RF_EQlocation3D.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9d0b9c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is an example for applying the random forest method for earthquake location\n",
    "#\n",
    "# Prepared by Yangkang Chen, 02/07/2025\n",
    "# \n",
    "# References:\n",
    "# Chen et al., 2022, RFloc3D: a machine learning method for 3D microseismic source location using P- and S-wave arrivals, IEEE TGRS\n",
    "# Chen et al., 2023, 3D microseismic monitoring using machine learning, JGR-SE\n",
    "# \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cdca791-7726-44a6-9125-7f5cccdf59ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "stname='smalldata/stations_locations_times_39.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "66ea5323",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['-104.03412 31.93553 9.67058467742\\n', '-103.8458 31.5542 7.26633602151\\n', '-104.301444 31.774145 15.921\\n', '-103.86697 31.37045 10.772687052\\n', '-103.66793 31.57935 12.9183947133\\n', '-104.50077 31.66864 11.1308938172\\n', '-104.514453 31.75299 10.9986653226\\n', '-103.9577 31.2133 12.1441532258\\n', '-103.754585 31.283607 15.4345878136\\n', '-103.510147 31.408922 13.134311828\\n', '-103.78112 30.94367 19.5108207885\\n', '-103.21825 31.6472 17.924516129\\n', '-103.3227 31.2804 18.6044601254\\n', '-103.2694 31.18697 19.0081312724\\n', '-103.252 31.12496 20.431140233\\n', '-103.099701 31.303087 20.4052508961\\n', '-103.06216 31.341918 19.5493391577\\n', '-103.3247 30.91978 24.5249546371\\n', '-104.98515 30.7866 25.3761102151\\n', '-102.76141 31.34023 24.6987197581\\n', '-103.63852 30.37448 27.6922150538\\n', '-102.90736 30.89174 26.9816572581\\n', '-102.54913 32.12009 26.7092600806\\n', '-102.25277 32.00061 31.0355129928\\n', '-102.037907 31.19806 34.7853046595\\n', '-101.7999756 31.9779827 37.1375515233\\n', '-101.8597158 32.6265397 39.6453951613\\n', '-102.12331 29.95471 44.4879702061\\n', '-101.50105 33.0662 46.0500166331\\n', '-101.12639 31.91436 46.6274937276\\n', '-101.12708 30.92195 45.9032871864\\n', '-101.060724 32.782579 48.8458225806\\n', '-100.94118 32.92429 51.0626861559\\n', '-100.91106 32.86452 52.7397535842\\n', '-100.86952 32.89878 51.227515233\\n', '-100.89172 33.09092 52.6062423835\\n', '-100.739412 32.672969 51.802108871\\n', '-100.72388 33.04918 55.6003100358\\n', '-100.92365 33.66653 55.9619231631\\n']\n"
     ]
    }
   ],
   "source": [
    "f = open(stname,'r');\n",
    "lines=f.readlines();\n",
    "print(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "de9095fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c88f28b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ['-104.03412', '31.93553', '9.67058467742']\n",
      "1 ['-103.8458', '31.5542', '7.26633602151']\n",
      "2 ['-104.301444', '31.774145', '15.921']\n",
      "3 ['-103.86697', '31.37045', '10.772687052']\n",
      "4 ['-103.66793', '31.57935', '12.9183947133']\n",
      "5 ['-104.50077', '31.66864', '11.1308938172']\n",
      "6 ['-104.514453', '31.75299', '10.9986653226']\n",
      "7 ['-103.9577', '31.2133', '12.1441532258']\n",
      "8 ['-103.754585', '31.283607', '15.4345878136']\n",
      "9 ['-103.510147', '31.408922', '13.134311828']\n",
      "10 ['-103.78112', '30.94367', '19.5108207885']\n",
      "11 ['-103.21825', '31.6472', '17.924516129']\n",
      "12 ['-103.3227', '31.2804', '18.6044601254']\n",
      "13 ['-103.2694', '31.18697', '19.0081312724']\n",
      "14 ['-103.252', '31.12496', '20.431140233']\n",
      "15 ['-103.099701', '31.303087', '20.4052508961']\n",
      "16 ['-103.06216', '31.341918', '19.5493391577']\n",
      "17 ['-103.3247', '30.91978', '24.5249546371']\n",
      "18 ['-104.98515', '30.7866', '25.3761102151']\n",
      "19 ['-102.76141', '31.34023', '24.6987197581']\n",
      "20 ['-103.63852', '30.37448', '27.6922150538']\n",
      "21 ['-102.90736', '30.89174', '26.9816572581']\n",
      "22 ['-102.54913', '32.12009', '26.7092600806']\n",
      "23 ['-102.25277', '32.00061', '31.0355129928']\n",
      "24 ['-102.037907', '31.19806', '34.7853046595']\n",
      "25 ['-101.7999756', '31.9779827', '37.1375515233']\n",
      "26 ['-101.8597158', '32.6265397', '39.6453951613']\n",
      "27 ['-102.12331', '29.95471', '44.4879702061']\n",
      "28 ['-101.50105', '33.0662', '46.0500166331']\n",
      "29 ['-101.12639', '31.91436', '46.6274937276']\n",
      "30 ['-101.12708', '30.92195', '45.9032871864']\n",
      "31 ['-101.060724', '32.782579', '48.8458225806']\n",
      "32 ['-100.94118', '32.92429', '51.0626861559']\n",
      "33 ['-100.91106', '32.86452', '52.7397535842']\n",
      "34 ['-100.86952', '32.89878', '51.227515233']\n",
      "35 ['-100.89172', '33.09092', '52.6062423835']\n",
      "36 ['-100.739412', '32.672969', '51.802108871']\n",
      "37 ['-100.72388', '33.04918', '55.6003100358']\n",
      "38 ['-100.92365', '33.66653', '55.9619231631']\n"
     ]
    }
   ],
   "source": [
    "lons=[]\n",
    "lats=[]\n",
    "times=[]\n",
    "for iline,line in enumerate(lines):\n",
    "    line=line.rstrip(\"\\n\").split(\" \")\n",
    "    print(iline,line)\n",
    "    lons.append(float(line[0]))\n",
    "    lats.append(float(line[1]))\n",
    "    times.append(float(line[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f8102d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of stations in the area of interest\n",
    "nsta=9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e2372cf0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minlon,maxlon: -104.514453 -103.66793\n",
      "minlat,maxlat: 31.2133 31.93553\n",
      "minx,maxx: 0 94.12904033270054\n",
      "miny,maxy: 0 80.30829262699997\n"
     ]
    }
   ],
   "source": [
    "lons=lons[0:nsta]\n",
    "lats=lats[0:nsta]\n",
    "times=times[0:nsta]\n",
    "minlon=min(lons)\n",
    "maxlon=max(lons)\n",
    "minlat=min(lats)\n",
    "maxlat=max(lats)\n",
    "print('minlon,maxlon:',minlon,maxlon)\n",
    "print('minlat,maxlat:',minlat,maxlat)\n",
    "#In Cartesian coordinates\n",
    "minx=0;miny=0;\n",
    "maxx=(maxlon-minlon)*111.1949\n",
    "maxy=(maxlat-minlat)*111.1949\n",
    "print('minx,maxx:',minx,maxx)\n",
    "print('miny,maxy:',miny,maxy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7c81a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Catalog location: longitude=-104.05, latitude=31.7, Depth=7.1 km\n",
    "evlat=31.7\n",
    "evlon=-104.05\n",
    "evdep=7.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d14d896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x108e98110>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lons,lats,'v',color='b',markersize=15)\n",
    "plt.plot(evlon,evlat,'*',color='r',markersize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e420f04c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load synthetic traveltimes\n",
    "import numpy as np\n",
    "\n",
    "evfile='./smalldata/locations_10000.txt'\n",
    "\n",
    "f=open(evfile,'r');\n",
    "evlabels=[line.rstrip(\"\\n\").split(\" \") for line in f.readlines()];\n",
    "evlabels=np.array(evlabels,dtype='float32').transpose()\n",
    "\n",
    "nevent=evlabels.shape[1];\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ab9e88d-6743-4994-affb-18ba0cf12e9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chenyk/aaspip.pyseistr/pyseistr/plot.py:160: UserWarning: The following kwargs were not used by contour: 'barlabel'\n",
      "  _ = ax.contourf(\n",
      "/Users/chenyk/aaspip.pyseistr/pyseistr/plot.py:165: UserWarning: The following kwargs were not used by contour: 'barlabel'\n",
      "  _ = ax.contourf(\n",
      "/Users/chenyk/aaspip.pyseistr/pyseistr/plot.py:170: UserWarning: The following kwargs were not used by contour: 'barlabel'\n",
      "  C = ax.contourf(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from pyseistr import asciiread,asciiwrite\n",
    "import matplotlib.pyplot as plt\n",
    "from pyseistr import plot3d\n",
    "\n",
    "vels=asciiread('smalldata/WTexas_vp.txt');\n",
    "z=[float(ii.split()[0]) for ii in vels];\n",
    "vel=[float(ii.split()[1]) for ii in vels];\n",
    "\n",
    "nz=101;nx=101;ny=101;dx=1.0;dy=1.0;dz=z[1]-z[0];\n",
    "\n",
    "\n",
    "vp=np.zeros([nz,nx,ny]);\n",
    "for ii in range(nx):\n",
    "    for jj in range(ny):\n",
    "        vp[:,ii,jj]=np.array(vel);\n",
    "\n",
    "\n",
    "plot3d(vp,cmap='jet',x=np.array(range(nx))*dx,y=np.array(range(ny))*dy,z=np.array(range(nz))*dz,barlabel='Velocity (km/s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2089910e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 10000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evlabels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a553112b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using pyekfmm\n",
    "\n",
    "# vfile='/Users/chenyk/chenyk/eqsource_ml/db1d_syn9_v2/vpmod.bin';\n",
    "\n",
    "# fd = open(vfile,'rb')\n",
    "# vp = np.fromfile(fd, dtype = np.float32) #synthetic time\n",
    "# vp=vp.reshape([101,101,101],order='F'); #zxy\n",
    "\n",
    "ii=50\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.subplot(1,2,1)\n",
    "ax=plt.imshow(vp[:,:,ii],extent=[minx,maxx,25,0],aspect='auto');\n",
    "plt.jet();\n",
    "plt.xlabel('X');plt.ylabel('Z');\n",
    "plt.colorbar(orientation='horizontal',shrink=0.6);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9cf1da3f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[47.065 ,  0.8031,  2.25  ],\n",
       "       [53.6541, 57.0201, 15.    ],\n",
       "       [ 0.    , 61.8387,  4.25  ],\n",
       "       [90.3648, 19.2744, 12.5   ],\n",
       "       [75.304 , 74.6883,  9.5   ]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shots=evlabels.transpose()\n",
    "shots[-5:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ec3a9d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[42.3585, 47.3829,  3.25  ],\n",
       "       [93.1887, 14.4558,  2.75  ],\n",
       "       [57.4193,  3.2124, 19.5   ]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shots[0:3,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9ee3757b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "event id= 0  is done\n",
      "\n",
      "event id= 50  is done\n",
      "\n",
      "event id= 100  is done\n",
      "\n",
      "event id= 150  is done\n",
      "\n",
      "event id= 200  is done\n",
      "\n",
      "event id= 250  is done\n",
      "\n",
      "event id= 300  is done\n",
      "\n",
      "event id= 350  is done\n",
      "\n",
      "event id= 400  is done\n",
      "\n",
      "event id= 450  is done\n",
      "\n",
      "event id= 500  is done\n",
      "\n",
      "event id= 550  is done\n",
      "\n",
      "event id= 600  is done\n",
      "\n",
      "event id= 650  is done\n",
      "\n",
      "event id= 700  is done\n",
      "\n",
      "event id= 750  is done\n",
      "\n",
      "event id= 800  is done\n",
      "\n",
      "event id= 850  is done\n",
      "\n",
      "event id= 900  is done\n",
      "\n",
      "event id= 950  is done\n",
      "\n",
      "event id= 1000  is done\n",
      "\n",
      "event id= 1050  is done\n",
      "\n",
      "event id= 1100  is done\n",
      "\n",
      "event id= 1150  is done\n",
      "\n",
      "event id= 1200  is done\n",
      "\n",
      "event id= 1250  is done\n",
      "\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
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      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shotevent id= 1300  is done\n",
      "\n",
      "event id= 1350  is done\n",
      "\n",
      "event id= 1400  is done\n",
      "\n",
      "event id= 1450  is done\n",
      "\n",
      "event id= 1500  is done\n",
      "\n",
      "event id= 1550  is done\n",
      "\n",
      "event id= 1600  is done\n",
      "\n",
      "event id= 1650  is done\n",
      "\n",
      "event id= 1700  is done\n",
      "\n",
      "event id= 1750  is done\n",
      "\n",
      "event id= 1800  is done\n",
      "\n",
      "event id= 1850  is done\n",
      "\n",
      "event id= 1900  is done\n",
      "\n",
      "event id= 1950  is done\n",
      "\n",
      "event id= 2000  is done\n",
      "\n",
      "event id= 2050  is done\n",
      "\n",
      "event id= 2100  is done\n",
      "\n",
      "event id= 2150  is done\n",
      "\n",
      "event id= 2200  is done\n",
      "\n",
      "event id= 2250  is done\n",
      "\n",
      "event id= 2300  is done\n",
      "\n",
      "event id= 2350  is done\n",
      "\n",
      "event id= 2400  is done\n",
      "\n",
      "event id= 2450  is done\n",
      "\n",
      "event id= 2500  is done\n",
      "\n",
      " 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
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      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 oevent id= 2550  is done\n",
      "\n",
      "event id= 2600  is done\n",
      "\n",
      "event id= 2650  is done\n",
      "\n",
      "event id= 2700  is done\n",
      "\n",
      "event id= 2750  is done\n",
      "\n",
      "event id= 2800  is done\n",
      "\n",
      "event id= 2850  is done\n",
      "\n",
      "event id= 2900  is done\n",
      "\n",
      "event id= 2950  is done\n",
      "\n",
      "event id= 3000  is done\n",
      "\n",
      "event id= 3050  is done\n",
      "\n",
      "event id= 3100  is done\n",
      "\n",
      "event id= 3150  is done\n",
      "\n",
      "event id= 3200  is done\n",
      "\n",
      "event id= 3250  is done\n",
      "\n",
      "event id= 3300  is done\n",
      "\n",
      "event id= 3350  is done\n",
      "\n",
      "event id= 3400  is done\n",
      "\n",
      "event id= 3450  is done\n",
      "\n",
      "event id= 3500  is done\n",
      "\n",
      "event id= 3550  is done\n",
      "\n",
      "event id= 3600  is done\n",
      "\n",
      "event id= 3650  is done\n",
      "\n",
      "event id= 3700  is done\n",
      "\n",
      "event id= 3750  is done\n",
      "\n",
      "f 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
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      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;event id= 3800  is done\n",
      "\n",
      "event id= 3850  is done\n",
      "\n",
      "event id= 3900  is done\n",
      "\n",
      "event id= 3950  is done\n",
      "\n",
      "event id= 4000  is done\n",
      "\n",
      "event id= 4050  is done\n",
      "\n",
      "event id= 4100  is done\n",
      "\n",
      "event id= 4150  is done\n",
      "\n",
      "event id= 4200  is done\n",
      "\n",
      "event id= 4250  is done\n",
      "\n",
      "event id= 4300  is done\n",
      "\n",
      "event id= 4350  is done\n",
      "\n",
      "event id= 4400  is done\n",
      "\n",
      "event id= 4450  is done\n",
      "\n",
      "event id= 4500  is done\n",
      "\n",
      "event id= 4550  is done\n",
      "\n",
      "event id= 4600  is done\n",
      "\n",
      "event id= 4650  is done\n",
      "\n",
      "event id= 4700  is done\n",
      "\n",
      "event id= 4750  is done\n",
      "\n",
      "event id= 4800  is done\n",
      "\n",
      "event id= 4850  is done\n",
      "\n",
      "event id= 4900  is done\n",
      "\n",
      "event id= 4950  is done\n",
      "\n",
      "event id= 5000  is done\n",
      "\n",
      "\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
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      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
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      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shoevent id= 5050  is done\n",
      "\n",
      "event id= 5100  is done\n",
      "\n",
      "event id= 5150  is done\n",
      "\n",
      "event id= 5200  is done\n",
      "\n",
      "event id= 5250  is done\n",
      "\n",
      "event id= 5300  is done\n",
      "\n",
      "event id= 5350  is done\n",
      "\n",
      "event id= 5400  is done\n",
      "\n",
      "event id= 5450  is done\n",
      "\n",
      "event id= 5500  is done\n",
      "\n",
      "event id= 5550  is done\n",
      "\n",
      "event id= 5600  is done\n",
      "\n",
      "event id= 5650  is done\n",
      "\n",
      "event id= 5700  is done\n",
      "\n",
      "event id= 5750  is done\n",
      "\n",
      "event id= 5800  is done\n",
      "\n",
      "event id= 5850  is done\n",
      "\n",
      "event id= 5900  is done\n",
      "\n",
      "event id= 5950  is done\n",
      "\n",
      "event id= 6000  is done\n",
      "\n",
      "event id= 6050  is done\n",
      "\n",
      "event id= 6100  is done\n",
      "\n",
      "event id= 6150  is done\n",
      "\n",
      "event id= 6200  is done\n",
      "\n",
      "event id= 6250  is done\n",
      "\n",
      "event id= 6300  is done\n",
      "\n",
      "t 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
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      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 event id= 6350  is done\n",
      "\n",
      "event id= 6400  is done\n",
      "\n",
      "event id= 6450  is done\n",
      "\n",
      "event id= 6500  is done\n",
      "\n",
      "event id= 6550  is done\n",
      "\n",
      "event id= 6600  is done\n",
      "\n",
      "event id= 6650  is done\n",
      "\n",
      "event id= 6700  is done\n",
      "\n",
      "event id= 6750  is done\n",
      "\n",
      "event id= 6800  is done\n",
      "\n",
      "event id= 6850  is done\n",
      "\n",
      "event id= 6900  is done\n",
      "\n",
      "event id= 6950  is done\n",
      "\n",
      "event id= 7000  is done\n",
      "\n",
      "event id= 7050  is done\n",
      "\n",
      "event id= 7100  is done\n",
      "\n",
      "event id= 7150  is done\n",
      "\n",
      "event id= 7200  is done\n",
      "\n",
      "event id= 7250  is done\n",
      "\n",
      "event id= 7300  is done\n",
      "\n",
      "event id= 7350  is done\n",
      "\n",
      "event id= 7400  is done\n",
      "\n",
      "event id= 7450  is done\n",
      "\n",
      "event id= 7500  is done\n",
      "\n",
      "event id= 7550  is done\n",
      "\n",
      "of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
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      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1event id= 7600  is done\n",
      "\n",
      "event id= 7650  is done\n",
      "\n",
      "event id= 7700  is done\n",
      "\n",
      "event id= 7750  is done\n",
      "\n",
      "event id= 7800  is done\n",
      "\n",
      "event id= 7850  is done\n",
      "\n",
      "event id= 7900  is done\n",
      "\n",
      "event id= 7950  is done\n",
      "\n",
      "event id= 8000  is done\n",
      "\n",
      "event id= 8050  is done\n",
      "\n",
      "event id= 8100  is done\n",
      "\n",
      "event id= 8150  is done\n",
      "\n",
      "event id= 8200  is done\n",
      "\n",
      "event id= 8250  is done\n",
      "\n",
      "event id= 8300  is done\n",
      "\n",
      "event id= 8350  is done\n",
      "\n",
      "event id= 8400  is done\n",
      "\n",
      "event id= 8450  is done\n",
      "\n",
      "event id= 8500  is done\n",
      "\n",
      "event id= 8550  is done\n",
      "\n",
      "event id= 8600  is done\n",
      "\n",
      "event id= 8650  is done\n",
      "\n",
      "event id= 8700  is done\n",
      "\n",
      "event id= 8750  is done\n",
      "\n",
      "event id= 8800  is done\n",
      "\n",
      ";\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
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      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shot 1 of 1;\n",
      "shevent id= 8850  is done\n",
      "\n",
      "event id= 8900  is done\n",
      "\n",
      "event id= 8950  is done\n",
      "\n",
      "event id= 9000  is done\n",
      "\n",
      "event id= 9050  is done\n",
      "\n",
      "event id= 9100  is done\n",
      "\n",
      "event id= 9150  is done\n",
      "\n",
      "event id= 9200  is done\n",
      "\n",
      "event id= 9250  is done\n",
      "\n",
      "event id= 9300  is done\n",
      "\n",
      "event id= 9350  is done\n",
      "\n",
      "event id= 9400  is done\n",
      "\n",
      "event id= 9450  is done\n",
      "\n",
      "event id= 9500  is done\n",
      "\n",
      "event id= 9550  is done\n",
      "\n",
      "event id= 9600  is done\n",
      "\n",
      "event id= 9650  is done\n",
      "\n",
      "event id= 9700  is done\n",
      "\n",
      "event id= 9750  is done\n",
      "\n",
      "event id= 9800  is done\n",
      "\n",
      "event id= 9850  is done\n",
      "\n",
      "event id= 9900  is done\n",
      "\n",
      "event id= 9950  is done\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pyekfmm as fmm\n",
    "\n",
    "vel=np.swapaxes(vp,0,2).reshape([101*101*101,1],order='F'); #transpose to [xyz]\n",
    "# t=fmm.eikonal_surf(vel,xyz=shots[0:4],ax=[0,0.9413,101],ay=[0,0.8031,101],az=[0,0.25,101],order=1) #xy-nevets \n",
    "# fmmtime=t.reshape(101,101,nevent,order='F'); #[x,y]\n",
    "# memory issue stopped\n",
    "\n",
    "fmmtimes=np.zeros([101,101,nevent],dtype='float32') #x,y,nevent\n",
    "for ii in range(nevent):\n",
    "    fmmtimes[:,:,ii]=fmm.eikonal_surf(vel,xyz=np.expand_dims(shots[ii,:],0),ay=[0,0.9413,101],ax=[0,0.8031,101],az=[0,0.25,101],order=1).reshape(101,101,order='F');\n",
    "    #a little inconsistent here (due to C-version X-Y inconsistency)\n",
    "    if np.mod(ii,50)==0:\n",
    "        print('event id=',ii,' is done\\n');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0771a904",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# nn=252;\n",
    "# a=np.array_equal(stimes[:,:,0:nn],fmmtimes[:,:,0:nn]);\n",
    "# print(\"Calculation result correct? \",a)\n",
    "\n",
    "stimes=fmmtimes;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06f0610a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.array_equal(stimes[:,:,253:],fmmtimes[:,:,253:])\n",
    "stimes[:,:,nn]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90a9c91b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fmmtimes[:,:,nn]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9064719",
   "metadata": {},
   "outputs": [],
   "source": [
    "fmmtimes[:,:,:].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dca92271",
   "metadata": {},
   "outputs": [],
   "source": [
    "stimes[:,:,:].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79c43633",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[73.4214 63.4449  0.    ]\n",
      "[53.4105799  74.35080347 23.68551445 71.99680744 94.12904033  1.52147982\n",
      "  0.         61.90809416 84.49344627]\n",
      "[80.30829263 37.90634141 62.36310369 17.47427854 40.70289315 50.63148577\n",
      " 60.01077558  0.          7.81777983]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x109851910>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ii=252\n",
    "print(evlabels[:,ii])\n",
    "evlabels_deg=evlabels.copy();\n",
    "evlabels_deg[0,ii]=minlon+evlabels_deg[0,ii]/111.1949\n",
    "evlabels_deg[1,ii]=minlat+evlabels_deg[1,ii]/111.1949\n",
    "\n",
    "## coordinates transformation\n",
    "lons_x=(np.array(lons)-minlon)*111.1949 #station longitudes in X\n",
    "lats_y=(np.array(lats)-minlat)*111.1949 #station latitudes in Y\n",
    "print(lons_x)\n",
    "print(lats_y)\n",
    "evlon_x=(evlon-minlon)*111.1949\n",
    "evlat_y=(evlat-minlat)*111.1949\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "plt.subplot(2,2,1)\n",
    "ax=plt.imshow(stimes[:,:,ii].T,extent=[minx,maxx,maxy,miny]);\n",
    "plt.gca().invert_yaxis()\n",
    "plt.jet();\n",
    "plt.xlabel('X');plt.ylabel('Y');\n",
    "plt.plot(evlabels[0,ii],evlabels[1,ii],'*',color='g',markersize=12)\n",
    "plt.colorbar(orientation='horizontal',shrink=0.6);\n",
    "plt.plot(lons_x,lats_y,'v',color='b',markersize=15)\n",
    "plt.plot(evlon_x,evlat_y,'*',color='r',markersize=15)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "ax=plt.imshow(stimes[:,:,ii].T,extent=[minlon,maxlon,maxlat,minlat]);\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('Lon');plt.ylabel('Lat');\n",
    "plt.plot(evlabels_deg[0,ii],evlabels_deg[1,ii],'*',color='g',markersize=12)\n",
    "plt.colorbar(orientation='horizontal',shrink=0.6);\n",
    "plt.plot(lons,lats,'v',color='b',markersize=15)\n",
    "plt.plot(evlon,evlat,'*',color='r',markersize=15)\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "ax=plt.imshow(fmmtimes[:,:,ii].T,extent=[minx,maxx,maxy,miny]);\n",
    "plt.gca().invert_yaxis()\n",
    "plt.jet();\n",
    "plt.xlabel('X');plt.ylabel('Y');\n",
    "plt.plot(evlabels[0,ii],evlabels[1,ii],'*',color='g',markersize=12)\n",
    "plt.colorbar(orientation='horizontal',shrink=0.6);\n",
    "plt.plot(lons_x,lats_y,'v',color='b',markersize=15)\n",
    "plt.plot(evlon_x,evlat_y,'*',color='r',markersize=15)\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "ax=plt.imshow(stimes[:,:,ii].T-fmmtimes[:,:,ii].T,extent=[minlon,maxlon,maxlat,minlat]);\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('Lon');plt.ylabel('Lat');\n",
    "plt.plot(evlabels_deg[0,ii],evlabels_deg[1,ii],'*',color='g',markersize=12)\n",
    "plt.colorbar(orientation='horizontal',shrink=0.6);\n",
    "plt.plot(lons,lats,'v',color='b',markersize=15)\n",
    "plt.plot(evlon,evlat,'*',color='r',markersize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d19320e0-a5c8-4f17-8c38-e224a4259416",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('fmmtimes',fmmtimes)\n",
    "\n",
    "fmmtimes=np.load('fmmtimes.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6781447",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from here, using \n",
    "stimes=fmmtimes;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b38f299c",
   "metadata": {},
   "outputs": [],
   "source": [
    "minx,maxx,maxy,miny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef92f463",
   "metadata": {},
   "outputs": [],
   "source": [
    "evlabels_deg[0,ii],evlabels_deg[1,ii]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c11c900c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[53.4105799  74.35080347 23.68551445 71.99680744 94.12904033  1.52147982\n",
      "  0.         61.90809416 84.49344627]\n",
      "[80.30829263 37.90634141 62.36310369 17.47427854 40.70289315 50.63148577\n",
      " 60.01077558  0.          7.81777983]\n",
      "0.9412904033270054 0.8030829262699997\n",
      "[ 56  78  25  76 100   1   0  65  89]\n",
      "[100  47  77  21  50  63  74   0   9]\n"
     ]
    }
   ],
   "source": [
    "## prepare training data\n",
    "print(lons_x)\n",
    "print(lats_y)\n",
    "dx=(maxx-minx)/100;\n",
    "dy=(maxy-miny)/100;\n",
    "dz=0.25\n",
    "print(dx,dy)\n",
    "\n",
    "indx=np.int16(lons_x/dx)\n",
    "indy=np.int16(lats_y/dy)\n",
    "print(indx)\n",
    "print(indy)\n",
    "\n",
    "lons_x2=indx*dx\n",
    "lats_y2=indy*dy\n",
    "data=np.zeros([3,nsta,nevent])\n",
    "for ii in range(nevent):\n",
    "    for jj in range(nsta):\n",
    "        data[0,jj,ii]=lons_x2[jj] #use grid position\n",
    "        data[1,jj,ii]=lats_y2[jj] #use grid position\n",
    "        data[2,jj,ii]=stimes[indx[jj],indy[jj],ii]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca43c66a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[:,:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "917653b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for ii in range(nevent):\n",
    "    ind=np.argsort(data[2,:,ii])\n",
    "    data[:,:,ii]=data[:,ind,ii]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1d28e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[:,:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16d37e56",
   "metadata": {},
   "outputs": [],
   "source": [
    "stimes[89,9,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1a06ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "evlabels[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed3f9e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data[2,1,1]) #simple verification\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ec2cc023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([93.1887, 14.4558,  2.75  ], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## label \n",
    "lab = evlabels\n",
    "lab.shape\n",
    "lab[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2064e782",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import h5py\n",
    "# import scipy.io\n",
    "# import numpy as np\n",
    "\n",
    "# #mat = scipy.io.loadmat(r'D:\\Research\\MicroseismicLocation\\Matlab\\ml_source_train_10000.mat')\n",
    "# mat = scipy.io.loadmat(r'/Users/chenyk/chenyk/texnet/traveltime/ml_source4/ml_source_train_100000_weiyuan4.mat')\n",
    "# data = mat['data222']\n",
    "# lab = mat['label_location']\n",
    "# np.shape(data)\n",
    "# (3, 38, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ee7b34b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "\n",
    "# Reading Input Lat and Long for the Stations and Events.\n",
    "lat=[]\n",
    "long=[]\n",
    "timdif=[]\n",
    "laball=[]\n",
    "loc=[]\n",
    "depall=[]\n",
    "lad = []\n",
    "lod = []\n",
    "for i in range(0,np.shape(data)[-1]):\n",
    "    # Time difference betweeen the first arrival time and the 9 stations.\n",
    "    t = data[2,:,i] - data[2,0,i] \n",
    "    t = t[1:]\n",
    "    timdif.append(t)\n",
    "    \n",
    "    # The target is the difference between the lat/long of the first station \n",
    "    # and the excat lat/long of the event. The depth is the excat value.\n",
    "    lad.append(lab[0,i] - data[0,0,i])\n",
    "    lod.append(lab[1,i] - data[1,0,i])\n",
    "    \n",
    "    # The lat/long of the 10 stations\n",
    "    lat.append(data[0,:,i])\n",
    "    long.append(data[1,:,i])\n",
    "    \n",
    "    \n",
    "    # The lat/long and depth of the events.\n",
    "    laball.append(lab[:,i])\n",
    "    depall.append(lab[2,i])\n",
    "    \n",
    "lat    = np.array(lat)\n",
    "long   = np.array(long)\n",
    "timdif = np.array(timdif)\n",
    "laball = np.array(laball)\n",
    "depall = np.array(depall)\n",
    "lad = np.array(lad)\n",
    "lod = np.array(lod)\n",
    "\n",
    "lad    = np.reshape(lad,(lad.shape[0],1))\n",
    "lod   = np.reshape(lod,(lod.shape[0],1))\n",
    "depall = np.reshape(depall,(depall.shape[0],1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1f53e60f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 26)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Concatenating the input features.\n",
    "p_all = np.concatenate([lat,long,timdif],axis=-1)\n",
    "\n",
    "# We randomly split the data, so I saved the random index for reproducing and comparing.\n",
    "np.random.seed(2021)\n",
    "indrand=np.random.permutation(nevent)\n",
    "\n",
    "# Reorder according to the randomaization.\n",
    "p_all = p_all[indrand]\n",
    "d = np.concatenate([lad,lod,depall],axis=-1) \n",
    "d = d[indrand]\n",
    "\n",
    "np.shape(p_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff807c70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done 168 tasks      | elapsed:    1.8s\n",
      "[Parallel(n_jobs=-1)]: Done 418 tasks      | elapsed:    4.2s\n",
      "[Parallel(n_jobs=-1)]: Done 768 tasks      | elapsed:    7.4s\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    9.7s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=1000, max_features=26, n_estimators=1000,\n",
       "                      n_jobs=-1, random_state=43, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=1000, max_features=26, n_estimators=1000,\n",
       "                      n_jobs=-1, random_state=43, verbose=1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_depth=1000, max_features=26, n_estimators=1000,\n",
       "                      n_jobs=-1, random_state=43, verbose=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training RF using 90% of the data.\n",
    "#regr = RandomForestRegressor(max_depth=100,random_state=0,verbose=1,criterion='mse',n_jobs=-1)\n",
    "regr = RandomForestRegressor(max_depth=1000,max_features=26,n_estimators=1000,oob_score=False,random_state=43,verbose=1,criterion='squared_error',n_jobs=-1)\n",
    "\n",
    "ix = int(len(p_all)*0.9)\n",
    "regr.fit(p_all[0:ix],d[0:ix])  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a22d1b02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model\n",
    "# import joblib\n",
    "# joblib.dump(regr, 'model.joblib') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "32f4a96f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=16)]: Done 1000 out of 1000 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.4931861278935522, 0.2623652159028241, 0.5872840833333333)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train result\n",
    "\n",
    "# Predicting the difference lat/long and excat depth.\n",
    "outtrain = regr.predict(p_all[0:ix])\n",
    "\n",
    "# Retriving the excat lat/long\n",
    "outtrain[:,0] = data[0,0,indrand][0:ix] + outtrain[:,0]\n",
    "outtrain[:,1] = data[1,0,indrand][0:ix] + outtrain[:,1]\n",
    "dx = laball[indrand][0:ix]\n",
    "\n",
    "# Error estimatin (MAE)\n",
    "errlattrain  = np.mean(np.abs( (outtrain[:,0]) - (dx[:,0]) ))\n",
    "errlongtrain = np.mean(np.abs( (outtrain[:,1]) - (dx[:,1]) ))\n",
    "errsepthtrain = np.mean(np.abs( (outtrain[:,2]) - (dx[:,2]) ))\n",
    "\n",
    "\n",
    "errlattrain,errlongtrain,errsepthtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27c7ff8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx,dy,dz= 0.9413 0.8031 0.25\n"
     ]
    }
   ],
   "source": [
    "print('dx,dy,dz=',0.9413,0.8031,0.25) ##comparison with model resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeaf1142",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_all[0:ix].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "238c55f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "46a07325",
   "metadata": {},
   "outputs": [],
   "source": [
    "times=np.array(times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3770cbea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        , -2.40424866,  6.25041532,  1.10210237,  3.24781004,\n",
       "        1.46030914,  1.32808065,  2.47356855,  5.76400314])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times-times[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1904b4e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "lons_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6397290",
   "metadata": {},
   "outputs": [],
   "source": [
    "lats_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a203fe84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test the robustness\n",
    "# times[6]=0\n",
    "# times[7]=16\n",
    "# times[8]=16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4d94c350",
   "metadata": {},
   "outputs": [],
   "source": [
    "inds=np.argsort(times)\n",
    "times=times[inds]\n",
    "lons_x=lons_x[inds]\n",
    "lats_y=lats_y[inds]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e2821f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_all = np.concatenate([lons_x,lats_y,times[1:]-times[0]],axis=-1) #here use grid positions of stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19409413",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.concatenate([lons_x,lats_y,times],axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f36061a",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bd6623a",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec4c964b",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_all[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f6a5b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dde8902",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f66dc29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 26)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_all=np.expand_dims(t_all, axis=0)\n",
    "t_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e73631b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load independently trained model\n",
    "# import joblib\n",
    "# regr = joblib.load('../../db1d_syn9_v2/model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fd3e28a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-104.02552029   31.68625527    7.70575   ]]\n",
      "Predicted [-104.02552029] [31.68625527] [7.70575]\n",
      "catalog: -104.05 31.7 7.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=16)]: Done 168 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=16)]: Done 418 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=16)]: Done 768 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=16)]: Done 1000 out of 1000 | elapsed:    0.2s finished\n"
     ]
    }
   ],
   "source": [
    "# test result\n",
    "\n",
    "# Predicting the difference lat/long and excat depth.\n",
    "outtest_xy = regr.predict(t_all)\n",
    "\n",
    "# Retriving the excat lat/long\n",
    "outtest_xy[:,0] = lons_x[0] + outtest_xy[:,0] #use grid positions of stations (after binning)\n",
    "outtest_xy[:,1] = lats_y[0] + outtest_xy[:,1] #use grid positions of stations (after binning)\n",
    "\n",
    "outtest=outtest_xy\n",
    "outtest[:,0]=minlon+outtest[:,0]/111.1949\n",
    "outtest[:,1]=minlat+outtest[:,1]/111.1949\n",
    "\n",
    "print(outtest)\n",
    "print('Predicted',outtest[:,0],outtest[:,1],outtest[:,2])\n",
    "print('catalog:',-104.05,31.7,7.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73763927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x154204500>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lons,lats,'v',color='b',markersize=15)\n",
    "plt.plot(evlon,evlat,'*',color='r',markersize=15)\n",
    "plt.plot(outtest[:,0],outtest[:,1],'p',color='g',markersize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f03ed0a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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