diff --git "a/example_fastapi.ipynb" "b/example_fastapi.ipynb" new file mode 100644--- /dev/null +++ "b/example_fastapi.ipynb" @@ -0,0 +1,1730 @@ +{ + "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": 2, + "metadata": {}, + "outputs": [], + "source": [ + "ADLOC_API_URL = \"http://127.0.0.1:8000\"\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": [ + "
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" + ], + "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 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on=\"station_id\", how=\"left\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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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
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200 rows × 8 columns

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" + ], + "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": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from app import run_gamma" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Associating 108 picks with 1 CPUs\n", + "." + ] + } + ], + "source": [ + "config[\"region\"] = \"Ridgecrest\"\n", + "config[\"event_index\"] = 1\n", + "\n", + "evengts_, picks_ = run_gamma(picks, stations, config)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "mapping_color = lambda x: f\"C{x}\" if x!= -1 else \"black\"\n", + "plt.scatter(picks_[\"timestamp\"], picks_[\"latitude\"], c=picks_[\"event_index\"].apply(mapping_color), s=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# call loalhost:8000/predict\n", + "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\"{ADLOC_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": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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timemagnitudesigma_timesigma_ampcov_time_ampgamma_scorenum_picksnum_p_picksnum_s_picksevent_indexlongitudelatitudedepth_km
02019-07-04T17:02:55.0084.339750.3261460.3280150.05017997.9997629849491-117.49596535.71035316.201628
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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 17:02:58.3040.9453700.000961P35.587-117.462694.510.000156
2CI.CLC..BH2019-07-04 17:02:58.4940.9764440.002152P35.816-117.598775.010.230912
3CI.CLC..HN2019-07-04 17:02:58.5040.9689910.002354P35.816-117.598775.010.748432
4CI.CLC..HH2019-07-04 17:02:58.5040.9790830.002593P35.816-117.598775.010.788955
.................................
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
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108 rows × 10 columns

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