-
Notifications
You must be signed in to change notification settings - Fork 0
/
full_data_picks_C185.py
257 lines (206 loc) · 8.83 KB
/
full_data_picks_C185.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
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
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']
sta_f = open('input/all_station_crossing_db_C185.txt','r')
# Loop through each station in text file that we already know comes within 2km of the nodes
for line in sta_f.readlines():
val = line.split(',')
date = val[0]
flight = val[1]
sta = val[5]
equipment = val[6][0:4]
tm = float(val[2])
if datetime.utcfromtimestamp(tm).month == 3:
flight_file = '/scratch/irseppi/nodal_data/flightradar24/' + str(date) + '_positions/' + str(date) + '_' + str(flight) + '.csv'
flight_data = pd.read_csv(flight_file, sep=",")
flight_latitudes = flight_data['latitude']
flight_longitudes = flight_data['longitude']
time = flight_data['snapshot_id']
speed = flight_data['speed']
altitude = flight_data['altitude']
for n in range(len(time)):
if str(tm) == str(time[n])+'.0':
spd = speed[n]
speed_mps = spd * 0.514444
alt = altitude[n]
alt_m = alt * 0.3048
index = n
for e in range(len(station)):
if station[e] == sta:
elevation = elevations[e]
clat, clon, dist_m, tmid = closest_encounter(flight_latitudes, flight_longitudes, index, time, seismo_latitudes[e], seismo_longitudes[e])
else:
continue
else:
continue
height_m = alt_m - elevation
tarrive = calc_time(tmid,dist_m,height_m)
ht = datetime.utcfromtimestamp(tarrive)
mins = ht.minute
secs = ht.second
month = ht.month
day = ht.day
h = ht.hour
h_u = str(h+1)
if h < 23:
day2 = str(day)
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+1)
if len(str(day)) == 1:
day = '0'+str(day)
day2 = day
try:
p = "/scratch/naalexeev/NODAL/2019-0"+str(month)+"-"+str(day)+"T"+str(h)+":00:00.000000Z.2019-0"+str(month)+"-"+str(day2)+"T"+str(h_u)+":00:00.000000Z."+str(sta)+".mseed"
tr = obspy.read(p)
except:
continue
tr[2].trim(tr[2].stats.starttime + (mins * 60) + secs - 120, tr[2].stats.starttime + (mins * 60) + secs + 120)
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()
# 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))
try_median = False
if try_median == True:
if isinstance(sta, int):
spec = np.zeros((a,b))
for col in range(0,b):
p = sorted(Sxx[:, col])
median = p[int(len(p)/2)]
for row in range(len(Sxx)):
spec[row][col] = 10 * np.log10(Sxx[row][col]) - ((10 * np.log10(MDF[row][col])) + ((10*np.log10(median))))
middle_index = len(times) // 2
middle_column = spec[:, middle_index]
vmin = 0
vmax = np.max(middle_column)
c = 343
tprime0 = 120
v0 = speed_mps
l = np.sqrt(dist_m**2 + (height_m)**2)
f0_array = [38, 57, 76, 96, 116, 135, 154, 173, 192, 211, 231]
#f0_array = [62,82,105,124,145,167,186,209,250]
tf = np.arange(0, 240, 1)
fig, ax1 = plt.subplots(1, 1)
# Plot spectrogram
cax = ax1.pcolormesh(times, frequencies, spec, shading='gouraud', cmap='pink_r', vmin=vmin, vmax=vmax)
ax1.set_xlabel('Time (s)')
for pp in range(len(f0_array)):
f0 = f0_array[pp]
ft = calc_ft(times, tprime0, f0, v0, l, c)
ax1.plot(times, ft, '#377eb8', ls = (0,(5,20)), linewidth=0.7)
ax1.axvline(x=120, color='black', linestyle='--', linewidth=0.7)
ax1.margins(x=0)
ax2 = fig.add_axes([0.9, 0.11, 0.015, 0.35])
plt.colorbar(mappable=cax, cax=ax2)
ax1.margins(x=0)
ax1.set_xlim(0, 240)
ax1.set_ylim(0, int(fs/2))
plt.show()
con = input("Do you want to use this? (y or n)")
if con == 'n':
continue
else:
corridor_width = 6
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:
max_amplitude_index,_ = find_peaks(tt, prominence = 15, wlen=10, height=vmax*0.1)
maxa = np.argmax(tt[max_amplitude_index])
max_amplitude_frequency = frequencies[int(max_amplitude_index[maxa])+int(np.round(lower[t_f],0))]
except:
continue
maxfreq.append(max_amplitude_frequency)
coord_inv.append((times[t_f], max_amplitude_frequency))
ttt.append(times[t_f])
except:
continue
if len(coord_inv) > 0:
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
else:
continue
if len(fobs) == 0:
print('No picks found')
continue
time_pick = False
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 = []
for i in range(len(tobs)):
if tobs[i] >= start_time and tobs[i] <= end_time:
ftobs.append(tobs[i])
ffobs.append(fobs[i])
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()
else:
continue