)+'-'+str(bp_u)+'Hz_T'+str(smooth_time_window)+'_Space'+str(smooth_space_window)
sta_lat=[]
sta_long=[]
for tr in stream_for_bp:
sta_lat.append(tr.stats.station_latitude)
sta_long.append(tr.stats.station_longitude)
spacing=source_grid_size
fig = pygmt.Figure()
figsize=("9c", "8c")
proj="M?"
#########################################################################################################################################
# Plotting rupture map
#########################################################################################################################################
with fig.subplot(nrows=1, ncols=1, figsize=figsize, autolabel="a)",
sharey=False,
sharex=False,):
energy_cmap=pygmt.makecpt(cmap="bilbao", series=[0.5, 1, 0.01])
df = pygmt.blockmean(data=cumulative_energy, region=region, spacing=spacing)
grd = pygmt.xyz2grd(data=df, region=region, spacing=spacing)
fig.grdimage(grid=grd,cmap=energy_cmap,projection=proj, region=region, frame=["af", "WSne"],\
panel=[0, 0])
fig.colorbar(frame=["a0.2", "x+lEnergy", "y+lrelative"])
fig.coast(shorelines=True,frame=True,)
#fig.plot(x=aftershock[:,1],y=aftershock[:,0],size=aftershock[:,3]/20,style='cc',fill = 'red',pen='black',)
#fig.plot(x=aftershock['longitude'],y=aftershock['latitude'],projection=proj,region=region,size=aftershock['mag']/50,style='cc',fill = 'red',pen='black',transparency=60)
try:
fig.plot(x=aftershocks['Lon'],y=aftershocks['Lat'],size=aftershocks['Mag']/50,style='cc',fill = 'black',pen='black')#,transparency=0)
except:
pass
fig.meca(spec=Focal_mech,projection=proj,region=region,scale="0.5c", longitude=event_long,latitude=event_lat,depth=event_depth,transparency=40,)
peak_cmap=pygmt.makecpt(cmap="seis", series=[STF_start, STF_end, (STF_end-STF_start)/10])
fig.plot(x=peak_energy[STF_start:STF_end,1],y=peak_energy[STF_start:STF_end,2],projection=proj,region=region, \
fill=peak_energy[STF_start:STF_end,0],cmap=True, \
no_clip=True,size=peak_energy[STF_start:STF_end,3]/scale,style='cc', pen='0.5p,black',transparency=40,)
#fig.plot(x=peak_energy[:,1],y=peak_energy[:,2],projection=proj,region=region, \
# fill=peak_energy[:,0],cmap=True, \
# no_clip=True,style='c0.1', pen='0.5p,black',transparency=40,)
fig.colorbar(cmap=peak_cmap,position="jBL+o0.4c/0.8c+h",box=False,frame=["x+l ", "y+lTime(s)"],scale=1,)
fig.plot(x=event_long,y=event_lat,style= 'a0.5c',fill = 'blue',pen='black',)
#fig.legend()
#########################################################################################################################################
# Left, two subplots
# Move plot origin by 1 cm above the height of the entire figure
#########################################################################################################################################
fig.shift_origin(xshift="w+2c")
with fig.subplot(nrows=2, ncols=2, figsize=figsize, autolabel="b)", frame="a",
sharey=False,
sharex=False,
margins=["0.4c", "0.4c"],):
#########################################################################################################################################
# Plotting STF
#########################################################################################################################################
fig.basemap(
region=[STF_start, STF_end, 0, 1], projection="X?", frame=["x+lTime (s)", "y+lAmplitude", str(title)], panel=[0, 0]
)
#fig.plot(x=STF_array[:,0],y=STF_array[:,1], pen='2p,black',)
fig.plot(x=stf_beam[:,1],y=stf_beam[:,0], pen='2p,black',)
#fig.plot(x=STF_array[:,0],y=STF_array[:,1], pen='2p,red',)
#########################################################################################################################################
# Plotting Ruptuer velocity
#########################################################################################################################################
peak_cmap=pygmt.makecpt(cmap="bilbao", series=[0, STF_end, (STF_end-STF_start)/15])
fig.basemap(
region=[STF_start, STF_end,0, np.max(dist_rupture2[STF_start:STF_end]) ], projection="X?", frame=["x+lTime (s)", "y+lDistance (km)"], panel=[1, 0]
)
dist_x=2.5*peak_energy[STF_start:STF_end,0]
fig.plot(y=dist_x,x=peak_energy[STF_start:STF_end,0], style= None,pen='0.5p,green',label='2.5 km/s')
dist_x=3.5*peak_energy[STF_start:STF_end,0]
fig.plot(y=dist_x,x=peak_energy[STF_start:STF_end,0], style= None,pen='0.5p,blue',label='3.5 km/s')
dist_x=4.5*peak_energy[STF_start:STF_end,0]
fig.plot(y=dist_x,x=peak_energy[STF_start:STF_end,0], style= None,pen='0.5p,red',label='4.5 km/s')
fig.legend(position="jBR+o0.0c", box=False)
fig.plot(y=dist_rupture2,x=peak_energy[:,0],size=peak_energy[:,3]/scale,style='cc',fill = 'orange',pen='black',
transparency=40,)
fig.plot(y=dist_rupture,x=peak_energy[:,0],size=peak_energy[:,3]/scale,style='cc',fill = 'blue',pen='black',
transparency=40,)
#########################################################################################################################################
# Plotting Traces
#########################################################################################################################################
fig.basemap(region=[0, STF_end+10, -1, len(stream_for_bp)], projection="X?", \
frame=["x+lTime (s)", "y+lTrace"], panel=[0, 1],)
count=0
for tr in stream_for_plot:
count=count+1
if tr.stats.station == ref_trace.stats.station:
#arrival=source[i][3]+tr[0].stats.Corr_shift
tr.filter('bandpass',freqmin=bp_l,freqmax=bp_u)
tr.trim(tr.stats['P_arrival']+tr.stats['Corr_shift']-10,tr.stats['P_arrival']+tr.stats['Corr_shift']+STF_end)
#tr.normalize()
cut = tr.data #bp_lib.cut_window(tr, t_corr, -5, STF_end)[0]
cut=cut*tr.stats['Corr_sign']*tr.stats['Corr_coeff']
cut=cut/np.max(cut) + count
time = np.arange(0, len(cut)/ tr.stats.sampling_rate, tr.stats.delta)
fig.plot(x=time,y=cut, pen='red',)
else:
#t_corr = tr.stats['P_arrival'] + tr.stats.Corr_shift
tr.filter('bandpass',freqmin=bp_l,freqmax=bp_u)
#tr.detrend("linear")
tr.trim(tr.stats['P_arrival']+tr.stats['Corr_shift']-10,tr.stats['P_arrival']+tr.stats['Corr_shift']+STF_end)
#tr.normalize()
cut = tr.data #bp_lib.cut_window(tr, t_corr, -5, STF_end)[0]
cut=cut*tr.stats['Corr_sign']*tr.stats['Corr_coeff']
cut=cut/np.max(cut) + count
time = np.arange(0, len(cut)/ tr.stats.sampling_rate, tr.stats.delta)
fig.plot(x=time,y=cut, pen='black',)
#########################################################################################################################################
# Plotting Stations
#########################################################################################################################################
projection='A'+str(event_long)+'/'+str(event_lat)+'/120/?'
fig.basemap(
region="g", projection=projection, frame=False,panel=[1,1],
)
# Plot the land as light gray, and the water as sky blue
#fig.coast(shorelines=True)
fig.coast(land="#666666", water="skyblue",)
fig.meca(spec=Focal_mech,scale="0.4c",longitude=event_long,latitude=event_lat,depth=event_depth)
fig.plot(x=sta_long,y=sta_lat,style='t0.2',fill = 'red',pen='black',)
fig.plot(x=sta_long[Ref_station_index],y=sta_lat[Ref_station_index],\
style= 't0.2',fill = 'blue',pen='black',)
fig.show()
fig.savefig(outdir+'/'+'T_Summary_'+str(bp_l)+'_'+str(bp_u)+'_'+str(Array_name)+'_T'+str(smooth_time_window)+'_Space'+str(smooth_space_window)+'.png',dpi=300)
#fig.savefig(outdir+'/'+'T_Summary_'+str(bp_l)+'_'+str(bp_u)+'_'+str(Array_name)+'.eps',dpi=300)