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simplified_inversion.py
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simplified_inversion.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import obspy
import datetime
from prelude import *
from scipy.signal import find_peaks, spectrogram
import scipy.linalg as la
seismo_data = pd.read_csv('input/all_sta.txt', sep="|")
seismo_latitudes = seismo_data['Latitude']
seismo_longitudes = seismo_data['Longitude']
station = seismo_data['Station']
elevations = seismo_data['Elevation']
flight_num = [530342801,528485724,528473220,528407493,528293430,531605202,531715679,529805251,529948401]
time = [1551066051,1550172833,1550168070,1550165577,1550089044,1551662362,1551736354,1550803701,1550867033]
sta = [1022,1272,1173,1283,1004,1010,1021,1006,1109]
day = [25,14,14,14,13,4,4,22,22]
month = [2,2,2,2,2,3,3,2,2]
for n in range(0,8):
ht = datetime.utcfromtimestamp(time[n])
mins = ht.minute
secs = ht.second
h = ht.hour
tim = 120
h_u = str(h+1)
if h < 23:
day2 = str(day[n])
if h < 10:
h_u = '0'+str(h+1)
h = '0'+str(h)
else:
h_u = str(h+1)
h = str(h)
else:
h_u = '00'
day2 = str(day[n]+1)
if len(str(day[n])) == 1:
day[n] = '0'+str(day[n])
day2 = day[n]
flight_data = pd.read_csv('/scratch/irseppi/nodal_data/flightradar24/20190'+str(month[n])+str(day[n])+'_positions/20190'+str(month[n])+str(day[n])+'_'+str(flight_num[n])+'.csv', sep=",")
flight_latitudes = flight_data['latitude']
flight_longitudes = flight_data['longitude']
tm = flight_data['snapshot_id']
speed = flight_data['speed']
alt = flight_data['altitude']
head = flight_data['heading']
for line in range(len(tm)):
if str(tm[line]) == str(time[n]):
speed = flight_data['speed'][line]
speed_mps = speed * 0.514444
alt = flight_data['altitude'][line]
alt_m = alt * 0.3048
for y in range(len(station)):
if str(station[y]) == str(sta[n]):
elevation = elevations[y]
dist = distance(seismo_latitudes[y], seismo_longitudes[y], flight_latitudes[line], flight_longitudes[line])
p = "/scratch/naalexeev/NODAL/2019-0"+str(month[n])+"-"+str(day[n])+"T"+str(h)+":00:00.000000Z.2019-0"+str(month[n])+"-"+str(day2)+"T"+str(h_u)+":00:00.000000Z."+str(station[y])+".mseed"
tr = obspy.read(p)
tr[2].trim(tr[2].stats.starttime + (mins * 60) + secs - tim, tr[2].stats.starttime + (mins * 60) + secs + tim)
data = tr[2][:]
fs = int(tr[2].stats.sampling_rate)
title = f'{tr[2].stats.network}.{tr[2].stats.station}.{tr[2].stats.location}.{tr[2].stats.channel} − starting {tr[2].stats["starttime"]}'
torg = tr[2].times()
clat, clon, dist_m, tmid = closest_encounter(flight_latitudes, flight_longitudes,line, tm, seismo_latitudes[y], seismo_longitudes[y])
height_m = alt_m - elevation
tarrive = 120+ (time[n] - calc_time(tmid,dist_m,height_m))
# Compute spectrogram
frequencies, times, Sxx = spectrogram(data, fs, scaling='density', nperseg=fs, noverlap=fs * .9, detrend = 'constant')
a, b = Sxx.shape
MDF = np.zeros((a,b))
for row in range(len(Sxx)):
m = len(Sxx[row])
p = sorted(Sxx[row])
median = p[int(m/2)]
for col in range(m):
MDF[row][col] = median
spec = 10 * np.log10(Sxx) - (10 * np.log10(MDF))
c = 343
if n == 0:
f0_array = [38, 57, 76, 96, 116, 135, 154, 173, 231]
tprime0 = 112
v0 = 63
l = 1645
if n == 1:
f0_array = [36, 55, 73, 109, 146, 164, 183, 218, 236, 254, 273]
tprime0 = 106
v0 = 106
l = 3176
if n == 2:
f0_array = [78,119,130, 258]
tprime0 = 93
v0 = 142
l = 4992
if n == 3:
f0_array = [35,70,103,119,133,139]
tprime0 = 115
v0 = 160
l = 3832
if n == 4:
f0_array = [14,27,40,53,67,80,94,108,122,135,147,161,174,187,202,225,240,248,270]
tprime0 = 140
v0 = 62
l = 504
if n == 5:
f0_array = [38, 57, 76, 96, 116, 135, 154, 173, 192, 211, 231]
tprime0 = 112
v0 = 53
l = 831
if n == 6:
f0_array = [19,40,59,79,100,120,140,160,180,200,221,241,261]
tprime0 = 118
v0 = 59
l = 479
if n == 7:
f0_array = [14,32,43,48,64,80,86,96,112,129,145,158,161,180,194,202,210,227,243,260,277]
tprime0 = 110
v0 = 89
l = 1307
c = 343
corridor_width = 6
if n == 3: #if it is a Boeing Jet
corridor_width = 3
elif n == 4: # if it is a helicopter
corridor_width = 4
elif n == 7: # if it is a C46: CURTISS COMMANDO
corridor_width = 3
middle_index = len(times) // 2
middle_column = spec[:, middle_index]
vmin = 0
vmax = np.max(middle_column)
w = len(f0_array)
mprior = []
mprior.append(v0)
mprior.append(l)
mprior.append(tprime0)
for i in range(w):
mprior.append(f0_array[i])
mprior = np.array(mprior)
peaks_assos = []
fobs = []
tobs = []
for i in range(len(f0_array)):
f0 = f0_array[i]
ft = calc_ft(times, tprime0, f0, v0, l, c)
maxfreq = []
coord_inv = []
ttt = []
f01 = f0 + corridor_width
f02 = f0 - corridor_width
upper = calc_ft(times, tprime0, f01, v0, l, c)
lower = calc_ft(times, tprime0, f02, v0, l, c)
for t_f in range(len(times)):
try:
tt = spec[int(np.round(lower[t_f],0)):int(np.round(upper[t_f],0)), t_f]
try:
if n != 2 or n != 3 or n != 4 or n != 7:
max_amplitude_index,_ = find_peaks(tt, prominence = 15, wlen=10, height=vmax*0.1)
elif n == 7:
max_amplitude_index,_ = find_peaks(tt, prominence = 1, wlen=25, height=vmax*0.2)
else:
max_amplitude_index,_ = find_peaks(tt, prominence = 25, wlen=5, height=vmax*0.5)
maxa = np.argmax(tt[max_amplitude_index])
max_amplitude_frequency = frequencies[int(max_amplitude_index[maxa])+int(np.round(lower[t_f],0))]
except:
if n == 3 or len(f0_array) > 11: #This is used for the boeing jet and any other flight with more than 11 fundamental frequencies
if np.max(tt) > vmax*0.4:
max_amplitude_index = np.argmax(tt)
max_amplitude_frequency = max_amplitude_index+int(np.round(lower[t_f],0))
else:
continue
else:
continue
maxfreq.append(max_amplitude_frequency)
coord_inv.append((times[t_f], max_amplitude_frequency))
ttt.append(times[t_f])
except:
continue
if f0 < 200:
coord_inv_array = np.array(coord_inv)
mtest = [f0,v0, l, tprime0]
mtest,_ = invert_f(mtest, coord_inv_array, num_iterations=4)
ft = calc_ft(ttt, mtest[3], mtest[0], mtest[1], mtest[2], c)
else:
ft = calc_ft(ttt, tprime0, f0, v0, l, c)
delf = np.array(ft) - np.array(maxfreq)
count = 0
for i in range(len(delf)):
if np.abs(delf[i]) <= (3):
fobs.append(maxfreq[i])
tobs.append(ttt[i])
count += 1
peaks_assos.append(count)
time_pick = True
if time_pick == True:
set_time = []
plt.figure()
plt.pcolormesh(times, frequencies, spec, shading='gouraud', cmap='pink_r', vmin=vmin, vmax=vmax)
plt.scatter(tobs,fobs, color='black', marker='x')
def onclick(event):
global coords
set_time.append(event.xdata)
plt.scatter(event.xdata, event.ydata, color='red', marker='x') # Add this line
plt.draw()
print('Clicked:', event.xdata, event.ydata)
cid = plt.gcf().canvas.mpl_connect('button_press_event', onclick)
plt.show(block=True)
start_time = set_time[0]
end_time = set_time[1]
ftobs = []
ffobs = []
peak_ass = []
cum = 0
for p in range(w):
count = 0
for j in range(cum,cum+peaks_assos[p]):
if tobs[j] >= start_time and tobs[j] <= end_time:
ftobs.append(tobs[j])
ffobs.append(fobs[j])
count += 1
cum = cum + peaks_assos[p]
peak_ass.append(count)
peaks_assos = peak_ass
tobs = ftobs
fobs = ffobs
plt.figure()
plt.pcolormesh(times, frequencies, spec, shading='gouraud', cmap='pink_r', vmin=vmin, vmax=vmax)
plt.scatter(tobs,fobs, color='black', marker='x')
plt.show()
qv = 0
num_iterations = 4
cprior = np.zeros((w+3,w+3))
for row in range(len(cprior)):
if row == 0:
cprior[row][row] = 20**2
elif row == 1:
cprior[row][row] = 500**2
elif row == 2:
cprior[row][row] = 20**2
else:
cprior[row][row] = 1**2
Cd = np.zeros((len(fobs), len(fobs)), int)
np.fill_diagonal(Cd, 3**2)
mnew = np.array(mprior)
while qv < num_iterations:
G = np.zeros((0,w+3))
fnew = []
cum = 0
for p in range(w):
new_row = np.zeros(w+3)
f0 = f0_array[p]
for j in range(cum,cum+peaks_assos[p]):
tprime = tobs[j]
t = ((tprime - tprime0)- np.sqrt((tprime-tprime0)**2-(1-v0**2/c**2)*((tprime-tprime0)**2-l**2/c**2)))/(1-v0**2/c**2)
ft0p = f0/(1+(v0/c)*(v0*t)/(np.sqrt(l**2+(v0*t)**2)))
f_derivef0, f_derivev0, f_derivel, f_derivetprime0 = df(f0,v0,l,tprime0, tobs[j])
new_row[0] = f_derivev0
new_row[1] = f_derivel
new_row[2] = f_derivetprime0
new_row[3+p] = f_derivef0
G = np.vstack((G, new_row))
fnew.append(ft0p)
cum = cum + peaks_assos[p]
m = np.array(mnew) + [email protected]@la.inv(G@[email protected]+Cd)@(np.array(fobs)- np.array(fnew))
mnew = m
v0 = mnew[0]
l = mnew[1]
tprime0 = mnew[2]
f0_array = mnew[3:]
print(m)
qv += 1
covm = la.inv([email protected](Cd)@G + la.inv(cprior))
closest_index = np.argmin(np.abs(tprime0 - times))
arrive_time = spec[:,closest_index]
for i in range(len(arrive_time)):
if arrive_time[i] < 0:
arrive_time[i] = 0
vmin = np.min(arrive_time)
vmax = np.max(arrive_time)
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=False, figsize=(8,6))
ax1.plot(torg, data, 'k', linewidth=0.5)
ax1.set_title(title)
ax1.margins(x=0)
ax1.set_position([0.125, 0.6, 0.775, 0.3]) # Move ax1 plot upwards
# Plot spectrogram
cax = ax2.pcolormesh(times, frequencies, spec, shading='gouraud', cmap='pink_r', vmin=vmin, vmax=vmax)
ax2.set_xlabel('Time (s)')
f0lab = []
ax2.axvline(x=tprime0, c = '#377eb8', ls = '--', linewidth=0.7,label='Estimated arrival: '+str(np.round(tprime0,2))+' s')
f0_array = sorted(f0_array)
covm = np.sqrt(np.diag(covm))
for pp in range(len(f0_array)):
f0 = f0_array[pp]
ft = calc_ft(times, tprime0, f0, v0, l, c)
ax2.plot(times, ft, '#377eb8', ls = (0,(5,20)), linewidth=0.7)
tprime = tprime0
t = ((tprime - tprime0)- np.sqrt((tprime-tprime0)**2-(1-v0**2/c**2)*((tprime-tprime0)**2-l**2/c**2)))/(1-v0**2/c**2)
ft0p = f0/(1+(v0/c)*(v0*t)/(np.sqrt(l**2+(v0*t)**2)))
ax2.scatter(tprime0, ft0p, color='black', marker='x', s=30)
f0lab.append(str(np.round(f0,2)) +'+/-' + str(np.round(covm[3+pp],2))+',')
if len(f0_array) > 16:
if pp == int(int(len(f0_array))/3):
f0lab.append('\n')
elif pp == int((int(len(f0_array))/3)+(int(len(f0_array))/3)):
f0lab.append('\n')
elif len(f0_array) > 8:
if pp == int(int(len(f0_array))/2):
f0lab.append('\n')
fss = 'x-small'
ax2.set_title("Final Model:\nt0'= "+str(np.round(tprime0,2))+' +/- ' + str(np.round(covm[2],2)) + ' sec, v0 = '+str(np.round(v0,2))+' +/- ' + str(np.round(covm[0],2)) +' m/s, l = '+str(np.round(l,2))+' +/- ' + str(np.round(covm[1],2)) +' m, \n' + 'f0 = '+' '.join(f0lab) +' Hz', fontsize=fss)
ax2.axvline(x=tarrive, c = '#e41a1c', ls = '--',linewidth=0.5,label='Wave arrvial: '+str(np.round(tarrive,2))+' s')
ax2.legend(loc='upper right',fontsize = 'x-small')
ax2.set_ylabel('Frequency (Hz)')
ax2.margins(x=0)
ax3 = fig.add_axes([0.9, 0.11, 0.015, 0.35])
plt.colorbar(mappable=cax, cax=ax3)
ax3.set_ylabel('Relative Amplitude (dB)')
ax2.margins(x=0)
ax2.set_xlim(0, 240)
ax2.set_ylim(0, int(fs/2))
# Plot overlay
spec2 = 10 * np.log10(MDF)
middle_column2 = spec2[:, middle_index]
vmin2 = np.min(middle_column2)
vmax2 = np.max(middle_column2)
# Create ax4 and plot on the same y-axis as ax2
ax4 = fig.add_axes([0.125, 0.11, 0.07, 0.35], sharey=ax2)
ax4.plot(middle_column2, frequencies, c='#ff7f00')
ax4.set_ylim(0, int(fs/2))
ax4.set_xlim(vmax2*1.1, vmin2)
ax4.tick_params(left=False, right=False, labelleft=False, labelbottom=False, bottom=False)
ax4.grid(axis='y')
plt.show()
BASE_DIR = '/scratch/irseppi/nodal_data/plane_info/5plane_spec/2019-0'+str(month[n])+'-'+str(day[n])+'/'+str(flight_num[n])+'/'+str(sta[n])+'/'
make_base_dir(BASE_DIR)
fig.savefig('/scratch/irseppi/nodal_data/plane_info/5plane_spec/2019-0'+str(month[n])+'-'+str(day[n])+'/'+str(flight_num[n])+'/'+str(sta[n])+'/'+str(time[n])+'_'+str(flight_num[n])+'.png')
plt.close()
fig = plt.figure(figsize=(10,6))
plt.grid()
plt.plot(frequencies, arrive_time, c='#377eb8')
for pp in range(len(f0_array)):
f0 = f0_array[pp]
if fs/2 < f0:
continue
tprime = tprime0
t = ((tprime - tprime0)- np.sqrt((tprime-tprime0)**2-(1-v0**2/c**2)*((tprime-tprime0)**2-l**2/c**2)))/(1-v0**2/c**2)
ft0p = f0/(1+(v0/c)*(v0*t)/(np.sqrt(l**2+(v0*t)**2)))
upper = int(ft0p + 5)
lower = int(ft0p - 5)
tt = spec[lower:upper, closest_index]
if upper > 250:
freqp = ft0p
ampp = np.interp(ft0p, frequencies, arrive_time)
elif lower < 0:
freqp = ft0p
ampp = np.interp(ft0p, frequencies, arrive_time)
else:
ampp = np.max(tt)
freqp = np.argmax(tt)+lower
plt.scatter(freqp, ampp, color='black', marker='x', s=100)
if isinstance(sta[n], int):
plt.text(freqp - 5, ampp + 0.8, freqp, fontsize=17, fontweight='bold')
else:
plt.text(freqp - 1, ampp + 0.8, freqp, fontsize=17, fontweight='bold')
plt.xlim(0, int(fs/2))
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.ylim(0,vmax*1.1)
plt.xlabel('Frequency (Hz)', fontsize=17)
plt.ylabel('Relative Amplitude at t = {:.2f} s (dB)'.format(tprime0), fontsize=17)
make_base_dir('/scratch/irseppi/nodal_data/plane_info/5spec/20190'+str(month[n])+str(day[n])+'/'+str(flight_num[n])+'/'+str(sta[n])+'/')
fig.savefig('/scratch/irseppi/nodal_data/plane_info/5spec/20190'+str(month[n])+str(day[n])+'/'+str(flight_num[n])+'/'+str(sta[n])+'/'+str(sta[n])+'_' + str(time[n]) + '.png')
plt.close()