{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Example" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import requests\n", "import json\n", "import pandas as pd\n", "import os\n", "import warnings\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# GAMMA_API_URL = \"http://127.0.0.1:8000\"\n", "GAMMA_API_URL = \"https://ai4eps-gamma.hf.space\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Prepare test data\n", "\n", "- Download test data: PhaseNet picks of the 2019 Ridgecrest earthquake sequence\n", "1. picks file: picks.json\n", "2. station information: stations.csv\n", "3. events in SCSN catalog: events.csv\n", "4. config file: config.pkl\n", "\n", "```bash\n", "wget https://github.com/wayneweiqiang/GMMA/releases/download/test_data/test_data.zip\n", "unzip test_data.zip\n", "```" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# !wget https://github.com/wayneweiqiang/GMMA/releases/download/test_data/test_data.zip\n", "# !unzip test_data.zip" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_dir = lambda x: os.path.join(\"test_data\", x)\n", "station_csv = data_dir(\"stations.csv\")\n", "pick_json = data_dir(\"picks.json\")\n", "catalog_csv = data_dir(\"catalog_gamma.csv\")\n", "picks_csv = data_dir(\"picks_gamma.csv\")\n", "if not os.path.exists(\"figures\"):\n", " os.makedirs(\"figures\")\n", "figure_dir = lambda x: os.path.join(\"figures\", x)\n", "\n", "## set config\n", "config = {'xlim_degree': [-118.004, -117.004], \n", " 'ylim_degree': [35.205, 36.205],\n", " 'z(km)': [0, 41]}\n", "\n", "## read stations\n", "stations = pd.read_csv(station_csv, delimiter=\"\\t\")\n", "stations = stations.rename(columns={\"station\":\"station_id\"})\n", "\n", "## read picks\n", "picks = pd.read_json(pick_json).sort_values(\"timestamp\").iloc[:200]\n", "picks[\"timestamp\"] = pd.to_datetime(picks[\"timestamp\"])\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idtimestampprobamptype
51368CI.WCS2..HH2019-07-04 17:00:00.0040.3719021.576248e-06p
50738CI.WBM..BH2019-07-04 17:00:00.0040.4294254.883445e-07p
51254CI.WCS2..BH2019-07-04 17:00:00.0040.5709321.388111e-06p
51643CI.WMF..BH2019-07-04 17:00:00.0040.3254801.930339e-07p
51727CI.WMF..HH2019-07-04 17:00:00.0040.4990701.795238e-07p
..................
52664CI.WRV2..EH2019-07-04 17:05:13.6340.5653246.402773e-07p
53676PB.B918..EH2019-07-04 17:05:13.7540.6215346.146262e-07s
53083CI.WVP2..HN2019-07-04 17:05:13.8940.8912104.123632e-06p
46727CI.CCC..HN2019-07-04 17:05:14.0040.7918525.821601e-06s
46543CI.CCC..HH2019-07-04 17:05:14.0040.8274455.868008e-06s
\n", "

200 rows × 5 columns

\n", "
" ], "text/plain": [ " id timestamp prob amp type\n", "51368 CI.WCS2..HH 2019-07-04 17:00:00.004 0.371902 1.576248e-06 p\n", "50738 CI.WBM..BH 2019-07-04 17:00:00.004 0.429425 4.883445e-07 p\n", "51254 CI.WCS2..BH 2019-07-04 17:00:00.004 0.570932 1.388111e-06 p\n", "51643 CI.WMF..BH 2019-07-04 17:00:00.004 0.325480 1.930339e-07 p\n", "51727 CI.WMF..HH 2019-07-04 17:00:00.004 0.499070 1.795238e-07 p\n", "... ... ... ... ... ...\n", "52664 CI.WRV2..EH 2019-07-04 17:05:13.634 0.565324 6.402773e-07 p\n", "53676 PB.B918..EH 2019-07-04 17:05:13.754 0.621534 6.146262e-07 s\n", "53083 CI.WVP2..HN 2019-07-04 17:05:13.894 0.891210 4.123632e-06 p\n", "46727 CI.CCC..HN 2019-07-04 17:05:14.004 0.791852 5.821601e-06 s\n", "46543 CI.CCC..HH 2019-07-04 17:05:14.004 0.827445 5.868008e-06 s\n", "\n", "[200 rows x 5 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "picks" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
station_idlongitudelatitudeelevation(m)unitcomponentresponse
0CI.CCC..BH-117.36535.525670.0m/sE,N,Z627368000.00,627368000.00,627368000.00
1CI.CCC..HH-117.36535.525670.0m/sE,N,Z627368000.00,627368000.00,627368000.00
2CI.CCC..HN-117.36535.525670.0m/s**2E,N,Z213979.00,214322.00,213808.00
3CI.CLC..BH-117.59835.816775.0m/sE,N,Z627368000.00,627368000.00,627368000.00
4CI.CLC..HH-117.59835.816775.0m/sE,N,Z627368000.00,627368000.00,627368000.00
5CI.CLC..HN-117.59835.816775.0m/s**2E,N,Z213945.00,213808.00,213740.00
6CI.DTP..BH-117.84635.267908.0m/sE,N,Z627368000.00,627368000.00,627368000.00
7CI.DTP..HH-117.84635.267908.0m/sE,N,Z627368000.00,627368000.00,627368000.00
8CI.DTP..HN-117.84635.267908.0m/s**2E,N,Z214399.00,213971.00,214484.00
9CI.JRC2..BH-117.80935.9821469.0m/sE,N,Z784866000.00,784866000.00,790478000.00
10CI.JRC2..HH-117.80935.9821469.0m/sE,N,Z784866000.00,784866000.00,790478000.00
11CI.JRC2..HN-117.80935.9821469.0m/s**2E,N,Z213808.00,213945.00,214185.00
12CI.LRL..BH-117.68235.4801340.0m/sE,N,Z628306000.00,629984000.00,627467000.00
13CI.LRL..HH-117.68235.4801340.0m/sE,N,Z628306000.00,629984000.00,627467000.00
14CI.LRL..HN-117.68235.4801340.0m/s**2E,N,Z213757.00,213671.00,213201.00
15CI.LRL.2C.HN-117.68235.4801340.0m/s**2E,N,Z213757.00,213671.00,213201.00
16CI.MPM..BH-117.48936.0581839.0m/sE,N,Z627368000.00,627368000.00,627368000.00
17CI.MPM..HH-117.48936.0581839.0m/sE,N,Z627368000.00,627368000.00,627368000.00
18CI.MPM..HN-117.48936.0581839.0m/s**2E,N,Z213911.00,214219.00,213911.00
19CI.Q0072.01.HN-117.66735.610695.0m/s**2E,N,Z256354.00,256354.00,256354.00
20CI.SLA..BH-117.28335.8911174.0m/sE,N,Z622338000.00,618992000.00,616482000.00
21CI.SLA..HH-117.28335.8911174.0m/sE,N,Z622338000.00,618992000.00,616482000.00
22CI.SLA..HN-117.28335.8911174.0m/s**2E,N,Z214253.00,213671.00,213979.00
23CI.SRT..BH-117.75135.692667.0m/sE,N,Z629145000.00,629145000.00,629145000.00
24CI.SRT..HH-117.75135.692667.0m/sE,N,Z629145000.00,629145000.00,629145000.00
25CI.SRT..HN-117.75135.692667.0m/s**2E,N,Z214056.00,213628.00,213842.00
26CI.TOW2..BH-117.76535.809685.0m/sE,N,Z626910000.00,626910000.00,626838000.00
27CI.TOW2..HH-117.76535.809685.0m/sE,N,Z626910000.00,626910000.00,626838000.00
28CI.TOW2..HN-117.76535.809685.0m/s**2E,N,Z213800.00,214142.00,214356.00
29CI.WBM..BH-117.89035.608892.0m/sE,N,Z314573000.00,314573000.00,314573000.00
30CI.WBM..HH-117.89035.608892.0m/sE,N,Z314573000.00,314573000.00,314573000.00
31CI.WBM..HN-117.89035.608892.0m/s**2E,N,Z213550.00,214064.00,213550.00
32CI.WBM.2C.HN-117.89035.608892.0m/s**2E,N,Z213550.00,214064.00,213550.00
33CI.WCS2..BH-117.76536.0251143.0m/sE,N,Z626910000.00,626910000.00,626838000.00
34CI.WCS2..HH-117.76536.0251143.0m/sE,N,Z626910000.00,626910000.00,626838000.00
35CI.WCS2..HN-117.76536.0251143.0m/s**2E,N,Z213757.00,213329.00,213415.00
36CI.WMF..BH-117.85536.1181537.4m/sE,N,Z625790000.00,627467000.00,625790000.00
37CI.WMF..HH-117.85536.1181537.4m/sE,N,Z625790000.00,627467000.00,625790000.00
38CI.WMF..HN-117.85536.1181537.4m/s**2E,N,Z213842.00,213842.00,213842.00
39CI.WMF.2C.HN-117.85536.1181537.4m/s**2E,N,Z213842.00,213842.00,213842.00
40CI.WNM..EH-117.90635.842974.3m/sZ69328700.00
41CI.WNM..HN-117.90635.842974.3m/s**2E,N,Z214021.00,213892.00,214021.00
42CI.WNM.2C.HN-117.90635.842974.3m/s**2E,N,Z214039.00,213911.00,214039.00
43CI.WRC2..BH-117.65035.948943.0m/sE,N,Z629145000.00,629145000.00,629145000.00
44CI.WRC2..HH-117.65035.948943.0m/sE,N,Z629145000.00,629145000.00,629145000.00
45CI.WRC2..HN-117.65035.948943.0m/s**2E,N,Z214227.00,213970.00,214056.00
46CI.WRV2..EH-117.89036.0081070.0m/sZ71450700.00
47CI.WRV2..HN-117.89036.0081070.0m/s**2E,N,Z213850.00,235188.00,235102.00
48CI.WRV2.2C.HN-117.89036.0081070.0m/s**2E,N,Z213868.00,235208.00,235122.00
49CI.WVP2..EH-117.81835.9491465.0m/sZ68041300.00
50CI.WVP2..HN-117.81835.9491465.0m/s**2E,N,Z213764.00,213550.00,213721.00
51CI.WVP2.2C.HN-117.81835.9491465.0m/s**2E,N,Z213782.00,213569.00,213740.00
52NP.1809..HN-117.95736.1101092.0m/s**2E,N,Z429497.00,429497.00,426141.00
53NP.5419..HN-117.66235.649689.0m/s**2E,N,Z426141.00,429497.00,429497.00
54PB.B916..EH-117.66836.1931859.9m/s1,2,Z781398000.00,781398000.00,781398000.00
55PB.B917..EH-117.25935.4051192.0m/s1,2,Z781398000.00,781398000.00,781398000.00
56PB.B918..EH-117.60235.9361042.6m/s1,2,Z781398000.00,781398000.00,781398000.00
57PB.B921..EH-117.46235.587694.5m/s1,2,Z781398000.00,781398000.00,781398000.00
\n", "
" ], "text/plain": [ " station_id longitude latitude elevation(m) unit component \\\n", "0 CI.CCC..BH -117.365 35.525 670.0 m/s E,N,Z \n", "1 CI.CCC..HH -117.365 35.525 670.0 m/s E,N,Z \n", "2 CI.CCC..HN -117.365 35.525 670.0 m/s**2 E,N,Z \n", "3 CI.CLC..BH -117.598 35.816 775.0 m/s E,N,Z \n", "4 CI.CLC..HH -117.598 35.816 775.0 m/s E,N,Z \n", "5 CI.CLC..HN -117.598 35.816 775.0 m/s**2 E,N,Z \n", "6 CI.DTP..BH -117.846 35.267 908.0 m/s E,N,Z \n", "7 CI.DTP..HH -117.846 35.267 908.0 m/s E,N,Z \n", "8 CI.DTP..HN -117.846 35.267 908.0 m/s**2 E,N,Z \n", "9 CI.JRC2..BH -117.809 35.982 1469.0 m/s E,N,Z \n", "10 CI.JRC2..HH -117.809 35.982 1469.0 m/s E,N,Z \n", "11 CI.JRC2..HN -117.809 35.982 1469.0 m/s**2 E,N,Z \n", "12 CI.LRL..BH -117.682 35.480 1340.0 m/s E,N,Z \n", "13 CI.LRL..HH -117.682 35.480 1340.0 m/s E,N,Z \n", "14 CI.LRL..HN -117.682 35.480 1340.0 m/s**2 E,N,Z \n", "15 CI.LRL.2C.HN -117.682 35.480 1340.0 m/s**2 E,N,Z \n", "16 CI.MPM..BH -117.489 36.058 1839.0 m/s E,N,Z \n", "17 CI.MPM..HH -117.489 36.058 1839.0 m/s E,N,Z \n", "18 CI.MPM..HN -117.489 36.058 1839.0 m/s**2 E,N,Z \n", "19 CI.Q0072.01.HN -117.667 35.610 695.0 m/s**2 E,N,Z \n", "20 CI.SLA..BH -117.283 35.891 1174.0 m/s E,N,Z \n", "21 CI.SLA..HH -117.283 35.891 1174.0 m/s E,N,Z \n", "22 CI.SLA..HN -117.283 35.891 1174.0 m/s**2 E,N,Z \n", "23 CI.SRT..BH -117.751 35.692 667.0 m/s E,N,Z \n", "24 CI.SRT..HH -117.751 35.692 667.0 m/s E,N,Z \n", "25 CI.SRT..HN -117.751 35.692 667.0 m/s**2 E,N,Z \n", "26 CI.TOW2..BH -117.765 35.809 685.0 m/s E,N,Z \n", "27 CI.TOW2..HH -117.765 35.809 685.0 m/s E,N,Z \n", "28 CI.TOW2..HN -117.765 35.809 685.0 m/s**2 E,N,Z \n", "29 CI.WBM..BH -117.890 35.608 892.0 m/s E,N,Z \n", "30 CI.WBM..HH -117.890 35.608 892.0 m/s E,N,Z \n", "31 CI.WBM..HN -117.890 35.608 892.0 m/s**2 E,N,Z \n", "32 CI.WBM.2C.HN -117.890 35.608 892.0 m/s**2 E,N,Z \n", "33 CI.WCS2..BH -117.765 36.025 1143.0 m/s E,N,Z \n", "34 CI.WCS2..HH -117.765 36.025 1143.0 m/s E,N,Z \n", "35 CI.WCS2..HN -117.765 36.025 1143.0 m/s**2 E,N,Z \n", "36 CI.WMF..BH -117.855 36.118 1537.4 m/s E,N,Z \n", "37 CI.WMF..HH -117.855 36.118 1537.4 m/s E,N,Z \n", "38 CI.WMF..HN -117.855 36.118 1537.4 m/s**2 E,N,Z \n", "39 CI.WMF.2C.HN -117.855 36.118 1537.4 m/s**2 E,N,Z \n", "40 CI.WNM..EH -117.906 35.842 974.3 m/s Z \n", "41 CI.WNM..HN -117.906 35.842 974.3 m/s**2 E,N,Z \n", "42 CI.WNM.2C.HN -117.906 35.842 974.3 m/s**2 E,N,Z \n", "43 CI.WRC2..BH -117.650 35.948 943.0 m/s E,N,Z \n", "44 CI.WRC2..HH -117.650 35.948 943.0 m/s E,N,Z \n", "45 CI.WRC2..HN -117.650 35.948 943.0 m/s**2 E,N,Z \n", "46 CI.WRV2..EH -117.890 36.008 1070.0 m/s Z \n", "47 CI.WRV2..HN -117.890 36.008 1070.0 m/s**2 E,N,Z \n", "48 CI.WRV2.2C.HN -117.890 36.008 1070.0 m/s**2 E,N,Z \n", "49 CI.WVP2..EH -117.818 35.949 1465.0 m/s Z \n", "50 CI.WVP2..HN -117.818 35.949 1465.0 m/s**2 E,N,Z \n", "51 CI.WVP2.2C.HN -117.818 35.949 1465.0 m/s**2 E,N,Z \n", "52 NP.1809..HN -117.957 36.110 1092.0 m/s**2 E,N,Z \n", "53 NP.5419..HN -117.662 35.649 689.0 m/s**2 E,N,Z \n", "54 PB.B916..EH -117.668 36.193 1859.9 m/s 1,2,Z \n", "55 PB.B917..EH -117.259 35.405 1192.0 m/s 1,2,Z \n", "56 PB.B918..EH -117.602 35.936 1042.6 m/s 1,2,Z \n", "57 PB.B921..EH -117.462 35.587 694.5 m/s 1,2,Z \n", "\n", " response \n", "0 627368000.00,627368000.00,627368000.00 \n", "1 627368000.00,627368000.00,627368000.00 \n", "2 213979.00,214322.00,213808.00 \n", "3 627368000.00,627368000.00,627368000.00 \n", "4 627368000.00,627368000.00,627368000.00 \n", "5 213945.00,213808.00,213740.00 \n", "6 627368000.00,627368000.00,627368000.00 \n", "7 627368000.00,627368000.00,627368000.00 \n", "8 214399.00,213971.00,214484.00 \n", "9 784866000.00,784866000.00,790478000.00 \n", "10 784866000.00,784866000.00,790478000.00 \n", "11 213808.00,213945.00,214185.00 \n", "12 628306000.00,629984000.00,627467000.00 \n", "13 628306000.00,629984000.00,627467000.00 \n", "14 213757.00,213671.00,213201.00 \n", "15 213757.00,213671.00,213201.00 \n", "16 627368000.00,627368000.00,627368000.00 \n", "17 627368000.00,627368000.00,627368000.00 \n", "18 213911.00,214219.00,213911.00 \n", "19 256354.00,256354.00,256354.00 \n", "20 622338000.00,618992000.00,616482000.00 \n", "21 622338000.00,618992000.00,616482000.00 \n", "22 214253.00,213671.00,213979.00 \n", "23 629145000.00,629145000.00,629145000.00 \n", "24 629145000.00,629145000.00,629145000.00 \n", "25 214056.00,213628.00,213842.00 \n", "26 626910000.00,626910000.00,626838000.00 \n", "27 626910000.00,626910000.00,626838000.00 \n", "28 213800.00,214142.00,214356.00 \n", "29 314573000.00,314573000.00,314573000.00 \n", "30 314573000.00,314573000.00,314573000.00 \n", "31 213550.00,214064.00,213550.00 \n", "32 213550.00,214064.00,213550.00 \n", "33 626910000.00,626910000.00,626838000.00 \n", "34 626910000.00,626910000.00,626838000.00 \n", "35 213757.00,213329.00,213415.00 \n", "36 625790000.00,627467000.00,625790000.00 \n", "37 625790000.00,627467000.00,625790000.00 \n", "38 213842.00,213842.00,213842.00 \n", "39 213842.00,213842.00,213842.00 \n", "40 69328700.00 \n", "41 214021.00,213892.00,214021.00 \n", "42 214039.00,213911.00,214039.00 \n", "43 629145000.00,629145000.00,629145000.00 \n", "44 629145000.00,629145000.00,629145000.00 \n", "45 214227.00,213970.00,214056.00 \n", "46 71450700.00 \n", "47 213850.00,235188.00,235102.00 \n", "48 213868.00,235208.00,235122.00 \n", "49 68041300.00 \n", "50 213764.00,213550.00,213721.00 \n", "51 213782.00,213569.00,213740.00 \n", "52 429497.00,429497.00,426141.00 \n", "53 426141.00,429497.00,429497.00 \n", "54 781398000.00,781398000.00,781398000.00 \n", "55 781398000.00,781398000.00,781398000.00 \n", "56 781398000.00,781398000.00,781398000.00 \n", "57 781398000.00,781398000.00,781398000.00 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stations" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "picks.rename(columns={\"id\": \"station_id\", \"timestamp\": \"phase_time\", \"prob\": \"phase_score\", \"amp\": \"phase_amplitude\", \"type\": \"phase_type\"}, inplace=True)\n", "stations.rename(columns={\"id\": \"station_id\", \"elevation(m)\": \"elevation_m\"}, inplace=True)\n", "stations.drop(columns=[\"unit\", \"component\", \"response\"], inplace=True, errors=\"ignore\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "picks[\"phase_type\"] = picks[\"phase_type\"].str.upper()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "picks = picks.merge(stations[[\"station_id\", \"latitude\", \"longitude\", \"elevation_m\"]], on=\"station_id\", how=\"left\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
station_idphase_timephase_scorephase_amplitudephase_typelatitudelongitudeelevation_m
0CI.WCS2..HH2019-07-04 17:00:00.0040.3719021.576248e-06P36.025-117.7651143.0
1CI.WBM..BH2019-07-04 17:00:00.0040.4294254.883445e-07P35.608-117.890892.0
2CI.WCS2..BH2019-07-04 17:00:00.0040.5709321.388111e-06P36.025-117.7651143.0
3CI.WMF..BH2019-07-04 17:00:00.0040.3254801.930339e-07P36.118-117.8551537.4
4CI.WMF..HH2019-07-04 17:00:00.0040.4990701.795238e-07P36.118-117.8551537.4
...........................
195CI.WRV2..EH2019-07-04 17:05:13.6340.5653246.402773e-07P36.008-117.8901070.0
196PB.B918..EH2019-07-04 17:05:13.7540.6215346.146262e-07S35.936-117.6021042.6
197CI.WVP2..HN2019-07-04 17:05:13.8940.8912104.123632e-06P35.949-117.8181465.0
198CI.CCC..HN2019-07-04 17:05:14.0040.7918525.821601e-06S35.525-117.365670.0
199CI.CCC..HH2019-07-04 17:05:14.0040.8274455.868008e-06S35.525-117.365670.0
\n", "

200 rows × 8 columns

\n", "
" ], "text/plain": [ " station_id phase_time phase_score phase_amplitude \\\n", "0 CI.WCS2..HH 2019-07-04 17:00:00.004 0.371902 1.576248e-06 \n", "1 CI.WBM..BH 2019-07-04 17:00:00.004 0.429425 4.883445e-07 \n", "2 CI.WCS2..BH 2019-07-04 17:00:00.004 0.570932 1.388111e-06 \n", "3 CI.WMF..BH 2019-07-04 17:00:00.004 0.325480 1.930339e-07 \n", "4 CI.WMF..HH 2019-07-04 17:00:00.004 0.499070 1.795238e-07 \n", ".. ... ... ... ... \n", "195 CI.WRV2..EH 2019-07-04 17:05:13.634 0.565324 6.402773e-07 \n", "196 PB.B918..EH 2019-07-04 17:05:13.754 0.621534 6.146262e-07 \n", "197 CI.WVP2..HN 2019-07-04 17:05:13.894 0.891210 4.123632e-06 \n", "198 CI.CCC..HN 2019-07-04 17:05:14.004 0.791852 5.821601e-06 \n", "199 CI.CCC..HH 2019-07-04 17:05:14.004 0.827445 5.868008e-06 \n", "\n", " phase_type latitude longitude elevation_m \n", "0 P 36.025 -117.765 1143.0 \n", "1 P 35.608 -117.890 892.0 \n", "2 P 36.025 -117.765 1143.0 \n", "3 P 36.118 -117.855 1537.4 \n", "4 P 36.118 -117.855 1537.4 \n", ".. ... ... ... ... \n", "195 P 36.008 -117.890 1070.0 \n", "196 S 35.936 -117.602 1042.6 \n", "197 P 35.949 -117.818 1465.0 \n", "198 S 35.525 -117.365 670.0 \n", "199 S 35.525 -117.365 670.0 \n", "\n", "[200 rows x 8 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "picks" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "picks = picks[(picks[\"phase_time\"] > pd.to_datetime(\"2019-07-04T17:02:00.000\")) & (picks[\"phase_time\"] < pd.to_datetime(\"2019-07-04T17:04:00.000\"))]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "color = {\"P\": \"red\", \"S\": \"blue\"}\n", "plt.scatter(picks[\"phase_time\"], picks[\"latitude\"], c=picks[\"phase_type\"].apply(lambda x: color[x]), s=10)\n", "plt.scatter([], [], c=\"red\", label=\"P\")\n", "plt.scatter([], [], c=\"blue\", label=\"S\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Associating 108 picks with 1 CPUs\n", ".[{'time': '2019-07-04T17:02:55.008', 'magnitude': 4.339747018742565, 'sigma_time': 0.32614604178710815, 'sigma_amp': 0.3280145301432932, 'cov_time_amp': 0.05017926672657749, 'gamma_score': 97.9995778560105, 'num_picks': 98, 'num_p_picks': 49, 'num_s_picks': 49, 'event_index': 1, 'x(km)': 0.7269972359810098, 'y(km)': 0.5939380031466018, 'z(km)': 16.20145420615579}]\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from app import run_gamma\n", "\n", "config[\"region\"] = \"Ridgecrest\"\n", "config[\"event_index\"] = 1\n", "\n", "evengts_, picks_ = run_gamma(picks, stations, config)\n", "\n", "plt.figure()\n", "mapping_color = lambda x: f\"C{x}\" if x!= -1 else \"black\"\n", "plt.scatter(picks_[\"phase_time\"], picks_[\"latitude\"], c=picks_[\"event_index\"].apply(mapping_color), s=10)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "picks_ = picks.copy()\n", "picks_[\"phase_time\"] = picks_[\"phase_time\"].apply(lambda x: x.isoformat())\n", "stations_ = stations.copy()\n", "\n", "picks_ = picks_.to_dict(orient=\"records\")\n", "stations_ = stations.to_dict(orient=\"records\")\n", "\n", "response = requests.post(f\"{GAMMA_API_URL}/predict/\", json={\"picks\": {\"data\":picks_}, \"stations\": {\"data\": stations_}, \"config\": config})\n", "\n", "if response.status_code == 200:\n", " result = response.json()\n", " # Process the result as needed\n", "else:\n", " print(f\"Request failed with status code: {response.status_code}\")\n", " print(f\"Error message: {response.text}\")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "events = pd.DataFrame(result[\"events\"])\n", "picks_ = pd.DataFrame(result[\"picks\"])\n", "picks_[\"phase_time\"] = pd.to_datetime(picks_[\"phase_time\"])\n", "\n", "plt.figure()\n", "mapping_color = lambda x: f\"C{x}\" if x!= -1 else \"black\"\n", "plt.scatter(picks_[\"phase_time\"], picks_[\"latitude\"], c=picks_[\"event_index\"].apply(mapping_color), s=10)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
timemagnitudesigma_timesigma_ampcov_time_ampgamma_scorenum_picksnum_p_picksnum_s_picksevent_indexlongitudelatitudedepth_km
02019-07-04T17:02:55.0084.3397470.3261460.3280150.05017997.9995789849491-117.49596635.71035316.201454
\n", "
" ], "text/plain": [ " time magnitude sigma_time sigma_amp cov_time_amp \\\n", "0 2019-07-04T17:02:55.008 4.339747 0.326146 0.328015 0.050179 \n", "\n", " gamma_score num_picks num_p_picks num_s_picks event_index longitude \\\n", "0 97.999578 98 49 49 1 -117.495966 \n", "\n", " latitude depth_km \n", "0 35.710353 16.201454 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "events" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
station_idphase_timephase_scorephase_amplitudephase_typelatitudelongitudeelevation_mevent_indexgamma_score
0CI.WCS2..HN2019-07-04 17:02:24.4740.3457360.000003S36.025-117.7651143.0-1-1.000000
1PB.B921..EH2019-07-04 17:02:58.3040.9453700.000961P35.587-117.462694.510.000016
2CI.CLC..BH2019-07-04 17:02:58.4940.9764440.002152P35.816-117.598775.010.185935
3CI.CLC..HN2019-07-04 17:02:58.5040.9689910.002354P35.816-117.598775.010.300784
4CI.CLC..HH2019-07-04 17:02:58.5040.9790830.002593P35.816-117.598775.010.306604
.................................
103CI.WCS2..HN2019-07-04 17:03:28.3540.4965330.000056P36.025-117.7651143.0-1-1.000000
104CI.JRC2..HN2019-07-04 17:03:28.7140.3838730.000084P35.982-117.8091469.0-1-1.000000
105CI.WNM..HN2019-07-04 17:03:40.2340.3131300.000019P35.842-117.906974.3-1-1.000000
106CI.MPM..HN2019-07-04 17:03:42.3940.3734620.000028P36.058-117.4891839.0-1-1.000000
107CI.DTP..HN2019-07-04 17:03:43.3540.4018610.000014P35.267-117.846908.0-1-1.000000
\n", "

108 rows × 10 columns

\n", "
" ], "text/plain": [ " station_id phase_time phase_score phase_amplitude \\\n", "0 CI.WCS2..HN 2019-07-04 17:02:24.474 0.345736 0.000003 \n", "1 PB.B921..EH 2019-07-04 17:02:58.304 0.945370 0.000961 \n", "2 CI.CLC..BH 2019-07-04 17:02:58.494 0.976444 0.002152 \n", "3 CI.CLC..HN 2019-07-04 17:02:58.504 0.968991 0.002354 \n", "4 CI.CLC..HH 2019-07-04 17:02:58.504 0.979083 0.002593 \n", ".. ... ... ... ... \n", "103 CI.WCS2..HN 2019-07-04 17:03:28.354 0.496533 0.000056 \n", "104 CI.JRC2..HN 2019-07-04 17:03:28.714 0.383873 0.000084 \n", "105 CI.WNM..HN 2019-07-04 17:03:40.234 0.313130 0.000019 \n", "106 CI.MPM..HN 2019-07-04 17:03:42.394 0.373462 0.000028 \n", "107 CI.DTP..HN 2019-07-04 17:03:43.354 0.401861 0.000014 \n", "\n", " phase_type latitude longitude elevation_m event_index gamma_score \n", "0 S 36.025 -117.765 1143.0 -1 -1.000000 \n", "1 P 35.587 -117.462 694.5 1 0.000016 \n", "2 P 35.816 -117.598 775.0 1 0.185935 \n", "3 P 35.816 -117.598 775.0 1 0.300784 \n", "4 P 35.816 -117.598 775.0 1 0.306604 \n", ".. ... ... ... ... ... ... \n", "103 P 36.025 -117.765 1143.0 -1 -1.000000 \n", "104 P 35.982 -117.809 1469.0 -1 -1.000000 \n", "105 P 35.842 -117.906 974.3 -1 -1.000000 \n", "106 P 36.058 -117.489 1839.0 -1 -1.000000 \n", "107 P 35.267 -117.846 908.0 -1 -1.000000 \n", "\n", "[108 rows x 10 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "picks_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "cd49b9d623d06aa0c5f872a997e70207e179b28bd8e4cd8fec363e5d29096c9c" } } }, "nbformat": 4, "nbformat_minor": 2 }