Spaces:
Runtime error
Runtime error
WIP on section plot
Browse files- .DS_Store +0 -0
- Gradio_app.ipynb +506 -0
- phasehunter/.DS_Store +0 -0
- phasehunter/__pycache__/data_preparation.cpython-311.pyc +0 -0
- phasehunter/__pycache__/model.cpython-311.pyc +0 -0
- phasehunter/app.py +0 -188
.DS_Store
CHANGED
Binary files a/.DS_Store and b/.DS_Store differ
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Gradio_app.ipynb
ADDED
@@ -0,0 +1,506 @@
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1 |
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{
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"cells": [
|
3 |
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{
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4 |
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"cell_type": "code",
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5 |
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"execution_count": 8,
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6 |
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"metadata": {},
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7 |
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"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Inventory created at 2023-03-31T20:48:41.380800Z\n",
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"\tCreated by: IRIS WEB SERVICE: fdsnws-station | version: 1.1.52\n",
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14 |
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"\t\t http://service.iris.edu/fdsnws/station/1/query?starttime=2019-07-...\n",
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15 |
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"\tSending institution: IRIS-DMC (IRIS-DMC)\n",
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16 |
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"\tContains:\n",
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"\t\tNetworks (7):\n",
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"\t\t\t8P, CI, LB, NN, NP, PB, SY\n",
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"\t\tStations (85):\n",
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20 |
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"\t\t\t8P.CAU08 (Monache Meadows, CA, USA)\n",
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21 |
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"\t\t\tCI.APL (Apollo)\n",
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22 |
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"\t\t\tCI.CCA (California City Airport)\n",
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23 |
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"\t\t\tCI.CCC (Christmas Canyon China Lake)\n",
|
24 |
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"\t\t\tCI.CGO (Cerro Gordo)\n",
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25 |
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"\t\t\tCI.CLC (China Lake)\n",
|
26 |
+
"\t\t\tCI.CWC (Cottonwood Creek)\n",
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27 |
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"\t\t\tCI.DAW (Darwin)\n",
|
28 |
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"\t\t\tCI.DTP (Desert Tortoise Park)\n",
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29 |
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"\t\t\tCI.GSC (Goldstone)\n",
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30 |
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"\t\t\tCI.HAR (Harper Dry Lake bed)\n",
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31 |
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"\t\t\tCI.ISA (Isabella)\n",
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32 |
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"\t\t\tCI.JRC2 (Joshua Ridge: China Lake)\n",
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33 |
+
"\t\t\tCI.LMR2 (Leuhmann Ridge Extension)\n",
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34 |
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"\t\t\tCI.LRL (Laurel Mtn)\n",
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35 |
+
"\t\t\tCI.MPM (Manuel Prospect Mine)\n",
|
36 |
+
"\t\t\tCI.MRS (Mars)\n",
|
37 |
+
"\t\t\tCI.Q0068 (Redwood Blvd, California City CA)\n",
|
38 |
+
"\t\t\tCI.Q0072 (Lakeland Street, Ridgecrest CA)\n",
|
39 |
+
"\t\t\tCI.SLA (Slate Mountain)\n",
|
40 |
+
"\t\t\tCI.SRT (Snort)\n",
|
41 |
+
"\t\t\tCI.TEH (Cattani Ranch)\n",
|
42 |
+
"\t\t\tCI.TOW2 (Tower 2)\n",
|
43 |
+
"\t\t\tCI.WAS2 (Alta Sierra 2)\n",
|
44 |
+
"\t\t\tCI.WBM (Bowman Road)\n",
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45 |
+
"\t\t\tCI.WBS (Bird Springs)\n",
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46 |
+
"\t\t\tCI.WCS2 (Coso Hot Springs 2)\n",
|
47 |
+
"\t\t\tCI.WHF (Hanning Flat)\n",
|
48 |
+
"\t\t\tCI.WLH2 (Little Horse 2)\n",
|
49 |
+
"\t\t\tCI.WMF (Mccloud Flat)\n",
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50 |
+
"\t\t\tCI.WNM (Nine Mile Canyon)\n",
|
51 |
+
"\t\t\tCI.WOR (Onyx Ranch)\n",
|
52 |
+
"\t\t\tCI.WRC2 (Renegade Canyon)\n",
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53 |
+
"\t\t\tCI.WRV2 (Rose Valley Canyon 2)\n",
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54 |
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"\t\t\tCI.WVP2 (Volcano Peak 2)\n",
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55 |
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"\t\t\tLB.DAC (Inyo County, Darwin, CA, USA)\n",
|
56 |
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"\t\t\tNN.GWY (Greenwater Valley, CA. (GPS 12/06/2000) w84gm)\n",
|
57 |
+
"\t\t\tNN.PAN (Panamint Range. (GPS 12/06/2000) w84gm)\n",
|
58 |
+
"\t\t\tNN.QSM (Queen of Sheba Mine, CA. (GPS 01/17/2001) w84gm)\n",
|
59 |
+
"\t\t\tNP.1035 (CA:Lake Isabella Dam)\n",
|
60 |
+
"\t\t\tNP.1809 (CA:Haiwee Rsvr;Pump Pl)\n",
|
61 |
+
"\t\t\tNP.5419 (CA:China Lake;Nav Weapon Ctr)\n",
|
62 |
+
"\t\t\tPB.B916 (marips916bcs2008, China Lake, CA, USA)\n",
|
63 |
+
"\t\t\tPB.B917 (tonyso917bcs2008, China Lake, CA, USA)\n",
|
64 |
+
"\t\t\tPB.B918 (mtsprn918bcs2008, China Lake, CA, USA)\n",
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65 |
+
"\t\t\tPB.B921 (randsb921bcs2008, China Lake, CA, USA)\n",
|
66 |
+
"\t\t\tSY.CCA (CCA synthetic)\n",
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67 |
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"\t\t\tSY.CCC (CCC synthetic)\n",
|
68 |
+
"\t\t\tSY.CGO (CGO synthetic)\n",
|
69 |
+
"\t\t\tSY.CLC (CLC synthetic)\n",
|
70 |
+
"\t\t\tSY.CWC (CWC synthetic)\n",
|
71 |
+
"\t\t\tSY.DAC (DAC synthetic)\n",
|
72 |
+
"\t\t\tSY.DAW (DAW synthetic)\n",
|
73 |
+
"\t\t\tSY.DTP (DTP synthetic)\n",
|
74 |
+
"\t\t\tSY.FPC (FPC synthetic)\n",
|
75 |
+
"\t\t\tSY.FSR (FSR synthetic)\n",
|
76 |
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"\t\t\tSY.GPO (GPO synthetic)\n",
|
77 |
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"\t\t\tSY.GSC (GSC synthetic)\n",
|
78 |
+
"\t\t\tSY.HAR (HAR synthetic)\n",
|
79 |
+
"\t\t\tSY.ISA (ISA synthetic)\n",
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80 |
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"\t\t\tSY.JRC (JRC synthetic)\n",
|
81 |
+
"\t\t\tSY.JRC2 (JRC2 synthetic)\n",
|
82 |
+
"\t\t\tSY.KRV3 (KRV3 synthetic)\n",
|
83 |
+
"\t\t\tSY.LMR (LMR synthetic)\n",
|
84 |
+
"\t\t\tSY.LMR2 (LMR2 synthetic)\n",
|
85 |
+
"\t\t\tSY.LRL (LRL synthetic)\n",
|
86 |
+
"\t\t\tSY.MPM (MPM synthetic)\n",
|
87 |
+
"\t\t\tSY.OVRO (OVRO synthetic)\n",
|
88 |
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"\t\t\tSY.RRC (RRC synthetic)\n",
|
89 |
+
"\t\t\tSY.SEV (SEV synthetic)\n",
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90 |
+
"\t\t\tSY.SLA (SLA synthetic)\n",
|
91 |
+
"\t\t\tSY.SRT (SRT synthetic)\n",
|
92 |
+
"\t\t\tSY.TEH (TEH synthetic)\n",
|
93 |
+
"\t\t\tSY.TOW2 (TOW2 synthetic)\n",
|
94 |
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"\t\t\tSY.WAS2 (WAS2 synthetic)\n",
|
95 |
+
"\t\t\tSY.WBM (WBM synthetic)\n",
|
96 |
+
"\t\t\tSY.WBP (WBP synthetic)\n",
|
97 |
+
"\t\t\tSY.WBS (WBS synthetic)\n",
|
98 |
+
"\t\t\tSY.WCS2 (WCS2 synthetic)\n",
|
99 |
+
"\t\t\tSY.WHF (WHF synthetic)\n",
|
100 |
+
"\t\t\tSY.WLH2 (WLH2 synthetic)\n",
|
101 |
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"\t\t\tSY.WMF (WMF synthetic)\n",
|
102 |
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"\t\t\tSY.WNM (WNM synthetic)\n",
|
103 |
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"\t\t\tSY.WOR (WOR synthetic)\n",
|
104 |
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"\t\t\tSY.WRC2 (WRC2 synthetic)\n",
|
105 |
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"\t\tChannels (0):\n",
|
106 |
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"\n"
|
107 |
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]
|
108 |
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}
|
109 |
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],
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110 |
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"source": [
|
111 |
+
"import obspy\n",
|
112 |
+
"from obspy.clients.fdsn import Client\n",
|
113 |
+
"\n",
|
114 |
+
"client_name = 'SCEDC'\n",
|
115 |
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"radius_km = 100\n",
|
116 |
+
"timestamp = '2019-07-04 17:33:49'\n",
|
117 |
+
"eq_lat = 35.766\n",
|
118 |
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"eq_lon = -117.605\n",
|
119 |
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"\n",
|
120 |
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"origin_time = obspy.UTCDateTime(timestamp)\n",
|
121 |
+
"\n",
|
122 |
+
"client = Client(\"IRIS\")\n",
|
123 |
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"inventory = client.get_stations(network=\"*\", station=\"*\", channel=\"*\",\n",
|
124 |
+
" starttime=origin_time, endtime=origin_time + 120,\n",
|
125 |
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" latitude=eq_lat, longitude=eq_lon, maxradius=radius_km/111.2)\n",
|
126 |
+
"print(inventory)\n"
|
127 |
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]
|
128 |
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},
|
129 |
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{
|
130 |
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"cell_type": "code",
|
131 |
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"execution_count": null,
|
132 |
+
"metadata": {},
|
133 |
+
"outputs": [],
|
134 |
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"source": [
|
135 |
+
"\n",
|
136 |
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"\n",
|
137 |
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"client = Client(client_name)\n",
|
138 |
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"window = radius_km / 111.2\n",
|
139 |
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"\n",
|
140 |
+
"assert eq_lat - window > -90 and eq_lat + window < 90, \"Latitude out of bounds\"\n",
|
141 |
+
"assert eq_lon - window > -180 and eq_lon + window < 180, \"Longitude out of bounds\"\n",
|
142 |
+
"\n",
|
143 |
+
"starttime = obspy.UTCDateTime(timestamp)\n",
|
144 |
+
"endtime = starttime + 120\n",
|
145 |
+
"\n",
|
146 |
+
"inv = client.get_stations(network=\"*\", station=\"*\", location=\"*\", channel=\"*H*\", \n",
|
147 |
+
" starttime=starttime, endtime=endtime, \n",
|
148 |
+
" minlatitude=(eq_lat-window), maxlatitude=(eq_lat+window),\n",
|
149 |
+
" minlongitude=(eq_lon-window), maxlongitude=(eq_lon+window), \n",
|
150 |
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" level='station')"
|
151 |
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]
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152 |
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"metadata": {},
|
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"outputs": [
|
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{
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"name": "stderr",
|
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"output_type": "stream",
|
161 |
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"text": [
|
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"/Users/anovosel/miniconda3/envs/phasehunter/lib/python3.11/site-packages/gradio/outputs.py:43: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
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163 |
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" warnings.warn(\n"
|
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]
|
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},
|
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{
|
167 |
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"name": "stdout",
|
168 |
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"output_type": "stream",
|
169 |
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"text": [
|
170 |
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"Running on local URL: http://127.0.0.1:7914\n",
|
171 |
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"\n",
|
172 |
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"To create a public link, set `share=True` in `launch()`.\n"
|
173 |
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]
|
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},
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{
|
176 |
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"data": {
|
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"text/html": [
|
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"<div><iframe src=\"http://127.0.0.1:7914/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": []
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},
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"execution_count": 64,
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"metadata": {},
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+
"output_type": "execute_result"
|
194 |
+
},
|
195 |
+
{
|
196 |
+
"name": "stdout",
|
197 |
+
"output_type": "stream",
|
198 |
+
"text": [
|
199 |
+
"torch.Size([256])\n"
|
200 |
+
]
|
201 |
+
}
|
202 |
+
],
|
203 |
+
"source": [
|
204 |
+
"# Gradio app that takes seismic waveform as input and marks 2 phases on the waveform as output.\n",
|
205 |
+
"\n",
|
206 |
+
"import gradio as gr\n",
|
207 |
+
"import numpy as np\n",
|
208 |
+
"from phasehunter.model import Onset_picker, Updated_onset_picker\n",
|
209 |
+
"from phasehunter.data_preparation import prepare_waveform\n",
|
210 |
+
"import torch\n",
|
211 |
+
"\n",
|
212 |
+
"from scipy.stats import gaussian_kde\n",
|
213 |
+
"\n",
|
214 |
+
"import obspy\n",
|
215 |
+
"from obspy.clients.fdsn import Client\n",
|
216 |
+
"from obspy.clients.fdsn.header import FDSNNoDataException, FDSNTimeoutException, FDSNInternalServerException\n",
|
217 |
+
"from obspy.geodetics.base import locations2degrees\n",
|
218 |
+
"from obspy.taup import TauPyModel\n",
|
219 |
+
"from obspy.taup.helper_classes import SlownessModelError\n",
|
220 |
+
"\n",
|
221 |
+
"from obspy.clients.fdsn.header import URL_MAPPINGS\n",
|
222 |
+
"\n",
|
223 |
+
"import matplotlib.pyplot as plt\n",
|
224 |
+
"\n",
|
225 |
+
"def make_prediction(waveform):\n",
|
226 |
+
" waveform = np.load(waveform)\n",
|
227 |
+
" processed_input = prepare_waveform(waveform)\n",
|
228 |
+
" \n",
|
229 |
+
" # Make prediction\n",
|
230 |
+
" with torch.no_grad():\n",
|
231 |
+
" output = model(processed_input)\n",
|
232 |
+
"\n",
|
233 |
+
" p_phase = output[:, 0]\n",
|
234 |
+
" s_phase = output[:, 1]\n",
|
235 |
+
"\n",
|
236 |
+
" return processed_input, p_phase, s_phase\n",
|
237 |
+
"\n",
|
238 |
+
"def mark_phases(waveform):\n",
|
239 |
+
" processed_input, p_phase, s_phase = make_prediction(waveform)\n",
|
240 |
+
"\n",
|
241 |
+
" # Create a plot of the waveform with the phases marked\n",
|
242 |
+
" if sum(processed_input[0][2] == 0): #if input is 1C\n",
|
243 |
+
" fig, ax = plt.subplots(nrows=2, figsize=(10, 2), sharex=True)\n",
|
244 |
+
"\n",
|
245 |
+
" ax[0].plot(processed_input[0][0])\n",
|
246 |
+
" ax[0].set_ylabel('Norm. Ampl.')\n",
|
247 |
+
"\n",
|
248 |
+
" else: #if input is 3C\n",
|
249 |
+
" fig, ax = plt.subplots(nrows=4, figsize=(10, 6), sharex=True)\n",
|
250 |
+
" ax[0].plot(processed_input[0][0])\n",
|
251 |
+
" ax[1].plot(processed_input[0][1])\n",
|
252 |
+
" ax[2].plot(processed_input[0][2])\n",
|
253 |
+
"\n",
|
254 |
+
" ax[0].set_ylabel('Z')\n",
|
255 |
+
" ax[1].set_ylabel('N')\n",
|
256 |
+
" ax[2].set_ylabel('E')\n",
|
257 |
+
"\n",
|
258 |
+
" p_phase_plot = p_phase*processed_input.shape[-1]\n",
|
259 |
+
" p_kde = gaussian_kde(p_phase_plot)\n",
|
260 |
+
" p_dist_space = np.linspace( min(p_phase_plot)-10, max(p_phase_plot)+10, 500 )\n",
|
261 |
+
" ax[-1].plot( p_dist_space, p_kde(p_dist_space), color='r')\n",
|
262 |
+
"\n",
|
263 |
+
" s_phase_plot = s_phase*processed_input.shape[-1]\n",
|
264 |
+
" s_kde = gaussian_kde(s_phase_plot)\n",
|
265 |
+
" s_dist_space = np.linspace( min(s_phase_plot)-10, max(s_phase_plot)+10, 500 )\n",
|
266 |
+
" ax[-1].plot( s_dist_space, s_kde(s_dist_space), color='b')\n",
|
267 |
+
"\n",
|
268 |
+
" for a in ax:\n",
|
269 |
+
" a.axvline(p_phase.mean()*processed_input.shape[-1], color='r', linestyle='--', label='P')\n",
|
270 |
+
" a.axvline(s_phase.mean()*processed_input.shape[-1], color='b', linestyle='--', label='S')\n",
|
271 |
+
"\n",
|
272 |
+
" ax[-1].set_xlabel('Time, samples')\n",
|
273 |
+
" ax[-1].set_ylabel('Uncert.')\n",
|
274 |
+
" ax[-1].legend()\n",
|
275 |
+
"\n",
|
276 |
+
" plt.subplots_adjust(hspace=0., wspace=0.)\n",
|
277 |
+
"\n",
|
278 |
+
" # Convert the plot to an image and return it\n",
|
279 |
+
" fig.canvas.draw()\n",
|
280 |
+
" image = np.array(fig.canvas.renderer.buffer_rgba())\n",
|
281 |
+
" plt.close(fig)\n",
|
282 |
+
" return image\n",
|
283 |
+
"\n",
|
284 |
+
"def predict_on_section(client_name, timestamp, eq_lat, eq_lon, radius_km, source_depth_km, velocity_model):\n",
|
285 |
+
" distances, t0s, st_lats, st_lons, waveforms = [], [], [], [], []\n",
|
286 |
+
" \n",
|
287 |
+
" taup_model = TauPyModel(model=velocity_model)\n",
|
288 |
+
" client = Client(client_name)\n",
|
289 |
+
"\n",
|
290 |
+
" window = radius_km / 111.2\n",
|
291 |
+
"\n",
|
292 |
+
" assert eq_lat - window > -90 and eq_lat + window < 90, \"Latitude out of bounds\"\n",
|
293 |
+
" assert eq_lon - window > -180 and eq_lon + window < 180, \"Longitude out of bounds\"\n",
|
294 |
+
"\n",
|
295 |
+
" starttime = obspy.UTCDateTime(timestamp)\n",
|
296 |
+
" endtime = starttime + 120\n",
|
297 |
+
"\n",
|
298 |
+
" inv = client.get_stations(network=\"*\", station=\"*\", location=\"*\", channel=\"*H*\", \n",
|
299 |
+
" starttime=starttime, endtime=endtime, \n",
|
300 |
+
" minlatitude=(eq_lat-window), maxlatitude=(eq_lat+window),\n",
|
301 |
+
" minlongitude=(eq_lon-window), maxlongitude=(eq_lon+window), \n",
|
302 |
+
" level='station')\n",
|
303 |
+
" \n",
|
304 |
+
" waveforms = []\n",
|
305 |
+
" for network in inv:\n",
|
306 |
+
" for station in network:\n",
|
307 |
+
" try:\n",
|
308 |
+
" distance = locations2degrees(eq_lat, eq_lon, station.latitude, station.longitude)\n",
|
309 |
+
"\n",
|
310 |
+
" arrivals = taup_model.get_travel_times(source_depth_in_km=source_depth_km, \n",
|
311 |
+
" distance_in_degree=distance, \n",
|
312 |
+
" phase_list=[\"P\", \"S\"])\n",
|
313 |
+
"\n",
|
314 |
+
" if len(arrivals) > 0:\n",
|
315 |
+
"\n",
|
316 |
+
" starttime = obspy.UTCDateTime(timestamp) + arrivals[0].time - 15\n",
|
317 |
+
" endtime = starttime + 60\n",
|
318 |
+
"\n",
|
319 |
+
" waveform = client.get_waveforms(network=network.code, station=station.code, location=\"*\", channel=\"*\", \n",
|
320 |
+
" starttime=starttime, endtime=endtime)\n",
|
321 |
+
" \n",
|
322 |
+
" waveform = waveform.select(channel=\"H[BH][ZNE]\")\n",
|
323 |
+
" waveform = waveform.merge(fill_value=0)\n",
|
324 |
+
" waveform = waveform[:3]\n",
|
325 |
+
" \n",
|
326 |
+
" len_check = [len(x.data) for x in waveform]\n",
|
327 |
+
" if len(set(len_check)) > 1:\n",
|
328 |
+
" continue\n",
|
329 |
+
"\n",
|
330 |
+
" if len(waveform) == 3:\n",
|
331 |
+
" waveform = prepare_waveform(np.stack([x.data for x in waveform]))\n",
|
332 |
+
" \n",
|
333 |
+
" distances.append(distance)\n",
|
334 |
+
" t0s.append(starttime)\n",
|
335 |
+
" st_lats.append(station.latitude)\n",
|
336 |
+
" st_lons.append(station.longitude)\n",
|
337 |
+
" waveforms.append(waveform)\n",
|
338 |
+
"\n",
|
339 |
+
" except (IndexError, FDSNNoDataException, FDSNTimeoutException):\n",
|
340 |
+
" continue\n",
|
341 |
+
"\n",
|
342 |
+
" with torch.no_grad():\n",
|
343 |
+
" waveforms_torch = torch.vstack(waveforms)\n",
|
344 |
+
" output = model(waveforms_torch)\n",
|
345 |
+
"\n",
|
346 |
+
" p_phases = output[:, 0]\n",
|
347 |
+
" s_phases = output[:, 1]\n",
|
348 |
+
"\n",
|
349 |
+
"\n",
|
350 |
+
" print(p_phases.shape)\n",
|
351 |
+
" # for i in range(len(waveforms)):\n",
|
352 |
+
" # current_P = P_batch[i::len(waveforms)].cpu()\n",
|
353 |
+
" # current_S_batch = S_batch[i::len(waveforms)].cpu()\n",
|
354 |
+
" # current_Pg_batch = Pg_batch[i::len(waveforms)].cpu()\n",
|
355 |
+
" # current_Sg_batch = Sg_batch[i::len(waveforms)].cpu()\n",
|
356 |
+
" # current_Pn_batch = Pn_batch[i::len(waveforms)].cpu()\n",
|
357 |
+
" # current_Sn_batch = Sn_batch[i::len(waveforms)].cpu()\n",
|
358 |
+
" \n",
|
359 |
+
" fig,ax = plt.subplots()\n",
|
360 |
+
" ax.scatter(st_lats, st_lons)\n",
|
361 |
+
" fig.canvas.draw()\n",
|
362 |
+
" image = np.array(fig.canvas.renderer.buffer_rgba())\n",
|
363 |
+
" plt.close(fig)\n",
|
364 |
+
"\n",
|
365 |
+
" return image\n",
|
366 |
+
"\n",
|
367 |
+
"\n",
|
368 |
+
"model = Onset_picker.load_from_checkpoint(\"./weights.ckpt\",\n",
|
369 |
+
" picker=Updated_onset_picker(),\n",
|
370 |
+
" learning_rate=3e-4)\n",
|
371 |
+
"model.eval()\n",
|
372 |
+
"\n",
|
373 |
+
"\n",
|
374 |
+
"\n",
|
375 |
+
"# # Create the Gradio interface\n",
|
376 |
+
"# gr.Interface(mark_phases, inputs, outputs, title='PhaseHunter').launch()\n",
|
377 |
+
"\n",
|
378 |
+
"\n",
|
379 |
+
"with gr.Blocks() as demo:\n",
|
380 |
+
" gr.Markdown(\"# PhaseHunter\")\n",
|
381 |
+
" gr.Markdown(\"\"\"This app allows one to detect P and S seismic phases along with uncertainty of the detection. \n",
|
382 |
+
" The app can be used in three ways: either by selecting one of the sample waveforms;\n",
|
383 |
+
" or by selecting an earthquake from the global earthquake catalogue;\n",
|
384 |
+
" or by uploading a waveform of interest.\n",
|
385 |
+
" \"\"\")\n",
|
386 |
+
" with gr.Tab(\"Default example\"):\n",
|
387 |
+
" # Define the input and output types for Gradio\n",
|
388 |
+
" inputs = gr.Dropdown(\n",
|
389 |
+
" [\"data/sample/sample_0.npy\", \n",
|
390 |
+
" \"data/sample/sample_1.npy\", \n",
|
391 |
+
" \"data/sample/sample_2.npy\"], \n",
|
392 |
+
" label=\"Sample waveform\", \n",
|
393 |
+
" info=\"Select one of the samples\",\n",
|
394 |
+
" value = \"data/sample/sample_0.npy\"\n",
|
395 |
+
" )\n",
|
396 |
+
"\n",
|
397 |
+
" button = gr.Button(\"Predict phases\")\n",
|
398 |
+
" outputs = gr.outputs.Image(label='Waveform with Phases Marked', type='numpy')\n",
|
399 |
+
" \n",
|
400 |
+
" button.click(mark_phases, inputs=inputs, outputs=outputs)\n",
|
401 |
+
" \n",
|
402 |
+
" with gr.Tab(\"Select earthquake from catalogue\"):\n",
|
403 |
+
" gr.Markdown('TEST')\n",
|
404 |
+
" \n",
|
405 |
+
" client_inputs = gr.Dropdown(\n",
|
406 |
+
" choices = list(URL_MAPPINGS.keys()), \n",
|
407 |
+
" label=\"FDSN Client\", \n",
|
408 |
+
" info=\"Select one of the available FDSN clients\",\n",
|
409 |
+
" value = \"IRIS\",\n",
|
410 |
+
" interactive=True\n",
|
411 |
+
" )\n",
|
412 |
+
" with gr.Row(): \n",
|
413 |
+
"\n",
|
414 |
+
" timestamp_inputs = gr.Textbox(value='2019-07-04 17:33:49',\n",
|
415 |
+
" placeholder='YYYY-MM-DD HH:MM:SS',\n",
|
416 |
+
" label=\"Timestamp\",\n",
|
417 |
+
" info=\"Timestamp of the earthquake\",\n",
|
418 |
+
" max_lines=1,\n",
|
419 |
+
" interactive=True)\n",
|
420 |
+
" \n",
|
421 |
+
" eq_lat_inputs = gr.Number(value=35.766, \n",
|
422 |
+
" label=\"Latitude\", \n",
|
423 |
+
" info=\"Latitude of the earthquake\",\n",
|
424 |
+
" interactive=True)\n",
|
425 |
+
" \n",
|
426 |
+
" eq_lon_inputs = gr.Number(value=-117.605,\n",
|
427 |
+
" label=\"Longitude\",\n",
|
428 |
+
" info=\"Longitude of the earthquake\",\n",
|
429 |
+
" interactive=True)\n",
|
430 |
+
" \n",
|
431 |
+
" source_depth_inputs = gr.Number(value=10,\n",
|
432 |
+
" label=\"Source depth (km)\",\n",
|
433 |
+
" info=\"Depth of the earthquake\",\n",
|
434 |
+
" interactive=True)\n",
|
435 |
+
" \n",
|
436 |
+
" radius_inputs = gr.Slider(minimum=1, \n",
|
437 |
+
" maximum=150, \n",
|
438 |
+
" value=50, label=\"Radius (km)\", \n",
|
439 |
+
" info=\"Select the radius around the earthquake to download data from\",\n",
|
440 |
+
" interactive=True)\n",
|
441 |
+
" \n",
|
442 |
+
" velocity_inputs = gr.Dropdown(\n",
|
443 |
+
" choices = ['1066a', '1066b', 'ak135', 'ak135f', 'herrin', 'iasp91', 'jb', 'prem', 'pwdk'], \n",
|
444 |
+
" label=\"1D velocity model\", \n",
|
445 |
+
" info=\"Velocity model for station selection\",\n",
|
446 |
+
" value = \"1066a\",\n",
|
447 |
+
" interactive=True\n",
|
448 |
+
" )\n",
|
449 |
+
" \n",
|
450 |
+
" \n",
|
451 |
+
" button = gr.Button(\"Predict phases\")\n",
|
452 |
+
" outputs_section = gr.outputs.Image(label='Waveforms with Phases Marked', type='numpy')\n",
|
453 |
+
" \n",
|
454 |
+
" button.click(predict_on_section, \n",
|
455 |
+
" inputs=[client_inputs, timestamp_inputs, \n",
|
456 |
+
" eq_lat_inputs, eq_lon_inputs, \n",
|
457 |
+
" radius_inputs, source_depth_inputs, velocity_inputs],\n",
|
458 |
+
" outputs=outputs_section)\n",
|
459 |
+
"\n",
|
460 |
+
" with gr.Tab(\"Predict on your own waveform\"):\n",
|
461 |
+
" gr.Markdown(\"\"\"\n",
|
462 |
+
" Please upload your waveform in .npy (numpy) format. \n",
|
463 |
+
" Your waveform should be sampled at 100 sps and have 3 (Z, N, E) or 1 (Z) channels.\n",
|
464 |
+
" \"\"\")\n",
|
465 |
+
"\n",
|
466 |
+
"\n",
|
467 |
+
"\n",
|
468 |
+
"demo.launch()"
|
469 |
+
]
|
470 |
+
},
|
471 |
+
{
|
472 |
+
"cell_type": "code",
|
473 |
+
"execution_count": null,
|
474 |
+
"metadata": {},
|
475 |
+
"outputs": [],
|
476 |
+
"source": []
|
477 |
+
}
|
478 |
+
],
|
479 |
+
"metadata": {
|
480 |
+
"kernelspec": {
|
481 |
+
"display_name": "phasehunter",
|
482 |
+
"language": "python",
|
483 |
+
"name": "python3"
|
484 |
+
},
|
485 |
+
"language_info": {
|
486 |
+
"codemirror_mode": {
|
487 |
+
"name": "ipython",
|
488 |
+
"version": 3
|
489 |
+
},
|
490 |
+
"file_extension": ".py",
|
491 |
+
"mimetype": "text/x-python",
|
492 |
+
"name": "python",
|
493 |
+
"nbconvert_exporter": "python",
|
494 |
+
"pygments_lexer": "ipython3",
|
495 |
+
"version": "3.11.2"
|
496 |
+
},
|
497 |
+
"orig_nbformat": 4,
|
498 |
+
"vscode": {
|
499 |
+
"interpreter": {
|
500 |
+
"hash": "6bf57068982d7b420bddaaf1d0614a7795947176033057024cf47d8ca2c1c4cd"
|
501 |
+
}
|
502 |
+
}
|
503 |
+
},
|
504 |
+
"nbformat": 4,
|
505 |
+
"nbformat_minor": 2
|
506 |
+
}
|
phasehunter/.DS_Store
ADDED
Binary file (6.15 kB). View file
|
|
phasehunter/__pycache__/data_preparation.cpython-311.pyc
ADDED
Binary file (9.14 kB). View file
|
|
phasehunter/__pycache__/model.cpython-311.pyc
ADDED
Binary file (16.4 kB). View file
|
|
phasehunter/app.py
DELETED
@@ -1,188 +0,0 @@
|
|
1 |
-
# Gradio app that takes seismic waveform as input and marks 2 phases on the waveform as output.
|
2 |
-
|
3 |
-
import gradio as gr
|
4 |
-
import numpy as np
|
5 |
-
from phasehunter.model import Onset_picker, Updated_onset_picker
|
6 |
-
from phasehunter.data_preparation import prepare_waveform
|
7 |
-
import torch
|
8 |
-
|
9 |
-
from scipy.stats import gaussian_kde
|
10 |
-
|
11 |
-
import obspy
|
12 |
-
from obspy.clients.fdsn import Client
|
13 |
-
from obspy.clients.fdsn.header import FDSNNoDataException, FDSNTimeoutException, FDSNInternalServerException
|
14 |
-
from obspy.geodetics.base import locations2degrees
|
15 |
-
from obspy.taup import TauPyModel
|
16 |
-
from obspy.taup.helper_classes import SlownessModelError
|
17 |
-
|
18 |
-
from obspy.clients.fdsn.header import URL_MAPPINGS
|
19 |
-
|
20 |
-
import matplotlib.pyplot as plt
|
21 |
-
|
22 |
-
def make_prediction(waveform):
|
23 |
-
waveform = np.load(waveform)
|
24 |
-
processed_input = prepare_waveform(waveform)
|
25 |
-
|
26 |
-
# Make prediction
|
27 |
-
with torch.no_grad():
|
28 |
-
output = model(processed_input)
|
29 |
-
|
30 |
-
p_phase = output[:, 0]
|
31 |
-
s_phase = output[:, 1]
|
32 |
-
|
33 |
-
return processed_input, p_phase, s_phase
|
34 |
-
|
35 |
-
def mark_phases(waveform):
|
36 |
-
processed_input, p_phase, s_phase = make_prediction(waveform)
|
37 |
-
|
38 |
-
# Create a plot of the waveform with the phases marked
|
39 |
-
if sum(processed_input[0][2] == 0): #if input is 1C
|
40 |
-
fig, ax = plt.subplots(nrows=2, figsize=(10, 2), sharex=True)
|
41 |
-
|
42 |
-
ax[0].plot(processed_input[0][0])
|
43 |
-
ax[0].set_ylabel('Norm. Ampl.')
|
44 |
-
|
45 |
-
else: #if input is 3C
|
46 |
-
fig, ax = plt.subplots(nrows=4, figsize=(10, 6), sharex=True)
|
47 |
-
ax[0].plot(processed_input[0][0])
|
48 |
-
ax[1].plot(processed_input[0][1])
|
49 |
-
ax[2].plot(processed_input[0][2])
|
50 |
-
|
51 |
-
ax[0].set_ylabel('Z')
|
52 |
-
ax[1].set_ylabel('N')
|
53 |
-
ax[2].set_ylabel('E')
|
54 |
-
|
55 |
-
p_phase_plot = p_phase*processed_input.shape[-1]
|
56 |
-
p_kde = gaussian_kde(p_phase_plot)
|
57 |
-
p_dist_space = np.linspace( min(p_phase_plot)-10, max(p_phase_plot)+10, 500 )
|
58 |
-
ax[-1].plot( p_dist_space, p_kde(p_dist_space), color='r')
|
59 |
-
|
60 |
-
s_phase_plot = s_phase*processed_input.shape[-1]
|
61 |
-
s_kde = gaussian_kde(s_phase_plot)
|
62 |
-
s_dist_space = np.linspace( min(s_phase_plot)-10, max(s_phase_plot)+10, 500 )
|
63 |
-
ax[-1].plot( s_dist_space, s_kde(s_dist_space), color='b')
|
64 |
-
|
65 |
-
for a in ax:
|
66 |
-
a.axvline(p_phase.mean()*processed_input.shape[-1], color='r', linestyle='--', label='P')
|
67 |
-
a.axvline(s_phase.mean()*processed_input.shape[-1], color='b', linestyle='--', label='S')
|
68 |
-
|
69 |
-
ax[-1].set_xlabel('Time, samples')
|
70 |
-
ax[-1].set_ylabel('Uncert.')
|
71 |
-
ax[-1].legend()
|
72 |
-
|
73 |
-
plt.subplots_adjust(hspace=0., wspace=0.)
|
74 |
-
|
75 |
-
# Convert the plot to an image and return it
|
76 |
-
fig.canvas.draw()
|
77 |
-
image = np.array(fig.canvas.renderer.buffer_rgba())
|
78 |
-
plt.close(fig)
|
79 |
-
return image
|
80 |
-
|
81 |
-
#??
|
82 |
-
|
83 |
-
def download_data(timestamp, eq_lat, eq_lon, client_name, radius_km):
|
84 |
-
client = Client(client_name)
|
85 |
-
window = radius_km / 111.2
|
86 |
-
|
87 |
-
assert eq_lat - window > -90 and eq_lat + window < 90, "Latitude out of bounds"
|
88 |
-
assert eq_lon - window > -180 and eq_lon + window < 180, "Longitude out of bounds"
|
89 |
-
|
90 |
-
starttime = obspy.UTCDateTime(timestamp)
|
91 |
-
endtime = startime + 120
|
92 |
-
|
93 |
-
inv = client.get_stations(network="*", station="*", location="*", channel="*H*",
|
94 |
-
starttime=obspy.UTCDateTime(starttime), endtime=endtime,
|
95 |
-
minlatitude=eq_lat-window, maxlatitude=eq_lat+window,
|
96 |
-
minlongitude=eq_lon-window, maxlongitude=eq_lon+window,
|
97 |
-
level='channel')
|
98 |
-
|
99 |
-
for network in inv:
|
100 |
-
for station in network:
|
101 |
-
print(station)
|
102 |
-
|
103 |
-
# waveform = client.get_waveforms(network=network.code, station=station.code, location="*", channel="*",
|
104 |
-
# starttime=obspy.UTCDateTime(start_date), endtime=obspy.UTCDateTime(end_date))
|
105 |
-
|
106 |
-
return 0
|
107 |
-
|
108 |
-
model = Onset_picker.load_from_checkpoint("./weights.ckpt",
|
109 |
-
picker=Updated_onset_picker(),
|
110 |
-
learning_rate=3e-4)
|
111 |
-
model.eval()
|
112 |
-
|
113 |
-
|
114 |
-
|
115 |
-
# # Create the Gradio interface
|
116 |
-
# gr.Interface(mark_phases, inputs, outputs, title='PhaseHunter').launch()
|
117 |
-
|
118 |
-
|
119 |
-
with gr.Blocks() as demo:
|
120 |
-
gr.Markdown("# PhaseHunter")
|
121 |
-
gr.Markdown("""This app allows one to detect P and S seismic phases along with uncertainty of the detection.
|
122 |
-
The app can be used in three ways: either by selecting one of the sample waveforms;
|
123 |
-
or by selecting an earthquake from the global earthquake catalogue;
|
124 |
-
or by uploading a waveform of interest.
|
125 |
-
""")
|
126 |
-
with gr.Tab("Default example"):
|
127 |
-
# Define the input and output types for Gradio
|
128 |
-
inputs = gr.Dropdown(
|
129 |
-
["data/sample/sample_0.npy",
|
130 |
-
"data/sample/sample_1.npy",
|
131 |
-
"data/sample/sample_2.npy"],
|
132 |
-
label="Sample waveform",
|
133 |
-
info="Select one of the samples",
|
134 |
-
value = "data/sample/sample_0.npy"
|
135 |
-
)
|
136 |
-
|
137 |
-
button = gr.Button("Predict phases")
|
138 |
-
outputs = gr.outputs.Image(label='Waveform with Phases Marked', type='numpy')
|
139 |
-
|
140 |
-
button.click(mark_phases, inputs=inputs, outputs=outputs)
|
141 |
-
|
142 |
-
with gr.Tab("Select earthquake from catalogue"):
|
143 |
-
gr.Markdown('TEST')
|
144 |
-
|
145 |
-
client_inputs = gr.Dropdown(
|
146 |
-
choices = list(URL_MAPPINGS.keys()),
|
147 |
-
label="FDSN Client",
|
148 |
-
info="Select one of the available FDSN clients",
|
149 |
-
value = "IRIS",
|
150 |
-
interactive=True
|
151 |
-
)
|
152 |
-
with gr.Row():
|
153 |
-
|
154 |
-
timestamp_inputs = gr.Textbox(value='2019-07-04 17:33:49',
|
155 |
-
placeholder='YYYY-MM-DD HH:MM:SS',
|
156 |
-
label="Timestamp",
|
157 |
-
info="Timestamp of the earthquake",
|
158 |
-
max_lines=1,
|
159 |
-
interactive=True)
|
160 |
-
|
161 |
-
eq_lat_inputs = gr.Number(value=35.766,
|
162 |
-
label="Latitude",
|
163 |
-
info="Latitude of the earthquake",
|
164 |
-
interactive=True)
|
165 |
-
|
166 |
-
eq_lo_inputs = gr.Number(value=117.605,
|
167 |
-
label="Longitude",
|
168 |
-
info="Longitude of the earthquake",
|
169 |
-
interactive=True)
|
170 |
-
|
171 |
-
radius_inputs = gr.Slider(minimum=1,
|
172 |
-
maximum=150,
|
173 |
-
value=50, label="Radius (km)",
|
174 |
-
info="Select the radius around the earthquake to download data from",
|
175 |
-
interactive=True)
|
176 |
-
|
177 |
-
button = gr.Button("Predict phases")
|
178 |
-
button.click(mark_phases, inputs=inputs, outputs=outputs)
|
179 |
-
|
180 |
-
with gr.Tab("Predict on your own waveform"):
|
181 |
-
gr.Markdown("""
|
182 |
-
Please upload your waveform in .npy (numpy) format.
|
183 |
-
Your waveform should be sampled at 100 sps and have 3 (Z, N, E) or 1 (Z) channels.
|
184 |
-
""")
|
185 |
-
|
186 |
-
button.click(download_data, inputs=[timestamp_inputs, eq_lat_inputs,eq_lo_inputs, radius_inputs], outputs=outputs)
|
187 |
-
|
188 |
-
demo.launch()
|
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